




'''simple docstring'''







import unittest

import numpy as np

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

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
   from transformers.models.electra.modeling_flax_electra import (
       FlaxElectraForCausalLM,
       FlaxElectraForMaskedLM,
       FlaxElectraForMultipleChoice,
       FlaxElectraForPreTraining,
       FlaxElectraForQuestionAnswering,
       FlaxElectraForSequenceClassification,
       FlaxElectraForTokenClassification,
       FlaxElectraModel,
   )


class      _a    (      unittest.TestCase					):
       def __init__(      self		:      Dict  ,							lowercase		:      Dict  ,							lowercase		:      Any=13  ,							lowercase		:      int=7  ,							lowercase		:      str=True  ,							lowercase		:      str=True  ,							lowercase		:      Tuple=True  ,							lowercase		:      Any=True  ,							lowercase		:      Union[str, Any]=99  ,							lowercase		:      Optional[int]=24  ,							lowercase		:      Optional[Any]=32  ,							lowercase		:      List[Any]=5  ,							lowercase		:      Optional[int]=4  ,							lowercase		:      Optional[Any]=37  ,							lowercase		:      Optional[int]="gelu"  ,							lowercase		:      Optional[Any]=0.1  ,							lowercase		:      Any=0.1  ,							lowercase		:      int=512  ,							lowercase		:      List[Any]=16  ,							lowercase		:      int=2  ,							lowercase		:      Tuple=0.02  ,							lowercase		:      List[str]=4  ,							):



           '''simple docstring'''

           UpperCAmelCase     			=						parent
           UpperCAmelCase     			=						batch_size
           UpperCAmelCase     			=						seq_length
           UpperCAmelCase     			=						is_training
           UpperCAmelCase     			=						use_attention_mask
           UpperCAmelCase     			=						use_token_type_ids
           UpperCAmelCase     			=						use_labels
           UpperCAmelCase     			=						vocab_size
           UpperCAmelCase     			=						hidden_size
           UpperCAmelCase     			=						embedding_size
           UpperCAmelCase     			=						num_hidden_layers
           UpperCAmelCase     			=						num_attention_heads
           UpperCAmelCase     			=						intermediate_size
           UpperCAmelCase     			=						hidden_act
           UpperCAmelCase     			=						hidden_dropout_prob
           UpperCAmelCase     			=						attention_probs_dropout_prob
           UpperCAmelCase     			=						max_position_embeddings
           UpperCAmelCase     			=						type_vocab_size
           UpperCAmelCase     			=						type_sequence_label_size
           UpperCAmelCase     			=						initializer_range
           UpperCAmelCase     			=						num_choices
       def        A (      self		:      str							):



           '''simple docstring'''

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

           UpperCAmelCase     			=						None
           if self.use_attention_mask:
               UpperCAmelCase     			=						random_attention_mask([self.batch_size, self.seq_length]							)

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

           UpperCAmelCase     			=						ElectraConfig(
               vocab_size=self.vocab_size  ,							hidden_size=self.hidden_size  ,							embedding_size=self.embedding_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 config, input_ids, token_type_ids, attention_mask







       def        A (      self		:      Any							):



           '''simple docstring'''

           UpperCAmelCase     			=						self.prepare_config_and_inputs()
           UpperCAmelCase			,	UpperCAmelCase			,	UpperCAmelCase			,	UpperCAmelCase     			=						config_and_inputs
           UpperCAmelCase     			=						{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
           return config, inputs_dict






@require_flax
class      _a    (      __a				,				unittest.TestCase					):
       __a							:	Dict  							=   True

       __a							:	List[Any]  							=   (
           (
               FlaxElectraModel,
               FlaxElectraForCausalLM,
               FlaxElectraForMaskedLM,
               FlaxElectraForPreTraining,
               FlaxElectraForTokenClassification,
               FlaxElectraForQuestionAnswering,
               FlaxElectraForMultipleChoice,
               FlaxElectraForSequenceClassification,
           )
           if is_flax_available()
           else ()
       )
       def        A (      self		:      Optional[Any]							):



           '''simple docstring'''

           UpperCAmelCase     			=						FlaxElectraModelTester(self							)







       @slow
       def        A (      self		:      Tuple							):



           '''simple docstring'''

           for model_class_name in self.all_model_classes:
               if model_class_name == FlaxElectraForMaskedLM:
                   UpperCAmelCase     			=						model_class_name.from_pretrained('''google/electra-small-generator'''							)
               else:
                   UpperCAmelCase     			=						model_class_name.from_pretrained('''google/electra-small-discriminator'''							)
               UpperCAmelCase     			=						model(np.ones((1, 1)							)							)
               self.assertIsNotNone(lowercase							)










'''simple docstring'''







def 		snake_case_				(_a      :				Any       ,  _a      :				Union[str, Any]	):
    if n < 0:
        return 0
    # table[i] represents the number of ways to get to amount i
    UpperCAmelCase     			=						[0] * (n + 1)

    # There is exactly 1 way to get to zero(You pick no coins).
    UpperCAmelCase     			=						1

    # Pick all coins one by one and update table[] values
    # after the index greater than or equal to the value of the
    # picked coin
    for coin_val in s:
        for j in range(_a       ,  n + 1	):
            table[j] += table[j - coin_val]

    return table[n]


if __name__ == "__main__":
   import doctest

   doctest.testmod()




