

import os
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
	from transformers.pipelines.conversational import Conversation

__A	:   Optional[Any]	=					logging.get_logger(__name__)

__A	:   Any	=					{'vocab_file': 'tokenizer.model'}

__A	:   Dict	=					{
    'vocab_file': {
        'hf-internal-testing/llama-tokenizer': 'https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer.model',
    },
    'tokenizer_file': {
        'hf-internal-testing/llama-tokenizer': 'https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer_config.json',
    },
}
__A	:   List[str]	=					{
    'hf-internal-testing/llama-tokenizer': 20_48,
}
__A	:   Optional[Any]	=					'▁'

__A,		__A	:   List[str]	=					'[INST]', '[/INST]'
__A,		__A	:   Optional[Any]	=					'<<SYS>>\n', '\n<</SYS>>\n\n'

# fmt: off
__A	:   Union[str, Any]	=					'You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your\\nanswers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure\\nthat your responses are socially unbiased and positive in nature.\n\nIf a question does not make any sense, or is not factually coherent, explain why instead of answering something not\\ncorrect. If you don\'t know the answer to a question, please don\'t share false information.'


class 			_SCREAMING_SNAKE_CASE      (  lowerCAmelCase__):
						_UpperCamelCase:Any	=		VOCAB_FILES_NAMES
						_UpperCamelCase:int	=		PRETRAINED_VOCAB_FILES_MAP
						_UpperCamelCase:Dict	=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_UpperCamelCase:Optional[Any]	=		["input_ids", "attention_mask"]







						def __init__(			self     ,						_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE="<unk>"     ,						_SCREAMING_SNAKE_CASE="<s>"     ,						_SCREAMING_SNAKE_CASE="</s>"     ,						_SCREAMING_SNAKE_CASE=None     ,						_SCREAMING_SNAKE_CASE = None     ,						_SCREAMING_SNAKE_CASE=True     ,						_SCREAMING_SNAKE_CASE=False     ,						_SCREAMING_SNAKE_CASE=False     ,						_SCREAMING_SNAKE_CASE=True     ,						**_SCREAMING_SNAKE_CASE     ,						)->							Union[str, Any]:
													lowerCamelCase_								={} if sp_model_kwargs is None else sp_model_kwargs
													lowerCamelCase_								=AddedToken(_SCREAMING_SNAKE_CASE     ,						lstrip=_SCREAMING_SNAKE_CASE     ,						rstrip=_SCREAMING_SNAKE_CASE							) if isinstance(_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE							) else bos_token
													lowerCamelCase_								=AddedToken(_SCREAMING_SNAKE_CASE     ,						lstrip=_SCREAMING_SNAKE_CASE     ,						rstrip=_SCREAMING_SNAKE_CASE							) if isinstance(_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE							) else eos_token
													lowerCamelCase_								=AddedToken(_SCREAMING_SNAKE_CASE     ,						lstrip=_SCREAMING_SNAKE_CASE     ,						rstrip=_SCREAMING_SNAKE_CASE							) if isinstance(_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE							) else unk_token
													lowerCamelCase_								=AddedToken(_SCREAMING_SNAKE_CASE     ,						lstrip=_SCREAMING_SNAKE_CASE     ,						rstrip=_SCREAMING_SNAKE_CASE							) if isinstance(_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE							) else pad_token
													super().__init__(
													    bos_token=_SCREAMING_SNAKE_CASE     ,						eos_token=_SCREAMING_SNAKE_CASE     ,						unk_token=_SCREAMING_SNAKE_CASE     ,						pad_token=_SCREAMING_SNAKE_CASE     ,						add_bos_token=_SCREAMING_SNAKE_CASE     ,						add_eos_token=_SCREAMING_SNAKE_CASE     ,						sp_model_kwargs=self.sp_model_kwargs     ,						clean_up_tokenization_spaces=_SCREAMING_SNAKE_CASE     ,						legacy=_SCREAMING_SNAKE_CASE     ,						**_SCREAMING_SNAKE_CASE     ,						)
													if legacy:
																				logger.warning_once(
																				    f'You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to'
																				    """ read the related pull request available at https://github.com/huggingface/transformers/pull/24565"""							)
													lowerCamelCase_								=legacy
													lowerCamelCase_								=vocab_file
													lowerCamelCase_								=add_bos_token
													lowerCamelCase_								=add_eos_token
													lowerCamelCase_								=spm.SentencePieceProcessor(**self.sp_model_kwargs							)
													self.sp_model.Load(_SCREAMING_SNAKE_CASE							)







						def __getstate__(			self							)->							Optional[Any]:
													lowerCamelCase_								=self.__dict__.copy()
													lowerCamelCase_								=None
													lowerCamelCase_								=self.sp_model.serialized_model_proto()
													return state







						def __setstate__(			self     ,						_SCREAMING_SNAKE_CASE							)->							List[Any]:
													lowerCamelCase_								=d
													lowerCamelCase_								=spm.SentencePieceProcessor(**self.sp_model_kwargs							)
													self.sp_model.LoadFromSerializedProto(self.sp_model_proto							)







						@property
						def        _snake_case				(			self							)->							Tuple:
													return self.sp_model.get_piece_size()







						def        _snake_case				(			self							)->							Any:
													lowerCamelCase_								={self.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE							): i for i in range(self.vocab_size							)}
													vocab.update(self.added_tokens_encoder							)
													return vocab







						def        _snake_case				(			self     ,						_SCREAMING_SNAKE_CASE     ,						**_SCREAMING_SNAKE_CASE							)->							List[str]:
													# Replace the SPIECE_UNDERLINE with a space to make sure SPIECE_UNDERLINE is only used at
													# the beginning of the text
													if not self.legacy:
																				lowerCamelCase_								=SPIECE_UNDERLINE + text.replace(_SCREAMING_SNAKE_CASE     ,						""" """							)
													return super().tokenize(_SCREAMING_SNAKE_CASE     ,						**_SCREAMING_SNAKE_CASE							)







						def        _snake_case				(			self     ,						_SCREAMING_SNAKE_CASE							)->							Optional[Any]:
													if not self.legacy:
																				lowerCamelCase_								=text.startswith(_SCREAMING_SNAKE_CASE							)
																				if is_first:
																											lowerCamelCase_								=text[1:]

													lowerCamelCase_								=self.sp_model.encode(_SCREAMING_SNAKE_CASE     ,						out_type=_SCREAMING_SNAKE_CASE							)

													if not self.legacy and not is_first and not text.startswith(""" """							) and tokens[0].startswith(_SCREAMING_SNAKE_CASE							):
																				lowerCamelCase_								=([tokens[0][1:]] if len(tokens[0]							) > 1 else []) + tokens[1:]
													return tokens







						def        _snake_case				(			self     ,						_SCREAMING_SNAKE_CASE							)->							Optional[Any]:
													return self.sp_model.piece_to_id(_SCREAMING_SNAKE_CASE							)







						def        _snake_case				(			self     ,						_SCREAMING_SNAKE_CASE							)->							List[Any]:
													lowerCamelCase_								=self.sp_model.IdToPiece(_SCREAMING_SNAKE_CASE							)
													return token







						def        _snake_case				(			self     ,						_SCREAMING_SNAKE_CASE							)->							List[Any]:
													lowerCamelCase_								=[]
													lowerCamelCase_								=""""""
													lowerCamelCase_								=False
													for i, token in enumerate(_SCREAMING_SNAKE_CASE							):
																				# make sure that special tokens are not decoded using sentencepiece model
																				if token in self.all_special_tokens:
																											if not prev_is_special and i != 0:
																																		out_string += " "
																											out_string += self.sp_model.decode(_SCREAMING_SNAKE_CASE							) + token
																											lowerCamelCase_								=True
																											lowerCamelCase_								=[]
																				else:
																											current_sub_tokens.append(_SCREAMING_SNAKE_CASE							)
																											lowerCamelCase_								=False
													out_string += self.sp_model.decode(_SCREAMING_SNAKE_CASE							)
													return out_string







						def        _snake_case				(			self     ,						_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE = None							)->							Tuple[str]:
													if not os.path.isdir(_SCREAMING_SNAKE_CASE							):
																				logger.error(f'Vocabulary path ({save_directory}) should be a directory'							)
																				return
													lowerCamelCase_								=os.path.join(
													    _SCREAMING_SNAKE_CASE     ,						(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]							)

													if os.path.abspath(self.vocab_file							) != os.path.abspath(_SCREAMING_SNAKE_CASE							) and os.path.isfile(self.vocab_file							):
																				copyfile(self.vocab_file     ,						_SCREAMING_SNAKE_CASE							)
													elif not os.path.isfile(self.vocab_file							):
																				with open(_SCREAMING_SNAKE_CASE     ,						"""wb"""							) as fi:
																											lowerCamelCase_								=self.sp_model.serialized_model_proto()
																											fi.write(_SCREAMING_SNAKE_CASE							)

													return (out_vocab_file,)







						def        _snake_case				(			self     ,						_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE=None							)->							Optional[Any]:
													lowerCamelCase_								=[self.bos_token_id] if self.add_bos_token else []
													lowerCamelCase_								=[self.eos_token_id] if self.add_eos_token else []

													lowerCamelCase_								=bos_token_id + token_ids_a + eos_token_id

													if token_ids_a is not None:
																				lowerCamelCase_								=output + bos_token_id + token_ids_a + eos_token_id

													return output







						def        _snake_case				(			self     ,						_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE = None     ,						_SCREAMING_SNAKE_CASE = False							)->							List[int]:
													if already_has_special_tokens:
																				return super().get_special_tokens_mask(
																				    token_ids_a=_SCREAMING_SNAKE_CASE     ,						token_ids_a=_SCREAMING_SNAKE_CASE     ,						already_has_special_tokens=_SCREAMING_SNAKE_CASE							)

													lowerCamelCase_								=[1] if self.add_bos_token else []
													lowerCamelCase_								=[1] if self.add_eos_token else []

													if token_ids_a is None:
																				return bos_token_id + ([0] * len(_SCREAMING_SNAKE_CASE							)) + eos_token_id
													return (
													    bos_token_id
													    + ([0] * len(_SCREAMING_SNAKE_CASE							))
													    + eos_token_id
													    + bos_token_id
													    + ([0] * len(_SCREAMING_SNAKE_CASE							))
													    + eos_token_id
													)







						def        _snake_case				(			self     ,						_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE = None							)->							List[int]:
													lowerCamelCase_								=[self.bos_token_id] if self.add_bos_token else []
													lowerCamelCase_								=[self.eos_token_id] if self.add_eos_token else []

													lowerCamelCase_								=[0] * len(bos_token_id + token_ids_a + eos_token_id							)

													if token_ids_a is not None:
																				output += [1] * len(bos_token_id + token_ids_a + eos_token_id							)

													return output







						def        _snake_case				(			self     ,						_SCREAMING_SNAKE_CASE							)->							List[int]:
													lowerCamelCase_								=list(conversation.iter_texts()							)
													if not all([is_user for is_user, msg in dialogue[::2]]							) or not all(
													    [not is_user for is_user, msg in dialogue[1::2]]							):
																				raise ValueError(
																				    """The model only supports 'user' and 'assistant' roles, starting with user and alternating (u/a/u/a/u...)"""							)

													lowerCamelCase_								=[]
													if len(conversation.past_user_inputs							) > 0:
																				if not conversation.past_user_inputs[0].startswith(_SCREAMING_SNAKE_CASE							) or E_SYS not in conversation.past_user_inputs[0]:
																											lowerCamelCase_								=(
																											    B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + conversation.past_user_inputs[0]
																											)
													elif not dialogue[0][1].startswith(_SCREAMING_SNAKE_CASE							) or E_SYS not in dialogue[0][1]:
																				lowerCamelCase_								=(dialogue[0][0], B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + dialogue[0][1])

													dialog_tokens += sum(
													    [
													        [self.bos_token_id]
													        + self.encode(
													            f'{B_INST} {(prompt[1]).strip()} {E_INST} {(answer[1]).strip()} '     ,						add_special_tokens=_SCREAMING_SNAKE_CASE							)
													        + [self.eos_token_id]
													        for prompt, answer in zip(dialogue[::2]     ,						dialogue[1::2]							)
													    ]     ,						[]     ,						)
													if not (dialogue[-1][0]):
																				raise ValueError(f'Last message must be from user, got {dialogue[-1]["role"]}'							)
													dialog_tokens += [self.bos_token_id] + self.encode(
													    f'{B_INST} {(dialogue[-1][1]).strip()} {E_INST}'     ,						add_special_tokens=_SCREAMING_SNAKE_CASE							)
													return dialog_tokens



import argparse
import os
from functools import reduce

import fairseq
import torch
from datasets import load_dataset

from transformers import WavaVecaProcessor, logging
from transformers.models.dataavec.configuration_dataavec_audio import DataaVecAudioConfig

# Copied from https://github.com/pytorch/fairseq/blob/main/examples/data2vec/models/data2vec_audio.py
from transformers.models.dataavec.dataavec_audio import DataaVecAudioModel as Dummy  # noqa: F401
from transformers.models.dataavec.modeling_dataavec_audio import DataaVecAudioForCTC, DataaVecAudioModel


logging.set_verbosity_info()
__A	:   Optional[Any]	=					logging.get_logger(__name__)

__A	:   Optional[int]	=					{
    'post_extract_proj': 'feature_projection.projection',
    'models.0.layer_norm': 'feature_projection.layer_norm',
    'self_attn.k_proj': 'encoder.layers.*.attention.k_proj',
    'self_attn.v_proj': 'encoder.layers.*.attention.v_proj',
    'self_attn.q_proj': 'encoder.layers.*.attention.q_proj',
    'self_attn.out_proj': 'encoder.layers.*.attention.out_proj',
    'self_attn_layer_norm': 'encoder.layers.*.layer_norm',
    'fc1': 'encoder.layers.*.feed_forward.intermediate_dense',
    'fc2': 'encoder.layers.*.feed_forward.output_dense',
    'final_layer_norm': 'encoder.layers.*.final_layer_norm',
    'encoder.layer_norm': 'encoder.layer_norm',
    'w2v_model.layer_norm': 'feature_projection.layer_norm',
    'w2v_encoder.proj': 'lm_head',
    'mask_emb': 'masked_spec_embed',
}
__A	:   Optional[int]	=					[
    'lm_head',
]




def        __UpperCamelCase     (      _A					:							Optional[Any]      ,  _A					:							Dict      ,  _A					:							Tuple      ,  _A					:							str      ,  _A					:							int		)				->List[str]:



							"""simple docstring"""
							for attribute in key.split("""."""		):
														lowerCamelCase_								=getattr(_A      ,  _A		)

							if weight_type is not None:
														lowerCamelCase_								=getattr(_A      ,  _A		).shape
							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
							else:
														lowerCamelCase_								=value

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




def        __UpperCamelCase     (      _A					:							Dict      ,  _A					:							Dict      ,  _A					:							Optional[Any]		)				->Any:



							"""simple docstring"""
							lowerCamelCase_								=[]
							lowerCamelCase_								=fairseq_model.state_dict()

							if not is_headless:
														lowerCamelCase_								=hf_model.dataavec_audio.feature_extractor
														lowerCamelCase_								=hf_model.dataavec_audio.encoder.pos_conv_embed

							else:
														lowerCamelCase_								=hf_model.feature_extractor
														lowerCamelCase_								=hf_model.encoder.pos_conv_embed

							for name, value in fairseq_dict.items():
														lowerCamelCase_								=False
														if "conv_layers" in name:
																					load_conv_layer(
																					    _A      ,  _A      ,  _A      ,  _A      ,  )
																					lowerCamelCase_								=True
														elif "pos_conv" in name:
																					load_pos_conv_layer(
																					    _A      ,  _A      ,  _A      ,  _A      ,  )
																					lowerCamelCase_								=True
														else:
																					for key, mapped_key in MAPPING.items():
																												if not is_headless:
																																			lowerCamelCase_								="""data2vec_audio.""" + 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(_A		)[0].split("""."""		)[-2]
																																										lowerCamelCase_								=mapped_key.replace("""*"""      ,  _A		)
																																			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
																																			set_recursively(_A      ,  _A      ,  _A      ,  _A      ,  _A		)
																												continue
														if not is_used:
																					unused_weights.append(_A		)

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




def        __UpperCamelCase     (      _A					:							Optional[Any]      ,  _A					:							Union[str, Any]		)				->Tuple:



							"""simple docstring"""
							lowerCamelCase_								=path.split("""."""		)
							return reduce(_A      ,  _A      ,  _A		)




def        __UpperCamelCase     (      _A					:							str      ,  _A					:							List[Any]      ,  _A					:							str      ,  _A					:							Dict		)				->Union[str, Any]:



							"""simple docstring"""
							lowerCamelCase_								=access_by_string(_A      ,  _A		)
							lowerCamelCase_								=hf_weight.data

							if fsq_value.shape != hf_value.shape:
														raise ValueError(f'{full_name} has size {fsq_value.shape}, but {hf_value.shape} was found.'		)
							lowerCamelCase_								=fsq_value
							logger.info(f'{full_name} was correctly initialized from {hf_weight_path}.'		)




def        __UpperCamelCase     (      _A					:							Union[str, Any]      ,  _A					:							Any      ,  _A					:							List[str]      ,  _A					:							List[Any]		)				->Union[str, Any]:



							"""simple docstring"""
							lowerCamelCase_								=full_name.split("""conv_layers."""		)[-1]
							lowerCamelCase_								=name.split("""."""		)
							lowerCamelCase_								=int(items[0]		)
							lowerCamelCase_								=int(items[1]		)

							lowerCamelCase_								=name.split("""."""		)[-1]
							if type_id == 0:
														lowerCamelCase_								="""conv"""
							elif type_id == 2:
														lowerCamelCase_								="""layer_norm"""
							else:
														unused_weights.append(_A		)
														return

							set_weights(_A      ,  _A      ,  _A      ,  f'conv_layers.{layer_id}.{layer_type}.{weight_type}'		)




def        __UpperCamelCase     (      _A					:							Union[str, Any]      ,  _A					:							Optional[int]      ,  _A					:							Optional[int]      ,  _A					:							List[Any]		)				->Tuple:



							"""simple docstring"""
							lowerCamelCase_								=full_name.split("""pos_conv."""		)[-1]
							lowerCamelCase_								=name.split("""."""		)
							lowerCamelCase_								=int(items[0]		)
							lowerCamelCase_								=int(items[1]		)

							lowerCamelCase_								=name.split("""."""		)[-1]
							if type_id != 0:
														unused_weights.append(_A		)
														return
							else:
														lowerCamelCase_								="""conv"""

							set_weights(_A      ,  _A      ,  _A      ,  f'layers.{layer_id}.{layer_type}.{weight_type}'		)




@torch.no_grad()
def        __UpperCamelCase     (      _A					:							Optional[Any]      ,  _A					:							Optional[int]      ,  _A					:							List[Any]=None      ,  _A					:							int=None      ,  _A					:							Union[str, Any]=True		)				->str:



							"""simple docstring"""
							if config_path is not None:
														lowerCamelCase_								=DataaVecAudioConfig.from_pretrained(_A		)
							else:
														lowerCamelCase_								=DataaVecAudioConfig()

							if not is_finetuned:
														# Modify final_proj layer name
														lowerCamelCase_								=DataaVecAudioModel(_A		)
														lowerCamelCase_								=os.path.dirname(_A		)

														lowerCamelCase_								=torch.load(_A		)
														lowerCamelCase_								=state_dict["""model"""].pop("""final_proj.0.weight"""		)
														lowerCamelCase_								=state_dict["""model"""].pop("""final_proj.0.bias"""		)
														lowerCamelCase_								=os.path.join(_A      ,  """converted.pt"""		)
														torch.save(_A      ,  _A		)
							else:
														lowerCamelCase_								=DataaVecAudioForCTC(_A		)
														lowerCamelCase_								=checkpoint_path

							def load_dataavec(_A					:							str		):
														lowerCamelCase_	,   lowerCamelCase_	,   lowerCamelCase_								=fairseq.checkpoint_utils.load_model_ensemble_and_task([path]		)
														return model[0].eval()

							lowerCamelCase_								=load_dataavec(_A		)

							recursively_load_weights(_A      ,  _A      ,  not is_finetuned		)

							lowerCamelCase_								=WavaVecaProcessor.from_pretrained("""facebook/wav2vec2-large-lv60"""		)

							lowerCamelCase_								=load_dataset("""patrickvonplaten/librispeech_asr_dummy"""      ,  """clean"""      ,  split="""validation"""		)
							lowerCamelCase_								=[x["""array"""] for x in ds[:4]["""audio"""]]

							lowerCamelCase_								=processor(_A      ,  return_tensors="""pt"""      ,  padding=_A		)

							lowerCamelCase_								=inputs.input_values
							lowerCamelCase_								=inputs.attention_mask
							#    input_values = inputs.input_values[:, :-1]
							#    attention_mask = inputs.attention_mask[:, :-1]

							hf_wavavec.eval()
							model.eval()
							if is_finetuned:
														lowerCamelCase_								=model(source=_A      ,  padding_mask=(1 - attention_mask)      ,  mask=_A      ,  features_only=_A		)[
														    """encoder_out"""
														].transpose(0      ,  1		)
														lowerCamelCase_								=hf_wavavec(_A      ,  attention_mask=_A		)["""logits"""]

														lowerCamelCase_								=torch.argmax(_A      ,  dim=-1		)
														lowerCamelCase_								=processor.batch_decode(_A		)

														print(f'Expected Output: {ds[:4]["text"]}, Pred: {output_string}'		)
							else:
														lowerCamelCase_								=model(source=_A      ,  padding_mask=(1 - attention_mask)      ,  mask=_A      ,  features_only=_A		)[
														    """layer_results"""
														][-1][0].transpose(0      ,  1		)
														lowerCamelCase_								=hf_wavavec(_A      ,  attention_mask=_A		)["""last_hidden_state"""]

							print(our_output.shape      ,  their_output.shape		)
							lowerCamelCase_								=torch.max(torch.abs(our_output - their_output		)		).item()
							print(f'max_absolute_diff = {max_absolute_diff}'		)  # ~ 1e-7
							lowerCamelCase_								=torch.allclose(_A      ,  _A      ,  atol=1E-3		)
							print("""Do both models output the same tensors?"""      ,  """🔥""" if success else """💩"""		)
							if not success:
														raise Exception("""Something went wRoNg"""		)

							hf_wavavec.save_pretrained(_A		)

							if is_finetuned:
														processor.save_pretrained(_A		)
							else:
														processor.feature_extractor.save_pretrained(_A		)


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'
	)
	__A	:   List[str]	=					parser.parse_args()
	convert_wavaveca_checkpoint(
	    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
	)
