
'''simple docstring'''

import collections
import inspect
import unittest

from transformers import SwinConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch
   from torch import nn

   from transformers import SwinBackbone, SwinForImageClassification, SwinForMaskedImageModeling, SwinModel
   from transformers.models.swin.modeling_swin import SWIN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
   from PIL import Image

   from transformers import AutoImageProcessor







class 	__lowerCAmelCase    :



     def __init__(self						,	lowerCAmelCase__						,	lowerCAmelCase__=1_3						,	lowerCAmelCase__=3_2						,	lowerCAmelCase__=2						,	lowerCAmelCase__=3						,	lowerCAmelCase__=1_6						,	lowerCAmelCase__=[1, 2, 1]						,	lowerCAmelCase__=[2, 2, 4]						,	lowerCAmelCase__=2						,	lowerCAmelCase__=2.0						,	lowerCAmelCase__=True						,	lowerCAmelCase__=0.0						,	lowerCAmelCase__=0.0						,	lowerCAmelCase__=0.1						,	lowerCAmelCase__="gelu"						,	lowerCAmelCase__=False						,	lowerCAmelCase__=True						,	lowerCAmelCase__=0.0_2						,	lowerCAmelCase__=1e-5						,	lowerCAmelCase__=True						,	lowerCAmelCase__=None						,	lowerCAmelCase__=True						,	lowerCAmelCase__=1_0						,	lowerCAmelCase__=8						,	lowerCAmelCase__=["stage1", "stage2"]						,	lowerCAmelCase__=[1, 2]						,	):
          _UpperCAmelCase					:  Any         =    parent
          _UpperCAmelCase					:  Tuple         =    batch_size
          _UpperCAmelCase					:  Optional[int]         =    image_size
          _UpperCAmelCase					:  Optional[int]         =    patch_size
          _UpperCAmelCase					:  str         =    num_channels
          _UpperCAmelCase					:  Optional[Any]         =    embed_dim
          _UpperCAmelCase					:  List[Any]         =    depths
          _UpperCAmelCase					:  Optional[int]         =    num_heads
          _UpperCAmelCase					:  List[Any]         =    window_size
          _UpperCAmelCase					:  Optional[Any]         =    mlp_ratio
          _UpperCAmelCase					:  Optional[int]         =    qkv_bias
          _UpperCAmelCase					:  Tuple         =    hidden_dropout_prob
          _UpperCAmelCase					:  Dict         =    attention_probs_dropout_prob
          _UpperCAmelCase					:  Optional[int]         =    drop_path_rate
          _UpperCAmelCase					:  Tuple         =    hidden_act
          _UpperCAmelCase					:  str         =    use_absolute_embeddings
          _UpperCAmelCase					:  Union[str, Any]         =    patch_norm
          _UpperCAmelCase					:  Dict         =    layer_norm_eps
          _UpperCAmelCase					:  Tuple         =    initializer_range
          _UpperCAmelCase					:  Any         =    is_training
          _UpperCAmelCase					:  Optional[Any]         =    scope
          _UpperCAmelCase					:  List[Any]         =    use_labels
          _UpperCAmelCase					:  Dict         =    type_sequence_label_size
          _UpperCAmelCase					:  str         =    encoder_stride
          _UpperCAmelCase					:  Union[str, Any]         =    out_features
          _UpperCAmelCase					:  Dict         =    out_indices



     def     snake_case_ (self	):
          _UpperCAmelCase					:  Dict         =    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

          _UpperCAmelCase					:  int         =    None
          if self.use_labels:
               _UpperCAmelCase					:  int         =    ids_tensor([self.batch_size]						,	self.type_sequence_label_size	)

          _UpperCAmelCase					:  List[str]         =    self.get_config()

          return config, pixel_values, labels



     def     snake_case_ (self	):
          return SwinConfig(
              image_size=self.image_size						,	patch_size=self.patch_size						,	num_channels=self.num_channels						,	embed_dim=self.embed_dim						,	depths=self.depths						,	num_heads=self.num_heads						,	window_size=self.window_size						,	mlp_ratio=self.mlp_ratio						,	qkv_bias=self.qkv_bias						,	hidden_dropout_prob=self.hidden_dropout_prob						,	attention_probs_dropout_prob=self.attention_probs_dropout_prob						,	drop_path_rate=self.drop_path_rate						,	hidden_act=self.hidden_act						,	use_absolute_embeddings=self.use_absolute_embeddings						,	path_norm=self.patch_norm						,	layer_norm_eps=self.layer_norm_eps						,	initializer_range=self.initializer_range						,	encoder_stride=self.encoder_stride						,	out_features=self.out_features						,	out_indices=self.out_indices						,	)



     def     snake_case_ (self						,	lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__	):
          _UpperCAmelCase					:  Optional[int]         =    SwinModel(config=_UpperCAmelCase	)
          model.to(_UpperCAmelCase	)
          model.eval()
          _UpperCAmelCase					:  Union[str, Any]         =    model(_UpperCAmelCase	)

          _UpperCAmelCase					:  int         =    ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths	) - 1))
          _UpperCAmelCase					:  int         =    int(config.embed_dim * 2 ** (len(config.depths	) - 1)	)

          self.parent.assertEqual(result.last_hidden_state.shape						,	(self.batch_size, expected_seq_len, expected_dim)	)



     def     snake_case_ (self						,	lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__	):
          _UpperCAmelCase					:  Dict         =    SwinBackbone(config=_UpperCAmelCase	)
          model.to(_UpperCAmelCase	)
          model.eval()
          _UpperCAmelCase					:  int         =    model(_UpperCAmelCase	)

          # verify hidden states
          self.parent.assertEqual(len(result.feature_maps	)						,	len(config.out_features	)	)
          self.parent.assertListEqual(list(result.feature_maps[0].shape	)						,	[self.batch_size, model.channels[0], 1_6, 1_6]	)

          # verify channels
          self.parent.assertEqual(len(model.channels	)						,	len(config.out_features	)	)

          # verify backbone works with out_features=None
          _UpperCAmelCase					:  List[Any]         =    None
          _UpperCAmelCase					:  Any         =    SwinBackbone(config=_UpperCAmelCase	)
          model.to(_UpperCAmelCase	)
          model.eval()
          _UpperCAmelCase					:  Dict         =    model(_UpperCAmelCase	)

          # verify feature maps
          self.parent.assertEqual(len(result.feature_maps	)						,	1	)
          self.parent.assertListEqual(list(result.feature_maps[0].shape	)						,	[self.batch_size, model.channels[-1], 4, 4]	)

          # verify channels
          self.parent.assertEqual(len(model.channels	)						,	1	)



     def     snake_case_ (self						,	lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__	):
          _UpperCAmelCase					:  Tuple         =    SwinForMaskedImageModeling(config=_UpperCAmelCase	)
          model.to(_UpperCAmelCase	)
          model.eval()
          _UpperCAmelCase					:  List[str]         =    model(_UpperCAmelCase	)
          self.parent.assertEqual(
              result.logits.shape						,	(self.batch_size, self.num_channels, self.image_size, self.image_size)	)

          # test greyscale images
          _UpperCAmelCase					:  str         =    1
          _UpperCAmelCase					:  Optional[int]         =    SwinForMaskedImageModeling(_UpperCAmelCase	)
          model.to(_UpperCAmelCase	)
          model.eval()

          _UpperCAmelCase					:  List[str]         =    floats_tensor([self.batch_size, 1, self.image_size, self.image_size]	)
          _UpperCAmelCase					:  Tuple         =    model(_UpperCAmelCase	)
          self.parent.assertEqual(result.logits.shape						,	(self.batch_size, 1, self.image_size, self.image_size)	)



     def     snake_case_ (self						,	lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__	):
          _UpperCAmelCase					:  Optional[int]         =    self.type_sequence_label_size
          _UpperCAmelCase					:  List[str]         =    SwinForImageClassification(_UpperCAmelCase	)
          model.to(_UpperCAmelCase	)
          model.eval()
          _UpperCAmelCase					:  Optional[Any]         =    model(_UpperCAmelCase						,	labels=_UpperCAmelCase	)
          self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.type_sequence_label_size)	)

          # test greyscale images
          _UpperCAmelCase					:  Optional[int]         =    1
          _UpperCAmelCase					:  List[str]         =    SwinForImageClassification(_UpperCAmelCase	)
          model.to(_UpperCAmelCase	)
          model.eval()

          _UpperCAmelCase					:  List[Any]         =    floats_tensor([self.batch_size, 1, self.image_size, self.image_size]	)
          _UpperCAmelCase					:  str         =    model(_UpperCAmelCase	)
          self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.type_sequence_label_size)	)



     def     snake_case_ (self	):
          _UpperCAmelCase					:  Optional[Any]         =    self.prepare_config_and_inputs()
          (
              (
              _UpperCAmelCase    
          )  ,     (
              _UpperCAmelCase    
          )  ,     (
              _UpperCAmelCase    
          )  ,         
          )					:  Tuple         =    config_and_inputs
          _UpperCAmelCase					:  List[str]         =    {"""pixel_values""": pixel_values}
          return config, inputs_dict







@require_torch
class 	__lowerCAmelCase    (  lowerCamelCase__       ,					lowerCamelCase__       ,					unittest.TestCase				):
     snake_case   :    List[Any]							      =      (
         (
             SwinModel,
             SwinBackbone,
             SwinForImageClassification,
             SwinForMaskedImageModeling,
         )
         if is_torch_available()
         else ()
     )
     snake_case   :    Any							      =      (
         {"""feature-extraction""": SwinModel, """image-classification""": SwinForImageClassification}
         if is_torch_available()
         else {}
     )
     snake_case   :    Union[str, Any]							      =      True

     snake_case   :    int							      =      False
     snake_case   :    Optional[int]							      =      False
     snake_case   :    Any							      =      False



     def     snake_case_ (self	):
          _UpperCAmelCase					:  Optional[int]         =    SwinModelTester(self	)
          _UpperCAmelCase					:  List[str]         =    ConfigTester(self						,	config_class=_UpperCAmelCase						,	embed_dim=3_7	)



     def     snake_case_ (self	):
          self.create_and_test_config_common_properties()
          self.config_tester.create_and_test_config_to_json_string()
          self.config_tester.create_and_test_config_to_json_file()
          self.config_tester.create_and_test_config_from_and_save_pretrained()
          self.config_tester.create_and_test_config_with_num_labels()
          self.config_tester.check_config_can_be_init_without_params()
          self.config_tester.check_config_arguments_init()



     def     snake_case_ (self	):
          return



     def     snake_case_ (self	):
          _UpperCAmelCase					:  List[str]         =    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*_UpperCAmelCase	)



     @unittest.skip(reason="""Got `CUDA error: misaligned address` with PyTorch 2.0.0."""	)
     def     snake_case_ (self	):
          pass



     def     snake_case_ (self	):
          super().test_training_gradient_checkpointing()



     def     snake_case_ (self	):
          _UpperCAmelCase					:  Dict         =    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_backbone(*_UpperCAmelCase	)



     def     snake_case_ (self	):
          _UpperCAmelCase					:  List[Any]         =    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_masked_image_modeling(*_UpperCAmelCase	)



     def     snake_case_ (self	):
          _UpperCAmelCase					:  Optional[int]         =    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*_UpperCAmelCase	)



     @unittest.skip(reason="""Swin does not use inputs_embeds"""	)
     def     snake_case_ (self	):
          pass



     @unittest.skip(reason="""Swin Transformer does not use feedforward chunking"""	)
     def     snake_case_ (self	):
          pass



     def     snake_case_ (self	):
          _UpperCAmelCase  ,     _UpperCAmelCase					:  Optional[Any]         =    self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
               _UpperCAmelCase					:  Optional[int]         =    model_class(_UpperCAmelCase	)
               self.assertIsInstance(model.get_input_embeddings()						,	(nn.Module)	)
               _UpperCAmelCase					:  int         =    model.get_output_embeddings()
               self.assertTrue(x is None or isinstance(_UpperCAmelCase						,	nn.Linear	)	)



     def     snake_case_ (self	):
          _UpperCAmelCase  ,     _UpperCAmelCase					:  Dict         =    self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
               _UpperCAmelCase					:  int         =    model_class(_UpperCAmelCase	)
               _UpperCAmelCase					:  List[str]         =    inspect.signature(model.forward	)
               # signature.parameters is an OrderedDict => so arg_names order is deterministic
               _UpperCAmelCase					:  str         =    [*signature.parameters.keys()]

               _UpperCAmelCase					:  Optional[int]         =    ["""pixel_values"""]
               self.assertListEqual(arg_names[:1]						,	_UpperCAmelCase	)



     def     snake_case_ (self	):
          _UpperCAmelCase  ,     _UpperCAmelCase					:  List[str]         =    self.model_tester.prepare_config_and_inputs_for_common()
          _UpperCAmelCase					:  int         =    True

          for model_class in self.all_model_classes:
               _UpperCAmelCase					:  List[Any]         =    True
               _UpperCAmelCase					:  Optional[int]         =    False
               _UpperCAmelCase					:  str         =    True
               _UpperCAmelCase					:  Optional[Any]         =    model_class(_UpperCAmelCase	)
               model.to(_UpperCAmelCase	)
               model.eval()
               with torch.no_grad():
                    _UpperCAmelCase					:  List[str]         =    model(**self._prepare_for_class(_UpperCAmelCase						,	_UpperCAmelCase	)	)
               _UpperCAmelCase					:  Optional[int]         =    outputs.attentions
               _UpperCAmelCase					:  str         =    len(self.model_tester.depths	)
               self.assertEqual(len(_UpperCAmelCase	)						,	_UpperCAmelCase	)

               # check that output_attentions also work using config
               del inputs_dict["output_attentions"]
               _UpperCAmelCase					:  List[Any]         =    True
               _UpperCAmelCase					:  Dict         =    config.window_size**2
               _UpperCAmelCase					:  Any         =    model_class(_UpperCAmelCase	)
               model.to(_UpperCAmelCase	)
               model.eval()
               with torch.no_grad():
                    _UpperCAmelCase					:  List[str]         =    model(**self._prepare_for_class(_UpperCAmelCase						,	_UpperCAmelCase	)	)
               _UpperCAmelCase					:  Tuple         =    outputs.attentions
               self.assertEqual(len(_UpperCAmelCase	)						,	_UpperCAmelCase	)

               self.assertListEqual(
                   list(attentions[0].shape[-3:]	)						,	[self.model_tester.num_heads[0], window_size_squared, window_size_squared]						,	)
               _UpperCAmelCase					:  List[str]         =    len(_UpperCAmelCase	)

               # Check attention is always last and order is fine
               _UpperCAmelCase					:  Union[str, Any]         =    True
               _UpperCAmelCase					:  Tuple         =    True
               _UpperCAmelCase					:  Optional[int]         =    model_class(_UpperCAmelCase	)
               model.to(_UpperCAmelCase	)
               model.eval()
               with torch.no_grad():
                    _UpperCAmelCase					:  Optional[int]         =    model(**self._prepare_for_class(_UpperCAmelCase						,	_UpperCAmelCase	)	)

               # also another +1 for reshaped_hidden_states
               _UpperCAmelCase					:  Optional[int]         =    1 if model_class.__name__ == """SwinBackbone""" else 2
               self.assertEqual(out_len + added_hidden_states						,	len(_UpperCAmelCase	)	)

               _UpperCAmelCase					:  int         =    outputs.attentions

               self.assertEqual(len(_UpperCAmelCase	)						,	_UpperCAmelCase	)

               self.assertListEqual(
                   list(self_attentions[0].shape[-3:]	)						,	[self.model_tester.num_heads[0], window_size_squared, window_size_squared]						,	)



     def     snake_case_ (self						,	lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__	):
          _UpperCAmelCase					:  Dict         =    model_class(_UpperCAmelCase	)
          model.to(_UpperCAmelCase	)
          model.eval()

          with torch.no_grad():
               _UpperCAmelCase					:  Union[str, Any]         =    model(**self._prepare_for_class(_UpperCAmelCase						,	_UpperCAmelCase	)	)

          _UpperCAmelCase					:  str         =    outputs.hidden_states

          _UpperCAmelCase					:  List[str]         =    getattr(
              self.model_tester						,	"""expected_num_hidden_layers"""						,	len(self.model_tester.depths	) + 1	)
          self.assertEqual(len(_UpperCAmelCase	)						,	_UpperCAmelCase	)

          # Swin has a different seq_length
          _UpperCAmelCase					:  int         =    (
              config.patch_size
              if isinstance(config.patch_size						,	collections.abc.Iterable	)
              else (config.patch_size, config.patch_size)
          )

          _UpperCAmelCase					:  str         =    (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

          self.assertListEqual(
              list(hidden_states[0].shape[-2:]	)						,	[num_patches, self.model_tester.embed_dim]						,	)

          if not model_class.__name__ == "SwinBackbone":
               _UpperCAmelCase					:  Optional[int]         =    outputs.reshaped_hidden_states
               self.assertEqual(len(_UpperCAmelCase	)						,	_UpperCAmelCase	)

               _UpperCAmelCase  ,     _UpperCAmelCase  ,     _UpperCAmelCase  ,     _UpperCAmelCase					:  Optional[int]         =    reshaped_hidden_states[0].shape
               _UpperCAmelCase					:  Dict         =    (
                   reshaped_hidden_states[0].view(_UpperCAmelCase						,	_UpperCAmelCase						,	height * width	).permute(0						,	2						,	1	)
               )
               self.assertListEqual(
                   list(reshaped_hidden_states.shape[-2:]	)						,	[num_patches, self.model_tester.embed_dim]						,	)



     def     snake_case_ (self	):
          _UpperCAmelCase  ,     _UpperCAmelCase					:  int         =    self.model_tester.prepare_config_and_inputs_for_common()

          _UpperCAmelCase					:  Tuple         =    (
              self.model_tester.image_size
              if isinstance(self.model_tester.image_size						,	collections.abc.Iterable	)
              else (self.model_tester.image_size, self.model_tester.image_size)
          )

          for model_class in self.all_model_classes:
               _UpperCAmelCase					:  Union[str, Any]         =    True
               self.check_hidden_states_output(_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase	)

               # check that output_hidden_states also work using config
               del inputs_dict["output_hidden_states"]
               _UpperCAmelCase					:  Any         =    True

               self.check_hidden_states_output(_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase	)



     def     snake_case_ (self	):
          _UpperCAmelCase  ,     _UpperCAmelCase					:  Dict         =    self.model_tester.prepare_config_and_inputs_for_common()
          _UpperCAmelCase					:  Optional[int]         =    3

          _UpperCAmelCase					:  Optional[int]         =    (
              self.model_tester.image_size
              if isinstance(self.model_tester.image_size						,	collections.abc.Iterable	)
              else (self.model_tester.image_size, self.model_tester.image_size)
          )
          _UpperCAmelCase					:  Optional[Any]         =    (
              config.patch_size
              if isinstance(config.patch_size						,	collections.abc.Iterable	)
              else (config.patch_size, config.patch_size)
          )

          _UpperCAmelCase					:  int         =    image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
          _UpperCAmelCase					:  Union[str, Any]         =    image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

          for model_class in self.all_model_classes:
               _UpperCAmelCase					:  Optional[Any]         =    True
               self.check_hidden_states_output(_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	(padded_height, padded_width)	)

               # check that output_hidden_states also work using config
               del inputs_dict["output_hidden_states"]
               _UpperCAmelCase					:  str         =    True
               self.check_hidden_states_output(_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	(padded_height, padded_width)	)



     @slow
     def     snake_case_ (self	):
          for model_name in SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               _UpperCAmelCase					:  Dict         =    SwinModel.from_pretrained(_UpperCAmelCase	)
               self.assertIsNotNone(_UpperCAmelCase	)



     def     snake_case_ (self	):
          _UpperCAmelCase  ,     _UpperCAmelCase					:  Union[str, Any]         =    self.model_tester.prepare_config_and_inputs_for_common()

          _UpperCAmelCase					:  Tuple         =    _config_zero_init(_UpperCAmelCase	)
          for model_class in self.all_model_classes:
               _UpperCAmelCase					:  List[Any]         =    model_class(config=_UpperCAmelCase	)
               for name, param in model.named_parameters():
                    if "embeddings" not in name and param.requires_grad:
                         self.assertIn(
                             ((param.data.mean() * 1e9).round() / 1e9).item()						,	[0.0, 1.0]						,	msg=F"Parameter {name} of model {model_class} seems not properly initialized"						,	)







@require_vision
@require_torch
class 	__lowerCAmelCase    (  unittest.TestCase				):



     @cached_property
     def     snake_case_ (self	):
          return (
              AutoImageProcessor.from_pretrained("""microsoft/swin-tiny-patch4-window7-224"""	)
              if is_vision_available()
              else None
          )



     @slow
     def     snake_case_ (self	):
          _UpperCAmelCase					:  List[Any]         =    SwinForImageClassification.from_pretrained("""microsoft/swin-tiny-patch4-window7-224"""	).to(_UpperCAmelCase	)
          _UpperCAmelCase					:  List[str]         =    self.default_image_processor

          _UpperCAmelCase					:  List[str]         =    Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""	)
          _UpperCAmelCase					:  List[str]         =    image_processor(images=_UpperCAmelCase						,	return_tensors="""pt"""	).to(_UpperCAmelCase	)

          # forward pass
          with torch.no_grad():
               _UpperCAmelCase					:  List[str]         =    model(**_UpperCAmelCase	)

          # verify the logits
          _UpperCAmelCase					:  List[Any]         =    torch.Size((1, 1_0_0_0)	)
          self.assertEqual(outputs.logits.shape						,	_UpperCAmelCase	)
          _UpperCAmelCase					:  Dict         =    torch.tensor([-0.0_9_4_8, -0.6_4_5_4, -0.0_9_2_1]	).to(_UpperCAmelCase	)
          self.assertTrue(torch.allclose(outputs.logits[0, :3]						,	_UpperCAmelCase						,	atol=1e-4	)	)







@require_torch
class 	__lowerCAmelCase    (  unittest.TestCase       ,					lowerCamelCase__				):
     snake_case   :    str							      =      (SwinBackbone,) if is_torch_available() else ()
     snake_case   :    Optional[Any]							      =      SwinConfig



     def     snake_case_ (self	):
          _UpperCAmelCase					:  Optional[int]         =    SwinModelTester(self	)






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase			=    {
    """configuration_big_bird""": ["""BIG_BIRD_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BigBirdConfig""", """BigBirdOnnxConfig"""],
}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase			=    ["""BigBirdTokenizer"""]

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase			=    ["""BigBirdTokenizerFast"""]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase			=    [
       """BIG_BIRD_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """BigBirdForCausalLM""",
       """BigBirdForMaskedLM""",
       """BigBirdForMultipleChoice""",
       """BigBirdForPreTraining""",
       """BigBirdForQuestionAnswering""",
       """BigBirdForSequenceClassification""",
       """BigBirdForTokenClassification""",
       """BigBirdLayer""",
       """BigBirdModel""",
       """BigBirdPreTrainedModel""",
       """load_tf_weights_in_big_bird""",
   ]

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase			=    [
       """FlaxBigBirdForCausalLM""",
       """FlaxBigBirdForMaskedLM""",
       """FlaxBigBirdForMultipleChoice""",
       """FlaxBigBirdForPreTraining""",
       """FlaxBigBirdForQuestionAnswering""",
       """FlaxBigBirdForSequenceClassification""",
       """FlaxBigBirdForTokenClassification""",
       """FlaxBigBirdModel""",
       """FlaxBigBirdPreTrainedModel""",
   ]

if TYPE_CHECKING:
   from .configuration_big_bird import BIG_BIRD_PRETRAINED_CONFIG_ARCHIVE_MAP, BigBirdConfig, BigBirdOnnxConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_big_bird import BigBirdTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_big_bird_fast import BigBirdTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_big_bird import (
          BIG_BIRD_PRETRAINED_MODEL_ARCHIVE_LIST,
          BigBirdForCausalLM,
          BigBirdForMaskedLM,
          BigBirdForMultipleChoice,
          BigBirdForPreTraining,
          BigBirdForQuestionAnswering,
          BigBirdForSequenceClassification,
          BigBirdForTokenClassification,
          BigBirdLayer,
          BigBirdModel,
          BigBirdPreTrainedModel,
          load_tf_weights_in_big_bird,
      )

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_big_bird import (
          FlaxBigBirdForCausalLM,
          FlaxBigBirdForMaskedLM,
          FlaxBigBirdForMultipleChoice,
          FlaxBigBirdForPreTraining,
          FlaxBigBirdForQuestionAnswering,
          FlaxBigBirdForSequenceClassification,
          FlaxBigBirdForTokenClassification,
          FlaxBigBirdModel,
          FlaxBigBirdPreTrainedModel,
      )

else:
   import sys

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