





import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWDConfig,
    SEWDForCTC,
    SEWDModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
a__		    =    logging.get_logger(__name__)

a__		    =    {
    '''post_extract_proj''': '''feature_projection''',
    '''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
    '''attention.self.query_proj''': '''encoder.encoder.layer.*.attention.self.query_proj''',
    '''attention.self.key_proj''': '''encoder.encoder.layer.*.attention.self.key_proj''',
    '''attention.self.value_proj''': '''encoder.encoder.layer.*.attention.self.value_proj''',
    '''attention.output.dense''': '''encoder.encoder.layer.*.attention.output.dense''',
    '''attention.output.LayerNorm''': '''encoder.encoder.layer.*.attention.output.LayerNorm''',
    '''intermediate.dense''': '''encoder.encoder.layer.*.intermediate.dense''',
    '''output.dense''': '''encoder.encoder.layer.*.output.dense''',
    '''output.LayerNorm''': '''encoder.encoder.layer.*.output.LayerNorm''',
    '''encoder.encoder.rel_embeddings''': '''encoder.encoder.rel_embeddings''',
    '''encoder.encoder.LayerNorm''': '''encoder.encoder.LayerNorm''',
    '''encoder.upsample.0''': '''encoder.upsample.projection''',
    '''encoder.layer_norm''': '''encoder.layer_norm''',
    '''w2v_model.layer_norm''': '''layer_norm''',
    '''w2v_encoder.proj''': '''lm_head''',
    '''mask_emb''': '''masked_spec_embed''',
}





def 							A__   (snake_case      :		List[Any]		,  snake_case      :		Any		,  snake_case      :		Any		,  snake_case      :		List[Any]		,  snake_case      :		Union[str, Any]							)    ->	Dict:
					for attribute in key.split("""."""							):
										__UpperCamelCase				:  Any								=			getattr(snake_case		,  snake_case							)

					if weight_type is not None:
										__UpperCamelCase				:  List[str]								=			getattr(snake_case		,  snake_case							).shape
					else:
										__UpperCamelCase				:  Dict								=			hf_pointer.shape

					assert hf_shape == value.shape, (
					    F'''Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'''
					    F''' {value.shape} for {full_name}'''
					)

					if weight_type == "weight":
										__UpperCamelCase				:  str								=			value
					elif weight_type == "weight_g":
										__UpperCamelCase				:  List[str]								=			value
					elif weight_type == "weight_v":
										__UpperCamelCase				:  Dict								=			value
					elif weight_type == "bias":
										__UpperCamelCase				:  Optional[int]								=			value
					else:
										__UpperCamelCase				:  List[Any]								=			value

					logger.info(F'''{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'''							)





def 							A__   (snake_case      :		Dict		,  snake_case      :		str		,  snake_case      :		Dict							)    ->	str:
					__UpperCamelCase				:  str								=			[]
					__UpperCamelCase				:  Dict								=			fairseq_model.state_dict()

					__UpperCamelCase				:  List[Any]								=			hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor

					for name, value in fairseq_dict.items():
										__UpperCamelCase				:  Dict								=			False
										if "conv_layers" in name:
															load_conv_layer(
															    snake_case		,  snake_case		,  snake_case		,  snake_case		,  hf_model.config.feat_extract_norm == """group"""		,  )
															__UpperCamelCase				:  Any								=			True
										else:
															for key, mapped_key in MAPPING.items():
																				__UpperCamelCase				:  Union[str, Any]								=			"""sew_d.""" + mapped_key if (is_finetuned and mapped_key != """lm_head""") else mapped_key

																				if key in name or key.split("""w2v_model."""							)[-1] == name.split("""."""							)[0]:
																									__UpperCamelCase				:  Any								=			True
																									if "*" in mapped_key:
																														__UpperCamelCase				:  Any								=			name.split(snake_case							)[0].split("""."""							)[-2]
																														if not layer_index.isnumeric():
																																			continue
																														__UpperCamelCase				:  Union[str, Any]								=			mapped_key.replace("""*"""		,  snake_case							)
																									if "weight_g" in name:
																														__UpperCamelCase				:  Optional[int]								=			"""weight_g"""
																									elif "weight_v" in name:
																														__UpperCamelCase				:  Optional[int]								=			"""weight_v"""
																									elif "weight" in name:
																														__UpperCamelCase				:  Any								=			"""weight"""
																									elif "bias" in name:
																														__UpperCamelCase				:  Optional[int]								=			"""bias"""
																									else:
																														__UpperCamelCase				:  Any								=			None
																									set_recursively(snake_case		,  snake_case		,  snake_case		,  snake_case		,  snake_case							)
																				continue
										if not is_used:
															unused_weights.append(snake_case							)

					logger.warning(F'''Unused weights: {unused_weights}'''							)





def 							A__   (snake_case      :		List[Any]		,  snake_case      :		int		,  snake_case      :		Union[str, Any]		,  snake_case      :		Optional[int]		,  snake_case      :		List[Any]							)    ->	str:
					__UpperCamelCase				:  int								=			full_name.split("""conv_layers."""							)[-1]
					__UpperCamelCase				:  Optional[int]								=			name.split("""."""							)
					__UpperCamelCase				:  Tuple								=			int(items[0]							)
					__UpperCamelCase				:  int								=			int(items[1]							)

					if type_id == 0:
										if "bias" in name:
															assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
															    F'''{full_name} has size {value.shape}, but'''
															    F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''
															)
															__UpperCamelCase				:  List[str]								=			value
															logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''							)
										elif "weight" in name:
															assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
															    F'''{full_name} has size {value.shape}, but'''
															    F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''
															)
															__UpperCamelCase				:  Union[str, Any]								=			value
															logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''							)
					elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
										if "bias" in name:
															assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
															    F'''{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'''
															    " found."
															)
															__UpperCamelCase				:  Optional[int]								=			value
															logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''							)
										elif "weight" in name:
															assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
															    F'''{full_name} has size {value.shape}, but'''
															    F''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''
															)
															__UpperCamelCase				:  str								=			value
															logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''							)
					else:
										unused_weights.append(snake_case							)





def 							A__   (snake_case      :		List[str]		,  snake_case      :		Optional[Any]							)    ->	List[str]:
					__UpperCamelCase				:  int								=			SEWDConfig()
					if is_finetuned:
										__UpperCamelCase				:  Optional[Any]								=			model.wav_encoder.wav_model.cfg
					else:
										__UpperCamelCase				:  List[str]								=			model.cfg

					__UpperCamelCase				:  Optional[int]								=			fs_config.conv_bias
					__UpperCamelCase				:  int								=			eval(fs_config.conv_feature_layers							)
					__UpperCamelCase				:  Tuple								=			[x[0] for x in conv_layers]
					__UpperCamelCase				:  List[str]								=			[x[1] for x in conv_layers]
					__UpperCamelCase				:  Any								=			[x[2] for x in conv_layers]
					__UpperCamelCase				:  Union[str, Any]								=			"""gelu"""
					__UpperCamelCase				:  Any								=			"""layer""" if fs_config.extractor_mode == """layer_norm""" else """group"""
					__UpperCamelCase				:  int								=			0.0
					__UpperCamelCase				:  Tuple								=			fs_config.activation_fn.name
					__UpperCamelCase				:  Union[str, Any]								=			fs_config.encoder_embed_dim
					__UpperCamelCase				:  List[Any]								=			0.02
					__UpperCamelCase				:  str								=			fs_config.encoder_ffn_embed_dim
					__UpperCamelCase				:  Optional[Any]								=			1e-5
					__UpperCamelCase				:  Optional[int]								=			fs_config.encoder_layerdrop
					__UpperCamelCase				:  Any								=			fs_config.encoder_attention_heads
					__UpperCamelCase				:  str								=			fs_config.conv_pos_groups
					__UpperCamelCase				:  Optional[int]								=			fs_config.conv_pos
					__UpperCamelCase				:  Any								=			len(snake_case							)
					__UpperCamelCase				:  Dict								=			fs_config.encoder_layers
					__UpperCamelCase				:  List[str]								=			fs_config.squeeze_factor
					# DeBERTa-specific parameters:
					__UpperCamelCase				:  List[Any]								=			fs_config.max_position_embeddings
					__UpperCamelCase				:  Optional[Any]								=			fs_config.position_buckets
					__UpperCamelCase				:  List[str]								=			fs_config.share_att_key
					__UpperCamelCase				:  Dict								=			fs_config.relative_attention
					__UpperCamelCase				:  Optional[int]								=			fs_config.position_biased_input
					__UpperCamelCase				:  Dict								=			tuple(fs_config.pos_att_type.split("""|"""							)							)
					__UpperCamelCase				:  List[str]								=			fs_config.norm_rel_ebd

					# take care of any params that are overridden by the Wav2VecCtc model
					if is_finetuned:
										__UpperCamelCase				:  str								=			model.cfg
										__UpperCamelCase				:  List[Any]								=			fs_config.final_dropout
										__UpperCamelCase				:  Union[str, Any]								=			fs_config.layerdrop
					__UpperCamelCase				:  Optional[Any]								=			fs_config.activation_dropout
					__UpperCamelCase				:  str								=			fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
					__UpperCamelCase				:  List[str]								=			fs_config.attention_dropout
					__UpperCamelCase				:  int								=			fs_config.dropout_input
					__UpperCamelCase				:  List[Any]								=			fs_config.dropout
					__UpperCamelCase				:  Union[str, Any]								=			fs_config.mask_channel_length
					__UpperCamelCase				:  Optional[Any]								=			fs_config.mask_channel_prob
					__UpperCamelCase				:  int								=			fs_config.mask_length
					__UpperCamelCase				:  int								=			fs_config.mask_prob

					__UpperCamelCase				:  Dict								=			"""Wav2Vec2FeatureExtractor"""
					__UpperCamelCase				:  str								=			"""Wav2Vec2CTCTokenizer"""

					return config





@torch.no_grad()
def 							A__   (snake_case      :		int		,  snake_case      :		Tuple		,  snake_case      :		Dict=None		,  snake_case      :		Union[str, Any]=None		,  snake_case      :		Tuple=True							)    ->	str:

					if is_finetuned:
										__UpperCamelCase				:  Any								=			fairseq.checkpoint_utils.load_model_ensemble_and_task(
										    [checkpoint_path]		,  arg_overrides={"""data""": """/""".join(dict_path.split("""/"""							)[:-1]							)}							)
					else:
										__UpperCamelCase				:  Optional[int]								=			fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]							)

					if config_path is not None:
										__UpperCamelCase				:  Optional[int]								=			SEWDConfig.from_pretrained(snake_case							)
					else:
										__UpperCamelCase				:  Dict								=			convert_config(model[0]		,  snake_case							)
					__UpperCamelCase				:  int								=			model[0].eval()

					__UpperCamelCase				:  List[Any]								=			True if config.feat_extract_norm == """layer""" else False
					__UpperCamelCase				:  Optional[int]								=			WavaVecaFeatureExtractor(
					    feature_size=1		,  sampling_rate=1_60_00		,  padding_value=0		,  do_normalize=snake_case		,  return_attention_mask=snake_case		,  )

					if is_finetuned:
										if dict_path:
															__UpperCamelCase				:  int								=			Dictionary.load(snake_case							)

															# important change bos & pad token id since CTC symbol is <pad> and
															# not <s> as in fairseq
															__UpperCamelCase				:  Optional[int]								=			target_dict.pad_index
															__UpperCamelCase				:  Tuple								=			target_dict.bos_index
															__UpperCamelCase				:  Tuple								=			target_dict.pad_index
															__UpperCamelCase				:  Tuple								=			target_dict.bos_index
															__UpperCamelCase				:  int								=			target_dict.eos_index
															__UpperCamelCase				:  str								=			len(target_dict.symbols							)
															__UpperCamelCase				:  int								=			os.path.join(snake_case		,  """vocab.json"""							)
															if not os.path.isdir(snake_case							):
																				logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(snake_case							)							)
																				return
															os.makedirs(snake_case		,  exist_ok=snake_case							)
															with open(snake_case		,  """w"""		,  encoding="""utf-8"""							) as vocab_handle:
																				json.dump(target_dict.indices		,  snake_case							)
															__UpperCamelCase				:  Dict								=			WavaVecaCTCTokenizer(
															    snake_case		,  unk_token=target_dict.unk_word		,  pad_token=target_dict.pad_word		,  bos_token=target_dict.bos_word		,  eos_token=target_dict.eos_word		,  word_delimiter_token="""|"""		,  do_lower_case=snake_case		,  )
															__UpperCamelCase				:  List[Any]								=			WavaVecaProcessor(feature_extractor=snake_case		,  tokenizer=snake_case							)
															processor.save_pretrained(snake_case							)

										__UpperCamelCase				:  str								=			SEWDForCTC(snake_case							)
					else:
										__UpperCamelCase				:  Any								=			SEWDModel(snake_case							)
										feature_extractor.save_pretrained(snake_case							)

					recursively_load_weights(snake_case		,  snake_case		,  snake_case							)

					hf_model.save_pretrained(snake_case							)


if __name__ == "__main__":
	a__		    =    argparse.ArgumentParser()
	parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
	parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
	parser.add_argument('''--dict_path''', default=None, type=str, help='''Path to dict of fine-tuned model''')
	parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
	parser.add_argument(
	    '''--is_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
	)
	a__		    =    parser.parse_args()
	convert_sew_checkpoint(
	    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
	)












import inspect
import unittest

from transformers import EfficientNetConfig
from transformers.testing_utils import is_pipeline_test, 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 EfficientNetForImageClassification, EfficientNetModel
	from transformers.models.efficientnet.modeling_efficientnet import EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
	from PIL import Image

	from transformers import AutoImageProcessor


class       SCREAMING_SNAKE_CASE_							:



	"""simple docstring"""


	def __init__(				self		:	Optional[int]				,    lowerCAmelCase		:	List[Any]				,    lowerCAmelCase		:	int=13				,    lowerCAmelCase		:	Union[str, Any]=32				,    lowerCAmelCase		:	List[Any]=3				,    lowerCAmelCase		:	Tuple=[3, 3, 5]				,    lowerCAmelCase		:	str=[32, 16, 24]				,    lowerCAmelCase		:	Dict=[16, 24, 40]				,    lowerCAmelCase		:	Optional[Any]=[1, 1, 2]				,    lowerCAmelCase		:	int=[1, 1, 2]				,    lowerCAmelCase		:	Any=[1, 6, 6]				,    lowerCAmelCase		:	str=True				,    lowerCAmelCase		:	Tuple=True				,    lowerCAmelCase		:	Dict=37				,    lowerCAmelCase		:	Any="gelu"				,    lowerCAmelCase		:	List[str]=10				,    )  ->					Optional[Any]:







						"""simple docstring"""







						__UpperCamelCase				:  List[Any]								=			parent
						__UpperCamelCase				:  Any								=			batch_size
						__UpperCamelCase				:  List[str]								=			image_size
						__UpperCamelCase				:  int								=			num_channels
						__UpperCamelCase				:  Tuple								=			kernel_sizes
						__UpperCamelCase				:  Union[str, Any]								=			in_channels
						__UpperCamelCase				:  int								=			out_channels
						__UpperCamelCase				:  List[str]								=			strides
						__UpperCamelCase				:  Optional[Any]								=			num_block_repeats
						__UpperCamelCase				:  Tuple								=			expand_ratios
						__UpperCamelCase				:  Optional[int]								=			is_training
						__UpperCamelCase				:  Dict								=			hidden_act
						__UpperCamelCase				:  Optional[int]								=			num_labels
						__UpperCamelCase				:  List[Any]								=			use_labels






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







						"""simple docstring"""







						__UpperCamelCase				:  Tuple								=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

						__UpperCamelCase				:  int								=			None
						if self.use_labels:
											__UpperCamelCase				:  str								=			ids_tensor([self.batch_size]				,    self.num_labels  )

						__UpperCamelCase				:  int								=			self.get_config()
						return config, pixel_values, labels






	def 				lowerCamelCase__    (				self		:	Tuple  )  ->					Any:







						"""simple docstring"""







						return EfficientNetConfig(
						    num_channels=self.num_channels				,    kernel_sizes=self.kernel_sizes				,    in_channels=self.in_channels				,    out_channels=self.out_channels				,    strides=self.strides				,    num_block_repeats=self.num_block_repeats				,    expand_ratios=self.expand_ratios				,    hidden_act=self.hidden_act				,    num_labels=self.num_labels				,    )






	def 				lowerCamelCase__    (				self		:	Optional[int]				,    lowerCAmelCase		:	int				,    lowerCAmelCase		:	Tuple				,    lowerCAmelCase		:	Optional[Any]  )  ->					Union[str, Any]:







						"""simple docstring"""







						__UpperCamelCase				:  int								=			EfficientNetModel(config=lowerCAmelCase  )
						model.to(lowerCAmelCase  )
						model.eval()
						__UpperCamelCase				:  Union[str, Any]								=			model(lowerCAmelCase  )
						# expected last hidden states: B, C, H // 4, W // 4
						self.parent.assertEqual(
						    result.last_hidden_state.shape				,    (self.batch_size, config.hidden_dim, self.image_size // 4, self.image_size // 4)				,    )






	def 				lowerCamelCase__    (				self		:	int				,    lowerCAmelCase		:	Union[str, Any]				,    lowerCAmelCase		:	Optional[Any]				,    lowerCAmelCase		:	str  )  ->					Optional[int]:







						"""simple docstring"""







						__UpperCamelCase				:  List[Any]								=			EfficientNetForImageClassification(lowerCAmelCase  )
						model.to(lowerCAmelCase  )
						model.eval()
						__UpperCamelCase				:  Optional[int]								=			model(lowerCAmelCase				,    labels=lowerCAmelCase  )
						self.parent.assertEqual(result.logits.shape				,    (self.batch_size, self.num_labels)  )






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







						"""simple docstring"""







						__UpperCamelCase				:  Optional[int]								=			self.prepare_config_and_inputs()
						__UpperCamelCase			, __UpperCamelCase			, __UpperCamelCase				:  str								=			config_and_inputs
						__UpperCamelCase				:  Optional[Any]								=			{"""pixel_values""": pixel_values}
						return config, inputs_dict






@require_torch
class       SCREAMING_SNAKE_CASE_							(					_UpperCamelCase  ,     _UpperCamelCase  ,     unittest.TestCase      ):



	"""simple docstring"""


	__magic_name__      :					List[Any]			  =		(EfficientNetModel, EfficientNetForImageClassification) if is_torch_available() else ()
	__magic_name__      :					Tuple			  =		(
	    {'feature-extraction': EfficientNetModel, 'image-classification': EfficientNetForImageClassification}
	    if is_torch_available()
	    else {}
	)

	__magic_name__      :					Union[str, Any]			  =		False
	__magic_name__      :					Dict			  =		False
	__magic_name__      :					str			  =		False
	__magic_name__      :					Union[str, Any]			  =		False
	__magic_name__      :					int			  =		False






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







						"""simple docstring"""







						__UpperCamelCase				:  List[str]								=			EfficientNetModelTester(self  )
						__UpperCamelCase				:  Optional[Any]								=			ConfigTester(
						    self				,    config_class=lowerCAmelCase				,    has_text_modality=lowerCAmelCase				,    hidden_size=37  )






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







						"""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		:	Optional[Any]  )  ->					Dict:







						"""simple docstring"""







						return






	@unittest.skip(reason="""EfficientNet does not use inputs_embeds"""  )
	def 				lowerCamelCase__    (				self		:	str  )  ->					List[str]:







						"""simple docstring"""







						pass






	@unittest.skip(reason="""EfficientNet does not support input and output embeddings"""  )
	def 				lowerCamelCase__    (				self		:	Optional[Any]  )  ->					Any:







						"""simple docstring"""







						pass






	@unittest.skip(reason="""EfficientNet does not use feedforward chunking"""  )
	def 				lowerCamelCase__    (				self		:	Tuple  )  ->					Optional[int]:







						"""simple docstring"""







						pass






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







						"""simple docstring"""







						__UpperCamelCase			, __UpperCamelCase				:  Dict								=			self.model_tester.prepare_config_and_inputs_for_common()

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

											__UpperCamelCase				:  Union[str, Any]								=			["""pixel_values"""]
											self.assertListEqual(arg_names[:1]				,    lowerCAmelCase  )






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







						"""simple docstring"""







						__UpperCamelCase				:  Optional[int]								=			self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*lowerCAmelCase  )






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







						"""simple docstring"""







						def check_hidden_states_output(lowerCAmelCase		:	Optional[Any]				,    lowerCAmelCase		:	str				,    lowerCAmelCase		:	Any  ):
											__UpperCamelCase				:  int								=			model_class(lowerCAmelCase  )
											model.to(lowerCAmelCase  )
											model.eval()

											with torch.no_grad():
																__UpperCamelCase				:  Tuple								=			model(**self._prepare_for_class(lowerCAmelCase				,    lowerCAmelCase  )  )

											__UpperCamelCase				:  Any								=			outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
											__UpperCamelCase				:  Union[str, Any]								=			sum(config.num_block_repeats  ) * 4
											self.assertEqual(len(lowerCAmelCase  )				,    lowerCAmelCase  )

											# EfficientNet'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]				,    )

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

						for model_class in self.all_model_classes:
											__UpperCamelCase				:  Union[str, Any]								=			True
											check_hidden_states_output(lowerCAmelCase				,    lowerCAmelCase				,    lowerCAmelCase  )

											# check that output_hidden_states also work using config
											del inputs_dict["output_hidden_states"]
											__UpperCamelCase				:  Optional[Any]								=			True

											check_hidden_states_output(lowerCAmelCase				,    lowerCAmelCase				,    lowerCAmelCase  )






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







						"""simple docstring"""







						__UpperCamelCase				:  Optional[Any]								=			self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase  )






	@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""  )
	def 				lowerCamelCase__    (				self		:	Optional[int]  )  ->					Dict:







						"""simple docstring"""







						pass






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







						"""simple docstring"""







						for model_name in EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											__UpperCamelCase				:  Union[str, Any]								=			EfficientNetModel.from_pretrained(lowerCAmelCase  )
											self.assertIsNotNone(lowerCAmelCase  )






	@is_pipeline_test
	@require_vision
	@slow
	def 				lowerCamelCase__    (				self		:	List[str]  )  ->					Union[str, Any]:







						"""simple docstring"""







						super().test_pipeline_image_classification()






def 							A__   ()    ->	Union[str, Any]:
					__UpperCamelCase				:  List[str]								=			Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""							)
					return image







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



	"""simple docstring"""


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







						"""simple docstring"""







						return AutoImageProcessor.from_pretrained("""google/efficientnet-b7"""  ) if is_vision_available() else None






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







						"""simple docstring"""







						__UpperCamelCase				:  Optional[Any]								=			EfficientNetForImageClassification.from_pretrained("""google/efficientnet-b7"""  ).to(lowerCAmelCase  )

						__UpperCamelCase				:  Any								=			self.default_image_processor
						__UpperCamelCase				:  Any								=			prepare_img()
						__UpperCamelCase				:  Optional[Any]								=			image_processor(images=lowerCAmelCase				,    return_tensors="""pt"""  ).to(lowerCAmelCase  )

						# forward pass
						with torch.no_grad():
											__UpperCamelCase				:  Tuple								=			model(**lowerCAmelCase  )

						# verify the logits
						__UpperCamelCase				:  Optional[int]								=			torch.Size((1, 1000)  )
						self.assertEqual(outputs.logits.shape				,    lowerCAmelCase  )

						__UpperCamelCase				:  int								=			torch.tensor([0.00_01, 0.00_02, 0.00_02]  ).to(lowerCAmelCase  )
						self.assertTrue(torch.allclose(outputs.logits[0, :3]				,    lowerCAmelCase				,    atol=1E-4  )  )






