




'''simple docstring'''







import argparse
import json

import torch

from diffusers import AutoencoderKL, DDPMPipeline, DDPMScheduler, UNetaDModel, VQModel





def 		snake_case_				(_a      :				Optional[Any]       ,  _a      :				Optional[Any]=1	):
    if n_shave_prefix_segments >= 0:
        return ".".join(path.split('''.'''	)[n_shave_prefix_segments:]	)
    else:
        return ".".join(path.split('''.'''	)[:n_shave_prefix_segments]	)





def 		snake_case_				(_a      :				Optional[Any]       ,  _a      :				List[str]=0	):
    UpperCAmelCase     			=						[]
    for old_item in old_list:
        UpperCAmelCase     			=						old_item
        UpperCAmelCase     			=						new_item.replace('''block.'''       ,  '''resnets.'''	)
        UpperCAmelCase     			=						new_item.replace('''conv_shorcut'''       ,  '''conv1'''	)
        UpperCAmelCase     			=						new_item.replace('''in_shortcut'''       ,  '''conv_shortcut'''	)
        UpperCAmelCase     			=						new_item.replace('''temb_proj'''       ,  '''time_emb_proj'''	)

        UpperCAmelCase     			=						shave_segments(_a       ,  n_shave_prefix_segments=_a	)

        mapping.append({'''old''': old_item, '''new''': new_item}	)

    return mapping





def 		snake_case_				(_a      :				Dict       ,  _a      :				Tuple=0       ,  _a      :				int=False	):
    UpperCAmelCase     			=						[]
    for old_item in old_list:
        UpperCAmelCase     			=						old_item

        # In `model.mid`, the layer is called `attn`.
        if not in_mid:
            UpperCAmelCase     			=						new_item.replace('''attn'''       ,  '''attentions'''	)
        UpperCAmelCase     			=						new_item.replace('''.k.'''       ,  '''.key.'''	)
        UpperCAmelCase     			=						new_item.replace('''.v.'''       ,  '''.value.'''	)
        UpperCAmelCase     			=						new_item.replace('''.q.'''       ,  '''.query.'''	)

        UpperCAmelCase     			=						new_item.replace('''proj_out'''       ,  '''proj_attn'''	)
        UpperCAmelCase     			=						new_item.replace('''norm'''       ,  '''group_norm'''	)

        UpperCAmelCase     			=						shave_segments(_a       ,  n_shave_prefix_segments=_a	)
        mapping.append({'''old''': old_item, '''new''': new_item}	)

    return mapping





def 		snake_case_				(_a      :				List[Any]       ,  _a      :				int       ,  _a      :				Any       ,  _a      :				Any=None       ,  _a      :				List[str]=None       ,  _a      :				Dict=None	):
    assert isinstance(_a       ,  _a	), "Paths should be a list of dicts containing 'old' and 'new' keys."

    if attention_paths_to_split is not None:
        if config is None:
            raise ValueError('''Please specify the config if setting \'attention_paths_to_split\' to \'True\'.'''	)

        for path, path_map in attention_paths_to_split.items():
            UpperCAmelCase     			=						old_checkpoint[path]
            UpperCAmelCase     			=						old_tensor.shape[0] // 3

            UpperCAmelCase     			=						(-1, channels) if len(old_tensor.shape	) == 3 else (-1)

            UpperCAmelCase     			=						old_tensor.shape[0] // config.get('''num_head_channels'''       ,  1	) // 3

            UpperCAmelCase     			=						old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]	)
            UpperCAmelCase			,	UpperCAmelCase			,	UpperCAmelCase     			=						old_tensor.split(channels // num_heads       ,  dim=1	)

            UpperCAmelCase     			=						query.reshape(_a	).squeeze()
            UpperCAmelCase     			=						key.reshape(_a	).squeeze()
            UpperCAmelCase     			=						value.reshape(_a	).squeeze()

    for path in paths:
        UpperCAmelCase     			=						path['''new''']

        if attention_paths_to_split is not None and new_path in attention_paths_to_split:
            continue

        UpperCAmelCase     			=						new_path.replace('''down.'''       ,  '''down_blocks.'''	)
        UpperCAmelCase     			=						new_path.replace('''up.'''       ,  '''up_blocks.'''	)

        if additional_replacements is not None:
            for replacement in additional_replacements:
                UpperCAmelCase     			=						new_path.replace(replacement['''old''']       ,  replacement['''new''']	)

        if "attentions" in new_path:
            UpperCAmelCase     			=						old_checkpoint[path['''old''']].squeeze()
        else:
            UpperCAmelCase     			=						old_checkpoint[path['''old''']]





def 		snake_case_				(_a      :				List[str]       ,  _a      :				int	):
    UpperCAmelCase     			=						{}

    UpperCAmelCase     			=						checkpoint['''temb.dense.0.weight''']
    UpperCAmelCase     			=						checkpoint['''temb.dense.0.bias''']
    UpperCAmelCase     			=						checkpoint['''temb.dense.1.weight''']
    UpperCAmelCase     			=						checkpoint['''temb.dense.1.bias''']

    UpperCAmelCase     			=						checkpoint['''norm_out.weight''']
    UpperCAmelCase     			=						checkpoint['''norm_out.bias''']

    UpperCAmelCase     			=						checkpoint['''conv_in.weight''']
    UpperCAmelCase     			=						checkpoint['''conv_in.bias''']
    UpperCAmelCase     			=						checkpoint['''conv_out.weight''']
    UpperCAmelCase     			=						checkpoint['''conv_out.bias''']

    UpperCAmelCase     			=						len({'''.'''.join(layer.split('''.'''	)[:2]	) for layer in checkpoint if '''down''' in layer}	)
    UpperCAmelCase     			=						{
        layer_id: [key for key in checkpoint if F"down.{layer_id}" in key] for layer_id in range(_a	)
    }

    UpperCAmelCase     			=						len({'''.'''.join(layer.split('''.'''	)[:2]	) for layer in checkpoint if '''up''' in layer}	)
    UpperCAmelCase     			=						{layer_id: [key for key in checkpoint if F"up.{layer_id}" in key] for layer_id in range(_a	)}

    for i in range(_a	):
        UpperCAmelCase     			=						(i - 1) // (config['''layers_per_block'''] + 1)

        if any('''downsample''' in layer for layer in down_blocks[i]	):
            UpperCAmelCase     			=						checkpoint[
                F"down.{i}.downsample.op.weight"
            ]
            UpperCAmelCase     			=						checkpoint[F"down.{i}.downsample.op.bias"]
        #            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.weight'] = checkpoint[f'down.{i}.downsample.conv.weight']
        #            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.bias'] = checkpoint[f'down.{i}.downsample.conv.bias']

        if any('''block''' in layer for layer in down_blocks[i]	):
            UpperCAmelCase     			=						len(
                {'''.'''.join(shave_segments(_a       ,  2	).split('''.'''	)[:2]	) for layer in down_blocks[i] if '''block''' in layer}	)
            UpperCAmelCase     			=						{
                layer_id: [key for key in down_blocks[i] if F"block.{layer_id}" in key]
                for layer_id in range(_a	)
            }

            if num_blocks > 0:
                for j in range(config['''layers_per_block''']	):
                    UpperCAmelCase     			=						renew_resnet_paths(blocks[j]	)
                    assign_to_checkpoint(_a       ,  _a       ,  _a	)

        if any('''attn''' in layer for layer in down_blocks[i]	):
            UpperCAmelCase     			=						len(
                {'''.'''.join(shave_segments(_a       ,  2	).split('''.'''	)[:2]	) for layer in down_blocks[i] if '''attn''' in layer}	)
            UpperCAmelCase     			=						{
                layer_id: [key for key in down_blocks[i] if F"attn.{layer_id}" in key]
                for layer_id in range(_a	)
            }

            if num_attn > 0:
                for j in range(config['''layers_per_block''']	):
                    UpperCAmelCase     			=						renew_attention_paths(attns[j]	)
                    assign_to_checkpoint(_a       ,  _a       ,  _a       ,  config=_a	)

    UpperCAmelCase     			=						[key for key in checkpoint if '''mid.block_1''' in key]
    UpperCAmelCase     			=						[key for key in checkpoint if '''mid.block_2''' in key]
    UpperCAmelCase     			=						[key for key in checkpoint if '''mid.attn_1''' in key]

    # Mid new 2
    UpperCAmelCase     			=						renew_resnet_paths(_a	)
    assign_to_checkpoint(
        _a       ,  _a       ,  _a       ,  additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''block_1''', '''new''': '''resnets.0'''}]       ,  )

    UpperCAmelCase     			=						renew_resnet_paths(_a	)
    assign_to_checkpoint(
        _a       ,  _a       ,  _a       ,  additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''block_2''', '''new''': '''resnets.1'''}]       ,  )

    UpperCAmelCase     			=						renew_attention_paths(_a       ,  in_mid=_a	)
    assign_to_checkpoint(
        _a       ,  _a       ,  _a       ,  additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''attn_1''', '''new''': '''attentions.0'''}]       ,  )

    for i in range(_a	):
        UpperCAmelCase     			=						num_up_blocks - 1 - i

        if any('''upsample''' in layer for layer in up_blocks[i]	):
            UpperCAmelCase     			=						checkpoint[
                F"up.{i}.upsample.conv.weight"
            ]
            UpperCAmelCase     			=						checkpoint[F"up.{i}.upsample.conv.bias"]

        if any('''block''' in layer for layer in up_blocks[i]	):
            UpperCAmelCase     			=						len(
                {'''.'''.join(shave_segments(_a       ,  2	).split('''.'''	)[:2]	) for layer in up_blocks[i] if '''block''' in layer}	)
            UpperCAmelCase     			=						{
                layer_id: [key for key in up_blocks[i] if F"block.{layer_id}" in key] for layer_id in range(_a	)
            }

            if num_blocks > 0:
                for j in range(config['''layers_per_block'''] + 1	):
                    UpperCAmelCase     			=						{'''old''': F"up_blocks.{i}", '''new''': F"up_blocks.{block_id}"}
                    UpperCAmelCase     			=						renew_resnet_paths(blocks[j]	)
                    assign_to_checkpoint(_a       ,  _a       ,  _a       ,  additional_replacements=[replace_indices]	)

        if any('''attn''' in layer for layer in up_blocks[i]	):
            UpperCAmelCase     			=						len(
                {'''.'''.join(shave_segments(_a       ,  2	).split('''.'''	)[:2]	) for layer in up_blocks[i] if '''attn''' in layer}	)
            UpperCAmelCase     			=						{
                layer_id: [key for key in up_blocks[i] if F"attn.{layer_id}" in key] for layer_id in range(_a	)
            }

            if num_attn > 0:
                for j in range(config['''layers_per_block'''] + 1	):
                    UpperCAmelCase     			=						{'''old''': F"up_blocks.{i}", '''new''': F"up_blocks.{block_id}"}
                    UpperCAmelCase     			=						renew_attention_paths(attns[j]	)
                    assign_to_checkpoint(_a       ,  _a       ,  _a       ,  additional_replacements=[replace_indices]	)

    UpperCAmelCase     			=						{k.replace('''mid_new_2'''       ,  '''mid_block'''	): v for k, v in new_checkpoint.items()}
    return new_checkpoint





def 		snake_case_				(_a      :				Optional[Any]       ,  _a      :				Any	):
    UpperCAmelCase     			=						{}

    UpperCAmelCase     			=						checkpoint['''encoder.norm_out.weight''']
    UpperCAmelCase     			=						checkpoint['''encoder.norm_out.bias''']

    UpperCAmelCase     			=						checkpoint['''encoder.conv_in.weight''']
    UpperCAmelCase     			=						checkpoint['''encoder.conv_in.bias''']
    UpperCAmelCase     			=						checkpoint['''encoder.conv_out.weight''']
    UpperCAmelCase     			=						checkpoint['''encoder.conv_out.bias''']

    UpperCAmelCase     			=						checkpoint['''decoder.norm_out.weight''']
    UpperCAmelCase     			=						checkpoint['''decoder.norm_out.bias''']

    UpperCAmelCase     			=						checkpoint['''decoder.conv_in.weight''']
    UpperCAmelCase     			=						checkpoint['''decoder.conv_in.bias''']
    UpperCAmelCase     			=						checkpoint['''decoder.conv_out.weight''']
    UpperCAmelCase     			=						checkpoint['''decoder.conv_out.bias''']

    UpperCAmelCase     			=						len({'''.'''.join(layer.split('''.'''	)[:3]	) for layer in checkpoint if '''down''' in layer}	)
    UpperCAmelCase     			=						{
        layer_id: [key for key in checkpoint if F"down.{layer_id}" in key] for layer_id in range(_a	)
    }

    UpperCAmelCase     			=						len({'''.'''.join(layer.split('''.'''	)[:3]	) for layer in checkpoint if '''up''' in layer}	)
    UpperCAmelCase     			=						{layer_id: [key for key in checkpoint if F"up.{layer_id}" in key] for layer_id in range(_a	)}

    for i in range(_a	):
        UpperCAmelCase     			=						(i - 1) // (config['''layers_per_block'''] + 1)

        if any('''downsample''' in layer for layer in down_blocks[i]	):
            UpperCAmelCase     			=						checkpoint[
                F"encoder.down.{i}.downsample.conv.weight"
            ]
            UpperCAmelCase     			=						checkpoint[
                F"encoder.down.{i}.downsample.conv.bias"
            ]

        if any('''block''' in layer for layer in down_blocks[i]	):
            UpperCAmelCase     			=						len(
                {'''.'''.join(shave_segments(_a       ,  3	).split('''.'''	)[:3]	) for layer in down_blocks[i] if '''block''' in layer}	)
            UpperCAmelCase     			=						{
                layer_id: [key for key in down_blocks[i] if F"block.{layer_id}" in key]
                for layer_id in range(_a	)
            }

            if num_blocks > 0:
                for j in range(config['''layers_per_block''']	):
                    UpperCAmelCase     			=						renew_resnet_paths(blocks[j]	)
                    assign_to_checkpoint(_a       ,  _a       ,  _a	)

        if any('''attn''' in layer for layer in down_blocks[i]	):
            UpperCAmelCase     			=						len(
                {'''.'''.join(shave_segments(_a       ,  3	).split('''.'''	)[:3]	) for layer in down_blocks[i] if '''attn''' in layer}	)
            UpperCAmelCase     			=						{
                layer_id: [key for key in down_blocks[i] if F"attn.{layer_id}" in key]
                for layer_id in range(_a	)
            }

            if num_attn > 0:
                for j in range(config['''layers_per_block''']	):
                    UpperCAmelCase     			=						renew_attention_paths(attns[j]	)
                    assign_to_checkpoint(_a       ,  _a       ,  _a       ,  config=_a	)

    UpperCAmelCase     			=						[key for key in checkpoint if '''mid.block_1''' in key]
    UpperCAmelCase     			=						[key for key in checkpoint if '''mid.block_2''' in key]
    UpperCAmelCase     			=						[key for key in checkpoint if '''mid.attn_1''' in key]

    # Mid new 2
    UpperCAmelCase     			=						renew_resnet_paths(_a	)
    assign_to_checkpoint(
        _a       ,  _a       ,  _a       ,  additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''block_1''', '''new''': '''resnets.0'''}]       ,  )

    UpperCAmelCase     			=						renew_resnet_paths(_a	)
    assign_to_checkpoint(
        _a       ,  _a       ,  _a       ,  additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''block_2''', '''new''': '''resnets.1'''}]       ,  )

    UpperCAmelCase     			=						renew_attention_paths(_a       ,  in_mid=_a	)
    assign_to_checkpoint(
        _a       ,  _a       ,  _a       ,  additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''attn_1''', '''new''': '''attentions.0'''}]       ,  )

    for i in range(_a	):
        UpperCAmelCase     			=						num_up_blocks - 1 - i

        if any('''upsample''' in layer for layer in up_blocks[i]	):
            UpperCAmelCase     			=						checkpoint[
                F"decoder.up.{i}.upsample.conv.weight"
            ]
            UpperCAmelCase     			=						checkpoint[
                F"decoder.up.{i}.upsample.conv.bias"
            ]

        if any('''block''' in layer for layer in up_blocks[i]	):
            UpperCAmelCase     			=						len(
                {'''.'''.join(shave_segments(_a       ,  3	).split('''.'''	)[:3]	) for layer in up_blocks[i] if '''block''' in layer}	)
            UpperCAmelCase     			=						{
                layer_id: [key for key in up_blocks[i] if F"block.{layer_id}" in key] for layer_id in range(_a	)
            }

            if num_blocks > 0:
                for j in range(config['''layers_per_block'''] + 1	):
                    UpperCAmelCase     			=						{'''old''': F"up_blocks.{i}", '''new''': F"up_blocks.{block_id}"}
                    UpperCAmelCase     			=						renew_resnet_paths(blocks[j]	)
                    assign_to_checkpoint(_a       ,  _a       ,  _a       ,  additional_replacements=[replace_indices]	)

        if any('''attn''' in layer for layer in up_blocks[i]	):
            UpperCAmelCase     			=						len(
                {'''.'''.join(shave_segments(_a       ,  3	).split('''.'''	)[:3]	) for layer in up_blocks[i] if '''attn''' in layer}	)
            UpperCAmelCase     			=						{
                layer_id: [key for key in up_blocks[i] if F"attn.{layer_id}" in key] for layer_id in range(_a	)
            }

            if num_attn > 0:
                for j in range(config['''layers_per_block'''] + 1	):
                    UpperCAmelCase     			=						{'''old''': F"up_blocks.{i}", '''new''': F"up_blocks.{block_id}"}
                    UpperCAmelCase     			=						renew_attention_paths(attns[j]	)
                    assign_to_checkpoint(_a       ,  _a       ,  _a       ,  additional_replacements=[replace_indices]	)

    UpperCAmelCase     			=						{k.replace('''mid_new_2'''       ,  '''mid_block'''	): v for k, v in new_checkpoint.items()}
    UpperCAmelCase     			=						checkpoint['''quant_conv.weight''']
    UpperCAmelCase     			=						checkpoint['''quant_conv.bias''']
    if "quantize.embedding.weight" in checkpoint:
        UpperCAmelCase     			=						checkpoint['''quantize.embedding.weight''']
    UpperCAmelCase     			=						checkpoint['''post_quant_conv.weight''']
    UpperCAmelCase     			=						checkpoint['''post_quant_conv.bias''']

    return new_checkpoint


if __name__ == "__main__":
   A					       =argparse.ArgumentParser()

   parser.add_argument(
       '--checkpoint_path', default=None, type=str, required=True, help='Path to the checkpoint to convert.'
   )

   parser.add_argument(
       '--config_file',
       default=None,
       type=str,
       required=True,
       help='The config json file corresponding to the architecture.',
   )

   parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')

   A					       =parser.parse_args()
   A					       =torch.load(args.checkpoint_path)

   with open(args.config_file) as f:
      A					       =json.loads(f.read())

   # unet case
   A					       ={key.split('.')[0] for key in checkpoint.keys()}
   if "encoder" in key_prefix_set and "decoder" in key_prefix_set:
      A					       =convert_vq_autoenc_checkpoint(checkpoint, config)
   else:
      A					       =convert_ddpm_checkpoint(checkpoint, config)

   if "ddpm" in config:
      del config["ddpm"]

   if config["_class_name"] == "VQModel":
      A					       =VQModel(**config)
      model.load_state_dict(converted_checkpoint)
      model.save_pretrained(args.dump_path)
   elif config["_class_name"] == "AutoencoderKL":
      A					       =AutoencoderKL(**config)
      model.load_state_dict(converted_checkpoint)
      model.save_pretrained(args.dump_path)
   else:
      A					       =UNetaDModel(**config)
      model.load_state_dict(converted_checkpoint)

      A					       =DDPMScheduler.from_config('/'.join(args.checkpoint_path.split('/')[:-1]))

      A					       =DDPMPipeline(unet=model, scheduler=scheduler)
      pipe.save_pretrained(args.dump_path)










'''simple docstring'''







import json
import os
from dataclasses import dataclass

import numpy as np
import pyarrow as pa

import datasets
from utils import get_duration


A					       =10_00_00_00_00_00
A					       =1_00_00

A       , A					       =os.path.split(__file__)
A					       =os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))





def 		snake_case_				(_a      :				int       ,  _a      :				int	):
    UpperCAmelCase     			=						pa.Table.from_pydict({'''col''': [0] * chunk_size}	)
    UpperCAmelCase     			=						pa.concat_tables([table] * (num_examples // chunk_size)	)
    return datasets.Dataset(_a       ,  fingerprint='''table_100B'''	)


@dataclass
class      _a    :
       __a							:	int
       __a							:	int
       __a							:	int
       __a							:	int
       def        A (      self		:      List[str]							):



           '''simple docstring'''

           UpperCAmelCase     			=						np.random.default_rng(self.seed							)
           UpperCAmelCase     			=						rng.integers(low=self.low  ,							high=self.high  ,							size=self.size							).tolist()
       def __iter__(      self		:      Tuple							):



           '''simple docstring'''

           return iter(self._sampled_values							)







       def __len__(      self		:      Dict							):



           '''simple docstring'''

           return self.size






@get_duration
def 		snake_case_				(_a      :				datasets.Dataset	):
    UpperCAmelCase     			=						dataset[0]





@get_duration
def 		snake_case_				(_a      :				datasets.Dataset	):
    UpperCAmelCase     			=						dataset[-1]





@get_duration
def 		snake_case_				(_a      :				datasets.Dataset	):
    UpperCAmelCase     			=						dataset[range(len(_a	) // 2       ,  len(_a	) // 2 + 1_0_2_4	)]





@get_duration
def 		snake_case_				(_a      :				datasets.Dataset	):
    UpperCAmelCase     			=						dataset[RandIter(0       ,  len(_a	)       ,  1_0_2_4       ,  seed=4_2	)]





def 		snake_case_				():
    UpperCAmelCase     			=						{'''num examples''': SPEED_TEST_N_EXAMPLES}
    UpperCAmelCase     			=						(get_first_row, get_last_row, get_batch_of_aaaa_rows, get_batch_of_aaaa_random_rows)
    print('''generating dataset'''	)
    UpperCAmelCase     			=						generate_100B_dataset(num_examples=_a       ,  chunk_size=_a	)
    print('''Functions'''	)
    for func in functions:
        print(func.__name__	)
        UpperCAmelCase     			=						func(_a	)

    with open(_a       ,  '''wb'''	) as f:
        f.write(json.dumps(_a	).encode('''utf-8'''	)	)


if __name__ == "__main__":  # useful to run the profiler
   benchmark_table_100B()




