






'''simple docstring'''
from typing import List, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging


a_ 		=       logging.get_logger(__name__)

a_ 		=       {
    '''huggingface/autoformer-tourism-monthly''': '''https://huggingface.co/huggingface/autoformer-tourism-monthly/resolve/main/config.json''',
}






class    __SCREAMING_SNAKE_CASE					(	__snake_case ):
 snake_case_     							=							"""autoformer"""
 snake_case_     							=							{
     """hidden_size""": """d_model""",
     """num_attention_heads""": """encoder_attention_heads""",
     """num_hidden_layers""": """encoder_layers""",
 }




 def __init__(					self		:							int    ,					__lowercase		:							Optional[int] = None    ,					__lowercase		:							Optional[int] = None    ,					__lowercase		:							str = "student_t"    ,					__lowercase		:							str = "nll"    ,					__lowercase		:							int = 1    ,					__lowercase		:							List[int] = [1, 2, 3, 4, 5, 6, 7]    ,					__lowercase		:							bool = True    ,					__lowercase		:							int = 0    ,					__lowercase		:							int = 0    ,					__lowercase		:							int = 0    ,					__lowercase		:							int = 0    ,					__lowercase		:							Optional[List[int]] = None    ,					__lowercase		:							Optional[List[int]] = None    ,					__lowercase		:							int = 64    ,					__lowercase		:							int = 2    ,					__lowercase		:							int = 2    ,					__lowercase		:							int = 2    ,					__lowercase		:							int = 2    ,					__lowercase		:							int = 32    ,					__lowercase		:							int = 32    ,					__lowercase		:							str = "gelu"    ,					__lowercase		:							float = 0.1    ,					__lowercase		:							float = 0.1    ,					__lowercase		:							float = 0.1    ,					__lowercase		:							float = 0.1    ,					__lowercase		:							float = 0.1    ,					__lowercase		:							int = 1_00    ,					__lowercase		:							float = 0.02    ,					__lowercase		:							bool = True    ,					__lowercase		:							Optional[int]=True    ,					__lowercase		:							int = 10    ,					__lowercase		:							int = 25    ,					__lowercase		:							int = 3    ,					**__lowercase		:							Any    ,					)		->  Dict:
       # time series specific configuration
       SCREAMING_SNAKE_CASE__   : Optional[int]													=prediction_length
       SCREAMING_SNAKE_CASE__   : str													=context_length if context_length is not None else prediction_length
       SCREAMING_SNAKE_CASE__   : Tuple													=distribution_output
       SCREAMING_SNAKE_CASE__   : int													=loss
       SCREAMING_SNAKE_CASE__   : Any													=input_size
       SCREAMING_SNAKE_CASE__   : Optional[int]													=num_time_features
       SCREAMING_SNAKE_CASE__   : List[Any]													=lags_sequence
       SCREAMING_SNAKE_CASE__   : Any													=scaling
       SCREAMING_SNAKE_CASE__   : Optional[int]													=num_dynamic_real_features
       SCREAMING_SNAKE_CASE__   : int													=num_static_real_features
       SCREAMING_SNAKE_CASE__   : Dict													=num_static_categorical_features
       if cardinality is not None and num_static_categorical_features > 0:
             if len(__SCREAMING_SNAKE_CASE					) != num_static_categorical_features:
                   raise ValueError(
                       '''The cardinality should be a list of the same length as `num_static_categorical_features`'''					)
             SCREAMING_SNAKE_CASE__   : List[Any]													=cardinality
       else:
             SCREAMING_SNAKE_CASE__   : Dict													=[0]
       if embedding_dimension is not None and num_static_categorical_features > 0:
             if len(__SCREAMING_SNAKE_CASE					) != num_static_categorical_features:
                   raise ValueError(
                       '''The embedding dimension should be a list of the same length as `num_static_categorical_features`'''					)
             SCREAMING_SNAKE_CASE__   : Tuple													=embedding_dimension
       else:
             SCREAMING_SNAKE_CASE__   : Tuple													=[min(50    ,					(cat + 1) // 2					) for cat in self.cardinality]
       SCREAMING_SNAKE_CASE__   : Union[str, Any]													=num_parallel_samples

       # Transformer architecture configuration
       SCREAMING_SNAKE_CASE__   : int													=input_size * len(self.lags_sequence					) + self._number_of_features
       SCREAMING_SNAKE_CASE__   : Optional[Any]													=d_model
       SCREAMING_SNAKE_CASE__   : List[Any]													=encoder_attention_heads
       SCREAMING_SNAKE_CASE__   : Optional[Any]													=decoder_attention_heads
       SCREAMING_SNAKE_CASE__   : Any													=encoder_ffn_dim
       SCREAMING_SNAKE_CASE__   : Optional[int]													=decoder_ffn_dim
       SCREAMING_SNAKE_CASE__   : Tuple													=encoder_layers
       SCREAMING_SNAKE_CASE__   : int													=decoder_layers

       SCREAMING_SNAKE_CASE__   : List[Any]													=dropout
       SCREAMING_SNAKE_CASE__   : Dict													=attention_dropout
       SCREAMING_SNAKE_CASE__   : Optional[Any]													=activation_dropout
       SCREAMING_SNAKE_CASE__   : Tuple													=encoder_layerdrop
       SCREAMING_SNAKE_CASE__   : List[Any]													=decoder_layerdrop

       SCREAMING_SNAKE_CASE__   : Optional[Any]													=activation_function
       SCREAMING_SNAKE_CASE__   : Tuple													=init_std

       SCREAMING_SNAKE_CASE__   : int													=use_cache

       # Autoformer
       SCREAMING_SNAKE_CASE__   : Any													=label_length
       SCREAMING_SNAKE_CASE__   : Optional[int]													=moving_average
       SCREAMING_SNAKE_CASE__   : List[Any]													=autocorrelation_factor

       super().__init__(is_encoder_decoder=__SCREAMING_SNAKE_CASE    ,					**__SCREAMING_SNAKE_CASE					)




 @property
 def    __magic_name__		(					self		:							str					)		->  int:
       return (
           sum(self.embedding_dimension					)
           + self.num_dynamic_real_features
           + self.num_time_features
           + self.num_static_real_features
           + self.input_size * 2  # the log1p(abs(loc)) and log(scale) features
       )







"""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		):






    def __init__(       self     :							Any       ,						__SCREAMING_SNAKE_CASE     :							Optional[int]       ,						__SCREAMING_SNAKE_CASE     :							List[str]=7       ,						__SCREAMING_SNAKE_CASE     :							Dict=3       ,						__SCREAMING_SNAKE_CASE     :							Dict=18       ,						__SCREAMING_SNAKE_CASE     :							Union[str, Any]=30       ,						__SCREAMING_SNAKE_CASE     :							Optional[Any]=400       ,						__SCREAMING_SNAKE_CASE     :							Dict=True       ,						__SCREAMING_SNAKE_CASE     :							Dict=None       ,						__SCREAMING_SNAKE_CASE     :							Any=True       ,						)     ->		str:
      lowerCamelCase_         =						size if size is not None else {'height': 18, 'width': 18}
      lowerCamelCase_         =						parent
      lowerCamelCase_         =						batch_size
      lowerCamelCase_         =						num_channels
      lowerCamelCase_         =						image_size
      lowerCamelCase_         =						min_resolution
      lowerCamelCase_         =						max_resolution
      lowerCamelCase_         =						do_resize
      lowerCamelCase_         =						size
      lowerCamelCase_         =						apply_ocr






    def    UpperCamelCase       (       self     :							int					)     ->		Tuple:
      return {"do_resize": self.do_resize, "size": self.size, "apply_ocr": self.apply_ocr}






@require_torch
@require_pytesseract
class      a						(					__snake_case  ,      unittest.TestCase		):
    SCREAMING_SNAKE_CASE						:     str						= LayoutLMvaImageProcessor if is_pytesseract_available() else None






    def    UpperCamelCase       (       self     :							List[str]					)     ->		int:
      lowerCamelCase_         =						LayoutLMvaImageProcessingTester(self					)






    @property
    def    UpperCamelCase       (       self     :							Optional[Any]					)     ->		Any:
      return self.image_processor_tester.prepare_image_processor_dict()






    def    UpperCamelCase       (       self     :							Tuple					)     ->		Optional[Any]:
      lowerCamelCase_         =						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    UpperCamelCase       (       self     :							Any					)     ->		Any:
      lowerCamelCase_         =						self.image_processing_class.from_dict(self.image_processor_dict					)
      self.assertEqual(image_processor.size       ,						{'height': 18, 'width': 18}					)

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






    def    UpperCamelCase       (       self     :							Dict					)     ->		Any:
      pass






    def    UpperCamelCase       (       self     :							int					)     ->		Dict:
      # Initialize image_processing
      lowerCamelCase_         =						self.image_processing_class(**self.image_processor_dict					)
      # create random PIL images
      lowerCamelCase_         =						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
      lowerCamelCase_         =						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
      lowerCamelCase_         =						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    UpperCamelCase       (       self     :							Optional[int]					)     ->		Optional[Any]:
      # Initialize image_processing
      lowerCamelCase_         =						self.image_processing_class(**self.image_processor_dict					)
      # create random numpy tensors
      lowerCamelCase_         =						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
      lowerCamelCase_         =						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
      lowerCamelCase_         =						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    UpperCamelCase       (       self     :							Dict					)     ->		int:
      # Initialize image_processing
      lowerCamelCase_         =						self.image_processing_class(**self.image_processor_dict					)
      # create random PyTorch tensors
      lowerCamelCase_         =						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
      lowerCamelCase_         =						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
      lowerCamelCase_         =						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    UpperCamelCase       (       self     :							Dict					)     ->		Any:
      # with apply_OCR = True
      lowerCamelCase_         =						LayoutLMvaImageProcessor()

      from datasets import load_dataset

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

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

      lowerCamelCase_         =						image_processing(__SCREAMING_SNAKE_CASE       ,						return_tensors='pt'					)

      self.assertEqual(encoding.pixel_values.shape       ,						(1, 3, 224, 224)					)
      self.assertEqual(len(encoding.words					)       ,						len(encoding.boxes					)					)

      # fmt: off
      # the words and boxes were obtained with Tesseract 4.1.1
      lowerCamelCase_         =						[['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
      lowerCamelCase_         =						[[[141, 57, 214, 69], [228, 58, 252, 69], [141, 75, 216, 88], [230, 79, 280, 88], [142, 260, 218, 273], [230, 261, 255, 273], [143, 279, 218, 290], [231, 282, 290, 291], [143, 342, 218, 354], [231, 345, 289, 355], [202, 362, 227, 373], [143, 379, 220, 392], [231, 382, 291, 394], [144, 714, 220, 726], [231, 715, 256, 726], [144, 732, 220, 745], [232, 736, 291, 747], [144, 769, 218, 782], [231, 770, 256, 782], [141, 788, 202, 801], [215, 791, 274, 804], [143, 826, 204, 838], [215, 826, 240, 838], [142, 844, 202, 857], [215, 847, 274, 859], [334, 57, 427, 69], [440, 57, 522, 69], [369, 75, 461, 88], [469, 75, 516, 88], [528, 76, 562, 88], [570, 76, 667, 88], [675, 75, 711, 87], [721, 79, 778, 88], [789, 75, 840, 88], [369, 97, 470, 107], [484, 94, 507, 106], [518, 94, 562, 107], [576, 94, 655, 110], [668, 94, 792, 109], [804, 95, 829, 107], [369, 113, 465, 125], [477, 116, 547, 125], [562, 113, 658, 125], [671, 116, 748, 125], [761, 113, 811, 125], [369, 131, 465, 143], [477, 133, 548, 143], [563, 130, 698, 145], [710, 130, 802, 146], [336, 171, 412, 183], [423, 171, 572, 183], [582, 170, 716, 184], [728, 171, 817, 187], [829, 171, 844, 186], [338, 197, 482, 212], [507, 196, 557, 209], [569, 196, 595, 208], [610, 196, 702, 209], [505, 214, 583, 226], [595, 214, 656, 227], [670, 215, 807, 227], [335, 259, 543, 274], [556, 259, 708, 272], [372, 279, 422, 291], [435, 279, 460, 291], [474, 279, 574, 292], [587, 278, 664, 291], [676, 278, 738, 291], [751, 279, 834, 291], [372, 298, 434, 310], [335, 341, 483, 354], [497, 341, 655, 354], [667, 341, 728, 354], [740, 341, 825, 354], [335, 360, 430, 372], [442, 360, 534, 372], [545, 359, 687, 372], [697, 360, 754, 372], [765, 360, 823, 373], [334, 378, 428, 391], [440, 378, 577, 394], [590, 378, 705, 391], [720, 378, 801, 391], [334, 397, 400, 409], [370, 416, 529, 429], [544, 416, 576, 432], [587, 416, 665, 428], [677, 416, 814, 429], [372, 435, 452, 450], [465, 434, 495, 447], [511, 434, 600, 447], [611, 436, 637, 447], [649, 436, 694, 451], [705, 438, 824, 447], [369, 453, 452, 466], [464, 454, 509, 466], [522, 453, 611, 469], [625, 453, 792, 469], [370, 472, 556, 488], [570, 472, 684, 487], [697, 472, 718, 485], [732, 472, 835, 488], [369, 490, 411, 503], [425, 490, 484, 503], [496, 490, 635, 506], [645, 490, 707, 503], [718, 491, 761, 503], [771, 490, 840, 503], [336, 510, 374, 521], [388, 510, 447, 522], [460, 510, 489, 521], [503, 510, 580, 522], [592, 509, 736, 525], [745, 509, 770, 522], [781, 509, 840, 522], [338, 528, 434, 541], [448, 528, 596, 541], [609, 527, 687, 540], [700, 528, 792, 541], [336, 546, 397, 559], [407, 546, 431, 559], [443, 546, 525, 560], [537, 546, 680, 562], [688, 546, 714, 559], [722, 546, 837, 562], [336, 565, 449, 581], [461, 565, 485, 577], [497, 565, 665, 581], [681, 565, 718, 577], [732, 565, 837, 580], [337, 584, 438, 597], [452, 583, 521, 596], [535, 584, 677, 599], [690, 583, 787, 596], [801, 583, 825, 596], [338, 602, 478, 615], [492, 602, 530, 614], [543, 602, 638, 615], [650, 602, 676, 614], [688, 602, 788, 615], [802, 602, 843, 614], [337, 621, 502, 633], [516, 621, 615, 637], [629, 621, 774, 636], [789, 621, 827, 633], [337, 639, 418, 652], [432, 640, 571, 653], [587, 639, 731, 655], [743, 639, 769, 652], [780, 639, 841, 652], [338, 658, 440, 673], [455, 658, 491, 670], [508, 658, 602, 671], [616, 658, 638, 670], [654, 658, 835, 674], [337, 677, 429, 689], [337, 714, 482, 726], [495, 714, 548, 726], [561, 714, 683, 726], [338, 770, 461, 782], [474, 769, 554, 785], [489, 788, 562, 803], [576, 788, 643, 801], [656, 787, 751, 804], [764, 788, 844, 801], [334, 825, 421, 838], [430, 824, 574, 838], [584, 824, 723, 841], [335, 844, 450, 857], [464, 843, 583, 860], [628, 862, 755, 875], [769, 861, 848, 878]]]  # noqa: E231
      # fmt: on

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

      # with apply_OCR = False
      lowerCamelCase_         =						LayoutLMvaImageProcessor(apply_ocr=__SCREAMING_SNAKE_CASE					)

      lowerCamelCase_         =						image_processing(__SCREAMING_SNAKE_CASE       ,						return_tensors='pt'					)

      self.assertEqual(encoding.pixel_values.shape       ,						(1, 3, 224, 224)					)




