import warnings
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 					lowercase__						(   UpperCamelCase_):
						UpperCamelCase_     =		["""image_processor""", """tokenizer"""]
						UpperCamelCase_     =		"""ViltImageProcessor"""
						UpperCamelCase_     =		("""BertTokenizer""", """BertTokenizerFast""")


						def __init__(       self	:				Optional[int]    ,		UpperCamelCase__	:				Optional[int]=None    ,		UpperCamelCase__	:				Tuple=None    ,		**UpperCamelCase__	:				Optional[Any]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    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.'''    ,		UpperCamelCase__    ,		)
																		SCREAMING_SNAKE_CASE							:    Dict							=							kwargs.pop('''feature_extractor'''						)

												SCREAMING_SNAKE_CASE							:    int							=							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__(UpperCamelCase__    ,		UpperCamelCase__						)
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							self.image_processor


						def __call__(       self	:				List[str]    ,		UpperCamelCase__	:				int    ,		UpperCamelCase__	:				Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None    ,		UpperCamelCase__	:				bool = True    ,		UpperCamelCase__	:				Union[bool, str, PaddingStrategy] = False    ,		UpperCamelCase__	:				Union[bool, str, TruncationStrategy] = None    ,		UpperCamelCase__	:				Optional[int] = None    ,		UpperCamelCase__	:				int = 0    ,		UpperCamelCase__	:				Optional[int] = None    ,		UpperCamelCase__	:				Optional[bool] = None    ,		UpperCamelCase__	:				Optional[bool] = None    ,		UpperCamelCase__	:				bool = False    ,		UpperCamelCase__	:				bool = False    ,		UpperCamelCase__	:				bool = False    ,		UpperCamelCase__	:				bool = False    ,		UpperCamelCase__	:				bool = True    ,		UpperCamelCase__	:				Optional[Union[str, TensorType]] = None    ,		**UpperCamelCase__	:				Union[str, Any]    ,		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    str							=							self.tokenizer(
												    text=UpperCamelCase__    ,		add_special_tokens=UpperCamelCase__    ,		padding=UpperCamelCase__    ,		truncation=UpperCamelCase__    ,		max_length=UpperCamelCase__    ,		stride=UpperCamelCase__    ,		pad_to_multiple_of=UpperCamelCase__    ,		return_token_type_ids=UpperCamelCase__    ,		return_attention_mask=UpperCamelCase__    ,		return_overflowing_tokens=UpperCamelCase__    ,		return_special_tokens_mask=UpperCamelCase__    ,		return_offsets_mapping=UpperCamelCase__    ,		return_length=UpperCamelCase__    ,		verbose=UpperCamelCase__    ,		return_tensors=UpperCamelCase__    ,		**UpperCamelCase__    ,		)
												# add pixel_values + pixel_mask
												SCREAMING_SNAKE_CASE							:    int							=							self.image_processor(UpperCamelCase__    ,		return_tensors=UpperCamelCase__						)
												encoding.update(UpperCamelCase__						)

												return encoding


						def        __A       (       self	:				Union[str, Any]    ,		*UpperCamelCase__	:				List[str]    ,		**UpperCamelCase__	:				int						):





												'''simple docstring'''





												return self.tokenizer.batch_decode(*UpperCamelCase__    ,		**UpperCamelCase__						)


						def        __A       (       self	:				str    ,		*UpperCamelCase__	:				Union[str, Any]    ,		**UpperCamelCase__	:				str						):





												'''simple docstring'''





												return self.tokenizer.decode(*UpperCamelCase__    ,		**UpperCamelCase__						)


						@property
						def        __A       (       self	:				Union[str, Any]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Optional[int]							=							self.tokenizer.model_input_names
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							self.image_processor.model_input_names
												return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names						)						)


						@property
						def        __A       (       self	:				Tuple						):





												'''simple docstring'''





												warnings.warn(
												    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''    ,		UpperCamelCase__    ,		)
												return self.image_processor_class



						@property
						def        __A       (       self	:				str						):





												'''simple docstring'''





												warnings.warn(
												    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''    ,		UpperCamelCase__    ,		)
												return self.image_processor






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

import transformers
from transformers import (
    CONFIG_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    CLIPConfig,
    CLIPImageProcessor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER


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

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_image_processing import CustomImageProcessor  # noqa E402





class 					lowercase__						(   unittest.TestCase):


						def        __A       (       self	:				List[Any]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Dict							=							0


						def        __A       (       self	:				Dict						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    str							=							AutoImageProcessor.from_pretrained('''openai/clip-vit-base-patch32'''						)
												self.assertIsInstance(UpperCamelCase__    ,		UpperCamelCase__						)


						def        __A       (       self	:				Dict						):





												'''simple docstring'''





												with tempfile.TemporaryDirectory() as tmpdirname:
																		SCREAMING_SNAKE_CASE							:    str							=							Path(UpperCamelCase__						) / '''preprocessor_config.json'''
																		SCREAMING_SNAKE_CASE							:    Dict							=							Path(UpperCamelCase__						) / '''config.json'''
																		json.dump(
																		    {'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''}    ,		open(UpperCamelCase__    ,		'''w'''						)    ,		)
																		json.dump({'''model_type''': '''clip'''}    ,		open(UpperCamelCase__    ,		'''w'''						)						)

																		SCREAMING_SNAKE_CASE							:    Any							=							AutoImageProcessor.from_pretrained(UpperCamelCase__						)
																		self.assertIsInstance(UpperCamelCase__    ,		UpperCamelCase__						)


						def        __A       (       self	:				int						):





												'''simple docstring'''





												with tempfile.TemporaryDirectory() as tmpdirname:
																		SCREAMING_SNAKE_CASE							:    Tuple							=							Path(UpperCamelCase__						) / '''preprocessor_config.json'''
																		SCREAMING_SNAKE_CASE							:    Dict							=							Path(UpperCamelCase__						) / '''config.json'''
																		json.dump(
																		    {'''feature_extractor_type''': '''CLIPFeatureExtractor''', '''processor_class''': '''CLIPProcessor'''}    ,		open(UpperCamelCase__    ,		'''w'''						)    ,		)
																		json.dump({'''model_type''': '''clip'''}    ,		open(UpperCamelCase__    ,		'''w'''						)						)

																		SCREAMING_SNAKE_CASE							:    Optional[Any]							=							AutoImageProcessor.from_pretrained(UpperCamelCase__						)
																		self.assertIsInstance(UpperCamelCase__    ,		UpperCamelCase__						)


						def        __A       (       self	:				List[str]						):





												'''simple docstring'''





												with tempfile.TemporaryDirectory() as tmpdirname:
																		SCREAMING_SNAKE_CASE							:    Dict							=							CLIPConfig()

																		# Create a dummy config file with image_proceesor_type
																		SCREAMING_SNAKE_CASE							:    Dict							=							Path(UpperCamelCase__						) / '''preprocessor_config.json'''
																		SCREAMING_SNAKE_CASE							:    List[str]							=							Path(UpperCamelCase__						) / '''config.json'''
																		json.dump(
																		    {'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''}    ,		open(UpperCamelCase__    ,		'''w'''						)    ,		)
																		json.dump({'''model_type''': '''clip'''}    ,		open(UpperCamelCase__    ,		'''w'''						)						)

																		# remove image_processor_type to make sure config.json alone is enough to load image processor locally
																		SCREAMING_SNAKE_CASE							:    List[Any]							=							AutoImageProcessor.from_pretrained(UpperCamelCase__						).to_dict()

																		config_dict.pop('''image_processor_type'''						)
																		SCREAMING_SNAKE_CASE							:    str							=							CLIPImageProcessor(**UpperCamelCase__						)

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

																		SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							AutoImageProcessor.from_pretrained(UpperCamelCase__						)

																		# make sure private variable is not incorrectly saved
																		SCREAMING_SNAKE_CASE							:    List[Any]							=							json.loads(config.to_json_string()						)
																		self.assertTrue('''_processor_class''' not in dict_as_saved						)

												self.assertIsInstance(UpperCamelCase__    ,		UpperCamelCase__						)


						def        __A       (       self	:				Tuple						):





												'''simple docstring'''





												with tempfile.TemporaryDirectory() as tmpdirname:
																		SCREAMING_SNAKE_CASE							:    Optional[int]							=							Path(UpperCamelCase__						) / '''preprocessor_config.json'''
																		json.dump(
																		    {'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''}    ,		open(UpperCamelCase__    ,		'''w'''						)    ,		)

																		SCREAMING_SNAKE_CASE							:    Dict							=							AutoImageProcessor.from_pretrained(UpperCamelCase__						)
																		self.assertIsInstance(UpperCamelCase__    ,		UpperCamelCase__						)


						def        __A       (       self	:				int						):





												'''simple docstring'''





												with self.assertRaisesRegex(
												    UpperCamelCase__    ,		'''clip-base is not a local folder and is not a valid model identifier'''						):
																		SCREAMING_SNAKE_CASE							:    List[str]							=							AutoImageProcessor.from_pretrained('''clip-base'''						)


						def        __A       (       self	:				List[str]						):





												'''simple docstring'''





												with self.assertRaisesRegex(
												    UpperCamelCase__    ,		r'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'''						):
																		SCREAMING_SNAKE_CASE							:    Tuple							=							AutoImageProcessor.from_pretrained(UpperCamelCase__    ,		revision='''aaaaaa'''						)


						def        __A       (       self	:				Dict						):





												'''simple docstring'''





												with self.assertRaisesRegex(
												    UpperCamelCase__    ,		'''hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.'''    ,		):
																		SCREAMING_SNAKE_CASE							:    Dict							=							AutoImageProcessor.from_pretrained('''hf-internal-testing/config-no-model'''						)


						def        __A       (       self	:				List[Any]						):





												'''simple docstring'''





												with self.assertRaises(UpperCamelCase__						):
																		SCREAMING_SNAKE_CASE							:    str							=							AutoImageProcessor.from_pretrained('''hf-internal-testing/test_dynamic_image_processor'''						)
												# If remote code is disabled, we can't load this config.
												with self.assertRaises(UpperCamelCase__						):
																		SCREAMING_SNAKE_CASE							:    List[Any]							=							AutoImageProcessor.from_pretrained(
																		    '''hf-internal-testing/test_dynamic_image_processor'''    ,		trust_remote_code=UpperCamelCase__						)

												SCREAMING_SNAKE_CASE							:    List[Any]							=							AutoImageProcessor.from_pretrained(
												    '''hf-internal-testing/test_dynamic_image_processor'''    ,		trust_remote_code=UpperCamelCase__						)
												self.assertEqual(image_processor.__class__.__name__    ,		'''NewImageProcessor'''						)

												# Test image processor can be reloaded.
												with tempfile.TemporaryDirectory() as tmp_dir:
																		image_processor.save_pretrained(UpperCamelCase__						)
																		SCREAMING_SNAKE_CASE							:    Optional[int]							=							AutoImageProcessor.from_pretrained(UpperCamelCase__    ,		trust_remote_code=UpperCamelCase__						)
												self.assertEqual(reloaded_image_processor.__class__.__name__    ,		'''NewImageProcessor'''						)


						def        __A       (       self	:				Optional[Any]						):





												'''simple docstring'''





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

																		with tempfile.TemporaryDirectory() as tmpdirname:
																								SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							Path(UpperCamelCase__						) / '''preprocessor_config.json'''
																								SCREAMING_SNAKE_CASE							:    Any							=							Path(UpperCamelCase__						) / '''config.json'''
																								json.dump(
																								    {'''feature_extractor_type''': '''CLIPFeatureExtractor''', '''processor_class''': '''CLIPProcessor'''}    ,		open(UpperCamelCase__    ,		'''w'''						)    ,		)
																								json.dump({'''model_type''': '''clip'''}    ,		open(UpperCamelCase__    ,		'''w'''						)						)

																								SCREAMING_SNAKE_CASE							:    Tuple							=							CustomImageProcessor.from_pretrained(UpperCamelCase__						)

																		# Now that the config is registered, it can be used as any other config with the auto-API
																		with tempfile.TemporaryDirectory() as tmp_dir:
																								image_processor.save_pretrained(UpperCamelCase__						)
																								SCREAMING_SNAKE_CASE							:    Any							=							AutoImageProcessor.from_pretrained(UpperCamelCase__						)
																								self.assertIsInstance(UpperCamelCase__    ,		UpperCamelCase__						)

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



						def        __A       (       self	:				Any						):





												'''simple docstring'''





												class 					lowercase__						(   UpperCamelCase_):
																		UpperCamelCase_     =		True

												try:
																		AutoConfig.register('''custom'''    ,		UpperCamelCase__						)
																		AutoImageProcessor.register(UpperCamelCase__    ,		UpperCamelCase__						)
																		# If remote code is not set, the default is to use local
																		SCREAMING_SNAKE_CASE							:    str							=							AutoImageProcessor.from_pretrained('''hf-internal-testing/test_dynamic_image_processor'''						)
																		self.assertEqual(image_processor.__class__.__name__    ,		'''NewImageProcessor'''						)
																		self.assertTrue(image_processor.is_local						)

																		# If remote code is disabled, we load the local one.
																		SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							AutoImageProcessor.from_pretrained(
																		    '''hf-internal-testing/test_dynamic_image_processor'''    ,		trust_remote_code=UpperCamelCase__						)
																		self.assertEqual(image_processor.__class__.__name__    ,		'''NewImageProcessor'''						)
																		self.assertTrue(image_processor.is_local						)

																		# If remote is enabled, we load from the Hub
																		SCREAMING_SNAKE_CASE							:    Optional[Any]							=							AutoImageProcessor.from_pretrained(
																		    '''hf-internal-testing/test_dynamic_image_processor'''    ,		trust_remote_code=UpperCamelCase__						)
																		self.assertEqual(image_processor.__class__.__name__    ,		'''NewImageProcessor'''						)
																		self.assertTrue(not hasattr(UpperCamelCase__    ,		'''is_local'''						)						)

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





