



"""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


lowercase__			   =  logging.get_logger(__name__)

lowercase__			   =  {
    "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 SCREAMING_SNAKE_CASE__					(		__snake_case      ):
      _lowerCAmelCase							=					"owlvit_text_model"



      def __init__(self						,     _lowercase=49408						,     _lowercase=512						,     _lowercase=2048						,     _lowercase=12						,     _lowercase=8						,     _lowercase=16						,     _lowercase="quick_gelu"						,     _lowercase=1e-5						,     _lowercase=0.0						,     _lowercase=0.02						,     _lowercase=1.0						,     _lowercase=0						,     _lowercase=49406						,     _lowercase=49407						,     **_lowercase						,     ):





             '''simple docstring'''

             super().__init__(pad_token_id=_lowercase						,     bos_token_id=_lowercase						,     eos_token_id=_lowercase						,     **_lowercase							)

             __a		:		Tuple									=       vocab_size
             __a		:		Union[str, Any]									=       hidden_size
             __a		:		Tuple									=       intermediate_size
             __a		:		Union[str, Any]									=       num_hidden_layers
             __a		:		Optional[Any]									=       num_attention_heads
             __a		:		Union[str, Any]									=       max_position_embeddings
             __a		:		List[Any]									=       hidden_act
             __a		:		Tuple									=       layer_norm_eps
             __a		:		Tuple									=       attention_dropout
             __a		:		List[Any]									=       initializer_range
             __a		:		List[Any]									=       initializer_factor





      @classmethod
      def     lowerCAmelCase__(cls						,     _lowercase						,     **_lowercase							):





             '''simple docstring'''

             cls._set_token_in_kwargs(_lowercase							)

             __a		:		List[Any]									=       cls.get_config_dict(_lowercase						,     **_lowercase							)

             # get the text config dict if we are loading from OwlViTConfig
             if config_dict.get("""model_type"""							) == "owlvit":
                    __a		:		Dict									=       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(_lowercase						,     **_lowercase							)







class SCREAMING_SNAKE_CASE__					(		__snake_case      ):
      _lowerCAmelCase							=					"owlvit_vision_model"



      def __init__(self						,     _lowercase=768						,     _lowercase=3072						,     _lowercase=12						,     _lowercase=12						,     _lowercase=3						,     _lowercase=768						,     _lowercase=32						,     _lowercase="quick_gelu"						,     _lowercase=1e-5						,     _lowercase=0.0						,     _lowercase=0.02						,     _lowercase=1.0						,     **_lowercase						,     ):





             '''simple docstring'''

             super().__init__(**_lowercase							)

             __a		:		Any									=       hidden_size
             __a		:		Tuple									=       intermediate_size
             __a		:		str									=       num_hidden_layers
             __a		:		int									=       num_attention_heads
             __a		:		int									=       num_channels
             __a		:		List[Any]									=       image_size
             __a		:		int									=       patch_size
             __a		:		Any									=       hidden_act
             __a		:		int									=       layer_norm_eps
             __a		:		Union[str, Any]									=       attention_dropout
             __a		:		str									=       initializer_range
             __a		:		Union[str, Any]									=       initializer_factor





      @classmethod
      def     lowerCAmelCase__(cls						,     _lowercase						,     **_lowercase							):





             '''simple docstring'''

             cls._set_token_in_kwargs(_lowercase							)

             __a		:		List[str]									=       cls.get_config_dict(_lowercase						,     **_lowercase							)

             # get the vision config dict if we are loading from OwlViTConfig
             if config_dict.get("""model_type"""							) == "owlvit":
                    __a		:		List[str]									=       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(_lowercase						,     **_lowercase							)







class SCREAMING_SNAKE_CASE__					(		__snake_case      ):
      _lowerCAmelCase							=					"owlvit"
      _lowerCAmelCase							=					True



      def __init__(self						,     _lowercase=None						,     _lowercase=None						,     _lowercase=512						,     _lowercase=2.6592						,     _lowercase=True						,     **_lowercase						,     ):





             '''simple docstring'''

             super().__init__(**_lowercase							)

             if text_config is None:
                    __a		:		Union[str, Any]									=       {}
                    logger.info("""text_config is None. Initializing the OwlViTTextConfig with default values."""							)

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

             __a		:		Tuple									=       OwlViTTextConfig(**_lowercase							)
             __a		:		Dict									=       OwlViTVisionConfig(**_lowercase							)

             __a		:		int									=       projection_dim
             __a		:		List[str]									=       logit_scale_init_value
             __a		:		List[str]									=       return_dict
             __a		:		Any									=       1.0



      @classmethod
      def     lowerCAmelCase__(cls						,     _lowercase						,     **_lowercase							):





             '''simple docstring'''

             cls._set_token_in_kwargs(_lowercase							)

             __a		:		List[Any]									=       cls.get_config_dict(_lowercase						,     **_lowercase							)

             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(_lowercase						,     **_lowercase							)



      @classmethod
      def     lowerCAmelCase__(cls						,     _lowercase						,     _lowercase						,     **_lowercase							):





             '''simple docstring'''

             __a		:		Any									=       {}
             __a		:		int									=       text_config
             __a		:		Any									=       vision_config

             return cls.from_dict(_lowercase						,     **_lowercase							)





      def     lowerCAmelCase__(self							):





             '''simple docstring'''

             __a		:		List[Any]									=       copy.deepcopy(self.__dict__							)
             __a		:		List[str]									=       self.text_config.to_dict()
             __a		:		Union[str, Any]									=       self.vision_config.to_dict()
             __a		:		List[str]									=       self.__class__.model_type
             return output







class SCREAMING_SNAKE_CASE__					(		__snake_case      ):



      @property
      def     lowerCAmelCase__(self							):





             '''simple docstring'''

             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							):





             '''simple docstring'''

             return OrderedDict(
                 [
                     ("""logits_per_image""", {0: """batch"""}),
                     ("""logits_per_text""", {0: """batch"""}),
                     ("""text_embeds""", {0: """batch"""}),
                     ("""image_embeds""", {0: """batch"""}),
                 ]							)



      @property
      def     lowerCAmelCase__(self							):





             '''simple docstring'''

             return 1e-4



      def     lowerCAmelCase__(self						,     _lowercase						,     _lowercase = -1						,     _lowercase = -1						,     _lowercase = None						,     ):





             '''simple docstring'''

             __a		:		Optional[int]									=       super().generate_dummy_inputs(
                 processor.tokenizer						,     batch_size=_lowercase						,     seq_length=_lowercase						,     framework=_lowercase							)
             __a		:		Optional[int]									=       super().generate_dummy_inputs(
                 processor.image_processor						,     batch_size=_lowercase						,     framework=_lowercase							)
             return {**text_input_dict, **image_input_dict}





      @property
      def     lowerCAmelCase__(self							):





             '''simple docstring'''

             return 14






"""simple docstring"""

import importlib

import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel





def       __magic_name__    (   _lowerCamelCase				: int  ,       _lowerCamelCase				: Optional[Any]=False					):
							__a		:		Dict									=       OmegaConf.load(_lowerCamelCase					)
							if display:
														print(yaml.dump(OmegaConf.to_container(_lowerCamelCase					)					)					)
							return config





def       __magic_name__    (   _lowerCamelCase				: str  ,       _lowerCamelCase				: Union[str, Any]=None  ,       _lowerCamelCase				: int=None					):
							if conf_path is None:
														__a		:		str									=       """./model_checkpoints/vqgan_only.yaml"""
							__a		:		List[Any]									=       load_config(_lowerCamelCase  ,       display=_lowerCamelCase					)
							__a		:		Dict									=       VQModel(**config.model.params					)
							if ckpt_path is None:
														__a		:		List[Any]									=       """./model_checkpoints/vqgan_only.pt"""
							__a		:		Tuple									=       torch.load(_lowerCamelCase  ,       map_location=_lowerCamelCase					)
							if ".ckpt" in ckpt_path:
														__a		:		List[str]									=       sd["""state_dict"""]
							model.load_state_dict(_lowerCamelCase  ,       strict=_lowerCamelCase					)
							model.to(_lowerCamelCase					)
							del sd
							return model





def       __magic_name__    (   _lowerCamelCase				: Optional[int]  ,       _lowerCamelCase				: List[str]					):
							__a							,     __a							,     __a		:		Tuple									=       model.encode(_lowerCamelCase					)
							print(F'''VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}'''					)
							__a		:		Union[str, Any]									=       model.decode(_lowerCamelCase					)
							return xrec





def       __magic_name__    (   _lowerCamelCase				: Optional[int]  ,       _lowerCamelCase				: Union[str, Any]=False					):
							__a							,     __a		:		Optional[Any]									=       string.rsplit("""."""  ,       1					)
							if reload:
														__a		:		Optional[Any]									=       importlib.import_module(_lowerCamelCase					)
														importlib.reload(_lowerCamelCase					)
							return getattr(importlib.import_module(_lowerCamelCase  ,       package=_lowerCamelCase					)  ,       cls					)





def       __magic_name__    (   _lowerCamelCase				: Any					):
							if "target" not in config:
														raise KeyError("""Expected key `target` to instantiate."""					)
							return get_obj_from_str(config["""target"""]					)(**config.get("""params"""  ,       {}					)					)





def       __magic_name__    (   _lowerCamelCase				: Any  ,       _lowerCamelCase				: Dict  ,       _lowerCamelCase				: int=True  ,       _lowerCamelCase				: int=True					):
							__a		:		Union[str, Any]									=       instantiate_from_config(_lowerCamelCase					)
							if sd is not None:
														model.load_state_dict(_lowerCamelCase					)
							if gpu:
														model.cuda()
							if eval_mode:
														model.eval()
							return {"model": model}







def       __magic_name__    (   _lowerCamelCase				: Any  ,       _lowerCamelCase				: Union[str, Any]  ,       _lowerCamelCase				: str  ,       _lowerCamelCase				: int					):
							# load the specified checkpoint
							if ckpt:
														__a		:		List[str]									=       torch.load(_lowerCamelCase  ,       map_location="""cpu"""					)
														__a		:		Any									=       pl_sd["""global_step"""]
														print(F'''loaded model from global step {global_step}.'''					)
							else:
														__a		:		List[Any]									=       {"""state_dict""": None}
														__a		:		Any									=       None
							__a		:		Union[str, Any]									=       load_model_from_config(config.model  ,       pl_sd["""state_dict"""]  ,       gpu=_lowerCamelCase  ,       eval_mode=_lowerCamelCase					)["""model"""]
							return model, global_step

