





"""simple docstring"""


import argparse

import torch

from transformers import ChineseCLIPConfig, ChineseCLIPModel





def 							a__			(  _UpperCAmelCase	,     _UpperCAmelCase	,     _UpperCAmelCase	)    ->  str:

 '''simple docstring'''






 snake_case__    							=				pt_weights[f"{prefix}.in_proj_weight"].chunk(3	,     dim=0	)
 snake_case__    							=				pt_weights[f"{prefix}.in_proj_bias"].chunk(3	,     dim=0	)

 snake_case__    							=				pt_weights[f"{prefix}.out_proj.weight"]
 snake_case__    							=				pt_weights[f"{prefix}.out_proj.bias"]

 snake_case__    							=				q_proj
 snake_case__    							=				q_proj_bias

 snake_case__    							=				k_proj
 snake_case__    							=				k_proj_bias

 snake_case__    							=				v_proj
 snake_case__    							=				v_proj_bias

 snake_case__    							=				out_proj_weights
 snake_case__    							=				out_proj_bias





def 							a__			(  _UpperCAmelCase	,     _UpperCAmelCase	,     _UpperCAmelCase	)    ->  Dict:

 '''simple docstring'''






 copy_linear(hf_mlp.fca	,     _SCREAMING_SNAKE_CASE	,     f"{prefix}.c_fc"	)
 copy_linear(hf_mlp.fca	,     _SCREAMING_SNAKE_CASE	,     f"{prefix}.c_proj"	)





def 							a__			(  _UpperCAmelCase	,     _UpperCAmelCase	,     _UpperCAmelCase	)    ->  int:

 '''simple docstring'''






 snake_case__    							=				pt_weights[f"{prefix}.weight"].data
 snake_case__    							=				pt_weights[f"{prefix}.bias"].data





def 							a__			(  _UpperCAmelCase	,     _UpperCAmelCase	,     _UpperCAmelCase	)    ->  int:

 '''simple docstring'''






 copy_linear(hf_layer.layer_norma	,     _SCREAMING_SNAKE_CASE	,     f"{prefix}.ln_1"	)
 copy_linear(hf_layer.layer_norma	,     _SCREAMING_SNAKE_CASE	,     f"{prefix}.ln_2"	)

 # copy MLP
 copy_mlp(hf_layer.mlp	,     _SCREAMING_SNAKE_CASE	,     f"{prefix}.mlp"	)

 # copy attn
 copy_attn_layer(hf_layer.self_attn	,     _SCREAMING_SNAKE_CASE	,     f"{prefix}.attn"	)





def 							a__			(  _UpperCAmelCase	,     _UpperCAmelCase	,     _UpperCAmelCase	)    ->  List[Any]:

 '''simple docstring'''






 for layer_id, hf_layer in enumerate(_SCREAMING_SNAKE_CASE	):
  copy_layer(_SCREAMING_SNAKE_CASE	,     _SCREAMING_SNAKE_CASE	,     f"{prefix}.{layer_id}"	)





def 							a__			(  _UpperCAmelCase	,     _UpperCAmelCase	)    ->  int:

 '''simple docstring'''






 snake_case__    							=				pt_weights['text_projection'].data.T

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





def 							a__			(  _UpperCAmelCase	,     _UpperCAmelCase	)    ->  List[str]:

 '''simple docstring'''






 snake_case__    							=				pt_weights['visual.proj'].data.T

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

 # copy embeddings
 snake_case__    							=				pt_weights['visual.conv1.weight'].data
 snake_case__    							=				pt_weights['visual.class_embedding'].data
 snake_case__    							=				pt_weights['visual.positional_embedding'].data

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





@torch.no_grad()
def 							a__			(  _UpperCAmelCase	,     _UpperCAmelCase	,     _UpperCAmelCase=None	)    ->  int:

 '''simple docstring'''






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

 snake_case__    							=				ChineseCLIPModel(_SCREAMING_SNAKE_CASE	).eval()

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

 copy_text_model_and_projection(_SCREAMING_SNAKE_CASE	,     _SCREAMING_SNAKE_CASE	)
 copy_vision_model_and_projection(_SCREAMING_SNAKE_CASE	,     _SCREAMING_SNAKE_CASE	)
 snake_case__    							=				pt_weights['logit_scale'].data

 hf_model.save_pretrained(_SCREAMING_SNAKE_CASE	)


if __name__ == "__main__":
   a__					:     Optional[int]							=				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."""
   )
   a__					:     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!""")








"""simple docstring"""


from typing import TYPE_CHECKING

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


a__					:     List[Any]							=				{
    """configuration_bart""": ["""BART_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BartConfig""", """BartOnnxConfig"""],
    """tokenization_bart""": ["""BartTokenizer"""],
}

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

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   a__					:     List[Any]							=				[
       """BART_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """BartForCausalLM""",
       """BartForConditionalGeneration""",
       """BartForQuestionAnswering""",
       """BartForSequenceClassification""",
       """BartModel""",
       """BartPretrainedModel""",
       """PretrainedBartModel""",
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   a__					:     List[str]							=				[
       """TFBartForConditionalGeneration""",
       """TFBartForSequenceClassification""",
       """TFBartModel""",
       """TFBartPretrainedModel""",
   ]

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   a__					:     Any							=				[
       """FlaxBartDecoderPreTrainedModel""",
       """FlaxBartForCausalLM""",
       """FlaxBartForConditionalGeneration""",
       """FlaxBartForQuestionAnswering""",
       """FlaxBartForSequenceClassification""",
       """FlaxBartModel""",
       """FlaxBartPreTrainedModel""",
   ]

if TYPE_CHECKING:
   from .configuration_bart import BART_PRETRAINED_CONFIG_ARCHIVE_MAP, BartConfig, BartOnnxConfig
   from .tokenization_bart import BartTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_bart_fast import BartTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_bart import (
          BART_PRETRAINED_MODEL_ARCHIVE_LIST,
          BartForCausalLM,
          BartForConditionalGeneration,
          BartForQuestionAnswering,
          BartForSequenceClassification,
          BartModel,
          BartPretrainedModel,
          PretrainedBartModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_bart import (
          TFBartForConditionalGeneration,
          TFBartForSequenceClassification,
          TFBartModel,
          TFBartPretrainedModel,
      )

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_bart import (
          FlaxBartDecoderPreTrainedModel,
          FlaxBartForCausalLM,
          FlaxBartForConditionalGeneration,
          FlaxBartForQuestionAnswering,
          FlaxBartForSequenceClassification,
          FlaxBartModel,
          FlaxBartPreTrainedModel,
      )

else:
   import sys

   a__					:     int							=				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

