import inspect
import os
from argparse import ArgumentParser, Namespace
from importlib import import_module

import huggingface_hub
import numpy as np
from packaging import version

from .. import (
    FEATURE_EXTRACTOR_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    PROCESSOR_MAPPING,
    TOKENIZER_MAPPING,
    AutoConfig,
    AutoFeatureExtractor,
    AutoImageProcessor,
    AutoProcessor,
    AutoTokenizer,
    is_datasets_available,
    is_tf_available,
    is_torch_available,
)
from ..utils import TF2_WEIGHTS_INDEX_NAME, TF2_WEIGHTS_NAME, logging
from . import BaseTransformersCLICommand


if is_tf_available():
				import tensorflow as tf

				tf.config.experimental.enable_tensor_float_aa_execution(False)

if is_torch_available():
				import torch

if is_datasets_available():
				from datasets import load_dataset


lowerCAmelCase__					:       str		=       5E-5  # larger error tolerance than in our internal tests, to avoid flaky user-facing errors
def 			UpperCamelCase__						(    A__   )       -> Union[str, Any]:
				return PTtoTFCommand(
				    args.model_name      ,					args.local_dir      ,					args.max_error      ,					args.new_weights      ,					args.no_pr      ,					args.push      ,					args.extra_commit_description      ,					args.override_model_class      ,					)





class 			__snake_case  (      _lowerCamelCase     ):




					@staticmethod
					def       __a      (       __UpperCamelCase		)					->  int:



									'''simple docstring'''







									snake_case__ :     List[Any] 							=				parser.add_parser(
									    'pt-to-tf'   ,	help=(
									        'CLI tool to run convert a transformers model from a PyTorch checkpoint to a TensorFlow checkpoint.'
									        ' Can also be used to validate existing weights without opening PRs, with --no-pr.'
									    )   ,	)
									train_parser.add_argument(
									    '--model-name'   ,	type=__UpperCamelCase   ,	required=__UpperCamelCase   ,	help='The model name, including owner/organization, as seen on the hub.'   ,	)
									train_parser.add_argument(
									    '--local-dir'   ,	type=__UpperCamelCase   ,	default=''   ,	help='Optional local directory of the model repository. Defaults to /tmp/{model_name}'   ,	)
									train_parser.add_argument(
									    '--max-error'   ,	type=__UpperCamelCase   ,	default=__UpperCamelCase   ,	help=(
									        F"""Maximum error tolerance. Defaults to {MAX_ERROR}. This flag should be avoided, use at your own risk."""
									    )   ,	)
									train_parser.add_argument(
									    '--new-weights'   ,	action='store_true'   ,	help='Optional flag to create new TensorFlow weights, even if they already exist.'   ,	)
									train_parser.add_argument(
									    '--no-pr'   ,	action='store_true'   ,	help='Optional flag to NOT open a PR with converted weights.'		)
									train_parser.add_argument(
									    '--push'   ,	action='store_true'   ,	help='Optional flag to push the weights directly to `main` (requires permissions)'   ,	)
									train_parser.add_argument(
									    '--extra-commit-description'   ,	type=__UpperCamelCase   ,	default=''   ,	help='Optional additional commit description to use when opening a PR (e.g. to tag the owner).'   ,	)
									train_parser.add_argument(
									    '--override-model-class'   ,	type=__UpperCamelCase   ,	default=__UpperCamelCase   ,	help='If you think you know better than the auto-detector, you can specify the model class here. '
									    'Can be either an AutoModel class or a specific model class like BertForSequenceClassification.'   ,	)
									train_parser.set_defaults(func=__UpperCamelCase		)




					@staticmethod
					def       __a      (       __UpperCamelCase   ,	__UpperCamelCase		)					->  Optional[Any]:



									'''simple docstring'''







									snake_case__ :     Optional[Any] 							=				set(pt_outputs.keys()		)
									snake_case__ :     int 							=				set(tf_outputs.keys()		)
									if pt_out_attrs != tf_out_attrs:
													raise ValueError(
													    F"""The model outputs have different attributes, aborting. (Pytorch: {pt_out_attrs}, TensorFlow:"""
													    F""" {tf_out_attrs})"""		)

									# 2. For each output attribute, computes the difference
									def _find_pt_tf_differences(__UpperCamelCase   ,	__UpperCamelCase   ,	__UpperCamelCase   ,	__UpperCamelCase=""		):
													# If the current attribute is a tensor, it is a leaf and we make the comparison. Otherwise, we will dig in
													# recursivelly, keeping the name of the attribute.
													if isinstance(__UpperCamelCase   ,	torch.Tensor		):
																	snake_case__ :     Any 							=				np.max(np.abs(pt_out.numpy() - tf_out.numpy()		)		)
																	snake_case__ :     Optional[Any] 							=				tensor_difference
													else:
																	snake_case__ :     Union[str, Any] 							=				attr_name
																	for i, pt_item in enumerate(__UpperCamelCase		):
																					# If it is a named attribute, we keep the name. Otherwise, just its index.
																					if isinstance(__UpperCamelCase   ,	__UpperCamelCase		):
																									snake_case__ :     List[Any] 							=				root_name + pt_item
																									snake_case__ :     Union[str, Any] 							=				tf_out[pt_item]
																									snake_case__ :     Union[str, Any] 							=				pt_out[pt_item]
																					else:
																									snake_case__ :     Optional[int] 							=				root_name + F"""[{i}]"""
																									snake_case__ :     int 							=				tf_out[i]
																					snake_case__ :     Any 							=				_find_pt_tf_differences(__UpperCamelCase   ,	__UpperCamelCase   ,	__UpperCamelCase   ,	__UpperCamelCase		)

													return differences

									return _find_pt_tf_differences(__UpperCamelCase   ,	__UpperCamelCase   ,	{}		)




					def __init__(       self   ,	__UpperCamelCase   ,	__UpperCamelCase   ,	__UpperCamelCase   ,	__UpperCamelCase   ,	__UpperCamelCase   ,	__UpperCamelCase   ,	__UpperCamelCase   ,	__UpperCamelCase   ,	*__UpperCamelCase   ,	)					->  List[Any]:



									'''simple docstring'''







									snake_case__ :     Dict 							=				logging.get_logger('transformers-cli/pt_to_tf'		)
									snake_case__ :     Any 							=				model_name
									snake_case__ :     Tuple 							=				local_dir if local_dir else os.path.join('/tmp'   ,	__UpperCamelCase		)
									snake_case__ :     Any 							=				max_error
									snake_case__ :     Tuple 							=				new_weights
									snake_case__ :     int 							=				no_pr
									snake_case__ :     Tuple 							=				push
									snake_case__ :     List[Any] 							=				extra_commit_description
									snake_case__ :     Tuple 							=				override_model_class




					def       __a      (       self   ,	__UpperCamelCase   ,	__UpperCamelCase   ,	__UpperCamelCase		)					->  Optional[Any]:



									'''simple docstring'''







									def _get_audio_input():
													snake_case__ :     Optional[Any] 							=				load_dataset('hf-internal-testing/librispeech_asr_dummy'   ,	'clean'   ,	split='validation'		)
													snake_case__ :     str 							=				ds.sort('id'		).select(range(2		)		)[:2]['audio']
													snake_case__ :     Union[str, Any] 							=				[x['array'] for x in speech_samples]
													return raw_samples

									snake_case__ :     Optional[Any] 							=				type(pt_model.config		)
									if model_config_class in PROCESSOR_MAPPING:
													snake_case__ :     Optional[int] 							=				AutoProcessor.from_pretrained(self._local_dir		)
													if model_config_class in TOKENIZER_MAPPING and processor.tokenizer.pad_token is None:
																	snake_case__ :     Optional[Any] 							=				processor.tokenizer.eos_token
									elif model_config_class in IMAGE_PROCESSOR_MAPPING:
													snake_case__ :     Any 							=				AutoImageProcessor.from_pretrained(self._local_dir		)
									elif model_config_class in FEATURE_EXTRACTOR_MAPPING:
													snake_case__ :     List[str] 							=				AutoFeatureExtractor.from_pretrained(self._local_dir		)
									elif model_config_class in TOKENIZER_MAPPING:
													snake_case__ :     Optional[Any] 							=				AutoTokenizer.from_pretrained(self._local_dir		)
													if processor.pad_token is None:
																	snake_case__ :     Optional[int] 							=				processor.eos_token
									else:
													raise ValueError(F"""Unknown data processing type (model config type: {model_config_class})"""		)

									snake_case__ :     Dict 							=				set(inspect.signature(pt_model.forward		).parameters.keys()		)
									snake_case__ :     Tuple 							=				{}
									if "input_ids" in model_forward_signature:
													processor_inputs.update(
													    {
													        'text': ['Hi there!', 'I am a batch with more than one row and different input lengths.'],
													        'padding': True,
													        'truncation': True,
													    }		)
									if "pixel_values" in model_forward_signature:
													snake_case__ :     Dict 							=				load_dataset('cifar10'   ,	'plain_text'   ,	split='test'		)[:2]['img']
													processor_inputs.update({'images': sample_images}		)
									if "input_features" in model_forward_signature:
													snake_case__ :     List[str] 							=				inspect.signature(processor.feature_extractor		).parameters
													# Pad to the largest input length by default but take feature extractor default
													# padding value if it exists e.g. "max_length" and is not False or None
													if "padding" in feature_extractor_signature:
																	snake_case__ :     Union[str, Any] 							=				feature_extractor_signature['padding'].default
																	if default_strategy is not False and default_strategy is not None:
																					snake_case__ :     Any 							=				default_strategy
																	else:
																					snake_case__ :     int 							=				True
													else:
																	snake_case__ :     Union[str, Any] 							=				True
													processor_inputs.update({'audio': _get_audio_input(), 'padding': padding_strategy}		)
									if "input_values" in model_forward_signature:  # Wav2Vec2 audio input
													processor_inputs.update({'audio': _get_audio_input(), 'padding': True}		)
									snake_case__ :     List[Any] 							=				processor(**__UpperCamelCase   ,	return_tensors='pt'		)
									snake_case__ :     Optional[int] 							=				processor(**__UpperCamelCase   ,	return_tensors='tf'		)

									# Extra input requirements, in addition to the input modality
									if (
									    config.is_encoder_decoder
									    or (hasattr(__UpperCamelCase   ,	'encoder'		) and hasattr(__UpperCamelCase   ,	'decoder'		))
									    or "decoder_input_ids" in tf_dummy_inputs
									):
													snake_case__ :     Tuple 							=				np.asarray([[1], [1]]   ,	dtype=__UpperCamelCase		) * (pt_model.config.decoder_start_token_id or 0)
													pt_input.update({'decoder_input_ids': torch.tensor(__UpperCamelCase		)}		)
													tf_input.update({'decoder_input_ids': tf.convert_to_tensor(__UpperCamelCase		)}		)

									return pt_input, tf_input




					def       __a      (       self		)					->  Optional[Any]:



									'''simple docstring'''







									if version.parse(huggingface_hub.__version__		) < version.parse('0.9.0'		):
													raise ImportError(
													    'The huggingface_hub version must be >= 0.9.0 to use this command. Please update your huggingface_hub'
													    ' installation.'		)
									else:
													from huggingface_hub import Repository, create_commit
													from huggingface_hub._commit_api import CommitOperationAdd

									# Fetch remote data
									snake_case__ :     Tuple 							=				Repository(local_dir=self._local_dir   ,	clone_from=self._model_name		)

									# Load config and get the appropriate architecture -- the latter is needed to convert the head's weights
									snake_case__ :     Optional[Any] 							=				AutoConfig.from_pretrained(self._local_dir		)
									snake_case__ :     Optional[Any] 							=				config.architectures
									if self._override_model_class is not None:
													if self._override_model_class.startswith('TF'		):
																	snake_case__ :     str 							=				[self._override_model_class[2:]]
													else:
																	snake_case__ :     Union[str, Any] 							=				[self._override_model_class]
													try:
																	snake_case__ :     List[str] 							=				getattr(import_module('transformers'		)   ,	architectures[0]		)
													except AttributeError:
																	raise ValueError(F"""Model class {self._override_model_class} not found in transformers."""		)
													try:
																	snake_case__ :     int 							=				getattr(import_module('transformers'		)   ,	'TF' + architectures[0]		)
													except AttributeError:
																	raise ValueError(F"""TF model class TF{self._override_model_class} not found in transformers."""		)
									elif architectures is None:  # No architecture defined -- use auto classes
													snake_case__ :     List[str] 							=				getattr(import_module('transformers'		)   ,	'AutoModel'		)
													snake_case__ :     List[str] 							=				getattr(import_module('transformers'		)   ,	'TFAutoModel'		)
													self._logger.warning('No detected architecture, using AutoModel/TFAutoModel'		)
									else:  # Architecture defined -- use it
													if len(__UpperCamelCase		) > 1:
																	raise ValueError(F"""More than one architecture was found, aborting. (architectures = {architectures})"""		)
													self._logger.warning(F"""Detected architecture: {architectures[0]}"""		)
													snake_case__ :     Dict 							=				getattr(import_module('transformers'		)   ,	architectures[0]		)
													try:
																	snake_case__ :     Union[str, Any] 							=				getattr(import_module('transformers'		)   ,	'TF' + architectures[0]		)
													except AttributeError:
																	raise AttributeError(F"""The TensorFlow equivalent of {architectures[0]} doesn't exist in transformers."""		)

        # Check the TF dummy inputs to see what keys we need in the forward pass
									snake_case__ :     Dict 							=				tf_class.from_config(__UpperCamelCase		)
									snake_case__ :     Any 							=				tf_from_pt_model.dummy_inputs

									del tf_from_pt_model  # Try to keep only one model in memory at a time

									# Load the model and get some basic inputs
									snake_case__ :     int 							=				pt_class.from_pretrained(self._local_dir		)
									pt_model.eval()

									snake_case__ :     List[str] 							=				self.get_inputs(__UpperCamelCase   ,	__UpperCamelCase   ,	__UpperCamelCase		)

									with torch.no_grad():
													snake_case__ :     str 							=				pt_model(**__UpperCamelCase   ,	output_hidden_states=__UpperCamelCase		)
									del pt_model  # will no longer be used, and may have a large memory footprint

									snake_case__ :     Dict 							=				tf_class.from_pretrained(self._local_dir   ,	from_pt=__UpperCamelCase		)
									snake_case__ :     Tuple 							=				tf_from_pt_model(**__UpperCamelCase   ,	output_hidden_states=__UpperCamelCase   ,	training=__UpperCamelCase		)

									# Confirms that cross loading PT weights into TF worked.
									snake_case__ :     int 							=				self.find_pt_tf_differences(__UpperCamelCase   ,	__UpperCamelCase		)
									snake_case__ :     Union[str, Any] 							=				{k: v for k, v in crossload_differences.items() if 'hidden' not in k}
									snake_case__ :     Dict 							=				{k: v for k, v in crossload_differences.items() if 'hidden' in k}
									if len(__UpperCamelCase		) == 0 and architectures is not None:
													raise ValueError(
													    F"""Something went wrong -- the config file has architectures ({architectures}), but no model head"""
													    ' output was found. All outputs start with \'hidden\''		)
									snake_case__ :     Optional[int] 							=				max(output_differences.values()		) if output_differences else 0.0
									snake_case__ :     int 							=				max(hidden_differences.values()		)
									if max_crossload_output_diff > self._max_error or max_crossload_hidden_diff > self._max_error:
													raise ValueError(
													    'The cross-loaded TensorFlow model has different outputs, something went wrong!\n'
													    + F"""\nList of maximum output differences above the threshold ({self._max_error}):\n"""
													    + '\n'.join([F"""{k}: {v:.3e}""" for k, v in output_differences.items() if v > self._max_error]		)
													    + F"""\n\nList of maximum hidden layer differences above the threshold ({self._max_error}):\n"""
													    + '\n'.join([F"""{k}: {v:.3e}""" for k, v in hidden_differences.items() if v > self._max_error]		)		)

									# Save the weights in a TF format (if needed) and confirms that the results are still good
									snake_case__ :     Any 							=				os.path.join(self._local_dir   ,	__UpperCamelCase		)
									snake_case__ :     Optional[Any] 							=				os.path.join(self._local_dir   ,	__UpperCamelCase		)
									if (not os.path.exists(__UpperCamelCase		) and not os.path.exists(__UpperCamelCase		)) or self._new_weights:
													tf_from_pt_model.save_pretrained(self._local_dir		)
									del tf_from_pt_model  # will no longer be used, and may have a large memory footprint

									snake_case__ :     List[str] 							=				tf_class.from_pretrained(self._local_dir		)
									snake_case__ :     Union[str, Any] 							=				tf_model(**__UpperCamelCase   ,	output_hidden_states=__UpperCamelCase		)

									snake_case__ :     Optional[Any] 							=				self.find_pt_tf_differences(__UpperCamelCase   ,	__UpperCamelCase		)
									snake_case__ :     Any 							=				{k: v for k, v in conversion_differences.items() if 'hidden' not in k}
									snake_case__ :     Any 							=				{k: v for k, v in conversion_differences.items() if 'hidden' in k}
									if len(__UpperCamelCase		) == 0 and architectures is not None:
													raise ValueError(
													    F"""Something went wrong -- the config file has architectures ({architectures}), but no model head"""
													    ' output was found. All outputs start with \'hidden\''		)
									snake_case__ :     int 							=				max(output_differences.values()		) if output_differences else 0.0
									snake_case__ :     Any 							=				max(hidden_differences.values()		)
									if max_conversion_output_diff > self._max_error or max_conversion_hidden_diff > self._max_error:
													raise ValueError(
													    'The converted TensorFlow model has different outputs, something went wrong!\n'
													    + F"""\nList of maximum output differences above the threshold ({self._max_error}):\n"""
													    + '\n'.join([F"""{k}: {v:.3e}""" for k, v in output_differences.items() if v > self._max_error]		)
													    + F"""\n\nList of maximum hidden layer differences above the threshold ({self._max_error}):\n"""
													    + '\n'.join([F"""{k}: {v:.3e}""" for k, v in hidden_differences.items() if v > self._max_error]		)		)

									snake_case__ :     Any 							=				'Update TF weights' if self._new_weights else 'Add TF weights'
									if self._push:
													repo.git_add(auto_lfs_track=__UpperCamelCase		)
													repo.git_commit(__UpperCamelCase		)
													repo.git_push(blocking=__UpperCamelCase		)  # this prints a progress bar with the upload
													self._logger.warning(F"""TF weights pushed into {self._model_name}"""		)
									elif not self._no_pr:
													self._logger.warning('Uploading the weights into a new PR...'		)
													snake_case__ :     Optional[int] 							=				(
													    'Model converted by the [`transformers`\' `pt_to_tf`'
													    ' CLI](https://github.com/huggingface/transformers/blob/main/src/transformers/commands/pt_to_tf.py). '
													    'All converted model outputs and hidden layers were validated against its PyTorch counterpart.\n\n'
													    F"""Maximum crossload output difference={max_crossload_output_diff:.3e}; """
													    F"""Maximum crossload hidden layer difference={max_crossload_hidden_diff:.3e};\n"""
													    F"""Maximum conversion output difference={max_conversion_output_diff:.3e}; """
													    F"""Maximum conversion hidden layer difference={max_conversion_hidden_diff:.3e};\n"""
													)
													if self._max_error > MAX_ERROR:
																	commit_descrition += (
																	    F"""\n\nCAUTION: The maximum admissible error was manually increased to {self._max_error}!"""
																	)
													if self._extra_commit_description:
																	commit_descrition += "\n\n" + self._extra_commit_description

													# sharded model -> adds all related files (index and .h5 shards)
													if os.path.exists(__UpperCamelCase		):
																	snake_case__ :     str 							=				[
																	    CommitOperationAdd(path_in_repo=__UpperCamelCase   ,	path_or_fileobj=__UpperCamelCase		)
																	]
																	for shard_path in tf.io.gfile.glob(self._local_dir + '/tf_model-*.h5'		):
																					operations += [
																					    CommitOperationAdd(path_in_repo=os.path.basename(__UpperCamelCase		)   ,	path_or_fileobj=__UpperCamelCase		)
																					]
													else:
																	snake_case__ :     List[Any] 							=				[CommitOperationAdd(path_in_repo=__UpperCamelCase   ,	path_or_fileobj=__UpperCamelCase		)]

													snake_case__ :     Tuple 							=				create_commit(
													    repo_id=self._model_name   ,	operations=__UpperCamelCase   ,	commit_message=__UpperCamelCase   ,	commit_description=__UpperCamelCase   ,	repo_type='model'   ,	create_pr=__UpperCamelCase   ,	).pr_url
													self._logger.warning(F"""PR open in {hub_pr_url}"""		)



import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
lowerCAmelCase__					:       Any		=       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''',
    '''mask_emb''': '''masked_spec_embed''',
}
def 			UpperCamelCase__						(    A__      ,			A__      ,			A__      ,			A__      ,			A__   )       -> Optional[int]:
				for attribute in key.split('.'   ):
								snake_case__ :     Optional[Any] 							=				getattr(A__      ,			A__   )

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

				logger.info(F"""{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}."""   )
def 			UpperCamelCase__						(    A__      ,			A__   )       -> List[str]:
				snake_case__ :     Tuple 							=				[]
				snake_case__ :     int 							=				fairseq_model.state_dict()

				snake_case__ :     int 							=				hf_model.feature_extractor

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

																if key in name:
																				snake_case__ :     int 							=				True
																				if "*" in mapped_key:
																								snake_case__ :     List[str] 							=				name.split(A__   )[0].split('.'   )[-2]
																								snake_case__ :     Dict 							=				mapped_key.replace('*'      ,			A__   )
																				if "weight_g" in name:
																								snake_case__ :     int 							=				'weight_g'
																				elif "weight_v" in name:
																								snake_case__ :     int 							=				'weight_v'
																				elif "weight" in name:
																								snake_case__ :     Any 							=				'weight'
																				elif "bias" in name:
																								snake_case__ :     Tuple 							=				'bias'
																				else:
																								snake_case__ :     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__      ,			A__      ,			A__      ,			A__      ,			A__   )       -> Tuple:
				snake_case__ :     Optional[int] 							=				full_name.split('conv_layers.'   )[-1]
				snake_case__ :     Dict 							=				name.split('.'   )
				snake_case__ :     str 							=				int(items[0]   )
				snake_case__ :     Optional[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."""
												)
												snake_case__ :     List[Any] 							=				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."""
												)
												snake_case__ :     Optional[int] 							=				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."
												)
												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."""
												)
												snake_case__ :     Union[str, Any] 							=				value
												logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""   )
				else:
								unused_weights.append(A__   )
def 			UpperCamelCase__						(    A__   )       -> Union[str, Any]:
				snake_case__ :     Optional[Any] 							=				HubertConfig()
				snake_case__ :     Dict 							=				model.config

				snake_case__ :     Optional[Any] 							=				fs_config.activation_dropout
				snake_case__ :     Union[str, Any] 							=				False
				snake_case__ :     Tuple 							=				fs_config.attention_dropout
				snake_case__ :     List[str] 							=				False
				snake_case__ :     Optional[int] 							=				eval(fs_config.extractor_conv_feature_layers   )
				snake_case__ :     Optional[int] 							=				[x[0] for x in conv_layers]
				snake_case__ :     Union[str, Any] 							=				[x[1] for x in conv_layers]
				snake_case__ :     Dict 							=				[x[2] for x in conv_layers]
				snake_case__ :     int 							=				'gelu'
				snake_case__ :     Dict 							=				'layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
				snake_case__ :     Any 							=				False
				snake_case__ :     List[Any] 							=				0.0
				snake_case__ :     Any 							=				0.0
				snake_case__ :     Union[str, Any] 							=				fs_config.activation_fn
				snake_case__ :     List[str] 							=				fs_config.dropout
				snake_case__ :     Dict 							=				fs_config.encoder_embed_dim
				snake_case__ :     Any 							=				0.0_2
				snake_case__ :     Union[str, Any] 							=				fs_config.encoder_ffn_embed_dim
				snake_case__ :     List[Any] 							=				1e-5
				snake_case__ :     Union[str, Any] 							=				0.0
				snake_case__ :     int 							=				fs_config.encoder_attention_heads
				snake_case__ :     Any 							=				fs_config.conv_pos_groups
				snake_case__ :     int 							=				fs_config.conv_pos
				snake_case__ :     Any 							=				len(A__   )
				snake_case__ :     str 							=				fs_config.encoder_layers

				return config
@torch.no_grad()
def 			UpperCamelCase__						(    A__      ,			A__=None   )       -> str:
				snake_case__ :     int 							=				distilhubert().model.model

				if config_path is not None:
								snake_case__ :     Optional[int] 							=				HubertConfig.from_pretrained(A__   )
				else:
								snake_case__ :     int 							=				convert_config(A__   )
				snake_case__ :     Dict 							=				model.eval()

				snake_case__ :     int 							=				WavaVecaFeatureExtractor(
				    feature_size=1      ,			sampling_rate=1_6000      ,			padding_value=0      ,			do_normalize=A__      ,			return_attention_mask=A__      ,			)
				snake_case__ :     Optional[int] 							=				HubertModel(A__   )

				recursively_load_weights(A__      ,			A__   )

				feature_extractor.save_pretrained(A__   )
				hf_model.save_pretrained(A__   )


if __name__ == "__main__":
				lowerCAmelCase__					:       Union[str, Any]		=       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''')
				lowerCAmelCase__					:       List[Any]		=       parser.parse_args()
				convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)


