
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


A		: 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 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	List[str]       )  ->     Any:
						_lowercase					       =					["""layers""", """blocks"""]
						for k in ignore_keys:
												state_dict.pop(snake_case__		,   snake_case__       )


A		: 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 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	str       )  ->     Tuple:
						_lowercase					       =					list(s_dict.keys()       )
						for key in keys:
												_lowercase					       =					key
												for k, v in WHISPER_MAPPING.items():
																		if k in key:
																								_lowercase					       =					new_key.replace(snake_case__		,   snake_case__       )

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

												_lowercase					       =					s_dict.pop(snake_case__       )
						return s_dict




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	Any       )  ->     Dict:
						_lowercase					       =					emb.weight.shape
						_lowercase					       =					nn.Linear(snake_case__		,   snake_case__		,   bias=snake_case__       )
						_lowercase					       =					emb.weight.data
						return lin_layer




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	Optional[Any]		,   SCREAMING_SNAKE_CASE_	:	Optional[int]       )  ->     Optional[Any]:
						os.makedirs(snake_case__		,   exist_ok=snake_case__       )
						_lowercase					       =					os.path.basename(snake_case__       )

						_lowercase					       =					url.split("""/"""       )[-2]
						_lowercase					       =					os.path.join(snake_case__		,   snake_case__       )

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

						if os.path.isfile(snake_case__       ):
												_lowercase					       =					open(snake_case__		,   """rb"""       ).read()
												if hashlib.shaaaa(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(snake_case__       ) as source, open(snake_case__		,   """wb"""       ) as output:
												with tqdm(
												    total=int(source.info().get("""Content-Length"""       )       )		,   ncols=80		,   unit="""iB"""		,   unit_scale=snake_case__		,   unit_divisor=10_24       ) as loop:
																		while True:
																								_lowercase					       =					source.read(81_92       )
																								if not buffer:
																														break

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

						_lowercase					       =					open(snake_case__		,   """rb"""       ).read()
						if hashlib.shaaaa(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 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	int		,   SCREAMING_SNAKE_CASE_	:	str       )  ->     Optional[int]:
						if ".pt" not in checkpoint_path:
												_lowercase					       =					_download(_MODELS[checkpoint_path]       )
						else:
												_lowercase					       =					torch.load(snake_case__		,   map_location="""cpu"""       )
						_lowercase					       =					original_checkpoint["""dims"""]
						_lowercase					       =					original_checkpoint["""model_state_dict"""]
						_lowercase					       =					state_dict["""decoder.token_embedding.weight"""]
						remove_ignore_keys_(snake_case__       )
						rename_keys(snake_case__       )
						_lowercase					       =					True
						_lowercase					       =					state_dict["""decoder.layers.0.fc1.weight"""].shape[0]

						_lowercase					       =					WhisperConfig(
						    vocab_size=dimensions["""n_vocab"""]		,   encoder_ffn_dim=snake_case__		,   decoder_ffn_dim=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"""]		,   )

						_lowercase					       =					WhisperForConditionalGeneration(snake_case__       )
						_lowercase					       =					model.model.load_state_dict(snake_case__		,   strict=snake_case__       )
						if len(snake_case__       ) > 0 and not set(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:
												_lowercase					       =					make_linear_from_emb(model.model.decoder.embed_tokens       )
						else:
												_lowercase					       =					proj_out_weights

						model.save_pretrained(snake_case__       )


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

		convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)







"""simple docstring"""
import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate
# and perform gradient accumulation
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


_lowerCAmelCase       =				16
_lowerCAmelCase       =				32






def       __UpperCamelCase						(		snake_case__ , snake_case__ = 16       ):
	A_		:    List[str]						     = AutoTokenizer.from_pretrained("""bert-base-cased"""       )
	A_		:    Optional[Any]						     = load_dataset("""glue""" , """mrpc"""       )

	def tokenize_function(snake_case__       ):
		# max_length=None => use the model max length (it's actually the default)
		A_		:    Tuple						     = tokenizer(examples["""sentence1"""] , examples["""sentence2"""] , truncation=snake_case__ , max_length=snake_case__       )
		return outputs

	# Apply the method we just defined to all the examples in all the splits of the dataset
	# starting with the main process first:
	with accelerator.main_process_first():
		A_		:    str						     = datasets.map(
		    snake_case__ , batched=snake_case__ , remove_columns=["""idx""", """sentence1""", """sentence2"""] , )

	# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
	# transformers library
	A_		:    int						     = tokenized_datasets.rename_column("""label""" , """labels"""       )

	def collate_fn(snake_case__       ):
		# On TPU it's best to pad everything to the same length or training will be very slow.
		A_		:    Optional[Any]						     = 128 if accelerator.distributed_type == DistributedType.TPU else None
		# When using mixed precision we want round multiples of 8/16
		if accelerator.mixed_precision == "fp8":
			A_		:    Tuple						     = 16
		elif accelerator.mixed_precision != "no":
			A_		:    List[Any]						     = 8
		else:
			A_		:    List[Any]						     = None

		return tokenizer.pad(
		    snake_case__ , padding="""longest""" , max_length=snake_case__ , pad_to_multiple_of=snake_case__ , return_tensors="""pt""" , )

	# Instantiate dataloaders.
	A_		:    Optional[Any]						     = DataLoader(
	    tokenized_datasets["""train"""] , shuffle=snake_case__ , collate_fn=snake_case__ , batch_size=snake_case__       )
	A_		:    List[str]						     = DataLoader(
	    tokenized_datasets["""validation"""] , shuffle=snake_case__ , collate_fn=snake_case__ , batch_size=snake_case__       )

	return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1":
							from accelerate.test_utils.training import mocked_dataloaders

							_lowerCAmelCase       =				mocked_dataloaders  # noqa: F811






def       __UpperCamelCase						(		snake_case__ , snake_case__       ):
	# For testing only
	if os.environ.get("""TESTING_MOCKED_DATALOADERS""" , snake_case__       ) == "1":
		A_		:    List[Any]						     = 2
	# New Code #
	A_		:    Tuple						     = int(args.gradient_accumulation_steps       )
	# Initialize accelerator
	A_		:    Any						     = Accelerator(
	    cpu=args.cpu , mixed_precision=args.mixed_precision , gradient_accumulation_steps=snake_case__       )
	if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
		raise NotImplementedError(
		    """Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`"""       )
	# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
	A_		:    Union[str, Any]						     = config["""lr"""]
	A_		:    Tuple						     = int(config["""num_epochs"""]       )
	A_		:    List[Any]						     = int(config["""seed"""]       )
	A_		:    Dict						     = int(config["""batch_size"""]       )

	A_		:    Tuple						     = evaluate.load("""glue""" , """mrpc"""       )

	set_seed(snake_case__       )
	A_     ,  A_		:    List[Any]						     = get_dataloaders(snake_case__ , snake_case__       )
	# Instantiate the model (we build the model here so that the seed also control new weights initialization)
	A_		:    Dict						     = AutoModelForSequenceClassification.from_pretrained("""bert-base-cased""" , return_dict=snake_case__       )

	# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
	# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
	# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
	A_		:    Union[str, Any]						     = model.to(accelerator.device       )

	# Instantiate optimizer
	A_		:    Optional[int]						     = AdamW(params=model.parameters() , lr=snake_case__       )

	# Instantiate scheduler
	A_		:    Union[str, Any]						     = get_linear_schedule_with_warmup(
	    optimizer=snake_case__ , num_warmup_steps=100 , num_training_steps=(len(snake_case__       ) * num_epochs) , )

	# Prepare everything
	# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
	# prepare method.
	A_     ,  A_     ,  A_     ,  A_     ,  A_		:    Optional[int]						     = accelerator.prepare(
	    snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__       )

	# Now we train the model
	for epoch in range(snake_case__       ):
		model.train()
		for step, batch in enumerate(snake_case__       ):
			# We could avoid this line since we set the accelerator with `device_placement=True`.
			batch.to(accelerator.device       )
			# New code #
			# We use the new `accumulate` context manager to perform gradient accumulation
			# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
			with accelerator.accumulate(snake_case__       ):
				A_		:    Optional[int]						     = model(**snake_case__       )
				A_		:    Tuple						     = output.loss
				accelerator.backward(snake_case__       )
				optimizer.step()
				lr_scheduler.step()
				optimizer.zero_grad()

		model.eval()
		for step, batch in enumerate(snake_case__       ):
			# We could avoid this line since we set the accelerator with `device_placement=True`.
			batch.to(accelerator.device       )
			with torch.no_grad():
				A_		:    List[str]						     = model(**snake_case__       )
			A_		:    List[str]						     = outputs.logits.argmax(dim=-1       )
			A_     ,  A_		:    List[Any]						     = accelerator.gather_for_metrics((predictions, batch["""labels"""])       )
			metric.add_batch(
			    predictions=snake_case__ , references=snake_case__ , )

		A_		:    str						     = metric.compute()
		# Use accelerator.print to print only on the main process.
		accelerator.print(F"""epoch {epoch}:""" , snake_case__       )






def       __UpperCamelCase						(		):
	A_		:    List[Any]						     = argparse.ArgumentParser(description="""Simple example of training script."""       )
	parser.add_argument(
	    """--mixed_precision""" , type=snake_case__ , default=snake_case__ , choices=["""no""", """fp16""", """bf16""", """fp8"""] , help="""Whether to use mixed precision. Choose"""
	    """between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
	    """and an Nvidia Ampere GPU.""" , )
	# New Code #
	parser.add_argument(
	    """--gradient_accumulation_steps""" , type=snake_case__ , default=1 , help="""The number of minibatches to be ran before gradients are accumulated.""" , )
	parser.add_argument("""--cpu""" , action="""store_true""" , help="""If passed, will train on the CPU."""       )
	A_		:    int						     = parser.parse_args()
	A_		:    Optional[Any]						     = {"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
	training_function(snake_case__ , snake_case__       )


if __name__ == "__main__":
							main()

