




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    lowercase_		(unittest.TestCase ):



						def __init__(			self	:   Optional[Any]					,							snake_case__	:   Tuple					,							snake_case__	:   Optional[Any]=7					,							snake_case__	:   Tuple=3					,							snake_case__	:   Any=18					,							snake_case__	:   Union[str, Any]=30					,							snake_case__	:   Optional[Any]=4_00					,							snake_case__	:   int=True					,							snake_case__	:   List[str]=None					,							snake_case__	:   int=True					,							):

								"""simple docstring"""

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






						def 	__a					(			self	:   Tuple  ):

								"""simple docstring"""

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



@require_torch
@require_pytesseract
class    lowercase_		(_lowerCAmelCase							,	unittest.TestCase ):
						lowerCAmelCase__			      =LayoutLMvaImageProcessor if is_pytesseract_available() else None



						def 	__a					(			self	:   Any  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							LayoutLMvaImageProcessingTester(self  )



						@property
						def 	__a					(			self	:   int  ):

								"""simple docstring"""

								return self.image_processor_tester.prepare_image_processor_dict()



						def 	__a					(			self	:   List[str]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							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 	__a					(			self	:   Optional[Any]  ):

								"""simple docstring"""

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

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



						def 	__a					(			self	:   Any  ):

								"""simple docstring"""

								pass



						def 	__a					(			self	:   Optional[Any]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							self.image_processing_class(**self.image_processor_dict  )
								# create random PIL images
								SCREAMING_SNAKE_CASE_					      =							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
								SCREAMING_SNAKE_CASE_					      =							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
								SCREAMING_SNAKE_CASE_					      =							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 	__a					(			self	:   int  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							self.image_processing_class(**self.image_processor_dict  )
								# create random numpy tensors
								SCREAMING_SNAKE_CASE_					      =							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
								SCREAMING_SNAKE_CASE_					      =							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
								SCREAMING_SNAKE_CASE_					      =							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 	__a					(			self	:   List[Any]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							self.image_processing_class(**self.image_processor_dict  )
								# create random PyTorch tensors
								SCREAMING_SNAKE_CASE_					      =							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
								SCREAMING_SNAKE_CASE_					      =							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
								SCREAMING_SNAKE_CASE_					      =							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 	__a					(			self	:   Optional[int]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							LayoutLMvaImageProcessor()

								from datasets import load_dataset

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

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

								SCREAMING_SNAKE_CASE_					      =							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
								SCREAMING_SNAKE_CASE_					      =							[['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
								SCREAMING_SNAKE_CASE_					      =							[[[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
								SCREAMING_SNAKE_CASE_					      =							LayoutLMvaImageProcessor(apply_ocr=snake_case__  )

								SCREAMING_SNAKE_CASE_					      =							image_processing(snake_case__					,							return_tensors='pt'  )

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







'''simple docstring'''



from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging


if TYPE_CHECKING:
 from ...feature_extraction_utils import FeatureExtractionMixin
 from ...tokenization_utils_base import PreTrainedTokenizerBase
 from ...utils import TensorType

__UpperCamelCase   :							List[str]         =   logging.get_logger(__name__)

__UpperCamelCase   :							Optional[int]         =   {
    """openai/whisper-base""": """https://huggingface.co/openai/whisper-base/resolve/main/config.json""",
}

# fmt: off
__UpperCamelCase   :							Optional[Any]         =   [
    1, 2, 7, 8, 9, 10, 14, 25,
    26, 27, 28, 29, 31, 58, 59, 60, 61, 62,
    63, 90, 91, 92, 93, 357, 366, 438, 532, 685,
    705, 796, 930, 1058, 1220, 1267, 1279, 1303, 1343, 1377,
    1391, 1635, 1782, 1875, 2162, 2361, 2488, 3467, 4008, 4211,
    4600, 4808, 5299, 5855, 6329, 7203, 9609, 9959, 10563, 10786,
    11420, 11709, 11907, 13163, 13697, 13700, 14808, 15306, 16410, 16791,
    17992, 19203, 19510, 20724, 22305, 22935, 27007, 30109, 30420, 33409,
    34949, 40283, 40493, 40549, 47282, 49146, 50257, 50359, 50360, 50361
]
__UpperCamelCase   :							List[str]         =   [
    1, 2, 7, 8, 9, 10, 14, 25,
    26, 27, 28, 29, 31, 58, 59, 60, 61, 62,
    63, 90, 91, 92, 93, 359, 503, 522, 542, 873,
    893, 902, 918, 922, 931, 1350, 1853, 1982, 2460, 2627,
    3246, 3253, 3268, 3536, 3846, 3961, 4183, 4667, 6585, 6647,
    7273, 9061, 9383, 10428, 10929, 11938, 12033, 12331, 12562, 13793,
    14157, 14635, 15265, 15618, 16553, 16604, 18362, 18956, 20075, 21675,
    22520, 26130, 26161, 26435, 28279, 29464, 31650, 32302, 32470, 36865,
    42863, 47425, 49870, 50254, 50258, 50360, 50361, 50362
]
class 				__SCREAMING_SNAKE_CASE							(				_lowerCAmelCase  ):
      __a	       ="whisper"
      __a	       =["past_key_values"]
      __a	       ={"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}




      def __init__(	self			, lowerCamelCase=5_1865			, lowerCamelCase=80			, lowerCamelCase=6			, lowerCamelCase=4			, lowerCamelCase=6			, lowerCamelCase=4			, lowerCamelCase=1536			, lowerCamelCase=1536			, lowerCamelCase=0.0			, lowerCamelCase=0.0			, lowerCamelCase=5_0257			, lowerCamelCase=True			, lowerCamelCase=True			, lowerCamelCase="gelu"			, lowerCamelCase=256			, lowerCamelCase=0.0			, lowerCamelCase=0.0			, lowerCamelCase=0.0			, lowerCamelCase=0.02			, lowerCamelCase=False			, lowerCamelCase=1500			, lowerCamelCase=448			, lowerCamelCase=5_0256			, lowerCamelCase=5_0256			, lowerCamelCase=5_0256			, lowerCamelCase=None			, lowerCamelCase=[220, 5_0256]			, lowerCamelCase=False			, lowerCamelCase=256			, lowerCamelCase=False			, lowerCamelCase=0.05			, lowerCamelCase=10			, lowerCamelCase=2			, lowerCamelCase=0.0			, lowerCamelCase=10			, lowerCamelCase=0			, lowerCamelCase=7			, **lowerCamelCase			, )				->Any:



         '''simple docstring'''
         __a					  =     vocab_size
         __a					  =     num_mel_bins
         __a					  =     d_model
         __a					  =     encoder_layers
         __a					  =     encoder_attention_heads
         __a					  =     decoder_layers
         __a					  =     decoder_attention_heads
         __a					  =     decoder_ffn_dim
         __a					  =     encoder_ffn_dim
         __a					  =     dropout
         __a					  =     attention_dropout
         __a					  =     activation_dropout
         __a					  =     activation_function
         __a					  =     init_std
         __a					  =     encoder_layerdrop
         __a					  =     decoder_layerdrop
         __a					  =     use_cache
         __a					  =     encoder_layers
         __a					  =     scale_embedding  # scale factor will be sqrt(d_model) if True
         __a					  =     max_source_positions
         __a					  =     max_target_positions

         # Audio Classification-specific parameters. Feel free to ignore for other classes.
         __a					  =     classifier_proj_size
         __a					  =     use_weighted_layer_sum

         # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
         __a					  =     apply_spec_augment
         __a					  =     mask_time_prob
         __a					  =     mask_time_length
         __a					  =     mask_time_min_masks
         __a					  =     mask_feature_prob
         __a					  =     mask_feature_length
         __a					  =     mask_feature_min_masks

         __a					  =     median_filter_width

         super().__init__(
             pad_token_id=lowerCamelCase			, bos_token_id=lowerCamelCase			, eos_token_id=lowerCamelCase			, is_encoder_decoder=lowerCamelCase			, decoder_start_token_id=lowerCamelCase			, suppress_tokens=lowerCamelCase			, begin_suppress_tokens=lowerCamelCase			, **lowerCamelCase			, )







class 				__SCREAMING_SNAKE_CASE							(				_lowerCAmelCase  ):




      @property
      def 			__UpperCamelCase				(	self   )				->Mapping[str, Mapping[int, str]]:



         '''simple docstring'''
         __a					  =     OrderedDict(
             [
                 ('input_features', {0: 'batch', 1: 'feature_size', 2: 'encoder_sequence'}),
             ]   )
         if self.use_past:
            __a					  =     {0: 'batch'}
         else:
            __a					  =     {0: 'batch', 1: 'decoder_sequence'}

         if self.use_past:
            self.fill_with_past_key_values_(lowerCamelCase			, direction='inputs'   )

         return common_inputs




      def 			__UpperCamelCase				(	self			, lowerCamelCase			, lowerCamelCase = -1			, lowerCamelCase = -1			, lowerCamelCase = False			, lowerCamelCase = None			, lowerCamelCase = 2_2050			, lowerCamelCase = 5.0			, lowerCamelCase = 220			, )				->Mapping[str, Any]:



         '''simple docstring'''
         __a					  =     OrderedDict()
         __a					  =     OnnxConfig.generate_dummy_inputs(
             self			, preprocessor=preprocessor.feature_extractor			, batch_size=lowerCamelCase			, framework=lowerCamelCase			, sampling_rate=lowerCamelCase			, time_duration=lowerCamelCase			, frequency=lowerCamelCase			, )
         __a					  =     encoder_inputs['input_features'].shape[2]
         __a					  =     encoder_sequence_length // 2 if self.use_past else seq_length

         __a					  =     super().generate_dummy_inputs(
             preprocessor.tokenizer			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase   )

         __a					  =     encoder_inputs.pop('input_features'   )
         __a					  =     decoder_inputs.pop('decoder_input_ids'   )

         if "past_key_values" in decoder_inputs:
            __a					  =     decoder_inputs.pop('past_key_values'   )

         return dummy_inputs






      @property
      def 			__UpperCamelCase				(	self   )				->float:



         '''simple docstring'''
         return 1e-3