"""simple docstring"""






import copy
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union


if TYPE_CHECKING:
						from ...processing_utils import ProcessorMixin
						from ...utils import TensorType

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


_A   					=    logging.get_logger(__name__)

_A   					=    {
    'google/owlvit-base-patch32': 'https://huggingface.co/google/owlvit-base-patch32/resolve/main/config.json',
    'google/owlvit-base-patch16': 'https://huggingface.co/google/owlvit-base-patch16/resolve/main/config.json',
    'google/owlvit-large-patch14': 'https://huggingface.co/google/owlvit-large-patch14/resolve/main/config.json',
}

class lowerCamelCase		(_SCREAMING_SNAKE_CASE      ):






					'''simple docstring'''


					a   	=		"owlvit_text_model"



					def __init__(						self			:				List[str]   , _snake_case			:				Optional[Any]=49408   , _snake_case			:				int=512   , _snake_case			:				List[Any]=2048   , _snake_case			:				Optional[int]=12   , _snake_case			:				Union[str, Any]=8   , _snake_case			:				Optional[Any]=16   , _snake_case			:				Tuple="quick_gelu"   , _snake_case			:				Optional[Any]=1e-5   , _snake_case			:				Any=0.0   , _snake_case			:				List[str]=0.02   , _snake_case			:				List[str]=1.0   , _snake_case			:				Optional[Any]=0   , _snake_case			:				Tuple=49406   , _snake_case			:				Union[str, Any]=49407   , **_snake_case			:				Optional[Any]   , )							->       Tuple:
									super().__init__(pad_token_id=_snake_case   , bos_token_id=_snake_case   , eos_token_id=_snake_case   , **_snake_case		)

									SCREAMING_SNAKE_CASE__		  =				vocab_size
									SCREAMING_SNAKE_CASE__		  =				hidden_size
									SCREAMING_SNAKE_CASE__		  =				intermediate_size
									SCREAMING_SNAKE_CASE__		  =				num_hidden_layers
									SCREAMING_SNAKE_CASE__		  =				num_attention_heads
									SCREAMING_SNAKE_CASE__		  =				max_position_embeddings
									SCREAMING_SNAKE_CASE__		  =				hidden_act
									SCREAMING_SNAKE_CASE__		  =				layer_norm_eps
									SCREAMING_SNAKE_CASE__		  =				attention_dropout
									SCREAMING_SNAKE_CASE__		  =				initializer_range
									SCREAMING_SNAKE_CASE__		  =				initializer_factor



					@classmethod
					def 			lowerCAmelCase_					(						cls			:				Tuple   , _snake_case			:				Union[str, os.PathLike]   , **_snake_case			:				Optional[int]		)							->       "PretrainedConfig":
									cls._set_token_in_kwargs(_snake_case		)

									SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				cls.get_config_dict(_snake_case   , **_snake_case		)

									# get the text config dict if we are loading from OwlViTConfig
									if config_dict.get("model_type"		) == "owlvit":
													SCREAMING_SNAKE_CASE__		  =				config_dict["text_config"]

									if "model_type" in config_dict and hasattr(cls   , "model_type"		) and config_dict["model_type"] != cls.model_type:
													logger.warning(
													    F"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
													    F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""		)

									return cls.from_dict(_snake_case   , **_snake_case		)



class lowerCamelCase		(_SCREAMING_SNAKE_CASE      ):






					'''simple docstring'''


					a   	=		"owlvit_vision_model"



					def __init__(						self			:				Any   , _snake_case			:				List[Any]=768   , _snake_case			:				Optional[Any]=3072   , _snake_case			:				List[str]=12   , _snake_case			:				Tuple=12   , _snake_case			:				int=3   , _snake_case			:				Dict=768   , _snake_case			:				Optional[int]=32   , _snake_case			:				Optional[Any]="quick_gelu"   , _snake_case			:				Union[str, Any]=1e-5   , _snake_case			:				Optional[Any]=0.0   , _snake_case			:				Optional[int]=0.02   , _snake_case			:				Optional[int]=1.0   , **_snake_case			:				Any   , )							->       Optional[int]:
									super().__init__(**_snake_case		)

									SCREAMING_SNAKE_CASE__		  =				hidden_size
									SCREAMING_SNAKE_CASE__		  =				intermediate_size
									SCREAMING_SNAKE_CASE__		  =				num_hidden_layers
									SCREAMING_SNAKE_CASE__		  =				num_attention_heads
									SCREAMING_SNAKE_CASE__		  =				num_channels
									SCREAMING_SNAKE_CASE__		  =				image_size
									SCREAMING_SNAKE_CASE__		  =				patch_size
									SCREAMING_SNAKE_CASE__		  =				hidden_act
									SCREAMING_SNAKE_CASE__		  =				layer_norm_eps
									SCREAMING_SNAKE_CASE__		  =				attention_dropout
									SCREAMING_SNAKE_CASE__		  =				initializer_range
									SCREAMING_SNAKE_CASE__		  =				initializer_factor



					@classmethod
					def 			lowerCAmelCase_					(						cls			:				Dict   , _snake_case			:				Union[str, os.PathLike]   , **_snake_case			:				int		)							->       "PretrainedConfig":
									cls._set_token_in_kwargs(_snake_case		)

									SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				cls.get_config_dict(_snake_case   , **_snake_case		)

									# get the vision config dict if we are loading from OwlViTConfig
									if config_dict.get("model_type"		) == "owlvit":
													SCREAMING_SNAKE_CASE__		  =				config_dict["vision_config"]

									if "model_type" in config_dict and hasattr(cls   , "model_type"		) and config_dict["model_type"] != cls.model_type:
													logger.warning(
													    F"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
													    F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""		)

									return cls.from_dict(_snake_case   , **_snake_case		)



class lowerCamelCase		(_SCREAMING_SNAKE_CASE      ):






					'''simple docstring'''


					a   	=		"owlvit"
					a   	=		True



					def __init__(						self			:				Tuple   , _snake_case			:				str=None   , _snake_case			:				List[Any]=None   , _snake_case			:				List[Any]=512   , _snake_case			:				Dict=2.6592   , _snake_case			:				int=True   , **_snake_case			:				List[Any]   , )							->       str:
									super().__init__(**_snake_case		)

									if text_config is None:
													SCREAMING_SNAKE_CASE__		  =				{}
													logger.info("text_config is None. Initializing the OwlViTTextConfig with default values."		)

									if vision_config is None:
													SCREAMING_SNAKE_CASE__		  =				{}
													logger.info("vision_config is None. initializing the OwlViTVisionConfig with default values."		)

									SCREAMING_SNAKE_CASE__		  =				OwlViTTextConfig(**_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				OwlViTVisionConfig(**_snake_case		)

									SCREAMING_SNAKE_CASE__		  =				projection_dim
									SCREAMING_SNAKE_CASE__		  =				logit_scale_init_value
									SCREAMING_SNAKE_CASE__		  =				return_dict
									SCREAMING_SNAKE_CASE__		  =				1.0



					@classmethod
					def 			lowerCAmelCase_					(						cls			:				Optional[int]   , _snake_case			:				Union[str, os.PathLike]   , **_snake_case			:				str		)							->       "PretrainedConfig":
									cls._set_token_in_kwargs(_snake_case		)

									SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				cls.get_config_dict(_snake_case   , **_snake_case		)

									if "model_type" in config_dict and hasattr(cls   , "model_type"		) and config_dict["model_type"] != cls.model_type:
													logger.warning(
													    F"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
													    F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""		)

									return cls.from_dict(_snake_case   , **_snake_case		)



					@classmethod
					def 			lowerCAmelCase_					(						cls			:				str   , _snake_case			:				Dict   , _snake_case			:				Dict   , **_snake_case			:				Optional[Any]		)							->       Dict:
									SCREAMING_SNAKE_CASE__		  =				{}
									SCREAMING_SNAKE_CASE__		  =				text_config
									SCREAMING_SNAKE_CASE__		  =				vision_config

									return cls.from_dict(_snake_case   , **_snake_case		)



					def 			lowerCAmelCase_					(						self			:				Optional[int]		)							->       str:
									SCREAMING_SNAKE_CASE__		  =				copy.deepcopy(self.__dict__		)
									SCREAMING_SNAKE_CASE__		  =				self.text_config.to_dict()
									SCREAMING_SNAKE_CASE__		  =				self.vision_config.to_dict()
									SCREAMING_SNAKE_CASE__		  =				self.__class__.model_type
									return output



class lowerCamelCase		(_SCREAMING_SNAKE_CASE      ):






					'''simple docstring'''


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



					@property
					def 			lowerCAmelCase_					(						self			:				List[Any]		)							->       Mapping[str, Mapping[int, str]]:
									return OrderedDict(
									    [
									        ("logits_per_image", {0: "batch"}),
									        ("logits_per_text", {0: "batch"}),
									        ("text_embeds", {0: "batch"}),
									        ("image_embeds", {0: "batch"}),
									    ]		)



					@property
					def 			lowerCAmelCase_					(						self			:				List[str]		)							->       float:
									return 1e-4



					def 			lowerCAmelCase_					(						self			:				Optional[Any]   , _snake_case			:				"ProcessorMixin"   , _snake_case			:				int = -1   , _snake_case			:				int = -1   , _snake_case			:				Optional["TensorType"] = None   , )							->       Mapping[str, Any]:
									SCREAMING_SNAKE_CASE__		  =				super().generate_dummy_inputs(
									    processor.tokenizer   , batch_size=_snake_case   , seq_length=_snake_case   , framework=_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				super().generate_dummy_inputs(
									    processor.image_processor   , batch_size=_snake_case   , framework=_snake_case		)
									return {**text_input_dict, **image_input_dict}



					@property
					def 			lowerCAmelCase_					(						self			:				str		)							->       int:
									return 14





"""simple docstring"""






import argparse
import os

import torch

from transformers.utils import WEIGHTS_NAME


_A   					=    ['small', 'medium', 'large']

_A   					=    'lm_head.decoder.weight'
_A   					=    'lm_head.weight'




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase       )							->   List[str]:
				SCREAMING_SNAKE_CASE__		  =				torch.load(__UpperCAmelCase       )
				SCREAMING_SNAKE_CASE__		  =				d.pop(__UpperCAmelCase       )
				os.makedirs(__UpperCAmelCase  ,					exist_ok=__UpperCAmelCase       )
				torch.save(__UpperCAmelCase  ,					os.path.join(__UpperCAmelCase  ,					__UpperCAmelCase       )       )


if __name__ == "__main__":
						_A   					=    argparse.ArgumentParser()
						parser.add_argument('--dialogpt_path', default='.', type=str)
						_A   					=    parser.parse_args()
						for MODEL in DIALOGPT_MODELS:
												_A   					=    os.path.join(args.dialogpt_path, F'{MODEL}_ft.pkl')
												_A   					=    F'./DialoGPT-{MODEL}'
												convert_dialogpt_checkpoint(
												    checkpoint_path,
												    pytorch_dump_folder_path,
												)




