






'''simple docstring'''



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							=						{
    '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 				lowercase_					(     __A			:							Tuple      )							->   Optional[Any]:



 """simple docstring"""




 lowercase       : Optional[int]						=['''layers''', '''blocks''']
 for k in ignore_keys:
  state_dict.pop(__A       ,   __A      )


SCREAMING_SNAKE_CASE							=						{
    '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 				lowercase_					(     __A			:							str      )							->   str:



 """simple docstring"""




 lowercase       : Optional[int]						=list(s_dict.keys()      )
 for key in keys:
  lowercase       : Union[str, Any]						=key
  for k, v in WHISPER_MAPPING.items():
   if k in key:
    lowercase       : Any						=new_key.replace(__A       ,   __A      )

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

  lowercase       : int						=s_dict.pop(__A      )
 return s_dict


def 				lowercase_					(     __A			:							str      )							->   str:



 """simple docstring"""




 lowercase	,		lowercase       : Dict						=emb.weight.shape
 lowercase       : List[Any]						=nn.Linear(__A       ,   __A       ,   bias=__A      )
 lowercase       : Union[str, Any]						=emb.weight.data
 return lin_layer


def 				lowercase_					(     __A			:							str       ,   __A			:							str      )							->   bytes:



 """simple docstring"""




 os.makedirs(__A       ,   exist_ok=__A      )
 lowercase       : Union[str, Any]						=os.path.basename(__A      )

 lowercase       : Union[str, Any]						=url.split('''/'''      )[-2]
 lowercase       : Union[str, Any]						=os.path.join(__A       ,   __A      )

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

 if os.path.isfile(__A      ):
  lowercase       : List[str]						=open(__A       ,   '''rb'''      ).read()
  if hashlib.shaaaa(__A      ).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(__A      ) as source, open(__A       ,   '''wb'''      ) as output:
  with tqdm(
      total=int(source.info().get('''Content-Length'''      )      )       ,   ncols=8_0       ,   unit='''iB'''       ,   unit_scale=__A       ,   unit_divisor=1_0_2_4      ) as loop:
   while True:
    lowercase       : Optional[Any]						=source.read(8_1_9_2      )
    if not buffer:
     break

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

 lowercase       : List[Any]						=open(__A       ,   '''rb'''      ).read()
 if hashlib.shaaaa(__A      ).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 				lowercase_					(     __A			:							Dict       ,   __A			:							Any      )							->   List[str]:



 """simple docstring"""




 if ".pt" not in checkpoint_path:
  lowercase       : List[Any]						=_download(_MODELS[checkpoint_path]      )
 else:
  lowercase       : List[Any]						=torch.load(__A       ,   map_location='''cpu'''      )
 lowercase       : List[Any]						=original_checkpoint['''dims''']
 lowercase       : Optional[int]						=original_checkpoint['''model_state_dict''']
 lowercase       : Any						=state_dict['''decoder.token_embedding.weight''']
 remove_ignore_keys_(__A      )
 rename_keys(__A      )
 lowercase       : Union[str, Any]						=True
 lowercase       : Optional[int]						=state_dict['''decoder.layers.0.fc1.weight'''].shape[0]

 lowercase       : Tuple						=WhisperConfig(
     vocab_size=dimensions['''n_vocab''']       ,   encoder_ffn_dim=__A       ,   decoder_ffn_dim=__A       ,   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''']       ,   )

 lowercase       : int						=WhisperForConditionalGeneration(__A      )
 lowercase	,		lowercase       : str						=model.model.load_state_dict(__A       ,   strict=__A      )
 if len(__A      ) > 0 and not set(__A      ) <= {
     "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:
  lowercase       : Dict						=make_linear_from_emb(model.model.decoder.embed_tokens      )
 else:
  lowercase       : List[Any]						=proj_out_weights

 model.save_pretrained(__A      )


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE							=						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							=						parser.parse_args()

      convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)











'''simple docstring'''



from queue import PriorityQueue
from typing import Any

import numpy as np


def 				lowercase_					(     __A			:							dict       ,   __A			:							str       ,   __A			:							set       ,   __A			:							set       ,   __A			:							dict       ,   __A			:							dict       ,   __A			:							PriorityQueue       ,   __A			:							dict       ,   __A			:							float | int       ,   )							->   float | int:



 """simple docstring"""




 for nxt, d in graph[v]:
  if nxt in visited_forward:
   continue
  lowercase       : str						=cst_fwd.get(__A       ,   np.inf      )
  lowercase       : str						=cst_fwd[v] + d
  if new_cost_f < old_cost_f:
   queue.put((new_cost_f, nxt)      )
   lowercase       : int						=new_cost_f
   lowercase       : Optional[int]						=v
  if nxt in visited_backward:
   if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
    lowercase       : List[Any]						=cst_fwd[v] + d + cst_bwd[nxt]
 return shortest_distance


def 				lowercase_					(     __A			:							str       ,   __A			:							str       ,   __A			:							dict       ,   __A			:							dict      )							->   int:



 """simple docstring"""




 lowercase       : Optional[Any]						=-1

 lowercase       : Tuple						=set()
 lowercase       : str						=set()
 lowercase       : List[str]						={source: 0}
 lowercase       : List[str]						={destination: 0}
 lowercase       : Tuple						={source: None}
 lowercase       : List[Any]						={destination: None}
 lowercase       : PriorityQueue[Any]						=PriorityQueue()
 lowercase       : PriorityQueue[Any]						=PriorityQueue()

 lowercase       : Any						=np.inf

 queue_forward.put((0, source)      )
 queue_backward.put((0, destination)      )

 if source == destination:
  return 0

 while not queue_forward.empty() and not queue_backward.empty():
  lowercase	,		lowercase       : int						=queue_forward.get()
  visited_forward.add(__A      )

  lowercase	,		lowercase       : int						=queue_backward.get()
  visited_backward.add(__A      )

  lowercase       : Dict						=pass_and_relaxation(
      __A       ,   __A       ,   __A       ,   __A       ,   __A       ,   __A       ,   __A       ,   __A       ,   __A       ,   )

  lowercase       : List[Any]						=pass_and_relaxation(
      __A       ,   __A       ,   __A       ,   __A       ,   __A       ,   __A       ,   __A       ,   __A       ,   __A       ,   )

  if cst_fwd[v_fwd] + cst_bwd[v_bwd] >= shortest_distance:
   break

 if shortest_distance != np.inf:
  lowercase       : Union[str, Any]						=shortest_distance
 return shortest_path_distance


SCREAMING_SNAKE_CASE							=						{
    'B': [['C', 1]],
    'C': [['D', 1]],
    'D': [['F', 1]],
    'E': [['B', 1], ['G', 2]],
    'F': [],
    'G': [['F', 1]],
}
SCREAMING_SNAKE_CASE							=						{
    'B': [['E', 1]],
    'C': [['B', 1]],
    'D': [['C', 1]],
    'F': [['D', 1], ['G', 1]],
    'E': [[None, np.inf]],
    'G': [['E', 2]],
}

if __name__ == "__main__":
      import doctest

      doctest.testmod()



