




'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


a__         =     {
    '''configuration_marian''': ['''MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MarianConfig''', '''MarianOnnxConfig'''],
}

try:
		if not is_sentencepiece_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__         =     ['''MarianTokenizer''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__         =     [
		    '''MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''MarianForCausalLM''',
		    '''MarianModel''',
		    '''MarianMTModel''',
		    '''MarianPreTrainedModel''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__         =     ['''TFMarianModel''', '''TFMarianMTModel''', '''TFMarianPreTrainedModel''']

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__         =     ['''FlaxMarianModel''', '''FlaxMarianMTModel''', '''FlaxMarianPreTrainedModel''']

if TYPE_CHECKING:
		from .configuration_marian import MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP, MarianConfig, MarianOnnxConfig

		try:
				if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_marian import MarianTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_marian import (
				    MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST,
				    MarianForCausalLM,
				    MarianModel,
				    MarianMTModel,
				    MarianPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_marian import TFMarianModel, TFMarianMTModel, TFMarianPreTrainedModel

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, FlaxMarianPreTrainedModel

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!")