
'''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 ViTImageProcessor






class 				lowerCAmelCase__     (						unittest.TestCase  ):






					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=[0.5, 0.5, 0.5]       ,							__SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]       ,							):


											"""simple docstring"""





											lowercase_			:  List[Any]									= size if size is not None else {'''height''': 18, '''width''': 18}
											lowercase_			:  Tuple									= parent
											lowercase_			:  Any									= batch_size
											lowercase_			:  Tuple									= num_channels
											lowercase_			:  List[Any]									= image_size
											lowercase_			:  List[Any]									= min_resolution
											lowercase_			:  Optional[Any]									= max_resolution
											lowercase_			:  List[Any]									= do_resize
											lowercase_			:  Dict									= size
											lowercase_			:  Optional[Any]									= do_normalize
											lowercase_			:  Any									= image_mean
											lowercase_			:  List[str]									= image_std






					def 			_snake_case					(     self	):


											"""simple docstring"""





											return {
											    "image_mean": self.image_mean,
											    "image_std": self.image_std,
											    "do_normalize": self.do_normalize,
											    "do_resize": self.do_resize,
											    "size": self.size,
											}







@require_torch
@require_vision
class 				lowerCAmelCase__     (						lowerCamelCase_					,		unittest.TestCase  ):
					lowerCAmelCase_       =  ViTImageProcessor if is_vision_available() else None






					def 			_snake_case					(     self	):


											"""simple docstring"""





											lowercase_			:  Tuple									= ViTImageProcessingTester(self	)






					@property
					def 			_snake_case					(     self	):


											"""simple docstring"""





											return self.image_processor_tester.prepare_image_processor_dict()






					def 			_snake_case					(     self	):


											"""simple docstring"""





											lowercase_			:  Optional[int]									= 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'''	)	)






					def 			_snake_case					(     self	):


											"""simple docstring"""





											lowercase_			:  str									= self.image_processing_class.from_dict(self.image_processor_dict	)
											self.assertEqual(image_processor.size       ,							{'''height''': 18, '''width''': 18}	)

											lowercase_			:  str									= self.image_processing_class.from_dict(self.image_processor_dict       ,							size=42	)
											self.assertEqual(image_processor.size       ,							{'''height''': 42, '''width''': 42}	)






					def 			_snake_case					(     self	):


											"""simple docstring"""





											pass






					def 			_snake_case					(     self	):


											"""simple docstring"""





											lowercase_			:  Optional[int]									= self.image_processing_class(**self.image_processor_dict	)
											# create random PIL images
											lowercase_			:  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
											lowercase_			:  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.size['''height'''],
											        self.image_processor_tester.size['''width'''],
											    )       ,							)

											# Test batched
											lowercase_			:  Union[str, 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.size['''height'''],
											        self.image_processor_tester.size['''width'''],
											    )       ,							)






					def 			_snake_case					(     self	):


											"""simple docstring"""





											lowercase_			:  List[str]									= self.image_processing_class(**self.image_processor_dict	)
											# create random numpy tensors
											lowercase_			:  Any									= 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
											lowercase_			:  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.size['''height'''],
											        self.image_processor_tester.size['''width'''],
											    )       ,							)

											# Test batched
											lowercase_			:  List[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.size['''height'''],
											        self.image_processor_tester.size['''width'''],
											    )       ,							)






					def 			_snake_case					(     self	):


											"""simple docstring"""





											lowercase_			:  Optional[Any]									= self.image_processing_class(**self.image_processor_dict	)
											# create random PyTorch tensors
											lowercase_			:  List[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
											lowercase_			:  Tuple									= 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.size['''height'''],
											        self.image_processor_tester.size['''width'''],
											    )       ,							)

											# Test batched
											lowercase_			:  Union[str, 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.size['''height'''],
											        self.image_processor_tester.size['''width'''],
											    )       ,							)





'''simple docstring'''







from typing import List

from ...processing_utils import ProcessorMixin
from ...utils import is_torch_available


if is_torch_available():
							import torch






class 				lowerCAmelCase__     (						lowerCamelCase_  ):
					lowerCAmelCase_       =  ['''image_processor''', '''tokenizer''']
					lowerCAmelCase_       =  '''OneFormerImageProcessor'''
					lowerCAmelCase_       =  ('''CLIPTokenizer''', '''CLIPTokenizerFast''')






					def __init__(     self       ,							__SCREAMING_SNAKE_CASE=None       ,							__SCREAMING_SNAKE_CASE=None       ,							__SCREAMING_SNAKE_CASE = 77       ,							__SCREAMING_SNAKE_CASE = 77       ,							**__SCREAMING_SNAKE_CASE	):


											"""simple docstring"""





											if image_processor is None:
																	raise ValueError('''You need to specify an `image_processor`.'''	)
											if tokenizer is None:
																	raise ValueError('''You need to specify a `tokenizer`.'''	)

											lowercase_			:  Dict									= max_seq_length
											lowercase_			:  Dict									= task_seq_length

											super().__init__(__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE	)






					def 			_snake_case					(     self       ,							__SCREAMING_SNAKE_CASE=None       ,							__SCREAMING_SNAKE_CASE=77	):


											"""simple docstring"""





											if text_list is None:
																	raise ValueError('''tokens cannot be None.'''	)

											lowercase_			:  str									= self.tokenizer(__SCREAMING_SNAKE_CASE       ,							padding='''max_length'''       ,							max_length=__SCREAMING_SNAKE_CASE       ,							truncation=__SCREAMING_SNAKE_CASE	)

											lowercase_		,				lowercase_			:  List[str]									= tokens['''attention_mask'''], tokens['''input_ids''']

											lowercase_			:  List[str]									= []
											for attn_mask, input_id in zip(__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE	):
																	lowercase_			:  Optional[Any]									= torch.tensor(__SCREAMING_SNAKE_CASE	) * torch.tensor(__SCREAMING_SNAKE_CASE	)
																	token_inputs.append(token.unsqueeze(0	)	)

											lowercase_			:  str									= torch.cat(__SCREAMING_SNAKE_CASE       ,							dim=0	)
											return token_inputs






					def __call__(     self       ,							__SCREAMING_SNAKE_CASE=None       ,							__SCREAMING_SNAKE_CASE=None       ,							__SCREAMING_SNAKE_CASE=None       ,							**__SCREAMING_SNAKE_CASE	):


											"""simple docstring"""





											if task_inputs is None:
																	raise ValueError('''You have to specify the task_input. Found None.'''	)
											elif images is None:
																	raise ValueError('''You have to specify the image. Found None.'''	)

											if not all(task in ['''semantic''', '''instance''', '''panoptic'''] for task in task_inputs	):
																	raise ValueError('''task_inputs must be semantic, instance, or panoptic.'''	)

											lowercase_			:  Optional[int]									= self.image_processor(__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE       ,							**__SCREAMING_SNAKE_CASE	)

											if isinstance(__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE	):
																	lowercase_			:  List[str]									= [task_inputs]

											if isinstance(__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE	) and all(isinstance(__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE	) for task_input in task_inputs	):
																	lowercase_			:  List[str]									= []
																	for task in task_inputs:
																							lowercase_			:  Tuple									= F'''the task is {task}'''
																							task_token_inputs.append(__SCREAMING_SNAKE_CASE	)
																	lowercase_			:  Tuple									= self._preprocess_text(__SCREAMING_SNAKE_CASE       ,							max_length=self.task_seq_length	)
											else:
																	raise TypeError('''Task Inputs should be a string or a list of strings.'''	)

											if hasattr(__SCREAMING_SNAKE_CASE       ,							'''text_inputs'''	):
																	lowercase_			:  Any									= encoded_inputs.text_inputs

																	lowercase_			:  int									= []
																	for texts in texts_list:
																							lowercase_			:  Any									= self._preprocess_text(__SCREAMING_SNAKE_CASE       ,							max_length=self.max_seq_length	)
																							text_inputs.append(text_input_list.unsqueeze(0	)	)

																	lowercase_			:  Tuple									= torch.cat(__SCREAMING_SNAKE_CASE       ,							dim=0	)

											return encoded_inputs






					def 			_snake_case					(     self       ,							__SCREAMING_SNAKE_CASE=None       ,							__SCREAMING_SNAKE_CASE=None       ,							__SCREAMING_SNAKE_CASE=None       ,							**__SCREAMING_SNAKE_CASE	):


											"""simple docstring"""





											if task_inputs is None:
																	raise ValueError('''You have to specify the task_input. Found None.'''	)
											elif images is None:
																	raise ValueError('''You have to specify the image. Found None.'''	)

											if not all(task in ['''semantic''', '''instance''', '''panoptic'''] for task in task_inputs	):
																	raise ValueError('''task_inputs must be semantic, instance, or panoptic.'''	)

											lowercase_			:  Dict									= self.image_processor.encode_inputs(__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE       ,							**__SCREAMING_SNAKE_CASE	)

											if isinstance(__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE	):
																	lowercase_			:  List[Any]									= [task_inputs]

											if isinstance(__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE	) and all(isinstance(__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE	) for task_input in task_inputs	):
																	lowercase_			:  int									= []
																	for task in task_inputs:
																							lowercase_			:  int									= F'''the task is {task}'''
																							task_token_inputs.append(__SCREAMING_SNAKE_CASE	)
																	lowercase_			:  str									= self._preprocess_text(__SCREAMING_SNAKE_CASE       ,							max_length=self.task_seq_length	)
											else:
																	raise TypeError('''Task Inputs should be a string or a list of strings.'''	)

											if hasattr(__SCREAMING_SNAKE_CASE       ,							'''text_inputs'''	):
																	lowercase_			:  Dict									= encoded_inputs.text_inputs

																	lowercase_			:  str									= []
																	for texts in texts_list:
																							lowercase_			:  List[str]									= self._preprocess_text(__SCREAMING_SNAKE_CASE       ,							max_length=self.max_seq_length	)
																							text_inputs.append(text_input_list.unsqueeze(0	)	)

																	lowercase_			:  Union[str, Any]									= torch.cat(__SCREAMING_SNAKE_CASE       ,							dim=0	)

											return encoded_inputs






					def 			_snake_case					(     self       ,							*__SCREAMING_SNAKE_CASE       ,							**__SCREAMING_SNAKE_CASE	):


											"""simple docstring"""





											return self.image_processor.post_process_semantic_segmentation(*__SCREAMING_SNAKE_CASE       ,							**__SCREAMING_SNAKE_CASE	)






					def 			_snake_case					(     self       ,							*__SCREAMING_SNAKE_CASE       ,							**__SCREAMING_SNAKE_CASE	):


											"""simple docstring"""





											return self.image_processor.post_process_instance_segmentation(*__SCREAMING_SNAKE_CASE       ,							**__SCREAMING_SNAKE_CASE	)






					def 			_snake_case					(     self       ,							*__SCREAMING_SNAKE_CASE       ,							**__SCREAMING_SNAKE_CASE	):


											"""simple docstring"""





											return self.image_processor.post_process_panoptic_segmentation(*__SCREAMING_SNAKE_CASE       ,							**__SCREAMING_SNAKE_CASE	)



