






'''simple docstring'''







import logging
import math
import os
from dataclasses import dataclass, field
from glob import glob
from typing import Optional

from torch.utils.data import ConcatDataset

import transformers
from transformers import (
    CONFIG_MAPPING,
    MODEL_WITH_LM_HEAD_MAPPING,
    AutoConfig,
    AutoModelWithLMHead,
    AutoTokenizer,
    DataCollatorForLanguageModeling,
    DataCollatorForPermutationLanguageModeling,
    DataCollatorForWholeWordMask,
    HfArgumentParser,
    LineByLineTextDataset,
    LineByLineWithRefDataset,
    PreTrainedTokenizer,
    TextDataset,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


__lowerCAmelCase									=   logging.getLogger(__name__)


__lowerCAmelCase									=   list(MODEL_WITH_LM_HEAD_MAPPING.keys())
__lowerCAmelCase									=   tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)





@dataclass
class 						SCREAMING_SNAKE_CASE       :
			snake_case__	=					field(
			    default=SCREAMING_SNAKE_CASE_						, metadata={
			        "help": (
			            "The model checkpoint for weights initialization. Leave None if you want to train a model from"
			            " scratch."
			        )
			    }						, )
			snake_case__	=					field(
			    default=SCREAMING_SNAKE_CASE_						, metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(SCREAMING_SNAKE_CASE_							)}						, )
			snake_case__	=					field(
			    default=SCREAMING_SNAKE_CASE_						, metadata={"help": "Pretrained config name or path if not the same as model_name"}							)
			snake_case__	=					field(
			    default=SCREAMING_SNAKE_CASE_						, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}							)
			snake_case__	=					field(
			    default=SCREAMING_SNAKE_CASE_						, metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}						, )





@dataclass
class 						SCREAMING_SNAKE_CASE       :
			snake_case__	=					field(
			    default=SCREAMING_SNAKE_CASE_						, metadata={"help": "The input training data file (a text file)."}							)
			snake_case__	=					field(
			    default=SCREAMING_SNAKE_CASE_						, metadata={
			        "help": (
			            "The input training data files (multiple files in glob format). "
			            "Very often splitting large files to smaller files can prevent tokenizer going out of memory"
			        )
			    }						, )
			snake_case__	=					field(
			    default=SCREAMING_SNAKE_CASE_						, metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."}						, )
			snake_case__	=					field(
			    default=SCREAMING_SNAKE_CASE_						, metadata={"help": "An optional input train ref data file for whole word mask in Chinese."}						, )
			snake_case__	=					field(
			    default=SCREAMING_SNAKE_CASE_						, metadata={"help": "An optional input eval ref data file for whole word mask in Chinese."}						, )
			snake_case__	=					field(
			    default=SCREAMING_SNAKE_CASE_						, metadata={"help": "Whether distinct lines of text in the dataset are to be handled as distinct sequences."}						, )

			snake_case__	=					field(
			    default=SCREAMING_SNAKE_CASE_						, metadata={"help": "Train with masked-language modeling loss instead of language modeling."}							)
			snake_case__	=					field(default=SCREAMING_SNAKE_CASE_						, metadata={"help": "Whether ot not to use whole word mask."}							)
			snake_case__	=					field(
			    default=0.15						, metadata={"help": "Ratio of tokens to mask for masked language modeling loss"}							)
			snake_case__	=					field(
			    default=1 / 6						, metadata={
			        "help": (
			            "Ratio of length of a span of masked tokens to surrounding context length for permutation language"
			            " modeling."
			        )
			    }						, )
			snake_case__	=					field(
			    default=5						, metadata={"help": "Maximum length of a span of masked tokens for permutation language modeling."}							)

			snake_case__	=					field(
			    default=-1						, metadata={
			        "help": (
			            "Optional input sequence length after tokenization."
			            "The training dataset will be truncated in block of this size for training."
			            "Default to the model max input length for single sentence inputs (take into account special tokens)."
			        )
			    }						, )
			snake_case__	=					field(
			    default=SCREAMING_SNAKE_CASE_						, metadata={"help": "Overwrite the cached training and evaluation sets"}							)





def 		_UpperCAmelCase (				__A				: DataTrainingArguments    ,		__A				: PreTrainedTokenizer    ,		__A				: bool = False    ,		__A				: Optional[str] = None    ,		):
				def _dataset(__A				: Any    ,		__A				: str=None   ):
								if args.line_by_line:
												if ref_path is not None:
																if not args.whole_word_mask or not args.mlm:
																				raise ValueError('''You need to set world whole masking and mlm to True for Chinese Whole Word Mask'''   )
																return LineByLineWithRefDataset(
																    tokenizer=__A    ,		file_path=__A    ,		block_size=args.block_size    ,		ref_path=__A    ,		)

												return LineByLineTextDataset(tokenizer=__A    ,		file_path=__A    ,		block_size=args.block_size   )
								else:
												return TextDataset(
												    tokenizer=__A    ,		file_path=__A    ,		block_size=args.block_size    ,		overwrite_cache=args.overwrite_cache    ,		cache_dir=__A    ,		)

				if evaluate:
								return _dataset(args.eval_data_file    ,		args.eval_ref_file   )
				elif args.train_data_files:
								return ConcatDataset([_dataset(__A   ) for f in glob(args.train_data_files   )]   )
				else:
								return _dataset(args.train_data_file    ,		args.train_ref_file   )





def 		_UpperCAmelCase (				):
				# See all possible arguments in src/transformers/training_args.py
				# or by passing the --help flag to this script.
				# We now keep distinct sets of args, for a cleaner separation of concerns.

				a_		:	Any									=   HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)   )
				a_					,      a_					,      a_		:	int									=   parser.parse_args_into_dataclasses()

				if data_args.eval_data_file is None and training_args.do_eval:
								raise ValueError(
								    '''Cannot do evaluation without an evaluation data file. Either supply a file to --eval_data_file '''
								    '''or remove the --do_eval argument.'''   )
				if (
				    os.path.exists(training_args.output_dir   )
				    and os.listdir(training_args.output_dir   )
				    and training_args.do_train
				    and not training_args.overwrite_output_dir
				):
								raise ValueError(
								    f'Output directory ({training_args.output_dir}) already exists and is not empty. Use'
								    ''' --overwrite_output_dir to overcome.'''   )

				# Setup logging
				logging.basicConfig(
				    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''    ,		datefmt='''%m/%d/%Y %H:%M:%S'''    ,		level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN    ,		)
				logger.warning(
				    '''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s'''    ,		training_args.local_rank    ,		training_args.device    ,		training_args.n_gpu    ,		bool(training_args.local_rank != -1   )    ,		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
				set_seed(training_args.seed   )

				# Load pretrained model and tokenizer
				#
				# Distributed training:
				# The .from_pretrained methods guarantee that only one local process can concurrently
				# download model & vocab.

				if model_args.config_name:
								a_		:	List[Any]									=   AutoConfig.from_pretrained(model_args.config_name    ,		cache_dir=model_args.cache_dir   )
				elif model_args.model_name_or_path:
								a_		:	List[Any]									=   AutoConfig.from_pretrained(model_args.model_name_or_path    ,		cache_dir=model_args.cache_dir   )
				else:
								a_		:	List[str]									=   CONFIG_MAPPING[model_args.model_type]()
								logger.warning('''You are instantiating a new config instance from scratch.'''   )

				if model_args.tokenizer_name:
								a_		:	int									=   AutoTokenizer.from_pretrained(model_args.tokenizer_name    ,		cache_dir=model_args.cache_dir   )
				elif model_args.model_name_or_path:
								a_		:	Optional[Any]									=   AutoTokenizer.from_pretrained(model_args.model_name_or_path    ,		cache_dir=model_args.cache_dir   )
				else:
								raise ValueError(
								    '''You are instantiating a new tokenizer from scratch. This is not supported, but 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_		:	Dict									=   AutoModelWithLMHead.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    ,		)
				else:
								logger.info('''Training new model from scratch'''   )
								a_		:	str									=   AutoModelWithLMHead.from_config(__A   )

				model.resize_token_embeddings(len(__A   )   )

				if config.model_type in ["bert", "roberta", "distilbert", "camembert"] and not data_args.mlm:
								raise ValueError(
								    '''BERT and RoBERTa-like models do not have LM heads but masked LM heads. They must be run using the'''
								    '''--mlm flag (masked language modeling).'''   )

				if data_args.block_size <= 0:
								a_		:	Union[str, Any]									=   tokenizer.max_len
								# Our input block size will be the max possible for the model
				else:
								a_		:	Union[str, Any]									=   min(data_args.block_size    ,		tokenizer.max_len   )

				# Get datasets

				a_		:	List[str]									=   (
				    get_dataset(__A    ,		tokenizer=__A    ,		cache_dir=model_args.cache_dir   ) if training_args.do_train else None
				)
				a_		:	Tuple									=   (
				    get_dataset(__A    ,		tokenizer=__A    ,		evaluate=__A    ,		cache_dir=model_args.cache_dir   )
				    if training_args.do_eval
				    else None
				)
				if config.model_type == "xlnet":
								a_		:	Optional[Any]									=   DataCollatorForPermutationLanguageModeling(
								    tokenizer=__A    ,		plm_probability=data_args.plm_probability    ,		max_span_length=data_args.max_span_length    ,		)
				else:
								if data_args.mlm and data_args.whole_word_mask:
												a_		:	Tuple									=   DataCollatorForWholeWordMask(
												    tokenizer=__A    ,		mlm_probability=data_args.mlm_probability   )
								else:
												a_		:	Any									=   DataCollatorForLanguageModeling(
												    tokenizer=__A    ,		mlm=data_args.mlm    ,		mlm_probability=data_args.mlm_probability   )

    # Initialize our Trainer
				a_		:	List[str]									=   Trainer(
				    model=__A    ,		args=__A    ,		data_collator=__A    ,		train_dataset=__A    ,		eval_dataset=__A    ,		prediction_loss_only=__A    ,		)

				# Training
				if training_args.do_train:
								a_		:	List[str]									=   (
								    model_args.model_name_or_path
								    if model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path   )
								    else None
								)
								trainer.train(model_path=__A   )
								trainer.save_model()
								# For convenience, we also re-save the tokenizer to the same directory,
								# so that you can share your model easily on huggingface.co/models =)
								if trainer.is_world_master():
												tokenizer.save_pretrained(training_args.output_dir   )

    # Evaluation
				a_		:	List[str]									=   {}
				if training_args.do_eval:
								logger.info('''*** Evaluate ***'''   )

								a_		:	Dict									=   trainer.evaluate()

								a_		:	Tuple									=   math.exp(eval_output['''eval_loss''']   )
								a_		:	List[str]									=   {'''perplexity''': perplexity}

								a_		:	List[str]									=   os.path.join(training_args.output_dir    ,		'''eval_results_lm.txt'''   )
								if trainer.is_world_master():
												with open(__A    ,		'''w'''   ) as writer:
																logger.info('''***** Eval results *****'''   )
																for key in sorted(result.keys()   ):
																				logger.info('''  %s = %s'''    ,		__A    ,		str(result[key]   )   )
																				writer.write('''%s = %s\n''' % (key, str(result[key]   ))   )

								results.update(__A   )

				return results





def 		_UpperCAmelCase (				__A				: Optional[Any]   ):
				# For xla_spawn (TPUs)
				main()


if __name__ == "__main__":
		main()












'''simple docstring'''







import json
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
		import torch

		if is_vision_available():
				from transformers import OneFormerImageProcessor
				from transformers.models.oneformer.image_processing_oneformer import binary_mask_to_rle
				from transformers.models.oneformer.modeling_oneformer import OneFormerForUniversalSegmentationOutput

if is_vision_available():
		from PIL import Image





def 		_UpperCAmelCase (				__A				: int    ,		__A				: Tuple="shi-labs/oneformer_demo"   ):
				with open(hf_hub_download(__A    ,		__A    ,		repo_type='''dataset'''   )    ,		'''r'''   ) as f:
								a_		:	Optional[Any]									=   json.load(__A   )
				a_		:	List[Any]									=   {}
				a_		:	List[Any]									=   []
				a_		:	Tuple									=   []
				for key, info in class_info.items():
								a_		:	Tuple									=   info['''name''']
								class_names.append(info['''name''']   )
								if info["isthing"]:
												thing_ids.append(int(__A   )   )
				a_		:	Optional[Any]									=   thing_ids
				a_		:	str									=   class_names
				return metadata





class 						SCREAMING_SNAKE_CASE       (       unittest.TestCase							):






			def __init__(			self   :      Tuple	,    __SCREAMING_SNAKE_CASE   :      Union[str, Any]	,    __SCREAMING_SNAKE_CASE   :      List[str]=7	,    __SCREAMING_SNAKE_CASE   :      List[Any]=3	,    __SCREAMING_SNAKE_CASE   :      List[Any]=30	,    __SCREAMING_SNAKE_CASE   :      str=400	,    __SCREAMING_SNAKE_CASE   :      List[str]=None	,    __SCREAMING_SNAKE_CASE   :      Optional[Any]=True	,    __SCREAMING_SNAKE_CASE   :      List[Any]=True	,    __SCREAMING_SNAKE_CASE   :      str=[0.5, 0.5, 0.5]	,    __SCREAMING_SNAKE_CASE   :      Optional[int]=[0.5, 0.5, 0.5]	,    __SCREAMING_SNAKE_CASE   :      Optional[int]=10	,    __SCREAMING_SNAKE_CASE   :      int=False	,    __SCREAMING_SNAKE_CASE   :      Optional[int]=255	,    __SCREAMING_SNAKE_CASE   :      List[Any]="shi-labs/oneformer_demo"	,    __SCREAMING_SNAKE_CASE   :      List[str]="ade20k_panoptic.json"	,    __SCREAMING_SNAKE_CASE   :      List[Any]=10	,    )      ->      Dict:
							a_		:	int									=   parent
							a_		:	Optional[Any]									=   batch_size
							a_		:	str									=   num_channels
							a_		:	Tuple									=   min_resolution
							a_		:	List[Any]									=   max_resolution
							a_		:	List[Any]									=   do_resize
							a_		:	Union[str, Any]									=   {'''shortest_edge''': 32, '''longest_edge''': 1333} if size is None else size
							a_		:	Dict									=   do_normalize
							a_		:	Union[str, Any]									=   image_mean
							a_		:	Dict									=   image_std
							a_		:	int									=   class_info_file
							a_		:	List[Any]									=   prepare_metadata(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )
							a_		:	Optional[int]									=   num_text
							a_		:	Any									=   repo_path

							# for the post_process_functions
							a_		:	List[str]									=   2
							a_		:	Tuple									=   10
							a_		:	Union[str, Any]									=   10
							a_		:	Dict									=   3
							a_		:	int									=   4
							a_		:	Optional[Any]									=   num_labels
							a_		:	Union[str, Any]									=   do_reduce_labels
							a_		:	Tuple									=   ignore_index






			def       SCREAMING_SNAKE_CASE   (			self   :      str       )      ->      int:
							return {
							    "do_resize": self.do_resize,
							    "size": self.size,
							    "do_normalize": self.do_normalize,
							    "image_mean": self.image_mean,
							    "image_std": self.image_std,
							    "num_labels": self.num_labels,
							    "do_reduce_labels": self.do_reduce_labels,
							    "ignore_index": self.ignore_index,
							    "class_info_file": self.class_info_file,
							    "metadata": self.metadata,
							    "num_text": self.num_text,
							}






			def       SCREAMING_SNAKE_CASE   (			self   :      Tuple	,    __SCREAMING_SNAKE_CASE   :      Any	,    __SCREAMING_SNAKE_CASE   :      List[str]=False       )      ->      Optional[Any]:
							if not batched:
											a_		:	List[Any]									=   image_inputs[0]
											if isinstance(__SCREAMING_SNAKE_CASE	,    Image.Image       ):
															a_					,      a_		:	List[str]									=   image.size
											else:
															a_					,      a_		:	Any									=   image.shape[1], image.shape[2]
											if w < h:
															a_		:	int									=   int(self.size['''shortest_edge'''] * h / w       )
															a_		:	Union[str, Any]									=   self.size['''shortest_edge''']
											elif w > h:
															a_		:	Any									=   self.size['''shortest_edge''']
															a_		:	Any									=   int(self.size['''shortest_edge'''] * w / h       )
											else:
															a_		:	Optional[int]									=   self.size['''shortest_edge''']
															a_		:	int									=   self.size['''shortest_edge''']

							else:
											a_		:	int									=   []
											for image in image_inputs:
															a_					,      a_		:	List[str]									=   self.get_expected_values([image]       )
															expected_values.append((expected_height, expected_width)       )
											a_		:	List[str]									=   max(__SCREAMING_SNAKE_CASE	,    key=lambda __SCREAMING_SNAKE_CASE       : item[0]       )[0]
											a_		:	str									=   max(__SCREAMING_SNAKE_CASE	,    key=lambda __SCREAMING_SNAKE_CASE       : item[1]       )[1]

							return expected_height, expected_width







			def       SCREAMING_SNAKE_CASE   (			self   :      Dict       )      ->      List[str]:
							return OneFormerForUniversalSegmentationOutput(
							    # +1 for null class
							    class_queries_logits=torch.randn((self.batch_size, self.num_queries, self.num_classes + 1)       )	,    masks_queries_logits=torch.randn((self.batch_size, self.num_queries, self.height, self.width)       )	,    )





@require_torch
@require_vision
class 						SCREAMING_SNAKE_CASE       (       SCREAMING_SNAKE_CASE_						, unittest.TestCase							):
			snake_case__	=					OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
			# only for test_image_processing_common.test_image_proc_to_json_string
			snake_case__	=					image_processing_class






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






			@property
			def       SCREAMING_SNAKE_CASE   (			self   :      Optional[Any]       )      ->      Any:
							return self.image_processing_tester.prepare_image_processor_dict()






			def       SCREAMING_SNAKE_CASE   (			self   :      List[str]       )      ->      Dict:
							a_		:	List[Any]									=   self.image_processing_class(**self.image_processor_dict       )
							self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE	,    '''image_mean'''       )       )
							self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE	,    '''image_std'''       )       )
							self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE	,    '''do_normalize'''       )       )
							self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE	,    '''do_resize'''       )       )
							self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE	,    '''size'''       )       )
							self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE	,    '''ignore_index'''       )       )
							self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE	,    '''class_info_file'''       )       )
							self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE	,    '''num_text'''       )       )
							self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE	,    '''repo_path'''       )       )
							self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE	,    '''metadata'''       )       )
							self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE	,    '''do_reduce_labels'''       )       )






			def       SCREAMING_SNAKE_CASE   (			self   :      Union[str, Any]       )      ->      str:
							pass






			def       SCREAMING_SNAKE_CASE   (			self   :      List[Any]       )      ->      Tuple:
							# Initialize image_processor
							a_		:	Union[str, Any]									=   self.image_processing_class(**self.image_processor_dict       )
							# create random PIL images
							a_		:	Optional[int]									=   prepare_image_inputs(self.image_processing_tester	,    equal_resolution=__SCREAMING_SNAKE_CASE       )
							for image in image_inputs:
											self.assertIsInstance(__SCREAMING_SNAKE_CASE	,    Image.Image       )

							# Test not batched input
							a_		:	Dict									=   image_processor(image_inputs[0]	,    ['''semantic''']	,    return_tensors='''pt'''       ).pixel_values

							a_					,      a_		:	Optional[int]									=   self.image_processing_tester.get_expected_values(__SCREAMING_SNAKE_CASE       )

							self.assertEqual(
							    encoded_images.shape	,    (1, self.image_processing_tester.num_channels, expected_height, expected_width)	,    )

							# Test batched
							a_					,      a_		:	Union[str, Any]									=   self.image_processing_tester.get_expected_values(__SCREAMING_SNAKE_CASE	,    batched=__SCREAMING_SNAKE_CASE       )

							a_		:	Any									=   image_processor(
							    __SCREAMING_SNAKE_CASE	,    ['''semantic'''] * len(__SCREAMING_SNAKE_CASE       )	,    return_tensors='''pt'''       ).pixel_values
							self.assertEqual(
							    encoded_images.shape	,    (
							        self.image_processing_tester.batch_size,
							        self.image_processing_tester.num_channels,
							        expected_height,
							        expected_width,
							    )	,    )






			def       SCREAMING_SNAKE_CASE   (			self   :      List[Any]       )      ->      str:
							# Initialize image_processor
							a_		:	Dict									=   self.image_processing_class(**self.image_processor_dict       )
							# create random numpy tensors
							a_		:	Tuple									=   prepare_image_inputs(self.image_processing_tester	,    equal_resolution=__SCREAMING_SNAKE_CASE	,    numpify=__SCREAMING_SNAKE_CASE       )
							for image in image_inputs:
											self.assertIsInstance(__SCREAMING_SNAKE_CASE	,    np.ndarray       )

							# Test not batched input
							a_		:	List[Any]									=   image_processor(image_inputs[0]	,    ['''semantic''']	,    return_tensors='''pt'''       ).pixel_values

							a_					,      a_		:	Union[str, Any]									=   self.image_processing_tester.get_expected_values(__SCREAMING_SNAKE_CASE       )

							self.assertEqual(
							    encoded_images.shape	,    (1, self.image_processing_tester.num_channels, expected_height, expected_width)	,    )

							# Test batched
							a_					,      a_		:	Union[str, Any]									=   self.image_processing_tester.get_expected_values(__SCREAMING_SNAKE_CASE	,    batched=__SCREAMING_SNAKE_CASE       )

							a_		:	Tuple									=   image_processor(
							    __SCREAMING_SNAKE_CASE	,    ['''semantic'''] * len(__SCREAMING_SNAKE_CASE       )	,    return_tensors='''pt'''       ).pixel_values
							self.assertEqual(
							    encoded_images.shape	,    (
							        self.image_processing_tester.batch_size,
							        self.image_processing_tester.num_channels,
							        expected_height,
							        expected_width,
							    )	,    )






			def       SCREAMING_SNAKE_CASE   (			self   :      int       )      ->      int:
							# Initialize image_processor
							a_		:	str									=   self.image_processing_class(**self.image_processor_dict       )
							# create random PyTorch tensors
							a_		:	Tuple									=   prepare_image_inputs(self.image_processing_tester	,    equal_resolution=__SCREAMING_SNAKE_CASE	,    torchify=__SCREAMING_SNAKE_CASE       )
							for image in image_inputs:
											self.assertIsInstance(__SCREAMING_SNAKE_CASE	,    torch.Tensor       )

							# Test not batched input
							a_		:	str									=   image_processor(image_inputs[0]	,    ['''semantic''']	,    return_tensors='''pt'''       ).pixel_values

							a_					,      a_		:	int									=   self.image_processing_tester.get_expected_values(__SCREAMING_SNAKE_CASE       )

							self.assertEqual(
							    encoded_images.shape	,    (1, self.image_processing_tester.num_channels, expected_height, expected_width)	,    )

							# Test batched
							a_					,      a_		:	Tuple									=   self.image_processing_tester.get_expected_values(__SCREAMING_SNAKE_CASE	,    batched=__SCREAMING_SNAKE_CASE       )

							a_		:	int									=   image_processor(
							    __SCREAMING_SNAKE_CASE	,    ['''semantic'''] * len(__SCREAMING_SNAKE_CASE       )	,    return_tensors='''pt'''       ).pixel_values
							self.assertEqual(
							    encoded_images.shape	,    (
							        self.image_processing_tester.batch_size,
							        self.image_processing_tester.num_channels,
							        expected_height,
							        expected_width,
							    )	,    )






			def       SCREAMING_SNAKE_CASE   (			self   :      Union[str, Any]	,    __SCREAMING_SNAKE_CASE   :      List[Any]=False	,    __SCREAMING_SNAKE_CASE   :      Optional[int]=False	,    __SCREAMING_SNAKE_CASE   :      str="np"       )      ->      Any:
							a_		:	Dict									=   self.image_processing_class(**self.image_processor_dict       )
							# prepare image and target
							a_		:	Optional[Any]									=   self.image_processing_tester.num_labels
							a_		:	Union[str, Any]									=   None
							a_		:	int									=   None
							a_		:	Dict									=   prepare_image_inputs(self.image_processing_tester	,    equal_resolution=__SCREAMING_SNAKE_CASE       )
							if with_segmentation_maps:
											a_		:	List[str]									=   num_labels
											if is_instance_map:
															a_		:	str									=   list(range(__SCREAMING_SNAKE_CASE       )       ) * 2
															a_		:	str									=   dict(enumerate(__SCREAMING_SNAKE_CASE       )       )
											a_		:	List[str]									=   [
											    np.random.randint(0	,    high * 2	,    (img.size[1], img.size[0])       ).astype(np.uinta       ) for img in image_inputs
											]
											if segmentation_type == "pil":
															a_		:	Any									=   [Image.fromarray(__SCREAMING_SNAKE_CASE       ) for annotation in annotations]

							a_		:	Dict									=   image_processor(
							    __SCREAMING_SNAKE_CASE	,    ['''semantic'''] * len(__SCREAMING_SNAKE_CASE       )	,    __SCREAMING_SNAKE_CASE	,    return_tensors='''pt'''	,    instance_id_to_semantic_id=__SCREAMING_SNAKE_CASE	,    pad_and_return_pixel_mask=__SCREAMING_SNAKE_CASE	,    )

							return inputs






			def       SCREAMING_SNAKE_CASE   (			self   :      List[str]       )      ->      Optional[Any]:
							pass






			def       SCREAMING_SNAKE_CASE   (			self   :      Dict       )      ->      List[str]:
							def common(__SCREAMING_SNAKE_CASE   :      int=False	,    __SCREAMING_SNAKE_CASE   :      List[str]=None       ):
											a_		:	Tuple									=   self.comm_get_image_processor_inputs(
											    with_segmentation_maps=__SCREAMING_SNAKE_CASE	,    is_instance_map=__SCREAMING_SNAKE_CASE	,    segmentation_type=__SCREAMING_SNAKE_CASE       )

											a_		:	List[Any]									=   inputs['''mask_labels''']
											a_		:	Any									=   inputs['''class_labels''']
											a_		:	Any									=   inputs['''pixel_values''']
											a_		:	Optional[Any]									=   inputs['''text_inputs''']

											# check the batch_size
											for mask_label, class_label, text_input in zip(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       ):
															self.assertEqual(mask_label.shape[0]	,    class_label.shape[0]       )
															# this ensure padding has happened
															self.assertEqual(mask_label.shape[1:]	,    pixel_values.shape[2:]       )
															self.assertEqual(len(__SCREAMING_SNAKE_CASE       )	,    self.image_processing_tester.num_text       )

							common()
							common(is_instance_map=__SCREAMING_SNAKE_CASE       )
							common(is_instance_map=__SCREAMING_SNAKE_CASE	,    segmentation_type='''pil'''       )
							common(is_instance_map=__SCREAMING_SNAKE_CASE	,    segmentation_type='''pil'''       )






			def       SCREAMING_SNAKE_CASE   (			self   :      Dict       )      ->      str:
							a_		:	int									=   np.zeros((20, 50)       )
							a_		:	Dict									=   1
							a_		:	Optional[Any]									=   1
							a_		:	Dict									=   1

							a_		:	Tuple									=   binary_mask_to_rle(__SCREAMING_SNAKE_CASE       )
							self.assertEqual(len(__SCREAMING_SNAKE_CASE       )	,    4       )
							self.assertEqual(rle[0]	,    21       )
							self.assertEqual(rle[1]	,    45       )






			def       SCREAMING_SNAKE_CASE   (			self   :      Optional[int]       )      ->      int:
							a_		:	List[str]									=   self.image_processing_class(
							    num_labels=self.image_processing_tester.num_classes	,    max_seq_length=77	,    task_seq_length=77	,    class_info_file='''ade20k_panoptic.json'''	,    num_text=self.image_processing_tester.num_text	,    repo_path='''shi-labs/oneformer_demo'''	,    )
							a_		:	Any									=   self.image_processing_tester.get_fake_oneformer_outputs()

							a_		:	str									=   fature_extractor.post_process_semantic_segmentation(__SCREAMING_SNAKE_CASE       )

							self.assertEqual(len(__SCREAMING_SNAKE_CASE       )	,    self.image_processing_tester.batch_size       )
							self.assertEqual(
							    segmentation[0].shape	,    (
							        self.image_processing_tester.height,
							        self.image_processing_tester.width,
							    )	,    )

							a_		:	Dict									=   [(1, 4) for i in range(self.image_processing_tester.batch_size       )]
							a_		:	List[str]									=   fature_extractor.post_process_semantic_segmentation(__SCREAMING_SNAKE_CASE	,    target_sizes=__SCREAMING_SNAKE_CASE       )

							self.assertEqual(segmentation[0].shape	,    target_sizes[0]       )






			def       SCREAMING_SNAKE_CASE   (			self   :      str       )      ->      Dict:
							a_		:	Optional[Any]									=   self.image_processing_class(
							    num_labels=self.image_processing_tester.num_classes	,    max_seq_length=77	,    task_seq_length=77	,    class_info_file='''ade20k_panoptic.json'''	,    num_text=self.image_processing_tester.num_text	,    repo_path='''shi-labs/oneformer_demo'''	,    )
							a_		:	Optional[int]									=   self.image_processing_tester.get_fake_oneformer_outputs()
							a_		:	Tuple									=   image_processor.post_process_instance_segmentation(__SCREAMING_SNAKE_CASE	,    threshold=0       )

							self.assertTrue(len(__SCREAMING_SNAKE_CASE       ) == self.image_processing_tester.batch_size       )
							for el in segmentation:
											self.assertTrue('''segmentation''' in el       )
											self.assertTrue('''segments_info''' in el       )
											self.assertEqual(type(el['''segments_info''']       )	,    __SCREAMING_SNAKE_CASE       )
											self.assertEqual(
											    el['''segmentation'''].shape	,    (self.image_processing_tester.height, self.image_processing_tester.width)       )







			def       SCREAMING_SNAKE_CASE   (			self   :      int       )      ->      Union[str, Any]:
							a_		:	List[Any]									=   self.image_processing_class(
							    num_labels=self.image_processing_tester.num_classes	,    max_seq_length=77	,    task_seq_length=77	,    class_info_file='''ade20k_panoptic.json'''	,    num_text=self.image_processing_tester.num_text	,    repo_path='''shi-labs/oneformer_demo'''	,    )
							a_		:	int									=   self.image_processing_tester.get_fake_oneformer_outputs()
							a_		:	str									=   image_processor.post_process_panoptic_segmentation(__SCREAMING_SNAKE_CASE	,    threshold=0       )

							self.assertTrue(len(__SCREAMING_SNAKE_CASE       ) == self.image_processing_tester.batch_size       )
							for el in segmentation:
											self.assertTrue('''segmentation''' in el       )
											self.assertTrue('''segments_info''' in el       )
											self.assertEqual(type(el['''segments_info''']       )	,    __SCREAMING_SNAKE_CASE       )
											self.assertEqual(
											    el['''segmentation'''].shape	,    (self.image_processing_tester.height, self.image_processing_tester.width)       )




