


import copy
import os
from typing import Union

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


lowerCAmelCase       		=    logging.get_logger(__name__)

lowerCAmelCase       		=    {
    """Salesforce/blip-vqa-base""": """https://huggingface.co/Salesforce/blip-vqa-base/resolve/main/config.json""",
    """Salesforce/blip-vqa-capfit-large""": (
        """https://huggingface.co/Salesforce/blip-vqa-base-capfit/resolve/main/config.json"""
    ),
    """Salesforce/blip-image-captioning-base""": (
        """https://huggingface.co/Salesforce/blip-image-captioning-base/resolve/main/config.json"""
    ),
    """Salesforce/blip-image-captioning-large""": (
        """https://huggingface.co/Salesforce/blip-image-captioning-large/resolve/main/config.json"""
    ),
    """Salesforce/blip-itm-base-coco""": """https://huggingface.co/Salesforce/blip-itm-base-coco/resolve/main/config.json""",
    """Salesforce/blip-itm-large-coco""": """https://huggingface.co/Salesforce/blip-itm-large-coco/resolve/main/config.json""",
    """Salesforce/blip-itm-base-flikr""": """https://huggingface.co/Salesforce/blip-itm-base-flikr/resolve/main/config.json""",
    """Salesforce/blip-itm-large-flikr""": (
        """https://huggingface.co/Salesforce/blip-itm-large-flikr/resolve/main/config.json"""
    ),
}




class 							lowerCamelCase   (  _UpperCamelCase ):
	_lowerCAmelCase     :		Tuple										=						'''blip_text_model'''


	def __init__(    self				,     lowercase__=3_0_5_2_4				,     lowercase__=7_6_8				,     lowercase__=7_6_8				,     lowercase__=3_0_7_2				,     lowercase__=7_6_8				,     lowercase__=1_2				,     lowercase__=8				,     lowercase__=5_1_2				,     lowercase__="gelu"				,     lowercase__=1e-12				,     lowercase__=0.0				,     lowercase__=0.0				,     lowercase__=0.0_2				,     lowercase__=3_0_5_2_2				,     lowercase__=2				,     lowercase__=0				,     lowercase__=1_0_2				,     lowercase__=True				,     lowercase__=True				,     **lowercase__				,     ):
							super().__init__(
							    pad_token_id=lowercase__				,     bos_token_id=lowercase__				,     eos_token_id=lowercase__				,     sep_token_id=lowercase__				,     **lowercase__				,     )

							__UpperCAmelCase			:							Dict	    =	vocab_size
							__UpperCAmelCase			:							Optional[Any]	    =	hidden_size
							__UpperCAmelCase			:							Optional[Any]	    =	encoder_hidden_size
							__UpperCAmelCase			:							str	    =	intermediate_size
							__UpperCAmelCase			:							Optional[Any]	    =	projection_dim
							__UpperCAmelCase			:							Any	    =	hidden_dropout_prob
							__UpperCAmelCase			:							List[str]	    =	num_hidden_layers
							__UpperCAmelCase			:							int	    =	num_attention_heads
							__UpperCAmelCase			:							int	    =	max_position_embeddings
							__UpperCAmelCase			:							Optional[Any]	    =	layer_norm_eps
							__UpperCAmelCase			:							List[str]	    =	hidden_act
							__UpperCAmelCase			:							Union[str, Any]	    =	initializer_range
							__UpperCAmelCase			:							Any	    =	attention_probs_dropout_prob
							__UpperCAmelCase			:							Union[str, Any]	    =	is_decoder
							__UpperCAmelCase			:							Tuple	    =	use_cache


	@classmethod
	def  A(    cls				,     lowercase__				,     **lowercase__):
							cls._set_token_in_kwargs(lowercase__)

							__UpperCAmelCase    ,      __UpperCAmelCase			:							Optional[Any]	    =	cls.get_config_dict(lowercase__				,     **lowercase__)

							# get the text config dict if we are loading from BlipConfig
							if config_dict.get('''model_type''') == "blip":
													__UpperCAmelCase			:							Tuple	    =	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 							lowerCamelCase   (  _UpperCamelCase ):
	_lowerCAmelCase     :		Optional[Any]										=						'''blip_vision_model'''


	def __init__(    self				,     lowercase__=7_6_8				,     lowercase__=3_0_7_2				,     lowercase__=5_1_2				,     lowercase__=1_2				,     lowercase__=1_2				,     lowercase__=3_8_4				,     lowercase__=1_6				,     lowercase__="gelu"				,     lowercase__=1e-5				,     lowercase__=0.0				,     lowercase__=1e-10				,     **lowercase__				,     ):
							super().__init__(**lowercase__)

							__UpperCAmelCase			:							List[str]	    =	hidden_size
							__UpperCAmelCase			:							List[str]	    =	intermediate_size
							__UpperCAmelCase			:							Optional[int]	    =	projection_dim
							__UpperCAmelCase			:							Optional[int]	    =	num_hidden_layers
							__UpperCAmelCase			:							Union[str, Any]	    =	num_attention_heads
							__UpperCAmelCase			:							Dict	    =	patch_size
							__UpperCAmelCase			:							Optional[Any]	    =	image_size
							__UpperCAmelCase			:							int	    =	initializer_range
							__UpperCAmelCase			:							Union[str, Any]	    =	attention_dropout
							__UpperCAmelCase			:							int	    =	layer_norm_eps
							__UpperCAmelCase			:							Tuple	    =	hidden_act


	@classmethod
	def  A(    cls				,     lowercase__				,     **lowercase__):
							cls._set_token_in_kwargs(lowercase__)

							__UpperCAmelCase    ,      __UpperCAmelCase			:							Optional[Any]	    =	cls.get_config_dict(lowercase__				,     **lowercase__)

							# get the vision config dict if we are loading from BlipConfig
							if config_dict.get('''model_type''') == "blip":
													__UpperCAmelCase			:							Union[str, Any]	    =	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 							lowerCamelCase   (  _UpperCamelCase ):
	_lowerCAmelCase     :		Dict										=						'''blip'''
	_lowerCAmelCase     :		List[str]										=						True


	def __init__(    self				,     lowercase__=None				,     lowercase__=None				,     lowercase__=5_1_2				,     lowercase__=2.6_5_9_2				,     lowercase__=2_5_6				,     **lowercase__				,     ):
							super().__init__(**lowercase__)

							if text_config is None:
													__UpperCAmelCase			:							Optional[int]	    =	{}
													logger.info('''`text_config` is `None`. Initializing the `BlipTextConfig` with default values.''')

							if vision_config is None:
													__UpperCAmelCase			:							int	    =	{}
													logger.info('''`vision_config` is `None`. Initializing the `BlipVisionConfig` with default values.''')

							__UpperCAmelCase			:							Union[str, Any]	    =	BlipTextConfig(**lowercase__)
							__UpperCAmelCase			:							Union[str, Any]	    =	BlipVisionConfig(**lowercase__)

							__UpperCAmelCase			:							List[str]	    =	self.vision_config.hidden_size

							__UpperCAmelCase			:							List[str]	    =	projection_dim
							__UpperCAmelCase			:							List[Any]	    =	logit_scale_init_value
							__UpperCAmelCase			:							Tuple	    =	1.0
							__UpperCAmelCase			:							int	    =	0.0_2
							__UpperCAmelCase			:							Optional[int]	    =	image_text_hidden_size


	@classmethod
	def  A(    cls				,     lowercase__				,     lowercase__				,     **lowercase__):
							return cls(text_config=text_config.to_dict()				,     vision_config=vision_config.to_dict()				,     **lowercase__)


	def  A(    self):
							__UpperCAmelCase			:							Optional[int]	    =	copy.deepcopy(self.__dict__)
							__UpperCAmelCase			:							Union[str, Any]	    =	self.text_config.to_dict()
							__UpperCAmelCase			:							List[Any]	    =	self.vision_config.to_dict()
							__UpperCAmelCase			:							Optional[int]	    =	self.__class__.model_type
							return output





import importlib

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




def     __SCREAMING_SNAKE_CASE							(       lowercase_      ,       lowercase_=False      )		->     Optional[Any]:




						'''simple docstring'''







						__UpperCAmelCase			:							Optional[Any]	    =	OmegaConf.load(lowercase_      )
						if display:
												print(yaml.dump(OmegaConf.to_container(lowercase_      )      )      )
						return config




def     __SCREAMING_SNAKE_CASE							(       lowercase_      ,       lowercase_=None      ,       lowercase_=None      )		->     Union[str, Any]:




						'''simple docstring'''







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




def     __SCREAMING_SNAKE_CASE							(       lowercase_      ,       lowercase_      )		->     List[Any]:




						'''simple docstring'''







						__UpperCAmelCase    ,      __UpperCAmelCase    ,      __UpperCAmelCase			:							Optional[Any]	    =	model.encode(lowercase_      )
						print(f"VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}"      )
						__UpperCAmelCase			:							List[str]	    =	model.decode(lowercase_      )
						return xrec




def     __SCREAMING_SNAKE_CASE							(       lowercase_      ,       lowercase_=False      )		->     int:




						'''simple docstring'''







						__UpperCAmelCase    ,      __UpperCAmelCase			:							Optional[int]	    =	string.rsplit('''.'''      ,       1      )
						if reload:
												__UpperCAmelCase			:							Optional[int]	    =	importlib.import_module(lowercase_      )
												importlib.reload(lowercase_      )
						return getattr(importlib.import_module(lowercase_      ,       package=lowercase_      )      ,       cls      )




def     __SCREAMING_SNAKE_CASE							(       lowercase_      )		->     Union[str, Any]:




						'''simple docstring'''







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




def     __SCREAMING_SNAKE_CASE							(       lowercase_      ,       lowercase_      ,       lowercase_=True      ,       lowercase_=True      )		->     int:




						'''simple docstring'''







						__UpperCAmelCase			:							Dict	    =	instantiate_from_config(lowercase_      )
						if sd is not None:
												model.load_state_dict(lowercase_      )
						if gpu:
												model.cuda()
						if eval_mode:
												model.eval()
						return {"model": model}





def     __SCREAMING_SNAKE_CASE							(       lowercase_      ,       lowercase_      ,       lowercase_      ,       lowercase_      )		->     Optional[Any]:




						'''simple docstring'''







						if ckpt:
												__UpperCAmelCase			:							Optional[int]	    =	torch.load(lowercase_      ,       map_location='''cpu'''      )
												__UpperCAmelCase			:							Union[str, Any]	    =	pl_sd['''global_step''']
												print(f"loaded model from global step {global_step}."      )
						else:
												__UpperCAmelCase			:							Union[str, Any]	    =	{'''state_dict''': None}
												__UpperCAmelCase			:							Optional[int]	    =	None
						__UpperCAmelCase			:							str	    =	load_model_from_config(config.model      ,       pl_sd['''state_dict''']      ,       gpu=lowercase_      ,       eval_mode=lowercase_      )['''model''']
						return model, global_step

