



import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


a_   :str        =      {
    "sample_size": 32,
    "in_channels": 3,
    "out_channels": 3,
    "layers_per_block": 2,
    "num_class_embeds": 1_000,
    "block_out_channels": [32, 64],
    "attention_head_dim": 8,
    "down_block_types": [
        "ResnetDownsampleBlock2D",
        "AttnDownBlock2D",
    ],
    "up_block_types": [
        "AttnUpBlock2D",
        "ResnetUpsampleBlock2D",
    ],
    "resnet_time_scale_shift": "scale_shift",
    "upsample_type": "resnet",
    "downsample_type": "resnet",
}

a_   :Dict        =      {
    "sample_size": 64,
    "in_channels": 3,
    "out_channels": 3,
    "layers_per_block": 3,
    "num_class_embeds": 1_000,
    "block_out_channels": [192, 192 * 2, 192 * 3, 192 * 4],
    "attention_head_dim": 64,
    "down_block_types": [
        "ResnetDownsampleBlock2D",
        "AttnDownBlock2D",
        "AttnDownBlock2D",
        "AttnDownBlock2D",
    ],
    "up_block_types": [
        "AttnUpBlock2D",
        "AttnUpBlock2D",
        "AttnUpBlock2D",
        "ResnetUpsampleBlock2D",
    ],
    "resnet_time_scale_shift": "scale_shift",
    "upsample_type": "resnet",
    "downsample_type": "resnet",
}

a_   :Union[str, Any]        =      {
    "sample_size": 256,
    "in_channels": 3,
    "out_channels": 3,
    "layers_per_block": 2,
    "num_class_embeds": None,
    "block_out_channels": [256, 256, 256 * 2, 256 * 2, 256 * 4, 256 * 4],
    "attention_head_dim": 64,
    "down_block_types": [
        "ResnetDownsampleBlock2D",
        "ResnetDownsampleBlock2D",
        "ResnetDownsampleBlock2D",
        "AttnDownBlock2D",
        "AttnDownBlock2D",
        "AttnDownBlock2D",
    ],
    "up_block_types": [
        "AttnUpBlock2D",
        "AttnUpBlock2D",
        "AttnUpBlock2D",
        "ResnetUpsampleBlock2D",
        "ResnetUpsampleBlock2D",
        "ResnetUpsampleBlock2D",
    ],
    "resnet_time_scale_shift": "default",
    "upsample_type": "resnet",
    "downsample_type": "resnet",
}

a_   :int        =      {
    "num_train_timesteps": 40,
    "sigma_min": 0.0_02,
    "sigma_max": 80.0,
}

a_   :int        =      {
    "num_train_timesteps": 201,
    "sigma_min": 0.0_02,
    "sigma_max": 80.0,
}

a_   :Dict        =      {
    "num_train_timesteps": 151,
    "sigma_min": 0.0_02,
    "sigma_max": 80.0,
}
def        lowercase_							(A : Optional[int]      ):
       if isinstance(A		,					A      ):
              return v
       if v.lower() in ("yes", "true", "t", "y", "1"):
              return True
       elif v.lower() in ("no", "false", "f", "n", "0"):
              return False
       else:
              raise argparse.ArgumentTypeError('boolean value expected'      )
def        lowercase_							(A : Union[str, Any]		,					A : List[str]		,					A : str		,					A : Union[str, Any]		,					A : Any=False      ):
       snake_case__							:	List[str]   	=	checkpoint[F'''{old_prefix}.in_layers.0.weight''']
       snake_case__							:	Dict   	=	checkpoint[F'''{old_prefix}.in_layers.0.bias''']
       snake_case__							:	Any   	=	checkpoint[F'''{old_prefix}.in_layers.2.weight''']
       snake_case__							:	Tuple   	=	checkpoint[F'''{old_prefix}.in_layers.2.bias''']
       snake_case__							:	List[str]   	=	checkpoint[F'''{old_prefix}.emb_layers.1.weight''']
       snake_case__							:	Optional[Any]   	=	checkpoint[F'''{old_prefix}.emb_layers.1.bias''']
       snake_case__							:	Optional[int]   	=	checkpoint[F'''{old_prefix}.out_layers.0.weight''']
       snake_case__							:	Optional[Any]   	=	checkpoint[F'''{old_prefix}.out_layers.0.bias''']
       snake_case__							:	Tuple   	=	checkpoint[F'''{old_prefix}.out_layers.3.weight''']
       snake_case__							:	List[Any]   	=	checkpoint[F'''{old_prefix}.out_layers.3.bias''']

       if has_skip:
              snake_case__							:	Union[str, Any]   	=	checkpoint[F'''{old_prefix}.skip_connection.weight''']
              snake_case__							:	Any   	=	checkpoint[F'''{old_prefix}.skip_connection.bias''']

       return new_checkpoint
def        lowercase_							(A : Union[str, Any]		,					A : List[str]		,					A : Optional[Any]		,					A : Optional[Any]		,					A : List[Any]=None      ):
       snake_case__	,				snake_case__	,				snake_case__							:	Optional[Any]   	=	checkpoint[F'''{old_prefix}.qkv.weight'''].chunk(3		,					dim=0      )
       snake_case__	,				snake_case__	,				snake_case__							:	List[str]   	=	checkpoint[F'''{old_prefix}.qkv.bias'''].chunk(3		,					dim=0      )

       snake_case__							:	Tuple   	=	checkpoint[F'''{old_prefix}.norm.weight''']
       snake_case__							:	Union[str, Any]   	=	checkpoint[F'''{old_prefix}.norm.bias''']

       snake_case__							:	Union[str, Any]   	=	weight_q.squeeze(-1      ).squeeze(-1      )
       snake_case__							:	int   	=	bias_q.squeeze(-1      ).squeeze(-1      )
       snake_case__							:	str   	=	weight_k.squeeze(-1      ).squeeze(-1      )
       snake_case__							:	int   	=	bias_k.squeeze(-1      ).squeeze(-1      )
       snake_case__							:	Optional[Any]   	=	weight_v.squeeze(-1      ).squeeze(-1      )
       snake_case__							:	Union[str, Any]   	=	bias_v.squeeze(-1      ).squeeze(-1      )

       snake_case__							:	Tuple   	=	(
           checkpoint[F'''{old_prefix}.proj_out.weight'''].squeeze(-1      ).squeeze(-1      )
       )
       snake_case__							:	Optional[Any]   	=	checkpoint[F'''{old_prefix}.proj_out.bias'''].squeeze(-1      ).squeeze(-1      )

       return new_checkpoint
def        lowercase_							(A : str		,					A : str      ):
       snake_case__							:	Union[str, Any]   	=	torch.load(A		,					map_location='cpu'      )
       snake_case__							:	List[Any]   	=	{}

       snake_case__							:	str   	=	checkpoint['time_embed.0.weight']
       snake_case__							:	Tuple   	=	checkpoint['time_embed.0.bias']
       snake_case__							:	str   	=	checkpoint['time_embed.2.weight']
       snake_case__							:	Any   	=	checkpoint['time_embed.2.bias']

       if unet_config["num_class_embeds"] is not None:
              snake_case__							:	Tuple   	=	checkpoint['label_emb.weight']

       snake_case__							:	Any   	=	checkpoint['input_blocks.0.0.weight']
       snake_case__							:	Tuple   	=	checkpoint['input_blocks.0.0.bias']

       snake_case__							:	List[str]   	=	unet_config['down_block_types']
       snake_case__							:	Any   	=	unet_config['layers_per_block']
       snake_case__							:	str   	=	unet_config['attention_head_dim']
       snake_case__							:	Tuple   	=	unet_config['block_out_channels']
       snake_case__							:	str   	=	1
       snake_case__							:	str   	=	channels_list[0]

       for i, layer_type in enumerate(A      ):
              snake_case__							:	Dict   	=	channels_list[i]
              snake_case__							:	Optional[int]   	=	current_channels != prev_channels
              if layer_type == "ResnetDownsampleBlock2D":
                     for j in range(A      ):
                            snake_case__							:	Any   	=	F'''down_blocks.{i}.resnets.{j}'''
                            snake_case__							:	str   	=	F'''input_blocks.{current_layer}.0'''
                            snake_case__							:	List[str]   	=	True if j == 0 and downsample_block_has_skip else False
                            snake_case__							:	str   	=	convert_resnet(A		,					A		,					A		,					A		,					has_skip=A      )
                            current_layer += 1

              elif layer_type == "AttnDownBlock2D":
                     for j in range(A      ):
                            snake_case__							:	Union[str, Any]   	=	F'''down_blocks.{i}.resnets.{j}'''
                            snake_case__							:	Dict   	=	F'''input_blocks.{current_layer}.0'''
                            snake_case__							:	Optional[int]   	=	True if j == 0 and downsample_block_has_skip else False
                            snake_case__							:	Any   	=	convert_resnet(A		,					A		,					A		,					A		,					has_skip=A      )
                            snake_case__							:	Any   	=	F'''down_blocks.{i}.attentions.{j}'''
                            snake_case__							:	str   	=	F'''input_blocks.{current_layer}.1'''
                            snake_case__							:	Optional[int]   	=	convert_attention(
                                A		,					A		,					A		,					A		,					A      )
                            current_layer += 1

              if i != len(A      ) - 1:
                     snake_case__							:	Dict   	=	F'''down_blocks.{i}.downsamplers.0'''
                     snake_case__							:	str   	=	F'''input_blocks.{current_layer}.0'''
                     snake_case__							:	Union[str, Any]   	=	convert_resnet(A		,					A		,					A		,					A      )
                     current_layer += 1

              snake_case__							:	List[Any]   	=	current_channels

       # hardcoded the mid-block for now
       snake_case__							:	int   	=	'mid_block.resnets.0'
       snake_case__							:	Union[str, Any]   	=	'middle_block.0'
       snake_case__							:	Optional[Any]   	=	convert_resnet(A		,					A		,					A		,					A      )
       snake_case__							:	Union[str, Any]   	=	'mid_block.attentions.0'
       snake_case__							:	Any   	=	'middle_block.1'
       snake_case__							:	str   	=	convert_attention(A		,					A		,					A		,					A		,					A      )
       snake_case__							:	Dict   	=	'mid_block.resnets.1'
       snake_case__							:	Any   	=	'middle_block.2'
       snake_case__							:	Union[str, Any]   	=	convert_resnet(A		,					A		,					A		,					A      )

       snake_case__							:	List[Any]   	=	0
       snake_case__							:	str   	=	unet_config['up_block_types']

       for i, layer_type in enumerate(A      ):
              if layer_type == "ResnetUpsampleBlock2D":
                     for j in range(layers_per_block + 1      ):
                            snake_case__							:	List[Any]   	=	F'''up_blocks.{i}.resnets.{j}'''
                            snake_case__							:	Any   	=	F'''output_blocks.{current_layer}.0'''
                            snake_case__							:	Optional[int]   	=	convert_resnet(A		,					A		,					A		,					A		,					has_skip=A      )
                            current_layer += 1

                     if i != len(A      ) - 1:
                            snake_case__							:	Tuple   	=	F'''up_blocks.{i}.upsamplers.0'''
                            snake_case__							:	List[Any]   	=	F'''output_blocks.{current_layer-1}.1'''
                            snake_case__							:	Any   	=	convert_resnet(A		,					A		,					A		,					A      )
              elif layer_type == "AttnUpBlock2D":
                     for j in range(layers_per_block + 1      ):
                            snake_case__							:	Any   	=	F'''up_blocks.{i}.resnets.{j}'''
                            snake_case__							:	Tuple   	=	F'''output_blocks.{current_layer}.0'''
                            snake_case__							:	Tuple   	=	convert_resnet(A		,					A		,					A		,					A		,					has_skip=A      )
                            snake_case__							:	Union[str, Any]   	=	F'''up_blocks.{i}.attentions.{j}'''
                            snake_case__							:	Optional[Any]   	=	F'''output_blocks.{current_layer}.1'''
                            snake_case__							:	int   	=	convert_attention(
                                A		,					A		,					A		,					A		,					A      )
                            current_layer += 1

                     if i != len(A      ) - 1:
                            snake_case__							:	Optional[int]   	=	F'''up_blocks.{i}.upsamplers.0'''
                            snake_case__							:	List[Any]   	=	F'''output_blocks.{current_layer-1}.2'''
                            snake_case__							:	List[Any]   	=	convert_resnet(A		,					A		,					A		,					A      )

       snake_case__							:	int   	=	checkpoint['out.0.weight']
       snake_case__							:	str   	=	checkpoint['out.0.bias']
       snake_case__							:	List[Any]   	=	checkpoint['out.2.weight']
       snake_case__							:	Optional[Any]   	=	checkpoint['out.2.bias']

       return new_checkpoint


if __name__ == "__main__":
 a_   :Any        =      argparse.ArgumentParser()

 parser.add_argument("--unet_path", default=None, type=str, required=True, help="Path to the unet.pt to convert.")
 parser.add_argument(
     "--dump_path", default=None, type=str, required=True, help="Path to output the converted UNet model."
 )
 parser.add_argument("--class_cond", default=True, type=str, help="Whether the model is class-conditional.")

 a_   :Any        =      parser.parse_args()
 a_   :Optional[Any]        =      strabool(args.class_cond)

 a_   :Optional[Any]        =      os.path.basename(args.unet_path)
 print(F"""Checkpoint: {ckpt_name}""")

 # Get U-Net config
 if "imagenet64" in ckpt_name:
  a_   :str        =      IMAGENET_64_UNET_CONFIG
 elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
  a_   :Optional[int]        =      LSUN_256_UNET_CONFIG
 elif "test" in ckpt_name:
  a_   :Dict        =      TEST_UNET_CONFIG
 else:
  raise ValueError(F"""Checkpoint type {ckpt_name} is not currently supported.""")

 if not args.class_cond:
  a_   :List[Any]        =      None

 a_   :int        =      con_pt_to_diffuser(args.unet_path, unet_config)

 a_   :List[Any]        =      UNetaDModel(**unet_config)
 image_unet.load_state_dict(converted_unet_ckpt)

 # Get scheduler config
 if "cd" in ckpt_name or "test" in ckpt_name:
  a_   :Tuple        =      CD_SCHEDULER_CONFIG
 elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
  a_   :List[str]        =      CT_IMAGENET_64_SCHEDULER_CONFIG
 elif "ct" in ckpt_name and "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
  a_   :List[Any]        =      CT_LSUN_256_SCHEDULER_CONFIG
 else:
  raise ValueError(F"""Checkpoint type {ckpt_name} is not currently supported.""")

 a_   :List[Any]        =      CMStochasticIterativeScheduler(**scheduler_config)

 a_   :Any        =      ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
 consistency_model.save_pretrained(args.dump_path)











import argparse

import torch

from transformers import LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert
from transformers.utils import logging


logging.set_verbosity_info()
def        lowercase_							(A : str		,					A : List[Any]		,					A : Any      ):
       # Initialise PyTorch model
       snake_case__							:	List[Any]   	=	LxmertConfig.from_json_file(A      )
       print(F'''Building PyTorch model from configuration: {config}'''      )
       snake_case__							:	List[str]   	=	LxmertForPreTraining(A      )

       # Load weights from tf checkpoint
       load_tf_weights_in_lxmert(A		,					A		,					A      )

       # Save pytorch-model
       print(F'''Save PyTorch model to {pytorch_dump_path}'''      )
       torch.save(model.state_dict()		,					A      )


if __name__ == "__main__":
 a_   :Union[str, Any]        =      argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
 )
 parser.add_argument(
     "--config_file",
     default=None,
     type=str,
     required=True,
     help="The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.",
 )
 parser.add_argument(
     "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
 )
 a_   :Optional[int]        =      parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)






