import argparse

import torch

from transformers import ChineseCLIPConfig, ChineseCLIPModel






def  UpperCAmelCase__(    lowerCamelCase_				,						lowerCamelCase_				,						lowerCamelCase_	):



			"""simple docstring"""





			_snake_case							,			_snake_case							,			_snake_case														=   pt_weights[f'{prefix}.in_proj_weight'].chunk(3				,						dim=0	)
			_snake_case							,			_snake_case							,			_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  UpperCAmelCase__(    lowerCamelCase_				,						lowerCamelCase_				,						lowerCamelCase_	):



			"""simple docstring"""





			copy_linear(hf_mlp.fca				,						lowerCamelCase_				,						f'{prefix}.c_fc'	)
			copy_linear(hf_mlp.fca				,						lowerCamelCase_				,						f'{prefix}.c_proj'	)






def  UpperCAmelCase__(    lowerCamelCase_				,						lowerCamelCase_				,						lowerCamelCase_	):



			"""simple docstring"""





			_snake_case														=   pt_weights[f'{prefix}.weight'].data
			_snake_case														=   pt_weights[f'{prefix}.bias'].data






def  UpperCAmelCase__(    lowerCamelCase_				,						lowerCamelCase_				,						lowerCamelCase_	):



			"""simple docstring"""





			copy_linear(hf_layer.layer_norma				,						lowerCamelCase_				,						f'{prefix}.ln_1'	)
			copy_linear(hf_layer.layer_norma				,						lowerCamelCase_				,						f'{prefix}.ln_2'	)

			# copy MLP
			copy_mlp(hf_layer.mlp				,						lowerCamelCase_				,						f'{prefix}.mlp'	)

			# copy attn
			copy_attn_layer(hf_layer.self_attn				,						lowerCamelCase_				,						f'{prefix}.attn'	)






def  UpperCAmelCase__(    lowerCamelCase_				,						lowerCamelCase_				,						lowerCamelCase_	):



			"""simple docstring"""





			for layer_id, hf_layer in enumerate(lowerCamelCase_	):
						copy_layer(lowerCamelCase_				,						lowerCamelCase_				,						f'{prefix}.{layer_id}'	)






def  UpperCAmelCase__(    lowerCamelCase_				,						lowerCamelCase_	):



			"""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  UpperCAmelCase__(    lowerCamelCase_				,						lowerCamelCase_	):



			"""simple docstring"""





			_snake_case														=   pt_weights["visual.proj"].data.T

			# copy layer norms
			copy_linear(hf_model.vision_model.pre_layrnorm				,						lowerCamelCase_				,						"visual.ln_pre"	)
			copy_linear(hf_model.vision_model.post_layernorm				,						lowerCamelCase_				,						"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				,						lowerCamelCase_				,						"visual.transformer.resblocks"	)






@torch.no_grad()
def  UpperCAmelCase__(    lowerCamelCase_				,						lowerCamelCase_				,						lowerCamelCase_=None	):



			"""simple docstring"""





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

			_snake_case														=   ChineseCLIPModel(lowerCamelCase_	).eval()

			_snake_case														=   torch.load(lowerCamelCase_				,						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(lowerCamelCase_				,						lowerCamelCase_	)
			copy_vision_model_and_projection(lowerCamelCase_				,						lowerCamelCase_	)
			_snake_case														=   pt_weights["logit_scale"].data

			hf_model.save_pretrained(lowerCamelCase_	)


if __name__ == "__main__":
			_SCREAMING_SNAKE_CASE:    Dict     						=							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.'''
			)
			_SCREAMING_SNAKE_CASE:    Any     						=							parser.parse_args()

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


def  UpperCAmelCase__(    lowerCamelCase_	):



			"""simple docstring"""





			_snake_case														=   len(lowerCamelCase_	)
			_snake_case														=   [0] * length
			_snake_case														=   [[False for i in range(lowerCamelCase_	)] for j in range(lowerCamelCase_	)]
			for i, c in enumerate(lowerCamelCase_	):
						_snake_case														=   i
						for j in range(i + 1	):
									if c == string[j] and (i - j < 2 or is_palindromic[j + 1][i - 1]):
												_snake_case														=   True
												_snake_case														=   min(lowerCamelCase_				,						0 if j == 0 else (cut[j - 1] + 1)	)
						_snake_case														=   mincut
			return cut[length - 1]


if __name__ == "__main__":
			_SCREAMING_SNAKE_CASE:    Dict     						=							input('''Enter the string: ''').strip()
			_SCREAMING_SNAKE_CASE:    str     						=							find_minimum_partitions(s)
			print(F"Minimum number of partitions required for the '{s}' is {ans}")

