





import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError

from transformers import AutoImageProcessor, ViTImageProcessor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test


sys.path.append(str(Path(__file__).parent.parent / 'utils'))

from test_module.custom_image_processing import CustomImageProcessor  # noqa E402


_A :   str  			=  get_tests_dir('fixtures')


class        __SCREAMING_SNAKE_CASE      (			unittest.TestCase ):







       def 						__lowerCamelCase   (				self					:     List[str]      )       ->List[Any]:
          # A mock response for an HTTP head request to emulate server down
          lowerCamelCase__       :						Tuple        =							mock.Mock()
          lowerCamelCase__       :						Tuple        =							5_0_0
          lowerCamelCase__       :						List[str]        =							{}
          lowerCamelCase__       :						Tuple        =							HTTPError
          lowerCamelCase__       :						Any        =							{}

          # Download this model to make sure it's in the cache.
          lowerCamelCase__       :						Optional[Any]        =							ViTImageProcessor.from_pretrained('''hf-internal-testing/tiny-random-vit'''      )
          # Under the mock environment we get a 500 error when trying to reach the model.
          with mock.patch('''requests.Session.request'''		, return_value=A      ) as mock_head:
             lowerCamelCase__       :						Optional[Any]        =							ViTImageProcessor.from_pretrained('''hf-internal-testing/tiny-random-vit'''      )
             # This check we did call the fake head request
             mock_head.assert_called()







       def 						__lowerCamelCase   (				self					:     Union[str, Any]      )       ->List[Any]:
          # This test is for deprecated behavior and can be removed in v5
          lowerCamelCase__       :						str        =							ViTImageProcessor.from_pretrained(
              '''https://huggingface.co/hf-internal-testing/tiny-random-vit/resolve/main/preprocessor_config.json'''      )







       def 						__lowerCamelCase   (				self					:     Optional[Any]      )       ->Optional[int]:
          with self.assertRaises(A      ):
             # config is in subfolder, the following should not work without specifying the subfolder
             lowerCamelCase__       :						List[str]        =							AutoImageProcessor.from_pretrained('''hf-internal-testing/stable-diffusion-all-variants'''      )

          lowerCamelCase__       :						Any        =							AutoImageProcessor.from_pretrained(
              '''hf-internal-testing/stable-diffusion-all-variants'''		, subfolder='''feature_extractor'''      )

          self.assertIsNotNone(A      )


@is_staging_test
class        __SCREAMING_SNAKE_CASE      (			unittest.TestCase ):







       @classmethod
       def 						__lowerCamelCase   (				cls					:     int      )       ->Optional[int]:
          lowerCamelCase__       :						List[Any]        =							TOKEN
          HfFolder.save_token(A      )







       @classmethod
       def 						__lowerCamelCase   (				cls					:     List[Any]      )       ->Optional[Any]:
          try:
             delete_repo(token=cls._token		, repo_id='''test-image-processor'''      )
          except HTTPError:
             pass

          try:
             delete_repo(token=cls._token		, repo_id='''valid_org/test-image-processor-org'''      )
          except HTTPError:
             pass

          try:
             delete_repo(token=cls._token		, repo_id='''test-dynamic-image-processor'''      )
          except HTTPError:
             pass







       def 						__lowerCamelCase   (				self					:     Dict      )       ->Optional[Any]:
          lowerCamelCase__       :						List[str]        =							ViTImageProcessor.from_pretrained(A      )
          image_processor.push_to_hub('''test-image-processor'''		, use_auth_token=self._token      )

          lowerCamelCase__       :						int        =							ViTImageProcessor.from_pretrained(F"{USER}/test-image-processor"      )
          for k, v in image_processor.__dict__.items():
             self.assertEqual(A		, getattr(A		, A      )      )

          # Reset repo
          delete_repo(token=self._token		, repo_id='''test-image-processor'''      )

          # Push to hub via save_pretrained
          with tempfile.TemporaryDirectory() as tmp_dir:
             image_processor.save_pretrained(
                 A		, repo_id='''test-image-processor'''		, push_to_hub=A		, use_auth_token=self._token      )

          lowerCamelCase__       :						Dict        =							ViTImageProcessor.from_pretrained(F"{USER}/test-image-processor"      )
          for k, v in image_processor.__dict__.items():
             self.assertEqual(A		, getattr(A		, A      )      )







       def 						__lowerCamelCase   (				self					:     Tuple      )       ->Union[str, Any]:
          lowerCamelCase__       :						Dict        =							ViTImageProcessor.from_pretrained(A      )
          image_processor.push_to_hub('''valid_org/test-image-processor'''		, use_auth_token=self._token      )

          lowerCamelCase__       :						Any        =							ViTImageProcessor.from_pretrained('''valid_org/test-image-processor'''      )
          for k, v in image_processor.__dict__.items():
             self.assertEqual(A		, getattr(A		, A      )      )

          # Reset repo
          delete_repo(token=self._token		, repo_id='''valid_org/test-image-processor'''      )

          # Push to hub via save_pretrained
          with tempfile.TemporaryDirectory() as tmp_dir:
             image_processor.save_pretrained(
                 A		, repo_id='''valid_org/test-image-processor-org'''		, push_to_hub=A		, use_auth_token=self._token      )

          lowerCamelCase__       :						Dict        =							ViTImageProcessor.from_pretrained('''valid_org/test-image-processor-org'''      )
          for k, v in image_processor.__dict__.items():
             self.assertEqual(A		, getattr(A		, A      )      )







       def 						__lowerCamelCase   (				self					:     List[str]      )       ->Optional[int]:
          CustomImageProcessor.register_for_auto_class()
          lowerCamelCase__       :						List[Any]        =							CustomImageProcessor.from_pretrained(A      )

          image_processor.push_to_hub('''test-dynamic-image-processor'''		, use_auth_token=self._token      )

          # This has added the proper auto_map field to the config
          self.assertDictEqual(
              image_processor.auto_map		, {'''AutoImageProcessor''': '''custom_image_processing.CustomImageProcessor'''}		, )

          lowerCamelCase__       :						List[Any]        =							AutoImageProcessor.from_pretrained(
              F"{USER}/test-dynamic-image-processor"		, trust_remote_code=A      )
          # Can't make an isinstance check because the new_image_processor is from the CustomImageProcessor class of a dynamic module
          self.assertEqual(new_image_processor.__class__.__name__		, '''CustomImageProcessor'''      )










from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


_A :   Tuple  			=  logging.get_logger(__name__)

_A :   List[str]  			=  {
    'shi-labs/nat-mini-in1k-224': 'https://huggingface.co/shi-labs/nat-mini-in1k-224/resolve/main/config.json',
    # See all Nat models at https://huggingface.co/models?filter=nat
}


class        __SCREAMING_SNAKE_CASE      (			lowerCAmelCase_					,lowerCAmelCase_ ):
       _UpperCAmelCase				:  Dict			   =   "nat"

       _UpperCAmelCase				:  Tuple			   =   {
           "num_attention_heads": "num_heads",
           "num_hidden_layers": "num_layers",
       }







       def __init__(				self					:     List[Any]		, A					:     Union[str, Any]=4		, A					:     str=3		, A					:     List[Any]=6_4		, A					:     Optional[Any]=[3, 4, 6, 5]		, A					:     int=[2, 4, 8, 1_6]		, A					:     Optional[int]=7		, A					:     List[Any]=3.0		, A					:     str=True		, A					:     str=0.0		, A					:     Any=0.0		, A					:     int=0.1		, A					:     Tuple="gelu"		, A					:     List[Any]=0.02		, A					:     str=1e-5		, A					:     Optional[int]=0.0		, A					:     Optional[Any]=None		, A					:     Dict=None		, **A					:     str		, )       ->Union[str, Any]:
          super().__init__(**A      )

          lowerCamelCase__       :						Any        =							patch_size
          lowerCamelCase__       :						Optional[Any]        =							num_channels
          lowerCamelCase__       :						Any        =							embed_dim
          lowerCamelCase__       :						str        =							depths
          lowerCamelCase__       :						Union[str, Any]        =							len(A      )
          lowerCamelCase__       :						int        =							num_heads
          lowerCamelCase__       :						Optional[int]        =							kernel_size
          lowerCamelCase__       :						Optional[int]        =							mlp_ratio
          lowerCamelCase__       :						List[Any]        =							qkv_bias
          lowerCamelCase__       :						Tuple        =							hidden_dropout_prob
          lowerCamelCase__       :						List[str]        =							attention_probs_dropout_prob
          lowerCamelCase__       :						List[Any]        =							drop_path_rate
          lowerCamelCase__       :						Union[str, Any]        =							hidden_act
          lowerCamelCase__       :						Union[str, Any]        =							layer_norm_eps
          lowerCamelCase__       :						List[Any]        =							initializer_range
          # we set the hidden_size attribute in order to make Nat work with VisionEncoderDecoderModel
          # this indicates the channel dimension after the last stage of the model
          lowerCamelCase__       :						str        =							int(embed_dim * 2 ** (len(A      ) - 1)      )
          lowerCamelCase__       :						Dict        =							layer_scale_init_value
          lowerCamelCase__       :						Dict        =							['''stem'''] + [F"stage{idx}" for idx in range(1		, len(A      ) + 1      )]
          lowerCamelCase__       ,							lowerCamelCase__       :						str        =							get_aligned_output_features_output_indices(
              out_features=A		, out_indices=A		, stage_names=self.stage_names      )



