"""simple docstring"""







from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_a					:					List[Any]=			logging.get_logger(__name__)

_a					:					List[Any]=			{
    "kssteven/ibert-roberta-base": "https://huggingface.co/kssteven/ibert-roberta-base/resolve/main/config.json",
    "kssteven/ibert-roberta-large": "https://huggingface.co/kssteven/ibert-roberta-large/resolve/main/config.json",
    "kssteven/ibert-roberta-large-mnli": (
        "https://huggingface.co/kssteven/ibert-roberta-large-mnli/resolve/main/config.json"
    ),
}

class 		UpperCamelCase      (   lowercase						):






				"""simple docstring"""




				UpperCAmelCase    :				List[str]   =		"""ibert"""



				def __init__(self    :				str     ,  _A    :				List[Any]=3_05_22     ,  _A    :				List[str]=7_68     ,  _A    :				Optional[Any]=12     ,  _A    :				Any=12     ,  _A    :				Any=30_72     ,  _A    :				Any="gelu"     ,  _A    :				int=0.1     ,  _A    :				Union[str, Any]=0.1     ,  _A    :				int=5_12     ,  _A    :				str=2     ,  _A    :				Any=0.02     ,  _A    :				Dict=1E-12     ,  _A    :				List[str]=1     ,  _A    :				List[Any]=0     ,  _A    :				Any=2     ,  _A    :				Optional[Any]="absolute"     ,  _A    :				List[Any]=False     ,  _A    :				Tuple="none"     ,  **_A    :				List[str]     ,  )   ->     List[Any]:
					super().__init__(pad_token_id=_A     ,  bos_token_id=_A     ,  eos_token_id=_A     ,  **_A)

					__snake_case      :							int						=			vocab_size
					__snake_case      :							List[Any]						=			hidden_size
					__snake_case      :							int						=			num_hidden_layers
					__snake_case      :							Union[str, Any]						=			num_attention_heads
					__snake_case      :							int						=			hidden_act
					__snake_case      :							Dict						=			intermediate_size
					__snake_case      :							Optional[Any]						=			hidden_dropout_prob
					__snake_case      :							Optional[Any]						=			attention_probs_dropout_prob
					__snake_case      :							Dict						=			max_position_embeddings
					__snake_case      :							Dict						=			type_vocab_size
					__snake_case      :							Tuple						=			initializer_range
					__snake_case      :							List[str]						=			layer_norm_eps
					__snake_case      :							str						=			position_embedding_type
					__snake_case      :							Optional[int]						=			quant_mode
					__snake_case      :							int						=			force_dequant







class 		UpperCamelCase      (   lowercase						):






				"""simple docstring"""




				@property
				def  _lowercase					(self    :				List[str])   ->     Mapping[str, Mapping[int, str]]:
					if self.task == "multiple-choice":
						__snake_case      :							Tuple						=			{0: 'batch', 1: 'choice', 2: 'sequence'}
					else:
						__snake_case      :							Optional[int]						=			{0: 'batch', 1: 'sequence'}
					return OrderedDict(
					    [
					        ('input_ids', dynamic_axis),
					        ('attention_mask', dynamic_axis),
					    ])






"""simple docstring"""







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 		UpperCamelCase      (   unittest.TestCase						):



				def  _lowercase					(self    :				Union[str, Any])   ->     Optional[int]:
					__snake_case      :							Optional[Any]						=			0



				def  _lowercase					(self    :				Tuple)   ->     int:
					__snake_case      :							Optional[Any]						=			AutoImageProcessor.from_pretrained('openai/clip-vit-base-patch32')
					self.assertIsInstance(_A     ,  _A)



				def  _lowercase					(self    :				str)   ->     List[str]:
					with tempfile.TemporaryDirectory() as tmpdirname:
						__snake_case      :							List[str]						=			Path(_A) / 'preprocessor_config.json'
						__snake_case      :							Optional[Any]						=			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      :							Optional[int]						=			AutoImageProcessor.from_pretrained(_A)
						self.assertIsInstance(_A     ,  _A)



				def  _lowercase					(self    :				Any)   ->     Optional[int]:
					# Ensure we can load the image processor from the feature extractor config
					with tempfile.TemporaryDirectory() as tmpdirname:
						__snake_case      :							Any						=			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      :							Tuple						=			AutoImageProcessor.from_pretrained(_A)
						self.assertIsInstance(_A     ,  _A)



				def  _lowercase					(self    :				List[Any])   ->     Optional[Any]:
					with tempfile.TemporaryDirectory() as tmpdirname:
						__snake_case      :							str						=			CLIPConfig()

						# Create a dummy config file with image_proceesor_type
						__snake_case      :							List[Any]						=			Path(_A) / 'preprocessor_config.json'
						__snake_case      :							Optional[Any]						=			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      :							List[str]						=			AutoImageProcessor.from_pretrained(_A).to_dict()

						config_dict.pop('image_processor_type')
						__snake_case      :							Optional[int]						=			CLIPImageProcessor(**_A)

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

						__snake_case      :							Optional[int]						=			AutoImageProcessor.from_pretrained(_A)

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

					self.assertIsInstance(_A     ,  _A)



				def  _lowercase					(self    :				Union[str, Any])   ->     Optional[Any]:
					with tempfile.TemporaryDirectory() as tmpdirname:
						__snake_case      :							int						=			Path(_A) / 'preprocessor_config.json'
						json.dump(
						    {'image_processor_type': 'CLIPImageProcessor', 'processor_class': 'CLIPProcessor'}     ,  open(_A     ,  'w')     ,  )

						__snake_case      :							List[str]						=			AutoImageProcessor.from_pretrained(_A)
						self.assertIsInstance(_A     ,  _A)



				def  _lowercase					(self    :				Optional[int])   ->     Dict:
					with self.assertRaisesRegex(
					    _A     ,  'clip-base is not a local folder and is not a valid model identifier'):
						__snake_case      :							Tuple						=			AutoImageProcessor.from_pretrained('clip-base')



				def  _lowercase					(self    :				str)   ->     int:
					with self.assertRaisesRegex(
					    _A     ,  r'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'):
						__snake_case      :							str						=			AutoImageProcessor.from_pretrained(_A     ,  revision='aaaaaa')



				def  _lowercase					(self    :				List[Any])   ->     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    :				Optional[int])   ->     List[str]:
					# If remote code is not set, we will time out when asking whether to load the model.
					with self.assertRaises(_A):
						__snake_case      :							Any						=			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      :							Tuple						=			AutoImageProcessor.from_pretrained(
						    'hf-internal-testing/test_dynamic_image_processor'     ,  trust_remote_code=_A)

					__snake_case      :							Union[str, Any]						=			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      :							Optional[int]						=			AutoImageProcessor.from_pretrained(_A     ,  trust_remote_code=_A)
					self.assertEqual(reloaded_image_processor.__class__.__name__     ,  'NewImageProcessor')



				def  _lowercase					(self    :				int)   ->     Optional[int]:
					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      :							Dict						=			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      :							Tuple						=			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      :							Tuple						=			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    :				List[Any])   ->     Tuple:

					class 		UpperCamelCase      (   lowercase						):
									UpperCAmelCase    :				str   =		True

					try:
						AutoConfig.register('custom'     ,  _A)
						AutoImageProcessor.register(_A     ,  _A)
						# If remote code is not set, the default is to use local
						__snake_case      :							Tuple						=			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      :							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(image_processor.is_local)

						# If remote is enabled, we load from the Hub
						__snake_case      :							List[Any]						=			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]





