





'''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()
__UpperCamelCase					    =	logging.get_logger(__name__)

__UpperCamelCase					    =	{
    "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",
}
__UpperCamelCase					    =	[
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
    "projector",
    "classifier",
]







def 					_a						(       _lowerCamelCase ) ->				Any:



			"""simple docstring"""


			__snake_case					:      Any							      =     {}
			with open(lowerCamelCase_    ,	"""r""" ) as file:
						for line_number, line in enumerate(lowerCamelCase_ ):
									__snake_case					:      List[Any]							      =     line.strip()
									if line:
												__snake_case					:      List[Any]							      =     line.split()
												__snake_case					:      Optional[Any]							      =     line_number
												__snake_case					:      Any							      =     words[0]
												__snake_case					:      Union[str, Any]							      =     value
			return result







def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase ) ->				str:



			"""simple docstring"""


			for attribute in key.split(""".""" ):
						__snake_case					:      Optional[Any]							      =     getattr(lowerCamelCase_    ,	lowerCamelCase_ )

			__snake_case					:      Any							      =     None
			for param_key in PARAM_MAPPING.keys():
						if full_name.endswith(lowerCamelCase_ ):
									__snake_case					:      str							      =     PARAM_MAPPING[full_name.split(""".""" )[-1]]
									__snake_case					:      int							      =     """param"""

			if weight_type is not None and weight_type != "param":
						__snake_case					:      Tuple							      =     getattr(lowerCamelCase_    ,	lowerCamelCase_ ).shape
			elif weight_type is not None and weight_type == "param":
						__snake_case					:      List[Any]							      =     hf_pointer
						for attribute in hf_param_name.split(""".""" ):
									__snake_case					:      Optional[Any]							      =     getattr(lowerCamelCase_    ,	lowerCamelCase_ )
						__snake_case					:      Optional[int]							      =     shape_pointer.shape

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

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







def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase ) ->				Optional[int]:



			"""simple docstring"""


			__snake_case					:      str							      =     None
			for param_key in PARAM_MAPPING.keys():
						if full_name.endswith(lowerCamelCase_ ):
									__snake_case					:      Dict							      =     PARAM_MAPPING[full_name.split(""".""" )[-1]]
									__snake_case					:      Tuple							      =     """param"""

			if weight_type is not None and weight_type != "param":
						__snake_case					:      Any							      =     """.""".join([key, weight_type] )
			elif weight_type is not None and weight_type == "param":
						__snake_case					:      Optional[Any]							      =     """.""".join([key, hf_param_name] )
			else:
						__snake_case					:      Tuple							      =     key

			__snake_case					:      int							      =     value if """lm_head""" in full_key else value[0]


__UpperCamelCase					    =	{
    "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 					_a						(       _lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase=None    ,	_lowerCamelCase=None ) ->				str:



			"""simple docstring"""


			__snake_case					:      Dict							      =     False
			for key, mapped_key in MAPPING.items():
						__snake_case					:      Any							      =     """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]:
									__snake_case					:      Union[str, Any]							      =     True
									if "*" in mapped_key:
												__snake_case					:      str							      =     name.split(lowerCamelCase_ )[0].split(""".""" )[-2]
												__snake_case					:      int							      =     mapped_key.replace("""*"""    ,	lowerCamelCase_ )
									if "weight_g" in name:
												__snake_case					:      Any							      =     """weight_g"""
									elif "weight_v" in name:
												__snake_case					:      Optional[int]							      =     """weight_v"""
									elif "bias" in name:
												__snake_case					:      Union[str, Any]							      =     """bias"""
									elif "weight" in name:
												# TODO: don't match quantizer.weight_proj
												__snake_case					:      List[Any]							      =     """weight"""
									else:
												__snake_case					:      Optional[int]							      =     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 					_a						(       _lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase ) ->				List[Any]:



			"""simple docstring"""


			__snake_case					:      str							      =     []
			__snake_case					:      Dict							      =     fairseq_model.state_dict()

			__snake_case					:      Dict							      =     hf_model.wavaveca.feature_extractor

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

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







def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase ) ->				Union[str, Any]:



			"""simple docstring"""


			__snake_case					:      Dict							      =     full_name.split("""conv_layers.""" )[-1]
			__snake_case					:      Dict							      =     name.split(""".""" )
			__snake_case					:      Optional[Any]							      =     int(items[0] )
			__snake_case					:      List[str]							      =     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.''' )
									__snake_case					:      Optional[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.''' )
									__snake_case					:      List[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.''' )
									__snake_case					:      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.''' )
									__snake_case					:      List[Any]							      =     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    ,	_lowerCamelCase=False ) ->				int:



			"""simple docstring"""


			if config_path is not None:
						__snake_case					:      List[Any]							      =     WavaVecaConfig.from_pretrained(lowerCamelCase_ )
			else:
						__snake_case					:      Dict							      =     WavaVecaConfig()

			if is_seq_class:
						__snake_case					:      Any							      =     read_txt_into_dict(lowerCamelCase_ )
						__snake_case					:      Optional[Any]							      =     idalabel
						__snake_case					:      List[str]							      =     WavaVecaForSequenceClassification(lowerCamelCase_ )
						__snake_case					:      str							      =     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:
									__snake_case					:      int							      =     Dictionary.load(lowerCamelCase_ )

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

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

						__snake_case					:      Union[str, Any]							      =     WavaVecaForCTC(lowerCamelCase_ )
			else:
						__snake_case					:      Union[str, Any]							      =     WavaVecaForPreTraining(lowerCamelCase_ )

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

						__snake_case					:      str							      =     fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]    ,	task=lowerCamelCase_ )

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

			recursively_load_weights(lowerCamelCase_    ,	lowerCamelCase_    ,	not is_finetuned )

			hf_wavavec.save_pretrained(lowerCamelCase_ )


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

	__UpperCamelCase					    =	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 __future__ import annotations



def 		__A		(  lowerCamelCase_			,			lowerCamelCase_	):


		"""simple docstring"""



		if partitions <= 0:
				raise ValueError("""partitions must be a positive number!"""	)
		if partitions > number_of_bytes:
				raise ValueError("""partitions can not > number_of_bytes!"""	)
		SCREAMING_SNAKE_CASE :				int		=			number_of_bytes // partitions
		SCREAMING_SNAKE_CASE :				int		=			[]
		for i in range(lowerCamelCase_	):
				SCREAMING_SNAKE_CASE :				Optional[Any]		=			i * bytes_per_partition + 1
				SCREAMING_SNAKE_CASE :				Optional[int]		=			(
				    number_of_bytes if i == partitions - 1 else (i + 1) * bytes_per_partition
				)
				allocation_list.append(f'''{start_bytes}-{end_bytes}'''	)
		return allocation_list


if __name__ == "__main__":
							import doctest

							doctest.testmod()



