






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  _lowercase   :
      def __init__(     self				,			a				,			):
       snake_case__  :    List[str]							       =parent
       snake_case__  :    Any							       =1_3
       snake_case__  :    Optional[int]							       =7
       snake_case__  :    int							       =True
       snake_case__  :    List[Any]							       =True
       snake_case__  :    Optional[int]							       =True
       snake_case__  :    Union[str, Any]							       =9_9
       snake_case__  :    List[Any]							       =3_2
       snake_case__  :    Tuple							       =2
       snake_case__  :    Union[str, Any]							       =4
       snake_case__  :    List[Any]							       =3_7
       snake_case__  :    str							       ="""gelu"""
       snake_case__  :    int							       =0.1
       snake_case__  :    str							       =0.1
       snake_case__  :    List[Any]							       =5_1_2
       snake_case__  :    List[str]							       =1_6
       snake_case__  :    str							       =2
       snake_case__  :    Union[str, Any]							       =0.02
       snake_case__  :    str							       =3
       snake_case__  :    Any							       =4
       snake_case__  :    int							       =None
      def 						lowercase__					(     self   ):
       snake_case__  :    List[str]							       =ids_tensor([self.batch_size, self.seq_length]				,			self.vocab_size   )

       snake_case__  :    Union[str, Any]							       =None
       if self.use_input_mask:
        snake_case__  :    Optional[int]							       =random_attention_mask([self.batch_size, self.seq_length]   )

       snake_case__  :    List[str]							       =None
       snake_case__  :    Optional[Any]							       =None
       snake_case__  :    Dict							       =None
       if self.use_labels:
        snake_case__  :    Tuple							       =ids_tensor([self.batch_size]				,			self.type_sequence_label_size   )
        snake_case__  :    List[Any]							       =ids_tensor([self.batch_size, self.seq_length]				,			self.num_labels   )
        snake_case__  :    int							       =ids_tensor([self.batch_size]				,			self.num_choices   )

       snake_case__  :    Any							       =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 						lowercase__					(     self   ):
       (
           (
           snake_case__
       )       ,   (
           snake_case__
       )       ,   (
           snake_case__
       )       ,   (
           snake_case__
       )       ,   (
           snake_case__
       )       ,   (
           snake_case__
       )       ,   
       )  :    int							       =self.prepare_config_and_inputs()

       snake_case__  :    Any							       =True
       snake_case__  :    int							       =floats_tensor([self.batch_size, self.seq_length, self.hidden_size]   )
       snake_case__  :    Tuple							       =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 						lowercase__					(     self				,			a				,			a				,			a				,			a				,			a				,			a   ):
       snake_case__  :    Optional[Any]							       =TFEsmModel(config=a   )
       snake_case__  :    Any							       ={"""input_ids""": input_ids, """attention_mask""": input_mask}
       snake_case__  :    Dict							       =model(a   )

       snake_case__  :    Tuple							       =[input_ids, input_mask]
       snake_case__  :    Optional[int]							       =model(a   )

       snake_case__  :    Union[str, Any]							       =model(a   )

       self.parent.assertEqual(result.last_hidden_state.shape				,			(self.batch_size, self.seq_length, self.hidden_size)   )
      def 						lowercase__					(     self				,			a				,			a				,			a				,			a				,			a				,			a				,			a				,			a				,			):
       snake_case__  :    str							       =True

       snake_case__  :    Optional[int]							       =TFEsmModel(config=a   )
       snake_case__  :    Optional[int]							       ={
           """input_ids""": input_ids,
           """attention_mask""": input_mask,
           """encoder_hidden_states""": encoder_hidden_states,
           """encoder_attention_mask""": encoder_attention_mask,
       }
       snake_case__  :    Tuple							       =model(a   )

       snake_case__  :    Optional[int]							       =[input_ids, input_mask]
       snake_case__  :    Dict							       =model(a				,			encoder_hidden_states=a   )

       # Also check the case where encoder outputs are not passed
       snake_case__  :    List[str]							       =model(a				,			attention_mask=a   )

       self.parent.assertEqual(result.last_hidden_state.shape				,			(self.batch_size, self.seq_length, self.hidden_size)   )
      def 						lowercase__					(     self				,			a				,			a				,			a				,			a				,			a				,			a   ):
       snake_case__  :    Dict							       =TFEsmForMaskedLM(config=a   )
       snake_case__  :    Optional[Any]							       =model([input_ids, input_mask]   )
       self.parent.assertEqual(result.logits.shape				,			(self.batch_size, self.seq_length, self.vocab_size)   )
      def 						lowercase__					(     self				,			a				,			a				,			a				,			a				,			a				,			a   ):
       snake_case__  :    int							       =self.num_labels
       snake_case__  :    List[str]							       =TFEsmForTokenClassification(config=a   )
       snake_case__  :    Any							       ={"""input_ids""": input_ids, """attention_mask""": input_mask}
       snake_case__  :    List[Any]							       =model(a   )
       self.parent.assertEqual(result.logits.shape				,			(self.batch_size, self.seq_length, self.num_labels)   )



      def 						lowercase__					(     self   ):
       snake_case__  :    int							       =self.prepare_config_and_inputs()
       (
           (
           snake_case__
       )       ,   (
           snake_case__
       )       ,   (
           snake_case__
       )       ,   (
           snake_case__
       )       ,   (
           snake_case__
       )       ,   (
           snake_case__
       )       ,   
       )  :    List[Any]							       =config_and_inputs
       snake_case__  :    List[Any]							       ={"""input_ids""": input_ids, """attention_mask""": input_mask}
       return config, inputs_dict







@require_tf
class  _lowercase   (   _A						,				_A						,				unittest.TestCase						):
      _a							:						str										=							(
          (
              TFEsmModel,
              TFEsmForMaskedLM,
              TFEsmForSequenceClassification,
              TFEsmForTokenClassification,
          )
          if is_tf_available()
          else ()
      )
      _a							:						int										=							(
          {
              'feature-extraction': TFEsmModel,
              'fill-mask': TFEsmForMaskedLM,
              'text-classification': TFEsmForSequenceClassification,
              'token-classification': TFEsmForTokenClassification,
              'zero-shot': TFEsmForSequenceClassification,
          }
          if is_tf_available()
          else {}
      )
      _a							:						int										=							False
      _a							:						Tuple										=							False
      def 						lowercase__					(     self   ):
       snake_case__  :    Optional[int]							       =TFEsmModelTester(self   )
       snake_case__  :    Tuple							       =ConfigTester(self				,			config_class=a				,			hidden_size=3_7   )
      def 						lowercase__					(     self   ):
       self.config_tester.run_common_tests()
      def 						lowercase__					(     self   ):
       snake_case__  :    int							       =self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*a   )
      def 						lowercase__					(     self   ):
       snake_case__  :    int							       =self.model_tester.prepare_config_and_inputs_for_decoder()
       self.model_tester.create_and_check_model_as_decoder(*a   )
      def 						lowercase__					(     self   ):
       snake_case__  :    Tuple							       =self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_lm(*a   )
      def 						lowercase__					(     self   ):
       snake_case__  :    List[Any]							       =self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_token_classification(*a   )
      @slow
      def 						lowercase__					(     self   ):
       for model_name in TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        snake_case__  :    Dict							       =TFEsmModel.from_pretrained(a   )
        self.assertIsNotNone(a   )
      @unittest.skip("""Protein models do not support embedding resizing."""   )
      def 						lowercase__					(     self   ):
       pass
      @unittest.skip("""Protein models do not support embedding resizing."""   )
      def 						lowercase__					(     self   ):
       pass



      def 						lowercase__					(     self   ):
       snake_case__       ,   snake_case__  :    str							       =self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
        snake_case__  :    List[str]							       =model_class(a   )
        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
         snake_case__  :    int							       =model.get_bias()
         assert isinstance(a				,			a   )
         for k, v in name.items():
          assert isinstance(a				,			tf.Variable   )
        else:
         snake_case__  :    List[Any]							       =model.get_output_embeddings()
         assert x is None
         snake_case__  :    str							       =model.get_bias()
         assert name is None







@require_tf
class  _lowercase   (   unittest.TestCase						):
      @slow
      def 						lowercase__					(     self   ):
       snake_case__  :    Tuple							       =TFEsmForMaskedLM.from_pretrained("""facebook/esm2_t6_8M_UR50D"""   )

       snake_case__  :    Optional[Any]							       =tf.constant([[0, 1, 2, 3, 4, 5]]   )
       snake_case__  :    Tuple							       =model(a   )[0]
       snake_case__  :    Optional[Any]							       =[1, 6, 3_3]
       self.assertEqual(list(output.numpy().shape   )				,			a   )
       # compare the actual values for a slice.
       snake_case__  :    List[str]							       =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 						lowercase__					(     self   ):
       snake_case__  :    Optional[int]							       =TFEsmModel.from_pretrained("""facebook/esm2_t6_8M_UR50D"""   )

       snake_case__  :    Tuple							       =tf.constant([[0, 6, 4, 1_3, 5, 4, 1_6, 1_2, 1_1, 7, 2]]   )
       snake_case__  :    Union[str, Any]							       =model(a   )[0]
       # compare the actual values for a slice.
       snake_case__  :    Any							       =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   )   )













import math







def 			A__							(      _a  :					list		,  _a  :					int = 0		,  _a  :					int = 0	):





 '''simple docstring'''







 snake_case__  :    Tuple							       =end or len(_a	)
 for i in range(_a		,  _a	):
  snake_case__  :    int							       =i
  snake_case__  :    int							       =array[i]
  while temp_index != start and temp_index_value < array[temp_index - 1]:
   snake_case__  :    Dict							       =array[temp_index - 1]
   temp_index -= 1
  snake_case__  :    Dict							       =temp_index_value
 return array







def 			A__							(      _a  :					list		,  _a  :					int		,  _a  :					int	):  # Max Heap





 '''simple docstring'''







 snake_case__  :    Tuple							       =index
 snake_case__  :    Dict							       =2 * index + 1  # Left Node
 snake_case__  :    Union[str, Any]							       =2 * index + 2  # Right Node

 if left_index < heap_size and array[largest] < array[left_index]:
  snake_case__  :    Dict							       =left_index

 if right_index < heap_size and array[largest] < array[right_index]:
  snake_case__  :    List[Any]							       =right_index

 if largest != index:
  snake_case__       ,   snake_case__  :    List[Any]							       =array[largest], array[index]
  heapify(_a		,  _a		,  _a	)







def 			A__							(      _a  :					list	):





 '''simple docstring'''







 snake_case__  :    List[str]							       =len(_a	)

 for i in range(n // 2		,  -1		,  -1	):
  heapify(_a		,  _a		,  _a	)

 for i in range(n - 1		,  0		,  -1	):
  snake_case__       ,   snake_case__  :    Tuple							       =array[0], array[i]
  heapify(_a		,  0		,  _a	)

 return array







def 			A__							(      _a  :					list		,  _a  :					int		,  _a  :					int		,  _a  :					int	):





 '''simple docstring'''







 if (array[first_index] > array[middle_index]) != (
     array[first_index] > array[last_index]
 ):
  return array[first_index]
 elif (array[middle_index] > array[first_index]) != (
     array[middle_index] > array[last_index]
 ):
  return array[middle_index]
 else:
  return array[last_index]







def 			A__							(      _a  :					list		,  _a  :					int		,  _a  :					int		,  _a  :					int	):





 '''simple docstring'''







 snake_case__  :    Dict							       =low
 snake_case__  :    Union[str, Any]							       =high
 while True:
  while array[i] < pivot:
   i += 1
  j -= 1
  while pivot < array[j]:
   j -= 1
  if i >= j:
   return i
  snake_case__       ,   snake_case__  :    List[Any]							       =array[j], array[i]
  i += 1







def 			A__							(      _a  :					list	):





 '''simple docstring'''







 if len(_a	) == 0:
  return array
 snake_case__  :    Optional[int]							       =2 * math.ceil(math.loga(len(_a	)	)	)
 snake_case__  :    Tuple							       =16
 return intro_sort(_a		,  0		,  len(_a	)		,  _a		,  _a	)







def 			A__							(      _a  :					list		,  _a  :					int		,  _a  :					int		,  _a  :					int		,  _a  :					int	):





 '''simple docstring'''







 while end - start > size_threshold:
  if max_depth == 0:
   return heap_sort(_a	)
  max_depth -= 1
  snake_case__  :    Dict							       =median_of_a(_a		,  _a		,  start + ((end - start) // 2) + 1		,  end - 1	)
  snake_case__  :    List[str]							       =partition(_a		,  _a		,  _a		,  _a	)
  intro_sort(_a		,  _a		,  _a		,  _a		,  _a	)
  snake_case__  :    Union[str, Any]							       =p
 return insertion_sort(_a		,  _a		,  _a	)


if __name__ == "__main__":
       import doctest

       doctest.testmod()

       __lowerCamelCase	:		Optional[int]										=   input("""Enter numbers separated by a comma : """).strip()
       __lowerCamelCase	:		Dict										=   [float(item) for item in user_input.split(""",""")]
       print(sort(unsorted))





