
"""simple docstring"""





import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union

# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...feature_extraction_utils import FeatureExtractionMixin
from ...utils import CONFIG_NAME, FEATURE_EXTRACTOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
    CONFIG_MAPPING_NAMES,
    AutoConfig,
    model_type_to_module_name,
    replace_list_option_in_docstrings,
)


UpperCamelCase_					:						Union[str, Any]				= logging.get_logger(__name__)

UpperCamelCase_					:						str				= OrderedDict(
    [
        ('''audio-spectrogram-transformer''', '''ASTFeatureExtractor'''),
        ('''beit''', '''BeitFeatureExtractor'''),
        ('''chinese_clip''', '''ChineseCLIPFeatureExtractor'''),
        ('''clap''', '''ClapFeatureExtractor'''),
        ('''clip''', '''CLIPFeatureExtractor'''),
        ('''clipseg''', '''ViTFeatureExtractor'''),
        ('''conditional_detr''', '''ConditionalDetrFeatureExtractor'''),
        ('''convnext''', '''ConvNextFeatureExtractor'''),
        ('''cvt''', '''ConvNextFeatureExtractor'''),
        ('''data2vec-audio''', '''Wav2Vec2FeatureExtractor'''),
        ('''data2vec-vision''', '''BeitFeatureExtractor'''),
        ('''deformable_detr''', '''DeformableDetrFeatureExtractor'''),
        ('''deit''', '''DeiTFeatureExtractor'''),
        ('''detr''', '''DetrFeatureExtractor'''),
        ('''dinat''', '''ViTFeatureExtractor'''),
        ('''donut-swin''', '''DonutFeatureExtractor'''),
        ('''dpt''', '''DPTFeatureExtractor'''),
        ('''encodec''', '''EncodecFeatureExtractor'''),
        ('''flava''', '''FlavaFeatureExtractor'''),
        ('''glpn''', '''GLPNFeatureExtractor'''),
        ('''groupvit''', '''CLIPFeatureExtractor'''),
        ('''hubert''', '''Wav2Vec2FeatureExtractor'''),
        ('''imagegpt''', '''ImageGPTFeatureExtractor'''),
        ('''layoutlmv2''', '''LayoutLMv2FeatureExtractor'''),
        ('''layoutlmv3''', '''LayoutLMv3FeatureExtractor'''),
        ('''levit''', '''LevitFeatureExtractor'''),
        ('''maskformer''', '''MaskFormerFeatureExtractor'''),
        ('''mctct''', '''MCTCTFeatureExtractor'''),
        ('''mobilenet_v1''', '''MobileNetV1FeatureExtractor'''),
        ('''mobilenet_v2''', '''MobileNetV2FeatureExtractor'''),
        ('''mobilevit''', '''MobileViTFeatureExtractor'''),
        ('''nat''', '''ViTFeatureExtractor'''),
        ('''owlvit''', '''OwlViTFeatureExtractor'''),
        ('''perceiver''', '''PerceiverFeatureExtractor'''),
        ('''poolformer''', '''PoolFormerFeatureExtractor'''),
        ('''regnet''', '''ConvNextFeatureExtractor'''),
        ('''resnet''', '''ConvNextFeatureExtractor'''),
        ('''segformer''', '''SegformerFeatureExtractor'''),
        ('''sew''', '''Wav2Vec2FeatureExtractor'''),
        ('''sew-d''', '''Wav2Vec2FeatureExtractor'''),
        ('''speech_to_text''', '''Speech2TextFeatureExtractor'''),
        ('''speecht5''', '''SpeechT5FeatureExtractor'''),
        ('''swiftformer''', '''ViTFeatureExtractor'''),
        ('''swin''', '''ViTFeatureExtractor'''),
        ('''swinv2''', '''ViTFeatureExtractor'''),
        ('''table-transformer''', '''DetrFeatureExtractor'''),
        ('''timesformer''', '''VideoMAEFeatureExtractor'''),
        ('''tvlt''', '''TvltFeatureExtractor'''),
        ('''unispeech''', '''Wav2Vec2FeatureExtractor'''),
        ('''unispeech-sat''', '''Wav2Vec2FeatureExtractor'''),
        ('''van''', '''ConvNextFeatureExtractor'''),
        ('''videomae''', '''VideoMAEFeatureExtractor'''),
        ('''vilt''', '''ViltFeatureExtractor'''),
        ('''vit''', '''ViTFeatureExtractor'''),
        ('''vit_mae''', '''ViTFeatureExtractor'''),
        ('''vit_msn''', '''ViTFeatureExtractor'''),
        ('''wav2vec2''', '''Wav2Vec2FeatureExtractor'''),
        ('''wav2vec2-conformer''', '''Wav2Vec2FeatureExtractor'''),
        ('''wavlm''', '''Wav2Vec2FeatureExtractor'''),
        ('''whisper''', '''WhisperFeatureExtractor'''),
        ('''xclip''', '''CLIPFeatureExtractor'''),
        ('''yolos''', '''YolosFeatureExtractor'''),
    ]
)

UpperCamelCase_					:						int				= _LazyAutoMapping(CONFIG_MAPPING_NAMES, FEATURE_EXTRACTOR_MAPPING_NAMES)
def 							A_     (__a				):

					'''simple docstring'''





					for module_name, extractors in FEATURE_EXTRACTOR_MAPPING_NAMES.items():
										if class_name in extractors:
															A_				 =						model_type_to_module_name(__a				)

															A_				 =						importlib.import_module(f'.{module_name}'				,   "transformers.models"				)
															try:
																				return getattr(__a				,   __a				)
															except AttributeError:
																				continue

					for _, extractor in FEATURE_EXTRACTOR_MAPPING._extra_content.items():
										if getattr(__a				,   "__name__"				,   __a				) == class_name:
															return extractor

    # We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
    # init and we return the proper dummy to get an appropriate error message.
					A_				 =						importlib.import_module("transformers"				)
					if hasattr(__a				,   __a				):
										return getattr(__a				,   __a				)

					return None
def 							A_     (__a				,   __a = None				,   __a = False				,   __a = False				,   __a = None				,   __a = None				,   __a = None				,   __a = False				,   **__a				,   ):

					'''simple docstring'''





					A_				 =						get_file_from_repo(
					    __a				,   __a				,   cache_dir=__a				,   force_download=__a				,   resume_download=__a				,   proxies=__a				,   use_auth_token=__a				,   revision=__a				,   local_files_only=__a				,   )
					if resolved_config_file is None:
										logger.info(
										    "Could not locate the feature extractor configuration file, will try to use the model config instead."				)
										return {}

					with open(__a				,   encoding="utf-8"				) as reader:
										return json.load(__a				)




class __lowerCAmelCase				:




			"""simple docstring"""







			def __init__(				self						:					List[Any]	)      -> Tuple:







								"""simple docstring"""
								raise EnvironmentError(
								    "AutoFeatureExtractor is designed to be instantiated "
								    "using the `AutoFeatureExtractor.from_pretrained(pretrained_model_name_or_path)` method."	)




			@classmethod
			@replace_list_option_in_docstrings(_snake_case	)
			def      lowerCamelCase__     (				cls						:					Optional[int]       ,   _snake_case						:					List[Any]       ,   **_snake_case						:					List[Any]	)      -> str:







								"""simple docstring"""
								A_				 =						kwargs.pop("config"       ,   _snake_case	)
								A_				 =						kwargs.pop("trust_remote_code"       ,   _snake_case	)
								A_				 =						True

								A_ ,    A_				 =						FeatureExtractionMixin.get_feature_extractor_dict(_snake_case       ,   **_snake_case	)
								A_				 =						config_dict.get("feature_extractor_type"       ,   _snake_case	)
								A_				 =						None
								if "AutoFeatureExtractor" in config_dict.get("auto_map"       ,   {}	):
													A_				 =						config_dict["auto_map"]["AutoFeatureExtractor"]

								# If we don't find the feature extractor class in the feature extractor config, let's try the model config.
								if feature_extractor_class is None and feature_extractor_auto_map is None:
													if not isinstance(_snake_case       ,   _snake_case	):
																		A_				 =						AutoConfig.from_pretrained(_snake_case       ,   **_snake_case	)
													# It could be in `config.feature_extractor_type``
													A_				 =						getattr(_snake_case       ,   "feature_extractor_type"       ,   _snake_case	)
													if hasattr(_snake_case       ,   "auto_map"	) and "AutoFeatureExtractor" in config.auto_map:
																		A_				 =						config.auto_map["AutoFeatureExtractor"]

								if feature_extractor_class is not None:
													A_				 =						feature_extractor_class_from_name(_snake_case	)

								A_				 =						feature_extractor_auto_map is not None
								A_				 =						feature_extractor_class is not None or type(_snake_case	) in FEATURE_EXTRACTOR_MAPPING
								A_				 =						resolve_trust_remote_code(
								    _snake_case       ,   _snake_case       ,   _snake_case       ,   _snake_case	)

								if has_remote_code and trust_remote_code:
													A_				 =						get_class_from_dynamic_module(
													    _snake_case       ,   _snake_case       ,   **_snake_case	)
													A_				 =						kwargs.pop("code_revision"       ,   _snake_case	)
													if os.path.isdir(_snake_case	):
																		feature_extractor_class.register_for_auto_class()
													return feature_extractor_class.from_dict(_snake_case       ,   **_snake_case	)
								elif feature_extractor_class is not None:
													return feature_extractor_class.from_dict(_snake_case       ,   **_snake_case	)
								# Last try: we use the FEATURE_EXTRACTOR_MAPPING.
								elif type(_snake_case	) in FEATURE_EXTRACTOR_MAPPING:
													A_				 =						FEATURE_EXTRACTOR_MAPPING[type(_snake_case	)]
													return feature_extractor_class.from_dict(_snake_case       ,   **_snake_case	)

								raise ValueError(
								    F'Unrecognized feature extractor in {pretrained_model_name_or_path}. Should have a '
								    F'`feature_extractor_type` key in its {FEATURE_EXTRACTOR_NAME} of {CONFIG_NAME}, or one of the following '
								    F'`model_type` keys in its {CONFIG_NAME}: {", ".join(c for c in FEATURE_EXTRACTOR_MAPPING_NAMES.keys()	)}'	)






			@staticmethod
			def      lowerCamelCase__     (				_snake_case						:					Optional[int]       ,   _snake_case						:					Optional[int]	)      -> Optional[int]:







								"""simple docstring"""
								FEATURE_EXTRACTOR_MAPPING.register(_snake_case       ,   _snake_case	)






"""simple docstring"""





import inspect
import unittest

from transformers import RegNetConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

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 torch import nn

		from transformers import RegNetForImageClassification, RegNetModel
		from transformers.models.regnet.modeling_regnet import REGNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import AutoImageProcessor



class __lowerCAmelCase				:




			"""simple docstring"""







			def __init__(				self						:					Optional[int]       ,   _snake_case						:					Any       ,   _snake_case						:					int=3       ,   _snake_case						:					List[Any]=32       ,   _snake_case						:					str=3       ,   _snake_case						:					Any=10       ,   _snake_case						:					List[Any]=[10, 20, 30, 40]       ,   _snake_case						:					Dict=[1, 1, 2, 1]       ,   _snake_case						:					Dict=True       ,   _snake_case						:					Any=True       ,   _snake_case						:					List[str]="relu"       ,   _snake_case						:					List[Any]=3       ,   _snake_case						:					List[str]=None       ,   )      -> int:







								"""simple docstring"""
								A_				 =						parent
								A_				 =						batch_size
								A_				 =						image_size
								A_				 =						num_channels
								A_				 =						embeddings_size
								A_				 =						hidden_sizes
								A_				 =						depths
								A_				 =						is_training
								A_				 =						use_labels
								A_				 =						hidden_act
								A_				 =						num_labels
								A_				 =						scope
								A_				 =						len(_snake_case	)




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







								"""simple docstring"""
								A_				 =						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

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

								A_				 =						self.get_config()

								return config, pixel_values, labels




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







								"""simple docstring"""
								return RegNetConfig(
								    num_channels=self.num_channels       ,   embeddings_size=self.embeddings_size       ,   hidden_sizes=self.hidden_sizes       ,   depths=self.depths       ,   hidden_act=self.hidden_act       ,   num_labels=self.num_labels       ,   )




			def      lowerCamelCase__     (				self						:					Union[str, Any]       ,   _snake_case						:					str       ,   _snake_case						:					Tuple       ,   _snake_case						:					Tuple	)      -> List[str]:







								"""simple docstring"""
								A_				 =						RegNetModel(config=_snake_case	)
								model.to(_snake_case	)
								model.eval()
								A_				 =						model(_snake_case	)
								# expected last hidden states: B, C, H // 32, W // 32
								self.parent.assertEqual(
								    result.last_hidden_state.shape       ,   (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)       ,   )




			def      lowerCamelCase__     (				self						:					Dict       ,   _snake_case						:					Tuple       ,   _snake_case						:					Optional[Any]       ,   _snake_case						:					Optional[Any]	)      -> Optional[Any]:







								"""simple docstring"""
								A_				 =						self.num_labels
								A_				 =						RegNetForImageClassification(_snake_case	)
								model.to(_snake_case	)
								model.eval()
								A_				 =						model(_snake_case       ,   labels=_snake_case	)
								self.parent.assertEqual(result.logits.shape       ,   (self.batch_size, self.num_labels)	)






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







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





@require_torch
class __lowerCAmelCase				(					_lowercase     ,				_lowercase     ,				unittest.TestCase      ):




			"""simple docstring"""







			snake_case	      =				(RegNetModel, RegNetForImageClassification) if is_torch_available() else ()
			snake_case	      =				(
			    {"feature-extraction": RegNetModel, "image-classification": RegNetForImageClassification}
			    if is_torch_available()
			    else {}
			)

			snake_case	      =				False
			snake_case	      =				False
			snake_case	      =				False
			snake_case	      =				False




			def      lowerCamelCase__     (				self						:					str	)      -> str:







								"""simple docstring"""
								A_				 =						RegNetModelTester(self	)
								A_				 =						ConfigTester(self       ,   config_class=_snake_case       ,   has_text_modality=_snake_case	)




			def      lowerCamelCase__     (				self						:					Tuple	)      -> List[Any]:







								"""simple docstring"""
								self.create_and_test_config_common_properties()
								self.config_tester.create_and_test_config_to_json_string()
								self.config_tester.create_and_test_config_to_json_file()
								self.config_tester.create_and_test_config_from_and_save_pretrained()
								self.config_tester.create_and_test_config_with_num_labels()
								self.config_tester.check_config_can_be_init_without_params()
								self.config_tester.check_config_arguments_init()




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







								"""simple docstring"""
								return




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







								"""simple docstring"""
								pass




			@unittest.skip(reason="RegNet does not support input and output embeddings"	)
			def      lowerCamelCase__     (				self						:					int	)      -> Union[str, Any]:







								"""simple docstring"""
								pass




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







								"""simple docstring"""
								A_ ,    A_				 =						self.model_tester.prepare_config_and_inputs_for_common()

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

													A_				 =						["pixel_values"]
													self.assertListEqual(arg_names[:1]       ,   _snake_case	)




			def      lowerCamelCase__     (				self						:					Any	)      -> int:







								"""simple docstring"""
								A_				 =						self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*_snake_case	)




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







								"""simple docstring"""
								A_ ,    A_				 =						self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
													A_				 =						model_class(config=_snake_case	)
													for name, module in model.named_modules():
																		if isinstance(_snake_case       ,   (nn.BatchNormad, nn.GroupNorm)	):
																							self.assertTrue(
																							    torch.all(module.weight == 1	)       ,   msg=F'Parameter {name} of model {model_class} seems not properly initialized'       ,   )
																							self.assertTrue(
																							    torch.all(module.bias == 0	)       ,   msg=F'Parameter {name} of model {model_class} seems not properly initialized'       ,   )




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







								"""simple docstring"""
								def check_hidden_states_output(_snake_case						:					Tuple       ,   _snake_case						:					Optional[int]       ,   _snake_case						:					Optional[int]	):
													A_				 =						model_class(_snake_case	)
													model.to(_snake_case	)
													model.eval()

													with torch.no_grad():
																		A_				 =						model(**self._prepare_for_class(_snake_case       ,   _snake_case	)	)

													A_				 =						outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

													A_				 =						self.model_tester.num_stages
													self.assertEqual(len(_snake_case	)       ,   expected_num_stages + 1	)

													# RegNet's feature maps are of shape (batch_size, num_channels, height, width)
													self.assertListEqual(
													    list(hidden_states[0].shape[-2:]	)       ,   [self.model_tester.image_size // 2, self.model_tester.image_size // 2]       ,   )

								A_ ,    A_				 =						self.model_tester.prepare_config_and_inputs_for_common()
								A_				 =						["basic", "bottleneck"]
								for model_class in self.all_model_classes:
													for layer_type in layers_type:
																		A_				 =						layer_type
																		A_				 =						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"]
																		A_				 =						True

																		check_hidden_states_output(_snake_case       ,   _snake_case       ,   _snake_case	)




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







								"""simple docstring"""
								A_				 =						self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*_snake_case	)






			@slow
			def      lowerCamelCase__     (				self						:					Optional[Any]	)      -> int:







								"""simple docstring"""
								for model_name in REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													A_				 =						RegNetModel.from_pretrained(_snake_case	)
													self.assertIsNotNone(_snake_case	)





def 							A_     ():

					'''simple docstring'''





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




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




			"""simple docstring"""







			@cached_property
			def      lowerCamelCase__     (				self						:					Union[str, Any]	)      -> int:







								"""simple docstring"""
								return (
								    AutoImageProcessor.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]	)
								    if is_vision_available()
								    else None
								)






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







								"""simple docstring"""
								A_				 =						RegNetForImageClassification.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]	).to(_snake_case	)

								A_				 =						self.default_image_processor
								A_				 =						prepare_img()
								A_				 =						image_processor(images=_snake_case       ,   return_tensors="pt"	).to(_snake_case	)

								# forward pass
								with torch.no_grad():
													A_				 =						model(**_snake_case	)

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

								A_				 =						torch.tensor([-0.4_1_8_0, -1.5_0_5_1, -3.4_8_3_6]	).to(_snake_case	)

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




