

'''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 						lowerCAmelCase__   :



 """simple docstring"""



 def __init__(		self					:       Any					,							__SCREAMING_SNAKE_CASE					:       str					,							)  -> Tuple:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      parent
      __SCREAMING_SNAKE_CASE     =      13
      __SCREAMING_SNAKE_CASE     =      7
      __SCREAMING_SNAKE_CASE     =      True
      __SCREAMING_SNAKE_CASE     =      True
      __SCREAMING_SNAKE_CASE     =      True
      __SCREAMING_SNAKE_CASE     =      99
      __SCREAMING_SNAKE_CASE     =      32
      __SCREAMING_SNAKE_CASE     =      2
      __SCREAMING_SNAKE_CASE     =      4
      __SCREAMING_SNAKE_CASE     =      37
      __SCREAMING_SNAKE_CASE     =      """gelu"""
      __SCREAMING_SNAKE_CASE     =      0.1
      __SCREAMING_SNAKE_CASE     =      0.1
      __SCREAMING_SNAKE_CASE     =      512
      __SCREAMING_SNAKE_CASE     =      16
      __SCREAMING_SNAKE_CASE     =      2
      __SCREAMING_SNAKE_CASE     =      0.02
      __SCREAMING_SNAKE_CASE     =      3
      __SCREAMING_SNAKE_CASE     =      4
      __SCREAMING_SNAKE_CASE     =      None
 def       UpperCAmelCase__		(		self					:       Optional[Any]			)  -> str:


      """simple docstring"""




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

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

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

      __SCREAMING_SNAKE_CASE     =      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       UpperCAmelCase__		(		self					:       Union[str, Any]			)  -> Union[str, Any]:


      """simple docstring"""




      (
          (
          __SCREAMING_SNAKE_CASE
      )   ,			(
          __SCREAMING_SNAKE_CASE
      )   ,			(
          __SCREAMING_SNAKE_CASE
      )   ,			(
          __SCREAMING_SNAKE_CASE
      )   ,			(
          __SCREAMING_SNAKE_CASE
      )   ,			(
          __SCREAMING_SNAKE_CASE
      )   ,			
      )     =      self.prepare_config_and_inputs()

      __SCREAMING_SNAKE_CASE     =      True
      __SCREAMING_SNAKE_CASE     =      floats_tensor([self.batch_size, self.seq_length, self.hidden_size]			)
      __SCREAMING_SNAKE_CASE     =      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       UpperCAmelCase__		(		self					:       List[Any]					,							__SCREAMING_SNAKE_CASE					:       Tuple					,							__SCREAMING_SNAKE_CASE					:       str					,							__SCREAMING_SNAKE_CASE					:       str					,							__SCREAMING_SNAKE_CASE					:       Tuple					,							__SCREAMING_SNAKE_CASE					:       Tuple					,							__SCREAMING_SNAKE_CASE					:       int			)  -> List[Any]:


      """simple docstring"""




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

      __SCREAMING_SNAKE_CASE     =      [input_ids, input_mask]
      __SCREAMING_SNAKE_CASE     =      model(__SCREAMING_SNAKE_CASE			)

      __SCREAMING_SNAKE_CASE     =      model(__SCREAMING_SNAKE_CASE			)

      self.parent.assertEqual(result.last_hidden_state.shape					,							(self.batch_size, self.seq_length, self.hidden_size)			)
 def       UpperCAmelCase__		(		self					:       Union[str, Any]					,							__SCREAMING_SNAKE_CASE					:       Any					,							__SCREAMING_SNAKE_CASE					:       List[Any]					,							__SCREAMING_SNAKE_CASE					:       Union[str, Any]					,							__SCREAMING_SNAKE_CASE					:       Dict					,							__SCREAMING_SNAKE_CASE					:       Union[str, Any]					,							__SCREAMING_SNAKE_CASE					:       Union[str, Any]					,							__SCREAMING_SNAKE_CASE					:       List[Any]					,							__SCREAMING_SNAKE_CASE					:       Optional[Any]					,							)  -> int:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      True

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

      __SCREAMING_SNAKE_CASE     =      [input_ids, input_mask]
      __SCREAMING_SNAKE_CASE     =      model(__SCREAMING_SNAKE_CASE					,							encoder_hidden_states=__SCREAMING_SNAKE_CASE			)

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

      self.parent.assertEqual(result.last_hidden_state.shape					,							(self.batch_size, self.seq_length, self.hidden_size)			)
 def       UpperCAmelCase__		(		self					:       Optional[int]					,							__SCREAMING_SNAKE_CASE					:       str					,							__SCREAMING_SNAKE_CASE					:       Optional[Any]					,							__SCREAMING_SNAKE_CASE					:       int					,							__SCREAMING_SNAKE_CASE					:       Optional[Any]					,							__SCREAMING_SNAKE_CASE					:       int					,							__SCREAMING_SNAKE_CASE					:       List[Any]			)  -> int:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      TFEsmForMaskedLM(config=__SCREAMING_SNAKE_CASE			)
      __SCREAMING_SNAKE_CASE     =      model([input_ids, input_mask]			)
      self.parent.assertEqual(result.logits.shape					,							(self.batch_size, self.seq_length, self.vocab_size)			)
 def       UpperCAmelCase__		(		self					:       Optional[Any]					,							__SCREAMING_SNAKE_CASE					:       Any					,							__SCREAMING_SNAKE_CASE					:       Optional[int]					,							__SCREAMING_SNAKE_CASE					:       Dict					,							__SCREAMING_SNAKE_CASE					:       Union[str, Any]					,							__SCREAMING_SNAKE_CASE					:       List[str]					,							__SCREAMING_SNAKE_CASE					:       int			)  -> List[str]:


      """simple docstring"""




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


 def       UpperCAmelCase__		(		self					:       List[Any]			)  -> int:


      """simple docstring"""




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







@require_tf
class 						lowerCAmelCase__   (      a ,					a ,					unittest.TestCase					):



 """simple docstring"""



 lowerCAmelCase__  					=      (
     (
         TFEsmModel,
         TFEsmForMaskedLM,
         TFEsmForSequenceClassification,
         TFEsmForTokenClassification,
     )
     if is_tf_available()
     else ()
 )
 lowerCAmelCase__  					=      (
     {
         "feature-extraction": TFEsmModel,
         "fill-mask": TFEsmForMaskedLM,
         "text-classification": TFEsmForSequenceClassification,
         "token-classification": TFEsmForTokenClassification,
         "zero-shot": TFEsmForSequenceClassification,
     }
     if is_tf_available()
     else {}
 )
 lowerCAmelCase__  					=      False
 lowerCAmelCase__  					=      False
 def       UpperCAmelCase__		(		self					:       Dict			)  -> Any:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      TFEsmModelTester(self			)
      __SCREAMING_SNAKE_CASE     =      ConfigTester(self					,							config_class=__SCREAMING_SNAKE_CASE					,							hidden_size=37			)
 def       UpperCAmelCase__		(		self					:       Optional[Any]			)  -> Any:


      """simple docstring"""




      self.config_tester.run_common_tests()
 def       UpperCAmelCase__		(		self					:       List[str]			)  -> List[str]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE			)
 def       UpperCAmelCase__		(		self					:       Any			)  -> Optional[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      self.model_tester.prepare_config_and_inputs_for_decoder()
      self.model_tester.create_and_check_model_as_decoder(*__SCREAMING_SNAKE_CASE			)
 def       UpperCAmelCase__		(		self					:       Optional[Any]			)  -> int:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_masked_lm(*__SCREAMING_SNAKE_CASE			)
 def       UpperCAmelCase__		(		self					:       Union[str, Any]			)  -> Tuple:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_token_classification(*__SCREAMING_SNAKE_CASE			)
 @slow
 def       UpperCAmelCase__		(		self					:       str			)  -> int:


      """simple docstring"""




      for model_name in TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           __SCREAMING_SNAKE_CASE     =      TFEsmModel.from_pretrained(__SCREAMING_SNAKE_CASE			)
           self.assertIsNotNone(__SCREAMING_SNAKE_CASE			)
 @unittest.skip("""Protein models do not support embedding resizing."""			)
 def       UpperCAmelCase__		(		self					:       Optional[Any]			)  -> Tuple:


      """simple docstring"""




      pass
 @unittest.skip("""Protein models do not support embedding resizing."""			)
 def       UpperCAmelCase__		(		self					:       List[Any]			)  -> Tuple:


      """simple docstring"""




      pass


 def       UpperCAmelCase__		(		self					:       str			)  -> Tuple:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE     =      self.model_tester.prepare_config_and_inputs_for_common()

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







@require_tf
class 						lowerCAmelCase__   (      unittest.TestCase					):



 """simple docstring"""



 @slow
 def       UpperCAmelCase__		(		self					:       Tuple			)  -> Any:


      """simple docstring"""




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

      __SCREAMING_SNAKE_CASE     =      tf.constant([[0, 1, 2, 3, 4, 5]]			)
      __SCREAMING_SNAKE_CASE     =      model(__SCREAMING_SNAKE_CASE			)[0]
      __SCREAMING_SNAKE_CASE     =      [1, 6, 33]
      self.assertEqual(list(output.numpy().shape			)					,							__SCREAMING_SNAKE_CASE			)
      # compare the actual values for a slice.
      __SCREAMING_SNAKE_CASE     =      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       UpperCAmelCase__		(		self					:       Optional[Any]			)  -> str:


      """simple docstring"""




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

      __SCREAMING_SNAKE_CASE     =      tf.constant([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]]			)
      __SCREAMING_SNAKE_CASE     =      model(__SCREAMING_SNAKE_CASE			)[0]
      # compare the actual values for a slice.
      __SCREAMING_SNAKE_CASE     =      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			)			)









'''simple docstring'''



from itertools import count







def    a__       (  a__ = 50						):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     =      [1] * min_block_length

     for n in count(a__						):
          fill_count_functions.append(1						)

          for block_length in range(a__      ,    n + 1						):
               for block_start in range(n - block_length						):
                    fill_count_functions[n] += fill_count_functions[
                        n - block_start - block_length - 1
                    ]

               fill_count_functions[n] += 1

          if fill_count_functions[n] > 1_00_00_00:
               break

     return n


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






