



from math import pi

def     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__ ):


      """simple docstring"""



      return 2 * pi * radius * (angle / 360)


if __name__ == "__main__":
     print(arc_length(9_0, 1_0))




from __future__ import annotations

import unittest

from transformers import 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 numpy
     import tensorflow as tf

     from transformers import (
         TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
         TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
         TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
         BertConfig,
         DPRConfig,
         TFDPRContextEncoder,
         TFDPRQuestionEncoder,
         TFDPRReader,
     )





class    lowerCamelCase__  :
     """simple docstring"""



     def __init__(self				,			__a				,			__a=13				,			__a=7				,			__a=True				,			__a=True				,			__a=True				,			__a=True				,			__a=99				,			__a=32				,			__a=2				,			__a=4				,			__a=37				,			__a="gelu"				,			__a=0.1				,			__a=0.1				,			__a=5_12				,			__a=16				,			__a=2				,			__a=0.02				,			__a=3				,			__a=4				,			__a=None				,			__a=0				,			):






           '''simple docstring'''



           lowerCamelCase								=		parent
           lowerCamelCase								=		batch_size
           lowerCamelCase								=		seq_length
           lowerCamelCase								=		is_training
           lowerCamelCase								=		use_input_mask
           lowerCamelCase								=		use_token_type_ids
           lowerCamelCase								=		use_labels
           lowerCamelCase								=		vocab_size
           lowerCamelCase								=		hidden_size
           lowerCamelCase								=		num_hidden_layers
           lowerCamelCase								=		num_attention_heads
           lowerCamelCase								=		intermediate_size
           lowerCamelCase								=		hidden_act
           lowerCamelCase								=		hidden_dropout_prob
           lowerCamelCase								=		attention_probs_dropout_prob
           lowerCamelCase								=		max_position_embeddings
           lowerCamelCase								=		type_vocab_size
           lowerCamelCase								=		type_sequence_label_size
           lowerCamelCase								=		initializer_range
           lowerCamelCase								=		num_labels
           lowerCamelCase								=		num_choices
           lowerCamelCase								=		scope
           lowerCamelCase								=		projection_dim







     def 						_a      (self    ):






           '''simple docstring'''



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

           lowerCamelCase								=		None
           if self.use_input_mask:
                 # follow test_modeling_tf_ctrl.py
                 lowerCamelCase								=		random_attention_mask([self.batch_size, self.seq_length]    )

           lowerCamelCase								=		None
           if self.use_token_type_ids:
                 lowerCamelCase								=		ids_tensor([self.batch_size, self.seq_length]				,			self.type_vocab_size    )

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

           lowerCamelCase								=		BertConfig(
               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				,			is_decoder=__a				,			initializer_range=self.initializer_range				,			)
           lowerCamelCase								=		DPRConfig(projection_dim=self.projection_dim				,			**config.to_dict()    )

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







     def 						_a      (self				,			__a				,			__a				,			__a				,			__a				,			__a				,			__a				,			__a    ):






           '''simple docstring'''



           lowerCamelCase								=		TFDPRContextEncoder(config=__a    )
           lowerCamelCase								=		model(__a				,			attention_mask=__a				,			token_type_ids=__a    )
           lowerCamelCase								=		model(__a				,			token_type_ids=__a    )
           lowerCamelCase								=		model(__a    )
           self.parent.assertEqual(result.pooler_output.shape				,			(self.batch_size, self.projection_dim or self.hidden_size)    )







     def 						_a      (self				,			__a				,			__a				,			__a				,			__a				,			__a				,			__a				,			__a    ):






           '''simple docstring'''



           lowerCamelCase								=		TFDPRQuestionEncoder(config=__a    )
           lowerCamelCase								=		model(__a				,			attention_mask=__a				,			token_type_ids=__a    )
           lowerCamelCase								=		model(__a				,			token_type_ids=__a    )
           lowerCamelCase								=		model(__a    )
           self.parent.assertEqual(result.pooler_output.shape				,			(self.batch_size, self.projection_dim or self.hidden_size)    )







     def 						_a      (self				,			__a				,			__a				,			__a				,			__a				,			__a				,			__a				,			__a    ):






           '''simple docstring'''



           lowerCamelCase								=		TFDPRReader(config=__a    )
           lowerCamelCase								=		model(__a				,			attention_mask=__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)    )
           self.parent.assertEqual(result.relevance_logits.shape				,			(self.batch_size,)    )







     def 						_a      (self    ):






           '''simple docstring'''



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





@require_tf
class    lowerCamelCase__  (						UpperCAmelCase_       ,    UpperCAmelCase_       ,    unittest.TestCase):
     """simple docstring"""



     _A       =			(
         (
             TFDPRContextEncoder,
             TFDPRQuestionEncoder,
             TFDPRReader,
         )
         if is_tf_available()
         else ()
     )
     _A       =			{'feature-extraction': TFDPRQuestionEncoder} if is_tf_available() else {}

     _A       =			False
     _A       =			False
     _A       =			False
     _A       =			False
     _A       =			False







     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		TFDPRModelTester(self    )
           lowerCamelCase								=		ConfigTester(self				,			config_class=__a				,			hidden_size=37    )







     def 						_a      (self    ):






           '''simple docstring'''



           self.config_tester.run_common_tests()







     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_dpr_context_encoder(*__a    )







     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_dpr_question_encoder(*__a    )







     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_dpr_reader(*__a    )







     @slow
     def 						_a      (self    ):






           '''simple docstring'''



           for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 lowerCamelCase								=		TFDPRContextEncoder.from_pretrained(__a    )
                 self.assertIsNotNone(__a    )

           for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 lowerCamelCase								=		TFDPRContextEncoder.from_pretrained(__a    )
                 self.assertIsNotNone(__a    )

           for model_name in TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 lowerCamelCase								=		TFDPRQuestionEncoder.from_pretrained(__a    )
                 self.assertIsNotNone(__a    )

           for model_name in TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 lowerCamelCase								=		TFDPRReader.from_pretrained(__a    )
                 self.assertIsNotNone(__a    )





@require_tf
class    lowerCamelCase__  (						unittest.TestCase):
     """simple docstring"""



     @slow
     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		TFDPRQuestionEncoder.from_pretrained("facebook/dpr-question_encoder-single-nq-base"    )

           lowerCamelCase								=		tf.constant(
               [[1_01, 75_92, 10_10, 20_03, 20_26, 38_99, 1_01_40, 10_29, 1_02]]    )  # [CLS] hello, is my dog cute? [SEP]
           lowerCamelCase								=		model(__a    )[0]  # embedding shape = (1, 768)
           # compare the actual values for a slice.
           lowerCamelCase								=		tf.constant(
               [
                   [
                       0.0323_6253,
                       0.1275_3335,
                       0.1681_8509,
                       0.0027_9786,
                       0.389_6933,
                       0.2426_4945,
                       0.217_8971,
                       -0.0233_5227,
                       -0.0848_1959,
                       -0.1432_4117,
                   ]
               ]    )
           self.assertTrue(numpy.allclose(output[:, :10].numpy()				,			expected_slice.numpy()				,			atol=1E-4    )    )