
'''simple docstring'''






from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable


UpperCAmelCase_       :   Tuple   =       {
    """configuration_gpt_neox_japanese""": ["""GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoXJapaneseConfig"""],
    """tokenization_gpt_neox_japanese""": ["""GPTNeoXJapaneseTokenizer"""],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase_       :   Optional[int]   =       [
     """GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """GPTNeoXJapaneseForCausalLM""",
     """GPTNeoXJapaneseLayer""",
     """GPTNeoXJapaneseModel""",
     """GPTNeoXJapanesePreTrainedModel""",
 ]


if TYPE_CHECKING:
 from .configuration_gpt_neox_japanese import GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXJapaneseConfig
 from .tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_gpt_neox_japanese import (
      GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
      GPTNeoXJapaneseForCausalLM,
      GPTNeoXJapaneseLayer,
      GPTNeoXJapaneseModel,
      GPTNeoXJapanesePreTrainedModel,
  )


else:
 import sys

 UpperCAmelCase_       :   Any   =       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

'''simple docstring'''






import unittest

import numpy as np

from transformers import AlbertConfig, 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():
 import jax.numpy as jnp

 from transformers.models.albert.modeling_flax_albert import (
     FlaxAlbertForMaskedLM,
     FlaxAlbertForMultipleChoice,
     FlaxAlbertForPreTraining,
     FlaxAlbertForQuestionAnswering,
     FlaxAlbertForSequenceClassification,
     FlaxAlbertForTokenClassification,
     FlaxAlbertModel,
 )

class  UpperCAmelCase__						(       unittest.TestCase ):



    def __init__(	self      :       Union[str, Any],__A      :       Union[str, Any],__A      :       Optional[int]=1_3,__A      :       Optional[Any]=7,__A      :       int=True,__A      :       Any=True,__A      :       Dict=True,__A      :       List[Any]=True,__A      :       Optional[Any]=9_9,__A      :       List[str]=3_2,__A      :       int=5,__A      :       str=4,__A      :       int=3_7,__A      :       Optional[Any]="gelu",__A      :       Union[str, Any]=0.1,__A      :       str=0.1,__A      :       Any=5_1_2,__A      :       Any=1_6,__A      :       Optional[int]=2,__A      :       int=0.02,__A      :       Optional[int]=4,):
          _lowerCamelCase				:   Union[str, Any]								=   parent
          _lowerCamelCase				:   Tuple								=   batch_size
          _lowerCamelCase				:   List[str]								=   seq_length
          _lowerCamelCase				:   Optional[int]								=   is_training
          _lowerCamelCase				:   str								=   use_attention_mask
          _lowerCamelCase				:   str								=   use_token_type_ids
          _lowerCamelCase				:   List[str]								=   use_labels
          _lowerCamelCase				:   Tuple								=   vocab_size
          _lowerCamelCase				:   Dict								=   hidden_size
          _lowerCamelCase				:   str								=   num_hidden_layers
          _lowerCamelCase				:   List[str]								=   num_attention_heads
          _lowerCamelCase				:   Optional[Any]								=   intermediate_size
          _lowerCamelCase				:   Optional[int]								=   hidden_act
          _lowerCamelCase				:   Any								=   hidden_dropout_prob
          _lowerCamelCase				:   List[Any]								=   attention_probs_dropout_prob
          _lowerCamelCase				:   Tuple								=   max_position_embeddings
          _lowerCamelCase				:   str								=   type_vocab_size
          _lowerCamelCase				:   str								=   type_sequence_label_size
          _lowerCamelCase				:   int								=   initializer_range
          _lowerCamelCase				:   List[Any]								=   num_choices



    def   lowerCamelCase_  (	self      :       Optional[int]     ):
          _lowerCamelCase				:   List[Any]								=   ids_tensor([self.batch_size, self.seq_length],self.vocab_size     )

          _lowerCamelCase				:   Union[str, Any]								=   None
          if self.use_attention_mask:
                _lowerCamelCase				:   Tuple								=   random_attention_mask([self.batch_size, self.seq_length]     )

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

          _lowerCamelCase				:   int								=   AlbertConfig(
              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=__A,initializer_range=self.initializer_range,)

          return config, input_ids, token_type_ids, attention_mask



    def   lowerCamelCase_  (	self      :       Dict     ):
          _lowerCamelCase				:   str								=   self.prepare_config_and_inputs()
          _lowerCamelCase						,		_lowerCamelCase						,		_lowerCamelCase						,		_lowerCamelCase				:   Union[str, Any]								=   config_and_inputs
          _lowerCamelCase				:   Any								=   {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
          return config, inputs_dict


@require_flax
class  UpperCAmelCase__						(       A						,	unittest.TestCase ):
    lowerCAmelCase_				    =		(
        (
            FlaxAlbertModel,
            FlaxAlbertForPreTraining,
            FlaxAlbertForMaskedLM,
            FlaxAlbertForMultipleChoice,
            FlaxAlbertForQuestionAnswering,
            FlaxAlbertForSequenceClassification,
            FlaxAlbertForTokenClassification,
            FlaxAlbertForQuestionAnswering,
        )
        if is_flax_available()
        else ()
    )



    def   lowerCamelCase_  (	self      :       Any     ):
          _lowerCamelCase				:   List[str]								=   FlaxAlbertModelTester(self     )



    @slow
    def   lowerCamelCase_  (	self      :       Union[str, Any]     ):
          for model_class_name in self.all_model_classes:
                _lowerCamelCase				:   str								=   model_class_name.from_pretrained("albert-base-v2"     )
                _lowerCamelCase				:   Any								=   model(np.ones((1, 1)     )     )
                self.assertIsNotNone(__A     )


@require_flax
class  UpperCAmelCase__						(       unittest.TestCase ):



    @slow
    def   lowerCamelCase_  (	self      :       Tuple     ):
          _lowerCamelCase				:   Optional[int]								=   FlaxAlbertModel.from_pretrained("albert-base-v2"     )
          _lowerCamelCase				:   int								=   np.array([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]]     )
          _lowerCamelCase				:   Union[str, Any]								=   np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]     )
          _lowerCamelCase				:   Optional[Any]								=   model(__A,attention_mask=__A     )[0]
          _lowerCamelCase				:   Dict								=   (1, 1_1, 7_6_8)
          self.assertEqual(output.shape,__A     )
          _lowerCamelCase				:   Tuple								=   np.array(
              [[[-0.6513, 1.5035, -0.2766], [-0.6515, 1.5046, -0.2780], [-0.6512, 1.5049, -0.2784]]]     )

          self.assertTrue(jnp.allclose(output[:, 1:4, 1:4],__A,atol=1e-4     )     )