



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


UpperCAmelCase_							:      Dict      =  logging.get_logger(__name__)

# General docstring
UpperCAmelCase_							:      Optional[Any]      =  "RegNetConfig"

# Base docstring
UpperCAmelCase_							:      List[str]      =  "facebook/regnet-y-040"
UpperCAmelCase_							:      Optional[int]      =  [1, 1_088, 7, 7]

# Image classification docstring
UpperCAmelCase_							:      Dict      =  "facebook/regnet-y-040"
UpperCAmelCase_							:      Any      =  "tabby, tabby cat"

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






class      UpperCamelCase    (						tf.keras.layers.Layer     ):



	def __init__(       self			,	UpperCAmelCase__			,	UpperCAmelCase__ = 3			,	UpperCAmelCase__ = 1			,	UpperCAmelCase__ = 1			,	UpperCAmelCase__ = "relu"			,	**UpperCAmelCase__			,	):
							super().__init__(**UpperCAmelCase__		)
							# The padding and conv has been verified in
							# https://colab.research.google.com/gist/sayakpaul/854bc10eeaf21c9ee2119e0b9f3841a7/scratchpad.ipynb
							A__							=       tf.keras.layers.ZeroPaddingaD(padding=kernel_size // 2		)
							A__							=       tf.keras.layers.ConvaD(
							    filters=UpperCAmelCase__			,	kernel_size=UpperCAmelCase__			,	strides=UpperCAmelCase__			,	padding="VALID"			,	groups=UpperCAmelCase__			,	use_bias=UpperCAmelCase__			,	name="convolution"			,	)
							A__							=       tf.keras.layers.BatchNormalization(epsilon=1e-5			,	momentum=0.9			,	name="normalization"		)
							A__							=       ACTaFN[activation] if activation is not None else tf.identity



	def 			__A							(       self			,	UpperCAmelCase__		):
							A__							=       self.convolution(self.padding(UpperCAmelCase__		)		)
							A__							=       self.normalization(UpperCAmelCase__		)
							A__							=       self.activation(UpperCAmelCase__		)
							return hidden_state






class      UpperCamelCase    (						tf.keras.layers.Layer     ):



	def __init__(       self			,	UpperCAmelCase__			,	**UpperCAmelCase__		):
							super().__init__(**UpperCAmelCase__		)
							A__							=       config.num_channels
							A__							=       TFRegNetConvLayer(
							    out_channels=config.embedding_size			,	kernel_size=3			,	stride=2			,	activation=config.hidden_act			,	name="embedder"			,	)



	def 			__A							(       self			,	UpperCAmelCase__		):
							A__							=       shape_list(UpperCAmelCase__		)[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)
							A__							=       tf.transpose(UpperCAmelCase__			,	perm=(0, 2, 3, 1)		)
							A__							=       self.embedder(UpperCAmelCase__		)
							return hidden_state






class      UpperCamelCase    (						tf.keras.layers.Layer     ):



	def __init__(       self			,	UpperCAmelCase__			,	UpperCAmelCase__ = 2			,	**UpperCAmelCase__		):
							super().__init__(**UpperCAmelCase__		)
							A__							=       tf.keras.layers.ConvaD(
							    filters=UpperCAmelCase__			,	kernel_size=1			,	strides=UpperCAmelCase__			,	use_bias=UpperCAmelCase__			,	name="convolution"		)
							A__							=       tf.keras.layers.BatchNormalization(epsilon=1e-5			,	momentum=0.9			,	name="normalization"		)



	def 			__A							(       self			,	UpperCAmelCase__			,	UpperCAmelCase__ = False		):
							return self.normalization(self.convolution(UpperCAmelCase__		)			,	training=UpperCAmelCase__		)






class      UpperCamelCase    (						tf.keras.layers.Layer     ):



	def __init__(       self			,	UpperCAmelCase__			,	UpperCAmelCase__			,	**UpperCAmelCase__		):
							super().__init__(**UpperCAmelCase__		)
							A__							=       tf.keras.layers.GlobalAveragePoolingaD(keepdims=UpperCAmelCase__			,	name="pooler"		)
							A__							=       [
							    tf.keras.layers.ConvaD(filters=UpperCAmelCase__			,	kernel_size=1			,	activation="relu"			,	name="attention.0"		),
							    tf.keras.layers.ConvaD(filters=UpperCAmelCase__			,	kernel_size=1			,	activation="sigmoid"			,	name="attention.2"		),
							]



	def 			__A							(       self			,	UpperCAmelCase__		):
							# [batch_size, h, w, num_channels] -> [batch_size, 1, 1, num_channels]
							A__							=       self.pooler(UpperCAmelCase__		)
							for layer_module in self.attention:
													A__							=       layer_module(UpperCAmelCase__		)
							A__							=       hidden_state * pooled
							return hidden_state






class      UpperCamelCase    (						tf.keras.layers.Layer     ):



	def __init__(       self			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__ = 1			,	**UpperCAmelCase__		):
							super().__init__(**UpperCAmelCase__		)
							A__							=       in_channels != out_channels or stride != 1
							A__							=       max(1			,	out_channels // config.groups_width		)
							A__							=       (
							    TFRegNetShortCut(UpperCAmelCase__			,	stride=UpperCAmelCase__			,	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.
							A__							=       [
							    TFRegNetConvLayer(UpperCAmelCase__			,	kernel_size=1			,	activation=config.hidden_act			,	name="layer.0"		),
							    TFRegNetConvLayer(
							        UpperCAmelCase__			,	stride=UpperCAmelCase__			,	groups=UpperCAmelCase__			,	activation=config.hidden_act			,	name="layer.1"		),
							    TFRegNetConvLayer(UpperCAmelCase__			,	kernel_size=1			,	activation=UpperCAmelCase__			,	name="layer.2"		),
							]
							A__							=       ACTaFN[config.hidden_act]



	def 			__A							(       self			,	UpperCAmelCase__		):
							A__							=       hidden_state
							for layer_module in self.layers:
													A__							=       layer_module(UpperCAmelCase__		)
							A__							=       self.shortcut(UpperCAmelCase__		)
							hidden_state += residual
							A__							=       self.activation(UpperCAmelCase__		)
							return hidden_state






class      UpperCamelCase    (						tf.keras.layers.Layer     ):



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



	def 			__A							(       self			,	UpperCAmelCase__		):
							A__							=       hidden_state
							for layer_module in self.layers:
													A__							=       layer_module(UpperCAmelCase__		)
							A__							=       self.shortcut(UpperCAmelCase__		)
							hidden_state += residual
							A__							=       self.activation(UpperCAmelCase__		)
							return hidden_state






class      UpperCamelCase    (						tf.keras.layers.Layer     ):



	def __init__(       self			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__ = 2			,	UpperCAmelCase__ = 2			,	**UpperCAmelCase__		):
							super().__init__(**UpperCAmelCase__		)

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



	def 			__A							(       self			,	UpperCAmelCase__		):
							for layer_module in self.layers:
													A__							=       layer_module(UpperCAmelCase__		)
							return hidden_state






class      UpperCamelCase    (						tf.keras.layers.Layer     ):



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



	def 			__A							(       self			,	UpperCAmelCase__			,	UpperCAmelCase__ = False			,	UpperCAmelCase__ = True		):
							A__							=       () if output_hidden_states else None

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

													A__							=       stage_module(UpperCAmelCase__		)

							if output_hidden_states:
													A__							=       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=UpperCAmelCase__			,	hidden_states=UpperCAmelCase__		)






@keras_serializable
class      UpperCamelCase    (						tf.keras.layers.Layer     ):
	lowerCAmelCase	:   Dict				=	RegNetConfig



	def __init__(       self			,	UpperCAmelCase__			,	**UpperCAmelCase__		):
							super().__init__(**UpperCAmelCase__		)
							A__							=       config
							A__							=       TFRegNetEmbeddings(UpperCAmelCase__			,	name="embedder"		)
							A__							=       TFRegNetEncoder(UpperCAmelCase__			,	name="encoder"		)
							A__							=       tf.keras.layers.GlobalAveragePoolingaD(keepdims=UpperCAmelCase__			,	name="pooler"		)



	@unpack_inputs
	def 			__A							(       self			,	UpperCAmelCase__			,	UpperCAmelCase__ = None			,	UpperCAmelCase__ = None			,	UpperCAmelCase__ = False			,	):
							A__							=       (
							    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
							)
							A__							=       return_dict if return_dict is not None else self.config.use_return_dict

							A__							=       self.embedder(UpperCAmelCase__			,	training=UpperCAmelCase__		)

							A__							=       self.encoder(
							    UpperCAmelCase__			,	output_hidden_states=UpperCAmelCase__			,	return_dict=UpperCAmelCase__			,	training=UpperCAmelCase__		)

							A__							=       encoder_outputs[0]
							A__							=       self.pooler(UpperCAmelCase__		)

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

							# Change the other hidden state outputs to NCHW as well
							if output_hidden_states:
													A__							=       tuple([tf.transpose(UpperCAmelCase__			,	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=UpperCAmelCase__			,	pooler_output=UpperCAmelCase__			,	hidden_states=hidden_states if output_hidden_states else encoder_outputs.hidden_states			,	)






class      UpperCamelCase    (						_UpperCAmelCase     ):
	lowerCAmelCase	:   str				=	RegNetConfig
	lowerCAmelCase	:   str				=	"""regnet"""
	lowerCAmelCase	:   str				=	"""pixel_values"""



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





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

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






@add_start_docstrings(
    """The bare RegNet model outputting raw features without any specific head on top."""       ,    _UpperCAmelCase       ,    )
class      UpperCamelCase    (						_UpperCAmelCase     ):



	def __init__(       self			,	UpperCAmelCase__			,	*UpperCAmelCase__			,	**UpperCAmelCase__		):
							super().__init__(UpperCAmelCase__			,	*UpperCAmelCase__			,	**UpperCAmelCase__		)
							A__							=       TFRegNetMainLayer(UpperCAmelCase__			,	name="regnet"		)



	@unpack_inputs
	@add_start_docstrings_to_model_forward(UpperCAmelCase__		)
	@add_code_sample_docstrings(
	    checkpoint=_CHECKPOINT_FOR_DOC			,	output_type=UpperCAmelCase__			,	config_class=_CONFIG_FOR_DOC			,	modality="vision"			,	expected_output=_EXPECTED_OUTPUT_SHAPE			,	)
	def 			__A							(       self			,	UpperCAmelCase__			,	UpperCAmelCase__ = None			,	UpperCAmelCase__ = None			,	UpperCAmelCase__=False			,	):
							A__							=       (
							    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
							)
							A__							=       return_dict if return_dict is not None else self.config.use_return_dict

							A__							=       self.regnet(
							    pixel_values=UpperCAmelCase__			,	output_hidden_states=UpperCAmelCase__			,	return_dict=UpperCAmelCase__			,	training=UpperCAmelCase__			,	)
							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(
    """
    RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
    ImageNet.
    """       ,    _UpperCAmelCase       ,    )
class      UpperCamelCase    (						_UpperCAmelCase       ,    _UpperCAmelCase     ):



	def __init__(       self			,	UpperCAmelCase__			,	*UpperCAmelCase__			,	**UpperCAmelCase__		):
							super().__init__(UpperCAmelCase__			,	*UpperCAmelCase__			,	**UpperCAmelCase__		)
							A__							=       config.num_labels
							A__							=       TFRegNetMainLayer(UpperCAmelCase__			,	name="regnet"		)
							# classification head
							A__							=       [
							    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(UpperCAmelCase__		)
	@add_code_sample_docstrings(
	    checkpoint=_IMAGE_CLASS_CHECKPOINT			,	output_type=UpperCAmelCase__			,	config_class=_CONFIG_FOR_DOC			,	expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT			,	)
	def 			__A							(       self			,	UpperCAmelCase__ = None			,	UpperCAmelCase__ = None			,	UpperCAmelCase__ = None			,	UpperCAmelCase__ = None			,	UpperCAmelCase__=False			,	):
							A__							=       (
							    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
							)
							A__							=       return_dict if return_dict is not None else self.config.use_return_dict

							A__							=       self.regnet(
							    UpperCAmelCase__			,	output_hidden_states=UpperCAmelCase__			,	return_dict=UpperCAmelCase__			,	training=UpperCAmelCase__		)

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

							A__							=       self.classifier[0](UpperCAmelCase__		)
							A__							=       self.classifier[1](UpperCAmelCase__		)

							A__							=       None if labels is None else self.hf_compute_loss(labels=UpperCAmelCase__			,	logits=UpperCAmelCase__		)

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

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










import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionAttendAndExcitePipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_numpy, skip_mps, slow
from diffusers.utils.testing_utils import require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


UpperCAmelCase_							:      Dict      =  False






@skip_mps
class      UpperCamelCase    (						_UpperCAmelCase       ,    _UpperCAmelCase       ,    _UpperCAmelCase       ,    unittest.TestCase     ):
	lowerCAmelCase	:   Optional[Any]				=	StableDiffusionAttendAndExcitePipeline
	lowerCAmelCase	:   Optional[Any]				=	False
	lowerCAmelCase	:   Any				=	TEXT_TO_IMAGE_PARAMS
	lowerCAmelCase	:   Dict				=	TEXT_TO_IMAGE_BATCH_PARAMS.union({"""token_indices"""}     )
	lowerCAmelCase	:   List[str]				=	TEXT_TO_IMAGE_IMAGE_PARAMS
	lowerCAmelCase	:   List[Any]				=	TEXT_TO_IMAGE_IMAGE_PARAMS



	@classmethod
	def 			__A							(       cls		):
							super().setUpClass()
							torch.use_deterministic_algorithms(UpperCAmelCase__		)



	@classmethod
	def 			__A							(       cls		):
							super().tearDownClass()
							torch.use_deterministic_algorithms(UpperCAmelCase__		)



	def 			__A							(       self		):
							torch.manual_seed(0		)
							A__							=       UNetaDConditionModel(
							    block_out_channels=(32, 64)			,	layers_per_block=1			,	sample_size=32			,	in_channels=4			,	out_channels=4			,	down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")			,	up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")			,	cross_attention_dim=32			,	attention_head_dim=(2, 4)			,	use_linear_projection=UpperCAmelCase__			,	)
							A__							=       DDIMScheduler(
							    beta_start=0.00_085			,	beta_end=0.012			,	beta_schedule="scaled_linear"			,	clip_sample=UpperCAmelCase__			,	set_alpha_to_one=UpperCAmelCase__			,	)
							torch.manual_seed(0		)
							A__							=       AutoencoderKL(
							    block_out_channels=[32, 64]			,	in_channels=3			,	out_channels=3			,	down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]			,	up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]			,	latent_channels=4			,	sample_size=128			,	)
							torch.manual_seed(0		)
							A__							=       CLIPTextConfig(
							    bos_token_id=0			,	eos_token_id=2			,	hidden_size=32			,	intermediate_size=37			,	layer_norm_eps=1e-0_5			,	num_attention_heads=4			,	num_hidden_layers=5			,	pad_token_id=1			,	vocab_size=1_000			,	hidden_act="gelu"			,	projection_dim=512			,	)
							A__							=       CLIPTextModel(UpperCAmelCase__		)
							A__							=       CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"		)

							A__							=       {
							    "unet": unet,
							    "scheduler": scheduler,
							    "vae": vae,
							    "text_encoder": text_encoder,
							    "tokenizer": tokenizer,
							    "safety_checker": None,
							    "feature_extractor": None,
							}

							return components



	def 			__A							(       self			,	UpperCAmelCase__			,	UpperCAmelCase__=0		):
							if str(UpperCAmelCase__		).startswith("mps"		):
													A__							=       torch.manual_seed(UpperCAmelCase__		)
							else:
													A__							=       torch.Generator(device=UpperCAmelCase__		).manual_seed(UpperCAmelCase__		)
							A__							=       A__							=       {
							    "prompt": "a cat and a frog",
							    "token_indices": [2, 5],
							    "generator": generator,
							    "num_inference_steps": 1,
							    "guidance_scale": 6.0,
							    "output_type": "numpy",
							    "max_iter_to_alter": 2,
							    "thresholds": {0: 0.7},
							}
							return inputs



	def 			__A							(       self		):
							A__							=       "cpu"

							A__							=       self.get_dummy_components()
							A__							=       self.pipeline_class(**UpperCAmelCase__		)
							pipe.to(UpperCAmelCase__		)
							pipe.set_progress_bar_config(disable=UpperCAmelCase__		)

							A__							=       self.get_dummy_inputs(UpperCAmelCase__		)
							A__							=       pipe(**UpperCAmelCase__		).images
							A__							=       image[0, -3:, -3:, -1]

							self.assertEqual(image.shape			,	(1, 64, 64, 3)		)
							A__							=       np.array(
							    [0.63_905_364, 0.62_897_307, 0.48_599_017, 0.5_133_624, 0.5_550_048, 0.45_769_516, 0.50_326_973, 0.5_023_139, 0.45_384_496]		)
							A__							=       np.abs(image_slice.flatten() - expected_slice		).max()
							self.assertLessEqual(UpperCAmelCase__			,	1e-3		)



	def 			__A							(       self		):
							super().test_cpu_offload_forward_pass(expected_max_diff=5e-4		)



	def 			__A							(       self		):
							# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
							self._test_inference_batch_consistent(batch_sizes=[1, 2]		)



	def 			__A							(       self		):
							self._test_inference_batch_single_identical(batch_size=2			,	expected_max_diff=7e-4		)



	def 			__A							(       self		):
							super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3		)



	def 			__A							(       self		):
							super().test_pt_np_pil_outputs_equivalent(expected_max_diff=5e-4		)



	def 			__A							(       self		):
							super().test_save_load_local(expected_max_difference=5e-4		)



	def 			__A							(       self		):
							super().test_save_load_optional_components(expected_max_difference=4e-4		)






@require_torch_gpu
@slow
class      UpperCamelCase    (						unittest.TestCase     ):



	@classmethod
	def 			__A							(       cls		):
							super().setUpClass()
							torch.use_deterministic_algorithms(UpperCAmelCase__		)



	@classmethod
	def 			__A							(       cls		):
							super().tearDownClass()
							torch.use_deterministic_algorithms(UpperCAmelCase__		)



	def 			__A							(       self		):
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()



	def 			__A							(       self		):
							A__							=       torch.manual_seed(51		)

							A__							=       StableDiffusionAttendAndExcitePipeline.from_pretrained(
							    "CompVis/stable-diffusion-v1-4"			,	safety_checker=UpperCAmelCase__			,	torch_dtype=torch.floataa		)
							pipe.to("cuda"		)

							A__							=       "a painting of an elephant with glasses"
							A__							=       [5, 7]

							A__							=       pipe(
							    prompt=UpperCAmelCase__			,	token_indices=UpperCAmelCase__			,	guidance_scale=7.5			,	generator=UpperCAmelCase__			,	num_inference_steps=5			,	max_iter_to_alter=5			,	output_type="numpy"			,	).images[0]

							A__							=       load_numpy(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/attend-and-excite/elephant_glasses.npy"		)
							assert np.abs((expected_image - image).max()		) < 5e-1





