





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


lowerCAmelCase_    =    {
    '''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			)		->						List[Any]:






				"""simple docstring"""


				snake_case_						:   List[str]           =  ['''layers''', '''blocks''']
				for k in ignore_keys:
								state_dict.pop(_UpperCamelCase			,    _UpperCamelCase			)


lowerCAmelCase_    =    {
    '''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			)		->						Dict:






				"""simple docstring"""


				snake_case_						:   Optional[Any]           =  list(s_dict.keys()			)
				for key in keys:
								snake_case_						:   Dict           =  key
								for k, v in WHISPER_MAPPING.items():
												if k in key:
																snake_case_						:   str           =  new_key.replace(_UpperCamelCase			,    _UpperCamelCase			)

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

								snake_case_						:   Optional[int]           =  s_dict.pop(_UpperCamelCase			)
				return s_dict






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						int:






				"""simple docstring"""


				snake_case_      ,							snake_case_						:   List[Any]           =  emb.weight.shape
				snake_case_						:   List[str]           =  nn.Linear(_UpperCamelCase			,    _UpperCamelCase			,    bias=_UpperCamelCase			)
				snake_case_						:   Tuple           =  emb.weight.data
				return lin_layer






def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			)		->						bytes:






				"""simple docstring"""


				os.makedirs(_UpperCamelCase			,    exist_ok=_UpperCamelCase			)
				snake_case_						:   Any           =  os.path.basename(_UpperCamelCase			)

				snake_case_						:   List[Any]           =  url.split('''/'''			)[-2]
				snake_case_						:   Dict           =  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			):
								snake_case_						:   Union[str, Any]           =  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=1_024			) as loop:
												while True:
																snake_case_						:   List[Any]           =  source.read(8_192			)
																if not buffer:
																				break

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

				snake_case_						:   List[Any]           =  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 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			)		->						Optional[int]:






				"""simple docstring"""


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

				snake_case_						:   Tuple           =  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''']			,    )

				snake_case_						:   str           =  WhisperForConditionalGeneration(_UpperCamelCase			)
				snake_case_      ,							snake_case_						:   Optional[Any]           =  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:
								snake_case_						:   List[Any]           =  make_linear_from_emb(model.model.decoder.embed_tokens			)
				else:
								snake_case_						:   str           =  proj_out_weights

				model.save_pretrained(_UpperCamelCase			)


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

	convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)







from typing import List, Optional

from tokenizers import ByteLevelBPETokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer


lowerCAmelCase_    =    logging.get_logger(__name__)

lowerCAmelCase_    =    {
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
}

lowerCAmelCase_    =    {
    '''vocab_file''': {
        '''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json'''
    },
    '''merges_file''': {
        '''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt'''
    },
    '''tokenizer_config_file''': {
        '''facebook/blenderbot_small-90M''': (
            '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json'''
        )
    },
}

lowerCAmelCase_    =    {
    '''facebook/blenderbot_small-90M''': 5_1_2,
}



class  __lowerCAmelCase	(      _a					):
			lowerCamelCase_					:					int	      =				VOCAB_FILES_NAMES
			lowerCamelCase_					:					List[Any]	      =				PRETRAINED_VOCAB_FILES_MAP
			lowerCamelCase_					:					int	      =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCamelCase_					:					Optional[Any]	      =				BlenderbotSmallTokenizer






			def __init__(self ,							__magic_name__=None ,							__magic_name__=None ,							__magic_name__="<|endoftext|>" ,							__magic_name__="<|endoftext|>" ,							__magic_name__="<|endoftext|>" ,							__magic_name__=False ,							__magic_name__=True ,							**__magic_name__ ,							)					->       Union[str, Any]:


							'''simple docstring'''



							super().__init__(
							    ByteLevelBPETokenizer(
							        vocab=__magic_name__ ,							merges=__magic_name__ ,							add_prefix_space=__magic_name__ ,							trim_offsets=__magic_name__ ,							) ,							bos_token=__magic_name__ ,							eos_token=__magic_name__ ,							unk_token=__magic_name__ ,							**__magic_name__ ,							)
							snake_case_						:   Optional[int]           =  add_prefix_space






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__=None   )					->       Any:


							'''simple docstring'''



							snake_case_						:   Optional[int]           =  [self.bos_token_id] + token_ids_a + [self.eos_token_id]
							if token_ids_a is None:
											return output

							return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ = None   )					->       List[int]:


							'''simple docstring'''



							snake_case_						:   List[str]           =  [self.sep_token_id]
							snake_case_						:   Optional[Any]           =  [self.cls_token_id]

							if token_ids_a is None:
											return len(cls + token_ids_a + sep   ) * [0]
							return len(cls + token_ids_a + sep + sep + token_ids_a + sep   ) * [0]
