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     lowerCAmelCase_     (       unittest.TestCase ):







							def  UpperCamelCase_						(   self      :     List[str]		):
												_UpperCamelCase			     =     0







							def  UpperCamelCase_						(   self      :     Tuple		):
												_UpperCamelCase			     =     AutoImageProcessor.from_pretrained('''openai/clip-vit-base-patch32'''		)
												self.assertIsInstance(_A ,			_A		)







							def  UpperCamelCase_						(   self      :     str		):
												with tempfile.TemporaryDirectory() as tmpdirname:
																	_UpperCamelCase			     =     Path(_A		) / '''preprocessor_config.json'''
																	_UpperCamelCase			     =     Path(_A		) / '''config.json'''
																	json.dump(
																	    {'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''} ,			open(_A ,			'''w'''		) ,			)
																	json.dump({'''model_type''': '''clip'''} ,			open(_A ,			'''w'''		)		)

																	_UpperCamelCase			     =     AutoImageProcessor.from_pretrained(_A		)
																	self.assertIsInstance(_A ,			_A		)







							def  UpperCamelCase_						(   self      :     Tuple		):
												# Ensure we can load the image processor from the feature extractor config
												with tempfile.TemporaryDirectory() as tmpdirname:
																	_UpperCamelCase			     =     Path(_A		) / '''preprocessor_config.json'''
																	_UpperCamelCase			     =     Path(_A		) / '''config.json'''
																	json.dump(
																	    {'''feature_extractor_type''': '''CLIPFeatureExtractor''', '''processor_class''': '''CLIPProcessor'''} ,			open(_A ,			'''w'''		) ,			)
																	json.dump({'''model_type''': '''clip'''} ,			open(_A ,			'''w'''		)		)

																	_UpperCamelCase			     =     AutoImageProcessor.from_pretrained(_A		)
																	self.assertIsInstance(_A ,			_A		)







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

																	# Create a dummy config file with image_proceesor_type
																	_UpperCamelCase			     =     Path(_A		) / '''preprocessor_config.json'''
																	_UpperCamelCase			     =     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
																	_UpperCamelCase			     =     AutoImageProcessor.from_pretrained(_A		).to_dict()

																	config_dict.pop('''image_processor_type'''		)
																	_UpperCamelCase			     =     CLIPImageProcessor(**_A		)

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

																	_UpperCamelCase			     =     AutoImageProcessor.from_pretrained(_A		)

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

												self.assertIsInstance(_A ,			_A		)







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

																	_UpperCamelCase			     =     AutoImageProcessor.from_pretrained(_A		)
																	self.assertIsInstance(_A ,			_A		)







							def  UpperCamelCase_						(   self      :     List[Any]		):
												with self.assertRaisesRegex(
												    _A ,			'''clip-base is not a local folder and is not a valid model identifier'''		):
																	_UpperCamelCase			     =     AutoImageProcessor.from_pretrained('''clip-base'''		)







							def  UpperCamelCase_						(   self      :     Dict		):
												with self.assertRaisesRegex(
												    _A ,			R'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'''		):
																	_UpperCamelCase			     =     AutoImageProcessor.from_pretrained(_A ,			revision='''aaaaaa'''		)







							def  UpperCamelCase_						(   self      :     Union[str, Any]		):
												with self.assertRaisesRegex(
												    _A ,			'''hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.''' ,			):
																	_UpperCamelCase			     =     AutoImageProcessor.from_pretrained('''hf-internal-testing/config-no-model'''		)







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

												_UpperCamelCase			     =     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		)
																	_UpperCamelCase			     =     AutoImageProcessor.from_pretrained(_A ,			trust_remote_code=_A		)
												self.assertEqual(reloaded_image_processor.__class__.__name__ ,			'''NewImageProcessor'''		)







							def  UpperCamelCase_						(   self      :     List[Any]		):
												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:
																						_UpperCamelCase			     =     Path(_A		) / '''preprocessor_config.json'''
																						_UpperCamelCase			     =     Path(_A		) / '''config.json'''
																						json.dump(
																						    {'''feature_extractor_type''': '''CLIPFeatureExtractor''', '''processor_class''': '''CLIPProcessor'''} ,			open(_A ,			'''w'''		) ,			)
																						json.dump({'''model_type''': '''clip'''} ,			open(_A ,			'''w'''		)		)

																						_UpperCamelCase			     =     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		)
																						_UpperCamelCase			     =     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  UpperCamelCase_						(   self      :     Optional[Any]		):
												class     lowerCAmelCase_     (       __lowercase ):
																			UpperCAmelCase				   =     True

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








"""simple docstring"""





from collections import OrderedDict
from typing import List, Mapping

from packaging import version

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


__lowerCamelCase							=  logging.get_logger(__name__)

__lowerCamelCase							=  {
    'google/efficientnet-b7': 'https://huggingface.co/google/efficientnet-b7/resolve/main/config.json',
}



class       __A		(   SCREAMING_SNAKE_CASE_			):
					UpperCAmelCase__								=				"efficientnet"

					def __init__(			self  :      Optional[Any]     ,		__snake_case  :      int = 3     ,		__snake_case  :      int = 6_0_0     ,		__snake_case  :      float = 2.0     ,		__snake_case  :      float = 3.1     ,		__snake_case  :      int = 8     ,		__snake_case  :      List[int] = [3, 3, 5, 3, 5, 5, 3]     ,		__snake_case  :      List[int] = [3_2, 1_6, 2_4, 4_0, 8_0, 1_1_2, 1_9_2]     ,		__snake_case  :      List[int] = [1_6, 2_4, 4_0, 8_0, 1_1_2, 1_9_2, 3_2_0]     ,		__snake_case  :      List[int] = []     ,		__snake_case  :      List[int] = [1, 2, 2, 2, 1, 2, 1]     ,		__snake_case  :      List[int] = [1, 2, 2, 3, 3, 4, 1]     ,		__snake_case  :      List[int] = [1, 6, 6, 6, 6, 6, 6]     ,		__snake_case  :      float = 0.25     ,		__snake_case  :      str = "swish"     ,		__snake_case  :      int = 2_5_6_0     ,		__snake_case  :      str = "mean"     ,		__snake_case  :      float = 0.02     ,		__snake_case  :      float = 0.001     ,		__snake_case  :      float = 0.99     ,		__snake_case  :      float = 0.5     ,		__snake_case  :      float = 0.2     ,		**__snake_case  :      List[Any]     ,		)			->							List[Any]:
											super().__init__(**__snake_case					)

											__magic_name__:	str							=       num_channels
											__magic_name__:	List[str]							=       image_size
											__magic_name__:	List[str]							=       width_coefficient
											__magic_name__:	Optional[Any]							=       depth_coefficient
											__magic_name__:	Tuple							=       depth_divisor
											__magic_name__:	Dict							=       kernel_sizes
											__magic_name__:	int							=       in_channels
											__magic_name__:	str							=       out_channels
											__magic_name__:	Dict							=       depthwise_padding
											__magic_name__:	Union[str, Any]							=       strides
											__magic_name__:	Dict							=       num_block_repeats
											__magic_name__:	Tuple							=       expand_ratios
											__magic_name__:	List[str]							=       squeeze_expansion_ratio
											__magic_name__:	Any							=       hidden_act
											__magic_name__:	Tuple							=       hidden_dim
											__magic_name__:	int							=       pooling_type
											__magic_name__:	int							=       initializer_range
											__magic_name__:	List[str]							=       batch_norm_eps
											__magic_name__:	str							=       batch_norm_momentum
											__magic_name__:	List[str]							=       dropout_rate
											__magic_name__:	Dict							=       drop_connect_rate
											__magic_name__:	Optional[Any]							=       sum(__snake_case					) * 4



class       __A		(   SCREAMING_SNAKE_CASE_			):
					UpperCAmelCase__								=				version.parse("1.11"			)

					@property
					def   lowerCamelCase__   (			self  :      List[str]					)			->							Mapping[str, Mapping[int, str]]:
											return OrderedDict(
											    [
											        ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
											    ]					)






					@property
					def   lowerCamelCase__   (			self  :      List[Any]					)			->							float:
											return 1E-5



