



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 		_lowercase		(							unittest.TestCase						):

   def __init__(   self      :							Dict				, __lowerCAmelCase      :							Dict				, __lowerCAmelCase      :							Any=13				, __lowerCAmelCase      :							int=7				, __lowerCAmelCase      :							str=True				, __lowerCAmelCase      :							str=True				, __lowerCAmelCase      :							Tuple=True				, __lowerCAmelCase      :							Any=True				, __lowerCAmelCase      :							Union[str, Any]=99				, __lowerCAmelCase      :							Optional[int]=24				, __lowerCAmelCase      :							Optional[Any]=32				, __lowerCAmelCase      :							List[Any]=5				, __lowerCAmelCase      :							Optional[int]=4				, __lowerCAmelCase      :							Optional[Any]=37				, __lowerCAmelCase      :							Optional[int]="gelu"				, __lowerCAmelCase      :							Optional[Any]=0.1				, __lowerCAmelCase      :							Any=0.1				, __lowerCAmelCase      :							int=512				, __lowerCAmelCase      :							List[Any]=16				, __lowerCAmelCase      :							int=2				, __lowerCAmelCase      :							Tuple=0.0_2				, __lowerCAmelCase      :							List[str]=4				, )							->					Tuple:


    """simple docstring"""






    a							=		parent
    a							=		batch_size
    a							=		seq_length
    a							=		is_training
    a							=		use_attention_mask
    a							=		use_token_type_ids
    a							=		use_labels
    a							=		vocab_size
    a							=		hidden_size
    a							=		embedding_size
    a							=		num_hidden_layers
    a							=		num_attention_heads
    a							=		intermediate_size
    a							=		hidden_act
    a							=		hidden_dropout_prob
    a							=		attention_probs_dropout_prob
    a							=		max_position_embeddings
    a							=		type_vocab_size
    a							=		type_sequence_label_size
    a							=		initializer_range
    a							=		num_choices

   def 		A				(   self      :							str				)							->					Optional[int]:


    """simple docstring"""






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

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

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

    a							=		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				)							->					str:


    """simple docstring"""






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



@require_flax
class 		_lowercase		(							__a, unittest.TestCase						):
   _UpperCAmelCase  						=      True

   _UpperCAmelCase  						=      (
       (
           FlaxElectraModel,
           FlaxElectraForCausalLM,
           FlaxElectraForMaskedLM,
           FlaxElectraForPreTraining,
           FlaxElectraForTokenClassification,
           FlaxElectraForQuestionAnswering,
           FlaxElectraForMultipleChoice,
           FlaxElectraForSequenceClassification,
       )
       if is_flax_available()
       else ()
   )

   def 		A				(   self      :							Optional[Any]				)							->					str:


    """simple docstring"""






    a							=		FlaxElectraModelTester(self				)




   @slow
   def 		A				(   self      :							Tuple				)							->					Any:


    """simple docstring"""






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







'''simple docstring'''







from __future__ import annotations





def 		snake_case_				(_a      :				list[list[int]]       ,  _a      :				list[list[int]]	):
    assert len(_a	) == len(_a	)

    UpperCAmelCase     			=						len(_a	)
    UpperCAmelCase     			=						list(range(_a	)	)
    UpperCAmelCase     			=						[-1] * n  # who the donor has donated to
    UpperCAmelCase     			=						[-1] * n  # who the recipient has received from
    UpperCAmelCase     			=						[0] * n

    while unmatched_donors:
        UpperCAmelCase     			=						unmatched_donors[0]
        UpperCAmelCase     			=						donor_pref[donor]
        UpperCAmelCase     			=						donor_preference[num_donations[donor]]
        num_donations[donor] += 1
        UpperCAmelCase     			=						recipient_pref[recipient]
        UpperCAmelCase     			=						rec_record[recipient]

        if prev_donor != -1:
            if rec_preference.index(_a	) > rec_preference.index(_a	):
                UpperCAmelCase     			=						donor
                UpperCAmelCase     			=						recipient
                unmatched_donors.append(_a	)
                unmatched_donors.remove(_a	)
        else:
            UpperCAmelCase     			=						donor
            UpperCAmelCase     			=						recipient
            unmatched_donors.remove(_a	)
    return donor_record




