


"""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 AutoFeatureExtractor, WavaVecaFeatureExtractor
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_feature_extraction import CustomFeatureExtractor  # noqa E402


lowerCAmelCase__      				=   get_tests_dir('''fixtures''')


class       SCREAMING_SNAKE_CASE__				(		unittest.TestCase				):
       """simple docstring"""


       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     Tuple								=							mock.Mock()
         lowerCAmelCase     :     List[str]								=							500
         lowerCAmelCase     :     List[str]								=							{}
         lowerCAmelCase     :     Optional[int]								=							HTTPError
         lowerCAmelCase     :     Tuple								=							{}

         # Download this model to make sure it's in the cache.
         lowerCAmelCase     :     str								=							WavaVecaFeatureExtractor.from_pretrained("hf-internal-testing/tiny-random-wav2vec2"						)
         # Under the mock environment we get a 500 error when trying to reach the model.
         with mock.patch("requests.Session.request"		,     return_value=snake_case__						) as mock_head:
           lowerCAmelCase     :     Any								=							WavaVecaFeatureExtractor.from_pretrained("hf-internal-testing/tiny-random-wav2vec2"						)
           # This check we did call the fake head request
           mock_head.assert_called()







       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     List[str]								=							WavaVecaFeatureExtractor.from_pretrained(
             "https://huggingface.co/hf-internal-testing/tiny-random-wav2vec2/resolve/main/preprocessor_config.json"						)


@is_staging_test
class       SCREAMING_SNAKE_CASE__				(		unittest.TestCase				):
       """simple docstring"""


       @classmethod
       def        lowercase__							(						cls						):






         """simple docstring"""





         lowerCAmelCase     :     Optional[Any]								=							TOKEN
         HfFolder.save_token(snake_case__						)

       @classmethod
       def        lowercase__							(						cls						):






         """simple docstring"""





         try:
           delete_repo(token=cls._token		,     repo_id="test-feature-extractor"						)
         except HTTPError:
           pass

         try:
           delete_repo(token=cls._token		,     repo_id="valid_org/test-feature-extractor-org"						)
         except HTTPError:
           pass

         try:
           delete_repo(token=cls._token		,     repo_id="test-dynamic-feature-extractor"						)
         except HTTPError:
           pass

       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     str								=							WavaVecaFeatureExtractor.from_pretrained(snake_case__						)
         feature_extractor.push_to_hub("test-feature-extractor"		,     use_auth_token=self._token						)

         lowerCAmelCase     :     List[Any]								=							WavaVecaFeatureExtractor.from_pretrained(f"""{USER}/test-feature-extractor"""						)
         for k, v in feature_extractor.__dict__.items():
           self.assertEqual(snake_case__		,     getattr(snake_case__		,     snake_case__						)						)

         # Reset repo
         delete_repo(token=self._token		,     repo_id="test-feature-extractor"						)

         # Push to hub via save_pretrained
         with tempfile.TemporaryDirectory() as tmp_dir:
           feature_extractor.save_pretrained(
               snake_case__		,     repo_id="test-feature-extractor"		,     push_to_hub=snake_case__		,     use_auth_token=self._token						)

         lowerCAmelCase     :     int								=							WavaVecaFeatureExtractor.from_pretrained(f"""{USER}/test-feature-extractor"""						)
         for k, v in feature_extractor.__dict__.items():
           self.assertEqual(snake_case__		,     getattr(snake_case__		,     snake_case__						)						)

       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     int								=							WavaVecaFeatureExtractor.from_pretrained(snake_case__						)
         feature_extractor.push_to_hub("valid_org/test-feature-extractor"		,     use_auth_token=self._token						)

         lowerCAmelCase     :     Optional[Any]								=							WavaVecaFeatureExtractor.from_pretrained("valid_org/test-feature-extractor"						)
         for k, v in feature_extractor.__dict__.items():
           self.assertEqual(snake_case__		,     getattr(snake_case__		,     snake_case__						)						)

         # Reset repo
         delete_repo(token=self._token		,     repo_id="valid_org/test-feature-extractor"						)

         # Push to hub via save_pretrained
         with tempfile.TemporaryDirectory() as tmp_dir:
           feature_extractor.save_pretrained(
               snake_case__		,     repo_id="valid_org/test-feature-extractor-org"		,     push_to_hub=snake_case__		,     use_auth_token=self._token						)

         lowerCAmelCase     :     Dict								=							WavaVecaFeatureExtractor.from_pretrained("valid_org/test-feature-extractor-org"						)
         for k, v in feature_extractor.__dict__.items():
           self.assertEqual(snake_case__		,     getattr(snake_case__		,     snake_case__						)						)







       def        lowercase__							(						self						):






         """simple docstring"""





         CustomFeatureExtractor.register_for_auto_class()
         lowerCAmelCase     :     Tuple								=							CustomFeatureExtractor.from_pretrained(snake_case__						)

         feature_extractor.push_to_hub("test-dynamic-feature-extractor"		,     use_auth_token=self._token						)

         # This has added the proper auto_map field to the config
         self.assertDictEqual(
             feature_extractor.auto_map		,     {"AutoFeatureExtractor": "custom_feature_extraction.CustomFeatureExtractor"}		,     )

         lowerCAmelCase     :     str								=							AutoFeatureExtractor.from_pretrained(
             f"""{USER}/test-dynamic-feature-extractor"""		,     trust_remote_code=snake_case__						)
         # Can't make an isinstance check because the new_feature_extractor is from the CustomFeatureExtractor class of a dynamic module
         self.assertEqual(new_feature_extractor.__class__.__name__		,     "CustomFeatureExtractor"						)









"""simple docstring"""







from typing import TYPE_CHECKING

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


lowerCAmelCase__      				=   {
    '''configuration_pix2struct''': [
        '''PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''Pix2StructConfig''',
        '''Pix2StructTextConfig''',
        '''Pix2StructVisionConfig''',
    ],
    '''processing_pix2struct''': ['''Pix2StructProcessor'''],
}

try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCAmelCase__      				=   ['''Pix2StructImageProcessor''']


try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCAmelCase__      				=   [
         '''PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''Pix2StructPreTrainedModel''',
         '''Pix2StructForConditionalGeneration''',
         '''Pix2StructVisionModel''',
         '''Pix2StructTextModel''',
     ]

if TYPE_CHECKING:
     from .configuration_pixastruct import (
         PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP,
         PixaStructConfig,
         PixaStructTextConfig,
         PixaStructVisionConfig,
     )
     from .processing_pixastruct import PixaStructProcessor

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .image_processing_pixastruct import PixaStructImageProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_pixastruct import (
              PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST,
              PixaStructForConditionalGeneration,
              PixaStructPreTrainedModel,
              PixaStructTextModel,
              PixaStructVisionModel,
          )

else:
     import sys

     lowerCAmelCase__      				=   _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





