"""simple docstring"""


from __future__ import annotations

import unittest

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

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


if is_tf_available():
 import numpy
 import tensorflow as tf

 from transformers.models.esm.modeling_tf_esm import (
     TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
     TFEsmForMaskedLM,
     TFEsmForSequenceClassification,
     TFEsmForTokenClassification,
     TFEsmModel,
 )






class    a__    :


   def __init__(     self,				_UpperCAmelCase,				):
        '''simple docstring'''



        lowercase__				 =							parent
        lowercase__				 =							13
        lowercase__				 =							7
        lowercase__				 =							True
        lowercase__				 =							True
        lowercase__				 =							True
        lowercase__				 =							99
        lowercase__				 =							32
        lowercase__				 =							2
        lowercase__				 =							4
        lowercase__				 =							37
        lowercase__				 =							"gelu"
        lowercase__				 =							0.1
        lowercase__				 =							0.1
        lowercase__				 =							512
        lowercase__				 =							16
        lowercase__				 =							2
        lowercase__				 =							0.02
        lowercase__				 =							3
        lowercase__				 =							4
        lowercase__				 =							None


   def 	snake_case__	(     self  ):
        '''simple docstring'''



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

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

        lowercase__				 =							None
        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.seq_length],				self.num_labels  )
             lowercase__				 =							ids_tensor([self.batch_size],				self.num_choices  )

        lowercase__				 =							EsmConfig(
            vocab_size=self.vocab_size,				hidden_size=self.hidden_size,				num_hidden_layers=self.num_hidden_layers,				pad_token_id=1,				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 config, input_ids, input_mask, sequence_labels, token_labels, choice_labels


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        (
            (
            lowercase__
        ) ,						(
            lowercase__
        ) ,						(
            lowercase__
        ) ,						(
            lowercase__
        ) ,						(
            lowercase__
        ) ,						(
            lowercase__
        ) ,						
        )				 =							self.prepare_config_and_inputs()

        lowercase__				 =							True
        lowercase__				 =							floats_tensor([self.batch_size, self.seq_length, self.hidden_size]  )
        lowercase__				 =							ids_tensor([self.batch_size, self.seq_length],				vocab_size=2  )

        return (
            config,
            input_ids,
            input_mask,
            sequence_labels,
            token_labels,
            choice_labels,
            encoder_hidden_states,
            encoder_attention_mask,
        )


   def 	snake_case__	(     self,				_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase  ):
        '''simple docstring'''



        lowercase__				 =							TFEsmModel(config=_UpperCAmelCase  )
        lowercase__				 =							{"input_ids": input_ids, "attention_mask": input_mask}
        lowercase__				 =							model(_UpperCAmelCase  )

        lowercase__				 =							[input_ids, input_mask]
        lowercase__				 =							model(_UpperCAmelCase  )

        lowercase__				 =							model(_UpperCAmelCase  )

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


   def 	snake_case__	(     self,				_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				):
        '''simple docstring'''



        lowercase__				 =							True

        lowercase__				 =							TFEsmModel(config=_UpperCAmelCase  )
        lowercase__				 =							{
            "input_ids": input_ids,
            "attention_mask": input_mask,
            "encoder_hidden_states": encoder_hidden_states,
            "encoder_attention_mask": encoder_attention_mask,
        }
        lowercase__				 =							model(_UpperCAmelCase  )

        lowercase__				 =							[input_ids, input_mask]
        lowercase__				 =							model(_UpperCAmelCase,				encoder_hidden_states=_UpperCAmelCase  )

        # Also check the case where encoder outputs are not passed
        lowercase__				 =							model(_UpperCAmelCase,				attention_mask=_UpperCAmelCase  )

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


   def 	snake_case__	(     self,				_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase  ):
        '''simple docstring'''



        lowercase__				 =							TFEsmForMaskedLM(config=_UpperCAmelCase  )
        lowercase__				 =							model([input_ids, input_mask]  )
        self.parent.assertEqual(result.logits.shape,				(self.batch_size, self.seq_length, self.vocab_size)  )


   def 	snake_case__	(     self,				_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase  ):
        '''simple docstring'''



        lowercase__				 =							self.num_labels
        lowercase__				 =							TFEsmForTokenClassification(config=_UpperCAmelCase  )
        lowercase__				 =							{"input_ids": input_ids, "attention_mask": input_mask}
        lowercase__				 =							model(_UpperCAmelCase  )
        self.parent.assertEqual(result.logits.shape,				(self.batch_size, self.seq_length, self.num_labels)  )


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							self.prepare_config_and_inputs()
        (
            (
            lowercase__
        ) ,						(
            lowercase__
        ) ,						(
            lowercase__
        ) ,						(
            lowercase__
        ) ,						(
            lowercase__
        ) ,						(
            lowercase__
        ) ,						
        )				 =							config_and_inputs
        lowercase__				 =							{"input_ids": input_ids, "attention_mask": input_mask}
        return config, inputs_dict






@require_tf
class    a__    (   _a					,					_a					,					unittest.TestCase	):
   snake_case_							=      (
       (
           TFEsmModel,
           TFEsmForMaskedLM,
           TFEsmForSequenceClassification,
           TFEsmForTokenClassification,
       )
       if is_tf_available()
       else ()
   )
   snake_case_							=      (
       {
           "feature-extraction": TFEsmModel,
           "fill-mask": TFEsmForMaskedLM,
           "text-classification": TFEsmForSequenceClassification,
           "token-classification": TFEsmForTokenClassification,
           "zero-shot": TFEsmForSequenceClassification,
       }
       if is_tf_available()
       else {}
   )
   snake_case_							=      False
   snake_case_							=      False


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							TFEsmModelTester(self  )
        lowercase__				 =							ConfigTester(self,				config_class=_UpperCAmelCase,				hidden_size=37  )


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        self.config_tester.run_common_tests()


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*_UpperCAmelCase  )


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							self.model_tester.prepare_config_and_inputs_for_decoder()
        self.model_tester.create_and_check_model_as_decoder(*_UpperCAmelCase  )


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_lm(*_UpperCAmelCase  )


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_token_classification(*_UpperCAmelCase  )


   @slow
   def 	snake_case__	(     self  ):
        '''simple docstring'''



        for model_name in TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             lowercase__				 =							TFEsmModel.from_pretrained(_UpperCAmelCase  )
             self.assertIsNotNone(_UpperCAmelCase  )


   @unittest.skip("Protein models do not support embedding resizing."  )
   def 	snake_case__	(     self  ):
        '''simple docstring'''



        pass


   @unittest.skip("Protein models do not support embedding resizing."  )
   def 	snake_case__	(     self  ):
        '''simple docstring'''



        pass


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__ ,						lowercase__				 =							self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
             lowercase__				 =							model_class(_UpperCAmelCase  )
             assert isinstance(model.get_input_embeddings(),				tf.keras.layers.Layer  )
             if model_class is TFEsmForMaskedLM:
                  # Output embedding test differs from the main test because they're a matrix, not a layer
                  lowercase__				 =							model.get_bias()
                  assert isinstance(_UpperCAmelCase,				_UpperCAmelCase  )
                  for k, v in name.items():
                       assert isinstance(_UpperCAmelCase,				tf.Variable  )
             else:
                  lowercase__				 =							model.get_output_embeddings()
                  assert x is None
                  lowercase__				 =							model.get_bias()
                  assert name is None






@require_tf
class    a__    (   unittest.TestCase	):


   @slow
   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							TFEsmForMaskedLM.from_pretrained("facebook/esm2_t6_8M_UR50D"  )

        lowercase__				 =							tf.constant([[0, 1, 2, 3, 4, 5]]  )
        lowercase__				 =							model(_UpperCAmelCase  )[0]
        lowercase__				 =							[1, 6, 33]
        self.assertEqual(list(output.numpy().shape  ),				_UpperCAmelCase  )
        # compare the actual values for a slice.
        lowercase__				 =							tf.constant(
            [
                [
                    [8.921_518, -10.589_814, -6.4_671_307],
                    [-6.3_967_156, -13.911_377, -1.1_211_915],
                    [-7.781_247, -13.951_557, -3.740_592],
                ]
            ]  )
        self.assertTrue(numpy.allclose(output[:, :3, :3].numpy(),				expected_slice.numpy(),				atol=1E-2  )  )


   @slow
   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							TFEsmModel.from_pretrained("facebook/esm2_t6_8M_UR50D"  )

        lowercase__				 =							tf.constant([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]]  )
        lowercase__				 =							model(_UpperCAmelCase  )[0]
        # compare the actual values for a slice.
        lowercase__				 =							tf.constant(
            [
                [
                    [0.14_443_092, 0.54_125_327, 0.3_247_739],
                    [0.30_340_484, 0.00_526_676, 0.31_077_722],
                    [0.32_278_043, -0.24_987_096, 0.3_414_628],
                ]
            ]  )
        self.assertTrue(numpy.allclose(output[:, :3, :3].numpy(),				expected_slice.numpy(),				atol=1E-4  )  )





"""simple docstring"""


from __future__ import annotations

import math







def 					__a   (		A      ):






     '''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


lowerCAmelCase_:					Optional[Any]     =					[num for num in range(3, 1_0_0_0_0_1, 2) if not is_prime(num)]







def 					__a   (		A      ):






     '''simple docstring'''



     if not isinstance(A    ,				A      ):
          raise ValueError("n must be an integer"      )
     if n <= 0:
          raise ValueError("n must be >= 0"      )

     lowercase__				 =							[]
     for num in range(len(A      )      ):
          lowercase__				 =							0
          while 2 * i * i <= odd_composites[num]:
               lowercase__				 =							odd_composites[num] - 2 * i * i
               if is_prime(A      ):
                    break
               i += 1
          else:
               list_nums.append(odd_composites[num]      )
               if len(A      ) == n:
                    return list_nums

     return []







def 					__a   (		):






     '''simple docstring'''



     return compute_nums(1      )[0]


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




