

import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin






class 	__magic_name__    (			a_      ):
			SCREAMING_SNAKE_CASE							   =						["image_processor", "tokenizer"]
			SCREAMING_SNAKE_CASE							   =						"AutoImageProcessor"
			SCREAMING_SNAKE_CASE							   =						"AutoTokenizer"





			def __init__(		self					,  __snake_case=None					,  __snake_case=None					,  **__snake_case					)   -> Dict:




						'''simple docstring'''
						__a        =None
						if "feature_extractor" in kwargs:
									warnings.warn(
									    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
									    ' instead.'					,  _lowerCamelCase					,  )
									__a        =kwargs.pop('feature_extractor'					)

						__a        =image_processor if image_processor is not None else feature_extractor
						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`.'					)

						super().__init__(_lowerCamelCase					,  _lowerCamelCase					)
						__a        =self.image_processor
						__a        =False





			def __call__(		self					,  *__snake_case					,  **__snake_case					)   -> List[str]:




						'''simple docstring'''
						# For backward compatibility
						if self._in_target_context_manager:
									return self.current_processor(*_lowerCamelCase					,  **_lowerCamelCase					)

						__a        =kwargs.pop('images'					,  _lowerCamelCase					)
						__a        =kwargs.pop('text'					,  _lowerCamelCase					)
						if len(_lowerCamelCase					) > 0:
									__a        =args[0]
									__a        =args[1:]

						if images is None and text is None:
									raise ValueError('You need to specify either an `images` or `text` input to process.'					)

						if images is not None:
									__a        =self.image_processor(_lowerCamelCase					,  *_lowerCamelCase					,  **_lowerCamelCase					)
						if text is not None:
									__a        =self.tokenizer(_lowerCamelCase					,  **_lowerCamelCase					)

						if text is None:
									return inputs
						elif images is None:
									return encodings
						else:
									__a        =encodings['input_ids']
									return inputs





			def 		__magic_name__    (		self					,  *__snake_case					,  **__snake_case					)   -> str:




						'''simple docstring'''
						return self.tokenizer.batch_decode(*_lowerCamelCase					,  **_lowerCamelCase					)





			def 		__magic_name__    (		self					,  *__snake_case					,  **__snake_case					)   -> Any:




						'''simple docstring'''
						return self.tokenizer.decode(*_lowerCamelCase					,  **_lowerCamelCase					)





			@contextmanager
			def 		__magic_name__    (		self					)   -> str:




						'''simple docstring'''
						warnings.warn(
						    '`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
						    'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
						    'your images inputs, or in a separate call.'					)
						__a        =True
						__a        =self.tokenizer
						yield
						__a        =self.image_processor
						__a        =False





			def 		__magic_name__    (		self					,  __snake_case					,  __snake_case=False					,  __snake_case=None					)   -> Optional[Any]:




						'''simple docstring'''
						if added_vocab is None:
									__a        =self.tokenizer.get_added_vocab()

						__a        ={}

						while tokens:
									__a        =re.search(R'<s_(.*?)>'					,  _lowerCamelCase					,  re.IGNORECASE					)
									if start_token is None:
												break
									__a        =start_token.group(1					)
									__a        =re.search(Rf'</s_{key}>'					,  _lowerCamelCase					,  re.IGNORECASE					)
									__a        =start_token.group()
									if end_token is None:
												__a        =tokens.replace(_lowerCamelCase					,  ''					)
									else:
												__a        =end_token.group()
												__a        =re.escape(_lowerCamelCase					)
												__a        =re.escape(_lowerCamelCase					)
												__a        =re.search(f'{start_token_escaped}(.*?){end_token_escaped}'					,  _lowerCamelCase					,  re.IGNORECASE					)
												if content is not None:
															__a        =content.group(1					).strip()
															if r"<s_" in content and r"</s_" in content:  # non-leaf node
																		__a        =self.tokenajson(_lowerCamelCase					,  is_inner_value=_lowerCamelCase					,  added_vocab=_lowerCamelCase					)
																		if value:
																					if len(_lowerCamelCase					) == 1:
																								__a        =value[0]
																					__a        =value
															else:  # leaf nodes
																		__a        =[]
																		for leaf in content.split(R'<sep/>'					):
																					__a        =leaf.strip()
																					if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
																								__a        =leaf[1:-2]  # for categorical special tokens
																					output[key].append(_lowerCamelCase					)
																		if len(output[key]					) == 1:
																					__a        =output[key][0]

												__a        =tokens[tokens.find(_lowerCamelCase					) + len(_lowerCamelCase					) :].strip()
												if tokens[:6] == r"<sep/>":  # non-leaf nodes
															return [output] + self.tokenajson(tokens[6:]					,  is_inner_value=_lowerCamelCase					,  added_vocab=_lowerCamelCase					)

						if len(_lowerCamelCase					):
									return [output] if is_inner_value else output
						else:
									return [] if is_inner_value else {"text_sequence": tokens}





			@property
			def 		__magic_name__    (		self					)   -> List[str]:




						'''simple docstring'''
						warnings.warn(
						    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'					,  _lowerCamelCase					,  )
						return self.image_processor_class





			@property
			def 		__magic_name__    (		self					)   -> Any:




						'''simple docstring'''
						warnings.warn(
						    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'					,  _lowerCamelCase					,  )
						return self.image_processor














"""simple docstring"""



import unittest

import numpy as np

from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision

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 DPTImageProcessor

class        a     (   unittest.TestCase	):







    def __init__(			self							,		_lowerCamelCase							,		_lowerCamelCase=7							,		_lowerCamelCase=3							,		_lowerCamelCase=1_8							,		_lowerCamelCase=3_0							,		_lowerCamelCase=4_0_0							,		_lowerCamelCase=True							,		_lowerCamelCase=None							,		_lowerCamelCase=True							,		_lowerCamelCase=[0.5, 0.5, 0.5]							,		_lowerCamelCase=[0.5, 0.5, 0.5]							,		):
      lowercase							    =       size if size is not None else {'height': 1_8, 'width': 1_8}
      lowercase							    =       parent
      lowercase							    =       batch_size
      lowercase							    =       num_channels
      lowercase							    =       image_size
      lowercase							    =       min_resolution
      lowercase							    =       max_resolution
      lowercase							    =       do_resize
      lowercase							    =       size
      lowercase							    =       do_normalize
      lowercase							    =       image_mean
      lowercase							    =       image_std







    def 			UpperCamelCase_      (			self       ):
      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        a     (   a_,				unittest.TestCase	):
    UpperCAmelCase_ :     Optional[int]	     =DPTImageProcessor if is_vision_available() else None







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       DPTImageProcessingTester(self       )







    @property
    def 			UpperCamelCase_      (			self       ):
      return self.image_processor_tester.prepare_image_processor_dict()







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       self.image_processing_class(**self.image_processor_dict       )
      self.assertTrue(hasattr(_lowerCamelCase							,		'image_mean'       )       )
      self.assertTrue(hasattr(_lowerCamelCase							,		'image_std'       )       )
      self.assertTrue(hasattr(_lowerCamelCase							,		'do_normalize'       )       )
      self.assertTrue(hasattr(_lowerCamelCase							,		'do_resize'       )       )
      self.assertTrue(hasattr(_lowerCamelCase							,		'size'       )       )







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       self.image_processing_class.from_dict(self.image_processor_dict       )
      self.assertEqual(image_processor.size							,		{'height': 1_8, 'width': 1_8}       )

      lowercase							    =       self.image_processing_class.from_dict(self.image_processor_dict							,		size=4_2       )
      self.assertEqual(image_processor.size							,		{'height': 4_2, 'width': 4_2}       )







    def 			UpperCamelCase_      (			self       ):
      # Initialize image_processing
      lowercase							    =       self.image_processing_class(**self.image_processor_dict       )
      # create random PIL images
      lowercase							    =       prepare_image_inputs(self.image_processor_tester							,		equal_resolution=_lowerCamelCase       )
      for image in image_inputs:
        self.assertIsInstance(_lowerCamelCase							,		Image.Image       )

      # Test not batched input
      lowercase							    =       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							    =       image_processing(_lowerCamelCase							,		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 			UpperCamelCase_      (			self       ):
      # Initialize image_processing
      lowercase							    =       self.image_processing_class(**self.image_processor_dict       )
      # create random numpy tensors
      lowercase							    =       prepare_image_inputs(self.image_processor_tester							,		equal_resolution=_lowerCamelCase							,		numpify=_lowerCamelCase       )
      for image in image_inputs:
        self.assertIsInstance(_lowerCamelCase							,		np.ndarray       )

      # Test not batched input
      lowercase							    =       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							    =       image_processing(_lowerCamelCase							,		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 			UpperCamelCase_      (			self       ):
      # Initialize image_processing
      lowercase							    =       self.image_processing_class(**self.image_processor_dict       )
      # create random PyTorch tensors
      lowercase							    =       prepare_image_inputs(self.image_processor_tester							,		equal_resolution=_lowerCamelCase							,		torchify=_lowerCamelCase       )
      for image in image_inputs:
        self.assertIsInstance(_lowerCamelCase							,		torch.Tensor       )

      # Test not batched input
      lowercase							    =       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							    =       image_processing(_lowerCamelCase							,		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'],
          )							,		)


