

"""simple docstring"""



import argparse
import json

import torch

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





def 			_snake_case  (     UpperCAmelCase_	:       List[Any]       , UpperCAmelCase_	:       Dict=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  (     UpperCAmelCase_	:       Tuple       , UpperCAmelCase_	:       Union[str, Any]=0      ):
     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_	:       Any       , UpperCAmelCase_	:       Any=0       , UpperCAmelCase_	:       Optional[int]=False      ):
     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_	:       Union[str, Any]       , UpperCAmelCase_	:       str       , UpperCAmelCase_	:       int       , UpperCAmelCase_	:       Tuple=None       , UpperCAmelCase_	:       str=None       , UpperCAmelCase_	:       Optional[int]=None      ):
     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_	:       Union[str, Any]       , UpperCAmelCase_	:       Any      ):
     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_	:       Any       , UpperCAmelCase_	:       Dict      ):
     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__":
 SCREAMING_SNAKE_CASE_       :						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.')

 SCREAMING_SNAKE_CASE_       :						List[str]					=    parser.parse_args()
 SCREAMING_SNAKE_CASE_       :						str					=    torch.load(args.checkpoint_path)

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

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

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

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

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

  SCREAMING_SNAKE_CASE_       :						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,
}






