






'''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 	SCREAMING_SNAKE_CASE					(					unittest.TestCase       ):






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







			def 			SCREAMING_SNAKE_CASE      ( self      :			List[Any]					)			->			Optional[Any]:
							return {"do_resize": self.do_resize, "size": self.size, "apply_ocr": self.apply_ocr}





@require_torch
@require_pytesseract
class 	SCREAMING_SNAKE_CASE					(					SCREAMING_SNAKE_CASE_	,   unittest.TestCase       ):
			snake_case__  =				LayoutLMvaImageProcessor if is_pytesseract_available() else None






			def 			SCREAMING_SNAKE_CASE      ( self      :			Optional[Any]					)			->			Any:
							a_		:  Optional[int]											=      LayoutLMvaImageProcessingTester(self					)






			@property
			def 			SCREAMING_SNAKE_CASE      ( self      :			List[Any]					)			->			Dict:
							return self.image_processor_tester.prepare_image_processor_dict()






			def 			SCREAMING_SNAKE_CASE      ( self      :			Tuple					)			->			List[Any]:
							a_		:  Union[str, Any]											=      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 			SCREAMING_SNAKE_CASE      ( self      :			Union[str, Any]					)			->			int:
							a_		:  Union[str, Any]											=      self.image_processing_class.from_dict(self.image_processor_dict					)
							self.assertEqual(image_processor.size  ,						{'''height''': 18, '''width''': 18}					)

							a_		:  Union[str, Any]											=      self.image_processing_class.from_dict(self.image_processor_dict  ,						size=42					)
							self.assertEqual(image_processor.size  ,						{'''height''': 42, '''width''': 42}					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			int					)			->			Union[str, Any]:
							pass






			def 			SCREAMING_SNAKE_CASE      ( self      :			Any					)			->			Optional[int]:
							# Initialize image_processing
							a_		:  Optional[int]											=      self.image_processing_class(**self.image_processor_dict					)
							# create random PIL images
							a_		:  Tuple											=      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
							a_		:  Optional[int]											=      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
							a_		:  Optional[Any]											=      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 			SCREAMING_SNAKE_CASE      ( self      :			Dict					)			->			List[str]:
							# Initialize image_processing
							a_		:  Union[str, Any]											=      self.image_processing_class(**self.image_processor_dict					)
							# create random numpy tensors
							a_		:  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
							a_		:  Tuple											=      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_		:  Dict											=      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 			SCREAMING_SNAKE_CASE      ( self      :			Optional[int]					)			->			int:
							# Initialize image_processing
							a_		:  List[str]											=      self.image_processing_class(**self.image_processor_dict					)
							# create random PyTorch tensors
							a_		:  Optional[Any]											=      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
							a_		:  List[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_		:  Union[str, Any]											=      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 			SCREAMING_SNAKE_CASE      ( self      :			List[str]					)			->			int:
							# with apply_OCR = True
							a_		:  Union[str, Any]											=      LayoutLMvaImageProcessor()

							from datasets import load_dataset

							a_		:  List[str]											=      load_dataset('''hf-internal-testing/fixtures_docvqa'''  ,						split='''test'''					)

							a_		:  Optional[int]											=      Image.open(ds[0]['''file''']					).convert('''RGB'''					)

							a_		:  List[Any]											=      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
							a_		:  str											=      [['''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_		:  Dict											=      [[[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
							a_		:  Optional[int]											=      LayoutLMvaImageProcessor(apply_ocr=__SCREAMING_SNAKE_CASE					)

							a_		:  str											=      image_processing(__SCREAMING_SNAKE_CASE  ,						return_tensors='''pt'''					)

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












'''simple docstring'''







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

from packaging import version


if TYPE_CHECKING:
		from ... import PreTrainedTokenizer, TensorType

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import is_torch_available, logging


__lowerCAmelCase       		=      logging.get_logger(__name__)

__lowerCAmelCase       		=      {
    'bigscience/bloom': 'https://huggingface.co/bigscience/bloom/resolve/main/config.json',
    'bigscience/bloom-560m': 'https://huggingface.co/bigscience/bloom-560m/blob/main/config.json',
    'bigscience/bloom-1b1': 'https://huggingface.co/bigscience/bloom-1b1/blob/main/config.json',
    'bigscience/bloom-1b7': 'https://huggingface.co/bigscience/bloom-1b7/blob/main/config.json',
    'bigscience/bloom-3b': 'https://huggingface.co/bigscience/bloom-3b/blob/main/config.json',
    'bigscience/bloom-7b1': 'https://huggingface.co/bigscience/bloom-7b1/blob/main/config.json',
}





class 	SCREAMING_SNAKE_CASE					(					SCREAMING_SNAKE_CASE_       ):
			snake_case__  =				"bloom"
			snake_case__  =				["past_key_values"]
			snake_case__  =				{
			    "num_hidden_layers": "n_layer",
			    "num_attention_heads": "n_head",
			}






			def __init__( self      :			Union[str, Any]  ,						__SCREAMING_SNAKE_CASE      :			int=25_0880  ,						__SCREAMING_SNAKE_CASE      :			Dict=64  ,						__SCREAMING_SNAKE_CASE      :			Tuple=2  ,						__SCREAMING_SNAKE_CASE      :			int=8  ,						__SCREAMING_SNAKE_CASE      :			Any=1e-5  ,						__SCREAMING_SNAKE_CASE      :			Optional[Any]=0.02  ,						__SCREAMING_SNAKE_CASE      :			Union[str, Any]=True  ,						__SCREAMING_SNAKE_CASE      :			int=1  ,						__SCREAMING_SNAKE_CASE      :			Any=2  ,						__SCREAMING_SNAKE_CASE      :			Optional[Any]=False  ,						__SCREAMING_SNAKE_CASE      :			Optional[Any]=0.0  ,						__SCREAMING_SNAKE_CASE      :			str=0.0  ,						__SCREAMING_SNAKE_CASE      :			List[Any]=1  ,						__SCREAMING_SNAKE_CASE      :			List[str]=False  ,						**__SCREAMING_SNAKE_CASE      :			str  ,						)			->			Any:
							a_		:  Optional[int]											=      vocab_size
							# Backward compatibility with n_embed kwarg
							a_		:  Any											=      kwargs.pop('''n_embed'''  ,						__SCREAMING_SNAKE_CASE					)
							a_		:  Optional[int]											=      hidden_size if n_embed is None else n_embed
							a_		:  int											=      n_layer
							a_		:  str											=      n_head
							a_		:  Optional[int]											=      layer_norm_epsilon
							a_		:  Dict											=      initializer_range
							a_		:  List[str]											=      use_cache
							a_		:  Dict											=      pretraining_tp
							a_		:  Optional[Any]											=      apply_residual_connection_post_layernorm
							a_		:  Optional[Any]											=      hidden_dropout
							a_		:  List[str]											=      attention_dropout

							a_		:  Dict											=      bos_token_id
							a_		:  Optional[int]											=      eos_token_id
							a_		:  Any											=      slow_but_exact

							super().__init__(bos_token_id=__SCREAMING_SNAKE_CASE  ,						eos_token_id=__SCREAMING_SNAKE_CASE  ,						**__SCREAMING_SNAKE_CASE					)





class 	SCREAMING_SNAKE_CASE					(					SCREAMING_SNAKE_CASE_       ):
			snake_case__  =				version.parse("1.12"       )






			def __init__( self      :			Tuple  ,						__SCREAMING_SNAKE_CASE      :			PretrainedConfig  ,						__SCREAMING_SNAKE_CASE      :			str = "default"  ,						__SCREAMING_SNAKE_CASE      :			List[PatchingSpec] = None  ,						__SCREAMING_SNAKE_CASE      :			bool = False  ,						)			->			Optional[Any]:
							super().__init__(__SCREAMING_SNAKE_CASE  ,						task=__SCREAMING_SNAKE_CASE  ,						patching_specs=__SCREAMING_SNAKE_CASE  ,						use_past=__SCREAMING_SNAKE_CASE					)
							if not getattr(self._config  ,						'''pad_token_id'''  ,						__SCREAMING_SNAKE_CASE					):
											# TODO: how to do that better?
											a_		:  Tuple											=      0






			@property
			def 			SCREAMING_SNAKE_CASE      ( self      :			Tuple					)			->			Mapping[str, Mapping[int, str]]:
							a_		:  Optional[Any]											=      OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}}					)
							if self.use_past:
											# BLOOM stores values on dynamic axis 2. For more details see: https://github.com/huggingface/transformers/pull/18344
											self.fill_with_past_key_values_(__SCREAMING_SNAKE_CASE  ,						direction='''inputs'''  ,						inverted_values_shape=__SCREAMING_SNAKE_CASE					)
											a_		:  Union[str, Any]											=      {0: '''batch''', 1: '''past_sequence + sequence'''}
							else:
											a_		:  Union[str, Any]											=      {0: '''batch''', 1: '''sequence'''}

							return common_inputs






			@property
			def 			SCREAMING_SNAKE_CASE      ( self      :			Any					)			->			int:
							return self._config.n_layer






			@property
			def 			SCREAMING_SNAKE_CASE      ( self      :			int					)			->			int:
							return self._config.n_head






			@property
			def 			SCREAMING_SNAKE_CASE      ( self      :			int					)			->			float:
							return 1e-3






			def 			SCREAMING_SNAKE_CASE      ( self      :			Dict  ,						__SCREAMING_SNAKE_CASE      :			"PreTrainedTokenizer"  ,						__SCREAMING_SNAKE_CASE      :			int = -1  ,						__SCREAMING_SNAKE_CASE      :			int = -1  ,						__SCREAMING_SNAKE_CASE      :			bool = False  ,						__SCREAMING_SNAKE_CASE      :			Optional["TensorType"] = None  ,						)			->			Mapping[str, Any]:
							a_		:  Dict											=      super(__SCREAMING_SNAKE_CASE  ,						self					).generate_dummy_inputs(
							    __SCREAMING_SNAKE_CASE  ,						batch_size=__SCREAMING_SNAKE_CASE  ,						seq_length=__SCREAMING_SNAKE_CASE  ,						is_pair=__SCREAMING_SNAKE_CASE  ,						framework=__SCREAMING_SNAKE_CASE					)

							# We need to order the input in the way they appears in the forward()
							a_		:  Union[str, Any]											=      OrderedDict({'''input_ids''': common_inputs['''input_ids''']}					)

							# Need to add the past_keys
							if self.use_past:
											if not is_torch_available():
															raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''					)
											else:
															import torch

															a_				,			a_		:  Any											=      common_inputs['''input_ids'''].shape
															# Not using the same length for past_key_values
															a_		:  str											=      seqlen + 2
															a_		:  Any											=      self._config.hidden_size // self.num_attention_heads
															a_		:  Optional[int]											=      (
															    batch * self.num_attention_heads,
															    head_dim,
															    past_key_values_length,
															)
															a_		:  Any											=      (
															    batch * self.num_attention_heads,
															    past_key_values_length,
															    head_dim,
															)
															a_		:  List[str]											=      [
															    (torch.zeros(__SCREAMING_SNAKE_CASE					), torch.zeros(__SCREAMING_SNAKE_CASE					)) for _ in range(self.num_layers					)
															]

							a_		:  Union[str, Any]											=      common_inputs['''attention_mask''']
							if self.use_past:
											a_		:  Optional[int]											=      ordered_inputs['''attention_mask'''].dtype
											a_		:  List[Any]											=      torch.cat(
											    [ordered_inputs['''attention_mask'''], torch.ones(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE  ,						dtype=__SCREAMING_SNAKE_CASE					)]  ,						dim=1					)

							return ordered_inputs







			@property
			def 			SCREAMING_SNAKE_CASE      ( self      :			Optional[int]					)			->			int:
							return 13




