

'''simple docstring'''
import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


_snake_case     =  {
    "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",
}

_snake_case     =  {
    "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",
}

_snake_case     =  {
    "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",
}

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

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

_snake_case     =  {
    "num_train_timesteps": 151,
    "sigma_min": 0.0_0_2,
    "sigma_max": 8_0.0,
}







def 					_A		(	snake_case     )			->	int:
   if isinstance(snake_case		,							snake_case     ):
      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		(	snake_case		,							snake_case		,							snake_case		,							snake_case		,							snake_case=False     )			->	Union[str, Any]:
   _lowercase			:      Dict			     =   checkpoint[F'''{old_prefix}.in_layers.0.weight''']
   _lowercase			:      Any			     =   checkpoint[F'''{old_prefix}.in_layers.0.bias''']
   _lowercase			:      Optional[Any]			     =   checkpoint[F'''{old_prefix}.in_layers.2.weight''']
   _lowercase			:      List[Any]			     =   checkpoint[F'''{old_prefix}.in_layers.2.bias''']
   _lowercase			:      Optional[Any]			     =   checkpoint[F'''{old_prefix}.emb_layers.1.weight''']
   _lowercase			:      Optional[Any]			     =   checkpoint[F'''{old_prefix}.emb_layers.1.bias''']
   _lowercase			:      Union[str, Any]			     =   checkpoint[F'''{old_prefix}.out_layers.0.weight''']
   _lowercase			:      Dict			     =   checkpoint[F'''{old_prefix}.out_layers.0.bias''']
   _lowercase			:      str			     =   checkpoint[F'''{old_prefix}.out_layers.3.weight''']
   _lowercase			:      str			     =   checkpoint[F'''{old_prefix}.out_layers.3.bias''']

   if has_skip:
      _lowercase			:      List[Any]			     =   checkpoint[F'''{old_prefix}.skip_connection.weight''']
      _lowercase			:      Optional[Any]			     =   checkpoint[F'''{old_prefix}.skip_connection.bias''']

   return new_checkpoint







def 					_A		(	snake_case		,							snake_case		,							snake_case		,							snake_case		,							snake_case=None     )			->	List[str]:
   _lowercase			:      Dict			     =   checkpoint[F'''{old_prefix}.qkv.weight'''].chunk(3		,							dim=0     )
   _lowercase			:      Optional[Any]			     =   checkpoint[F'''{old_prefix}.qkv.bias'''].chunk(3		,							dim=0     )

   _lowercase			:      List[str]			     =   checkpoint[F'''{old_prefix}.norm.weight''']
   _lowercase			:      List[Any]			     =   checkpoint[F'''{old_prefix}.norm.bias''']

   _lowercase			:      Dict			     =   weight_q.squeeze(-1     ).squeeze(-1     )
   _lowercase			:      List[Any]			     =   bias_q.squeeze(-1     ).squeeze(-1     )
   _lowercase			:      List[Any]			     =   weight_k.squeeze(-1     ).squeeze(-1     )
   _lowercase			:      Optional[Any]			     =   bias_k.squeeze(-1     ).squeeze(-1     )
   _lowercase			:      Tuple			     =   weight_v.squeeze(-1     ).squeeze(-1     )
   _lowercase			:      str			     =   bias_v.squeeze(-1     ).squeeze(-1     )

   _lowercase			:      Optional[int]			     =   (
       checkpoint[F'''{old_prefix}.proj_out.weight'''].squeeze(-1     ).squeeze(-1     )
   )
   _lowercase			:      List[str]			     =   checkpoint[F'''{old_prefix}.proj_out.bias'''].squeeze(-1     ).squeeze(-1     )

   return new_checkpoint







def 					_A		(	snake_case		,							snake_case     )			->	Union[str, Any]:
   _lowercase			:      Tuple			     =   torch.load(snake_case		,							map_location="cpu"     )
   _lowercase			:      str			     =   {}

   _lowercase			:      int			     =   checkpoint["""time_embed.0.weight"""]
   _lowercase			:      Union[str, Any]			     =   checkpoint["""time_embed.0.bias"""]
   _lowercase			:      List[Any]			     =   checkpoint["""time_embed.2.weight"""]
   _lowercase			:      str			     =   checkpoint["""time_embed.2.bias"""]

   if unet_config["num_class_embeds"] is not None:
      _lowercase			:      List[Any]			     =   checkpoint["""label_emb.weight"""]

   _lowercase			:      Optional[Any]			     =   checkpoint["""input_blocks.0.0.weight"""]
   _lowercase			:      List[str]			     =   checkpoint["""input_blocks.0.0.bias"""]

   _lowercase			:      Optional[Any]			     =   unet_config["""down_block_types"""]
   _lowercase			:      Any			     =   unet_config["""layers_per_block"""]
   _lowercase			:      Any			     =   unet_config["""attention_head_dim"""]
   _lowercase			:      Optional[int]			     =   unet_config["""block_out_channels"""]
   _lowercase			:      Tuple			     =   1
   _lowercase			:      Dict			     =   channels_list[0]

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

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

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

      _lowercase			:      List[str]			     =   current_channels

   # hardcoded the mid-block for now
   _lowercase			:      Any			     =   """mid_block.resnets.0"""
   _lowercase			:      Optional[Any]			     =   """middle_block.0"""
   _lowercase			:      Optional[Any]			     =   convert_resnet(snake_case		,							snake_case		,							snake_case		,							snake_case     )
   _lowercase			:      str			     =   """mid_block.attentions.0"""
   _lowercase			:      Optional[int]			     =   """middle_block.1"""
   _lowercase			:      Dict			     =   convert_attention(snake_case		,							snake_case		,							snake_case		,							snake_case		,							snake_case     )
   _lowercase			:      Any			     =   """mid_block.resnets.1"""
   _lowercase			:      Tuple			     =   """middle_block.2"""
   _lowercase			:      Tuple			     =   convert_resnet(snake_case		,							snake_case		,							snake_case		,							snake_case     )

   _lowercase			:      List[Any]			     =   0
   _lowercase			:      str			     =   unet_config["""up_block_types"""]

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

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

         if i != len(snake_case     ) - 1:
            _lowercase			:      int			     =   F'''up_blocks.{i}.upsamplers.0'''
            _lowercase			:      Dict			     =   F'''output_blocks.{current_layer-1}.2'''
            _lowercase			:      Union[str, Any]			     =   convert_resnet(snake_case		,							snake_case		,							snake_case		,							snake_case     )

   _lowercase			:      List[Any]			     =   checkpoint["""out.0.weight"""]
   _lowercase			:      str			     =   checkpoint["""out.0.bias"""]
   _lowercase			:      str			     =   checkpoint["""out.2.weight"""]
   _lowercase			:      Tuple			     =   checkpoint["""out.2.bias"""]

   return new_checkpoint


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

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

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

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

    if not args.class_cond:
        _snake_case     =  None

    _snake_case     =  con_pt_to_diffuser(args.unet_path, unet_config)

    _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:
        _snake_case     =  CD_SCHEDULER_CONFIG
    elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
        _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)):
        _snake_case     =  CT_LSUN_256_SCHEDULER_CONFIG
    else:
        raise ValueError(F'''Checkpoint type {ckpt_name} is not currently supported.''')

    _snake_case     =  CMStochasticIterativeScheduler(**scheduler_config)

    _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_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


A:   str								=		{
    "configuration_distilbert": [
        "DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "DistilBertConfig",
        "DistilBertOnnxConfig",
    ],
    "tokenization_distilbert": ["DistilBertTokenizer"],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A:   Dict								=		["DistilBertTokenizerFast"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A:   List[Any]								=		[
          "DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "DistilBertForMaskedLM",
          "DistilBertForMultipleChoice",
          "DistilBertForQuestionAnswering",
          "DistilBertForSequenceClassification",
          "DistilBertForTokenClassification",
          "DistilBertModel",
          "DistilBertPreTrainedModel",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A:   Dict								=		[
          "TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFDistilBertForMaskedLM",
          "TFDistilBertForMultipleChoice",
          "TFDistilBertForQuestionAnswering",
          "TFDistilBertForSequenceClassification",
          "TFDistilBertForTokenClassification",
          "TFDistilBertMainLayer",
          "TFDistilBertModel",
          "TFDistilBertPreTrainedModel",
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A:   int								=		[
          "FlaxDistilBertForMaskedLM",
          "FlaxDistilBertForMultipleChoice",
          "FlaxDistilBertForQuestionAnswering",
          "FlaxDistilBertForSequenceClassification",
          "FlaxDistilBertForTokenClassification",
          "FlaxDistilBertModel",
          "FlaxDistilBertPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_distilbert import (
          DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
          DistilBertConfig,
          DistilBertOnnxConfig,
      )
      from .tokenization_distilbert import DistilBertTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_distilbert_fast import DistilBertTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_distilbert import (
                DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                DistilBertForMaskedLM,
                DistilBertForMultipleChoice,
                DistilBertForQuestionAnswering,
                DistilBertForSequenceClassification,
                DistilBertForTokenClassification,
                DistilBertModel,
                DistilBertPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_distilbert import (
                TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFDistilBertForMaskedLM,
                TFDistilBertForMultipleChoice,
                TFDistilBertForQuestionAnswering,
                TFDistilBertForSequenceClassification,
                TFDistilBertForTokenClassification,
                TFDistilBertMainLayer,
                TFDistilBertModel,
                TFDistilBertPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_distilbert import (
                FlaxDistilBertForMaskedLM,
                FlaxDistilBertForMultipleChoice,
                FlaxDistilBertForQuestionAnswering,
                FlaxDistilBertForSequenceClassification,
                FlaxDistilBertForTokenClassification,
                FlaxDistilBertModel,
                FlaxDistilBertPreTrainedModel,
            )

else:
      import sys

      A:   Optional[int]								=		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
