



"""simple docstring"""

from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType




class  _UpperCAmelCase (    lowerCAmelCase__):
 _lowerCAmelCase     :    str	      =	["""image_processor""", """tokenizer"""]
 _lowerCAmelCase     :    str	      =	"""BridgeTowerImageProcessor"""
 _lowerCAmelCase     :    Tuple	      =	("""RobertaTokenizer""", """RobertaTokenizerFast""")

 def __init__(  self	: Dict						,  lowercase_	: Optional[Any]						,  lowercase_	: Any       ):
     super().__init__(lowercase_						,  lowercase_       )

 def __call__(  self	: List[str]						,  lowercase_	: Optional[int]						,  lowercase_	: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None						,  lowercase_	: bool = True						,  lowercase_	: Union[bool, str, PaddingStrategy] = False						,  lowercase_	: Union[bool, str, TruncationStrategy] = None						,  lowercase_	: Optional[int] = None						,  lowercase_	: int = 0						,  lowercase_	: Optional[int] = None						,  lowercase_	: Optional[bool] = None						,  lowercase_	: Optional[bool] = None						,  lowercase_	: bool = False						,  lowercase_	: bool = False						,  lowercase_	: bool = False						,  lowercase_	: bool = False						,  lowercase_	: bool = True						,  lowercase_	: Optional[Union[str, TensorType]] = None						,  **lowercase_	: Union[str, Any]						,  ):
     snake_case_				:  Dict       =			self.tokenizer(
         text=lowercase_						,  add_special_tokens=lowercase_						,  padding=lowercase_						,  truncation=lowercase_						,  max_length=lowercase_						,  stride=lowercase_						,  pad_to_multiple_of=lowercase_						,  return_token_type_ids=lowercase_						,  return_attention_mask=lowercase_						,  return_overflowing_tokens=lowercase_						,  return_special_tokens_mask=lowercase_						,  return_offsets_mapping=lowercase_						,  return_length=lowercase_						,  verbose=lowercase_						,  return_tensors=lowercase_						,  **lowercase_						,  )
     # add pixel_values + pixel_mask
     snake_case_				:  List[str]       =			self.image_processor(
         lowercase_						,  return_tensors=lowercase_						,  do_normalize=lowercase_						,  do_center_crop=lowercase_						,  **lowercase_       )
     encoding.update(lowercase_       )

     return encoding

 def 		_snake_case      (  self	: Optional[int]						,  *lowercase_	: Union[str, Any]						,  **lowercase_	: str       ):
     return self.tokenizer.batch_decode(*lowercase_						,  **lowercase_       )

 def 		_snake_case      (  self	: int						,  *lowercase_	: List[Any]						,  **lowercase_	: Tuple       ):
     return self.tokenizer.decode(*lowercase_						,  **lowercase_       )






 @property
 def 		_snake_case      (  self	: str       ):
     snake_case_				:  Union[str, Any]       =			self.tokenizer.model_input_names
     snake_case_				:  List[str]       =			self.image_processor.model_input_names
     return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names       )       )










"""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


lowercase__					:						Any												=			get_tests_dir('''fixtures''')




class  _UpperCAmelCase (    unittest.TestCase):

 def 		_snake_case      (  self	: Tuple       ):
     # A mock response for an HTTP head request to emulate server down
     snake_case_				:  str       =			mock.Mock()
     snake_case_				:  Optional[Any]       =			500
     snake_case_				:  str       =			{}
     snake_case_				:  Optional[int]       =			HTTPError
     snake_case_				:  Tuple       =			{}

     # Download this model to make sure it's in the cache.
     snake_case_				:  List[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=lowercase_       ) as mock_head:
         snake_case_				:  int       =			WavaVecaFeatureExtractor.from_pretrained('''hf-internal-testing/tiny-random-wav2vec2'''       )
         # This check we did call the fake head request
         mock_head.assert_called()






 def 		_snake_case      (  self	: Union[str, Any]       ):
     # This test is for deprecated behavior and can be removed in v5
     snake_case_				:  str       =			WavaVecaFeatureExtractor.from_pretrained(
         '''https://huggingface.co/hf-internal-testing/tiny-random-wav2vec2/resolve/main/preprocessor_config.json'''       )





@is_staging_test
class  _UpperCAmelCase (    unittest.TestCase):

 @classmethod
 def 		_snake_case      (  cls	: int       ):
     snake_case_				:  Dict       =			TOKEN
     HfFolder.save_token(lowercase_       )

 @classmethod
 def 		_snake_case      (  cls	: str       ):
     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 		_snake_case      (  self	: Any       ):
     snake_case_				:  int       =			WavaVecaFeatureExtractor.from_pretrained(lowercase_       )
     feature_extractor.push_to_hub('''test-feature-extractor'''						,  use_auth_token=self._token       )

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

     # 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(
             lowercase_						,  repo_id='''test-feature-extractor'''						,  push_to_hub=lowercase_						,  use_auth_token=self._token       )

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

 def 		_snake_case      (  self	: Dict       ):
     snake_case_				:  Union[str, Any]       =			WavaVecaFeatureExtractor.from_pretrained(lowercase_       )
     feature_extractor.push_to_hub('''valid_org/test-feature-extractor'''						,  use_auth_token=self._token       )

     snake_case_				:  Optional[int]       =			WavaVecaFeatureExtractor.from_pretrained('''valid_org/test-feature-extractor'''       )
     for k, v in feature_extractor.__dict__.items():
         self.assertEqual(lowercase_						,  getattr(lowercase_						,  lowercase_       )       )

     # 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(
             lowercase_						,  repo_id='''valid_org/test-feature-extractor-org'''						,  push_to_hub=lowercase_						,  use_auth_token=self._token       )

     snake_case_				:  int       =			WavaVecaFeatureExtractor.from_pretrained('''valid_org/test-feature-extractor-org'''       )
     for k, v in feature_extractor.__dict__.items():
         self.assertEqual(lowercase_						,  getattr(lowercase_						,  lowercase_       )       )






 def 		_snake_case      (  self	: str       ):
     CustomFeatureExtractor.register_for_auto_class()
     snake_case_				:  Optional[Any]       =			CustomFeatureExtractor.from_pretrained(lowercase_       )

     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'''}						,  )

     snake_case_				:  Any       =			AutoFeatureExtractor.from_pretrained(
         f"{USER}/test-dynamic-feature-extractor"						,  trust_remote_code=lowercase_       )
     # 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'''       )





