






"""simple docstring"""




import argparse

import torch

from transformers import ChineseCLIPConfig, ChineseCLIPModel






def 						_SCREAMING_SNAKE_CASE							( UpperCamelCase     :	Any		,				UpperCamelCase     :	Optional[Any]		,				UpperCamelCase     :	Union[str, Any]    )     ->   int:
  A__						,A__						,A__											=							pt_weights[F"""{prefix}.in_proj_weight"""].chunk(3		,				dim=0    )
  A__						,A__						,A__											=							pt_weights[F"""{prefix}.in_proj_bias"""].chunk(3		,				dim=0    )

  A__											=							pt_weights[F"""{prefix}.out_proj.weight"""]
  A__											=							pt_weights[F"""{prefix}.out_proj.bias"""]

  A__											=							q_proj
  A__											=							q_proj_bias

  A__											=							k_proj
  A__											=							k_proj_bias

  A__											=							v_proj
  A__											=							v_proj_bias

  A__											=							out_proj_weights
  A__											=							out_proj_bias






def 						_SCREAMING_SNAKE_CASE							( UpperCamelCase     :	Optional[int]		,				UpperCamelCase     :	str		,				UpperCamelCase     :	Any    )     ->   Optional[int]:
  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     :	str		,				UpperCamelCase     :	Optional[int]		,				UpperCamelCase     :	List[str]    )     ->   int:
  A__											=							pt_weights[F"""{prefix}.weight"""].data
  A__											=							pt_weights[F"""{prefix}.bias"""].data






def 						_SCREAMING_SNAKE_CASE							( UpperCamelCase     :	List[str]		,				UpperCamelCase     :	str		,				UpperCamelCase     :	Optional[Any]    )     ->   Optional[Any]:
  # copy layer norms
  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     :	Dict		,				UpperCamelCase     :	Any		,				UpperCamelCase     :	List[Any]    )     ->   Dict:
  for layer_id, hf_layer in enumerate(UpperCamelCase    ):
    copy_layer(UpperCamelCase		,				UpperCamelCase		,				F"""{prefix}.{layer_id}"""    )






def 						_SCREAMING_SNAKE_CASE							( UpperCamelCase     :	Tuple		,				UpperCamelCase     :	Any    )     ->   List[str]:
  # copy projection
  A__											=							pt_weights["""text_projection"""].data.T

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






def 						_SCREAMING_SNAKE_CASE							( UpperCamelCase     :	Tuple		,				UpperCamelCase     :	List[Any]    )     ->   Optional[Any]:
  # copy projection
  A__											=							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
  A__											=							pt_weights["""visual.conv1.weight"""].data
  A__											=							pt_weights["""visual.class_embedding"""].data
  A__											=							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     :	str		,				UpperCamelCase     :	Dict		,				UpperCamelCase     :	List[str]=None    )     ->   List[str]:
  assert config_path is not None, "Please specify the ChineseCLIP model config of the corresponding model size."
  A__											=							ChineseCLIPConfig.from_pretrained(UpperCamelCase    )

  A__											=							ChineseCLIPModel(UpperCamelCase    ).eval()

  A__											=							torch.load(UpperCamelCase		,				map_location="""cpu"""    )["""state_dict"""]
  A__											=							{(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    )
  A__											=							pt_weights["""logit_scale"""].data

  hf_model.save_pretrained(UpperCamelCase    )


if __name__ == "__main__":
      lowerCamelCase__			=   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."
      )
      lowerCamelCase__			=   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"""




import argparse

import torch
from flax.training.checkpoints import restore_checkpoint

from transformers import FNetConfig, FNetForPreTraining
from transformers.utils import logging


logging.set_verbosity_info()






def 						_SCREAMING_SNAKE_CASE							( UpperCamelCase     :	Any		,				UpperCamelCase     :	Optional[Any]		,				UpperCamelCase     :	Dict    ):
  # Initialise PyTorch model
  A__											=							FNetConfig.from_json_file(UpperCamelCase    )
  print(F"""Building PyTorch model from configuration: {config}"""    )
  A__											=							FNetForPreTraining(UpperCamelCase    )

  A__											=							restore_checkpoint(UpperCamelCase		,				UpperCamelCase    )
  A__											=							checkpoint_dict["""target"""]

  # Embeddings
  # Position IDs
  A__											=							fnet_pretraining_model.state_dict()

  A__											=							state_dict["""fnet.embeddings.position_ids"""]
  A__											=							{"""fnet.embeddings.position_ids""": position_ids}
  # Embedding Layers
  A__											=							torch.tensor(
      pretrained_model_params["""encoder"""]["""embedder"""]["""word"""]["""embedding"""]    )
  A__											=							torch.tensor(
      pretrained_model_params["""encoder"""]["""embedder"""]["""position"""]["""embedding"""][0]    )
  A__											=							torch.tensor(
      pretrained_model_params["""encoder"""]["""embedder"""]["""type"""]["""embedding"""]    )
  A__											=							torch.tensor(
      pretrained_model_params["""encoder"""]["""embedder"""]["""hidden_mapping_in"""]["""kernel"""]    ).T
  A__											=							torch.tensor(
      pretrained_model_params["""encoder"""]["""embedder"""]["""hidden_mapping_in"""]["""bias"""]    )
  A__											=							torch.tensor(
      pretrained_model_params["""encoder"""]["""embedder"""]["""layer_norm"""]["""scale"""]    )
  A__											=							torch.tensor(
      pretrained_model_params["""encoder"""]["""embedder"""]["""layer_norm"""]["""bias"""]    )

  # Encoder Layers
  for layer in range(config.num_hidden_layers    ):
    A__											=							torch.tensor(
        pretrained_model_params["""encoder"""][F"""encoder_{layer}"""]["""mixing_layer_norm"""]["""scale"""]    )
    A__											=							torch.tensor(
        pretrained_model_params["""encoder"""][F"""encoder_{layer}"""]["""mixing_layer_norm"""]["""bias"""]    )

    A__											=							torch.tensor(
        pretrained_model_params["""encoder"""][F"""feed_forward_{layer}"""]["""intermediate"""]["""kernel"""]    ).T
    A__											=							torch.tensor(
        pretrained_model_params["""encoder"""][F"""feed_forward_{layer}"""]["""intermediate"""]["""bias"""]    )

    A__											=							torch.tensor(
        pretrained_model_params["""encoder"""][F"""feed_forward_{layer}"""]["""output"""]["""kernel"""]    ).T
    A__											=							torch.tensor(
        pretrained_model_params["""encoder"""][F"""feed_forward_{layer}"""]["""output"""]["""bias"""]    )

    A__											=							torch.tensor(
        pretrained_model_params["""encoder"""][F"""encoder_{layer}"""]["""output_layer_norm"""]["""scale"""]    )
    A__											=							torch.tensor(
        pretrained_model_params["""encoder"""][F"""encoder_{layer}"""]["""output_layer_norm"""]["""bias"""]    )

  # Pooler Layers
  A__											=							torch.tensor(pretrained_model_params["""encoder"""]["""pooler"""]["""kernel"""]    ).T
  A__											=							torch.tensor(pretrained_model_params["""encoder"""]["""pooler"""]["""bias"""]    )

  # Masked LM Layers
  A__											=							torch.tensor(
      pretrained_model_params["""predictions_dense"""]["""kernel"""]    ).T
  A__											=							torch.tensor(
      pretrained_model_params["""predictions_dense"""]["""bias"""]    )
  A__											=							torch.tensor(
      pretrained_model_params["""predictions_layer_norm"""]["""scale"""]    )
  A__											=							torch.tensor(
      pretrained_model_params["""predictions_layer_norm"""]["""bias"""]    )
  A__											=							torch.tensor(
      pretrained_model_params["""encoder"""]["""embedder"""]["""word"""]["""embedding"""]    )
  A__											=							torch.tensor(
      pretrained_model_params["""predictions_output"""]["""output_bias"""]    )
  A__											=							torch.tensor(pretrained_model_params["""predictions_output"""]["""output_bias"""]    )

  # Seq Relationship Layers
  A__											=							torch.tensor(
      pretrained_model_params["""classification"""]["""output_kernel"""]    )
  A__											=							torch.tensor(
      pretrained_model_params["""classification"""]["""output_bias"""]    )

  # Load State Dict
  fnet_pretraining_model.load_state_dict(UpperCamelCase    )

  # Save PreTrained
  print(F"""Saving pretrained model to {save_path}"""    )
  fnet_pretraining_model.save_pretrained(UpperCamelCase    )


if __name__ == "__main__":
      lowerCamelCase__			=   argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          "--flax_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
      )
      parser.add_argument(
          "--fnet_config_file",
          default=None,
          type=str,
          required=True,
          help=(
              "The config json file corresponding to the pre-trained FNet model. \n"
              "This specifies the model architecture."
          ),
      )
      parser.add_argument("--save_path", default=None, type=str, required=True, help="Path to the output model.")
      lowerCamelCase__			=   parser.parse_args()
      convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)




