
"""simple docstring"""



import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List

import git
import torch
from torch.utils.data import Dataset

from transformers import BartTokenizer, RagTokenizer, TaTokenizer






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					Dict     ,  SCREAMING_SNAKE_CASE:					Any     ,  SCREAMING_SNAKE_CASE:					Any     ,  SCREAMING_SNAKE_CASE:					str     ,  SCREAMING_SNAKE_CASE:					Union[str, Any]=True     ,  SCREAMING_SNAKE_CASE:					Optional[Any]="pt"						):






							"""simple docstring"""



							_lowerCAmelCase  	=			{'add_prefix_space': True} if isinstance(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE						) and not line.startswith(' '						) else {}
							_lowerCAmelCase  	=			padding_side
							return tokenizer(
							    [line]     ,  max_length=SCREAMING_SNAKE_CASE     ,  padding='max_length' if pad_to_max_length else None     ,  truncation=SCREAMING_SNAKE_CASE     ,  return_tensors=SCREAMING_SNAKE_CASE     ,  add_special_tokens=SCREAMING_SNAKE_CASE     ,  **SCREAMING_SNAKE_CASE     ,  )






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					Tuple     ,  SCREAMING_SNAKE_CASE:					Tuple     ,  SCREAMING_SNAKE_CASE:					Optional[Any]=None     ,  ):






							"""simple docstring"""



							_lowerCAmelCase  	=			input_ids.ne(SCREAMING_SNAKE_CASE						).any(dim=0						)
							if attention_mask is None:
														return input_ids[:, keep_column_mask]
							else:
														return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])
class        _SCREAMING_SNAKE_CASE  (  UpperCAmelCase							):






				'''simple docstring'''



				def __init__(					self	:			Optional[Any]				,     UpperCAmelCase_	:			List[str]				,     UpperCAmelCase_	:			Union[str, Any]				,     UpperCAmelCase_	:			List[Any]				,     UpperCAmelCase_	:			int				,     UpperCAmelCase_	:			List[str]="train"				,     UpperCAmelCase_	:			Any=None				,     UpperCAmelCase_	:			List[Any]=None				,     UpperCAmelCase_	:			Union[str, Any]=None				,     UpperCAmelCase_	:			Union[str, Any]=""				,     )		->  Optional[int]:







											"""simple docstring"""




											super().__init__()
											_lowerCAmelCase  	=			Path(UpperCAmelCase_  ).joinpath(type_path + '.source'  )
											_lowerCAmelCase  	=			Path(UpperCAmelCase_  ).joinpath(type_path + '.target'  )
											_lowerCAmelCase  	=			self.get_char_lens(self.src_file  )
											_lowerCAmelCase  	=			max_source_length
											_lowerCAmelCase  	=			max_target_length
											assert min(self.src_lens  ) > 0, F"""found empty line in {self.src_file}"""
											_lowerCAmelCase  	=			tokenizer
											_lowerCAmelCase  	=			prefix
											if n_obs is not None:
																		_lowerCAmelCase  	=			self.src_lens[:n_obs]
											_lowerCAmelCase  	=			src_lang
											_lowerCAmelCase  	=			tgt_lang


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







											"""simple docstring"""




											return len(self.src_lens  )


				def __getitem__(					self	:			Tuple				,     UpperCAmelCase_	:			List[str]  )		->  Dict[str, torch.Tensor]:







											"""simple docstring"""




											_lowerCAmelCase  	=			index + 1  # linecache starts at 1
											_lowerCAmelCase  	=			self.prefix + linecache.getline(str(self.src_file  )				,     UpperCAmelCase_  ).rstrip('\n'  )
											_lowerCAmelCase  	=			linecache.getline(str(self.tgt_file  )				,     UpperCAmelCase_  ).rstrip('\n'  )
											assert source_line, F"""empty source line for index {index}"""
											assert tgt_line, F"""empty tgt line for index {index}"""

											# Need to add eos token manually for T5
											if isinstance(self.tokenizer				,     UpperCAmelCase_  ):
																		source_line += self.tokenizer.eos_token
																		tgt_line += self.tokenizer.eos_token

											# Pad source and target to the right
											_lowerCAmelCase  	=			(
											    self.tokenizer.question_encoder if isinstance(self.tokenizer				,     UpperCAmelCase_  ) else self.tokenizer
											)
											_lowerCAmelCase  	=			self.tokenizer.generator if isinstance(self.tokenizer				,     UpperCAmelCase_  ) else self.tokenizer

											_lowerCAmelCase  	=			encode_line(UpperCAmelCase_				,     UpperCAmelCase_				,     self.max_source_length				,     'right'  )
											_lowerCAmelCase  	=			encode_line(UpperCAmelCase_				,     UpperCAmelCase_				,     self.max_target_length				,     'right'  )

											_lowerCAmelCase  	=			source_inputs['input_ids'].squeeze()
											_lowerCAmelCase  	=			target_inputs['input_ids'].squeeze()
											_lowerCAmelCase  	=			source_inputs['attention_mask'].squeeze()
											return {
											    "input_ids": source_ids,
											    "attention_mask": src_mask,
											    "decoder_input_ids": target_ids,
											}


				@staticmethod
				def 				__lowerCamelCase	(					UpperCAmelCase_	:			Union[str, Any]  )		->  Dict:







											"""simple docstring"""




											return [len(UpperCAmelCase_  ) for x in Path(UpperCAmelCase_  ).open().readlines()]







				def 				__lowerCamelCase	(					self	:			Tuple				,     UpperCAmelCase_	:			Union[str, Any]  )		->  Dict[str, torch.Tensor]:







											"""simple docstring"""




											_lowerCAmelCase  	=			torch.stack([x['input_ids'] for x in batch]  )
											_lowerCAmelCase  	=			torch.stack([x['attention_mask'] for x in batch]  )
											_lowerCAmelCase  	=			torch.stack([x['decoder_input_ids'] for x in batch]  )
											_lowerCAmelCase  	=			(
											    self.tokenizer.generator.pad_token_id
											    if isinstance(self.tokenizer				,     UpperCAmelCase_  )
											    else self.tokenizer.pad_token_id
											)
											_lowerCAmelCase  	=			(
											    self.tokenizer.question_encoder.pad_token_id
											    if isinstance(self.tokenizer				,     UpperCAmelCase_  )
											    else self.tokenizer.pad_token_id
											)
											_lowerCAmelCase  	=			trim_batch(UpperCAmelCase_				,     UpperCAmelCase_  )
											_lowerCAmelCase	,		_lowerCAmelCase  	=			trim_batch(UpperCAmelCase_				,     UpperCAmelCase_				,     attention_mask=UpperCAmelCase_  )
											_lowerCAmelCase  	=			{
											    'input_ids': source_ids,
											    'attention_mask': source_mask,
											    'decoder_input_ids': y,
											}
											return batch






_snake_case          =						getLogger(__name__)






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					List[List]						):






							"""simple docstring"""



							return list(itertools.chain.from_iterable(SCREAMING_SNAKE_CASE						)						)






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					str						):






							"""simple docstring"""



							_lowerCAmelCase  	=			get_git_info()
							save_json(SCREAMING_SNAKE_CASE     ,  os.path.join(SCREAMING_SNAKE_CASE     ,  'git_log.json'						)						)






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					Optional[Any]     ,  SCREAMING_SNAKE_CASE:					int     ,  SCREAMING_SNAKE_CASE:					Dict=4     ,  **SCREAMING_SNAKE_CASE:					Any						):






							"""simple docstring"""



							with open(SCREAMING_SNAKE_CASE     ,  'w'						) as f:
														json.dump(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE     ,  indent=SCREAMING_SNAKE_CASE     ,  **SCREAMING_SNAKE_CASE						)






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					Dict						):






							"""simple docstring"""



							with open(SCREAMING_SNAKE_CASE						) as f:
														return json.load(SCREAMING_SNAKE_CASE						)






def 	__snake_case  (    ):






							"""simple docstring"""



							_lowerCAmelCase  	=			git.Repo(search_parent_directories=SCREAMING_SNAKE_CASE						)
							_lowerCAmelCase  	=			{
							    'repo_id': str(SCREAMING_SNAKE_CASE						),
							    'repo_sha': str(repo.head.object.hexsha						),
							    'repo_branch': str(repo.active_branch						),
							    'hostname': str(socket.gethostname()						),
							}
							return repo_infos






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					Callable     ,  SCREAMING_SNAKE_CASE:					Iterable						):






							"""simple docstring"""



							return list(map(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE						)						)






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					Dict     ,  SCREAMING_SNAKE_CASE:					Tuple						):






							"""simple docstring"""



							with open(SCREAMING_SNAKE_CASE     ,  'wb'						) as f:
														return pickle.dump(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE						)






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					str						):






							"""simple docstring"""



							def remove_articles(SCREAMING_SNAKE_CASE:					Optional[Any]						):
														return re.sub(R'\b(a|an|the)\b'     ,  ' '     ,  SCREAMING_SNAKE_CASE						)

							def white_space_fix(SCREAMING_SNAKE_CASE:					Any						):
														return " ".join(text.split()						)

							def remove_punc(SCREAMING_SNAKE_CASE:					Union[str, Any]						):
														_lowerCAmelCase  	=			set(string.punctuation						)
														return "".join(ch for ch in text if ch not in exclude						)

							def lower(SCREAMING_SNAKE_CASE:					str						):
														return text.lower()

							return white_space_fix(remove_articles(remove_punc(lower(SCREAMING_SNAKE_CASE						)						)						)						)






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					str     ,  SCREAMING_SNAKE_CASE:					Tuple						):






							"""simple docstring"""



							_lowerCAmelCase  	=			normalize_answer(SCREAMING_SNAKE_CASE						).split()
							_lowerCAmelCase  	=			normalize_answer(SCREAMING_SNAKE_CASE						).split()
							_lowerCAmelCase  	=			Counter(SCREAMING_SNAKE_CASE						) & Counter(SCREAMING_SNAKE_CASE						)
							_lowerCAmelCase  	=			sum(common.values()						)
							if num_same == 0:
														return 0
							_lowerCAmelCase  	=			1.0 * num_same / len(SCREAMING_SNAKE_CASE						)
							_lowerCAmelCase  	=			1.0 * num_same / len(SCREAMING_SNAKE_CASE						)
							_lowerCAmelCase  	=			(2 * precision * recall) / (precision + recall)
							return fa






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					str     ,  SCREAMING_SNAKE_CASE:					int						):






							"""simple docstring"""



							return normalize_answer(SCREAMING_SNAKE_CASE						) == normalize_answer(SCREAMING_SNAKE_CASE						)






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					List[str]     ,  SCREAMING_SNAKE_CASE:					List[str]						):






							"""simple docstring"""



							assert len(SCREAMING_SNAKE_CASE						) == len(SCREAMING_SNAKE_CASE						)
							_lowerCAmelCase  	=			0
							for hypo, pred in zip(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE						):
														em += exact_match_score(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE						)
							if len(SCREAMING_SNAKE_CASE						) > 0:
														em /= len(SCREAMING_SNAKE_CASE						)
							return {"em": em}






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					Any						):






							"""simple docstring"""



							return model_prefix.startswith('rag'						)






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					Union[str, Any]     ,  SCREAMING_SNAKE_CASE:					Union[str, Any]     ,  SCREAMING_SNAKE_CASE:					Union[str, Any]						):






							"""simple docstring"""



							_lowerCAmelCase  	=			{p: p for p in extra_params}
							# T5 models don't have `dropout` param, they have `dropout_rate` instead
							_lowerCAmelCase  	=			'dropout_rate'
							for p in extra_params:
														if getattr(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE						):
																					if not hasattr(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE						) and not hasattr(SCREAMING_SNAKE_CASE     ,  equivalent_param[p]						):
																												logger.info('config doesn\'t have a `{}` attribute'.format(SCREAMING_SNAKE_CASE						)						)
																												delattr(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE						)
																												continue
																					_lowerCAmelCase  	=			p if hasattr(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE						) else equivalent_param[p]
																					setattr(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE     ,  getattr(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE						)						)
																					delattr(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE						)
							return hparams, config








"""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'],
											    )				,     )






