import unittest

from transformers import XLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
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 import (
          XLMForMultipleChoice,
          XLMForQuestionAnswering,
          XLMForQuestionAnsweringSimple,
          XLMForSequenceClassification,
          XLMForTokenClassification,
          XLMModel,
          XLMWithLMHeadModel,
      )
      from transformers.models.xlm.modeling_xlm import XLM_PRETRAINED_MODEL_ARCHIVE_LIST

class       snake_case					:




  def __init__(      self     :	Tuple     , a_     :	Optional[Any]     , a_     :	List[Any]=13     , a_     :	Tuple=7     , a_     :	Dict=True     , a_     :	List[str]=True     , a_     :	str=True     , a_     :	str=True     , a_     :	Any=True     , a_     :	str=False     , a_     :	int=False     , a_     :	Union[str, Any]=False     , a_     :	Tuple=2     , a_     :	int=99     , a_     :	Optional[Any]=0     , a_     :	int=32     , a_     :	Dict=5     , a_     :	Tuple=4     , a_     :	Any=0.1     , a_     :	Union[str, Any]=0.1     , a_     :	str=512     , a_     :	List[str]=2     , a_     :	Tuple=0.02     , a_     :	Dict=2     , a_     :	Dict=4     , a_     :	Union[str, Any]="last"     , a_     :	str=True     , a_     :	Optional[int]=None     , a_     :	Dict=0     , )->       Tuple:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		parent
       SCREAMING_SNAKE_CASE__				:				Tuple			=		batch_size
       SCREAMING_SNAKE_CASE__				:				Tuple			=		seq_length
       SCREAMING_SNAKE_CASE__				:				Any			=		is_training
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		use_input_lengths
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		use_token_type_ids
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		use_labels
       SCREAMING_SNAKE_CASE__				:				Dict			=		gelu_activation
       SCREAMING_SNAKE_CASE__				:				Any			=		sinusoidal_embeddings
       SCREAMING_SNAKE_CASE__				:				Tuple			=		causal
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		asm
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		n_langs
       SCREAMING_SNAKE_CASE__				:				Any			=		vocab_size
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		n_special
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		hidden_size
       SCREAMING_SNAKE_CASE__				:				Dict			=		num_hidden_layers
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		num_attention_heads
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		hidden_dropout_prob
       SCREAMING_SNAKE_CASE__				:				str			=		attention_probs_dropout_prob
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		max_position_embeddings
       SCREAMING_SNAKE_CASE__				:				Any			=		type_sequence_label_size
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		initializer_range
       SCREAMING_SNAKE_CASE__				:				str			=		num_labels
       SCREAMING_SNAKE_CASE__				:				str			=		num_choices
       SCREAMING_SNAKE_CASE__				:				int			=		summary_type
       SCREAMING_SNAKE_CASE__				:				Dict			=		use_proj
       SCREAMING_SNAKE_CASE__				:				Any			=		scope
       SCREAMING_SNAKE_CASE__				:				Dict			=		bos_token_id




  def 	__lowercase(      self     :	str				)->       Optional[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Dict			=		ids_tensor([self.batch_size, self.seq_length]     , self.vocab_size				)
       SCREAMING_SNAKE_CASE__				:				int			=		random_attention_mask([self.batch_size, self.seq_length]				)

       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		None
       if self.use_input_lengths:
            SCREAMING_SNAKE_CASE__				:				Dict			=		(
                ids_tensor([self.batch_size]     , vocab_size=2				) + self.seq_length - 2
            )  # small variation of seq_length

       SCREAMING_SNAKE_CASE__				:				List[str]			=		None
       if self.use_token_type_ids:
            SCREAMING_SNAKE_CASE__				:				List[Any]			=		ids_tensor([self.batch_size, self.seq_length]     , self.n_langs				)

       SCREAMING_SNAKE_CASE__				:				Any			=		None
       SCREAMING_SNAKE_CASE__				:				Dict			=		None
       SCREAMING_SNAKE_CASE__				:				Dict			=		None
       if self.use_labels:
            SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		ids_tensor([self.batch_size]     , self.type_sequence_label_size				)
            SCREAMING_SNAKE_CASE__				:				Dict			=		ids_tensor([self.batch_size, self.seq_length]     , self.num_labels				)
            SCREAMING_SNAKE_CASE__				:				Dict			=		ids_tensor([self.batch_size]     , 2				).float()
            SCREAMING_SNAKE_CASE__				:				Dict			=		ids_tensor([self.batch_size]     , self.num_choices				)

       SCREAMING_SNAKE_CASE__				:				Any			=		self.get_config()

       return (
           config,
           input_ids,
           token_type_ids,
           input_lengths,
           sequence_labels,
           token_labels,
           is_impossible_labels,
           choice_labels,
           input_mask,
       )




  def 	__lowercase(      self     :	Dict				)->       Optional[Any]:




       """simple docstring"""
       return XLMConfig(
           vocab_size=self.vocab_size     , n_special=self.n_special     , emb_dim=self.hidden_size     , n_layers=self.num_hidden_layers     , n_heads=self.num_attention_heads     , dropout=self.hidden_dropout_prob     , attention_dropout=self.attention_probs_dropout_prob     , gelu_activation=self.gelu_activation     , sinusoidal_embeddings=self.sinusoidal_embeddings     , asm=self.asm     , causal=self.causal     , n_langs=self.n_langs     , max_position_embeddings=self.max_position_embeddings     , initializer_range=self.initializer_range     , summary_type=self.summary_type     , use_proj=self.use_proj     , num_labels=self.num_labels     , bos_token_id=self.bos_token_id     , )




  def 	__lowercase(      self     :	Optional[int]     , a_     :	List[Any]     , a_     :	int     , a_     :	Optional[Any]     , a_     :	Union[str, Any]     , a_     :	List[str]     , a_     :	List[str]     , a_     :	str     , a_     :	List[str]     , a_     :	List[Any]     , )->       Tuple:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				List[str]			=		XLMModel(config=a_				)
       model.to(a_				)
       model.eval()
       SCREAMING_SNAKE_CASE__				:				Dict			=		model(a_     , lengths=a_     , langs=a_				)
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		model(a_     , langs=a_				)
       SCREAMING_SNAKE_CASE__				:				List[str]			=		model(a_				)
       self.parent.assertEqual(result.last_hidden_state.shape     , (self.batch_size, self.seq_length, self.hidden_size)				)




  def 	__lowercase(      self     :	Union[str, Any]     , a_     :	List[Any]     , a_     :	Optional[int]     , a_     :	int     , a_     :	str     , a_     :	int     , a_     :	Optional[Any]     , a_     :	Optional[int]     , a_     :	Tuple     , a_     :	List[str]     , )->       List[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Dict			=		XLMWithLMHeadModel(a_				)
       model.to(a_				)
       model.eval()

       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		model(a_     , token_type_ids=a_     , labels=a_				)
       self.parent.assertEqual(result.loss.shape     , ()				)
       self.parent.assertEqual(result.logits.shape     , (self.batch_size, self.seq_length, self.vocab_size)				)




  def 	__lowercase(      self     :	Any     , a_     :	Union[str, Any]     , a_     :	Union[str, Any]     , a_     :	Union[str, Any]     , a_     :	Optional[int]     , a_     :	Tuple     , a_     :	List[Any]     , a_     :	Optional[int]     , a_     :	int     , a_     :	Union[str, Any]     , )->       str:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		XLMForQuestionAnsweringSimple(a_				)
       model.to(a_				)
       model.eval()

       SCREAMING_SNAKE_CASE__				:				List[Any]			=		model(a_				)

       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		model(a_     , start_positions=a_     , end_positions=a_				)
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		outputs
       self.parent.assertEqual(result.start_logits.shape     , (self.batch_size, self.seq_length)				)
       self.parent.assertEqual(result.end_logits.shape     , (self.batch_size, self.seq_length)				)




  def 	__lowercase(      self     :	str     , a_     :	Optional[Any]     , a_     :	str     , a_     :	str     , a_     :	List[str]     , a_     :	Union[str, Any]     , a_     :	List[Any]     , a_     :	Optional[Any]     , a_     :	Optional[Any]     , a_     :	List[Any]     , )->       Union[str, Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				List[str]			=		XLMForQuestionAnswering(a_				)
       model.to(a_				)
       model.eval()

       SCREAMING_SNAKE_CASE__				:				Dict			=		model(a_				)

       SCREAMING_SNAKE_CASE__				:				int			=		model(
           a_     , start_positions=a_     , end_positions=a_     , cls_index=a_     , is_impossible=a_     , p_mask=a_     , )

       SCREAMING_SNAKE_CASE__				:				Any			=		model(
           a_     , start_positions=a_     , end_positions=a_     , cls_index=a_     , is_impossible=a_     , )

       ((SCREAMING_SNAKE_CASE__)			,							)				:				Dict			=		result_with_labels.to_tuple()

       SCREAMING_SNAKE_CASE__				:				Dict			=		model(a_     , start_positions=a_     , end_positions=a_				)

       ((SCREAMING_SNAKE_CASE__)			,							)				:				Dict			=		result_with_labels.to_tuple()

       self.parent.assertEqual(result_with_labels.loss.shape     , ()				)
       self.parent.assertEqual(result.start_top_log_probs.shape     , (self.batch_size, model.config.start_n_top)				)
       self.parent.assertEqual(result.start_top_index.shape     , (self.batch_size, model.config.start_n_top)				)
       self.parent.assertEqual(
           result.end_top_log_probs.shape     , (self.batch_size, model.config.start_n_top * model.config.end_n_top)				)
       self.parent.assertEqual(
           result.end_top_index.shape     , (self.batch_size, model.config.start_n_top * model.config.end_n_top)				)
       self.parent.assertEqual(result.cls_logits.shape     , (self.batch_size,)				)




  def 	__lowercase(      self     :	Tuple     , a_     :	str     , a_     :	Optional[Any]     , a_     :	Dict     , a_     :	List[Any]     , a_     :	Union[str, Any]     , a_     :	Union[str, Any]     , a_     :	Optional[int]     , a_     :	Any     , a_     :	List[str]     , )->       List[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Tuple			=		XLMForSequenceClassification(a_				)
       model.to(a_				)
       model.eval()

       SCREAMING_SNAKE_CASE__				:				int			=		model(a_				)
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		model(a_     , labels=a_				)
       self.parent.assertEqual(result.loss.shape     , ()				)
       self.parent.assertEqual(result.logits.shape     , (self.batch_size, self.type_sequence_label_size)				)




  def 	__lowercase(      self     :	Any     , a_     :	Any     , a_     :	Optional[Any]     , a_     :	Dict     , a_     :	int     , a_     :	str     , a_     :	Tuple     , a_     :	int     , a_     :	Optional[int]     , a_     :	int     , )->       List[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				int			=		self.num_labels
       SCREAMING_SNAKE_CASE__				:				Tuple			=		XLMForTokenClassification(a_				)
       model.to(a_				)
       model.eval()

       SCREAMING_SNAKE_CASE__				:				int			=		model(a_     , attention_mask=a_     , labels=a_				)
       self.parent.assertEqual(result.logits.shape     , (self.batch_size, self.seq_length, self.num_labels)				)




  def 	__lowercase(      self     :	List[Any]     , a_     :	str     , a_     :	List[Any]     , a_     :	str     , a_     :	int     , a_     :	Any     , a_     :	Dict     , a_     :	int     , a_     :	List[Any]     , a_     :	Optional[Any]     , )->       Tuple:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Dict			=		self.num_choices
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		XLMForMultipleChoice(config=a_				)
       model.to(a_				)
       model.eval()
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		input_ids.unsqueeze(1				).expand(-1     , self.num_choices     , -1				).contiguous()
       SCREAMING_SNAKE_CASE__				:				str			=		token_type_ids.unsqueeze(1				).expand(-1     , self.num_choices     , -1				).contiguous()
       SCREAMING_SNAKE_CASE__				:				List[str]			=		input_mask.unsqueeze(1				).expand(-1     , self.num_choices     , -1				).contiguous()
       SCREAMING_SNAKE_CASE__				:				int			=		model(
           a_     , attention_mask=a_     , token_type_ids=a_     , labels=a_     , )
       self.parent.assertEqual(result.logits.shape     , (self.batch_size, self.num_choices)				)




  def 	__lowercase(      self     :	Tuple				)->       List[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		self.prepare_config_and_inputs()
       (
           (
           SCREAMING_SNAKE_CASE__
       )			,							(
           SCREAMING_SNAKE_CASE__
       )			,							(
           SCREAMING_SNAKE_CASE__
       )			,							(
           SCREAMING_SNAKE_CASE__
       )			,							(
           SCREAMING_SNAKE_CASE__
       )			,							(
           SCREAMING_SNAKE_CASE__
       )			,							(
           SCREAMING_SNAKE_CASE__
       )			,							(
           SCREAMING_SNAKE_CASE__
       )			,							(
           SCREAMING_SNAKE_CASE__
       )			,							
       )				:				Tuple			=		config_and_inputs
       SCREAMING_SNAKE_CASE__				:				Tuple			=		{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'lengths': input_lengths}
       return config, inputs_dict




@require_torch
class       snake_case					(      UpperCamelCase_   ,						UpperCamelCase_   ,						UpperCamelCase_   ,						unittest.TestCase						):
  lowercase_			=     (
      (
          XLMModel,
          XLMWithLMHeadModel,
          XLMForQuestionAnswering,
          XLMForSequenceClassification,
          XLMForQuestionAnsweringSimple,
          XLMForTokenClassification,
          XLMForMultipleChoice,
      )
      if is_torch_available()
      else ()
  )
  lowercase_			=     (
      (XLMWithLMHeadModel,) if is_torch_available() else ()
  )  # TODO (PVP): Check other models whether language generation is also applicable
  lowercase_			=     (
      {
          'feature-extraction': XLMModel,
          'fill-mask': XLMWithLMHeadModel,
          'question-answering': XLMForQuestionAnsweringSimple,
          'text-classification': XLMForSequenceClassification,
          'text-generation': XLMWithLMHeadModel,
          'token-classification': XLMForTokenClassification,
          'zero-shot': XLMForSequenceClassification,
      }
      if is_torch_available()
      else {}
  )




  def 	__lowercase(      self     :	Any     , a_     :	Any     , a_     :	Dict     , a_     :	List[Any]     , a_     :	List[str]     , a_     :	str				)->       Tuple:




       """simple docstring"""
       if (
           pipeline_test_casse_name == "QAPipelineTests"
           and tokenizer_name is not None
           and not tokenizer_name.endswith('Fast'				)
       ):
            # `QAPipelineTests` fails for a few models when the slower tokenizer are used.
            # (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
            # TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
            return True

       return False




  def 	__lowercase(      self     :	Optional[int]     , a_     :	Dict     , a_     :	List[str]     , a_     :	Dict=False				)->       List[str]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				int			=		super()._prepare_for_class(a_     , a_     , return_labels=a_				)

       if return_labels:
            if model_class.__name__ == "XLMForQuestionAnswering":
                 SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		torch.zeros(
                     self.model_tester.batch_size     , dtype=torch.long     , device=a_				)
                 SCREAMING_SNAKE_CASE__				:				List[Any]			=		torch.zeros(
                     self.model_tester.batch_size     , dtype=torch.long     , device=a_				)

       return inputs_dict




  def 	__lowercase(      self     :	List[Any]				)->       List[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Dict			=		XLMModelTester(self				)
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		ConfigTester(self     , config_class=a_     , emb_dim=37				)




  def 	__lowercase(      self     :	Union[str, Any]				)->       Dict:




       """simple docstring"""
       self.config_tester.run_common_tests()




  def 	__lowercase(      self     :	str				)->       List[str]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_xlm_model(*a_				)




  def 	__lowercase(      self     :	Dict				)->       str:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Dict			=		self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_xlm_lm_head(*a_				)




  def 	__lowercase(      self     :	Tuple				)->       Any:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_xlm_simple_qa(*a_				)




  def 	__lowercase(      self     :	Tuple				)->       Tuple:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				int			=		self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_xlm_qa(*a_				)




  def 	__lowercase(      self     :	List[str]				)->       Any:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_xlm_sequence_classif(*a_				)




  def 	__lowercase(      self     :	int				)->       Optional[int]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_xlm_token_classif(*a_				)




  def 	__lowercase(      self     :	Dict				)->       Optional[int]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				int			=		self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_xlm_for_multiple_choice(*a_				)




  def 	__lowercase(      self     :	Optional[Any]     , a_     :	Any     , a_     :	Any     , a_     :	str     , a_     :	Tuple     , a_     :	str     , a_     :	Optional[Any]=False     , a_     :	Dict=1				)->       List[Any]:




       """simple docstring"""
       self.assertIsInstance(a_     , a_				)
       self.assertListEqual(
           [isinstance(a_     , a_				) for iter_attentions in attentions]     , [True] * len(a_				)				)
       self.assertEqual(len(a_				)     , (max_length - min_length) * num_beam_groups				)

       for idx, iter_attentions in enumerate(a_				):
            # adds PAD dummy token
            SCREAMING_SNAKE_CASE__				:				Dict			=		min_length + idx + 1
            SCREAMING_SNAKE_CASE__				:				str			=		min_length + idx + 1

            SCREAMING_SNAKE_CASE__				:				Tuple			=		(
                batch_size * num_beam_groups,
                config.num_attention_heads,
                tgt_len,
                src_len,
            )
            # check attn size
            self.assertListEqual(
                [layer_attention.shape for layer_attention in iter_attentions]     , [expected_shape] * len(a_				)				)




  def 	__lowercase(      self     :	List[str]     , a_     :	List[str]     , a_     :	Dict     , a_     :	Union[str, Any]     , a_     :	List[Any]     , a_     :	Any     , a_     :	Dict=False     , a_     :	List[str]=1				)->       List[str]:




       """simple docstring"""
       self.assertIsInstance(a_     , a_				)
       self.assertListEqual(
           [isinstance(a_     , a_				) for iter_hidden_states in hidden_states]     , [True] * len(a_				)     , )
       self.assertEqual(len(a_				)     , (max_length - min_length) * num_beam_groups				)

       for idx, iter_hidden_states in enumerate(a_				):
            # adds PAD dummy token
            SCREAMING_SNAKE_CASE__				:				List[str]			=		min_length + idx + 1
            SCREAMING_SNAKE_CASE__				:				List[Any]			=		(batch_size * num_beam_groups, seq_len, config.hidden_size)
            # check hidden size
            self.assertListEqual(
                [layer_hidden_states.shape for layer_hidden_states in iter_hidden_states]     , [expected_shape] * len(a_				)     , )
       pass




  @slow
  def 	__lowercase(      self     :	Optional[Any]				)->       List[Any]:




       """simple docstring"""
       for model_name in XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            SCREAMING_SNAKE_CASE__				:				Optional[int]			=		XLMModel.from_pretrained(a_				)
            self.assertIsNotNone(a_				)




@require_torch
class       snake_case					(      unittest.TestCase						):




  @slow
  def 	__lowercase(      self     :	Optional[Any]				)->       Any:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		XLMWithLMHeadModel.from_pretrained('xlm-mlm-en-2048'				)
       model.to(a_				)
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		torch.tensor([[14, 447]]     , dtype=torch.long     , device=a_				)  # the president
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		[
           14,
           447,
           14,
           447,
           14,
           447,
           14,
           447,
           14,
           447,
           14,
           447,
           14,
           447,
           14,
           447,
           14,
           447,
           14,
           447,
       ]  # the president the president the president the president the president the president the president the president the president the president
       # TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		model.generate(a_     , do_sample=a_				)
       self.assertListEqual(output_ids[0].cpu().numpy().tolist()     , a_				)




from math import factorial, radians







def     _a				(      lowercase__  :			float     ,  lowercase__  :			int = 18     ,  lowercase__  :			int = 10 ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		angle_in_degrees - ((angle_in_degrees // 360.0) * 360.0)

     # Converting from degrees to radians
     SCREAMING_SNAKE_CASE__				:				int			=		radians(lowercase__ )

     SCREAMING_SNAKE_CASE__				:				List[Any]			=		angle_in_radians
     SCREAMING_SNAKE_CASE__				:				Optional[int]			=		3
     SCREAMING_SNAKE_CASE__				:				Optional[int]			=		-1

     for _ in range(lowercase__ ):
          result += (b * (angle_in_radians**a)) / factorial(lowercase__ )

          SCREAMING_SNAKE_CASE__				:				Any			=		-b  # One positive term and the next will be negative and so on...
          a += 2  # Increased by 2 for every term.

     return round(lowercase__     ,  lowercase__ )


if __name__ == "__main__":
      __import__("doctest").testmod()



