

from __future__ import annotations

import unittest

from transformers import RoFormerConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
    import tensorflow as tf

    from transformers import (
        TFRoFormerForCausalLM,
        TFRoFormerForMaskedLM,
        TFRoFormerForMultipleChoice,
        TFRoFormerForQuestionAnswering,
        TFRoFormerForSequenceClassification,
        TFRoFormerForTokenClassification,
        TFRoFormerModel,
    )
    from transformers.models.roformer.modeling_tf_roformer import (
        TFRoFormerSelfAttention,
        TFRoFormerSinusoidalPositionalEmbedding,
    )

class     __snake_case    :
       """simple docstring"""







       def __init__(		self      , _UpperCamelCase      , _UpperCamelCase=13      , _UpperCamelCase=7      , _UpperCamelCase=True      , _UpperCamelCase=True      , _UpperCamelCase=True      , _UpperCamelCase=True      , _UpperCamelCase=99      , _UpperCamelCase=32      , _UpperCamelCase=2      , _UpperCamelCase=4      , _UpperCamelCase=37      , _UpperCamelCase="gelu"      , _UpperCamelCase=0.1      , _UpperCamelCase=0.1      , _UpperCamelCase=5_12      , _UpperCamelCase=16      , _UpperCamelCase=2      , _UpperCamelCase=0.02      , _UpperCamelCase=3      , _UpperCamelCase=4      , _UpperCamelCase=None      , )      ->							Union[str, Any]:






              """simple docstring"""


              __snake_case       	=							parent
              __snake_case       	=							13
              __snake_case       	=							7
              __snake_case       	=							True
              __snake_case       	=							True
              __snake_case       	=							True
              __snake_case       	=							True
              __snake_case       	=							99
              __snake_case       	=							32
              __snake_case       	=							2
              __snake_case       	=							4
              __snake_case       	=							37
              __snake_case       	=							"""gelu"""
              __snake_case       	=							0.1
              __snake_case       	=							0.1
              __snake_case       	=							5_12
              __snake_case       	=							16
              __snake_case       	=							2
              __snake_case       	=							0.02
              __snake_case       	=							3
              __snake_case       	=							4
              __snake_case       	=							None


       def 			a		(		self   )      ->							List[Any]:






              """simple docstring"""


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

              __snake_case       	=							None
              if self.use_input_mask:
                     __snake_case       	=							random_attention_mask([self.batch_size, self.seq_length]   )

              __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
              __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       	=							ids_tensor([self.batch_size]      , self.num_choices   )

              __snake_case       	=							RoFormerConfig(
                  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      , return_dict=_UpperCamelCase      , )

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


       def 			a		(		self      , _UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase   )      ->							Optional[int]:






              """simple docstring"""


              __snake_case       	=							TFRoFormerModel(config=_UpperCamelCase   )
              __snake_case       	=							{"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}

              __snake_case       	=							[input_ids, input_mask]
              __snake_case       	=							model(_UpperCamelCase   )

              __snake_case       	=							model(_UpperCamelCase   )

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


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






              """simple docstring"""


              __snake_case       	=							True
              __snake_case       	=							TFRoFormerForCausalLM(config=_UpperCamelCase   )
              __snake_case       	=							{
                  """input_ids""": input_ids,
                  """attention_mask""": input_mask,
                  """token_type_ids""": token_type_ids,
              }
              __snake_case       	=							model(_UpperCamelCase   )["""logits"""]
              self.parent.assertListEqual(
                  list(prediction_scores.numpy().shape   )      , [self.batch_size, self.seq_length, self.vocab_size]   )


       def 			a		(		self      , _UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase   )      ->							str:






              """simple docstring"""


              __snake_case       	=							TFRoFormerForMaskedLM(config=_UpperCamelCase   )
              __snake_case       	=							{
                  """input_ids""": input_ids,
                  """attention_mask""": input_mask,
                  """token_type_ids""": token_type_ids,
              }
              __snake_case       	=							model(_UpperCamelCase   )
              self.parent.assertEqual(result.logits.shape      , (self.batch_size, self.seq_length, self.vocab_size)   )


       def 			a		(		self      , _UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase   )      ->							Optional[Any]:






              """simple docstring"""


              __snake_case       	=							self.num_labels
              __snake_case       	=							TFRoFormerForSequenceClassification(config=_UpperCamelCase   )
              __snake_case       	=							{
                  """input_ids""": input_ids,
                  """attention_mask""": input_mask,
                  """token_type_ids""": token_type_ids,
              }

              __snake_case       	=							model(_UpperCamelCase   )
              self.parent.assertEqual(result.logits.shape      , (self.batch_size, self.num_labels)   )


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






              """simple docstring"""


              __snake_case       	=							self.num_choices
              __snake_case       	=							TFRoFormerForMultipleChoice(config=_UpperCamelCase   )
              __snake_case       	=							tf.tile(tf.expand_dims(_UpperCamelCase      , 1   )      , (1, self.num_choices, 1)   )
              __snake_case       	=							tf.tile(tf.expand_dims(_UpperCamelCase      , 1   )      , (1, self.num_choices, 1)   )
              __snake_case       	=							tf.tile(tf.expand_dims(_UpperCamelCase      , 1   )      , (1, self.num_choices, 1)   )
              __snake_case       	=							{
                  """input_ids""": multiple_choice_inputs_ids,
                  """attention_mask""": multiple_choice_input_mask,
                  """token_type_ids""": multiple_choice_token_type_ids,
              }
              __snake_case       	=							model(_UpperCamelCase   )
              self.parent.assertEqual(result.logits.shape      , (self.batch_size, self.num_choices)   )


       def 			a		(		self      , _UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase   )      ->							Tuple:






              """simple docstring"""


              __snake_case       	=							self.num_labels
              __snake_case       	=							TFRoFormerForTokenClassification(config=_UpperCamelCase   )
              __snake_case       	=							{
                  """input_ids""": input_ids,
                  """attention_mask""": input_mask,
                  """token_type_ids""": token_type_ids,
              }
              __snake_case       	=							model(_UpperCamelCase   )
              self.parent.assertEqual(result.logits.shape      , (self.batch_size, self.seq_length, self.num_labels)   )


       def 			a		(		self      , _UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase   )      ->							List[str]:






              """simple docstring"""


              __snake_case       	=							TFRoFormerForQuestionAnswering(config=_UpperCamelCase   )
              __snake_case       	=							{
                  """input_ids""": input_ids,
                  """attention_mask""": input_mask,
                  """token_type_ids""": token_type_ids,
              }

              __snake_case       	=							model(_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   )      ->							Dict:






              """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, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
              return config, inputs_dict






@require_tf
class     __snake_case    (				snake_case__			,	snake_case__			,	unittest.TestCase  ):
       """simple docstring"""







       __SCREAMING_SNAKE_CASE				    =							(
           (
               TFRoFormerModel,
               TFRoFormerForCausalLM,
               TFRoFormerForMaskedLM,
               TFRoFormerForQuestionAnswering,
               TFRoFormerForSequenceClassification,
               TFRoFormerForTokenClassification,
               TFRoFormerForMultipleChoice,
           )
           if is_tf_available()
           else ()
       )
       __SCREAMING_SNAKE_CASE				    =							(
           {
               "feature-extraction": TFRoFormerModel,
               "fill-mask": TFRoFormerForMaskedLM,
               "question-answering": TFRoFormerForQuestionAnswering,
               "text-classification": TFRoFormerForSequenceClassification,
               "text-generation": TFRoFormerForCausalLM,
               "token-classification": TFRoFormerForTokenClassification,
               "zero-shot": TFRoFormerForSequenceClassification,
           }
           if is_tf_available()
           else {}
       )

       __SCREAMING_SNAKE_CASE				    =							False
       __SCREAMING_SNAKE_CASE				    =							False


       def 			a		(		self      , _UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase   )      ->							Dict:






              """simple docstring"""


              if pipeline_test_casse_name == "TextGenerationPipelineTests":
                     return True

              return False


       def 			a		(		self   )      ->							Dict:






              """simple docstring"""


              __snake_case       	=							TFRoFormerModelTester(self   )
              __snake_case       	=							ConfigTester(self      , config_class=_UpperCamelCase      , hidden_size=37   )


       def 			a		(		self   )      ->							Optional[Any]:






              """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(*_UpperCamelCase   )


       def 			a		(		self   )      ->							List[Any]:






              """simple docstring"""


              __snake_case       	=							self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_for_masked_lm(*_UpperCamelCase   )


       def 			a		(		self   )      ->							List[str]:






              """simple docstring"""


              __snake_case       	=							self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_lm_head(*_UpperCamelCase   )


       def 			a		(		self   )      ->							int:






              """simple docstring"""


              __snake_case       	=							self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_for_multiple_choice(*_UpperCamelCase   )


       def 			a		(		self   )      ->							Optional[int]:






              """simple docstring"""


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


       def 			a		(		self   )      ->							Tuple:






              """simple docstring"""


              __snake_case       	=							self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_for_sequence_classification(*_UpperCamelCase   )


       def 			a		(		self   )      ->							List[str]:






              """simple docstring"""


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






       @slow
       def 			a		(		self   )      ->							Optional[Any]:






              """simple docstring"""


              __snake_case       	=							TFRoFormerModel.from_pretrained("""junnyu/roformer_chinese_base"""   )
              self.assertIsNotNone(_UpperCamelCase   )






@require_tf
class     __snake_case    (				unittest.TestCase  ):
       """simple docstring"""







       @slow
       def 			a		(		self   )      ->							int:






              """simple docstring"""


              __snake_case       	=							TFRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base"""   )
              __snake_case       	=							tf.constant([[0, 1, 2, 3, 4, 5]]   )
              __snake_case       	=							model(_UpperCamelCase   )[0]

              # TODO Replace vocab size
              __snake_case       	=							5_00_00

              __snake_case       	=							[1, 6, vocab_size]
              self.assertEqual(output.shape      , _UpperCamelCase   )

              print(output[:, :3, :3]   )

              # TODO Replace values below with what was printed above.
              __snake_case       	=							tf.constant(
                  [
                      [
                          [-0.1205_3341, -1.026_4901, 0.2922_1946],
                          [-1.513_3783, 0.19_7433, 0.1519_0607],
                          [-5.013_5403, -3.90_0256, -0.8403_8764],
                      ]
                  ]   )
              tf.debugging.assert_near(output[:, :3, :3]      , _UpperCamelCase      , atol=1E-4   )






@require_tf
class     __snake_case    (				unittest.TestCase  ):
       """simple docstring"""







       __SCREAMING_SNAKE_CASE				    =							1e-4


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






              """simple docstring"""


              __snake_case       	=							tf.constant([[4, 10]]   )
              __snake_case       	=							TFRoFormerSinusoidalPositionalEmbedding(num_positions=6      , embedding_dim=6   )

              __snake_case       	=							emba(input_ids.shape   )
              __snake_case       	=							tf.constant(
                  [[0.0000, 0.0000, 0.0000, 1.0000, 1.0000, 1.0000], [0.8415, 0.0464, 0.0022, 0.5403, 0.9989, 1.0000]]   )

              tf.debugging.assert_near(_UpperCamelCase      , _UpperCamelCase      , atol=self.tolerance   )






       def 			a		(		self   )      ->							int:






              """simple docstring"""


              __snake_case       	=							tf.constant(
                  [
                      [0.0000, 0.0000, 0.0000, 0.0000, 0.0000],
                      [0.8415, 0.8219, 0.8020, 0.7819, 0.7617],
                      [0.9093, 0.9364, 0.9581, 0.9749, 0.9870],
                  ]   )
              __snake_case       	=							TFRoFormerSinusoidalPositionalEmbedding(num_positions=5_12      , embedding_dim=5_12   )
              emba([2, 16, 5_12]   )
              __snake_case       	=							emba.weight[:3, :5]

              tf.debugging.assert_near(_UpperCamelCase      , _UpperCamelCase      , atol=self.tolerance   )






@require_tf
class     __snake_case    (				unittest.TestCase  ):
       """simple docstring"""







       __SCREAMING_SNAKE_CASE				    =							1e-4


       def 			a		(		self   )      ->							Any:






              """simple docstring"""


              __snake_case       	=							tf.reshape(tf.range(2 * 12 * 16 * 64      , dtype=tf.floataa   )      , shape=(2, 12, 16, 64)   ) / 1_00

              __snake_case       	=							-tf.reshape(tf.range(2 * 12 * 16 * 64      , dtype=tf.floataa   )      , shape=(2, 12, 16, 64)   ) / 1_00

              __snake_case       	=							TFRoFormerSinusoidalPositionalEmbedding(num_positions=32      , embedding_dim=64   )
              __snake_case       	=							embed_positions([2, 16, 7_68]   )[None, None, :, :]

              __snake_case	,    __snake_case       	=							TFRoFormerSelfAttention.apply_rotary_position_embeddings(
                  _UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase   )

              __snake_case       	=							tf.constant(
                  [
                      [0.0000, 0.0100, 0.0200, 0.0300, 0.0400, 0.0500, 0.0600, 0.0700],
                      [-0.2012, 0.8897, 0.0263, 0.9401, 0.2074, 0.9463, 0.3481, 0.9343],
                      [-1.7057, 0.6271, -1.2145, 1.3897, -0.6303, 1.7647, -0.1173, 1.8985],
                      [-2.1731, -1.6397, -2.7358, 0.2854, -2.1840, 1.7183, -1.3018, 2.4871],
                      [0.2717, -3.6173, -2.9206, -2.1988, -3.6638, 0.3858, -2.9155, 2.2980],
                      [3.9859, -2.1580, -0.7984, -4.4904, -4.1181, -2.0252, -4.4782, 1.1253],
                  ]   )
              __snake_case       	=							tf.constant(
                  [
                      [0.0000, -0.0100, -0.0200, -0.0300, -0.0400, -0.0500, -0.0600, -0.0700],
                      [0.2012, -0.8897, -0.0263, -0.9401, -0.2074, -0.9463, -0.3481, -0.9343],
                      [1.7057, -0.6271, 1.2145, -1.3897, 0.6303, -1.7647, 0.1173, -1.8985],
                      [2.1731, 1.6397, 2.7358, -0.2854, 2.1840, -1.7183, 1.3018, -2.4871],
                      [-0.2717, 3.6173, 2.9206, 2.1988, 3.6638, -0.3858, 2.9155, -2.2980],
                      [-3.9859, 2.1580, 0.7984, 4.4904, 4.1181, 2.0252, 4.4782, -1.1253],
                  ]   )

              tf.debugging.assert_near(query_layer[0, 0, :6, :8]      , _UpperCamelCase      , atol=self.tolerance   )
              tf.debugging.assert_near(key_layer[0, 0, :6, :8]      , _UpperCamelCase      , atol=self.tolerance   )




from math import isqrt, loga







def 						lowerCamelCase__      ( __A    :int		):


       """simple docstring"""




       __snake_case       	=							[True] * max_number
       for i in range(2	,isqrt(max_number - 1		) + 1		):
              if is_prime[i]:
                     for j in range(i**2	,__A	,__A		):
                            __snake_case       	=							False

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







def 						lowerCamelCase__      ( __A    :int = 8_0_0_8_0_0	,__A    :int = 8_0_0_8_0_0		):


       """simple docstring"""




       __snake_case       	=							degree * loga(__A		)
       __snake_case       	=							int(__A		)
       __snake_case       	=							calculate_prime_numbers(__A		)

       __snake_case       	=							0
       __snake_case       	=							0
       __snake_case       	=							len(__A		) - 1
       while left < right:
              while (
                  prime_numbers[right] * loga(prime_numbers[left]		)
                  + prime_numbers[left] * loga(prime_numbers[right]		)
                  > upper_bound
              ):
                     right -= 1
              hybrid_integers_count += right - left
              left += 1

       return hybrid_integers_count


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

