import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


SCREAMING_SNAKE_CASE			:						Any				  =		{
    """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""",
}

SCREAMING_SNAKE_CASE			:						Dict				  =		{
    """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""",
}

SCREAMING_SNAKE_CASE			:						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""",
}

SCREAMING_SNAKE_CASE			:						str				  =		{
    """num_train_timesteps""": 40,
    """sigma_min""": 0.0_0_2,
    """sigma_max""": 8_0.0,
}

SCREAMING_SNAKE_CASE			:						Dict				  =		{
    """num_train_timesteps""": 201,
    """sigma_min""": 0.0_0_2,
    """sigma_max""": 8_0.0,
}

SCREAMING_SNAKE_CASE			:						List[str]				  =		{
    """num_train_timesteps""": 151,
    """sigma_min""": 0.0_0_2,
    """sigma_max""": 8_0.0,
}



def 	__A		(      _A       ):



 """simple docstring"""


 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 	__A		(      _A				,					_A				,					_A				,					_A				,					_A=False       ):



 """simple docstring"""


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

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

 return new_checkpoint



def 	__A		(      _A				,					_A				,					_A				,					_A				,					_A=None       ):



 """simple docstring"""


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

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

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

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

 return new_checkpoint



def 	__A		(      _A				,					_A       ):



 """simple docstring"""


 __a							=	torch.load(_A				,					map_location="cpu"       )
 __a							=	{}

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

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

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

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

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

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

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

  __a							=	current_channels

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

 __a							=	0
 __a							=	unet_config["up_block_types"]

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

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

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

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

 return new_checkpoint


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE			:						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.""")

  SCREAMING_SNAKE_CASE			:						Any				  =		parser.parse_args()
  SCREAMING_SNAKE_CASE			:						Dict				  =		strabool(args.class_cond)

  SCREAMING_SNAKE_CASE			:						Optional[int]				  =		os.path.basename(args.unet_path)
  print(f'''Checkpoint: {ckpt_name}''')

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

  if not args.class_cond:
    SCREAMING_SNAKE_CASE			:						int				  =		None

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

  SCREAMING_SNAKE_CASE			:						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:
    SCREAMING_SNAKE_CASE			:						Optional[Any]				  =		CD_SCHEDULER_CONFIG
  elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
    SCREAMING_SNAKE_CASE			:						Union[str, Any]				  =		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			:						Dict				  =		CT_LSUN_256_SCHEDULER_CONFIG
  else:
    raise ValueError(f'''Checkpoint type {ckpt_name} is not currently supported.''')

  SCREAMING_SNAKE_CASE			:						int				  =		CMStochasticIterativeScheduler(**scheduler_config)

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



from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE			:						Optional[int]				  =		{
    """configuration_mctct""": ["""MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MCTCTConfig"""],
    """feature_extraction_mctct""": ["""MCTCTFeatureExtractor"""],
    """processing_mctct""": ["""MCTCTProcessor"""],
}


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE			:						Optional[Any]				  =		[
      """MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """MCTCTForCTC""",
      """MCTCTModel""",
      """MCTCTPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_mctct import MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP, MCTCTConfig
  from .feature_extraction_mctct import MCTCTFeatureExtractor
  from .processing_mctct import MCTCTProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_mctct import MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST, MCTCTForCTC, MCTCTModel, MCTCTPreTrainedModel

else:
  import sys

  SCREAMING_SNAKE_CASE			:						Union[str, Any]				  =		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


