"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


a							={
    'configuration_imagegpt': ['IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ImageGPTConfig', 'ImageGPTOnnxConfig']
}

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a							=['ImageGPTFeatureExtractor']
	a							=['ImageGPTImageProcessor']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a							=[
	    'IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'ImageGPTForCausalImageModeling',
	    'ImageGPTForImageClassification',
	    'ImageGPTModel',
	    'ImageGPTPreTrainedModel',
	    'load_tf_weights_in_imagegpt',
	]


if TYPE_CHECKING:
	from .configuration_imagegpt import IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, ImageGPTConfig, ImageGPTOnnxConfig

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .feature_extraction_imagegpt import ImageGPTFeatureExtractor
		from .image_processing_imagegpt import ImageGPTImageProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_imagegpt import (
		    IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    ImageGPTForCausalImageModeling,
		    ImageGPTForImageClassification,
		    ImageGPTModel,
		    ImageGPTPreTrainedModel,
		    load_tf_weights_in_imagegpt,
		)

else:
	import sys

	a							=_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



"""simple docstring"""


import copy

from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging


a							=logging.get_logger(__name__)






class    __UpperCAmelCase						(	__lowerCAmelCase	):
				A__      :   Any  							=       '''hybrid-clip'''
				A__      :   Optional[Any]  							=       True

				def __init__(      self	, _lowerCamelCase=512	, **_lowerCamelCase      ):
										super().__init__(**_lowerCamelCase      )

										if "text_config" not in kwargs:
																raise ValueError("`text_config` can not be `None`."      )

										if "vision_config" not in kwargs:
																raise ValueError("`vision_config` can not be `None`."      )

										lowerCamelCase__					=kwargs.pop("text_config"      )
										lowerCamelCase__					=kwargs.pop("vision_config"      )

										lowerCamelCase__					=text_config.pop("model_type"      )
										lowerCamelCase__					=vision_config.pop("model_type"      )

										from transformers import AutoConfig

										lowerCamelCase__					=AutoConfig.for_model(_lowerCamelCase	, **_lowerCamelCase      )

										if vision_model_type == "clip":
																lowerCamelCase__					=AutoConfig.for_model(_lowerCamelCase	, **_lowerCamelCase      ).vision_config
										elif vision_model_type == "clip_vision_model":
																from transformers import CLIPVisionConfig

																lowerCamelCase__					=CLIPVisionConfig(**_lowerCamelCase      )
										else:
																lowerCamelCase__					=AutoConfig.for_model(_lowerCamelCase	, **_lowerCamelCase      )

										lowerCamelCase__					=projection_dim
										lowerCamelCase__					=1.0

				@classmethod
				def 		_a	(      cls	, _lowerCamelCase	, _lowerCamelCase	, **_lowerCamelCase      ):
										return cls(text_config=text_config.to_dict()	, vision_config=vision_config.to_dict()	, **_lowerCamelCase      )






				def 		_a	(      self      ):
										lowerCamelCase__					=copy.deepcopy(self.__dict__      )
										lowerCamelCase__					=self.text_config.to_dict()
										lowerCamelCase__					=self.vision_config.to_dict()
										lowerCamelCase__					=self.__class__.model_type
										return output


