"""simple docstring"""





from __future__ import annotations

import unittest

from transformers import RoFormerConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
       import tensorflow as tf

       from transformers import (
           TFRoFormerForCausalLM,
           TFRoFormerForMaskedLM,
           TFRoFormerForMultipleChoice,
           TFRoFormerForQuestionAnswering,
           TFRoFormerForSequenceClassification,
           TFRoFormerForTokenClassification,
           TFRoFormerModel,
       )
       from transformers.models.roformer.modeling_tf_roformer import (
           TFRoFormerSelfAttention,
           TFRoFormerSinusoidalPositionalEmbedding,
       )


class   UpperCAmelCase_			:


      def __init__(     self	,    a	,    a=1_3	,    a=7	,    a=True	,    a=True	,    a=True	,    a=True	,    a=9_9	,    a=3_2	,    a=2	,    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	,    ) ->      List[str]:
             lowercase__    :	Optional[Any]     					=			parent
             lowercase__    :	List[Any]     					=			1_3
             lowercase__    :	Union[str, Any]     					=			7
             lowercase__    :	List[str]     					=			True
             lowercase__    :	Dict     					=			True
             lowercase__    :	Tuple     					=			True
             lowercase__    :	str     					=			True
             lowercase__    :	Dict     					=			9_9
             lowercase__    :	Any     					=			3_2
             lowercase__    :	Dict     					=			2
             lowercase__    :	int     					=			4
             lowercase__    :	Optional[Any]     					=			3_7
             lowercase__    :	Any     					=			'gelu'
             lowercase__    :	Optional[int]     					=			0.1
             lowercase__    :	Optional[int]     					=			0.1
             lowercase__    :	Dict     					=			5_1_2
             lowercase__    :	Union[str, Any]     					=			1_6
             lowercase__    :	Tuple     					=			2
             lowercase__    :	Dict     					=			0.02
             lowercase__    :	Any     					=			3
             lowercase__    :	Optional[int]     					=			4
             lowercase__    :	str     					=			None


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

             lowercase__    :	Union[str, Any]     					=			None
             if self.use_input_mask:
                    lowercase__    :	List[str]     					=			random_attention_mask([self.batch_size, self.seq_length]							)

             lowercase__    :	Tuple     					=			None
             if self.use_token_type_ids:
                    lowercase__    :	Union[str, Any]     					=			ids_tensor([self.batch_size, self.seq_length]	,    self.type_vocab_size							)

             lowercase__    :	int     					=			None
             lowercase__    :	Any     					=			None
             lowercase__    :	Tuple     					=			None
             if self.use_labels:
                    lowercase__    :	Dict     					=			ids_tensor([self.batch_size]	,    self.type_sequence_label_size							)
                    lowercase__    :	Tuple     					=			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]     					=			RoFormerConfig(
                 vocab_size=self.vocab_size	,    hidden_size=self.hidden_size	,    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	,    return_dict=a	,    )

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


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a	,    a	,    a	,    a	,    a							) ->      Optional[int]:
             lowercase__    :	str     					=			TFRoFormerModel(config=a							)
             lowercase__    :	Union[str, Any]     					=			{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}

             lowercase__    :	Union[str, Any]     					=			[input_ids, input_mask]
             lowercase__    :	Optional[int]     					=			model(a							)

             lowercase__    :	Tuple     					=			model(a							)

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


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a	,    a	,    a	,    a	,    a							) ->      Optional[Any]:
             lowercase__    :	Optional[Any]     					=			True
             lowercase__    :	Any     					=			TFRoFormerForCausalLM(config=a							)
             lowercase__    :	Dict     					=			{
                 'input_ids': input_ids,
                 'attention_mask': input_mask,
                 'token_type_ids': token_type_ids,
             }
             lowercase__    :	Optional[int]     					=			model(a							)['logits']
             self.parent.assertListEqual(
                 list(prediction_scores.numpy().shape							)	,    [self.batch_size, self.seq_length, self.vocab_size]							)


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a	,    a	,    a	,    a	,    a							) ->      List[str]:
             lowercase__    :	Tuple     					=			TFRoFormerForMaskedLM(config=a							)
             lowercase__    :	Optional[Any]     					=			{
                 'input_ids': input_ids,
                 'attention_mask': input_mask,
                 'token_type_ids': token_type_ids,
             }
             lowercase__    :	Any     					=			model(a							)
             self.parent.assertEqual(result.logits.shape	,    (self.batch_size, self.seq_length, self.vocab_size)							)


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a	,    a	,    a	,    a	,    a							) ->      int:
             lowercase__    :	Optional[Any]     					=			self.num_labels
             lowercase__    :	Dict     					=			TFRoFormerForSequenceClassification(config=a							)
             lowercase__    :	str     					=			{
                 'input_ids': input_ids,
                 'attention_mask': input_mask,
                 'token_type_ids': token_type_ids,
             }

             lowercase__    :	int     					=			model(a							)
             self.parent.assertEqual(result.logits.shape	,    (self.batch_size, self.num_labels)							)


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a	,    a	,    a	,    a	,    a							) ->      str:
             lowercase__    :	Optional[int]     					=			self.num_choices
             lowercase__    :	Tuple     					=			TFRoFormerForMultipleChoice(config=a							)
             lowercase__    :	str     					=			tf.tile(tf.expand_dims(a	,    1							)	,    (1, self.num_choices, 1)							)
             lowercase__    :	Optional[Any]     					=			tf.tile(tf.expand_dims(a	,    1							)	,    (1, self.num_choices, 1)							)
             lowercase__    :	Tuple     					=			tf.tile(tf.expand_dims(a	,    1							)	,    (1, self.num_choices, 1)							)
             lowercase__    :	Union[str, Any]     					=			{
                 'input_ids': multiple_choice_inputs_ids,
                 'attention_mask': multiple_choice_input_mask,
                 'token_type_ids': multiple_choice_token_type_ids,
             }
             lowercase__    :	Union[str, Any]     					=			model(a							)
             self.parent.assertEqual(result.logits.shape	,    (self.batch_size, self.num_choices)							)


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a	,    a	,    a	,    a	,    a							) ->      List[str]:
             lowercase__    :	Any     					=			self.num_labels
             lowercase__    :	Dict     					=			TFRoFormerForTokenClassification(config=a							)
             lowercase__    :	Tuple     					=			{
                 'input_ids': input_ids,
                 'attention_mask': input_mask,
                 'token_type_ids': token_type_ids,
             }
             lowercase__    :	int     					=			model(a							)
             self.parent.assertEqual(result.logits.shape	,    (self.batch_size, self.seq_length, self.num_labels)							)


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a	,    a	,    a	,    a	,    a							) ->      Dict:
             lowercase__    :	Optional[int]     					=			TFRoFormerForQuestionAnswering(config=a							)
             lowercase__    :	List[Any]     					=			{
                 'input_ids': input_ids,
                 'attention_mask': input_mask,
                 'token_type_ids': token_type_ids,
             }

             lowercase__    :	Dict     					=			model(a							)
             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 	_UpperCAmelCase (     self							) ->      List[Any]:
             lowercase__    :	Tuple     					=			self.prepare_config_and_inputs()
             (
                 (
                 lowercase__
             )					,   (
                 lowercase__
             )					,   (
                 lowercase__
             )					,   (
                 lowercase__
             )					,   (
                 lowercase__
             )					,   (
                 lowercase__
             )					,   (
                 lowercase__
             )					,   
             )    :	List[Any]     					=			config_and_inputs
             lowercase__    :	Optional[int]     					=			{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
             return config, inputs_dict





@require_tf
class   UpperCAmelCase_			(					_a				,							_a				,							unittest.TestCase):
      lowerCamelCase__	:					List[Any]										=						(
          (
              TFRoFormerModel,
              TFRoFormerForCausalLM,
              TFRoFormerForMaskedLM,
              TFRoFormerForQuestionAnswering,
              TFRoFormerForSequenceClassification,
              TFRoFormerForTokenClassification,
              TFRoFormerForMultipleChoice,
          )
          if is_tf_available()
          else ()
      )
      lowerCamelCase__	:					List[str]										=						(
          {
              "feature-extraction": TFRoFormerModel,
              "fill-mask": TFRoFormerForMaskedLM,
              "question-answering": TFRoFormerForQuestionAnswering,
              "text-classification": TFRoFormerForSequenceClassification,
              "text-generation": TFRoFormerForCausalLM,
              "token-classification": TFRoFormerForTokenClassification,
              "zero-shot": TFRoFormerForSequenceClassification,
          }
          if is_tf_available()
          else {}
      )

      lowerCamelCase__	:					Union[str, Any]										=						False
      lowerCamelCase__	:					Any										=						False


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a	,    a	,    a							) ->      Union[str, Any]:
             if pipeline_test_casse_name == "TextGenerationPipelineTests":
                    return True

             return False


      def 	_UpperCAmelCase (     self							) ->      Union[str, Any]:
             lowercase__    :	Optional[int]     					=			TFRoFormerModelTester(self							)
             lowercase__    :	List[Any]     					=			ConfigTester(self	,    config_class=a	,    hidden_size=3_7							)


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


      def 	_UpperCAmelCase (     self							) ->      str:
             lowercase__    :	Tuple     					=			self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*a							)


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


      def 	_UpperCAmelCase (     self							) ->      int:
             lowercase__    :	Dict     					=			self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_lm_head(*a							)


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


      def 	_UpperCAmelCase (     self							) ->      List[Any]:
             lowercase__    :	List[str]     					=			self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_question_answering(*a							)


      def 	_UpperCAmelCase (     self							) ->      Optional[Any]:
             lowercase__    :	Dict     					=			self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_sequence_classification(*a							)


      def 	_UpperCAmelCase (     self							) ->      Dict:
             lowercase__    :	Union[str, Any]     					=			self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_token_classification(*a							)


      @slow
      def 	_UpperCAmelCase (     self							) ->      List[str]:
             lowercase__    :	List[str]     					=			TFRoFormerModel.from_pretrained('junnyu/roformer_chinese_base'							)
             self.assertIsNotNone(a							)





@require_tf
class   UpperCAmelCase_			(					unittest.TestCase):


      @slow
      def 	_UpperCAmelCase (     self							) ->      Any:
             lowercase__    :	List[Any]     					=			TFRoFormerForMaskedLM.from_pretrained('junnyu/roformer_chinese_base'							)
             lowercase__    :	List[str]     					=			tf.constant([[0, 1, 2, 3, 4, 5]]							)
             lowercase__    :	Optional[int]     					=			model(a							)[0]

             # TODO Replace vocab size
             lowercase__    :	Any     					=			5_0_0_0_0

             lowercase__    :	List[Any]     					=			[1, 6, vocab_size]
             self.assertEqual(output.shape	,    a							)

             print(output[:, :3, :3]							)

             # TODO Replace values below with what was printed above.
             lowercase__    :	Optional[Any]     					=			tf.constant(
                 [
                     [
                         [-0.12_053_341, -1.0_264_901, 0.29_221_946],
                         [-1.5_133_783, 0.197_433, 0.15_190_607],
                         [-5.0_135_403, -3.900_256, -0.84_038_764],
                     ]
                 ]							)
             tf.debugging.assert_near(output[:, :3, :3]	,    a	,    atol=1e-4							)





@require_tf
class   UpperCAmelCase_			(					unittest.TestCase):
      lowerCamelCase__	:					List[str]										=						1E-4


      def 	_UpperCAmelCase (     self							) ->      List[str]:
             lowercase__    :	Union[str, Any]     					=			tf.constant([[4, 1_0]]							)
             lowercase__    :	int     					=			TFRoFormerSinusoidalPositionalEmbedding(num_positions=6	,    embedding_dim=6							)

             lowercase__    :	List[str]     					=			emba(input_ids.shape							)
             lowercase__    :	Optional[Any]     					=			tf.constant(
                 [[0.0_000, 0.0_000, 0.0_000, 1.0_000, 1.0_000, 1.0_000], [0.8_415, 0.0_464, 0.0_022, 0.5_403, 0.9_989, 1.0_000]]							)

             tf.debugging.assert_near(a	,    a	,    atol=self.tolerance							)


      def 	_UpperCAmelCase (     self							) ->      Any:
             lowercase__    :	Optional[Any]     					=			tf.constant(
                 [
                     [0.0_000, 0.0_000, 0.0_000, 0.0_000, 0.0_000],
                     [0.8_415, 0.8_219, 0.8_020, 0.7_819, 0.7_617],
                     [0.9_093, 0.9_364, 0.9_581, 0.9_749, 0.9_870],
                 ]							)
             lowercase__    :	Dict     					=			TFRoFormerSinusoidalPositionalEmbedding(num_positions=5_1_2	,    embedding_dim=5_1_2							)
             emba([2, 1_6, 5_1_2]							)
             lowercase__    :	Any     					=			emba.weight[:3, :5]

             tf.debugging.assert_near(a	,    a	,    atol=self.tolerance							)





@require_tf
class   UpperCAmelCase_			(					unittest.TestCase):
      lowerCamelCase__	:					Union[str, Any]										=						1E-4


      def 	_UpperCAmelCase (     self							) ->      Any:
             # 2,12,16,64
             lowercase__    :	List[Any]     					=			tf.reshape(tf.range(2 * 1_2 * 1_6 * 6_4	,    dtype=tf.floataa							)	,    shape=(2, 1_2, 1_6, 6_4)							) / 1_0_0

             lowercase__    :	int     					=			-tf.reshape(tf.range(2 * 1_2 * 1_6 * 6_4	,    dtype=tf.floataa							)	,    shape=(2, 1_2, 1_6, 6_4)							) / 1_0_0

             lowercase__    :	List[Any]     					=			TFRoFormerSinusoidalPositionalEmbedding(num_positions=3_2	,    embedding_dim=6_4							)
             lowercase__    :	str     					=			embed_positions([2, 1_6, 7_6_8]							)[None, None, :, :]

             lowercase__					,   lowercase__    :	List[str]     					=			TFRoFormerSelfAttention.apply_rotary_position_embeddings(
                 a	,    a	,    a							)

             lowercase__    :	List[Any]     					=			tf.constant(
                 [
                     [0.0_000, 0.0_100, 0.0_200, 0.0_300, 0.0_400, 0.0_500, 0.0_600, 0.0_700],
                     [-0.2_012, 0.8_897, 0.0_263, 0.9_401, 0.2_074, 0.9_463, 0.3_481, 0.9_343],
                     [-1.7_057, 0.6_271, -1.2_145, 1.3_897, -0.6_303, 1.7_647, -0.1_173, 1.8_985],
                     [-2.1_731, -1.6_397, -2.7_358, 0.2_854, -2.1_840, 1.7_183, -1.3_018, 2.4_871],
                     [0.2_717, -3.6_173, -2.9_206, -2.1_988, -3.6_638, 0.3_858, -2.9_155, 2.2_980],
                     [3.9_859, -2.1_580, -0.7_984, -4.4_904, -4.1_181, -2.0_252, -4.4_782, 1.1_253],
                 ]							)
             lowercase__    :	List[Any]     					=			tf.constant(
                 [
                     [0.0_000, -0.0_100, -0.0_200, -0.0_300, -0.0_400, -0.0_500, -0.0_600, -0.0_700],
                     [0.2_012, -0.8_897, -0.0_263, -0.9_401, -0.2_074, -0.9_463, -0.3_481, -0.9_343],
                     [1.7_057, -0.6_271, 1.2_145, -1.3_897, 0.6_303, -1.7_647, 0.1_173, -1.8_985],
                     [2.1_731, 1.6_397, 2.7_358, -0.2_854, 2.1_840, -1.7_183, 1.3_018, -2.4_871],
                     [-0.2_717, 3.6_173, 2.9_206, 2.1_988, 3.6_638, -0.3_858, 2.9_155, -2.2_980],
                     [-3.9_859, 2.1_580, 0.7_984, 4.4_904, 4.1_181, 2.0_252, 4.4_782, -1.1_253],
                 ]							)

             tf.debugging.assert_near(query_layer[0, 0, :6, :8]	,    a	,    atol=self.tolerance							)
             tf.debugging.assert_near(key_layer[0, 0, :6, :8]	,    a	,    atol=self.tolerance							)


"""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))

