




"""simple docstring"""




import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple

import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner import Split, TokenClassificationDataset, TokenClassificationTask

import transformers
from transformers import (
    AutoConfig,
    AutoModelForTokenClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


a_        =       logging.getLogger(__name__)

@dataclass
class   UpperCAmelCase_    :
		UpperCamelCase							=field(
		    metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}		)
		UpperCamelCase							=field(
		    default=snake_case   ,					metadata={"help": "Pretrained config name or path if not the same as model_name"}		)
		UpperCamelCase							=field(
		    default="NER"   ,					metadata={"help": "Task type to fine tune in training (e.g. NER, POS, etc)"}		)
		UpperCamelCase							=field(
		    default=snake_case   ,					metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}		)
		UpperCamelCase							=field(default=snake_case   ,					metadata={"help": "Set this flag to use fast tokenization."}		)
		# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
		# or just modify its tokenizer_config.json.
		UpperCamelCase							=field(
		    default=snake_case   ,					metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}   ,					)


@dataclass
class   UpperCAmelCase_    :
		UpperCamelCase							=field(
		    metadata={"help": "The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."}		)
		UpperCamelCase							=field(
		    default=snake_case   ,					metadata={"help": "Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."}   ,					)
		UpperCamelCase							=field(
		    default=1_28   ,					metadata={
		        "help": (
		            "The maximum total input sequence length after tokenization. Sequences longer "
		            "than this will be truncated, sequences shorter will be padded."
		        )
		    }   ,					)
		UpperCamelCase							=field(
		    default=snake_case   ,					metadata={"help": "Overwrite the cached training and evaluation sets"}		)







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.

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

			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.'''	)

			__lowercase			:  int								=       import_module('''tasks'''	)
			try:
						__lowercase			:  List[Any]								=       getattr(__UpperCamelCase	,				model_args.task_type	)
						__lowercase			:  TokenClassificationTask								=       token_classification_task_clazz()
			except AttributeError:
						raise ValueError(
						    f"""Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. """
						    f"""Available tasks classes are: {TokenClassificationTask.__subclasses__()}"""	)

			# 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'''	,				__UpperCamelCase	)

			# Set seed
			set_seed(training_args.seed	)

			# Prepare CONLL-2003 task
			__lowercase			:  Dict								=       token_classification_task.get_labels(data_args.labels	)
			__lowercase			:  Dict[int, str]								=       dict(enumerate(__UpperCamelCase	)	)
			__lowercase			:  Optional[int]								=       len(__UpperCamelCase	)

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

			__lowercase			:  int								=       AutoConfig.from_pretrained(
			    model_args.config_name if model_args.config_name else model_args.model_name_or_path	,				num_labels=__UpperCamelCase	,				idalabel=__UpperCamelCase	,				labelaid={label: i for i, label in enumerate(__UpperCamelCase	)}	,				cache_dir=model_args.cache_dir	,				)
			__lowercase			:  str								=       AutoTokenizer.from_pretrained(
			    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path	,				cache_dir=model_args.cache_dir	,				use_fast=model_args.use_fast	,				)
			__lowercase			:  Dict								=       AutoModelForTokenClassification.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	,				)

			# Get datasets
			__lowercase			:  List[str]								=       (
			    TokenClassificationDataset(
			        token_classification_task=__UpperCamelCase	,				data_dir=data_args.data_dir	,				tokenizer=__UpperCamelCase	,				labels=__UpperCamelCase	,				model_type=config.model_type	,				max_seq_length=data_args.max_seq_length	,				overwrite_cache=data_args.overwrite_cache	,				mode=Split.train	,				)
			    if training_args.do_train
			    else None
			)
			__lowercase			:  List[str]								=       (
			    TokenClassificationDataset(
			        token_classification_task=__UpperCamelCase	,				data_dir=data_args.data_dir	,				tokenizer=__UpperCamelCase	,				labels=__UpperCamelCase	,				model_type=config.model_type	,				max_seq_length=data_args.max_seq_length	,				overwrite_cache=data_args.overwrite_cache	,				mode=Split.dev	,				)
			    if training_args.do_eval
			    else None
			)

			def align_predictions(__UpperCamelCase	,				__UpperCamelCase	) -> Tuple[List[int], List[int]]:
						__lowercase			:  int								=       np.argmax(__UpperCamelCase	,				axis=2	)

						__lowercase			,__lowercase			:  Optional[Any]								=       preds.shape

						__lowercase			:  Any								=       [[] for _ in range(__UpperCamelCase	)]
						__lowercase			:  List[str]								=       [[] for _ in range(__UpperCamelCase	)]

						for i in range(__UpperCamelCase	):
									for j in range(__UpperCamelCase	):
												if label_ids[i, j] != nn.CrossEntropyLoss().ignore_index:
															out_label_list[i].append(label_map[label_ids[i][j]]	)
															preds_list[i].append(label_map[preds[i][j]]	)

						return preds_list, out_label_list

			def compute_metrics(__UpperCamelCase	) -> Dict:
						__lowercase			,__lowercase			:  str								=       align_predictions(p.predictions	,				p.label_ids	)
						return {
						    "accuracy_score": accuracy_score(__UpperCamelCase	,				__UpperCamelCase	),
						    "precision": precision_score(__UpperCamelCase	,				__UpperCamelCase	),
						    "recall": recall_score(__UpperCamelCase	,				__UpperCamelCase	),
						    "f1": fa_score(__UpperCamelCase	,				__UpperCamelCase	),
						}

			# Data collator
			__lowercase			:  Tuple								=       DataCollatorWithPadding(__UpperCamelCase	,				pad_to_multiple_of=8	) if training_args.fpaa else None

			# Initialize our Trainer
			__lowercase			:  Tuple								=       Trainer(
			    model=__UpperCamelCase	,				args=__UpperCamelCase	,				train_dataset=__UpperCamelCase	,				eval_dataset=__UpperCamelCase	,				compute_metrics=__UpperCamelCase	,				data_collator=__UpperCamelCase	,				)

			# Training
			if training_args.do_train:
						trainer.train(
						    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path	) else None	)
						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_process_zero():
									tokenizer.save_pretrained(training_args.output_dir	)

    # Evaluation
			__lowercase			:  Union[str, Any]								=       {}
			if training_args.do_eval:
						logger.info('''*** Evaluate ***'''	)

						__lowercase			:  List[Any]								=       trainer.evaluate()

						__lowercase			:  Optional[Any]								=       os.path.join(training_args.output_dir	,				'''eval_results.txt'''	)
						if trainer.is_world_process_zero():
									with open(__UpperCamelCase	,				'''w'''	) as writer:
												logger.info('''***** Eval results *****'''	)
												for key, value in result.items():
															logger.info('''  %s = %s'''	,				__UpperCamelCase	,				__UpperCamelCase	)
															writer.write('''%s = %s\n''' % (key, value)	)

									results.update(__UpperCamelCase	)

    # Predict
			if training_args.do_predict:
						__lowercase			:  List[str]								=       TokenClassificationDataset(
						    token_classification_task=__UpperCamelCase	,				data_dir=data_args.data_dir	,				tokenizer=__UpperCamelCase	,				labels=__UpperCamelCase	,				model_type=config.model_type	,				max_seq_length=data_args.max_seq_length	,				overwrite_cache=data_args.overwrite_cache	,				mode=Split.test	,				)

						__lowercase			,__lowercase			,__lowercase			:  Any								=       trainer.predict(__UpperCamelCase	)
						__lowercase			,__lowercase			:  int								=       align_predictions(__UpperCamelCase	,				__UpperCamelCase	)

						__lowercase			:  Tuple								=       os.path.join(training_args.output_dir	,				'''test_results.txt'''	)
						if trainer.is_world_process_zero():
									with open(__UpperCamelCase	,				'''w'''	) as writer:
												for key, value in metrics.items():
															logger.info('''  %s = %s'''	,				__UpperCamelCase	,				__UpperCamelCase	)
															writer.write('''%s = %s\n''' % (key, value)	)

        # Save predictions
						__lowercase			:  Optional[int]								=       os.path.join(training_args.output_dir	,				'''test_predictions.txt'''	)
						if trainer.is_world_process_zero():
									with open(__UpperCamelCase	,				'''w'''	) as writer:
												with open(os.path.join(data_args.data_dir	,				'''test.txt'''	)	,				'''r'''	) as f:
															token_classification_task.write_predictions_to_file(__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	)

			return results







def      __UpperCAmelCase     ( __UpperCamelCase	):
			# 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)       )




