





'''simple docstring'''

import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


lowercase_					= {
    """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""",
}

lowercase_					= {
    """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""",
}

lowercase_					= {
    """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""",
}

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

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

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





def lowerCamelCase						(     __lowerCamelCase :    Dict    )						->Optional[int]:
    if isinstance(__lowerCamelCase					,       __lowerCamelCase    ):
        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 lowerCamelCase						(     __lowerCamelCase :    str					,       __lowerCamelCase :    str					,       __lowerCamelCase :    str					,       __lowerCamelCase :    List[str]					,       __lowerCamelCase :    Optional[int]=False    )						->List[Any]:
    _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 lowerCamelCase						(     __lowerCamelCase :    str					,       __lowerCamelCase :    Union[str, Any]					,       __lowerCamelCase :    Optional[int]					,       __lowerCamelCase :    str					,       __lowerCamelCase :    Union[str, Any]=None    )						->int:
    _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 lowerCamelCase						(     __lowerCamelCase :    str					,       __lowerCamelCase :    Tuple    )						->List[str]:
    _SCREAMING_SNAKE_CASE									=			torch.load(__lowerCamelCase					,       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(__lowerCamelCase    ):
        _SCREAMING_SNAKE_CASE									=			channels_list[i]
        _SCREAMING_SNAKE_CASE									=			current_channels != prev_channels
        if layer_type == "ResnetDownsampleBlock2D":
            for j in range(__lowerCamelCase    ):
                _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(__lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       has_skip=__lowerCamelCase    )
                current_layer += 1

        elif layer_type == "AttnDownBlock2D":
            for j in range(__lowerCamelCase    ):
                _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(__lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       has_skip=__lowerCamelCase    )
                _SCREAMING_SNAKE_CASE									=			F'down_blocks.{i}.attentions.{j}'
                _SCREAMING_SNAKE_CASE									=			F'input_blocks.{current_layer}.1'
                _SCREAMING_SNAKE_CASE									=			convert_attention(
                    __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase    )
                current_layer += 1

        if i != len(__lowerCamelCase    ) - 1:
            _SCREAMING_SNAKE_CASE									=			F'down_blocks.{i}.downsamplers.0'
            _SCREAMING_SNAKE_CASE									=			F'input_blocks.{current_layer}.0'
            _SCREAMING_SNAKE_CASE									=			convert_resnet(__lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase    )
            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(__lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase    )
    _SCREAMING_SNAKE_CASE									=			"""mid_block.attentions.0"""
    _SCREAMING_SNAKE_CASE									=			"""middle_block.1"""
    _SCREAMING_SNAKE_CASE									=			convert_attention(__lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase    )
    _SCREAMING_SNAKE_CASE									=			"""mid_block.resnets.1"""
    _SCREAMING_SNAKE_CASE									=			"""middle_block.2"""
    _SCREAMING_SNAKE_CASE									=			convert_resnet(__lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase    )

    _SCREAMING_SNAKE_CASE									=			0
    _SCREAMING_SNAKE_CASE									=			unet_config["""up_block_types"""]

    for i, layer_type in enumerate(__lowerCamelCase    ):
        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(__lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       has_skip=__lowerCamelCase    )
                current_layer += 1

            if i != len(__lowerCamelCase    ) - 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(__lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase    )
        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(__lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       has_skip=__lowerCamelCase    )
                _SCREAMING_SNAKE_CASE									=			F'up_blocks.{i}.attentions.{j}'
                _SCREAMING_SNAKE_CASE									=			F'output_blocks.{current_layer}.1'
                _SCREAMING_SNAKE_CASE									=			convert_attention(
                    __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase    )
                current_layer += 1

            if i != len(__lowerCamelCase    ) - 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(__lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase    )

    _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__":
      lowercase_					= 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.""")

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

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

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

      if not args.class_cond:
            lowercase_					= None

      lowercase_					= con_pt_to_diffuser(args.unet_path, unet_config)

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

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

      lowercase_					= CMStochasticIterativeScheduler(**scheduler_config)

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










'''simple docstring'''

import argparse
import logging
import pickle
from collections import Counter


logging.basicConfig(
    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)
lowercase_					= logging.getLogger(__name__)

if __name__ == "__main__":
      lowercase_					= argparse.ArgumentParser(
          description="""Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)"""
      )
      parser.add_argument(
          """--data_file""", type=str, default="""data/dump.bert-base-uncased.pickle""", help="""The binarized dataset."""
      )
      parser.add_argument(
          """--token_counts_dump""", type=str, default="""data/token_counts.bert-base-uncased.pickle""", help="""The dump file."""
      )
      parser.add_argument("""--vocab_size""", default=30_522, type=int)
      lowercase_					= parser.parse_args()

      logger.info(f"""Loading data from {args.data_file}""")
      with open(args.data_file, """rb""") as fp:
            lowercase_					= pickle.load(fp)

      logger.info("""Counting occurrences for MLM.""")
      lowercase_					= Counter()
      for tk_ids in data:
            counter.update(tk_ids)
      lowercase_					= [0] * args.vocab_size
      for k, v in counter.items():
            lowercase_					= v

      logger.info(f"""Dump to {args.token_counts_dump}""")
      with open(args.token_counts_dump, """wb""") as handle:
            pickle.dump(counts, handle, protocol=pickle.HIGHEST_PROTOCOL)



