

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


a_     :    int     		=   {
    """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 							__lowerCAmelCase     (							_UpperCamelCase		:	Union[str, Any]						)		->				Union[str, Any]:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      ["""layers""", """blocks"""]
 for k in ignore_keys:
  state_dict.pop(lowercase_		,				lowercase_						)


a_     :    Optional[Any]     		=   {
    """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 							__lowerCAmelCase     (							_UpperCamelCase		:	Tuple						)		->				int:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      list(s_dict.keys()						)
 for key in keys:
  SCREAMING_SNAKE_CASE		    =      key
  for k, v in WHISPER_MAPPING.items():
   if k in key:
    SCREAMING_SNAKE_CASE		    =      new_key.replace(lowercase_		,				lowercase_						)

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

  SCREAMING_SNAKE_CASE		    =      s_dict.pop(lowercase_						)
 return s_dict


def 							__lowerCAmelCase     (							_UpperCamelCase		:	Any						)		->				List[str]:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      emb.weight.shape
 SCREAMING_SNAKE_CASE		    =      nn.Linear(lowercase_		,				lowercase_		,				bias=lowercase_						)
 SCREAMING_SNAKE_CASE		    =      emb.weight.data
 return lin_layer


def 							__lowerCAmelCase     (							_UpperCamelCase		:	List[Any]		,				_UpperCamelCase		:	Any						)		->				bytes:
 '''simple docstring'''


 os.makedirs(lowercase_		,				exist_ok=lowercase_						)
 SCREAMING_SNAKE_CASE		    =      os.path.basename(lowercase_						)

 SCREAMING_SNAKE_CASE		    =      url.split('/'						)[-2]
 SCREAMING_SNAKE_CASE		    =      os.path.join(lowercase_		,				lowercase_						)

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

 if os.path.isfile(lowercase_						):
  SCREAMING_SNAKE_CASE		    =      open(lowercase_		,				'rb'						).read()
  if hashlib.shaaaa(lowercase_						).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(lowercase_						) as source, open(lowercase_		,				'wb'						) as output:
  with tqdm(
      total=int(source.info().get('Content-Length'						)						)		,				ncols=80		,				unit='iB'		,				unit_scale=lowercase_		,				unit_divisor=10_24						) as loop:
   while True:
    SCREAMING_SNAKE_CASE		    =      source.read(81_92						)
    if not buffer:
     break

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

 SCREAMING_SNAKE_CASE		    =      open(lowercase_		,				'rb'						).read()
 if hashlib.shaaaa(lowercase_						).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 							__lowerCAmelCase     (							_UpperCamelCase		:	str		,				_UpperCamelCase		:	Union[str, Any]						)		->				List[Any]:
 '''simple docstring'''


 if ".pt" not in checkpoint_path:
  SCREAMING_SNAKE_CASE		    =      _download(_MODELS[checkpoint_path]						)
 else:
  SCREAMING_SNAKE_CASE		    =      torch.load(lowercase_		,				map_location='cpu'						)
 SCREAMING_SNAKE_CASE		    =      original_checkpoint["""dims"""]
 SCREAMING_SNAKE_CASE		    =      original_checkpoint["""model_state_dict"""]
 SCREAMING_SNAKE_CASE		    =      state_dict["""decoder.token_embedding.weight"""]
 remove_ignore_keys_(lowercase_						)
 rename_keys(lowercase_						)
 SCREAMING_SNAKE_CASE		    =      True
 SCREAMING_SNAKE_CASE		    =      state_dict["""decoder.layers.0.fc1.weight"""].shape[0]

 SCREAMING_SNAKE_CASE		    =      WhisperConfig(
     vocab_size=dimensions['n_vocab']		,				encoder_ffn_dim=lowercase_		,				decoder_ffn_dim=lowercase_		,				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']		,				)

 SCREAMING_SNAKE_CASE		    =      WhisperForConditionalGeneration(lowercase_						)
 SCREAMING_SNAKE_CASE		    =      model.model.load_state_dict(lowercase_		,				strict=lowercase_						)
 if len(lowercase_						) > 0 and not set(lowercase_						) <= {
     "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:
  SCREAMING_SNAKE_CASE		    =      make_linear_from_emb(model.model.decoder.embed_tokens						)
 else:
  SCREAMING_SNAKE_CASE		    =      proj_out_weights

 model.save_pretrained(lowercase_						)


if __name__ == "__main__":
      a_     :    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.")
      a_     :    Any     		=   parser.parse_args()

      convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)



from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding



class    _snake_case (						UpperCAmelCase_				):
 __lowerCAmelCase	:  Any	   =      ['image_processor', 'tokenizer']
 __lowerCAmelCase	:  Union[str, Any]	   =      'AutoImageProcessor'
 __lowerCAmelCase	:  int	   =      'AutoTokenizer'


 def __init__(       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   super().__init__(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)
   lowercase__	: Union[str, Any]     		=   self.image_processor


 def __call__(       self       ,	SCREAMING_SNAKE_CASE_=None       ,	SCREAMING_SNAKE_CASE_=None       ,	SCREAMING_SNAKE_CASE_=None       ,	**SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   if text is None and images is None:
     raise ValueError("""You have to specify either text or images. Both cannot be none.""")

   if text is not None:
     lowercase__	: List[str]     		=   self.tokenizer(SCREAMING_SNAKE_CASE_       ,	return_tensors=SCREAMING_SNAKE_CASE_       ,	**SCREAMING_SNAKE_CASE_)

   if images is not None:
     lowercase__	: Optional[int]     		=   self.image_processor(SCREAMING_SNAKE_CASE_       ,	return_tensors=SCREAMING_SNAKE_CASE_       ,	**SCREAMING_SNAKE_CASE_)

   if text is not None and images is not None:
     lowercase__	: Union[str, Any]     		=   image_features.pixel_values
     return encoding
   elif text is not None:
     return encoding
   else:
     return BatchEncoding(data=dict(**SCREAMING_SNAKE_CASE_)       ,	tensor_type=SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self       ,	*SCREAMING_SNAKE_CASE_       ,	**SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE_       ,	**SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self       ,	*SCREAMING_SNAKE_CASE_       ,	**SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   return self.tokenizer.decode(*SCREAMING_SNAKE_CASE_       ,	**SCREAMING_SNAKE_CASE_)



 @property
 def   lowercase__  (       self):


   '''simple docstring'''




   return ["input_ids", "attention_mask", "pixel_values"]






