





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


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 lowerCamelCase__    (							snake_case_	:  Tuple       )      ->				Optional[int]:
   __snake_case							       =   ['''layers''', '''blocks''']
   for k in ignore_keys:
      state_dict.pop(snake_case_       ,      snake_case_       )


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 lowerCamelCase__    (							snake_case_	:  Optional[Any]       )      ->				Optional[Any]:
   __snake_case							       =   list(s_dict.keys()       )
   for key in keys:
      __snake_case							       =   key
      for k, v in WHISPER_MAPPING.items():
         if k in key:
            __snake_case							       =   new_key.replace(snake_case_       ,      snake_case_       )

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

      __snake_case							       =   s_dict.pop(snake_case_       )
   return s_dict






def lowerCamelCase__    (							snake_case_	:  str       )      ->				int:
   __snake_case       ,	__snake_case							       =   emb.weight.shape
   __snake_case							       =   nn.Linear(snake_case_       ,      snake_case_       ,      bias=snake_case_       )
   __snake_case							       =   emb.weight.data
   return lin_layer






def lowerCamelCase__    (							snake_case_	:  str       ,      snake_case_	:  str       )      ->				bytes:
   os.makedirs(snake_case_       ,      exist_ok=snake_case_       )
   __snake_case							       =   os.path.basename(snake_case_       )

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

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

   if os.path.isfile(snake_case_       ):
      __snake_case							       =   open(snake_case_       ,      '''rb'''       ).read()
      if hashlib.shaaaa(snake_case_       ).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(snake_case_       ) as source, open(snake_case_       ,      '''wb'''       ) as output:
      with tqdm(
          total=int(source.info().get('''Content-Length'''       )       )       ,      ncols=80       ,      unit='''iB'''       ,      unit_scale=snake_case_       ,      unit_divisor=1024       ) as loop:
         while True:
            __snake_case							       =   source.read(8192       )
            if not buffer:
               break

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

   __snake_case							       =   open(snake_case_       ,      '''rb'''       ).read()
   if hashlib.shaaaa(snake_case_       ).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__    (							snake_case_	:  Tuple       ,      snake_case_	:  Tuple       )      ->				List[Any]:
   if ".pt" not in checkpoint_path:
      __snake_case							       =   _download(_MODELS[checkpoint_path]       )
   else:
      __snake_case							       =   torch.load(snake_case_       ,      map_location='''cpu'''       )
   __snake_case							       =   original_checkpoint['''dims''']
   __snake_case							       =   original_checkpoint['''model_state_dict''']
   __snake_case							       =   state_dict['''decoder.token_embedding.weight''']
   remove_ignore_keys_(snake_case_       )
   rename_keys(snake_case_       )
   __snake_case							       =   True
   __snake_case							       =   state_dict['''decoder.layers.0.fc1.weight'''].shape[0]

   __snake_case							       =   WhisperConfig(
       vocab_size=dimensions['''n_vocab''']       ,      encoder_ffn_dim=snake_case_       ,      decoder_ffn_dim=snake_case_       ,      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''']       ,      )

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

   model.save_pretrained(snake_case_       )


if __name__ == "__main__":
    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.')
    snake_case_				=			parser.parse_args()

    convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)





'''simple docstring'''



import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey





class    UpperCAmelCase_  :





    """simple docstring"""






    def __init__(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)				->     int:
         if dst_width < 0 or dst_height < 0:
              raise ValueError('Destination width/height should be > 0'	)

         __lowerCamelCase   :	Dict								=		img
         __lowerCamelCase   :	Any								=		img.shape[1]
         __lowerCamelCase   :	Optional[int]								=		img.shape[0]
         __lowerCamelCase   :	Dict								=		dst_width
         __lowerCamelCase   :	str								=		dst_height

         __lowerCamelCase   :	Dict								=		self.src_w / self.dst_w
         __lowerCamelCase   :	List[Any]								=		self.src_h / self.dst_h

         __lowerCamelCase   :	Optional[int]								=		(
             np.ones((self.dst_h, self.dst_w, 3)			,     np.uinta	) * 2_55
         )







    def       lowercase_				(							self	)				->     List[Any]:
         for i in range(self.dst_h	):
              for j in range(self.dst_w	):
                   __lowerCamelCase   :	Union[str, Any]								=		self.img[self.get_y(SCREAMING_SNAKE_CASE_	)][self.get_x(SCREAMING_SNAKE_CASE_	)]







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_	)				->     int:
         return int(self.ratio_x * x	)







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_	)				->     int:
         return int(self.ratio_y * y	)


if __name__ == "__main__":
      A__						,					A__		:					Optional[Any]												=  800, 600
      A__		:					List[str]												=  imread("""image_data/lena.jpg""", 1)
      A__		:					List[Any]												=  NearestNeighbour(im, dst_w, dst_h)
      n.process()

      imshow(
          f'''Image resized from: {im.shape[1]}x{im.shape[0]} to {dst_w}x{dst_h}''', n.output
      )
      waitKey(0)
      destroyAllWindows()






