






"""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 							lowerCAmelCase__       (    unittest.TestCase				):






      '''simple docstring'''

      def __init__(		self  :						int		,			lowercase_  :						int		,			lowercase_  :						Dict=7		,			lowercase_  :						Tuple=3		,			lowercase_  :						List[str]=18		,			lowercase_  :						str=30		,			lowercase_  :						Union[str, Any]=400		,			lowercase_  :						Tuple=True		,			lowercase_  :						Optional[int]=None		,			lowercase_  :						Union[str, Any]=True		,			):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	size if size is not None else {'''height''': 18, '''width''': 18}
          SCREAMING_SNAKE_CASE_   :						str							=	parent
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	batch_size
          SCREAMING_SNAKE_CASE_   :						List[str]							=	num_channels
          SCREAMING_SNAKE_CASE_   :						Any							=	image_size
          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	min_resolution
          SCREAMING_SNAKE_CASE_   :						Tuple							=	max_resolution
          SCREAMING_SNAKE_CASE_   :						str							=	do_resize
          SCREAMING_SNAKE_CASE_   :						Any							=	size
          SCREAMING_SNAKE_CASE_   :						Tuple							=	apply_ocr



      def _SCREAMING_SNAKE_CASE				(		self  :						Optional[Any]):




          '''simple docstring'''


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




@require_torch
@require_pytesseract
class 							lowerCAmelCase__       (    UpperCAmelCase__       ,	unittest.TestCase				):






      '''simple docstring'''

      __UpperCamelCase							=						LayoutLMvaImageProcessor if is_pytesseract_available() else None
      def _SCREAMING_SNAKE_CASE				(		self  :						Optional[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	LayoutLMvaImageProcessingTester(self)
      @property
      def _SCREAMING_SNAKE_CASE				(		self  :						int):




          '''simple docstring'''


          return self.image_processor_tester.prepare_image_processor_dict()
      def _SCREAMING_SNAKE_CASE				(		self  :						str):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Dict							=	self.image_processing_class(**self.image_processor_dict)
          self.assertTrue(hasattr(lowercase_		,			'''do_resize'''))
          self.assertTrue(hasattr(lowercase_		,			'''size'''))
          self.assertTrue(hasattr(lowercase_		,			'''apply_ocr'''))
      def _SCREAMING_SNAKE_CASE				(		self  :						int):




          '''simple docstring'''


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

          SCREAMING_SNAKE_CASE_   :						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  :						Any):




          '''simple docstring'''


          pass
      def _SCREAMING_SNAKE_CASE				(		self  :						Any):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						List[Any]							=	self.image_processing_class(**self.image_processor_dict)
          # create random PIL images
          SCREAMING_SNAKE_CASE_   :						int							=	prepare_image_inputs(self.image_processor_tester		,			equal_resolution=lowercase_)
          for image in image_inputs:
              self.assertIsInstance(lowercase_		,			Image.Image)

          # Test not batched input
          SCREAMING_SNAKE_CASE_   :						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		,			lowercase_)
          self.assertIsInstance(encoding.boxes		,			lowercase_)

          # Test batched
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	image_processing(lowercase_		,			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  :						Union[str, Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						str							=	self.image_processing_class(**self.image_processor_dict)
          # create random numpy tensors
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	prepare_image_inputs(self.image_processor_tester		,			equal_resolution=lowercase_		,			numpify=lowercase_)
          for image in image_inputs:
              self.assertIsInstance(lowercase_		,			np.ndarray)

          # Test not batched input
          SCREAMING_SNAKE_CASE_   :						Dict							=	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_   :						Tuple							=	image_processing(lowercase_		,			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  :						str):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Tuple							=	self.image_processing_class(**self.image_processor_dict)
          # create random PyTorch tensors
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	prepare_image_inputs(self.image_processor_tester		,			equal_resolution=lowercase_		,			torchify=lowercase_)
          for image in image_inputs:
              self.assertIsInstance(lowercase_		,			torch.Tensor)

          # Test not batched input
          SCREAMING_SNAKE_CASE_   :						Optional[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
          SCREAMING_SNAKE_CASE_   :						str							=	image_processing(lowercase_		,			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[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	LayoutLMvaImageProcessor()

          from datasets import load_dataset

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

          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	Image.open(ds[0]['''file''']).convert('''RGB''')

          SCREAMING_SNAKE_CASE_   :						Tuple							=	image_processing(lowercase_		,			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
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	[['''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_   :						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		,			lowercase_)
          self.assertListEqual(encoding.boxes		,			lowercase_)

          # with apply_OCR = False
          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	LayoutLMvaImageProcessor(apply_ocr=lowercase_)

          SCREAMING_SNAKE_CASE_   :						Tuple							=	image_processing(lowercase_		,			return_tensors='''pt''')

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










"""simple docstring"""






import unittest

from transformers import AutoTokenizer, FalconConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import (
      FalconForCausalLM,
      FalconForQuestionAnswering,
      FalconForSequenceClassification,
      FalconForTokenClassification,
      FalconModel,
  )




class 							lowerCAmelCase__       :






      '''simple docstring'''

      def __init__(		self  :						Any		,			lowercase_  :						Dict		,			lowercase_  :						str=3		,			lowercase_  :						Dict=7		,			lowercase_  :						Any=True		,			lowercase_  :						List[Any]=True		,			lowercase_  :						Union[str, Any]=False		,			lowercase_  :						Optional[int]=True		,			lowercase_  :						Union[str, Any]=99		,			lowercase_  :						Dict=32		,			lowercase_  :						Union[str, Any]=5		,			lowercase_  :						Optional[Any]=4		,			lowercase_  :						List[str]=37		,			lowercase_  :						int="gelu"		,			lowercase_  :						int=0.1		,			lowercase_  :						List[str]=0.1		,			lowercase_  :						Any=512		,			lowercase_  :						List[Any]=16		,			lowercase_  :						Union[str, Any]=2		,			lowercase_  :						Dict=0.02		,			lowercase_  :						Optional[int]=3		,			lowercase_  :						Dict=4		,			lowercase_  :						Optional[int]=None		,			):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						List[Any]							=	parent
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	batch_size
          SCREAMING_SNAKE_CASE_   :						List[str]							=	seq_length
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	is_training
          SCREAMING_SNAKE_CASE_   :						Dict							=	use_input_mask
          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	use_token_type_ids
          SCREAMING_SNAKE_CASE_   :						str							=	use_labels
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	vocab_size
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	hidden_size
          SCREAMING_SNAKE_CASE_   :						str							=	num_hidden_layers
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	num_attention_heads
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	intermediate_size
          SCREAMING_SNAKE_CASE_   :						int							=	hidden_act
          SCREAMING_SNAKE_CASE_   :						Dict							=	hidden_dropout_prob
          SCREAMING_SNAKE_CASE_   :						str							=	attention_probs_dropout_prob
          SCREAMING_SNAKE_CASE_   :						str							=	max_position_embeddings
          SCREAMING_SNAKE_CASE_   :						Dict							=	type_vocab_size
          SCREAMING_SNAKE_CASE_   :						str							=	type_sequence_label_size
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	initializer_range
          SCREAMING_SNAKE_CASE_   :						int							=	num_labels
          SCREAMING_SNAKE_CASE_   :						Dict							=	num_choices
          SCREAMING_SNAKE_CASE_   :						Tuple							=	scope
      def _SCREAMING_SNAKE_CASE				(		self  :						List[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	ids_tensor([self.batch_size, self.seq_length]		,			self.vocab_size)

          SCREAMING_SNAKE_CASE_   :						Tuple							=	None
          if self.use_input_mask:
              SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	random_attention_mask([self.batch_size, self.seq_length])

          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	None

          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	None
          SCREAMING_SNAKE_CASE_   :						Dict							=	None
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	None
          if self.use_labels:
              SCREAMING_SNAKE_CASE_   :						List[Any]							=	ids_tensor([self.batch_size]		,			self.type_sequence_label_size)
              SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	ids_tensor([self.batch_size, self.seq_length]		,			self.num_labels)
              SCREAMING_SNAKE_CASE_   :						str							=	ids_tensor([self.batch_size]		,			self.num_choices)

          SCREAMING_SNAKE_CASE_   :						Dict							=	self.get_config()

          return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
      def _SCREAMING_SNAKE_CASE				(		self  :						Union[str, Any]):




          '''simple docstring'''


          return FalconConfig(
              vocab_size=self.vocab_size		,			hidden_size=self.hidden_size		,			num_hidden_layers=self.num_hidden_layers		,			num_attention_heads=self.num_attention_heads		,			intermediate_size=self.intermediate_size		,			hidden_act=self.hidden_act		,			hidden_dropout_prob=self.hidden_dropout_prob		,			attention_probs_dropout_prob=self.attention_probs_dropout_prob		,			max_position_embeddings=self.max_position_embeddings		,			type_vocab_size=self.type_vocab_size		,			is_decoder=lowercase_		,			initializer_range=self.initializer_range		,			pad_token_id=1		,			new_decoder_architecture=lowercase_		,			)
      def _SCREAMING_SNAKE_CASE				(		self  :						Tuple		,			lowercase_  :						List[Any]		,			lowercase_  :						int		,			lowercase_  :						List[str]		,			lowercase_  :						Any		,			lowercase_  :						Optional[Any]		,			lowercase_  :						Union[str, Any]		,			lowercase_  :						Any):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Tuple							=	FalconModel(config=lowercase_)
          model.to(lowercase_)
          model.eval()
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	model(lowercase_		,			attention_mask=lowercase_)
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	model(lowercase_)
          self.parent.assertEqual(result.last_hidden_state.shape		,			(self.batch_size, self.seq_length, self.hidden_size))
      def _SCREAMING_SNAKE_CASE				(		self  :						Tuple		,			lowercase_  :						int		,			lowercase_  :						Dict		,			lowercase_  :						int		,			lowercase_  :						str		,			lowercase_  :						str		,			lowercase_  :						int		,			lowercase_  :						Tuple		,			lowercase_  :						Dict		,			lowercase_  :						int		,			):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Tuple							=	True
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	FalconModel(lowercase_)
          model.to(lowercase_)
          model.eval()
          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	model(
              lowercase_		,			attention_mask=lowercase_		,			encoder_hidden_states=lowercase_		,			encoder_attention_mask=lowercase_		,			)
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	model(
              lowercase_		,			attention_mask=lowercase_		,			encoder_hidden_states=lowercase_		,			)
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	model(lowercase_		,			attention_mask=lowercase_)
          self.parent.assertEqual(result.last_hidden_state.shape		,			(self.batch_size, self.seq_length, self.hidden_size))
      def _SCREAMING_SNAKE_CASE				(		self  :						Dict		,			lowercase_  :						Union[str, Any]		,			lowercase_  :						Tuple		,			lowercase_  :						int		,			lowercase_  :						Optional[Any]		,			lowercase_  :						Dict		,			lowercase_  :						Dict		,			lowercase_  :						Dict		,			lowercase_  :						List[str]		,			lowercase_  :						List[str]		,			):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						int							=	FalconForCausalLM(config=lowercase_)
          model.to(lowercase_)
          model.eval()
          SCREAMING_SNAKE_CASE_   :						Any							=	model(lowercase_		,			attention_mask=lowercase_		,			labels=lowercase_)
          self.parent.assertEqual(result.logits.shape		,			(self.batch_size, self.seq_length, self.vocab_size))
      def _SCREAMING_SNAKE_CASE				(		self  :						List[str]		,			lowercase_  :						Any		,			lowercase_  :						int		,			lowercase_  :						str		,			lowercase_  :						Tuple		,			lowercase_  :						List[str]		,			lowercase_  :						Optional[int]		,			lowercase_  :						Tuple		,			lowercase_  :						Optional[int]		,			lowercase_  :						str		,			):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Dict							=	True
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	True
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	FalconForCausalLM(config=lowercase_)
          model.to(lowercase_)
          model.eval()

          # first forward pass
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	model(
              lowercase_		,			attention_mask=lowercase_		,			encoder_hidden_states=lowercase_		,			encoder_attention_mask=lowercase_		,			use_cache=lowercase_		,			)
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	outputs.past_key_values

          # create hypothetical multiple next token and extent to next_input_ids
          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	ids_tensor((self.batch_size, 3)		,			config.vocab_size)
          SCREAMING_SNAKE_CASE_   :						List[str]							=	ids_tensor((self.batch_size, 3)		,			vocab_size=2)

          # append to next input_ids and
          SCREAMING_SNAKE_CASE_   :						Dict							=	torch.cat([input_ids, next_tokens]		,			dim=-1)
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	torch.cat([input_mask, next_mask]		,			dim=-1)

          SCREAMING_SNAKE_CASE_   :						List[str]							=	model(
              lowercase_		,			attention_mask=lowercase_		,			encoder_hidden_states=lowercase_		,			encoder_attention_mask=lowercase_		,			output_hidden_states=lowercase_		,			)['''hidden_states'''][0]
          SCREAMING_SNAKE_CASE_   :						Any							=	model(
              lowercase_		,			attention_mask=lowercase_		,			encoder_hidden_states=lowercase_		,			encoder_attention_mask=lowercase_		,			past_key_values=lowercase_		,			output_hidden_states=lowercase_		,			)['''hidden_states'''][0]

          # select random slice
          SCREAMING_SNAKE_CASE_   :						int							=	ids_tensor((1,)		,			output_from_past.shape[-1]).item()
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	output_from_no_past[:, -3:, random_slice_idx].detach()
          SCREAMING_SNAKE_CASE_   :						Dict							=	output_from_past[:, :, random_slice_idx].detach()

          self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])

          # test that outputs are equal for slice
          self.parent.assertTrue(torch.allclose(lowercase_		,			lowercase_		,			atol=1e-3))



      def _SCREAMING_SNAKE_CASE				(		self  :						Union[str, Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Tuple							=	self.prepare_config_and_inputs()
          (
              (
              SCREAMING_SNAKE_CASE_
          )		,    (
              SCREAMING_SNAKE_CASE_
          )		,    (
              SCREAMING_SNAKE_CASE_
          )		,    (
              SCREAMING_SNAKE_CASE_
          )		,    (
              SCREAMING_SNAKE_CASE_
          )		,    (
              SCREAMING_SNAKE_CASE_
          )		,    (
              SCREAMING_SNAKE_CASE_
          )		,    
          )   :						Tuple							=	config_and_inputs
          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	{'''input_ids''': input_ids, '''attention_mask''': input_mask}
          return config, inputs_dict




@require_torch
class 							lowerCAmelCase__       (    UpperCAmelCase__       ,	UpperCAmelCase__       ,	UpperCAmelCase__       ,	unittest.TestCase				):






      '''simple docstring'''

      __UpperCamelCase							=						(
          (
              FalconModel,
              FalconForCausalLM,
              FalconForSequenceClassification,
              FalconForTokenClassification,
              FalconForQuestionAnswering,
          )
          if is_torch_available()
          else ()
      )
      __UpperCamelCase							=						(FalconForCausalLM,) if is_torch_available() else ()
      __UpperCamelCase							=						(
          {
              "feature-extraction": FalconModel,
              "text-classification": FalconForSequenceClassification,
              "text-generation": FalconForCausalLM,
              "question-answering": FalconForQuestionAnswering,
              "token-classification": FalconForTokenClassification,
              "zero-shot": FalconForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )
      __UpperCamelCase							=						False
      __UpperCamelCase							=						False
      def _SCREAMING_SNAKE_CASE				(		self  :						Optional[int]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						str							=	FalconModelTester(self)
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	ConfigTester(self		,			config_class=lowercase_		,			hidden_size=37)
      def _SCREAMING_SNAKE_CASE				(		self  :						Tuple):




          '''simple docstring'''


          self.config_tester.run_common_tests()
      def _SCREAMING_SNAKE_CASE				(		self  :						int):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						List[Any]							=	self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*lowercase_)
      def _SCREAMING_SNAKE_CASE				(		self  :						int):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_		,    *SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	self.model_tester.prepare_config_and_inputs()
          for alibi in [True, False]:
              SCREAMING_SNAKE_CASE_   :						Optional[int]							=	alibi
              self.model_tester.create_and_check_model(lowercase_		,			*lowercase_)
      def _SCREAMING_SNAKE_CASE				(		self  :						Tuple):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_   :						List[str]							=	self.model_tester.prepare_config_and_inputs_for_common()
          SCREAMING_SNAKE_CASE_   :						Tuple							=	3
          SCREAMING_SNAKE_CASE_   :						Tuple							=	input_dict['''input_ids''']
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	input_ids.ne(1).to(lowercase_)
          SCREAMING_SNAKE_CASE_   :						Dict							=	ids_tensor([self.model_tester.batch_size]		,			self.model_tester.type_sequence_label_size)
          SCREAMING_SNAKE_CASE_   :						Tuple							=	FalconForSequenceClassification(lowercase_)
          model.to(lowercase_)
          model.eval()
          SCREAMING_SNAKE_CASE_   :						List[str]							=	model(lowercase_		,			attention_mask=lowercase_		,			labels=lowercase_)
          self.assertEqual(result.logits.shape		,			(self.model_tester.batch_size, self.model_tester.num_labels))
      def _SCREAMING_SNAKE_CASE				(		self  :						int):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_   :						int							=	self.model_tester.prepare_config_and_inputs_for_common()
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	3
          SCREAMING_SNAKE_CASE_   :						int							=	'''single_label_classification'''
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	input_dict['''input_ids''']
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	input_ids.ne(1).to(lowercase_)
          SCREAMING_SNAKE_CASE_   :						Dict							=	ids_tensor([self.model_tester.batch_size]		,			self.model_tester.type_sequence_label_size)
          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	FalconForSequenceClassification(lowercase_)
          model.to(lowercase_)
          model.eval()
          SCREAMING_SNAKE_CASE_   :						List[str]							=	model(lowercase_		,			attention_mask=lowercase_		,			labels=lowercase_)
          self.assertEqual(result.logits.shape		,			(self.model_tester.batch_size, self.model_tester.num_labels))
      def _SCREAMING_SNAKE_CASE				(		self  :						Dict):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	self.model_tester.prepare_config_and_inputs_for_common()
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	input_dict['''input_ids''']
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	FalconForCausalLM(lowercase_)
          model.to(lowercase_)
          model.eval()
          SCREAMING_SNAKE_CASE_   :						str							=	model(lowercase_		,			use_cache=lowercase_)
          SCREAMING_SNAKE_CASE_   :						Any							=	input_ids.shape[0]
          SCREAMING_SNAKE_CASE_   :						Dict							=	model._convert_to_rw_cache(result.past_key_values)
          SCREAMING_SNAKE_CASE_   :						int							=	model._convert_cache_to_standard_format(lowercase_		,			lowercase_)
          for layer in range(len(lowercase_)):
              for tensor_idx in range(2):
                  self.assertTrue(rw_cache[layer][tensor_idx].ndim == 3)
                  self.assertTrue(result.past_key_values[layer][tensor_idx].ndim == 4)
                  self.assertTrue(
                      torch.all(result.past_key_values[layer][tensor_idx] == standard_cache[layer][tensor_idx]))
      def _SCREAMING_SNAKE_CASE				(		self  :						Tuple):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_   :						Optional[int]							=	self.model_tester.prepare_config_and_inputs_for_common()
          SCREAMING_SNAKE_CASE_   :						int							=	3
          SCREAMING_SNAKE_CASE_   :						Tuple							=	'''multi_label_classification'''
          SCREAMING_SNAKE_CASE_   :						Tuple							=	input_dict['''input_ids''']
          SCREAMING_SNAKE_CASE_   :						List[str]							=	input_ids.ne(1).to(lowercase_)
          SCREAMING_SNAKE_CASE_   :						Tuple							=	ids_tensor(
              [self.model_tester.batch_size, config.num_labels]		,			self.model_tester.type_sequence_label_size).to(torch.float)
          SCREAMING_SNAKE_CASE_   :						int							=	FalconForSequenceClassification(lowercase_)
          model.to(lowercase_)
          model.eval()
          SCREAMING_SNAKE_CASE_   :						Tuple							=	model(lowercase_		,			attention_mask=lowercase_		,			labels=lowercase_)
          self.assertEqual(result.logits.shape		,			(self.model_tester.batch_size, self.model_tester.num_labels))



      def _SCREAMING_SNAKE_CASE				(		self  :						Union[str, Any]):




          '''simple docstring'''


          for model_class in self.all_generative_model_classes:
              SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_   :						Optional[int]							=	self.model_tester.prepare_config_and_inputs_for_common()

              # If it doesn't support cache, pass the test
              if not hasattr(lowercase_		,			'''use_cache'''):
                  return

              SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	model_class(lowercase_).to(lowercase_)
              if "use_cache" not in inputs:
                  SCREAMING_SNAKE_CASE_   :						Dict							=	True
              SCREAMING_SNAKE_CASE_   :						str							=	model(**lowercase_)

              # If "past_key_values" is not returned, pass the test (e.g. RWKV uses a different cache name and format)
              if "past_key_values" not in outputs:
                  return

              SCREAMING_SNAKE_CASE_   :						Tuple							=	(
                  getattr(lowercase_		,			'''decoder_layers'''		,			lowercase_)
                  or getattr(lowercase_		,			'''num_decoder_layers'''		,			lowercase_)
                  or config.num_hidden_layers
              )
              SCREAMING_SNAKE_CASE_   :						str							=	getattr(lowercase_		,			'''num_kv_heads'''		,			config.num_attention_heads)
              SCREAMING_SNAKE_CASE_   :						Tuple							=	getattr(lowercase_		,			'''d_model'''		,			config.hidden_size)
              SCREAMING_SNAKE_CASE_   :						Dict							=	embed_dim // num_attention_heads

              SCREAMING_SNAKE_CASE_   :						int							=	outputs['''past_key_values''']
              self.assertEqual(len(lowercase_)		,			lowercase_)

              SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_   :						List[Any]							=	inputs['''input_ids'''].shape
              for i in range(lowercase_):
                  if config.new_decoder_architecture:
                      SCREAMING_SNAKE_CASE_   :						Dict							=	config.num_attention_heads
                  elif config.multi_query:
                      SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	1
                  self.assertEqual(len(past_kv[0])		,			2)  # K V for the decoder = 2
                  self.assertEqual(
                      past_kv[i][0].shape		,			(batch_size, num_attention_heads, seq_length, per_head_embed_dim))
                  self.assertEqual(
                      past_kv[i][1].shape		,			(batch_size, num_attention_heads, seq_length, per_head_embed_dim))




@require_torch
class 							lowerCAmelCase__       (    unittest.TestCase				):






      '''simple docstring'''

      @slow
      def _SCREAMING_SNAKE_CASE				(		self  :						List[str]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Tuple							=	AutoTokenizer.from_pretrained('''Rocketknight1/falcon-rw-1b''')
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	FalconForCausalLM.from_pretrained('''Rocketknight1/falcon-rw-1b''')
          model.eval()
          model.to(lowercase_)
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	tokenizer('''My favorite food is'''		,			return_tensors='''pt''').to(lowercase_)

          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	(
              '''My favorite food is pizza. I love it so much that I have a pizza party every year for my birthday.'''
          )

          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	model.generate(**lowercase_		,			do_sample=lowercase_		,			max_new_tokens=19)
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	tokenizer.batch_decode(lowercase_)[0]

          self.assertEqual(lowercase_		,			lowercase_)
      @slow
      def _SCREAMING_SNAKE_CASE				(		self  :						Union[str, Any]):




          '''simple docstring'''


          for repo in ["Rocketknight1/tiny-random-falcon-7b", "Rocketknight1/tiny-random-falcon-40b"]:
              SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	AutoTokenizer.from_pretrained(lowercase_)
              SCREAMING_SNAKE_CASE_   :						str							=	FalconForCausalLM.from_pretrained(lowercase_)
              model.eval()
              model.to(lowercase_)
              SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	tokenizer('''My favorite food is'''		,			return_tensors='''pt''').to(lowercase_)

              # We just test that these run without errors - the models are randomly initialized
              # and so the actual text outputs will be garbage
              model.generate(**lowercase_		,			do_sample=lowercase_		,			max_new_tokens=4)
              model.generate(**lowercase_		,			do_sample=lowercase_		,			max_new_tokens=4)
              model.generate(**lowercase_		,			num_beams=2		,			max_new_tokens=4)



      @slow
      def _SCREAMING_SNAKE_CASE				(		self  :						str):




          '''simple docstring'''


          with torch.no_grad():
              for repo in [
                  "Rocketknight1/falcon-rw-1b",
                  "Rocketknight1/tiny-random-falcon-7b",
                  "Rocketknight1/tiny-random-falcon-40b",
              ]:
                  SCREAMING_SNAKE_CASE_   :						List[str]							=	AutoTokenizer.from_pretrained(lowercase_)
                  SCREAMING_SNAKE_CASE_   :						Tuple							=	FalconForCausalLM.from_pretrained(lowercase_)
                  model.eval()
                  model.to(device=lowercase_)
                  SCREAMING_SNAKE_CASE_   :						int							=	tokenizer('''My favorite food is'''		,			return_tensors='''pt''').to(lowercase_)

                  # Test results are the same with and without cache
                  SCREAMING_SNAKE_CASE_   :						Any							=	model.generate(**lowercase_		,			do_sample=lowercase_		,			max_new_tokens=20		,			use_cache=lowercase_)
                  SCREAMING_SNAKE_CASE_   :						Dict							=	model.generate(**lowercase_		,			do_sample=lowercase_		,			max_new_tokens=20		,			use_cache=lowercase_)
                  self.assertTrue((outputs_cache - outputs_no_cache).sum().item() == 0)


