




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_		:					Union[str, Any]          =  {
    "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_ (						lowerCamelCase ):
				__magic_name__						:						str										=["""layers""", """blocks"""]
				for k in ignore_keys:
								state_dict.pop(lowerCamelCase						,  lowerCamelCase )


UpperCAmelCase_		:					Optional[Any]          =  {
    "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_ (						lowerCamelCase ):
				__magic_name__						:						List[str]										=list(s_dict.keys() )
				for key in keys:
								__magic_name__						:						Optional[Any]										=key
								for k, v in WHISPER_MAPPING.items():
												if k in key:
																__magic_name__						:						Tuple										=new_key.replace(lowerCamelCase						,  lowerCamelCase )

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

								__magic_name__						:						int										=s_dict.pop(lowerCamelCase )
				return s_dict




def 		lowerCAmelCase_ (						lowerCamelCase ):
				__magic_name__					,			__magic_name__						:						Optional[Any]										=emb.weight.shape
				__magic_name__						:						Optional[Any]										=nn.Linear(lowerCamelCase						,  lowerCamelCase						,  bias=lowerCamelCase )
				__magic_name__						:						Any										=emb.weight.data
				return lin_layer




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase ):
				os.makedirs(lowerCamelCase						,  exist_ok=lowerCamelCase )
				__magic_name__						:						str										=os.path.basename(lowerCamelCase )

				__magic_name__						:						Any										=url.split("""/""" )[-2]
				__magic_name__						:						int										=os.path.join(lowerCamelCase						,  lowerCamelCase )

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

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

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

				__magic_name__						:						Optional[int]										=open(lowerCamelCase						,  """rb""" ).read()
				if hashlib.shaaaa(lowerCamelCase ).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_ (						lowerCamelCase						,  lowerCamelCase ):
				if ".pt" not in checkpoint_path:
								__magic_name__						:						int										=_download(_MODELS[checkpoint_path] )
				else:
								__magic_name__						:						Tuple										=torch.load(lowerCamelCase						,  map_location="""cpu""" )
				__magic_name__						:						Dict										=original_checkpoint["""dims"""]
				__magic_name__						:						List[Any]										=original_checkpoint["""model_state_dict"""]
				__magic_name__						:						Dict										=state_dict["""decoder.token_embedding.weight"""]
				remove_ignore_keys_(lowerCamelCase )
				rename_keys(lowerCamelCase )
				__magic_name__						:						Optional[Any]										=True
				__magic_name__						:						Dict										=state_dict["""decoder.layers.0.fc1.weight"""].shape[0]

				__magic_name__						:						Dict										=WhisperConfig(
				    vocab_size=dimensions["""n_vocab"""]						,  encoder_ffn_dim=lowerCamelCase						,  decoder_ffn_dim=lowerCamelCase						,  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"""]						,  )

				__magic_name__						:						Tuple										=WhisperForConditionalGeneration(lowerCamelCase )
				__magic_name__					,			__magic_name__						:						Union[str, Any]										=model.model.load_state_dict(lowerCamelCase						,  strict=lowerCamelCase )
				if len(lowerCamelCase ) > 0 and not set(lowerCamelCase ) <= {
				    "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:
								__magic_name__						:						List[Any]										=make_linear_from_emb(model.model.decoder.embed_tokens )
				else:
								__magic_name__						:						Optional[Any]										=proj_out_weights

				model.save_pretrained(lowerCamelCase )


if __name__ == "__main__":
							UpperCAmelCase_		:					Union[str, Any]          =  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_		:					Dict          =  parser.parse_args()

							convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)










import unittest

from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script




@require_cpu
class 		__A   (      unittest.TestCase      ):

						def      A__    (		self :Tuple			):
										'''simple docstring'''

										debug_launcher(test_script.main			)


						def      A__    (		self :Dict			):
										'''simple docstring'''

										debug_launcher(test_ops.main			)




