

import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig


lowerCamelCase      				=     {
    '''facebook/maskformer-swin-base-ade''': (
        '''https://huggingface.co/facebook/maskformer-swin-base-ade/blob/main/config.json'''
    )
    # See all MaskFormer models at https://huggingface.co/models?filter=maskformer
}

lowerCamelCase      				=     logging.get_logger(__name__)





class     snake_case_			(				_UpperCamelCase							):


							"""simple docstring"""





							__UpperCAmelCase       ="maskformer"
							__UpperCAmelCase       ={"hidden_size": "mask_feature_size"}
							__UpperCAmelCase       =["resnet", "swin"]
							__UpperCAmelCase       =["detr"]




							def __init__( self					,						_A = 2_5_6					,						_A = 2_5_6					,						_A = 0.1					,						_A = False					,						_A = None					,						_A = None					,						_A = 0.02					,						_A = 1.0					,						_A = 1.0					,						_A = 1.0					,						_A = 20.0					,						_A = None					,						**_A					,						):
												if backbone_config is None:
																	# fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
																	__lowerCAmelCase					=							SwinConfig(
																	    image_size=3_8_4					,						in_channels=3					,						patch_size=4					,						embed_dim=1_2_8					,						depths=[2, 2, 1_8, 2]					,						num_heads=[4, 8, 1_6, 3_2]					,						window_size=1_2					,						drop_path_rate=0.3					,						out_features=['stage1', 'stage2', 'stage3', 'stage4']					,						)

												if isinstance(__a					,						__a							):
																	__lowerCAmelCase					=							backbone_config.pop('model_type'							)
																	__lowerCAmelCase					=							CONFIG_MAPPING[backbone_model_type]
																	__lowerCAmelCase					=							config_class.from_dict(__a							)

												# verify that the backbone is supported
												if backbone_config.model_type not in self.backbones_supported:
																	logger.warning_once(
																	    F"""Backbone {backbone_config.model_type} is not a supported model and may not be compatible with MaskFormer. """
																	    F"""Supported model types: {','.join(self.backbones_supported							)}"""							)

												if decoder_config is None:
																	# fall back to https://huggingface.co/facebook/detr-resnet-50
																	__lowerCAmelCase					=							DetrConfig()
												else:
																	# verify that the decoder is supported
																	__lowerCAmelCase					=							(
																	    decoder_config.pop('model_type'							) if isinstance(__a					,						__a							) else decoder_config.model_type
																	)
																	if decoder_type not in self.decoders_supported:
																						raise ValueError(
																						    F"""Transformer Decoder {decoder_type} not supported, please use one of"""
																						    F""" {','.join(self.decoders_supported							)}"""							)
																	if isinstance(__a					,						__a							):
																						__lowerCAmelCase					=							CONFIG_MAPPING[decoder_type]
																						__lowerCAmelCase					=							config_class.from_dict(__a							)

												__lowerCAmelCase					=							backbone_config
												__lowerCAmelCase					=							decoder_config
												# main feature dimension for the model
												__lowerCAmelCase					=							fpn_feature_size
												__lowerCAmelCase					=							mask_feature_size
												# initializer
												__lowerCAmelCase					=							init_std
												__lowerCAmelCase					=							init_xavier_std
												# Hungarian matcher && loss
												__lowerCAmelCase					=							cross_entropy_weight
												__lowerCAmelCase					=							dice_weight
												__lowerCAmelCase					=							mask_weight
												__lowerCAmelCase					=							use_auxiliary_loss
												__lowerCAmelCase					=							no_object_weight
												__lowerCAmelCase					=							output_auxiliary_logits

												__lowerCAmelCase					=							self.decoder_config.encoder_attention_heads
												__lowerCAmelCase					=							self.decoder_config.num_hidden_layers
												super().__init__(**__a							)




							@classmethod
							def        A__  ( cls					,						_A					,						_A					,						**_A							):
												return cls(
												    backbone_config=__a					,						decoder_config=__a					,						**__a					,						)




							def        A__  ( self							):
												__lowerCAmelCase					=							copy.deepcopy(self.__dict__							)
												__lowerCAmelCase					=							self.backbone_config.to_dict()
												__lowerCAmelCase					=							self.decoder_config.to_dict()
												__lowerCAmelCase					=							self.__class__.model_type
												return output









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


lowerCamelCase      				=     logging.getLogger(__name__)
lowerCamelCase      				=     list(MODEL_FOR_MASKED_LM_MAPPING.keys())
lowerCamelCase      				=     tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)





@dataclass
class     snake_case_			:


							"""simple docstring"""





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




							def        A__  ( self							):
												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     snake_case_			:


							"""simple docstring"""





							__UpperCAmelCase       =field(
							    default=_a			, metadata={"""help""": """The name of the dataset to use (via the datasets library)."""}							)
							__UpperCAmelCase       =field(
							    default=_a			, metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""}							)
							__UpperCAmelCase       =field(default=_a			, metadata={"""help""": """The input training data file (a text file)."""}							)
							__UpperCAmelCase       =field(
							    default=_a			, metadata={"""help""": """An optional input evaluation data file to evaluate the perplexity on (a text file)."""}			, )
							__UpperCAmelCase       =field(
							    default=_a			, metadata={"""help""": """An optional input train ref data file for whole word masking in Chinese."""}			, )
							__UpperCAmelCase       =field(
							    default=_a			, metadata={"""help""": """An optional input validation ref data file for whole word masking in Chinese."""}			, )
							__UpperCAmelCase       =field(
							    default=_a			, metadata={"""help""": """Overwrite the cached training and evaluation sets"""}							)
							__UpperCAmelCase       =field(
							    default=5			, metadata={
							        """help""": """The percentage of the train set used as validation set in case there's no validation split"""
							    }			, )
							__UpperCAmelCase       =field(
							    default=_a			, 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."""
							        )
							    }			, )
							__UpperCAmelCase       =field(
							    default=_a			, metadata={"""help""": """The number of processes to use for the preprocessing."""}			, )
							__UpperCAmelCase       =field(
							    default=0.15			, metadata={"""help""": """Ratio of tokens to mask for masked language modeling loss"""}							)
							__UpperCAmelCase       =field(
							    default=_a			, 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							):
												if self.train_file is not None:
																	__lowerCAmelCase					=							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:
																	__lowerCAmelCase					=							self.validation_file.split('.'							)[-1]
																	assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."


def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






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

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


def        __lowercase    (  ):

					"""simple docstring"""






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

					# Detecting last checkpoint.
					__lowerCAmelCase					=							None
					if os.path.isdir(training_args.output_dir      ) and training_args.do_train and not training_args.overwrite_output_dir:
										__lowerCAmelCase					=							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.
										__lowerCAmelCase					=							load_dataset(data_args.dataset_name		,					data_args.dataset_config_name      )
										if "validation" not in datasets.keys():
															__lowerCAmelCase					=							load_dataset(
															    data_args.dataset_name		,					data_args.dataset_config_name		,					split=F"""train[:{data_args.validation_split_percentage}%]"""		,					)
															__lowerCAmelCase					=							load_dataset(
															    data_args.dataset_name		,					data_args.dataset_config_name		,					split=F"""train[{data_args.validation_split_percentage}%:]"""		,					)
					else:
										__lowerCAmelCase					=							{}
										if data_args.train_file is not None:
															__lowerCAmelCase					=							data_args.train_file
										if data_args.validation_file is not None:
															__lowerCAmelCase					=							data_args.validation_file
										__lowerCAmelCase					=							data_args.train_file.split('.'      )[-1]
										if extension == "txt":
															__lowerCAmelCase					=							'text'
										__lowerCAmelCase					=							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.
					__lowerCAmelCase					=							{
					    '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:
										__lowerCAmelCase					=							AutoConfig.from_pretrained(model_args.config_name		,					**UpperCAmelCase__      )
					elif model_args.model_name_or_path:
										__lowerCAmelCase					=							AutoConfig.from_pretrained(model_args.model_name_or_path		,					**UpperCAmelCase__      )
					else:
										__lowerCAmelCase					=							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}"""      )

					__lowerCAmelCase					=							{
					    '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:
										__lowerCAmelCase					=							AutoTokenizer.from_pretrained(model_args.tokenizer_name		,					**UpperCAmelCase__      )
					elif model_args.model_name_or_path:
										__lowerCAmelCase					=							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:
										__lowerCAmelCase					=							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'      )
										__lowerCAmelCase					=							AutoModelForMaskedLM.from_config(UpperCAmelCase__      )

					model.resize_token_embeddings(len(UpperCAmelCase__      )      )

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

					__lowerCAmelCase					=							'max_length' if data_args.pad_to_max_length else False

					def tokenize_function(UpperCAmelCase__      ):
										# Remove empty lines
										__lowerCAmelCase					=							[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      )

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

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

					# Initialize our Trainer
					__lowerCAmelCase					=							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:
															__lowerCAmelCase					=							last_checkpoint
										elif model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path      ):
															__lowerCAmelCase					=							model_args.model_name_or_path
										else:
															__lowerCAmelCase					=							None
										__lowerCAmelCase					=							trainer.train(resume_from_checkpoint=UpperCAmelCase__      )
										trainer.save_model()  # Saves the tokenizer too for easy upload

										__lowerCAmelCase					=							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
					__lowerCAmelCase					=							{}
					if training_args.do_eval:
										logger.info('*** Evaluate ***'      )

										__lowerCAmelCase					=							trainer.evaluate()

										__lowerCAmelCase					=							math.exp(eval_output['eval_loss']      )
										__lowerCAmelCase					=							perplexity

										__lowerCAmelCase					=							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        __lowercase    (  UpperCAmelCase__      ):

					"""simple docstring"""






					main()


if __name__ == "__main__":
		main()






