






import unittest

import numpy as np

from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
					import torch

if is_pytesseract_available():
					from PIL import Image

					from transformers import LayoutLMvaImageProcessor
class      UpperCamelCase (				unittest.TestCase ):







		"""simple docstring"""



		def __init__(  self		:       int						,_SCREAMING_SNAKE_CASE		:       Tuple						,_SCREAMING_SNAKE_CASE		:       Any=7						,_SCREAMING_SNAKE_CASE		:       Dict=3						,_SCREAMING_SNAKE_CASE		:       Optional[Any]=1_8						,_SCREAMING_SNAKE_CASE		:       Optional[Any]=3_0						,_SCREAMING_SNAKE_CASE		:       Union[str, Any]=4_0_0						,_SCREAMING_SNAKE_CASE		:       int=True						,_SCREAMING_SNAKE_CASE		:       List[Any]=None						,_SCREAMING_SNAKE_CASE		:       List[Any]=True						,)    ->   Optional[int]:


						'''simple docstring'''
						A	 =      size if size is not None else {'height': 1_8, 'width': 1_8}
						A	 =      parent
						A	 =      batch_size
						A	 =      num_channels
						A	 =      image_size
						A	 =      min_resolution
						A	 =      max_resolution
						A	 =      do_resize
						A	 =      size
						A	 =      apply_ocr







		def       A(  self		:       Dict		)    ->   str:


						'''simple docstring'''
						return {"do_resize": self.do_resize, "size": self.size, "apply_ocr": self.apply_ocr}


@require_torch
@require_pytesseract
class      UpperCamelCase (				snake_case__			,       unittest.TestCase ):







		"""simple docstring"""



		snake_case    =   LayoutLMvaImageProcessor if is_pytesseract_available() else None
		def       A(  self		:       Union[str, Any]		)    ->   Optional[Any]:


						'''simple docstring'''
						A	 =      LayoutLMvaImageProcessingTester(self		)
		@property
		def       A(  self		:       str		)    ->   str:


						'''simple docstring'''
						return self.image_processor_tester.prepare_image_processor_dict()
		def       A(  self		:       Dict		)    ->   Optional[Any]:


						'''simple docstring'''
						A	 =      self.image_processing_class(**self.image_processor_dict		)
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE						,'do_resize'		)		)
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE						,'size'		)		)
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE						,'apply_ocr'		)		)
		def       A(  self		:       int		)    ->   Any:


						'''simple docstring'''
						A	 =      self.image_processing_class.from_dict(self.image_processor_dict		)
						self.assertEqual(image_processor.size						,{'height': 1_8, 'width': 1_8}		)

						A	 =      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       A(  self		:       List[str]		)    ->   Any:


						'''simple docstring'''
						pass
		def       A(  self		:       List[Any]		)    ->   int:


						'''simple docstring'''
						A	 =      self.image_processing_class(**self.image_processor_dict		)
						# create random PIL images
						A	 =      prepare_image_inputs(self.image_processor_tester						,equal_resolution=_SCREAMING_SNAKE_CASE		)
						for image in image_inputs:
										self.assertIsInstance(_SCREAMING_SNAKE_CASE						,Image.Image		)

						# Test not batched input
						A	 =      image_processing(image_inputs[0]						,return_tensors='pt'		)
						self.assertEqual(
						    encoding.pixel_values.shape						,(
						        1,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.size['height'],
						        self.image_processor_tester.size['width'],
						    )						,)

						self.assertIsInstance(encoding.words						,_SCREAMING_SNAKE_CASE		)
						self.assertIsInstance(encoding.boxes						,_SCREAMING_SNAKE_CASE		)

						# Test batched
						A	 =      image_processing(_SCREAMING_SNAKE_CASE						,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       A(  self		:       Optional[int]		)    ->   List[Any]:


						'''simple docstring'''
						A	 =      self.image_processing_class(**self.image_processor_dict		)
						# create random numpy tensors
						A	 =      prepare_image_inputs(self.image_processor_tester						,equal_resolution=_SCREAMING_SNAKE_CASE						,numpify=_SCREAMING_SNAKE_CASE		)
						for image in image_inputs:
										self.assertIsInstance(_SCREAMING_SNAKE_CASE						,np.ndarray		)

						# Test not batched input
						A	 =      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
						A	 =      image_processing(_SCREAMING_SNAKE_CASE						,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       A(  self		:       Dict		)    ->   List[Any]:


						'''simple docstring'''
						A	 =      self.image_processing_class(**self.image_processor_dict		)
						# create random PyTorch tensors
						A	 =      prepare_image_inputs(self.image_processor_tester						,equal_resolution=_SCREAMING_SNAKE_CASE						,torchify=_SCREAMING_SNAKE_CASE		)
						for image in image_inputs:
										self.assertIsInstance(_SCREAMING_SNAKE_CASE						,torch.Tensor		)

						# Test not batched input
						A	 =      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
						A	 =      image_processing(_SCREAMING_SNAKE_CASE						,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       A(  self		:       List[str]		)    ->   Dict:


						'''simple docstring'''
						A	 =      LayoutLMvaImageProcessor()

						from datasets import load_dataset

						A	 =      load_dataset('hf-internal-testing/fixtures_docvqa'						,split='test'		)

						A	 =      Image.open(ds[0]['file']		).convert('RGB'		)

						A	 =      image_processing(_SCREAMING_SNAKE_CASE						,return_tensors='pt'		)

						self.assertEqual(encoding.pixel_values.shape						,(1, 3, 2_2_4, 2_2_4)		)
						self.assertEqual(len(encoding.words		)						,len(encoding.boxes		)		)

						# fmt: off
						# the words and boxes were obtained with Tesseract 4.1.1
						A	 =      [['11:14', 'to', '11:39', 'a.m', '11:39', 'to', '11:44', 'a.m.', '11:44', 'a.m.', 'to', '12:25', 'p.m.', '12:25', 'to', '12:58', 'p.m.', '12:58', 'to', '4:00', 'p.m.', '2:00', 'to', '5:00', 'p.m.', 'Coffee', 'Break', 'Coffee', 'will', 'be', 'served', 'for', 'men', 'and', 'women', 'in', 'the', 'lobby', 'adjacent', 'to', 'exhibit', 'area.', 'Please', 'move', 'into', 'exhibit', 'area.', '(Exhibits', 'Open)', 'TRRF', 'GENERAL', 'SESSION', '(PART', '|)', 'Presiding:', 'Lee', 'A.', 'Waller', 'TRRF', 'Vice', 'President', '“Introductory', 'Remarks”', 'Lee', 'A.', 'Waller,', 'TRRF', 'Vice', 'Presi-', 'dent', 'Individual', 'Interviews', 'with', 'TRRF', 'Public', 'Board', 'Members', 'and', 'Sci-', 'entific', 'Advisory', 'Council', 'Mem-', 'bers', 'Conducted', 'by', 'TRRF', 'Treasurer', 'Philip', 'G.', 'Kuehn', 'to', 'get', 'answers', 'which', 'the', 'public', 'refrigerated', 'warehousing', 'industry', 'is', 'looking', 'for.', 'Plus', 'questions', 'from', 'the', 'floor.', 'Dr.', 'Emil', 'M.', 'Mrak,', 'University', 'of', 'Cal-', 'ifornia,', 'Chairman,', 'TRRF', 'Board;', 'Sam', 'R.', 'Cecil,', 'University', 'of', 'Georgia', 'College', 'of', 'Agriculture;', 'Dr.', 'Stanley', 'Charm,', 'Tufts', 'University', 'School', 'of', 'Medicine;', 'Dr.', 'Robert', 'H.', 'Cotton,', 'ITT', 'Continental', 'Baking', 'Company;', 'Dr.', 'Owen', 'Fennema,', 'University', 'of', 'Wis-', 'consin;', 'Dr.', 'Robert', 'E.', 'Hardenburg,', 'USDA.', 'Questions', 'and', 'Answers', 'Exhibits', 'Open', 'Capt.', 'Jack', 'Stoney', 'Room', 'TRRF', 'Scientific', 'Advisory', 'Council', 'Meeting', 'Ballroom', 'Foyer']]  # noqa: E231
						A	 =      [[[1_4_1, 5_7, 2_1_4, 6_9], [2_2_8, 5_8, 2_5_2, 6_9], [1_4_1, 7_5, 2_1_6, 8_8], [2_3_0, 7_9, 2_8_0, 8_8], [1_4_2, 2_6_0, 2_1_8, 2_7_3], [2_3_0, 2_6_1, 2_5_5, 2_7_3], [1_4_3, 2_7_9, 2_1_8, 2_9_0], [2_3_1, 2_8_2, 2_9_0, 2_9_1], [1_4_3, 3_4_2, 2_1_8, 3_5_4], [2_3_1, 3_4_5, 2_8_9, 3_5_5], [2_0_2, 3_6_2, 2_2_7, 3_7_3], [1_4_3, 3_7_9, 2_2_0, 3_9_2], [2_3_1, 3_8_2, 2_9_1, 3_9_4], [1_4_4, 7_1_4, 2_2_0, 7_2_6], [2_3_1, 7_1_5, 2_5_6, 7_2_6], [1_4_4, 7_3_2, 2_2_0, 7_4_5], [2_3_2, 7_3_6, 2_9_1, 7_4_7], [1_4_4, 7_6_9, 2_1_8, 7_8_2], [2_3_1, 7_7_0, 2_5_6, 7_8_2], [1_4_1, 7_8_8, 2_0_2, 8_0_1], [2_1_5, 7_9_1, 2_7_4, 8_0_4], [1_4_3, 8_2_6, 2_0_4, 8_3_8], [2_1_5, 8_2_6, 2_4_0, 8_3_8], [1_4_2, 8_4_4, 2_0_2, 8_5_7], [2_1_5, 8_4_7, 2_7_4, 8_5_9], [3_3_4, 5_7, 4_2_7, 6_9], [4_4_0, 5_7, 5_2_2, 6_9], [3_6_9, 7_5, 4_6_1, 8_8], [4_6_9, 7_5, 5_1_6, 8_8], [5_2_8, 7_6, 5_6_2, 8_8], [5_7_0, 7_6, 6_6_7, 8_8], [6_7_5, 7_5, 7_1_1, 8_7], [7_2_1, 7_9, 7_7_8, 8_8], [7_8_9, 7_5, 8_4_0, 8_8], [3_6_9, 9_7, 4_7_0, 1_0_7], [4_8_4, 9_4, 5_0_7, 1_0_6], [5_1_8, 9_4, 5_6_2, 1_0_7], [5_7_6, 9_4, 6_5_5, 1_1_0], [6_6_8, 9_4, 7_9_2, 1_0_9], [8_0_4, 9_5, 8_2_9, 1_0_7], [3_6_9, 1_1_3, 4_6_5, 1_2_5], [4_7_7, 1_1_6, 5_4_7, 1_2_5], [5_6_2, 1_1_3, 6_5_8, 1_2_5], [6_7_1, 1_1_6, 7_4_8, 1_2_5], [7_6_1, 1_1_3, 8_1_1, 1_2_5], [3_6_9, 1_3_1, 4_6_5, 1_4_3], [4_7_7, 1_3_3, 5_4_8, 1_4_3], [5_6_3, 1_3_0, 6_9_8, 1_4_5], [7_1_0, 1_3_0, 8_0_2, 1_4_6], [3_3_6, 1_7_1, 4_1_2, 1_8_3], [4_2_3, 1_7_1, 5_7_2, 1_8_3], [5_8_2, 1_7_0, 7_1_6, 1_8_4], [7_2_8, 1_7_1, 8_1_7, 1_8_7], [8_2_9, 1_7_1, 8_4_4, 1_8_6], [3_3_8, 1_9_7, 4_8_2, 2_1_2], [5_0_7, 1_9_6, 5_5_7, 2_0_9], [5_6_9, 1_9_6, 5_9_5, 2_0_8], [6_1_0, 1_9_6, 7_0_2, 2_0_9], [5_0_5, 2_1_4, 5_8_3, 2_2_6], [5_9_5, 2_1_4, 6_5_6, 2_2_7], [6_7_0, 2_1_5, 8_0_7, 2_2_7], [3_3_5, 2_5_9, 5_4_3, 2_7_4], [5_5_6, 2_5_9, 7_0_8, 2_7_2], [3_7_2, 2_7_9, 4_2_2, 2_9_1], [4_3_5, 2_7_9, 4_6_0, 2_9_1], [4_7_4, 2_7_9, 5_7_4, 2_9_2], [5_8_7, 2_7_8, 6_6_4, 2_9_1], [6_7_6, 2_7_8, 7_3_8, 2_9_1], [7_5_1, 2_7_9, 8_3_4, 2_9_1], [3_7_2, 2_9_8, 4_3_4, 3_1_0], [3_3_5, 3_4_1, 4_8_3, 3_5_4], [4_9_7, 3_4_1, 6_5_5, 3_5_4], [6_6_7, 3_4_1, 7_2_8, 3_5_4], [7_4_0, 3_4_1, 8_2_5, 3_5_4], [3_3_5, 3_6_0, 4_3_0, 3_7_2], [4_4_2, 3_6_0, 5_3_4, 3_7_2], [5_4_5, 3_5_9, 6_8_7, 3_7_2], [6_9_7, 3_6_0, 7_5_4, 3_7_2], [7_6_5, 3_6_0, 8_2_3, 3_7_3], [3_3_4, 3_7_8, 4_2_8, 3_9_1], [4_4_0, 3_7_8, 5_7_7, 3_9_4], [5_9_0, 3_7_8, 7_0_5, 3_9_1], [7_2_0, 3_7_8, 8_0_1, 3_9_1], [3_3_4, 3_9_7, 4_0_0, 4_0_9], [3_7_0, 4_1_6, 5_2_9, 4_2_9], [5_4_4, 4_1_6, 5_7_6, 4_3_2], [5_8_7, 4_1_6, 6_6_5, 4_2_8], [6_7_7, 4_1_6, 8_1_4, 4_2_9], [3_7_2, 4_3_5, 4_5_2, 4_5_0], [4_6_5, 4_3_4, 4_9_5, 4_4_7], [5_1_1, 4_3_4, 6_0_0, 4_4_7], [6_1_1, 4_3_6, 6_3_7, 4_4_7], [6_4_9, 4_3_6, 6_9_4, 4_5_1], [7_0_5, 4_3_8, 8_2_4, 4_4_7], [3_6_9, 4_5_3, 4_5_2, 4_6_6], [4_6_4, 4_5_4, 5_0_9, 4_6_6], [5_2_2, 4_5_3, 6_1_1, 4_6_9], [6_2_5, 4_5_3, 7_9_2, 4_6_9], [3_7_0, 4_7_2, 5_5_6, 4_8_8], [5_7_0, 4_7_2, 6_8_4, 4_8_7], [6_9_7, 4_7_2, 7_1_8, 4_8_5], [7_3_2, 4_7_2, 8_3_5, 4_8_8], [3_6_9, 4_9_0, 4_1_1, 5_0_3], [4_2_5, 4_9_0, 4_8_4, 5_0_3], [4_9_6, 4_9_0, 6_3_5, 5_0_6], [6_4_5, 4_9_0, 7_0_7, 5_0_3], [7_1_8, 4_9_1, 7_6_1, 5_0_3], [7_7_1, 4_9_0, 8_4_0, 5_0_3], [3_3_6, 5_1_0, 3_7_4, 5_2_1], [3_8_8, 5_1_0, 4_4_7, 5_2_2], [4_6_0, 5_1_0, 4_8_9, 5_2_1], [5_0_3, 5_1_0, 5_8_0, 5_2_2], [5_9_2, 5_0_9, 7_3_6, 5_2_5], [7_4_5, 5_0_9, 7_7_0, 5_2_2], [7_8_1, 5_0_9, 8_4_0, 5_2_2], [3_3_8, 5_2_8, 4_3_4, 5_4_1], [4_4_8, 5_2_8, 5_9_6, 5_4_1], [6_0_9, 5_2_7, 6_8_7, 5_4_0], [7_0_0, 5_2_8, 7_9_2, 5_4_1], [3_3_6, 5_4_6, 3_9_7, 5_5_9], [4_0_7, 5_4_6, 4_3_1, 5_5_9], [4_4_3, 5_4_6, 5_2_5, 5_6_0], [5_3_7, 5_4_6, 6_8_0, 5_6_2], [6_8_8, 5_4_6, 7_1_4, 5_5_9], [7_2_2, 5_4_6, 8_3_7, 5_6_2], [3_3_6, 5_6_5, 4_4_9, 5_8_1], [4_6_1, 5_6_5, 4_8_5, 5_7_7], [4_9_7, 5_6_5, 6_6_5, 5_8_1], [6_8_1, 5_6_5, 7_1_8, 5_7_7], [7_3_2, 5_6_5, 8_3_7, 5_8_0], [3_3_7, 5_8_4, 4_3_8, 5_9_7], [4_5_2, 5_8_3, 5_2_1, 5_9_6], [5_3_5, 5_8_4, 6_7_7, 5_9_9], [6_9_0, 5_8_3, 7_8_7, 5_9_6], [8_0_1, 5_8_3, 8_2_5, 5_9_6], [3_3_8, 6_0_2, 4_7_8, 6_1_5], [4_9_2, 6_0_2, 5_3_0, 6_1_4], [5_4_3, 6_0_2, 6_3_8, 6_1_5], [6_5_0, 6_0_2, 6_7_6, 6_1_4], [6_8_8, 6_0_2, 7_8_8, 6_1_5], [8_0_2, 6_0_2, 8_4_3, 6_1_4], [3_3_7, 6_2_1, 5_0_2, 6_3_3], [5_1_6, 6_2_1, 6_1_5, 6_3_7], [6_2_9, 6_2_1, 7_7_4, 6_3_6], [7_8_9, 6_2_1, 8_2_7, 6_3_3], [3_3_7, 6_3_9, 4_1_8, 6_5_2], [4_3_2, 6_4_0, 5_7_1, 6_5_3], [5_8_7, 6_3_9, 7_3_1, 6_5_5], [7_4_3, 6_3_9, 7_6_9, 6_5_2], [7_8_0, 6_3_9, 8_4_1, 6_5_2], [3_3_8, 6_5_8, 4_4_0, 6_7_3], [4_5_5, 6_5_8, 4_9_1, 6_7_0], [5_0_8, 6_5_8, 6_0_2, 6_7_1], [6_1_6, 6_5_8, 6_3_8, 6_7_0], [6_5_4, 6_5_8, 8_3_5, 6_7_4], [3_3_7, 6_7_7, 4_2_9, 6_8_9], [3_3_7, 7_1_4, 4_8_2, 7_2_6], [4_9_5, 7_1_4, 5_4_8, 7_2_6], [5_6_1, 7_1_4, 6_8_3, 7_2_6], [3_3_8, 7_7_0, 4_6_1, 7_8_2], [4_7_4, 7_6_9, 5_5_4, 7_8_5], [4_8_9, 7_8_8, 5_6_2, 8_0_3], [5_7_6, 7_8_8, 6_4_3, 8_0_1], [6_5_6, 7_8_7, 7_5_1, 8_0_4], [7_6_4, 7_8_8, 8_4_4, 8_0_1], [3_3_4, 8_2_5, 4_2_1, 8_3_8], [4_3_0, 8_2_4, 5_7_4, 8_3_8], [5_8_4, 8_2_4, 7_2_3, 8_4_1], [3_3_5, 8_4_4, 4_5_0, 8_5_7], [4_6_4, 8_4_3, 5_8_3, 8_6_0], [6_2_8, 8_6_2, 7_5_5, 8_7_5], [7_6_9, 8_6_1, 8_4_8, 8_7_8]]]  # noqa: E231
						# fmt: on

						self.assertListEqual(encoding.words						,_SCREAMING_SNAKE_CASE		)
						self.assertListEqual(encoding.boxes						,_SCREAMING_SNAKE_CASE		)

						# with apply_OCR = False
						A	 =      LayoutLMvaImageProcessor(apply_ocr=_SCREAMING_SNAKE_CASE		)

						A	 =      image_processing(_SCREAMING_SNAKE_CASE						,return_tensors='pt'		)

						self.assertEqual(encoding.pixel_values.shape						,(1, 3, 2_2_4, 2_2_4)		)













from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
					from PIL import Image

					from ..image_utils import load_image

if is_tf_available():
					from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_VISION_2_SEQ_MAPPING

if is_torch_available():
					import torch

					from ..models.auto.modeling_auto import MODEL_FOR_VISION_2_SEQ_MAPPING

lowerCAmelCase_           = logging.get_logger(__name__)
@add_end_docstrings(snake_case__ )
class      UpperCamelCase (				snake_case__ ):







		"""simple docstring"""



		def __init__(  self		:       str						,*_SCREAMING_SNAKE_CASE		:       List[Any]						,**_SCREAMING_SNAKE_CASE		:       List[Any]		)    ->   Dict:


						'''simple docstring'''
						super().__init__(*_SCREAMING_SNAKE_CASE						,**_SCREAMING_SNAKE_CASE		)
						requires_backends(self						,'vision'		)
						self.check_model_type(
						    TF_MODEL_FOR_VISION_2_SEQ_MAPPING if self.framework == 'tf' else MODEL_FOR_VISION_2_SEQ_MAPPING		)
		def       A(  self		:       Union[str, Any]						,_SCREAMING_SNAKE_CASE		:       List[Any]=None						,_SCREAMING_SNAKE_CASE		:       List[str]=None						,_SCREAMING_SNAKE_CASE		:       int=None		)    ->   Any:


						'''simple docstring'''
						A	 =      {}
						A	 =      {}

						if prompt is not None:
										A	 =      prompt

						if generate_kwargs is not None:
										A	 =      generate_kwargs
						if max_new_tokens is not None:
										if "generate_kwargs" not in forward_kwargs:
														A	 =      {}
										if "max_new_tokens" in forward_kwargs["generate_kwargs"]:
														raise ValueError(
														    '\'max_new_tokens\' is defined twice, once in \'generate_kwargs\' and once as a direct parameter,'
														    ' please use only one'		)
										A	 =      max_new_tokens
						return preprocess_params, forward_kwargs, {}
		def __call__(  self		:       int						,_SCREAMING_SNAKE_CASE		:       Union[str, List[str], "Image.Image", List["Image.Image"]]						,**_SCREAMING_SNAKE_CASE		:       List[str]		)    ->   Union[str, Any]:


						'''simple docstring'''
						return super().__call__(_SCREAMING_SNAKE_CASE						,**_SCREAMING_SNAKE_CASE		)
		def       A(  self		:       Dict						,_SCREAMING_SNAKE_CASE		:       List[Any]						,_SCREAMING_SNAKE_CASE		:       Optional[Any]=None		)    ->   int:


						'''simple docstring'''
						A	 =      load_image(_SCREAMING_SNAKE_CASE		)

						if prompt is not None:
										if not isinstance(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		):
														raise ValueError(
														    f'Received an invalid text input, got - {type(_SCREAMING_SNAKE_CASE		)} - but expected a single string. '
														    'Note also that one single text can be provided for conditional image to text generation.'		)

										A	 =      self.model.config.model_type

										if model_type == "git":
														A	 =      self.image_processor(images=_SCREAMING_SNAKE_CASE						,return_tensors=self.framework		)
														A	 =      self.tokenizer(text=_SCREAMING_SNAKE_CASE						,add_special_tokens=_SCREAMING_SNAKE_CASE		).input_ids
														A	 =      [self.tokenizer.cls_token_id] + input_ids
														A	 =      torch.tensor(_SCREAMING_SNAKE_CASE		).unsqueeze(0		)
														model_inputs.update({'input_ids': input_ids}		)

										elif model_type == "pix2struct":
														A	 =      self.image_processor(images=_SCREAMING_SNAKE_CASE						,header_text=_SCREAMING_SNAKE_CASE						,return_tensors=self.framework		)

										elif model_type != "vision-encoder-decoder":
														# vision-encoder-decoder does not support conditional generation
														A	 =      self.image_processor(images=_SCREAMING_SNAKE_CASE						,return_tensors=self.framework		)
														A	 =      self.tokenizer(_SCREAMING_SNAKE_CASE						,return_tensors=self.framework		)
														model_inputs.update(_SCREAMING_SNAKE_CASE		)

										else:
														raise ValueError(f'Model type {model_type} does not support conditional text generation'		)

						else:
										A	 =      self.image_processor(images=_SCREAMING_SNAKE_CASE						,return_tensors=self.framework		)

						if self.model.config.model_type == "git" and prompt is None:
										A	 =      None

						return model_inputs
		def       A(  self		:       str						,_SCREAMING_SNAKE_CASE		:       str						,_SCREAMING_SNAKE_CASE		:       Dict=None		)    ->   List[str]:


						'''simple docstring'''
						# Git model sets `model_inputs["input_ids"] = None` in `preprocess` (when `prompt=None`). In batch model, the
						# pipeline will group them into a list of `None`, which fail `_forward`. Avoid this by checking it first.
						if (
						    "input_ids" in model_inputs
						    and isinstance(model_inputs['input_ids']						,_SCREAMING_SNAKE_CASE		)
						    and all(x is None for x in model_inputs['input_ids']		)
						):
										A	 =      None

						if generate_kwargs is None:
										A	 =      {}
						# FIXME: We need to pop here due to a difference in how `generation.py` and `generation.tf_utils.py`
						#  parse inputs. In the Tensorflow version, `generate` raises an error if we don't use `input_ids` whereas
						#  the PyTorch version matches it with `self.model.main_input_name` or `self.model.encoder.main_input_name`
						#  in the `_prepare_model_inputs` method.
						A	 =      model_inputs.pop(self.model.main_input_name		)
						A	 =      self.model.generate(_SCREAMING_SNAKE_CASE						,**_SCREAMING_SNAKE_CASE						,**_SCREAMING_SNAKE_CASE		)
						return model_outputs







		def       A(  self		:       Dict						,_SCREAMING_SNAKE_CASE		:       Dict		)    ->   Optional[int]:


						'''simple docstring'''
						A	 =      []
						for output_ids in model_outputs:
										A	 =      {
										    'generated_text': self.tokenizer.decode(
										        _SCREAMING_SNAKE_CASE						,skip_special_tokens=_SCREAMING_SNAKE_CASE						,)
										}
										records.append(_SCREAMING_SNAKE_CASE		)
						return records





