


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  ):







 """simple docstring"""





 def __init__(      self       :			Dict							,     _a       :			int							,     _a       :			Dict=1_3							,     _a       :			Optional[int]=7							,     _a       :			Optional[int]=True							,     _a       :			Union[str, Any]=True							,     _a       :			Optional[Any]=True							,     _a       :			Any=True							,     _a       :			Tuple=9_9							,     _a       :			Optional[int]=2_4							,     _a       :			int=3_2							,     _a       :			Union[str, Any]=5							,     _a       :			Any=4							,     _a       :			Union[str, Any]=3_7							,     _a       :			Tuple="gelu"							,     _a       :			int=0.1							,     _a       :			str=0.1							,     _a       :			Union[str, Any]=5_1_2							,     _a       :			Dict=1_6							,     _a       :			int=2							,     _a       :			int=0.0_2							,     _a       :			List[str]=4							,     )     ->      List[Any]:
      UpperCAmelCase    :					Any			=      parent
      UpperCAmelCase    :					str			=      batch_size
      UpperCAmelCase    :					Tuple			=      seq_length
      UpperCAmelCase    :					int			=      is_training
      UpperCAmelCase    :					List[str]			=      use_attention_mask
      UpperCAmelCase    :					Dict			=      use_token_type_ids
      UpperCAmelCase    :					Any			=      use_labels
      UpperCAmelCase    :					Optional[int]			=      vocab_size
      UpperCAmelCase    :					Dict			=      hidden_size
      UpperCAmelCase    :					Optional[int]			=      embedding_size
      UpperCAmelCase    :					Optional[Any]			=      num_hidden_layers
      UpperCAmelCase    :					Optional[int]			=      num_attention_heads
      UpperCAmelCase    :					str			=      intermediate_size
      UpperCAmelCase    :					List[str]			=      hidden_act
      UpperCAmelCase    :					str			=      hidden_dropout_prob
      UpperCAmelCase    :					Any			=      attention_probs_dropout_prob
      UpperCAmelCase    :					Tuple			=      max_position_embeddings
      UpperCAmelCase    :					Optional[int]			=      type_vocab_size
      UpperCAmelCase    :					Dict			=      type_sequence_label_size
      UpperCAmelCase    :					str			=      initializer_range
      UpperCAmelCase    :					Optional[Any]			=      num_choices
 def 	_UpperCamelCase     (      self       :			Dict					)     ->      Dict:
      UpperCAmelCase    :					str			=      ids_tensor([self.batch_size, self.seq_length]							,     self.vocab_size					)

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

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

      UpperCAmelCase    :					Union[str, Any]			=      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 	_UpperCamelCase     (      self       :			Dict					)     ->      Tuple:
      UpperCAmelCase    :					Dict			=      self.prepare_config_and_inputs()
      UpperCAmelCase	,			UpperCAmelCase	,			UpperCAmelCase	,			UpperCAmelCase    :					Union[str, Any]			=      config_and_inputs
      UpperCAmelCase    :					int			=      {"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  ):







 """simple docstring"""





 _UpperCamelCase					:			Dict     	=       True

 _UpperCamelCase					:			str     	=       (
     (
         FlaxElectraModel,
         FlaxElectraForCausalLM,
         FlaxElectraForMaskedLM,
         FlaxElectraForPreTraining,
         FlaxElectraForTokenClassification,
         FlaxElectraForQuestionAnswering,
         FlaxElectraForMultipleChoice,
         FlaxElectraForSequenceClassification,
     )
     if is_flax_available()
     else ()
 )
 def 	_UpperCamelCase     (      self       :			Any					)     ->      Dict:
      UpperCAmelCase    :					Dict			=      FlaxElectraModelTester(self					)

 @slow
 def 	_UpperCamelCase     (      self       :			List[Any]					)     ->      Optional[int]:
      for model_class_name in self.all_model_classes:
           if model_class_name == FlaxElectraForMaskedLM:
                UpperCAmelCase    :					List[Any]			=      model_class_name.from_pretrained("google/electra-small-generator"					)
           else:
                UpperCAmelCase    :					Any			=      model_class_name.from_pretrained("google/electra-small-discriminator"					)
           UpperCAmelCase    :					List[str]			=      model(np.ones((1, 1)					)					)
           self.assertIsNotNone(_a					)







import logging

# standard libraries
import sys

import numpy as np
import pytest  # type: ignore

# Custom/local libraries
from matrix import matrix_operation as matop

_snake_case				=			[[1_2, 1_0], [3, 9]]
_snake_case				=			[[3, 4], [7, 4]]
_snake_case				=			[[3, 0, 2], [2, 0, -2], [0, 1, 1]]
_snake_case				=			[[3, 0, -2], [2, 0, 2], [0, 1, 1]]
_snake_case				=			[[3, 0, 2], [2, 0, -2], [0, 1, 1], [2, 0, -2]]
_snake_case				=			[1]
_snake_case				=			[2]

_snake_case				=			logging.getLogger()
_snake_case				=			logging.DEBUG
_snake_case				=			logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)







@pytest.mark.mat_ops
@pytest.mark.parametrize(
    ("mat1", "mat2")		,		[(mat_a, mat_b), (mat_c, mat_d), (mat_d, mat_e), (mat_f, mat_h)]			)
def 	A (		A__		,		A__			):
     if (np.array(A__			)).shape < (2, 2) or (np.array(A__			)).shape < (2, 2):
          with pytest.raises(A__			):
               logger.info(f'\n\t{test_addition.__name__} returned integer'			)
               matop.add(A__		,		A__			)
     elif (np.array(A__			)).shape == (np.array(A__			)).shape:
          logger.info(f'\n\t{test_addition.__name__} with same matrix dims'			)
          UpperCAmelCase    :					Tuple			=      (np.array(A__			) + np.array(A__			)).tolist()
          UpperCAmelCase    :					Optional[Any]			=      matop.add(A__		,		A__			)
          assert theo == act
     else:
          with pytest.raises(A__			):
               logger.info(f'\n\t{test_addition.__name__} with different matrix dims'			)
               matop.add(A__		,		A__			)







@pytest.mark.mat_ops
@pytest.mark.parametrize(
    ("mat1", "mat2")		,		[(mat_a, mat_b), (mat_c, mat_d), (mat_d, mat_e), (mat_f, mat_h)]			)
def 	A (		A__		,		A__			):
     if (np.array(A__			)).shape < (2, 2) or (np.array(A__			)).shape < (2, 2):
          with pytest.raises(A__			):
               logger.info(f'\n\t{test_subtraction.__name__} returned integer'			)
               matop.subtract(A__		,		A__			)
     elif (np.array(A__			)).shape == (np.array(A__			)).shape:
          logger.info(f'\n\t{test_subtraction.__name__} with same matrix dims'			)
          UpperCAmelCase    :					Tuple			=      (np.array(A__			) - np.array(A__			)).tolist()
          UpperCAmelCase    :					Optional[int]			=      matop.subtract(A__		,		A__			)
          assert theo == act
     else:
          with pytest.raises(A__			):
               logger.info(f'\n\t{test_subtraction.__name__} with different matrix dims'			)
               assert matop.subtract(A__		,		A__			)







@pytest.mark.mat_ops
@pytest.mark.parametrize(
    ("mat1", "mat2")		,		[(mat_a, mat_b), (mat_c, mat_d), (mat_d, mat_e), (mat_f, mat_h)]			)
def 	A (		A__		,		A__			):
     if (np.array(A__			)).shape < (2, 2) or (np.array(A__			)).shape < (2, 2):
          logger.info(f'\n\t{test_multiplication.__name__} returned integer'			)
          with pytest.raises(A__			):
               matop.add(A__		,		A__			)
     elif (np.array(A__			)).shape == (np.array(A__			)).shape:
          logger.info(f'\n\t{test_multiplication.__name__} meets dim requirements'			)
          UpperCAmelCase    :					List[str]			=      (np.matmul(A__		,		A__			)).tolist()
          UpperCAmelCase    :					int			=      matop.multiply(A__		,		A__			)
          assert theo == act
     else:
          with pytest.raises(A__			):
               logger.info(
                   f'\n\t{test_multiplication.__name__} does not meet dim requirements'			)
               assert matop.subtract(A__		,		A__			)







@pytest.mark.mat_ops
def 	A (		):
     UpperCAmelCase    :					int			=      (3.5 * np.array(A__			)).tolist()
     UpperCAmelCase    :					Optional[int]			=      matop.scalar_multiply(A__		,		3.5			)
     assert theo == act







@pytest.mark.mat_ops
def 	A (		):
     UpperCAmelCase    :					List[Any]			=      (np.identity(5			)).tolist()
     UpperCAmelCase    :					Optional[int]			=      matop.identity(5			)
     assert theo == act







@pytest.mark.mat_ops
@pytest.mark.parametrize("mat"		,		[mat_a, mat_b, mat_c, mat_d, mat_e, mat_f]			)
def 	A (		A__			):
     if (np.array(A__			)).shape < (2, 2):
          with pytest.raises(A__			):
               logger.info(f'\n\t{test_transpose.__name__} returned integer'			)
               matop.transpose(A__			)
     else:
          UpperCAmelCase    :					Dict			=      (np.transpose(A__			)).tolist()
          UpperCAmelCase    :					List[str]			=      matop.transpose(A__		,		return_map=A__			)
          assert theo == act



