
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType







class   lowerCamelCase	(						A_						):
     UpperCAmelCase__       :							int     =	["image_processor", "tokenizer"]
     UpperCAmelCase__       :							List[Any]     =	"Pix2StructImageProcessor"
     UpperCAmelCase__       :							int     =	("T5Tokenizer", "T5TokenizerFast")





     def __init__(self   :					str       ,					_A   :					Any       ,					_A   :					Any     )		->		Optional[Any]:
         snake_case				  =      False
         super().__init__(_A       ,					_A     )





     def __call__(self   :					Optional[int]       ,					_A   :					List[Any]=None       ,					_A   :					Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None       ,					_A   :					bool = True       ,					_A   :					Union[bool, str, PaddingStrategy] = False       ,					_A   :					Union[bool, str, TruncationStrategy] = None       ,					_A   :					Optional[int] = None       ,					_A   :					Optional[int] = 2_0_4_8       ,					_A   :					int = 0       ,					_A   :					Optional[int] = None       ,					_A   :					Optional[bool] = None       ,					_A   :					bool = False       ,					_A   :					bool = False       ,					_A   :					bool = False       ,					_A   :					bool = False       ,					_A   :					bool = False       ,					_A   :					bool = True       ,					_A   :					Optional[Union[str, TensorType]] = None       ,					**_A   :					List[Any]       ,					)		->		BatchEncoding:
         if images is None and text is None:
             raise ValueError("You have to specify either images or text."     )

         # Get only text
         if images is None and not self.image_processor.is_vqa:
             snake_case				  =      self.tokenizer
             snake_case				  =      self.tokenizer(
                 text=_A       ,					add_special_tokens=_A       ,					padding=_A       ,					truncation=_A       ,					max_length=_A       ,					stride=_A       ,					pad_to_multiple_of=_A       ,					return_attention_mask=_A       ,					return_overflowing_tokens=_A       ,					return_special_tokens_mask=_A       ,					return_offsets_mapping=_A       ,					return_token_type_ids=_A       ,					return_length=_A       ,					verbose=_A       ,					return_tensors=_A       ,					**_A       ,					)
             return text_encoding

         if not self.image_processor.is_vqa:
             # add pixel_values
             snake_case				  =      self.image_processor(
                 _A       ,					return_tensors=_A       ,					max_patches=_A       ,					**_A     )
         else:
             # add pixel_values and bbox
             snake_case				  =      self.image_processor(
                 _A       ,					return_tensors=_A       ,					max_patches=_A       ,					header_text=_A       ,					**_A     )

         if text is not None and not self.image_processor.is_vqa:
             snake_case				  =      self.tokenizer(
                 text=_A       ,					add_special_tokens=_A       ,					padding=_A       ,					truncation=_A       ,					max_length=_A       ,					stride=_A       ,					pad_to_multiple_of=_A       ,					return_attention_mask=_A       ,					return_overflowing_tokens=_A       ,					return_special_tokens_mask=_A       ,					return_offsets_mapping=_A       ,					return_token_type_ids=_A       ,					return_length=_A       ,					verbose=_A       ,					return_tensors=_A       ,					**_A       ,					)

             if "attention_mask" in text_encoding:
                 snake_case				  =      text_encoding.pop("attention_mask"     )
             if "input_ids" in text_encoding:
                 snake_case				  =      text_encoding.pop("input_ids"     )
         else:
             snake_case				  =      None

         if text_encoding is not None:
             encoding_image_processor.update(_A     )

         return encoding_image_processor





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





     def 	UpperCAmelCase(self   :					List[Any]       ,					*_A   :					Tuple       ,					**_A   :					Optional[Any]     )		->		List[Any]:
         return self.tokenizer.decode(*_A       ,					**_A     )





     @property
     def 	UpperCAmelCase(self   :					str     )		->		Any:
         snake_case				  =      self.tokenizer.model_input_names
         snake_case				  =      self.image_processor.model_input_names
         return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names     )     )



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"],
             )       ,					)

