





'''simple docstring'''



import json
import os
import re
import unicodedata
from typing import Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer, _is_control, _is_punctuation, _is_whitespace
from ...utils import logging


snake_case_   :  Any								=      logging.get_logger(__name__)

snake_case_   :  List[str]								=      {
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
}

snake_case_   :  Union[str, Any]								=      {
    '''vocab_file''': {'''openai-gpt''': '''https://huggingface.co/openai-gpt/resolve/main/vocab.json'''},
    '''merges_file''': {'''openai-gpt''': '''https://huggingface.co/openai-gpt/resolve/main/merges.txt'''},
}

snake_case_   :  Tuple								=      {
    '''openai-gpt''': 512,
}



def  lowercase__(		_UpperCamelCase  : List[str]				)->   List[str]:






					"""simple docstring"""

					_UpperCamelCase											=   text.strip()
					if not text:
										return []
					_UpperCamelCase											=   text.split()
					return tokens

class     A_ (  lowerCAmelCase_  ):

			'''simple docstring'''





			def __init__(					self			,     A_=True			,     A_=None			,     A_=True			,     A_=None			,     A_=True			,     ):
								if never_split is None:
													_UpperCamelCase											=   []
								_UpperCamelCase											=   do_lower_case
								_UpperCamelCase											=   set(A_   )
								_UpperCamelCase											=   tokenize_chinese_chars
								_UpperCamelCase											=   strip_accents
								_UpperCamelCase											=   do_split_on_punc




			def       a	(					self			,     A_			,     A_=None   ):
								_UpperCamelCase											=   self.never_split.union(set(A_   )   ) if never_split else self.never_split
								_UpperCamelCase											=   self._clean_text(A_   )

								# This was added on November 1st, 2018 for the multilingual and Chinese
								# models. This is also applied to the English models now, but it doesn't
								# matter since the English models were not trained on any Chinese data
								# and generally don't have any Chinese data in them (there are Chinese
								# characters in the vocabulary because Wikipedia does have some Chinese
								# words in the English Wikipedia.).
								if self.tokenize_chinese_chars:
													_UpperCamelCase											=   self._tokenize_chinese_chars(A_   )
								# prevents treating the same character with different unicode codepoints as different characters
								_UpperCamelCase											=   unicodedata.normalize("NFC"			,     A_   )
								_UpperCamelCase											=   whitespace_tokenize(A_   )
								_UpperCamelCase											=   []
								for token in orig_tokens:
													if token not in never_split:
																		if self.do_lower_case:
																							_UpperCamelCase											=   token.lower()
																							if self.strip_accents is not False:
																												_UpperCamelCase											=   self._run_strip_accents(A_   )
																		elif self.strip_accents:
																							_UpperCamelCase											=   self._run_strip_accents(A_   )
													split_tokens.extend(self._run_split_on_punc(A_			,     A_   )   )

								_UpperCamelCase											=   whitespace_tokenize(" ".join(A_   )   )
								return output_tokens




			def       a	(					self			,     A_   ):
								_UpperCamelCase											=   unicodedata.normalize("NFD"			,     A_   )
								_UpperCamelCase											=   []
								for char in text:
													_UpperCamelCase											=   unicodedata.category(A_   )
													if cat == "Mn":
																		continue
													output.append(A_   )
								return "".join(A_   )




			def       a	(					self			,     A_			,     A_=None   ):
								if not self.do_split_on_punc or (never_split is not None and text in never_split):
													return [text]
								_UpperCamelCase											=   list(A_   )
								_UpperCamelCase											=   0
								_UpperCamelCase											=   True
								_UpperCamelCase											=   []
								while i < len(A_   ):
													_UpperCamelCase											=   chars[i]
													if _is_punctuation(A_   ):
																		output.append([char]   )
																		_UpperCamelCase											=   True
													else:
																		if start_new_word:
																							output.append([]   )
																		_UpperCamelCase											=   False
																		output[-1].append(A_   )
													i += 1

								return ["".join(A_   ) for x in output]




			def       a	(					self			,     A_   ):
								_UpperCamelCase											=   []
								for char in text:
													_UpperCamelCase											=   ord(A_   )
													if self._is_chinese_char(A_   ):
																		output.append(" "   )
																		output.append(A_   )
																		output.append(" "   )
													else:
																		output.append(A_   )
								return "".join(A_   )




			def       a	(					self			,     A_   ):
								# This defines a "chinese character" as anything in the CJK Unicode block:
								#   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
								#
								# Note that the CJK Unicode block is NOT all Japanese and Korean characters,
								# despite its name. The modern Korean Hangul alphabet is a different block,
								# as is Japanese Hiragana and Katakana. Those alphabets are used to write
								# space-separated words, so they are not treated specially and handled
								# like the all of the other languages.
								if (
								    (cp >= 0X4_e00 and cp <= 0X9_fff)
								    or (cp >= 0X3_400 and cp <= 0X4_dbf)  #
								    or (cp >= 0X20_000 and cp <= 0X2a_6df)  #
								    or (cp >= 0X2a_700 and cp <= 0X2b_73f)  #
								    or (cp >= 0X2b_740 and cp <= 0X2b_81f)  #
								    or (cp >= 0X2b_820 and cp <= 0X2c_eaf)  #
								    or (cp >= 0Xf_900 and cp <= 0Xf_aff)
								    or (cp >= 0X2f_800 and cp <= 0X2f_a1f)  #
								):  #
													return True

								return False




			def       a	(					self			,     A_   ):
								_UpperCamelCase											=   []
								for char in text:
													_UpperCamelCase											=   ord(A_   )
													if cp == 0 or cp == 0Xf_ffd or _is_control(A_   ):
																		continue
													if _is_whitespace(A_   ):
																		output.append(" "   )
													else:
																		output.append(A_   )
								return "".join(A_   )




def  lowercase__(		_UpperCamelCase  : List[str]				)->   Tuple:






					"""simple docstring"""

					_UpperCamelCase											=   set()
					_UpperCamelCase											=   word[0]
					for char in word[1:]:
										pairs.add((prev_char, char)				)
										_UpperCamelCase											=   char
					return pairs



def  lowercase__(		_UpperCamelCase  : int				)->   Optional[Any]:






					"""simple docstring"""

					_UpperCamelCase											=   text.replace("—" ,   "-"				)
					_UpperCamelCase											=   text.replace("–" ,   "-"				)
					_UpperCamelCase											=   text.replace("―" ,   "-"				)
					_UpperCamelCase											=   text.replace("…" ,   "..."				)
					_UpperCamelCase											=   text.replace("´" ,   "'"				)
					_UpperCamelCase											=   re.sub(R"(-+|~+|!+|\"+|;+|\?+|\++|,+|\)+|\(+|\\+|\/+|\*+|\[+|\]+|}+|{+|\|+|_+)" ,   R" \1 " ,   _UpperCamelCase				)
					_UpperCamelCase											=   re.sub(R"\s*\n\s*" ,   " \n " ,   _UpperCamelCase				)
					_UpperCamelCase											=   re.sub(R"[^\S\n]+" ,   " " ,   _UpperCamelCase				)
					return text.strip()

class     A_ (  lowerCAmelCase_  ):

			'''simple docstring'''





			_lowerCAmelCase						=    VOCAB_FILES_NAMES
			_lowerCAmelCase						=    PRETRAINED_VOCAB_FILES_MAP
			_lowerCAmelCase						=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			_lowerCAmelCase						=    ["""input_ids""", """attention_mask"""]




			def __init__(					self			,     A_			,     A_			,     A_="<unk>"			,     **A_   ):
								super().__init__(unk_token=A_			,     **A_   )

								try:
													import ftfy
													from spacy.lang.en import English

													_UpperCamelCase											=   English()
													_UpperCamelCase											=   _nlp.tokenizer
													_UpperCamelCase											=   ftfy.fix_text
								except ImportError:
													logger.warning("ftfy or spacy is not installed using BERT BasicTokenizer instead of SpaCy & ftfy."   )
													_UpperCamelCase											=   BasicTokenizer(do_lower_case=A_   )
													_UpperCamelCase											=   None

								with open(A_			,     encoding="utf-8"   ) as vocab_handle:
													_UpperCamelCase											=   json.load(A_   )
								_UpperCamelCase											=   {v: k for k, v in self.encoder.items()}
								with open(A_			,     encoding="utf-8"   ) as merges_handle:
													_UpperCamelCase											=   merges_handle.read().split("\n"   )[1:-1]
								_UpperCamelCase											=   [tuple(merge.split()   ) for merge in merges]
								_UpperCamelCase											=   dict(zip(A_			,     range(len(A_   )   )   )   )
								_UpperCamelCase											=   {}




			@property
			def       a	(					self   ):
								return True




			@property
			def       a	(					self   ):
								return len(self.encoder   )




			def       a	(					self   ):
								return dict(self.encoder			,     **self.added_tokens_encoder   )




			def       a	(					self			,     A_   ):
								_UpperCamelCase											=   tuple(token[:-1]   ) + (token[-1] + "</w>",)
								if token in self.cache:
													return self.cache[token]
								_UpperCamelCase											=   get_pairs(A_   )

								if not pairs:
													return token + "</w>"

								while True:
													_UpperCamelCase											=   min(A_			,     key=lambda A_   : self.bpe_ranks.get(A_			,     float("inf"   )   )   )
													if bigram not in self.bpe_ranks:
																		break
													_UpperCamelCase				,	_UpperCamelCase											=   bigram
													_UpperCamelCase											=   []
													_UpperCamelCase											=   0
													while i < len(A_   ):
																		try:
																							_UpperCamelCase											=   word.index(A_			,     A_   )
																		except ValueError:
																							new_word.extend(word[i:]   )
																							break
																		else:
																							new_word.extend(word[i:j]   )
																							_UpperCamelCase											=   j

																		if word[i] == first and i < len(A_   ) - 1 and word[i + 1] == second:
																							new_word.append(first + second   )
																							i += 2
																		else:
																							new_word.append(word[i]   )
																							i += 1
													_UpperCamelCase											=   tuple(A_   )
													_UpperCamelCase											=   new_word
													if len(A_   ) == 1:
																		break
													else:
																		_UpperCamelCase											=   get_pairs(A_   )
								_UpperCamelCase											=   " ".join(A_   )
								if word == "\n  </w>":
													_UpperCamelCase											=   "\n</w>"
								_UpperCamelCase											=   word
								return word




			def       a	(					self			,     A_   ):
								_UpperCamelCase											=   []
								if self.fix_text is None:
													# Using BERT's BasicTokenizer
													_UpperCamelCase											=   self.nlp.tokenize(A_   )
													for token in text:
																		split_tokens.extend(list(self.bpe(A_   ).split(" "   )   )   )
								else:
													# Using SpaCy & ftfy (original tokenization process of OpenAI GPT)
													_UpperCamelCase											=   self.nlp(text_standardize(self.fix_text(A_   )   )   )
													for token in text:
																		split_tokens.extend(list(self.bpe(token.text.lower()   ).split(" "   )   )   )
								return split_tokens




			def       a	(					self			,     A_   ):
								return self.encoder.get(A_			,     self.encoder.get(self.unk_token   )   )




			def       a	(					self			,     A_   ):
								return self.decoder.get(A_			,     self.unk_token   )




			def       a	(					self			,     A_   ):
								_UpperCamelCase											=   "".join(A_   ).replace("</w>"			,     " "   ).strip()
								return out_string




			def       a	(					self			,     A_			,     A_ = None   ):
								if not os.path.isdir(A_   ):
													logger.error(F"Vocabulary path ({save_directory}) should be a directory"   )
													return
								_UpperCamelCase											=   os.path.join(
								    A_			,     (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]   )
								_UpperCamelCase											=   os.path.join(
								    A_			,     (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]   )

								with open(A_			,     "w"			,     encoding="utf-8"   ) as f:
													f.write(json.dumps(self.encoder			,     indent=2			,     sort_keys=A_			,     ensure_ascii=A_   ) + "\n"   )

								_UpperCamelCase											=   0
								with open(A_			,     "w"			,     encoding="utf-8"   ) as writer:
													writer.write("#version: 0.2\n"   )
													for bpe_tokens, token_index in sorted(self.bpe_ranks.items()			,     key=lambda A_   : kv[1]   ):
																		if index != token_index:
																							logger.warning(
																							    F"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
																							    " Please check that the tokenizer is not corrupted!"   )
																							_UpperCamelCase											=   token_index
																		writer.write(" ".join(A_   ) + "\n"   )
																		index += 1

								return vocab_file, merge_file










'''simple docstring'''



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

snake_case_   :  Optional[Any]								=      {
    '''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''',
}
snake_case_   :  Optional[Any]								=      [
    '''lm_head''',
]



def  lowercase__(		_UpperCamelCase  : List[Any] ,   _UpperCamelCase  : Optional[Any] ,   _UpperCamelCase  : Tuple ,   _UpperCamelCase  : Tuple ,   _UpperCamelCase  : Optional[Any]				)->   Tuple:






					"""simple docstring"""

					for attribute in key.split("."				):
										_UpperCamelCase											=   getattr(_UpperCamelCase ,   _UpperCamelCase				)

					if weight_type is not None:
										_UpperCamelCase											=   getattr(_UpperCamelCase ,   _UpperCamelCase				).shape
					else:
										_UpperCamelCase											=   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":
										_UpperCamelCase											=   value
					elif weight_type == "weight_g":
										_UpperCamelCase											=   value
					elif weight_type == "weight_v":
										_UpperCamelCase											=   value
					elif weight_type == "bias":
										_UpperCamelCase											=   value
					else:
										_UpperCamelCase											=   value

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



def  lowercase__(		_UpperCamelCase  : Tuple ,   _UpperCamelCase  : Optional[Any] ,   _UpperCamelCase  : List[str]				)->   List[str]:






					"""simple docstring"""

					_UpperCamelCase											=   []
					_UpperCamelCase											=   fairseq_model.state_dict()

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

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

					for name, value in fairseq_dict.items():
										_UpperCamelCase											=   False
										if "conv_layers" in name:
															load_conv_layer(
															    _UpperCamelCase ,   _UpperCamelCase ,   _UpperCamelCase ,   _UpperCamelCase ,   )
															_UpperCamelCase											=   True
										elif "pos_conv" in name:
															load_pos_conv_layer(
															    _UpperCamelCase ,   _UpperCamelCase ,   _UpperCamelCase ,   _UpperCamelCase ,   )
															_UpperCamelCase											=   True
										else:
															for key, mapped_key in MAPPING.items():
																				if not is_headless:
																									_UpperCamelCase											=   "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]:
																									_UpperCamelCase											=   True
																									if "*" in mapped_key:
																														_UpperCamelCase											=   name.split(_UpperCamelCase				)[0].split("."				)[-2]
																														_UpperCamelCase											=   mapped_key.replace("*" ,   _UpperCamelCase				)
																									if "weight_g" in name:
																														_UpperCamelCase											=   "weight_g"
																									elif "weight_v" in name:
																														_UpperCamelCase											=   "weight_v"
																									elif "bias" in name:
																														_UpperCamelCase											=   "bias"
																									elif "weight" in name:
																														# TODO: don't match quantizer.weight_proj
																														_UpperCamelCase											=   "weight"
																									else:
																														_UpperCamelCase											=   None
																									set_recursively(_UpperCamelCase ,   _UpperCamelCase ,   _UpperCamelCase ,   _UpperCamelCase ,   _UpperCamelCase				)
																				continue
										if not is_used:
															unused_weights.append(_UpperCamelCase				)

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



def  lowercase__(		_UpperCamelCase  : List[Any] ,   _UpperCamelCase  : Tuple				)->   int:






					"""simple docstring"""

					_UpperCamelCase											=   path.split("."				)
					return reduce(_UpperCamelCase ,   _UpperCamelCase ,   _UpperCamelCase				)



def  lowercase__(		_UpperCamelCase  : str ,   _UpperCamelCase  : List[Any] ,   _UpperCamelCase  : Dict ,   _UpperCamelCase  : List[str]				)->   List[str]:






					"""simple docstring"""

					_UpperCamelCase											=   access_by_string(_UpperCamelCase ,   _UpperCamelCase				)
					_UpperCamelCase											=   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."				)
					_UpperCamelCase											=   fsq_value
					logger.info(f"{full_name} was correctly initialized from {hf_weight_path}."				)



def  lowercase__(		_UpperCamelCase  : Tuple ,   _UpperCamelCase  : Union[str, Any] ,   _UpperCamelCase  : int ,   _UpperCamelCase  : str				)->   Dict:






					"""simple docstring"""

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

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

					set_weights(_UpperCamelCase ,   _UpperCamelCase ,   _UpperCamelCase ,   f"conv_layers.{layer_id}.{layer_type}.{weight_type}"				)



def  lowercase__(		_UpperCamelCase  : Union[str, Any] ,   _UpperCamelCase  : List[Any] ,   _UpperCamelCase  : Tuple ,   _UpperCamelCase  : int				)->   Dict:






					"""simple docstring"""

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

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

					set_weights(_UpperCamelCase ,   _UpperCamelCase ,   _UpperCamelCase ,   f"layers.{layer_id}.{layer_type}.{weight_type}"				)



@torch.no_grad()
def  lowercase__(		_UpperCamelCase  : List[Any] ,   _UpperCamelCase  : Tuple ,   _UpperCamelCase  : Optional[Any]=None ,   _UpperCamelCase  : List[Any]=None ,   _UpperCamelCase  : Optional[Any]=True				)->   Optional[int]:






					"""simple docstring"""

					if config_path is not None:
										_UpperCamelCase											=   DataaVecAudioConfig.from_pretrained(_UpperCamelCase				)
					else:
										_UpperCamelCase											=   DataaVecAudioConfig()

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

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

					def load_dataavec(_UpperCamelCase  : List[Any]				):
										_UpperCamelCase				,	_UpperCamelCase				,	_UpperCamelCase											=   fairseq.checkpoint_utils.load_model_ensemble_and_task([path]				)
										return model[0].eval()

					_UpperCamelCase											=   load_dataavec(_UpperCamelCase				)

					recursively_load_weights(_UpperCamelCase ,   _UpperCamelCase ,   not is_finetuned				)

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

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

					_UpperCamelCase											=   processor(_UpperCamelCase ,   return_tensors="pt" ,   padding=_UpperCamelCase				)

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

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

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

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

					print(our_output.shape ,   their_output.shape				)
					_UpperCamelCase											=   torch.max(torch.abs(our_output - their_output				)				).item()
					print(f"max_absolute_diff = {max_absolute_diff}"				)  # ~ 1e-7
					_UpperCamelCase											=   torch.allclose(_UpperCamelCase ,   _UpperCamelCase ,   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(_UpperCamelCase				)

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


if __name__ == "__main__":
			snake_case_   :  int								=      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'''
			)
			snake_case_   :  Any								=      parser.parse_args()
			convert_wavaveca_checkpoint(
			    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
			)



