


'''simple docstring'''




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 					a			(				unittest.TestCase  ):
 '''simple docstring'''




 def __init__(    self		,      lowerCamelCase_		,      lowerCamelCase_=7		,      lowerCamelCase_=3		,      lowerCamelCase_=1_8		,      lowerCamelCase_=3_0		,      lowerCamelCase_=4_0_0		,      lowerCamelCase_=True		,      lowerCamelCase_=None		,      lowerCamelCase_=True		,      )      ->    Optional[int]:
    _a					:  int						=  size if size is not None else {'height': 1_8, 'width': 1_8}
    _a					:  List[str]						=  parent
    _a					:  List[Any]						=  batch_size
    _a					:  Optional[int]						=  num_channels
    _a					:  Union[str, Any]						=  image_size
    _a					:  Optional[Any]						=  min_resolution
    _a					:  Dict						=  max_resolution
    _a					:  Tuple						=  do_resize
    _a					:  int						=  size
    _a					:  List[str]						=  apply_ocr




 def   __UpperCamelCase   (    self      )      ->    List[str]:
    return {"do_resize": self.do_resize, "size": self.size, "apply_ocr": self.apply_ocr}







@require_torch
@require_pytesseract
class 					a			(				snake_case__			,							unittest.TestCase  ):
 '''simple docstring'''




 __lowerCAmelCase						: List[Any]							  =		LayoutLMvaImageProcessor if is_pytesseract_available() else None




 def   __UpperCamelCase   (    self      )      ->    Any:
    _a					:  Dict						=  LayoutLMvaImageProcessingTester(self      )




 @property
 def   __UpperCamelCase   (    self      )      ->    Any:
    return self.image_processor_tester.prepare_image_processor_dict()




 def   __UpperCamelCase   (    self      )      ->    int:
    _a					:  Tuple						=  self.image_processing_class(**self.image_processor_dict      )
    self.assertTrue(hasattr(lowerCamelCase_		,      'do_resize'      )      )
    self.assertTrue(hasattr(lowerCamelCase_		,      'size'      )      )
    self.assertTrue(hasattr(lowerCamelCase_		,      'apply_ocr'      )      )




 def   __UpperCamelCase   (    self      )      ->    Any:
    _a					:  Any						=  self.image_processing_class.from_dict(self.image_processor_dict      )
    self.assertEqual(image_processor.size		,      {'height': 1_8, 'width': 1_8}      )

    _a					:  Dict						=  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      )      ->    Any:
    pass




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

    # Test not batched input
    _a					:  Dict						=  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		,      lowerCamelCase_      )
    self.assertIsInstance(encoding.boxes		,      lowerCamelCase_      )

    # Test batched
    _a					:  List[str]						=  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      )      ->    int:
    # Initialize image_processing
    _a					:  Optional[Any]						=  self.image_processing_class(**self.image_processor_dict      )
    # create random numpy tensors
    _a					:  Optional[Any]						=  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
    _a					:  Any						=  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					:  List[str]						=  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      )      ->    Tuple:
    # Initialize image_processing
    _a					:  Optional[Any]						=  self.image_processing_class(**self.image_processor_dict      )
    # create random PyTorch tensors
    _a					:  List[str]						=  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
    _a					:  Any						=  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					:  Any						=  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      )      ->    Tuple:
    # with apply_OCR = True
    _a					:  Dict						=  LayoutLMvaImageProcessor()

    from datasets import load_dataset

    _a					:  Tuple						=  load_dataset('hf-internal-testing/fixtures_docvqa'		,      split='test'      )

    _a					:  Dict						=  Image.open(ds[0]['file']      ).convert('RGB'      )

    _a					:  List[str]						=  image_processing(lowerCamelCase_		,      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					:  int						=  [['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					:  Any						=  [[[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		,      lowerCamelCase_      )
    self.assertListEqual(encoding.boxes		,      lowerCamelCase_      )

    # with apply_OCR = False
    _a					:  str						=  LayoutLMvaImageProcessor(apply_ocr=lowerCamelCase_      )

    _a					:  int						=  image_processing(lowerCamelCase_		,      return_tensors='pt'      )

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






'''simple docstring'''




from __future__ import annotations

import unittest

import numpy as np

from transformers import OPTConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
 import tensorflow as tf

 from transformers import GPTaTokenizer, TFOPTForCausalLM, TFOPTModel


def    UpperCAmelCase_  (       A		,	A		,	A=None		,	A=None      ):
   '''simple docstring'''





   if attention_mask is None:
      _a					:  List[Any]						=  tf.cast(tf.math.not_equal(A		,	config.pad_token_id      )		,	tf.inta      )
   return {"input_ids": input_ids, "attention_mask": attention_mask}







@require_tf
class 					a			:
 '''simple docstring'''




 __lowerCAmelCase						: List[str]							  =		OPTConfig
 __lowerCAmelCase						: Union[str, Any]							  =		{}
 __lowerCAmelCase						: Any							  =		"""gelu"""




 def __init__(    self		,      lowerCamelCase_		,      lowerCamelCase_=1_3		,      lowerCamelCase_=7		,      lowerCamelCase_=True		,      lowerCamelCase_=False		,      lowerCamelCase_=9_9		,      lowerCamelCase_=1_6		,      lowerCamelCase_=2		,      lowerCamelCase_=4		,      lowerCamelCase_=4		,      lowerCamelCase_="gelu"		,      lowerCamelCase_=0.1		,      lowerCamelCase_=0.1		,      lowerCamelCase_=2_0		,      lowerCamelCase_=2		,      lowerCamelCase_=1		,      lowerCamelCase_=0		,      lowerCamelCase_=1_6		,      lowerCamelCase_=1_6		,      )      ->    List[Any]:
    _a					:  Tuple						=  parent
    _a					:  List[str]						=  batch_size
    _a					:  str						=  seq_length
    _a					:  Optional[Any]						=  is_training
    _a					:  List[str]						=  use_labels
    _a					:  Optional[int]						=  vocab_size
    _a					:  int						=  hidden_size
    _a					:  List[Any]						=  num_hidden_layers
    _a					:  Optional[int]						=  num_attention_heads
    _a					:  int						=  intermediate_size
    _a					:  Union[str, Any]						=  hidden_act
    _a					:  Any						=  hidden_dropout_prob
    _a					:  List[Any]						=  attention_probs_dropout_prob
    _a					:  int						=  max_position_embeddings
    _a					:  Optional[Any]						=  eos_token_id
    _a					:  int						=  pad_token_id
    _a					:  Optional[int]						=  bos_token_id
    _a					:  Dict						=  embed_dim
    _a					:  Union[str, Any]						=  word_embed_proj_dim
    _a					:  str						=  False




 def   __UpperCamelCase   (    self      )      ->    Union[str, Any]:
    _a					:  Optional[int]						=  ids_tensor([self.batch_size, self.seq_length - 1]		,      self.vocab_size      )
    _a					:  Optional[int]						=  tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size      )		,      1      )
    _a					:  Union[str, Any]						=  tf.concat([input_ids, eos_tensor]		,      axis=1      )

    _a					:  Optional[int]						=  self.config_cls(
        vocab_size=self.vocab_size		,      hidden_size=self.hidden_size		,      num_hidden_layers=self.num_hidden_layers		,      num_attention_heads=self.num_attention_heads		,      ffn_dim=self.intermediate_size		,      dropout=self.hidden_dropout_prob		,      attention_dropout=self.attention_probs_dropout_prob		,      max_position_embeddings=self.max_position_embeddings		,      eos_token_id=self.eos_token_id		,      bos_token_id=self.bos_token_id		,      pad_token_id=self.pad_token_id		,      embed_dim=self.embed_dim		,      word_embed_proj_dim=self.word_embed_proj_dim		,      is_encoder_decoder=lowerCamelCase_		,      **self.config_updates		,      )
    _a					:  Any						=  prepare_opt_inputs_dict(lowerCamelCase_		,      lowerCamelCase_      )
    return config, inputs_dict




 def   __UpperCamelCase   (    self		,      lowerCamelCase_		,      lowerCamelCase_      )      ->    str:
    _a					:  List[str]						=  TFOPTModel(config=lowerCamelCase_      )
    _a					:  Any						=  inputs_dict['input_ids']

    _a					:  Union[str, Any]						=  input_ids[:1, :]
    _a					:  Any						=  inputs_dict['attention_mask'][:1, :]
    _a					:  str						=  1

    # first forward pass
    _a					:  str						=  model(lowerCamelCase_		,      attention_mask=lowerCamelCase_		,      use_cache=lowerCamelCase_      )

    _a		,						_a					:  int						=  outputs.to_tuple()

    # create hypothetical next token and extent to next_input_ids
    _a					:  List[Any]						=  ids_tensor((self.batch_size, 3)		,      config.vocab_size      )
    _a					:  List[Any]						=  tf.cast(ids_tensor((self.batch_size, 3)		,      2      )		,      tf.inta      )

    # append to next input_ids and
    _a					:  List[str]						=  tf.concat([input_ids, next_tokens]		,      axis=-1      )
    _a					:  Dict						=  tf.concat([attention_mask, next_attn_mask]		,      axis=-1      )

    _a					:  Union[str, Any]						=  model(lowerCamelCase_		,      attention_mask=lowerCamelCase_      )[0]
    _a					:  int						=  model(lowerCamelCase_		,      attention_mask=lowerCamelCase_		,      past_key_values=lowerCamelCase_      )[0]

    self.parent.assertEqual(next_tokens.shape[1]		,      output_from_past.shape[1]      )

    # select random slice
    _a					:  Dict						=  int(ids_tensor((1,)		,      output_from_past.shape[-1]      )      )
    _a					:  Optional[Any]						=  output_from_no_past[:, -3:, random_slice_idx]
    _a					:  Dict						=  output_from_past[:, :, random_slice_idx]

    # test that outputs are equal for slice
    tf.debugging.assert_near(lowerCamelCase_		,      lowerCamelCase_		,      rtol=1e-3      )







@require_tf
class 					a			(				snake_case__			,							snake_case__			,							unittest.TestCase  ):
 '''simple docstring'''




 __lowerCAmelCase						: List[Any]							  =		(TFOPTModel, TFOPTForCausalLM) if is_tf_available() else ()
 __lowerCAmelCase						: int							  =		(TFOPTForCausalLM,) if is_tf_available() else ()
 __lowerCAmelCase						: str							  =		(
     {"""feature-extraction""": TFOPTModel, """text-generation""": TFOPTForCausalLM} if is_tf_available() else {}
 )
 __lowerCAmelCase						: List[Any]							  =		False
 __lowerCAmelCase						: List[str]							  =		False
 __lowerCAmelCase						: Optional[Any]							  =		False
 __lowerCAmelCase						: List[str]							  =		10




 def   __UpperCamelCase   (    self      )      ->    str:
    _a					:  str						=  TFOPTModelTester(self      )
    _a					:  Optional[int]						=  ConfigTester(self		,      config_class=lowerCamelCase_      )




 def   __UpperCamelCase   (    self      )      ->    Optional[Any]:
    self.config_tester.run_common_tests()




 def   __UpperCamelCase   (    self      )      ->    List[str]:
    _a					:  List[Any]						=  self.model_tester.prepare_config_and_inputs_for_common()
    self.model_tester.check_decoder_model_past_large_inputs(*lowerCamelCase_      )




 def   __UpperCamelCase   (    self      )      ->    Any:
    _a		,						_a					:  Optional[int]						=  self.model_tester.prepare_config_and_inputs_for_common()

    def _get_word_embedding_weight(lowerCamelCase_		,      lowerCamelCase_      ):
       if hasattr(lowerCamelCase_		,      'weight'      ):
          return embedding_layer.weight
       else:
          # Here we build the word embeddings weights if not exists.
          # And then we retry to get the attribute once built.
          model.build()
          if hasattr(lowerCamelCase_		,      'weight'      ):
             return embedding_layer.weight
          else:
             return None

    for model_class in self.all_model_classes:
       for size in [config.vocab_size - 1_0, config.vocab_size + 1_0]:
          # build the embeddings
          _a					:  Union[str, Any]						=  model_class(config=lowerCamelCase_      )
          _a					:  Optional[int]						=  _get_word_embedding_weight(lowerCamelCase_		,      model.get_input_embeddings()      )
          _a					:  Optional[int]						=  _get_word_embedding_weight(lowerCamelCase_		,      model.get_output_embeddings()      )

          # reshape the embeddings
          model.resize_token_embeddings(lowerCamelCase_      )
          _a					:  str						=  _get_word_embedding_weight(lowerCamelCase_		,      model.get_input_embeddings()      )
          _a					:  Union[str, Any]						=  _get_word_embedding_weight(lowerCamelCase_		,      model.get_output_embeddings()      )

          # check that the resized embeddings size matches the desired size.
          _a					:  Optional[int]						=  size if size is not None else config.vocab_size

          self.assertEqual(new_input_embeddings.shape[0]		,      lowerCamelCase_      )

          # check that weights remain the same after resizing
          _a					:  Optional[int]						=  True
          for pa, pa in zip(old_input_embeddings.value()		,      new_input_embeddings.value()      ):
             if tf.math.reduce_sum(tf.math.abs(pa - pa      )      ) > 0:
                _a					:  List[str]						=  False
          self.assertTrue(lowerCamelCase_      )

          if old_output_embeddings is not None and new_output_embeddings is not None:
             self.assertEqual(new_output_embeddings.shape[0]		,      lowerCamelCase_      )

             _a					:  Optional[Any]						=  True
             for pa, pa in zip(old_output_embeddings.value()		,      new_output_embeddings.value()      ):
                if tf.math.reduce_sum(tf.math.abs(pa - pa      )      ) > 0:
                   _a					:  Optional[Any]						=  False
             self.assertTrue(lowerCamelCase_      )





def    UpperCAmelCase_  (       A      ):
   '''simple docstring'''





   return tf.constant(A		,	dtype=tf.intaa      )







@require_tf
class 					a			(				unittest.TestCase  ):
 '''simple docstring'''




 __lowerCAmelCase						: Optional[Any]							  =		99




 def   __UpperCamelCase   (    self      )      ->    List[str]:
    _a					:  List[str]						=  tf.ones((4, 1)		,      dtype=tf.intaa      ) * 2
    _a					:  Any						=  tf.concat([ids_tensor((4, 6)		,      self.vocab_size - 3      ) + 3, eos_column_vector]		,      axis=1      )
    _a					:  Tuple						=  input_ids.shape[0]
    _a					:  Any						=  OPTConfig(
        vocab_size=self.vocab_size		,      hidden_size=2_4		,      num_hidden_layers=2		,      num_attention_heads=2		,      ffn_dim=3_2		,      max_position_embeddings=4_8		,      eos_token_id=2		,      pad_token_id=1		,      bos_token_id=0		,      )
    return config, input_ids, batch_size







@require_sentencepiece
@require_tf
class 					a			(				unittest.TestCase  ):
 '''simple docstring'''




 @slow
 def   __UpperCamelCase   (    self      )      ->    Union[str, Any]:
    _a					:  Any						=  TFOPTModel.from_pretrained('facebook/opt-350m'      )
    _a					:  Union[str, Any]						=  _long_tensor([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]      )
    _a					:  Any						=  tf.not_equal(lowerCamelCase_		,      model.config.pad_token_id      )
    with tf.GradientTape():
       _a					:  Dict						=  model(input_ids=lowerCamelCase_		,      attention_mask=lowerCamelCase_      ).last_hidden_state
    _a					:  List[Any]						=  (1, 1_1, 5_1_2)
    self.assertEqual(output.shape		,      lowerCamelCase_      )
    _a					:  int						=  tf.constant(
        [[-0.2873, -1.9218, -0.3033], [-1.2710, -0.1338, -0.1902], [0.4095, 0.1214, -1.3121]]      )
    self.assertTrue(np.allclose(output[:, :3, :3]		,      lowerCamelCase_		,      atol=4e-3      )      )

    _a					:  Union[str, Any]						=  tf.function(lowerCamelCase_		,      jit_compile=lowerCamelCase_      )
    _a					:  List[str]						=  xla_generate(lowerCamelCase_		,      lowerCamelCase_      )[0]
    self.assertTrue(np.allclose(output[:, :3, :3]		,      lowerCamelCase_		,      atol=4e-2      )      )







@require_tf
@slow
class 					a			(				unittest.TestCase  ):
 '''simple docstring'''




 def   __UpperCamelCase   (    self      )      ->    Any:
    super().setUp()
    _a					:  Any						=  'facebook/opt-350m'




 def   __UpperCamelCase   (    self      )      ->    str:
    _a					:  List[Any]						=  TFOPTForCausalLM.from_pretrained(self.path_model      )
    _a					:  List[str]						=  GPTaTokenizer.from_pretrained(self.path_model      )

    _a					:  Dict						=  [
        'Today is a beautiful day and I want to',
        'In the city of',
        'Paris is the capital of France and',
        'Computers and mobile phones have taken',
    ]
    # verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
    _a					:  Union[str, Any]						=  tokenizer(lowerCamelCase_		,      return_tensors='tf'		,      padding=lowerCamelCase_		,      add_special_tokens=lowerCamelCase_      )
    _a					:  List[Any]						=  tf.math.reduce_mean(model(inputs.input_ids		,      attention_mask=inputs.attention_mask      )[0]		,      axis=-1      )
    _a					:  int						=  tf.constant(
        [
            [1.3851, -13.8923, -10.5229, -10.7533, -0.2309, -10.2384, -0.5365, -9.0947, -5.1670],
            [-4.7073, -10.6276, -3.9415, -21.5242, -0.2822, -0.2822, -0.2822, -0.2822, -0.2822],
            [0.6247, -3.4229, -8.9179, -1.4297, -14.1650, 1.4146, -9.0218, -0.2703, -0.2703],
            [6.4783, -1.9913, -10.7926, -2.3336, 1.5092, -0.9974, -6.8213, 1.3477, 1.3477],
        ]      )
    self.assertTrue(np.allclose(lowerCamelCase_		,      lowerCamelCase_		,      atol=1e-4      )      )

    _a					:  Any						=  tf.function(lowerCamelCase_		,      jit_compile=lowerCamelCase_      )
    _a					:  List[Any]						=  tf.math.reduce_mean(xla_generate(inputs.input_ids		,      attention_mask=inputs.attention_mask      )[0]		,      axis=-1      )
    self.assertTrue(np.allclose(lowerCamelCase_		,      lowerCamelCase_		,      atol=1e-4      )      )







@require_tf
@slow
class 					a			(				unittest.TestCase  ):
 '''simple docstring'''




 @property
 def   __UpperCamelCase   (    self      )      ->    Optional[int]:
    return [
        "Today is a beautiful day and I want",
        "In the city of",
        "Paris is the capital of France and",
        "Computers and mobile phones have taken",
    ]




 def   __UpperCamelCase   (    self      )      ->    List[Any]:
    _a					:  Any						=  'facebook/opt-125m'

    _a					:  List[str]						=  [
        'Today is a beautiful day and I want to',
        'In the city of New York, the city',
        'Paris is the capital of France and the capital',
        'Computers and mobile phones have taken over the',
    ]

    _a					:  str						=  []
    _a					:  Optional[Any]						=  GPTaTokenizer.from_pretrained(lowerCamelCase_      )
    _a					:  Tuple						=  TFOPTForCausalLM.from_pretrained(lowerCamelCase_      )

    for prompt in self.prompts:
       _a					:  Tuple						=  tokenizer(lowerCamelCase_		,      return_tensors='tf'      ).input_ids

       _a					:  Union[str, Any]						=  model.generate(lowerCamelCase_		,      max_length=1_0      )

       _a					:  Any						=  tokenizer.batch_decode(lowerCamelCase_		,      skip_special_tokens=lowerCamelCase_      )
       predicted_outputs += generated_string

    self.assertListEqual(lowerCamelCase_		,      lowerCamelCase_      )




 def   __UpperCamelCase   (    self      )      ->    Tuple:
    _a					:  int						=  'facebook/opt-350m'

    _a					:  Dict						=  GPTaTokenizer.from_pretrained(lowerCamelCase_      )
    _a					:  Any						=  TFOPTForCausalLM.from_pretrained(lowerCamelCase_      )

    _a					:  Any						=  'left'

    # use different length sentences to test batching
    _a					:  Optional[Any]						=  [
        'Hello, my dog is a little',
        'Today, I',
    ]

    _a					:  List[Any]						=  tokenizer(lowerCamelCase_		,      return_tensors='tf'		,      padding=lowerCamelCase_      )
    _a					:  Dict						=  inputs['input_ids']

    _a					:  Tuple						=  model.generate(input_ids=lowerCamelCase_		,      attention_mask=inputs['attention_mask']      )

    _a					:  Dict						=  tokenizer(sentences[0]		,      return_tensors='tf'      ).input_ids
    _a					:  Union[str, Any]						=  model.generate(input_ids=lowerCamelCase_      )

    _a					:  Any						=  inputs_non_padded.shape[-1] - tf.math.reduce_sum(
        tf.cast(inputs['attention_mask'][-1]		,      tf.intaa      )      )
    _a					:  Union[str, Any]						=  tokenizer(sentences[1]		,      return_tensors='tf'      ).input_ids
    _a					:  str						=  model.generate(input_ids=lowerCamelCase_		,      max_length=model.config.max_length - num_paddings      )

    _a					:  str						=  tokenizer.batch_decode(lowerCamelCase_		,      skip_special_tokens=lowerCamelCase_      )
    _a					:  Optional[int]						=  tokenizer.decode(output_non_padded[0]		,      skip_special_tokens=lowerCamelCase_      )
    _a					:  Any						=  tokenizer.decode(output_padded[0]		,      skip_special_tokens=lowerCamelCase_      )

    _a					:  Tuple						=  [
        'Hello, my dog is a little bit of a dork.\nI\'m a little bit',
        'Today, I was in the middle of a conversation with a friend about the',
    ]
    self.assertListEqual(lowerCamelCase_		,      lowerCamelCase_      )
    self.assertListEqual(lowerCamelCase_		,      [non_padded_sentence, padded_sentence]      )




 def   __UpperCamelCase   (    self      )      ->    Union[str, Any]:
    _a					:  str						=  'facebook/opt-350m'

    _a					:  List[str]						=  [
        'Today is a beautiful day and I want to',
        'In the city of San Francisco, the city',
        'Paris is the capital of France and the capital',
        'Computers and mobile phones have taken over the',
    ]

    _a					:  List[str]						=  []
    _a					:  Union[str, Any]						=  GPTaTokenizer.from_pretrained(lowerCamelCase_      )
    _a					:  int						=  TFOPTForCausalLM.from_pretrained(lowerCamelCase_      )

    for prompt in self.prompts:
       _a					:  List[str]						=  tokenizer(lowerCamelCase_		,      return_tensors='tf'      ).input_ids

       _a					:  List[Any]						=  model.generate(lowerCamelCase_		,      max_length=1_0      )

       _a					:  Any						=  tokenizer.batch_decode(lowerCamelCase_		,      skip_special_tokens=lowerCamelCase_      )
       predicted_outputs += generated_string

    self.assertListEqual(lowerCamelCase_		,      lowerCamelCase_      )


