






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 							SCREAMING_SNAKE_CASE__						(    unittest.TestCase						):





					"""simple docstring"""






					def 	_lowercase					( self					:  Optional[Any]	):
						snake_case__			:				str       =  0



					def 	_lowercase					( self					:  Dict	):
						snake_case__			:				Optional[int]       =  AutoImageProcessor.from_pretrained("openai/clip-vit-base-patch32"	)
						self.assertIsInstance(__A							,		__A	)



					def 	_lowercase					( self					:  Union[str, Any]	):
						with tempfile.TemporaryDirectory() as tmpdirname:
							snake_case__			:				List[Any]       =  Path(__A	) / "preprocessor_config.json"
							snake_case__			:				int       =  Path(__A	) / "config.json"
							json.dump(
							    {"image_processor_type": "CLIPImageProcessor", "processor_class": "CLIPProcessor"}							,		open(__A							,		"w"	)							,		)
							json.dump({"model_type": "clip"}							,		open(__A							,		"w"	)	)

							snake_case__			:				Any       =  AutoImageProcessor.from_pretrained(__A	)
							self.assertIsInstance(__A							,		__A	)



					def 	_lowercase					( self					:  Union[str, Any]	):
						# Ensure we can load the image processor from the feature extractor config
						with tempfile.TemporaryDirectory() as tmpdirname:
							snake_case__			:				Dict       =  Path(__A	) / "preprocessor_config.json"
							snake_case__			:				int       =  Path(__A	) / "config.json"
							json.dump(
							    {"feature_extractor_type": "CLIPFeatureExtractor", "processor_class": "CLIPProcessor"}							,		open(__A							,		"w"	)							,		)
							json.dump({"model_type": "clip"}							,		open(__A							,		"w"	)	)

							snake_case__			:				List[str]       =  AutoImageProcessor.from_pretrained(__A	)
							self.assertIsInstance(__A							,		__A	)



					def 	_lowercase					( self					:  List[str]	):
						with tempfile.TemporaryDirectory() as tmpdirname:
							snake_case__			:				List[str]       =  CLIPConfig()

							# Create a dummy config file with image_proceesor_type
							snake_case__			:				Dict       =  Path(__A	) / "preprocessor_config.json"
							snake_case__			:				str       =  Path(__A	) / "config.json"
							json.dump(
							    {"image_processor_type": "CLIPImageProcessor", "processor_class": "CLIPProcessor"}							,		open(__A							,		"w"	)							,		)
							json.dump({"model_type": "clip"}							,		open(__A							,		"w"	)	)

							# remove image_processor_type to make sure config.json alone is enough to load image processor locally
							snake_case__			:				Tuple       =  AutoImageProcessor.from_pretrained(__A	).to_dict()

							config_dict.pop("image_processor_type"	)
							snake_case__			:				Tuple       =  CLIPImageProcessor(**__A	)

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

							snake_case__			:				List[Any]       =  AutoImageProcessor.from_pretrained(__A	)

							# make sure private variable is not incorrectly saved
							snake_case__			:				Dict       =  json.loads(config.to_json_string()	)
							self.assertTrue("_processor_class" not in dict_as_saved	)

						self.assertIsInstance(__A							,		__A	)



					def 	_lowercase					( self					:  Tuple	):
						with tempfile.TemporaryDirectory() as tmpdirname:
							snake_case__			:				List[str]       =  Path(__A	) / "preprocessor_config.json"
							json.dump(
							    {"image_processor_type": "CLIPImageProcessor", "processor_class": "CLIPProcessor"}							,		open(__A							,		"w"	)							,		)

							snake_case__			:				Dict       =  AutoImageProcessor.from_pretrained(__A	)
							self.assertIsInstance(__A							,		__A	)



					def 	_lowercase					( self					:  Union[str, Any]	):
						with self.assertRaisesRegex(
						    __A							,		"clip-base is not a local folder and is not a valid model identifier"	):
							snake_case__			:				Union[str, Any]       =  AutoImageProcessor.from_pretrained("clip-base"	)



					def 	_lowercase					( self					:  Any	):
						with self.assertRaisesRegex(
						    __A							,		R"aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"	):
							snake_case__			:				List[Any]       =  AutoImageProcessor.from_pretrained(__A							,		revision="aaaaaa"	)



					def 	_lowercase					( self					:  List[str]	):
						with self.assertRaisesRegex(
						    __A							,		"hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json."							,		):
							snake_case__			:				List[Any]       =  AutoImageProcessor.from_pretrained("hf-internal-testing/config-no-model"	)



					def 	_lowercase					( self					:  Tuple	):
						# If remote code is not set, we will time out when asking whether to load the model.
						with self.assertRaises(__A	):
							snake_case__			:				int       =  AutoImageProcessor.from_pretrained("hf-internal-testing/test_dynamic_image_processor"	)
						# If remote code is disabled, we can't load this config.
						with self.assertRaises(__A	):
							snake_case__			:				Union[str, Any]       =  AutoImageProcessor.from_pretrained(
							    "hf-internal-testing/test_dynamic_image_processor"							,		trust_remote_code=__A	)

						snake_case__			:				Optional[int]       =  AutoImageProcessor.from_pretrained(
						    "hf-internal-testing/test_dynamic_image_processor"							,		trust_remote_code=__A	)
						self.assertEqual(image_processor.__class__.__name__							,		"NewImageProcessor"	)

						# Test image processor can be reloaded.
						with tempfile.TemporaryDirectory() as tmp_dir:
							image_processor.save_pretrained(__A	)
							snake_case__			:				Dict       =  AutoImageProcessor.from_pretrained(__A							,		trust_remote_code=__A	)
						self.assertEqual(reloaded_image_processor.__class__.__name__							,		"NewImageProcessor"	)



					def 	_lowercase					( self					:  str	):
						try:
							AutoConfig.register("custom"							,		__A	)
							AutoImageProcessor.register(__A							,		__A	)
							# Trying to register something existing in the Transformers library will raise an error
							with self.assertRaises(__A	):
								AutoImageProcessor.register(__A							,		__A	)

							with tempfile.TemporaryDirectory() as tmpdirname:
								snake_case__			:				Tuple       =  Path(__A	) / "preprocessor_config.json"
								snake_case__			:				List[Any]       =  Path(__A	) / "config.json"
								json.dump(
								    {"feature_extractor_type": "CLIPFeatureExtractor", "processor_class": "CLIPProcessor"}							,		open(__A							,		"w"	)							,		)
								json.dump({"model_type": "clip"}							,		open(__A							,		"w"	)	)

								snake_case__			:				int       =  CustomImageProcessor.from_pretrained(__A	)

							# 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(__A	)
								snake_case__			:				Optional[int]       =  AutoImageProcessor.from_pretrained(__A	)
								self.assertIsInstance(__A							,		__A	)

						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 	_lowercase					( self					:  int	):



						class 							SCREAMING_SNAKE_CASE__						(    UpperCamelCase_						):





											"""simple docstring"""






											a_     =		True

						try:
							AutoConfig.register("custom"							,		__A	)
							AutoImageProcessor.register(__A							,		__A	)
							# If remote code is not set, the default is to use local
							snake_case__			:				List[Any]       =  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.
							snake_case__			:				List[str]       =  AutoImageProcessor.from_pretrained(
							    "hf-internal-testing/test_dynamic_image_processor"							,		trust_remote_code=__A	)
							self.assertEqual(image_processor.__class__.__name__							,		"NewImageProcessor"	)
							self.assertTrue(image_processor.is_local	)

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











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 							SCREAMING_SNAKE_CASE__						(    UpperCamelCase_						):





					"""simple docstring"""






					a_     =		["image_processor", "tokenizer"]
					a_     =		"ViltImageProcessor"
					a_     =		("BertTokenizer", "BertTokenizerFast")



					def __init__( self					:  Optional[int]							,		__A					:  Optional[int]=None							,		__A					:  Optional[Any]=None							,		**__A					:  int	):
						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."							,		__A							,		)
							snake_case__			:				Tuple       =  kwargs.pop("feature_extractor"	)

						snake_case__			:				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__(__A							,		__A	)
						snake_case__			:				Tuple       =  self.image_processor



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

						return encoding



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



					def 	_lowercase					( self					:  Dict							,		*__A					:  str							,		**__A					:  str	):
						return self.tokenizer.decode(*__A							,		**__A	)



					@property
					def 	_lowercase					( self					:  str	):
						snake_case__			:				Optional[Any]       =  self.tokenizer.model_input_names
						snake_case__			:				Any       =  self.image_processor.model_input_names
						return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names	)	)



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






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



