import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


UpperCAmelCase      		=					logging.get_logger(__name__)

UpperCAmelCase      		=					{
    '''shi-labs/oneformer_ade20k_swin_tiny''': (
        '''https://huggingface.co/shi-labs/oneformer_ade20k_swin_tiny/blob/main/config.json'''
    ),
    # See all OneFormer models at https://huggingface.co/models?filter=oneformer
}




class  __lowerCAmelCase	(							a_    ):



 '''simple docstring'''
 _lowerCamelCase    :						Tuple					     =			'oneformer'
 _lowerCamelCase    :						Optional[int]					     =			{'hidden_size': 'hidden_dim'}

 def __init__(      self							:		str							,      lowerCamelCase							:		Optional[Dict] = None							,      lowerCamelCase							:		int = 2_5_5							,      lowerCamelCase							:		int = 1_5_0							,      lowerCamelCase							:		int = 0.1							,      lowerCamelCase							:		float = 2.0							,      lowerCamelCase							:		float = 5.0							,      lowerCamelCase							:		float = 5.0							,      lowerCamelCase							:		float = 0.5							,      lowerCamelCase							:		float = 0.0_7							,      lowerCamelCase							:		int = 1_2_5_4_4							,      lowerCamelCase							:		float = 3.0							,      lowerCamelCase							:		float = 0.7_5							,      lowerCamelCase							:		float = 0.0_2							,      lowerCamelCase							:		float = 1.0							,      lowerCamelCase							:		float = 1e-05							,      lowerCamelCase							:		bool = False							,      lowerCamelCase							:		bool = True							,      lowerCamelCase							:		bool = True							,      lowerCamelCase							:		Optional[list] = [4, 8, 1_6, 3_2]							,      lowerCamelCase							:		int = 7_7							,      lowerCamelCase							:		int = 2_5_6							,      lowerCamelCase							:		int = 7_7							,      lowerCamelCase							:		int = 6							,      lowerCamelCase							:		int = 4_9_4_0_8							,      lowerCamelCase							:		int = 2							,      lowerCamelCase							:		int = 1_6							,      lowerCamelCase							:		int = 2_5_6							,      lowerCamelCase							:		int = 2_5_6							,      lowerCamelCase							:		int = 2_5_6							,      lowerCamelCase							:		int = 1_0_2_4							,      lowerCamelCase							:		str = "GN"							,      lowerCamelCase							:		int = 6							,      lowerCamelCase							:		int = 1_0							,      lowerCamelCase							:		bool = True							,      lowerCamelCase							:		int = 8							,      lowerCamelCase							:		float = 0.1							,      lowerCamelCase							:		int = 2_0_4_8							,      lowerCamelCase							:		bool = False							,      lowerCamelCase							:		bool = False							,      lowerCamelCase							:		int = 2							,      lowerCamelCase							:		int = 4							,      **lowerCamelCase							:		Union[str, Any]							,      )			->				Optional[Any]:
        if backbone_config is None:
               logger.info('`backbone_config` is unset. Initializing the config with the default `Swin` backbone.'	)
               _UpperCamelCase  :							Union[str, Any]	    =	CONFIG_MAPPING['swin'](
                   image_size=2_2_4							,      in_channels=3							,      patch_size=4							,      embed_dim=9_6							,      depths=[2, 2, 6, 2]							,      num_heads=[3, 6, 1_2, 2_4]							,      window_size=7							,      drop_path_rate=0.3							,      use_absolute_embeddings=lowerCamelCase							,      out_features=['stage1', 'stage2', 'stage3', 'stage4']							,      )
        elif isinstance(lowerCamelCase							,      lowerCamelCase	):
               _UpperCamelCase  :							int	    =	backbone_config.get('model_type'	)
               _UpperCamelCase  :							Optional[int]	    =	CONFIG_MAPPING[backbone_model_type]
               _UpperCamelCase  :							List[str]	    =	config_class.from_dict(lowerCamelCase	)

        _UpperCamelCase  :							Optional[int]	    =	backbone_config

        _UpperCamelCase  :							Dict	    =	ignore_value
        _UpperCamelCase  :							Optional[Any]	    =	num_queries
        _UpperCamelCase  :							int	    =	no_object_weight
        _UpperCamelCase  :							Optional[int]	    =	class_weight
        _UpperCamelCase  :							List[str]	    =	mask_weight
        _UpperCamelCase  :							Tuple	    =	dice_weight
        _UpperCamelCase  :							int	    =	contrastive_weight
        _UpperCamelCase  :							Tuple	    =	contrastive_temperature
        _UpperCamelCase  :							Tuple	    =	train_num_points
        _UpperCamelCase  :							List[Any]	    =	oversample_ratio
        _UpperCamelCase  :							int	    =	importance_sample_ratio
        _UpperCamelCase  :							List[Any]	    =	init_std
        _UpperCamelCase  :							Union[str, Any]	    =	init_xavier_std
        _UpperCamelCase  :							List[str]	    =	layer_norm_eps
        _UpperCamelCase  :							Any	    =	is_training
        _UpperCamelCase  :							Any	    =	use_auxiliary_loss
        _UpperCamelCase  :							Tuple	    =	output_auxiliary_logits
        _UpperCamelCase  :							Optional[Any]	    =	strides
        _UpperCamelCase  :							Optional[int]	    =	task_seq_len
        _UpperCamelCase  :							Tuple	    =	text_encoder_width
        _UpperCamelCase  :							Optional[int]	    =	text_encoder_context_length
        _UpperCamelCase  :							Dict	    =	text_encoder_num_layers
        _UpperCamelCase  :							Optional[int]	    =	text_encoder_vocab_size
        _UpperCamelCase  :							int	    =	text_encoder_proj_layers
        _UpperCamelCase  :							Dict	    =	text_encoder_n_ctx
        _UpperCamelCase  :							int	    =	conv_dim
        _UpperCamelCase  :							Optional[Any]	    =	mask_dim
        _UpperCamelCase  :							Optional[Any]	    =	hidden_dim
        _UpperCamelCase  :							Tuple	    =	encoder_feedforward_dim
        _UpperCamelCase  :							int	    =	norm
        _UpperCamelCase  :							List[str]	    =	encoder_layers
        _UpperCamelCase  :							List[Any]	    =	decoder_layers
        _UpperCamelCase  :							List[str]	    =	use_task_norm
        _UpperCamelCase  :							Tuple	    =	num_attention_heads
        _UpperCamelCase  :							Dict	    =	dropout
        _UpperCamelCase  :							Union[str, Any]	    =	dim_feedforward
        _UpperCamelCase  :							Union[str, Any]	    =	pre_norm
        _UpperCamelCase  :							Union[str, Any]	    =	enforce_input_proj
        _UpperCamelCase  :							Optional[int]	    =	query_dec_layers
        _UpperCamelCase  :							Dict	    =	common_stride
        _UpperCamelCase  :							str	    =	decoder_layers

        super().__init__(**lowerCamelCase	)

 def        __snake_case       (      self							:		Union[str, Any]	)			->				Dict[str, any]:
        _UpperCamelCase  :							Any	    =	copy.deepcopy(self.__dict__	)
        _UpperCamelCase  :							int	    =	self.backbone_config.to_dict()
        _UpperCamelCase  :							Optional[Any]	    =	self.__class__.model_type
        return output




import argparse

import torch

from transformers import ChineseCLIPConfig, ChineseCLIPModel


def SCREAMING_SNAKE_CASE					(  lowercase_:						List[str]		,    lowercase_:						Dict		,    lowercase_:						List[Any]					):
       _UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase  :							List[Any]	    =	pt_weights[F"{prefix}.in_proj_weight"].chunk(3		,    dim=0					)
       _UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase  :							Dict	    =	pt_weights[F"{prefix}.in_proj_bias"].chunk(3		,    dim=0					)

       _UpperCamelCase  :							List[str]	    =	pt_weights[F"{prefix}.out_proj.weight"]
       _UpperCamelCase  :							Any	    =	pt_weights[F"{prefix}.out_proj.bias"]

       _UpperCamelCase  :							List[Any]	    =	q_proj
       _UpperCamelCase  :							Union[str, Any]	    =	q_proj_bias

       _UpperCamelCase  :							str	    =	k_proj
       _UpperCamelCase  :							List[str]	    =	k_proj_bias

       _UpperCamelCase  :							Dict	    =	v_proj
       _UpperCamelCase  :							str	    =	v_proj_bias

       _UpperCamelCase  :							List[str]	    =	out_proj_weights
       _UpperCamelCase  :							Any	    =	out_proj_bias


def SCREAMING_SNAKE_CASE					(  lowercase_:						List[str]		,    lowercase_:						List[Any]		,    lowercase_:						Any					):
       copy_linear(hf_mlp.fca		,    lowercase_		,    F"{prefix}.c_fc"					)
       copy_linear(hf_mlp.fca		,    lowercase_		,    F"{prefix}.c_proj"					)


def SCREAMING_SNAKE_CASE					(  lowercase_:						Optional[Any]		,    lowercase_:						List[str]		,    lowercase_:						Any					):
       _UpperCamelCase  :							Dict	    =	pt_weights[F"{prefix}.weight"].data
       _UpperCamelCase  :							List[Any]	    =	pt_weights[F"{prefix}.bias"].data


def SCREAMING_SNAKE_CASE					(  lowercase_:						Any		,    lowercase_:						Optional[int]		,    lowercase_:						Union[str, Any]					):
       # copy layer norms
       copy_linear(hf_layer.layer_norma		,    lowercase_		,    F"{prefix}.ln_1"					)
       copy_linear(hf_layer.layer_norma		,    lowercase_		,    F"{prefix}.ln_2"					)

       # copy MLP
       copy_mlp(hf_layer.mlp		,    lowercase_		,    F"{prefix}.mlp"					)

       # copy attn
       copy_attn_layer(hf_layer.self_attn		,    lowercase_		,    F"{prefix}.attn"					)


def SCREAMING_SNAKE_CASE					(  lowercase_:						str		,    lowercase_:						str		,    lowercase_:						int					):
       for layer_id, hf_layer in enumerate(lowercase_					):
              copy_layer(lowercase_		,    lowercase_		,    F"{prefix}.{layer_id}"					)


def SCREAMING_SNAKE_CASE					(  lowercase_:						Union[str, Any]		,    lowercase_:						List[Any]					):
       # copy projection
       _UpperCamelCase  :							List[str]	    =	pt_weights['text_projection'].data.T

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


def SCREAMING_SNAKE_CASE					(  lowercase_:						str		,    lowercase_:						Any					):
       # copy projection
       _UpperCamelCase  :							List[Any]	    =	pt_weights['visual.proj'].data.T

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

       # copy embeddings
       _UpperCamelCase  :							List[Any]	    =	pt_weights['visual.conv1.weight'].data
       _UpperCamelCase  :							List[Any]	    =	pt_weights['visual.class_embedding'].data
       _UpperCamelCase  :							int	    =	pt_weights['visual.positional_embedding'].data

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


@torch.no_grad()
def SCREAMING_SNAKE_CASE					(  lowercase_:						Optional[Any]		,    lowercase_:						Optional[int]		,    lowercase_:						List[Any]=None					):
       assert config_path is not None, "Please specify the ChineseCLIP model config of the corresponding model size."
       _UpperCamelCase  :							Optional[Any]	    =	ChineseCLIPConfig.from_pretrained(lowercase_					)

       _UpperCamelCase  :							List[Any]	    =	ChineseCLIPModel(lowercase_					).eval()

       _UpperCamelCase  :							List[Any]	    =	torch.load(lowercase_		,    map_location='cpu'					)['state_dict']
       _UpperCamelCase  :							Union[str, Any]	    =	{(name[7:] if name.startswith('module.'					) else name): value for name, value in pt_weights.items()}

       copy_text_model_and_projection(lowercase_		,    lowercase_					)
       copy_vision_model_and_projection(lowercase_		,    lowercase_					)
       _UpperCamelCase  :							List[Any]	    =	pt_weights['logit_scale'].data

       hf_model.save_pretrained(lowercase_					)


if __name__ == "__main__":
 UpperCAmelCase      		=					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.'''
 )
 UpperCAmelCase      		=					parser.parse_args()

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



