


"""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 __snake_case		:







   """simple docstring"""




   def __init__(		self				:List[str]					,	UpperCamelCase__				:List[str]					,	):
          _a							 =					parent
          _a							 =					13
          _a							 =					7
          _a							 =					True
          _a							 =					True
          _a							 =					True
          _a							 =					99
          _a							 =					32
          _a							 =					2
          _a							 =					4
          _a							 =					37
          _a							 =					"gelu"
          _a							 =					0.1
          _a							 =					0.1
          _a							 =					512
          _a							 =					16
          _a							 =					2
          _a							 =					0.02
          _a							 =					3
          _a							 =					4
          _a							 =					None




   def SCREAMING_SNAKE_CASE_   (		self				:List[Any]							):
          _a							 =					ids_tensor([self.batch_size, self.seq_length]					,	self.vocab_size							)

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

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

          _a							 =					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 SCREAMING_SNAKE_CASE_   (		self				:int							):
          (
              (
              _a			
          ) ,			(
              _a			
          ) ,			(
              _a			
          ) ,			(
              _a			
          ) ,			(
              _a			
          ) ,			(
              _a			
          ) ,						
          )							 =					self.prepare_config_and_inputs()

          _a							 =					True
          _a							 =					floats_tensor([self.batch_size, self.seq_length, self.hidden_size]							)
          _a							 =					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 SCREAMING_SNAKE_CASE_   (		self				:str					,	UpperCamelCase__				:Any					,	UpperCamelCase__				:Optional[int]					,	UpperCamelCase__				:List[str]					,	UpperCamelCase__				:str					,	UpperCamelCase__				:List[Any]					,	UpperCamelCase__				:Tuple							):
          _a							 =					TFEsmModel(config=snake_case_							)
          _a							 =					{"input_ids": input_ids, "attention_mask": input_mask}
          _a							 =					model(snake_case_							)

          _a							 =					[input_ids, input_mask]
          _a							 =					model(snake_case_							)

          _a							 =					model(snake_case_							)

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




   def SCREAMING_SNAKE_CASE_   (		self				:List[Any]					,	UpperCamelCase__				:List[Any]					,	UpperCamelCase__				:Union[str, Any]					,	UpperCamelCase__				:List[Any]					,	UpperCamelCase__				:Tuple					,	UpperCamelCase__				:Optional[int]					,	UpperCamelCase__				:Tuple					,	UpperCamelCase__				:Tuple					,	UpperCamelCase__				:int					,	):
          _a							 =					True

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

          _a							 =					[input_ids, input_mask]
          _a							 =					model(snake_case_					,	encoder_hidden_states=snake_case_							)

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

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




   def SCREAMING_SNAKE_CASE_   (		self				:Tuple					,	UpperCamelCase__				:Union[str, Any]					,	UpperCamelCase__				:str					,	UpperCamelCase__				:Optional[int]					,	UpperCamelCase__				:Optional[Any]					,	UpperCamelCase__				:Optional[Any]					,	UpperCamelCase__				:Any							):
          _a							 =					TFEsmForMaskedLM(config=snake_case_							)
          _a							 =					model([input_ids, input_mask]							)
          self.parent.assertEqual(result.logits.shape					,	(self.batch_size, self.seq_length, self.vocab_size)							)




   def SCREAMING_SNAKE_CASE_   (		self				:List[Any]					,	UpperCamelCase__				:Union[str, Any]					,	UpperCamelCase__				:List[str]					,	UpperCamelCase__				:Any					,	UpperCamelCase__				:Dict					,	UpperCamelCase__				:Dict					,	UpperCamelCase__				:List[str]							):
          _a							 =					self.num_labels
          _a							 =					TFEsmForTokenClassification(config=snake_case_							)
          _a							 =					{"input_ids": input_ids, "attention_mask": input_mask}
          _a							 =					model(snake_case_							)
          self.parent.assertEqual(result.logits.shape					,	(self.batch_size, self.seq_length, self.num_labels)							)




   def SCREAMING_SNAKE_CASE_   (		self				:Union[str, Any]							):
          _a							 =					self.prepare_config_and_inputs()
          (
              (
              _a			
          ) ,			(
              _a			
          ) ,			(
              _a			
          ) ,			(
              _a			
          ) ,			(
              _a			
          ) ,			(
              _a			
          ) ,						
          )							 =					config_and_inputs
          _a							 =					{"input_ids": input_ids, "attention_mask": input_mask}
          return config, inputs_dict






@require_tf
class __snake_case		(       __snake_case					,							__snake_case					,							unittest.TestCase   ):







   """simple docstring"""




   lowerCAmelCase_					: Optional[Any]  						=      (
       (
           TFEsmModel,
           TFEsmForMaskedLM,
           TFEsmForSequenceClassification,
           TFEsmForTokenClassification,
       )
       if is_tf_available()
       else ()
   )
   lowerCAmelCase_					: Optional[Any]  						=      (
       {
           'feature-extraction': TFEsmModel,
           'fill-mask': TFEsmForMaskedLM,
           'text-classification': TFEsmForSequenceClassification,
           'token-classification': TFEsmForTokenClassification,
           'zero-shot': TFEsmForSequenceClassification,
       }
       if is_tf_available()
       else {}
   )
   lowerCAmelCase_					: Union[str, Any]  						=      False
   lowerCAmelCase_					: int  						=      False




   def SCREAMING_SNAKE_CASE_   (		self				:Any							):
          _a							 =					TFEsmModelTester(self							)
          _a							 =					ConfigTester(self					,	config_class=snake_case_					,	hidden_size=37							)




   def SCREAMING_SNAKE_CASE_   (		self				:int							):
          self.config_tester.run_common_tests()




   def SCREAMING_SNAKE_CASE_   (		self				:Any							):
          _a							 =					self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*snake_case_							)




   def SCREAMING_SNAKE_CASE_   (		self				:str							):
          _a							 =					self.model_tester.prepare_config_and_inputs_for_decoder()
          self.model_tester.create_and_check_model_as_decoder(*snake_case_							)




   def SCREAMING_SNAKE_CASE_   (		self				:List[Any]							):
          _a							 =					self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_masked_lm(*snake_case_							)




   def SCREAMING_SNAKE_CASE_   (		self				:List[str]							):
          _a							 =					self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_token_classification(*snake_case_							)




   @slow
   def SCREAMING_SNAKE_CASE_   (		self				:str							):
          for model_name in TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 _a							 =					TFEsmModel.from_pretrained(snake_case_							)
                 self.assertIsNotNone(snake_case_							)




   @unittest.skip("Protein models do not support embedding resizing."							)
   def SCREAMING_SNAKE_CASE_   (		self				:Any							):
          pass




   @unittest.skip("Protein models do not support embedding resizing."							)
   def SCREAMING_SNAKE_CASE_   (		self				:Any							):
          pass




   def SCREAMING_SNAKE_CASE_   (		self				:List[Any]							):
          _a ,			_a							 =					self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
                 _a							 =					model_class(snake_case_							)
                 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
                        _a							 =					model.get_bias()
                        assert isinstance(snake_case_					,	snake_case_							)
                        for k, v in name.items():
                               assert isinstance(snake_case_					,	tf.Variable							)
                 else:
                        _a							 =					model.get_output_embeddings()
                        assert x is None
                        _a							 =					model.get_bias()
                        assert name is None






@require_tf
class __snake_case		(       unittest.TestCase   ):







   """simple docstring"""




   @slow
   def SCREAMING_SNAKE_CASE_   (		self				:Dict							):
          _a							 =					TFEsmForMaskedLM.from_pretrained("facebook/esm2_t6_8M_UR50D"							)

          _a							 =					tf.constant([[0, 1, 2, 3, 4, 5]]							)
          _a							 =					model(snake_case_							)[0]
          _a							 =					[1, 6, 33]
          self.assertEqual(list(output.numpy().shape							)					,	snake_case_							)
          # compare the actual values for a slice.
          _a							 =					tf.constant(
              [
                  [
                      [8.921518, -10.589814, -6.4671307],
                      [-6.3967156, -13.911377, -1.1211915],
                      [-7.781247, -13.951557, -3.740592],
                  ]
              ]							)
          self.assertTrue(numpy.allclose(output[:, :3, :3].numpy()					,	expected_slice.numpy()					,	atol=1E-2							)							)




   @slow
   def SCREAMING_SNAKE_CASE_   (		self				:Any							):
          _a							 =					TFEsmModel.from_pretrained("facebook/esm2_t6_8M_UR50D"							)

          _a							 =					tf.constant([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]]							)
          _a							 =					model(snake_case_							)[0]
          # compare the actual values for a slice.
          _a							 =					tf.constant(
              [
                  [
                      [0.14443092, 0.54125327, 0.3247739],
                      [0.30340484, 0.00526676, 0.31077722],
                      [0.32278043, -0.24987096, 0.3414628],
                  ]
              ]							)
          self.assertTrue(numpy.allclose(output[:, :3, :3].numpy()					,	expected_slice.numpy()					,	atol=1E-4							)							)







from __future__ import annotations

from fractions import Fraction
from math import gcd, sqrt





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE				)							-> bool:




    """simple docstring"""




    _A     =							int(number**0.5				)
    return number == sq * sq





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> tuple[int, int]:




    """simple docstring"""




    _A     =							x_num * y_den * z_den + y_num * x_den * z_den + z_num * x_den * y_den
    _A     =							x_den * y_den * z_den
    _A     =							gcd(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)
    top //= hcf
    bottom //= hcf
    return top, bottom





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE = 35				)							-> int:




    """simple docstring"""




    _A     =							set()
    _A     =							42
    _A     =							Fraction(0				)
    _A     =							42

    for x_num in range(1							,     order + 1				):
        for x_den in range(x_num + 1							,     order + 1				):
            for y_num in range(1							,     order + 1				):
                for y_den in range(y_num + 1							,     order + 1				):
                    # n=1
                    _A     =							x_num * y_den + x_den * y_num
                    _A     =							x_den * y_den
                    _A     =							gcd(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)
                    z_num //= hcf
                    z_den //= hcf
                    if 0 < z_num < z_den <= order:
                        _A     =							add_three(
                            _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)
                        unique_s.add(_SCREAMING_SNAKE_CASE				)

                    # n=2
                    _A     =							(
                        x_num * x_num * y_den * y_den + x_den * x_den * y_num * y_num
                    )
                    _A     =							x_den * x_den * y_den * y_den
                    if is_sq(_SCREAMING_SNAKE_CASE				) and is_sq(_SCREAMING_SNAKE_CASE				):
                        _A     =							int(sqrt(_SCREAMING_SNAKE_CASE				)				)
                        _A     =							int(sqrt(_SCREAMING_SNAKE_CASE				)				)
                        _A     =							gcd(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)
                        z_num //= hcf
                        z_den //= hcf
                        if 0 < z_num < z_den <= order:
                            _A     =							add_three(
                                _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)
                            unique_s.add(_SCREAMING_SNAKE_CASE				)

                    # n=-1
                    _A     =							x_num * y_num
                    _A     =							x_den * y_num + x_num * y_den
                    _A     =							gcd(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)
                    z_num //= hcf
                    z_den //= hcf
                    if 0 < z_num < z_den <= order:
                        _A     =							add_three(
                            _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)
                        unique_s.add(_SCREAMING_SNAKE_CASE				)

                    # n=2
                    _A     =							x_num * x_num * y_num * y_num
                    _A     =							(
                        x_den * x_den * y_num * y_num + x_num * x_num * y_den * y_den
                    )
                    if is_sq(_SCREAMING_SNAKE_CASE				) and is_sq(_SCREAMING_SNAKE_CASE				):
                        _A     =							int(sqrt(_SCREAMING_SNAKE_CASE				)				)
                        _A     =							int(sqrt(_SCREAMING_SNAKE_CASE				)				)
                        _A     =							gcd(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)
                        z_num //= hcf
                        z_den //= hcf
                        if 0 < z_num < z_den <= order:
                            _A     =							add_three(
                                _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)
                            unique_s.add(_SCREAMING_SNAKE_CASE				)

    for num, den in unique_s:
        total += Fraction(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)

    return total.denominator + total.numerator


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





