


'''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


snake_case_						:			Optional[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(   a__):
				_SCREAMING_SNAKE_CASE			  =["""layers""", """blocks"""]
				for k in ignore_keys:
								state_dict.pop(__lowerCAmelCase	,__lowerCAmelCase)


snake_case_						:			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(   a__):
				_SCREAMING_SNAKE_CASE			  =list(s_dict.keys())
				for key in keys:
								_SCREAMING_SNAKE_CASE			  =key
								for k, v in WHISPER_MAPPING.items():
												if k in key:
																_SCREAMING_SNAKE_CASE			  =new_key.replace(__lowerCAmelCase	,__lowerCAmelCase)

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

								_SCREAMING_SNAKE_CASE			  =s_dict.pop(__lowerCAmelCase)
				return s_dict







def 						lowerCamelCase(   a__):
				_SCREAMING_SNAKE_CASE			  =emb.weight.shape
				_SCREAMING_SNAKE_CASE			  =nn.Linear(__lowerCAmelCase	,__lowerCAmelCase	,bias=__lowerCAmelCase)
				_SCREAMING_SNAKE_CASE			  =emb.weight.data
				return lin_layer







def 						lowerCamelCase(   a__	,a__):
				os.makedirs(__lowerCAmelCase	,exist_ok=__lowerCAmelCase)
				_SCREAMING_SNAKE_CASE			  =os.path.basename(__lowerCAmelCase)

				_SCREAMING_SNAKE_CASE			  =url.split('''/''')[-2]
				_SCREAMING_SNAKE_CASE			  =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):
								_SCREAMING_SNAKE_CASE			  =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:
																_SCREAMING_SNAKE_CASE			  =source.read(8192)
																if not buffer:
																				break

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

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

				_SCREAMING_SNAKE_CASE			  =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''']	,)

				_SCREAMING_SNAKE_CASE			  =WhisperForConditionalGeneration(__lowerCAmelCase)
				_SCREAMING_SNAKE_CASE			  =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:
								_SCREAMING_SNAKE_CASE			  =make_linear_from_emb(model.model.decoder.embed_tokens)
				else:
								_SCREAMING_SNAKE_CASE			  =proj_out_weights

				model.save_pretrained(__lowerCAmelCase)


if __name__ == "__main__":
							snake_case_						:			Tuple													=							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_						:			int													=							parser.parse_args()

							convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)



import os
import sys
import tempfile

import torch

from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment







def 						lowerCamelCase(   a__	,a__=()	,a__=None	,a__="no"	,a__="29500"):
				_SCREAMING_SNAKE_CASE			  =False
				_SCREAMING_SNAKE_CASE			  =False
				if any(key.startswith('''KAGGLE''') for key in os.environ.keys()):
								_SCREAMING_SNAKE_CASE			  =True
				elif "IPython" in sys.modules:
								_SCREAMING_SNAKE_CASE			  ='''google.colab''' in str(sys.modules['''IPython'''].get_ipython())

				try:
								_SCREAMING_SNAKE_CASE			  =PrecisionType(mixed_precision.lower())
				except ValueError:
								raise ValueError(
								    f"Unknown mixed_precision mode: {args.mixed_precision.lower()}. Choose between {PrecisionType.list()}.")

				if (in_colab or in_kaggle) and (os.environ.get('''TPU_NAME'''	,a__) is not None):
								# TPU launch
								import torch_xla.distributed.xla_multiprocessing as xmp

								if len(AcceleratorState._shared_state) > 0:
												raise ValueError(
												    '''To train on TPU in Colab or Kaggle Kernel, the `Accelerator` should only be initialized inside '''
												    '''your training function. Restart your notebook and make sure no cells initializes an '''
												    '''`Accelerator`.''')
								if num_processes is None:
												_SCREAMING_SNAKE_CASE			  =8

								_SCREAMING_SNAKE_CASE			  =PrepareForLaunch(a__	,distributed_type='''TPU''')
								print(f"Launching a training on {num_processes} TPU cores.")
								xmp.spawn(a__	,args=a__	,nprocs=a__	,start_method='''fork''')
				elif in_colab:
								# No need for a distributed launch otherwise as it's either CPU or one GPU.
								if torch.cuda.is_available():
												print('''Launching training on one GPU.''')
								else:
												print('''Launching training on one CPU.''')
								function(*a__)
				else:
								if num_processes is None:
												raise ValueError(
												    '''You have to specify the number of GPUs you would like to use, add `num_processes=...` to your call.''')

								if num_processes > 1:
												# Multi-GPU launch
												from torch.multiprocessing import start_processes
												from torch.multiprocessing.spawn import ProcessRaisedException

												if len(AcceleratorState._shared_state) > 0:
																raise ValueError(
																    '''To launch a multi-GPU training from your notebook, the `Accelerator` should only be initialized '''
																    '''inside your training function. Restart your notebook and make sure no cells initializes an '''
																    '''`Accelerator`.''')

												if torch.cuda.is_initialized():
																raise ValueError(
																    '''To launch a multi-GPU training from your notebook, you need to avoid running any instruction '''
																    '''using `torch.cuda` in any cell. Restart your notebook and make sure no cells use any CUDA '''
																    '''function.''')

												# torch.distributed will expect a few environment variable to be here. We set the ones common to each
												# process here (the other ones will be set be the launcher).
												with patch_environment(
												    world_size=a__	,master_addr='''127.0.01'''	,master_port=a__	,mixed_precision=a__):
																_SCREAMING_SNAKE_CASE			  =PrepareForLaunch(a__	,distributed_type='''MULTI_GPU''')
																print(f"Launching training on {num_processes} GPUs.")
																try:
																				start_processes(a__	,args=a__	,nprocs=a__	,start_method='''fork''')
																except ProcessRaisedException as e:
																				if "Cannot re-initialize CUDA in forked subprocess" in e.args[0]:
																								raise RuntimeError(
																								    '''CUDA has been initialized before the `notebook_launcher` could create a forked subprocess. '''
																								    '''This likely stems from an outside import causing issues once the `notebook_launcher()` is called. '''
																								    '''Please review your imports and test them when running the `notebook_launcher()` to identify '''
																								    '''which one is problematic.''') from e

								else:
												# No need for a distributed launch otherwise as it's either CPU, GPU or MPS.
												if is_mps_available():
																_SCREAMING_SNAKE_CASE			  ='''1'''
																print('''Launching training on MPS.''')
												elif torch.cuda.is_available():
																print('''Launching training on one GPU.''')
												else:
																print('''Launching training on CPU.''')
												function(*a__)







def 						lowerCamelCase(   a__	,a__=()	,a__=2):
				from torch.multiprocessing import start_processes

				with tempfile.NamedTemporaryFile() as tmp_file:
								# torch.distributed will expect a few environment variable to be here. We set the ones common to each
								# process here (the other ones will be set be the launcher).
								with patch_environment(
								    world_size=a__	,master_addr='''127.0.01'''	,master_port='''29500'''	,accelerate_mixed_precision='''no'''	,accelerate_debug_rdv_file=tmp_file.name	,accelerate_use_cpu='''yes'''	,):
												_SCREAMING_SNAKE_CASE			  =PrepareForLaunch(a__	,debug=a__)
												start_processes(a__	,args=a__	,nprocs=a__	,start_method='''fork''')