

'''simple docstring'''

import copy

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


lowerCAmelCase_     =      logging.get_logger(__name__)







class lowerCamelCase   (    __lowerCAmelCase				):
				snake_case_							     = '''hybrid-clip'''
				snake_case_							     = True

				def __init__(       self,						lowercase_=512,						**lowercase_ )    ->							List[Any]:
					super().__init__(**lowercase_ )

					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`.' )

					snake_case							=					kwargs.pop('text_config' )
					snake_case							=					kwargs.pop('vision_config' )

					snake_case							=					text_config.pop('model_type' )
					snake_case							=					vision_config.pop('model_type' )

					from transformers import AutoConfig

					snake_case							=					AutoConfig.for_model(lowercase_,						**lowercase_ )

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

						snake_case							=					CLIPVisionConfig(**lowercase_ )
					else:
						snake_case							=					AutoConfig.for_model(lowercase_,						**lowercase_ )

					snake_case							=					projection_dim
					snake_case							=					1.0

				@classmethod
				def 					_lowerCamelCase    (       cls,						lowercase_,						lowercase_,						**lowercase_ )    ->							Optional[Any]:
					return cls(text_config=text_config.to_dict(),						vision_config=vision_config.to_dict(),						**lowercase_ )






				def 					_lowerCamelCase    (       self )    ->							List[str]:
					snake_case							=					copy.deepcopy(self.__dict__ )
					snake_case							=					self.text_config.to_dict()
					snake_case							=					self.vision_config.to_dict()
					snake_case							=					self.__class__.model_type
					return output













"""simple docstring"""





from typing import TYPE_CHECKING

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


UpperCamelCase_						:					Tuple      				=			{"""configuration_detr""": ["""DETR_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DetrConfig""", """DetrOnnxConfig"""]}

try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase_						:					Tuple      				=			["""DetrFeatureExtractor"""]
    UpperCamelCase_						:					int      				=			["""DetrImageProcessor"""]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase_						:					Dict      				=			[
        """DETR_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """DetrForObjectDetection""",
        """DetrForSegmentation""",
        """DetrModel""",
        """DetrPreTrainedModel""",
    ]


if TYPE_CHECKING:
    from .configuration_detr import DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DetrConfig, DetrOnnxConfig

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .feature_extraction_detr import DetrFeatureExtractor
        from .image_processing_detr import DetrImageProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_detr import (
            DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
            DetrForObjectDetection,
            DetrForSegmentation,
            DetrModel,
            DetrPreTrainedModel,
        )

else:
    import sys

    UpperCamelCase_						:					List[str]      				=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


