

import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING


snake_case_        =			logging.get_logger(__name__)

snake_case_        =			{
    '''Salesforce/instruct-blip-flan-t5''': '''https://huggingface.co/Salesforce/instruct-blip-flan-t5/resolve/main/config.json''',
}


class 				SCREAMING_SNAKE_CASE__ (__snake_case					):
    __lowerCamelCase						:	List[str]														=					"""instructblip_vision_model"""





    def __init__(				self						,       a=1408						,       a=6144						,       a=39						,       a=16						,       a=224						,       a=14						,       a="gelu"						,       a=1e-6						,       a=0.0						,       a=1e-10						,       a=True						,       **a						,       ):
         super().__init__(**a)

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





    @classmethod
    def 				snake_case_    (				cls						,       a						,       **a):
         cls._set_token_in_kwargs(a)

         lowercase__				,			lowercase__   :       Any				=	cls.get_config_dict(a						,       **a)

         # get the vision config dict if we are loading from InstructBlipConfig
         if config_dict.get('model_type') == "instructblip":
              lowercase__   :       Tuple				=	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(a						,       **a)



class 				SCREAMING_SNAKE_CASE__ (__snake_case					):
    __lowerCamelCase						:	Union[str, Any]														=					"""instructblip_qformer"""





    def __init__(				self						,       a=3_0522						,       a=768						,       a=12						,       a=12						,       a=3072						,       a="gelu"						,       a=0.1						,       a=0.1						,       a=512						,       a=0.02						,       a=1e-12						,       a=0						,       a="absolute"						,       a=2						,       a=1408						,       **a						,       ):
         super().__init__(pad_token_id=a						,       **a)

         lowercase__   :       List[str]				=	vocab_size
         lowercase__   :       Optional[int]				=	hidden_size
         lowercase__   :       List[str]				=	num_hidden_layers
         lowercase__   :       Tuple				=	num_attention_heads
         lowercase__   :       str				=	hidden_act
         lowercase__   :       str				=	intermediate_size
         lowercase__   :       Tuple				=	hidden_dropout_prob
         lowercase__   :       Any				=	attention_probs_dropout_prob
         lowercase__   :       Dict				=	max_position_embeddings
         lowercase__   :       str				=	initializer_range
         lowercase__   :       List[Any]				=	layer_norm_eps
         lowercase__   :       List[Any]				=	position_embedding_type
         lowercase__   :       List[str]				=	cross_attention_frequency
         lowercase__   :       Any				=	encoder_hidden_size





    @classmethod
    def 				snake_case_    (				cls						,       a						,       **a):
         cls._set_token_in_kwargs(a)

         lowercase__				,			lowercase__   :       List[Any]				=	cls.get_config_dict(a						,       **a)

         # get the qformer config dict if we are loading from InstructBlipConfig
         if config_dict.get('model_type') == "instructblip":
              lowercase__   :       Union[str, Any]				=	config_dict['qformer_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(a						,       **a)



class 				SCREAMING_SNAKE_CASE__ (__snake_case					):
    __lowerCamelCase						:	Optional[int]														=					"""instructblip"""
    __lowerCamelCase						:	Dict														=					True





    def __init__(				self						,       a=None						,       a=None						,       a=None						,       a=32						,       **a):
         super().__init__(**a)

         if vision_config is None:
              lowercase__   :       Any				=	{}
              logger.info('vision_config is None. initializing the InstructBlipVisionConfig with default values.')

         if qformer_config is None:
              lowercase__   :       Union[str, Any]				=	{}
              logger.info('qformer_config is None. Initializing the InstructBlipQFormerConfig with default values.')

         if text_config is None:
              lowercase__   :       Dict				=	{}
              logger.info('text_config is None. Initializing the text config with default values (`OPTConfig`).')

         lowercase__   :       List[str]				=	InstructBlipVisionConfig(**a)
         lowercase__   :       str				=	InstructBlipQFormerConfig(**a)
         lowercase__   :       Dict				=	text_config['model_type'] if 'model_type' in text_config else 'opt'
         lowercase__   :       Optional[Any]				=	CONFIG_MAPPING[text_model_type](**a)

         lowercase__   :       int				=	self.text_config.tie_word_embeddings
         lowercase__   :       Any				=	self.text_config.is_encoder_decoder

         lowercase__   :       Dict				=	num_query_tokens
         lowercase__   :       List[Any]				=	self.vision_config.hidden_size
         lowercase__   :       Any				=	self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
         lowercase__   :       List[str]				=	1.0
         lowercase__   :       Optional[Any]				=	0.02





    @classmethod
    def 				snake_case_    (				cls						,       a						,       a						,       a						,       **a						,       ):
         return cls(
             vision_config=vision_config.to_dict()						,       qformer_config=qformer_config.to_dict()						,       text_config=text_config.to_dict()						,       **a						,       )





    def 				snake_case_    (				self):
         lowercase__   :       int				=	copy.deepcopy(self.__dict__)
         lowercase__   :       str				=	self.vision_config.to_dict()
         lowercase__   :       Dict				=	self.qformer_config.to_dict()
         lowercase__   :       Union[str, Any]				=	self.text_config.to_dict()
         lowercase__   :       Optional[Any]				=	self.__class__.model_type
         return output




import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


snake_case_        =			False
snake_case_        =			True
snake_case_        =			False


if __name__ == "__main__":
       snake_case_        =			argparse.ArgumentParser()

       parser.add_argument(
           '''--repo_path''',
           default=None,
           type=str,
           required=True,
           help='''The config json file corresponding to the architecture.''',
       )

       parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')

       snake_case_        =			parser.parse_args()

       snake_case_        =			{
           '''image_size''': '''sample_size''',
           '''num_res_blocks''': '''layers_per_block''',
           '''block_channels''': '''block_out_channels''',
           '''down_blocks''': '''down_block_types''',
           '''up_blocks''': '''up_block_types''',
           '''downscale_freq_shift''': '''freq_shift''',
           '''resnet_num_groups''': '''norm_num_groups''',
           '''resnet_act_fn''': '''act_fn''',
           '''resnet_eps''': '''norm_eps''',
           '''num_head_channels''': '''attention_head_dim''',
       }

       snake_case_        =			{
           '''time_steps''': '''time_proj''',
           '''mid''': '''mid_block''',
           '''downsample_blocks''': '''down_blocks''',
           '''upsample_blocks''': '''up_blocks''',
       }

       snake_case_        =			'''''' if has_file(args.repo_path, '''config.json''') else '''unet'''

       with open(os.path.join(args.repo_path, subfolder, '''config.json'''), '''r''', encoding='''utf-8''') as reader:
              snake_case_        =			reader.read()
              snake_case_        =			json.loads(text)

       if do_only_config:
              for key in config_parameters_to_change.keys():
                     config.pop(key, None)

       if has_file(args.repo_path, '''config.json'''):
              snake_case_        =			UNetaDModel(**config)
       else:
              snake_case_        =			UNetaDConditionModel if '''ldm-text2im-large-256''' in args.repo_path else UNetaDModel
              snake_case_        =			class_name(**config)

       if do_only_config:
              model.save_config(os.path.join(args.repo_path, subfolder))

       snake_case_        =			dict(model.config)

       if do_only_renaming:
              for key, value in config_parameters_to_change.items():
                     if key in config:
                            snake_case_        =			config[key]
                            del config[key]

              snake_case_        =			[k.replace('''UNetRes''', '''''') for k in config['''down_block_types''']]
              snake_case_        =			[k.replace('''UNetRes''', '''''') for k in config['''up_block_types''']]

       if do_only_weights:
              snake_case_        =			torch.load(os.path.join(args.repo_path, subfolder, '''diffusion_pytorch_model.bin'''))

              snake_case_        =			{}
              for param_key, param_value in state_dict.items():
                     if param_key.endswith('''.op.bias''') or param_key.endswith('''.op.weight'''):
                            continue
                     snake_case_        =			False
                     for key, new_key in key_parameters_to_change.items():
                            if not has_changed and param_key.split('''.''')[0] == key:
                                   snake_case_        =			param_value
                                   snake_case_        =			True
                     if not has_changed:
                            snake_case_        =			param_value

              model.load_state_dict(new_state_dict)
              model.save_pretrained(os.path.join(args.repo_path, subfolder))

