
import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin







class   lowerCamelCase	(						A_						):
     UpperCAmelCase__       :							List[str]     =	["image_processor", "tokenizer"]
     UpperCAmelCase__       :							List[str]     =	"AutoImageProcessor"
     UpperCAmelCase__       :							int     =	"AutoTokenizer"





     def __init__(self   :					Dict       ,					_A   :					Union[str, Any]=None       ,					_A   :					List[str]=None       ,					**_A   :					str     )		->		Optional[Any]:
         snake_case				  =      None
         if "feature_extractor" in kwargs:
             warnings.warn(
                 "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
                 " instead."       ,					_A       ,					)
             snake_case				  =      kwargs.pop("feature_extractor"     )

         snake_case				  =      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__(_A       ,					_A     )
         snake_case				  =      self.image_processor
         snake_case				  =      False





     def __call__(self   :					Dict       ,					*_A   :					List[str]       ,					**_A   :					Any     )		->		Optional[int]:
         # For backward compatibility
         if self._in_target_context_manager:
             return self.current_processor(*_A       ,					**_A     )

         snake_case				  =      kwargs.pop("images"       ,					_A     )
         snake_case				  =      kwargs.pop("text"       ,					_A     )
         if len(_A     ) > 0:
             snake_case				  =      args[0]
             snake_case				  =      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:
             snake_case				  =      self.image_processor(_A       ,					*_A       ,					**_A     )
         if text is not None:
             snake_case				  =      self.tokenizer(_A       ,					**_A     )

         if text is None:
             return inputs
         elif images is None:
             return encodings
         else:
             snake_case				  =      encodings["input_ids"]
             return inputs





     def 	UpperCAmelCase(self   :					str       ,					*_A   :					List[Any]       ,					**_A   :					Dict     )		->		int:
         return self.tokenizer.batch_decode(*_A       ,					**_A     )





     def 	UpperCAmelCase(self   :					str       ,					*_A   :					Dict       ,					**_A   :					Union[str, Any]     )		->		List[str]:
         return self.tokenizer.decode(*_A       ,					**_A     )





     @contextmanager
     def 	UpperCAmelCase(self   :					Optional[Any]     )		->		Any:
         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."     )
         snake_case				  =      True
         snake_case				  =      self.tokenizer
         yield
         snake_case				  =      self.image_processor
         snake_case				  =      False





     def 	UpperCAmelCase(self   :					Any       ,					_A   :					Dict       ,					_A   :					Dict=False       ,					_A   :					Tuple=None     )		->		Optional[int]:
         if added_vocab is None:
             snake_case				  =      self.tokenizer.get_added_vocab()

         snake_case				  =      {}

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

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

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





     @property
     def 	UpperCAmelCase(self   :					Any     )		->		Tuple:
         warnings.warn(
             "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."       ,					_A       ,					)
         return self.image_processor_class





     @property
     def 	UpperCAmelCase(self   :					Any     )		->		int:
         warnings.warn(
             "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."       ,					_A       ,					)
         return self.image_processor



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 MobileNetVaImageProcessor







class   lowerCamelCase	(						unittest.TestCase						):





     def __init__(self   :					Optional[int]       ,					_A   :					Dict       ,					_A   :					Optional[int]=7       ,					_A   :					Union[str, Any]=3       ,					_A   :					Tuple=1_8       ,					_A   :					str=3_0       ,					_A   :					Union[str, Any]=4_0_0       ,					_A   :					Optional[Any]=True       ,					_A   :					Union[str, Any]=None       ,					_A   :					str=True       ,					_A   :					Optional[int]=None       ,					)		->		int:
         snake_case				  =      size if size is not None else {"shortest_edge": 2_0}
         snake_case				  =      crop_size if crop_size is not None else {"height": 1_8, "width": 1_8}
         snake_case				  =      parent
         snake_case				  =      batch_size
         snake_case				  =      num_channels
         snake_case				  =      image_size
         snake_case				  =      min_resolution
         snake_case				  =      max_resolution
         snake_case				  =      do_resize
         snake_case				  =      size
         snake_case				  =      do_center_crop
         snake_case				  =      crop_size





     def 	UpperCAmelCase(self   :					str     )		->		int:
         return {
             "do_resize": self.do_resize,
             "size": self.size,
             "do_center_crop": self.do_center_crop,
             "crop_size": self.crop_size,
         }







@require_torch
@require_vision
class   lowerCamelCase	(						A_							,				unittest.TestCase						):
     UpperCAmelCase__       :							Optional[int]     =	MobileNetVaImageProcessor if is_vision_available() else None





     def 	UpperCAmelCase(self   :					Dict     )		->		List[Any]:
         snake_case				  =      MobileNetVaImageProcessingTester(self     )





     @property
     def 	UpperCAmelCase(self   :					Union[str, Any]     )		->		Tuple:
         return self.image_processor_tester.prepare_image_processor_dict()





     def 	UpperCAmelCase(self   :					Tuple     )		->		Tuple:
         snake_case				  =      self.image_processing_class(**self.image_processor_dict     )
         self.assertTrue(hasattr(_A       ,					"do_resize"     )     )
         self.assertTrue(hasattr(_A       ,					"size"     )     )
         self.assertTrue(hasattr(_A       ,					"do_center_crop"     )     )
         self.assertTrue(hasattr(_A       ,					"crop_size"     )     )





     def 	UpperCAmelCase(self   :					int     )		->		List[Any]:
         snake_case				  =      self.image_processing_class.from_dict(self.image_processor_dict     )
         self.assertEqual(image_processor.size       ,					{"shortest_edge": 2_0}     )
         self.assertEqual(image_processor.crop_size       ,					{"height": 1_8, "width": 1_8}     )

         snake_case				  =      self.image_processing_class.from_dict(self.image_processor_dict       ,					size=4_2       ,					crop_size=8_4     )
         self.assertEqual(image_processor.size       ,					{"shortest_edge": 4_2}     )
         self.assertEqual(image_processor.crop_size       ,					{"height": 8_4, "width": 8_4}     )





     def 	UpperCAmelCase(self   :					Any     )		->		Tuple:
         pass





     def 	UpperCAmelCase(self   :					Tuple     )		->		Tuple:
         # Initialize image_processing
         snake_case				  =      self.image_processing_class(**self.image_processor_dict     )
         # create random PIL images
         snake_case				  =      prepare_image_inputs(self.image_processor_tester       ,					equal_resolution=_A     )
         for image in image_inputs:
             self.assertIsInstance(_A       ,					Image.Image     )

         # Test not batched input
         snake_case				  =      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				  =      image_processing(_A       ,					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 	UpperCAmelCase(self   :					Tuple     )		->		List[Any]:
         # Initialize image_processing
         snake_case				  =      self.image_processing_class(**self.image_processor_dict     )
         # create random numpy tensors
         snake_case				  =      prepare_image_inputs(self.image_processor_tester       ,					equal_resolution=_A       ,					numpify=_A     )
         for image in image_inputs:
             self.assertIsInstance(_A       ,					np.ndarray     )

         # Test not batched input
         snake_case				  =      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				  =      image_processing(_A       ,					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 	UpperCAmelCase(self   :					List[str]     )		->		List[str]:
         # Initialize image_processing
         snake_case				  =      self.image_processing_class(**self.image_processor_dict     )
         # create random PyTorch tensors
         snake_case				  =      prepare_image_inputs(self.image_processor_tester       ,					equal_resolution=_A       ,					torchify=_A     )
         for image in image_inputs:
             self.assertIsInstance(_A       ,					torch.Tensor     )

         # Test not batched input
         snake_case				  =      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				  =      image_processing(_A       ,					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"],
             )       ,					)

