






"""simple docstring"""



import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

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


A_    =							logging.get_logger(__name__)


A_    =							{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt'''}

# See all BART models at https://huggingface.co/models?filter=bart
A_    =							{
    '''vocab_file''': {
        '''facebook/bart-base''': '''https://huggingface.co/facebook/bart-base/resolve/main/vocab.json''',
        '''facebook/bart-large''': '''https://huggingface.co/facebook/bart-large/resolve/main/vocab.json''',
        '''facebook/bart-large-mnli''': '''https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json''',
        '''facebook/bart-large-cnn''': '''https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json''',
        '''facebook/bart-large-xsum''': '''https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json''',
        '''yjernite/bart_eli5''': '''https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''facebook/bart-base''': '''https://huggingface.co/facebook/bart-base/resolve/main/merges.txt''',
        '''facebook/bart-large''': '''https://huggingface.co/facebook/bart-large/resolve/main/merges.txt''',
        '''facebook/bart-large-mnli''': '''https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt''',
        '''facebook/bart-large-cnn''': '''https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt''',
        '''facebook/bart-large-xsum''': '''https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt''',
        '''yjernite/bart_eli5''': '''https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt''',
    },
}

A_    =							{
    '''facebook/bart-base''': 1024,
    '''facebook/bart-large''': 1024,
    '''facebook/bart-large-mnli''': 1024,
    '''facebook/bart-large-cnn''': 1024,
    '''facebook/bart-large-xsum''': 1024,
    '''yjernite/bart_eli5''': 1024,
}




@lru_cache()
def      _lowerCAmelCase		(							)		->Optional[Any]:
						A__						:  Dict		  =  (
						    list(range(ord("""!"""      ),						ord("""~"""      ) + 1      )      ) + list(range(ord("""¡"""      ),						ord("""¬"""      ) + 1      )      ) + list(range(ord("""®"""      ),						ord("""ÿ"""      ) + 1      )      )
						)
						A__						:  int		  =  bs[:]
						A__						:  Union[str, Any]		  =  0
						for b in range(2**8      ):
												if b not in bs:
																		bs.append(UpperCAmelCase__      )
																		cs.append(2**8 + n      )
																		n += 1
						A__						:  Union[str, Any]		  =  [chr(UpperCAmelCase__      ) for n in cs]
						return dict(zip(UpperCAmelCase__,						UpperCAmelCase__      )      )




def      _lowerCAmelCase		(							UpperCAmelCase__       :					List[str]      )		->Any:
						A__						:  str		  =  set()
						A__						:  List[Any]		  =  word[0]
						for char in word[1:]:
												pairs.add((prev_char, char)      )
												A__						:  int		  =  char
						return pairs






class 							__SCREAMING_SNAKE_CASE						(				UpperCamelCase		):
							snake_case_     =							VOCAB_FILES_NAMES
							snake_case_     =							PRETRAINED_VOCAB_FILES_MAP
							snake_case_     =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							snake_case_     =							['input_ids', 'attention_mask']


							def __init__(			self      :						Any			,    snake_case      :						int			,    snake_case      :						Optional[Any]			,    snake_case      :						int="replace"			,    snake_case      :						Optional[Any]="<s>"			,    snake_case      :						Union[str, Any]="</s>"			,    snake_case      :						Any="</s>"			,    snake_case      :						Optional[Any]="<s>"			,    snake_case      :						Tuple="<unk>"			,    snake_case      :						Optional[int]="<pad>"			,    snake_case      :						Tuple="<mask>"			,    snake_case      :						Tuple=False			,    **snake_case      :						Dict			,    ):
													'''simple docstring'''





													A__						:  Tuple		  =  AddedToken(snake_case			,    lstrip=snake_case			,    rstrip=snake_case							) if isinstance(snake_case			,    snake_case							) else bos_token
													A__						:  List[str]		  =  AddedToken(snake_case			,    lstrip=snake_case			,    rstrip=snake_case							) if isinstance(snake_case			,    snake_case							) else eos_token
													A__						:  Any		  =  AddedToken(snake_case			,    lstrip=snake_case			,    rstrip=snake_case							) if isinstance(snake_case			,    snake_case							) else sep_token
													A__						:  Any		  =  AddedToken(snake_case			,    lstrip=snake_case			,    rstrip=snake_case							) if isinstance(snake_case			,    snake_case							) else cls_token
													A__						:  List[str]		  =  AddedToken(snake_case			,    lstrip=snake_case			,    rstrip=snake_case							) if isinstance(snake_case			,    snake_case							) else unk_token
													A__						:  List[Any]		  =  AddedToken(snake_case			,    lstrip=snake_case			,    rstrip=snake_case							) if isinstance(snake_case			,    snake_case							) else pad_token

													# Mask token behave like a normal word, i.e. include the space before it
													A__						:  Dict		  =  AddedToken(snake_case			,    lstrip=snake_case			,    rstrip=snake_case							) if isinstance(snake_case			,    snake_case							) else mask_token

													super().__init__(
													    errors=snake_case			,    bos_token=snake_case			,    eos_token=snake_case			,    unk_token=snake_case			,    sep_token=snake_case			,    cls_token=snake_case			,    pad_token=snake_case			,    mask_token=snake_case			,    add_prefix_space=snake_case			,    **snake_case			,    )

													with open(snake_case			,    encoding="""utf-8"""							) as vocab_handle:
																			A__						:  Dict		  =  json.load(snake_case							)
													A__						:  Optional[Any]		  =  {v: k for k, v in self.encoder.items()}
													A__						:  Dict		  =  errors  # how to handle errors in decoding
													A__						:  Dict		  =  bytes_to_unicode()
													A__						:  List[str]		  =  {v: k for k, v in self.byte_encoder.items()}
													with open(snake_case			,    encoding="""utf-8"""							) as merges_handle:
																			A__						:  Union[str, Any]		  =  merges_handle.read().split("""\n"""							)[1:-1]
													A__						:  Optional[int]		  =  [tuple(merge.split()							) for merge in bpe_merges]
													A__						:  List[str]		  =  dict(zip(snake_case			,    range(len(snake_case							)							)							)							)
													A__						:  int		  =  {}
													A__						:  List[Any]		  =  add_prefix_space

													# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
													A__						:  Optional[Any]		  =  re.compile(r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"""							)


							@property
							def 						_UpperCamelCase       (			self      :						Optional[Any]							):
													'''simple docstring'''





													return len(self.encoder							)


							def 						_UpperCamelCase       (			self      :						Tuple							):
													'''simple docstring'''





													return dict(self.encoder			,    **self.added_tokens_encoder							)


							def 						_UpperCamelCase       (			self      :						Union[str, Any]			,    snake_case      :						Optional[Any]							):
													'''simple docstring'''





													if token in self.cache:
																			return self.cache[token]
													A__						:  Optional[int]		  =  tuple(snake_case							)
													A__						:  Optional[int]		  =  get_pairs(snake_case							)

													if not pairs:
																			return token

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

																									if word[i] == first and i < len(snake_case							) - 1 and word[i + 1] == second:
																															new_word.append(first + second							)
																															i += 2
																									else:
																															new_word.append(word[i]							)
																															i += 1
																			A__						:  int		  =  tuple(snake_case							)
																			A__						:  Tuple		  =  new_word
																			if len(snake_case							) == 1:
																									break
																			else:
																									A__						:  str		  =  get_pairs(snake_case							)
													A__						:  Any		  =  """ """.join(snake_case							)
													A__						:  Union[str, Any]		  =  word
													return word


							def 						_UpperCamelCase       (			self      :						List[Any]			,    snake_case      :						Dict							):
													'''simple docstring'''





													A__						:  Any		  =  []
													for token in re.findall(self.pat			,    snake_case							):
																			A__						:  int		  =  """""".join(
																			    self.byte_encoder[b] for b in token.encode("""utf-8"""							)							)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
																			bpe_tokens.extend(bpe_token for bpe_token in self.bpe(snake_case							).split(""" """							)							)
													return bpe_tokens


							def 						_UpperCamelCase       (			self      :						Optional[int]			,    snake_case      :						Optional[Any]							):
													'''simple docstring'''





													return self.encoder.get(snake_case			,    self.encoder.get(self.unk_token							)							)


							def 						_UpperCamelCase       (			self      :						List[str]			,    snake_case      :						str							):
													'''simple docstring'''





													return self.decoder.get(snake_case							)


							def 						_UpperCamelCase       (			self      :						Any			,    snake_case      :						Any							):
													'''simple docstring'''





													A__						:  str		  =  """""".join(snake_case							)
													A__						:  int		  =  bytearray([self.byte_decoder[c] for c in text]							).decode("""utf-8"""			,    errors=self.errors							)
													return text


							def 						_UpperCamelCase       (			self      :						List[str]			,    snake_case      :						str			,    snake_case      :						Optional[str] = None							):
													'''simple docstring'''





													if not os.path.isdir(snake_case							):
																			logger.error(F'Vocabulary path ({save_directory}) should be a directory'							)
																			return
													A__						:  List[Any]		  =  os.path.join(
													    snake_case			,    (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]							)
													A__						:  Tuple		  =  os.path.join(
													    snake_case			,    (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]							)

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

													A__						:  List[str]		  =  0
													with open(snake_case			,    """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 snake_case							: 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!"""							)
																															A__						:  Union[str, Any]		  =  token_index
																									writer.write(""" """.join(snake_case							) + """\n"""							)
																									index += 1

													return vocab_file, merge_file


							def 						_UpperCamelCase       (			self      :						List[str]			,    snake_case      :						List[int]			,    snake_case      :						Optional[List[int]] = None							):
													'''simple docstring'''





													if token_ids_a is None:
																			return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
													A__						:  List[str]		  =  [self.cls_token_id]
													A__						:  str		  =  [self.sep_token_id]
													return cls + token_ids_a + sep + sep + token_ids_a + sep


							def 						_UpperCamelCase       (			self      :						List[str]			,    snake_case      :						List[int]			,    snake_case      :						Optional[List[int]] = None			,    snake_case      :						bool = False							):
													'''simple docstring'''





													if already_has_special_tokens:
																			return super().get_special_tokens_mask(
																			    token_ids_a=snake_case			,    token_ids_a=snake_case			,    already_has_special_tokens=snake_case							)

													if token_ids_a is None:
																			return [1] + ([0] * len(snake_case							)) + [1]
													return [1] + ([0] * len(snake_case							)) + [1, 1] + ([0] * len(snake_case							)) + [1]


							def 						_UpperCamelCase       (			self      :						str			,    snake_case      :						List[int]			,    snake_case      :						Optional[List[int]] = None							):
													'''simple docstring'''





													A__						:  int		  =  [self.sep_token_id]
													A__						:  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]


							def 						_UpperCamelCase       (			self      :						Optional[int]			,    snake_case      :						Dict			,    snake_case      :						Optional[Any]=False			,    **snake_case      :						List[str]							):
													'''simple docstring'''





													A__						:  Optional[Any]		  =  kwargs.pop("""add_prefix_space"""			,    self.add_prefix_space							)
													if (is_split_into_words or add_prefix_space) and (len(snake_case							) > 0 and not text[0].isspace()):
																			A__						:  Optional[int]		  =  """ """ + text
													return (text, kwargs)














"""simple docstring"""



import argparse

import fairseq
import torch
from torch import nn

from transformers import (
    MBartaaTokenizer,
    MBartConfig,
    MBartForCausalLM,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


logging.set_verbosity_info()
A_    =							logging.get_logger(__name__)

A_    =							{
    '''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''',
    '''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''',
}
A_    =							[
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]




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

						if weight_type is not None:
												A__						:  Union[str, Any]		  =  getattr(UpperCAmelCase__,						UpperCAmelCase__      ).shape
						else:
												A__						:  Optional[int]		  =  hf_pointer.shape

						assert hf_shape == value.shape, (
						    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__						:  List[Any]		  =  value
						elif weight_type == "weight_g":
												A__						:  Any		  =  value
						elif weight_type == "weight_v":
												A__						:  Optional[int]		  =  value
						elif weight_type == "bias":
												A__						:  Any		  =  value
						else:
												A__						:  Any		  =  value

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




def      _lowerCAmelCase		(							UpperCAmelCase__       :					Optional[Any],						UpperCAmelCase__       :					Any      )		->Union[str, Any]:
						A__						:  Dict		  =  []
						A__						:  int		  =  fairseq_model.state_dict()

						A__						:  Dict		  =  hf_model.feature_extractor
						A__						:  Any		  =  hf_model.adapter

						for name, value in fairseq_dict.items():
												A__						:  Optional[int]		  =  False
												if "conv_layers" in name:
																		load_conv_layer(
																		    UpperCAmelCase__,						UpperCAmelCase__,						UpperCAmelCase__,						UpperCAmelCase__,						hf_model.config.feat_extract_norm == """group""",						)
																		A__						:  Union[str, Any]		  =  True
												elif any(x in name for x in ["""adaptor""", """w2v_encoder.proj.""", """w2v_proj_ln."""]      ):
																		load_adapter(UpperCAmelCase__,						UpperCAmelCase__,						UpperCAmelCase__,						UpperCAmelCase__      )
																		A__						:  int		  =  True
												else:
																		for key, mapped_key in MAPPING.items():
																								if key in name or key.split("""w2v_model."""      )[-1] == name.split("""."""      )[0]:
																														A__						:  List[Any]		  =  True
																														if "*" in mapped_key:
																																				A__						:  Union[str, Any]		  =  name.split(UpperCAmelCase__      )[0].split("""."""      )[-2]
																																				A__						:  Union[str, Any]		  =  mapped_key.replace("""*""",						UpperCAmelCase__      )
																														if "weight_g" in name:
																																				A__						:  List[str]		  =  """weight_g"""
																														elif "weight_v" in name:
																																				A__						:  Optional[Any]		  =  """weight_v"""
																														elif "bias" in name:
																																				A__						:  Optional[int]		  =  """bias"""
																														elif "weight" in name:
																																				A__						:  int		  =  """weight"""
																														else:
																																				A__						:  Optional[Any]		  =  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      _lowerCAmelCase		(							UpperCAmelCase__       :					int,						UpperCAmelCase__       :					Optional[Any],						UpperCAmelCase__       :					List[Any],						UpperCAmelCase__       :					List[Any],						UpperCAmelCase__       :					Any      )		->List[Any]:
						A__						:  Dict		  =  full_name.split("""conv_layers."""      )[-1]
						A__						:  Optional[int]		  =  name.split("""."""      )
						A__						:  int		  =  int(items[0]      )
						A__						:  Tuple		  =  int(items[1]      )

						if type_id == 0:
												if "bias" in name:
																		assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
																		    f'{full_name} has size {value.shape}, but'
																		    f' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'
																		)
																		A__						:  Optional[Any]		  =  value
																		logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.'      )
												elif "weight" in name:
																		assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
																		    f'{full_name} has size {value.shape}, but'
																		    f' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'
																		)
																		A__						:  Optional[int]		  =  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:
																		assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
																		    f'{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'
																		    " found."
																		)
																		A__						:  Optional[int]		  =  value
																		logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'      )
												elif "weight" in name:
																		assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
																		    f'{full_name} has size {value.shape}, but'
																		    f' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'
																		)
																		A__						:  Any		  =  value
																		logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'      )
						else:
												unused_weights.append(UpperCAmelCase__      )




def      _lowerCAmelCase		(							UpperCAmelCase__       :					Tuple,						UpperCAmelCase__       :					Tuple,						UpperCAmelCase__       :					List[str],						UpperCAmelCase__       :					Dict      )		->str:
						A__						:  Tuple		  =  full_name.split("""adaptor."""      )[-1]
						A__						:  Optional[int]		  =  name.split("""."""      )

						if items[1].isdigit():
												A__						:  Optional[Any]		  =  int(items[1]      )
						else:
												A__						:  Any		  =  None

						if "adaptor" not in full_name:
												if "proj_ln" in full_name:
																		# has to be layer norm
																		if "bias" in name:
																								assert (
																								    value.shape == adapter.proj_layer_norm.bias.data.shape
																								), f'{full_name} has size {value.shape}, but {adapter.proj_layer_norm.bias.data.shape} was found.'
																								A__						:  Union[str, Any]		  =  value
																								logger.info(f'Adapter proj layer norm bias was initialized from {full_name}.'      )
																		if "weight" in name:
																								assert (
																								    value.shape == adapter.proj_layer_norm.weight.data.shape
																								), f'{full_name} has size {value.shape}, but {adapter.proj_layer_norm.weight.data.shape} was found.'
																								A__						:  Tuple		  =  value
												else:
																		# has to be projection layer
																		if "bias" in name:
																								assert (
																								    value.shape == adapter.proj.bias.data.shape
																								), f'{full_name} has size {value.shape}, but {adapter.proj.bias.data.shape} was found.'
																								A__						:  str		  =  value
																								logger.info(f'Adapter proj layer bias was initialized from {full_name}.'      )
																		if "weight" in name:
																								assert (
																								    value.shape == adapter.proj.weight.data.shape
																								), f'{full_name} has size {value.shape}, but {adapter.proj.weight.data.shape} was found.'
																								A__						:  Union[str, Any]		  =  value
																								logger.info(f'Adapter proj layer weight was initialized from {full_name}.'      )
						elif isinstance(UpperCAmelCase__,						UpperCAmelCase__      ):
												if "bias" in name:
																		assert (
																		    value.shape == adapter.layers[layer_id].conv.bias.data.shape
																		), f'{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.bias.data.shape} was found.'
																		A__						:  Dict		  =  value
																		logger.info(f'Adapter layer {layer_id} bias was initialized from {full_name}.'      )
												elif "weight" in name:
																		assert (
																		    value.shape == adapter.layers[layer_id].conv.weight.data.shape
																		), f'{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.weight.data.shape} was found.'
																		A__						:  Optional[int]		  =  value
																		logger.info(f'Adapter layer {layer_id} bias was initialized from {full_name}.'      )
						else:
												unused_weights.append(UpperCAmelCase__      )




def      _lowerCAmelCase		(							UpperCAmelCase__       :					Union[str, Any]      )		->List[Any]:
						A__  ,  A__						:  Union[str, Any]		  =  emb.weight.shape
						A__						:  List[str]		  =  nn.Linear(UpperCAmelCase__,						UpperCAmelCase__,						bias=UpperCAmelCase__      )
						A__						:  List[Any]		  =  emb.weight.data
						return lin_layer




@torch.no_grad()
def      _lowerCAmelCase		(							UpperCAmelCase__       :					Optional[int],						UpperCAmelCase__       :					Any,						UpperCAmelCase__       :					Optional[int],						UpperCAmelCase__       :					Tuple,						UpperCAmelCase__       :					Optional[int],						UpperCAmelCase__       :					Dict,						UpperCAmelCase__       :					Union[str, Any],						UpperCAmelCase__       :					List[Any],						UpperCAmelCase__       :					int,						UpperCAmelCase__       :					Optional[int],						UpperCAmelCase__       :					Dict,						)		->str:
						A__						:  Tuple		  =  WavaVecaConfig.from_pretrained(
						    UpperCAmelCase__,						add_adapter=UpperCAmelCase__,						adapter_stride=UpperCAmelCase__,						adapter_kernel_size=UpperCAmelCase__,						use_auth_token=UpperCAmelCase__,						output_hidden_size=UpperCAmelCase__,						)
						A__						:  List[Any]		  =  MBartConfig.from_pretrained(UpperCAmelCase__      )

						# load model
						A__  ,  A__  ,  A__						:  str		  =  fairseq.checkpoint_utils.load_model_ensemble_and_task(
						    [checkpoint_path],						arg_overrides={
						        """config_yaml""": config_yaml_path,
						        """data""": """/""".join(dict_path.split("""/"""      )[:-1]      ),
						        """w2v_path""": checkpoint_path,
						        """load_pretrained_decoder_from""": None,
						    },						)
						A__						:  List[Any]		  =  model[0].eval()

						# load feature extractor
						A__						:  Any		  =  WavaVecaFeatureExtractor.from_pretrained(UpperCAmelCase__,						use_auth_token=UpperCAmelCase__      )

						# set weights for wav2vec2 encoder
						A__						:  Dict		  =  WavaVecaModel(UpperCAmelCase__      )

						recursively_load_weights_wavaveca(model.encoder,						UpperCAmelCase__      )

						# load decoder weights
						A__						:  Any		  =  MBartForCausalLM(UpperCAmelCase__      )
						A__  ,  A__						:  Union[str, Any]		  =  hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict(),						strict=UpperCAmelCase__      )
						logger.warning(f'The following keys are missing when loading the decoder weights: {missing_keys}'      )
						logger.warning(f'The following keys are unexpected when loading the decoder weights: {unexpected_keys}'      )

						A__						:  Dict		  =  SpeechEncoderDecoderModel(encoder=UpperCAmelCase__,						decoder=UpperCAmelCase__      )
						A__						:  Optional[Any]		  =  False

						A__						:  Optional[Any]		  =  MBartaaTokenizer(UpperCAmelCase__      )
						tokenizer.save_pretrained(UpperCAmelCase__      )

						A__						:  Dict		  =  hf_wavavec.config.to_dict()
						A__						:  List[Any]		  =  tokenizer.pad_token_id
						A__						:  Optional[Any]		  =  tokenizer.bos_token_id
						A__						:  List[Any]		  =  tokenizer.eos_token_id
						A__						:  Tuple		  =  """mbart50"""
						A__						:  List[str]		  =  """wav2vec2"""

						A__						:  Optional[int]		  =  tokenizer.eos_token_id
						A__						:  int		  =  2_5_0_0_0_4
						A__						:  Dict		  =  tokenizer.eos_token_id

						A__						:  str		  =  SpeechEncoderDecoderConfig.from_dict(UpperCAmelCase__      )

						hf_wavavec.save_pretrained(UpperCAmelCase__      )
						feature_extractor.save_pretrained(UpperCAmelCase__      )


if __name__ == "__main__":
						A_    =							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_yaml_path''', default=None, type=str, help='''Path to yaml file of fine-tuned model''')
						parser.add_argument(
						    '''--encoder_config_path''',
						    default='''facebook/wav2vec2-xls-r-1b''',
						    type=str,
						    help='''Path to hf encoder wav2vec2 checkpoint config''',
						)
						parser.add_argument(
						    '''--decoder_config_path''',
						    default='''facebook/mbart-large-50-one-to-many-mmt''',
						    type=str,
						    help='''Path to hf decoder checkpoint config''',
						)
						parser.add_argument('''--add_adapter''', default=True, type=bool, help='''whethere to add model adapter layers''')
						parser.add_argument('''--adapter_stride''', default=2, type=int, help='''stride of adapter layers''')
						parser.add_argument('''--adapter_kernel_size''', default=3, type=int, help='''kernel size of adapter layers''')
						parser.add_argument('''--encoder_output_dim''', default=1024, type=int, help='''encoder output dim''')
						parser.add_argument('''--start_token_id''', default=25_0004, type=int, help='''`decoder_start_token_id` of model config''')

						A_    =							parser.parse_args()
						convert_wavaveca_checkpoint(
						    args.checkpoint_path,
						    args.pytorch_dump_folder_path,
						    args.dict_path,
						    args.config_yaml_path,
						    encoder_config_path=args.encoder_config_path,
						    decoder_config_path=args.decoder_config_path,
						    add_adapter=args.add_adapter,
						    adapter_kernel_size=args.adapter_kernel_size,
						    adapter_stride=args.adapter_stride,
						    decoder_start_token_id=args.start_token_id,
						    encoder_output_dim=args.encoder_output_dim,
						)






