






'''simple docstring'''

import unittest

import numpy as np

from transformers import BertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
      from transformers.models.bert.modeling_flax_bert import (
          FlaxBertForMaskedLM,
          FlaxBertForMultipleChoice,
          FlaxBertForNextSentencePrediction,
          FlaxBertForPreTraining,
          FlaxBertForQuestionAnswering,
          FlaxBertForSequenceClassification,
          FlaxBertForTokenClassification,
          FlaxBertModel,
      )




class      A     (   unittest.TestCase    ):






 """simple docstring"""





 def __init__(				self      ,  __lowerCAmelCase      ,  __lowerCAmelCase=13      ,  __lowerCAmelCase=7      ,  __lowerCAmelCase=True      ,  __lowerCAmelCase=True      ,  __lowerCAmelCase=True      ,  __lowerCAmelCase=True      ,  __lowerCAmelCase=99      ,  __lowerCAmelCase=32      ,  __lowerCAmelCase=5      ,  __lowerCAmelCase=4      ,  __lowerCAmelCase=37      ,  __lowerCAmelCase="gelu"      ,  __lowerCAmelCase=0.1      ,  __lowerCAmelCase=0.1      ,  __lowerCAmelCase=5_12      ,  __lowerCAmelCase=16      ,  __lowerCAmelCase=2      ,  __lowerCAmelCase=0.02      ,  __lowerCAmelCase=4      ,  ):
       UpperCamelCase_					: Tuple            =				parent
       UpperCamelCase_					: List[Any]            =				batch_size
       UpperCamelCase_					: Tuple            =				seq_length
       UpperCamelCase_					: Tuple            =				is_training
       UpperCamelCase_					: str            =				use_attention_mask
       UpperCamelCase_					: Dict            =				use_token_type_ids
       UpperCamelCase_					: Optional[Any]            =				use_labels
       UpperCamelCase_					: Union[str, Any]            =				vocab_size
       UpperCamelCase_					: List[str]            =				hidden_size
       UpperCamelCase_					: List[str]            =				num_hidden_layers
       UpperCamelCase_					: int            =				num_attention_heads
       UpperCamelCase_					: Optional[Any]            =				intermediate_size
       UpperCamelCase_					: int            =				hidden_act
       UpperCamelCase_					: Union[str, Any]            =				hidden_dropout_prob
       UpperCamelCase_					: Tuple            =				attention_probs_dropout_prob
       UpperCamelCase_					: str            =				max_position_embeddings
       UpperCamelCase_					: Dict            =				type_vocab_size
       UpperCamelCase_					: Tuple            =				type_sequence_label_size
       UpperCamelCase_					: int            =				initializer_range
       UpperCamelCase_					: List[Any]            =				num_choices
 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: Tuple            =				ids_tensor([self.batch_size, self.seq_length]      ,  self.vocab_size			)

       UpperCamelCase_					: List[Any]            =				None
       if self.use_attention_mask:
             UpperCamelCase_					: int            =				random_attention_mask([self.batch_size, self.seq_length]			)

       UpperCamelCase_					: List[Any]            =				None
       if self.use_token_type_ids:
             UpperCamelCase_					: Dict            =				ids_tensor([self.batch_size, self.seq_length]      ,  self.type_vocab_size			)

       UpperCamelCase_					: Dict            =				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=__lowerCAmelCase      ,  initializer_range=self.initializer_range      ,  )

       return config, input_ids, token_type_ids, attention_mask
 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: Any            =				self.prepare_config_and_inputs()
       UpperCamelCase_     ,      UpperCamelCase_     ,      UpperCamelCase_     ,      UpperCamelCase_					: Any            =				config_and_inputs
       UpperCamelCase_					: Any            =				{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
       return config, inputs_dict





 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: Union[str, Any]            =				self.prepare_config_and_inputs()
       UpperCamelCase_     ,      UpperCamelCase_     ,      UpperCamelCase_     ,      UpperCamelCase_					: int            =				config_and_inputs

       UpperCamelCase_					: Any            =				True
       UpperCamelCase_					: Optional[int]            =				floats_tensor([self.batch_size, self.seq_length, self.hidden_size]			)
       UpperCamelCase_					: Union[str, Any]            =				ids_tensor([self.batch_size, self.seq_length]      ,  vocab_size=2			)

       return (
           config,
           input_ids,
           attention_mask,
           encoder_hidden_states,
           encoder_attention_mask,
       )






@require_flax
class      A     (   SCREAMING_SNAKE_CASE__, unittest.TestCase    ):






 """simple docstring"""





 __a      :    Any			  =    True

 __a      :    Union[str, Any]			  =    (
     (
         FlaxBertModel,
         FlaxBertForPreTraining,
         FlaxBertForMaskedLM,
         FlaxBertForMultipleChoice,
         FlaxBertForQuestionAnswering,
         FlaxBertForNextSentencePrediction,
         FlaxBertForSequenceClassification,
         FlaxBertForTokenClassification,
         FlaxBertForQuestionAnswering,
     )
     if is_flax_available()
     else ()
 )
 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: Any            =				FlaxBertModelTester(self			)





 @slow
 def 	_UpperCAmelCase							(				self			):
       # Only check this for base model, not necessary for all model classes.
       # This will also help speed-up tests.
       UpperCamelCase_					: Optional[int]            =				FlaxBertModel.from_pretrained("""bert-base-cased"""			)
       UpperCamelCase_					: Union[str, Any]            =				model(np.ones((1, 1)			)			)
       self.assertIsNotNone(__lowerCAmelCase			)














'''simple docstring'''

def 				snake_case       (  a_				:			str     , a_				:			Optional[int]						)							->					Any:





      """simple docstring"""

      UpperCamelCase_					: Tuple            =				(boundary[1] - boundary[0]) / steps
      UpperCamelCase_					: Dict            =				boundary[0]
      UpperCamelCase_					: Any            =				boundary[1]
      UpperCamelCase_					: Union[str, Any]            =				make_points(a_     , a_     , a_						)
      UpperCamelCase_					: Any            =				0.0
      y += (h / 2.0) * f(a_						)
      for i in x_i:
            # print(i)
            y += h * f(a_						)
      y += (h / 2.0) * f(a_						)
      return y



def 				snake_case       (  a_				:			Tuple     , a_				:			Any     , a_				:			Tuple						)							->					Tuple:





      """simple docstring"""

      UpperCamelCase_					: Optional[int]            =				a + h
      while x < (b - h):
            yield x
            UpperCamelCase_					: List[str]            =				x + h



def 				snake_case       (  a_				:			List[str]						)							->					Tuple:  # enter your function here





      """simple docstring"""

      UpperCamelCase_					: int            =				(x - 0) * (x - 0)
      return y



def 				snake_case       (  )							->					Dict:





      """simple docstring"""

      UpperCamelCase_					: Optional[Any]            =				0.0  # Lower bound of integration
      UpperCamelCase_					: Optional[int]            =				1.0  # Upper bound of integration
      UpperCamelCase_					: Optional[Any]            =				10.0  # define number of steps or resolution
      UpperCamelCase_					: Optional[Any]            =				[a, b]  # define boundary of integration
      UpperCamelCase_					: Any            =				method_a(a_     , a_						)
      print(f"y = {y}"						)


if __name__ == "__main__":
      main()






