





'''simple docstring'''







# Copyright (c) 2021-, NVIDIA CORPORATION.  All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

####################################################################################################

#
# Note: If when running this conversion script you're getting an exception:
#     ModuleNotFoundError: No module named 'megatron.model.enums'
# you need to tell python where to find the clone of Megatron-LM, e.g.:
#
# cd /tmp
# git clone https://github.com/NVIDIA/Megatron-LM
# PYTHONPATH=/tmp/Megatron-LM python src/transformers/models/megatron_bert/convert_megatron_bert_checkpoint.py ...
#
# if you already have it cloned elsewhere, simply adjust the path to the existing path
#
# If the training was done using a Megatron-LM fork, e.g.,
# https://github.com/microsoft/Megatron-DeepSpeed/ then chances are that you need to have that one
# in your path, i.e., /path/to/Megatron-DeepSpeed/
#

import argparse
import os
import re
import zipfile

import torch

from transformers import MegatronBertConfig





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      Union[str, Any],      SCREAMING_SNAKE_CASE__   :      Dict,      SCREAMING_SNAKE_CASE__   :      Tuple=0 )			->						Any:
						# Format the message.
						if name is None:
												UpperCAmelCase_      :   Tuple      =	None
						else:
												UpperCAmelCase_      :   Dict      =	'''.''' * max(0,      spaces - 2 ) + '''# {:''' + str(50 - spaces ) + '''s}'''
												UpperCAmelCase_      :   List[str]      =	fmt.format(SCREAMING_SNAKE_CASE__ )

						# Print and recurse (if needed).
						if isinstance(SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__ ):
												if msg is not None:
																		print(SCREAMING_SNAKE_CASE__ )
												for k in val.keys():
																		recursive_print(SCREAMING_SNAKE_CASE__,      val[k],      spaces + 2 )
						elif isinstance(SCREAMING_SNAKE_CASE__,      torch.Tensor ):
												print(SCREAMING_SNAKE_CASE__,      ''':''',      val.size() )
						else:
												print(SCREAMING_SNAKE_CASE__,      ''':''',      SCREAMING_SNAKE_CASE__ )





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      Any,      SCREAMING_SNAKE_CASE__   :      Any,      SCREAMING_SNAKE_CASE__   :      Tuple,      SCREAMING_SNAKE_CASE__   :      Any,      SCREAMING_SNAKE_CASE__   :      Union[str, Any] )			->						List[Any]:
						# Permutes layout of param tensor to [num_splits * num_heads * hidden_size, :]
						# for compatibility with later versions of NVIDIA Megatron-LM.
						# The inverse operation is performed inside Megatron-LM to read checkpoints:
						# https://github.com/NVIDIA/Megatron-LM/blob/v2.4/megatron/checkpointing.py#L209
						# If param is the weight tensor of the self-attention block, the returned tensor
						# will have to be transposed one more time to be read by HuggingFace BERT.
						UpperCAmelCase_      :   Optional[Any]      =	param.size()
						if checkpoint_version == 1.0:
												# version 1.0 stores [num_heads * hidden_size * num_splits, :]
												UpperCAmelCase_      :   Tuple      =	(num_heads, hidden_size, num_splits) + input_shape[1:]
												UpperCAmelCase_      :   Any      =	param.view(*SCREAMING_SNAKE_CASE__ )
												UpperCAmelCase_      :   Optional[int]      =	param.transpose(0,      2 )
												UpperCAmelCase_      :   str      =	param.transpose(1,      2 ).contiguous()
						elif checkpoint_version >= 2.0:
												# other versions store [num_heads * num_splits * hidden_size, :]
												UpperCAmelCase_      :   str      =	(num_heads, num_splits, hidden_size) + input_shape[1:]
												UpperCAmelCase_      :   Any      =	param.view(*SCREAMING_SNAKE_CASE__ )
												UpperCAmelCase_      :   Any      =	param.transpose(0,      1 ).contiguous()
						UpperCAmelCase_      :   Optional[int]      =	param.view(*SCREAMING_SNAKE_CASE__ )
						return param





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      int,      SCREAMING_SNAKE_CASE__   :      int,      SCREAMING_SNAKE_CASE__   :      Dict )			->						Tuple:
						# The converted output model.
						UpperCAmelCase_      :   List[Any]      =	{}

						# old versions did not store training args
						UpperCAmelCase_      :   Optional[Any]      =	input_state_dict.get('''args''',      SCREAMING_SNAKE_CASE__ )
						if ds_args is not None:
												# do not make the user write a config file when the exact dimensions/sizes are already in the checkpoint
												# from pprint import pprint
												# pprint(vars(ds_args))

												UpperCAmelCase_      :   List[str]      =	ds_args.tokenizer_type
												UpperCAmelCase_      :   Any      =	ds_args.padded_vocab_size
												UpperCAmelCase_      :   List[str]      =	ds_args.max_position_embeddings
												UpperCAmelCase_      :   int      =	ds_args.hidden_size
												UpperCAmelCase_      :   Optional[Any]      =	ds_args.num_layers
												UpperCAmelCase_      :   Union[str, Any]      =	ds_args.num_attention_heads
												UpperCAmelCase_      :   Optional[Any]      =	ds_args.ffn_hidden_size if '''ffn_hidden_size''' in ds_args else 4 * ds_args.hidden_size
												# pprint(config)

						# The number of heads.
						UpperCAmelCase_      :   Tuple      =	config.num_attention_heads
						# The hidden_size per head.
						UpperCAmelCase_      :   Dict      =	config.hidden_size // heads
						# Megatron-LM checkpoint version
						if "checkpoint_version" in input_state_dict.keys():
												UpperCAmelCase_      :   Optional[Any]      =	input_state_dict['''checkpoint_version''']
						else:
												UpperCAmelCase_      :   Dict      =	0.0

						# The model.
						UpperCAmelCase_      :   str      =	input_state_dict['''model''']
						# The language model.
						UpperCAmelCase_      :   str      =	model['''language_model''']
						# The embeddings.
						UpperCAmelCase_      :   Any      =	lm['''embedding''']

						# The word embeddings.
						UpperCAmelCase_      :   Dict      =	embeddings['''word_embeddings''']['''weight''']
						# Truncate the embedding table to vocab_size rows.
						UpperCAmelCase_      :   List[str]      =	word_embeddings[: config.vocab_size, :]
						# Store the word embeddings.
						UpperCAmelCase_      :   Union[str, Any]      =	word_embeddings

						# The position embeddings.
						UpperCAmelCase_      :   Any      =	embeddings['''position_embeddings''']['''weight''']
						assert pos_embeddings.size(0 ) == config.max_position_embeddings and pos_embeddings.size(1 ) == config.hidden_size
						# Store the position embeddings.
						UpperCAmelCase_      :   List[Any]      =	pos_embeddings

						# The token-type embeddings.
						UpperCAmelCase_      :   Tuple      =	embeddings['''tokentype_embeddings''']['''weight''']
						# Store the position embeddings.
						UpperCAmelCase_      :   Union[str, Any]      =	tokentype_embeddings

						# The transformer.
						UpperCAmelCase_      :   Optional[Any]      =	lm['''transformer'''] if '''transformer''' in lm.keys() else lm['''encoder''']

						# The regex to extract layer names.
						UpperCAmelCase_      :   str      =	re.compile(R'''layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)''' )

						# The simple map of names for "automated" rules.
						UpperCAmelCase_      :   List[str]      =	{
						    '''attention.dense''': '''.attention.output.dense.''',
						    '''self_attention.dense''': '''.attention.output.dense.''',
						    '''mlp.dense_h_to_4h''': '''.intermediate.dense.''',
						    '''mlp.dense_4h_to_h''': '''.output.dense.''',
						}

						# Keep track of the attention/query/value tensor.
						UpperCAmelCase_      :   str      =	None

						# Extract the layers.
						for key, val in transformer.items():
												# Match the name.
												UpperCAmelCase_      :   int      =	layer_re.match(SCREAMING_SNAKE_CASE__ )

												# Stop if that's not a layer
												if m is None:
																		break

												# The index of the layer.
												UpperCAmelCase_      :   List[str]      =	int(m.group(1 ) )
												# The name of the operation.
												UpperCAmelCase_      :   str      =	m.group(2 )
												# Is it a weight or a bias?
												UpperCAmelCase_      :   Any      =	m.group(3 )

												# The name of the layer.
												UpperCAmelCase_      :   Any      =	F"""bert.encoder.layer.{layer_idx}"""

												# For layernorm(s), simply store the layer norm.
												if op_name.endswith('''layernorm''' ):
																		UpperCAmelCase_      :   Dict      =	'''attention.ln''' if op_name.startswith('''input''' ) else '''ln'''
																		UpperCAmelCase_      :   List[Any]      =	val

												# Transpose the QKV matrix.
												elif (
												    op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
												) and weight_or_bias == "weight":
																		# Make sure the QKV pointer is nil.
																		assert attention_qkv_weight is None, ""

																		UpperCAmelCase_      :   List[str]      =	fix_query_key_value_ordering(SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__,      3,      SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__ )
																		# Store the tensor as we need the bias as well to interleave QKV and biases.
																		UpperCAmelCase_      :   Union[str, Any]      =	out_val

												# Transpose the bias.
												elif (
												    op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
												) and weight_or_bias == "bias":
																		# Make sure we read the weight tensor.
																		assert attention_qkv_weight is not None, ""

																		# Split the QKV matrix into Q, K and V. Megatron stores Q,K,V interleaved.
																		UpperCAmelCase_      :   Tuple      =	attention_qkv_weight[0 * config.hidden_size : 1 * config.hidden_size, :]
																		UpperCAmelCase_      :   List[str]      =	attention_qkv_weight[1 * config.hidden_size : 2 * config.hidden_size, :]
																		UpperCAmelCase_      :   Optional[int]      =	attention_qkv_weight[2 * config.hidden_size : 3 * config.hidden_size, :]

																		UpperCAmelCase_      :   str      =	fix_query_key_value_ordering(SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__,      3,      SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__ )
																		# Split the bias.
																		UpperCAmelCase_      :   Dict      =	out_val[0 * config.hidden_size : 1 * config.hidden_size]
																		UpperCAmelCase_      :   str      =	out_val[1 * config.hidden_size : 2 * config.hidden_size]
																		UpperCAmelCase_      :   Optional[Any]      =	out_val[2 * config.hidden_size : 3 * config.hidden_size]

																		# Store.
																		UpperCAmelCase_      :   Any      =	q
																		UpperCAmelCase_      :   Optional[int]      =	q_bias
																		UpperCAmelCase_      :   int      =	k
																		UpperCAmelCase_      :   Dict      =	k_bias
																		UpperCAmelCase_      :   Any      =	v
																		UpperCAmelCase_      :   Optional[Any]      =	v_bias

																		# Clear the stored tensor.
																		UpperCAmelCase_      :   Tuple      =	None

												# Copy weights and biases as is.
												elif weight_or_bias in ["weight", "bias"]:
																		UpperCAmelCase_      :   Tuple      =	megatron_to_transformers[op_name]
																		UpperCAmelCase_      :   Dict      =	val

    # The final layernorm.
						UpperCAmelCase_      :   Union[str, Any]      =	transformer['''final_layernorm.weight''']
						UpperCAmelCase_      :   Tuple      =	transformer['''final_layernorm.bias''']

						# The pooler.
						UpperCAmelCase_      :   int      =	lm['''pooler''']

						# Store the matrix and the bias.
						UpperCAmelCase_      :   Any      =	pooler['''dense.weight''']
						UpperCAmelCase_      :   List[str]      =	pooler['''dense.bias''']

						# The LM head from Megatron (for RACE).
						UpperCAmelCase_      :   Optional[int]      =	model['''lm_head''']

						# The transform matrix.
						UpperCAmelCase_      :   List[str]      =	lm_head['''dense.weight''']
						UpperCAmelCase_      :   Any      =	lm_head['''dense.bias''']

						# The transform LN.
						UpperCAmelCase_      :   str      =	lm_head['''layernorm.weight''']
						UpperCAmelCase_      :   Optional[Any]      =	lm_head['''layernorm.bias''']

						# For the decoder, we replicate the weights.
						UpperCAmelCase_      :   List[Any]      =	word_embeddings
						UpperCAmelCase_      :   List[str]      =	lm_head['''bias''']

						# The classifier from Megatron (for MLNI).
						UpperCAmelCase_      :   str      =	model['''binary_head''']

						# Store the classifier.
						UpperCAmelCase_      :   Optional[int]      =	binary_head['''weight''']
						UpperCAmelCase_      :   Tuple      =	binary_head['''bias''']

						# It should be done!
						return output_state_dict





def 							lowerCamelCase_   (	)			->						int:
						# Create the argument parser.
						UpperCAmelCase_      :   List[Any]      =	argparse.ArgumentParser()
						parser.add_argument('''--print-checkpoint-structure''',      action='''store_true''' )
						parser.add_argument('''path_to_checkpoint''',      type=SCREAMING_SNAKE_CASE__,      help='''Path to the ZIP file containing the checkpoint''' )
						parser.add_argument(
						    '''--config_file''',      default='''''',      type=SCREAMING_SNAKE_CASE__,      help='''An optional config json file describing the pre-trained model.''',      )
						UpperCAmelCase_      :   Any      =	parser.parse_args()

						# Extract the basename.
						UpperCAmelCase_      :   Optional[Any]      =	os.path.dirname(args.path_to_checkpoint )

						# Load the model.
						# the .zip is very optional, let's keep it for backward compatibility
						print(F"""Extracting PyTorch state dictionary from \"{args.path_to_checkpoint}\"""" )
						if args.path_to_checkpoint.endswith('''.zip''' ):
												with zipfile.ZipFile(args.path_to_checkpoint,      '''r''' ) as checkpoint:
																		with checkpoint.open('''release/mp_rank_00/model_optim_rng.pt''' ) as pytorch_dict:
																								UpperCAmelCase_      :   List[Any]      =	torch.load(SCREAMING_SNAKE_CASE__,      map_location='''cpu''' )
						else:
												UpperCAmelCase_      :   List[Any]      =	torch.load(args.path_to_checkpoint,      map_location='''cpu''' )

						if args.config_file == "":
												# Default config of megatron-bert 345m
												UpperCAmelCase_      :   str      =	MegatronBertConfig()

												# different megatron-bert-*-345m models have different vocab sizes, so override the default
												# config (which is for megatron-bert-cased-345m) with the actual vocab dimension
												UpperCAmelCase_      :   Any      =	input_state_dict['''model''']['''lm_head''']['''bias'''].numel()
						else:
												UpperCAmelCase_      :   Union[str, Any]      =	MegatronBertConfig.from_json_file(args.config_file )

						# Convert.
						print('''Converting''' )
						UpperCAmelCase_      :   Optional[Any]      =	convert_megatron_checkpoint(SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__ )

						# Print the structure of converted state dict.
						if args.print_checkpoint_structure:
												recursive_print(SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__ )

						# Store the config to file.
						print('''Saving config''' )
						config.save_pretrained(SCREAMING_SNAKE_CASE__ )

						# Store the state_dict to file.
						UpperCAmelCase_      :   List[str]      =	os.path.join(SCREAMING_SNAKE_CASE__,      '''pytorch_model.bin''' )
						print(F"""Saving checkpoint to \"{output_checkpoint_file}\"""" )
						torch.save(SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__ )


####################################################################################################

if __name__ == "__main__":
					main()

####################################################################################################








import argparse
import os
from functools import reduce

import fairseq
import torch
from datasets import load_dataset

from transformers import WavaVecaProcessor, logging
from transformers.models.dataavec.configuration_dataavec_audio import DataaVecAudioConfig

# Copied from https://github.com/pytorch/fairseq/blob/main/examples/data2vec/models/data2vec_audio.py
from transformers.models.dataavec.dataavec_audio import DataaVecAudioModel as Dummy  # noqa: F401
from transformers.models.dataavec.modeling_dataavec_audio import DataaVecAudioForCTC, DataaVecAudioModel


logging.set_verbosity_info()
__A	:   Optional[Any]	=					logging.get_logger(__name__)

__A	:   Optional[int]	=					{
    'post_extract_proj': 'feature_projection.projection',
    'models.0.layer_norm': 'feature_projection.layer_norm',
    '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',
}
__A	:   Optional[int]	=					[
    'lm_head',
]




def        __UpperCamelCase     (      _A					:							Optional[Any]      ,  _A					:							Dict      ,  _A					:							Tuple      ,  _A					:							str      ,  _A					:							int		)				->List[str]:



							"""simple docstring"""
							for attribute in key.split("""."""		):
														lowerCamelCase_								=getattr(_A      ,  _A		)

							if weight_type is not None:
														lowerCamelCase_								=getattr(_A      ,  _A		).shape
							else:
														lowerCamelCase_								=hf_pointer.shape

							if hf_shape != value.shape:
														raise ValueError(
														    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":
														lowerCamelCase_								=value
							elif weight_type == "weight_g":
														lowerCamelCase_								=value
							elif weight_type == "weight_v":
														lowerCamelCase_								=value
							elif weight_type == "bias":
														lowerCamelCase_								=value
							else:
														lowerCamelCase_								=value

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




def        __UpperCamelCase     (      _A					:							Dict      ,  _A					:							Dict      ,  _A					:							Optional[Any]		)				->Any:



							"""simple docstring"""
							lowerCamelCase_								=[]
							lowerCamelCase_								=fairseq_model.state_dict()

							if not is_headless:
														lowerCamelCase_								=hf_model.dataavec_audio.feature_extractor
														lowerCamelCase_								=hf_model.dataavec_audio.encoder.pos_conv_embed

							else:
														lowerCamelCase_								=hf_model.feature_extractor
														lowerCamelCase_								=hf_model.encoder.pos_conv_embed

							for name, value in fairseq_dict.items():
														lowerCamelCase_								=False
														if "conv_layers" in name:
																					load_conv_layer(
																					    _A      ,  _A      ,  _A      ,  _A      ,  )
																					lowerCamelCase_								=True
														elif "pos_conv" in name:
																					load_pos_conv_layer(
																					    _A      ,  _A      ,  _A      ,  _A      ,  )
																					lowerCamelCase_								=True
														else:
																					for key, mapped_key in MAPPING.items():
																												if not is_headless:
																																			lowerCamelCase_								="""data2vec_audio.""" + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
																												if key in name or key.split("""w2v_model."""		)[-1] == name.split("""."""		)[0]:
																																			lowerCamelCase_								=True
																																			if "*" in mapped_key:
																																										lowerCamelCase_								=name.split(_A		)[0].split("""."""		)[-2]
																																										lowerCamelCase_								=mapped_key.replace("""*"""      ,  _A		)
																																			if "weight_g" in name:
																																										lowerCamelCase_								="""weight_g"""
																																			elif "weight_v" in name:
																																										lowerCamelCase_								="""weight_v"""
																																			elif "bias" in name:
																																										lowerCamelCase_								="""bias"""
																																			elif "weight" in name:
																																										# TODO: don't match quantizer.weight_proj
																																										lowerCamelCase_								="""weight"""
																																			else:
																																										lowerCamelCase_								=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					:							Optional[Any]      ,  _A					:							Union[str, Any]		)				->Tuple:



							"""simple docstring"""
							lowerCamelCase_								=path.split("""."""		)
							return reduce(_A      ,  _A      ,  _A		)




def        __UpperCamelCase     (      _A					:							str      ,  _A					:							List[Any]      ,  _A					:							str      ,  _A					:							Dict		)				->Union[str, Any]:



							"""simple docstring"""
							lowerCamelCase_								=access_by_string(_A      ,  _A		)
							lowerCamelCase_								=hf_weight.data

							if fsq_value.shape != hf_value.shape:
														raise ValueError(f'{full_name} has size {fsq_value.shape}, but {hf_value.shape} was found.'		)
							lowerCamelCase_								=fsq_value
							logger.info(f'{full_name} was correctly initialized from {hf_weight_path}.'		)




def        __UpperCamelCase     (      _A					:							Union[str, Any]      ,  _A					:							Any      ,  _A					:							List[str]      ,  _A					:							List[Any]		)				->Union[str, Any]:



							"""simple docstring"""
							lowerCamelCase_								=full_name.split("""conv_layers."""		)[-1]
							lowerCamelCase_								=name.split("""."""		)
							lowerCamelCase_								=int(items[0]		)
							lowerCamelCase_								=int(items[1]		)

							lowerCamelCase_								=name.split("""."""		)[-1]
							if type_id == 0:
														lowerCamelCase_								="""conv"""
							elif type_id == 2:
														lowerCamelCase_								="""layer_norm"""
							else:
														unused_weights.append(_A		)
														return

							set_weights(_A      ,  _A      ,  _A      ,  f'conv_layers.{layer_id}.{layer_type}.{weight_type}'		)




def        __UpperCamelCase     (      _A					:							Union[str, Any]      ,  _A					:							Optional[int]      ,  _A					:							Optional[int]      ,  _A					:							List[Any]		)				->Tuple:



							"""simple docstring"""
							lowerCamelCase_								=full_name.split("""pos_conv."""		)[-1]
							lowerCamelCase_								=name.split("""."""		)
							lowerCamelCase_								=int(items[0]		)
							lowerCamelCase_								=int(items[1]		)

							lowerCamelCase_								=name.split("""."""		)[-1]
							if type_id != 0:
														unused_weights.append(_A		)
														return
							else:
														lowerCamelCase_								="""conv"""

							set_weights(_A      ,  _A      ,  _A      ,  f'layers.{layer_id}.{layer_type}.{weight_type}'		)




@torch.no_grad()
def        __UpperCamelCase     (      _A					:							Optional[Any]      ,  _A					:							Optional[int]      ,  _A					:							List[Any]=None      ,  _A					:							int=None      ,  _A					:							Union[str, Any]=True		)				->str:



							"""simple docstring"""
							if config_path is not None:
														lowerCamelCase_								=DataaVecAudioConfig.from_pretrained(_A		)
							else:
														lowerCamelCase_								=DataaVecAudioConfig()

							if not is_finetuned:
														# Modify final_proj layer name
														lowerCamelCase_								=DataaVecAudioModel(_A		)
														lowerCamelCase_								=os.path.dirname(_A		)

														lowerCamelCase_								=torch.load(_A		)
														lowerCamelCase_								=state_dict["""model"""].pop("""final_proj.0.weight"""		)
														lowerCamelCase_								=state_dict["""model"""].pop("""final_proj.0.bias"""		)
														lowerCamelCase_								=os.path.join(_A      ,  """converted.pt"""		)
														torch.save(_A      ,  _A		)
							else:
														lowerCamelCase_								=DataaVecAudioForCTC(_A		)
														lowerCamelCase_								=checkpoint_path

							def load_dataavec(_A					:							str		):
														lowerCamelCase_	,   lowerCamelCase_	,   lowerCamelCase_								=fairseq.checkpoint_utils.load_model_ensemble_and_task([path]		)
														return model[0].eval()

							lowerCamelCase_								=load_dataavec(_A		)

							recursively_load_weights(_A      ,  _A      ,  not is_finetuned		)

							lowerCamelCase_								=WavaVecaProcessor.from_pretrained("""facebook/wav2vec2-large-lv60"""		)

							lowerCamelCase_								=load_dataset("""patrickvonplaten/librispeech_asr_dummy"""      ,  """clean"""      ,  split="""validation"""		)
							lowerCamelCase_								=[x["""array"""] for x in ds[:4]["""audio"""]]

							lowerCamelCase_								=processor(_A      ,  return_tensors="""pt"""      ,  padding=_A		)

							lowerCamelCase_								=inputs.input_values
							lowerCamelCase_								=inputs.attention_mask
							#    input_values = inputs.input_values[:, :-1]
							#    attention_mask = inputs.attention_mask[:, :-1]

							hf_wavavec.eval()
							model.eval()
							if is_finetuned:
														lowerCamelCase_								=model(source=_A      ,  padding_mask=(1 - attention_mask)      ,  mask=_A      ,  features_only=_A		)[
														    """encoder_out"""
														].transpose(0      ,  1		)
														lowerCamelCase_								=hf_wavavec(_A      ,  attention_mask=_A		)["""logits"""]

														lowerCamelCase_								=torch.argmax(_A      ,  dim=-1		)
														lowerCamelCase_								=processor.batch_decode(_A		)

														print(f'Expected Output: {ds[:4]["text"]}, Pred: {output_string}'		)
							else:
														lowerCamelCase_								=model(source=_A      ,  padding_mask=(1 - attention_mask)      ,  mask=_A      ,  features_only=_A		)[
														    """layer_results"""
														][-1][0].transpose(0      ,  1		)
														lowerCamelCase_								=hf_wavavec(_A      ,  attention_mask=_A		)["""last_hidden_state"""]

							print(our_output.shape      ,  their_output.shape		)
							lowerCamelCase_								=torch.max(torch.abs(our_output - their_output		)		).item()
							print(f'max_absolute_diff = {max_absolute_diff}'		)  # ~ 1e-7
							lowerCamelCase_								=torch.allclose(_A      ,  _A      ,  atol=1E-3		)
							print("""Do both models output the same tensors?"""      ,  """🔥""" if success else """💩"""		)
							if not success:
														raise Exception("""Something went wRoNg"""		)

							hf_wavavec.save_pretrained(_A		)

							if is_finetuned:
														processor.save_pretrained(_A		)
							else:
														processor.feature_extractor.save_pretrained(_A		)


if __name__ == "__main__":
	__A	:   Optional[Any]	=					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'
	)
	__A	:   List[str]	=					parser.parse_args()
	convert_wavaveca_checkpoint(
	    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
	)
