


'''simple docstring'''

# Note: if you intend to run this script make sure you look under scripts/fsmt/
# to locate the appropriate script to do the work correctly. There is a set of scripts to:
# - download and prepare data and run the conversion script
# - perform eval to get the best hparam into the config
# - generate model_cards - useful if you have multiple models from the same paper

import argparse
import json
import os
import re
from collections import OrderedDict
from os.path import basename, dirname

import fairseq
import torch
from fairseq import hub_utils
from fairseq.data.dictionary import Dictionary

from transformers import FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging


logging.set_verbosity_warning()

lowerCAmelCase				:Union[str, Any]        =      2

# based on the results of a search on a range of `num_beams`, `length_penalty` and `early_stopping`
# values against wmt19 test data to obtain the best BLEU scores, we will use the following defaults:
#
# * `num_beams`: 5 (higher scores better, but requires more memory/is slower, can be adjusted by users)
# * `early_stopping`: `False` consistently scored better
# * `length_penalty` varied, so will assign the best one depending on the model
lowerCAmelCase				:Dict        =      {
    # fairseq:
    '''wmt19-ru-en''': {'''length_penalty''': 1.1},
    '''wmt19-en-ru''': {'''length_penalty''': 1.15},
    '''wmt19-en-de''': {'''length_penalty''': 1.0},
    '''wmt19-de-en''': {'''length_penalty''': 1.1},
    # allenai:
    '''wmt16-en-de-dist-12-1''': {'''length_penalty''': 0.6},
    '''wmt16-en-de-dist-6-1''': {'''length_penalty''': 0.6},
    '''wmt16-en-de-12-1''': {'''length_penalty''': 0.8},
    '''wmt19-de-en-6-6-base''': {'''length_penalty''': 0.6},
    '''wmt19-de-en-6-6-big''': {'''length_penalty''': 0.6},
}

# this remaps the different models to their organization names
lowerCAmelCase				:List[Any]        =      {}
for m in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
							lowerCAmelCase				:Optional[int]        =      '''facebook'''
for m in [
    "wmt16-en-de-dist-12-1",
    "wmt16-en-de-dist-6-1",
    "wmt16-en-de-12-1",
    "wmt19-de-en-6-6-base",
    "wmt19-de-en-6-6-big",
]:
							lowerCAmelCase				:List[Any]        =      '''allenai'''





def lowerCamelCase      (      lowerCAmelCase    :     List[str]		):





	"""simple docstring"""







	__magic_name__	:   int					     =					dict((re.sub(R'@@$'		,		''		,		lowerCAmelCase		), v) if k.endswith('@@'		) else (re.sub(R'$'		,		'</w>'		,		lowerCAmelCase		), v) for k, v in d.items()		)
	__magic_name__	:   Union[str, Any]					     =					'<s> <pad> </s> <unk>'.split()
	# restore the special tokens
	for k in keep_keys:
		del da[f'{k}</w>']
		__magic_name__	:   List[Any]					     =					d[k]  # restore
	return da





def lowerCamelCase      (      lowerCAmelCase    :     Any		,		lowerCAmelCase    :     int		):





	"""simple docstring"""







	assert os.path.exists(lowerCAmelCase		)
	os.makedirs(lowerCAmelCase		,		exist_ok=lowerCAmelCase		)
	print(f'Writing results to {pytorch_dump_folder_path}'		)

	# handle various types of models

	__magic_name__	:   Any					     =					basename(lowerCAmelCase		)
	__magic_name__	:   Any					     =					dirname(lowerCAmelCase		)

	__magic_name__	:   Union[str, Any]					     =					fairseq.model_parallel.models.transformer.ModelParallelTransformerModel
	__magic_name__	:   Dict					     =					cls.hub_models()
	__magic_name__	:   Dict					     =					{'bpe': 'fastbpe', 'tokenizer': 'moses'}
	__magic_name__	:   str					     =					'.'
	# note: since the model dump is old, fairseq has upgraded its model some
	# time later, and it does a whole lot of rewrites and splits on the saved
	# weights, therefore we can't use torch.load() directly on the model file.
	# see: upgrade_state_dict(state_dict) in fairseq_model.py
	print(f'using checkpoint {checkpoint_file}'		)
	__magic_name__	:   List[Any]					     =					hub_utils.from_pretrained(
	    lowerCAmelCase		,		lowerCAmelCase		,		lowerCAmelCase		,		archive_map=lowerCAmelCase		,		**lowerCAmelCase		)

	__magic_name__	:   Tuple					     =					vars(chkpt['args']['model']		)

	__magic_name__	:   Dict					     =					args['source_lang']
	__magic_name__	:   Optional[int]					     =					args['target_lang']

	__magic_name__	:   Optional[Any]					     =					dirname(lowerCAmelCase		)
	__magic_name__	:   Union[str, Any]					     =					basename(lowerCAmelCase		)

	# dicts
	__magic_name__	:   Tuple					     =					os.path.join(lowerCAmelCase		,		f'dict.{src_lang}.txt'		)
	__magic_name__	:   int					     =					os.path.join(lowerCAmelCase		,		f'dict.{tgt_lang}.txt'		)

	__magic_name__	:   str					     =					Dictionary.load(lowerCAmelCase		)
	__magic_name__	:   str					     =					rewrite_dict_keys(src_dict.indices		)
	__magic_name__	:   int					     =					len(lowerCAmelCase		)
	__magic_name__	:   str					     =					os.path.join(lowerCAmelCase		,		'vocab-src.json'		)
	print(f'Generating {src_vocab_file} of {src_vocab_size} of {src_lang} records'		)
	with open(lowerCAmelCase		,		'w'		,		encoding='utf-8'		) as f:
		f.write(json.dumps(lowerCAmelCase		,		ensure_ascii=lowerCAmelCase		,		indent=lowerCAmelCase		)		)

	# detect whether this is a do_lower_case situation, which can be derived by checking whether we
	# have at least one uppercase letter in the source vocab
	__magic_name__	:   Optional[Any]					     =					True
	for k in src_vocab.keys():
		if not k.islower():
			__magic_name__	:   Dict					     =					False
			break

	__magic_name__	:   Tuple					     =					Dictionary.load(lowerCAmelCase		)
	__magic_name__	:   List[str]					     =					rewrite_dict_keys(tgt_dict.indices		)
	__magic_name__	:   Any					     =					len(lowerCAmelCase		)
	__magic_name__	:   Tuple					     =					os.path.join(lowerCAmelCase		,		'vocab-tgt.json'		)
	print(f'Generating {tgt_vocab_file} of {tgt_vocab_size} of {tgt_lang} records'		)
	with open(lowerCAmelCase		,		'w'		,		encoding='utf-8'		) as f:
		f.write(json.dumps(lowerCAmelCase		,		ensure_ascii=lowerCAmelCase		,		indent=lowerCAmelCase		)		)

	# merges_file (bpecodes)
	__magic_name__	:   Optional[Any]					     =					os.path.join(lowerCAmelCase		,		VOCAB_FILES_NAMES['merges_file']		)
	for fn in ["bpecodes", "code"]:  # older fairseq called the merges file "code"
		__magic_name__	:   Union[str, Any]					     =					os.path.join(lowerCAmelCase		,		lowerCAmelCase		)
		if os.path.exists(lowerCAmelCase		):
			break
	with open(lowerCAmelCase		,		encoding='utf-8'		) as fin:
		__magic_name__	:   int					     =					fin.read()
	__magic_name__	:   str					     =					re.sub(R' \d+$'		,		''		,		lowerCAmelCase		,		0		,		re.M		)  # remove frequency number
	print(f'Generating {merges_file}'		)
	with open(lowerCAmelCase		,		'w'		,		encoding='utf-8'		) as fout:
		fout.write(lowerCAmelCase		)

	# model config
	__magic_name__	:   List[str]					     =					os.path.join(lowerCAmelCase		,		'config.json'		)

	# validate bpe/tokenizer config, as currently it's hardcoded to moses+fastbpe -
	# may have to modify the tokenizer if a different type is used by a future model
	assert args["bpe"] == "fastbpe", f'need to extend tokenizer to support bpe={args["bpe"]}'
	assert args["tokenizer"] == "moses", f'need to extend tokenizer to support bpe={args["tokenizer"]}'

	__magic_name__	:   Optional[Any]					     =					{
	    'architectures': ['FSMTForConditionalGeneration'],
	    'model_type': 'fsmt',
	    'activation_dropout': args['activation_dropout'],
	    'activation_function': 'relu',
	    'attention_dropout': args['attention_dropout'],
	    'd_model': args['decoder_embed_dim'],
	    'dropout': args['dropout'],
	    'init_std': 0.02,
	    'max_position_embeddings': args['max_source_positions'],
	    'num_hidden_layers': args['encoder_layers'],
	    'src_vocab_size': src_vocab_size,
	    'tgt_vocab_size': tgt_vocab_size,
	    'langs': [src_lang, tgt_lang],
	    'encoder_attention_heads': args['encoder_attention_heads'],
	    'encoder_ffn_dim': args['encoder_ffn_embed_dim'],
	    'encoder_layerdrop': args['encoder_layerdrop'],
	    'encoder_layers': args['encoder_layers'],
	    'decoder_attention_heads': args['decoder_attention_heads'],
	    'decoder_ffn_dim': args['decoder_ffn_embed_dim'],
	    'decoder_layerdrop': args['decoder_layerdrop'],
	    'decoder_layers': args['decoder_layers'],
	    'bos_token_id': 0,
	    'pad_token_id': 1,
	    'eos_token_id': 2,
	    'is_encoder_decoder': True,
	    'scale_embedding': not args['no_scale_embedding'],
	    'tie_word_embeddings': args['share_all_embeddings'],
	}

	# good hparam defaults to start with
	__magic_name__	:   List[str]					     =					5
	__magic_name__	:   str					     =					False
	if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
		__magic_name__	:   Union[str, Any]					     =					best_score_hparams[model_dir]['length_penalty']
	else:
		__magic_name__	:   Any					     =					1.0

	print(f'Generating {fsmt_model_config_file}'		)
	with open(lowerCAmelCase		,		'w'		,		encoding='utf-8'		) as f:
		f.write(json.dumps(lowerCAmelCase		,		ensure_ascii=lowerCAmelCase		,		indent=lowerCAmelCase		)		)

	# tokenizer config
	__magic_name__	:   str					     =					os.path.join(lowerCAmelCase		,		lowerCAmelCase		)

	__magic_name__	:   Any					     =					{
	    'langs': [src_lang, tgt_lang],
	    'model_max_length': 1024,
	    'do_lower_case': do_lower_case,
	}

	print(f'Generating {fsmt_tokenizer_config_file}'		)
	with open(lowerCAmelCase		,		'w'		,		encoding='utf-8'		) as f:
		f.write(json.dumps(lowerCAmelCase		,		ensure_ascii=lowerCAmelCase		,		indent=lowerCAmelCase		)		)

	# model
	__magic_name__	:   str					     =					chkpt['models'][0]
	__magic_name__	:   Tuple					     =					model.state_dict()

	# rename keys to start with 'model.'
	__magic_name__	:   Any					     =					OrderedDict(('model.' + k, v) for k, v in model_state_dict.items()		)

	# remove unneeded keys
	__magic_name__	:   int					     =					[
	    'model.model',
	    'model.encoder.version',
	    'model.decoder.version',
	    'model.encoder_embed_tokens.weight',
	    'model.decoder_embed_tokens.weight',
	    'model.encoder.embed_positions._float_tensor',
	    'model.decoder.embed_positions._float_tensor',
	]
	for k in ignore_keys:
		model_state_dict.pop(lowerCAmelCase		,		lowerCAmelCase		)

	__magic_name__	:   Optional[int]					     =					FSMTConfig.from_pretrained(lowerCAmelCase		)
	__magic_name__	:   List[Any]					     =					FSMTForConditionalGeneration(lowerCAmelCase		)

	# check that it loads ok
	model_new.load_state_dict(lowerCAmelCase		,		strict=lowerCAmelCase		)

	# save
	__magic_name__	:   Optional[int]					     =					os.path.join(lowerCAmelCase		,		lowerCAmelCase		)
	print(f'Generating {pytorch_weights_dump_path}'		)
	torch.save(lowerCAmelCase		,		lowerCAmelCase		)

	print('Conversion is done!'		)
	print('\nLast step is to upload the files to s3'		)
	print(f'cd {data_root}'		)
	print(f'transformers-cli upload {model_dir}'		)


if __name__ == "__main__":
							lowerCAmelCase				:Optional[int]        =      argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '''--fsmt_checkpoint_path''',
							    default=None,
							    type=str,
							    required=True,
							    help=(
							        '''Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,'''
							        ''' bpecodes, etc.'''
							    ),
							)
							parser.add_argument(
							    '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
							)
							lowerCAmelCase				:Optional[int]        =      parser.parse_args()
							convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)



'''simple docstring'''

import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConfig,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaForPreTraining,
    WavaVecaProcessor,
    logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification


logging.set_verbosity_info()
lowerCAmelCase				:str        =      logging.get_logger(__name__)

lowerCAmelCase				:str        =      {
    '''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''',
    '''adapter_layer''': '''encoder.layers.*.adapter_layer''',
    '''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''': '''lm_head''',
    '''mask_emb''': '''masked_spec_embed''',
    '''pooling_layer.linear''': '''projector''',
    '''pooling_layer.projection''': '''classifier''',
}
lowerCAmelCase				:List[str]        =      [
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
    '''projector''',
    '''classifier''',
]





def lowerCamelCase      (      lowerCAmelCase    :     Tuple		):





	"""simple docstring"""







	__magic_name__	:   Any					     =					{}
	with open(lowerCAmelCase		,		'r'		) as file:
		for line_number, line in enumerate(lowerCAmelCase		):
			__magic_name__	:   Optional[Any]					     =					line.strip()
			if line:
				__magic_name__	:   Optional[int]					     =					line.split()
				__magic_name__	:   Any					     =					line_number
				__magic_name__	:   Union[str, Any]					     =					words[0]
				__magic_name__	:   Dict					     =					value
	return result





def lowerCamelCase      (      lowerCAmelCase    :     Optional[int]		,		lowerCAmelCase    :     List[Any]		,		lowerCAmelCase    :     Optional[int]		,		lowerCAmelCase    :     Optional[Any]		,		lowerCAmelCase    :     Tuple		):





	"""simple docstring"""







	for attribute in key.split('.'		):
		__magic_name__	:   Optional[Any]					     =					getattr(lowerCAmelCase		,		lowerCAmelCase		)

	__magic_name__	:   Tuple					     =					None
	for param_key in PARAM_MAPPING.keys():
		if full_name.endswith(lowerCAmelCase		):
			__magic_name__	:   Optional[Any]					     =					PARAM_MAPPING[full_name.split('.'		)[-1]]
			__magic_name__	:   List[Any]					     =					'param'

	if weight_type is not None and weight_type != "param":
		__magic_name__	:   List[str]					     =					getattr(lowerCAmelCase		,		lowerCAmelCase		).shape
	elif weight_type is not None and weight_type == "param":
		__magic_name__	:   Tuple					     =					hf_pointer
		for attribute in hf_param_name.split('.'		):
			__magic_name__	:   str					     =					getattr(lowerCAmelCase		,		lowerCAmelCase		)
		__magic_name__	:   Union[str, Any]					     =					shape_pointer.shape

		# let's reduce dimension
		__magic_name__	:   int					     =					value[0]
	else:
		__magic_name__	:   Optional[Any]					     =					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":
		__magic_name__	:   Optional[Any]					     =					value
	elif weight_type == "weight_g":
		__magic_name__	:   List[str]					     =					value
	elif weight_type == "weight_v":
		__magic_name__	:   Optional[int]					     =					value
	elif weight_type == "bias":
		__magic_name__	:   Optional[Any]					     =					value
	elif weight_type == "param":
		for attribute in hf_param_name.split('.'		):
			__magic_name__	:   Optional[int]					     =					getattr(lowerCAmelCase		,		lowerCAmelCase		)
		__magic_name__	:   List[str]					     =					value
	else:
		__magic_name__	:   int					     =					value

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





def lowerCamelCase      (      lowerCAmelCase    :     List[Any]		,		lowerCAmelCase    :     str		,		lowerCAmelCase    :     List[str]		,		lowerCAmelCase    :     str		,		lowerCAmelCase    :     str		):





	"""simple docstring"""







	__magic_name__	:   Optional[int]					     =					None
	for param_key in PARAM_MAPPING.keys():
		if full_name.endswith(lowerCAmelCase		):
			__magic_name__	:   List[Any]					     =					PARAM_MAPPING[full_name.split('.'		)[-1]]
			__magic_name__	:   Dict					     =					'param'

	if weight_type is not None and weight_type != "param":
		__magic_name__	:   Union[str, Any]					     =					'.'.join([key, weight_type]		)
	elif weight_type is not None and weight_type == "param":
		__magic_name__	:   Optional[Any]					     =					'.'.join([key, hf_param_name]		)
	else:
		__magic_name__	:   int					     =					key

	__magic_name__	:   int					     =					value if 'lm_head' in full_key else value[0]


lowerCAmelCase				:int        =      {
    '''W_a''': '''linear_1.weight''',
    '''W_b''': '''linear_2.weight''',
    '''b_a''': '''linear_1.bias''',
    '''b_b''': '''linear_2.bias''',
    '''ln_W''': '''norm.weight''',
    '''ln_b''': '''norm.bias''',
}





def lowerCamelCase      (      lowerCAmelCase    :     Optional[Any]		,		lowerCAmelCase    :     List[Any]		,		lowerCAmelCase    :     Tuple=None		,		lowerCAmelCase    :     Tuple=None		):





	"""simple docstring"""







	__magic_name__	:   Dict					     =					False
	for key, mapped_key in MAPPING.items():
		__magic_name__	:   int					     =					'wav2vec2.' + 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]:
			__magic_name__	:   Union[str, Any]					     =					True
			if "*" in mapped_key:
				__magic_name__	:   List[Any]					     =					name.split(lowerCAmelCase		)[0].split('.'		)[-2]
				__magic_name__	:   List[str]					     =					mapped_key.replace('*'		,		lowerCAmelCase		)
			if "weight_g" in name:
				__magic_name__	:   str					     =					'weight_g'
			elif "weight_v" in name:
				__magic_name__	:   Optional[int]					     =					'weight_v'
			elif "bias" in name:
				__magic_name__	:   int					     =					'bias'
			elif "weight" in name:
				# TODO: don't match quantizer.weight_proj
				__magic_name__	:   List[str]					     =					'weight'
			else:
				__magic_name__	:   Any					     =					None
			if hf_dict is not None:
				rename_dict(lowerCAmelCase		,		lowerCAmelCase		,		lowerCAmelCase		,		lowerCAmelCase		,		lowerCAmelCase		)
			else:
				set_recursively(lowerCAmelCase		,		lowerCAmelCase		,		lowerCAmelCase		,		lowerCAmelCase		,		lowerCAmelCase		)
			return is_used
	return is_used





def lowerCamelCase      (      lowerCAmelCase    :     Union[str, Any]		,		lowerCAmelCase    :     Optional[int]		,		lowerCAmelCase    :     Dict		):





	"""simple docstring"""







	__magic_name__	:   Union[str, Any]					     =					[]
	__magic_name__	:   Any					     =					fairseq_model.state_dict()

	__magic_name__	:   Optional[int]					     =					hf_model.wavaveca.feature_extractor

	for name, value in fairseq_dict.items():
		__magic_name__	:   Optional[Any]					     =					False
		if "conv_layers" in name:
			load_conv_layer(
			    lowerCAmelCase		,		lowerCAmelCase		,		lowerCAmelCase		,		lowerCAmelCase		,		hf_model.config.feat_extract_norm == 'group'		,		)
			__magic_name__	:   Optional[int]					     =					True
		else:
			__magic_name__	:   str					     =					load_wavaveca_layer(lowerCAmelCase		,		lowerCAmelCase		,		lowerCAmelCase		)
		if not is_used:
			unused_weights.append(lowerCAmelCase		)

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





def lowerCamelCase      (      lowerCAmelCase    :     List[str]		,		lowerCAmelCase    :     Dict		,		lowerCAmelCase    :     Any		,		lowerCAmelCase    :     Any		,		lowerCAmelCase    :     int		):





	"""simple docstring"""







	__magic_name__	:   Any					     =					full_name.split('conv_layers.'		)[-1]
	__magic_name__	:   int					     =					name.split('.'		)
	__magic_name__	:   Any					     =					int(items[0]		)
	__magic_name__	:   Any					     =					int(items[1]		)

	if type_id == 0:
		if "bias" in name:
			if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
				raise ValueError(
				    f'{full_name} has size {value.shape}, but'
				    f' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'		)
			__magic_name__	:   Union[str, Any]					     =					value
			logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.'		)
		elif "weight" in name:
			if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
				raise ValueError(
				    f'{full_name} has size {value.shape}, but'
				    f' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'		)
			__magic_name__	:   str					     =					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:
			if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
				raise ValueError(
				    f'{full_name} has size {value.shape}, but'
				    f' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.'		)
			__magic_name__	:   Optional[int]					     =					value
			logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'		)
		elif "weight" in name:
			if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
				raise ValueError(
				    f'{full_name} has size {value.shape}, but'
				    f' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.'		)
			__magic_name__	:   Dict					     =					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 lowerCamelCase      (      lowerCAmelCase    :     Tuple		,		lowerCAmelCase    :     Optional[int]		,		lowerCAmelCase    :     Dict=None		,		lowerCAmelCase    :     Any=None		,		lowerCAmelCase    :     Optional[Any]=True		,		lowerCAmelCase    :     Any=False		):





	"""simple docstring"""







	if config_path is not None:
		__magic_name__	:   int					     =					WavaVecaConfig.from_pretrained(lowerCAmelCase		)
	else:
		__magic_name__	:   List[str]					     =					WavaVecaConfig()

	if is_seq_class:
		__magic_name__	:   Any					     =					read_txt_into_dict(lowerCAmelCase		)
		__magic_name__	:   Optional[Any]					     =					idalabel
		__magic_name__	:   Union[str, Any]					     =					WavaVecaForSequenceClassification(lowerCAmelCase		)
		__magic_name__	:   Optional[int]					     =					WavaVecaFeatureExtractor(
		    feature_size=1		,		sampling_rate=1_6000		,		padding_value=0		,		do_normalize=lowerCAmelCase		,		return_attention_mask=lowerCAmelCase		,		)
		feature_extractor.save_pretrained(lowerCAmelCase		)

	elif is_finetuned:
		if dict_path:
			__magic_name__	:   str					     =					Dictionary.load(lowerCAmelCase		)

			# important change bos & pad token id since CTC symbol is <pad> and
			# not <s> as in fairseq
			__magic_name__	:   Dict					     =					target_dict.pad_index
			__magic_name__	:   Union[str, Any]					     =					target_dict.bos_index
			__magic_name__	:   Union[str, Any]					     =					target_dict.eos_index
			__magic_name__	:   Union[str, Any]					     =					len(target_dict.symbols		)
			__magic_name__	:   Dict					     =					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		)
			__magic_name__	:   List[Any]					     =					target_dict.indices

			# fairseq has the <pad> and <s> switched
			__magic_name__	:   Any					     =					0
			__magic_name__	:   Optional[int]					     =					1
			with open(lowerCAmelCase		,		'w'		,		encoding='utf-8'		) as vocab_handle:
				json.dump(lowerCAmelCase		,		lowerCAmelCase		)
			__magic_name__	:   List[str]					     =					WavaVecaCTCTokenizer(
			    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		,		)
			__magic_name__	:   Tuple					     =					True if config.feat_extract_norm == 'layer' else False
			__magic_name__	:   Optional[Any]					     =					WavaVecaFeatureExtractor(
			    feature_size=1		,		sampling_rate=1_6000		,		padding_value=0		,		do_normalize=lowerCAmelCase		,		return_attention_mask=lowerCAmelCase		,		)
			__magic_name__	:   List[Any]					     =					WavaVecaProcessor(feature_extractor=lowerCAmelCase		,		tokenizer=lowerCAmelCase		)
			processor.save_pretrained(lowerCAmelCase		)

		__magic_name__	:   Dict					     =					WavaVecaForCTC(lowerCAmelCase		)
	else:
		__magic_name__	:   Tuple					     =					WavaVecaForPreTraining(lowerCAmelCase		)

	if is_finetuned or is_seq_class:
		__magic_name__     ,    __magic_name__     ,    __magic_name__	:   str					     =					fairseq.checkpoint_utils.load_model_ensemble_and_task(
		    [checkpoint_path]		,		arg_overrides={'data': '/'.join(dict_path.split('/'		)[:-1]		)}		)
	else:
		__magic_name__	:   Optional[Any]					     =					argparse.Namespace(task='audio_pretraining'		)
		__magic_name__	:   Dict					     =					fairseq.tasks.setup_task(lowerCAmelCase		)

		__magic_name__     ,    __magic_name__     ,    __magic_name__	:   Optional[Any]					     =					fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]		,		task=lowerCAmelCase		)

	__magic_name__	:   Any					     =					model[0].eval()

	recursively_load_weights(lowerCAmelCase		,		lowerCAmelCase		,		not is_finetuned		)

	hf_wavavec.save_pretrained(lowerCAmelCase		)


if __name__ == "__main__":
							lowerCAmelCase				:str        =      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'''
							)
							parser.add_argument(
							    '''--is_seq_class''',
							    action='''store_true''',
							    help='''Whether the model to convert is a fine-tuned sequence classification model or not''',
							)
							lowerCAmelCase				:Dict        =      parser.parse_args()

							lowerCAmelCase				:Tuple        =      not args.not_finetuned and not args.is_seq_class
							convert_wavaveca_checkpoint(
							    args.checkpoint_path,
							    args.pytorch_dump_folder_path,
							    args.config_path,
							    args.dict_path,
							    is_finetuned,
							    args.is_seq_class,
							)