




'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


a__         =     {
    "configuration_realm": ["REALM_PRETRAINED_CONFIG_ARCHIVE_MAP", "RealmConfig"],
    "tokenization_realm": ["RealmTokenizer"],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__         =     ["RealmTokenizerFast"]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__         =     [
		    "REALM_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "RealmEmbedder",
		    "RealmForOpenQA",
		    "RealmKnowledgeAugEncoder",
		    "RealmPreTrainedModel",
		    "RealmReader",
		    "RealmScorer",
		    "load_tf_weights_in_realm",
		]
		a__         =     ["RealmRetriever"]


if TYPE_CHECKING:
		from .configuration_realm import REALM_PRETRAINED_CONFIG_ARCHIVE_MAP, RealmConfig
		from .tokenization_realm import RealmTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_realm import RealmTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_realm import (
				    REALM_PRETRAINED_MODEL_ARCHIVE_LIST,
				    RealmEmbedder,
				    RealmForOpenQA,
				    RealmKnowledgeAugEncoder,
				    RealmPreTrainedModel,
				    RealmReader,
				    RealmScorer,
				    load_tf_weights_in_realm,
				)
				from .retrieval_realm import RealmRetriever


else:
		import sys

		a__         =     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



'''simple docstring'''

import argparse

import torch

from transformers import ChineseCLIPConfig, ChineseCLIPModel



def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					int     ,		_UpperCamelCase   :					List[str]     ,		_UpperCamelCase   :					List[str] )		->       List[str]:





   '''simple docstring'''







   UpperCamelCase__    ,		UpperCamelCase__    ,		UpperCamelCase__			    =    pt_weights[F'{prefix}.in_proj_weight'].chunk(3     ,		dim=0 )
   UpperCamelCase__    ,		UpperCamelCase__    ,		UpperCamelCase__			    =    pt_weights[F'{prefix}.in_proj_bias'].chunk(3     ,		dim=0 )

   UpperCamelCase__			    =    pt_weights[F'{prefix}.out_proj.weight']
   UpperCamelCase__			    =    pt_weights[F'{prefix}.out_proj.bias']

   UpperCamelCase__			    =    q_proj
   UpperCamelCase__			    =    q_proj_bias

   UpperCamelCase__			    =    k_proj
   UpperCamelCase__			    =    k_proj_bias

   UpperCamelCase__			    =    v_proj
   UpperCamelCase__			    =    v_proj_bias

   UpperCamelCase__			    =    out_proj_weights
   UpperCamelCase__			    =    out_proj_bias



def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					Union[str, Any]     ,		_UpperCamelCase   :					List[str]     ,		_UpperCamelCase   :					Dict )		->       Tuple:





   '''simple docstring'''







   copy_linear(hf_mlp.fca     ,		_UpperCamelCase     ,		F'{prefix}.c_fc' )
   copy_linear(hf_mlp.fca     ,		_UpperCamelCase     ,		F'{prefix}.c_proj' )



def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					Tuple     ,		_UpperCamelCase   :					str     ,		_UpperCamelCase   :					Optional[int] )		->       Any:





   '''simple docstring'''







   UpperCamelCase__			    =    pt_weights[F'{prefix}.weight'].data
   UpperCamelCase__			    =    pt_weights[F'{prefix}.bias'].data



def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					int     ,		_UpperCamelCase   :					List[Any]     ,		_UpperCamelCase   :					Any )		->       Union[str, Any]:





   '''simple docstring'''







   copy_linear(hf_layer.layer_norma     ,		_UpperCamelCase     ,		F'{prefix}.ln_1' )
   copy_linear(hf_layer.layer_norma     ,		_UpperCamelCase     ,		F'{prefix}.ln_2' )

   # copy MLP
   copy_mlp(hf_layer.mlp     ,		_UpperCamelCase     ,		F'{prefix}.mlp' )

   # copy attn
   copy_attn_layer(hf_layer.self_attn     ,		_UpperCamelCase     ,		F'{prefix}.attn' )



def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					Tuple     ,		_UpperCamelCase   :					Optional[int]     ,		_UpperCamelCase   :					Optional[int] )		->       List[Any]:





   '''simple docstring'''







   for layer_id, hf_layer in enumerate(_UpperCamelCase ):
      copy_layer(_UpperCamelCase     ,		_UpperCamelCase     ,		F'{prefix}.{layer_id}' )



def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					Tuple     ,		_UpperCamelCase   :					Dict )		->       Tuple:





   '''simple docstring'''







   UpperCamelCase__			    =    pt_weights["text_projection"].data.T

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



def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					List[str]     ,		_UpperCamelCase   :					Dict )		->       List[Any]:





   '''simple docstring'''







   UpperCamelCase__			    =    pt_weights["visual.proj"].data.T

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

   # copy embeddings
   UpperCamelCase__			    =    pt_weights["visual.conv1.weight"].data
   UpperCamelCase__			    =    pt_weights["visual.class_embedding"].data
   UpperCamelCase__			    =    pt_weights["visual.positional_embedding"].data

   # copy encoder
   copy_layers(hf_model.vision_model.encoder.layers     ,		_UpperCamelCase     ,		"visual.transformer.resblocks" )



@torch.no_grad()
def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					int     ,		_UpperCamelCase   :					Union[str, Any]     ,		_UpperCamelCase   :					Any=None )		->       List[str]:





   '''simple docstring'''







   assert config_path is not None, "Please specify the ChineseCLIP model config of the corresponding model size."
   UpperCamelCase__			    =    ChineseCLIPConfig.from_pretrained(_UpperCamelCase )

   UpperCamelCase__			    =    ChineseCLIPModel(_UpperCamelCase ).eval()

   UpperCamelCase__			    =    torch.load(_UpperCamelCase     ,		map_location="cpu" )["state_dict"]
   UpperCamelCase__			    =    {(name[7:] if name.startswith("module." ) else name): value for name, value in pt_weights.items()}

   copy_text_model_and_projection(_UpperCamelCase     ,		_UpperCamelCase )
   copy_vision_model_and_projection(_UpperCamelCase     ,		_UpperCamelCase )
   UpperCamelCase__			    =    pt_weights["logit_scale"].data

   hf_model.save_pretrained(_UpperCamelCase )


if __name__ == "__main__":
       __lowercase:		Tuple    						=  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."
       )
       __lowercase:		Optional[int]    						=  parser.parse_args()

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