"""simple docstring"""





import unittest

from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch

       from transformers.models.esm.modeling_esmfold import EsmForProteinFolding


class   UpperCAmelCase_			:


      def __init__(     self	,    a	,    a=1_3	,    a=7	,    a=False	,    a=True	,    a=False	,    a=False	,    a=1_9	,    a=3_2	,    a=5	,    a=4	,    a=3_7	,    a="gelu"	,    a=0.1	,    a=0.1	,    a=5_1_2	,    a=1_6	,    a=2	,    a=0.02	,    a=3	,    a=4	,    a=None	,    ) ->      Union[str, Any]:
             lowercase__    :	Optional[Any]     					=			parent
             lowercase__    :	Optional[Any]     					=			batch_size
             lowercase__    :	str     					=			seq_length
             lowercase__    :	Optional[Any]     					=			is_training
             lowercase__    :	int     					=			use_input_mask
             lowercase__    :	List[str]     					=			use_token_type_ids
             lowercase__    :	List[Any]     					=			use_labels
             lowercase__    :	Any     					=			vocab_size
             lowercase__    :	Optional[int]     					=			hidden_size
             lowercase__    :	Tuple     					=			num_hidden_layers
             lowercase__    :	Tuple     					=			num_attention_heads
             lowercase__    :	str     					=			intermediate_size
             lowercase__    :	Union[str, Any]     					=			hidden_act
             lowercase__    :	Optional[Any]     					=			hidden_dropout_prob
             lowercase__    :	Any     					=			attention_probs_dropout_prob
             lowercase__    :	Any     					=			max_position_embeddings
             lowercase__    :	str     					=			type_vocab_size
             lowercase__    :	Union[str, Any]     					=			type_sequence_label_size
             lowercase__    :	Dict     					=			initializer_range
             lowercase__    :	Tuple     					=			num_labels
             lowercase__    :	Any     					=			num_choices
             lowercase__    :	List[Any]     					=			scope


      def 	_UpperCAmelCase (     self							) ->      Dict:
             lowercase__    :	List[Any]     					=			ids_tensor([self.batch_size, self.seq_length]	,    self.vocab_size							)

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

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

             lowercase__    :	Union[str, Any]     					=			self.get_config()

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


      def 	_UpperCAmelCase (     self							) ->      Union[str, Any]:
             lowercase__    :	Tuple     					=			EsmConfig(
                 vocab_size=3_3	,    hidden_size=self.hidden_size	,    pad_token_id=1	,    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	,    is_folding_model=a	,    esmfold_config={'trunk': {'num_blocks': 2}, 'fp16_esm': False}	,    )
             return config


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a	,    a	,    a	,    a							) ->      str:
             lowercase__    :	List[str]     					=			EsmForProteinFolding(config=a							).float()
             model.to(a							)
             model.eval()
             lowercase__    :	Dict     					=			model(a	,    attention_mask=a							)
             lowercase__    :	List[str]     					=			model(a							)
             lowercase__    :	str     					=			model(a							)

             self.parent.assertEqual(result.positions.shape	,    (8, self.batch_size, self.seq_length, 1_4, 3)							)
             self.parent.assertEqual(result.angles.shape	,    (8, self.batch_size, self.seq_length, 7, 2)							)


      def 	_UpperCAmelCase (     self							) ->      Optional[Any]:
             lowercase__    :	Optional[Any]     					=			self.prepare_config_and_inputs()
             (
                 (
                 lowercase__
             )					,   (
                 lowercase__
             )					,   (
                 lowercase__
             )					,   (
                 lowercase__
             )					,   (
                 lowercase__
             )					,   (
                 lowercase__
             )					,   
             )    :	Optional[int]     					=			config_and_inputs
             lowercase__    :	Any     					=			{'input_ids': input_ids, 'attention_mask': input_mask}
             return config, inputs_dict





@require_torch
class   UpperCAmelCase_			(					_a				,							_a				,							unittest.TestCase):
      lowerCamelCase__	:					Any										=						False

      lowerCamelCase__	:					List[Any]										=						(EsmForProteinFolding,) if is_torch_available() else ()
      lowerCamelCase__	:					List[str]										=						()
      lowerCamelCase__	:					Optional[int]										=						{} if is_torch_available() else {}
      lowerCamelCase__	:					Optional[Any]										=						False


      def 	_UpperCAmelCase (     self							) ->      Dict:
             lowercase__    :	Optional[Any]     					=			EsmFoldModelTester(self							)
             lowercase__    :	Optional[int]     					=			ConfigTester(self	,    config_class=a	,    hidden_size=3_7							)


      def 	_UpperCAmelCase (     self							) ->      Tuple:
             self.config_tester.run_common_tests()


      def 	_UpperCAmelCase (     self							) ->      str:
             lowercase__    :	Optional[int]     					=			self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*a							)


      @unittest.skip('Does not support attention outputs'							)
      def 	_UpperCAmelCase (     self							) ->      Tuple:
             pass


      @unittest.skip
      def 	_UpperCAmelCase (     self							) ->      Optional[int]:
             pass


      @unittest.skip('Esm does not support embedding resizing'							)
      def 	_UpperCAmelCase (     self							) ->      Optional[Any]:
             pass


      @unittest.skip('Esm does not support embedding resizing'							)
      def 	_UpperCAmelCase (     self							) ->      int:
             pass


      @unittest.skip('ESMFold does not support passing input embeds!'							)
      def 	_UpperCAmelCase (     self							) ->      List[Any]:
             pass


      @unittest.skip('ESMFold does not support head pruning.'							)
      def 	_UpperCAmelCase (     self							) ->      List[str]:
             pass


      @unittest.skip('ESMFold does not support head pruning.'							)
      def 	_UpperCAmelCase (     self							) ->      Dict:
             pass


      @unittest.skip('ESMFold does not support head pruning.'							)
      def 	_UpperCAmelCase (     self							) ->      Any:
             pass


      @unittest.skip('ESMFold does not support head pruning.'							)
      def 	_UpperCAmelCase (     self							) ->      Optional[Any]:
             pass


      @unittest.skip('ESMFold does not support head pruning.'							)
      def 	_UpperCAmelCase (     self							) ->      List[str]:
             pass


      @unittest.skip('ESMFold does not output hidden states in the normal way.'							)
      def 	_UpperCAmelCase (     self							) ->      Dict:
             pass


      @unittest.skip('ESMfold does not output hidden states in the normal way.'							)
      def 	_UpperCAmelCase (     self							) ->      str:
             pass


      @unittest.skip('ESMFold only has one output format.'							)
      def 	_UpperCAmelCase (     self							) ->      int:
             pass


      @unittest.skip('This test doesn\'t work for ESMFold and doesn\'t test core functionality'							)
      def 	_UpperCAmelCase (     self							) ->      Optional[Any]:
             pass


      @unittest.skip('ESMFold does not support input chunking.'							)
      def 	_UpperCAmelCase (     self							) ->      List[Any]:
             pass


      @unittest.skip('ESMFold doesn\'t respect you and it certainly doesn\'t respect your initialization arguments.'							)
      def 	_UpperCAmelCase (     self							) ->      str:
             pass


      @unittest.skip('ESMFold doesn\'t support torchscript compilation.'							)
      def 	_UpperCAmelCase (     self							) ->      Optional[int]:
             pass


      @unittest.skip('ESMFold doesn\'t support torchscript compilation.'							)
      def 	_UpperCAmelCase (     self							) ->      Optional[int]:
             pass


      @unittest.skip('ESMFold doesn\'t support torchscript compilation.'							)
      def 	_UpperCAmelCase (     self							) ->      Any:
             pass


      @unittest.skip('ESMFold doesn\'t support data parallel.'							)
      def 	_UpperCAmelCase (     self							) ->      Dict:
             pass


      @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'							)
      def 	_UpperCAmelCase (     self							) ->      List[str]:
             pass





@require_torch
class   UpperCAmelCase_			(					_a):


      @slow
      def 	_UpperCAmelCase (     self							) ->      Union[str, Any]:
             lowercase__    :	Optional[Any]     					=			EsmForProteinFolding.from_pretrained('facebook/esmfold_v1'							).float()
             model.eval()
             lowercase__    :	str     					=			torch.tensor([[0, 6, 4, 1_3, 5, 4, 1_6, 1_2, 1_1, 7, 2]]							)
             lowercase__    :	Union[str, Any]     					=			model(a							)['positions']
             lowercase__    :	Any     					=			torch.tensor([2.5_828, 0.7_993, -10.9_334]	,    dtype=torch.floataa							)
             self.assertTrue(torch.allclose(position_outputs[0, 0, 0, 0]	,    a	,    atol=1e-4							)							)


"""simple docstring"""





from collections.abc import Sequence





def        a_  (   _lowerCAmelCase						:				Sequence[float]     ,				_lowerCAmelCase						:				float  ):





       '''simple docstring'''

       return sum(c * (x**i) for i, c in enumerate(_lowerCAmelCase  )  )





def        a_  (   _lowerCAmelCase						:				Sequence[float]     ,				_lowerCAmelCase						:				float  ):





       '''simple docstring'''

       lowercase__    :	int     					=			0.0
       for coeff in reversed(_lowerCAmelCase  ):
              lowercase__    :	List[Any]     					=			result * x + coeff
       return result


if __name__ == "__main__":
       _UpperCamelCase     :		int											=					(0.0, 0.0, 5.0, 9.3, 7.0)
       _UpperCamelCase     :		Dict											=					1_0.0
       print(evaluate_poly(poly, x))
       print(horner(poly, x))

