



'''simple docstring'''



import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    UniSpeechConfig,
    UniSpeechForCTC,
    UniSpeechForPreTraining,
    WavaVecaFeatureExtractor,
    WavaVecaPhonemeCTCTokenizer,
    WavaVecaProcessor,
    logging,
)


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

lowerCamelCase	 =       {
    """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""",
    """quantizer.weight_proj""": """quantizer.weight_proj""",
    """quantizer.vars""": """quantizer.codevectors""",
    """project_q""": """project_q""",
    """final_proj""": """project_hid""",
    """w2v_encoder.proj""": """ctc_proj""",
    """mask_emb""": """masked_spec_embed""",
}
lowerCamelCase	 =       [
    """ctc_proj""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]



def   _A       (   _lowerCAmelCase  ,   _lowerCAmelCase  ,   _lowerCAmelCase  ,   _lowerCAmelCase  ,   _lowerCAmelCase  ,   _lowerCAmelCase					):



							"""simple docstring"""



							for attribute in key.split('.'					):
														if is_finetuned:
																					if attribute in ["quantizer", "project_q", "project_hid"]:
																												# those layers are only relevant for pretraining and should be dropped
																												return

																					if attribute == "ctc_proj":
																												# we should rename `ctc_proj` to `lm_head` for fine-tuned phoneme models
																												__lowercase       	='lm_head'

														__lowercase       	=getattr(_lowerCAmelCase  ,   _lowerCAmelCase					)

							if weight_type is not None:
														__lowercase       	=getattr(_lowerCAmelCase  ,   _lowerCAmelCase					).shape
							else:
														__lowercase       	=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       	=value
							elif weight_type == "weight_g":
														__lowercase       	=value
							elif weight_type == "weight_v":
														__lowercase       	=value
							elif weight_type == "bias":
														__lowercase       	=value
							else:
														__lowercase       	=value

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



def   _A       (   _lowerCAmelCase  ,   _lowerCAmelCase  ,   _lowerCAmelCase					):



							"""simple docstring"""



							__lowercase       	=[]
							__lowercase       	=fairseq_model.state_dict()

							__lowercase       	=hf_model.unispeech.feature_extractor

							for name, value in fairseq_dict.items():
														__lowercase       	=False
														if "conv_layers" in name:
																					load_conv_layer(
																					    _lowerCAmelCase  ,   _lowerCAmelCase  ,   _lowerCAmelCase  ,   _lowerCAmelCase  ,   hf_model.config.feat_extract_norm == 'group'  ,   )
																					__lowercase       	=True
														else:
																					for key, mapped_key in MAPPING.items():
																												__lowercase       	='unispeech.' + 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]:
																																			__lowercase       	=True
																																			if "*" in mapped_key:
																																										__lowercase       	=name.split(_lowerCAmelCase					)[0].split('.'					)[-2]
																																										__lowercase       	=mapped_key.replace('*'  ,   _lowerCAmelCase					)
																																			if "weight_g" in name:
																																										__lowercase       	='weight_g'
																																			elif "weight_v" in name:
																																										__lowercase       	='weight_v'
																																			elif "bias" in name:
																																										__lowercase       	='bias'
																																			elif "weight" in name:
																																										# TODO: don't match quantizer.weight_proj
																																										__lowercase       	='weight'
																																			else:
																																										__lowercase       	=None
																																			set_recursively(_lowerCAmelCase  ,   _lowerCAmelCase  ,   _lowerCAmelCase  ,   _lowerCAmelCase  ,   _lowerCAmelCase  ,   _lowerCAmelCase					)
																												continue
														if not is_used:
																					unused_weights.append(_lowerCAmelCase					)

							logger.warning(f"""Unused weights: {unused_weights}"""					)



def   _A       (   _lowerCAmelCase  ,   _lowerCAmelCase  ,   _lowerCAmelCase  ,   _lowerCAmelCase  ,   _lowerCAmelCase					):



							"""simple docstring"""



							__lowercase       	=full_name.split('conv_layers.'					)[-1]
							__lowercase       	=name.split('.'					)
							__lowercase       	=int(items[0]					)
							__lowercase       	=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       	=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       	=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       	=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       	=value
																					logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""					)
							else:
														unused_weights.append(_lowerCAmelCase					)



@torch.no_grad()
def   _A       (   _lowerCAmelCase  ,   _lowerCAmelCase  ,   _lowerCAmelCase=None  ,   _lowerCAmelCase=None  ,   _lowerCAmelCase=True					):



							"""simple docstring"""



							if config_path is not None:
														__lowercase       	=UniSpeechConfig.from_pretrained(_lowerCAmelCase					)
							else:
														__lowercase       	=UniSpeechConfig()

							if is_finetuned:
														if dict_path:
																					__lowercase       	=Dictionary.load_from_json(_lowerCAmelCase					)

																					# important change bos & pad token id since CTC symbol is <pad> and
																					# not <s> as in fairseq
																					__lowercase       	=target_dict.pad_index
																					__lowercase       	=target_dict.bos_index
																					__lowercase       	=target_dict.eos_index
																					__lowercase       	=len(target_dict.symbols					)
																					__lowercase       	=os.path.join(_lowerCAmelCase  ,   'vocab.json'					)
																					if not os.path.isdir(_lowerCAmelCase					):
																												logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(_lowerCAmelCase					)					)
																												return
																					os.makedirs(_lowerCAmelCase  ,   exist_ok=_lowerCAmelCase					)
																					__lowercase       	=target_dict.indices

																					# fairseq has the <pad> and <s> switched
																					__lowercase       	=42
																					__lowercase       	=43
																					with open(_lowerCAmelCase  ,   'w'  ,   encoding='utf-8'					) as vocab_handle:
																												json.dump(_lowerCAmelCase  ,   _lowerCAmelCase					)
																					__lowercase       	=WavaVecaPhonemeCTCTokenizer(
																					    _lowerCAmelCase  ,   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=_lowerCAmelCase  ,   )
																					__lowercase       	=True if config.feat_extract_norm == 'layer' else False
																					__lowercase       	=WavaVecaFeatureExtractor(
																					    feature_size=1  ,   sampling_rate=16_000  ,   padding_value=0  ,   do_normalize=_lowerCAmelCase  ,   return_attention_mask=_lowerCAmelCase  ,   )
																					__lowercase       	=WavaVecaProcessor(feature_extractor=_lowerCAmelCase  ,   tokenizer=_lowerCAmelCase					)
																					processor.save_pretrained(_lowerCAmelCase					)

														__lowercase       	=UniSpeechForCTC(_lowerCAmelCase					)
							else:
														__lowercase       	=UniSpeechForPreTraining(_lowerCAmelCase					)

							if is_finetuned:
														__lowercase	,				__lowercase	,				__lowercase       	=fairseq.checkpoint_utils.load_model_ensemble_and_task(
														    [checkpoint_path]  ,   arg_overrides={'data': '/'.join(dict_path.split('/'					)[:-1]					), 'w2v_path': checkpoint_path}					)
							else:
														__lowercase	,				__lowercase	,				__lowercase       	=fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]					)

							__lowercase       	=model[0].eval()

							recursively_load_weights(_lowerCAmelCase  ,   _lowerCAmelCase  ,   _lowerCAmelCase					)

							hf_unispeech.save_pretrained(_lowerCAmelCase					)


if __name__ == "__main__":
		lowerCamelCase	 =       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	 =       parser.parse_args()
		convert_unispeech_checkpoint(
		    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
		)








'''simple docstring'''



# Copyright 2021 The HuggingFace Team. 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.

import argparse
import os

from accelerate.utils import ComputeEnvironment

from .cluster import get_cluster_input
from .config_args import cache_dir, default_config_file, default_yaml_config_file, load_config_from_file  # noqa: F401
from .config_utils import _ask_field, _ask_options, _convert_compute_environment  # noqa: F401
from .sagemaker import get_sagemaker_input


lowerCamelCase	 =       """Launches a series of prompts to create and save a `default_config.yaml` configuration file for your training system. Should always be ran first on your machine"""



def   _A       (   ):



							"""simple docstring"""



							__lowercase       	=_ask_options(
							    'In which compute environment are you running?'  ,   ['This machine', 'AWS (Amazon SageMaker)']  ,   _convert_compute_environment  ,   )
							if compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
														__lowercase       	=get_sagemaker_input()
							else:
														__lowercase       	=get_cluster_input()
							return config



def   _A       (   _lowerCAmelCase=None					):



							"""simple docstring"""



							if subparsers is not None:
														__lowercase       	=subparsers.add_parser('config'  ,   description=_lowerCAmelCase					)
							else:
														__lowercase       	=argparse.ArgumentParser('Accelerate config command'  ,   description=_lowerCAmelCase					)

							parser.add_argument(
							    '--config_file'  ,   default=_lowerCAmelCase  ,   help=(
							        'The path to use to store the config file. Will default to a file named default_config.yaml in the cache '
							        'location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '
							        'such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '
							        'with \'huggingface\'.'
							    )  ,   )

							if subparsers is not None:
														parser.set_defaults(func=_lowerCAmelCase					)
							return parser



def   _A       (   _lowerCAmelCase					):



							"""simple docstring"""



							__lowercase       	=get_user_input()
							if args.config_file is not None:
														__lowercase       	=args.config_file
							else:
														if not os.path.isdir(_lowerCAmelCase					):
																					os.makedirs(_lowerCAmelCase					)
														__lowercase       	=default_yaml_config_file

							if config_file.endswith('.json'					):
														config.to_json_file(_lowerCAmelCase					)
							else:
														config.to_yaml_file(_lowerCAmelCase					)
							print(f"""accelerate configuration saved at {config_file}"""					)



def   _A       (   ):



							"""simple docstring"""



							__lowercase       	=config_command_parser()
							__lowercase       	=parser.parse_args()
							config_command(_lowerCAmelCase					)


if __name__ == "__main__":
		main()



