
"""simple docstring"""



from dataclasses import dataclass, field
from typing import Optional
@dataclass
class        _SCREAMING_SNAKE_CASE  :






				'''simple docstring'''



				SCREAMING_SNAKE_CASE_:							Optional[str]							=					field(
				    default="codeparrot/codeparrot"      ,				metadata={"help": "Model name or path of model to be trained."}							)
				SCREAMING_SNAKE_CASE_:							Optional[str]							=					field(
				    default="./"      ,				metadata={"help": "Save dir where model repo is cloned and models updates are saved to."}							)
				SCREAMING_SNAKE_CASE_:							Optional[str]							=					field(
				    default="codeparrot/codeparrot-clean-train"      ,				metadata={"help": "Name or path of training dataset."}							)
				SCREAMING_SNAKE_CASE_:							Optional[str]							=					field(
				    default="codeparrot/codeparrot-clean-valid"      ,				metadata={"help": "Name or path of validation dataset."}							)
				SCREAMING_SNAKE_CASE_:							Optional[int]							=					field(default=2      ,				metadata={"help": "Batch size for training."}							)
				SCREAMING_SNAKE_CASE_:							Optional[int]							=					field(default=2      ,				metadata={"help": "Batch size for evaluation."}							)
				SCREAMING_SNAKE_CASE_:							Optional[float]							=					field(default=0.1      ,				metadata={"help": "Value of weight decay."}							)
				SCREAMING_SNAKE_CASE_:							Optional[int]							=					field(
				    default=1_0_0_0_0      ,				metadata={"help": "Size of buffer used to shuffle streaming dataset."}							)
				SCREAMING_SNAKE_CASE_:							Optional[float]							=					field(default=2e-4      ,				metadata={"help": "Learning rate fo training."}							)
				SCREAMING_SNAKE_CASE_:							Optional[str]							=					field(default="cosine"      ,				metadata={"help": "Learning rate."}							)
				SCREAMING_SNAKE_CASE_:							Optional[int]							=					field(
				    default=7_5_0      ,				metadata={"help": "Number of warmup steps in the learning rate schedule."}							)
				SCREAMING_SNAKE_CASE_:							Optional[int]							=					field(
				    default=1_6      ,				metadata={"help": "Number of gradient accumulation steps."}							)
				SCREAMING_SNAKE_CASE_:							Optional[bool]							=					field(
				    default=UpperCAmelCase      ,				metadata={"help": "Use gradient checkpointing to reduce memory footprint."}							)
				SCREAMING_SNAKE_CASE_:							Optional[int]							=					field(default=5_0_0_0_0      ,				metadata={"help": "Maximum number of training steps."}							)
				SCREAMING_SNAKE_CASE_:							Optional[int]							=					field(
				    default=-1      ,				metadata={"help": "Maximum number of evaluation steps. If -1 the full dataset is evaluated."}							)
				SCREAMING_SNAKE_CASE_:							Optional[int]							=					field(default=1_0_2_4      ,				metadata={"help": "Sequence lengths used for training."}							)
				SCREAMING_SNAKE_CASE_:							Optional[int]							=					field(default=1      ,				metadata={"help": "Training seed."}							)
				SCREAMING_SNAKE_CASE_:							Optional[int]							=					field(
				    default=1_0_2_4      ,				metadata={"help": "Interval to save checkpoints. Measured as number of forward passes not training steps."}      ,				)
				SCREAMING_SNAKE_CASE_:							Optional[str]							=					field(
				    default=UpperCAmelCase      ,				metadata={"help": "States path if the training should continue from a checkpoint folder."}							)
				SCREAMING_SNAKE_CASE_:							Optional[bool]							=					field(default=UpperCAmelCase      ,				metadata={"help": "If True the data is pretokenized."}							)






@dataclass
class        _SCREAMING_SNAKE_CASE  :






				'''simple docstring'''



				SCREAMING_SNAKE_CASE_:							Optional[str]							=					field(
				    default="codeparrot/codeparrot"      ,				metadata={"help": "Model name or path of model to be evaluated."}							)
				SCREAMING_SNAKE_CASE_:							Optional[str]							=					field(
				    default="codeparrot/codeparrot-clean-valid"      ,				metadata={"help": "Name or path of validation dataset."}							)
				SCREAMING_SNAKE_CASE_:							Optional[int]							=					field(default=2      ,				metadata={"help": "Batch size used for evaluation."}							)
				SCREAMING_SNAKE_CASE_:							Optional[int]							=					field(
				    default=-1      ,				metadata={"help": "Maximum number of evaluation steps. If -1 the full dataset is evaluated."}							)
				SCREAMING_SNAKE_CASE_:							Optional[int]							=					field(default=1_0_2_4      ,				metadata={"help": "Length of sequences to be evaluated."}							)
				SCREAMING_SNAKE_CASE_:							Optional[int]							=					field(default=1      ,				metadata={"help": "Random seed used for evaluation."}							)






@dataclass
class        _SCREAMING_SNAKE_CASE  :






				'''simple docstring'''



				SCREAMING_SNAKE_CASE_:							Optional[str]							=					field(
				    default="codeparrot/codeparrot"      ,				metadata={"help": "Model name or path of model to be evaluated."}							)
				SCREAMING_SNAKE_CASE_:							Optional[int]							=					field(default=UpperCAmelCase      ,				metadata={"help": "Number of workers used for code evaluation."}							)
				SCREAMING_SNAKE_CASE_:							Optional[int]							=					field(
				    default=UpperCAmelCase      ,				metadata={"help": "The number of human-eval tasks to run. If not included all tasks are evaluated."}      ,				)
				SCREAMING_SNAKE_CASE_:							Optional[bool]							=					field(
				    default=UpperCAmelCase      ,				metadata={"help": "Sample from the language model's output distribution."}							)
				SCREAMING_SNAKE_CASE_:							Optional[float]							=					field(default=0.2      ,				metadata={"help": "Sampling temperature used for generation."}							)
				SCREAMING_SNAKE_CASE_:							Optional[int]							=					field(default=2_5_6      ,				metadata={"help": "Maximum number of newly generated tokens."}							)
				SCREAMING_SNAKE_CASE_:							Optional[int]							=					field(default=0      ,				metadata={"help": "Top-k parameter used for generation."}							)
				SCREAMING_SNAKE_CASE_:							Optional[float]							=					field(default=0.9_5      ,				metadata={"help": "Top-p parameter used for nucleus sampling."}							)
				SCREAMING_SNAKE_CASE_:							Optional[int]							=					field(default=1_0      ,				metadata={"help": "Number of generations to run in parallel."}							)
				SCREAMING_SNAKE_CASE_:							Optional[int]							=					field(
				    default=2_0_0      ,				metadata={"help": "Number of completions to generate for each sample."}							)
				SCREAMING_SNAKE_CASE_:							Optional[int]							=					field(default=1      ,				metadata={"help": "Random seed used for evaluation."}							)
				SCREAMING_SNAKE_CASE_:							Optional[str]							=					field(
				    default="eval_results.json"      ,				metadata={"help": "Random seed used for evaluation."}							)
				SCREAMING_SNAKE_CASE_:							Optional[str]							=					field(
				    default="0"      ,				metadata={"help": "Allow `code_eval` to execute Python code on machine"}							)
				SCREAMING_SNAKE_CASE_:							Optional[int]							=					field(
				    default=-1      ,				metadata={
				        "help": (
				            "Determine which device to run the `text-generation` Pipeline on. -1 is CPU and any zero or positive"
				            " number corresponds to which GPU device id to run on."
				        )
				    }      ,				)






@dataclass
class        _SCREAMING_SNAKE_CASE  :






				'''simple docstring'''



				SCREAMING_SNAKE_CASE_:							Optional[int]							=					field(
				    default=UpperCAmelCase      ,				metadata={
				        "help": "The number of CPU cores to use for parallel preprocessing. Default uses the maximum available."
				    }      ,				)
				SCREAMING_SNAKE_CASE_:							Optional[str]							=					field(
				    default="transformersbook/codeparrot"      ,				metadata={"help": "Folder or name of dataset to process."}							)
				SCREAMING_SNAKE_CASE_:							Optional[str]							=					field(
				    default="codeparrot-clean"      ,				metadata={"help": "Folder to save processed processed dataset."}							)
				SCREAMING_SNAKE_CASE_:							Optional[int]							=					field(
				    default=1_0_0_0_0_0      ,				metadata={"help": "Number of files to save per JSON output file."}							)
				SCREAMING_SNAKE_CASE_:							Optional[str]							=					field(default="content"      ,				metadata={"help": "Column containing text data to process."}							)
				SCREAMING_SNAKE_CASE_:							Optional[float]							=					field(
				    default=1_0_0_0      ,				metadata={"help": "Maximum line length in file, otherwise file is filtered."}							)
				SCREAMING_SNAKE_CASE_:							Optional[float]							=					field(
				    default=1_0_0      ,				metadata={"help": "Maximum mean line length in file, otherwise file is filtered."}							)
				SCREAMING_SNAKE_CASE_:							Optional[float]							=					field(
				    default=0.2_5      ,				metadata={"help": "Maximum fraction of non-alphanumeric characters, otherwise file is filtered."}							)
				SCREAMING_SNAKE_CASE_:							Optional[float]							=					field(
				    default=1.5      ,				metadata={"help": "Minimum character token ratio for the file, otherwise file is filtered."}							)
				SCREAMING_SNAKE_CASE_:							Optional[float]							=					field(
				    default=0.7      ,				metadata={"help": "Probability for filtering config, test and uncommon files."}							)
				SCREAMING_SNAKE_CASE_:							Optional[str]							=					field(
				    default="codeparrot/codeparrot"      ,				metadata={"help": "Name or path to the tokenizer."}      ,				)
				SCREAMING_SNAKE_CASE_:							Optional[bool]							=					field(
				    default=UpperCAmelCase      ,				metadata={"help": "If True, near-duplicate samples are removed."}							)
				SCREAMING_SNAKE_CASE_:							Optional[float]							=					field(
				    default=0.8_5      ,				metadata={"help": "Jaccard threshold for near-duplicate samples."}							)






@dataclass
class        _SCREAMING_SNAKE_CASE  :






				'''simple docstring'''



				SCREAMING_SNAKE_CASE_:							Optional[str]							=					field(
				    default="gpt2"      ,				metadata={"help": "Base tokenizer to build new tokenizer from."}							)
				SCREAMING_SNAKE_CASE_:							Optional[str]							=					field(
				    default="transformersbook/codeparrot-train"      ,				metadata={"help": "Dataset to train tokenizer on."}							)
				SCREAMING_SNAKE_CASE_:							Optional[str]							=					field(default="content"      ,				metadata={"help": "Column containing text data to process."}							)
				SCREAMING_SNAKE_CASE_:							Optional[int]							=					field(default=2_0_0_0_0_0      ,				metadata={"help": "Number of examples to train tokenizer on."}							)
				SCREAMING_SNAKE_CASE_:							Optional[int]							=					field(
				    default=3_2_7_6_8      ,				metadata={"help": "Number of examples to train the tokenizer on."}							)
				SCREAMING_SNAKE_CASE_:							Optional[str]							=					field(default="codeparrot"      ,				metadata={"help": "Name of new tokenizer."}							)
				SCREAMING_SNAKE_CASE_:							Optional[bool]							=					field(default=UpperCAmelCase      ,				metadata={"help": "Push saved tokenizer to the hub."}							)






@dataclass
class        _SCREAMING_SNAKE_CASE  :






				'''simple docstring'''



				SCREAMING_SNAKE_CASE_:							Optional[str]							=					field(
				    default="codeparrot/codeparrot"      ,				metadata={"help": "Name or path to the tokenizer."}							)
				SCREAMING_SNAKE_CASE_:							Optional[str]							=					field(
				    default="codeparrot/codeparrot-clean-train"      ,				metadata={"help": "Name or path to the dataset to pretokenize."}							)
				SCREAMING_SNAKE_CASE_:							Optional[str]							=					field(
				    default="tokenized-codeparrot-train"      ,				metadata={"help": "Repo name of the pretokenized data."}							)
				SCREAMING_SNAKE_CASE_:							Optional[int]							=					field(default=UpperCAmelCase      ,				metadata={"help": "Number of workers used for code evaluation."}							)






@dataclass
class        _SCREAMING_SNAKE_CASE  :






				'''simple docstring'''



				SCREAMING_SNAKE_CASE_:							Optional[str]							=					field(
				    default="gpt2-large"      ,				metadata={"help": "Configuration to use for model initialization."}							)
				SCREAMING_SNAKE_CASE_:							Optional[str]							=					field(
				    default="codeparrot/codeparrot"      ,				metadata={"help": "Tokenizer attached to model."}							)
				SCREAMING_SNAKE_CASE_:							Optional[str]							=					field(default="codeparrot"      ,				metadata={"help": "Name of the created model."}							)
				SCREAMING_SNAKE_CASE_:							Optional[bool]							=					field(default=UpperCAmelCase      ,				metadata={"help": "Push saved tokenizer to the hub."}							)








"""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


if is_torch_available():
	import torch

if is_vision_available():
	from PIL import Image

	from transformers import ChineseCLIPImageProcessor
class        _SCREAMING_SNAKE_CASE  (  unittest.TestCase							):






				'''simple docstring'''



				def __init__(					self	:			Optional[Any]				,     UpperCAmelCase_	:			int				,     UpperCAmelCase_	:			Dict=7				,     UpperCAmelCase_	:			List[Any]=3				,     UpperCAmelCase_	:			Tuple=18				,     UpperCAmelCase_	:			List[str]=30				,     UpperCAmelCase_	:			Union[str, Any]=400				,     UpperCAmelCase_	:			Optional[Any]=True				,     UpperCAmelCase_	:			Tuple=None				,     UpperCAmelCase_	:			Optional[int]=True				,     UpperCAmelCase_	:			Any=None				,     UpperCAmelCase_	:			Tuple=True				,     UpperCAmelCase_	:			List[Any]=[0.48145466, 0.4578275, 0.40821073]				,     UpperCAmelCase_	:			List[Any]=[0.26862954, 0.26130258, 0.27577711]				,     UpperCAmelCase_	:			Optional[int]=True				,     )		->  Tuple:







											"""simple docstring"""




											_lowerCAmelCase  	=			size if size is not None else {'height': 224, 'width': 224}
											_lowerCAmelCase  	=			crop_size if crop_size is not None else {'height': 18, 'width': 18}
											_lowerCAmelCase  	=			parent
											_lowerCAmelCase  	=			batch_size
											_lowerCAmelCase  	=			num_channels
											_lowerCAmelCase  	=			image_size
											_lowerCAmelCase  	=			min_resolution
											_lowerCAmelCase  	=			max_resolution
											_lowerCAmelCase  	=			do_resize
											_lowerCAmelCase  	=			size
											_lowerCAmelCase  	=			do_center_crop
											_lowerCAmelCase  	=			crop_size
											_lowerCAmelCase  	=			do_normalize
											_lowerCAmelCase  	=			image_mean
											_lowerCAmelCase  	=			image_std
											_lowerCAmelCase  	=			do_convert_rgb


				def 				__lowerCamelCase	(					self	:			Optional[int]  )		->  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_normalize": self.do_normalize,
											    "image_mean": self.image_mean,
											    "image_std": self.image_std,
											    "do_convert_rgb": self.do_convert_rgb,
											}







				def 				__lowerCamelCase	(					self	:			Dict				,     UpperCAmelCase_	:			Dict=False				,     UpperCAmelCase_	:			int=False				,     UpperCAmelCase_	:			Dict=False  )		->  str:







											"""simple docstring"""




											assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

											if equal_resolution:
																		_lowerCAmelCase  	=			[]
																		for i in range(self.batch_size  ):
																									image_inputs.append(
																									    np.random.randint(
																									        255				,     size=(self.num_channels, self.max_resolution, self.max_resolution)				,     dtype=np.uinta  )  )
											else:
																		_lowerCAmelCase  	=			[]
																		for i in range(self.batch_size  ):
																									_lowerCAmelCase	,		_lowerCAmelCase  	=			np.random.choice(np.arange(self.min_resolution				,     self.max_resolution  )				,     2  )
																									image_inputs.append(np.random.randint(255				,     size=(self.num_channels, width, height)				,     dtype=np.uinta  )  )

											if not numpify and not torchify:
																		# PIL expects the channel dimension as last dimension
																		_lowerCAmelCase  	=			[Image.fromarray(np.moveaxis(UpperCAmelCase_				,     0				,     -1  )  ) for x in image_inputs]

											if torchify:
																		_lowerCAmelCase  	=			[torch.from_numpy(UpperCAmelCase_  ) for x in image_inputs]

											return image_inputs






@require_torch
@require_vision
class        _SCREAMING_SNAKE_CASE  (  UpperCAmelCase      ,				unittest.TestCase							):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE_:							List[Any]							=					ChineseCLIPImageProcessor if is_vision_available() else None


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







											"""simple docstring"""




											_lowerCAmelCase  	=			ChineseCLIPImageProcessingTester(self				,     do_center_crop=UpperCAmelCase_  )


				@property
				def 				__lowerCamelCase	(					self	:			Union[str, Any]  )		->  int:







											"""simple docstring"""




											return self.image_processor_tester.prepare_image_processor_dict()


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







											"""simple docstring"""




											_lowerCAmelCase  	=			self.image_processing_class(**self.image_processor_dict  )
											self.assertTrue(hasattr(UpperCAmelCase_				,     'do_resize'  )  )
											self.assertTrue(hasattr(UpperCAmelCase_				,     'size'  )  )
											self.assertTrue(hasattr(UpperCAmelCase_				,     'do_center_crop'  )  )
											self.assertTrue(hasattr(UpperCAmelCase_				,     'center_crop'  )  )
											self.assertTrue(hasattr(UpperCAmelCase_				,     'do_normalize'  )  )
											self.assertTrue(hasattr(UpperCAmelCase_				,     'image_mean'  )  )
											self.assertTrue(hasattr(UpperCAmelCase_				,     'image_std'  )  )
											self.assertTrue(hasattr(UpperCAmelCase_				,     'do_convert_rgb'  )  )


				def 				__lowerCamelCase	(					self	:			Dict  )		->  Tuple:







											"""simple docstring"""




											_lowerCAmelCase  	=			self.image_processing_class.from_dict(self.image_processor_dict  )
											self.assertEqual(image_processor.size				,     {'height': 224, 'width': 224}  )
											self.assertEqual(image_processor.crop_size				,     {'height': 18, 'width': 18}  )

											_lowerCAmelCase  	=			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 				__lowerCamelCase	(					self	:			Union[str, Any]  )		->  List[str]:







											"""simple docstring"""




											pass


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







											"""simple docstring"""




											_lowerCAmelCase  	=			self.image_processing_class(**self.image_processor_dict  )
											# create random PIL images
											_lowerCAmelCase  	=			self.image_processor_tester.prepare_inputs(equal_resolution=UpperCAmelCase_  )
											for image in image_inputs:
																		self.assertIsInstance(UpperCAmelCase_				,     Image.Image  )

											# Test not batched input
											_lowerCAmelCase  	=			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
											_lowerCAmelCase  	=			image_processing(UpperCAmelCase_				,     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 				__lowerCamelCase	(					self	:			List[str]  )		->  int:







											"""simple docstring"""




											_lowerCAmelCase  	=			self.image_processing_class(**self.image_processor_dict  )
											# create random numpy tensors
											_lowerCAmelCase  	=			self.image_processor_tester.prepare_inputs(equal_resolution=UpperCAmelCase_				,     numpify=UpperCAmelCase_  )
											for image in image_inputs:
																		self.assertIsInstance(UpperCAmelCase_				,     np.ndarray  )

											# Test not batched input
											_lowerCAmelCase  	=			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
											_lowerCAmelCase  	=			image_processing(UpperCAmelCase_				,     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 				__lowerCamelCase	(					self	:			List[Any]  )		->  Dict:







											"""simple docstring"""




											_lowerCAmelCase  	=			self.image_processing_class(**self.image_processor_dict  )
											# create random PyTorch tensors
											_lowerCAmelCase  	=			self.image_processor_tester.prepare_inputs(equal_resolution=UpperCAmelCase_				,     torchify=UpperCAmelCase_  )
											for image in image_inputs:
																		self.assertIsInstance(UpperCAmelCase_				,     torch.Tensor  )

											# Test not batched input
											_lowerCAmelCase  	=			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
											_lowerCAmelCase  	=			image_processing(UpperCAmelCase_				,     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'],
											    )				,     )






@require_torch
@require_vision
class        _SCREAMING_SNAKE_CASE  (  UpperCAmelCase      ,				unittest.TestCase							):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE_:							str							=					ChineseCLIPImageProcessor if is_vision_available() else None


				def 				__lowerCamelCase	(					self	:			List[str]  )		->  List[Any]:







											"""simple docstring"""




											_lowerCAmelCase  	=			ChineseCLIPImageProcessingTester(self				,     num_channels=4				,     do_center_crop=UpperCAmelCase_  )
											_lowerCAmelCase  	=			3


				@property
				def 				__lowerCamelCase	(					self	:			Union[str, Any]  )		->  Dict:







											"""simple docstring"""




											return self.image_processor_tester.prepare_image_processor_dict()


				def 				__lowerCamelCase	(					self	:			Optional[Any]  )		->  Dict:







											"""simple docstring"""




											_lowerCAmelCase  	=			self.image_processing_class(**self.image_processor_dict  )
											self.assertTrue(hasattr(UpperCAmelCase_				,     'do_resize'  )  )
											self.assertTrue(hasattr(UpperCAmelCase_				,     'size'  )  )
											self.assertTrue(hasattr(UpperCAmelCase_				,     'do_center_crop'  )  )
											self.assertTrue(hasattr(UpperCAmelCase_				,     'center_crop'  )  )
											self.assertTrue(hasattr(UpperCAmelCase_				,     'do_normalize'  )  )
											self.assertTrue(hasattr(UpperCAmelCase_				,     'image_mean'  )  )
											self.assertTrue(hasattr(UpperCAmelCase_				,     'image_std'  )  )
											self.assertTrue(hasattr(UpperCAmelCase_				,     'do_convert_rgb'  )  )


				def 				__lowerCamelCase	(					self	:			List[Any]  )		->  Optional[int]:







											"""simple docstring"""




											pass







				def 				__lowerCamelCase	(					self	:			int  )		->  List[Any]:







											"""simple docstring"""




											_lowerCAmelCase  	=			self.image_processing_class(**self.image_processor_dict  )
											# create random PIL images
											_lowerCAmelCase  	=			self.image_processor_tester.prepare_inputs(equal_resolution=UpperCAmelCase_  )
											for image in image_inputs:
																		self.assertIsInstance(UpperCAmelCase_				,     Image.Image  )

											# Test not batched input
											_lowerCAmelCase  	=			image_processing(image_inputs[0]				,     return_tensors='pt'  ).pixel_values
											self.assertEqual(
											    encoded_images.shape				,     (
											        1,
											        self.expected_encoded_image_num_channels,
											        self.image_processor_tester.crop_size['height'],
											        self.image_processor_tester.crop_size['width'],
											    )				,     )

											# Test batched
											_lowerCAmelCase  	=			image_processing(UpperCAmelCase_				,     return_tensors='pt'  ).pixel_values
											self.assertEqual(
											    encoded_images.shape				,     (
											        self.image_processor_tester.batch_size,
											        self.expected_encoded_image_num_channels,
											        self.image_processor_tester.crop_size['height'],
											        self.image_processor_tester.crop_size['width'],
											    )				,     )






