


'''simple docstring'''






import inspect
import tempfile
import unittest

from huggingface_hub import hf_hub_download

from transformers import is_torch_available
from transformers.testing_utils import is_flaky, require_torch, slow, torch_device

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


__A :  int    							=				1E-4

if is_torch_available():
  import torch

  from transformers import AutoformerConfig, AutoformerForPrediction, AutoformerModel
  from transformers.models.autoformer.modeling_autoformer import AutoformerDecoder, AutoformerEncoder






@require_torch
class        __snake_case	:







    """simple docstring"""



    def __init__(       self   :  Optional[int]   ,						lowerCamelCase   :  List[Any]   ,						lowerCamelCase   :  List[Any]=16   ,						lowerCamelCase   :  List[str]=13   ,						lowerCamelCase   :  Union[str, Any]=7   ,						lowerCamelCase   :  List[Any]=14   ,						lowerCamelCase   :  Dict=10   ,						lowerCamelCase   :  List[Any]=19   ,						lowerCamelCase   :  List[Any]=5   ,						lowerCamelCase   :  str=4   ,						lowerCamelCase   :  Optional[int]=True   ,						lowerCamelCase   :  List[Any]=16   ,						lowerCamelCase   :  Optional[int]=2   ,						lowerCamelCase   :  Optional[int]=4   ,						lowerCamelCase   :  List[str]=4   ,						lowerCamelCase   :  Optional[int]="gelu"   ,						lowerCamelCase   :  Any=0.1   ,						lowerCamelCase   :  Optional[Any]=0.1   ,						lowerCamelCase   :  List[str]=[1, 2, 3, 4, 5]   ,						lowerCamelCase   :  int=25   ,						lowerCamelCase   :  List[Any]=5   ,						)       ->  Optional[Any]:
      lowerCAmelCase_      :				Dict      =       d_model
      lowerCAmelCase_      :				Any      =       parent
      lowerCAmelCase_      :				List[str]      =       batch_size
      lowerCAmelCase_      :				Optional[Any]      =       prediction_length
      lowerCAmelCase_      :				Any      =       context_length
      lowerCAmelCase_      :				Optional[Any]      =       cardinality
      lowerCAmelCase_      :				Union[str, Any]      =       num_time_features
      lowerCAmelCase_      :				str      =       lags_sequence
      lowerCAmelCase_      :				str      =       embedding_dimension
      lowerCAmelCase_      :				List[Any]      =       is_training
      lowerCAmelCase_      :				Any      =       hidden_size
      lowerCAmelCase_      :				Optional[int]      =       num_hidden_layers
      lowerCAmelCase_      :				Optional[Any]      =       num_attention_heads
      lowerCAmelCase_      :				Tuple      =       intermediate_size
      lowerCAmelCase_      :				int      =       hidden_act
      lowerCAmelCase_      :				Optional[Any]      =       hidden_dropout_prob
      lowerCAmelCase_      :				Optional[Any]      =       attention_probs_dropout_prob

      lowerCAmelCase_      :				List[str]      =       context_length
      lowerCAmelCase_      :				List[str]      =       prediction_length + label_length
      lowerCAmelCase_      :				str      =       label_length

      lowerCAmelCase_      :				Tuple      =       moving_average
      lowerCAmelCase_      :				Optional[int]      =       autocorrelation_factor

    def __lowercase       (       self   :  Any		)       ->  Any:
      return AutoformerConfig(
          d_model=self.d_model   ,						encoder_layers=self.num_hidden_layers   ,						decoder_layers=self.num_hidden_layers   ,						encoder_attention_heads=self.num_attention_heads   ,						decoder_attention_heads=self.num_attention_heads   ,						encoder_ffn_dim=self.intermediate_size   ,						decoder_ffn_dim=self.intermediate_size   ,						dropout=self.hidden_dropout_prob   ,						attention_dropout=self.attention_probs_dropout_prob   ,						prediction_length=self.prediction_length   ,						context_length=self.context_length   ,						label_length=self.label_length   ,						lags_sequence=self.lags_sequence   ,						num_time_features=self.num_time_features   ,						num_static_categorical_features=1   ,						cardinality=[self.cardinality]   ,						embedding_dimension=[self.embedding_dimension]   ,						moving_average=self.moving_average   ,						)

    def __lowercase       (       self   :  Optional[Any]   ,						lowerCamelCase   :  Union[str, Any]		)       ->  Optional[Any]:
      lowerCAmelCase_      :				Any      =       config.context_length + max(config.lags_sequence		)

      lowerCAmelCase_      :				Tuple      =       ids_tensor([self.batch_size, 1]   ,						config.cardinality[0]		)
      lowerCAmelCase_      :				Dict      =       floats_tensor([self.batch_size, _past_length, config.num_time_features]		)
      lowerCAmelCase_      :				Optional[int]      =       floats_tensor([self.batch_size, _past_length]		)
      lowerCAmelCase_      :				Dict      =       floats_tensor([self.batch_size, _past_length]		) > 0.5

      # decoder inputs
      lowerCAmelCase_      :				Any      =       floats_tensor([self.batch_size, config.prediction_length, config.num_time_features]		)
      lowerCAmelCase_      :				List[Any]      =       floats_tensor([self.batch_size, config.prediction_length]		)

      lowerCAmelCase_      :				List[Any]      =       {
          """past_values""": past_values,
          """static_categorical_features""": static_categorical_features,
          """past_time_features""": past_time_features,
          """past_observed_mask""": past_observed_mask,
          """future_time_features""": future_time_features,
          """future_values""": future_values,
      }
      return inputs_dict

    def __lowercase       (       self   :  Any		)       ->  Optional[int]:
      lowerCAmelCase_      :				Any      =       self.get_config()
      lowerCAmelCase_      :				List[Any]      =       self.prepare_autoformer_inputs_dict(lowerCamelCase		)
      return config, inputs_dict

    def __lowercase       (       self   :  str		)       ->  Dict:
      lowerCAmelCase_,							lowerCAmelCase_      :				List[Any]      =       self.prepare_config_and_inputs()
      return config, inputs_dict





    def __lowercase       (       self   :  Optional[int]   ,						lowerCamelCase   :  str   ,						lowerCamelCase   :  List[str]		)       ->  Tuple:
      lowerCAmelCase_      :				Dict      =       AutoformerModel(config=lowerCamelCase		).to(lowerCamelCase		).eval()
      lowerCAmelCase_      :				List[str]      =       model(**lowerCamelCase		)

      lowerCAmelCase_      :				Tuple      =       outputs.encoder_last_hidden_state
      lowerCAmelCase_      :				Union[str, Any]      =       outputs.last_hidden_state

      with tempfile.TemporaryDirectory() as tmpdirname:
        lowerCAmelCase_      :				str      =       model.get_encoder()
        encoder.save_pretrained(lowerCamelCase		)
        lowerCAmelCase_      :				Dict      =       AutoformerEncoder.from_pretrained(lowerCamelCase		).to(lowerCamelCase		)

      lowerCAmelCase_,							lowerCAmelCase_,							lowerCAmelCase_,							lowerCAmelCase_,							lowerCAmelCase_      :				List[Any]      =       model.create_network_inputs(**lowerCamelCase		)
      lowerCAmelCase_,							lowerCAmelCase_      :				Union[str, Any]      =       model.decomposition_layer(transformer_inputs[:, : config.context_length, ...]		)

      lowerCAmelCase_      :				List[str]      =       torch.cat(
          (transformer_inputs[:, : config.context_length, ...], feature[:, : config.context_length, ...])   ,						dim=-1   ,						)
      lowerCAmelCase_      :				List[Any]      =       encoder(inputs_embeds=lowerCamelCase		)[0]
      self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1E-3		)

      lowerCAmelCase_      :				List[Any]      =       (
          torch.mean(transformer_inputs[:, : config.context_length, ...]   ,						dim=1		)
          .unsqueeze(1		)
          .repeat(1   ,						config.prediction_length   ,						1		)
      )
      lowerCAmelCase_      :				Any      =       torch.zeros(
          [transformer_inputs.shape[0], config.prediction_length, transformer_inputs.shape[2]]   ,						device=enc_input.device   ,						)

      lowerCAmelCase_      :				Optional[Any]      =       torch.cat(
          (
              torch.cat((seasonal_input[:, -config.label_length :, ...], zeros)   ,						dim=1		),
              feature[:, config.context_length - config.label_length :, ...],
          )   ,						dim=-1   ,						)
      lowerCAmelCase_      :				str      =       torch.cat(
          (
              torch.cat((trend_input[:, -config.label_length :, ...], mean)   ,						dim=1		),
              feature[:, config.context_length - config.label_length :, ...],
          )   ,						dim=-1   ,						)

      with tempfile.TemporaryDirectory() as tmpdirname:
        lowerCAmelCase_      :				List[Any]      =       model.get_decoder()
        decoder.save_pretrained(lowerCamelCase		)
        lowerCAmelCase_      :				List[Any]      =       AutoformerDecoder.from_pretrained(lowerCamelCase		).to(lowerCamelCase		)

      lowerCAmelCase_      :				Tuple      =       decoder(
          trend=lowerCamelCase   ,						inputs_embeds=lowerCamelCase   ,						encoder_hidden_states=lowerCamelCase   ,						)[0]

      self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1E-3		)







@require_torch
class        __snake_case	(       _SCREAMING_SNAKE_CASE     ,_SCREAMING_SNAKE_CASE     ,unittest.TestCase):







    """simple docstring"""



    lowercase       			= (AutoformerModel, AutoformerForPrediction) if is_torch_available() else ()
    lowercase       			= (AutoformerForPrediction,) if is_torch_available() else ()
    lowercase       			= {'feature-extraction': AutoformerModel} if is_torch_available() else {}
    lowercase       			= False
    lowercase       			= False
    lowercase       			= False
    lowercase       			= False
    lowercase       			= False
    lowercase       			= False

    def __lowercase       (       self   :  Union[str, Any]		)       ->  int:
      lowerCAmelCase_      :				List[str]      =       AutoformerModelTester(self		)
      lowerCAmelCase_      :				List[Any]      =       ConfigTester(self   ,						config_class=lowerCamelCase   ,						has_text_modality=lowerCamelCase		)

    def __lowercase       (       self   :  int		)       ->  Optional[Any]:
      self.config_tester.run_common_tests()

    def __lowercase       (       self   :  List[str]		)       ->  Any:
      lowerCAmelCase_,							lowerCAmelCase_      :				Any      =       self.model_tester.prepare_config_and_inputs()
      for model_class in self.all_model_classes:
        lowerCAmelCase_      :				Optional[Any]      =       model_class(lowerCamelCase		)

        with tempfile.TemporaryDirectory() as tmpdirname:
          model.save_pretrained(lowerCamelCase		)
          lowerCAmelCase_,							lowerCAmelCase_      :				Optional[int]      =       model_class.from_pretrained(lowerCamelCase   ,						output_loading_info=lowerCamelCase		)
        self.assertEqual(info["""missing_keys"""]   ,						[]		)

    def __lowercase       (       self   :  List[Any]		)       ->  Dict:
      lowerCAmelCase_      :				Optional[Any]      =       self.model_tester.prepare_config_and_inputs_for_common()
      self.model_tester.check_encoder_decoder_model_standalone(*lowerCamelCase		)

    @unittest.skip(reason="""Model has no tokens embeddings"""		)
    def __lowercase       (       self   :  List[str]		)       ->  Dict:
      pass

    def __lowercase       (       self   :  Union[str, Any]		)       ->  Union[str, Any]:
      lowerCAmelCase_      :				str      =       inspect.signature(getattr(lowerCamelCase   ,						"""forward"""		)		)
      # The main input is the name of the argument after `self`
      lowerCAmelCase_      :				Dict      =       list(model_signature.parameters.keys()		)[1]
      self.assertEqual(AutoformerModel.main_input_name   ,						lowerCamelCase		)

    def __lowercase       (       self   :  List[Any]		)       ->  int:
      lowerCAmelCase_,							lowerCAmelCase_      :				List[Any]      =       self.model_tester.prepare_config_and_inputs_for_common()

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

        lowerCAmelCase_      :				Any      =       [
            """past_values""",
            """past_time_features""",
            """past_observed_mask""",
            """static_categorical_features""",
            """static_real_features""",
            """future_values""",
            """future_time_features""",
        ]

        if model.__class__.__name__ in ["AutoformerForPrediction"]:
          expected_arg_names.append("""future_observed_mask"""		)

        expected_arg_names.extend(
            [
                """decoder_attention_mask""",
                """head_mask""",
                """decoder_head_mask""",
                """cross_attn_head_mask""",
                """encoder_outputs""",
                """past_key_values""",
                """output_hidden_states""",
                """output_attentions""",
                """use_cache""",
                """return_dict""",
            ]		)

        self.assertListEqual(arg_names[: len(lowerCamelCase		)]   ,						lowerCamelCase		)

    def __lowercase       (       self   :  List[Any]		)       ->  str:
      lowerCAmelCase_,							lowerCAmelCase_      :				Any      =       self.model_tester.prepare_config_and_inputs_for_common()
      lowerCAmelCase_      :				Tuple      =       True

      lowerCAmelCase_      :				str      =       getattr(self.model_tester   ,						"""seq_length"""   ,						lowerCamelCase		)
      lowerCAmelCase_      :				Tuple      =       getattr(self.model_tester   ,						"""decoder_seq_length"""   ,						lowerCamelCase		)
      lowerCAmelCase_      :				Tuple      =       getattr(self.model_tester   ,						"""encoder_seq_length"""   ,						lowerCamelCase		)
      lowerCAmelCase_      :				Tuple      =       getattr(self.model_tester   ,						"""d_model"""   ,						lowerCamelCase		)
      lowerCAmelCase_      :				Optional[Any]      =       getattr(self.model_tester   ,						"""num_attention_heads"""   ,						lowerCamelCase		)
      lowerCAmelCase_      :				List[Any]      =       d_model // num_attention_heads

      for model_class in self.all_model_classes:
        lowerCAmelCase_      :				str      =       True
        lowerCAmelCase_      :				Union[str, Any]      =       False
        lowerCAmelCase_      :				Union[str, Any]      =       True
        lowerCAmelCase_      :				Any      =       model_class(lowerCamelCase		)
        model.to(lowerCamelCase		)
        model.eval()
        with torch.no_grad():
          lowerCAmelCase_      :				Optional[Any]      =       model(**self._prepare_for_class(lowerCamelCase   ,						lowerCamelCase		)		)
        lowerCAmelCase_      :				List[str]      =       outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
        self.assertEqual(len(lowerCamelCase		)   ,						self.model_tester.num_hidden_layers		)

        # check that output_attentions also work using config
        del inputs_dict["output_attentions"]
        lowerCAmelCase_      :				List[Any]      =       True
        lowerCAmelCase_      :				Optional[int]      =       model_class(lowerCamelCase		)
        model.to(lowerCamelCase		)
        model.eval()
        with torch.no_grad():
          lowerCAmelCase_      :				int      =       model(**self._prepare_for_class(lowerCamelCase   ,						lowerCamelCase		)		)
        lowerCAmelCase_      :				List[str]      =       outputs.encoder_attentions
        self.assertEqual(len(lowerCamelCase		)   ,						self.model_tester.num_hidden_layers		)

        self.assertListEqual(
            list(attentions[0].shape[-3:]		)   ,						[self.model_tester.num_attention_heads, encoder_seq_length, dim]   ,						)
        lowerCAmelCase_      :				Optional[int]      =       len(lowerCamelCase		)

        lowerCAmelCase_      :				Optional[int]      =       7

        if "last_hidden_state" in outputs:
          correct_outlen += 1

        if "trend" in outputs:
          correct_outlen += 1

        if "past_key_values" in outputs:
          correct_outlen += 1  # past_key_values have been returned

        if "loss" in outputs:
          correct_outlen += 1

        if "params" in outputs:
          correct_outlen += 1

        self.assertEqual(lowerCamelCase   ,						lowerCamelCase		)

        # decoder attentions
        lowerCAmelCase_      :				Union[str, Any]      =       outputs.decoder_attentions
        self.assertIsInstance(lowerCamelCase   ,						(list, tuple)		)
        self.assertEqual(len(lowerCamelCase		)   ,						self.model_tester.num_hidden_layers		)
        self.assertListEqual(
            list(decoder_attentions[0].shape[-3:]		)   ,						[self.model_tester.num_attention_heads, decoder_seq_length, dim]   ,						)

        # cross attentions
        lowerCAmelCase_      :				Optional[Any]      =       outputs.cross_attentions
        self.assertIsInstance(lowerCamelCase   ,						(list, tuple)		)
        self.assertEqual(len(lowerCamelCase		)   ,						self.model_tester.num_hidden_layers		)
        self.assertListEqual(
            list(cross_attentions[0].shape[-3:]		)   ,						[self.model_tester.num_attention_heads, decoder_seq_length, dim]   ,						)

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

      self.assertEqual(out_len + 2   ,						len(lowerCamelCase		)		)

      lowerCAmelCase_      :				Tuple      =       outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions

      self.assertEqual(len(lowerCamelCase		)   ,						self.model_tester.num_hidden_layers		)
      self.assertListEqual(
          list(self_attentions[0].shape[-3:]		)   ,						[self.model_tester.num_attention_heads, encoder_seq_length, dim]   ,						)





    @is_flaky()
    def __lowercase       (       self   :  List[Any]		)       ->  Optional[int]:
      super().test_retain_grad_hidden_states_attentions()







def     UpperCamelCase_      (   A__ :       Union[str, Any]="train-batch.pt" ):
  '''simple docstring'''





  lowerCAmelCase_      :				Union[str, Any]      =       hf_hub_download(repo_id="""hf-internal-testing/tourism-monthly-batch"""							,   filename=A__							,   repo_type="""dataset""" )
  lowerCAmelCase_      :				int      =       torch.load(A__							,   map_location=A__ )
  return batch






@require_torch
@slow
class        __snake_case	(       unittest.TestCase):







    """simple docstring"""



    def __lowercase       (       self   :  Optional[int]		)       ->  int:
      lowerCAmelCase_      :				Union[str, Any]      =       AutoformerModel.from_pretrained("""huggingface/autoformer-tourism-monthly"""		).to(lowerCamelCase		)
      lowerCAmelCase_      :				int      =       prepare_batch()

      with torch.no_grad():
        lowerCAmelCase_      :				int      =       model(
            past_values=batch["""past_values"""]   ,						past_time_features=batch["""past_time_features"""]   ,						past_observed_mask=batch["""past_observed_mask"""]   ,						static_categorical_features=batch["""static_categorical_features"""]   ,						future_values=batch["""future_values"""]   ,						future_time_features=batch["""future_time_features"""]   ,						)[0]

      lowerCAmelCase_      :				Tuple      =       torch.Size(
          (64, model.config.prediction_length + model.config.label_length, model.config.feature_size)		)
      self.assertEqual(output.shape   ,						lowerCamelCase		)

      lowerCAmelCase_      :				int      =       torch.tensor(
          [[0.3_593, -1.3_398, 0.6_330], [0.2_279, 1.5_396, -0.1_792], [0.0_450, 1.3_225, -0.2_335]]   ,						device=lowerCamelCase		)
      self.assertTrue(torch.allclose(output[0, :3, :3]   ,						lowerCamelCase   ,						atol=lowerCamelCase		)		)

    def __lowercase       (       self   :  List[Any]		)       ->  Union[str, Any]:
      lowerCAmelCase_      :				Optional[int]      =       AutoformerForPrediction.from_pretrained("""huggingface/autoformer-tourism-monthly"""		).to(lowerCamelCase		)
      lowerCAmelCase_      :				List[str]      =       prepare_batch("""val-batch.pt"""		)
      with torch.no_grad():
        lowerCAmelCase_      :				List[Any]      =       model(
            past_values=batch["""past_values"""]   ,						past_time_features=batch["""past_time_features"""]   ,						past_observed_mask=batch["""past_observed_mask"""]   ,						static_categorical_features=batch["""static_categorical_features"""]   ,						).encoder_last_hidden_state
      lowerCAmelCase_      :				Any      =       torch.Size((64, model.config.context_length, model.config.d_model)		)
      self.assertEqual(output.shape   ,						lowerCamelCase		)

      lowerCAmelCase_      :				Optional[int]      =       torch.tensor(
          [[-0.0_734, -0.9_036, 0.8_358], [4.7_186, 2.4_113, 1.9_581], [1.7_953, 2.3_558, 1.2_970]]   ,						device=lowerCamelCase		)
      self.assertTrue(torch.allclose(output[0, :3, :3]   ,						lowerCamelCase   ,						atol=lowerCamelCase		)		)





    def __lowercase       (       self   :  Union[str, Any]		)       ->  List[str]:
      lowerCAmelCase_      :				List[Any]      =       AutoformerForPrediction.from_pretrained("""huggingface/autoformer-tourism-monthly"""		).to(lowerCamelCase		)
      lowerCAmelCase_      :				Union[str, Any]      =       prepare_batch("""val-batch.pt"""		)
      with torch.no_grad():
        lowerCAmelCase_      :				int      =       model.generate(
            static_categorical_features=batch["""static_categorical_features"""]   ,						past_time_features=batch["""past_time_features"""]   ,						past_values=batch["""past_values"""]   ,						future_time_features=batch["""future_time_features"""]   ,						past_observed_mask=batch["""past_observed_mask"""]   ,						)
      lowerCAmelCase_      :				Tuple      =       torch.Size((64, model.config.num_parallel_samples, model.config.prediction_length)		)
      self.assertEqual(outputs.sequences.shape   ,						lowerCamelCase		)

      lowerCAmelCase_      :				Optional[Any]      =       torch.tensor([3_130.6_763, 4_056.5_293, 7_053.0_786]   ,						device=lowerCamelCase		)
      lowerCAmelCase_      :				int      =       outputs.sequences.mean(dim=1		)
      self.assertTrue(torch.allclose(mean_prediction[0, -3:]   ,						lowerCamelCase   ,						rtol=1E-1		)		)







'''simple docstring'''






from typing import TYPE_CHECKING

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


__A :  List[str]    							=				{
    "configuration_owlvit": [
        "OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "OwlViTConfig",
        "OwlViTOnnxConfig",
        "OwlViTTextConfig",
        "OwlViTVisionConfig",
    ],
    "processing_owlvit": ["OwlViTProcessor"],
}


try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __A :  Optional[int]    							=				["OwlViTFeatureExtractor"]
  __A :  str    							=				["OwlViTImageProcessor"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __A :  Tuple    							=				[
      "OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
      "OwlViTModel",
      "OwlViTPreTrainedModel",
      "OwlViTTextModel",
      "OwlViTVisionModel",
      "OwlViTForObjectDetection",
  ]

if TYPE_CHECKING:
  from .configuration_owlvit import (
      OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
      OwlViTConfig,
      OwlViTOnnxConfig,
      OwlViTTextConfig,
      OwlViTVisionConfig,
  )
  from .processing_owlvit import OwlViTProcessor

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_owlvit import OwlViTFeatureExtractor
    from .image_processing_owlvit import OwlViTImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_owlvit import (
        OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
        OwlViTForObjectDetection,
        OwlViTModel,
        OwlViTPreTrainedModel,
        OwlViTTextModel,
        OwlViTVisionModel,
    )

else:
  import sys

  __A :  List[Any]    							=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



