

"""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


_a							=							{
    '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  (  UpperCamelCase_					:	Optional[int])     ->						Tuple:
   '''simple docstring'''


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


_a							=							{
    '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  (  UpperCamelCase_					:	List[Any])     ->						Union[str, Any]:
   '''simple docstring'''


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

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

      __lowercase								=						s_dict.pop(UpperCamelCase_)
   return s_dict




def   _A  (  UpperCamelCase_					:	Dict)     ->						List[Any]:
   '''simple docstring'''


   __lowercase		,__lowercase								=						emb.weight.shape
   __lowercase								=						nn.Linear(UpperCamelCase_,      UpperCamelCase_,      bias=UpperCamelCase_)
   __lowercase								=						emb.weight.data
   return lin_layer




def   _A  (  UpperCamelCase_					:	str,      UpperCamelCase_					:	str)     ->						bytes:
   '''simple docstring'''


   os.makedirs(UpperCamelCase_,      exist_ok=UpperCamelCase_)
   __lowercase								=						os.path.basename(UpperCamelCase_)

   __lowercase								=						url.split("/")[-2]
   __lowercase								=						os.path.join(UpperCamelCase_,      UpperCamelCase_)

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

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

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

   __lowercase								=						open(UpperCamelCase_,      "rb").read()
   if hashlib.shaaaa(UpperCamelCase_).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  (  UpperCamelCase_					:	List[Any],      UpperCamelCase_					:	str)     ->						Union[str, Any]:
   '''simple docstring'''


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

   __lowercase								=						WhisperConfig(
       vocab_size=dimensions["n_vocab"],      encoder_ffn_dim=UpperCamelCase_,      decoder_ffn_dim=UpperCamelCase_,      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								=						WhisperForConditionalGeneration(UpperCamelCase_)
   __lowercase		,__lowercase								=						model.model.load_state_dict(UpperCamelCase_,      strict=UpperCamelCase_)
   if len(UpperCamelCase_) > 0 and not set(UpperCamelCase_) <= {
       "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								=						make_linear_from_emb(model.model.decoder.embed_tokens)
   else:
      __lowercase								=						proj_out_weights

   model.save_pretrained(UpperCamelCase_)


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

   convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)







"""simple docstring"""





from unittest import TestCase

from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters




def   _A  (  )     ->						Dict:
   '''simple docstring'''


   __lowercase								=						{
       "repo_name": ["test_repo1", "test_repo2", "test_repo3"],
       "path": ["test_1.py", "test_2.py", "unit_test.py"],
       "content": ["a " * 20, "a " * 30, "b " * 7],
   }
   __lowercase								=						Dataset.from_dict(UpperCamelCase_)
   return dataset




class  _lowerCAmelCase  (     lowercase       ):







 """simple docstring"""







 def  _lowercase			(      self							:      int     ):
    __lowercase								=						get_dataset()
    __lowercase								=						make_duplicate_clusters(UpperCAmelCase__, 0.85     )
    self.assertEqual(len(duplicate_clusters[0]     ), 2     )



 def  _lowercase			(      self							:      Any     ):
    __lowercase								=						get_dataset()
    __lowercase		,__lowercase								=						deduplicate_dataset(UpperCAmelCase__     )
    self.assertEqual(len(UpperCAmelCase__     ), 2     )
    print(UpperCAmelCase__     )
    self.assertEqual(duplicate_clusters[0][0]["copies"], 2     )
    self.assertEqual(duplicate_clusters[0][0]["is_extreme"], UpperCAmelCase__     )




