





import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

from datasets import Dataset, load_dataset

import transformers
from transformers import (
    CONFIG_MAPPING,
    MODEL_FOR_MASKED_LM_MAPPING,
    AutoConfig,
    AutoModelForMaskedLM,
    AutoTokenizer,
    DataCollatorForWholeWordMask,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process


A    :					Optional[int] =      logging.getLogger(__name__)
A    :					List[Any] =      list(MODEL_FOR_MASKED_LM_MAPPING.keys())
A    :					str =      tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)


@dataclass
class   A			:
			'''simple docstring'''




			__lowerCamelCase	:       Optional[str]							=    field(
			    default=SCREAMING_SNAKE_CASE   ,     metadata={
			        '''help''': (
			            '''The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch.'''
			        )
			    }   ,     )
			__lowerCamelCase	:       Optional[str]							=    field(
			    default=SCREAMING_SNAKE_CASE   ,     metadata={'''help''': '''If training from scratch, pass a model type from the list: ''' + ''', '''.join(SCREAMING_SNAKE_CASE    )}   ,     )
			__lowerCamelCase	:       Optional[str]							=    field(
			    default=SCREAMING_SNAKE_CASE   ,     metadata={
			        '''help''': (
			            '''Override some existing default config settings when a model is trained from scratch. Example: '''
			            '''n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index'''
			        )
			    }   ,     )
			__lowerCamelCase	:       Optional[str]							=    field(
			    default=SCREAMING_SNAKE_CASE   ,     metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}    )
			__lowerCamelCase	:       Optional[str]							=    field(
			    default=SCREAMING_SNAKE_CASE   ,     metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}    )
			__lowerCamelCase	:       Optional[str]							=    field(
			    default=SCREAMING_SNAKE_CASE   ,     metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}   ,     )
			__lowerCamelCase	:       bool							=    field(
			    default=SCREAMING_SNAKE_CASE   ,     metadata={'''help''': '''Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'''}   ,     )
			__lowerCamelCase	:       str							=    field(
			    default='''main'''   ,     metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''}   ,     )
			__lowerCamelCase	:       bool							=    field(
			    default=SCREAMING_SNAKE_CASE   ,     metadata={
			        '''help''': (
			            '''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
			            '''with private models).'''
			        )
			    }   ,     )






			def    a_       (			self   :    Any   )	->	Tuple:







					"""simple docstring"""






					if self.config_overrides is not None and (self.config_name is not None or self.model_name_or_path is not None):
							raise ValueError(
							    """--config_overrides can't be used in combination with --config_name or --model_name_or_path"""   )


@dataclass
class   A			:
			'''simple docstring'''




			__lowerCamelCase	:       Optional[str]							=    field(
			    default=SCREAMING_SNAKE_CASE   ,     metadata={'''help''': '''The name of the dataset to use (via the datasets library).'''}    )
			__lowerCamelCase	:       Optional[str]							=    field(
			    default=SCREAMING_SNAKE_CASE   ,     metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}    )
			__lowerCamelCase	:       Optional[str]							=    field(default=SCREAMING_SNAKE_CASE   ,     metadata={'''help''': '''The input training data file (a text file).'''}    )
			__lowerCamelCase	:       Optional[str]							=    field(
			    default=SCREAMING_SNAKE_CASE   ,     metadata={'''help''': '''An optional input evaluation data file to evaluate the perplexity on (a text file).'''}   ,     )
			__lowerCamelCase	:       Optional[str]							=    field(
			    default=SCREAMING_SNAKE_CASE   ,     metadata={'''help''': '''An optional input train ref data file for whole word masking in Chinese.'''}   ,     )
			__lowerCamelCase	:       Optional[str]							=    field(
			    default=SCREAMING_SNAKE_CASE   ,     metadata={'''help''': '''An optional input validation ref data file for whole word masking in Chinese.'''}   ,     )
			__lowerCamelCase	:       bool							=    field(
			    default=SCREAMING_SNAKE_CASE   ,     metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}    )
			__lowerCamelCase	:       Optional[int]							=    field(
			    default=5   ,     metadata={
			        '''help''': '''The percentage of the train set used as validation set in case there\'s no validation split'''
			    }   ,     )
			__lowerCamelCase	:       Optional[int]							=    field(
			    default=SCREAMING_SNAKE_CASE   ,     metadata={
			        '''help''': (
			            '''The maximum total input sequence length after tokenization. Sequences longer '''
			            '''than this will be truncated. Default to the max input length of the model.'''
			        )
			    }   ,     )
			__lowerCamelCase	:       Optional[int]							=    field(
			    default=SCREAMING_SNAKE_CASE   ,     metadata={'''help''': '''The number of processes to use for the preprocessing.'''}   ,     )
			__lowerCamelCase	:       float							=    field(
			    default=0.15   ,     metadata={'''help''': '''Ratio of tokens to mask for masked language modeling loss'''}    )
			__lowerCamelCase	:       bool							=    field(
			    default=SCREAMING_SNAKE_CASE   ,     metadata={
			        '''help''': (
			            '''Whether to pad all samples to `max_seq_length`. '''
			            '''If False, will pad the samples dynamically when batching to the maximum length in the batch.'''
			        )
			    }   ,     )






			def    a_       (			self   :    List[str]   )	->	Optional[int]:







					"""simple docstring"""






					if self.train_file is not None:
							A__        =   self.train_file.split("""."""   )[-1]
							assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file."
					if self.validation_file is not None:
							A__        =   self.validation_file.split("""."""   )[-1]
							assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."







def    __lowerCamelCase					(							__a   :Optional[Any]    ,     __a   :Optional[int]    )		->				str:






		"""simple docstring"""

		with open(__a    ,     """r"""    ,     encoding="""utf-8"""    ) as f:
				A__        =   [json.loads(__a    ) for line in f.read().splitlines() if (len(__a    ) > 0 and not line.isspace())]
		assert len(__a    ) == len(__a    )

		A__        =   {c: dataset[c] for c in dataset.column_names}
		A__        =   refs
		return Dataset.from_dict(__a    )







def    __lowerCamelCase					(							)		->				Optional[int]:






		"""simple docstring"""

		A__        =   HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)    )
		if len(sys.argv    ) == 2 and sys.argv[1].endswith(""".json"""    ):
				# If we pass only one argument to the script and it's the path to a json file,
				# let's parse it to get our arguments.
				A__      ,			A__      ,			A__        =   parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]    )    )
		else:
				A__      ,			A__      ,			A__        =   parser.parse_args_into_dataclasses()

		# Detecting last checkpoint.
		A__        =   None
		if os.path.isdir(training_args.output_dir    ) and training_args.do_train and not training_args.overwrite_output_dir:
				A__        =   get_last_checkpoint(training_args.output_dir    )
				if last_checkpoint is None and len(os.listdir(training_args.output_dir    )    ) > 0:
						raise ValueError(
						    F'Output directory ({training_args.output_dir}) already exists and is not empty. '
						    """Use --overwrite_output_dir to overcome."""    )
				elif last_checkpoint is not None:
						logger.info(
						    F'Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '
						    """the `--output_dir` or add `--overwrite_output_dir` to train from scratch."""    )

    # Setup logging
		logging.basicConfig(
		    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""    ,     datefmt="""%m/%d/%Y %H:%M:%S"""    ,     handlers=[logging.StreamHandler(sys.stdout    )]    ,     )
		logger.setLevel(logging.INFO if is_main_process(training_args.local_rank    ) else logging.WARN    )

		# Log on each process the small summary:
		logger.warning(
		    F'Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'
		    + F'distributed training: {bool(training_args.local_rank != -1    )}, 16-bits training: {training_args.fpaa}'    )
		# Set the verbosity to info of the Transformers logger (on main process only):
		if is_main_process(training_args.local_rank    ):
				transformers.utils.logging.set_verbosity_info()
				transformers.utils.logging.enable_default_handler()
				transformers.utils.logging.enable_explicit_format()
		logger.info("""Training/evaluation parameters %s"""    ,     __a    )

		# Set seed before initializing model.
		set_seed(training_args.seed    )

		# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
		# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
		# (the dataset will be downloaded automatically from the datasets Hub).
		#
		# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
		# 'text' is found. You can easily tweak this behavior (see below).
		#
		# In distributed training, the load_dataset function guarantee that only one local process can concurrently
		# download the dataset.
		if data_args.dataset_name is not None:
				# Downloading and loading a dataset from the hub.
				A__        =   load_dataset(data_args.dataset_name    ,     data_args.dataset_config_name    )
				if "validation" not in datasets.keys():
						A__        =   load_dataset(
						    data_args.dataset_name    ,     data_args.dataset_config_name    ,     split=F'train[:{data_args.validation_split_percentage}%]'    ,     )
						A__        =   load_dataset(
						    data_args.dataset_name    ,     data_args.dataset_config_name    ,     split=F'train[{data_args.validation_split_percentage}%:]'    ,     )
		else:
				A__        =   {}
				if data_args.train_file is not None:
						A__        =   data_args.train_file
				if data_args.validation_file is not None:
						A__        =   data_args.validation_file
				A__        =   data_args.train_file.split("""."""    )[-1]
				if extension == "txt":
						A__        =   """text"""
				A__        =   load_dataset(__a    ,     data_files=__a    )
		# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
		# https://huggingface.co/docs/datasets/loading_datasets.html.

		# Load pretrained model and tokenizer
		#
		# Distributed training:
		# The .from_pretrained methods guarantee that only one local process can concurrently
		# download model & vocab.
		A__        =   {
		    """cache_dir""": model_args.cache_dir,
		    """revision""": model_args.model_revision,
		    """use_auth_token""": True if model_args.use_auth_token else None,
		}
		if model_args.config_name:
				A__        =   AutoConfig.from_pretrained(model_args.config_name    ,     **__a    )
		elif model_args.model_name_or_path:
				A__        =   AutoConfig.from_pretrained(model_args.model_name_or_path    ,     **__a    )
		else:
				A__        =   CONFIG_MAPPING[model_args.model_type]()
				logger.warning("""You are instantiating a new config instance from scratch."""    )
				if model_args.config_overrides is not None:
						logger.info(F'Overriding config: {model_args.config_overrides}'    )
						config.update_from_string(model_args.config_overrides    )
						logger.info(F'New config: {config}'    )

		A__        =   {
		    """cache_dir""": model_args.cache_dir,
		    """use_fast""": model_args.use_fast_tokenizer,
		    """revision""": model_args.model_revision,
		    """use_auth_token""": True if model_args.use_auth_token else None,
		}
		if model_args.tokenizer_name:
				A__        =   AutoTokenizer.from_pretrained(model_args.tokenizer_name    ,     **__a    )
		elif model_args.model_name_or_path:
				A__        =   AutoTokenizer.from_pretrained(model_args.model_name_or_path    ,     **__a    )
		else:
				raise ValueError(
				    """You are instantiating a new tokenizer from scratch. This is not supported by this script."""
				    """You can do it from another script, save it, and load it from here, using --tokenizer_name."""    )

		if model_args.model_name_or_path:
				A__        =   AutoModelForMaskedLM.from_pretrained(
				    model_args.model_name_or_path    ,     from_tf=bool(""".ckpt""" in model_args.model_name_or_path    )    ,     config=__a    ,     cache_dir=model_args.cache_dir    ,     revision=model_args.model_revision    ,     use_auth_token=True if model_args.use_auth_token else None    ,     )
		else:
				logger.info("""Training new model from scratch"""    )
				A__        =   AutoModelForMaskedLM.from_config(__a    )

		model.resize_token_embeddings(len(__a    )    )

		# Preprocessing the datasets.
		# First we tokenize all the texts.
		if training_args.do_train:
				A__        =   datasets["""train"""].column_names
		else:
				A__        =   datasets["""validation"""].column_names
		A__        =   """text""" if """text""" in column_names else column_names[0]

		A__        =   """max_length""" if data_args.pad_to_max_length else False

		def tokenize_function(__a   :Union[str, Any]    ):
				# Remove empty lines
				A__        =   [line for line in examples["""text"""] if len(__a    ) > 0 and not line.isspace()]
				return tokenizer(examples["""text"""]    ,     padding=__a    ,     truncation=__a    ,     max_length=data_args.max_seq_length    )

		A__        =   datasets.map(
		    __a    ,     batched=__a    ,     num_proc=data_args.preprocessing_num_workers    ,     remove_columns=[text_column_name]    ,     load_from_cache_file=not data_args.overwrite_cache    ,     )

		# Add the chinese references if provided
		if data_args.train_ref_file is not None:
				A__        =   add_chinese_references(tokenized_datasets["""train"""]    ,     data_args.train_ref_file    )
		if data_args.validation_ref_file is not None:
				A__        =   add_chinese_references(
				    tokenized_datasets["""validation"""]    ,     data_args.validation_ref_file    )
		# If we have ref files, need to avoid it removed by trainer
		A__        =   data_args.train_ref_file or data_args.validation_ref_file
		if has_ref:
				A__        =   False

		# Data collator
		# This one will take care of randomly masking the tokens.
		A__        =   DataCollatorForWholeWordMask(tokenizer=__a    ,     mlm_probability=data_args.mlm_probability    )

		# Initialize our Trainer
		A__        =   Trainer(
		    model=__a    ,     args=__a    ,     train_dataset=tokenized_datasets["""train"""] if training_args.do_train else None    ,     eval_dataset=tokenized_datasets["""validation"""] if training_args.do_eval else None    ,     tokenizer=__a    ,     data_collator=__a    ,     )

		# Training
		if training_args.do_train:
				if last_checkpoint is not None:
						A__        =   last_checkpoint
				elif model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path    ):
						A__        =   model_args.model_name_or_path
				else:
						A__        =   None
				A__        =   trainer.train(resume_from_checkpoint=__a    )
				trainer.save_model()  # Saves the tokenizer too for easy upload

				A__        =   os.path.join(training_args.output_dir    ,     """train_results.txt"""    )
				if trainer.is_world_process_zero():
						with open(__a    ,     """w"""    ) as writer:
								logger.info("""***** Train results *****"""    )
								for key, value in sorted(train_result.metrics.items()    ):
										logger.info(F'  {key} = {value}'    )
										writer.write(F'{key} = {value}\n'    )

            # Need to save the state, since Trainer.save_model saves only the tokenizer with the model
						trainer.state.save_to_json(os.path.join(training_args.output_dir    ,     """trainer_state.json"""    )    )

    # Evaluation
		A__        =   {}
		if training_args.do_eval:
				logger.info("""*** Evaluate ***"""    )

				A__        =   trainer.evaluate()

				A__        =   math.exp(eval_output["""eval_loss"""]    )
				A__        =   perplexity

				A__        =   os.path.join(training_args.output_dir    ,     """eval_results_mlm_wwm.txt"""    )
				if trainer.is_world_process_zero():
						with open(__a    ,     """w"""    ) as writer:
								logger.info("""***** Eval results *****"""    )
								for key, value in sorted(results.items()    ):
										logger.info(F'  {key} = {value}'    )
										writer.write(F'{key} = {value}\n'    )

		return results







def    __lowerCamelCase					(							__a   :Tuple    )		->				Optional[Any]:






		"""simple docstring"""

		main()


if __name__ == "__main__":
					main()










import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM







def    __lowerCamelCase					(							__a   :Dict    )		->				Any:






		"""simple docstring"""

		A__        =   [
		    """decoder.version""",
		    """decoder.output_projection.weight""",
		    """_float_tensor""",
		    """decoder.embed_positions._float_tensor""",
		]
		for k in ignore_keys:
				state_dict.pop(__a    ,     __a    )







def    __lowerCamelCase					(							__a   :str    )		->				Union[str, Any]:






		"""simple docstring"""

		A__      ,			A__        =   emb.weight.shape
		A__        =   nn.Linear(__a    ,     __a    ,     bias=__a    )
		A__        =   emb.weight.data
		return lin_layer







def    __lowerCamelCase					(							__a   :str    )		->				List[str]:






		"""simple docstring"""

		A__        =   torch.load(__a    ,     map_location="""cpu"""    )
		A__        =   Namespace(**checkpoint["""cfg"""]["""model"""]    )
		A__        =   checkpoint["""model"""]
		remove_ignore_keys_(__a    )
		A__        =   state_dict["""decoder.embed_tokens.weight"""].shape[0]

		A__        =   {key.replace("""decoder"""    ,     """model"""    ): val for key, val in state_dict.items()}

		A__        =   XGLMConfig(
		    vocab_size=__a    ,     max_position_embeddings=args.max_target_positions    ,     num_layers=args.decoder_layers    ,     attention_heads=args.decoder_attention_heads    ,     ffn_dim=args.decoder_ffn_embed_dim    ,     d_model=args.decoder_embed_dim    ,     layerdrop=args.decoder_layerdrop    ,     dropout=args.dropout    ,     attention_dropout=args.attention_dropout    ,     activation_dropout=args.activation_dropout    ,     activation_function="""gelu"""    ,     scale_embedding=not args.no_scale_embedding    ,     tie_word_embeddings=args.share_decoder_input_output_embed    ,     )

		A__        =   XGLMForCausalLM(__a    )
		A__        =   model.load_state_dict(__a    ,     strict=__a    )
		print(__a    )
		A__        =   make_linear_from_emb(model.model.embed_tokens    )

		return model


if __name__ == "__main__":
					A    :					int =      argparse.ArgumentParser()
					# Required parameters
					parser.add_argument('''fairseq_path''', type=str, help='''path to a model.pt on local filesystem.''')
					parser.add_argument('''pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
					A    :					str =      parser.parse_args()
					A    :					str =      convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
					model.save_pretrained(args.pytorch_dump_folder_path)



