'''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


UpperCamelCase_      	=	{
    '''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__				:  str		)			->	List[str]:


							'''simple docstring'''






							SCREAMING_SNAKE_CASE__     :str						    = ['layers', 'blocks']
							for k in ignore_keys:
														state_dict.pop(SCREAMING_SNAKE_CASE_				,						SCREAMING_SNAKE_CASE_		)


UpperCamelCase_      	=	{
    '''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		)			->	Any:


							'''simple docstring'''






							SCREAMING_SNAKE_CASE__     :Optional[int]						    = list(s_dict.keys()		)
							for key in keys:
														SCREAMING_SNAKE_CASE__     :List[Any]						    = key
														for k, v in WHISPER_MAPPING.items():
																					if k in key:
																												SCREAMING_SNAKE_CASE__     :int						    = new_key.replace(SCREAMING_SNAKE_CASE_				,						SCREAMING_SNAKE_CASE_		)

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

														SCREAMING_SNAKE_CASE__     :Optional[Any]						    = s_dict.pop(SCREAMING_SNAKE_CASE_		)
							return s_dict


def    lowerCamelCase       (			UpperCAmelCase__				:  List[Any]		)			->	str:


							'''simple docstring'''






							SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :int						    = emb.weight.shape
							SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = nn.Linear(SCREAMING_SNAKE_CASE_				,						SCREAMING_SNAKE_CASE_				,						bias=SCREAMING_SNAKE_CASE_		)
							SCREAMING_SNAKE_CASE__     :Any						    = emb.weight.data
							return lin_layer


def    lowerCamelCase       (			UpperCAmelCase__				:  List[str]				,						UpperCAmelCase__				:  List[str]		)			->	bytes:


							'''simple docstring'''






							os.makedirs(SCREAMING_SNAKE_CASE_				,						exist_ok=SCREAMING_SNAKE_CASE_		)
							SCREAMING_SNAKE_CASE__     :Optional[int]						    = os.path.basename(SCREAMING_SNAKE_CASE_		)

							SCREAMING_SNAKE_CASE__     :Optional[Any]						    = url.split('/'		)[-2]
							SCREAMING_SNAKE_CASE__     :Optional[Any]						    = os.path.join(SCREAMING_SNAKE_CASE_				,						SCREAMING_SNAKE_CASE_		)

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

							if os.path.isfile(SCREAMING_SNAKE_CASE_		):
														SCREAMING_SNAKE_CASE__     :Dict						    = open(SCREAMING_SNAKE_CASE_				,						'rb'		).read()
														if hashlib.shaaaa(SCREAMING_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(SCREAMING_SNAKE_CASE_		) as source, open(SCREAMING_SNAKE_CASE_				,						'wb'		) as output:
														with tqdm(
														    total=int(source.info().get('Content-Length'		)		)				,						ncols=8_0				,						unit='iB'				,						unit_scale=SCREAMING_SNAKE_CASE_				,						unit_divisor=1_0_2_4		) as loop:
																					while True:
																												SCREAMING_SNAKE_CASE__     :Dict						    = source.read(8_1_9_2		)
																												if not buffer:
																																			break

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

							SCREAMING_SNAKE_CASE__     :Optional[int]						    = open(SCREAMING_SNAKE_CASE_				,						'rb'		).read()
							if hashlib.shaaaa(SCREAMING_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       (			UpperCAmelCase__				:  Union[str, Any]				,						UpperCAmelCase__				:  Optional[Any]		)			->	Optional[Any]:


							'''simple docstring'''






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

							SCREAMING_SNAKE_CASE__     :List[Any]						    = WhisperConfig(
							    vocab_size=dimensions['n_vocab']				,						encoder_ffn_dim=SCREAMING_SNAKE_CASE_				,						decoder_ffn_dim=SCREAMING_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']				,						)

							SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = WhisperForConditionalGeneration(SCREAMING_SNAKE_CASE_		)
							SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :List[Any]						    = model.model.load_state_dict(SCREAMING_SNAKE_CASE_				,						strict=SCREAMING_SNAKE_CASE_		)
							if len(SCREAMING_SNAKE_CASE_		) > 0 and not set(SCREAMING_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:
														SCREAMING_SNAKE_CASE__     :int						    = make_linear_from_emb(model.model.decoder.embed_tokens		)
							else:
														SCREAMING_SNAKE_CASE__     :Tuple						    = proj_out_weights

							model.save_pretrained(SCREAMING_SNAKE_CASE_		)


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

		convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)







'''simple docstring'''


def   A_      (       SCREAMING_SNAKE_CASE_						)			->bool:
  if num < 0:
    return False

  lowercase_     =       num
  lowercase_     =       0
  while num > 0:
    lowercase_     =       rev_num * 10 + (num % 10)
    num //= 10

  return num_copy == rev_num


if __name__ == "__main__":
       import doctest

       doctest.testmod()

