






'''simple docstring'''

import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


SCREAMING_SNAKE_CASE__     =       {
    'sample_size': 3_2,
    'in_channels': 3,
    'out_channels': 3,
    'layers_per_block': 2,
    'num_class_embeds': 1_0_0_0,
    'block_out_channels': [3_2, 6_4],
    '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',
}

SCREAMING_SNAKE_CASE__     =       {
    'sample_size': 6_4,
    'in_channels': 3,
    'out_channels': 3,
    'layers_per_block': 3,
    'num_class_embeds': 1_0_0_0,
    'block_out_channels': [1_9_2, 1_9_2 * 2, 1_9_2 * 3, 1_9_2 * 4],
    'attention_head_dim': 6_4,
    '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',
}

SCREAMING_SNAKE_CASE__     =       {
    'sample_size': 2_5_6,
    'in_channels': 3,
    'out_channels': 3,
    'layers_per_block': 2,
    'num_class_embeds': None,
    'block_out_channels': [2_5_6, 2_5_6, 2_5_6 * 2, 2_5_6 * 2, 2_5_6 * 4, 2_5_6 * 4],
    'attention_head_dim': 6_4,
    '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',
}

SCREAMING_SNAKE_CASE__     =       {
    'num_train_timesteps': 4_0,
    'sigma_min': 0.0_02,
    'sigma_max': 80.0,
}

SCREAMING_SNAKE_CASE__     =       {
    'num_train_timesteps': 2_0_1,
    'sigma_min': 0.0_02,
    'sigma_max': 80.0,
}

SCREAMING_SNAKE_CASE__     =       {
    'num_train_timesteps': 1_5_1,
    'sigma_min': 0.0_02,
    'sigma_max': 80.0,
}







def    lowercase__			(				__UpperCamelCase   )->			Dict:
      if isinstance(__UpperCamelCase		,						__UpperCamelCase   ):
            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__			(				__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase=False   )->			Any:
      UpperCamelCase      = checkpoint[F"{old_prefix}.in_layers.0.weight"]
      UpperCamelCase      = checkpoint[F"{old_prefix}.in_layers.0.bias"]
      UpperCamelCase      = checkpoint[F"{old_prefix}.in_layers.2.weight"]
      UpperCamelCase      = checkpoint[F"{old_prefix}.in_layers.2.bias"]
      UpperCamelCase      = checkpoint[F"{old_prefix}.emb_layers.1.weight"]
      UpperCamelCase      = checkpoint[F"{old_prefix}.emb_layers.1.bias"]
      UpperCamelCase      = checkpoint[F"{old_prefix}.out_layers.0.weight"]
      UpperCamelCase      = checkpoint[F"{old_prefix}.out_layers.0.bias"]
      UpperCamelCase      = checkpoint[F"{old_prefix}.out_layers.3.weight"]
      UpperCamelCase      = checkpoint[F"{old_prefix}.out_layers.3.bias"]

      if has_skip:
            UpperCamelCase      = checkpoint[F"{old_prefix}.skip_connection.weight"]
            UpperCamelCase      = checkpoint[F"{old_prefix}.skip_connection.bias"]

      return new_checkpoint







def    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase=None   )->			List[Any]:
      UpperCamelCase  ,UpperCamelCase  ,UpperCamelCase      = checkpoint[F"{old_prefix}.qkv.weight"].chunk(3		,						dim=0   )
      UpperCamelCase  ,UpperCamelCase  ,UpperCamelCase      = checkpoint[F"{old_prefix}.qkv.bias"].chunk(3		,						dim=0   )

      UpperCamelCase      = checkpoint[F"{old_prefix}.norm.weight"]
      UpperCamelCase      = checkpoint[F"{old_prefix}.norm.bias"]

      UpperCamelCase      = weight_q.squeeze(-1   ).squeeze(-1   )
      UpperCamelCase      = bias_q.squeeze(-1   ).squeeze(-1   )
      UpperCamelCase      = weight_k.squeeze(-1   ).squeeze(-1   )
      UpperCamelCase      = bias_k.squeeze(-1   ).squeeze(-1   )
      UpperCamelCase      = weight_v.squeeze(-1   ).squeeze(-1   )
      UpperCamelCase      = bias_v.squeeze(-1   ).squeeze(-1   )

      UpperCamelCase      = (
          checkpoint[F"{old_prefix}.proj_out.weight"].squeeze(-1   ).squeeze(-1   )
      )
      UpperCamelCase      = checkpoint[F"{old_prefix}.proj_out.bias"].squeeze(-1   ).squeeze(-1   )

      return new_checkpoint







def    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase   )->			Any:
      UpperCamelCase      = torch.load(__UpperCamelCase		,						map_location="""cpu"""   )
      UpperCamelCase      = {}

      UpperCamelCase      = checkpoint["""time_embed.0.weight"""]
      UpperCamelCase      = checkpoint["""time_embed.0.bias"""]
      UpperCamelCase      = checkpoint["""time_embed.2.weight"""]
      UpperCamelCase      = checkpoint["""time_embed.2.bias"""]

      if unet_config["num_class_embeds"] is not None:
            UpperCamelCase      = checkpoint["""label_emb.weight"""]

      UpperCamelCase      = checkpoint["""input_blocks.0.0.weight"""]
      UpperCamelCase      = checkpoint["""input_blocks.0.0.bias"""]

      UpperCamelCase      = unet_config["""down_block_types"""]
      UpperCamelCase      = unet_config["""layers_per_block"""]
      UpperCamelCase      = unet_config["""attention_head_dim"""]
      UpperCamelCase      = unet_config["""block_out_channels"""]
      UpperCamelCase      = 1
      UpperCamelCase      = channels_list[0]

      for i, layer_type in enumerate(__UpperCamelCase   ):
            UpperCamelCase      = channels_list[i]
            UpperCamelCase      = current_channels != prev_channels
            if layer_type == "ResnetDownsampleBlock2D":
                  for j in range(__UpperCamelCase   ):
                        UpperCamelCase      = F"down_blocks.{i}.resnets.{j}"
                        UpperCamelCase      = F"input_blocks.{current_layer}.0"
                        UpperCamelCase      = True if j == 0 and downsample_block_has_skip else False
                        UpperCamelCase      = convert_resnet(__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase		,						has_skip=__UpperCamelCase   )
                        current_layer += 1

            elif layer_type == "AttnDownBlock2D":
                  for j in range(__UpperCamelCase   ):
                        UpperCamelCase      = F"down_blocks.{i}.resnets.{j}"
                        UpperCamelCase      = F"input_blocks.{current_layer}.0"
                        UpperCamelCase      = True if j == 0 and downsample_block_has_skip else False
                        UpperCamelCase      = convert_resnet(__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase		,						has_skip=__UpperCamelCase   )
                        UpperCamelCase      = F"down_blocks.{i}.attentions.{j}"
                        UpperCamelCase      = F"input_blocks.{current_layer}.1"
                        UpperCamelCase      = convert_attention(
                            __UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase   )
                        current_layer += 1

            if i != len(__UpperCamelCase   ) - 1:
                  UpperCamelCase      = F"down_blocks.{i}.downsamplers.0"
                  UpperCamelCase      = F"input_blocks.{current_layer}.0"
                  UpperCamelCase      = convert_resnet(__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase   )
                  current_layer += 1

            UpperCamelCase      = current_channels

      # hardcoded the mid-block for now
      UpperCamelCase      = """mid_block.resnets.0"""
      UpperCamelCase      = """middle_block.0"""
      UpperCamelCase      = convert_resnet(__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase   )
      UpperCamelCase      = """mid_block.attentions.0"""
      UpperCamelCase      = """middle_block.1"""
      UpperCamelCase      = convert_attention(__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase   )
      UpperCamelCase      = """mid_block.resnets.1"""
      UpperCamelCase      = """middle_block.2"""
      UpperCamelCase      = convert_resnet(__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase   )

      UpperCamelCase      = 0
      UpperCamelCase      = unet_config["""up_block_types"""]

      for i, layer_type in enumerate(__UpperCamelCase   ):
            if layer_type == "ResnetUpsampleBlock2D":
                  for j in range(layers_per_block + 1   ):
                        UpperCamelCase      = F"up_blocks.{i}.resnets.{j}"
                        UpperCamelCase      = F"output_blocks.{current_layer}.0"
                        UpperCamelCase      = convert_resnet(__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase		,						has_skip=__UpperCamelCase   )
                        current_layer += 1

                  if i != len(__UpperCamelCase   ) - 1:
                        UpperCamelCase      = F"up_blocks.{i}.upsamplers.0"
                        UpperCamelCase      = F"output_blocks.{current_layer-1}.1"
                        UpperCamelCase      = convert_resnet(__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase   )
            elif layer_type == "AttnUpBlock2D":
                  for j in range(layers_per_block + 1   ):
                        UpperCamelCase      = F"up_blocks.{i}.resnets.{j}"
                        UpperCamelCase      = F"output_blocks.{current_layer}.0"
                        UpperCamelCase      = convert_resnet(__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase		,						has_skip=__UpperCamelCase   )
                        UpperCamelCase      = F"up_blocks.{i}.attentions.{j}"
                        UpperCamelCase      = F"output_blocks.{current_layer}.1"
                        UpperCamelCase      = convert_attention(
                            __UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase   )
                        current_layer += 1

                  if i != len(__UpperCamelCase   ) - 1:
                        UpperCamelCase      = F"up_blocks.{i}.upsamplers.0"
                        UpperCamelCase      = F"output_blocks.{current_layer-1}.2"
                        UpperCamelCase      = convert_resnet(__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase   )

      UpperCamelCase      = checkpoint["""out.0.weight"""]
      UpperCamelCase      = checkpoint["""out.0.bias"""]
      UpperCamelCase      = checkpoint["""out.2.weight"""]
      UpperCamelCase      = checkpoint["""out.2.bias"""]

      return new_checkpoint


if __name__ == "__main__":
     SCREAMING_SNAKE_CASE__     =       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.')

     SCREAMING_SNAKE_CASE__     =       parser.parse_args()
     SCREAMING_SNAKE_CASE__     =       strabool(args.class_cond)

     SCREAMING_SNAKE_CASE__     =       os.path.basename(args.unet_path)
     print(f'Checkpoint: {ckpt_name}')

     # Get U-Net config
     if "imagenet64" in ckpt_name:
          SCREAMING_SNAKE_CASE__     =       IMAGENET_64_UNET_CONFIG
     elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
          SCREAMING_SNAKE_CASE__     =       LSUN_256_UNET_CONFIG
     elif "test" in ckpt_name:
          SCREAMING_SNAKE_CASE__     =       TEST_UNET_CONFIG
     else:
          raise ValueError(f'Checkpoint type {ckpt_name} is not currently supported.')

     if not args.class_cond:
          SCREAMING_SNAKE_CASE__     =       None

     SCREAMING_SNAKE_CASE__     =       con_pt_to_diffuser(args.unet_path, unet_config)

     SCREAMING_SNAKE_CASE__     =       UNetaDModel(**unet_config)
     image_unet.load_state_dict(converted_unet_ckpt)

     # Get scheduler config
     if "cd" in ckpt_name or "test" in ckpt_name:
          SCREAMING_SNAKE_CASE__     =       CD_SCHEDULER_CONFIG
     elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
          SCREAMING_SNAKE_CASE__     =       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)):
          SCREAMING_SNAKE_CASE__     =       CT_LSUN_256_SCHEDULER_CONFIG
     else:
          raise ValueError(f'Checkpoint type {ckpt_name} is not currently supported.')

     SCREAMING_SNAKE_CASE__     =       CMStochasticIterativeScheduler(**scheduler_config)

     SCREAMING_SNAKE_CASE__     =       ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
     consistency_model.save_pretrained(args.dump_path)








'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE__     =       {
    'configuration_nllb_moe': [
        'NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'NllbMoeConfig',
    ]
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     SCREAMING_SNAKE_CASE__     =       [
         'NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST',
         'NllbMoeForConditionalGeneration',
         'NllbMoeModel',
         'NllbMoePreTrainedModel',
         'NllbMoeTop2Router',
         'NllbMoeSparseMLP',
     ]


if TYPE_CHECKING:
     from .configuration_nllb_moe import (
         NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP,
         NllbMoeConfig,
     )

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_nllb_moe import (
              NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST,
              NllbMoeForConditionalGeneration,
              NllbMoeModel,
              NllbMoePreTrainedModel,
              NllbMoeSparseMLP,
              NllbMoeTopaRouter,
          )


else:
     import sys

     SCREAMING_SNAKE_CASE__     =       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
