





'''simple docstring'''






import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


A__							:  Any    ={
    """sample_size""": 32,
    """in_channels""": 3,
    """out_channels""": 3,
    """layers_per_block""": 2,
    """num_class_embeds""": 10_00,
    """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""": 10_00,
    """block_out_channels""": [1_92, 1_92 * 2, 1_92 * 3, 1_92 * 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__							:  Any    ={
    """sample_size""": 2_56,
    """in_channels""": 3,
    """out_channels""": 3,
    """layers_per_block""": 2,
    """num_class_embeds""": None,
    """block_out_channels""": [2_56, 2_56, 2_56 * 2, 2_56 * 2, 2_56 * 4, 2_56 * 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__							:  str    ={
    """num_train_timesteps""": 40,
    """sigma_min""": 0.002,
    """sigma_max""": 80.0,
}

A__							:  Dict    ={
    """num_train_timesteps""": 2_01,
    """sigma_min""": 0.002,
    """sigma_max""": 80.0,
}

A__							:  List[str]    ={
    """num_train_timesteps""": 1_51,
    """sigma_min""": 0.002,
    """sigma_max""": 80.0,
}
def   UpperCamelCase__ (						lowerCAmelCase    ):





      """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   UpperCamelCase__ (						lowerCAmelCase				,      lowerCAmelCase				,      lowerCAmelCase				,      lowerCAmelCase				,      lowerCAmelCase=False    ):





      """simple docstring"""


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

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

      return new_checkpoint
def   UpperCamelCase__ (						lowerCAmelCase				,      lowerCAmelCase				,      lowerCAmelCase				,      lowerCAmelCase				,      lowerCAmelCase=None    ):





      """simple docstring"""


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

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

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

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

      return new_checkpoint
def   UpperCamelCase__ (						lowerCAmelCase				,      lowerCAmelCase    ):





      """simple docstring"""


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

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

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

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

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

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

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

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

            _lowerCAmelCase					=				current_channels

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

      _lowerCAmelCase					=				0
      _lowerCAmelCase					=				unet_config["""up_block_types"""]

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

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

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

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

      return new_checkpoint


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

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

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

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

      if not args.class_cond:
            A__							:  int    =None

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

      A__							:  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:
            A__							:  Optional[Any]    =CD_SCHEDULER_CONFIG
      elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
            A__							:  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)):
            A__							:  Dict    =CT_LSUN_256_SCHEDULER_CONFIG
      else:
            raise ValueError(F"""Checkpoint type {ckpt_name} is not currently supported.""")

      A__							:  int    =CMStochasticIterativeScheduler(**scheduler_config)

      A__							:  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_flax_available,
    is_tensorflow_text_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE			:						Optional[int]				  =		{
    """configuration_bert""": ["""BERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BertConfig""", """BertOnnxConfig"""],
    """tokenization_bert""": ["""BasicTokenizer""", """BertTokenizer""", """WordpieceTokenizer"""],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE			:						Optional[int]				  =		["""BertTokenizerFast"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE			:						List[Any]				  =		[
      """BERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """BertForMaskedLM""",
      """BertForMultipleChoice""",
      """BertForNextSentencePrediction""",
      """BertForPreTraining""",
      """BertForQuestionAnswering""",
      """BertForSequenceClassification""",
      """BertForTokenClassification""",
      """BertLayer""",
      """BertLMHeadModel""",
      """BertModel""",
      """BertPreTrainedModel""",
      """load_tf_weights_in_bert""",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE			:						Union[str, Any]				  =		[
      """TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """TFBertEmbeddings""",
      """TFBertForMaskedLM""",
      """TFBertForMultipleChoice""",
      """TFBertForNextSentencePrediction""",
      """TFBertForPreTraining""",
      """TFBertForQuestionAnswering""",
      """TFBertForSequenceClassification""",
      """TFBertForTokenClassification""",
      """TFBertLMHeadModel""",
      """TFBertMainLayer""",
      """TFBertModel""",
      """TFBertPreTrainedModel""",
  ]
try:
  if not is_tensorflow_text_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE			:						Optional[Any]				  =		["""TFBertTokenizer"""]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE			:						Any				  =		[
      """FlaxBertForCausalLM""",
      """FlaxBertForMaskedLM""",
      """FlaxBertForMultipleChoice""",
      """FlaxBertForNextSentencePrediction""",
      """FlaxBertForPreTraining""",
      """FlaxBertForQuestionAnswering""",
      """FlaxBertForSequenceClassification""",
      """FlaxBertForTokenClassification""",
      """FlaxBertModel""",
      """FlaxBertPreTrainedModel""",
  ]

if TYPE_CHECKING:
  from .configuration_bert import BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, BertConfig, BertOnnxConfig
  from .tokenization_bert import BasicTokenizer, BertTokenizer, WordpieceTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_bert_fast import BertTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_bert import (
        BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        BertForMaskedLM,
        BertForMultipleChoice,
        BertForNextSentencePrediction,
        BertForPreTraining,
        BertForQuestionAnswering,
        BertForSequenceClassification,
        BertForTokenClassification,
        BertLayer,
        BertLMHeadModel,
        BertModel,
        BertPreTrainedModel,
        load_tf_weights_in_bert,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_bert import (
        TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFBertEmbeddings,
        TFBertForMaskedLM,
        TFBertForMultipleChoice,
        TFBertForNextSentencePrediction,
        TFBertForPreTraining,
        TFBertForQuestionAnswering,
        TFBertForSequenceClassification,
        TFBertForTokenClassification,
        TFBertLMHeadModel,
        TFBertMainLayer,
        TFBertModel,
        TFBertPreTrainedModel,
    )

  try:
    if not is_tensorflow_text_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_bert_tf import TFBertTokenizer

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_bert import (
        FlaxBertForCausalLM,
        FlaxBertForMaskedLM,
        FlaxBertForMultipleChoice,
        FlaxBertForNextSentencePrediction,
        FlaxBertForPreTraining,
        FlaxBertForQuestionAnswering,
        FlaxBertForSequenceClassification,
        FlaxBertForTokenClassification,
        FlaxBertModel,
        FlaxBertPreTrainedModel,
    )

else:
  import sys

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


