






'''simple docstring'''





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


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


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







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







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





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase					):




	'''simple docstring'''







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

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





def 				snake_case  (     ):




	'''simple docstring'''







	__lowercase             =  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.
		__lowercase       ,						__lowercase       ,						__lowercase             =  parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]					)					)
	else:
		__lowercase       ,						__lowercase       ,						__lowercase             =  parser.parse_args_into_dataclasses()

	# Detecting last checkpoint.
	__lowercase             =  None
	if os.path.isdir(training_args.output_dir					) and training_args.do_train and not training_args.overwrite_output_dir:
		__lowercase             =  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"""				,  lowerCamelCase					)

	# 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.
		__lowercase             =  load_dataset(data_args.dataset_name				,  data_args.dataset_config_name					)
		if "validation" not in datasets.keys():
			__lowercase             =  load_dataset(
			    data_args.dataset_name				,  data_args.dataset_config_name				,  split=F'train[:{data_args.validation_split_percentage}%]'				,  )
			__lowercase             =  load_dataset(
			    data_args.dataset_name				,  data_args.dataset_config_name				,  split=F'train[{data_args.validation_split_percentage}%:]'				,  )
	else:
		__lowercase             =  {}
		if data_args.train_file is not None:
			__lowercase             =  data_args.train_file
		if data_args.validation_file is not None:
			__lowercase             =  data_args.validation_file
		__lowercase             =  data_args.train_file.split("""."""					)[-1]
		if extension == "txt":
			__lowercase             =  """text"""
		__lowercase             =  load_dataset(lowerCamelCase				,  data_files=lowerCamelCase					)
	# 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.
	__lowercase             =  {
	    """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:
		__lowercase             =  AutoConfig.from_pretrained(model_args.config_name				,  **lowerCamelCase					)
	elif model_args.model_name_or_path:
		__lowercase             =  AutoConfig.from_pretrained(model_args.model_name_or_path				,  **lowerCamelCase					)
	else:
		__lowercase             =  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}'					)

	__lowercase             =  {
	    """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:
		__lowercase             =  AutoTokenizer.from_pretrained(model_args.tokenizer_name				,  **lowerCamelCase					)
	elif model_args.model_name_or_path:
		__lowercase             =  AutoTokenizer.from_pretrained(model_args.model_name_or_path				,  **lowerCamelCase					)
	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:
		__lowercase             =  AutoModelForMaskedLM.from_pretrained(
		    model_args.model_name_or_path				,  from_tf=bool(""".ckpt""" in model_args.model_name_or_path					)				,  config=lowerCamelCase				,  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"""					)
		__lowercase             =  AutoModelForMaskedLM.from_config(lowerCamelCase					)

	model.resize_token_embeddings(len(lowerCamelCase					)					)

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

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

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

	__lowercase             =  datasets.map(
	    lowerCamelCase				,  batched=lowerCamelCase				,  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:
		__lowercase             =  add_chinese_references(tokenized_datasets["""train"""]				,  data_args.train_ref_file					)
	if data_args.validation_ref_file is not None:
		__lowercase             =  add_chinese_references(
		    tokenized_datasets["""validation"""]				,  data_args.validation_ref_file					)
	# If we have ref files, need to avoid it removed by trainer
	__lowercase             =  data_args.train_ref_file or data_args.validation_ref_file
	if has_ref:
		__lowercase             =  False

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

	# Initialize our Trainer
	__lowercase             =  Trainer(
	    model=lowerCamelCase				,  args=lowerCamelCase				,  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=lowerCamelCase				,  data_collator=lowerCamelCase				,  )

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

		__lowercase             =  os.path.join(training_args.output_dir				,  """train_results.txt"""					)
		if trainer.is_world_process_zero():
			with open(lowerCamelCase				,  """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
	__lowercase             =  {}
	if training_args.do_eval:
		logger.info("""*** Evaluate ***"""					)

		__lowercase             =  trainer.evaluate()

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

		__lowercase             =  os.path.join(training_args.output_dir				,  """eval_results_mlm_wwm.txt"""					)
		if trainer.is_world_process_zero():
			with open(lowerCamelCase				,  """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 				snake_case  (     lowerCamelCase					):




	'''simple docstring'''







	main()


if __name__ == "__main__":
						main()









import random
import unittest

import torch

from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin


@skip_mps
class        __UpperCamelCase    (     _lowerCAmelCase   ,			_lowerCAmelCase   ,			unittest.TestCase   ):
	__snake_case	:Any   =       IFInpaintingPipeline
	__snake_case	:str   =       TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'width', 'height'}
	__snake_case	:Optional[int]   =       TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
	__snake_case	:str   =       PipelineTesterMixin.required_optional_params - {'latents'}







	def  _a   (  self      :							Union[str, Any]			)		-> Tuple:
		"""simple docstring"""







		return self._get_dummy_components()







	def  _a   (  self      :							Optional[int]       ,				_lowerCAmelCase      :							List[str]       ,				_lowerCAmelCase      :							Dict=0			)		-> Any:
		"""simple docstring"""







		if str(_lowerCAmelCase			).startswith("""mps"""			):
			__lowercase             =  torch.manual_seed(_lowerCAmelCase			)
		else:
			__lowercase             =  torch.Generator(device=_lowerCAmelCase			).manual_seed(_lowerCAmelCase			)

		__lowercase             =  floats_tensor((1, 3, 32, 32)       ,				rng=random.Random(_lowerCAmelCase			)			).to(_lowerCAmelCase			)
		__lowercase             =  floats_tensor((1, 3, 32, 32)       ,				rng=random.Random(_lowerCAmelCase			)			).to(_lowerCAmelCase			)

		__lowercase             =  {
		    """prompt""": """A painting of a squirrel eating a burger""",
		    """image""": image,
		    """mask_image""": mask_image,
		    """generator""": generator,
		    """num_inference_steps""": 2,
		    """output_type""": """numpy""",
		}

		return inputs







	@unittest.skipIf(
	    torch_device != """cuda""" or not is_xformers_available()       ,				reason="""XFormers attention is only available with CUDA and `xformers` installed"""       ,				)
	def  _a   (  self      :							Tuple			)		-> Dict:
		"""simple docstring"""







		self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3			)







	def  _a   (  self      :							Union[str, Any]			)		-> Dict:
		"""simple docstring"""







		self._test_save_load_optional_components()







	@unittest.skipIf(torch_device != """cuda"""       ,				reason="""float16 requires CUDA"""			)
	def  _a   (  self      :							List[str]			)		-> Tuple:
		"""simple docstring"""







		super().test_save_load_floataa(expected_max_diff=1e-1			)







	def  _a   (  self      :							Tuple			)		-> Optional[Any]:
		"""simple docstring"""







		self._test_attention_slicing_forward_pass(expected_max_diff=1e-2			)







	def  _a   (  self      :							str			)		-> Optional[int]:
		"""simple docstring"""







		self._test_save_load_local()







	def  _a   (  self      :							List[str]			)		-> List[Any]:
		"""simple docstring"""







		self._test_inference_batch_single_identical(
		    expected_max_diff=1e-2       ,				)

