


from typing import List, Optional, Tuple, Union

import torch
from torch import nn
from torch.nn import CrossEntropyLoss

from ... import AutoBackbone
from ...modeling_outputs import SemanticSegmenterOutput
from ...modeling_utils import PreTrainedModel
from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, replace_return_docstrings
from ...utils.backbone_utils import BackboneMixin
from .configuration_upernet import UperNetConfig


lowerCAmelCase__  :Optional[Any]             =							[
    '''openmmlab/upernet-convnext-tiny''',
    # See all UperNet models at https://huggingface.co/models?filter=upernet
]

# General docstring
lowerCAmelCase__  :Union[str, Any]             =							'''UperNetConfig'''

class 		__a				(    nn.Module      ):
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE = 0      ,   _SCREAMING_SNAKE_CASE = False      ,   _SCREAMING_SNAKE_CASE = 1      ,   )			->	None:




									"""simple docstring"""





									super().__init__()
									_UpperCAmelCase					=      nn.Convad(
									    in_channels=_SCREAMING_SNAKE_CASE      ,   out_channels=_SCREAMING_SNAKE_CASE      ,   kernel_size=_SCREAMING_SNAKE_CASE      ,   padding=_SCREAMING_SNAKE_CASE      ,   bias=_SCREAMING_SNAKE_CASE      ,   dilation=_SCREAMING_SNAKE_CASE      ,   )
									_UpperCAmelCase					=      nn.BatchNormad(_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      nn.ReLU()






		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE						)			->	torch.Tensor:




									"""simple docstring"""





									_UpperCAmelCase					=      self.conv(_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      self.batch_norm(_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      self.activation(_SCREAMING_SNAKE_CASE						)

									return output



class 		__a				(    nn.Module      ):
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)			->	None:




									"""simple docstring"""





									super().__init__()
									_UpperCAmelCase					=      [
									    nn.AdaptiveAvgPoolad(_SCREAMING_SNAKE_CASE						),
									    UperNetConvModule(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   kernel_size=1						),
									]
									for i, layer in enumerate(self.layers						):
																self.add_module(str(_SCREAMING_SNAKE_CASE						)      ,   _SCREAMING_SNAKE_CASE						)






		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE						)			->	torch.Tensor:




									"""simple docstring"""





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



class 		__a				(    nn.Module      ):
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)			->	None:




									"""simple docstring"""





									super().__init__()
									_UpperCAmelCase					=      pool_scales
									_UpperCAmelCase					=      align_corners
									_UpperCAmelCase					=      in_channels
									_UpperCAmelCase					=      channels
									_UpperCAmelCase					=      []
									for i, pool_scale in enumerate(_SCREAMING_SNAKE_CASE						):
																_UpperCAmelCase					=      UperNetPyramidPoolingBlock(pool_scale=_SCREAMING_SNAKE_CASE      ,   in_channels=_SCREAMING_SNAKE_CASE      ,   channels=_SCREAMING_SNAKE_CASE						)
																self.blocks.append(_SCREAMING_SNAKE_CASE						)
																self.add_module(str(_SCREAMING_SNAKE_CASE						)      ,   _SCREAMING_SNAKE_CASE						)






		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE						)			->	List[torch.Tensor]:




									"""simple docstring"""





									_UpperCAmelCase					=      []
									for ppm in self.blocks:
																_UpperCAmelCase					=      ppm(_SCREAMING_SNAKE_CASE						)
																_UpperCAmelCase					=      nn.functional.interpolate(
																    _SCREAMING_SNAKE_CASE      ,   size=x.size()[2:]      ,   mode='bilinear'      ,   align_corners=self.align_corners						)
																ppm_outs.append(_SCREAMING_SNAKE_CASE						)
									return ppm_outs



class 		__a				(    nn.Module      ):
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)			->	str:




									"""simple docstring"""





									super().__init__()

									_UpperCAmelCase					=      config
									_UpperCAmelCase					=      config.pool_scales  # e.g. (1, 2, 3, 6)
									_UpperCAmelCase					=      in_channels
									_UpperCAmelCase					=      config.hidden_size
									_UpperCAmelCase					=      False
									_UpperCAmelCase					=      nn.Convad(self.channels      ,   config.num_labels      ,   kernel_size=1						)

									# PSP Module
									_UpperCAmelCase					=      UperNetPyramidPoolingModule(
									    self.pool_scales      ,   self.in_channels[-1]      ,   self.channels      ,   align_corners=self.align_corners      ,   )
									_UpperCAmelCase					=      UperNetConvModule(
									    self.in_channels[-1] + len(self.pool_scales						) * self.channels      ,   self.channels      ,   kernel_size=3      ,   padding=1      ,   )
									# FPN Module
									_UpperCAmelCase					=      nn.ModuleList()
									_UpperCAmelCase					=      nn.ModuleList()
									for in_channels in self.in_channels[:-1]:  # skip the top layer
																_UpperCAmelCase					=      UperNetConvModule(_SCREAMING_SNAKE_CASE      ,   self.channels      ,   kernel_size=1						)
																_UpperCAmelCase					=      UperNetConvModule(self.channels      ,   self.channels      ,   kernel_size=3      ,   padding=1						)
																self.lateral_convs.append(_SCREAMING_SNAKE_CASE						)
																self.fpn_convs.append(_SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase					=      UperNetConvModule(
									    len(self.in_channels						) * self.channels      ,   self.channels      ,   kernel_size=3      ,   padding=1      ,   )
		def      UpperCAmelCase__    (      self						)			->	Optional[int]:




									"""simple docstring"""





									self.apply(self._init_weights						)
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE						)			->	Dict:




									"""simple docstring"""





									if isinstance(_SCREAMING_SNAKE_CASE      ,   nn.Convad						):
																module.weight.data.normal_(mean=0.0      ,   std=self.config.initializer_range						)
																if module.bias is not None:
																							module.bias.data.zero_()
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE						)			->	Optional[int]:




									"""simple docstring"""





									_UpperCAmelCase					=      inputs[-1]
									_UpperCAmelCase					=      [x]
									psp_outs.extend(self.psp_modules(_SCREAMING_SNAKE_CASE						)						)
									_UpperCAmelCase					=      torch.cat(_SCREAMING_SNAKE_CASE      ,   dim=1						)
									_UpperCAmelCase					=      self.bottleneck(_SCREAMING_SNAKE_CASE						)

									return output






		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE						)			->	torch.Tensor:




									"""simple docstring"""





									_UpperCAmelCase					=      [lateral_conv(encoder_hidden_states[i]						) for i, lateral_conv in enumerate(self.lateral_convs						)]

									laterals.append(self.psp_forward(_SCREAMING_SNAKE_CASE						)						)

									# build top-down path
									_UpperCAmelCase					=      len(_SCREAMING_SNAKE_CASE						)
									for i in range(used_backbone_levels - 1      ,   0      ,   -1						):
																_UpperCAmelCase					=      laterals[i - 1].shape[2:]
																_UpperCAmelCase					=      laterals[i - 1] + nn.functional.interpolate(
																    laterals[i]      ,   size=_SCREAMING_SNAKE_CASE      ,   mode='bilinear'      ,   align_corners=self.align_corners						)

									# build outputs
									_UpperCAmelCase					=      [self.fpn_convs[i](laterals[i]						) for i in range(used_backbone_levels - 1						)]
									# append psp feature
									fpn_outs.append(laterals[-1]						)

									for i in range(used_backbone_levels - 1      ,   0      ,   -1						):
																_UpperCAmelCase					=      nn.functional.interpolate(
																    fpn_outs[i]      ,   size=fpn_outs[0].shape[2:]      ,   mode='bilinear'      ,   align_corners=self.align_corners						)
									_UpperCAmelCase					=      torch.cat(_SCREAMING_SNAKE_CASE      ,   dim=1						)
									_UpperCAmelCase					=      self.fpn_bottleneck(_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      self.classifier(_SCREAMING_SNAKE_CASE						)

									return output



class 		__a				(    nn.Module      ):
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE = 2      ,   _SCREAMING_SNAKE_CASE = 3      ,   _SCREAMING_SNAKE_CASE = 1						)			->	None:




									"""simple docstring"""





									super().__init__()

									_UpperCAmelCase					=      config
									_UpperCAmelCase					=      config.auxiliary_in_channels
									_UpperCAmelCase					=      config.auxiliary_channels
									_UpperCAmelCase					=      config.auxiliary_num_convs
									_UpperCAmelCase					=      config.auxiliary_concat_input
									_UpperCAmelCase					=      in_index

									_UpperCAmelCase					=      (kernel_size // 2) * dilation
									_UpperCAmelCase					=      []
									convs.append(
									    UperNetConvModule(
									        self.in_channels      ,   self.channels      ,   kernel_size=_SCREAMING_SNAKE_CASE      ,   padding=_SCREAMING_SNAKE_CASE      ,   dilation=_SCREAMING_SNAKE_CASE						)						)
									for i in range(self.num_convs - 1						):
																convs.append(
																    UperNetConvModule(
																        self.channels      ,   self.channels      ,   kernel_size=_SCREAMING_SNAKE_CASE      ,   padding=_SCREAMING_SNAKE_CASE      ,   dilation=_SCREAMING_SNAKE_CASE						)						)
									if self.num_convs == 0:
																_UpperCAmelCase					=      nn.Identity()
									else:
																_UpperCAmelCase					=      nn.Sequential(*_SCREAMING_SNAKE_CASE						)
									if self.concat_input:
																_UpperCAmelCase					=      UperNetConvModule(
																    self.in_channels + self.channels      ,   self.channels      ,   kernel_size=_SCREAMING_SNAKE_CASE      ,   padding=kernel_size // 2						)

									_UpperCAmelCase					=      nn.Convad(self.channels      ,   config.num_labels      ,   kernel_size=1						)
		def      UpperCAmelCase__    (      self						)			->	Dict:




									"""simple docstring"""





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




									"""simple docstring"""





									if isinstance(_SCREAMING_SNAKE_CASE      ,   nn.Convad						):
																module.weight.data.normal_(mean=0.0      ,   std=self.config.initializer_range						)
																if module.bias is not None:
																							module.bias.data.zero_()






		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE						)			->	torch.Tensor:




									"""simple docstring"""





									_UpperCAmelCase					=      encoder_hidden_states[self.in_index]
									_UpperCAmelCase					=      self.convs(_SCREAMING_SNAKE_CASE						)
									if self.concat_input:
																_UpperCAmelCase					=      self.conv_cat(torch.cat([hidden_states, output]      ,   dim=1						)						)
									_UpperCAmelCase					=      self.classifier(_SCREAMING_SNAKE_CASE						)
									return output



class 		__a				(    UpperCAmelCase      ):
		_a     :  List[str]           =     UperNetConfig
		_a     :  List[Any]           =     'pixel_values'
		_a     :  Optional[Any]           =     True
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE						)			->	Optional[int]:




									"""simple docstring"""





									if isinstance(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						):
																module.backbone.init_weights()
																module.decode_head.init_weights()
																module.auxiliary_head.init_weights()
		def      UpperCAmelCase__    (      self						)			->	str:




									"""simple docstring"""





									self.backbone.init_weights()
									self.decode_head.init_weights()
									self.auxiliary_head.init_weights()






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




									"""simple docstring"""





									if isinstance(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						):
																_UpperCAmelCase					=      value



lowerCAmelCase__  :Optional[int]             =							R'''
    Parameters:
    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
    it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
    behavior.
        config ([`UperNetConfig`]): 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 [`~PreTrainedModel.from_pretrained`] method to load the model weights.
'''

lowerCAmelCase__  :Any             =							R'''
    Args:
        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using
            [`AutoImageProcessor`]. See [`SegformerImageProcessor.__call__`] for details.
        output_attentions (`bool`, *optional*):
            Whether or not to return the attentions tensors of all attention layers in case the backbone has them. See
            `attentions` under returned tensors for more detail.
        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers of the backbone. 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(
    'UperNet framework leveraging any vision backbone e.g. for ADE20k, CityScapes.'   ,				UpperCAmelCase   ,				)
class 		__a				(    UpperCAmelCase      ):
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE						)			->	Optional[int]:




									"""simple docstring"""





									super().__init__(_SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase					=      AutoBackbone.from_config(config.backbone_config						)

									# Semantic segmentation head(s)
									_UpperCAmelCase					=      UperNetHead(_SCREAMING_SNAKE_CASE      ,   in_channels=self.backbone.channels						)
									_UpperCAmelCase					=      UperNetFCNHead(_SCREAMING_SNAKE_CASE						) if config.use_auxiliary_head else None

									# Initialize weights and apply final processing
									self.post_init()






		@add_start_docstrings_to_model_forward(UPERNET_INPUTS_DOCSTRING.format('batch_size, sequence_length'						)						)
		@replace_return_docstrings(output_type=_SCREAMING_SNAKE_CASE      ,   config_class=_CONFIG_FOR_DOC						)
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   )			->	Union[tuple, SemanticSegmenterOutput]:




									"""simple docstring"""





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

									_UpperCAmelCase					=      self.backbone.forward_with_filtered_kwargs(
									    _SCREAMING_SNAKE_CASE      ,   output_hidden_states=_SCREAMING_SNAKE_CASE      ,   output_attentions=_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      outputs.feature_maps

									_UpperCAmelCase					=      self.decode_head(_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      nn.functional.interpolate(_SCREAMING_SNAKE_CASE      ,   size=pixel_values.shape[2:]      ,   mode='bilinear'      ,   align_corners=_SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase					=      None
									if self.auxiliary_head is not None:
																_UpperCAmelCase					=      self.auxiliary_head(_SCREAMING_SNAKE_CASE						)
																_UpperCAmelCase					=      nn.functional.interpolate(
																    _SCREAMING_SNAKE_CASE      ,   size=pixel_values.shape[2:]      ,   mode='bilinear'      ,   align_corners=_SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase					=      None
									if labels is not None:
																if self.config.num_labels == 1:
																							raise ValueError('The number of labels should be greater than one'						)
																else:
																							# compute weighted loss
																							_UpperCAmelCase					=      CrossEntropyLoss(ignore_index=self.config.loss_ignore_index						)
																							_UpperCAmelCase					=      loss_fct(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)
																							_UpperCAmelCase					=      loss_fct(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)
																							_UpperCAmelCase					=      main_loss + self.config.auxiliary_loss_weight * auxiliary_loss

									if not return_dict:
																if output_hidden_states:
																							_UpperCAmelCase					=      (logits,) + outputs[1:]
																else:
																							_UpperCAmelCase					=      (logits,) + outputs[2:]
																return ((loss,) + output) if loss is not None else output

									return SemanticSegmenterOutput(
									    loss=_SCREAMING_SNAKE_CASE      ,   logits=_SCREAMING_SNAKE_CASE      ,   hidden_states=outputs.hidden_states      ,   attentions=outputs.attentions      ,   )




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						)
