


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       lowerCamelCase__   :





     def __init__(self		,     UpperCAmelCase		,     UpperCAmelCase=2		,     UpperCAmelCase=3		,     UpperCAmelCase=4		,     UpperCAmelCase=2		,     UpperCAmelCase=7		,     UpperCAmelCase=True		,     UpperCAmelCase=True		,     UpperCAmelCase=True		,     UpperCAmelCase=True		,     UpperCAmelCase=9_9		,     UpperCAmelCase=3_6		,     UpperCAmelCase=3		,     UpperCAmelCase=4		,     UpperCAmelCase=3_7		,     UpperCAmelCase="gelu"		,     UpperCAmelCase=0.1		,     UpperCAmelCase=0.1		,     UpperCAmelCase=5_1_2		,     UpperCAmelCase=1_6		,     UpperCAmelCase=2		,     UpperCAmelCase=0.02		,     UpperCAmelCase=6		,     UpperCAmelCase=6		,     UpperCAmelCase=3		,     UpperCAmelCase=4		,     UpperCAmelCase=None		,     UpperCAmelCase=1_0_0_0		,     )     ->   Tuple:
      _lowercase							      =parent
      _lowercase							      =batch_size
      _lowercase							      =num_channels
      _lowercase							      =image_size
      _lowercase							      =patch_size
      _lowercase							      =text_seq_length
      _lowercase							      =is_training
      _lowercase							      =use_input_mask
      _lowercase							      =use_token_type_ids
      _lowercase							      =use_labels
      _lowercase							      =vocab_size
      _lowercase							      =hidden_size
      _lowercase							      =num_hidden_layers
      _lowercase							      =num_attention_heads
      _lowercase							      =intermediate_size
      _lowercase							      =hidden_act
      _lowercase							      =hidden_dropout_prob
      _lowercase							      =attention_probs_dropout_prob
      _lowercase							      =max_position_embeddings
      _lowercase							      =type_vocab_size
      _lowercase							      =type_sequence_label_size
      _lowercase							      =initializer_range
      _lowercase							      =coordinate_size
      _lowercase							      =shape_size
      _lowercase							      =num_labels
      _lowercase							      =num_choices
      _lowercase							      =scope
      _lowercase							      =range_bbox

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





     def      __A					(self       )     ->   Any:
      _lowercase							      =ids_tensor([self.batch_size, self.text_seq_length]		,     self.vocab_size       )

      _lowercase							      =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]:
         _lowercase							      =bbox[i, j, 3]
         _lowercase							      =bbox[i, j, 1]
         _lowercase							      =t
        if bbox[i, j, 2] < bbox[i, j, 0]:
         _lowercase							      =bbox[i, j, 2]
         _lowercase							      =bbox[i, j, 0]
         _lowercase							      =t

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

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

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

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

      _lowercase							      =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      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase       )     ->   Optional[Any]:
      _lowercase							      =LayoutLMvaModel(config=UpperCAmelCase_       )
      model.to(UpperCAmelCase_       )
      model.eval()

      # text + image
      _lowercase							      =model(UpperCAmelCase_		,     pixel_values=UpperCAmelCase_       )
      _lowercase							      =model(
          UpperCAmelCase_		,     bbox=UpperCAmelCase_		,     pixel_values=UpperCAmelCase_		,     attention_mask=UpperCAmelCase_		,     token_type_ids=UpperCAmelCase_       )
      _lowercase							      =model(UpperCAmelCase_		,     bbox=UpperCAmelCase_		,     pixel_values=UpperCAmelCase_		,     token_type_ids=UpperCAmelCase_       )
      _lowercase							      =model(UpperCAmelCase_		,     bbox=UpperCAmelCase_		,     pixel_values=UpperCAmelCase_       )

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

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

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





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase       )     ->   List[str]:
      _lowercase							      =self.num_labels
      _lowercase							      =LayoutLMvaForSequenceClassification(UpperCAmelCase_       )
      model.to(UpperCAmelCase_       )
      model.eval()
      _lowercase							      =model(
          UpperCAmelCase_		,     bbox=UpperCAmelCase_		,     pixel_values=UpperCAmelCase_		,     attention_mask=UpperCAmelCase_		,     token_type_ids=UpperCAmelCase_		,     labels=UpperCAmelCase_		,     )
      self.parent.assertEqual(result.logits.shape		,     (self.batch_size, self.num_labels)       )





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase       )     ->   Optional[Any]:
      _lowercase							      =self.num_labels
      _lowercase							      =LayoutLMvaForTokenClassification(config=UpperCAmelCase_       )
      model.to(UpperCAmelCase_       )
      model.eval()
      _lowercase							      =model(
          UpperCAmelCase_		,     bbox=UpperCAmelCase_		,     pixel_values=UpperCAmelCase_		,     attention_mask=UpperCAmelCase_		,     token_type_ids=UpperCAmelCase_		,     labels=UpperCAmelCase_		,     )
      self.parent.assertEqual(result.logits.shape		,     (self.batch_size, self.text_seq_length, self.num_labels)       )





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase       )     ->   Tuple:
      _lowercase							      =LayoutLMvaForQuestionAnswering(config=UpperCAmelCase_       )
      model.to(UpperCAmelCase_       )
      model.eval()
      _lowercase							      =model(
          UpperCAmelCase_		,     bbox=UpperCAmelCase_		,     pixel_values=UpperCAmelCase_		,     attention_mask=UpperCAmelCase_		,     token_type_ids=UpperCAmelCase_		,     start_positions=UpperCAmelCase_		,     end_positions=UpperCAmelCase_		,     )
      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      __A					(self       )     ->   Optional[Any]:
      _lowercase							      =self.prepare_config_and_inputs()
      (
          _lowercase					
      )							      =config_and_inputs
      _lowercase							      ={
          "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       lowerCamelCase__   (       __SCREAMING_SNAKE_CASE   ,							__SCREAMING_SNAKE_CASE   ,							unittest.TestCase):
     SCREAMING_SNAKE_CASE__      			=			False
     SCREAMING_SNAKE_CASE__      			=			False
     SCREAMING_SNAKE_CASE__      			=			False

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





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase       )     ->   Optional[int]:
      return True





     def      __A					(self       )     ->   Dict:
      _lowercase							      =LayoutLMvaModelTester(self       )
      _lowercase							      =ConfigTester(self		,     config_class=UpperCAmelCase_		,     hidden_size=3_7       )





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

      return inputs_dict





     def      __A					(self       )     ->   int:
      self.config_tester.run_common_tests()





     def      __A					(self       )     ->   List[str]:
      _lowercase							      =self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*UpperCAmelCase_       )





     def      __A					(self       )     ->   str:
      _lowercase							      =self.model_tester.prepare_config_and_inputs()
      for type in ["absolute", "relative_key", "relative_key_query"]:
       _lowercase							      =type
       self.model_tester.create_and_check_model(*UpperCAmelCase_       )





     def      __A					(self       )     ->   List[Any]:
      _lowercase							      =self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_sequence_classification(*UpperCAmelCase_       )





     def      __A					(self       )     ->   Optional[Any]:
      _lowercase							      =self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_token_classification(*UpperCAmelCase_       )





     def      __A					(self       )     ->   Optional[int]:
      _lowercase							      =self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_question_answering(*UpperCAmelCase_       )





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



      for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       _lowercase							      =LayoutLMvaModel.from_pretrained(UpperCAmelCase_       )
       self.assertIsNotNone(UpperCAmelCase_       )



def 				UpperCAmelCase_					(							)     ->			Dict:







 """simple docstring"""


 _lowercase							      =Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''						)
 return image







@require_torch
class       lowerCamelCase__   (       unittest.TestCase):





     @cached_property
     def      __A					(self       )     ->   str:
      return LayoutLMvaImageProcessor(apply_ocr=UpperCAmelCase_       ) if is_vision_available() else None





     @slow
     def      __A					(self       )     ->   Optional[Any]:
      _lowercase							      =LayoutLMvaModel.from_pretrained('''microsoft/layoutlmv3-base'''       ).to(UpperCAmelCase_       )

      _lowercase							      =self.default_image_processor
      _lowercase							      =prepare_img()
      _lowercase							      =image_processor(images=UpperCAmelCase_		,     return_tensors='''pt'''       ).pixel_values.to(UpperCAmelCase_       )

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

      # forward pass
      _lowercase							      =model(
          input_ids=input_ids.to(UpperCAmelCase_       )		,     bbox=bbox.to(UpperCAmelCase_       )		,     pixel_values=pixel_values.to(UpperCAmelCase_       )		,     )

      # verify the logits
      _lowercase							      =torch.Size((1, 1_9_9, 7_6_8)       )
      self.assertEqual(outputs.last_hidden_state.shape		,     UpperCAmelCase_       )

      _lowercase							      =torch.tensor(
          [[-0.0529, 0.3618, 0.1632], [-0.1587, -0.1667, -0.0400], [-0.1557, -0.1671, -0.0505]]       ).to(UpperCAmelCase_       )

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








import itertools
import math





def      lowerCAmelCase_ (       __a		)     ->		bool:







  """simple docstring"""





  if 1 < number < 4:
    # 2 and 3 are primes
    return True
  elif number < 2 or number % 2 == 0 or number % 3 == 0:
    # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
    return False

  # All primes number are in format of 6k +/- 1
  for i in range(5	,    int(math.sqrt(__a		) + 1		)	,    6		):
    if number % i == 0 or number % (i + 2) == 0:
      return False
  return True





def      lowerCAmelCase_ (       )     ->		str:







  """simple docstring"""





  lowerCamelCase__:      Optional[int]			  =2
  while True:
    if is_prime(__a		):
      yield num
    num += 1





def      lowerCAmelCase_ (       __a = 10001		)     ->		int:







  """simple docstring"""





  return next(itertools.islice(prime_generator()	,    nth - 1	,    __a		)		)


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

