




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










"""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)




