





"""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 					_SCREAMING_SNAKE_CASE(   unittest.TestCase  ):




       def __init__(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__=13		,SCREAMING_SNAKE_CASE__=7		,SCREAMING_SNAKE_CASE__=True		,SCREAMING_SNAKE_CASE__=True		,SCREAMING_SNAKE_CASE__=True		,SCREAMING_SNAKE_CASE__=True		,SCREAMING_SNAKE_CASE__=99		,SCREAMING_SNAKE_CASE__=32		,SCREAMING_SNAKE_CASE__=5		,SCREAMING_SNAKE_CASE__=4		,SCREAMING_SNAKE_CASE__=37		,SCREAMING_SNAKE_CASE__="gelu"		,SCREAMING_SNAKE_CASE__=0.1		,SCREAMING_SNAKE_CASE__=0.1		,SCREAMING_SNAKE_CASE__=5_12		,SCREAMING_SNAKE_CASE__=16		,SCREAMING_SNAKE_CASE__=2		,SCREAMING_SNAKE_CASE__=0.0_2		,SCREAMING_SNAKE_CASE__=4		,)			->      Optional[int]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :str						=  parent
             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  batch_size
             __SCREAMING_SNAKE_CASE   :List[str]						=  seq_length
             __SCREAMING_SNAKE_CASE   :str						=  is_training
             __SCREAMING_SNAKE_CASE   :Tuple						=  use_attention_mask
             __SCREAMING_SNAKE_CASE   :int						=  use_token_type_ids
             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  use_labels
             __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  vocab_size
             __SCREAMING_SNAKE_CASE   :int						=  hidden_size
             __SCREAMING_SNAKE_CASE   :Dict						=  num_hidden_layers
             __SCREAMING_SNAKE_CASE   :Dict						=  num_attention_heads
             __SCREAMING_SNAKE_CASE   :List[str]						=  intermediate_size
             __SCREAMING_SNAKE_CASE   :List[str]						=  hidden_act
             __SCREAMING_SNAKE_CASE   :str						=  hidden_dropout_prob
             __SCREAMING_SNAKE_CASE   :Dict						=  attention_probs_dropout_prob
             __SCREAMING_SNAKE_CASE   :str						=  max_position_embeddings
             __SCREAMING_SNAKE_CASE   :Tuple						=  type_vocab_size
             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  type_sequence_label_size
             __SCREAMING_SNAKE_CASE   :List[Any]						=  initializer_range
             __SCREAMING_SNAKE_CASE   :Optional[int]						=  num_choices




       def     _UpperCamelCase     (						self							)			->      Tuple:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :Any						=  ids_tensor([self.batch_size, self.seq_length]		,self.vocab_size							)

             __SCREAMING_SNAKE_CASE   :str						=  None
             if self.use_attention_mask:
                   __SCREAMING_SNAKE_CASE   :Optional[Any]						=  random_attention_mask([self.batch_size, self.seq_length]							)

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

             __SCREAMING_SNAKE_CASE   :Tuple						=  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=SCREAMING_SNAKE_CASE__		,initializer_range=self.initializer_range		,)

             return config, input_ids, token_type_ids, attention_mask




       def     _UpperCamelCase     (						self							)			->      Tuple:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :Any						=  self.prepare_config_and_inputs()
             __SCREAMING_SNAKE_CASE				,__SCREAMING_SNAKE_CASE				,__SCREAMING_SNAKE_CASE				,__SCREAMING_SNAKE_CASE   :Tuple						=  config_and_inputs
             __SCREAMING_SNAKE_CASE   :int						=  {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
             return config, inputs_dict






@require_flax
class 					_SCREAMING_SNAKE_CASE(   A    ,	unittest.TestCase  ):
       SCREAMING_SNAKE_CASE_   :      Optional[Any]							=  (
           (
               FlaxAlbertModel,
               FlaxAlbertForPreTraining,
               FlaxAlbertForMaskedLM,
               FlaxAlbertForMultipleChoice,
               FlaxAlbertForQuestionAnswering,
               FlaxAlbertForSequenceClassification,
               FlaxAlbertForTokenClassification,
               FlaxAlbertForQuestionAnswering,
           )
           if is_flax_available()
           else ()
       )




       def     _UpperCamelCase     (						self							)			->      Dict:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :Any						=  FlaxAlbertModelTester(self							)




       @slow
       def     _UpperCamelCase     (						self							)			->      Dict:





             """simple docstring"""






             for model_class_name in self.all_model_classes:
                   __SCREAMING_SNAKE_CASE   :Optional[Any]						=  model_class_name.from_pretrained('''albert-base-v2'''							)
                   __SCREAMING_SNAKE_CASE   :int						=  model(np.ones((1, 1)							)							)
                   self.assertIsNotNone(SCREAMING_SNAKE_CASE__							)






@require_flax
class 					_SCREAMING_SNAKE_CASE(   unittest.TestCase  ):




       @slow
       def     _UpperCamelCase     (						self							)			->      Optional[int]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :str						=  FlaxAlbertModel.from_pretrained('''albert-base-v2'''							)
             __SCREAMING_SNAKE_CASE   :List[Any]						=  np.array([[0, 3_45, 2_32, 3_28, 7_40, 1_40, 16_95, 69, 60_78, 15_88, 2]]							)
             __SCREAMING_SNAKE_CASE   :Dict						=  np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]							)
             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  model(SCREAMING_SNAKE_CASE__		,attention_mask=SCREAMING_SNAKE_CASE__							)[0]
             __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  (1, 11, 7_68)
             self.assertEqual(output.shape		,SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE   :List[str]						=  np.array(
                 [[[-0.6_5_1_3, 1.5_0_3_5, -0.2_7_6_6], [-0.6_5_1_5, 1.5_0_4_6, -0.2_7_8_0], [-0.6_5_1_2, 1.5_0_4_9, -0.2_7_8_4]]]							)

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






"""simple docstring"""
from typing import TYPE_CHECKING

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


lowerCamelCase_								=		{"configuration_dpt": ["DPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DPTConfig"]}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowerCamelCase_								=		["DPTFeatureExtractor"]
  lowerCamelCase_								=		["DPTImageProcessor"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowerCamelCase_								=		[
      "DPT_PRETRAINED_MODEL_ARCHIVE_LIST",
      "DPTForDepthEstimation",
      "DPTForSemanticSegmentation",
      "DPTModel",
      "DPTPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_dpt import DPT_PRETRAINED_CONFIG_ARCHIVE_MAP, DPTConfig

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_dpt import DPTFeatureExtractor
    from .image_processing_dpt import DPTImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_dpt import (
        DPT_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPTForDepthEstimation,
        DPTForSemanticSegmentation,
        DPTModel,
        DPTPreTrainedModel,
    )


else:
  import sys

  lowerCamelCase_								=		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)