



'''simple docstring'''



import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    HubertConfig,
    HubertForCTC,
    HubertModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
lowerCAmelCase_     :       Dict								=						logging.get_logger(__name__)

lowerCAmelCase_     :       int								=						{
    'post_extract_proj': 'feature_projection.projection',
    'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
    'self_attn.k_proj': 'encoder.layers.*.attention.k_proj',
    'self_attn.v_proj': 'encoder.layers.*.attention.v_proj',
    'self_attn.q_proj': 'encoder.layers.*.attention.q_proj',
    'self_attn.out_proj': 'encoder.layers.*.attention.out_proj',
    'self_attn_layer_norm': 'encoder.layers.*.layer_norm',
    'fc1': 'encoder.layers.*.feed_forward.intermediate_dense',
    'fc2': 'encoder.layers.*.feed_forward.output_dense',
    'final_layer_norm': 'encoder.layers.*.final_layer_norm',
    'encoder.layer_norm': 'encoder.layer_norm',
    'w2v_model.layer_norm': 'feature_projection.layer_norm',
    'w2v_encoder.proj': 'lm_head',
    'mask_emb': 'masked_spec_embed',
}
def 					UpperCAmelCase   (						A  :				int					,		A  :				List[Any]					,		A  :				Any					,		A  :				List[Any]					,		A  :				List[str]				):
							for attribute in key.split('''.'''				):
														SCREAMING_SNAKE_CASE					:      Tuple     					=			getattr(A					,		A				)

							if weight_type is not None:
														SCREAMING_SNAKE_CASE					:      str     					=			getattr(A					,		A				).shape
							else:
														SCREAMING_SNAKE_CASE					:      str     					=			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":
														SCREAMING_SNAKE_CASE					:      Tuple     					=			value
							elif weight_type == "weight_g":
														SCREAMING_SNAKE_CASE					:      Tuple     					=			value
							elif weight_type == "weight_v":
														SCREAMING_SNAKE_CASE					:      Tuple     					=			value
							elif weight_type == "bias":
														SCREAMING_SNAKE_CASE					:      str     					=			value
							else:
														SCREAMING_SNAKE_CASE					:      Dict     					=			value

							logger.info(F"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}."""				)
def 					UpperCAmelCase   (						A  :				List[str]					,		A  :				List[str]					,		A  :				str				):
							SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			[]
							SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			fairseq_model.state_dict()

							SCREAMING_SNAKE_CASE					:      Optional[int]     					=			hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor

							for name, value in fairseq_dict.items():
														SCREAMING_SNAKE_CASE					:      Dict     					=			False
														if "conv_layers" in name:
																					load_conv_layer(
																					    A					,		A					,		A					,		A					,		hf_model.config.feat_extract_norm == '''group'''					,		)
																					SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			True
														else:
																					for key, mapped_key in MAPPING.items():
																												SCREAMING_SNAKE_CASE					:      List[str]     					=			'''hubert.''' + 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] and not is_finetuned):
																																			SCREAMING_SNAKE_CASE					:      Any     					=			True
																																			if "*" in mapped_key:
																																										SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			name.split(A				)[0].split('''.'''				)[-2]
																																										SCREAMING_SNAKE_CASE					:      Any     					=			mapped_key.replace('''*'''					,		A				)
																																			if "weight_g" in name:
																																										SCREAMING_SNAKE_CASE					:      Dict     					=			'''weight_g'''
																																			elif "weight_v" in name:
																																										SCREAMING_SNAKE_CASE					:      List[Any]     					=			'''weight_v'''
																																			elif "weight" in name:
																																										SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			'''weight'''
																																			elif "bias" in name:
																																										SCREAMING_SNAKE_CASE					:      Tuple     					=			'''bias'''
																																			else:
																																										SCREAMING_SNAKE_CASE					:      Optional[int]     					=			None
																																			set_recursively(A					,		A					,		A					,		A					,		A				)
																												continue
														if not is_used:
																					unused_weights.append(A				)

							logger.warning(F"""Unused weights: {unused_weights}"""				)
def 					UpperCAmelCase   (						A  :				Dict					,		A  :				List[Any]					,		A  :				Any					,		A  :				Dict					,		A  :				Optional[Any]				):
							SCREAMING_SNAKE_CASE					:      Optional[int]     					=			full_name.split('''conv_layers.'''				)[-1]
							SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			name.split('''.'''				)
							SCREAMING_SNAKE_CASE					:      int     					=			int(items[0]				)
							SCREAMING_SNAKE_CASE					:      Any     					=			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."""
																					)
																					SCREAMING_SNAKE_CASE					:      Dict     					=			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."""
																					)
																					SCREAMING_SNAKE_CASE					:      Tuple     					=			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."
																					)
																					SCREAMING_SNAKE_CASE					:      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."""
																					)
																					SCREAMING_SNAKE_CASE					:      str     					=			value
																					logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""				)
							else:
														unused_weights.append(A				)
@torch.no_grad()
def 					UpperCAmelCase   (						A  :				Tuple					,		A  :				Any					,		A  :				Dict=None					,		A  :				str=None					,		A  :				Any=True				):
							if config_path is not None:
														SCREAMING_SNAKE_CASE					:      Tuple     					=			HubertConfig.from_pretrained(A				)
							else:
														SCREAMING_SNAKE_CASE					:      List[str]     					=			HubertConfig()

							if is_finetuned:
														if dict_path:
																					SCREAMING_SNAKE_CASE					:      Dict     					=			Dictionary.load(A				)

																					# important change bos & pad token id since CTC symbol is <pad> and
																					# not <s> as in fairseq
																					SCREAMING_SNAKE_CASE					:      List[str]     					=			target_dict.pad_index
																					SCREAMING_SNAKE_CASE					:      List[str]     					=			target_dict.bos_index
																					SCREAMING_SNAKE_CASE					:      Optional[int]     					=			target_dict.eos_index
																					SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			len(target_dict.symbols				)
																					SCREAMING_SNAKE_CASE					:      Tuple     					=			os.path.join(A					,		'''vocab.json'''				)
																					if not os.path.isdir(A				):
																												logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(A				)				)
																												return
																					os.makedirs(A					,		exist_ok=A				)
																					with open(A					,		'''w'''					,		encoding='''utf-8'''				) as vocab_handle:
																												json.dump(target_dict.indices					,		A				)
																					SCREAMING_SNAKE_CASE					:      Any     					=			WavaVecaCTCTokenizer(
																					    A					,		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=A					,		)
																					SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			True if config.feat_extract_norm == '''layer''' else False
																					SCREAMING_SNAKE_CASE					:      Any     					=			WavaVecaFeatureExtractor(
																					    feature_size=1					,		sampling_rate=16000					,		padding_value=0					,		do_normalize=A					,		return_attention_mask=A					,		)
																					SCREAMING_SNAKE_CASE					:      str     					=			WavaVecaProcessor(feature_extractor=A					,		tokenizer=A				)
																					processor.save_pretrained(A				)

														SCREAMING_SNAKE_CASE					:      Any     					=			HubertForCTC(A				)
							else:
														SCREAMING_SNAKE_CASE					:      List[Any]     					=			HubertModel(A				)

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

							SCREAMING_SNAKE_CASE					:      Dict     					=			model[0].eval()

							recursively_load_weights(A					,		A					,		A				)

							hf_wavavec.save_pretrained(A				)


if __name__ == "__main__":
							lowerCAmelCase_     :       Optional[int]								=						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(
							    '--not_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
							)
							lowerCAmelCase_     :       Union[str, Any]								=						parser.parse_args()
							convert_hubert_checkpoint(
							    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
							)









'''simple docstring'''



from __future__ import annotations

import inspect
import unittest
from typing import List, Tuple

from transformers import RegNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

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 tensorflow as tf

							from transformers import TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST, TFRegNetForImageClassification, TFRegNetModel


if is_vision_available():
							from PIL import Image

							from transformers import AutoImageProcessor
class 	lowerCamelCase_							:







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

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Tuple     					=			parent
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			batch_size
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			image_size
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			num_channels
													SCREAMING_SNAKE_CASE					:      str     					=			embeddings_size
													SCREAMING_SNAKE_CASE					:      str     					=			hidden_sizes
													SCREAMING_SNAKE_CASE					:      Tuple     					=			depths
													SCREAMING_SNAKE_CASE					:      Dict     					=			is_training
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			use_labels
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			hidden_act
													SCREAMING_SNAKE_CASE					:      str     					=			num_labels
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			scope
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			len(lowerCAmelCase__				)







						def 					__lowercase    (  self   :      str				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

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

													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			self.get_config()
													return config, pixel_values, labels







						def 					__lowercase    (  self   :      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 					__lowercase    (  self   :      Dict				,    lowerCAmelCase__   :      Tuple				,    lowerCAmelCase__   :      Union[str, Any]				,    lowerCAmelCase__   :      Optional[int]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      int     					=			TFRegNetModel(config=lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      int     					=			model(lowerCAmelCase__				,    training=lowerCAmelCase__				)
													# 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 					__lowercase    (  self   :      int				,    lowerCAmelCase__   :      str				,    lowerCAmelCase__   :      int				,    lowerCAmelCase__   :      List[str]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			self.num_labels
													SCREAMING_SNAKE_CASE					:      Any     					=			TFRegNetForImageClassification(lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      Dict     					=			model(lowerCAmelCase__				,    labels=lowerCAmelCase__				,    training=lowerCAmelCase__				)
													self.parent.assertEqual(result.logits.shape				,    (self.batch_size, self.num_labels)				)







						def 					__lowercase    (  self   :      List[Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      int     					=			self.prepare_config_and_inputs()
													SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      List[str]     					=			config_and_inputs
													SCREAMING_SNAKE_CASE					:      Tuple     					=			{'''pixel_values''': pixel_values}
													return config, inputs_dict


@require_tf
class 	lowerCamelCase_							(					snake_case_    ,				snake_case_    ,				unittest.TestCase			):
						_lowerCAmelCase     :   str	     =				(TFRegNetModel, TFRegNetForImageClassification) if is_tf_available() else ()
						_lowerCAmelCase     :   str	     =				(
						    {'feature-extraction': TFRegNetModel, 'image-classification': TFRegNetForImageClassification}
						    if is_tf_available()
						    else {}
						)

						_lowerCAmelCase     :   Tuple	     =				False
						_lowerCAmelCase     :   List[Any]	     =				False
						_lowerCAmelCase     :   List[str]	     =				False
						_lowerCAmelCase     :   str	     =				False
						_lowerCAmelCase     :   str	     =				False







						def 					__lowercase    (  self   :      Union[str, Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Tuple     					=			TFRegNetModelTester(self				)
													SCREAMING_SNAKE_CASE					:      Dict     					=			ConfigTester(self				,    config_class=lowerCAmelCase__				,    has_text_modality=lowerCAmelCase__				)







						def 					__lowercase    (  self   :      List[str]				):

													"""simple docstring"""
													return







						@unittest.skip(reason='''RegNet does not use inputs_embeds'''				)
						def 					__lowercase    (  self   :      Optional[int]				):

													"""simple docstring"""
													pass







						@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 					__lowercase    (  self   :      int				):

													"""simple docstring"""
													super().test_keras_fit()







						@unittest.skip(reason='''RegNet does not support input and output embeddings'''				)
						def 					__lowercase    (  self   :      Dict				):

													"""simple docstring"""
													pass







						def 					__lowercase    (  self   :      int				):

													"""simple docstring"""
													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					:      int     					=			model_class(lowerCAmelCase__				)
																				SCREAMING_SNAKE_CASE					:      Any     					=			inspect.signature(model.call				)
																				# signature.parameters is an OrderedDict => so arg_names order is deterministic
																				SCREAMING_SNAKE_CASE					:      Tuple     					=			[*signature.parameters.keys()]

																				SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			['''pixel_values''']
																				self.assertListEqual(arg_names[:1]				,    lowerCAmelCase__				)







						def 					__lowercase    (  self   :      Any				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_model(*lowerCAmelCase__				)







						def 					__lowercase    (  self   :      Dict				):

													"""simple docstring"""
													def check_hidden_states_output(lowerCAmelCase__   :      Any				,    lowerCAmelCase__   :      int				,    lowerCAmelCase__   :      Tuple				):
																				SCREAMING_SNAKE_CASE					:      List[Any]     					=			model_class(lowerCAmelCase__				)
																				SCREAMING_SNAKE_CASE					:      Dict     					=			model(**self._prepare_for_class(lowerCAmelCase__				,    lowerCAmelCase__				)				,    training=lowerCAmelCase__				)

																				SCREAMING_SNAKE_CASE					:      int     					=			outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

																				SCREAMING_SNAKE_CASE					:      int     					=			self.model_tester.num_stages
																				self.assertEqual(len(lowerCAmelCase__				)				,    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]				,    )

													SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			self.model_tester.prepare_config_and_inputs_for_common()
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			['''basic''', '''bottleneck''']
													for model_class in self.all_model_classes:
																				for layer_type in layers_type:
																											SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			layer_type
																											SCREAMING_SNAKE_CASE					:      Tuple     					=			True
																											check_hidden_states_output(lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__				)

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

																											check_hidden_states_output(lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__				)







						def 					__lowercase    (  self   :      Union[str, Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			self.model_tester.prepare_config_and_inputs_for_common()

													def check_equivalence(lowerCAmelCase__   :      Tuple				,    lowerCAmelCase__   :      int				,    lowerCAmelCase__   :      Dict				,    lowerCAmelCase__   :      str={}				):
																				SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			model(lowerCAmelCase__				,    return_dict=lowerCAmelCase__				,    **lowerCAmelCase__				)
																				SCREAMING_SNAKE_CASE					:      Any     					=			model(lowerCAmelCase__				,    return_dict=lowerCAmelCase__				,    **lowerCAmelCase__				).to_tuple()

																				def recursive_check(lowerCAmelCase__   :      Dict				,    lowerCAmelCase__   :      str				):
																											if isinstance(lowerCAmelCase__				,    (List, Tuple)				):
																																		for tuple_iterable_value, dict_iterable_value in zip(lowerCAmelCase__				,    lowerCAmelCase__				):
																																									recursive_check(lowerCAmelCase__				,    lowerCAmelCase__				)
																											elif tuple_object is None:
																																		return
																											else:
																																		self.assertTrue(
																																		    all(tf.equal(lowerCAmelCase__				,    lowerCAmelCase__				)				)				,    msg=(
																																		        '''Tuple and dict output are not equal. Difference:'''
																																		        F""" {tf.math.reduce_max(tf.abs(tuple_object - dict_object				)				)}"""
																																		    )				,    )

																											recursive_check(lowerCAmelCase__				,    lowerCAmelCase__				)

													for model_class in self.all_model_classes:
																				SCREAMING_SNAKE_CASE					:      List[Any]     					=			model_class(lowerCAmelCase__				)

																				SCREAMING_SNAKE_CASE					:      int     					=			self._prepare_for_class(lowerCAmelCase__				,    lowerCAmelCase__				)
																				SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			self._prepare_for_class(lowerCAmelCase__				,    lowerCAmelCase__				)
																				check_equivalence(lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__				)

																				SCREAMING_SNAKE_CASE					:      Optional[int]     					=			self._prepare_for_class(lowerCAmelCase__				,    lowerCAmelCase__				,    return_labels=lowerCAmelCase__				)
																				SCREAMING_SNAKE_CASE					:      str     					=			self._prepare_for_class(lowerCAmelCase__				,    lowerCAmelCase__				,    return_labels=lowerCAmelCase__				)
																				check_equivalence(lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__				)

																				SCREAMING_SNAKE_CASE					:      Tuple     					=			self._prepare_for_class(lowerCAmelCase__				,    lowerCAmelCase__				)
																				SCREAMING_SNAKE_CASE					:      Dict     					=			self._prepare_for_class(lowerCAmelCase__				,    lowerCAmelCase__				)
																				check_equivalence(lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__				,    {'''output_hidden_states''': True}				)

																				SCREAMING_SNAKE_CASE					:      Any     					=			self._prepare_for_class(lowerCAmelCase__				,    lowerCAmelCase__				,    return_labels=lowerCAmelCase__				)
																				SCREAMING_SNAKE_CASE					:      List[Any]     					=			self._prepare_for_class(lowerCAmelCase__				,    lowerCAmelCase__				,    return_labels=lowerCAmelCase__				)
																				check_equivalence(lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__				,    {'''output_hidden_states''': True}				)







						def 					__lowercase    (  self   :      List[Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Dict     					=			self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase__				)







						@slow
						def 					__lowercase    (  self   :      Any				):

													"""simple docstring"""
													for model_name in TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																				SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			TFRegNetModel.from_pretrained(lowerCAmelCase__				)
																				self.assertIsNotNone(lowerCAmelCase__				)


def 					UpperCAmelCase   (						):
							SCREAMING_SNAKE_CASE					:      List[Any]     					=			Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''				)
							return image






@require_tf
@require_vision
class 	lowerCamelCase_							(					unittest.TestCase			):







						@cached_property
						def 					__lowercase    (  self   :      List[str]				):

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







						@slow
						def 					__lowercase    (  self   :      str				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      str     					=			TFRegNetForImageClassification.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]				)

													SCREAMING_SNAKE_CASE					:      Dict     					=			self.default_image_processor
													SCREAMING_SNAKE_CASE					:      int     					=			prepare_img()
													SCREAMING_SNAKE_CASE					:      List[str]     					=			image_processor(images=lowerCAmelCase__				,    return_tensors='''tf'''				)

													# forward pass
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			model(**lowerCAmelCase__				,    training=lowerCAmelCase__				)

													# verify the logits
													SCREAMING_SNAKE_CASE					:      List[str]     					=			tf.TensorShape((1, 10_00)				)
													self.assertEqual(outputs.logits.shape				,    lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      List[str]     					=			tf.constant([-0.4180, -1.5051, -3.4836]				)

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




