






"""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   UpperCamelCase_ (       unittest.TestCase  ):



  def __init__(      self ,		snake_case__ ,		snake_case__=7 ,		snake_case__=3 ,		snake_case__=18 ,		snake_case__=30 ,		snake_case__=4_00 ,		snake_case__=True ,		snake_case__=None ,		snake_case__=True ,		)			->			Dict:



         """simple docstring"""





         UpperCAmelCase	    =			size if size is not None else {"""height""": 18, """width""": 18}
         UpperCAmelCase	    =			parent
         UpperCAmelCase	    =			batch_size
         UpperCAmelCase	    =			num_channels
         UpperCAmelCase	    =			image_size
         UpperCAmelCase	    =			min_resolution
         UpperCAmelCase	    =			max_resolution
         UpperCAmelCase	    =			do_resize
         UpperCAmelCase	    =			size
         UpperCAmelCase	    =			apply_ocr



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



         """simple docstring"""





         return {"do_resize": self.do_resize, "size": self.size, "apply_ocr": self.apply_ocr}





@require_torch
@require_pytesseract
class   UpperCamelCase_ (       a_   ,					unittest.TestCase  ):
  _A       :					Optional[Any]				=	LayoutLMvaImageProcessor if is_pytesseract_available() else None



  def 		UpperCamelCase_				(      self				)			->			Tuple:



         """simple docstring"""





         UpperCAmelCase	    =			LayoutLMvaImageProcessingTester(self				)



  @property
  def 		UpperCamelCase_				(      self				)			->			Tuple:



         """simple docstring"""





         return self.image_processor_tester.prepare_image_processor_dict()



  def 		UpperCamelCase_				(      self				)			->			Union[str, Any]:



         """simple docstring"""





         UpperCAmelCase	    =			self.image_processing_class(**self.image_processor_dict				)
         self.assertTrue(hasattr(snake_case__ ,		"""do_resize"""				)				)
         self.assertTrue(hasattr(snake_case__ ,		"""size"""				)				)
         self.assertTrue(hasattr(snake_case__ ,		"""apply_ocr"""				)				)



  def 		UpperCamelCase_				(      self				)			->			str:



         """simple docstring"""





         UpperCAmelCase	    =			self.image_processing_class.from_dict(self.image_processor_dict				)
         self.assertEqual(image_processor.size ,		{"""height""": 18, """width""": 18}				)

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



  def 		UpperCamelCase_				(      self				)			->			Any:



         """simple docstring"""





         pass



  def 		UpperCamelCase_				(      self				)			->			List[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=snake_case__				)
         for image in image_inputs:
                self.assertIsInstance(snake_case__ ,		Image.Image				)

         # Test not batched input
         UpperCAmelCase	    =			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 ,		snake_case__				)
         self.assertIsInstance(encoding.boxes ,		snake_case__				)

         # Test batched
         UpperCAmelCase	    =			image_processing(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				)			->			Union[str, Any]:



         """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=snake_case__ ,		numpify=snake_case__				)
         for image in image_inputs:
                self.assertIsInstance(snake_case__ ,		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(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				)			->			List[Any]:



         """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=snake_case__ ,		torchify=snake_case__				)
         for image in image_inputs:
                self.assertIsInstance(snake_case__ ,		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(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				)			->			List[Any]:



         """simple docstring"""





         UpperCAmelCase	    =			LayoutLMvaImageProcessor()

         from datasets import load_dataset

         UpperCAmelCase	    =			load_dataset("""hf-internal-testing/fixtures_docvqa""" ,		split="""test"""				)

         UpperCAmelCase	    =			Image.open(ds[0]["""file"""]				).convert("""RGB"""				)

         UpperCAmelCase	    =			image_processing(snake_case__ ,		return_tensors="""pt"""				)

         self.assertEqual(encoding.pixel_values.shape ,		(1, 3, 2_24, 2_24)				)
         self.assertEqual(len(encoding.words				) ,		len(encoding.boxes				)				)

         # fmt: off
         # the words and boxes were obtained with Tesseract 4.1.1
         UpperCAmelCase	    =			[["""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
         UpperCAmelCase	    =			[[[1_41, 57, 2_14, 69], [2_28, 58, 2_52, 69], [1_41, 75, 2_16, 88], [2_30, 79, 2_80, 88], [1_42, 2_60, 2_18, 2_73], [2_30, 2_61, 2_55, 2_73], [1_43, 2_79, 2_18, 2_90], [2_31, 2_82, 2_90, 2_91], [1_43, 3_42, 2_18, 3_54], [2_31, 3_45, 2_89, 3_55], [2_02, 3_62, 2_27, 3_73], [1_43, 3_79, 2_20, 3_92], [2_31, 3_82, 2_91, 3_94], [1_44, 7_14, 2_20, 7_26], [2_31, 7_15, 2_56, 7_26], [1_44, 7_32, 2_20, 7_45], [2_32, 7_36, 2_91, 7_47], [1_44, 7_69, 2_18, 7_82], [2_31, 7_70, 2_56, 7_82], [1_41, 7_88, 2_02, 8_01], [2_15, 7_91, 2_74, 8_04], [1_43, 8_26, 2_04, 8_38], [2_15, 8_26, 2_40, 8_38], [1_42, 8_44, 2_02, 8_57], [2_15, 8_47, 2_74, 8_59], [3_34, 57, 4_27, 69], [4_40, 57, 5_22, 69], [3_69, 75, 4_61, 88], [4_69, 75, 5_16, 88], [5_28, 76, 5_62, 88], [5_70, 76, 6_67, 88], [6_75, 75, 7_11, 87], [7_21, 79, 7_78, 88], [7_89, 75, 8_40, 88], [3_69, 97, 4_70, 1_07], [4_84, 94, 5_07, 1_06], [5_18, 94, 5_62, 1_07], [5_76, 94, 6_55, 1_10], [6_68, 94, 7_92, 1_09], [8_04, 95, 8_29, 1_07], [3_69, 1_13, 4_65, 1_25], [4_77, 1_16, 5_47, 1_25], [5_62, 1_13, 6_58, 1_25], [6_71, 1_16, 7_48, 1_25], [7_61, 1_13, 8_11, 1_25], [3_69, 1_31, 4_65, 1_43], [4_77, 1_33, 5_48, 1_43], [5_63, 1_30, 6_98, 1_45], [7_10, 1_30, 8_02, 1_46], [3_36, 1_71, 4_12, 1_83], [4_23, 1_71, 5_72, 1_83], [5_82, 1_70, 7_16, 1_84], [7_28, 1_71, 8_17, 1_87], [8_29, 1_71, 8_44, 1_86], [3_38, 1_97, 4_82, 2_12], [5_07, 1_96, 5_57, 2_09], [5_69, 1_96, 5_95, 2_08], [6_10, 1_96, 7_02, 2_09], [5_05, 2_14, 5_83, 2_26], [5_95, 2_14, 6_56, 2_27], [6_70, 2_15, 8_07, 2_27], [3_35, 2_59, 5_43, 2_74], [5_56, 2_59, 7_08, 2_72], [3_72, 2_79, 4_22, 2_91], [4_35, 2_79, 4_60, 2_91], [4_74, 2_79, 5_74, 2_92], [5_87, 2_78, 6_64, 2_91], [6_76, 2_78, 7_38, 2_91], [7_51, 2_79, 8_34, 2_91], [3_72, 2_98, 4_34, 3_10], [3_35, 3_41, 4_83, 3_54], [4_97, 3_41, 6_55, 3_54], [6_67, 3_41, 7_28, 3_54], [7_40, 3_41, 8_25, 3_54], [3_35, 3_60, 4_30, 3_72], [4_42, 3_60, 5_34, 3_72], [5_45, 3_59, 6_87, 3_72], [6_97, 3_60, 7_54, 3_72], [7_65, 3_60, 8_23, 3_73], [3_34, 3_78, 4_28, 3_91], [4_40, 3_78, 5_77, 3_94], [5_90, 3_78, 7_05, 3_91], [7_20, 3_78, 8_01, 3_91], [3_34, 3_97, 4_00, 4_09], [3_70, 4_16, 5_29, 4_29], [5_44, 4_16, 5_76, 4_32], [5_87, 4_16, 6_65, 4_28], [6_77, 4_16, 8_14, 4_29], [3_72, 4_35, 4_52, 4_50], [4_65, 4_34, 4_95, 4_47], [5_11, 4_34, 6_00, 4_47], [6_11, 4_36, 6_37, 4_47], [6_49, 4_36, 6_94, 4_51], [7_05, 4_38, 8_24, 4_47], [3_69, 4_53, 4_52, 4_66], [4_64, 4_54, 5_09, 4_66], [5_22, 4_53, 6_11, 4_69], [6_25, 4_53, 7_92, 4_69], [3_70, 4_72, 5_56, 4_88], [5_70, 4_72, 6_84, 4_87], [6_97, 4_72, 7_18, 4_85], [7_32, 4_72, 8_35, 4_88], [3_69, 4_90, 4_11, 5_03], [4_25, 4_90, 4_84, 5_03], [4_96, 4_90, 6_35, 5_06], [6_45, 4_90, 7_07, 5_03], [7_18, 4_91, 7_61, 5_03], [7_71, 4_90, 8_40, 5_03], [3_36, 5_10, 3_74, 5_21], [3_88, 5_10, 4_47, 5_22], [4_60, 5_10, 4_89, 5_21], [5_03, 5_10, 5_80, 5_22], [5_92, 5_09, 7_36, 5_25], [7_45, 5_09, 7_70, 5_22], [7_81, 5_09, 8_40, 5_22], [3_38, 5_28, 4_34, 5_41], [4_48, 5_28, 5_96, 5_41], [6_09, 5_27, 6_87, 5_40], [7_00, 5_28, 7_92, 5_41], [3_36, 5_46, 3_97, 5_59], [4_07, 5_46, 4_31, 5_59], [4_43, 5_46, 5_25, 5_60], [5_37, 5_46, 6_80, 5_62], [6_88, 5_46, 7_14, 5_59], [7_22, 5_46, 8_37, 5_62], [3_36, 5_65, 4_49, 5_81], [4_61, 5_65, 4_85, 5_77], [4_97, 5_65, 6_65, 5_81], [6_81, 5_65, 7_18, 5_77], [7_32, 5_65, 8_37, 5_80], [3_37, 5_84, 4_38, 5_97], [4_52, 5_83, 5_21, 5_96], [5_35, 5_84, 6_77, 5_99], [6_90, 5_83, 7_87, 5_96], [8_01, 5_83, 8_25, 5_96], [3_38, 6_02, 4_78, 6_15], [4_92, 6_02, 5_30, 6_14], [5_43, 6_02, 6_38, 6_15], [6_50, 6_02, 6_76, 6_14], [6_88, 6_02, 7_88, 6_15], [8_02, 6_02, 8_43, 6_14], [3_37, 6_21, 5_02, 6_33], [5_16, 6_21, 6_15, 6_37], [6_29, 6_21, 7_74, 6_36], [7_89, 6_21, 8_27, 6_33], [3_37, 6_39, 4_18, 6_52], [4_32, 6_40, 5_71, 6_53], [5_87, 6_39, 7_31, 6_55], [7_43, 6_39, 7_69, 6_52], [7_80, 6_39, 8_41, 6_52], [3_38, 6_58, 4_40, 6_73], [4_55, 6_58, 4_91, 6_70], [5_08, 6_58, 6_02, 6_71], [6_16, 6_58, 6_38, 6_70], [6_54, 6_58, 8_35, 6_74], [3_37, 6_77, 4_29, 6_89], [3_37, 7_14, 4_82, 7_26], [4_95, 7_14, 5_48, 7_26], [5_61, 7_14, 6_83, 7_26], [3_38, 7_70, 4_61, 7_82], [4_74, 7_69, 5_54, 7_85], [4_89, 7_88, 5_62, 8_03], [5_76, 7_88, 6_43, 8_01], [6_56, 7_87, 7_51, 8_04], [7_64, 7_88, 8_44, 8_01], [3_34, 8_25, 4_21, 8_38], [4_30, 8_24, 5_74, 8_38], [5_84, 8_24, 7_23, 8_41], [3_35, 8_44, 4_50, 8_57], [4_64, 8_43, 5_83, 8_60], [6_28, 8_62, 7_55, 8_75], [7_69, 8_61, 8_48, 8_78]]]  # noqa: E231
         # fmt: on

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

         # with apply_OCR = False
         UpperCAmelCase	    =			LayoutLMvaImageProcessor(apply_ocr=snake_case__				)

         UpperCAmelCase	    =			image_processing(snake_case__ ,		return_tensors="""pt"""				)

         self.assertEqual(encoding.pixel_values.shape ,		(1, 3, 2_24, 2_24)				)








"""simple docstring"""







from typing import List, Optional, Union

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


lowerCAmelCase_	:    str            =      logging.get_logger(__name__)

lowerCAmelCase_	:    Optional[int]            =      {
    '''huggingface/time-series-transformer-tourism-monthly''': (
        '''https://huggingface.co/huggingface/time-series-transformer-tourism-monthly/resolve/main/config.json'''
    ),
    # See all TimeSeriesTransformer models at https://huggingface.co/models?filter=time_series_transformer
}





class   UpperCamelCase_ (       a_  ):
  _A       :					int				=	'time_series_transformer'
  _A       :					List[Any]				=	{
      'hidden_size': 'd_model',
      'num_attention_heads': 'encoder_attention_heads',
      'num_hidden_layers': 'encoder_layers',
  }



  def __init__(      self ,		snake_case__ = None ,		snake_case__ = None ,		snake_case__ = "student_t" ,		snake_case__ = "nll" ,		snake_case__ = 1 ,		snake_case__ = [1, 2, 3, 4, 5, 6, 7] ,		snake_case__ = "mean" ,		snake_case__ = 0 ,		snake_case__ = 0 ,		snake_case__ = 0 ,		snake_case__ = 0 ,		snake_case__ = None ,		snake_case__ = None ,		snake_case__ = 32 ,		snake_case__ = 32 ,		snake_case__ = 2 ,		snake_case__ = 2 ,		snake_case__ = 2 ,		snake_case__ = 2 ,		snake_case__ = True ,		snake_case__ = "gelu" ,		snake_case__ = 64 ,		snake_case__ = 0.1 ,		snake_case__ = 0.1 ,		snake_case__ = 0.1 ,		snake_case__ = 0.1 ,		snake_case__ = 0.1 ,		snake_case__ = 1_00 ,		snake_case__ = 0.02 ,		snake_case__=True ,		**snake_case__ ,		)			->			Union[str, Any]:



         """simple docstring"""





         UpperCAmelCase	    =			prediction_length
         UpperCAmelCase	    =			context_length or prediction_length
         UpperCAmelCase	    =			distribution_output
         UpperCAmelCase	    =			loss
         UpperCAmelCase	    =			input_size
         UpperCAmelCase	    =			num_time_features
         UpperCAmelCase	    =			lags_sequence
         UpperCAmelCase	    =			scaling
         UpperCAmelCase	    =			num_dynamic_real_features
         UpperCAmelCase	    =			num_static_real_features
         UpperCAmelCase	    =			num_static_categorical_features
         if cardinality and num_static_categorical_features > 0:
                if len(snake_case__				) != num_static_categorical_features:
                       raise ValueError(
                           """The cardinality should be a list of the same length as `num_static_categorical_features`"""				)
                UpperCAmelCase	    =			cardinality
         else:
                UpperCAmelCase	    =			[0]
         if embedding_dimension and num_static_categorical_features > 0:
                if len(snake_case__				) != num_static_categorical_features:
                       raise ValueError(
                           """The embedding dimension should be a list of the same length as `num_static_categorical_features`"""				)
                UpperCAmelCase	    =			embedding_dimension
         else:
                UpperCAmelCase	    =			[min(50 ,		(cat + 1) // 2				) for cat in self.cardinality]
         UpperCAmelCase	    =			num_parallel_samples

         # Transformer architecture configuration
         UpperCAmelCase	    =			input_size * len(snake_case__				) + self._number_of_features
         UpperCAmelCase	    =			d_model
         UpperCAmelCase	    =			encoder_attention_heads
         UpperCAmelCase	    =			decoder_attention_heads
         UpperCAmelCase	    =			encoder_ffn_dim
         UpperCAmelCase	    =			decoder_ffn_dim
         UpperCAmelCase	    =			encoder_layers
         UpperCAmelCase	    =			decoder_layers

         UpperCAmelCase	    =			dropout
         UpperCAmelCase	    =			attention_dropout
         UpperCAmelCase	    =			activation_dropout
         UpperCAmelCase	    =			encoder_layerdrop
         UpperCAmelCase	    =			decoder_layerdrop

         UpperCAmelCase	    =			activation_function
         UpperCAmelCase	    =			init_std

         UpperCAmelCase	    =			use_cache

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



  @property
  def 		UpperCamelCase_				(      self				)			->			int:



         """simple docstring"""





         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
         )
