
"""simple docstring"""

import copy
import json
import os
import tempfile

from transformers import is_torch_available

from .test_configuration_utils import config_common_kwargs






class 	_lowercase       (    __UpperCAmelCase		):




	def __init__(			self		,			UpperCAmelCase_		,			UpperCAmelCase_=None		,			UpperCAmelCase_=True		,			UpperCAmelCase_=None		,			**UpperCAmelCase_ )		->							Optional[Any]:
			lowerCamelCase					:      Any          =   parent
			lowerCamelCase					:      str          =   config_class
			lowerCamelCase					:      List[str]          =   has_text_modality
			lowerCamelCase					:      Tuple          =   kwargs
			lowerCamelCase					:      Dict          =   common_properties




	def       _UpperCamelCase   (			self )		->							Any:
			lowerCamelCase					:      Any          =   self.config_class(**self.inputs_dict )
			lowerCamelCase					:      Optional[Any]          =   (
			    ['hidden_size', 'num_attention_heads', 'num_hidden_layers']
			    if self.common_properties is None
			    else self.common_properties
			)

			# Add common fields for text models
			if self.has_text_modality:
					common_properties.extend(['vocab_size'] )

			# Test that config has the common properties as getters
			for prop in common_properties:
					self.parent.assertTrue(hasattr(UpperCAmelCase_		,			UpperCAmelCase_ )		,			msg=F"""`{prop}` does not exist""" )

			# Test that config has the common properties as setter
			for idx, name in enumerate(UpperCAmelCase_ ):
					try:
							setattr(UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_ )
							self.parent.assertEqual(
							    getattr(UpperCAmelCase_		,			UpperCAmelCase_ )		,			UpperCAmelCase_		,			msg=F"""`{name} value {idx} expected, but was {getattr(UpperCAmelCase_		,			UpperCAmelCase_ )}""" )
					except NotImplementedError:
							# Some models might not be able to implement setters for common_properties
							# In that case, a NotImplementedError is raised
							pass

        # Test if config class can be called with Config(prop_name=..)
			for idx, name in enumerate(UpperCAmelCase_ ):
					try:
							lowerCamelCase					:      Tuple          =   self.config_class(**{name: idx} )
							self.parent.assertEqual(
							    getattr(UpperCAmelCase_		,			UpperCAmelCase_ )		,			UpperCAmelCase_		,			msg=F"""`{name} value {idx} expected, but was {getattr(UpperCAmelCase_		,			UpperCAmelCase_ )}""" )
					except NotImplementedError:
							# Some models might not be able to implement setters for common_properties
							# In that case, a NotImplementedError is raised
							pass




	def       _UpperCamelCase   (			self )		->							Tuple:
			lowerCamelCase					:      List[Any]          =   self.config_class(**self.inputs_dict )
			lowerCamelCase					:      Optional[Any]          =   json.loads(config.to_json_string() )
			for key, value in self.inputs_dict.items():
					self.parent.assertEqual(obj[key]		,			UpperCAmelCase_ )




	def       _UpperCamelCase   (			self )		->							str:
			lowerCamelCase					:      str          =   self.config_class(**self.inputs_dict )

			with tempfile.TemporaryDirectory() as tmpdirname:
					lowerCamelCase					:      Union[str, Any]          =   os.path.join(UpperCAmelCase_		,			'config.json' )
					config_first.to_json_file(UpperCAmelCase_ )
					lowerCamelCase					:      Optional[Any]          =   self.config_class.from_json_file(UpperCAmelCase_ )

			self.parent.assertEqual(config_second.to_dict()		,			config_first.to_dict() )




	def       _UpperCamelCase   (			self )		->							Optional[Any]:
			lowerCamelCase					:      str          =   self.config_class(**self.inputs_dict )

			with tempfile.TemporaryDirectory() as tmpdirname:
					config_first.save_pretrained(UpperCAmelCase_ )
					lowerCamelCase					:      Any          =   self.config_class.from_pretrained(UpperCAmelCase_ )

			self.parent.assertEqual(config_second.to_dict()		,			config_first.to_dict() )




	def       _UpperCamelCase   (			self )		->							str:
			lowerCamelCase					:      List[Any]          =   self.config_class(**self.inputs_dict )

			lowerCamelCase					:      List[str]          =   'test'
			with tempfile.TemporaryDirectory() as tmpdirname:
					lowerCamelCase					:      Optional[int]          =   os.path.join(UpperCAmelCase_		,			UpperCAmelCase_ )
					config_first.save_pretrained(UpperCAmelCase_ )
					lowerCamelCase					:      List[Any]          =   self.config_class.from_pretrained(UpperCAmelCase_		,			subfolder=UpperCAmelCase_ )

			self.parent.assertEqual(config_second.to_dict()		,			config_first.to_dict() )




	def       _UpperCamelCase   (			self )		->							List[Any]:
			lowerCamelCase					:      int          =   self.config_class(**self.inputs_dict		,			num_labels=5 )
			self.parent.assertEqual(len(config.idalabel )		,			5 )
			self.parent.assertEqual(len(config.labelaid )		,			5 )

			lowerCamelCase					:      Optional[int]          =   3
			self.parent.assertEqual(len(config.idalabel )		,			3 )
			self.parent.assertEqual(len(config.labelaid )		,			3 )




	def       _UpperCamelCase   (			self )		->							Union[str, Any]:
			if self.config_class.is_composition:
					return
			lowerCamelCase					:      List[Any]          =   self.config_class()
			self.parent.assertIsNotNone(UpperCAmelCase_ )




	def       _UpperCamelCase   (			self )		->							int:
			lowerCamelCase					:      Union[str, Any]          =   copy.deepcopy(UpperCAmelCase_ )
			lowerCamelCase					:      Any          =   self.config_class(**UpperCAmelCase_ )
			lowerCamelCase					:      List[Any]          =   []
			for key, value in config_common_kwargs.items():
					if key == "torch_dtype":
							if not is_torch_available():
									continue
							else:
									import torch

									if config.torch_dtype != torch.floataa:
											wrong_values.append(('torch_dtype', config.torch_dtype, torch.floataa) )
					elif getattr(UpperCAmelCase_		,			UpperCAmelCase_ ) != value:
							wrong_values.append((key, getattr(UpperCAmelCase_		,			UpperCAmelCase_ ), value) )

			if len(UpperCAmelCase_ ) > 0:
					lowerCamelCase					:      Union[str, Any]          =   '\n'.join([F"""- {v[0]}: got {v[1]} instead of {v[2]}""" for v in wrong_values] )
					raise ValueError(F"""The following keys were not properly set in the config:\n{errors}""" )




	def       _UpperCamelCase   (			self )		->							List[Any]:
			self.create_and_test_config_common_properties()
			self.create_and_test_config_to_json_string()
			self.create_and_test_config_to_json_file()
			self.create_and_test_config_from_and_save_pretrained()
			self.create_and_test_config_from_and_save_pretrained_subfolder()
			self.create_and_test_config_with_num_labels()
			self.check_config_can_be_init_without_params()
			self.check_config_arguments_init()








"""simple docstring"""

import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class 	_lowercase       (    __UpperCAmelCase		):
	lowercase_				=     ['image_processor', 'tokenizer']
	lowercase_				=     'ChineseCLIPImageProcessor'
	lowercase_				=     ('BertTokenizer', 'BertTokenizerFast')




	def __init__(			self		,			UpperCAmelCase_=None		,			UpperCAmelCase_=None		,			**UpperCAmelCase_ )		->							List[str]:
			lowerCamelCase					:      Tuple          =   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_		,			)
					lowerCamelCase					:      int          =   kwargs.pop('feature_extractor' )

			lowerCamelCase					:      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__(UpperCAmelCase_		,			UpperCAmelCase_ )
			lowerCamelCase					:      Tuple          =   self.image_processor




	def __call__(			self		,			UpperCAmelCase_=None		,			UpperCAmelCase_=None		,			UpperCAmelCase_=None		,			**UpperCAmelCase_ )		->							str:

			if text is None and images is None:
					raise ValueError('You have to specify either text or images. Both cannot be none.' )

			if text is not None:
					lowerCamelCase					:      Any          =   self.tokenizer(UpperCAmelCase_		,			return_tensors=UpperCAmelCase_		,			**UpperCAmelCase_ )

			if images is not None:
					lowerCamelCase					:      Dict          =   self.image_processor(UpperCAmelCase_		,			return_tensors=UpperCAmelCase_		,			**UpperCAmelCase_ )

			if text is not None and images is not None:
					lowerCamelCase					:      Tuple          =   image_features.pixel_values
					return encoding
			elif text is not None:
					return encoding
			else:
					return BatchEncoding(data=dict(**UpperCAmelCase_ )		,			tensor_type=UpperCAmelCase_ )




	def       _UpperCamelCase   (			self		,			*UpperCAmelCase_		,			**UpperCAmelCase_ )		->							List[str]:
			return self.tokenizer.batch_decode(*UpperCAmelCase_		,			**UpperCAmelCase_ )




	def       _UpperCamelCase   (			self		,			*UpperCAmelCase_		,			**UpperCAmelCase_ )		->							Optional[int]:
			return self.tokenizer.decode(*UpperCAmelCase_		,			**UpperCAmelCase_ )




	@property
	def       _UpperCamelCase   (			self )		->							Optional[Any]:
			lowerCamelCase					:      List[Any]          =   self.tokenizer.model_input_names
			lowerCamelCase					:      Union[str, Any]          =   self.image_processor.model_input_names
			return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )




	@property
	def       _UpperCamelCase   (			self )		->							List[str]:
			warnings.warn(
			    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'		,			UpperCAmelCase_		,			)
			return self.image_processor_class






