




'''simple docstring'''


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


UpperCAmelCase_ 		=      get_tests_dir("fixtures")





class    __lowercase						(  unittest.TestCase ):







   def 		UpperCamelCase__			(	self      )				->    Union[str, Any]:
     # A mock response for an HTTP head request to emulate server down
     __a			=					mock.Mock()
     __a			=					500
     __a			=					{}
     __a			=					HTTPError
     __a			=					{}

     # Download this model to make sure it's in the cache.
     __a			=					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=UpperCamelCase      ) as mock_head:
       __a			=					ViTImageProcessor.from_pretrained('hf-internal-testing/tiny-random-vit'      )
       # This check we did call the fake head request
       mock_head.assert_called()







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







   def 		UpperCamelCase__			(	self      )				->    Tuple:
     with self.assertRaises(UpperCamelCase      ):
       # config is in subfolder, the following should not work without specifying the subfolder
       __a			=					AutoImageProcessor.from_pretrained('hf-internal-testing/stable-diffusion-all-variants'      )

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

     self.assertIsNotNone(UpperCamelCase      )





@is_staging_test
class    __lowercase						(  unittest.TestCase ):







   @classmethod
   def 		UpperCamelCase__			(	cls      )				->    Optional[Any]:
     __a			=					TOKEN
     HfFolder.save_token(UpperCamelCase      )







   @classmethod
   def 		UpperCamelCase__			(	cls      )				->    Union[str, 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 		UpperCamelCase__			(	self      )				->    List[Any]:
     __a			=					ViTImageProcessor.from_pretrained(UpperCamelCase      )
     image_processor.push_to_hub('test-image-processor'      ,							use_auth_token=self._token      )

     __a			=					ViTImageProcessor.from_pretrained(f"{USER}/test-image-processor"      )
     for k, v in image_processor.__dict__.items():
       self.assertEqual(UpperCamelCase      ,							getattr(UpperCamelCase      ,							UpperCamelCase      )      )

     # 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(
           UpperCamelCase      ,							repo_id='test-image-processor'      ,							push_to_hub=UpperCamelCase      ,							use_auth_token=self._token      )

     __a			=					ViTImageProcessor.from_pretrained(f"{USER}/test-image-processor"      )
     for k, v in image_processor.__dict__.items():
       self.assertEqual(UpperCamelCase      ,							getattr(UpperCamelCase      ,							UpperCamelCase      )      )







   def 		UpperCamelCase__			(	self      )				->    str:
     __a			=					ViTImageProcessor.from_pretrained(UpperCamelCase      )
     image_processor.push_to_hub('valid_org/test-image-processor'      ,							use_auth_token=self._token      )

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

     # 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(
           UpperCamelCase      ,							repo_id='valid_org/test-image-processor-org'      ,							push_to_hub=UpperCamelCase      ,							use_auth_token=self._token      )

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







   def 		UpperCamelCase__			(	self      )				->    int:
     CustomImageProcessor.register_for_auto_class()
     __a			=					CustomImageProcessor.from_pretrained(UpperCamelCase      )

     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'}      ,							)

     __a			=					AutoImageProcessor.from_pretrained(
         f"{USER}/test-dynamic-image-processor"      ,							trust_remote_code=UpperCamelCase      )
     # 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'      )









'''simple docstring'''


from typing import TYPE_CHECKING

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


UpperCAmelCase_ 		=      {
    "configuration_roformer": ["ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoFormerConfig", "RoFormerOnnxConfig"],
    "tokenization_roformer": ["RoFormerTokenizer"],
}

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

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_ 		=      [
      "ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "RoFormerForCausalLM",
      "RoFormerForMaskedLM",
      "RoFormerForMultipleChoice",
      "RoFormerForQuestionAnswering",
      "RoFormerForSequenceClassification",
      "RoFormerForTokenClassification",
      "RoFormerLayer",
      "RoFormerModel",
      "RoFormerPreTrainedModel",
      "load_tf_weights_in_roformer",
  ]


try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_ 		=      [
      "TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TFRoFormerForCausalLM",
      "TFRoFormerForMaskedLM",
      "TFRoFormerForMultipleChoice",
      "TFRoFormerForQuestionAnswering",
      "TFRoFormerForSequenceClassification",
      "TFRoFormerForTokenClassification",
      "TFRoFormerLayer",
      "TFRoFormerModel",
      "TFRoFormerPreTrainedModel",
  ]


try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_ 		=      [
      "FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "FlaxRoFormerForMaskedLM",
      "FlaxRoFormerForMultipleChoice",
      "FlaxRoFormerForQuestionAnswering",
      "FlaxRoFormerForSequenceClassification",
      "FlaxRoFormerForTokenClassification",
      "FlaxRoFormerModel",
      "FlaxRoFormerPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
  from .tokenization_roformer import RoFormerTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_roformer_fast import RoFormerTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_roformer import (
        ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        RoFormerForCausalLM,
        RoFormerForMaskedLM,
        RoFormerForMultipleChoice,
        RoFormerForQuestionAnswering,
        RoFormerForSequenceClassification,
        RoFormerForTokenClassification,
        RoFormerLayer,
        RoFormerModel,
        RoFormerPreTrainedModel,
        load_tf_weights_in_roformer,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_roformer import (
        TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFRoFormerForCausalLM,
        TFRoFormerForMaskedLM,
        TFRoFormerForMultipleChoice,
        TFRoFormerForQuestionAnswering,
        TFRoFormerForSequenceClassification,
        TFRoFormerForTokenClassification,
        TFRoFormerLayer,
        TFRoFormerModel,
        TFRoFormerPreTrainedModel,
    )

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_roformer import (
        FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        FlaxRoFormerForMaskedLM,
        FlaxRoFormerForMultipleChoice,
        FlaxRoFormerForQuestionAnswering,
        FlaxRoFormerForSequenceClassification,
        FlaxRoFormerForTokenClassification,
        FlaxRoFormerModel,
        FlaxRoFormerPreTrainedModel,
    )


else:
  import sys

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



