


'''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 DeiTImageProcessor
class   __UpperCamelCase		(  unittest.TestCase							):





							def __init__(     self						:Tuple     ,_UpperCamelCase						:Union[str, Any]     ,_UpperCamelCase						:List[Any]=7     ,_UpperCamelCase						:Tuple=3     ,_UpperCamelCase						:List[Any]=1_8     ,_UpperCamelCase						:Optional[Any]=3_0     ,_UpperCamelCase						:List[Any]=4_0_0     ,_UpperCamelCase						:int=True     ,_UpperCamelCase						:List[Any]=None     ,_UpperCamelCase						:int=True     ,_UpperCamelCase						:Optional[Any]=None     ,_UpperCamelCase						:List[str]=True     ,_UpperCamelCase						:Optional[int]=[0.5, 0.5, 0.5]     ,_UpperCamelCase						:List[Any]=[0.5, 0.5, 0.5]     ,):
									snake_case_						:							List[Any]					=  size if size is not None else {'height': 2_0, 'width': 2_0}
									snake_case_						:							Optional[Any]					=  crop_size if crop_size is not None else {'height': 1_8, 'width': 1_8}

									snake_case_						:							List[Any]					=  parent
									snake_case_						:							Union[str, Any]					=  batch_size
									snake_case_						:							List[Any]					=  num_channels
									snake_case_						:							Union[str, Any]					=  image_size
									snake_case_						:							Tuple					=  min_resolution
									snake_case_						:							str					=  max_resolution
									snake_case_						:							int					=  do_resize
									snake_case_						:							str					=  size
									snake_case_						:							Optional[Any]					=  do_center_crop
									snake_case_						:							Union[str, Any]					=  crop_size
									snake_case_						:							Dict					=  do_normalize
									snake_case_						:							str					=  image_mean
									snake_case_						:							Optional[Any]					=  image_std





							def 						a__      (     self						:str	):
									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,
									}





@require_torch
@require_vision
class   __UpperCamelCase		(  a_  ,     unittest.TestCase							):
							lowercase	:						Optional[int]      						=     DeiTImageProcessor if is_vision_available() else None
							lowercase	:						Any      						=     True





							def 						a__      (     self						:Union[str, Any]	):
									snake_case_						:							List[Any]					=  DeiTImageProcessingTester(self	)





							@property
							def 						a__      (     self						:List[Any]	):
									return self.image_processor_tester.prepare_image_processor_dict()





							def 						a__      (     self						:int	):
									snake_case_						:							Any					=  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"""	)	)





							def 						a__      (     self						:str	):
									snake_case_						:							Dict					=  self.image_processing_class.from_dict(self.image_processor_dict	)
									self.assertEqual(image_processor.size     ,{"""height""": 2_0, """width""": 2_0}	)
									self.assertEqual(image_processor.crop_size     ,{"""height""": 1_8, """width""": 1_8}	)

									snake_case_						:							Optional[Any]					=  self.image_processing_class.from_dict(self.image_processor_dict     ,size=4_2     ,crop_size=8_4	)
									self.assertEqual(image_processor.size     ,{"""height""": 4_2, """width""": 4_2}	)
									self.assertEqual(image_processor.crop_size     ,{"""height""": 8_4, """width""": 8_4}	)





							def 						a__      (     self						:Dict	):
									pass





							def 						a__      (     self						:List[str]	):
									# Initialize image_processing
									snake_case_						:							Optional[Any]					=  self.image_processing_class(**self.image_processor_dict	)
									# create random PIL images
									snake_case_						:							Optional[Any]					=  prepare_image_inputs(self.image_processor_tester     ,equal_resolution=_UpperCamelCase	)
									for image in image_inputs:
											self.assertIsInstance(_UpperCamelCase     ,Image.Image	)

									# Test not batched input
									snake_case_						:							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
									snake_case_						:							Tuple					=  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 						a__      (     self						:int	):
									# Initialize image_processing
									snake_case_						:							Optional[Any]					=  self.image_processing_class(**self.image_processor_dict	)
									# create random numpy tensors
									snake_case_						:							Dict					=  prepare_image_inputs(self.image_processor_tester     ,equal_resolution=_UpperCamelCase     ,numpify=_UpperCamelCase	)
									for image in image_inputs:
											self.assertIsInstance(_UpperCamelCase     ,np.ndarray	)

									# Test not batched input
									snake_case_						:							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
									snake_case_						:							Tuple					=  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 						a__      (     self						:Optional[Any]	):
									# Initialize image_processing
									snake_case_						:							Dict					=  self.image_processing_class(**self.image_processor_dict	)
									# create random PyTorch tensors
									snake_case_						:							List[str]					=  prepare_image_inputs(self.image_processor_tester     ,equal_resolution=_UpperCamelCase     ,torchify=_UpperCamelCase	)
									for image in image_inputs:
											self.assertIsInstance(_UpperCamelCase     ,torch.Tensor	)

									# Test not batched input
									snake_case_						:							List[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
									snake_case_						:							List[str]					=  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"""],
									    )     ,)
from typing import List

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


if is_torch_available():
 import torch




class       __lowerCAmelCase     (				a_  ):



 '''simple docstring'''
 _lowerCamelCase  :		Dict 			=       ['image_processor', 'tokenizer']
 _lowerCamelCase  :		Tuple 			=       'OneFormerImageProcessor'
 _lowerCamelCase  :		Dict 			=       ('CLIPTokenizer', 'CLIPTokenizerFast')

 def __init__(	self				:						str				,	lowerCamelCase				:						Any=None				,	lowerCamelCase				:						List[Any]=None				,	lowerCamelCase				:						int = 7_7				,	lowerCamelCase				:						int = 7_7				,	**lowerCamelCase				:						Union[str, Any]     )       ->							Any:
        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`.'     )

        _UpperCamelCase   :				int       =     max_seq_length
        _UpperCamelCase   :				Optional[Any]       =     task_seq_length

        super().__init__(lowerCamelCase				,	lowerCamelCase     )

 def     __snake_case    (	self				:						List[str]				,	lowerCamelCase				:						int=None				,	lowerCamelCase				:						Optional[int]=7_7     )       ->							Tuple:
        if text_list is None:
               raise ValueError('tokens cannot be None.'     )

        _UpperCamelCase   :				Dict       =     self.tokenizer(lowerCamelCase				,	padding='max_length'				,	max_length=lowerCamelCase				,	truncation=lowerCamelCase     )

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

        _UpperCamelCase   :				Optional[int]       =     []
        for attn_mask, input_id in zip(lowerCamelCase				,	lowerCamelCase     ):
               _UpperCamelCase   :				int       =     torch.tensor(lowerCamelCase     ) * torch.tensor(lowerCamelCase     )
               token_inputs.append(token.unsqueeze(0     )     )

        _UpperCamelCase   :				Dict       =     torch.cat(lowerCamelCase				,	dim=0     )
        return token_inputs

 def __call__(	self				:						Union[str, Any]				,	lowerCamelCase				:						int=None				,	lowerCamelCase				:						str=None				,	lowerCamelCase				:						Any=None				,	**lowerCamelCase				:						List[Any]     )       ->							List[Any]:

        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.'     )

        _UpperCamelCase   :				Optional[Any]       =     self.image_processor(lowerCamelCase				,	lowerCamelCase				,	lowerCamelCase				,	**lowerCamelCase     )

        if isinstance(lowerCamelCase				,	lowerCamelCase     ):
               _UpperCamelCase   :				int       =     [task_inputs]

        if isinstance(lowerCamelCase				,	lowerCamelCase     ) and all(isinstance(lowerCamelCase				,	lowerCamelCase     ) for task_input in task_inputs     ):
               _UpperCamelCase   :				str       =     []
               for task in task_inputs:
                      _UpperCamelCase   :				Optional[Any]       =     f"the task is {task}"
                      task_token_inputs.append(lowerCamelCase     )
               _UpperCamelCase   :				Dict       =     self._preprocess_text(lowerCamelCase				,	max_length=self.task_seq_length     )
        else:
               raise TypeError('Task Inputs should be a string or a list of strings.'     )

        if hasattr(lowerCamelCase				,	'text_inputs'     ):
               _UpperCamelCase   :				str       =     encoded_inputs.text_inputs

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

               _UpperCamelCase   :				Optional[int]       =     torch.cat(lowerCamelCase				,	dim=0     )

        return encoded_inputs

 def     __snake_case    (	self				:						List[str]				,	lowerCamelCase				:						str=None				,	lowerCamelCase				:						str=None				,	lowerCamelCase				:						int=None				,	**lowerCamelCase				:						List[str]     )       ->							Tuple:

        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.'     )

        _UpperCamelCase   :				Any       =     self.image_processor.encode_inputs(lowerCamelCase				,	lowerCamelCase				,	lowerCamelCase				,	**lowerCamelCase     )

        if isinstance(lowerCamelCase				,	lowerCamelCase     ):
               _UpperCamelCase   :				int       =     [task_inputs]

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

        if hasattr(lowerCamelCase				,	'text_inputs'     ):
               _UpperCamelCase   :				Optional[int]       =     encoded_inputs.text_inputs

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

               _UpperCamelCase   :				Optional[Any]       =     torch.cat(lowerCamelCase				,	dim=0     )

        return encoded_inputs

 def     __snake_case    (	self				:						Dict				,	*lowerCamelCase				:						Tuple				,	**lowerCamelCase				:						Any     )       ->							str:
        return self.image_processor.post_process_semantic_segmentation(*lowerCamelCase				,	**lowerCamelCase     )

 def     __snake_case    (	self				:						Union[str, Any]				,	*lowerCamelCase				:						Optional[int]				,	**lowerCamelCase				:						Any     )       ->							Any:
        return self.image_processor.post_process_instance_segmentation(*lowerCamelCase				,	**lowerCamelCase     )

 def     __snake_case    (	self				:						Dict				,	*lowerCamelCase				:						List[str]				,	**lowerCamelCase				:						int     )       ->							List[Any]:
        return self.image_processor.post_process_panoptic_segmentation(*lowerCamelCase				,	**lowerCamelCase     )



