


from typing import List, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


snake_case_  :     str							  =							logging.get_logger(__name__)

snake_case_  :     Optional[int]							  =							{
    '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/config.json''',
    # See all LED models at https://huggingface.co/models?filter=led
}

class   A__							(						UpperCamelCase__					):
      UpperCAmelCase     =  "led"
      UpperCAmelCase     =  {
          "num_attention_heads": "encoder_attention_heads",
          "hidden_size": "d_model",
          "attention_probs_dropout_prob": "attention_dropout",
          "initializer_range": "init_std",
      }




      def __init__(    self   : Any			,      _a   : Tuple=5_0265			,      _a   : List[str]=1_6384			,      _a   : List[str]=1024			,      _a   : Dict=12			,      _a   : Tuple=4096			,      _a   : Optional[Any]=16			,      _a   : Tuple=12			,      _a   : Any=4096			,      _a   : List[Any]=16			,      _a   : str=0.0			,      _a   : Optional[int]=0.0			,      _a   : Dict=True			,      _a   : Dict=True			,      _a   : Optional[int]="gelu"			,      _a   : Dict=1024			,      _a   : List[str]=0.1			,      _a   : Union[str, Any]=0.0			,      _a   : Tuple=0.0			,      _a   : str=0.02			,      _a   : str=2			,      _a   : Union[str, Any]=0.0			,      _a   : Optional[Any]=1			,      _a   : Optional[int]=0			,      _a   : Optional[int]=2			,      _a   : Union[List[int], int] = 512			,      **_a   : Optional[int]			,      )							->       int:


          """simple docstring"""





          _SCREAMING_SNAKE_CASE     					=vocab_size
          _SCREAMING_SNAKE_CASE     					=max_encoder_position_embeddings
          _SCREAMING_SNAKE_CASE     					=max_decoder_position_embeddings
          _SCREAMING_SNAKE_CASE     					=d_model
          _SCREAMING_SNAKE_CASE     					=encoder_ffn_dim
          _SCREAMING_SNAKE_CASE     					=encoder_layers
          _SCREAMING_SNAKE_CASE     					=encoder_attention_heads
          _SCREAMING_SNAKE_CASE     					=decoder_ffn_dim
          _SCREAMING_SNAKE_CASE     					=decoder_layers
          _SCREAMING_SNAKE_CASE     					=decoder_attention_heads
          _SCREAMING_SNAKE_CASE     					=dropout
          _SCREAMING_SNAKE_CASE     					=attention_dropout
          _SCREAMING_SNAKE_CASE     					=activation_dropout
          _SCREAMING_SNAKE_CASE     					=activation_function
          _SCREAMING_SNAKE_CASE     					=init_std
          _SCREAMING_SNAKE_CASE     					=encoder_layerdrop
          _SCREAMING_SNAKE_CASE     					=decoder_layerdrop
          _SCREAMING_SNAKE_CASE     					=classifier_dropout
          _SCREAMING_SNAKE_CASE     					=use_cache
          _SCREAMING_SNAKE_CASE     					=encoder_layers
          _SCREAMING_SNAKE_CASE     					=attention_window

          super().__init__(
              pad_token_id=_a			,      bos_token_id=_a			,      eos_token_id=_a			,      is_encoder_decoder=_a			,      decoder_start_token_id=_a			,      **_a			,      )



import argparse

import torch

from transformers import ChineseCLIPConfig, ChineseCLIPModel







def   lowerCamelCase(				a__		,a__		,a__):
				_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE     					=pt_weights[f"{prefix}.in_proj_weight"].chunk(3		,dim=0)
				_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE     					=pt_weights[f"{prefix}.in_proj_bias"].chunk(3		,dim=0)

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

				_SCREAMING_SNAKE_CASE     					=q_proj
				_SCREAMING_SNAKE_CASE     					=q_proj_bias

				_SCREAMING_SNAKE_CASE     					=k_proj
				_SCREAMING_SNAKE_CASE     					=k_proj_bias

				_SCREAMING_SNAKE_CASE     					=v_proj
				_SCREAMING_SNAKE_CASE     					=v_proj_bias

				_SCREAMING_SNAKE_CASE     					=out_proj_weights
				_SCREAMING_SNAKE_CASE     					=out_proj_bias







def   lowerCamelCase(				a__		,a__		,a__):
				copy_linear(hf_mlp.fca		,a__		,f"{prefix}.c_fc")
				copy_linear(hf_mlp.fca		,a__		,f"{prefix}.c_proj")







def   lowerCamelCase(				a__		,a__		,a__):
				_SCREAMING_SNAKE_CASE     					=pt_weights[f"{prefix}.weight"].data
				_SCREAMING_SNAKE_CASE     					=pt_weights[f"{prefix}.bias"].data







def   lowerCamelCase(				a__		,a__		,a__):
				# copy layer norms
				copy_linear(hf_layer.layer_norma		,a__		,f"{prefix}.ln_1")
				copy_linear(hf_layer.layer_norma		,a__		,f"{prefix}.ln_2")

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

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







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







def   lowerCamelCase(				a__		,a__):
				# copy projection
				_SCREAMING_SNAKE_CASE     					=pt_weights['''text_projection'''].data.T

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







def   lowerCamelCase(				a__		,a__):
				# copy projection
				_SCREAMING_SNAKE_CASE     					=pt_weights['''visual.proj'''].data.T

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

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

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







@torch.no_grad()
def   lowerCamelCase(				a__		,a__		,a__=None):
				assert config_path is not None, "Please specify the ChineseCLIP model config of the corresponding model size."
				_SCREAMING_SNAKE_CASE     					=ChineseCLIPConfig.from_pretrained(a__)

				_SCREAMING_SNAKE_CASE     					=ChineseCLIPModel(a__).eval()

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

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

				hf_model.save_pretrained(a__)


if __name__ == "__main__":
							snake_case_  :     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.'''
							)
							snake_case_  :     List[str]							  =							parser.parse_args()

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