





import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
  from .tokenization_pegasus import PegasusTokenizer
else:
  SCREAMING_SNAKE_CASE__       :			Union[str, Any]			      =						None


SCREAMING_SNAKE_CASE__       :			Any			      =						logging.get_logger(__name__)


SCREAMING_SNAKE_CASE__       :			List[Any]			      =						"""▁"""

SCREAMING_SNAKE_CASE__       :			Tuple			      =						{"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

SCREAMING_SNAKE_CASE__       :			List[str]			      =						{
    """vocab_file""": {"""google/pegasus-xsum""": """https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"""},
    """tokenizer_file""": {
        """google/pegasus-xsum""": """https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json"""
    },
}

SCREAMING_SNAKE_CASE__       :			Optional[Any]			      =						{
    """google/pegasus-xsum""": 5_1_2,
}







class   __lowerCAmelCase			(					_UpperCamelCase  ):
   _UpperCamelCase :						Union[str, Any]     		=				VOCAB_FILES_NAMES
   _UpperCamelCase :						Dict     		=				PRETRAINED_VOCAB_FILES_MAP
   _UpperCamelCase :						str     		=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _UpperCamelCase :						Dict     		=				PegasusTokenizer
   _UpperCamelCase :						Union[str, Any]     		=				["""input_ids""", """attention_mask"""]





   def __init__( self					,			snake_case=None					,			snake_case=None					,			snake_case="<pad>"					,			snake_case="</s>"					,			snake_case="<unk>"					,			snake_case="<mask_2>"					,			snake_case="<mask_1>"					,			snake_case=None					,			snake_case=103					,			**snake_case					,			)				->  List[str]:
      """simple docstring"""





      a__	:						Optional[Any]      		=  offset

      if additional_special_tokens is not None:
         if not isinstance(snake_case					,			snake_case      ):
            raise TypeError(
                F"""additional_special_tokens should be of type {type(snake_case      )}, but is"""
                F""" {type(snake_case      )}"""      )

         a__	:						Optional[Any]      		=  (
             ([mask_token_sent] + additional_special_tokens)
             if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
             else additional_special_tokens
         )
         # fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
         additional_special_tokens_extended += [
             F"""<unk_{i}>""" for i in range(len(snake_case      )					,			self.offset - 1      )
         ]

         if len(set(snake_case      )      ) != len(snake_case      ):
            raise ValueError(
                "Please make sure that the provided additional_special_tokens do not contain an incorrectly"
                F""" shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}."""      )
         a__	:						List[str]      		=  additional_special_tokens_extended
      else:
         a__	:						Tuple      		=  [mask_token_sent] if mask_token_sent is not None else []
         additional_special_tokens += [F"""<unk_{i}>""" for i in range(2					,			self.offset      )]

      super().__init__(
          snake_case					,			tokenizer_file=snake_case					,			pad_token=snake_case					,			eos_token=snake_case					,			unk_token=snake_case					,			mask_token=snake_case					,			mask_token_sent=snake_case					,			offset=snake_case					,			additional_special_tokens=snake_case					,			**snake_case					,			)
      a__	:						int      		=  vocab_file
      a__	:						Union[str, Any]      		=  False if not self.vocab_file else True





   def 	_snake_case ( self					,			snake_case      )				->  Tuple:
      """simple docstring"""





      a__	:						Tuple      		=  set(self.all_special_ids      )  # call it once instead of inside list comp
      all_special_ids.remove(self.unk_token_id      )  # <unk> is only sometimes special

      if all_special_ids != set(range(len(self.additional_special_tokens      ) + 3      )      ):
         raise ValueError(
             "There should be 3 special tokens: mask_token, pad_token, and eos_token +"
             F""" {len(self.additional_special_tokens      )} additional_special_tokens, but got {all_special_ids}"""      )

      return [1 if x in all_special_ids else 0 for x in seq]





   def 	_snake_case ( self					,			snake_case					,			snake_case = None					,			snake_case = False      )				->  List[int]:
      """simple docstring"""





      if already_has_special_tokens:
         return self._special_token_mask(snake_case      )
      elif token_ids_a is None:
         return self._special_token_mask(snake_case      ) + [1]
      else:
         return self._special_token_mask(token_ids_a + token_ids_a      ) + [1]





   def 	_snake_case ( self					,			snake_case					,			snake_case=None      )				->  List[int]:
      """simple docstring"""





      if token_ids_a is None:
         return token_ids_a + [self.eos_token_id]
      # We don't expect to process pairs, but leave the pair logic for API consistency
      return token_ids_a + token_ids_a + [self.eos_token_id]





   def 	_snake_case ( self					,			snake_case					,			snake_case = None      )				->  Tuple[str]:
      """simple docstring"""





      if not self.can_save_slow_tokenizer:
         raise ValueError(
             "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
             "tokenizer."      )

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

      if os.path.abspath(self.vocab_file      ) != os.path.abspath(snake_case      ):
         copyfile(self.vocab_file					,			snake_case      )

      return (out_vocab_file,)








import argparse
import hashlib
import os
import urllib
import warnings

import torch
from torch import nn
from tqdm import tqdm

from transformers import WhisperConfig, WhisperForConditionalGeneration


SCREAMING_SNAKE_CASE__       :			List[str]			      =						{
    """tiny.en""": """https://openaipublic.azureedge.net/main/whisper/models/d3dd57d32accea0b295c96e26691aa14d8822fac7d9d27d5dc00b4ca2826dd03/tiny.en.pt""",
    """tiny""": """https://openaipublic.azureedge.net/main/whisper/models/65147644a518d12f04e32d6f3b26facc3f8dd46e5390956a9424a650c0ce22b9/tiny.pt""",
    """base.en""": """https://openaipublic.azureedge.net/main/whisper/models/25a8566e1d0c1e2231d1c762132cd20e0f96a85d16145c3a00adf5d1ac670ead/base.en.pt""",
    """base""": """https://openaipublic.azureedge.net/main/whisper/models/ed3a0b6b1c0edf879ad9b11b1af5a0e6ab5db9205f891f668f8b0e6c6326e34e/base.pt""",
    """small.en""": """https://openaipublic.azureedge.net/main/whisper/models/f953ad0fd29cacd07d5a9eda5624af0f6bcf2258be67c92b79389873d91e0872/small.en.pt""",
    """small""": """https://openaipublic.azureedge.net/main/whisper/models/9ecf779972d90ba49c06d968637d720dd632c55bbf19d441fb42bf17a411e794/small.pt""",
    """medium.en""": """https://openaipublic.azureedge.net/main/whisper/models/d7440d1dc186f76616474e0ff0b3b6b879abc9d1a4926b7adfa41db2d497ab4f/medium.en.pt""",
    """medium""": """https://openaipublic.azureedge.net/main/whisper/models/345ae4da62f9b3d59415adc60127b97c714f32e89e936602e85993674d08dcb1/medium.pt""",
    """large""": """https://openaipublic.azureedge.net/main/whisper/models/e4b87e7e0bf463eb8e6956e646f1e277e901512310def2c24bf0e11bd3c28e9a/large.pt""",
    """large-v2""": """https://openaipublic.azureedge.net/main/whisper/models/81f7c96c852ee8fc832187b0132e569d6c3065a3252ed18e56effd0b6a73e524/large-v2.pt""",
}






def       _A  (      lowerCamelCase   ):
   a__	:						Optional[int]      		=  ["layers", "blocks"]
   for k in ignore_keys:
      state_dict.pop(lowerCamelCase							, lowerCamelCase   )


SCREAMING_SNAKE_CASE__       :			List[str]			      =						{
    """blocks""": """layers""",
    """mlp.0""": """fc1""",
    """mlp.2""": """fc2""",
    """mlp_ln""": """final_layer_norm""",
    """.attn.query""": """.self_attn.q_proj""",
    """.attn.key""": """.self_attn.k_proj""",
    """.attn.value""": """.self_attn.v_proj""",
    """.attn_ln""": """.self_attn_layer_norm""",
    """.attn.out""": """.self_attn.out_proj""",
    """.cross_attn.query""": """.encoder_attn.q_proj""",
    """.cross_attn.key""": """.encoder_attn.k_proj""",
    """.cross_attn.value""": """.encoder_attn.v_proj""",
    """.cross_attn_ln""": """.encoder_attn_layer_norm""",
    """.cross_attn.out""": """.encoder_attn.out_proj""",
    """decoder.ln.""": """decoder.layer_norm.""",
    """encoder.ln.""": """encoder.layer_norm.""",
    """token_embedding""": """embed_tokens""",
    """encoder.positional_embedding""": """encoder.embed_positions.weight""",
    """decoder.positional_embedding""": """decoder.embed_positions.weight""",
    """ln_post""": """layer_norm""",
}






def       _A  (      lowerCamelCase   ):
   a__	:						Tuple      		=  list(s_dict.keys()   )
   for key in keys:
      a__	:						Optional[Any]      		=  key
      for k, v in WHISPER_MAPPING.items():
         if k in key:
            a__	:						Optional[int]      		=  new_key.replace(lowerCamelCase							, lowerCamelCase   )

      print(F"""{key} -> {new_key}"""   )

      a__	:						Dict      		=  s_dict.pop(lowerCamelCase   )
   return s_dict






def       _A  (      lowerCamelCase   ):
   a__		,     a__	:						Any      		=  emb.weight.shape
   a__	:						Optional[Any]      		=  nn.Linear(lowerCamelCase							, lowerCamelCase							, bias=lowerCamelCase   )
   a__	:						Optional[Any]      		=  emb.weight.data
   return lin_layer






def       _A  (      lowerCamelCase							, lowerCamelCase   ):
   os.makedirs(lowerCamelCase							, exist_ok=lowerCamelCase   )
   a__	:						Optional[Any]      		=  os.path.basename(lowerCamelCase   )

   a__	:						List[Any]      		=  url.split("/"   )[-2]
   a__	:						Tuple      		=  os.path.join(lowerCamelCase							, lowerCamelCase   )

   if os.path.exists(lowerCamelCase   ) and not os.path.isfile(lowerCamelCase   ):
      raise RuntimeError(F"""{download_target} exists and is not a regular file"""   )

   if os.path.isfile(lowerCamelCase   ):
      a__	:						Any      		=  open(lowerCamelCase							, "rb"   ).read()
      if hashlib.shaaaa(lowerCamelCase   ).hexdigest() == expected_shaaaa:
         return model_bytes
      else:
         warnings.warn(F"""{download_target} exists, but the SHA256 checksum does not match; re-downloading the file"""   )

   with urllib.request.urlopen(lowerCamelCase   ) as source, open(lowerCamelCase							, "wb"   ) as output:
      with tqdm(
          total=int(source.info().get("Content-Length"   )   )							, ncols=80							, unit="iB"							, unit_scale=lowerCamelCase							, unit_divisor=1024   ) as loop:
         while True:
            a__	:						Optional[Any]      		=  source.read(8192   )
            if not buffer:
               break

            output.write(lowerCamelCase   )
            loop.update(len(lowerCamelCase   )   )

   a__	:						Optional[int]      		=  open(lowerCamelCase							, "rb"   ).read()
   if hashlib.shaaaa(lowerCamelCase   ).hexdigest() != expected_shaaaa:
      raise RuntimeError(
          "Model has been downloaded but the SHA256 checksum does not not match. Please retry loading the model."   )

   return model_bytes






def       _A  (      lowerCamelCase							, lowerCamelCase   ):
   if ".pt" not in checkpoint_path:
      a__	:						str      		=  _download(_MODELS[checkpoint_path]   )
   else:
      a__	:						str      		=  torch.load(lowerCamelCase							, map_location="cpu"   )
   a__	:						Dict      		=  original_checkpoint["dims"]
   a__	:						Optional[int]      		=  original_checkpoint["model_state_dict"]
   a__	:						Any      		=  state_dict["decoder.token_embedding.weight"]
   remove_ignore_keys_(lowerCamelCase   )
   rename_keys(lowerCamelCase   )
   a__	:						Optional[Any]      		=  True
   a__	:						Optional[Any]      		=  state_dict["decoder.layers.0.fc1.weight"].shape[0]

   a__	:						Tuple      		=  WhisperConfig(
       vocab_size=dimensions["n_vocab"]							, encoder_ffn_dim=lowerCamelCase							, decoder_ffn_dim=lowerCamelCase							, num_mel_bins=dimensions["n_mels"]							, d_model=dimensions["n_audio_state"]							, max_target_positions=dimensions["n_text_ctx"]							, encoder_layers=dimensions["n_audio_layer"]							, encoder_attention_heads=dimensions["n_audio_head"]							, decoder_layers=dimensions["n_text_layer"]							, decoder_attention_heads=dimensions["n_text_state"]							, max_source_positions=dimensions["n_audio_ctx"]							, )

   a__	:						Optional[Any]      		=  WhisperForConditionalGeneration(lowerCamelCase   )
   a__		,     a__	:						Tuple      		=  model.model.load_state_dict(lowerCamelCase							, strict=lowerCamelCase   )
   if len(lowerCamelCase   ) > 0 and not set(lowerCamelCase   ) <= {
       "encoder.embed_positions.weights",
       "decoder.embed_positions.weights",
   }:
      raise ValueError(
          "Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights`  are allowed to be missing,"
          F""" but all the following weights are missing {missing}"""   )

   if tie_embeds:
      a__	:						Any      		=  make_linear_from_emb(model.model.decoder.embed_tokens   )
   else:
      a__	:						str      		=  proj_out_weights

   model.save_pretrained(lowerCamelCase   )


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE__       :			Tuple			      =						argparse.ArgumentParser()
  # # Required parameters
  parser.add_argument("""--checkpoint_path""", type=str, help="""Patht to the downloaded checkpoints""")
  parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
  SCREAMING_SNAKE_CASE__       :			List[str]			      =						parser.parse_args()

  convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)

