


from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


UpperCAmelCase_		:       Tuple							       =		logging.get_logger(__name__)

UpperCAmelCase_		:       str							       =		{
    'facebook/m2m100_418M': 'https://huggingface.co/facebook/m2m100_418M/resolve/main/config.json',
    # See all M2M100 models at https://huggingface.co/models?filter=m2m_100
}





class    lowercase__  (						_snake_case ):





							'''simple docstring'''


							A_							:				Optional[Any]				     =  """m2m_100"""
							A_							:				Any				     =  ["""past_key_values"""]
							A_							:				List[Any]				     =  {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}






							def __init__(   self   , __snake_case=12_8112   , __snake_case=1024   , __snake_case=12   , __snake_case=4096   , __snake_case=16   , __snake_case=12   , __snake_case=4096   , __snake_case=16   , __snake_case=0.05   , __snake_case=0.05   , __snake_case=True   , __snake_case=True   , __snake_case="relu"   , __snake_case=1024   , __snake_case=0.1   , __snake_case=0.1   , __snake_case=0.0   , __snake_case=0.02   , __snake_case=2   , __snake_case=True   , __snake_case=1   , __snake_case=0   , __snake_case=2   , **__snake_case   , ):
											_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				vocab_size
											_SCREAMING_SNAKE_CASE			:	Any							=				max_position_embeddings
											_SCREAMING_SNAKE_CASE			:	List[str]							=				d_model
											_SCREAMING_SNAKE_CASE			:	Optional[int]							=				encoder_ffn_dim
											_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				encoder_layers
											_SCREAMING_SNAKE_CASE			:	Tuple							=				encoder_attention_heads
											_SCREAMING_SNAKE_CASE			:	Tuple							=				decoder_ffn_dim
											_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				decoder_layers
											_SCREAMING_SNAKE_CASE			:	Dict							=				decoder_attention_heads
											_SCREAMING_SNAKE_CASE			:	Any							=				dropout
											_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				attention_dropout
											_SCREAMING_SNAKE_CASE			:	List[str]							=				activation_dropout
											_SCREAMING_SNAKE_CASE			:	Tuple							=				activation_function
											_SCREAMING_SNAKE_CASE			:	List[Any]							=				init_std
											_SCREAMING_SNAKE_CASE			:	int							=				encoder_layerdrop
											_SCREAMING_SNAKE_CASE			:	List[str]							=				decoder_layerdrop
											_SCREAMING_SNAKE_CASE			:	Tuple							=				use_cache
											_SCREAMING_SNAKE_CASE			:	Dict							=				encoder_layers
											_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				scale_embedding  # scale factor will be sqrt(d_model) if True

											super().__init__(
											    pad_token_id=__snake_case   , bos_token_id=__snake_case   , eos_token_id=__snake_case   , is_encoder_decoder=__snake_case   , decoder_start_token_id=__snake_case   , **__snake_case   , )





class    lowercase__  (						_snake_case ):





							'''simple docstring'''


							@property
							def UpperCAmelCase_     (   self  ):
											_SCREAMING_SNAKE_CASE			:	str							=				OrderedDict(
											    [
											        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
											        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
											    ]  )

											if self.use_past:
															_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				{0: """batch"""}
															_SCREAMING_SNAKE_CASE			:	Dict							=				{0: """batch""", 1: """past_decoder_sequence + sequence"""}
											else:
															_SCREAMING_SNAKE_CASE			:	str							=				{0: """batch""", 1: """decoder_sequence"""}
															_SCREAMING_SNAKE_CASE			:	List[Any]							=				{0: """batch""", 1: """decoder_sequence"""}

											if self.use_past:
															self.fill_with_past_key_values_(__snake_case   , direction="""inputs"""  )
											return common_inputs






							def UpperCAmelCase_     (   self   , __snake_case   , __snake_case = -1   , __snake_case = -1   , __snake_case = False   , __snake_case = None   , ):
											# Copied from OnnxConfig.generate_dummy_inputs
											# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
											# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
											_SCREAMING_SNAKE_CASE			:	Tuple							=				compute_effective_axis_dimension(
											    __snake_case   , fixed_dimension=OnnxConfig.default_fixed_batch   , num_token_to_add=0  )

											# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
											_SCREAMING_SNAKE_CASE			:	Optional[int]							=				tokenizer.num_special_tokens_to_add(__snake_case  )
											_SCREAMING_SNAKE_CASE			:	int							=				compute_effective_axis_dimension(
											    __snake_case   , fixed_dimension=OnnxConfig.default_fixed_sequence   , num_token_to_add=__snake_case  )

											# Generate dummy inputs according to compute batch and sequence
											_SCREAMING_SNAKE_CASE			:	Tuple							=				[""" """.join([tokenizer.unk_token]  ) * seq_length] * batch_size
											_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				dict(tokenizer(__snake_case   , return_tensors=__snake_case  )  )
											return common_inputs






							def UpperCAmelCase_     (   self   , __snake_case   , __snake_case = -1   , __snake_case = -1   , __snake_case = False   , __snake_case = None   , ):
											_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
											    __snake_case   , __snake_case   , __snake_case   , __snake_case   , __snake_case  )

											# Generate decoder inputs
											_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				seq_length if not self.use_past else 1
											_SCREAMING_SNAKE_CASE			:	int							=				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
											    __snake_case   , __snake_case   , __snake_case   , __snake_case   , __snake_case  )
											_SCREAMING_SNAKE_CASE			:	Any							=				{f"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
											_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				dict(**__snake_case   , **__snake_case  )

											if self.use_past:
															if not is_torch_available():
																			raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""  )
															else:
																			import torch
															_SCREAMING_SNAKE_CASE			:	int							=				common_inputs["""input_ids"""].shape
															_SCREAMING_SNAKE_CASE			:	int							=				common_inputs["""decoder_input_ids"""].shape[1]
															_SCREAMING_SNAKE_CASE			:	Optional[int]							=				self.num_attention_heads
															_SCREAMING_SNAKE_CASE			:	int							=				(
															    batch,
															    num_encoder_attention_heads,
															    encoder_seq_length,
															    self._config.hidden_size // num_encoder_attention_heads,
															)
															_SCREAMING_SNAKE_CASE			:	List[Any]							=				decoder_seq_length + 3
															_SCREAMING_SNAKE_CASE			:	Tuple							=				(
															    batch,
															    num_decoder_attention_heads,
															    decoder_past_length,
															    self._config.hidden_size // num_decoder_attention_heads,
															)

															_SCREAMING_SNAKE_CASE			:	int							=				torch.cat(
															    [common_inputs["""decoder_attention_mask"""], torch.ones(__snake_case   , __snake_case  )]   , dim=1  )

															_SCREAMING_SNAKE_CASE			:	Optional[int]							=				[]
															# If the number of encoder and decoder layers are present in the model configuration, both are considered
															_SCREAMING_SNAKE_CASE			:	List[Any]							=				self.num_layers
															_SCREAMING_SNAKE_CASE			:	int							=				min(__snake_case   , __snake_case  )
															_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				max(__snake_case   , __snake_case  ) - min_num_layers
															_SCREAMING_SNAKE_CASE			:	List[str]							=				"""encoder""" if num_encoder_layers > num_decoder_layers else """decoder"""

															for _ in range(__snake_case  ):
																			common_inputs["past_key_values"].append(
																			    (
																			        torch.zeros(__snake_case  ),
																			        torch.zeros(__snake_case  ),
																			        torch.zeros(__snake_case  ),
																			        torch.zeros(__snake_case  ),
																			    )  )
															# TODO: test this.
															_SCREAMING_SNAKE_CASE			:	Optional[int]							=				encoder_shape if remaining_side_name == """encoder""" else decoder_shape
															for _ in range(__snake_case   , __snake_case  ):
																			common_inputs["past_key_values"].append((torch.zeros(__snake_case  ), torch.zeros(__snake_case  ))  )
											return common_inputs



							A_							:				Union[str, Any]				     =  _generate_dummy_inputs_for_default_and_seqaseq_lm








'''simple docstring'''


from __future__ import annotations

import inspect
import unittest

from transformers import MobileViTConfig
from transformers.file_utils import is_tf_available, is_vision_available
from transformers.testing_utils import require_tf, slow

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


if is_tf_available():
				import numpy as np
				import tensorflow as tf

				from transformers import TFMobileViTForImageClassification, TFMobileViTForSemanticSegmentation, TFMobileViTModel
				from transformers.models.mobilevit.modeling_tf_mobilevit import TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
				from PIL import Image

				from transformers import MobileViTImageProcessor





class    lowercase__  (						_snake_case ):





							'''simple docstring'''


							def UpperCAmelCase_     (   self  ):
											_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				self.config_class(**self.inputs_dict  )
											self.parent.assertTrue(hasattr(__snake_case   , """hidden_sizes"""  )  )
											self.parent.assertTrue(hasattr(__snake_case   , """neck_hidden_sizes"""  )  )
											self.parent.assertTrue(hasattr(__snake_case   , """num_attention_heads"""  )  )





class    lowercase__  :





							'''simple docstring'''


							def __init__(   self   , __snake_case   , __snake_case=13   , __snake_case=32   , __snake_case=2   , __snake_case=3   , __snake_case=640   , __snake_case=4   , __snake_case="silu"   , __snake_case=3   , __snake_case=32   , __snake_case=0.1   , __snake_case=0.1   , __snake_case=0.1   , __snake_case=0.02   , __snake_case=True   , __snake_case=True   , __snake_case=10   , __snake_case=None   , ):
											_SCREAMING_SNAKE_CASE			:	Optional[int]							=				parent
											_SCREAMING_SNAKE_CASE			:	Any							=				batch_size
											_SCREAMING_SNAKE_CASE			:	str							=				image_size
											_SCREAMING_SNAKE_CASE			:	str							=				patch_size
											_SCREAMING_SNAKE_CASE			:	Any							=				num_channels
											_SCREAMING_SNAKE_CASE			:	List[Any]							=				last_hidden_size
											_SCREAMING_SNAKE_CASE			:	Any							=				num_attention_heads
											_SCREAMING_SNAKE_CASE			:	Any							=				hidden_act
											_SCREAMING_SNAKE_CASE			:	Any							=				conv_kernel_size
											_SCREAMING_SNAKE_CASE			:	int							=				output_stride
											_SCREAMING_SNAKE_CASE			:	List[Any]							=				hidden_dropout_prob
											_SCREAMING_SNAKE_CASE			:	int							=				attention_probs_dropout_prob
											_SCREAMING_SNAKE_CASE			:	Any							=				classifier_dropout_prob
											_SCREAMING_SNAKE_CASE			:	List[Any]							=				use_labels
											_SCREAMING_SNAKE_CASE			:	List[str]							=				is_training
											_SCREAMING_SNAKE_CASE			:	Dict							=				num_labels
											_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				initializer_range
											_SCREAMING_SNAKE_CASE			:	List[str]							=				scope






							def UpperCAmelCase_     (   self  ):
											_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

											_SCREAMING_SNAKE_CASE			:	str							=				None
											_SCREAMING_SNAKE_CASE			:	int							=				None
											if self.use_labels:
															_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				ids_tensor([self.batch_size]   , self.num_labels  )
															_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				ids_tensor([self.batch_size, self.image_size, self.image_size]   , self.num_labels  )

											_SCREAMING_SNAKE_CASE			:	Dict							=				self.get_config()

											return config, pixel_values, labels, pixel_labels






							def UpperCAmelCase_     (   self  ):
											return MobileViTConfig(
											    image_size=self.image_size   , patch_size=self.patch_size   , num_channels=self.num_channels   , num_attention_heads=self.num_attention_heads   , hidden_act=self.hidden_act   , conv_kernel_size=self.conv_kernel_size   , output_stride=self.output_stride   , hidden_dropout_prob=self.hidden_dropout_prob   , attention_probs_dropout_prob=self.attention_probs_dropout_prob   , classifier_dropout_prob=self.classifier_dropout_prob   , initializer_range=self.initializer_range   , )






							def UpperCAmelCase_     (   self   , __snake_case   , __snake_case   , __snake_case   , __snake_case  ):
											_SCREAMING_SNAKE_CASE			:	Any							=				TFMobileViTModel(config=__snake_case  )
											_SCREAMING_SNAKE_CASE			:	Any							=				model(__snake_case   , training=__snake_case  )
											_SCREAMING_SNAKE_CASE			:	Dict							=				self.image_size // self.output_stride
											self.parent.assertEqual(
											    result.last_hidden_state.shape   , (self.batch_size, self.last_hidden_size, expected_height, expected_width)  )






							def UpperCAmelCase_     (   self   , __snake_case   , __snake_case   , __snake_case   , __snake_case  ):
											_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				self.num_labels
											_SCREAMING_SNAKE_CASE			:	Any							=				TFMobileViTForImageClassification(__snake_case  )
											_SCREAMING_SNAKE_CASE			:	Dict							=				model(__snake_case   , labels=__snake_case   , training=__snake_case  )
											self.parent.assertEqual(result.logits.shape   , (self.batch_size, self.num_labels)  )






							def UpperCAmelCase_     (   self   , __snake_case   , __snake_case   , __snake_case   , __snake_case  ):
											_SCREAMING_SNAKE_CASE			:	Tuple							=				self.num_labels
											_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				TFMobileViTForSemanticSegmentation(__snake_case  )
											_SCREAMING_SNAKE_CASE			:	Tuple							=				self.image_size // self.output_stride

											_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				model(__snake_case   , training=__snake_case  )
											self.parent.assertEqual(
											    result.logits.shape   , (self.batch_size, self.num_labels, expected_height, expected_width)  )

											_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				model(__snake_case   , labels=__snake_case   , training=__snake_case  )
											self.parent.assertEqual(
											    result.logits.shape   , (self.batch_size, self.num_labels, expected_height, expected_width)  )






							def UpperCAmelCase_     (   self  ):
											_SCREAMING_SNAKE_CASE			:	str							=				self.prepare_config_and_inputs()
											_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE			:	int							=				config_and_inputs
											_SCREAMING_SNAKE_CASE			:	Tuple							=				{"""pixel_values""": pixel_values}
											return config, inputs_dict





@require_tf
class    lowercase__  (						_snake_case					,					_snake_case					,					unittest.TestCase ):





							'''simple docstring'''


							A_							:				List[Any]				     =  (
							    (TFMobileViTModel, TFMobileViTForImageClassification, TFMobileViTForSemanticSegmentation)
							    if is_tf_available()
							    else ()
							)
							A_							:				List[str]				     =  (
							    {"""feature-extraction""": TFMobileViTModel, """image-classification""": TFMobileViTForImageClassification}
							    if is_tf_available()
							    else {}
							)

							A_							:				str				     =  False
							A_							:				int				     =  False
							A_							:				Union[str, Any]				     =  False
							A_							:				List[str]				     =  False
							A_							:				str				     =  False






							def UpperCAmelCase_     (   self  ):
											_SCREAMING_SNAKE_CASE			:	List[Any]							=				TFMobileViTModelTester(self  )
											_SCREAMING_SNAKE_CASE			:	Dict							=				TFMobileViTConfigTester(self   , config_class=__snake_case   , has_text_modality=__snake_case  )






							def UpperCAmelCase_     (   self  ):
											self.config_tester.run_common_tests()






							@unittest.skip(reason="""MobileViT does not use inputs_embeds"""  )
							def UpperCAmelCase_     (   self  ):
											pass






							@unittest.skip(reason="""MobileViT does not support input and output embeddings"""  )
							def UpperCAmelCase_     (   self  ):
											pass






							@unittest.skip(reason="""MobileViT does not output attentions"""  )
							def UpperCAmelCase_     (   self  ):
											pass






							def UpperCAmelCase_     (   self  ):
											_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE			:	str							=				self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
															_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				model_class(__snake_case  )
															_SCREAMING_SNAKE_CASE			:	int							=				inspect.signature(model.call  )
															# signature.parameters is an OrderedDict => so arg_names order is deterministic
															_SCREAMING_SNAKE_CASE			:	str							=				[*signature.parameters.keys()]

															_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				["""pixel_values"""]
															self.assertListEqual(arg_names[:1]   , __snake_case  )






							def UpperCAmelCase_     (   self  ):
											_SCREAMING_SNAKE_CASE			:	List[Any]							=				self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*__snake_case  )






							def UpperCAmelCase_     (   self  ):
											def check_hidden_states_output(__snake_case   , __snake_case   , __snake_case  ):
															_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				model_class(__snake_case  )

															_SCREAMING_SNAKE_CASE			:	List[str]							=				model(**self._prepare_for_class(__snake_case   , __snake_case  )  )

															_SCREAMING_SNAKE_CASE			:	Optional[int]							=				outputs.hidden_states

															_SCREAMING_SNAKE_CASE			:	Dict							=				5
															self.assertEqual(len(__snake_case  )   , __snake_case  )

															# MobileViT's feature maps are of shape (batch_size, num_channels, height, width)
															# with the width and height being successively divided by 2.
															_SCREAMING_SNAKE_CASE			:	int							=				2
															for i in range(len(__snake_case  )  ):
																			self.assertListEqual(
																			    list(hidden_states[i].shape[-2:]  )   , [self.model_tester.image_size // divisor, self.model_tester.image_size // divisor]   , )
																			divisor *= 2

															self.assertEqual(self.model_tester.output_stride   , divisor // 2  )

											_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
															_SCREAMING_SNAKE_CASE			:	List[Any]							=				True
															check_hidden_states_output(__snake_case   , __snake_case   , __snake_case  )

															# check that output_hidden_states also work using config
															del inputs_dict["output_hidden_states"]
															_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				True

															check_hidden_states_output(__snake_case   , __snake_case   , __snake_case  )






							def UpperCAmelCase_     (   self  ):
											_SCREAMING_SNAKE_CASE			:	List[str]							=				self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_classification(*__snake_case  )






							def UpperCAmelCase_     (   self  ):
											_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_semantic_segmentation(*__snake_case  )






							@unittest.skipIf(
							    not is_tf_available() or len(tf.config.list_physical_devices("""GPU"""  )  ) == 0   , reason="""TF does not support backprop for grouped convolutions on CPU."""   , )
							def UpperCAmelCase_     (   self  ):
											super().test_dataset_conversion()






							def UpperCAmelCase_     (   self   , __snake_case   , __snake_case   , __snake_case=2e-1   , __snake_case=2e-1  ):
											self.assertTrue(np.allclose(__snake_case   , __snake_case   , atol=__snake_case   , rtol=__snake_case  )  )






							@unittest.skipIf(
							    not is_tf_available() or len(tf.config.list_physical_devices("""GPU"""  )  ) == 0   , reason="""TF does not support backprop for grouped convolutions on CPU."""   , )
							@slow
							def UpperCAmelCase_     (   self  ):
											_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE			:	Optional[int]							=				self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
															# Since `TFMobileViTModel` cannot operate with the default `fit()` method.
															if model_class.__name__ != "TFMobileViTModel":
																			_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				model_class(__snake_case  )
																			if getattr(__snake_case   , """hf_compute_loss"""   , __snake_case  ):
																							super().test_keras_fit()






							def UpperCAmelCase_     (   self  ):
											_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE			:	Dict							=				self.model_tester.prepare_config_and_inputs_for_common()
											for model_class in self.all_model_classes:
															# set an ignore index to correctly test the masked loss used in
															# `TFMobileViTForSemanticSegmentation`.
															if model_class.__name__ != "TFMobileViTForSemanticSegmentation":
																			_SCREAMING_SNAKE_CASE			:	int							=				5

															_SCREAMING_SNAKE_CASE			:	Dict							=				model_class(__snake_case  )
															if getattr(__snake_case   , """hf_compute_loss"""   , __snake_case  ):
																			# The number of elements in the loss should be the same as the number of elements in the label
																			_SCREAMING_SNAKE_CASE			:	Optional[int]							=				self._prepare_for_class(inputs_dict.copy()   , __snake_case   , return_labels=__snake_case  )
																			_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				prepared_for_class[
																			    sorted(prepared_for_class.keys() - inputs_dict.keys()   , reverse=__snake_case  )[0]
																			]
																			_SCREAMING_SNAKE_CASE			:	List[Any]							=				added_label.shape.as_list()[:1]

																			# Test that model correctly compute the loss with kwargs
																			_SCREAMING_SNAKE_CASE			:	int							=				self._prepare_for_class(inputs_dict.copy()   , __snake_case   , return_labels=__snake_case  )
																			_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				{"""input_ids""", """pixel_values""", """input_features"""}
																			_SCREAMING_SNAKE_CASE			:	List[str]							=				possible_input_names.intersection(set(__snake_case  )  ).pop()
																			_SCREAMING_SNAKE_CASE			:	int							=				prepared_for_class.pop(__snake_case  )

																			_SCREAMING_SNAKE_CASE			:	List[Any]							=				model(__snake_case   , **__snake_case  )[0]
																			self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]  )

																			# Test that model correctly compute the loss when we mask some positions
																			_SCREAMING_SNAKE_CASE			:	List[Any]							=				self._prepare_for_class(inputs_dict.copy()   , __snake_case   , return_labels=__snake_case  )
																			_SCREAMING_SNAKE_CASE			:	Dict							=				{"""input_ids""", """pixel_values""", """input_features"""}
																			_SCREAMING_SNAKE_CASE			:	Tuple							=				possible_input_names.intersection(set(__snake_case  )  ).pop()
																			_SCREAMING_SNAKE_CASE			:	List[str]							=				prepared_for_class.pop(__snake_case  )
																			if "labels" in prepared_for_class:
																							_SCREAMING_SNAKE_CASE			:	int							=				prepared_for_class["""labels"""].numpy()
																							if len(labels.shape  ) > 1 and labels.shape[1] != 1:
																											# labels[0] = -100
																											_SCREAMING_SNAKE_CASE			:	int							=				tf.convert_to_tensor(__snake_case  )
																											_SCREAMING_SNAKE_CASE			:	Optional[int]							=				model(__snake_case   , **__snake_case  )[0]
																											self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]  )
																											self.assertTrue(not np.any(np.isnan(loss.numpy()  )  )  )

                # Test that model correctly compute the loss with a dict
																			_SCREAMING_SNAKE_CASE			:	int							=				self._prepare_for_class(inputs_dict.copy()   , __snake_case   , return_labels=__snake_case  )
																			_SCREAMING_SNAKE_CASE			:	List[Any]							=				model(__snake_case  )[0]
																			self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]  )

																			# Test that model correctly compute the loss with a tuple
																			_SCREAMING_SNAKE_CASE			:	Optional[int]							=				self._prepare_for_class(inputs_dict.copy()   , __snake_case   , return_labels=__snake_case  )

																			# Get keys that were added with the _prepare_for_class function
																			_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				prepared_for_class.keys() - inputs_dict.keys()
																			_SCREAMING_SNAKE_CASE			:	Any							=				inspect.signature(model.call  ).parameters
																			_SCREAMING_SNAKE_CASE			:	Dict							=				list(signature.keys()  )

																			# Create a dictionary holding the location of the tensors in the tuple
																			_SCREAMING_SNAKE_CASE			:	str							=				{0: input_name}
																			for label_key in label_keys:
																							_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				signature_names.index(__snake_case  )
																							_SCREAMING_SNAKE_CASE			:	int							=				label_key
																			_SCREAMING_SNAKE_CASE			:	List[str]							=				sorted(tuple_index_mapping.items()  )
																			# Initialize a list with their default values, update the values and convert to a tuple
																			_SCREAMING_SNAKE_CASE			:	List[str]							=				[]

																			for name in signature_names:
																							if name != "kwargs":
																											list_input.append(signature[name].default  )

																			for index, value in sorted_tuple_index_mapping:
																							_SCREAMING_SNAKE_CASE			:	List[str]							=				prepared_for_class[value]

																			_SCREAMING_SNAKE_CASE			:	Optional[int]							=				tuple(__snake_case  )

																			# Send to model
																			_SCREAMING_SNAKE_CASE			:	List[Any]							=				model(tuple_input[:-1]  )[0]

																			self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]  )






							@slow
							def UpperCAmelCase_     (   self  ):
											for model_name in TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															_SCREAMING_SNAKE_CASE			:	List[Any]							=				TFMobileViTModel.from_pretrained(__snake_case  )
															self.assertIsNotNone(__snake_case  )







def       snake_case_       (  ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE			:	Tuple							=				Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""					)
				return image







@require_tf
class    lowercase__  (						unittest.TestCase ):





							'''simple docstring'''


							@slow
							def UpperCAmelCase_     (   self  ):
											_SCREAMING_SNAKE_CASE			:	Dict							=				TFMobileViTForImageClassification.from_pretrained("""apple/mobilevit-xx-small"""  )

											_SCREAMING_SNAKE_CASE			:	List[Any]							=				MobileViTImageProcessor.from_pretrained("""apple/mobilevit-xx-small"""  )
											_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				prepare_img()
											_SCREAMING_SNAKE_CASE			:	Optional[int]							=				image_processor(images=__snake_case   , return_tensors="""tf"""  )

											# forward pass
											_SCREAMING_SNAKE_CASE			:	Optional[int]							=				model(**__snake_case   , training=__snake_case  )

											# verify the logits
											_SCREAMING_SNAKE_CASE			:	int							=				tf.TensorShape((1, 1000)  )
											self.assertEqual(outputs.logits.shape   , __snake_case  )

											_SCREAMING_SNAKE_CASE			:	Optional[int]							=				tf.constant([-1.9364, -1.2327, -0.4653]  )

											tf.debugging.assert_near(outputs.logits[0, :3]   , __snake_case   , atol=1e-4   , rtol=1e-04  )






							@slow
							def UpperCAmelCase_     (   self  ):
											# `from_pt` will be removed
											_SCREAMING_SNAKE_CASE			:	Tuple							=				TFMobileViTForSemanticSegmentation.from_pretrained("""apple/deeplabv3-mobilevit-xx-small"""  )

											_SCREAMING_SNAKE_CASE			:	int							=				MobileViTImageProcessor.from_pretrained("""apple/deeplabv3-mobilevit-xx-small"""  )

											_SCREAMING_SNAKE_CASE			:	Dict							=				prepare_img()
											_SCREAMING_SNAKE_CASE			:	Optional[int]							=				image_processor(images=__snake_case   , return_tensors="""tf"""  )

											# forward pass
											_SCREAMING_SNAKE_CASE			:	Any							=				model(inputs.pixel_values   , training=__snake_case  )
											_SCREAMING_SNAKE_CASE			:	List[Any]							=				outputs.logits

											# verify the logits
											_SCREAMING_SNAKE_CASE			:	List[Any]							=				tf.TensorShape((1, 21, 32, 32)  )
											self.assertEqual(logits.shape   , __snake_case  )

											_SCREAMING_SNAKE_CASE			:	List[Any]							=				tf.constant(
											    [
											        [[6.9713, 6.9786, 7.2422], [7.2893, 7.2825, 7.4446], [7.6580, 7.8797, 7.9420]],
											        [[-10.6869, -10.3250, -10.3471], [-10.4228, -9.9868, -9.7132], [-11.0405, -11.0221, -10.7318]],
											        [[-3.3089, -2.8539, -2.6740], [-3.2706, -2.5621, -2.5108], [-3.2534, -2.6615, -2.6651]],
											    ]  )

											tf.debugging.assert_near(logits[0, :3, :3, :3]   , __snake_case   , rtol=1e-4   , atol=1e-4  )




