





import unittest

from transformers import LiltConfig, 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
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import (
        LiltForQuestionAnswering,
        LiltForSequenceClassification,
        LiltForTokenClassification,
        LiltModel,
    )
    from transformers.models.lilt.modeling_lilt import LILT_PRETRAINED_MODEL_ARCHIVE_LIST

class 		SCREAMING_SNAKE_CASE__    :


      def __init__(self	:				str    ,      a__	:				Optional[Any]    ,      a__	:				Tuple=13    ,      a__	:				Any=7    ,      a__	:				Dict=True    ,      a__	:				Optional[int]=True    ,      a__	:				Dict=True    ,      a__	:				str=True    ,      a__	:				Optional[Any]=99    ,      a__	:				Tuple=24    ,      a__	:				Any=2    ,      a__	:				Dict=6    ,      a__	:				Dict=37    ,      a__	:				List[str]="gelu"    ,      a__	:				str=0.1    ,      a__	:				Optional[Any]=0.1    ,      a__	:				Optional[Any]=512    ,      a__	:				Dict=16    ,      a__	:				Union[str, Any]=2    ,      a__	:				int=0.0_2    ,      a__	:				int=3    ,      a__	:				int=None    ,      a__	:				str=1000    ,      ):





         """simple docstring"""



         __snake_case								=       parent
         __snake_case								=       batch_size
         __snake_case								=       seq_length
         __snake_case								=       is_training
         __snake_case								=       use_input_mask
         __snake_case								=       use_token_type_ids
         __snake_case								=       use_labels
         __snake_case								=       vocab_size
         __snake_case								=       hidden_size
         __snake_case								=       num_hidden_layers
         __snake_case								=       num_attention_heads
         __snake_case								=       intermediate_size
         __snake_case								=       hidden_act
         __snake_case								=       hidden_dropout_prob
         __snake_case								=       attention_probs_dropout_prob
         __snake_case								=       max_position_embeddings
         __snake_case								=       type_vocab_size
         __snake_case								=       type_sequence_label_size
         __snake_case								=       initializer_range
         __snake_case								=       num_labels
         __snake_case								=       scope
         __snake_case								=       range_bbox


      def        a			(self	:				Dict				):





         """simple docstring"""



         __snake_case								=       ids_tensor([self.batch_size, self.seq_length]    ,      self.vocab_size				)

         __snake_case								=       ids_tensor([self.batch_size, self.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]:
                  __snake_case								=       bbox[i, j, 3]
                  __snake_case								=       bbox[i, j, 1]
                  __snake_case								=       t
               if bbox[i, j, 2] < bbox[i, j, 0]:
                  __snake_case								=       bbox[i, j, 2]
                  __snake_case								=       bbox[i, j, 0]
                  __snake_case								=       t

         __snake_case								=       None
         if self.use_input_mask:
            __snake_case								=       ids_tensor([self.batch_size, self.seq_length]    ,      vocab_size=2				)

         __snake_case								=       None
         if self.use_token_type_ids:
            __snake_case								=       ids_tensor([self.batch_size, self.seq_length]    ,      self.type_vocab_size				)

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

         __snake_case								=       self.get_config()

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


      def        a			(self	:				Optional[Any]				):





         """simple docstring"""



         return LiltConfig(
             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    ,      )


      def        a			(self	:				Union[str, Any]    ,      a__	:				int    ,      a__	:				int    ,      a__	:				List[str]    ,      a__	:				str    ,      a__	:				Tuple    ,      a__	:				Any    ,      a__	:				Any    ,      ):





         """simple docstring"""



         __snake_case								=       LiltModel(config=a__				)
         model.to(a__				)
         model.eval()
         __snake_case								=       model(a__    ,      bbox=a__    ,      attention_mask=a__    ,      token_type_ids=a__				)
         __snake_case								=       model(a__    ,      bbox=a__    ,      token_type_ids=a__				)
         __snake_case								=       model(a__    ,      bbox=a__				)

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


      def        a			(self	:				Tuple    ,      a__	:				Union[str, Any]    ,      a__	:				List[Any]    ,      a__	:				Optional[int]    ,      a__	:				Union[str, Any]    ,      a__	:				Optional[int]    ,      a__	:				int    ,      a__	:				Optional[int]    ,      ):





         """simple docstring"""



         __snake_case								=       self.num_labels
         __snake_case								=       LiltForTokenClassification(config=a__				)
         model.to(a__				)
         model.eval()
         __snake_case								=       model(
             a__    ,      bbox=a__    ,      attention_mask=a__    ,      token_type_ids=a__    ,      labels=a__				)
         self.parent.assertEqual(result.logits.shape    ,      (self.batch_size, self.seq_length, self.num_labels)				)


      def        a			(self	:				List[str]    ,      a__	:				Tuple    ,      a__	:				List[str]    ,      a__	:				str    ,      a__	:				List[str]    ,      a__	:				int    ,      a__	:				str    ,      a__	:				Optional[int]    ,      ):





         """simple docstring"""



         __snake_case								=       LiltForQuestionAnswering(config=a__				)
         model.to(a__				)
         model.eval()
         __snake_case								=       model(
             a__    ,      bbox=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        a			(self	:				Optional[Any]				):





         """simple docstring"""



         __snake_case								=       self.prepare_config_and_inputs()
         (
             (
             __snake_case
         )	,     (
             __snake_case
         )	,     (
             __snake_case
         )	,     (
             __snake_case
         )	,     (
             __snake_case
         )	,     (
             __snake_case
         )	,     (
             __snake_case
         )	,     
         )								=       config_and_inputs
         __snake_case								=       {
             '''input_ids''': input_ids,
             '''bbox''': bbox,
             '''token_type_ids''': token_type_ids,
             '''attention_mask''': input_mask,
         }
         return config, inputs_dict






@require_torch
class 		SCREAMING_SNAKE_CASE__    (	_UpperCAmelCase						,				_UpperCAmelCase						,				_UpperCAmelCase						,				unittest.TestCase  ):
      A_						:    Optional[int]							=   (
          (
              LiltModel,
              LiltForSequenceClassification,
              LiltForTokenClassification,
              LiltForQuestionAnswering,
          )
          if is_torch_available()
          else ()
      )
      A_						:    Union[str, Any]							=   (
          {
              'feature-extraction': LiltModel,
              'question-answering': LiltForQuestionAnswering,
              'text-classification': LiltForSequenceClassification,
              'token-classification': LiltForTokenClassification,
              'zero-shot': LiltForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )
      A_						:    int							=   False
      A_						:    str							=   False


      def        a			(self	:				Union[str, Any]    ,      a__	:				Optional[Any]    ,      a__	:				Union[str, Any]    ,      a__	:				Any    ,      a__	:				Any    ,      a__	:				List[str]				):





         """simple docstring"""



         return True


      def        a			(self	:				Optional[int]				):





         """simple docstring"""



         __snake_case								=       LiltModelTester(self				)
         __snake_case								=       ConfigTester(self    ,      config_class=a__    ,      hidden_size=37				)


      def        a			(self	:				int				):





         """simple docstring"""



         self.config_tester.run_common_tests()


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





         """simple docstring"""



         __snake_case								=       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*a__				)


      def        a			(self	:				int				):





         """simple docstring"""



         __snake_case								=       self.model_tester.prepare_config_and_inputs()
         for type in ["absolute", "relative_key", "relative_key_query"]:
            __snake_case								=       type
            self.model_tester.create_and_check_model(*a__				)


      def        a			(self	:				Any				):





         """simple docstring"""



         __snake_case								=       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_token_classification(*a__				)


      def        a			(self	:				Any				):





         """simple docstring"""



         __snake_case								=       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_question_answering(*a__				)


      @slow
      def        a			(self	:				int				):





         """simple docstring"""



         for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            __snake_case								=       LiltModel.from_pretrained(a__				)
            self.assertIsNotNone(a__				)






@require_torch
@slow
class 		SCREAMING_SNAKE_CASE__    (	unittest.TestCase  ):


      def        a			(self	:				Any				):





         """simple docstring"""



         __snake_case								=       LiltModel.from_pretrained('''SCUT-DLVCLab/lilt-roberta-en-base'''				).to(a__				)

         __snake_case								=       torch.tensor([[1, 2]]    ,      device=a__				)
         __snake_case								=       torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]]]    ,      device=a__				)

         # forward pass
         with torch.no_grad():
            __snake_case								=       model(input_ids=a__    ,      bbox=a__				)

         __snake_case								=       torch.Size([1, 2, 768]				)
         __snake_case								=       torch.tensor(
             [[-0.0_6_5_3, 0.0_9_5_0, -0.0_0_6_1], [-0.0_5_4_5, 0.0_9_2_6, -0.0_3_2_4]]    ,      device=a__    ,      )

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








from __future__ import annotations

snake_case_           =      [True] * 1000001
snake_case_           =      2
while i * i <= 1000000:
    if seive[i]:
        for j in range(i * i, 1000001, i):
            snake_case_           =      False
    i += 1






def lowerCamelCase__   (							snake_case_     :  int	)				->    bool:
   return seive[n]






def lowerCamelCase__   (							snake_case_     :  int	)				->    bool:
   return any(digit in '''02468''' for digit in str(snake_case_	)	)






def lowerCamelCase__   (							snake_case_     :  int = 100_0000	)				->    list[int]:
   __snake_case								=       [2]  # result already includes the number 2.
   for num in range(3	,				limit + 1	,				2	):
      if is_prime(snake_case_	) and not contains_an_even_digit(snake_case_	):
         __snake_case								=       str(snake_case_	)
         __snake_case								=       [int(str_num[j:] + str_num[:j]	) for j in range(len(snake_case_	)	)]
         if all(is_prime(snake_case_	) for i in list_nums	):
            result.append(snake_case_	)
   return result






def lowerCamelCase__   (							)				->    int:
   return len(find_circular_primes()	)


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

