'''simple docstring'''





from __future__ import annotations

import unittest

from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
							import tensorflow as tf

							from transformers import TFAutoModelForSeqaSeqLM, TFPegasusForConditionalGeneration, TFPegasusModel


@require_tf
class 						lowercase    :
							lowercase__						:       Tuple								=							PegasusConfig
							lowercase__						:       Dict								=							{}
							lowercase__						:       str								=							"""gelu"""




							def __init__(						self   :					Union[str, Any]	,							_UpperCamelCase   :					Union[str, Any]	,							_UpperCamelCase   :					Dict=13	,							_UpperCamelCase   :					List[Any]=7	,							_UpperCamelCase   :					str=True	,							_UpperCamelCase   :					Tuple=False	,							_UpperCamelCase   :					List[str]=99	,							_UpperCamelCase   :					Optional[Any]=32	,							_UpperCamelCase   :					Tuple=2	,							_UpperCamelCase   :					List[Any]=4	,							_UpperCamelCase   :					Optional[Any]=37	,							_UpperCamelCase   :					int=0.1	,							_UpperCamelCase   :					Optional[Any]=0.1	,							_UpperCamelCase   :					List[Any]=40	,							_UpperCamelCase   :					Tuple=2	,							_UpperCamelCase   :					Union[str, Any]=1	,							_UpperCamelCase   :					Union[str, Any]=0	,							)					->				int:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       parent
												SCREAMING_SNAKE_CASE    			=       batch_size
												SCREAMING_SNAKE_CASE    			=       seq_length
												SCREAMING_SNAKE_CASE    			=       is_training
												SCREAMING_SNAKE_CASE    			=       use_labels
												SCREAMING_SNAKE_CASE    			=       vocab_size
												SCREAMING_SNAKE_CASE    			=       hidden_size
												SCREAMING_SNAKE_CASE    			=       num_hidden_layers
												SCREAMING_SNAKE_CASE    			=       num_attention_heads
												SCREAMING_SNAKE_CASE    			=       intermediate_size

												SCREAMING_SNAKE_CASE    			=       hidden_dropout_prob
												SCREAMING_SNAKE_CASE    			=       attention_probs_dropout_prob
												SCREAMING_SNAKE_CASE    			=       max_position_embeddings
												SCREAMING_SNAKE_CASE    			=       eos_token_id
												SCREAMING_SNAKE_CASE    			=       pad_token_id
												SCREAMING_SNAKE_CASE    			=       bos_token_id




							def 	__snake_case(						self   :					Optional[int]      )					->				Optional[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       ids_tensor([self.batch_size, self.seq_length - 1]	,							self.vocab_size      )
												SCREAMING_SNAKE_CASE    			=       tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size      )	,							1      )
												SCREAMING_SNAKE_CASE    			=       tf.concat([input_ids, eos_tensor]	,							axis=1      )

												SCREAMING_SNAKE_CASE    			=       ids_tensor([self.batch_size, self.seq_length]	,							self.vocab_size      )

												SCREAMING_SNAKE_CASE    			=       self.config_cls(
												    vocab_size=self.vocab_size	,							d_model=self.hidden_size	,							encoder_layers=self.num_hidden_layers	,							decoder_layers=self.num_hidden_layers	,							encoder_attention_heads=self.num_attention_heads	,							decoder_attention_heads=self.num_attention_heads	,							encoder_ffn_dim=self.intermediate_size	,							decoder_ffn_dim=self.intermediate_size	,							dropout=self.hidden_dropout_prob	,							attention_dropout=self.attention_probs_dropout_prob	,							max_position_embeddings=self.max_position_embeddings	,							eos_token_ids=[2]	,							bos_token_id=self.bos_token_id	,							pad_token_id=self.pad_token_id	,							decoder_start_token_id=self.pad_token_id	,							**self.config_updates	,							)
												SCREAMING_SNAKE_CASE    			=       prepare_pegasus_inputs_dict(_UpperCamelCase	,							_UpperCamelCase	,							_UpperCamelCase      )
												return config, inputs_dict





							def 	__snake_case(						self   :					str	,							_UpperCamelCase   :					Any	,							_UpperCamelCase   :					int      )					->				Optional[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       TFPegasusModel(config=_UpperCamelCase      ).get_decoder()
												SCREAMING_SNAKE_CASE    			=       inputs_dict["input_ids"]

												SCREAMING_SNAKE_CASE    			=       input_ids[:1, :]
												SCREAMING_SNAKE_CASE    			=       inputs_dict["attention_mask"][:1, :]
												SCREAMING_SNAKE_CASE    			=       inputs_dict["head_mask"]
												SCREAMING_SNAKE_CASE    			=       1

												# first forward pass
												SCREAMING_SNAKE_CASE    			=       model(_UpperCamelCase	,							attention_mask=_UpperCamelCase	,							head_mask=_UpperCamelCase	,							use_cache=_UpperCamelCase      )

												SCREAMING_SNAKE_CASE    			=       outputs.to_tuple()

												# create hypothetical next token and extent to next_input_ids
												SCREAMING_SNAKE_CASE    			=       ids_tensor((self.batch_size, 3)	,							config.vocab_size      )
												SCREAMING_SNAKE_CASE    			=       tf.cast(ids_tensor((self.batch_size, 3)	,							2      )	,							tf.inta      )

												# append to next input_ids and
												SCREAMING_SNAKE_CASE    			=       tf.concat([input_ids, next_tokens]	,							axis=-1      )
												SCREAMING_SNAKE_CASE    			=       tf.concat([attention_mask, next_attn_mask]	,							axis=-1      )

												SCREAMING_SNAKE_CASE    			=       model(_UpperCamelCase	,							attention_mask=_UpperCamelCase      )[0]
												SCREAMING_SNAKE_CASE    			=       model(_UpperCamelCase	,							attention_mask=_UpperCamelCase	,							past_key_values=_UpperCamelCase      )[0]

												self.parent.assertEqual(next_tokens.shape[1]	,							output_from_past.shape[1]      )

												# select random slice
												SCREAMING_SNAKE_CASE    			=       int(ids_tensor((1,)	,							output_from_past.shape[-1]      )      )
												SCREAMING_SNAKE_CASE    			=       output_from_no_past[:, -3:, random_slice_idx]
												SCREAMING_SNAKE_CASE    			=       output_from_past[:, :, random_slice_idx]

												# test that outputs are equal for slice
												tf.debugging.assert_near(_UpperCamelCase	,							_UpperCamelCase	,							rtol=1e-3      )





def 	__lowerCamelCase							(UpperCAmelCase__     :							Dict      , UpperCAmelCase__     :							Union[str, Any]      , UpperCAmelCase__     :							Any      , UpperCAmelCase__     :							Dict=None      , UpperCAmelCase__     :							Union[str, Any]=None      , UpperCAmelCase__     :							Union[str, Any]=None      , UpperCAmelCase__     :							Optional[int]=None      , UpperCAmelCase__     :							Optional[int]=None      , ):


					if attention_mask is None:
										SCREAMING_SNAKE_CASE    			=       tf.cast(tf.math.not_equal(__snake_case      , config.pad_token_id  )      , tf.inta  )
					if decoder_attention_mask is None:
										SCREAMING_SNAKE_CASE    			=       tf.concat(
										    [
										        tf.ones(decoder_input_ids[:, :1].shape      , dtype=tf.inta  ),
										        tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]      , config.pad_token_id  )      , tf.inta  ),
										    ]      , axis=-1      , )
					if head_mask is None:
										SCREAMING_SNAKE_CASE    			=       tf.ones((config.encoder_layers, config.encoder_attention_heads)  )
					if decoder_head_mask is None:
										SCREAMING_SNAKE_CASE    			=       tf.ones((config.decoder_layers, config.decoder_attention_heads)  )
					if cross_attn_head_mask is None:
										SCREAMING_SNAKE_CASE    			=       tf.ones((config.decoder_layers, config.decoder_attention_heads)  )
					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,
					}


@require_tf
class 						lowercase    (     _A				,       _A				,       unittest.TestCase     ):
							lowercase__						:       Union[str, Any]								=							(TFPegasusForConditionalGeneration, TFPegasusModel) if is_tf_available() else ()
							lowercase__						:       str								=							(TFPegasusForConditionalGeneration,) if is_tf_available() else ()
							lowercase__						:       str								=							(
							    {
							        """conversational""": TFPegasusForConditionalGeneration,
							        """feature-extraction""": TFPegasusModel,
							        """summarization""": TFPegasusForConditionalGeneration,
							        """text2text-generation""": TFPegasusForConditionalGeneration,
							        """translation""": TFPegasusForConditionalGeneration,
							    }
							    if is_tf_available()
							    else {}
							)
							lowercase__						:       List[Any]								=							True
							lowercase__						:       Optional[int]								=							False
							lowercase__						:       Optional[int]								=							False




							def 	__snake_case(						self   :					Optional[int]      )					->				Optional[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       TFPegasusModelTester(self      )
												SCREAMING_SNAKE_CASE    			=       ConfigTester(self	,							config_class=_UpperCamelCase      )




							def 	__snake_case(						self   :					Dict      )					->				Any:



												'''simple docstring'''

												self.config_tester.run_common_tests()





							def 	__snake_case(						self   :					Any      )					->				Optional[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.model_tester.prepare_config_and_inputs_for_common()
												self.model_tester.check_decoder_model_past_large_inputs(*_UpperCamelCase      )



@require_sentencepiece
@require_tokenizers
@require_tf
class 						lowercase    (     unittest.TestCase     ):
							lowercase__						:       List[Any]								=							[
							    """ PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.""",
							    """ The London trio are up for best UK act and best album, as well as getting two nominations in the best song category.\"We got told like this morning \'Oh I think you\'re nominated\'\", said Dappy.\"And I was like \'Oh yeah, which one?\' And now we\'ve got nominated for four awards. I mean, wow!\"Bandmate Fazer added: \"We thought it\'s best of us to come down and mingle with everyone and say hello to the cameras. And now we find we\'ve got four nominations.\"The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn\'t be too disappointed if they didn\'t win this time around.\"At the end of the day we\'re grateful to be where we are in our careers.\"If it don\'t happen then it don\'t happen - live to fight another day and keep on making albums and hits for the fans.\"Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a  re-working of The Killers\' All These Things That I\'ve Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year\'s Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border.\"We just done Edinburgh the other day,\" said Dappy.\"We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!\" """,
							]
							lowercase__						:       Optional[int]								=							[
							    """California\'s largest electricity provider has cut power to hundreds of thousands of customers in an effort to"""
							    """ reduce the risk of wildfires.""",
							    """N-Dubz have revealed they\'re \"grateful\" to have been nominated for four Mobo Awards.""",
							]  # differs slightly from pytorch, likely due to numerical differences in linear layers
							lowercase__						:       Tuple								=							"""google/pegasus-xsum"""




							@cached_property
							def 	__snake_case(						self   :					Dict      )					->				Tuple:



												'''simple docstring'''

												return AutoTokenizer.from_pretrained(self.model_name      )




							@cached_property
							def 	__snake_case(						self   :					Dict      )					->				Dict:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name      )
												return model




							def 	__snake_case(						self   :					Tuple	,							**_UpperCamelCase   :					Optional[Any]      )					->				Any:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.translate_src_text(**_UpperCamelCase      )
												assert self.expected_text == generated_words




							def 	__snake_case(						self   :					List[str]	,							**_UpperCamelCase   :					Optional[Any]      )					->				str:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.tokenizer(self.src_text	,							**_UpperCamelCase	,							padding=_UpperCamelCase	,							return_tensors="tf"      )
												SCREAMING_SNAKE_CASE    			=       self.model.generate(
												    model_inputs.input_ids	,							attention_mask=model_inputs.attention_mask	,							num_beams=2	,							use_cache=_UpperCamelCase	,							)
												SCREAMING_SNAKE_CASE    			=       self.tokenizer.batch_decode(generated_ids.numpy()	,							skip_special_tokens=_UpperCamelCase      )
												return generated_words





							@slow
							def 	__snake_case(						self   :					List[Any]      )					->				Any:



												'''simple docstring'''

												self._assert_generated_batch_equal_expected()





import inspect
import unittest

from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch
							from torch import nn

							from transformers import ViTHybridForImageClassification, ViTHybridImageProcessor, ViTHybridModel
							from transformers.models.vit_hybrid.modeling_vit_hybrid import VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
							from PIL import Image


class 						lowercase    :




							def __init__(						self   :					Any	,							_UpperCamelCase   :					Any	,							_UpperCamelCase   :					Dict=13	,							_UpperCamelCase   :					List[Any]=64	,							_UpperCamelCase   :					Union[str, Any]=2	,							_UpperCamelCase   :					int=3	,							_UpperCamelCase   :					Union[str, Any]=True	,							_UpperCamelCase   :					Optional[int]=True	,							_UpperCamelCase   :					Tuple=32	,							_UpperCamelCase   :					str=5	,							_UpperCamelCase   :					Tuple=4	,							_UpperCamelCase   :					Any=37	,							_UpperCamelCase   :					List[str]="gelu"	,							_UpperCamelCase   :					int=0.1	,							_UpperCamelCase   :					int=0.1	,							_UpperCamelCase   :					Optional[int]=10	,							_UpperCamelCase   :					Tuple=0.0_2	,							_UpperCamelCase   :					Union[str, Any]=[1, 16, 4, 4]	,							_UpperCamelCase   :					Optional[Any]=None	,							)					->				Union[str, Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       parent
												SCREAMING_SNAKE_CASE    			=       batch_size
												SCREAMING_SNAKE_CASE    			=       image_size
												SCREAMING_SNAKE_CASE    			=       patch_size
												SCREAMING_SNAKE_CASE    			=       num_channels
												SCREAMING_SNAKE_CASE    			=       is_training
												SCREAMING_SNAKE_CASE    			=       use_labels
												SCREAMING_SNAKE_CASE    			=       hidden_size
												SCREAMING_SNAKE_CASE    			=       num_hidden_layers
												SCREAMING_SNAKE_CASE    			=       num_attention_heads
												SCREAMING_SNAKE_CASE    			=       intermediate_size
												SCREAMING_SNAKE_CASE    			=       hidden_act
												SCREAMING_SNAKE_CASE    			=       hidden_dropout_prob
												SCREAMING_SNAKE_CASE    			=       attention_probs_dropout_prob
												SCREAMING_SNAKE_CASE    			=       type_sequence_label_size
												SCREAMING_SNAKE_CASE    			=       initializer_range
												SCREAMING_SNAKE_CASE    			=       scope
												SCREAMING_SNAKE_CASE    			=       backbone_featmap_shape

												# in ViT hybrid, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
												# the number of patches is based on the feature map of the backbone, which by default uses an output stride
												# of 32, which means that the feature map has a spatial resolution of 1/32 of the input image size
												SCREAMING_SNAKE_CASE    			=       (self.image_size // 32) ** 2
												SCREAMING_SNAKE_CASE    			=       num_patches + 1




							def 	__snake_case(						self   :					Union[str, Any]      )					->				Optional[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

												SCREAMING_SNAKE_CASE    			=       None
												if self.use_labels:
																	SCREAMING_SNAKE_CASE    			=       ids_tensor([self.batch_size]	,							self.type_sequence_label_size      )

												SCREAMING_SNAKE_CASE    			=       self.get_config()

												return config, pixel_values, labels




							def 	__snake_case(						self   :					Dict      )					->				List[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       {
												    "global_padding": "same",
												    "layer_type": "bottleneck",
												    "depths": [3, 4, 9],
												    "out_features": ["stage1", "stage2", "stage3"],
												    "embedding_dynamic_padding": True,
												    "hidden_sizes": [4, 8, 16, 32],
												    "num_groups": 2,
												}

												return ViTHybridConfig(
												    image_size=self.image_size	,							patch_size=self.patch_size	,							num_channels=self.num_channels	,							hidden_size=self.hidden_size	,							num_hidden_layers=self.num_hidden_layers	,							num_attention_heads=self.num_attention_heads	,							intermediate_size=self.intermediate_size	,							hidden_act=self.hidden_act	,							hidden_dropout_prob=self.hidden_dropout_prob	,							attention_probs_dropout_prob=self.attention_probs_dropout_prob	,							is_decoder=_UpperCamelCase	,							initializer_range=self.initializer_range	,							backbone_featmap_shape=self.backbone_featmap_shape	,							backbone_config=_UpperCamelCase	,							)




							def 	__snake_case(						self   :					Dict	,							_UpperCamelCase   :					int	,							_UpperCamelCase   :					Union[str, Any]	,							_UpperCamelCase   :					Optional[int]      )					->				int:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       ViTHybridModel(config=_UpperCamelCase      )
												model.to(_UpperCamelCase      )
												model.eval()
												SCREAMING_SNAKE_CASE    			=       model(_UpperCamelCase      )
												self.parent.assertEqual(result.last_hidden_state.shape	,							(self.batch_size, self.seq_length, self.hidden_size)      )




							def 	__snake_case(						self   :					Any	,							_UpperCamelCase   :					List[Any]	,							_UpperCamelCase   :					List[Any]	,							_UpperCamelCase   :					Tuple      )					->				Optional[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.type_sequence_label_size
												SCREAMING_SNAKE_CASE    			=       ViTHybridForImageClassification(_UpperCamelCase      )
												model.to(_UpperCamelCase      )
												model.eval()
												SCREAMING_SNAKE_CASE    			=       model(_UpperCamelCase	,							labels=_UpperCamelCase      )
												self.parent.assertEqual(result.logits.shape	,							(self.batch_size, self.type_sequence_label_size)      )





							def 	__snake_case(						self   :					str      )					->				List[str]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.prepare_config_and_inputs()
												SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       config_and_inputs
												SCREAMING_SNAKE_CASE    			=       {"pixel_values": pixel_values}
												return config, inputs_dict



@require_torch
class 						lowercase    (     a				,       a				,       unittest.TestCase     ):
							lowercase__						:       Optional[int]								=							(ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
							lowercase__						:       List[Any]								=							(
							    {"""feature-extraction""": ViTHybridModel, """image-classification""": ViTHybridForImageClassification}
							    if is_torch_available()
							    else {}
							)
							lowercase__						:       int								=							False
							lowercase__						:       Any								=							False
							lowercase__						:       Optional[int]								=							False




							def 	__snake_case(						self   :					Union[str, Any]      )					->				Tuple:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       ViTHybridModelTester(self      )
												SCREAMING_SNAKE_CASE    			=       ConfigTester(self	,							config_class=_UpperCamelCase	,							has_text_modality=_UpperCamelCase	,							hidden_size=37      )




							def 	__snake_case(						self   :					Optional[Any]      )					->				Any:



												'''simple docstring'''

												self.config_tester.run_common_tests()




							@unittest.skip(reason="ViT does not use inputs_embeds"      )
							def 	__snake_case(						self   :					Optional[int]      )					->				Optional[Any]:



												'''simple docstring'''

												pass




							def 	__snake_case(						self   :					List[Any]      )					->				Optional[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																	SCREAMING_SNAKE_CASE    			=       model_class(_UpperCamelCase      )
																	self.assertIsInstance(model.get_input_embeddings()	,							(nn.Module)      )
																	SCREAMING_SNAKE_CASE    			=       model.get_output_embeddings()
																	self.assertTrue(x is None or isinstance(_UpperCamelCase	,							nn.Linear      )      )




							def 	__snake_case(						self   :					Optional[int]      )					->				Optional[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																	SCREAMING_SNAKE_CASE    			=       model_class(_UpperCamelCase      )
																	SCREAMING_SNAKE_CASE    			=       inspect.signature(model.forward      )
																	# signature.parameters is an OrderedDict => so arg_names order is deterministic
																	SCREAMING_SNAKE_CASE    			=       [*signature.parameters.keys()]

																	SCREAMING_SNAKE_CASE    			=       ["pixel_values"]
																	self.assertListEqual(arg_names[:1]	,							_UpperCamelCase      )




							def 	__snake_case(						self   :					List[str]      )					->				Optional[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*_UpperCamelCase      )




							def 	__snake_case(						self   :					Dict      )					->				List[str]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_image_classification(*_UpperCamelCase      )




							def 	__snake_case(						self   :					Optional[int]      )					->				List[str]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       self.model_tester.prepare_config_and_inputs_for_common()

												SCREAMING_SNAKE_CASE    			=       _config_zero_init(_UpperCamelCase      )
												for model_class in self.all_model_classes:
																	SCREAMING_SNAKE_CASE    			=       model_class(config=_UpperCamelCase      )
																	# Skip the check for the backbone
																	for name, module in model.named_modules():
																						if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
																											SCREAMING_SNAKE_CASE    			=       [F"{name}.{key}" for key in module.state_dict().keys()]
																											break

																	for name, param in model.named_parameters():
																						if param.requires_grad:
																											if name in backbone_params:
																																continue
																											self.assertIn(
																											    ((param.data.mean() * 1e9).round() / 1e9).item()	,							[0.0, 1.0]	,							msg=F"Parameter {name} of model {model_class} seems not properly initialized"	,							)





							@slow
							def 	__snake_case(						self   :					Any      )					->				List[Any]:



												'''simple docstring'''

												for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	SCREAMING_SNAKE_CASE    			=       ViTHybridModel.from_pretrained(_UpperCamelCase      )
																	self.assertIsNotNone(_UpperCamelCase      )





def 	__lowerCamelCase							():
					SCREAMING_SNAKE_CASE    			=       Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"  )
					return image


@require_torch
@require_vision
class 						lowercase    (     unittest.TestCase     ):




							@cached_property
							def 	__snake_case(						self   :					List[Any]      )					->				Any:



												'''simple docstring'''

												return (
												    ViTHybridImageProcessor.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]      )
												    if is_vision_available()
												    else None
												)




							@slow
							def 	__snake_case(						self   :					Tuple      )					->				str:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]      ).to(
												    _UpperCamelCase      )

												SCREAMING_SNAKE_CASE    			=       self.default_image_processor
												SCREAMING_SNAKE_CASE    			=       prepare_img()
												SCREAMING_SNAKE_CASE    			=       image_processor(images=_UpperCamelCase	,							return_tensors="pt"      ).to(_UpperCamelCase      )

												# forward pass
												with torch.no_grad():
																	SCREAMING_SNAKE_CASE    			=       model(**_UpperCamelCase      )

												# verify the logits
												SCREAMING_SNAKE_CASE    			=       torch.Size((1, 1_000)      )
												self.assertEqual(outputs.logits.shape	,							_UpperCamelCase      )

												SCREAMING_SNAKE_CASE    			=       torch.tensor([-1.9_0_9_0, -0.4_9_9_3, -0.2_3_8_9]      ).to(_UpperCamelCase      )

												self.assertTrue(torch.allclose(outputs.logits[0, :3]	,							_UpperCamelCase	,							atol=1e-4      )      )





							@slow
							@require_accelerate
							def 	__snake_case(						self   :					Optional[Any]      )					->				Tuple:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       ViTHybridImageProcessor.from_pretrained("google/vit-hybrid-base-bit-384"      )
												SCREAMING_SNAKE_CASE    			=       ViTHybridForImageClassification.from_pretrained("google/vit-hybrid-base-bit-384"	,							device_map="auto"      )

												SCREAMING_SNAKE_CASE    			=       prepare_img()

												SCREAMING_SNAKE_CASE    			=       image_processor(images=_UpperCamelCase	,							return_tensors="pt"      )
												SCREAMING_SNAKE_CASE    			=       model(**_UpperCamelCase      )
												SCREAMING_SNAKE_CASE    			=       outputs.logits
												# model predicts one of the 1000 ImageNet classes
												SCREAMING_SNAKE_CASE    			=       logits.argmax(-1      ).item()

												self.assertTrue(model.config.idalabel[predicted_class_idx]	,							"tabby, tabby cat"      )




