
import copy
import unittest

from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch

					from transformers import (
					    MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
					    MODEL_FOR_QUESTION_ANSWERING_MAPPING,
					    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
					    MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
					    LayoutLMvaConfig,
					    LayoutLMvaForQuestionAnswering,
					    LayoutLMvaForSequenceClassification,
					    LayoutLMvaForTokenClassification,
					    LayoutLMvaModel,
					)
					from transformers.models.layoutlmva.modeling_layoutlmva import LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
					from PIL import Image

					from transformers import LayoutLMvaImageProcessor





class 				UpperCAmelCase :




	'''simple docstring'''





	def __init__(			self   :					str				,A   :					int				,A   :					int=2				,A   :					Optional[Any]=3				,A   :					Dict=4				,A   :					Optional[int]=2				,A   :					Union[str, Any]=7				,A   :					List[str]=True				,A   :					Union[str, Any]=True				,A   :					Optional[int]=True				,A   :					Optional[int]=True				,A   :					Tuple=99				,A   :					Optional[int]=36				,A   :					Dict=3				,A   :					str=4				,A   :					Optional[Any]=37				,A   :					Dict="gelu"				,A   :					Dict=0.1				,A   :					Union[str, Any]=0.1				,A   :					Union[str, Any]=5_12				,A   :					Any=16				,A   :					Union[str, Any]=2				,A   :					List[Any]=0.02				,A   :					List[Any]=6				,A   :					Optional[int]=6				,A   :					List[Any]=3				,A   :					Union[str, Any]=4				,A   :					Tuple=None				,A   :					List[str]=10_00				,):
					__A       =					parent
					__A       =					batch_size
					__A       =					num_channels
					__A       =					image_size
					__A       =					patch_size
					__A       =					text_seq_length
					__A       =					is_training
					__A       =					use_input_mask
					__A       =					use_token_type_ids
					__A       =					use_labels
					__A       =					vocab_size
					__A       =					hidden_size
					__A       =					num_hidden_layers
					__A       =					num_attention_heads
					__A       =					intermediate_size
					__A       =					hidden_act
					__A       =					hidden_dropout_prob
					__A       =					attention_probs_dropout_prob
					__A       =					max_position_embeddings
					__A       =					type_vocab_size
					__A       =					type_sequence_label_size
					__A       =					initializer_range
					__A       =					coordinate_size
					__A       =					shape_size
					__A       =					num_labels
					__A       =					num_choices
					__A       =					scope
					__A       =					range_bbox

					# LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
					__A       =					text_seq_length
					__A       =					(image_size // patch_size) ** 2 + 1
					__A       =					self.text_seq_length + self.image_seq_length






	def 							UpperCamelCase_    (			self   :					int					):
					__A       =					ids_tensor([self.batch_size, self.text_seq_length]				,self.vocab_size					)

					__A       =					ids_tensor([self.batch_size, self.text_seq_length, 4]				,self.range_bbox					)
					# Ensure that bbox is legal
					for i in range(bbox.shape[0]					):
									for j in range(bbox.shape[1]					):
													if bbox[i, j, 3] < bbox[i, j, 1]:
																	__A       =					bbox[i, j, 3]
																	__A       =					bbox[i, j, 1]
																	__A       =					t
													if bbox[i, j, 2] < bbox[i, j, 0]:
																	__A       =					bbox[i, j, 2]
																	__A       =					bbox[i, j, 0]
																	__A       =					t

					__A       =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

					__A       =					None
					if self.use_input_mask:
									__A       =					random_attention_mask([self.batch_size, self.text_seq_length]					)

					__A       =					None
					if self.use_token_type_ids:
									__A       =					ids_tensor([self.batch_size, self.text_seq_length]				,self.type_vocab_size					)

					__A       =					None
					__A       =					None
					if self.use_labels:
									__A       =					ids_tensor([self.batch_size]				,self.type_sequence_label_size					)
									__A       =					ids_tensor([self.batch_size, self.text_seq_length]				,self.num_labels					)

					__A       =					LayoutLMvaConfig(
					    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				,initializer_range=self.initializer_range				,coordinate_size=self.coordinate_size				,shape_size=self.shape_size				,input_size=self.image_size				,patch_size=self.patch_size				,)

					return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels






	def 							UpperCamelCase_    (			self   :					Optional[int]				,A   :					List[str]				,A   :					Any				,A   :					Dict				,A   :					List[Any]				,A   :					Optional[int]				,A   :					Any				,A   :					Dict				,A   :					List[Any]					):
					__A       =					LayoutLMvaModel(config=A					)
					model.to(A					)
					model.eval()

					# text + image
					__A       =					model(A				,pixel_values=A					)
					__A       =					model(
					    A				,bbox=A				,pixel_values=A				,attention_mask=A				,token_type_ids=A					)
					__A       =					model(A				,bbox=A				,pixel_values=A				,token_type_ids=A					)
					__A       =					model(A				,bbox=A				,pixel_values=A					)

					self.parent.assertEqual(result.last_hidden_state.shape				,(self.batch_size, self.seq_length, self.hidden_size)					)

					# text only
					__A       =					model(A					)
					self.parent.assertEqual(
					    result.last_hidden_state.shape				,(self.batch_size, self.text_seq_length, self.hidden_size)					)

					# image only
					__A       =					model(pixel_values=A					)
					self.parent.assertEqual(
					    result.last_hidden_state.shape				,(self.batch_size, self.image_seq_length, self.hidden_size)					)






	def 							UpperCamelCase_    (			self   :					Optional[int]				,A   :					Dict				,A   :					List[str]				,A   :					Any				,A   :					List[Any]				,A   :					Any				,A   :					Any				,A   :					Dict				,A   :					Optional[Any]					):
					__A       =					self.num_labels
					__A       =					LayoutLMvaForSequenceClassification(A					)
					model.to(A					)
					model.eval()
					__A       =					model(
					    A				,bbox=A				,pixel_values=A				,attention_mask=A				,token_type_ids=A				,labels=A				,)
					self.parent.assertEqual(result.logits.shape				,(self.batch_size, self.num_labels)					)






	def 							UpperCamelCase_    (			self   :					str				,A   :					Optional[Any]				,A   :					Dict				,A   :					str				,A   :					Tuple				,A   :					Union[str, Any]				,A   :					List[Any]				,A   :					Any				,A   :					Union[str, Any]					):
					__A       =					self.num_labels
					__A       =					LayoutLMvaForTokenClassification(config=A					)
					model.to(A					)
					model.eval()
					__A       =					model(
					    A				,bbox=A				,pixel_values=A				,attention_mask=A				,token_type_ids=A				,labels=A				,)
					self.parent.assertEqual(result.logits.shape				,(self.batch_size, self.text_seq_length, self.num_labels)					)






	def 							UpperCamelCase_    (			self   :					Optional[int]				,A   :					Optional[Any]				,A   :					int				,A   :					str				,A   :					List[str]				,A   :					int				,A   :					List[str]				,A   :					List[str]				,A   :					Dict					):
					__A       =					LayoutLMvaForQuestionAnswering(config=A					)
					model.to(A					)
					model.eval()
					__A       =					model(
					    A				,bbox=A				,pixel_values=A				,attention_mask=A				,token_type_ids=A				,start_positions=A				,end_positions=A				,)
					self.parent.assertEqual(result.start_logits.shape				,(self.batch_size, self.seq_length)					)
					self.parent.assertEqual(result.end_logits.shape				,(self.batch_size, self.seq_length)					)







	def 							UpperCamelCase_    (			self   :					str					):
					__A       =					self.prepare_config_and_inputs()
					(
					    (
					    __A
					)     ,				(
					    __A
					)     ,				(
					    __A
					)     ,				(
					    __A
					)     ,				(
					    __A
					)     ,				(
					    __A
					)     ,				(
					    __A
					)     ,				(
					    __A
					)     ,				
					)       =					config_and_inputs
					__A       =					{
					    "input_ids": input_ids,
					    "bbox": bbox,
					    "pixel_values": pixel_values,
					    "token_type_ids": token_type_ids,
					    "attention_mask": input_mask,
					}
					return config, inputs_dict





@require_torch
class 				UpperCAmelCase (    __SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE     ,     unittest.TestCase    ):




	'''simple docstring'''





	snake_case_          =	False
	snake_case_          =	False
	snake_case_          =	False

	snake_case_          =	(
	    (
	        LayoutLMvaModel,
	        LayoutLMvaForSequenceClassification,
	        LayoutLMvaForTokenClassification,
	        LayoutLMvaForQuestionAnswering,
	    )
	    if is_torch_available()
	    else ()
	)
	snake_case_          =	(
	    {"document-question-answering": LayoutLMvaForQuestionAnswering, "feature-extraction": LayoutLMvaModel}
	    if is_torch_available()
	    else {}
	)






	def 							UpperCamelCase_    (			self   :					str				,A   :					Any				,A   :					Any				,A   :					Tuple				,A   :					List[Any]				,A   :					Optional[Any]					):
					# `DocumentQuestionAnsweringPipeline` is expected to work with this model, but it combines the text and visual
					# embedding along the sequence dimension (dim 1), which causes an error during post-processing as `p_mask` has
					# the sequence dimension of the text embedding only.
					# (see the line `embedding_output = torch.cat([embedding_output, visual_embeddings], dim=1)`)
					return True






	def 							UpperCamelCase_    (			self   :					Union[str, Any]					):
					__A       =					LayoutLMvaModelTester(self					)
					__A       =					ConfigTester(self				,config_class=A				,hidden_size=37					)






	def 							UpperCamelCase_    (			self   :					List[Any]				,A   :					int				,A   :					List[str]				,A   :					Dict=False					):
					__A       =					copy.deepcopy(A					)
					if model_class in get_values(A					):
									__A       =					{
									    k: v.unsqueeze(1					).expand(-1				,self.model_tester.num_choices				,-1					).contiguous()
									    if isinstance(A				,torch.Tensor					) and v.ndim > 1
									    else v
									    for k, v in inputs_dict.items()
									}
					if return_labels:
									if model_class in get_values(A					):
													__A       =					torch.ones(self.model_tester.batch_size				,dtype=torch.long				,device=A					)
									elif model_class in get_values(A					):
													__A       =					torch.zeros(
													    self.model_tester.batch_size				,dtype=torch.long				,device=A					)
													__A       =					torch.zeros(
													    self.model_tester.batch_size				,dtype=torch.long				,device=A					)
									elif model_class in [
									    *get_values(A					),
									]:
													__A       =					torch.zeros(
													    self.model_tester.batch_size				,dtype=torch.long				,device=A					)
									elif model_class in [
									    *get_values(A					),
									]:
													__A       =					torch.zeros(
													    (self.model_tester.batch_size, self.model_tester.text_seq_length)				,dtype=torch.long				,device=A				,)

					return inputs_dict






	def 							UpperCamelCase_    (			self   :					List[Any]					):
					self.config_tester.run_common_tests()






	def 							UpperCamelCase_    (			self   :					Union[str, Any]					):
					__A       =					self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*A					)






	def 							UpperCamelCase_    (			self   :					str					):
					__A       =					self.model_tester.prepare_config_and_inputs()
					for type in ["absolute", "relative_key", "relative_key_query"]:
									__A       =					type
									self.model_tester.create_and_check_model(*A					)






	def 							UpperCamelCase_    (			self   :					Optional[Any]					):
					__A       =					self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_sequence_classification(*A					)






	def 							UpperCamelCase_    (			self   :					Optional[Any]					):
					__A       =					self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_token_classification(*A					)






	def 							UpperCamelCase_    (			self   :					str					):
					__A       =					self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_question_answering(*A					)







	@slow
	def 							UpperCamelCase_    (			self   :					Optional[int]					):
					for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									__A       =					LayoutLMvaModel.from_pretrained(A					)
									self.assertIsNotNone(A					)





def 		UpperCAmelCase     (      )						->      Dict:







				"""simple docstring"""



				__A       =					Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
				return image





@require_torch
class 				UpperCAmelCase (    unittest.TestCase    ):




	'''simple docstring'''





	@cached_property
	def 							UpperCamelCase_    (			self   :					Any					):
					return LayoutLMvaImageProcessor(apply_ocr=A					) if is_vision_available() else None







	@slow
	def 							UpperCamelCase_    (			self   :					Dict					):
					__A       =					LayoutLMvaModel.from_pretrained("microsoft/layoutlmv3-base"					).to(A					)

					__A       =					self.default_image_processor
					__A       =					prepare_img()
					__A       =					image_processor(images=A				,return_tensors="pt"					).pixel_values.to(A					)

					__A       =					torch.tensor([[1, 2]]					)
					__A       =					torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]]					).unsqueeze(0					)

					# forward pass
					__A       =					model(
					    input_ids=input_ids.to(A					)				,bbox=bbox.to(A					)				,pixel_values=pixel_values.to(A					)				,)

					# verify the logits
					__A       =					torch.Size((1, 1_99, 7_68)					)
					self.assertEqual(outputs.last_hidden_state.shape				,A					)

					__A       =					torch.tensor(
					    [[-0.05_29, 0.36_18, 0.16_32], [-0.15_87, -0.16_67, -0.04_00], [-0.15_57, -0.16_71, -0.05_05]]					).to(A					)

					self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]				,A				,atol=1E-4					)					)

"""simple docstring"""


from math import isqrt






def      __UpperCAmelCase       (      lowercase		):







  """simple docstring"""
  _UpperCAmelCase    			=   [True] * max_number
  for i in range(2 ,isqrt(max_number - 1		) + 1		):
    if is_prime[i]:
      for j in range(i**2 ,lowercase ,lowercase		):
        _UpperCAmelCase    			=   False

  return [i for i in range(2 ,lowercase		) if is_prime[i]]






def      __UpperCAmelCase       (      lowercase = 10**8		):







  """simple docstring"""
  _UpperCAmelCase    			=   calculate_prime_numbers(max_number // 2		)

  _UpperCAmelCase    			=   0
  _UpperCAmelCase    			=   0
  _UpperCAmelCase    			=   len(lowercase		) - 1
  while left <= right:
    while prime_numbers[left] * prime_numbers[right] >= max_number:
      right -= 1
    semiprimes_count += right - left + 1
    left += 1

  return semiprimes_count


if __name__ == "__main__":
    print(F'''{solution() = }''')
