
"""simple docstring"""




import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional

import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm

import transformers
from transformers import AutoConfig, set_seed
from transformers.trainer_utils import IntervalStrategy


UpperCAmelCase__   :					Union[str, Any]												=				logging.getLogger(__name__)

UpperCAmelCase__   :					List[Any]												=				'pytorch_model.bin'



@dataclasses.dataclass
class 			lowerCAmelCase_		:


				"""simple docstring"""



				__UpperCamelCase   :		str       =     dataclasses.field(
				    metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models.'''}						)
				__UpperCamelCase   :		Optional[str]       =     dataclasses.field(
				    default=a__				,      metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co.'''}				,      )






@dataclasses.dataclass
class 			lowerCAmelCase_		:


				"""simple docstring"""



				__UpperCamelCase   :		str       =     dataclasses.field(metadata={'''help''': '''A csv or a json file containing the training data.'''}						)
				__UpperCamelCase   :		str       =     dataclasses.field(metadata={'''help''': '''A csv or a json file containing the data to predict on.'''}						)
				__UpperCamelCase   :		Optional[str]       =     dataclasses.field(
				    default=a__				,      metadata={'''help''': '''A csv or a json file containing the validation data.'''}						)
				__UpperCamelCase   :		Optional[str]       =     dataclasses.field(
				    default=a__				,      metadata={'''help''': '''The name of the task to train on.'''}				,      )
				__UpperCamelCase   :		Optional[List[str]]       =     dataclasses.field(
				    default=a__				,      metadata={'''help''': '''The list of labels for the task.'''}						)






@dataclasses.dataclass
class 			lowerCAmelCase_		:


				"""simple docstring"""



				__UpperCamelCase   :		str       =     dataclasses.field(
				    metadata={'''help''': '''The output directory where the model predictions and checkpoints will be written.'''}						)
				__UpperCamelCase   :		Optional[str]       =     dataclasses.field(
				    default='''accuracy'''				,      metadata={'''help''': '''The evaluation metric used for the task.'''}						)
				__UpperCamelCase   :		Optional[str]       =     dataclasses.field(
				    default='''no'''				,      metadata={
				        '''help''': '''The evaluation strategy to adopt during training. Possible values are: ["no", "step", "epoch]'''
				    }				,      )
				__UpperCamelCase   :		Optional[int]       =     dataclasses.field(
				    default=10				,      metadata={'''help''': '''Number of evaluation calls with no improvement after which training will be stopped.'''}				,      )
				__UpperCamelCase   :		Optional[float]       =     dataclasses.field(
				    default=0.0				,      metadata={
				        '''help''': '''How much the specified evaluation metric must improve to satisfy early stopping conditions.'''
				    }				,      )
				__UpperCamelCase   :		Optional[bool]       =     dataclasses.field(
				    default=a__				,      metadata={'''help''': '''Whether to filter the pseudo-labeled data based on the confidence score.'''}				,      )
				__UpperCamelCase   :		Optional[bool]       =     dataclasses.field(
				    default=a__				,      metadata={'''help''': '''Whether to filter the pseudo-labeled data based on the validation performance.'''}				,      )
				__UpperCamelCase   :		Optional[bool]       =     dataclasses.field(
				    default=a__				,      metadata={'''help''': '''Whether to fine-tune on labeled data after pseudo training.'''}				,      )
				__UpperCamelCase   :		Optional[float]       =     dataclasses.field(
				    default=0.0				,      metadata={'''help''': '''Confidence threshold for pseudo-labeled data filtering.'''}				,      )
				__UpperCamelCase   :		Optional[int]       =     dataclasses.field(
				    default=100				,      metadata={'''help''': '''Number of evaluation calls with no improvement after which training will be stopped.'''}				,      )
				__UpperCamelCase   :		Optional[int]       =     dataclasses.field(
				    default=a__				,      metadata={'''help''': '''Random seed for initialization.'''}				,      )






def lowercase_						(     _snake_case     ,_snake_case     ,_snake_case     ,_snake_case     ,_snake_case     ,_snake_case		):
							SCREAMING_SNAKE_CASE__		: Optional[Any]     =     datasets.concatenate_datasets([infer_input, infer_output]     ,axis=1		)

							if args.do_filter_by_confidence:
														SCREAMING_SNAKE_CASE__		: str     =     dataset.filter(lambda _snake_case		: example["probability"] > args.confidence_threshold		)

							if args.do_filter_by_val_performance:
														assert eval_result >= 0.0 and eval_result <= 1.0
														SCREAMING_SNAKE_CASE__		: List[Any]     =     int(eval_result * len(_snake_case		)		)
														print(_snake_case		)
														SCREAMING_SNAKE_CASE__		: str     =     dataset.sort("""probability"""     ,reverse=_snake_case		)
														SCREAMING_SNAKE_CASE__		: List[Any]     =     dataset.select(range(_snake_case		)		)

							SCREAMING_SNAKE_CASE__		: Optional[Any]     =     dataset.remove_columns(["""label""", """probability"""]		)
							SCREAMING_SNAKE_CASE__		: Tuple     =     dataset.rename_column("""prediction"""     ,"""label"""		)
							SCREAMING_SNAKE_CASE__		: Optional[Any]     =     dataset.map(lambda _snake_case		: {"label": idalabel[example["label"]]}		)
							SCREAMING_SNAKE_CASE__		: str     =     dataset.shuffle(seed=args.seed		)

							SCREAMING_SNAKE_CASE__		: Tuple     =     os.path.join(_snake_case     ,f'''train_pseudo.{args.data_file_extension}'''		)
							if args.data_file_extension == "csv":
														dataset.to_csv(_snake_case     ,index=_snake_case		)
							else:
														dataset.to_json(_snake_case		)

def lowercase_						(     _snake_case     ,_snake_case     ,_snake_case     ,_snake_case     ,**_snake_case		):
							SCREAMING_SNAKE_CASE__		: Tuple     =     Accelerator()
							# Make one log on every process with the configuration for debugging.
							logging.basicConfig(
							    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""     ,datefmt="""%m/%d/%Y %H:%M:%S"""     ,level=logging.INFO     ,)
							logger.info(accelerator.state		)

							# Setup logging, we only want one process per machine to log things on the
							# screen. accelerator.is_local_main_process is only True for one process per
							# machine.
							logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR		)

							if accelerator.is_local_main_process:
														datasets.utils.logging.set_verbosity_warning()
														transformers.utils.logging.set_verbosity_info()
							else:
														datasets.utils.logging.set_verbosity_error()
														transformers.utils.logging.set_verbosity_error()

							SCREAMING_SNAKE_CASE__		: List[str]     =     STModelArguments(model_name_or_path=_snake_case		)
							SCREAMING_SNAKE_CASE__		: Optional[int]     =     STDataArguments(train_file=_snake_case     ,infer_file=_snake_case		)
							SCREAMING_SNAKE_CASE__		: Any     =     STTrainingArguments(output_dir=_snake_case		)
							SCREAMING_SNAKE_CASE__		: Any     =     argparse.Namespace()

							for arg_class in (model_args, data_args, training_args):
														for key, value in vars(_snake_case		).items():
																					setattr(_snake_case     ,_snake_case     ,_snake_case		)

							for key, value in kwargs.items():
														if hasattr(_snake_case     ,_snake_case		):
																					setattr(_snake_case     ,_snake_case     ,_snake_case		)

    # Sanity checks
							SCREAMING_SNAKE_CASE__		: str     =     {}
							SCREAMING_SNAKE_CASE__		: Dict     =     None

							# You need to provide the training data and the data to predict on
							assert args.train_file is not None
							assert args.infer_file is not None
							SCREAMING_SNAKE_CASE__		: List[Any]     =     args.train_file
							SCREAMING_SNAKE_CASE__		: Optional[Any]     =     args.infer_file

							if args.evaluation_strategy != IntervalStrategy.NO.value:
														assert args.eval_file is not None
														SCREAMING_SNAKE_CASE__		: List[str]     =     args.eval_file

							for key in data_files:
														SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     data_files[key].split("""."""		)[-1]
														assert extension in ["csv", "json"], f'''`{key}_file` should be a csv or a json file.'''
														if args.data_file_extension is None:
																					SCREAMING_SNAKE_CASE__		: Any     =     extension
														else:
																					assert extension == args.data_file_extension, f'''`{key}_file` should be a {args.data_file_extension} file`.'''

							assert (
							    args.eval_metric in datasets.list_metrics()
							), f'''{args.eval_metric} not in the list of supported metrics {datasets.list_metrics()}.'''

							# If passed along, set the training seed now.
							if args.seed is not None:
														set_seed(args.seed		)

							logger.info("""Creating the initial data directory for self-training..."""		)
							SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     f'''{args.output_dir}/self-train_iter-{{}}'''.format
							SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     data_dir_format(0		)

							if accelerator.is_main_process:
														if args.output_dir is not None:
																					os.makedirs(args.output_dir     ,exist_ok=_snake_case		)
																					os.makedirs(_snake_case     ,exist_ok=_snake_case		)
							accelerator.wait_for_everyone()

							SCREAMING_SNAKE_CASE__		: str     =     None
							SCREAMING_SNAKE_CASE__		: List[str]     =     None
							SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     0
							SCREAMING_SNAKE_CASE__		: List[Any]     =     False
							# Show the progress bar
							SCREAMING_SNAKE_CASE__		: Any     =     tqdm(range(args.max_selftrain_iterations		)     ,disable=not accelerator.is_local_main_process		)

							# Self-train
							for iteration in range(0     ,int(args.max_selftrain_iterations		)		):
														SCREAMING_SNAKE_CASE__		: Dict     =     data_dir_format(_snake_case		)
														assert os.path.exists(_snake_case		)

														# Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
														# iteration > 0
														SCREAMING_SNAKE_CASE__		: Dict     =     os.path.join(_snake_case     ,"""stage-1"""		)
														SCREAMING_SNAKE_CASE__		: Dict     =     {
														    """accelerator""": accelerator,
														    """model_name_or_path""": args.model_name_or_path,
														    """cache_dir""": args.cache_dir,
														    """do_train""": True,
														    """train_file""": data_files["""train"""] if iteration == 0 else data_files["""train_pseudo"""],
														    """do_eval""": True if args.eval_file is not None else False,
														    """eval_file""": data_files["""eval"""],
														    """do_predict""": True,
														    """infer_file""": data_files["""infer"""],
														    """task_name""": args.task_name,
														    """label_list""": args.label_list,
														    """output_dir""": current_output_dir,
														    """eval_metric""": args.eval_metric,
														    """evaluation_strategy""": args.evaluation_strategy,
														    """early_stopping_patience""": args.early_stopping_patience,
														    """early_stopping_threshold""": args.early_stopping_threshold,
														    """seed""": args.seed,
														}
														# Add additional training arguments
														for key, value in kwargs.items():
																					if key not in arguments_dict and not hasattr(_snake_case     ,_snake_case		):
																												arguments_dict.update({key: value}		)

														SCREAMING_SNAKE_CASE__		: Dict     =     os.path.join(_snake_case     ,"""best-checkpoint"""     ,_snake_case		)
														if os.path.exists(_snake_case		):
																					logger.info(
																					    """Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 1."""     ,_snake_case     ,_snake_case     ,)
														else:
																					logger.info("""***** Running self-training: iteration: %d, stage: 1 *****"""     ,_snake_case		)
																					finetune(**_snake_case		)
																					accelerator.wait_for_everyone()
																					assert os.path.exists(_snake_case		)
																					logger.info("""Self-training job completed: iteration: %d, stage: 1."""     ,_snake_case		)

														if iteration > 0 and args.finetune_on_labeled_data:
																					# Stage 2 (optional): fine-tuning on the original labeled data
																					SCREAMING_SNAKE_CASE__		: str     =     os.path.join(_snake_case     ,"""best-checkpoint"""		)
																					SCREAMING_SNAKE_CASE__		: Dict     =     os.path.join(_snake_case     ,"""stage-2"""		)
																					# Update arguments_dict
																					SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     model_path
																					SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     data_files["""train"""]
																					SCREAMING_SNAKE_CASE__		: Any     =     current_output_dir

																					SCREAMING_SNAKE_CASE__		: Optional[Any]     =     os.path.join(_snake_case     ,"""best-checkpoint"""     ,_snake_case		)
																					if os.path.exists(_snake_case		):
																												logger.info(
																												    """Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 2."""     ,_snake_case     ,_snake_case     ,)
																					else:
																												logger.info("""***** Running self-training: iteration: %d, stage: 2 *****"""     ,_snake_case		)
																												finetune(**_snake_case		)
																												accelerator.wait_for_everyone()
																												assert os.path.exists(_snake_case		)
																												logger.info("""Self-training job completed: iteration: %d, stage: 2."""     ,_snake_case		)

														SCREAMING_SNAKE_CASE__		: Tuple     =     iteration
														SCREAMING_SNAKE_CASE__		: Optional[int]     =     data_dir_format(iteration + 1		)

														SCREAMING_SNAKE_CASE__		: Dict     =     AutoConfig.from_pretrained(os.path.join(_snake_case     ,"""best-checkpoint"""		)		)
														SCREAMING_SNAKE_CASE__		: int     =     config.idalabel
														SCREAMING_SNAKE_CASE__		: List[Any]     =     os.path.join(_snake_case     ,"""eval_results_best-checkpoint.json"""		)
														SCREAMING_SNAKE_CASE__		: Tuple     =     os.path.join(_snake_case     ,"""test_results_best-checkpoint.json"""		)
														assert os.path.exists(_snake_case		)

														with open(_snake_case     ,"""r"""		) as f:
																					SCREAMING_SNAKE_CASE__		: Optional[int]     =     float(json.load(_snake_case		)[args.eval_metric]		)
														SCREAMING_SNAKE_CASE__		: List[str]     =     os.path.join(_snake_case     ,"""infer_output_best-checkpoint.csv"""		)
														assert os.path.exists(_snake_case		)
														# Loading the dataset from local csv or json files.
														SCREAMING_SNAKE_CASE__		: Optional[Any]     =     load_dataset(args.data_file_extension     ,data_files={"""data""": data_files["""infer"""]}		)["""data"""]
														SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     load_dataset("""csv"""     ,data_files={"""data""": infer_output_file}		)["""data"""]

														if accelerator.is_main_process:
																					os.makedirs(_snake_case     ,exist_ok=_snake_case		)
																					shutil.copy(_snake_case     ,os.path.join(_snake_case     ,f'''eval_results_iter-{iteration}.json'''		)		)
																					if os.path.exists(_snake_case		):
																												shutil.copy(_snake_case     ,os.path.join(_snake_case     ,f'''test_results_iter-{iteration}.json'''		)		)
																					create_pseudo_labeled_data(_snake_case     ,_snake_case     ,_snake_case     ,_snake_case     ,_snake_case     ,_snake_case		)
														accelerator.wait_for_everyone()

														SCREAMING_SNAKE_CASE__		: str     =     os.path.join(_snake_case     ,f'''train_pseudo.{args.data_file_extension}'''		)

														if args.evaluation_strategy != IntervalStrategy.NO.value:
																					SCREAMING_SNAKE_CASE__		: Tuple     =     eval_result

																					if best_iteration is None:
																												SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     new_iteration
																												SCREAMING_SNAKE_CASE__		: Any     =     new_eval_result
																					else:
																												if new_eval_result - best_eval_result > args.early_stopping_threshold:
																																			SCREAMING_SNAKE_CASE__		: Dict     =     new_iteration
																																			SCREAMING_SNAKE_CASE__		: Tuple     =     new_eval_result
																																			SCREAMING_SNAKE_CASE__		: List[str]     =     0
																												else:
																																			if new_eval_result == best_eval_result:
																																										SCREAMING_SNAKE_CASE__		: Optional[int]     =     new_iteration
																																										SCREAMING_SNAKE_CASE__		: Optional[int]     =     new_eval_result
																																			early_stopping_patience_counter += 1

																												if early_stopping_patience_counter >= args.early_stopping_patience:
																																			SCREAMING_SNAKE_CASE__		: List[Any]     =     True

														progress_bar.update(1		)

														if should_training_stop:
																					break

							if best_iteration is not None:
														# Save the best iteration
														logger.info("""Best iteration: %d"""     ,_snake_case		)
														logger.info("""Best evaluation result: %s = %f"""     ,args.eval_metric     ,_snake_case		)
														accelerator.wait_for_everyone()
														if accelerator.is_main_process:
																					shutil.copy(
																					    os.path.join(_snake_case     ,f'''eval_results_iter-{iteration}.json'''		)     ,os.path.join(_snake_case     ,"""eval_results_best-iteration.json"""		)     ,)
							else:
														# Assume that the last iteration is the best
														logger.info("""Best iteration: %d"""     ,args.max_selftrain_iterations - 1		)
														logger.info("""Best evaluation result: %s = %f"""     ,args.eval_metric     ,_snake_case		)
														accelerator.wait_for_everyone()
														if accelerator.is_main_process:
																					shutil.copy(
																					    os.path.join(_snake_case     ,f'''eval_results_iter-{args.max_selftrain_iterations - 1}.json'''		)     ,os.path.join(_snake_case     ,"""eval_results_best-iteration.json"""		)     ,)






"""simple docstring"""




import unittest

import numpy as np

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 PIL import Image

					from transformers import MobileViTImageProcessor



class 			lowerCAmelCase_		(unittest.TestCase						):


				"""simple docstring"""



				def __init__(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__=7     , SCREAMING_SNAKE_CASE__=3     , SCREAMING_SNAKE_CASE__=18     , SCREAMING_SNAKE_CASE__=30     , SCREAMING_SNAKE_CASE__=4_00     , SCREAMING_SNAKE_CASE__=True     , SCREAMING_SNAKE_CASE__=None     , SCREAMING_SNAKE_CASE__=True     , SCREAMING_SNAKE_CASE__=None     , SCREAMING_SNAKE_CASE__=True     , )      ->	List[Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: List[str]     =     size if size is not None else {"""shortest_edge""": 20}
											SCREAMING_SNAKE_CASE__		: List[Any]     =     crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
											SCREAMING_SNAKE_CASE__		: Any     =     parent
											SCREAMING_SNAKE_CASE__		: List[str]     =     batch_size
											SCREAMING_SNAKE_CASE__		: Dict     =     num_channels
											SCREAMING_SNAKE_CASE__		: List[Any]     =     image_size
											SCREAMING_SNAKE_CASE__		: Dict     =     min_resolution
											SCREAMING_SNAKE_CASE__		: Dict     =     max_resolution
											SCREAMING_SNAKE_CASE__		: Optional[int]     =     do_resize
											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     size
											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     do_center_crop
											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     crop_size
											SCREAMING_SNAKE_CASE__		: List[str]     =     do_flip_channel_order



				def 			__magic_name__					(self					)      ->	int:



											"""simple docstring"""




											return {
											    "do_resize": self.do_resize,
											    "size": self.size,
											    "do_center_crop": self.do_center_crop,
											    "crop_size": self.crop_size,
											    "do_flip_channel_order": self.do_flip_channel_order,
											}






@require_torch
@require_vision
class 			lowerCAmelCase_		(a__				,      unittest.TestCase						):


				"""simple docstring"""



				__UpperCamelCase   :		str       =     MobileViTImageProcessor if is_vision_available() else None



				def 			__magic_name__					(self					)      ->	int:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     MobileViTImageProcessingTester(self					)



				@property
				def 			__magic_name__					(self					)      ->	Any:



											"""simple docstring"""




											return self.image_processor_tester.prepare_image_processor_dict()



				def 			__magic_name__					(self					)      ->	Optional[Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     self.image_processing_class(**self.image_processor_dict					)
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__     , """do_resize"""					)					)
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__     , """size"""					)					)
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__     , """do_center_crop"""					)					)
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__     , """center_crop"""					)					)
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__     , """do_flip_channel_order"""					)					)



				def 			__magic_name__					(self					)      ->	Tuple:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Dict     =     self.image_processing_class.from_dict(self.image_processor_dict					)
											self.assertEqual(image_processor.size     , {"""shortest_edge""": 20}					)
											self.assertEqual(image_processor.crop_size     , {"""height""": 18, """width""": 18}					)

											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     self.image_processing_class.from_dict(self.image_processor_dict     , size=42     , crop_size=84					)
											self.assertEqual(image_processor.size     , {"""shortest_edge""": 42}					)
											self.assertEqual(image_processor.crop_size     , {"""height""": 84, """width""": 84}					)



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



											"""simple docstring"""




											pass



				def 			__magic_name__					(self					)      ->	Optional[int]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: List[str]     =     self.image_processing_class(**self.image_processor_dict					)
											# create random PIL images
											SCREAMING_SNAKE_CASE__		: List[str]     =     prepare_image_inputs(self.image_processor_tester     , equal_resolution=SCREAMING_SNAKE_CASE__					)
											for image in image_inputs:
																		self.assertIsInstance(SCREAMING_SNAKE_CASE__     , Image.Image					)

											# Test not batched input
											SCREAMING_SNAKE_CASE__		: Dict     =     image_processing(image_inputs[0]     , return_tensors="""pt"""					).pixel_values
											self.assertEqual(
											    encoded_images.shape     , (
											        1,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.crop_size["""height"""],
											        self.image_processor_tester.crop_size["""width"""],
											    )     , )

											# Test batched
											SCREAMING_SNAKE_CASE__		: List[str]     =     image_processing(SCREAMING_SNAKE_CASE__     , return_tensors="""pt"""					).pixel_values
											self.assertEqual(
											    encoded_images.shape     , (
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.crop_size["""height"""],
											        self.image_processor_tester.crop_size["""width"""],
											    )     , )



				def 			__magic_name__					(self					)      ->	int:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Optional[int]     =     self.image_processing_class(**self.image_processor_dict					)
											# create random numpy tensors
											SCREAMING_SNAKE_CASE__		: Tuple     =     prepare_image_inputs(self.image_processor_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
											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     image_processing(image_inputs[0]     , return_tensors="""pt"""					).pixel_values
											self.assertEqual(
											    encoded_images.shape     , (
											        1,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.crop_size["""height"""],
											        self.image_processor_tester.crop_size["""width"""],
											    )     , )

											# Test batched
											SCREAMING_SNAKE_CASE__		: Optional[int]     =     image_processing(SCREAMING_SNAKE_CASE__     , return_tensors="""pt"""					).pixel_values
											self.assertEqual(
											    encoded_images.shape     , (
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.crop_size["""height"""],
											        self.image_processor_tester.crop_size["""width"""],
											    )     , )



				def 			__magic_name__					(self					)      ->	Optional[int]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: List[Any]     =     self.image_processing_class(**self.image_processor_dict					)
											# create random PyTorch tensors
											SCREAMING_SNAKE_CASE__		: Any     =     prepare_image_inputs(self.image_processor_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
											SCREAMING_SNAKE_CASE__		: List[str]     =     image_processing(image_inputs[0]     , return_tensors="""pt"""					).pixel_values
											self.assertEqual(
											    encoded_images.shape     , (
											        1,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.crop_size["""height"""],
											        self.image_processor_tester.crop_size["""width"""],
											    )     , )

											# Test batched
											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     image_processing(SCREAMING_SNAKE_CASE__     , return_tensors="""pt"""					).pixel_values
											self.assertEqual(
											    encoded_images.shape     , (
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.crop_size["""height"""],
											        self.image_processor_tester.crop_size["""width"""],
											    )     , )




