code: 
  
  
  
  
  import dataclasses
  import enum
  import functools
  import math
  import re
  
  # The following script is adapted from the script of TaPas.
  # Original: https://github.com/google-research/tapas/master/wikisql_utils.py
  from typing import Any, List, Text
  
  
  __A :		Dict      							=    '''none'''
  __A :		Dict      							=    '''none'''
  def 					A_			(		snake_case_     :       Union[str, Any]						,snake_case_     :       Optional[Any]    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      UpperCamelCase				:   List[Any]          =							value.split(snake_case_    )
      return len(snake_case_    ) > 1 and any((len(snake_case_    ) == 3 for x in split)    )
  def 					A_			(		snake_case_     :       Dict    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      if isinstance(snake_case_						,snake_case_    ):
          return value
      if isinstance(snake_case_						,snake_case_    ):
          return float(snake_case_    )
      if not isinstance(snake_case_						,snake_case_    ):
          raise ValueError("""Argument value is not a string. Can't parse it as float"""    )
      UpperCamelCase				:   Dict          =							value
  
      try:
          # Example: 1,000.7
          if "." in sanitized and "," in sanitized:
              return float(sanitized.replace(""","""						,""""""    )    )
          # 1,000
          if "," in sanitized and _split_thousands(""","""						,snake_case_    ):
              return float(sanitized.replace(""","""						,""""""    )    )
          # 5,5556
          if "," in sanitized and sanitized.count(""","""    ) == 1 and not _split_thousands(""","""						,snake_case_    ):
              return float(sanitized.replace(""","""						,"""."""    )    )
          # 0.0.0.1
          if sanitized.count("""."""    ) > 1:
              return float(sanitized.replace("""."""						,""""""    )    )
          # 0,0,0,1
          if sanitized.count(""","""    ) > 1:
              return float(sanitized.replace(""","""						,""""""    )    )
          return float(snake_case_    )
      except ValueError:
          # Avoid adding the sanitized value in the error message.
          raise ValueError("""Unable to convert value to float"""    )
  def 					A_			(		snake_case_     :       List[str]    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      if answer is None:
          return None
      try:
          UpperCamelCase				:   Optional[Any]          =							convert_to_float(snake_case_    )
          if isinstance(snake_case_						,snake_case_    ) and math.isnan(snake_case_    ):
              return None
          return value
      except ValueError:
          return answer.lower()
  
  
  __A :		List[str]      							=    {
      '''text''': lambda x: x,
      '''real''': convert_to_float,
  }
  class 							lowerCamelCase		( enum.Enum				):
     lowercase       :  str      =				0
     lowercase       :  Tuple      =				1
     lowercase       :  str      =				2
     lowercase       :  Optional[int]      =				3
     lowercase       :  Optional[int]      =				4
     lowercase       :  List[str]      =				5
  
  
  
  
  
  
  
  class 							lowerCamelCase		( enum.Enum				):
     lowercase       :  List[Any]      =				0
     lowercase       :  List[Any]      =				1
     lowercase       :  Optional[Any]      =				2
  
  
  
  
  
  
  
  @dataclasses.dataclass
  class 							lowerCamelCase		:
     lowercase       :  Text
     lowercase       :  _Operator
     lowercase       :  Any
  
  
  
  
  
  
  
  __A :		Optional[int]      							=    re.compile(R'''\w+|[^\w\s]+''', re.UNICODE | re.MULTILINE | re.DOTALL)
  def 					A_			(		snake_case_     :       Optional[Any]    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      return list(_TOKENIZER.findall(x.lower()    )    )
  def 					A_			(		snake_case_     :       Optional[int]						,snake_case_     :       Any						,snake_case_     :       Optional[Any]    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      if operator == _Operator.EQUALS:
          return src == tgt
      elif operator == _Operator.GREATER:
          return src > tgt
      elif operator == _Operator.LESSER:
          return src < tgt
      raise ValueError(f'Unknown operator: {operator}'    )
  def 					A_			(		snake_case_     :       Union[str, Any]						,snake_case_     :       Union[str, Any]						,snake_case_     :       Optional[int]    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      UpperCamelCase				:   Dict          =							table["""types"""]
      return _TYPE_CONVERTER[types[column]](snake_case_    )
  def 					A_			(		snake_case_     :       Any    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      return isinstance(snake_case_						,snake_case_    )
  def 					A_			(		snake_case_     :       Optional[Any]						,snake_case_     :       List[str]						,snake_case_     :       Optional[Any]    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      for cond in conditions:
          UpperCamelCase				:   Optional[int]          =							row[cond.column]
  
          UpperCamelCase				:   Tuple          =							_parse_value(snake_case_						,cond.column						,cond.cmp_value    )
  
          if _is_string(snake_case_    ) and _is_string(snake_case_    ):
              UpperCamelCase				:   str          =							_normalize_for_match(snake_case_    )
              UpperCamelCase				:   List[str]          =							_normalize_for_match(snake_case_    )
  
          if not isinstance(snake_case_						,type(snake_case_    )    ):
              raise ValueError("""Type difference {} != {}""".format(type(snake_case_    )						,type(snake_case_    )    )    )
  
          if not _compare(cond.operator						,snake_case_						,snake_case_    ):
              return False
      return True
  def 					A_			(		snake_case_     :       List[Any]						,snake_case_     :       Any						,snake_case_     :       Tuple    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      if not answer_coordinates:
          if aggregation_op == _Aggregation.COUNT:
              return 0.0
          else:
              return EMPTY_ANSWER_AGG
  
      # Count can support non numeric answers.
      if aggregation_op == _Aggregation.COUNT:
          return float(len(snake_case_    )    )
  
      # If we have just one answer, if float returns it or try a conversion.
      UpperCamelCase				:   Dict          =							[table["""rows"""][i][j] for (i, j) in answer_coordinates]
      if len(snake_case_    ) == 1:
          try:
              return convert_to_float(values[0]    )
          except ValueError as e:
              if aggregation_op != _Aggregation.NONE:
                  raise e
  
      if aggregation_op == _Aggregation.NONE:
          return None
  
      # Other aggregation only support numeric values. Bail out if we have strings.
      if not all((isinstance(snake_case_						,(int, float)    ) for v in values)    ):
          return None
  
      if aggregation_op == _Aggregation.SUM:
          return float(sum(snake_case_    )    )
      elif aggregation_op == _Aggregation.AVERAGE:
          return sum(snake_case_    ) / len(snake_case_    )
      else:
          raise ValueError(f'Unknown aggregation: {aggregation_op}'    )
  def 					A_			(		snake_case_     :       Tuple						,snake_case_     :       int    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      UpperCamelCase				:   str          =							sql_query["""agg"""]
      if aggregation_op_index >= 3:
          UpperCamelCase				:   int          =							_Aggregation(snake_case_    )
      else:
          UpperCamelCase				:   Dict          =							_Aggregation.NONE
  
      UpperCamelCase				:   Dict          =							sql_query["""sel"""]
      UpperCamelCase				:   int          =							[
          _Condition(snake_case_						,_Operator(snake_case_    )						,snake_case_    )
          for column, operator, cmp_value in zip(
              sql_query["""conds"""]["""column_index"""]						,sql_query["""conds"""]["""operator_index"""]						,sql_query["""conds"""]["""condition"""]    )
      ]
  
      UpperCamelCase				:   List[str]          =							[]
      for row in range(len(table["""rows"""]    )    ):
          if _respect_conditions(snake_case_						,table["""rows"""][row]						,snake_case_    ):
              indices.append((row, target_column)    )
  
      if not indices:
          return [], aggregation_op
  
      if len(snake_case_    ) == 1:
          return indices, aggregation_op
  
      # Parsing of MIN/MAX.
      if aggregation_op_index in (1, 2):
          UpperCamelCase				:   Union[str, Any]          =							{2: min, 1: max}
          UpperCamelCase				:   List[str]          =							[(table["""rows"""][i][j], index) for index, (i, j) in enumerate(snake_case_    )]
          UpperCamelCase				:   Optional[int]          =							functools.reduce(operators[sql_query["""agg"""]]						,snake_case_    )
  
          UpperCamelCase				:   Optional[Any]          =							[indices[reduced[1]]]
          return ret, _Aggregation.NONE
  
      return indices, aggregation_op
  def 					A_			(		snake_case_     :       List[Any]						,snake_case_     :       Any						,snake_case_     :       List[str]    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      if float_answer is not None:
          return [str(snake_case_    )]
      return [str(table["""real_rows"""][r][c]    ) for r, c in answer_coordinates]
  
  
  def 					A_			(		snake_case_     :       Tuple						,snake_case_     :       List[str]    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      UpperCamelCase				:   Optional[int]          =							_get_answer_coordinates(snake_case_						,snake_case_    )
      UpperCamelCase				:   Dict          =							_get_float_answer(snake_case_						,snake_case_						,snake_case_    )
      UpperCamelCase				:   Tuple          =							_get_answer_text(snake_case_						,snake_case_						,snake_case_    )
      # keep the original data the same with TaPas
      if len(snake_case_    ) == 0:
          UpperCamelCase				:   Optional[int]          =							[EMPTY_ANSWER]
      return answer_text
  
  
  
  
  
style_context: 
  
  
  
  
  """simple docstring"""
  
  
  
  
  import argparse
  import os
  
  import torch
  from torchvision.datasets.utils import download_url
  
  from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel
  
  
  __A :		int      							=    {512: '''DiT-XL-2-512x512.pt''', 256: '''DiT-XL-2-256x256.pt'''}
  def 					A_			(		snake_case_     :       Dict    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      UpperCamelCase				:   Any          =							f'pretrained_models/{model_name}'
      if not os.path.isfile(snake_case_    ):
          os.makedirs("""pretrained_models"""						,exist_ok=snake_case_    )
          UpperCamelCase				:   Dict          =							f'https://dl.fbaipublicfiles.com/DiT/models/{model_name}'
          download_url(snake_case_						,"""pretrained_models"""    )
      UpperCamelCase				:   List[Any]          =							torch.load(snake_case_						,map_location=lambda snake_case_						,snake_case_    : storage    )
      return model
  def 					A_			(		snake_case_     :       List[str]    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      UpperCamelCase				:   int          =							download_model(pretrained_models[args.image_size]    )
  
      UpperCamelCase				:   str          =							state_dict["""x_embedder.proj.weight"""]
      UpperCamelCase				:   Optional[Any]          =							state_dict["""x_embedder.proj.bias"""]
      state_dict.pop("""x_embedder.proj.weight"""    )
      state_dict.pop("""x_embedder.proj.bias"""    )
  
      for depth in range(2_8    ):
          UpperCamelCase				:   Optional[int]          =							state_dict[
              """t_embedder.mlp.0.weight"""
          ]
          UpperCamelCase				:   Optional[Any]          =							state_dict[
              """t_embedder.mlp.0.bias"""
          ]
          UpperCamelCase				:   str          =							state_dict[
              """t_embedder.mlp.2.weight"""
          ]
          UpperCamelCase				:   Optional[int]          =							state_dict[
              """t_embedder.mlp.2.bias"""
          ]
          UpperCamelCase				:   int          =							state_dict[
              """y_embedder.embedding_table.weight"""
          ]
  
          UpperCamelCase				:   List[Any]          =							state_dict[
              f'blocks.{depth}.adaLN_modulation.1.weight'
          ]
          UpperCamelCase				:   Any          =							state_dict[
              f'blocks.{depth}.adaLN_modulation.1.bias'
          ]
  
          UpperCamelCase       ,							UpperCamelCase       ,							UpperCamelCase				:   Dict          =							torch.chunk(state_dict[f'blocks.{depth}.attn.qkv.weight']						,3						,dim=0    )
          UpperCamelCase       ,							UpperCamelCase       ,							UpperCamelCase				:   Dict          =							torch.chunk(state_dict[f'blocks.{depth}.attn.qkv.bias']						,3						,dim=0    )
  
          UpperCamelCase				:   int          =							q
          UpperCamelCase				:   Tuple          =							q_bias
          UpperCamelCase				:   List[Any]          =							k
          UpperCamelCase				:   int          =							k_bias
          UpperCamelCase				:   List[str]          =							v
          UpperCamelCase				:   str          =							v_bias
  
          UpperCamelCase				:   Any          =							state_dict[
              f'blocks.{depth}.attn.proj.weight'
          ]
          UpperCamelCase				:   Optional[Any]          =							state_dict[f'blocks.{depth}.attn.proj.bias']
  
          UpperCamelCase				:   str          =							state_dict[f'blocks.{depth}.mlp.fc1.weight']
          UpperCamelCase				:   Tuple          =							state_dict[f'blocks.{depth}.mlp.fc1.bias']
          UpperCamelCase				:   str          =							state_dict[f'blocks.{depth}.mlp.fc2.weight']
          UpperCamelCase				:   Tuple          =							state_dict[f'blocks.{depth}.mlp.fc2.bias']
  
          state_dict.pop(f'blocks.{depth}.attn.qkv.weight'    )
          state_dict.pop(f'blocks.{depth}.attn.qkv.bias'    )
          state_dict.pop(f'blocks.{depth}.attn.proj.weight'    )
          state_dict.pop(f'blocks.{depth}.attn.proj.bias'    )
          state_dict.pop(f'blocks.{depth}.mlp.fc1.weight'    )
          state_dict.pop(f'blocks.{depth}.mlp.fc1.bias'    )
          state_dict.pop(f'blocks.{depth}.mlp.fc2.weight'    )
          state_dict.pop(f'blocks.{depth}.mlp.fc2.bias'    )
          state_dict.pop(f'blocks.{depth}.adaLN_modulation.1.weight'    )
          state_dict.pop(f'blocks.{depth}.adaLN_modulation.1.bias'    )
  
      state_dict.pop("""t_embedder.mlp.0.weight"""    )
      state_dict.pop("""t_embedder.mlp.0.bias"""    )
      state_dict.pop("""t_embedder.mlp.2.weight"""    )
      state_dict.pop("""t_embedder.mlp.2.bias"""    )
      state_dict.pop("""y_embedder.embedding_table.weight"""    )
  
      UpperCamelCase				:   List[str]          =							state_dict["""final_layer.adaLN_modulation.1.weight"""]
      UpperCamelCase				:   str          =							state_dict["""final_layer.adaLN_modulation.1.bias"""]
      UpperCamelCase				:   Tuple          =							state_dict["""final_layer.linear.weight"""]
      UpperCamelCase				:   List[Any]          =							state_dict["""final_layer.linear.bias"""]
  
      state_dict.pop("""final_layer.linear.weight"""    )
      state_dict.pop("""final_layer.linear.bias"""    )
      state_dict.pop("""final_layer.adaLN_modulation.1.weight"""    )
      state_dict.pop("""final_layer.adaLN_modulation.1.bias"""    )
  
      # DiT XL/2
      UpperCamelCase				:   int          =							TransformeraDModel(
          sample_size=args.image_size // 8						,num_layers=2_8						,attention_head_dim=7_2						,in_channels=4						,out_channels=8						,patch_size=2						,attention_bias=snake_case_						,num_attention_heads=1_6						,activation_fn="""gelu-approximate"""						,num_embeds_ada_norm=1_0_0_0						,norm_type="""ada_norm_zero"""						,norm_elementwise_affine=snake_case_						,)
      transformer.load_state_dict(snake_case_						,strict=snake_case_    )
  
      UpperCamelCase				:   str          =							DDIMScheduler(
          num_train_timesteps=1_0_0_0						,beta_schedule="""linear"""						,prediction_type="""epsilon"""						,clip_sample=snake_case_						,)
  
      UpperCamelCase				:   Optional[Any]          =							AutoencoderKL.from_pretrained(args.vae_model    )
  
      UpperCamelCase				:   List[Any]          =							DiTPipeline(transformer=snake_case_						,vae=snake_case_						,scheduler=snake_case_    )
  
      if args.save:
          pipeline.save_pretrained(args.checkpoint_path    )
  
  
  if __name__ == "__main__":
    __A :		Any      							=    argparse.ArgumentParser()
  
    parser.add_argument(
        '''--image_size''',
        default=256,
        type=int,
        required=False,
        help='''Image size of pretrained model, either 256 or 512.''',
    )
    parser.add_argument(
        '''--vae_model''',
        default='''stabilityai/sd-vae-ft-ema''',
        type=str,
        required=False,
        help='''Path to pretrained VAE model, either stabilityai/sd-vae-ft-mse or stabilityai/sd-vae-ft-ema.''',
    )
    parser.add_argument(
        '''--save''', default=True, type=bool, required=False, help='''Whether to save the converted pipeline or not.'''
    )
    parser.add_argument(
        '''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the output pipeline.'''
    )
  
    __A :		Dict      							=    parser.parse_args()
    main(args)
  
  
  
  
  