




'''simple docstring'''
from __future__ import annotations

import unittest

from transformers import DebertaConfig, 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 (
      TFDebertaForMaskedLM,
      TFDebertaForQuestionAnswering,
      TFDebertaForSequenceClassification,
      TFDebertaForTokenClassification,
      TFDebertaModel,
  )






class 			SCREAMING_SNAKE_CASE:

       def __init__(       self   ,						lowerCamelCase__   ,						lowerCamelCase__=13   ,						lowerCamelCase__=7   ,						lowerCamelCase__=True   ,						lowerCamelCase__=True   ,						lowerCamelCase__=True   ,						lowerCamelCase__=True   ,						lowerCamelCase__=99   ,						lowerCamelCase__=32   ,						lowerCamelCase__=2   ,						lowerCamelCase__=4   ,						lowerCamelCase__=37   ,						lowerCamelCase__="gelu"   ,						lowerCamelCase__=0.1   ,						lowerCamelCase__=0.1   ,						lowerCamelCase__=512   ,						lowerCamelCase__=16   ,						lowerCamelCase__=2   ,						lowerCamelCase__=0.02   ,						lowerCamelCase__=3   ,						lowerCamelCase__=4   ,						lowerCamelCase__=None   ,						)       ->   Any:

           """simple docstring"""



           __lowercase     							=		parent
           __lowercase     							=		13
           __lowercase     							=		7
           __lowercase     							=		True
           __lowercase     							=		True
           __lowercase     							=		True
           __lowercase     							=		True
           __lowercase     							=		99
           __lowercase     							=		32
           __lowercase     							=		2
           __lowercase     							=		4
           __lowercase     							=		37
           __lowercase     							=		"""gelu"""
           __lowercase     							=		0.1
           __lowercase     							=		0.1
           __lowercase     							=		512
           __lowercase     							=		16
           __lowercase     							=		False
           __lowercase     							=		-1
           __lowercase     							=		True
           __lowercase     							=		2
           __lowercase     							=		0.02
           __lowercase     							=		3
           __lowercase     							=		4
           __lowercase     							=		None

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

           """simple docstring"""



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

           __lowercase     							=		None
           if self.use_input_mask:
               __lowercase     							=		random_attention_mask([self.batch_size, self.seq_length]	)

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

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

           __lowercase     							=		DebertaConfig(
               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   ,						relative_attention=self.relative_attention   ,						max_relative_positions=self.max_relative_positions   ,						position_biased_input=self.position_biased_input   ,						initializer_range=self.initializer_range   ,						return_dict=lowerCamelCase__   ,						)

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

       def        snake_case__     (       self   ,						lowerCamelCase__   ,						lowerCamelCase__   ,						lowerCamelCase__   ,						lowerCamelCase__   ,						lowerCamelCase__   ,						lowerCamelCase__   ,						lowerCamelCase__	)       ->   Union[str, Any]:

           """simple docstring"""



           __lowercase     							=		TFDebertaModel(config=lowerCamelCase__	)
           __lowercase     							=		{"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}

           __lowercase     							=		[input_ids, input_mask]
           __lowercase     							=		model(lowerCamelCase__	)

           __lowercase     							=		model(lowerCamelCase__	)

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

       def        snake_case__     (       self   ,						lowerCamelCase__   ,						lowerCamelCase__   ,						lowerCamelCase__   ,						lowerCamelCase__   ,						lowerCamelCase__   ,						lowerCamelCase__   ,						lowerCamelCase__	)       ->   int:

           """simple docstring"""



           __lowercase     							=		TFDebertaForMaskedLM(config=lowerCamelCase__	)
           __lowercase     							=		{
               """input_ids""": input_ids,
               """attention_mask""": input_mask,
               """token_type_ids""": token_type_ids,
           }
           __lowercase     							=		model(lowerCamelCase__	)
           self.parent.assertEqual(result.logits.shape   ,						(self.batch_size, self.seq_length, self.vocab_size)	)

       def        snake_case__     (       self   ,						lowerCamelCase__   ,						lowerCamelCase__   ,						lowerCamelCase__   ,						lowerCamelCase__   ,						lowerCamelCase__   ,						lowerCamelCase__   ,						lowerCamelCase__	)       ->   int:

           """simple docstring"""



           __lowercase     							=		self.num_labels
           __lowercase     							=		TFDebertaForSequenceClassification(config=lowerCamelCase__	)
           __lowercase     							=		{
               """input_ids""": input_ids,
               """attention_mask""": input_mask,
               """token_type_ids""": token_type_ids,
           }

           __lowercase     							=		model(lowerCamelCase__	)
           self.parent.assertEqual(result.logits.shape   ,						(self.batch_size, self.num_labels)	)

       def        snake_case__     (       self   ,						lowerCamelCase__   ,						lowerCamelCase__   ,						lowerCamelCase__   ,						lowerCamelCase__   ,						lowerCamelCase__   ,						lowerCamelCase__   ,						lowerCamelCase__	)       ->   Dict:

           """simple docstring"""



           __lowercase     							=		self.num_labels
           __lowercase     							=		TFDebertaForTokenClassification(config=lowerCamelCase__	)
           __lowercase     							=		{
               """input_ids""": input_ids,
               """attention_mask""": input_mask,
               """token_type_ids""": token_type_ids,
           }
           __lowercase     							=		model(lowerCamelCase__	)
           self.parent.assertEqual(result.logits.shape   ,						(self.batch_size, self.seq_length, self.num_labels)	)

       def        snake_case__     (       self   ,						lowerCamelCase__   ,						lowerCamelCase__   ,						lowerCamelCase__   ,						lowerCamelCase__   ,						lowerCamelCase__   ,						lowerCamelCase__   ,						lowerCamelCase__	)       ->   Any:

           """simple docstring"""



           __lowercase     							=		TFDebertaForQuestionAnswering(config=lowerCamelCase__	)
           __lowercase     							=		{
               """input_ids""": input_ids,
               """attention_mask""": input_mask,
               """token_type_ids""": token_type_ids,
           }

           __lowercase     							=		model(lowerCamelCase__	)
           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        snake_case__     (       self	)       ->   Any:

           """simple docstring"""



           __lowercase     							=		self.prepare_config_and_inputs()
           (
               (
               __lowercase
           )							,(
               __lowercase
           )							,(
               __lowercase
           )							,(
               __lowercase
           )							,(
               __lowercase
           )							,(
               __lowercase
           )							,(
               __lowercase
           )							,
           )     							=		config_and_inputs
           __lowercase     							=		{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
           return config, inputs_dict






@require_tf
class 			SCREAMING_SNAKE_CASE(							__A  , __A  , unittest.TestCase	):
       snake_case_					:	Any       =   (
           (
               TFDebertaModel,
               TFDebertaForMaskedLM,
               TFDebertaForQuestionAnswering,
               TFDebertaForSequenceClassification,
               TFDebertaForTokenClassification,
           )
           if is_tf_available()
           else ()
       )
       snake_case_					:	List[Any]       =   (
           {
               """feature-extraction""": TFDebertaModel,
               """fill-mask""": TFDebertaForMaskedLM,
               """question-answering""": TFDebertaForQuestionAnswering,
               """text-classification""": TFDebertaForSequenceClassification,
               """token-classification""": TFDebertaForTokenClassification,
               """zero-shot""": TFDebertaForSequenceClassification,
           }
           if is_tf_available()
           else {}
       )

       snake_case_					:	Any       =   False
       snake_case_					:	List[Any]       =   False

       def        snake_case__     (       self	)       ->   Tuple:

           """simple docstring"""



           __lowercase     							=		TFDebertaModelTester(self	)
           __lowercase     							=		ConfigTester(self   ,						config_class=lowerCamelCase__   ,						hidden_size=37	)

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

           """simple docstring"""



           self.config_tester.run_common_tests()

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

           """simple docstring"""



           __lowercase     							=		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*lowerCamelCase__	)

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

           """simple docstring"""



           __lowercase     							=		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_masked_lm(*lowerCamelCase__	)

       def        snake_case__     (       self	)       ->   Any:

           """simple docstring"""



           __lowercase     							=		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_question_answering(*lowerCamelCase__	)

       def        snake_case__     (       self	)       ->   Tuple:

           """simple docstring"""



           __lowercase     							=		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_sequence_classification(*lowerCamelCase__	)

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

           """simple docstring"""



           __lowercase     							=		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_token_classification(*lowerCamelCase__	)




       @slow
       def        snake_case__     (       self	)       ->   int:

           """simple docstring"""



           __lowercase     							=		TFDebertaModel.from_pretrained("""kamalkraj/deberta-base"""	)
           self.assertIsNotNone(lowerCamelCase__	)






@require_tf
class 			SCREAMING_SNAKE_CASE(							unittest.TestCase	):

       @unittest.skip(reason="""Model not available yet"""	)
       def        snake_case__     (       self	)       ->   Union[str, Any]:

           """simple docstring"""



           pass




       @slow
       def        snake_case__     (       self	)       ->   str:

           """simple docstring"""



           __lowercase     							=		TFDebertaModel.from_pretrained("""kamalkraj/deberta-base"""	)
           __lowercase     							=		tf.constant([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]	)
           __lowercase     							=		tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]	)
           __lowercase     							=		model(lowerCamelCase__   ,						attention_mask=lowerCamelCase__	)[0]

           __lowercase     							=		tf.constant(
               [
                   [
                       [-0.59_85_58_96, -0.80_55_25_66, -0.8_46_21_35],
                       [1.4_48_40_25, -0.93_48_37_94, -0.80_59_30_85],
                       [0.3_12_27_41, 0.00_31_60_59, -1.4_13_13_77],
                   ]
               ]	)
           tf.debugging.assert_near(output[:, 1:4, 1:4]   ,						lowerCamelCase__   ,						atol=1E-4	)










'''simple docstring'''
def 							snake_case_    (  a__						:      int     ):



    """simple docstring"""




    __lowercase     							=		1
    __lowercase     							=		0
    __lowercase     							=		1
    __lowercase     							=		1
    __lowercase     							=		3
    __lowercase     							=		3
    __lowercase     							=		limit
    __lowercase     							=		0

    __lowercase     							=		""""""
    while counter != decimal + 1:
        if 4 * q + r - t < n * t:
            result += str(a__     )
            if counter == 0:
                result += "."

            if decimal == counter:
                break

            counter += 1
            __lowercase     							=		10 * (r - n * t)
            __lowercase     							=		((10 * (3 * q + r)) // t) - 10 * n
            q *= 10
            __lowercase     							=		nr
        else:
            __lowercase     							=		(2 * q + r) * l
            __lowercase     							=		(q * (7 * k) + 2 + (r * l)) // (t * l)
            q *= k
            t *= l
            l += 2
            k += 1
            __lowercase     							=		nn
            __lowercase     							=		nr
    return result
def 							snake_case_    (  ):



    """simple docstring"""




    print(f'{calculate_pi(50     ) = }'     )
    import doctest

    doctest.testmod()


if __name__ == "__main__":
  main()




