

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


_lowerCAmelCase					:			Optional[int]  				=			get_tests_dir("fixtures")
_lowerCAmelCase					:			Union[str, Any]  				=			get_tests_dir("fixtures/dummy_feature_extractor_config.json")
_lowerCAmelCase					:			Union[str, Any]  				=			get_tests_dir("fixtures/dummy-config.json")






class 	__magic_name__    (			unittest.TestCase      ):





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




						'''simple docstring'''
						__a        =0





			def 		__magic_name__    (		self					)   -> str:




						'''simple docstring'''
						__a        =AutoFeatureExtractor.from_pretrained('facebook/wav2vec2-base-960h'					)
						self.assertIsInstance(__snake_case					,  __snake_case					)





			def 		__magic_name__    (		self					)   -> Tuple:




						'''simple docstring'''
						__a        =AutoFeatureExtractor.from_pretrained(__snake_case					)
						self.assertIsInstance(__snake_case					,  __snake_case					)





			def 		__magic_name__    (		self					)   -> Tuple:




						'''simple docstring'''
						with tempfile.TemporaryDirectory() as tmpdirname:
									__a        =WavaVecaConfig()

									# remove feature_extractor_type to make sure config.json alone is enough to load feature processor locally
									__a        =AutoFeatureExtractor.from_pretrained(__snake_case					).to_dict()

									config_dict.pop('feature_extractor_type'					)
									__a        =WavaVecaFeatureExtractor(**__snake_case					)

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

									__a        =AutoFeatureExtractor.from_pretrained(__snake_case					)

									# make sure private variable is not incorrectly saved
									__a        =json.loads(config.to_json_string()					)
									self.assertTrue('_processor_class' not in dict_as_saved					)

						self.assertIsInstance(__snake_case					,  __snake_case					)





			def 		__magic_name__    (		self					)   -> Dict:




						'''simple docstring'''
						__a        =AutoFeatureExtractor.from_pretrained(__snake_case					)
						self.assertIsInstance(__snake_case					,  __snake_case					)





			def 		__magic_name__    (		self					)   -> List[Any]:




						'''simple docstring'''
						with self.assertRaisesRegex(
						    __snake_case					,  'bert-base is not a local folder and is not a valid model identifier'					):
									__a        =AutoFeatureExtractor.from_pretrained('bert-base'					)





			def 		__magic_name__    (		self					)   -> str:




						'''simple docstring'''
						with self.assertRaisesRegex(
						    __snake_case					,  R'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'					):
									__a        =AutoFeatureExtractor.from_pretrained(__snake_case					,  revision='aaaaaa'					)





			def 		__magic_name__    (		self					)   -> List[Any]:




						'''simple docstring'''
						with self.assertRaisesRegex(
						    __snake_case					,  'hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.'					,  ):
									__a        =AutoFeatureExtractor.from_pretrained('hf-internal-testing/config-no-model'					)





			def 		__magic_name__    (		self					)   -> int:




						'''simple docstring'''
						# If remote code is not set, we will time out when asking whether to load the model.
						with self.assertRaises(__snake_case					):
									__a        =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					):
									__a        =AutoFeatureExtractor.from_pretrained(
									    'hf-internal-testing/test_dynamic_feature_extractor'					,  trust_remote_code=__snake_case					)

						__a        =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					)
									__a        =AutoFeatureExtractor.from_pretrained(__snake_case					,  trust_remote_code=__snake_case					)
						self.assertEqual(reloaded_feature_extractor.__class__.__name__					,  'NewFeatureExtractor'					)





			def 		__magic_name__    (		self					)   -> Optional[int]:




						'''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
									__a        =CustomFeatureExtractor.from_pretrained(__snake_case					)
									with tempfile.TemporaryDirectory() as tmp_dir:
												feature_extractor.save_pretrained(__snake_case					)
												__a        =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 		__magic_name__    (		self					)   -> Dict:




						'''simple docstring'''






						class 	__magic_name__    (			lowerCAmelCase_      ):
									SCREAMING_SNAKE_CASE							   =						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
									__a        =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.
									__a        =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
									__a        =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]









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 	__magic_name__    (			lowerCAmelCase_      ):
			SCREAMING_SNAKE_CASE							   =						['image_processor', 'tokenizer']
			SCREAMING_SNAKE_CASE							   =						'BridgeTowerImageProcessor'
			SCREAMING_SNAKE_CASE							   =						('RobertaTokenizer', 'RobertaTokenizerFast')





			def __init__(		self					,  __snake_case					,  __snake_case					)   -> Optional[int]:




						'''simple docstring'''
						super().__init__(__snake_case					,  __snake_case					)





			def __call__(		self					,  __snake_case					,  __snake_case = None					,  __snake_case = True					,  __snake_case = False					,  __snake_case = None					,  __snake_case = None					,  __snake_case = 0					,  __snake_case = None					,  __snake_case = None					,  __snake_case = None					,  __snake_case = False					,  __snake_case = False					,  __snake_case = False					,  __snake_case = False					,  __snake_case = True					,  __snake_case = None					,  **__snake_case					,  )   -> BatchEncoding:




						'''simple docstring'''
						__a        =self.tokenizer(
						    text=__snake_case					,  add_special_tokens=__snake_case					,  padding=__snake_case					,  truncation=__snake_case					,  max_length=__snake_case					,  stride=__snake_case					,  pad_to_multiple_of=__snake_case					,  return_token_type_ids=__snake_case					,  return_attention_mask=__snake_case					,  return_overflowing_tokens=__snake_case					,  return_special_tokens_mask=__snake_case					,  return_offsets_mapping=__snake_case					,  return_length=__snake_case					,  verbose=__snake_case					,  return_tensors=__snake_case					,  **__snake_case					,  )
						# add pixel_values + pixel_mask
						__a        =self.image_processor(
						    __snake_case					,  return_tensors=__snake_case					,  do_normalize=__snake_case					,  do_center_crop=__snake_case					,  **__snake_case					)
						encoding.update(__snake_case					)

						return encoding





			def 		__magic_name__    (		self					,  *__snake_case					,  **__snake_case					)   -> str:




						'''simple docstring'''
						return self.tokenizer.batch_decode(*__snake_case					,  **__snake_case					)





			def 		__magic_name__    (		self					,  *__snake_case					,  **__snake_case					)   -> Optional[Any]:




						'''simple docstring'''
						return self.tokenizer.decode(*__snake_case					,  **__snake_case					)





			@property
			def 		__magic_name__    (		self					)   -> Union[str, Any]:




						'''simple docstring'''
						__a        =self.tokenizer.model_input_names
						__a        =self.image_processor.model_input_names
						return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names					)					)






