
"""simple docstring"""



import json
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
	import torch

	if is_vision_available():
		from transformers import OneFormerImageProcessor
		from transformers.models.oneformer.image_processing_oneformer import binary_mask_to_rle
		from transformers.models.oneformer.modeling_oneformer import OneFormerForUniversalSegmentationOutput

if is_vision_available():
	from PIL import Image






def 						__snake_case	(     SCREAMING_SNAKE_CASE:       Any  ,	SCREAMING_SNAKE_CASE:       Union[str, Any]="shi-labs/oneformer_demo"    ):






							"""simple docstring"""



							with open(hf_hub_download(lowercase__  ,	lowercase__  ,	repo_type='dataset'    )  ,	'r'    ) as f:
														_lowerCAmelCase							=      json.load(lowercase__    )
							_lowerCAmelCase							=      {}
							_lowerCAmelCase							=      []
							_lowerCAmelCase							=      []
							for key, info in class_info.items():
														_lowerCAmelCase							=      info["name"]
														class_names.append(info['name']    )
														if info["isthing"]:
																					thing_ids.append(int(lowercase__    )    )
							_lowerCAmelCase							=      thing_ids
							_lowerCAmelCase							=      class_names
							return metadata
class 							_SCREAMING_SNAKE_CASE	(	unittest.TestCase				):






				'''simple docstring'''



				def __init__(       self						:      List[Any]					,  UpperCAmelCase_						:      List[Any]					,  UpperCAmelCase_						:      str=7					,  UpperCAmelCase_						:      Optional[Any]=3					,  UpperCAmelCase_						:      Tuple=30					,  UpperCAmelCase_						:      List[str]=400					,  UpperCAmelCase_						:      int=None					,  UpperCAmelCase_						:      Optional[Any]=True					,  UpperCAmelCase_						:      str=True					,  UpperCAmelCase_						:      str=[0.5, 0.5, 0.5]					,  UpperCAmelCase_						:      Dict=[0.5, 0.5, 0.5]					,  UpperCAmelCase_						:      Optional[int]=10					,  UpperCAmelCase_						:      Dict=False					,  UpperCAmelCase_						:      Optional[int]=255					,  UpperCAmelCase_						:      Union[str, Any]="shi-labs/oneformer_demo"					,  UpperCAmelCase_						:      Any="ade20k_panoptic.json"					,  UpperCAmelCase_						:      Optional[Any]=10					,  )		->	Any:







											"""simple docstring"""




											_lowerCAmelCase							=      parent
											_lowerCAmelCase							=      batch_size
											_lowerCAmelCase							=      num_channels
											_lowerCAmelCase							=      min_resolution
											_lowerCAmelCase							=      max_resolution
											_lowerCAmelCase							=      do_resize
											_lowerCAmelCase							=      {"shortest_edge": 32, "longest_edge": 1_333} if size is None else size
											_lowerCAmelCase							=      do_normalize
											_lowerCAmelCase							=      image_mean
											_lowerCAmelCase							=      image_std
											_lowerCAmelCase							=      class_info_file
											_lowerCAmelCase							=      prepare_metadata(UpperCAmelCase_					,  UpperCAmelCase_	)
											_lowerCAmelCase							=      num_text
											_lowerCAmelCase							=      repo_path

											# for the post_process_functions
											_lowerCAmelCase							=      2
											_lowerCAmelCase							=      10
											_lowerCAmelCase							=      10
											_lowerCAmelCase							=      3
											_lowerCAmelCase							=      4
											_lowerCAmelCase							=      num_labels
											_lowerCAmelCase							=      do_reduce_labels
											_lowerCAmelCase							=      ignore_index


				def 					__lowerCamelCase						(       self						:      List[Any]	)		->	Optional[int]:







											"""simple docstring"""




											return {
											    "do_resize": self.do_resize,
											    "size": self.size,
											    "do_normalize": self.do_normalize,
											    "image_mean": self.image_mean,
											    "image_std": self.image_std,
											    "num_labels": self.num_labels,
											    "do_reduce_labels": self.do_reduce_labels,
											    "ignore_index": self.ignore_index,
											    "class_info_file": self.class_info_file,
											    "metadata": self.metadata,
											    "num_text": self.num_text,
											}


				def 					__lowerCamelCase						(       self						:      Optional[int]					,  UpperCAmelCase_						:      List[str]					,  UpperCAmelCase_						:      Union[str, Any]=False	)		->	Any:







											"""simple docstring"""




											if not batched:
																		_lowerCAmelCase							=      image_inputs[0]
																		if isinstance(UpperCAmelCase_					,  Image.Image	):
																									_lowerCAmelCase							=      image.size
																		else:
																									_lowerCAmelCase							=      image.shape[1], image.shape[2]
																		if w < h:
																									_lowerCAmelCase							=      int(self.size['shortest_edge'] * h / w	)
																									_lowerCAmelCase							=      self.size["shortest_edge"]
																		elif w > h:
																									_lowerCAmelCase							=      self.size["shortest_edge"]
																									_lowerCAmelCase							=      int(self.size['shortest_edge'] * w / h	)
																		else:
																									_lowerCAmelCase							=      self.size["shortest_edge"]
																									_lowerCAmelCase							=      self.size["shortest_edge"]

											else:
																		_lowerCAmelCase							=      []
																		for image in image_inputs:
																									_lowerCAmelCase							=      self.get_expected_values([image]	)
																									expected_values.append((expected_height, expected_width)	)
																		_lowerCAmelCase							=      max(UpperCAmelCase_					,  key=lambda UpperCAmelCase_	: item[0]	)[0]
																		_lowerCAmelCase							=      max(UpperCAmelCase_					,  key=lambda UpperCAmelCase_	: item[1]	)[1]

											return expected_height, expected_width







				def 					__lowerCamelCase						(       self						:      List[Any]	)		->	Tuple:







											"""simple docstring"""




											return OneFormerForUniversalSegmentationOutput(
											    # +1 for null class
											    class_queries_logits=torch.randn((self.batch_size, self.num_queries, self.num_classes + 1)	)					,  masks_queries_logits=torch.randn((self.batch_size, self.num_queries, self.height, self.width)	)					,  )






@require_torch
@require_vision
class 							_SCREAMING_SNAKE_CASE	(	UpperCAmelCase   ,					unittest.TestCase				):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE_:				List[str]				=       OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
				# only for test_image_processing_common.test_image_proc_to_json_string
				SCREAMING_SNAKE_CASE_:				Union[str, Any]				=       image_processing_class


				def 					__lowerCamelCase						(       self						:      str	)		->	List[str]:







											"""simple docstring"""




											_lowerCAmelCase							=      OneFormerImageProcessorTester(self	)


				@property
				def 					__lowerCamelCase						(       self						:      List[Any]	)		->	int:







											"""simple docstring"""




											return self.image_processing_tester.prepare_image_processor_dict()


				def 					__lowerCamelCase						(       self						:      Tuple	)		->	List[str]:







											"""simple docstring"""




											_lowerCAmelCase							=      self.image_processing_class(**self.image_processor_dict	)
											self.assertTrue(hasattr(UpperCAmelCase_					,  'image_mean'	)	)
											self.assertTrue(hasattr(UpperCAmelCase_					,  'image_std'	)	)
											self.assertTrue(hasattr(UpperCAmelCase_					,  'do_normalize'	)	)
											self.assertTrue(hasattr(UpperCAmelCase_					,  'do_resize'	)	)
											self.assertTrue(hasattr(UpperCAmelCase_					,  'size'	)	)
											self.assertTrue(hasattr(UpperCAmelCase_					,  'ignore_index'	)	)
											self.assertTrue(hasattr(UpperCAmelCase_					,  'class_info_file'	)	)
											self.assertTrue(hasattr(UpperCAmelCase_					,  'num_text'	)	)
											self.assertTrue(hasattr(UpperCAmelCase_					,  'repo_path'	)	)
											self.assertTrue(hasattr(UpperCAmelCase_					,  'metadata'	)	)
											self.assertTrue(hasattr(UpperCAmelCase_					,  'do_reduce_labels'	)	)


				def 					__lowerCamelCase						(       self						:      str	)		->	Tuple:







											"""simple docstring"""




											pass


				def 					__lowerCamelCase						(       self						:      Optional[int]	)		->	Optional[Any]:







											"""simple docstring"""




											_lowerCAmelCase							=      self.image_processing_class(**self.image_processor_dict	)
											# create random PIL images
											_lowerCAmelCase							=      prepare_image_inputs(self.image_processing_tester					,  equal_resolution=UpperCAmelCase_	)
											for image in image_inputs:
																		self.assertIsInstance(UpperCAmelCase_					,  Image.Image	)

											# Test not batched input
											_lowerCAmelCase							=      image_processor(image_inputs[0]					,  ['semantic']					,  return_tensors='pt'	).pixel_values

											_lowerCAmelCase							=      self.image_processing_tester.get_expected_values(UpperCAmelCase_	)

											self.assertEqual(
											    encoded_images.shape					,  (1, self.image_processing_tester.num_channels, expected_height, expected_width)					,  )

											# Test batched
											_lowerCAmelCase							=      self.image_processing_tester.get_expected_values(UpperCAmelCase_					,  batched=UpperCAmelCase_	)

											_lowerCAmelCase							=      image_processor(
											    UpperCAmelCase_					,  ['semantic'] * len(UpperCAmelCase_	)					,  return_tensors='pt'	).pixel_values
											self.assertEqual(
											    encoded_images.shape					,  (
											        self.image_processing_tester.batch_size,
											        self.image_processing_tester.num_channels,
											        expected_height,
											        expected_width,
											    )					,  )


				def 					__lowerCamelCase						(       self						:      Dict	)		->	Optional[Any]:







											"""simple docstring"""




											_lowerCAmelCase							=      self.image_processing_class(**self.image_processor_dict	)
											# create random numpy tensors
											_lowerCAmelCase							=      prepare_image_inputs(self.image_processing_tester					,  equal_resolution=UpperCAmelCase_					,  numpify=UpperCAmelCase_	)
											for image in image_inputs:
																		self.assertIsInstance(UpperCAmelCase_					,  np.ndarray	)

											# Test not batched input
											_lowerCAmelCase							=      image_processor(image_inputs[0]					,  ['semantic']					,  return_tensors='pt'	).pixel_values

											_lowerCAmelCase							=      self.image_processing_tester.get_expected_values(UpperCAmelCase_	)

											self.assertEqual(
											    encoded_images.shape					,  (1, self.image_processing_tester.num_channels, expected_height, expected_width)					,  )

											# Test batched
											_lowerCAmelCase							=      self.image_processing_tester.get_expected_values(UpperCAmelCase_					,  batched=UpperCAmelCase_	)

											_lowerCAmelCase							=      image_processor(
											    UpperCAmelCase_					,  ['semantic'] * len(UpperCAmelCase_	)					,  return_tensors='pt'	).pixel_values
											self.assertEqual(
											    encoded_images.shape					,  (
											        self.image_processing_tester.batch_size,
											        self.image_processing_tester.num_channels,
											        expected_height,
											        expected_width,
											    )					,  )


				def 					__lowerCamelCase						(       self						:      int	)		->	List[str]:







											"""simple docstring"""




											_lowerCAmelCase							=      self.image_processing_class(**self.image_processor_dict	)
											# create random PyTorch tensors
											_lowerCAmelCase							=      prepare_image_inputs(self.image_processing_tester					,  equal_resolution=UpperCAmelCase_					,  torchify=UpperCAmelCase_	)
											for image in image_inputs:
																		self.assertIsInstance(UpperCAmelCase_					,  torch.Tensor	)

											# Test not batched input
											_lowerCAmelCase							=      image_processor(image_inputs[0]					,  ['semantic']					,  return_tensors='pt'	).pixel_values

											_lowerCAmelCase							=      self.image_processing_tester.get_expected_values(UpperCAmelCase_	)

											self.assertEqual(
											    encoded_images.shape					,  (1, self.image_processing_tester.num_channels, expected_height, expected_width)					,  )

											# Test batched
											_lowerCAmelCase							=      self.image_processing_tester.get_expected_values(UpperCAmelCase_					,  batched=UpperCAmelCase_	)

											_lowerCAmelCase							=      image_processor(
											    UpperCAmelCase_					,  ['semantic'] * len(UpperCAmelCase_	)					,  return_tensors='pt'	).pixel_values
											self.assertEqual(
											    encoded_images.shape					,  (
											        self.image_processing_tester.batch_size,
											        self.image_processing_tester.num_channels,
											        expected_height,
											        expected_width,
											    )					,  )


				def 					__lowerCamelCase						(       self						:      Tuple					,  UpperCAmelCase_						:      str=False					,  UpperCAmelCase_						:      Tuple=False					,  UpperCAmelCase_						:      str="np"	)		->	Optional[Any]:







											"""simple docstring"""




											_lowerCAmelCase							=      self.image_processing_class(**self.image_processor_dict	)
											# prepare image and target
											_lowerCAmelCase							=      self.image_processing_tester.num_labels
											_lowerCAmelCase							=      None
											_lowerCAmelCase							=      None
											_lowerCAmelCase							=      prepare_image_inputs(self.image_processing_tester					,  equal_resolution=UpperCAmelCase_	)
											if with_segmentation_maps:
																		_lowerCAmelCase							=      num_labels
																		if is_instance_map:
																									_lowerCAmelCase							=      list(range(UpperCAmelCase_	)	) * 2
																									_lowerCAmelCase							=      dict(enumerate(UpperCAmelCase_	)	)
																		_lowerCAmelCase							=      [
																		    np.random.randint(0					,  high * 2					,  (img.size[1], img.size[0])	).astype(np.uinta	) for img in image_inputs
																		]
																		if segmentation_type == "pil":
																									_lowerCAmelCase							=      [Image.fromarray(UpperCAmelCase_	) for annotation in annotations]

											_lowerCAmelCase							=      image_processor(
											    UpperCAmelCase_					,  ['semantic'] * len(UpperCAmelCase_	)					,  UpperCAmelCase_					,  return_tensors='pt'					,  instance_id_to_semantic_id=UpperCAmelCase_					,  pad_and_return_pixel_mask=UpperCAmelCase_					,  )

											return inputs


				def 					__lowerCamelCase						(       self						:      Tuple	)		->	Union[str, Any]:







											"""simple docstring"""




											pass


				def 					__lowerCamelCase						(       self						:      Dict	)		->	Optional[int]:







											"""simple docstring"""




											def common(UpperCAmelCase_						:      List[str]=False					,  UpperCAmelCase_						:      Optional[int]=None	):
																		_lowerCAmelCase							=      self.comm_get_image_processor_inputs(
																		    with_segmentation_maps=UpperCAmelCase_					,  is_instance_map=UpperCAmelCase_					,  segmentation_type=UpperCAmelCase_	)

																		_lowerCAmelCase							=      inputs["mask_labels"]
																		_lowerCAmelCase							=      inputs["class_labels"]
																		_lowerCAmelCase							=      inputs["pixel_values"]
																		_lowerCAmelCase							=      inputs["text_inputs"]

																		# check the batch_size
																		for mask_label, class_label, text_input in zip(UpperCAmelCase_					,  UpperCAmelCase_					,  UpperCAmelCase_	):
																									self.assertEqual(mask_label.shape[0]					,  class_label.shape[0]	)
																									# this ensure padding has happened
																									self.assertEqual(mask_label.shape[1:]					,  pixel_values.shape[2:]	)
																									self.assertEqual(len(UpperCAmelCase_	)					,  self.image_processing_tester.num_text	)

											common()
											common(is_instance_map=UpperCAmelCase_	)
											common(is_instance_map=UpperCAmelCase_					,  segmentation_type='pil'	)
											common(is_instance_map=UpperCAmelCase_					,  segmentation_type='pil'	)


				def 					__lowerCamelCase						(       self						:      Tuple	)		->	Optional[int]:







											"""simple docstring"""




											_lowerCAmelCase							=      np.zeros((20, 50)	)
											_lowerCAmelCase							=      1
											_lowerCAmelCase							=      1
											_lowerCAmelCase							=      1

											_lowerCAmelCase							=      binary_mask_to_rle(UpperCAmelCase_	)
											self.assertEqual(len(UpperCAmelCase_	)					,  4	)
											self.assertEqual(rle[0]					,  21	)
											self.assertEqual(rle[1]					,  45	)


				def 					__lowerCamelCase						(       self						:      Union[str, Any]	)		->	Any:







											"""simple docstring"""




											_lowerCAmelCase							=      self.image_processing_class(
											    num_labels=self.image_processing_tester.num_classes					,  max_seq_length=77					,  task_seq_length=77					,  class_info_file='ade20k_panoptic.json'					,  num_text=self.image_processing_tester.num_text					,  repo_path='shi-labs/oneformer_demo'					,  )
											_lowerCAmelCase							=      self.image_processing_tester.get_fake_oneformer_outputs()

											_lowerCAmelCase							=      fature_extractor.post_process_semantic_segmentation(UpperCAmelCase_	)

											self.assertEqual(len(UpperCAmelCase_	)					,  self.image_processing_tester.batch_size	)
											self.assertEqual(
											    segmentation[0].shape					,  (
											        self.image_processing_tester.height,
											        self.image_processing_tester.width,
											    )					,  )

											_lowerCAmelCase							=      [(1, 4) for i in range(self.image_processing_tester.batch_size	)]
											_lowerCAmelCase							=      fature_extractor.post_process_semantic_segmentation(UpperCAmelCase_					,  target_sizes=UpperCAmelCase_	)

											self.assertEqual(segmentation[0].shape					,  target_sizes[0]	)


				def 					__lowerCamelCase						(       self						:      List[str]	)		->	Optional[Any]:







											"""simple docstring"""




											_lowerCAmelCase							=      self.image_processing_class(
											    num_labels=self.image_processing_tester.num_classes					,  max_seq_length=77					,  task_seq_length=77					,  class_info_file='ade20k_panoptic.json'					,  num_text=self.image_processing_tester.num_text					,  repo_path='shi-labs/oneformer_demo'					,  )
											_lowerCAmelCase							=      self.image_processing_tester.get_fake_oneformer_outputs()
											_lowerCAmelCase							=      image_processor.post_process_instance_segmentation(UpperCAmelCase_					,  threshold=0	)

											self.assertTrue(len(UpperCAmelCase_	) == self.image_processing_tester.batch_size	)
											for el in segmentation:
																		self.assertTrue('segmentation' in el	)
																		self.assertTrue('segments_info' in el	)
																		self.assertEqual(type(el['segments_info']	)					,  UpperCAmelCase_	)
																		self.assertEqual(
																		    el['segmentation'].shape					,  (self.image_processing_tester.height, self.image_processing_tester.width)	)







				def 					__lowerCamelCase						(       self						:      int	)		->	List[str]:







											"""simple docstring"""




											_lowerCAmelCase							=      self.image_processing_class(
											    num_labels=self.image_processing_tester.num_classes					,  max_seq_length=77					,  task_seq_length=77					,  class_info_file='ade20k_panoptic.json'					,  num_text=self.image_processing_tester.num_text					,  repo_path='shi-labs/oneformer_demo'					,  )
											_lowerCAmelCase							=      self.image_processing_tester.get_fake_oneformer_outputs()
											_lowerCAmelCase							=      image_processor.post_process_panoptic_segmentation(UpperCAmelCase_					,  threshold=0	)

											self.assertTrue(len(UpperCAmelCase_	) == self.image_processing_tester.batch_size	)
											for el in segmentation:
																		self.assertTrue('segmentation' in el	)
																		self.assertTrue('segments_info' in el	)
																		self.assertEqual(type(el['segments_info']	)					,  UpperCAmelCase_	)
																		self.assertEqual(
																		    el['segmentation'].shape					,  (self.image_processing_tester.height, self.image_processing_tester.width)	)









"""simple docstring"""

import json
import sys
import tempfile
import unittest
from pathlib import Path

import transformers
from transformers import (
    CONFIG_MAPPING,
    FEATURE_EXTRACTOR_MAPPING,
    AutoConfig,
    AutoFeatureExtractor,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, get_tests_dir


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

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_feature_extraction import CustomFeatureExtractor  # noqa E402


_UpperCamelCase		     =    get_tests_dir("""fixtures""")
_UpperCamelCase		     =    get_tests_dir("""fixtures/dummy_feature_extractor_config.json""")
_UpperCamelCase		     =    get_tests_dir("""fixtures/dummy-config.json""")






class 			__a					(						unittest.TestCase       ):





       """simple docstring"""


       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				Optional[int] 			=				0






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				Optional[int] 			=				AutoFeatureExtractor.from_pretrained("facebook/wav2vec2-base-960h"   )
        self.assertIsInstance(snake_case			,					snake_case   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				Tuple 			=				AutoFeatureExtractor.from_pretrained(snake_case   )
        self.assertIsInstance(snake_case			,					snake_case   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        with tempfile.TemporaryDirectory() as tmpdirname:
         lowerCAmelCase__   :				int 			=				WavaVecaConfig()

         # remove feature_extractor_type to make sure config.json alone is enough to load feature processor locally
         lowerCAmelCase__   :				Union[str, Any] 			=				AutoFeatureExtractor.from_pretrained(snake_case   ).to_dict()

         config_dict.pop("feature_extractor_type"   )
         lowerCAmelCase__   :				Any 			=				WavaVecaFeatureExtractor(**snake_case   )

         # save in new folder
         model_config.save_pretrained(snake_case   )
         config.save_pretrained(snake_case   )

         lowerCAmelCase__   :				List[str] 			=				AutoFeatureExtractor.from_pretrained(snake_case   )

         # make sure private variable is not incorrectly saved
         lowerCAmelCase__   :				Optional[int] 			=				json.loads(config.to_json_string()   )
         self.assertTrue("_processor_class" not in dict_as_saved   )

        self.assertIsInstance(snake_case			,					snake_case   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				List[str] 			=				AutoFeatureExtractor.from_pretrained(snake_case   )
        self.assertIsInstance(snake_case			,					snake_case   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        with self.assertRaisesRegex(
            snake_case			,					"bert-base is not a local folder and is not a valid model identifier"   ):
         lowerCAmelCase__   :				Optional[int] 			=				AutoFeatureExtractor.from_pretrained("bert-base"   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        with self.assertRaisesRegex(
            snake_case			,					R"aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"   ):
         lowerCAmelCase__   :				Optional[Any] 			=				AutoFeatureExtractor.from_pretrained(snake_case			,					revision="aaaaaa"   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        with self.assertRaisesRegex(
            snake_case			,					"hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json."			,					):
         lowerCAmelCase__   :				Dict 			=				AutoFeatureExtractor.from_pretrained("hf-internal-testing/config-no-model"   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        with self.assertRaises(snake_case   ):
         lowerCAmelCase__   :				Tuple 			=				AutoFeatureExtractor.from_pretrained(
             "hf-internal-testing/test_dynamic_feature_extractor"   )
        # If remote code is disabled, we can't load this config.
        with self.assertRaises(snake_case   ):
         lowerCAmelCase__   :				str 			=				AutoFeatureExtractor.from_pretrained(
             "hf-internal-testing/test_dynamic_feature_extractor"			,					trust_remote_code=snake_case   )

        lowerCAmelCase__   :				Optional[int] 			=				AutoFeatureExtractor.from_pretrained(
            "hf-internal-testing/test_dynamic_feature_extractor"			,					trust_remote_code=snake_case   )
        self.assertEqual(feature_extractor.__class__.__name__			,					"NewFeatureExtractor"   )

        # Test feature extractor can be reloaded.
        with tempfile.TemporaryDirectory() as tmp_dir:
         feature_extractor.save_pretrained(snake_case   )
         lowerCAmelCase__   :				Optional[Any] 			=				AutoFeatureExtractor.from_pretrained(snake_case			,					trust_remote_code=snake_case   )
        self.assertEqual(reloaded_feature_extractor.__class__.__name__			,					"NewFeatureExtractor"   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        try:
         AutoConfig.register("custom"			,					snake_case   )
         AutoFeatureExtractor.register(snake_case			,					snake_case   )
         # Trying to register something existing in the Transformers library will raise an error
         with self.assertRaises(snake_case   ):
          AutoFeatureExtractor.register(snake_case			,					snake_case   )

         # Now that the config is registered, it can be used as any other config with the auto-API
         lowerCAmelCase__   :				List[Any] 			=				CustomFeatureExtractor.from_pretrained(snake_case   )
         with tempfile.TemporaryDirectory() as tmp_dir:
          feature_extractor.save_pretrained(snake_case   )
          lowerCAmelCase__   :				Any 			=				AutoFeatureExtractor.from_pretrained(snake_case   )
          self.assertIsInstance(snake_case			,					snake_case   )

        finally:
         if "custom" in CONFIG_MAPPING._extra_content:
          del CONFIG_MAPPING._extra_content["custom"]
         if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
          del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        class 			__a					(						__magic_name__       ):





               """simple docstring"""


               __UpperCamelCase					:      Optional[int]    =						True

        try:
         AutoConfig.register("custom"			,					snake_case   )
         AutoFeatureExtractor.register(snake_case			,					snake_case   )
         # If remote code is not set, the default is to use local
         lowerCAmelCase__   :				List[Any] 			=				AutoFeatureExtractor.from_pretrained(
             "hf-internal-testing/test_dynamic_feature_extractor"   )
         self.assertEqual(feature_extractor.__class__.__name__			,					"NewFeatureExtractor"   )
         self.assertTrue(feature_extractor.is_local   )

         # If remote code is disabled, we load the local one.
         lowerCAmelCase__   :				Dict 			=				AutoFeatureExtractor.from_pretrained(
             "hf-internal-testing/test_dynamic_feature_extractor"			,					trust_remote_code=snake_case   )
         self.assertEqual(feature_extractor.__class__.__name__			,					"NewFeatureExtractor"   )
         self.assertTrue(feature_extractor.is_local   )

         # If remote is enabled, we load from the Hub
         lowerCAmelCase__   :				Tuple 			=				AutoFeatureExtractor.from_pretrained(
             "hf-internal-testing/test_dynamic_feature_extractor"			,					trust_remote_code=snake_case   )
         self.assertEqual(feature_extractor.__class__.__name__			,					"NewFeatureExtractor"   )
         self.assertTrue(not hasattr(snake_case			,					"is_local"   )   )

        finally:
         if "custom" in CONFIG_MAPPING._extra_content:
          del CONFIG_MAPPING._extra_content["custom"]
         if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
          del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]






