


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


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






@dataclass
class       _UpperCamelCase    :

				"""simple docstring"""







				lowerCAmelCase 		= field(
				    default=__snake_case    ,		metadata={
				        'help': (
				            'The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch.'
				        )
				    }    ,		)
				lowerCAmelCase 		= field(
				    default=__snake_case    ,		metadata={'help': 'If training from scratch, pass a model type from the list: ' + ', '.join(__snake_case				)}    ,		)
				lowerCAmelCase 		= field(
				    default=__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 		= field(
				    default=__snake_case    ,		metadata={'help': 'Pretrained config name or path if not the same as model_name'}				)
				lowerCAmelCase 		= field(
				    default=__snake_case    ,		metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}				)
				lowerCAmelCase 		= field(
				    default=__snake_case    ,		metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}    ,		)
				lowerCAmelCase 		= field(
				    default=__snake_case    ,		metadata={'help': 'Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'}    ,		)
				lowerCAmelCase 		= field(
				    default='main'    ,		metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'}    ,		)
				lowerCAmelCase 		= field(
				    default=__snake_case    ,		metadata={
				        'help': (
				            'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
				            'with private models).'
				        )
				    }    ,		)

				def      _UpperCAmelCase					(						self   )	->				Union[str, Any]:
					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       _UpperCamelCase    :

				"""simple docstring"""







				lowerCAmelCase 		= field(
				    default=__snake_case    ,		metadata={'help': 'The name of the dataset to use (via the datasets library).'}				)
				lowerCAmelCase 		= field(
				    default=__snake_case    ,		metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'}				)
				lowerCAmelCase 		= field(default=__snake_case    ,		metadata={'help': 'The input training data file (a text file).'}				)
				lowerCAmelCase 		= field(
				    default=__snake_case    ,		metadata={'help': 'An optional input evaluation data file to evaluate the perplexity on (a text file).'}    ,		)
				lowerCAmelCase 		= field(
				    default=__snake_case    ,		metadata={'help': 'An optional input train ref data file for whole word masking in Chinese.'}    ,		)
				lowerCAmelCase 		= field(
				    default=__snake_case    ,		metadata={'help': 'An optional input validation ref data file for whole word masking in Chinese.'}    ,		)
				lowerCAmelCase 		= field(
				    default=__snake_case    ,		metadata={'help': 'Overwrite the cached training and evaluation sets'}				)
				lowerCAmelCase 		= field(
				    default=5    ,		metadata={
				        'help': 'The percentage of the train set used as validation set in case there\'s no validation split'
				    }    ,		)
				lowerCAmelCase 		= field(
				    default=__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 		= field(
				    default=__snake_case    ,		metadata={'help': 'The number of processes to use for the preprocessing.'}    ,		)
				lowerCAmelCase 		= field(
				    default=0.15    ,		metadata={'help': 'Ratio of tokens to mask for masked language modeling loss'}				)
				lowerCAmelCase 		= field(
				    default=__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      _UpperCAmelCase					(						self   )	->				Optional[int]:
					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				(   UpperCamelCase__:     Optional[Any]							,					UpperCamelCase__:     Optional[Any]						) ->   Any:





	"""simple docstring"""







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

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





def   _lowerCAmelCase				(   ) ->   List[Any]:





	"""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"""							,					UpperCamelCase__						)

	# 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(UpperCamelCase__							,					data_files=UpperCamelCase__						)
	# 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							,					**UpperCamelCase__						)
	elif model_args.model_name_or_path:
		A								=				AutoConfig.from_pretrained(model_args.model_name_or_path							,					**UpperCamelCase__						)
	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							,					**UpperCamelCase__						)
	elif model_args.model_name_or_path:
		A								=				AutoTokenizer.from_pretrained(model_args.model_name_or_path							,					**UpperCamelCase__						)
	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=UpperCamelCase__							,					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(UpperCamelCase__						)

	model.resize_token_embeddings(len(UpperCamelCase__						)						)

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

	A								=				datasets.map(
	    UpperCamelCase__							,					batched=UpperCamelCase__							,					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=UpperCamelCase__							,					mlm_probability=data_args.mlm_probability						)

	# Initialize our Trainer
	A								=				Trainer(
	    model=UpperCamelCase__							,					args=UpperCamelCase__							,					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=UpperCamelCase__							,					data_collator=UpperCamelCase__							,					)

	# 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=UpperCamelCase__						)
		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(UpperCamelCase__							,					"""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(UpperCamelCase__							,					"""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				(   UpperCamelCase__:     Any						) ->   List[str]:





	"""simple docstring"""







	main()


if __name__ == "__main__":
				main()







import unittest
from typing import Tuple

import torch

from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch






@require_torch
class       _UpperCamelCase    :

				"""simple docstring"""







				@property
				def      _UpperCAmelCase					(						self   )	->				Any:
					return self.get_dummy_input()

				@property
				def      _UpperCAmelCase					(						self   )	->				Union[str, Any]:
					if self.block_type == "down":
						return (4, 32, 16, 16)
					elif self.block_type == "mid":
						return (4, 32, 32, 32)
					elif self.block_type == "up":
						return (4, 32, 64, 64)

					raise ValueError(f'\'{self.block_type}\' is not a supported block_type. Set it to \'up\', \'mid\', or \'down\'.'   )

				def      _UpperCAmelCase					(						self				,   a__=True				,   a__=False				,   a__=False				,   a__=False				,   )	->				Optional[Any]:
					A								=				4
					A								=				32
					A								=				(32, 32)

					A								=				torch.manual_seed(0   )
					A								=				torch.device(a__   )
					A								=				(batch_size, num_channels) + sizes
					A								=				randn_tensor(a__				,   generator=a__				,   device=a__   )
					A								=				{"""hidden_states""": hidden_states}

					if include_temb:
						A								=				128
						A								=				randn_tensor((batch_size, temb_channels)				,   generator=a__				,   device=a__   )

					if include_res_hidden_states_tuple:
						A								=				torch.manual_seed(1   )
						A								=				(randn_tensor(a__				,   generator=a__				,   device=a__   ),)

					if include_encoder_hidden_states:
						A								=				floats_tensor((batch_size, 32, 32)   ).to(a__   )

					if include_skip_sample:
						A								=				randn_tensor(((batch_size, 3) + sizes)				,   generator=a__				,   device=a__   )

					return dummy_input

				def      _UpperCAmelCase					(						self   )	->				int:
					A								=				{
					    """in_channels""": 32,
					    """out_channels""": 32,
					    """temb_channels""": 128,
					}
					if self.block_type == "up":
						A								=				32

					if self.block_type == "mid":
						init_dict.pop("""out_channels"""   )

					A								=				self.dummy_input
					return init_dict, inputs_dict

				def      _UpperCAmelCase					(						self				,   a__   )	->				Optional[int]:
					A					,			A								=				self.prepare_init_args_and_inputs_for_common()
					A								=				self.block_class(**a__   )
					unet_block.to(a__   )
					unet_block.eval()

					with torch.no_grad():
						A								=				unet_block(**a__   )

					if isinstance(a__				,   a__   ):
						A								=				output[0]

					self.assertEqual(output.shape				,   self.output_shape   )

					A								=				output[0, -1, -3:, -3:]
					A								=				torch.tensor(a__   ).to(a__   )
					assert torch_all_close(output_slice.flatten()				,   a__				,   atol=5e-3   )

				@unittest.skipIf(torch_device == """mps"""				,   """Training is not supported in mps"""   )
				def      _UpperCAmelCase					(						self   )	->				str:
					A					,			A								=				self.prepare_init_args_and_inputs_for_common()
					A								=				self.block_class(**a__   )
					model.to(a__   )
					model.train()
					A								=				model(**a__   )

					if isinstance(a__				,   a__   ):
						A								=				output[0]

					A								=				torch.device(a__   )
					A								=				randn_tensor(output.shape				,   device=a__   )
					A								=				torch.nn.functional.mse_loss(a__				,   a__   )
					loss.backward()



