
import argparse

import torch

from transformers import ChineseCLIPConfig, ChineseCLIPModel


def     a__			(    snake_case   ,	snake_case   ,	snake_case    ):





  """simple docstring"""
  __SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE     :					Dict			=						pt_weights[F'''{prefix}.in_proj_weight'''].chunk(3   ,	dim=0    )
  __SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE     :					Tuple			=						pt_weights[F'''{prefix}.in_proj_bias'''].chunk(3   ,	dim=0    )

  __SCREAMING_SNAKE_CASE     :					Optional[int]			=						pt_weights[F'''{prefix}.out_proj.weight''']
  __SCREAMING_SNAKE_CASE     :					Optional[int]			=						pt_weights[F'''{prefix}.out_proj.bias''']

  __SCREAMING_SNAKE_CASE     :					Any			=						q_proj
  __SCREAMING_SNAKE_CASE     :					Tuple			=						q_proj_bias

  __SCREAMING_SNAKE_CASE     :					Tuple			=						k_proj
  __SCREAMING_SNAKE_CASE     :					str			=						k_proj_bias

  __SCREAMING_SNAKE_CASE     :					Dict			=						v_proj
  __SCREAMING_SNAKE_CASE     :					Tuple			=						v_proj_bias

  __SCREAMING_SNAKE_CASE     :					List[Any]			=						out_proj_weights
  __SCREAMING_SNAKE_CASE     :					Tuple			=						out_proj_bias


def     a__			(    snake_case   ,	snake_case   ,	snake_case    ):





  """simple docstring"""
  copy_linear(hf_mlp.fca   ,	snake_case   ,	F'''{prefix}.c_fc'''    )
  copy_linear(hf_mlp.fca   ,	snake_case   ,	F'''{prefix}.c_proj'''    )


def     a__			(    snake_case   ,	snake_case   ,	snake_case    ):





  """simple docstring"""
  __SCREAMING_SNAKE_CASE     :					List[str]			=						pt_weights[F'''{prefix}.weight'''].data
  __SCREAMING_SNAKE_CASE     :					List[str]			=						pt_weights[F'''{prefix}.bias'''].data


def     a__			(    snake_case   ,	snake_case   ,	snake_case    ):





  """simple docstring"""
  # copy layer norms
  copy_linear(hf_layer.layer_norma   ,	snake_case   ,	F'''{prefix}.ln_1'''    )
  copy_linear(hf_layer.layer_norma   ,	snake_case   ,	F'''{prefix}.ln_2'''    )

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

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


def     a__			(    snake_case   ,	snake_case   ,	snake_case    ):





  """simple docstring"""
  for layer_id, hf_layer in enumerate(snake_case    ):
    copy_layer(snake_case   ,	snake_case   ,	F'''{prefix}.{layer_id}'''    )


def     a__			(    snake_case   ,	snake_case    ):





  """simple docstring"""
  # copy projection
  __SCREAMING_SNAKE_CASE     :					Dict			=						pt_weights['''text_projection'''].data.T

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


def     a__			(    snake_case   ,	snake_case    ):





  """simple docstring"""
  # copy projection
  __SCREAMING_SNAKE_CASE     :					str			=						pt_weights['''visual.proj'''].data.T

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

  # copy embeddings
  __SCREAMING_SNAKE_CASE     :					List[str]			=						pt_weights['''visual.conv1.weight'''].data
  __SCREAMING_SNAKE_CASE     :					List[str]			=						pt_weights['''visual.class_embedding'''].data
  __SCREAMING_SNAKE_CASE     :					Tuple			=						pt_weights['''visual.positional_embedding'''].data

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


@torch.no_grad()
def     a__			(    snake_case   ,	snake_case   ,	snake_case=None    ):





  """simple docstring"""
  assert config_path is not None, "Please specify the ChineseCLIP model config of the corresponding model size."
  __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						ChineseCLIPConfig.from_pretrained(snake_case    )

  __SCREAMING_SNAKE_CASE     :					List[Any]			=						ChineseCLIPModel(snake_case    ).eval()

  __SCREAMING_SNAKE_CASE     :					str			=						torch.load(snake_case   ,	map_location='''cpu'''    )['''state_dict''']
  __SCREAMING_SNAKE_CASE     :					Optional[int]			=						{(name[7:] if name.startswith('''module.'''    ) else name): value for name, value in pt_weights.items()}

  copy_text_model_and_projection(snake_case   ,	snake_case    )
  copy_vision_model_and_projection(snake_case   ,	snake_case    )
  __SCREAMING_SNAKE_CASE     :					List[str]			=						pt_weights['''logit_scale'''].data

  hf_model.save_pretrained(snake_case    )


if __name__ == "__main__":
   lowercase_											=	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_											=	parser.parse_args()

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


from typing import TYPE_CHECKING

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


lowercase_											=	{
    """configuration_led""": ["""LED_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LEDConfig"""],
    """tokenization_led""": ["""LEDTokenizer"""],
}

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

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowercase_											=	[
       """LED_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """LEDForConditionalGeneration""",
       """LEDForQuestionAnswering""",
       """LEDForSequenceClassification""",
       """LEDModel""",
       """LEDPreTrainedModel""",
   ]


try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowercase_											=	["""TFLEDForConditionalGeneration""", """TFLEDModel""", """TFLEDPreTrainedModel"""]


if TYPE_CHECKING:
   from .configuration_led import LED_PRETRAINED_CONFIG_ARCHIVE_MAP, LEDConfig
   from .tokenization_led import LEDTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_led_fast import LEDTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_led import (
          LED_PRETRAINED_MODEL_ARCHIVE_LIST,
          LEDForConditionalGeneration,
          LEDForQuestionAnswering,
          LEDForSequenceClassification,
          LEDModel,
          LEDPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_led import TFLEDForConditionalGeneration, TFLEDModel, TFLEDPreTrainedModel

else:
   import sys

   lowercase_											=	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
