
'''simple docstring'''





import argparse
import json

import torch

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





def 							snake_case				(		UpperCAmelCase   ,	UpperCAmelCase=1						)->      List[str]:







      """simple docstring"""
      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				(		UpperCAmelCase   ,	UpperCAmelCase=0						)->      Optional[int]:







      """simple docstring"""
      __A													=      []
      for old_item in old_list:
            __A													=      old_item
            __A													=      new_item.replace('block.'   ,	'resnets.'						)
            __A													=      new_item.replace('conv_shorcut'   ,	'conv1'						)
            __A													=      new_item.replace('in_shortcut'   ,	'conv_shortcut'						)
            __A													=      new_item.replace('temb_proj'   ,	'time_emb_proj'						)

            __A													=      shave_segments(UpperCAmelCase_   ,	n_shave_prefix_segments=UpperCAmelCase_						)

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

      return mapping





def 							snake_case				(		UpperCAmelCase   ,	UpperCAmelCase=0   ,	UpperCAmelCase=False						)->      Any:







      """simple docstring"""
      __A													=      []
      for old_item in old_list:
            __A													=      old_item

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

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

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

      return mapping





def 							snake_case				(		UpperCAmelCase   ,	UpperCAmelCase   ,	UpperCAmelCase   ,	UpperCAmelCase=None   ,	UpperCAmelCase=None   ,	UpperCAmelCase=None						)->      str:







      """simple docstring"""
      assert isinstance(UpperCAmelCase_   ,	UpperCAmelCase_						), "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():
                  __A													=      old_checkpoint[path]
                  __A													=      old_tensor.shape[0] // 3

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

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

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

                  __A													=      query.reshape(UpperCAmelCase_						).squeeze()
                  __A													=      key.reshape(UpperCAmelCase_						).squeeze()
                  __A													=      value.reshape(UpperCAmelCase_						).squeeze()

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

            if attention_paths_to_split is not None and new_path in attention_paths_to_split:
                  continue

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

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

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





def 							snake_case				(		UpperCAmelCase   ,	UpperCAmelCase						)->      Optional[Any]:







      """simple docstring"""
      __A													=      {}

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

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

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

      __A													=      len({'.'.join(layer.split('.'						)[:2]						) for layer in checkpoint if 'down' in layer}						)
      __A													=      {
          layer_id: [key for key in checkpoint if f'down.{layer_id}' in key] for layer_id in range(UpperCAmelCase_						)
      }

      __A													=      len({'.'.join(layer.split('.'						)[:2]						) for layer in checkpoint if 'up' in layer}						)
      __A													=      {layer_id: [key for key in checkpoint if f'up.{layer_id}' in key] for layer_id in range(UpperCAmelCase_						)}

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

            if any('downsample' in layer for layer in down_blocks[i]						):
                  __A													=      checkpoint[
                      f'down.{i}.downsample.op.weight'
                  ]
                  __A													=      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]						):
                  __A													=      len(
                      {'.'.join(shave_segments(UpperCAmelCase_   ,	2						).split('.'						)[:2]						) for layer in down_blocks[i] if 'block' in layer}						)
                  __A													=      {
                      layer_id: [key for key in down_blocks[i] if f'block.{layer_id}' in key]
                      for layer_id in range(UpperCAmelCase_						)
                  }

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

            if any('attn' in layer for layer in down_blocks[i]						):
                  __A													=      len(
                      {'.'.join(shave_segments(UpperCAmelCase_   ,	2						).split('.'						)[:2]						) for layer in down_blocks[i] if 'attn' in layer}						)
                  __A													=      {
                      layer_id: [key for key in down_blocks[i] if f'attn.{layer_id}' in key]
                      for layer_id in range(UpperCAmelCase_						)
                  }

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

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

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

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

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

      for i in range(UpperCAmelCase_						):
            __A													=      num_up_blocks - 1 - i

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

            if any('block' in layer for layer in up_blocks[i]						):
                  __A													=      len(
                      {'.'.join(shave_segments(UpperCAmelCase_   ,	2						).split('.'						)[:2]						) for layer in up_blocks[i] if 'block' in layer}						)
                  __A													=      {
                      layer_id: [key for key in up_blocks[i] if f'block.{layer_id}' in key] for layer_id in range(UpperCAmelCase_						)
                  }

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

            if any('attn' in layer for layer in up_blocks[i]						):
                  __A													=      len(
                      {'.'.join(shave_segments(UpperCAmelCase_   ,	2						).split('.'						)[:2]						) for layer in up_blocks[i] if 'attn' in layer}						)
                  __A													=      {
                      layer_id: [key for key in up_blocks[i] if f'attn.{layer_id}' in key] for layer_id in range(UpperCAmelCase_						)
                  }

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

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





def 							snake_case				(		UpperCAmelCase   ,	UpperCAmelCase						)->      List[Any]:







      """simple docstring"""
      __A													=      {}

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

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

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

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

      __A													=      len({'.'.join(layer.split('.'						)[:3]						) for layer in checkpoint if 'down' in layer}						)
      __A													=      {
          layer_id: [key for key in checkpoint if f'down.{layer_id}' in key] for layer_id in range(UpperCAmelCase_						)
      }

      __A													=      len({'.'.join(layer.split('.'						)[:3]						) for layer in checkpoint if 'up' in layer}						)
      __A													=      {layer_id: [key for key in checkpoint if f'up.{layer_id}' in key] for layer_id in range(UpperCAmelCase_						)}

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

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

            if any('block' in layer for layer in down_blocks[i]						):
                  __A													=      len(
                      {'.'.join(shave_segments(UpperCAmelCase_   ,	3						).split('.'						)[:3]						) for layer in down_blocks[i] if 'block' in layer}						)
                  __A													=      {
                      layer_id: [key for key in down_blocks[i] if f'block.{layer_id}' in key]
                      for layer_id in range(UpperCAmelCase_						)
                  }

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

            if any('attn' in layer for layer in down_blocks[i]						):
                  __A													=      len(
                      {'.'.join(shave_segments(UpperCAmelCase_   ,	3						).split('.'						)[:3]						) for layer in down_blocks[i] if 'attn' in layer}						)
                  __A													=      {
                      layer_id: [key for key in down_blocks[i] if f'attn.{layer_id}' in key]
                      for layer_id in range(UpperCAmelCase_						)
                  }

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

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

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

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

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

      for i in range(UpperCAmelCase_						):
            __A													=      num_up_blocks - 1 - i

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

            if any('block' in layer for layer in up_blocks[i]						):
                  __A													=      len(
                      {'.'.join(shave_segments(UpperCAmelCase_   ,	3						).split('.'						)[:3]						) for layer in up_blocks[i] if 'block' in layer}						)
                  __A													=      {
                      layer_id: [key for key in up_blocks[i] if f'block.{layer_id}' in key] for layer_id in range(UpperCAmelCase_						)
                  }

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

            if any('attn' in layer for layer in up_blocks[i]						):
                  __A													=      len(
                      {'.'.join(shave_segments(UpperCAmelCase_   ,	3						).split('.'						)[:3]						) for layer in up_blocks[i] if 'attn' in layer}						)
                  __A													=      {
                      layer_id: [key for key in up_blocks[i] if f'attn.{layer_id}' in key] for layer_id in range(UpperCAmelCase_						)
                  }

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

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

      return new_checkpoint


if __name__ == "__main__":
 a__		: List[Any]  							=							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__		: List[str]  							=							parser.parse_args()
 a__		: str  							=							torch.load(args.checkpoint_path)

 with open(args.config_file) as f:
  a__		: Optional[Any]  							=							json.loads(f.read())

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

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

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

  a__		: Optional[int]  							=							DDPMScheduler.from_config("/".join(args.checkpoint_path.split("/")[:-1]))

  a__		: Tuple  							=							DDPMPipeline(unet=model, scheduler=scheduler)
  pipe.save_pretrained(args.dump_path)









"""simple docstring"""



import os

from ...utils import logging
from .utils import DataProcessor, InputExample


SCREAMING_SNAKE_CASE_       :						Dict					=    logging.get_logger(__name__)



class        a    (							_lowerCamelCase					):



    """simple docstring"""



    def __init__(  self:			Tuple     ,    UpperCamelCase:			Optional[Any]     ,    UpperCamelCase:			Optional[int]=None							):






         """simple docstring"""


         A__				      =	language
         A__				      =	train_language

    def 					UpperCamelCase						(  self:			Union[str, Any]     ,    UpperCamelCase:			str							):






         """simple docstring"""


         A__				      =	self.language if self.train_language is None else self.train_language
         A__				      =	self._read_tsv(os.path.join(UpperCamelCase     ,    f"""XNLI-MT-1.0/multinli/multinli.train.{lg}.tsv"""							)							)
         A__				      =	[]
         for i, line in enumerate(UpperCamelCase							):
              if i == 0:
                   continue
              A__				      =	f"""train-{i}"""
              A__				      =	line[0]
              A__				      =	line[1]
              A__				      =	"""contradiction""" if line[2] == """contradictory""" else line[2]
              if not isinstance(UpperCamelCase     ,    UpperCamelCase							):
                   raise ValueError(f"""Training input {text_a} is not a string"""							)
              if not isinstance(UpperCamelCase     ,    UpperCamelCase							):
                   raise ValueError(f"""Training input {text_b} is not a string"""							)
              if not isinstance(UpperCamelCase     ,    UpperCamelCase							):
                   raise ValueError(f"""Training label {label} is not a string"""							)
              examples.append(InputExample(guid=UpperCamelCase     ,    text_a=UpperCamelCase     ,    text_b=UpperCamelCase     ,    label=UpperCamelCase							)							)
         return examples

    def 					UpperCamelCase						(  self:			Dict     ,    UpperCamelCase:			Union[str, Any]							):






         """simple docstring"""


         A__				      =	self._read_tsv(os.path.join(UpperCamelCase     ,    """XNLI-1.0/xnli.test.tsv"""							)							)
         A__				      =	[]
         for i, line in enumerate(UpperCamelCase							):
              if i == 0:
                   continue
              A__				      =	line[0]
              if language != self.language:
                   continue
              A__				      =	f"""test-{i}"""
              A__				      =	line[6]
              A__				      =	line[7]
              A__				      =	line[1]
              if not isinstance(UpperCamelCase     ,    UpperCamelCase							):
                   raise ValueError(f"""Training input {text_a} is not a string"""							)
              if not isinstance(UpperCamelCase     ,    UpperCamelCase							):
                   raise ValueError(f"""Training input {text_b} is not a string"""							)
              if not isinstance(UpperCamelCase     ,    UpperCamelCase							):
                   raise ValueError(f"""Training label {label} is not a string"""							)
              examples.append(InputExample(guid=UpperCamelCase     ,    text_a=UpperCamelCase     ,    text_b=UpperCamelCase     ,    label=UpperCamelCase							)							)
         return examples





    def 					UpperCamelCase						(  self:			Tuple							):






         """simple docstring"""


         return ["contradiction", "entailment", "neutral"]

SCREAMING_SNAKE_CASE_       :						int					=    {
    'xnli': XnliProcessor,
}

SCREAMING_SNAKE_CASE_       :						List[Any]					=    {
    'xnli': 'classification',
}

SCREAMING_SNAKE_CASE_       :						str					=    {
    'xnli': 3,
}






