




import logging
import pathlib
import re
import sys
from dataclasses import dataclass, field
from typing import Any, Callable, Dict, List, Optional, Set, Union

import datasets
import librosa
import numpy as np
import torch
from lang_trans import arabic
from packaging import version
from torch import nn

from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaProcessor,
    is_apex_available,
    trainer_utils,
)


if is_apex_available():
		from apex import amp

if version.parse(version.parse(torch.__version__).base_version) >= version.parse('1.6'):
		__lowerCAmelCase						:							Tuple			     =						True
		from torch.cuda.amp import autocast


__lowerCAmelCase						:							Any			     =						logging.getLogger(__name__)




@dataclass
class        snake_case__				:
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			:				str									=   field(
				    metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}  )
				SCREAMING_SNAKE_CASE_			:				Optional[str]									=   field(
				    default=_a       ,			metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}       ,			)
				SCREAMING_SNAKE_CASE_			:				Optional[bool]									=   field(
				    default=_a       ,			metadata={"""help""": """Whether to freeze the feature extractor layers of the model."""}  )
				SCREAMING_SNAKE_CASE_			:				Optional[bool]									=   field(
				    default=_a       ,			metadata={"""help""": """Whether to log verbose messages or not."""}       ,			)




def      __magic_name__							(					A						:   int,    A						:   str  ):






			'''simple docstring'''







			logging.basicConfig(
			    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",    datefmt="%m/%d/%Y %H:%M:%S",    handlers=[logging.StreamHandler(sys.stdout  )],    )
			a       =     logging.WARNING
			if model_args.verbose_logging:
						a       =     logging.DEBUG
			elif trainer_utils.is_main_process(training_args.local_rank  ):
						a       =     logging.INFO
			logger.setLevel(A  )




@dataclass
class        snake_case__				:
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			:				str									=   field(
				    default=_a       ,			metadata={"""help""": """The name of the dataset to use (via the datasets library)."""}  )
				SCREAMING_SNAKE_CASE_			:				Optional[str]									=   field(
				    default=_a       ,			metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""}  )
				SCREAMING_SNAKE_CASE_			:				Optional[str]									=   field(
				    default="""train"""       ,			metadata={
				        """help""": """The name of the training data set split to use (via the datasets library). Defaults to 'train'"""
				    }       ,			)
				SCREAMING_SNAKE_CASE_			:				Optional[str]									=   field(
				    default="""validation"""       ,			metadata={
				        """help""": (
				            """The name of the validation data set split to use (via the datasets library). Defaults to 'validation'"""
				        )
				    }       ,			)
				SCREAMING_SNAKE_CASE_			:				Optional[str]									=   field(
				    default="""text"""       ,			metadata={"""help""": """Column in the dataset that contains label (target text). Defaults to 'text'"""}       ,			)
				SCREAMING_SNAKE_CASE_			:				Optional[str]									=   field(
				    default="""file"""       ,			metadata={"""help""": """Column in the dataset that contains speech file path. Defaults to 'file'"""}       ,			)
				SCREAMING_SNAKE_CASE_			:				Optional[bool]									=   field(
				    default=_a       ,			metadata={"""help""": """Resample loaded audio to target feature extractor's sampling rate or not."""}       ,			)
				SCREAMING_SNAKE_CASE_			:				Optional[float]									=   field(
				    default=_a       ,			metadata={"""help""": """Filters out examples longer than specified. Defaults to no filtering."""}       ,			)
				SCREAMING_SNAKE_CASE_			:				Optional[str]									=   field(
				    default="""librispeech"""       ,			metadata={
				        """help""": (
				            """Orthography used for normalization and tokenization: 'librispeech' (default), 'timit', or"""
				            """ 'buckwalter'."""
				        )
				    }       ,			)
				SCREAMING_SNAKE_CASE_			:				bool									=   field(
				    default=_a       ,			metadata={"""help""": """Overwrite the cached preprocessed datasets or not."""}  )
				SCREAMING_SNAKE_CASE_			:				Optional[int]									=   field(
				    default=_a       ,			metadata={"""help""": """The number of processes to use for the preprocessing."""}       ,			)




@dataclass
class        snake_case__				:
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			:				bool									=   False
				SCREAMING_SNAKE_CASE_			:				Optional[str]									=   None
				SCREAMING_SNAKE_CASE_			:				Optional[str]									=   "|"
				SCREAMING_SNAKE_CASE_			:				Optional[Dict[str, str]]									=   field(default_factory=_a  )
				SCREAMING_SNAKE_CASE_			:				Optional[Set[str]]									=   field(default_factory=_a  )
				SCREAMING_SNAKE_CASE_			:				Optional[Callable[[str], str]]									=   None





				@classmethod
				def 				__UpperCAmelCase				( cls							:	Union[str, Any]   , __lowerCamelCase							:	List[str]				)   ->	Optional[int]:
							if name == "librispeech":
										return cls()
							if name == "timit":
										return cls(
										    do_lower_case=snake_case_   , translation_table=str.maketrans({"-": " "}				)   , )
							if name == "buckwalter":
										a       =     {
										    "-": " ",  # sometimes used to represent pauses
										    "^": "v",  # fixing "tha" in arabic_speech_corpus dataset
										}
										return cls(
										    vocab_file=pathlib.Path(snake_case_				).parent.joinpath("vocab/buckwalter.json"				)   , word_delimiter_token="/"   , translation_table=str.maketrans(snake_case_				)   , words_to_remove={"sil"}   , untransliterator=arabic.buckwalter.untransliterate   , )
							raise ValueError(f"""Unsupported orthography: \'{name}\'."""				)





				def 				__UpperCAmelCase				( self							:	Tuple   , __lowerCamelCase							:	Any				)   ->	str:
							# TODO(elgeish) return a pipeline (e.g., from jiwer) instead? Or rely on branch predictor as is
							if len(self.translation_table				) > 0:
										a       =     text.translate(self.translation_table				)
							if len(self.words_to_remove				) == 0:
										a       =     " ".join(text.split()				)  # clean up whitespaces
							else:
										a       =     " ".join(w for w in text.split() if w not in self.words_to_remove				)  # and clean up whilespaces
							return text





				def 				__UpperCAmelCase				( self							:	Union[str, Any]   , __lowerCamelCase							:	Any				)   ->	WavaVecaProcessor:
							a       =     WavaVecaFeatureExtractor.from_pretrained(
							    model_args.model_name_or_path   , cache_dir=model_args.cache_dir				)
							if self.vocab_file:
										a       =     WavaVecaCTCTokenizer(
										    self.vocab_file   , cache_dir=model_args.cache_dir   , do_lower_case=self.do_lower_case   , word_delimiter_token=self.word_delimiter_token   , )
							else:
										a       =     WavaVecaCTCTokenizer.from_pretrained(
										    model_args.model_name_or_path   , cache_dir=model_args.cache_dir   , do_lower_case=self.do_lower_case   , word_delimiter_token=self.word_delimiter_token   , )
							return WavaVecaProcessor(snake_case_   , snake_case_				)




@dataclass
class        snake_case__				:
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			:				WavaVecaProcessor
				SCREAMING_SNAKE_CASE_			:				Union[bool, str]									=   True
				SCREAMING_SNAKE_CASE_			:				Optional[int]									=   None
				SCREAMING_SNAKE_CASE_			:				Optional[int]									=   None
				SCREAMING_SNAKE_CASE_			:				Optional[int]									=   None
				SCREAMING_SNAKE_CASE_			:				Optional[int]									=   None





				def __call__( self							:	List[Any]   , __lowerCamelCase							:	Optional[int]				)   ->	Dict[str, torch.Tensor]:
							# split inputs and labels since they have to be of different lenghts and need
							# different padding methods
							a       =     [{"input_values": feature["input_values"]} for feature in features]
							a       =     [{"input_ids": feature["labels"]} for feature in features]

							a       =     self.processor.pad(
							    snake_case_   , padding=self.padding   , max_length=self.max_length   , pad_to_multiple_of=self.pad_to_multiple_of   , return_tensors="pt"   , )
							a       =     self.processor.pad(
							    labels=snake_case_   , padding=self.padding   , max_length=self.max_length_labels   , pad_to_multiple_of=self.pad_to_multiple_of_labels   , return_tensors="pt"   , )

							# replace padding with -100 to ignore loss correctly
							a       =     labels_batch["input_ids"].masked_fill(labels_batch.attention_mask.ne(1				)   , -1_00				)

							a       =     labels

							return batch




class        snake_case__				(_a  ):
				"""simple docstring"""



				def 				__UpperCAmelCase				( self							:	Optional[int]   , __lowerCamelCase							:	List[str]   , __lowerCamelCase							:	List[str]				)   ->	torch.Tensor:
							model.train()
							a       =     self._prepare_inputs(snake_case_				)

							if self.use_amp:
										with autocast():
													a       =     self.compute_loss(snake_case_   , snake_case_				)
							else:
										a       =     self.compute_loss(snake_case_   , snake_case_				)

							if self.args.n_gpu > 1:
										if model.module.config.ctc_loss_reduction == "mean":
													a       =     loss.mean()
										elif model.module.config.ctc_loss_reduction == "sum":
													a       =     loss.sum() / (inputs["labels"] >= 0).sum()
										else:
													raise ValueError(f"""{model.config.ctc_loss_reduction} is not valid. Choose one of [\'mean\', \'sum\']"""				)

							if self.args.gradient_accumulation_steps > 1:
										a       =     loss / self.args.gradient_accumulation_steps

							if self.use_amp:
										self.scaler.scale(snake_case_				).backward()
							elif self.use_apex:
										with amp.scale_loss(snake_case_   , self.optimizer				) as scaled_loss:
													scaled_loss.backward()
							elif self.deepspeed:
										self.deepspeed.backward(snake_case_				)
							else:
										loss.backward()

							return loss.detach()




def      __magic_name__							(					):






			'''simple docstring'''







			a       =     HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)  )

			a       ,						a       ,						a       =     parser.parse_args_into_dataclasses()
			configure_logger(A,    A  )

			a       =     Orthography.from_name(data_args.orthography.lower()  )
			a       =     orthography.create_processor(A  )
			a       =     WavaVecaForCTC.from_pretrained(
			    model_args.model_name_or_path,    cache_dir=model_args.cache_dir,    gradient_checkpointing=training_args.gradient_checkpointing,    vocab_size=len(processor.tokenizer  ),    )

			a       =     datasets.load_dataset(
			    data_args.dataset_name,    data_args.dataset_config_name,    split=data_args.train_split_name  )
			a       =     datasets.load_dataset(
			    data_args.dataset_name,    data_args.dataset_config_name,    split=data_args.validation_split_name  )

			a       =     datasets.load_metric("wer"  )
			a       =     processor.feature_extractor.sampling_rate if data_args.target_feature_extractor_sampling_rate else None
			a       =     "".join(t for t in processor.tokenizer.get_vocab().keys() if len(A  ) == 1  )
			a       =     re.compile(  # remove characters not in vocabulary
			    RF"""[^\s{re.escape(A  )}]""",    flags=re.IGNORECASE if processor.tokenizer.do_lower_case else 0,    )
			a       =     []

			def prepare_example(A						:   Optional[Any]  ):  # TODO(elgeish) make use of multiprocessing?
						a       ,						a       =     librosa.load(example[data_args.speech_file_column],    sr=A  )
						if data_args.max_duration_in_seconds is not None:
									a       =     len(example["speech"]  ) / example["sampling_rate"]
						# Normalize and clean up text; order matters!
						a       =     orthography.preprocess_for_training(example[data_args.target_text_column]  )
						a       =     vocabulary_text_cleaner.sub("",    A  )
						if updated_text != example[data_args.target_text_column]:
									text_updates.append((example[data_args.target_text_column], updated_text)  )
									a       =     updated_text
						return example

			a       =     train_dataset.map(A,    remove_columns=[data_args.speech_file_column]  )
			a       =     val_dataset.map(A,    remove_columns=[data_args.speech_file_column]  )

			if data_args.max_duration_in_seconds is not None:

						def filter_by_max_duration(A						:   Optional[int]  ):
									return example["duration_in_seconds"] <= data_args.max_duration_in_seconds

						a       =     len(A  )
						a       =     len(A  )
						a       =     train_dataset.filter(A,    remove_columns=["duration_in_seconds"]  )
						a       =     val_dataset.filter(A,    remove_columns=["duration_in_seconds"]  )
						if len(A  ) > old_train_size:
									logger.warning(
									    F"""Filtered out {len(A  ) - old_train_size} train example(s) longer than"""
									    F""" {data_args.max_duration_in_seconds} second(s)."""  )
						if len(A  ) > old_val_size:
									logger.warning(
									    F"""Filtered out {len(A  ) - old_val_size} validation example(s) longer than"""
									    F""" {data_args.max_duration_in_seconds} second(s)."""  )
			logger.info(F"""Split sizes: {len(A  )} train and {len(A  )} validation."""  )

			logger.warning(F"""Updated {len(A  )} transcript(s) using \'{data_args.orthography}\' orthography rules."""  )
			if logger.isEnabledFor(logging.DEBUG  ):
						for original_text, updated_text in text_updates:
									logger.debug(F"""Updated text: \"{original_text}\" -> \"{updated_text}\""""  )
			a       =     None

			def prepare_dataset(A						:   List[str]  ):
						# check that all files have the correct sampling rate
						assert (
						    len(set(batch["sampling_rate"]  )  ) == 1
						), F"""Make sure all inputs have the same sampling rate of {processor.feature_extractor.sampling_rate}."""

						a       =     processor(
						    audio=batch["speech"],    text=batch[data_args.target_text_column],    sampling_rate=batch["sampling_rate"][0]  )
						batch.update(A  )
						return batch

			a       =     train_dataset.map(
			    A,    batch_size=training_args.per_device_train_batch_size,    batched=A,    num_proc=data_args.preprocessing_num_workers,    )
			a       =     val_dataset.map(
			    A,    batch_size=training_args.per_device_train_batch_size,    batched=A,    num_proc=data_args.preprocessing_num_workers,    )

			a       =     DataCollatorCTCWithPadding(processor=A,    padding=A  )

			def compute_metrics(A						:   List[str]  ):
						a       =     pred.predictions
						a       =     np.argmax(A,    axis=-1  )

						a       =     processor.tokenizer.pad_token_id

						a       =     processor.batch_decode(A  )
						# we do not want to group tokens when computing the metrics
						a       =     processor.batch_decode(pred.label_ids,    group_tokens=A  )
						if logger.isEnabledFor(logging.DEBUG  ):
									for reference, predicted in zip(A,    A  ):
												logger.debug(F"""reference: \"{reference}\""""  )
												logger.debug(F"""predicted: \"{predicted}\""""  )
												if orthography.untransliterator is not None:
															logger.debug(F"""reference (untransliterated): \"{orthography.untransliterator(A  )}\""""  )
															logger.debug(F"""predicted (untransliterated): \"{orthography.untransliterator(A  )}\""""  )

						a       =     wer_metric.compute(predictions=A,    references=A  )

						return {"wer": wer}

			if model_args.freeze_feature_extractor:
						model.freeze_feature_extractor()

			a       =     CTCTrainer(
			    model=A,    data_collator=A,    args=A,    compute_metrics=A,    train_dataset=A,    eval_dataset=A,    tokenizer=processor.feature_extractor,    )

			trainer.train()


if __name__ == "__main__":
		main()














"""simple docstring"""





import argparse
import os
from os.path import isfile

import torch

from transformers import GPTaConfig







def 	lowerCAmelCase_(			SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE=0       )       ->						str:
    """simple docstring"""

    if name is None:
        UpperCamelCase__								=     None
    else:
        UpperCamelCase__								=     '.' * max(0			,						spaces - 2       ) + '# {:' + str(50 - spaces       ) + 's}'
        UpperCamelCase__								=     fmt.format(SCREAMING_SNAKE_CASE       )

    # Print and recurse (if needed).
    if isinstance(SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       ):
        if msg is not None:
            print(SCREAMING_SNAKE_CASE       )
        for k in val.keys():
            recursive_print(SCREAMING_SNAKE_CASE			,						val[k]			,						spaces + 2       )
    elif isinstance(SCREAMING_SNAKE_CASE			,						torch.Tensor       ):
        print(SCREAMING_SNAKE_CASE			,						':'			,						val.size()       )
    else:
        print(SCREAMING_SNAKE_CASE			,						':'			,						SCREAMING_SNAKE_CASE       )







def 	lowerCAmelCase_(			SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       )       ->						Tuple:
    """simple docstring"""

    UpperCamelCase__								=     param.size()
    # other versions store [num_heads * num_splits * hidden_size, :]
    UpperCamelCase__								=     (num_heads, num_splits, hidden_size) + input_shape[1:]
    UpperCamelCase__								=     param.view(*SCREAMING_SNAKE_CASE       )
    UpperCamelCase__								=     param.transpose(0			,						1       ).contiguous()
    UpperCamelCase__								=     param.view(*SCREAMING_SNAKE_CASE       )
    return param







def 	lowerCAmelCase_(			SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       )       ->						Any:
    """simple docstring"""

    UpperCamelCase__								=     config.n_positions
    UpperCamelCase__								=     config.n_layer
    UpperCamelCase__								=     config.vocab_size
    UpperCamelCase__								=     config.n_head
    UpperCamelCase__								=     config.n_embd // config.n_head

    UpperCamelCase__								=     sd_megatron['model.language_model.embedding.word_embeddings.weight'][:vocab_size, :]
    UpperCamelCase__								=     {
        'transformer.wte.weight': word_embeddings,
        'transformer.wpe.weight': sd_megatron['model.language_model.embedding.position_embeddings.weight'],
        'transformer.ln_f.weight': sd_megatron['model.language_model.encoder.final_layernorm.weight'],
        'transformer.ln_f.bias': sd_megatron['model.language_model.encoder.final_layernorm.bias'],
    }

    UpperCamelCase__								=     'model.language_model.encoder.layers.'
    for i in range(SCREAMING_SNAKE_CASE       ):
        UpperCamelCase__								=     torch.tril(torch.ones((n_positions, n_positions)			,						dtype=torch.bool       )       )
        UpperCamelCase__								=     causal_mask.view(1			,						1			,						SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       )
        UpperCamelCase__								=     causal_mask
        UpperCamelCase__								=     torch.tensor(-1E4			,						dtype=torch.bfloataa       )

        UpperCamelCase__								=     sd_megatron[F'{pf}{i}.input_layernorm.weight']
        UpperCamelCase__								=     sd_megatron[F'{pf}{i}.input_layernorm.bias']

        UpperCamelCase__								=     sd_megatron[F'{pf}{i}.self_attention.query_key_value.weight']
        UpperCamelCase__								=     fix_query_key_value_ordering(SCREAMING_SNAKE_CASE			,						3			,						SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       )
        UpperCamelCase__								=     vala.transpose(0			,						1       ).contiguous()

        UpperCamelCase__								=     sd_megatron[F'{pf}{i}.self_attention.query_key_value.bias']
        UpperCamelCase__								=     fix_query_key_value_ordering(SCREAMING_SNAKE_CASE			,						3			,						SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       )
        UpperCamelCase__								=     vala

        UpperCamelCase__								=     sd_megatron[F'{pf}{i}.self_attention.dense.weight'].transpose(
            0			,						1       )
        UpperCamelCase__								=     sd_megatron[F'{pf}{i}.self_attention.dense.bias']
        UpperCamelCase__								=     sd_megatron[F'{pf}{i}.post_attention_layernorm.weight']
        UpperCamelCase__								=     sd_megatron[F'{pf}{i}.post_attention_layernorm.bias']
        UpperCamelCase__								=     sd_megatron[F'{pf}{i}.mlp.dense_h_to_4h.weight'].transpose(0			,						1       )
        UpperCamelCase__								=     sd_megatron[F'{pf}{i}.mlp.dense_h_to_4h.bias']
        UpperCamelCase__								=     sd_megatron[F'{pf}{i}.mlp.dense_4h_to_h.weight'].transpose(
            0			,						1       )
        UpperCamelCase__								=     sd_megatron[F'{pf}{i}.mlp.dense_4h_to_h.bias']

    # For LM head, transformers' wants the matrix to weight embeddings.
    UpperCamelCase__								=     word_embeddings

    return sd_hf







def 	lowerCAmelCase_(			SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       )       ->						int:
    """simple docstring"""

    UpperCamelCase__								=     6_40_00
    UpperCamelCase__								=     config_megatron['encoder_seq_length']
    UpperCamelCase__								=     config_megatron['hidden_size']
    UpperCamelCase__								=     config_megatron['num_layers']
    UpperCamelCase__								=     config_megatron['num_attention_heads']
    UpperCamelCase__								=     config_megatron['ffn_hidden_size']
    UpperCamelCase__								=     'gelu'
    UpperCamelCase__								=     0.1
    UpperCamelCase__								=     0.1
    UpperCamelCase__								=     0.1
    UpperCamelCase__								=     config_megatron['layernorm_epsilon']  # 1e-5
    UpperCamelCase__								=     config_megatron['init_method_std']  # 0.02
    UpperCamelCase__								=     config_megatron['apply_query_key_layer_scaling']  # True
    UpperCamelCase__								=     True
    UpperCamelCase__								=     True

    # This identifies the 6.7B (7B) model which uses a different tokenizer
    if config_megatron["hidden_size"] == 40_96:
        UpperCamelCase__								=     1  # <|endoftext|>
        UpperCamelCase__								=     1  # <|endoftext|>
        UpperCamelCase__								=     0  # <unk>
    else:
        UpperCamelCase__								=     2  # <s>
        UpperCamelCase__								=     3  # <|endoftext|>
        UpperCamelCase__								=     0  # <pad>

    return config_hf







def 	lowerCAmelCase_(			SCREAMING_SNAKE_CASE       )       ->						str:
    """simple docstring"""

    print(SCREAMING_SNAKE_CASE       )

    UpperCamelCase__								=     args.checkpoint_path
    UpperCamelCase__								=     args.save_path
    if isfile(SCREAMING_SNAKE_CASE       ):
        raise FileNotFoundError(F'ERROR! could not find file {checkpoint_path}'       )

    # Load the model.
    UpperCamelCase__								=     torch.load(SCREAMING_SNAKE_CASE			,						map_location='cpu'       )

    # Load the config.
    UpperCamelCase__								=     checkpoint['hyper_parameters']['cfg']
    UpperCamelCase__								=     GPTaConfig()
    UpperCamelCase__								=     copy_config(config_hf=SCREAMING_SNAKE_CASE			,						config_megatron=SCREAMING_SNAKE_CASE       )
    UpperCamelCase__								=     ['GPT2LMHeadModel']

    UpperCamelCase__								=     checkpoint['state_dict']

    # Convert.
    print('Converting'       )
    UpperCamelCase__								=     convert_megatron_checkpoint(SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       )

    # Print the structure of converted state dict.
    if args.print_checkpoint_structure:
        recursive_print(SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       )

    UpperCamelCase__								=     'GPTSw3Tokenizer'

    # Store the config to file.
    print('Saving config'       )
    config_hf.save_pretrained(SCREAMING_SNAKE_CASE       )

    # Store the state_dict to file.
    UpperCamelCase__								=     os.path.join(SCREAMING_SNAKE_CASE			,						'pytorch_model.bin'       )
    print(F'Saving checkpoint to "{output_checkpoint_file}"'       )
    torch.save(SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       )


if __name__ == "__main__":
  A__       :			Optional[Any]=						argparse.ArgumentParser()
  parser.add_argument(
      """--checkpoint_path""",
      type=str,
      required=True,
      help="""e.g. megatron_gpt--val_loss=2.42-step=38000-consumed_samples=54720000""",
  )
  parser.add_argument("""--save_path""", type=str, required=True, help="""e.g. /home/user/gpt-sw3/hf""")
  parser.add_argument("""--print-checkpoint-structure""", action="""store_true""")
  A__       :			Any=						parser.parse_args()
  main(_args)




