



"""simple docstring"""






import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
	import torch

if is_vision_available():
	from PIL import Image

	from transformers import ConditionalDetrImageProcessor



class 			A__   (  unittest.TestCase							):




	'''simple docstring'''





	def __init__(     self:		int       ,			_SCREAMING_SNAKE_CASE:		List[str]       ,			_SCREAMING_SNAKE_CASE:		Union[str, Any]=7       ,			_SCREAMING_SNAKE_CASE:		Tuple=3       ,			_SCREAMING_SNAKE_CASE:		Optional[Any]=30       ,			_SCREAMING_SNAKE_CASE:		int=400       ,			_SCREAMING_SNAKE_CASE:		Union[str, Any]=True       ,			_SCREAMING_SNAKE_CASE:		str=None       ,			_SCREAMING_SNAKE_CASE:		Optional[Any]=True       ,			_SCREAMING_SNAKE_CASE:		List[Any]=[0.5, 0.5, 0.5]       ,			_SCREAMING_SNAKE_CASE:		List[str]=[0.5, 0.5, 0.5]       ,			_SCREAMING_SNAKE_CASE:		Any=True       ,			_SCREAMING_SNAKE_CASE:		Tuple=1 / 255       ,			_SCREAMING_SNAKE_CASE:		str=True       ,			)				->       Tuple:

				"""simple docstring"""

				__lowerCAmelCase      :  str												=       size if size is not None else {"shortest_edge": 18, "longest_edge": 1333}
				__lowerCAmelCase      :  str												=       parent
				__lowerCAmelCase      :  Optional[int]												=       batch_size
				__lowerCAmelCase      :  Optional[int]												=       num_channels
				__lowerCAmelCase      :  str												=       min_resolution
				__lowerCAmelCase      :  List[Any]												=       max_resolution
				__lowerCAmelCase      :  Tuple												=       do_resize
				__lowerCAmelCase      :  List[str]												=       size
				__lowerCAmelCase      :  Union[str, Any]												=       do_normalize
				__lowerCAmelCase      :  str												=       image_mean
				__lowerCAmelCase      :  List[Any]												=       image_std
				__lowerCAmelCase      :  Optional[int]												=       do_rescale
				__lowerCAmelCase      :  Any												=       rescale_factor
				__lowerCAmelCase      :  Optional[Any]												=       do_pad

	def 							_SCREAMING_SNAKE_CASE   (     self:		str)				->       str:

				"""simple docstring"""

				return {
				    "do_resize": self.do_resize,
				    "size": self.size,
				    "do_normalize": self.do_normalize,
				    "image_mean": self.image_mean,
				    "image_std": self.image_std,
				    "do_rescale": self.do_rescale,
				    "rescale_factor": self.rescale_factor,
				    "do_pad": self.do_pad,
				}







	def 							_SCREAMING_SNAKE_CASE   (     self:		List[str]       ,			_SCREAMING_SNAKE_CASE:		Optional[int]       ,			_SCREAMING_SNAKE_CASE:		str=False)				->       Dict:

				"""simple docstring"""

				if not batched:
							__lowerCAmelCase      :  Optional[Any]												=       image_inputs[0]
							if isinstance(_SCREAMING_SNAKE_CASE       ,			Image.Image):
										__lowerCAmelCase						,	__lowerCAmelCase      :  Any												=       image.size
							else:
										__lowerCAmelCase						,	__lowerCAmelCase      :  Optional[Any]												=       image.shape[1], image.shape[2]
							if w < h:
										__lowerCAmelCase      :  Any												=       int(self.size["shortest_edge"] * h / w)
										__lowerCAmelCase      :  List[Any]												=       self.size["shortest_edge"]
							elif w > h:
										__lowerCAmelCase      :  str												=       self.size["shortest_edge"]
										__lowerCAmelCase      :  Optional[int]												=       int(self.size["shortest_edge"] * w / h)
							else:
										__lowerCAmelCase      :  List[str]												=       self.size["shortest_edge"]
										__lowerCAmelCase      :  str												=       self.size["shortest_edge"]

				else:
							__lowerCAmelCase      :  Optional[int]												=       []
							for image in image_inputs:
										__lowerCAmelCase						,	__lowerCAmelCase      :  Dict												=       self.get_expected_values([image])
										expected_values.append((expected_height, expected_width))
							__lowerCAmelCase      :  int												=       max(_SCREAMING_SNAKE_CASE       ,			key=lambda _SCREAMING_SNAKE_CASE: item[0])[0]
							__lowerCAmelCase      :  Optional[Any]												=       max(_SCREAMING_SNAKE_CASE       ,			key=lambda _SCREAMING_SNAKE_CASE: item[1])[1]

				return expected_height, expected_width






@require_torch
@require_vision
class 			A__   (  __SCREAMING_SNAKE_CASE  ,		unittest.TestCase							):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE          =	ConditionalDetrImageProcessor if is_vision_available() else None

	def 							_SCREAMING_SNAKE_CASE   (     self:		List[Any])				->       Tuple:

				"""simple docstring"""

				__lowerCAmelCase      :  str												=       ConditionalDetrImageProcessingTester(self)

	@property
	def 							_SCREAMING_SNAKE_CASE   (     self:		str)				->       int:

				"""simple docstring"""

				return self.image_processor_tester.prepare_image_processor_dict()

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

				"""simple docstring"""

				__lowerCAmelCase      :  Tuple												=       self.image_processing_class(**self.image_processor_dict)
				self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE       ,			"image_mean"))
				self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE       ,			"image_std"))
				self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE       ,			"do_normalize"))
				self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE       ,			"do_resize"))
				self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE       ,			"size"))

	def 							_SCREAMING_SNAKE_CASE   (     self:		Any)				->       Dict:

				"""simple docstring"""

				__lowerCAmelCase      :  Tuple												=       self.image_processing_class.from_dict(self.image_processor_dict)
				self.assertEqual(image_processor.size       ,			{"shortest_edge": 18, "longest_edge": 1333})
				self.assertEqual(image_processor.do_pad       ,			_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase      :  Dict												=       self.image_processing_class.from_dict(
				    self.image_processor_dict       ,			size=42       ,			max_size=84       ,			pad_and_return_pixel_mask=_SCREAMING_SNAKE_CASE)
				self.assertEqual(image_processor.size       ,			{"shortest_edge": 42, "longest_edge": 84})
				self.assertEqual(image_processor.do_pad       ,			_SCREAMING_SNAKE_CASE)

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

				"""simple docstring"""

				pass

	def 							_SCREAMING_SNAKE_CASE   (     self:		Tuple)				->       Dict:

				"""simple docstring"""

				__lowerCAmelCase      :  Union[str, Any]												=       self.image_processing_class(**self.image_processor_dict)
				# create random PIL images
				__lowerCAmelCase      :  Any												=       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      :  List[str]												=       image_processing(image_inputs[0]       ,			return_tensors="pt").pixel_values

				__lowerCAmelCase						,	__lowerCAmelCase      :  Optional[int]												=       self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE)

				self.assertEqual(
				    encoded_images.shape       ,			(1, self.image_processor_tester.num_channels, expected_height, expected_width)       ,			)

				# Test batched
				__lowerCAmelCase						,	__lowerCAmelCase      :  List[Any]												=       self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE       ,			batched=_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase      :  str												=       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,
				        expected_height,
				        expected_width,
				    )       ,			)

	def 							_SCREAMING_SNAKE_CASE   (     self:		int)				->       int:

				"""simple docstring"""

				__lowerCAmelCase      :  Dict												=       self.image_processing_class(**self.image_processor_dict)
				# create random numpy tensors
				__lowerCAmelCase      :  Union[str, Any]												=       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      :  Any												=       image_processing(image_inputs[0]       ,			return_tensors="pt").pixel_values

				__lowerCAmelCase						,	__lowerCAmelCase      :  List[str]												=       self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE)

				self.assertEqual(
				    encoded_images.shape       ,			(1, self.image_processor_tester.num_channels, expected_height, expected_width)       ,			)

				# Test batched
				__lowerCAmelCase      :  Any												=       image_processing(_SCREAMING_SNAKE_CASE       ,			return_tensors="pt").pixel_values

				__lowerCAmelCase						,	__lowerCAmelCase      :  List[Any]												=       self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE       ,			batched=_SCREAMING_SNAKE_CASE)

				self.assertEqual(
				    encoded_images.shape       ,			(
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        expected_height,
				        expected_width,
				    )       ,			)

	def 							_SCREAMING_SNAKE_CASE   (     self:		Optional[Any])				->       int:

				"""simple docstring"""

				__lowerCAmelCase      :  Union[str, Any]												=       self.image_processing_class(**self.image_processor_dict)
				# create random PyTorch tensors
				__lowerCAmelCase      :  List[str]												=       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      :  Tuple												=       image_processing(image_inputs[0]       ,			return_tensors="pt").pixel_values

				__lowerCAmelCase						,	__lowerCAmelCase      :  Optional[Any]												=       self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE)

				self.assertEqual(
				    encoded_images.shape       ,			(1, self.image_processor_tester.num_channels, expected_height, expected_width)       ,			)

				# Test batched
				__lowerCAmelCase      :  Optional[Any]												=       image_processing(_SCREAMING_SNAKE_CASE       ,			return_tensors="pt").pixel_values

				__lowerCAmelCase						,	__lowerCAmelCase      :  Optional[int]												=       self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE       ,			batched=_SCREAMING_SNAKE_CASE)

				self.assertEqual(
				    encoded_images.shape       ,			(
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        expected_height,
				        expected_width,
				    )       ,			)

	@slow
	def 							_SCREAMING_SNAKE_CASE   (     self:		Dict)				->       Union[str, Any]:

				"""simple docstring"""

				__lowerCAmelCase      :  List[Any]												=       Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
				with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"       ,			"r") as f:
							__lowerCAmelCase      :  List[Any]												=       json.loads(f.read())

				__lowerCAmelCase      :  Tuple												=       {"image_id": 3_9769, "annotations": target}

				# encode them
				__lowerCAmelCase      :  Optional[Any]												=       ConditionalDetrImageProcessor.from_pretrained("microsoft/conditional-detr-resnet-50")
				__lowerCAmelCase      :  int												=       image_processing(images=_SCREAMING_SNAKE_CASE       ,			annotations=_SCREAMING_SNAKE_CASE       ,			return_tensors="pt")

				# verify pixel values
				__lowerCAmelCase      :  str												=       torch.Size([1, 3, 800, 1066])
				self.assertEqual(encoding["pixel_values"].shape       ,			_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase      :  List[str]												=       torch.tensor([0.2796, 0.3138, 0.3481])
				self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]       ,			_SCREAMING_SNAKE_CASE       ,			atol=1e-4))

				# verify area
				__lowerCAmelCase      :  int												=       torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438])
				self.assertTrue(torch.allclose(encoding["labels"][0]["area"]       ,			_SCREAMING_SNAKE_CASE))
				# verify boxes
				__lowerCAmelCase      :  Optional[Any]												=       torch.Size([6, 4])
				self.assertEqual(encoding["labels"][0]["boxes"].shape       ,			_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase      :  str												=       torch.tensor([0.5503, 0.2765, 0.0604, 0.2215])
				self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]       ,			_SCREAMING_SNAKE_CASE       ,			atol=1e-3))
				# verify image_id
				__lowerCAmelCase      :  List[Any]												=       torch.tensor([3_9769])
				self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]       ,			_SCREAMING_SNAKE_CASE))
				# verify is_crowd
				__lowerCAmelCase      :  List[str]												=       torch.tensor([0, 0, 0, 0, 0, 0])
				self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]       ,			_SCREAMING_SNAKE_CASE))
				# verify class_labels
				__lowerCAmelCase      :  Tuple												=       torch.tensor([75, 75, 63, 65, 17, 17])
				self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]       ,			_SCREAMING_SNAKE_CASE))
				# verify orig_size
				__lowerCAmelCase      :  List[Any]												=       torch.tensor([480, 640])
				self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]       ,			_SCREAMING_SNAKE_CASE))
				# verify size
				__lowerCAmelCase      :  int												=       torch.tensor([800, 1066])
				self.assertTrue(torch.allclose(encoding["labels"][0]["size"]       ,			_SCREAMING_SNAKE_CASE))







	@slow
	def 							_SCREAMING_SNAKE_CASE   (     self:		Dict)				->       int:

				"""simple docstring"""

				__lowerCAmelCase      :  Optional[int]												=       Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
				with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"       ,			"r") as f:
							__lowerCAmelCase      :  Optional[int]												=       json.loads(f.read())

				__lowerCAmelCase      :  Optional[Any]												=       {"file_name": "000000039769.png", "image_id": 3_9769, "segments_info": target}

				__lowerCAmelCase      :  int												=       pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic")

				# encode them
				__lowerCAmelCase      :  List[Any]												=       ConditionalDetrImageProcessor(format="coco_panoptic")
				__lowerCAmelCase      :  List[Any]												=       image_processing(images=_SCREAMING_SNAKE_CASE       ,			annotations=_SCREAMING_SNAKE_CASE       ,			masks_path=_SCREAMING_SNAKE_CASE       ,			return_tensors="pt")

				# verify pixel values
				__lowerCAmelCase      :  str												=       torch.Size([1, 3, 800, 1066])
				self.assertEqual(encoding["pixel_values"].shape       ,			_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase      :  List[Any]												=       torch.tensor([0.2796, 0.3138, 0.3481])
				self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]       ,			_SCREAMING_SNAKE_CASE       ,			atol=1e-4))

				# verify area
				__lowerCAmelCase      :  Dict												=       torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147])
				self.assertTrue(torch.allclose(encoding["labels"][0]["area"]       ,			_SCREAMING_SNAKE_CASE))
				# verify boxes
				__lowerCAmelCase      :  List[Any]												=       torch.Size([6, 4])
				self.assertEqual(encoding["labels"][0]["boxes"].shape       ,			_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase      :  Any												=       torch.tensor([0.2625, 0.5437, 0.4688, 0.8625])
				self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]       ,			_SCREAMING_SNAKE_CASE       ,			atol=1e-3))
				# verify image_id
				__lowerCAmelCase      :  int												=       torch.tensor([3_9769])
				self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]       ,			_SCREAMING_SNAKE_CASE))
				# verify is_crowd
				__lowerCAmelCase      :  Optional[int]												=       torch.tensor([0, 0, 0, 0, 0, 0])
				self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]       ,			_SCREAMING_SNAKE_CASE))
				# verify class_labels
				__lowerCAmelCase      :  Optional[Any]												=       torch.tensor([17, 17, 63, 75, 75, 93])
				self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]       ,			_SCREAMING_SNAKE_CASE))
				# verify masks
				__lowerCAmelCase      :  Any												=       82_2873
				self.assertEqual(encoding["labels"][0]["masks"].sum().item()       ,			_SCREAMING_SNAKE_CASE)
				# verify orig_size
				__lowerCAmelCase      :  Tuple												=       torch.tensor([480, 640])
				self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]       ,			_SCREAMING_SNAKE_CASE))
				# verify size
				__lowerCAmelCase      :  Dict												=       torch.tensor([800, 1066])
				self.assertTrue(torch.allclose(encoding["labels"][0]["size"]       ,			_SCREAMING_SNAKE_CASE))




"""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

__snake_case							:   Optional[Any]      =    logging.get_logger(__name__)

__snake_case							:   Optional[Any]      =    {
    'openai/whisper-base': 'https://huggingface.co/openai/whisper-base/resolve/main/config.json',
}

# fmt: off
__snake_case							:   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, 1_058, 1_220, 1_267, 1_279, 1_303, 1_343, 1_377,
    1_391, 1_635, 1_782, 1_875, 2_162, 2_361, 2_488, 3_467, 4_008, 4_211,
    4_600, 4_808, 5_299, 5_855, 6_329, 7_203, 9_609, 9_959, 10_563, 10_786,
    11_420, 11_709, 11_907, 13_163, 13_697, 13_700, 14_808, 15_306, 16_410, 16_791,
    17_992, 19_203, 19_510, 20_724, 22_305, 22_935, 27_007, 30_109, 30_420, 33_409,
    34_949, 40_283, 40_493, 40_549, 47_282, 49_146, 50_257, 50_359, 50_360, 50_361
]
__snake_case							:   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, 1_350, 1_853, 1_982, 2_460, 2_627,
    3_246, 3_253, 3_268, 3_536, 3_846, 3_961, 4_183, 4_667, 6_585, 6_647,
    7_273, 9_061, 9_383, 10_428, 10_929, 11_938, 12_033, 12_331, 12_562, 13_793,
    14_157, 14_635, 15_265, 15_618, 16_553, 16_604, 18_362, 18_956, 20_075, 21_675,
    22_520, 26_130, 26_161, 26_435, 28_279, 29_464, 31_650, 32_302, 32_470, 36_865,
    42_863, 47_425, 49_870, 50_254, 50_258, 50_360, 50_361, 50_362
]



class 			A__   (  __SCREAMING_SNAKE_CASE							):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE          =	'whisper'
	SCREAMING_SNAKE_CASE          =	['past_key_values']
	SCREAMING_SNAKE_CASE          =	{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}

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

				"""simple docstring"""

				__lowerCAmelCase      :  Optional[int]												=       vocab_size
				__lowerCAmelCase      :  Optional[Any]												=       num_mel_bins
				__lowerCAmelCase      :  int												=       d_model
				__lowerCAmelCase      :  List[Any]												=       encoder_layers
				__lowerCAmelCase      :  List[Any]												=       encoder_attention_heads
				__lowerCAmelCase      :  List[str]												=       decoder_layers
				__lowerCAmelCase      :  Tuple												=       decoder_attention_heads
				__lowerCAmelCase      :  Any												=       decoder_ffn_dim
				__lowerCAmelCase      :  Tuple												=       encoder_ffn_dim
				__lowerCAmelCase      :  List[str]												=       dropout
				__lowerCAmelCase      :  Union[str, Any]												=       attention_dropout
				__lowerCAmelCase      :  Union[str, Any]												=       activation_dropout
				__lowerCAmelCase      :  Dict												=       activation_function
				__lowerCAmelCase      :  Tuple												=       init_std
				__lowerCAmelCase      :  str												=       encoder_layerdrop
				__lowerCAmelCase      :  int												=       decoder_layerdrop
				__lowerCAmelCase      :  Optional[int]												=       use_cache
				__lowerCAmelCase      :  Union[str, Any]												=       encoder_layers
				__lowerCAmelCase      :  List[str]												=       scale_embedding  # scale factor will be sqrt(d_model) if True
				__lowerCAmelCase      :  int												=       max_source_positions
				__lowerCAmelCase      :  Any												=       max_target_positions

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

				# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
				__lowerCAmelCase      :  int												=       apply_spec_augment
				__lowerCAmelCase      :  Union[str, Any]												=       mask_time_prob
				__lowerCAmelCase      :  str												=       mask_time_length
				__lowerCAmelCase      :  int												=       mask_time_min_masks
				__lowerCAmelCase      :  List[Any]												=       mask_feature_prob
				__lowerCAmelCase      :  Tuple												=       mask_feature_length
				__lowerCAmelCase      :  Any												=       mask_feature_min_masks

				__lowerCAmelCase      :  Union[str, Any]												=       median_filter_width

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






class 			A__   (  __SCREAMING_SNAKE_CASE							):




	'''simple docstring'''





	@property
	def 							_SCREAMING_SNAKE_CASE   (     self:		List[Any])				->       Mapping[str, Mapping[int, str]]:

				"""simple docstring"""

				__lowerCAmelCase      :  List[str]												=       OrderedDict(
				    [
				        ("input_features", {0: "batch", 1: "feature_size", 2: "encoder_sequence"}),
				    ])
				if self.use_past:
							__lowerCAmelCase      :  Tuple												=       {0: "batch"}
				else:
							__lowerCAmelCase      :  Union[str, Any]												=       {0: "batch", 1: "decoder_sequence"}

				if self.use_past:
							self.fill_with_past_key_values_(_SCREAMING_SNAKE_CASE       ,			direction="inputs")

				return common_inputs

	def 							_SCREAMING_SNAKE_CASE   (     self:		Any       ,			_SCREAMING_SNAKE_CASE:		Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]       ,			_SCREAMING_SNAKE_CASE:		int = -1       ,			_SCREAMING_SNAKE_CASE:		int = -1       ,			_SCREAMING_SNAKE_CASE:		bool = False       ,			_SCREAMING_SNAKE_CASE:		Optional["TensorType"] = None       ,			_SCREAMING_SNAKE_CASE:		int = 2_2050       ,			_SCREAMING_SNAKE_CASE:		float = 5.0       ,			_SCREAMING_SNAKE_CASE:		int = 220       ,			)				->       Mapping[str, Any]:

				"""simple docstring"""

				__lowerCAmelCase      :  int												=       OrderedDict()
				__lowerCAmelCase      :  Optional[Any]												=       OnnxConfig.generate_dummy_inputs(
				    self       ,			preprocessor=preprocessor.feature_extractor       ,			batch_size=_SCREAMING_SNAKE_CASE       ,			framework=_SCREAMING_SNAKE_CASE       ,			sampling_rate=_SCREAMING_SNAKE_CASE       ,			time_duration=_SCREAMING_SNAKE_CASE       ,			frequency=_SCREAMING_SNAKE_CASE       ,			)
				__lowerCAmelCase      :  List[str]												=       encoder_inputs["input_features"].shape[2]
				__lowerCAmelCase      :  List[str]												=       encoder_sequence_length // 2 if self.use_past else seq_length

				__lowerCAmelCase      :  List[Any]												=       super().generate_dummy_inputs(
				    preprocessor.tokenizer       ,			_SCREAMING_SNAKE_CASE       ,			_SCREAMING_SNAKE_CASE       ,			_SCREAMING_SNAKE_CASE       ,			_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase      :  List[str]												=       encoder_inputs.pop("input_features")
				__lowerCAmelCase      :  List[Any]												=       decoder_inputs.pop("decoder_input_ids")

				if "past_key_values" in decoder_inputs:
							__lowerCAmelCase      :  int												=       decoder_inputs.pop("past_key_values")

				return dummy_inputs







	@property
	def 							_SCREAMING_SNAKE_CASE   (     self:		Union[str, Any])				->       float:

				"""simple docstring"""

				return 1e-3