

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						:int            = logging.get_logger(__name__)

__a						:Union[str, 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',
}
__a						:Dict            = [
    'lm_head',
]

def 			__snake_case	(   __UpperCamelCase  :   Tuple							,__UpperCamelCase  :   Tuple							,__UpperCamelCase  :   Dict							,__UpperCamelCase  :   Tuple							,__UpperCamelCase  :   Optional[Any]						):



      """simple docstring"""

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

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

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

def 			__snake_case	(   __UpperCamelCase  :   Tuple							,__UpperCamelCase  :   Dict							,__UpperCamelCase  :   Dict						):



      """simple docstring"""

      A_	     =							[]
      A_	     =							fairseq_model.state_dict()

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

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

      for name, value in fairseq_dict.items():
            A_	     =							False
            if "conv_layers" in name:
                  load_conv_layer(
                      UpperCamelCase__							,UpperCamelCase__							,UpperCamelCase__							,UpperCamelCase__							,)
                  A_	     =							True
            elif "pos_conv" in name:
                  load_pos_conv_layer(
                      UpperCamelCase__							,UpperCamelCase__							,UpperCamelCase__							,UpperCamelCase__							,)
                  A_	     =							True
            else:
                  for key, mapped_key in MAPPING.items():
                        if not is_headless:
                              A_	     =							'''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]:
                              A_	     =							True
                              if "*" in mapped_key:
                                    A_	     =							name.split(UpperCamelCase__						)[0].split("."						)[-2]
                                    A_	     =							mapped_key.replace("*"							,UpperCamelCase__						)
                              if "weight_g" in name:
                                    A_	     =							'''weight_g'''
                              elif "weight_v" in name:
                                    A_	     =							'''weight_v'''
                              elif "bias" in name:
                                    A_	     =							'''bias'''
                              elif "weight" in name:
                                    # TODO: don't match quantizer.weight_proj
                                    A_	     =							'''weight'''
                              else:
                                    A_	     =							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 			__snake_case	(   __UpperCamelCase  :   List[str]							,__UpperCamelCase  :   List[Any]						):



      """simple docstring"""

      A_	     =							path.split("."						)
      return reduce(UpperCamelCase__							,UpperCamelCase__							,UpperCamelCase__						)

def 			__snake_case	(   __UpperCamelCase  :   Tuple							,__UpperCamelCase  :   List[str]							,__UpperCamelCase  :   Tuple							,__UpperCamelCase  :   List[str]						):



      """simple docstring"""

      A_	     =							access_by_string(UpperCamelCase__							,UpperCamelCase__						)
      A_	     =							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.'''						)
      A_	     =							fsq_value
      logger.info(f'''{full_name} was correctly initialized from {hf_weight_path}.'''						)

def 			__snake_case	(   __UpperCamelCase  :   Union[str, Any]							,__UpperCamelCase  :   Optional[Any]							,__UpperCamelCase  :   Optional[Any]							,__UpperCamelCase  :   int						):



      """simple docstring"""

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

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

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

def 			__snake_case	(   __UpperCamelCase  :   Optional[int]							,__UpperCamelCase  :   Tuple							,__UpperCamelCase  :   int							,__UpperCamelCase  :   Union[str, Any]						):



      """simple docstring"""

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

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

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

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



      """simple docstring"""

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

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

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

      def load_dataavec(__UpperCamelCase  :   Optional[Any]						):
            A_	     =							fairseq.checkpoint_utils.load_model_ensemble_and_task([path]						)
            return model[0].eval()

      A_	     =							load_dataavec(UpperCamelCase__						)

      recursively_load_weights(UpperCamelCase__							,UpperCamelCase__							,not is_finetuned						)

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

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

      A_	     =							processor(UpperCamelCase__							,return_tensors="pt"							,padding=UpperCamelCase__						)

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

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

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

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

      print(our_output.shape							,their_output.shape						)
      A_	     =							torch.max(torch.abs(our_output - their_output						)						).item()
      print(f'''max_absolute_diff = {max_absolute_diff}'''						)  # ~ 1e-7
      A_	     =							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__":
      __a						:Union[str, 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						:Optional[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
      )









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

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


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

__a						:Union[str, Any]            = {
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
}

__a						:Union[str, Any]            = {
    'vocab_file': {
        'microsoft/biogpt': 'https://huggingface.co/microsoft/biogpt/resolve/main/vocab.json',
    },
    'merges_file': {'microsoft/biogpt': 'https://huggingface.co/microsoft/biogpt/resolve/main/merges.txt'},
}

__a						:str            = {
    'microsoft/biogpt': 1024,
}

def 			__snake_case	(   __UpperCamelCase  :   Any						):



      """simple docstring"""

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



class      _a				( snake_case_							):




      """simple docstring"""

      _lowerCamelCase					: Tuple					=					VOCAB_FILES_NAMES
      _lowerCamelCase					: Any					=					PRETRAINED_VOCAB_FILES_MAP
      _lowerCamelCase					: str					=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _lowerCamelCase					: List[Any]					=					['input_ids', 'attention_mask']





      def __init__(      self       :							Union[str, Any]	, UpperCAmelCase       :							Union[str, Any]	, UpperCAmelCase       :							Optional[int]	, UpperCAmelCase       :							List[Any]="<unk>"	, UpperCAmelCase       :							Dict="<s>"	, UpperCAmelCase       :							Tuple="</s>"	, UpperCAmelCase       :							Optional[int]="</s>"	, UpperCAmelCase       :							int="<pad>"	, **UpperCAmelCase       :							List[str]	, ):
            super().__init__(
                bos_token=UpperCAmelCase	, eos_token=UpperCAmelCase	, sep_token=UpperCAmelCase	, unk_token=UpperCAmelCase	, pad_token=UpperCAmelCase	, **UpperCAmelCase	, )

            try:
                  import sacremoses
            except ImportError:
                  raise ImportError(
                      "You need to install sacremoses to use BioGptTokenizer. "
                      "See https://pypi.org/project/sacremoses/ for installation."  )

            A_	     =							"en"
            A_	     =							sacremoses
            # cache of sm.MosesTokenizer instance
            A_	     =							{}
            A_	     =							{}
            with open(UpperCAmelCase	, encoding="utf-8"  ) as vocab_handle:
                  A_	     =							json.load(UpperCAmelCase  )
            A_	     =							{v: k for k, v in self.encoder.items()}
            with open(UpperCAmelCase	, encoding="utf-8"  ) as merges_handle:
                  A_	     =							merges_handle.read().split("\n"  )[:-1]
            A_	     =							[tuple(merge.split()[:2]  ) for merge in merges]
            A_	     =							dict(zip(UpperCAmelCase	, range(len(UpperCAmelCase  )  )  )  )
            A_	     =							{}





      @property
      def 		__A  (      self       :							int  ):
            return len(self.encoder  )





      def 		__A  (      self       :							Union[str, Any]  ):
            return dict(self.encoder	, **self.added_tokens_encoder  )





      def 		__A  (      self       :							Any	, UpperCAmelCase       :							Union[str, Any]	, UpperCAmelCase       :							Optional[Any]  ):
            if lang not in self.cache_moses_tokenizer:
                  A_	     =							self.sm.MosesTokenizer(lang=UpperCAmelCase  )
                  A_	     =							moses_tokenizer
            return self.cache_moses_tokenizer[lang].tokenize(
                UpperCAmelCase	, aggressive_dash_splits=UpperCAmelCase	, return_str=UpperCAmelCase	, escape=UpperCAmelCase  )





      def 		__A  (      self       :							List[Any]	, UpperCAmelCase       :							Tuple	, UpperCAmelCase       :							List[Any]  ):
            if lang not in self.cache_moses_detokenizer:
                  A_	     =							self.sm.MosesDetokenizer(lang=UpperCAmelCase  )
                  A_	     =							moses_detokenizer
            return self.cache_moses_detokenizer[lang].detokenize(UpperCAmelCase  )





      def 		__A  (      self       :							Optional[Any]	, UpperCAmelCase       :							int  ):
            A_	     =							tuple(token[:-1]  ) + (token[-1] + "</w>",)
            if token in self.cache:
                  return self.cache[token]
            A_	     =							get_pairs(UpperCAmelCase  )

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

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

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





      def 		__A  (      self       :							Optional[int]	, UpperCAmelCase       :							str	, UpperCAmelCase       :							Optional[Any]=False  ):
            if bypass_tokenizer:
                  A_	     =							text.split()
            else:
                  A_	     =							self.moses_tokenize(UpperCAmelCase	, self.lang  )

            A_	     =							[]
            for token in text:
                  if token:
                        split_tokens.extend(list(self.bpe(UpperCAmelCase  ).split(" "  )  )  )

            return split_tokens





      def 		__A  (      self       :							Dict	, UpperCAmelCase       :							Union[str, Any]  ):
            return self.encoder.get(UpperCAmelCase	, self.encoder.get(self.unk_token  )  )





      def 		__A  (      self       :							Optional[Any]	, UpperCAmelCase       :							List[str]  ):
            return self.decoder.get(UpperCAmelCase	, self.unk_token  )





      def 		__A  (      self       :							Dict	, UpperCAmelCase       :							Any  ):
            A_	     =							[t.replace(" "	, ""  ).replace("</w>"	, " "  ) for t in tokens]
            A_	     =							"".join(UpperCAmelCase  ).split()
            # detokenize
            A_	     =							self.moses_detokenize(UpperCAmelCase	, self.lang  )
            return text





      def 		__A  (      self       :							List[str]	, UpperCAmelCase       :							List[int]	, UpperCAmelCase       :							Optional[List[int]] = None  ):
            if token_ids_a is None:
                  return [self.sep_token_id] + token_ids_a
            A_	     =							[self.sep_token_id]
            return sep + token_ids_a + sep + token_ids_a





      def 		__A  (      self       :							Any	, UpperCAmelCase       :							List[int]	, UpperCAmelCase       :							Optional[List[int]] = None	, UpperCAmelCase       :							bool = False  ):
            if already_has_special_tokens:
                  return super().get_special_tokens_mask(
                      token_ids_a=UpperCAmelCase	, token_ids_a=UpperCAmelCase	, already_has_special_tokens=UpperCAmelCase  )
            # no bos used in fairseq
            if token_ids_a is not None:
                  return [1] + ([0] * len(UpperCAmelCase  )) + [1] + ([0] * len(UpperCAmelCase  ))
            return [1] + ([0] * len(UpperCAmelCase  ))





      def 		__A  (      self       :							Optional[Any]	, UpperCAmelCase       :							List[int]	, UpperCAmelCase       :							Optional[List[int]] = None  ):
            A_	     =							[self.sep_token_id]

            # no bos used in fairseq
            if token_ids_a is None:
                  return len(token_ids_a + sep  ) * [0]
            return len(token_ids_a + sep  ) * [0] + len(token_ids_a + sep  ) * [1]





      def 		__A  (      self       :							Union[str, Any]	, UpperCAmelCase       :							str	, UpperCAmelCase       :							Optional[str] = None  ):
            if not os.path.isdir(UpperCAmelCase  ):
                  logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''  )
                  return
            A_	     =							os.path.join(
                UpperCAmelCase	, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]  )
            A_	     =							os.path.join(
                UpperCAmelCase	, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]  )

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

            A_	     =							0
            with open(UpperCAmelCase	, "w"	, encoding="utf-8"  ) as writer:
                  for bpe_tokens, token_index in sorted(self.bpe_ranks.items()	, key=lambda UpperCAmelCase  : 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_	     =							token_index
                        writer.write(" ".join(UpperCAmelCase  ) + "\n"  )
                        index += 1

            return vocab_file, merge_file





      def __getstate__(      self       :							Union[str, Any]  ):
            A_	     =							self.__dict__.copy()
            A_	     =							None
            return state







      def __setstate__(      self       :							List[Any]	, UpperCAmelCase       :							Dict  ):
            A_	     =							d

            try:
                  import sacremoses
            except ImportError:
                  raise ImportError(
                      "You need to install sacremoses to use XLMTokenizer. "
                      "See https://pypi.org/project/sacremoses/ for installation."  )

            A_	     =							sacremoses