



"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


__a           =							logging.get_logger(__name__)

__a           =							{
    "facebook/opt-125m": "https://huggingface.co/facebook/opt-125m/blob/main/config.json",
    "facebook/opt-350m": "https://huggingface.co/facebook/opt-350m/blob/main/config.json",
    "facebook/opt-1.3b": "https://huggingface.co/facebook/opt-1.3b/blob/main/config.json",
    "facebook/opt-2.7b": "https://huggingface.co/facebook/opt-2.7b/blob/main/config.json",
    "facebook/opt-6.7b": "https://huggingface.co/facebook/opt-6.7b/blob/main/config.json",
    "facebook/opt-13b": "https://huggingface.co/facebook/opt-13b/blob/main/config.json",
}
class 							lowerCamelCase    (     _lowerCAmelCase							):






					'''simple docstring'''





					_A						:     Dict		=	"""opt"""
					_A						:     Optional[Any]		=	["""past_key_values"""]






					def __init__(			self:				Optional[int]		,	snake_case:				Optional[Any]=50_272		,	snake_case:				Tuple=768		,	snake_case:				Union[str, Any]=12		,	snake_case:				List[Any]=3_072		,	snake_case:				Optional[Any]=2_048		,	snake_case:				Dict=True		,	snake_case:				int=False		,	snake_case:				List[str]=None		,	snake_case:				Any=0.1		,	snake_case:				int=0.0		,	snake_case:				Tuple=12		,	snake_case:				Any="relu"		,	snake_case:				Tuple=0.0		,	snake_case:				str=0.0_2		,	snake_case:				int=True		,	snake_case:				Dict=1		,	snake_case:				Optional[Any]=2		,	snake_case:				Tuple=2		,	snake_case:				Optional[int]=True		,	snake_case:				Optional[Any]=True		,	**snake_case:				Optional[int]		,	)  ->      List[Any]:
											super().__init__(
											    pad_token_id=snake_case		,	bos_token_id=snake_case		,	eos_token_id=snake_case		,	**snake_case		,	)
											snake_case_					:Optional[int]		=     vocab_size
											snake_case_					:int		=     max_position_embeddings
											snake_case_					:Optional[int]		=     num_attention_heads
											snake_case_					:Tuple		=     word_embed_proj_dim if word_embed_proj_dim is not None else hidden_size
											snake_case_					:Tuple		=     ffn_dim
											snake_case_					:Optional[int]		=     hidden_size
											snake_case_					:int		=     num_hidden_layers
											snake_case_					:int		=     dropout
											snake_case_					:Optional[Any]		=     attention_dropout
											snake_case_					:int		=     activation_function
											snake_case_					:str		=     init_std
											snake_case_					:Optional[Any]		=     layerdrop
											snake_case_					:List[Any]		=     use_cache
											snake_case_					:Any		=     do_layer_norm_before
											# We keep these variables at `True` for backward compatibility.
											snake_case_					:List[Any]		=     enable_bias
											snake_case_					:List[str]		=     layer_norm_elementwise_affine

											# Note that the only purpose of `_remove_final_layer_norm` is to keep backward compatibility
											# with checkpoints that have been fine-tuned before transformers v4.20.1
											# see https://github.com/facebookresearch/metaseq/pull/164
											snake_case_					:Optional[Any]		=     _remove_final_layer_norm

import argparse

import torch

from transformers import ChineseCLIPConfig, ChineseCLIPModel


def SCREAMING_SNAKE_CASE					(  lowercase_:						List[str]		,    lowercase_:						Dict		,    lowercase_:						List[Any]					):
       _UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase  :							List[Any]	    =	pt_weights[F"{prefix}.in_proj_weight"].chunk(3		,    dim=0					)
       _UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase  :							Dict	    =	pt_weights[F"{prefix}.in_proj_bias"].chunk(3		,    dim=0					)

       _UpperCamelCase  :							List[str]	    =	pt_weights[F"{prefix}.out_proj.weight"]
       _UpperCamelCase  :							Any	    =	pt_weights[F"{prefix}.out_proj.bias"]

       _UpperCamelCase  :							List[Any]	    =	q_proj
       _UpperCamelCase  :							Union[str, Any]	    =	q_proj_bias

       _UpperCamelCase  :							str	    =	k_proj
       _UpperCamelCase  :							List[str]	    =	k_proj_bias

       _UpperCamelCase  :							Dict	    =	v_proj
       _UpperCamelCase  :							str	    =	v_proj_bias

       _UpperCamelCase  :							List[str]	    =	out_proj_weights
       _UpperCamelCase  :							Any	    =	out_proj_bias


def SCREAMING_SNAKE_CASE					(  lowercase_:						List[str]		,    lowercase_:						List[Any]		,    lowercase_:						Any					):
       copy_linear(hf_mlp.fca		,    lowercase_		,    F"{prefix}.c_fc"					)
       copy_linear(hf_mlp.fca		,    lowercase_		,    F"{prefix}.c_proj"					)


def SCREAMING_SNAKE_CASE					(  lowercase_:						Optional[Any]		,    lowercase_:						List[str]		,    lowercase_:						Any					):
       _UpperCamelCase  :							Dict	    =	pt_weights[F"{prefix}.weight"].data
       _UpperCamelCase  :							List[Any]	    =	pt_weights[F"{prefix}.bias"].data


def SCREAMING_SNAKE_CASE					(  lowercase_:						Any		,    lowercase_:						Optional[int]		,    lowercase_:						Union[str, Any]					):
       # copy layer norms
       copy_linear(hf_layer.layer_norma		,    lowercase_		,    F"{prefix}.ln_1"					)
       copy_linear(hf_layer.layer_norma		,    lowercase_		,    F"{prefix}.ln_2"					)

       # copy MLP
       copy_mlp(hf_layer.mlp		,    lowercase_		,    F"{prefix}.mlp"					)

       # copy attn
       copy_attn_layer(hf_layer.self_attn		,    lowercase_		,    F"{prefix}.attn"					)


def SCREAMING_SNAKE_CASE					(  lowercase_:						str		,    lowercase_:						str		,    lowercase_:						int					):
       for layer_id, hf_layer in enumerate(lowercase_					):
              copy_layer(lowercase_		,    lowercase_		,    F"{prefix}.{layer_id}"					)


def SCREAMING_SNAKE_CASE					(  lowercase_:						Union[str, Any]		,    lowercase_:						List[Any]					):
       # copy projection
       _UpperCamelCase  :							List[str]	    =	pt_weights['text_projection'].data.T

       # copy text encoder
       for name, param in hf_model.text_model.named_parameters():
              _UpperCamelCase  :							Tuple	    =	pt_weights[F"bert.{name}"].data


def SCREAMING_SNAKE_CASE					(  lowercase_:						str		,    lowercase_:						Any					):
       # copy projection
       _UpperCamelCase  :							List[Any]	    =	pt_weights['visual.proj'].data.T

       # copy layer norms
       copy_linear(hf_model.vision_model.pre_layrnorm		,    lowercase_		,    'visual.ln_pre'					)
       copy_linear(hf_model.vision_model.post_layernorm		,    lowercase_		,    'visual.ln_post'					)

       # copy embeddings
       _UpperCamelCase  :							List[Any]	    =	pt_weights['visual.conv1.weight'].data
       _UpperCamelCase  :							List[Any]	    =	pt_weights['visual.class_embedding'].data
       _UpperCamelCase  :							int	    =	pt_weights['visual.positional_embedding'].data

       # copy encoder
       copy_layers(hf_model.vision_model.encoder.layers		,    lowercase_		,    'visual.transformer.resblocks'					)


@torch.no_grad()
def SCREAMING_SNAKE_CASE					(  lowercase_:						Optional[Any]		,    lowercase_:						Optional[int]		,    lowercase_:						List[Any]=None					):
       assert config_path is not None, "Please specify the ChineseCLIP model config of the corresponding model size."
       _UpperCamelCase  :							Optional[Any]	    =	ChineseCLIPConfig.from_pretrained(lowercase_					)

       _UpperCamelCase  :							List[Any]	    =	ChineseCLIPModel(lowercase_					).eval()

       _UpperCamelCase  :							List[Any]	    =	torch.load(lowercase_		,    map_location='cpu'					)['state_dict']
       _UpperCamelCase  :							Union[str, Any]	    =	{(name[7:] if name.startswith('module.'					) else name): value for name, value in pt_weights.items()}

       copy_text_model_and_projection(lowercase_		,    lowercase_					)
       copy_vision_model_and_projection(lowercase_		,    lowercase_					)
       _UpperCamelCase  :							List[Any]	    =	pt_weights['logit_scale'].data

       hf_model.save_pretrained(lowercase_					)


if __name__ == "__main__":
 UpperCAmelCase      		=					argparse.ArgumentParser()
 parser.add_argument(
     '''--pytorch_dump_folder_path''',
     default=None,
     type=str,
     help='''Path to the output folder storing converted hf PyTorch model.''',
 )
 parser.add_argument(
     '''--checkpoint_path''', default=None, type=str, help='''Path to original github format ChineseCLIP checkpoint.'''
 )
 parser.add_argument(
     '''--config_path''', default=None, required=True, type=str, help='''Path to hf config.json of model to convert.'''
 )
 UpperCAmelCase      		=					parser.parse_args()

 convert_chinese_clip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)
 print('''The conversion is finished!''')



