import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


__lowerCamelCase      :			Optional[int]							       =		{
    '''sample_size''': 32,
    '''in_channels''': 3,
    '''out_channels''': 3,
    '''layers_per_block''': 2,
    '''num_class_embeds''': 1000,
    '''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''',
}

__lowerCamelCase      :			Any							       =		{
    '''sample_size''': 64,
    '''in_channels''': 3,
    '''out_channels''': 3,
    '''layers_per_block''': 3,
    '''num_class_embeds''': 1000,
    '''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''',
}

__lowerCamelCase      :			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''',
}

__lowerCamelCase      :			Tuple							       =		{
    '''num_train_timesteps''': 40,
    '''sigma_min''': 0.002,
    '''sigma_max''': 80.0,
}

__lowerCamelCase      :			Tuple							       =		{
    '''num_train_timesteps''': 201,
    '''sigma_min''': 0.002,
    '''sigma_max''': 80.0,
}

__lowerCamelCase      :			Optional[int]							       =		{
    '''num_train_timesteps''': 151,
    '''sigma_min''': 0.002,
    '''sigma_max''': 80.0,
}



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	int     )							-> List[str]:







  """simple docstring"""





  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      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	Optional[int]			,					__UpperCamelCase   :	Dict			,					__UpperCamelCase   :	int			,					__UpperCamelCase   :	Union[str, Any]			,					__UpperCamelCase   :	Dict=False     )							-> Dict:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				checkpoint[f"""{old_prefix}.in_layers.0.weight"""]
  SCREAMING_SNAKE_CASE__      							=				checkpoint[f"""{old_prefix}.in_layers.0.bias"""]
  SCREAMING_SNAKE_CASE__      							=				checkpoint[f"""{old_prefix}.in_layers.2.weight"""]
  SCREAMING_SNAKE_CASE__      							=				checkpoint[f"""{old_prefix}.in_layers.2.bias"""]
  SCREAMING_SNAKE_CASE__      							=				checkpoint[f"""{old_prefix}.emb_layers.1.weight"""]
  SCREAMING_SNAKE_CASE__      							=				checkpoint[f"""{old_prefix}.emb_layers.1.bias"""]
  SCREAMING_SNAKE_CASE__      							=				checkpoint[f"""{old_prefix}.out_layers.0.weight"""]
  SCREAMING_SNAKE_CASE__      							=				checkpoint[f"""{old_prefix}.out_layers.0.bias"""]
  SCREAMING_SNAKE_CASE__      							=				checkpoint[f"""{old_prefix}.out_layers.3.weight"""]
  SCREAMING_SNAKE_CASE__      							=				checkpoint[f"""{old_prefix}.out_layers.3.bias"""]

  if has_skip:
    SCREAMING_SNAKE_CASE__      							=				checkpoint[f"""{old_prefix}.skip_connection.weight"""]
    SCREAMING_SNAKE_CASE__      							=				checkpoint[f"""{old_prefix}.skip_connection.bias"""]

  return new_checkpoint



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	Any			,					__UpperCamelCase   :	List[Any]			,					__UpperCamelCase   :	Dict			,					__UpperCamelCase   :	Any			,					__UpperCamelCase   :	str=None     )							-> Optional[int]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      							=				checkpoint[f"""{old_prefix}.qkv.weight"""].chunk(3			,					dim=0     )
  SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      							=				checkpoint[f"""{old_prefix}.qkv.bias"""].chunk(3			,					dim=0     )

  SCREAMING_SNAKE_CASE__      							=				checkpoint[f"""{old_prefix}.norm.weight"""]
  SCREAMING_SNAKE_CASE__      							=				checkpoint[f"""{old_prefix}.norm.bias"""]

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

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

  return new_checkpoint



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	str			,					__UpperCamelCase   :	List[str]     )							-> Tuple:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				torch.load(__UpperCamelCase			,					map_location="""cpu"""     )
  SCREAMING_SNAKE_CASE__      							=				{}

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

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

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

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

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

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

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

    SCREAMING_SNAKE_CASE__      							=				current_channels

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

  SCREAMING_SNAKE_CASE__      							=				0
  SCREAMING_SNAKE_CASE__      							=				unet_config["""up_block_types"""]

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

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

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

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

  return new_checkpoint


if __name__ == "__main__":
   __lowerCamelCase      :			Dict							       =		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.''')

   __lowerCamelCase      :			int							       =		parser.parse_args()
   __lowerCamelCase      :			List[Any]							       =		strabool(args.class_cond)

   __lowerCamelCase      :			List[str]							       =		os.path.basename(args.unet_path)
   print(F"""Checkpoint: {ckpt_name}""")

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

   if not args.class_cond:
      __lowerCamelCase      :			Union[str, Any]							       =		None

   __lowerCamelCase      :			Union[str, Any]							       =		con_pt_to_diffuser(args.unet_path, unet_config)

   __lowerCamelCase      :			Dict							       =		UNetaDModel(**unet_config)
   image_unet.load_state_dict(converted_unet_ckpt)

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

   __lowerCamelCase      :			Optional[Any]							       =		CMStochasticIterativeScheduler(**scheduler_config)

   __lowerCamelCase      :			Union[str, Any]							       =		ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
   consistency_model.save_pretrained(args.dump_path)

import warnings

from ...utils import logging
from .image_processing_donut import DonutImageProcessor


__lowerCamelCase      :			str							       =		logging.get_logger(__name__)







class __snake_case    (						lowerCamelCase_		):




   def __init__(   self      :    List[str]	,     *_lowercase      :    Union[str, Any]	,     **_lowercase      :    Union[str, Any]				):



     """simple docstring"""






     warnings.warn(
         """The class DonutFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
         """ use DonutImageProcessor instead."""	,     _lowercase	,     )
     super().__init__(*_lowercase	,     **_lowercase				)
