






import warnings
from typing import List, Optional, Union

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



class 				SCREAMING_SNAKE_CASE__		(							UpperCamelCase_		):





					"""simple docstring"""






					a_      =						['image_processor', 'tokenizer']
					a_      =						'FlavaImageProcessor'
					a_      =						('BertTokenizer', 'BertTokenizerFast')



					def __init__(   self	:    List[Any]				,						__A	:    str=None				,						__A	:    int=None				,						**__A	:    Union[str, Any]			):
						snake_case__					: Union[str, Any]  =    None
						if "feature_extractor" in kwargs:
							warnings.warn(
							    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
							    " instead."				,						_SCREAMING_SNAKE_CASE				,						)
							snake_case__					: Union[str, Any]  =    kwargs.pop("feature_extractor"			)

						snake_case__					: List[Any]  =    image_processor if image_processor is not None else feature_extractor
						if image_processor is None:
							raise ValueError("You need to specify an `image_processor`."			)
						if tokenizer is None:
							raise ValueError("You need to specify a `tokenizer`."			)

						super().__init__(_SCREAMING_SNAKE_CASE				,						_SCREAMING_SNAKE_CASE			)
						snake_case__					: List[str]  =    self.image_processor



					def __call__(   self	:    List[Any]				,						__A	:    Union[str, Any] = None				,						__A	:    Union[str, Any] = None				,						__A	:    Union[str, Any] = True				,						__A	:    List[Any] = False				,						__A	:    List[Any] = False				,						__A	:    Dict = None				,						__A	:    Dict = 0				,						__A	:    Optional[Any] = None				,						__A	:    Optional[int] = None				,						__A	:    int = None				,						__A	:    Union[str, Any] = None				,						__A	:    Optional[int] = None				,						__A	:    Union[str, Any] = False				,						__A	:    int = False				,						__A	:    int = False				,						__A	:    str = False				,						__A	:    Optional[Any] = True				,						__A	:    List[str] = None				,						**__A	:    Dict				,						):





						if text is None and images is None:
							raise ValueError("You have to specify either text or images. Both cannot be none."			)

						if text is not None:
							snake_case__					: List[str]  =    self.tokenizer(
							    text=_SCREAMING_SNAKE_CASE				,						add_special_tokens=_SCREAMING_SNAKE_CASE				,						padding=_SCREAMING_SNAKE_CASE				,						truncation=_SCREAMING_SNAKE_CASE				,						max_length=_SCREAMING_SNAKE_CASE				,						stride=_SCREAMING_SNAKE_CASE				,						pad_to_multiple_of=_SCREAMING_SNAKE_CASE				,						return_token_type_ids=_SCREAMING_SNAKE_CASE				,						return_attention_mask=_SCREAMING_SNAKE_CASE				,						return_overflowing_tokens=_SCREAMING_SNAKE_CASE				,						return_special_tokens_mask=_SCREAMING_SNAKE_CASE				,						return_offsets_mapping=_SCREAMING_SNAKE_CASE				,						return_length=_SCREAMING_SNAKE_CASE				,						verbose=_SCREAMING_SNAKE_CASE				,						return_tensors=_SCREAMING_SNAKE_CASE				,						**_SCREAMING_SNAKE_CASE				,						)
						if images is not None:
							snake_case__					: Union[str, Any]  =    self.image_processor(
							    _SCREAMING_SNAKE_CASE				,						return_image_mask=_SCREAMING_SNAKE_CASE				,						return_codebook_pixels=_SCREAMING_SNAKE_CASE				,						return_tensors=_SCREAMING_SNAKE_CASE				,						**_SCREAMING_SNAKE_CASE				,						)

						if text is not None and images is not None:
							encoding.update(_SCREAMING_SNAKE_CASE			)
							return encoding
						elif text is not None:
							return encoding
						else:
							return BatchEncoding(data=dict(**_SCREAMING_SNAKE_CASE			)				,						tensor_type=_SCREAMING_SNAKE_CASE			)



					def 			_lowercase	(   self	:    List[str]				,						*__A	:    int				,						**__A	:    Union[str, Any]			):
						return self.tokenizer.batch_decode(*_SCREAMING_SNAKE_CASE				,						**_SCREAMING_SNAKE_CASE			)



					def 			_lowercase	(   self	:    Optional[int]				,						*__A	:    Tuple				,						**__A	:    Union[str, Any]			):
						return self.tokenizer.decode(*_SCREAMING_SNAKE_CASE				,						**_SCREAMING_SNAKE_CASE			)



					@property
					def 			_lowercase	(   self	:    Tuple			):
						snake_case__					: Dict  =    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			)			)



					@property
					def 			_lowercase	(   self	:    List[str]			):
						warnings.warn(
						    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."				,						_SCREAMING_SNAKE_CASE				,						)
						return self.image_processor_class






					@property
					def 			_lowercase	(   self	:    Union[str, Any]			):
						warnings.warn(
						    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."				,						_SCREAMING_SNAKE_CASE				,						)
						return self.image_processor







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__    :Optional[Any]       =				get_tests_dir('''fixtures''')

class 						__a (							unittest.TestCase     ):
		def       UpperCAmelCase__							(     self		)					->			Any:




									"""simple docstring"""





									_UpperCAmelCase											=     mock.Mock()
									_UpperCAmelCase											=     500
									_UpperCAmelCase											=     {}
									_UpperCAmelCase											=     HTTPError
									_UpperCAmelCase											=     {}

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






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




									"""simple docstring"""





									_UpperCAmelCase											=     WavaVecaFeatureExtractor.from_pretrained(
									    'https://huggingface.co/hf-internal-testing/tiny-random-wav2vec2/resolve/main/preprocessor_config.json'		)



@is_staging_test
class 						__a (							unittest.TestCase     ):
		@classmethod
		def       UpperCAmelCase__							(     cls		)					->			Tuple:




									"""simple docstring"""





									_UpperCAmelCase											=     TOKEN
									HfFolder.save_token(_SCREAMING_SNAKE_CASE		)
		@classmethod
		def       UpperCAmelCase__							(     cls		)					->			Tuple:




									"""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       UpperCAmelCase__							(     self		)					->			Optional[int]:




									"""simple docstring"""





									_UpperCAmelCase											=     WavaVecaFeatureExtractor.from_pretrained(_SCREAMING_SNAKE_CASE		)
									feature_extractor.push_to_hub('test-feature-extractor'     ,       use_auth_token=self._token		)

									_UpperCAmelCase											=     WavaVecaFeatureExtractor.from_pretrained(f'''{USER}/test-feature-extractor'''		)
									for k, v in feature_extractor.__dict__.items():
																self.assertEqual(_SCREAMING_SNAKE_CASE     ,       getattr(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_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(
																    _SCREAMING_SNAKE_CASE     ,       repo_id='test-feature-extractor'     ,       push_to_hub=_SCREAMING_SNAKE_CASE     ,       use_auth_token=self._token		)

									_UpperCAmelCase											=     WavaVecaFeatureExtractor.from_pretrained(f'''{USER}/test-feature-extractor'''		)
									for k, v in feature_extractor.__dict__.items():
																self.assertEqual(_SCREAMING_SNAKE_CASE     ,       getattr(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)		)
		def       UpperCAmelCase__							(     self		)					->			Optional[Any]:




									"""simple docstring"""





									_UpperCAmelCase											=     WavaVecaFeatureExtractor.from_pretrained(_SCREAMING_SNAKE_CASE		)
									feature_extractor.push_to_hub('valid_org/test-feature-extractor'     ,       use_auth_token=self._token		)

									_UpperCAmelCase											=     WavaVecaFeatureExtractor.from_pretrained('valid_org/test-feature-extractor'		)
									for k, v in feature_extractor.__dict__.items():
																self.assertEqual(_SCREAMING_SNAKE_CASE     ,       getattr(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_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(
																    _SCREAMING_SNAKE_CASE     ,       repo_id='valid_org/test-feature-extractor-org'     ,       push_to_hub=_SCREAMING_SNAKE_CASE     ,       use_auth_token=self._token		)

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






		def       UpperCAmelCase__							(     self		)					->			Tuple:




									"""simple docstring"""





									CustomFeatureExtractor.register_for_auto_class()
									_UpperCAmelCase											=     CustomFeatureExtractor.from_pretrained(_SCREAMING_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'}     ,       )

									_UpperCAmelCase											=     AutoFeatureExtractor.from_pretrained(
									    f'''{USER}/test-dynamic-feature-extractor'''     ,       trust_remote_code=_SCREAMING_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'		)
