



import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta import RobertaTokenizer


__UpperCAmelCase			  =			logging.get_logger(__name__)

__UpperCAmelCase			  =			{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

__UpperCAmelCase			  =			{
    '''vocab_file''': {
        '''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/vocab.json''',
        '''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/vocab.json''',
        '''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json''',
        '''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/vocab.json''',
        '''roberta-base-openai-detector''': '''https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json''',
        '''roberta-large-openai-detector''': (
            '''https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json'''
        ),
    },
    '''merges_file''': {
        '''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/merges.txt''',
        '''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/merges.txt''',
        '''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt''',
        '''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/merges.txt''',
        '''roberta-base-openai-detector''': '''https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt''',
        '''roberta-large-openai-detector''': (
            '''https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/tokenizer.json''',
        '''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/tokenizer.json''',
        '''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/tokenizer.json''',
        '''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/tokenizer.json''',
        '''roberta-base-openai-detector''': (
            '''https://huggingface.co/roberta-base-openai-detector/resolve/main/tokenizer.json'''
        ),
        '''roberta-large-openai-detector''': (
            '''https://huggingface.co/roberta-large-openai-detector/resolve/main/tokenizer.json'''
        ),
    },
}

__UpperCAmelCase			  =			{
    '''roberta-base''': 512,
    '''roberta-large''': 512,
    '''roberta-large-mnli''': 512,
    '''distilroberta-base''': 512,
    '''roberta-base-openai-detector''': 512,
    '''roberta-large-openai-detector''': 512,
}



class 							lowerCAmelCase_						(    a__				):
       UpperCAmelCase__							:   int  	=    VOCAB_FILES_NAMES
       UpperCAmelCase__							:   Dict  	=    PRETRAINED_VOCAB_FILES_MAP
       UpperCAmelCase__							:   List[str]  	=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       UpperCAmelCase__							:   str  	=    ["input_ids", "attention_mask"]
       UpperCAmelCase__							:   Dict  	=    RobertaTokenizer
       def __init__(     self,     SCREAMING_SNAKE_CASE_=None,     SCREAMING_SNAKE_CASE_=None,     SCREAMING_SNAKE_CASE_=None,     SCREAMING_SNAKE_CASE_="replace",     SCREAMING_SNAKE_CASE_="<s>",     SCREAMING_SNAKE_CASE_="</s>",     SCREAMING_SNAKE_CASE_="</s>",     SCREAMING_SNAKE_CASE_="<s>",     SCREAMING_SNAKE_CASE_="<unk>",     SCREAMING_SNAKE_CASE_="<pad>",     SCREAMING_SNAKE_CASE_="<mask>",     SCREAMING_SNAKE_CASE_=False,     SCREAMING_SNAKE_CASE_=True,     **SCREAMING_SNAKE_CASE_,     )	->						Optional[int]:
          super().__init__(
              SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     tokenizer_file=SCREAMING_SNAKE_CASE_,     errors=SCREAMING_SNAKE_CASE_,     bos_token=SCREAMING_SNAKE_CASE_,     eos_token=SCREAMING_SNAKE_CASE_,     sep_token=SCREAMING_SNAKE_CASE_,     cls_token=SCREAMING_SNAKE_CASE_,     unk_token=SCREAMING_SNAKE_CASE_,     pad_token=SCREAMING_SNAKE_CASE_,     mask_token=SCREAMING_SNAKE_CASE_,     add_prefix_space=SCREAMING_SNAKE_CASE_,     trim_offsets=SCREAMING_SNAKE_CASE_,     **SCREAMING_SNAKE_CASE_,     )

          UpperCamelCase       :    Tuple		      =      json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()			)
          if pre_tok_state.get('add_prefix_space',     SCREAMING_SNAKE_CASE_			) != add_prefix_space:
             UpperCamelCase       :    Dict		      =      getattr(SCREAMING_SNAKE_CASE_,     pre_tok_state.pop('type'			)			)
             UpperCamelCase       :    List[str]		      =      add_prefix_space
             UpperCamelCase       :    Dict		      =      pre_tok_class(**SCREAMING_SNAKE_CASE_			)

          UpperCamelCase       :    Dict		      =      add_prefix_space

          UpperCamelCase       :    Optional[Any]		      =      'post_processor'
          UpperCamelCase       :    Dict		      =      getattr(self.backend_tokenizer,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)
          if tokenizer_component_instance:
             UpperCamelCase       :    Optional[int]		      =      json.loads(tokenizer_component_instance.__getstate__()			)

             # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
             if "sep" in state:
                UpperCamelCase       :    Optional[Any]		      =      tuple(state['sep']			)
             if "cls" in state:
                UpperCamelCase       :    Optional[int]		      =      tuple(state['cls']			)

             UpperCamelCase       :    Any		      =      False

             if state.get('add_prefix_space',     SCREAMING_SNAKE_CASE_			) != add_prefix_space:
                UpperCamelCase       :    Optional[int]		      =      add_prefix_space
                UpperCamelCase       :    List[Any]		      =      True

             if state.get('trim_offsets',     SCREAMING_SNAKE_CASE_			) != trim_offsets:
                UpperCamelCase       :    Dict		      =      trim_offsets
                UpperCamelCase       :    Union[str, Any]		      =      True

             if changes_to_apply:
                UpperCamelCase       :    Tuple		      =      getattr(SCREAMING_SNAKE_CASE_,     state.pop('type'			)			)
                UpperCamelCase       :    Union[str, Any]		      =      component_class(**SCREAMING_SNAKE_CASE_			)
                setattr(self.backend_tokenizer,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)
       @property
       def    snake_case_			(     self			)	->						str:
          if self._mask_token is None:
             if self.verbose:
                logger.error('Using mask_token, but it is not set yet.'			)
             return None
          return str(self._mask_token			)
       @mask_token.setter
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_			)	->						List[Any]:
          UpperCamelCase       :    int		      =      AddedToken(SCREAMING_SNAKE_CASE_,     lstrip=SCREAMING_SNAKE_CASE_,     rstrip=SCREAMING_SNAKE_CASE_			) if isinstance(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			) else value
          UpperCamelCase       :    List[Any]		      =      value
       def    snake_case_			(     self,     *SCREAMING_SNAKE_CASE_,     **SCREAMING_SNAKE_CASE_			)	->						BatchEncoding:
          UpperCamelCase       :    Optional[int]		      =      kwargs.get('is_split_into_words',     SCREAMING_SNAKE_CASE_			)
          assert self.add_prefix_space or not is_split_into_words, (
              F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
              "to use it with pretokenized inputs."
          )

          return super()._batch_encode_plus(*SCREAMING_SNAKE_CASE_,     **SCREAMING_SNAKE_CASE_			)
       def    snake_case_			(     self,     *SCREAMING_SNAKE_CASE_,     **SCREAMING_SNAKE_CASE_			)	->						BatchEncoding:
          UpperCamelCase       :    Dict		      =      kwargs.get('is_split_into_words',     SCREAMING_SNAKE_CASE_			)

          assert self.add_prefix_space or not is_split_into_words, (
              F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
              "to use it with pretokenized inputs."
          )

          return super()._encode_plus(*SCREAMING_SNAKE_CASE_,     **SCREAMING_SNAKE_CASE_			)
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_ = None			)	->						Tuple[str]:
          UpperCamelCase       :    Dict		      =      self._tokenizer.model.save(SCREAMING_SNAKE_CASE_,     name=SCREAMING_SNAKE_CASE_			)
          return tuple(SCREAMING_SNAKE_CASE_			)
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_=None			)	->						Tuple:
          UpperCamelCase       :    Union[str, Any]		      =      [self.bos_token_id] + token_ids_a + [self.eos_token_id]
          if token_ids_a is None:
             return output

          return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]






       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_ = None			)	->						List[int]:
          UpperCamelCase       :    Dict		      =      [self.sep_token_id]
          UpperCamelCase       :    Optional[int]		      =      [self.cls_token_id]

          if token_ids_a is None:
             return len(cls + token_ids_a + sep			) * [0]
          return len(cls + token_ids_a + sep + sep + token_ids_a + sep			) * [0]






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()
A_			:					List[str]    			=     logging.get_logger(__name__)

A_			:					List[Any]    			=     {
    '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',
}
A_			:					str    			=     [
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
    'projector',
    'classifier',
]

def 		UpperCamelCase   (lowercase_:					List[Any]   )	-> List[Any]:
	A__				:       Optional[Any]	  =						{}
	with open(lowercase_       , """r"""   ) as file:
		for line_number, line in enumerate(lowercase_   ):
			A__				:       Any	  =						line.strip()
			if line:
				A__				:       Optional[int]	  =						line.split()
				A__				:       Any	  =						line_number
				A__				:       Tuple	  =						words[0]
				A__				:       Union[str, Any]	  =						value
	return result

def 		UpperCamelCase   (lowercase_:					List[str]       , lowercase_:					Tuple       , lowercase_:					int       , lowercase_:					Optional[int]       , lowercase_:					Optional[int]   )	-> Dict:
	for attribute in key.split("""."""   ):
		A__				:       Optional[int]	  =						getattr(lowercase_       , lowercase_   )

	A__				:       Optional[int]	  =						None
	for param_key in PARAM_MAPPING.keys():
		if full_name.endswith(lowercase_   ):
			A__				:       Dict	  =						PARAM_MAPPING[full_name.split("""."""   )[-1]]
			A__				:       str	  =						"""param"""

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

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

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

def 		UpperCamelCase   (lowercase_:					Any       , lowercase_:					Optional[Any]       , lowercase_:					Tuple       , lowercase_:					Any       , lowercase_:					Union[str, Any]   )	-> Tuple:
	A__				:       Any	  =						None
	for param_key in PARAM_MAPPING.keys():
		if full_name.endswith(lowercase_   ):
			A__				:       List[Any]	  =						PARAM_MAPPING[full_name.split("""."""   )[-1]]
			A__				:       List[str]	  =						"""param"""

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

	A__				:       Tuple	  =						value if """lm_head""" in full_key else value[0]


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

def 		UpperCamelCase   (lowercase_:					Union[str, Any]       , lowercase_:					Optional[int]       , lowercase_:					Any   )	-> Tuple:
	A__				:       List[Any]	  =						[]
	A__				:       Tuple	  =						fairseq_model.state_dict()

	A__				:       Any	  =						hf_model.wavaveca.feature_extractor

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

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

def 		UpperCamelCase   (lowercase_:					Optional[Any]       , lowercase_:					List[str]       , lowercase_:					Any       , lowercase_:					Union[str, Any]       , lowercase_:					Any   )	-> Dict:
	A__				:       str	  =						full_name.split("""conv_layers."""   )[-1]
	A__				:       Optional[Any]	  =						name.split("""."""   )
	A__				:       Optional[int]	  =						int(items[0]   )
	A__				:       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."""   )
			A__				:       int	  =						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."""   )
			A__				:       Any	  =						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."""   )
			A__				:       Union[str, Any]	  =						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."""   )
			A__				:       Dict	  =						value
			logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""   )
	else:
		unused_weights.append(lowercase_   )

@torch.no_grad()
def 		UpperCamelCase   (lowercase_:					List[Any]       , lowercase_:					int       , lowercase_:					str=None       , lowercase_:					Tuple=None       , lowercase_:					List[str]=True       , lowercase_:					Any=False   )	-> str:
	if config_path is not None:
		A__				:       Any	  =						WavaVecaConfig.from_pretrained(lowercase_   )
	else:
		A__				:       int	  =						WavaVecaConfig()

	if is_seq_class:
		A__				:       Optional[Any]	  =						read_txt_into_dict(lowercase_   )
		A__				:       List[str]	  =						idalabel
		A__				:       Any	  =						WavaVecaForSequenceClassification(lowercase_   )
		A__				:       str	  =						WavaVecaFeatureExtractor(
		    feature_size=1       , sampling_rate=16000       , padding_value=0       , do_normalize=lowercase_       , return_attention_mask=lowercase_       , )
		feature_extractor.save_pretrained(lowercase_   )

	elif is_finetuned:
		if dict_path:
			A__				:       List[Any]	  =						Dictionary.load(lowercase_   )

			# important change bos & pad token id since CTC symbol is <pad> and
			# not <s> as in fairseq
			A__				:       Tuple	  =						target_dict.pad_index
			A__				:       Union[str, Any]	  =						target_dict.bos_index
			A__				:       List[Any]	  =						target_dict.eos_index
			A__				:       List[str]	  =						len(target_dict.symbols   )
			A__				:       str	  =						os.path.join(lowercase_       , """vocab.json"""   )
			if not os.path.isdir(lowercase_   ):
				logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(lowercase_   )   )
				return
			os.makedirs(lowercase_       , exist_ok=lowercase_   )
			A__				:       Dict	  =						target_dict.indices

			# fairseq has the <pad> and <s> switched
			A__				:       List[str]	  =						0
			A__				:       Optional[Any]	  =						1
			with open(lowercase_       , """w"""       , encoding="""utf-8"""   ) as vocab_handle:
				json.dump(lowercase_       , lowercase_   )
			A__				:       List[str]	  =						WavaVecaCTCTokenizer(
			    lowercase_       , 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=lowercase_       , )
			A__				:       List[Any]	  =						True if config.feat_extract_norm == """layer""" else False
			A__				:       int	  =						WavaVecaFeatureExtractor(
			    feature_size=1       , sampling_rate=16000       , padding_value=0       , do_normalize=lowercase_       , return_attention_mask=lowercase_       , )
			A__				:       str	  =						WavaVecaProcessor(feature_extractor=lowercase_       , tokenizer=lowercase_   )
			processor.save_pretrained(lowercase_   )

		A__				:       Optional[Any]	  =						WavaVecaForCTC(lowercase_   )
	else:
		A__				:       str	  =						WavaVecaForPreTraining(lowercase_   )

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

		A__  ,  A__  ,  A__				:       List[str]	  =						fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]       , task=lowercase_   )

	A__				:       Tuple	  =						model[0].eval()

	recursively_load_weights(lowercase_       , lowercase_       , not is_finetuned   )

	hf_wavavec.save_pretrained(lowercase_   )


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'
	)
	parser.add_argument(
	    '--is_seq_class',
	    action='store_true',
	    help='Whether the model to convert is a fine-tuned sequence classification model or not',
	)
	A_			:					Any    			=     parser.parse_args()

	A_			:					List[Any]    			=     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,
	)



