_lowerCamelCase   :				List[Any]     			=						{
    '''Pillow''': '''Pillow<10.0.0''',
    '''accelerate''': '''accelerate>=0.20.3''',
    '''av''': '''av==9.2.0''',
    '''beautifulsoup4''': '''beautifulsoup4''',
    '''black''': '''black~=23.1''',
    '''codecarbon''': '''codecarbon==1.2.0''',
    '''cookiecutter''': '''cookiecutter==1.7.3''',
    '''dataclasses''': '''dataclasses''',
    '''datasets''': '''datasets!=2.5.0''',
    '''decord''': '''decord==0.6.0''',
    '''deepspeed''': '''deepspeed>=0.9.3''',
    '''diffusers''': '''diffusers''',
    '''dill''': '''dill<0.3.5''',
    '''evaluate''': '''evaluate>=0.2.0''',
    '''fairscale''': '''fairscale>0.3''',
    '''faiss-cpu''': '''faiss-cpu''',
    '''fastapi''': '''fastapi''',
    '''filelock''': '''filelock''',
    '''flax''': '''flax>=0.4.1,<=0.7.0''',
    '''ftfy''': '''ftfy''',
    '''fugashi''': '''fugashi>=1.0''',
    '''GitPython''': '''GitPython<3.1.19''',
    '''hf-doc-builder''': '''hf-doc-builder>=0.3.0''',
    '''huggingface-hub''': '''huggingface-hub>=0.14.1,<1.0''',
    '''importlib_metadata''': '''importlib_metadata''',
    '''ipadic''': '''ipadic>=1.0.0,<2.0''',
    '''isort''': '''isort>=5.5.4''',
    '''jax''': '''jax>=0.2.8,!=0.3.2,<=0.4.13''',
    '''jaxlib''': '''jaxlib>=0.1.65,<=0.4.13''',
    '''jieba''': '''jieba''',
    '''kenlm''': '''kenlm''',
    '''keras-nlp''': '''keras-nlp>=0.3.1''',
    '''librosa''': '''librosa''',
    '''nltk''': '''nltk''',
    '''natten''': '''natten>=0.14.6''',
    '''numpy''': '''numpy>=1.17''',
    '''onnxconverter-common''': '''onnxconverter-common''',
    '''onnxruntime-tools''': '''onnxruntime-tools>=1.4.2''',
    '''onnxruntime''': '''onnxruntime>=1.4.0''',
    '''opencv-python''': '''opencv-python''',
    '''optuna''': '''optuna''',
    '''optax''': '''optax>=0.0.8,<=0.1.4''',
    '''packaging''': '''packaging>=20.0''',
    '''parameterized''': '''parameterized''',
    '''phonemizer''': '''phonemizer''',
    '''protobuf''': '''protobuf''',
    '''psutil''': '''psutil''',
    '''pyyaml''': '''pyyaml>=5.1''',
    '''pydantic''': '''pydantic<2''',
    '''pytest''': '''pytest>=7.2.0''',
    '''pytest-timeout''': '''pytest-timeout''',
    '''pytest-xdist''': '''pytest-xdist''',
    '''python''': '''python>=3.8.0''',
    '''ray[tune]''': '''ray[tune]''',
    '''regex''': '''regex!=2019.12.17''',
    '''requests''': '''requests''',
    '''rhoknp''': '''rhoknp>=1.1.0,<1.3.1''',
    '''rjieba''': '''rjieba''',
    '''rouge-score''': '''rouge-score!=0.0.7,!=0.0.8,!=0.1,!=0.1.1''',
    '''ruff''': '''ruff>=0.0.241,<=0.0.259''',
    '''sacrebleu''': '''sacrebleu>=1.4.12,<2.0.0''',
    '''sacremoses''': '''sacremoses''',
    '''safetensors''': '''safetensors>=0.3.1''',
    '''sagemaker''': '''sagemaker>=2.31.0''',
    '''scikit-learn''': '''scikit-learn''',
    '''sentencepiece''': '''sentencepiece>=0.1.91,!=0.1.92''',
    '''sigopt''': '''sigopt''',
    '''starlette''': '''starlette''',
    '''sudachipy''': '''sudachipy>=0.6.6''',
    '''sudachidict_core''': '''sudachidict_core>=20220729''',
    '''tensorflow-cpu''': '''tensorflow-cpu>=2.6,<2.14''',
    '''tensorflow''': '''tensorflow>=2.6,<2.14''',
    '''tensorflow-text''': '''tensorflow-text<2.14''',
    '''tf2onnx''': '''tf2onnx''',
    '''timeout-decorator''': '''timeout-decorator''',
    '''timm''': '''timm''',
    '''tokenizers''': '''tokenizers>=0.11.1,!=0.11.3,<0.14''',
    '''torch''': '''torch>=1.9,!=1.12.0''',
    '''torchaudio''': '''torchaudio''',
    '''torchvision''': '''torchvision''',
    '''pyctcdecode''': '''pyctcdecode>=0.4.0''',
    '''tqdm''': '''tqdm>=4.27''',
    '''unidic''': '''unidic>=1.0.2''',
    '''unidic_lite''': '''unidic_lite>=1.0.7''',
    '''urllib3''': '''urllib3<2.0.0''',
    '''uvicorn''': '''uvicorn''',
}





import inspect
import unittest

from transformers import MobileNetVaConfig
from transformers.testing_utils import 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, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers import MobileNetVaForImageClassification, MobileNetVaModel
							from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
							from PIL import Image

							from transformers import MobileNetVaImageProcessor


class   lowercase      (       a       ):




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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.config_class(**self.inputs_dict )
												self.parent.assertTrue(hasattr(_UpperCamelCase		,							"tf_padding" ) )
												self.parent.assertTrue(hasattr(_UpperCamelCase		,							"depth_multiplier" ) )



class   lowercase      :




							def __init__(  self				:   Tuple		,							_UpperCamelCase				:   Optional[Any]		,							_UpperCamelCase				:   Any=13		,							_UpperCamelCase				:   Any=3		,							_UpperCamelCase				:   Union[str, Any]=32		,							_UpperCamelCase				:   Optional[Any]=0.2_5		,							_UpperCamelCase				:   int=8		,							_UpperCamelCase				:   str=True		,							_UpperCamelCase				:   Any=1_024		,							_UpperCamelCase				:   Tuple=32		,							_UpperCamelCase				:   List[str]="relu6"		,							_UpperCamelCase				:   Tuple=0.1		,							_UpperCamelCase				:   List[str]=0.0_2		,							_UpperCamelCase				:   int=True		,							_UpperCamelCase				:   int=True		,							_UpperCamelCase				:   Optional[Any]=10		,							_UpperCamelCase				:   List[str]=None		,							)   ->			Any:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    parent
												SCREAMING_SNAKE_CASE           =    batch_size
												SCREAMING_SNAKE_CASE           =    num_channels
												SCREAMING_SNAKE_CASE           =    image_size
												SCREAMING_SNAKE_CASE           =    depth_multiplier
												SCREAMING_SNAKE_CASE           =    min_depth
												SCREAMING_SNAKE_CASE           =    tf_padding
												SCREAMING_SNAKE_CASE           =    int(last_hidden_size * depth_multiplier )
												SCREAMING_SNAKE_CASE           =    output_stride
												SCREAMING_SNAKE_CASE           =    hidden_act
												SCREAMING_SNAKE_CASE           =    classifier_dropout_prob
												SCREAMING_SNAKE_CASE           =    use_labels
												SCREAMING_SNAKE_CASE           =    is_training
												SCREAMING_SNAKE_CASE           =    num_labels
												SCREAMING_SNAKE_CASE           =    initializer_range
												SCREAMING_SNAKE_CASE           =    scope




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



												'''simple docstring'''

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

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

												SCREAMING_SNAKE_CASE           =    self.get_config()

												return config, pixel_values, labels, pixel_labels




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



												'''simple docstring'''

												return MobileNetVaConfig(
												    num_channels=self.num_channels		,							image_size=self.image_size		,							depth_multiplier=self.depth_multiplier		,							min_depth=self.min_depth		,							tf_padding=self.tf_padding		,							hidden_act=self.hidden_act		,							classifier_dropout_prob=self.classifier_dropout_prob		,							initializer_range=self.initializer_range		,							)




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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    MobileNetVaModel(config=_UpperCamelCase )
												model.to(_UpperCamelCase )
												model.eval()
												SCREAMING_SNAKE_CASE           =    model(_UpperCamelCase )
												self.parent.assertEqual(
												    result.last_hidden_state.shape		,							(
												        self.batch_size,
												        self.last_hidden_size,
												        self.image_size // self.output_stride,
												        self.image_size // self.output_stride,
												    )		,							)




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



												'''simple docstring'''

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





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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.prepare_config_and_inputs()
												SCREAMING_SNAKE_CASE     ,				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__  :    Dict									=							(MobileNetVaModel, MobileNetVaForImageClassification) if is_torch_available() else ()
							lowercase__  :    Tuple									=							(
							    {"""feature-extraction""": MobileNetVaModel, """image-classification""": MobileNetVaForImageClassification}
							    if is_torch_available()
							    else {}
							)

							lowercase__  :    Union[str, Any]									=							False
							lowercase__  :    Union[str, Any]									=							False
							lowercase__  :    Tuple									=							False
							lowercase__  :    List[str]									=							False




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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    MobileNetVaModelTester(self )
												SCREAMING_SNAKE_CASE           =    MobileNetVaConfigTester(self		,							config_class=_UpperCamelCase		,							has_text_modality=_UpperCamelCase )




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



												'''simple docstring'''

												self.config_tester.run_common_tests()




							@unittest.skip(reason="MobileNetV1 does not use inputs_embeds" )
							def 		__snake_case(  self				:   Tuple )   ->			Tuple:



												'''simple docstring'''

												pass




							@unittest.skip(reason="MobileNetV1 does not support input and output embeddings" )
							def 		__snake_case(  self				:   Optional[int] )   ->			Dict:



												'''simple docstring'''

												pass




							@unittest.skip(reason="MobileNetV1 does not output attentions" )
							def 		__snake_case(  self				:   Any )   ->			List[str]:



												'''simple docstring'''

												pass




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



												'''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[Any] )   ->			Dict:



												'''simple docstring'''

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




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



												'''simple docstring'''

												def check_hidden_states_output(_UpperCamelCase				:   Any		,							_UpperCamelCase				:   Dict		,							_UpperCamelCase				:   Tuple ):
																	SCREAMING_SNAKE_CASE           =    model_class(_UpperCamelCase )
																	model.to(_UpperCamelCase )
																	model.eval()

																	with torch.no_grad():
																						SCREAMING_SNAKE_CASE           =    model(**self._prepare_for_class(_UpperCamelCase		,							_UpperCamelCase ) )

																	SCREAMING_SNAKE_CASE           =    outputs.hidden_states

																	SCREAMING_SNAKE_CASE           =    26
																	self.assertEqual(len(_UpperCamelCase )		,							_UpperCamelCase )

												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           =    True
																	check_hidden_states_output(_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase )

																	# check that output_hidden_states also work using config
																	del inputs_dict["output_hidden_states"]
																	SCREAMING_SNAKE_CASE           =    True

																	check_hidden_states_output(_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase )




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



												'''simple docstring'''

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





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



												'''simple docstring'''

												for model_name in MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	SCREAMING_SNAKE_CASE           =    MobileNetVaModel.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] )   ->			Optional[int]:



												'''simple docstring'''

												return (
												    MobileNetVaImageProcessor.from_pretrained("google/mobilenet_v1_1.0_224" ) if is_vision_available() else None
												)





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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    MobileNetVaForImageClassification.from_pretrained("google/mobilenet_v1_1.0_224" ).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_001) )
												self.assertEqual(outputs.logits.shape		,							_UpperCamelCase )

												SCREAMING_SNAKE_CASE           =    torch.tensor([-4.1_7_3_9, -1.1_2_3_3, 3.1_2_0_5] ).to(_UpperCamelCase )

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




