
'''simple docstring'''
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 			SCREAMING_SNAKE_CASE__      (     snake_case_):
				lowerCAmelCase_     			=							["""image_processor""", """tokenizer"""]
				lowerCAmelCase_     			=							"""Pix2StructImageProcessor"""
				lowerCAmelCase_     			=							("""T5Tokenizer""", """T5TokenizerFast""")







				def __init__(       self						,				A_						,				A_     )->		Any:







											'''simple docstring'''






											UpperCamelCase   			=     False
											super().__init__(A_						,				A_     )







				def __call__(       self						,				A_=None						,				A_ = None						,				A_ = True						,				A_ = False						,				A_ = None						,				A_ = None						,				A_ = 2048						,				A_ = 0						,				A_ = None						,				A_ = None						,				A_ = False						,				A_ = False						,				A_ = False						,				A_ = False						,				A_ = False						,				A_ = True						,				A_ = None						,				**A_						,				)->		BatchEncoding:







											'''simple docstring'''






											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:
																		UpperCamelCase   			=     self.tokenizer
																		UpperCamelCase   			=     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
																		UpperCamelCase   			=     self.image_processor(
																		    A_						,				return_tensors=A_						,				max_patches=A_						,				**A_     )
											else:
																		# add pixel_values and bbox
																		UpperCamelCase   			=     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:
																		UpperCamelCase   			=     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:
																									UpperCamelCase   			=     text_encoding.pop('attention_mask'     )
																		if "input_ids" in text_encoding:
																									UpperCamelCase   			=     text_encoding.pop('input_ids'     )
											else:
																		UpperCamelCase   			=     None

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

											return encoding_image_processor







				def 						UpperCAmelCase_    (       self						,				*A_						,				**A_     )->		Optional[Any]:







											'''simple docstring'''






											return self.tokenizer.batch_decode(*A_						,				**A_     )







				def 						UpperCAmelCase_    (       self						,				*A_						,				**A_     )->		List[str]:







											'''simple docstring'''






											return self.tokenizer.decode(*A_						,				**A_     )







				@property
				def 						UpperCAmelCase_    (       self     )->		Optional[int]:







											'''simple docstring'''






											UpperCamelCase   			=     self.tokenizer.model_input_names
											UpperCamelCase   			=     self.image_processor.model_input_names
											return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names     )     )






'''simple docstring'''
import unittest

import numpy as np

from transformers.testing_utils import is_flaky, 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 DonutImageProcessor


class 			SCREAMING_SNAKE_CASE__      (     unittest.TestCase):







				def __init__(       self						,				A_						,				A_=7						,				A_=3						,				A_=18						,				A_=30						,				A_=400						,				A_=True						,				A_=None						,				A_=True						,				A_=False						,				A_=True						,				A_=True						,				A_=[0.5, 0.5, 0.5]						,				A_=[0.5, 0.5, 0.5]						,				)->		Dict:







											'''simple docstring'''






											UpperCamelCase   			=     parent
											UpperCamelCase   			=     batch_size
											UpperCamelCase   			=     num_channels
											UpperCamelCase   			=     image_size
											UpperCamelCase   			=     min_resolution
											UpperCamelCase   			=     max_resolution
											UpperCamelCase   			=     do_resize
											UpperCamelCase   			=     size if size is not None else {'height': 18, 'width': 20}
											UpperCamelCase   			=     do_thumbnail
											UpperCamelCase   			=     do_align_axis
											UpperCamelCase   			=     do_pad
											UpperCamelCase   			=     do_normalize
											UpperCamelCase   			=     image_mean
											UpperCamelCase   			=     image_std







				def 						UpperCAmelCase_    (       self     )->		List[Any]:







											'''simple docstring'''






											return {
											    "do_resize": self.do_resize,
											    "size": self.size,
											    "do_thumbnail": self.do_thumbnail,
											    "do_align_long_axis": self.do_align_axis,
											    "do_pad": self.do_pad,
											    "do_normalize": self.do_normalize,
											    "image_mean": self.image_mean,
											    "image_std": self.image_std,
											}


@require_torch
@require_vision
class 			SCREAMING_SNAKE_CASE__      (     snake_case_							,       unittest.TestCase):
				lowerCAmelCase_     			=							DonutImageProcessor if is_vision_available() else None







				def 						UpperCAmelCase_    (       self     )->		str:







											'''simple docstring'''






											UpperCamelCase   			=     DonutImageProcessingTester(self     )







				@property
				def 						UpperCAmelCase_    (       self     )->		str:







											'''simple docstring'''






											return self.image_processor_tester.prepare_image_processor_dict()







				def 						UpperCAmelCase_    (       self     )->		Optional[int]:







											'''simple docstring'''






											UpperCamelCase   			=     self.image_processing_class(**self.image_processor_dict     )
											self.assertTrue(hasattr(A_						,				'do_resize'     )     )
											self.assertTrue(hasattr(A_						,				'size'     )     )
											self.assertTrue(hasattr(A_						,				'do_thumbnail'     )     )
											self.assertTrue(hasattr(A_						,				'do_align_long_axis'     )     )
											self.assertTrue(hasattr(A_						,				'do_pad'     )     )
											self.assertTrue(hasattr(A_						,				'do_normalize'     )     )
											self.assertTrue(hasattr(A_						,				'image_mean'     )     )
											self.assertTrue(hasattr(A_						,				'image_std'     )     )







				def 						UpperCAmelCase_    (       self     )->		Optional[int]:







											'''simple docstring'''






											UpperCamelCase   			=     self.image_processing_class.from_dict(self.image_processor_dict     )
											self.assertEqual(image_processor.size						,				{'height': 18, 'width': 20}     )

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

											# Previous config had dimensions in (width, height) order
											UpperCamelCase   			=     self.image_processing_class.from_dict(self.image_processor_dict						,				size=(42, 84)     )
											self.assertEqual(image_processor.size						,				{'height': 84, 'width': 42}     )







				def 						UpperCAmelCase_    (       self     )->		Tuple:







											'''simple docstring'''






											pass







				@is_flaky()
				def 						UpperCAmelCase_    (       self     )->		Any:







											'''simple docstring'''






											UpperCamelCase   			=     self.image_processing_class(**self.image_processor_dict     )
											# create random PIL images
											UpperCamelCase   			=     prepare_image_inputs(self.image_processor_tester						,				equal_resolution=A_     )
											for image in image_inputs:
																		self.assertIsInstance(A_						,				Image.Image     )

											# Test not batched input
											UpperCamelCase   			=     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
											UpperCamelCase   			=     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.size['height'],
											        self.image_processor_tester.size['width'],
											    )						,				)







				@is_flaky()
				def 						UpperCAmelCase_    (       self     )->		Optional[int]:







											'''simple docstring'''






											UpperCamelCase   			=     self.image_processing_class(**self.image_processor_dict     )
											# create random numpy tensors
											UpperCamelCase   			=     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
											UpperCamelCase   			=     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
											UpperCamelCase   			=     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.size['height'],
											        self.image_processor_tester.size['width'],
											    )						,				)







				@is_flaky()
				def 						UpperCAmelCase_    (       self     )->		Dict:







											'''simple docstring'''






											UpperCamelCase   			=     self.image_processing_class(**self.image_processor_dict     )
											# create random PyTorch tensors
											UpperCamelCase   			=     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
											UpperCamelCase   			=     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
											UpperCamelCase   			=     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.size['height'],
											        self.image_processor_tester.size['width'],
											    )						,				)




