
"""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()
_snake_case          =						logging.get_logger(__name__)

_snake_case          =						{
    '''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''',
}
_snake_case          =						[
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
    '''projector''',
    '''classifier''',
]






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					Any						):






							"""simple docstring"""



							_lowerCAmelCase  	=			{}
							with open(SCREAMING_SNAKE_CASE     ,  'r'						) as file:
														for line_number, line in enumerate(SCREAMING_SNAKE_CASE						):
																					_lowerCAmelCase  	=			line.strip()
																					if line:
																												_lowerCAmelCase  	=			line.split()
																												_lowerCAmelCase  	=			line_number
																												_lowerCAmelCase  	=			words[0]
																												_lowerCAmelCase  	=			value
							return result






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					Any     ,  SCREAMING_SNAKE_CASE:					List[Any]     ,  SCREAMING_SNAKE_CASE:					List[str]     ,  SCREAMING_SNAKE_CASE:					Union[str, Any]     ,  SCREAMING_SNAKE_CASE:					List[str]						):






							"""simple docstring"""



							for attribute in key.split('.'						):
														_lowerCAmelCase  	=			getattr(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE						)

							_lowerCAmelCase  	=			None
							for param_key in PARAM_MAPPING.keys():
														if full_name.endswith(SCREAMING_SNAKE_CASE						):
																					_lowerCAmelCase  	=			PARAM_MAPPING[full_name.split('.'						)[-1]]
																					_lowerCAmelCase  	=			'param'

							if weight_type is not None and weight_type != "param":
														_lowerCAmelCase  	=			getattr(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE						).shape
							elif weight_type is not None and weight_type == "param":
														_lowerCAmelCase  	=			hf_pointer
														for attribute in hf_param_name.split('.'						):
																					_lowerCAmelCase  	=			getattr(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE						)
														_lowerCAmelCase  	=			shape_pointer.shape

														# let's reduce dimension
														_lowerCAmelCase  	=			value[0]
							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
							elif weight_type == "param":
														for attribute in hf_param_name.split('.'						):
																					_lowerCAmelCase  	=			getattr(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE						)
														_lowerCAmelCase  	=			value
							else:
														_lowerCAmelCase  	=			value

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






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					Any     ,  SCREAMING_SNAKE_CASE:					Tuple     ,  SCREAMING_SNAKE_CASE:					List[Any]     ,  SCREAMING_SNAKE_CASE:					List[Any]     ,  SCREAMING_SNAKE_CASE:					Optional[int]						):






							"""simple docstring"""



							_lowerCAmelCase  	=			None
							for param_key in PARAM_MAPPING.keys():
														if full_name.endswith(SCREAMING_SNAKE_CASE						):
																					_lowerCAmelCase  	=			PARAM_MAPPING[full_name.split('.'						)[-1]]
																					_lowerCAmelCase  	=			'param'

							if weight_type is not None and weight_type != "param":
														_lowerCAmelCase  	=			'.'.join([key, weight_type]						)
							elif weight_type is not None and weight_type == "param":
														_lowerCAmelCase  	=			'.'.join([key, hf_param_name]						)
							else:
														_lowerCAmelCase  	=			key

							_lowerCAmelCase  	=			value if 'lm_head' in full_key else value[0]


_snake_case          =						{
    '''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 	__snake_case  (    SCREAMING_SNAKE_CASE:					str     ,  SCREAMING_SNAKE_CASE:					Dict     ,  SCREAMING_SNAKE_CASE:					Any=None     ,  SCREAMING_SNAKE_CASE:					Tuple=None						):






							"""simple docstring"""



							_lowerCAmelCase  	=			False
							for key, mapped_key in MAPPING.items():
														_lowerCAmelCase  	=			'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]:
																					_lowerCAmelCase  	=			True
																					if "*" in mapped_key:
																												_lowerCAmelCase  	=			name.split(SCREAMING_SNAKE_CASE						)[0].split('.'						)[-2]
																												_lowerCAmelCase  	=			mapped_key.replace('*'     ,  SCREAMING_SNAKE_CASE						)
																					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
																					if hf_dict is not None:
																												rename_dict(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE						)
																					else:
																												set_recursively(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE						)
																					return is_used
							return is_used






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					List[Any]     ,  SCREAMING_SNAKE_CASE:					Optional[Any]     ,  SCREAMING_SNAKE_CASE:					Dict						):






							"""simple docstring"""



							_lowerCAmelCase  	=			[]
							_lowerCAmelCase  	=			fairseq_model.state_dict()

							_lowerCAmelCase  	=			hf_model.wavaveca.feature_extractor

							for name, value in fairseq_dict.items():
														_lowerCAmelCase  	=			False
														if "conv_layers" in name:
																					load_conv_layer(
																					    SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE     ,  hf_model.config.feat_extract_norm == 'group'     ,  )
																					_lowerCAmelCase  	=			True
														else:
																					_lowerCAmelCase  	=			load_wavaveca_layer(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE						)
														if not is_used:
																					unused_weights.append(SCREAMING_SNAKE_CASE						)

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






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					Any     ,  SCREAMING_SNAKE_CASE:					Union[str, Any]     ,  SCREAMING_SNAKE_CASE:					Dict     ,  SCREAMING_SNAKE_CASE:					Optional[int]     ,  SCREAMING_SNAKE_CASE:					int						):






							"""simple docstring"""



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






@torch.no_grad()
def 	__snake_case  (    SCREAMING_SNAKE_CASE:					Optional[int]     ,  SCREAMING_SNAKE_CASE:					List[Any]     ,  SCREAMING_SNAKE_CASE:					List[str]=None     ,  SCREAMING_SNAKE_CASE:					List[str]=None     ,  SCREAMING_SNAKE_CASE:					Any=True     ,  SCREAMING_SNAKE_CASE:					Optional[int]=False						):






							"""simple docstring"""



							if config_path is not None:
														_lowerCAmelCase  	=			WavaVecaConfig.from_pretrained(SCREAMING_SNAKE_CASE						)
							else:
														_lowerCAmelCase  	=			WavaVecaConfig()

							if is_seq_class:
														_lowerCAmelCase  	=			read_txt_into_dict(SCREAMING_SNAKE_CASE						)
														_lowerCAmelCase  	=			idalabel
														_lowerCAmelCase  	=			WavaVecaForSequenceClassification(SCREAMING_SNAKE_CASE						)
														_lowerCAmelCase  	=			WavaVecaFeatureExtractor(
														    feature_size=1     ,  sampling_rate=1_6000     ,  padding_value=0     ,  do_normalize=SCREAMING_SNAKE_CASE     ,  return_attention_mask=SCREAMING_SNAKE_CASE     ,  )
														feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE						)

							elif is_finetuned:
														if dict_path:
																					_lowerCAmelCase  	=			Dictionary.load(SCREAMING_SNAKE_CASE						)

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

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

														_lowerCAmelCase  	=			WavaVecaForCTC(SCREAMING_SNAKE_CASE						)
							else:
														_lowerCAmelCase  	=			WavaVecaForPreTraining(SCREAMING_SNAKE_CASE						)

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

														_lowerCAmelCase	,		_lowerCAmelCase	,		_lowerCAmelCase  	=			fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]     ,  task=SCREAMING_SNAKE_CASE						)

							_lowerCAmelCase  	=			model[0].eval()

							recursively_load_weights(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE     ,  not is_finetuned						)

							hf_wavavec.save_pretrained(SCREAMING_SNAKE_CASE						)


if __name__ == "__main__":
	_snake_case          =						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''',
	)
	_snake_case          =						parser.parse_args()

	_snake_case          =						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,
	)








"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_snake_case          =						{
    '''configuration_roformer''': ['''ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RoFormerConfig''', '''RoFormerOnnxConfig'''],
    '''tokenization_roformer''': ['''RoFormerTokenizer'''],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_snake_case          =						['''RoFormerTokenizerFast''']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_snake_case          =						[
	    '''ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''RoFormerForCausalLM''',
	    '''RoFormerForMaskedLM''',
	    '''RoFormerForMultipleChoice''',
	    '''RoFormerForQuestionAnswering''',
	    '''RoFormerForSequenceClassification''',
	    '''RoFormerForTokenClassification''',
	    '''RoFormerLayer''',
	    '''RoFormerModel''',
	    '''RoFormerPreTrainedModel''',
	    '''load_tf_weights_in_roformer''',
	]


try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_snake_case          =						[
	    '''TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''TFRoFormerForCausalLM''',
	    '''TFRoFormerForMaskedLM''',
	    '''TFRoFormerForMultipleChoice''',
	    '''TFRoFormerForQuestionAnswering''',
	    '''TFRoFormerForSequenceClassification''',
	    '''TFRoFormerForTokenClassification''',
	    '''TFRoFormerLayer''',
	    '''TFRoFormerModel''',
	    '''TFRoFormerPreTrainedModel''',
	]


try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_snake_case          =						[
	    '''FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''FlaxRoFormerForMaskedLM''',
	    '''FlaxRoFormerForMultipleChoice''',
	    '''FlaxRoFormerForQuestionAnswering''',
	    '''FlaxRoFormerForSequenceClassification''',
	    '''FlaxRoFormerForTokenClassification''',
	    '''FlaxRoFormerModel''',
	    '''FlaxRoFormerPreTrainedModel''',
	]


if TYPE_CHECKING:
	from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
	from .tokenization_roformer import RoFormerTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_roformer_fast import RoFormerTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_roformer import (
		    ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    RoFormerForCausalLM,
		    RoFormerForMaskedLM,
		    RoFormerForMultipleChoice,
		    RoFormerForQuestionAnswering,
		    RoFormerForSequenceClassification,
		    RoFormerForTokenClassification,
		    RoFormerLayer,
		    RoFormerModel,
		    RoFormerPreTrainedModel,
		    load_tf_weights_in_roformer,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_roformer import (
		    TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFRoFormerForCausalLM,
		    TFRoFormerForMaskedLM,
		    TFRoFormerForMultipleChoice,
		    TFRoFormerForQuestionAnswering,
		    TFRoFormerForSequenceClassification,
		    TFRoFormerForTokenClassification,
		    TFRoFormerLayer,
		    TFRoFormerModel,
		    TFRoFormerPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_roformer import (
		    FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    FlaxRoFormerForMaskedLM,
		    FlaxRoFormerForMultipleChoice,
		    FlaxRoFormerForQuestionAnswering,
		    FlaxRoFormerForSequenceClassification,
		    FlaxRoFormerForTokenClassification,
		    FlaxRoFormerModel,
		    FlaxRoFormerPreTrainedModel,
		)


else:
	import sys

	_snake_case          =						_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






