


from typing import Optional, Tuple, Union

import tensorflow as tf

from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
    TFBaseModelOutputWithNoAttention,
    TFBaseModelOutputWithPoolingAndNoAttention,
    TFSequenceClassifierOutput,
)
from ...modeling_tf_utils import TFPreTrainedModel, TFSequenceClassificationLoss, keras_serializable, unpack_inputs
from ...tf_utils import shape_list
from ...utils import logging
from .configuration_regnet import RegNetConfig


lowerCAmelCase__  :Union[str, Any]             =							logging.get_logger(__name__)

# General docstring
lowerCAmelCase__  :int             =							'''RegNetConfig'''

# Base docstring
lowerCAmelCase__  :List[Any]             =							'''facebook/regnet-y-040'''
lowerCAmelCase__  :List[str]             =							[1, 1_0_8_8, 7, 7]

# Image classification docstring
lowerCAmelCase__  :Union[str, Any]             =							'''facebook/regnet-y-040'''
lowerCAmelCase__  :Optional[int]             =							'''tabby, tabby cat'''

lowerCAmelCase__  :int             =							[
    '''facebook/regnet-y-040''',
    # See all regnet models at https://huggingface.co/models?filter=regnet
]

class 		__a				(    tf.keras.layers.Layer      ):
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE = 3      ,   _SCREAMING_SNAKE_CASE = 1      ,   _SCREAMING_SNAKE_CASE = 1      ,   _SCREAMING_SNAKE_CASE = "relu"      ,   **_SCREAMING_SNAKE_CASE      ,   )			->	Any:




									"""simple docstring"""





									super().__init__(**_SCREAMING_SNAKE_CASE						)
									# The padding and conv has been verified in
									# https://colab.research.google.com/gist/sayakpaul/854bc10eeaf21c9ee2119e0b9f3841a7/scratchpad.ipynb
									_UpperCAmelCase					=      tf.keras.layers.ZeroPaddingaD(padding=kernel_size // 2						)
									_UpperCAmelCase					=      tf.keras.layers.ConvaD(
									    filters=_SCREAMING_SNAKE_CASE      ,   kernel_size=_SCREAMING_SNAKE_CASE      ,   strides=_SCREAMING_SNAKE_CASE      ,   padding='VALID'      ,   groups=_SCREAMING_SNAKE_CASE      ,   use_bias=_SCREAMING_SNAKE_CASE      ,   name='convolution'      ,   )
									_UpperCAmelCase					=      tf.keras.layers.BatchNormalization(epsilon=1e-5      ,   momentum=0.9      ,   name='normalization'						)
									_UpperCAmelCase					=      ACTaFN[activation] if activation is not None else tf.identity






		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE						)			->	int:




									"""simple docstring"""





									_UpperCAmelCase					=      self.convolution(self.padding(_SCREAMING_SNAKE_CASE						)						)
									_UpperCAmelCase					=      self.normalization(_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      self.activation(_SCREAMING_SNAKE_CASE						)
									return hidden_state



class 		__a				(    tf.keras.layers.Layer      ):
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE      ,   **_SCREAMING_SNAKE_CASE						)			->	Dict:




									"""simple docstring"""





									super().__init__(**_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      config.num_channels
									_UpperCAmelCase					=      TFRegNetConvLayer(
									    out_channels=config.embedding_size      ,   kernel_size=3      ,   stride=2      ,   activation=config.hidden_act      ,   name='embedder'      ,   )






		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE						)			->	List[Any]:




									"""simple docstring"""





									_UpperCAmelCase					=      shape_list(_SCREAMING_SNAKE_CASE						)[1]
									if tf.executing_eagerly() and num_channels != self.num_channels:
																raise ValueError(
																    'Make sure that the channel dimension of the pixel values match with the one set in the configuration.'						)

									# When running on CPU, `tf.keras.layers.Conv2D` doesn't support `NCHW` format.
									# So change the input format from `NCHW` to `NHWC`.
									# shape = (batch_size, in_height, in_width, in_channels=num_channels)
									_UpperCAmelCase					=      tf.transpose(_SCREAMING_SNAKE_CASE      ,   perm=(0, 2, 3, 1)						)
									_UpperCAmelCase					=      self.embedder(_SCREAMING_SNAKE_CASE						)
									return hidden_state



class 		__a				(    tf.keras.layers.Layer      ):
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE = 2      ,   **_SCREAMING_SNAKE_CASE						)			->	Union[str, Any]:




									"""simple docstring"""





									super().__init__(**_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      tf.keras.layers.ConvaD(
									    filters=_SCREAMING_SNAKE_CASE      ,   kernel_size=1      ,   strides=_SCREAMING_SNAKE_CASE      ,   use_bias=_SCREAMING_SNAKE_CASE      ,   name='convolution'						)
									_UpperCAmelCase					=      tf.keras.layers.BatchNormalization(epsilon=1e-5      ,   momentum=0.9      ,   name='normalization'						)






		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE = False						)			->	tf.Tensor:




									"""simple docstring"""





									return self.normalization(self.convolution(_SCREAMING_SNAKE_CASE						)      ,   training=_SCREAMING_SNAKE_CASE						)



class 		__a				(    tf.keras.layers.Layer      ):
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   **_SCREAMING_SNAKE_CASE						)			->	str:




									"""simple docstring"""





									super().__init__(**_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      tf.keras.layers.GlobalAveragePoolingaD(keepdims=_SCREAMING_SNAKE_CASE      ,   name='pooler'						)
									_UpperCAmelCase					=      [
									    tf.keras.layers.ConvaD(filters=_SCREAMING_SNAKE_CASE      ,   kernel_size=1      ,   activation='relu'      ,   name='attention.0'						),
									    tf.keras.layers.ConvaD(filters=_SCREAMING_SNAKE_CASE      ,   kernel_size=1      ,   activation='sigmoid'      ,   name='attention.2'						),
									]






		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE						)			->	List[Any]:




									"""simple docstring"""





									_UpperCAmelCase					=      self.pooler(_SCREAMING_SNAKE_CASE						)
									for layer_module in self.attention:
																_UpperCAmelCase					=      layer_module(_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      hidden_state * pooled
									return hidden_state



class 		__a				(    tf.keras.layers.Layer      ):
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE = 1      ,   **_SCREAMING_SNAKE_CASE						)			->	Optional[int]:




									"""simple docstring"""





									super().__init__(**_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      in_channels != out_channels or stride != 1
									_UpperCAmelCase					=      max(1      ,   out_channels // config.groups_width						)
									_UpperCAmelCase					=      (
									    TFRegNetShortCut(_SCREAMING_SNAKE_CASE      ,   stride=_SCREAMING_SNAKE_CASE      ,   name='shortcut'						)
									    if should_apply_shortcut
									    else tf.keras.layers.Activation('linear'      ,   name='shortcut'						)
									)
									# `self.layers` instead of `self.layer` because that is a reserved argument.
									_UpperCAmelCase					=      [
									    TFRegNetConvLayer(_SCREAMING_SNAKE_CASE      ,   kernel_size=1      ,   activation=config.hidden_act      ,   name='layer.0'						),
									    TFRegNetConvLayer(
									        _SCREAMING_SNAKE_CASE      ,   stride=_SCREAMING_SNAKE_CASE      ,   groups=_SCREAMING_SNAKE_CASE      ,   activation=config.hidden_act      ,   name='layer.1'						),
									    TFRegNetConvLayer(_SCREAMING_SNAKE_CASE      ,   kernel_size=1      ,   activation=_SCREAMING_SNAKE_CASE      ,   name='layer.2'						),
									]
									_UpperCAmelCase					=      ACTaFN[config.hidden_act]






		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE						)			->	Optional[int]:




									"""simple docstring"""





									_UpperCAmelCase					=      hidden_state
									for layer_module in self.layers:
																_UpperCAmelCase					=      layer_module(_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      self.shortcut(_SCREAMING_SNAKE_CASE						)
									hidden_state += residual
									_UpperCAmelCase					=      self.activation(_SCREAMING_SNAKE_CASE						)
									return hidden_state



class 		__a				(    tf.keras.layers.Layer      ):
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE = 1      ,   **_SCREAMING_SNAKE_CASE						)			->	str:




									"""simple docstring"""





									super().__init__(**_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      in_channels != out_channels or stride != 1
									_UpperCAmelCase					=      max(1      ,   out_channels // config.groups_width						)
									_UpperCAmelCase					=      (
									    TFRegNetShortCut(_SCREAMING_SNAKE_CASE      ,   stride=_SCREAMING_SNAKE_CASE      ,   name='shortcut'						)
									    if should_apply_shortcut
									    else tf.keras.layers.Activation('linear'      ,   name='shortcut'						)
									)
									_UpperCAmelCase					=      [
									    TFRegNetConvLayer(_SCREAMING_SNAKE_CASE      ,   kernel_size=1      ,   activation=config.hidden_act      ,   name='layer.0'						),
									    TFRegNetConvLayer(
									        _SCREAMING_SNAKE_CASE      ,   stride=_SCREAMING_SNAKE_CASE      ,   groups=_SCREAMING_SNAKE_CASE      ,   activation=config.hidden_act      ,   name='layer.1'						),
									    TFRegNetSELayer(_SCREAMING_SNAKE_CASE      ,   reduced_channels=int(round(in_channels / 4						)						)      ,   name='layer.2'						),
									    TFRegNetConvLayer(_SCREAMING_SNAKE_CASE      ,   kernel_size=1      ,   activation=_SCREAMING_SNAKE_CASE      ,   name='layer.3'						),
									]
									_UpperCAmelCase					=      ACTaFN[config.hidden_act]






		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE						)			->	Optional[Any]:




									"""simple docstring"""





									_UpperCAmelCase					=      hidden_state
									for layer_module in self.layers:
																_UpperCAmelCase					=      layer_module(_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      self.shortcut(_SCREAMING_SNAKE_CASE						)
									hidden_state += residual
									_UpperCAmelCase					=      self.activation(_SCREAMING_SNAKE_CASE						)
									return hidden_state



class 		__a				(    tf.keras.layers.Layer      ):
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE = 2      ,   _SCREAMING_SNAKE_CASE = 2      ,   **_SCREAMING_SNAKE_CASE						)			->	Optional[Any]:




									"""simple docstring"""





									super().__init__(**_SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase					=      TFRegNetXLayer if config.layer_type == 'x' else TFRegNetYLayer
									_UpperCAmelCase					=      [
									    # downsampling is done in the first layer with stride of 2
									    layer(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   stride=_SCREAMING_SNAKE_CASE      ,   name='layers.0'						),
									    *[layer(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   name=f'''layers.{i+1}'''						) for i in range(depth - 1						)],
									]






		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE						)			->	Any:




									"""simple docstring"""





									for layer_module in self.layers:
																_UpperCAmelCase					=      layer_module(_SCREAMING_SNAKE_CASE						)
									return hidden_state



class 		__a				(    tf.keras.layers.Layer      ):
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE      ,   **_SCREAMING_SNAKE_CASE						)			->	Tuple:




									"""simple docstring"""





									super().__init__(**_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      []
									# based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
									self.stages.append(
									    TFRegNetStage(
									        _SCREAMING_SNAKE_CASE      ,   config.embedding_size      ,   config.hidden_sizes[0]      ,   stride=2 if config.downsample_in_first_stage else 1      ,   depth=config.depths[0]      ,   name='stages.0'      ,   )						)
									_UpperCAmelCase					=      zip(config.hidden_sizes      ,   config.hidden_sizes[1:]						)
									for i, ((in_channels, out_channels), depth) in enumerate(zip(_SCREAMING_SNAKE_CASE      ,   config.depths[1:]						)						):
																self.stages.append(TFRegNetStage(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   depth=_SCREAMING_SNAKE_CASE      ,   name=f'''stages.{i+1}'''						)						)






		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE = False      ,   _SCREAMING_SNAKE_CASE = True						)			->	TFBaseModelOutputWithNoAttention:




									"""simple docstring"""





									_UpperCAmelCase					=      () if output_hidden_states else None

									for stage_module in self.stages:
																if output_hidden_states:
																							_UpperCAmelCase					=      hidden_states + (hidden_state,)

																_UpperCAmelCase					=      stage_module(_SCREAMING_SNAKE_CASE						)

									if output_hidden_states:
																_UpperCAmelCase					=      hidden_states + (hidden_state,)

									if not return_dict:
																return tuple(v for v in [hidden_state, hidden_states] if v is not None						)

									return TFBaseModelOutputWithNoAttention(last_hidden_state=_SCREAMING_SNAKE_CASE      ,   hidden_states=_SCREAMING_SNAKE_CASE						)



@keras_serializable
class 		__a				(    tf.keras.layers.Layer      ):
		_a     :  Optional[Any]           =     RegNetConfig
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE      ,   **_SCREAMING_SNAKE_CASE						)			->	str:




									"""simple docstring"""





									super().__init__(**_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      config
									_UpperCAmelCase					=      TFRegNetEmbeddings(_SCREAMING_SNAKE_CASE      ,   name='embedder'						)
									_UpperCAmelCase					=      TFRegNetEncoder(_SCREAMING_SNAKE_CASE      ,   name='encoder'						)
									_UpperCAmelCase					=      tf.keras.layers.GlobalAveragePoolingaD(keepdims=_SCREAMING_SNAKE_CASE      ,   name='pooler'						)






		@unpack_inputs
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = False      ,   )			->	TFBaseModelOutputWithPoolingAndNoAttention:




									"""simple docstring"""





									_UpperCAmelCase					=      (
									    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
									)
									_UpperCAmelCase					=      return_dict if return_dict is not None else self.config.use_return_dict

									_UpperCAmelCase					=      self.embedder(_SCREAMING_SNAKE_CASE      ,   training=_SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase					=      self.encoder(
									    _SCREAMING_SNAKE_CASE      ,   output_hidden_states=_SCREAMING_SNAKE_CASE      ,   return_dict=_SCREAMING_SNAKE_CASE      ,   training=_SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase					=      encoder_outputs[0]
									_UpperCAmelCase					=      self.pooler(_SCREAMING_SNAKE_CASE						)

									# Change to NCHW output format have uniformity in the modules
									_UpperCAmelCase					=      tf.transpose(_SCREAMING_SNAKE_CASE      ,   perm=(0, 3, 1, 2)						)
									_UpperCAmelCase					=      tf.transpose(_SCREAMING_SNAKE_CASE      ,   perm=(0, 3, 1, 2)						)

									# Change the other hidden state outputs to NCHW as well
									if output_hidden_states:
																_UpperCAmelCase					=      tuple([tf.transpose(_SCREAMING_SNAKE_CASE      ,   perm=(0, 3, 1, 2)						) for h in encoder_outputs[1]]						)

									if not return_dict:
																return (last_hidden_state, pooled_output) + encoder_outputs[1:]

									return TFBaseModelOutputWithPoolingAndNoAttention(
									    last_hidden_state=_SCREAMING_SNAKE_CASE      ,   pooler_output=_SCREAMING_SNAKE_CASE      ,   hidden_states=hidden_states if output_hidden_states else encoder_outputs.hidden_states      ,   )



class 		__a				(    UpperCAmelCase      ):
		_a     :  List[Any]           =     RegNetConfig
		_a     :  List[Any]           =     'regnet'
		_a     :  Optional[Any]           =     'pixel_values'
		@property
		def      UpperCAmelCase__    (      self						)			->	str:




									"""simple docstring"""





									return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 224, 224)      ,   dtype=tf.floataa						)}



lowerCAmelCase__  :Any             =							R'''
    Parameters:
    This model is a Tensorflow
    [tf.keras.layers.Layer](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer) sub-class. Use it as a
    regular Tensorflow Module and refer to the Tensorflow documentation for all matter related to general usage and
    behavior.
        config ([`RegNetConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.
'''

lowerCAmelCase__  :Any             =							R'''
    Args:
        pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
            [`ConveNextImageProcessor.__call__`] for details.
        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
'''

@add_start_docstrings(
    'The bare RegNet model outputting raw features without any specific head on top.'   ,				UpperCAmelCase   ,				)
class 		__a				(    UpperCAmelCase      ):
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE      ,   *_SCREAMING_SNAKE_CASE      ,   **_SCREAMING_SNAKE_CASE						)			->	str:




									"""simple docstring"""





									super().__init__(_SCREAMING_SNAKE_CASE      ,   *_SCREAMING_SNAKE_CASE      ,   **_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      TFRegNetMainLayer(_SCREAMING_SNAKE_CASE      ,   name='regnet'						)






		@unpack_inputs
		@add_start_docstrings_to_model_forward(_SCREAMING_SNAKE_CASE						)
		@add_code_sample_docstrings(
		    checkpoint=_CHECKPOINT_FOR_DOC      ,   output_type=_SCREAMING_SNAKE_CASE      ,   config_class=_CONFIG_FOR_DOC      ,   modality='vision'      ,   expected_output=_EXPECTED_OUTPUT_SHAPE      ,   )
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE=False      ,   )			->	Union[TFBaseModelOutputWithPoolingAndNoAttention, Tuple[tf.Tensor]]:




									"""simple docstring"""





									_UpperCAmelCase					=      (
									    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
									)
									_UpperCAmelCase					=      return_dict if return_dict is not None else self.config.use_return_dict

									_UpperCAmelCase					=      self.regnet(
									    pixel_values=_SCREAMING_SNAKE_CASE      ,   output_hidden_states=_SCREAMING_SNAKE_CASE      ,   return_dict=_SCREAMING_SNAKE_CASE      ,   training=_SCREAMING_SNAKE_CASE      ,   )
									if not return_dict:
																return (outputs[0],) + outputs[1:]

									return TFBaseModelOutputWithPoolingAndNoAttention(
									    last_hidden_state=outputs.last_hidden_state      ,   pooler_output=outputs.pooler_output      ,   hidden_states=outputs.hidden_states      ,   )



@add_start_docstrings(
    '\n    RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n    ImageNet.\n    '   ,				UpperCAmelCase   ,				)
class 		__a				(    UpperCAmelCase   ,				UpperCAmelCase      ):
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE      ,   *_SCREAMING_SNAKE_CASE      ,   **_SCREAMING_SNAKE_CASE						)			->	str:




									"""simple docstring"""





									super().__init__(_SCREAMING_SNAKE_CASE      ,   *_SCREAMING_SNAKE_CASE      ,   **_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      config.num_labels
									_UpperCAmelCase					=      TFRegNetMainLayer(_SCREAMING_SNAKE_CASE      ,   name='regnet'						)
									# classification head
									_UpperCAmelCase					=      [
									    tf.keras.layers.Flatten(),
									    tf.keras.layers.Dense(config.num_labels      ,   name='classifier.1'						) if config.num_labels > 0 else tf.identity,
									]






		@unpack_inputs
		@add_start_docstrings_to_model_forward(_SCREAMING_SNAKE_CASE						)
		@add_code_sample_docstrings(
		    checkpoint=_IMAGE_CLASS_CHECKPOINT      ,   output_type=_SCREAMING_SNAKE_CASE      ,   config_class=_CONFIG_FOR_DOC      ,   expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT      ,   )
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE=False      ,   )			->	Union[TFSequenceClassifierOutput, Tuple[tf.Tensor]]:




									"""simple docstring"""





									_UpperCAmelCase					=      (
									    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
									)
									_UpperCAmelCase					=      return_dict if return_dict is not None else self.config.use_return_dict

									_UpperCAmelCase					=      self.regnet(
									    _SCREAMING_SNAKE_CASE      ,   output_hidden_states=_SCREAMING_SNAKE_CASE      ,   return_dict=_SCREAMING_SNAKE_CASE      ,   training=_SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase					=      outputs.pooler_output if return_dict else outputs[1]

									_UpperCAmelCase					=      self.classifier[0](_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      self.classifier[1](_SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase					=      None if labels is None else self.hf_compute_loss(labels=_SCREAMING_SNAKE_CASE      ,   logits=_SCREAMING_SNAKE_CASE						)

									if not return_dict:
																_UpperCAmelCase					=      (logits,) + outputs[2:]
																return ((loss,) + output) if loss is not None else output

									return TFSequenceClassifierOutput(loss=_SCREAMING_SNAKE_CASE      ,   logits=_SCREAMING_SNAKE_CASE      ,   hidden_states=outputs.hidden_states						)




import tempfile
import unittest

from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
    torch_device,
)

from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch

		from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel

class 		__a				:
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE=99      ,   _SCREAMING_SNAKE_CASE=13      ,   _SCREAMING_SNAKE_CASE=7      ,   _SCREAMING_SNAKE_CASE=9      ,   _SCREAMING_SNAKE_CASE=True      ,   _SCREAMING_SNAKE_CASE=True      ,   _SCREAMING_SNAKE_CASE=False      ,   _SCREAMING_SNAKE_CASE=32      ,   _SCREAMING_SNAKE_CASE=5      ,   _SCREAMING_SNAKE_CASE=4      ,   _SCREAMING_SNAKE_CASE=37      ,   _SCREAMING_SNAKE_CASE=8      ,   _SCREAMING_SNAKE_CASE=0.1      ,   _SCREAMING_SNAKE_CASE=0.002      ,   _SCREAMING_SNAKE_CASE=1      ,   _SCREAMING_SNAKE_CASE=0      ,   _SCREAMING_SNAKE_CASE=0      ,   _SCREAMING_SNAKE_CASE=None      ,   _SCREAMING_SNAKE_CASE=None      ,   )			->	Tuple:




									"""simple docstring"""





									_UpperCAmelCase					=      parent
									_UpperCAmelCase					=      batch_size
									_UpperCAmelCase					=      encoder_seq_length
									_UpperCAmelCase					=      decoder_seq_length
									# For common tests
									_UpperCAmelCase					=      self.decoder_seq_length
									_UpperCAmelCase					=      is_training
									_UpperCAmelCase					=      use_attention_mask
									_UpperCAmelCase					=      use_labels
									_UpperCAmelCase					=      vocab_size
									_UpperCAmelCase					=      hidden_size
									_UpperCAmelCase					=      num_hidden_layers
									_UpperCAmelCase					=      num_attention_heads
									_UpperCAmelCase					=      d_ff
									_UpperCAmelCase					=      relative_attention_num_buckets
									_UpperCAmelCase					=      dropout_rate
									_UpperCAmelCase					=      initializer_factor
									_UpperCAmelCase					=      eos_token_id
									_UpperCAmelCase					=      pad_token_id
									_UpperCAmelCase					=      decoder_start_token_id
									_UpperCAmelCase					=      None
									_UpperCAmelCase					=      decoder_layers
		def      UpperCAmelCase__    (      self						)			->	List[str]:




									"""simple docstring"""





									return TaConfig.from_pretrained('google/umt5-base'						)
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE=None      ,   _SCREAMING_SNAKE_CASE=None      ,   _SCREAMING_SNAKE_CASE=None      ,   _SCREAMING_SNAKE_CASE=None      ,   _SCREAMING_SNAKE_CASE=None      ,   )			->	List[str]:




									"""simple docstring"""





									if attention_mask is None:
																_UpperCAmelCase					=      input_ids.ne(config.pad_token_id						)
									if decoder_attention_mask is None:
																_UpperCAmelCase					=      decoder_input_ids.ne(config.pad_token_id						)
									if head_mask is None:
																_UpperCAmelCase					=      torch.ones(config.num_hidden_layers      ,   config.num_attention_heads      ,   device=_SCREAMING_SNAKE_CASE						)
									if decoder_head_mask is None:
																_UpperCAmelCase					=      torch.ones(config.num_decoder_layers      ,   config.num_attention_heads      ,   device=_SCREAMING_SNAKE_CASE						)
									if cross_attn_head_mask is None:
																_UpperCAmelCase					=      torch.ones(
																    config.num_decoder_layers      ,   config.num_attention_heads      ,   device=_SCREAMING_SNAKE_CASE						)
									return {
									    "input_ids": input_ids,
									    "decoder_input_ids": decoder_input_ids,
									    "attention_mask": attention_mask,
									    "decoder_attention_mask": decoder_attention_mask,
									    "head_mask": head_mask,
									    "decoder_head_mask": decoder_head_mask,
									    "cross_attn_head_mask": cross_attn_head_mask,
									}
		def      UpperCAmelCase__    (      self						)			->	Union[str, Any]:




									"""simple docstring"""





									_UpperCAmelCase					=      ids_tensor([self.batch_size, self.encoder_seq_length]      ,   self.vocab_size						)
									_UpperCAmelCase					=      ids_tensor([self.batch_size, self.decoder_seq_length]      ,   self.vocab_size						)

									# we need to clamp the input ids here to avoid having pad token in between
									# this is because for NllbMoe the position_ids are prepared such that
									# all pad tokens have pos id = 2 and rest are between 2..seq_length
									# and the seq_length here is seq_length - num_pad_tokens
									# but when using past, there is no way of knowing if the past input ids had
									# pad tokens in them, which results in incorrect seq_lenth and which in turn results in
									# position_ids being off by num_pad_tokens in past input
									_UpperCAmelCase					=      input_ids.clamp(self.pad_token_id + 1						)
									_UpperCAmelCase					=      decoder_input_ids.clamp(self.pad_token_id + 1						)

									_UpperCAmelCase					=      self.get_config()
									_UpperCAmelCase					=      config.num_attention_heads
									_UpperCAmelCase					=      self.prepare_inputs_dict(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)
									return config, input_dict
		def      UpperCAmelCase__    (      self						)			->	Any:




									"""simple docstring"""





									_UpperCAmelCase		,	_UpperCAmelCase					=      self.prepare_config_and_inputs()
									return config, inputs_dict
		def      UpperCAmelCase__    (      self						)			->	Dict:




									"""simple docstring"""





									return TaConfig(
									    vocab_size=166      ,   d_model=self.hidden_size      ,   d_ff=self.d_ff      ,   d_kv=self.hidden_size // self.num_attention_heads      ,   num_layers=self.num_hidden_layers      ,   num_decoder_layers=self.decoder_layers      ,   num_heads=self.num_attention_heads      ,   relative_attention_num_buckets=self.relative_attention_num_buckets      ,   dropout_rate=self.dropout_rate      ,   initializer_factor=self.initializer_factor      ,   eos_token_id=self.eos_token_id      ,   bos_token_id=self.pad_token_id      ,   pad_token_id=self.pad_token_id      ,   decoder_start_token_id=self.decoder_start_token_id      ,   )
		def      UpperCAmelCase__    (      self						)			->	List[Any]:




									"""simple docstring"""





									return TaConfig(
									    vocab_size=self.vocab_size      ,   d_model=self.hidden_size      ,   d_ff=self.d_ff      ,   d_kv=self.hidden_size // self.num_attention_heads      ,   num_layers=self.num_hidden_layers      ,   num_decoder_layers=self.decoder_layers      ,   num_heads=self.num_attention_heads      ,   relative_attention_num_buckets=self.relative_attention_num_buckets      ,   dropout_rate=self.dropout_rate      ,   initializer_factor=self.initializer_factor      ,   eos_token_id=self.eos_token_id      ,   bos_token_id=self.pad_token_id      ,   pad_token_id=self.pad_token_id      ,   decoder_start_token_id=self.decoder_start_token_id      ,   )
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   )			->	str:




									"""simple docstring"""





									_UpperCAmelCase					=      UMTaModel(config=_SCREAMING_SNAKE_CASE						)
									model.to(_SCREAMING_SNAKE_CASE						)
									model.eval()
									_UpperCAmelCase					=      model(
									    input_ids=_SCREAMING_SNAKE_CASE      ,   decoder_input_ids=_SCREAMING_SNAKE_CASE      ,   attention_mask=_SCREAMING_SNAKE_CASE      ,   decoder_attention_mask=_SCREAMING_SNAKE_CASE      ,   )
									_UpperCAmelCase					=      model(input_ids=_SCREAMING_SNAKE_CASE      ,   decoder_input_ids=_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      result.last_hidden_state
									_UpperCAmelCase					=      result.past_key_values
									_UpperCAmelCase					=      result.encoder_last_hidden_state

									self.parent.assertEqual(encoder_output.size()      ,   (self.batch_size, self.encoder_seq_length, self.hidden_size)						)
									self.parent.assertEqual(decoder_output.size()      ,   (self.batch_size, self.decoder_seq_length, self.hidden_size)						)
									# There should be `num_layers` key value embeddings stored in decoder_past
									self.parent.assertEqual(len(_SCREAMING_SNAKE_CASE						)      ,   config.num_layers						)
									# There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
									self.parent.assertEqual(len(decoder_past[0]						)      ,   4						)
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   )			->	Optional[int]:




									"""simple docstring"""





									_UpperCAmelCase					=      UMTaModel(config=_SCREAMING_SNAKE_CASE						).get_decoder().to(_SCREAMING_SNAKE_CASE						).eval()
									# first forward pass
									_UpperCAmelCase					=      model(_SCREAMING_SNAKE_CASE      ,   use_cache=_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      model(_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      model(_SCREAMING_SNAKE_CASE      ,   use_cache=_SCREAMING_SNAKE_CASE						)

									self.parent.assertTrue(len(_SCREAMING_SNAKE_CASE						) == len(_SCREAMING_SNAKE_CASE						)						)
									self.parent.assertTrue(len(_SCREAMING_SNAKE_CASE						) == len(_SCREAMING_SNAKE_CASE						) + 1						)

									_UpperCAmelCase		,	_UpperCAmelCase					=      outputs.to_tuple()

									# create hypothetical next token and extent to next_input_ids
									_UpperCAmelCase					=      ids_tensor((self.batch_size, 1)      ,   config.vocab_size						)

									# append to next input_ids and
									_UpperCAmelCase					=      torch.cat([input_ids, next_tokens]      ,   dim=-1						)

									_UpperCAmelCase					=      model(_SCREAMING_SNAKE_CASE						)['last_hidden_state']
									_UpperCAmelCase					=      model(_SCREAMING_SNAKE_CASE      ,   past_key_values=_SCREAMING_SNAKE_CASE						)['last_hidden_state']

									# select random slice
									_UpperCAmelCase					=      ids_tensor((1,)      ,   output_from_past.shape[-1]						).item()
									_UpperCAmelCase					=      output_from_no_past[:, -1, random_slice_idx].detach()
									_UpperCAmelCase					=      output_from_past[:, 0, random_slice_idx].detach()

									# test that outputs are equal for slice
									self.parent.assertTrue(torch.allclose(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   atol=1e-3						)						)






		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   )			->	List[str]:




									"""simple docstring"""





									_UpperCAmelCase					=      UMTaModel(config=_SCREAMING_SNAKE_CASE						).to(_SCREAMING_SNAKE_CASE						).half().eval()
									_UpperCAmelCase					=      model(**_SCREAMING_SNAKE_CASE						)['last_hidden_state']
									self.parent.assertFalse(torch.isnan(_SCREAMING_SNAKE_CASE						).any().item()						)



@require_torch
class 		__a				(    UpperCAmelCase   ,				UpperCAmelCase   ,				UpperCAmelCase   ,				unittest.TestCase      ):
		_a     :  Union[str, Any]           =     (
		    (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
		)
		_a     :  List[Any]           =     (UMTaForConditionalGeneration,) if is_torch_available() else ()
		_a     :  Tuple           =     (
		    {
		        'conversational': UMTaForConditionalGeneration,
		        'feature-extraction': UMTaModel,
		        'summarization': UMTaForConditionalGeneration,
		        'text2text-generation': UMTaForConditionalGeneration,
		        'translation': UMTaForConditionalGeneration,
		        'question-answering': UMTaForQuestionAnswering,
		    }
		    if is_torch_available()
		    else {}
		)
		_a     :  List[str]           =     True
		_a     :  List[Any]           =     False
		_a     :  Tuple           =     False
		_a     :  List[Any]           =     True
		_a     :  str           =     True
		# The small UMT5 model needs higher percentages for CPU/MP tests
		_a     :  Tuple           =     [0.8, 0.9]
		def      UpperCAmelCase__    (      self						)			->	List[str]:




									"""simple docstring"""





									_UpperCAmelCase					=      UMTaModelTester(self						)
		@unittest.skip('Test has a segmentation fault on torch 1.8.0'						)
		def      UpperCAmelCase__    (      self						)			->	Union[str, Any]:




									"""simple docstring"""





									_UpperCAmelCase					=      self.model_tester.prepare_config_and_inputs()
									_UpperCAmelCase					=      UMTaModel(config_and_inputs[0]						).to(_SCREAMING_SNAKE_CASE						)
									with tempfile.TemporaryDirectory() as tmpdirname:
																torch.onnx.export(
																    _SCREAMING_SNAKE_CASE      ,   (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2])      ,   f'''{tmpdirname}/t5_test.onnx'''      ,   export_params=_SCREAMING_SNAKE_CASE      ,   opset_version=9      ,   input_names=['input_ids', 'decoder_input_ids']      ,   )
		@unittest.skipIf(torch_device == 'cpu'      ,   'Cant do half precision'						)
		def      UpperCAmelCase__    (      self						)			->	Tuple:




									"""simple docstring"""





									_UpperCAmelCase					=      self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model_fpaa_forward(*_SCREAMING_SNAKE_CASE						)
		def      UpperCAmelCase__    (      self						)			->	int:




									"""simple docstring"""





									_UpperCAmelCase					=      ['encoder_attentions', 'decoder_attentions', 'cross_attentions']
									_UpperCAmelCase					=      self.model_tester.prepare_config_and_inputs()
									_UpperCAmelCase					=      config_and_inputs[0]
									_UpperCAmelCase					=      UMTaForConditionalGeneration(_SCREAMING_SNAKE_CASE						).eval()
									model.to(_SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase					=      {
									    'head_mask': torch.zeros(config.num_layers      ,   config.num_heads      ,   device=_SCREAMING_SNAKE_CASE						),
									    'decoder_head_mask': torch.zeros(config.num_decoder_layers      ,   config.num_heads      ,   device=_SCREAMING_SNAKE_CASE						),
									    'cross_attn_head_mask': torch.zeros(config.num_decoder_layers      ,   config.num_heads      ,   device=_SCREAMING_SNAKE_CASE						),
									}

									for attn_name, (name, mask) in zip(_SCREAMING_SNAKE_CASE      ,   head_masking.items()						):
																_UpperCAmelCase					=      {name: mask}
																# Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
																if name == "head_mask":
																							_UpperCAmelCase					=      torch.ones(
																							    config.num_decoder_layers      ,   config.num_heads      ,   device=_SCREAMING_SNAKE_CASE						)

																_UpperCAmelCase					=      model.generate(
																    config_and_inputs[1]['input_ids']      ,   num_beams=1      ,   max_length=3      ,   output_attentions=_SCREAMING_SNAKE_CASE      ,   return_dict_in_generate=_SCREAMING_SNAKE_CASE      ,   **_SCREAMING_SNAKE_CASE      ,   )
																# We check the state of decoder_attentions and cross_attentions just from the last step
																_UpperCAmelCase					=      out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
																self.assertEqual(sum([w.sum().item() for w in attn_weights]						)      ,   0.0						)






		@unittest.skip('Does not work on the tiny model as we keep hitting edge cases.'						)
		def      UpperCAmelCase__    (      self						)			->	int:




									"""simple docstring"""





									pass



@require_torch
@require_sentencepiece
@require_tokenizers
class 		__a				(    unittest.TestCase      ):
		@slow
		@unittest.skip(
		    'Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged'						)
		def      UpperCAmelCase__    (      self						)			->	List[str]:




									"""simple docstring"""





									_UpperCAmelCase					=      UMTaForConditionalGeneration.from_pretrained('google/umt5-small'      ,   return_dict=_SCREAMING_SNAKE_CASE						).to(_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      AutoTokenizer.from_pretrained('google/umt5-small'      ,   use_fast=_SCREAMING_SNAKE_CASE      ,   legacy=_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      [
									    'Bonjour monsieur <extra_id_0> bien <extra_id_1>.',
									    'No se como puedo <extra_id_0>.',
									    'This is the reason why we <extra_id_0> them.',
									    'The <extra_id_0> walks in <extra_id_1>, seats',
									    'A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.',
									]
									_UpperCAmelCase					=      tokenizer(_SCREAMING_SNAKE_CASE      ,   return_tensors='pt'      ,   padding=_SCREAMING_SNAKE_CASE						).input_ids
									# fmt: off
									_UpperCAmelCase					=      torch.tensor(
									    [
									        [ 38530, 210703, 256299, 1410, 256298, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
									        [   826, 321, 671, 25922, 256299, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
									        [  1460, 339, 312, 19014, 10620, 758, 256299, 2355,274, 1, 0, 0, 0, 0, 0, 0,0, 0],
									        [   517, 256299, 14869, 281, 301, 256298, 275, 119983,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
									        [   320, 256299, 14869, 281, 2234, 289, 2275, 333,61391, 289, 256298, 543, 256297, 168714, 329, 256296,274, 1],
									    ]						)
									# fmt: on
									torch.testing.assert_allclose(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase					=      model.generate(input_ids.to(_SCREAMING_SNAKE_CASE						)						)
									_UpperCAmelCase					=      [
									    '<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>',
									    '<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
									    '<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
									    '<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
									    '<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
									]
									_UpperCAmelCase					=      tokenizer.batch_decode(_SCREAMING_SNAKE_CASE						)
									self.assertEqual(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)
