




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   :						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 		lowercase__   (		__A:	Tuple				):





					'''simple docstring'''






					__magic_name__							:	int    							=							['''layers''', '''blocks''']
					for k in ignore_keys:
										state_dict.pop(__A							,__A				)


__lowerCamelCase   :						Optional[int]				=			{
    '''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 		lowercase__   (		__A:	List[str]				):





					'''simple docstring'''






					__magic_name__							:	Tuple    							=							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__							:	Any    							=							new_key.replace(__A							,__A				)

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

										__magic_name__							:	Tuple    							=							s_dict.pop(__A				)
					return s_dict







def 		lowercase__   (		__A:	List[str]				):





					'''simple docstring'''






					__magic_name__							,    __magic_name__							:	str    							=							emb.weight.shape
					__magic_name__							:	List[Any]    							=							nn.Linear(__A							,__A							,bias=__A				)
					__magic_name__							:	int    							=							emb.weight.data
					return lin_layer







def 		lowercase__   (		__A:	str							,__A:	str				):





					'''simple docstring'''






					os.makedirs(__A							,exist_ok=__A				)
					__magic_name__							:	Tuple    							=							os.path.basename(__A				)

					__magic_name__							:	int    							=							url.split('''/'''				)[-2]
					__magic_name__							:	Optional[Any]    							=							os.path.join(__A							,__A				)

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

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

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

					__magic_name__							:	Union[str, Any]    							=							open(__A							,'''rb'''				).read()
					if hashlib.shaaaa(__A				).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 		lowercase__   (		__A:	Dict							,__A:	List[str]				):





					'''simple docstring'''






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

					__magic_name__							:	List[Any]    							=							WhisperConfig(
					    vocab_size=dimensions['''n_vocab''']							,encoder_ffn_dim=__A							,decoder_ffn_dim=__A							,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__							:	Union[str, Any]    							=							WhisperForConditionalGeneration(__A				)
					__magic_name__							,    __magic_name__							:	str    							=							model.model.load_state_dict(__A							,strict=__A				)
					if len(__A				) > 0 and not set(__A				) <= {
					    "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__							:	Dict    							=							make_linear_from_emb(model.model.decoder.embed_tokens				)
					else:
										__magic_name__							:	Any    							=							proj_out_weights

					model.save_pretrained(__A				)


if __name__ == "__main__":
	__lowerCamelCase   :						str				=			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   :						Any				=			parser.parse_args()

	convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)









import importlib.util
import json
import os
import warnings
from dataclasses import dataclass, field

import torch

from ..training_args import TrainingArguments
from ..utils import cached_property, is_sagemaker_dp_enabled, logging


__lowerCamelCase   :						List[Any]				=			logging.get_logger(__name__)







def 		lowercase__   (		):





					'''simple docstring'''






					__magic_name__							:	Any    							=							os.getenv('''SM_HP_MP_PARAMETERS'''							,'''{}'''				)
					try:
										# Parse it and check the field "partitions" is included, it is required for model parallel.
										__magic_name__							:	Tuple    							=							json.loads(__A				)
										if "partitions" not in smp_options:
															return False
					except json.JSONDecodeError:
										return False

					# Get the sagemaker specific framework parameters from mpi_options variable.
					__magic_name__							:	int    							=							os.getenv('''SM_FRAMEWORK_PARAMS'''							,'''{}'''				)
					try:
										# Parse it and check the field "sagemaker_distributed_dataparallel_enabled".
										__magic_name__							:	List[Any]    							=							json.loads(__A				)
										if not mpi_options.get('''sagemaker_mpi_enabled'''							,__A				):
															return False
					except json.JSONDecodeError:
										return False
					# Lastly, check if the `smdistributed` module is present.
					return importlib.util.find_spec('''smdistributed'''				) is not None


if is_sagemaker_model_parallel_available():
	import smdistributed.modelparallel.torch as smp

	smp.init()


@dataclass
class    lowerCamelCase    (		_lowerCamelCase						):






			'''simple docstring'''





			UpperCamelCase__								=field(
			    default=''''''				,metadata={'''help''': '''Used by the SageMaker launcher to send mp-specific args. Ignored in SageMakerTrainer'''}				,)

			def 							UpperCAmelCase__       ( self			:   str  )						->   List[str]:
								super().__post_init__()
								warnings.warn(
								    '''`SageMakerTrainingArguments` is deprecated and will be removed in v5 of Transformers. You can use '''
								    '''`TrainingArguments` instead.'''       ,						lowerCamelCase_       ,						)

			@cached_property
			def 							UpperCAmelCase__       ( self			:   int  )						->   "torch.device":
								logger.info('''PyTorch: setting up devices'''  )
								if torch.distributed.is_available() and torch.distributed.is_initialized() and self.local_rank == -1:
													logger.warning(
													    '''torch.distributed process group is initialized, but local_rank == -1. '''
													    '''In order to use Torch DDP, launch your script with `python -m torch.distributed.launch'''  )
								if self.no_cuda:
													__magic_name__							:	Optional[int]    							=							torch.device('''cpu'''  )
													__magic_name__							:	int    							=							0
								elif is_sagemaker_model_parallel_available():
													__magic_name__							:	Any    							=							smp.local_rank()
													__magic_name__							:	int    							=							torch.device('''cuda'''       ,						lowerCamelCase_  )
													__magic_name__							:	Optional[int]    							=							1
								elif is_sagemaker_dp_enabled():
													import smdistributed.dataparallel.torch.torch_smddp  # noqa: F401

													torch.distributed.init_process_group(backend='''smddp'''       ,						timeout=self.ddp_timeout_delta  )
													__magic_name__							:	Optional[int]    							=							int(os.getenv('''SMDATAPARALLEL_LOCAL_RANK'''  )  )
													__magic_name__							:	Optional[int]    							=							torch.device('''cuda'''       ,						self.local_rank  )
													__magic_name__							:	str    							=							1
								elif self.local_rank == -1:
													# if n_gpu is > 1 we'll use nn.DataParallel.
													# If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
													# Explicitly set CUDA to the first (index 0) CUDA device, otherwise `set_device` will
													# trigger an error that a device index is missing. Index 0 takes into account the
													# GPUs available in the environment, so `CUDA_VISIBLE_DEVICES=1,2` with `cuda:0`
													# will use the first GPU in that env, i.e. GPU#1
													__magic_name__							:	List[str]    							=							torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu'''  )
													# Sometimes the line in the postinit has not been run before we end up here, so just checking we're not at
													# the default value.
													__magic_name__							:	Dict    							=							torch.cuda.device_count()
								else:
													# Here, we'll use torch.distributed.
													# Initializes the distributed backend which will take care of synchronizing nodes/GPUs
													if not torch.distributed.is_initialized():
																		torch.distributed.init_process_group(backend='''nccl'''       ,						timeout=self.ddp_timeout_delta  )
													__magic_name__							:	str    							=							torch.device('''cuda'''       ,						self.local_rank  )
													__magic_name__							:	Dict    							=							1

								if device.type == "cuda":
													torch.cuda.set_device(lowerCamelCase_  )

								return device

			@property
			def 							UpperCAmelCase__       ( self			:   List[str]  )						->   int:
								if is_sagemaker_model_parallel_available():
													return smp.dp_size()

								return super().world_size

			@property
			def 							UpperCAmelCase__       ( self			:   Union[str, Any]  )						->   List[str]:
								return not is_sagemaker_model_parallel_available()


			@property
			def 							UpperCAmelCase__       ( self			:   Optional[int]  )						->   Tuple:
								return False



