
'''simple docstring'''







import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
_lowercase					:    Any       				=			logging.get_logger(__name__)

_lowercase					:    List[Any]       				=			{
    "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",
    "mask_emb": "masked_spec_embed",
}
def    snake_case_						(							__SCREAMING_SNAKE_CASE   :					Any , __SCREAMING_SNAKE_CASE   :					Tuple , __SCREAMING_SNAKE_CASE   :					int , __SCREAMING_SNAKE_CASE   :					List[Any] , __SCREAMING_SNAKE_CASE   :					List[str]				):



						"""simple docstring"""





						for attribute in key.split('''.'''				):
												lowercase_      :	Optional[int]						=			getattr(lowerCamelCase__ , lowerCamelCase__				)

						if weight_type is not None:
												lowercase_      :	List[Any]						=			getattr(lowerCamelCase__ , lowerCamelCase__				).shape
						else:
												lowercase_      :	List[Any]						=			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":
												lowercase_      :	int						=			value
						elif weight_type == "weight_g":
												lowercase_      :	Optional[Any]						=			value
						elif weight_type == "weight_v":
												lowercase_      :	str						=			value
						elif weight_type == "bias":
												lowercase_      :	Union[str, Any]						=			value
						else:
												lowercase_      :	Optional[int]						=			value

						logger.info(F'''{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.'''				)
def    snake_case_						(							__SCREAMING_SNAKE_CASE   :					Tuple , __SCREAMING_SNAKE_CASE   :					List[Any]				):



						"""simple docstring"""





						lowercase_      :	Tuple						=			[]
						lowercase_      :	int						=			fairseq_model.state_dict()

						lowercase_      :	int						=			hf_model.feature_extractor

						for name, value in fairseq_dict.items():
												lowercase_      :	Tuple						=			False
												if "conv_layers" in name:
																		load_conv_layer(
																		    lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , hf_model.config.feat_extract_norm == '''group''' , )
																		lowercase_      :	Optional[Any]						=			True
												else:
																		for key, mapped_key in MAPPING.items():
																								lowercase_      :	str						=			mapped_key

																								if key in name:
																														lowercase_      :	int						=			True
																														if "*" in mapped_key:
																																				lowercase_      :	Any						=			name.split(lowerCamelCase__				)[0].split('''.'''				)[-2]
																																				lowercase_      :	Union[str, Any]						=			mapped_key.replace('''*''' , lowerCamelCase__				)
																														if "weight_g" in name:
																																				lowercase_      :	Tuple						=			'''weight_g'''
																														elif "weight_v" in name:
																																				lowercase_      :	Optional[int]						=			'''weight_v'''
																														elif "weight" in name:
																																				lowercase_      :	str						=			'''weight'''
																														elif "bias" in name:
																																				lowercase_      :	Dict						=			'''bias'''
																														else:
																																				lowercase_      :	Optional[int]						=			None
																														set_recursively(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__				)
																								continue
												if not is_used:
																		unused_weights.append(lowerCamelCase__				)

						logger.warning(F'''Unused weights: {unused_weights}'''				)
def    snake_case_						(							__SCREAMING_SNAKE_CASE   :					Optional[int] , __SCREAMING_SNAKE_CASE   :					Dict , __SCREAMING_SNAKE_CASE   :					Union[str, Any] , __SCREAMING_SNAKE_CASE   :					Dict , __SCREAMING_SNAKE_CASE   :					int				):



						"""simple docstring"""





						lowercase_      :	List[Any]						=			full_name.split('''conv_layers.'''				)[-1]
						lowercase_      :	Optional[Any]						=			name.split('''.'''				)
						lowercase_      :	int						=			int(items[0]				)
						lowercase_      :	Optional[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.'''
																		)
																		lowercase_      :	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.'''
																		)
																		lowercase_      :	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."
																		)
																		lowercase_      :	Any						=			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.'''
																		)
																		lowercase_      :	Dict						=			value
																		logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''				)
						else:
												unused_weights.append(lowerCamelCase__				)
def    snake_case_						(							__SCREAMING_SNAKE_CASE   :					Optional[Any]				):



						"""simple docstring"""





						lowercase_      :	List[Any]						=			HubertConfig()
						lowercase_      :	Tuple						=			model.config

						lowercase_      :	Union[str, Any]						=			fs_config.activation_dropout
						lowercase_      :	Optional[int]						=			False
						lowercase_      :	str						=			fs_config.attention_dropout
						lowercase_      :	List[str]						=			False
						lowercase_      :	str						=			eval(fs_config.extractor_conv_feature_layers				)
						lowercase_      :	List[Any]						=			[x[0] for x in conv_layers]
						lowercase_      :	Dict						=			[x[1] for x in conv_layers]
						lowercase_      :	int						=			[x[2] for x in conv_layers]
						lowercase_      :	Dict						=			'''gelu'''
						lowercase_      :	Union[str, Any]						=			'''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
						lowercase_      :	int						=			False
						lowercase_      :	List[str]						=			0.0
						lowercase_      :	List[Any]						=			0.0
						lowercase_      :	List[str]						=			fs_config.activation_fn
						lowercase_      :	Tuple						=			fs_config.dropout
						lowercase_      :	Optional[Any]						=			fs_config.encoder_embed_dim
						lowercase_      :	List[str]						=			0.02
						lowercase_      :	Optional[int]						=			fs_config.encoder_ffn_embed_dim
						lowercase_      :	int						=			1E-5
						lowercase_      :	Any						=			0.0
						lowercase_      :	int						=			fs_config.encoder_attention_heads
						lowercase_      :	str						=			fs_config.conv_pos_groups
						lowercase_      :	List[Any]						=			fs_config.conv_pos
						lowercase_      :	Dict						=			len(lowerCamelCase__				)
						lowercase_      :	str						=			fs_config.encoder_layers

						return config
@torch.no_grad()
def    snake_case_						(							__SCREAMING_SNAKE_CASE   :					Union[str, Any] , __SCREAMING_SNAKE_CASE   :					int=None				):



						"""simple docstring"""





						lowercase_      :	List[Any]						=			distilhubert().model.model

						if config_path is not None:
												lowercase_      :	Optional[Any]						=			HubertConfig.from_pretrained(lowerCamelCase__				)
						else:
												lowercase_      :	Union[str, Any]						=			convert_config(lowerCamelCase__				)
						lowercase_      :	List[str]						=			model.eval()

						lowercase_      :	Union[str, Any]						=			WavaVecaFeatureExtractor(
						    feature_size=1 , sampling_rate=16000 , padding_value=0 , do_normalize=lowerCamelCase__ , return_attention_mask=lowerCamelCase__ , )
						lowercase_      :	Optional[Any]						=			HubertModel(lowerCamelCase__				)

						recursively_load_weights(lowerCamelCase__ , lowerCamelCase__				)

						feature_extractor.save_pretrained(lowerCamelCase__				)
						hf_model.save_pretrained(lowerCamelCase__				)


if __name__ == "__main__":
							_lowercase					:    str       				=			argparse.ArgumentParser()
							parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
							parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
							_lowercase					:    Dict       				=			parser.parse_args()
							convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)








"""simple docstring"""







import inspect
import unittest

from transformers import ResNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
							import jax
							import jax.numpy as jnp

							from transformers.models.resnet.modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel

if is_vision_available():
							from PIL import Image

							from transformers import AutoImageProcessor






class   UpperCAmelCase__		(   unittest.TestCase  ):



		def __init__(			self	:     Dict   ,				snake_case	:     Dict   ,				snake_case	:     Optional[Any]=3   ,				snake_case	:     Union[str, Any]=32   ,				snake_case	:     int=3   ,				snake_case	:     Any=10   ,				snake_case	:     int=[10, 20, 30, 40]   ,				snake_case	:     List[Any]=[1, 1, 2, 1]   ,				snake_case	:     List[str]=True   ,				snake_case	:     Any=True   ,				snake_case	:     str="relu"   ,				snake_case	:     List[str]=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        A_		(			self	:     Optional[int]					)      ->    Any:






									'''simple docstring'''

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

									A    =       self.get_config()

									return config, pixel_values



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






									'''simple docstring'''

									return ResNetConfig(
									    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   ,				image_size=self.image_size   ,				)



		def        A_		(			self	:     Optional[Any]   ,				snake_case	:     str   ,				snake_case	:     Any					)      ->    int:






									'''simple docstring'''

									A    =       FlaxResNetModel(config=snake_case					)
									A    =       model(snake_case					)

									# Output shape (b, c, h, w)
									self.parent.assertEqual(
									    result.last_hidden_state.shape   ,				(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)   ,				)



		def        A_		(			self	:     Dict   ,				snake_case	:     Tuple   ,				snake_case	:     Optional[Any]					)      ->    Tuple:






									'''simple docstring'''

									A    =       self.num_labels
									A    =       FlaxResNetForImageClassification(config=snake_case					)
									A    =       model(snake_case					)
									self.parent.assertEqual(result.logits.shape   ,				(self.batch_size, self.num_labels)					)



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






									'''simple docstring'''

									A    =       self.prepare_config_and_inputs()
									A   ,	A    =       config_and_inputs
									A    =       {'pixel_values': pixel_values}
									return config, inputs_dict






@require_flax
class   UpperCAmelCase__		(   UpperCamelCase ,unittest.TestCase  ):
		lowerCAmelCase_							:						List[str]							=  (FlaxResNetModel, FlaxResNetForImageClassification) if is_flax_available() else ()

		lowerCAmelCase_							:						Tuple							=  False
		lowerCAmelCase_							:						List[str]							=  False
		lowerCAmelCase_							:						List[str]							=  False



		def        A_		(			self	:     Optional[Any]					)      ->    None:






									'''simple docstring'''

									A    =       FlaxResNetModelTester(self					)
									A    =       ConfigTester(self   ,				config_class=snake_case   ,				has_text_modality=snake_case					)



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






									'''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        A_		(			self	:     Dict					)      ->    List[Any]:






									'''simple docstring'''

									return



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






									'''simple docstring'''

									A    =       self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*snake_case					)



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






									'''simple docstring'''

									A    =       self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*snake_case					)



		@unittest.skip(reason='ResNet does not use inputs_embeds'					)
		def        A_		(			self	:     Tuple					)      ->    str:






									'''simple docstring'''

									pass



		@unittest.skip(reason='ResNet does not support input and output embeddings'					)
		def        A_		(			self	:     Tuple					)      ->    Any:






									'''simple docstring'''

									pass



		def        A_		(			self	:     Optional[Any]					)      ->    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(snake_case					)
																A    =       inspect.signature(model.__call__					)
																# 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        A_		(			self	:     Any					)      ->    Tuple:






									'''simple docstring'''

									def check_hidden_states_output(snake_case	:     Tuple   ,				snake_case	:     Union[str, Any]   ,				snake_case	:     Optional[int]					):
																A    =       model_class(snake_case					)

																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					)



		@unittest.skip(reason='ResNet does not use feedforward chunking'					)
		def        A_		(			self	:     Optional[int]					)      ->    List[Any]:






									'''simple docstring'''

									pass



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






									'''simple docstring'''

									A   ,	A    =       self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
																with self.subTest(model_class.__name__					):
																							A    =       self._prepare_for_class(snake_case   ,				snake_case					)
																							A    =       model_class(snake_case					)

																							@jax.jit
																							def model_jitted(snake_case	:     Any   ,				**snake_case	:     int					):
																														return model(pixel_values=snake_case   ,				**snake_case					)

																							with self.subTest('JIT Enabled'					):
																														A    =       model_jitted(**snake_case					).to_tuple()

																							with self.subTest('JIT Disabled'					):
																														with jax.disable_jit():
																																					A    =       model_jitted(**snake_case					).to_tuple()

																							self.assertEqual(len(snake_case					)   ,				len(snake_case					)					)
																							for jitted_output, output in zip(snake_case   ,				snake_case					):
																														self.assertEqual(jitted_output.shape   ,				output.shape					)



def 	lowerCAmelCase__      (  )			->				int:
							A    =       Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'      )
							return image






@require_flax
class   UpperCAmelCase__		(   unittest.TestCase  ):



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






									'''simple docstring'''

									return AutoImageProcessor.from_pretrained('microsoft/resnet-50'					) if is_vision_available() else None



		@slow
		def        A_		(			self	:     Any					)      ->    Tuple:






									'''simple docstring'''

									A    =       FlaxResNetForImageClassification.from_pretrained('microsoft/resnet-50'					)

									A    =       self.default_image_processor
									A    =       prepare_img()
									A    =       image_processor(images=snake_case   ,				return_tensors='np'					)

									A    =       model(**snake_case					)

									# verify the logits
									A    =       (1, 1_000)
									self.assertEqual(outputs.logits.shape   ,				snake_case					)

									A    =       jnp.array([-11.1069, -9.7877, -8.3777]					)

									self.assertTrue(jnp.allclose(outputs.logits[0, :3]   ,				snake_case   ,				atol=1E-4					)					)



