import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


lowerCAmelCase__     						=			{
    '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__     						=			{
    '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__     						=			{
    '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__     						=			{
    'num_train_timesteps': 40,
    'sigma_min': 0.002,
    'sigma_max': 80.0,
}

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

lowerCAmelCase__     						=			{
    'num_train_timesteps': 151,
    'sigma_min': 0.002,
    'sigma_max': 80.0,
}






def 				__lowerCamelCase		(						lowerCAmelCase__     ):
       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__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__=False     ):
       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 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__=None     ):
       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 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__     ):
       lowerCAmelCase__					     = torch.load(lowerCAmelCase__     ,  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(lowerCAmelCase__     ):
              lowerCAmelCase__					     = channels_list[i]
              lowerCAmelCase__					     = current_channels != prev_channels
              if layer_type == "ResnetDownsampleBlock2D":
                     for j in range(lowerCAmelCase__     ):
                            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(lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  has_skip=lowerCAmelCase__     )
                            current_layer += 1

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

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

              lowerCAmelCase__					     = current_channels

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

       lowerCAmelCase__					     = 0
       lowerCAmelCase__					     = unet_config['up_block_types']

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

                     if i != len(lowerCAmelCase__     ) - 1:
                            lowerCAmelCase__					     = F"""up_blocks.{i}.upsamplers.0"""
                            lowerCAmelCase__					     = F"""output_blocks.{current_layer-1}.1"""
                            lowerCAmelCase__					     = convert_resnet(lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     )
              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(lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  has_skip=lowerCAmelCase__     )
                            lowerCAmelCase__					     = F"""up_blocks.{i}.attentions.{j}"""
                            lowerCAmelCase__					     = F"""output_blocks.{current_layer}.1"""
                            lowerCAmelCase__					     = convert_attention(
                                lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     )
                            current_layer += 1

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

       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__":
    lowerCAmelCase__     						=			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__     						=			parser.parse_args()
    lowerCAmelCase__     						=			strabool(args.class_cond)

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

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

    if not args.class_cond:
        lowerCAmelCase__     						=			None

    lowerCAmelCase__     						=			con_pt_to_diffuser(args.unet_path, unet_config)

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

    lowerCAmelCase__     						=			CMStochasticIterativeScheduler(**scheduler_config)

    lowerCAmelCase__     						=			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_tf_available, is_torch_available


lowerCAmelCase__     						=			{
    'configuration_transfo_xl': ['TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TransfoXLConfig'],
    'tokenization_transfo_xl': ['TransfoXLCorpus', 'TransfoXLTokenizer'],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase__     						=			[
        'TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST',
        'AdaptiveEmbedding',
        'TransfoXLForSequenceClassification',
        'TransfoXLLMHeadModel',
        'TransfoXLModel',
        'TransfoXLPreTrainedModel',
        'load_tf_weights_in_transfo_xl',
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase__     						=			[
        'TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST',
        'TFAdaptiveEmbedding',
        'TFTransfoXLForSequenceClassification',
        'TFTransfoXLLMHeadModel',
        'TFTransfoXLMainLayer',
        'TFTransfoXLModel',
        'TFTransfoXLPreTrainedModel',
    ]


if TYPE_CHECKING:
    from .configuration_transfo_xl import TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, TransfoXLConfig
    from .tokenization_transfo_xl import TransfoXLCorpus, TransfoXLTokenizer

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_transfo_xl import (
            TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
            AdaptiveEmbedding,
            TransfoXLForSequenceClassification,
            TransfoXLLMHeadModel,
            TransfoXLModel,
            TransfoXLPreTrainedModel,
            load_tf_weights_in_transfo_xl,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_transfo_xl import (
            TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFAdaptiveEmbedding,
            TFTransfoXLForSequenceClassification,
            TFTransfoXLLMHeadModel,
            TFTransfoXLMainLayer,
            TFTransfoXLModel,
            TFTransfoXLPreTrainedModel,
        )

else:
    import sys

    lowerCAmelCase__     						=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
