

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


_lowerCamelCase							="""none"""
_lowerCamelCase							="""none"""







def      snake_case__							(  lowerCAmelCase_,       lowerCAmelCase_     ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE 		=value.split(UpperCamelCase__     )
     return len(UpperCamelCase__     ) > 1 and any((len(UpperCamelCase__     ) == 3 for x in split)     )







def      snake_case__							(  lowerCAmelCase_     ):





     """simple docstring"""







     if isinstance(UpperCamelCase__,       UpperCamelCase__     ):
          return value
     if isinstance(UpperCamelCase__,       UpperCamelCase__     ):
          return float(UpperCamelCase__     )
     if not isinstance(UpperCamelCase__,       UpperCamelCase__     ):
          raise ValueError('Argument value is not a string. Can\'t parse it as float'     )
     SCREAMING_SNAKE_CASE 		=value

     try:
          # Example: 1,000.7
          if "." in sanitized and "," in sanitized:
               return float(sanitized.replace(',',       ''     )     )
          # 1,000
          if "," in sanitized and _split_thousands(',',       UpperCamelCase__     ):
               return float(sanitized.replace(',',       ''     )     )
          # 5,5556
          if "," in sanitized and sanitized.count(','     ) == 1 and not _split_thousands(',',       UpperCamelCase__     ):
               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(UpperCamelCase__     )
     except ValueError:
          # Avoid adding the sanitized value in the error message.
          raise ValueError('Unable to convert value to float'     )







def      snake_case__							(  lowerCAmelCase_     ):





     """simple docstring"""







     if answer is None:
          return None
     try:
          SCREAMING_SNAKE_CASE 		=convert_to_float(UpperCamelCase__     )
          if isinstance(UpperCamelCase__,       UpperCamelCase__     ) and math.isnan(UpperCamelCase__     ):
               return None
          return value
     except ValueError:
          return answer.lower()


_lowerCamelCase							={
    """text""": lambda x: x,
    """real""": convert_to_float,
}



class 						a_			(				enum.Enum	):



      """simple docstring"""







      __UpperCAmelCase     						= 0
      __UpperCAmelCase     						= 1
      __UpperCAmelCase     						= 2
      __UpperCAmelCase     						= 3
      __UpperCAmelCase     						= 4
      __UpperCAmelCase     						= 5







class 						a_			(				enum.Enum	):



      """simple docstring"""







      __UpperCAmelCase     						= 0
      __UpperCAmelCase     						= 1
      __UpperCAmelCase     						= 2







@dataclasses.dataclass
class 						a_			:



      """simple docstring"""







      __UpperCAmelCase     						= 42
      __UpperCAmelCase     						= 42
      __UpperCAmelCase     						= 42







_lowerCamelCase							=re.compile(R"\w+|[^\w\s]+", re.UNICODE | re.MULTILINE | re.DOTALL)







def      snake_case__							(  lowerCAmelCase_     ):





     """simple docstring"""







     return list(_TOKENIZER.findall(x.lower()     )     )







def      snake_case__							(  lowerCAmelCase_,       lowerCAmelCase_,       lowerCAmelCase_     ):





     """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      snake_case__							(  lowerCAmelCase_,       lowerCAmelCase_,       lowerCAmelCase_     ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE 		=table['types']
     return _TYPE_CONVERTER[types[column]](UpperCamelCase__     )







def      snake_case__							(  lowerCAmelCase_     ):





     """simple docstring"""







     return isinstance(UpperCamelCase__,       UpperCamelCase__     )







def      snake_case__							(  lowerCAmelCase_,       lowerCAmelCase_,       lowerCAmelCase_     ):





     """simple docstring"""







     for cond in conditions:
          SCREAMING_SNAKE_CASE 		=row[cond.column]

          SCREAMING_SNAKE_CASE 		=_parse_value(UpperCamelCase__,       cond.column,       cond.cmp_value     )

          if _is_string(UpperCamelCase__     ) and _is_string(UpperCamelCase__     ):
               SCREAMING_SNAKE_CASE 		=_normalize_for_match(UpperCamelCase__     )
               SCREAMING_SNAKE_CASE 		=_normalize_for_match(UpperCamelCase__     )

          if not isinstance(UpperCamelCase__,       type(UpperCamelCase__     )     ):
               raise ValueError('Type difference {} != {}'.format(type(UpperCamelCase__     ),       type(UpperCamelCase__     )     )     )

          if not _compare(cond.operator,       UpperCamelCase__,       UpperCamelCase__     ):
               return False
     return True







def      snake_case__							(  lowerCAmelCase_,       lowerCAmelCase_,       lowerCAmelCase_     ):





     """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(UpperCamelCase__     )     )

     # If we have just one answer, if float returns it or try a conversion.
     SCREAMING_SNAKE_CASE 		=[table['rows'][i][j] for (i, j) in answer_coordinates]
     if len(UpperCamelCase__     ) == 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(UpperCamelCase__,       (int, float)     ) for v in values)     ):
          return None

     if aggregation_op == _Aggregation.SUM:
          return float(sum(UpperCamelCase__     )     )
     elif aggregation_op == _Aggregation.AVERAGE:
          return sum(UpperCamelCase__     ) / len(UpperCamelCase__     )
     else:
          raise ValueError(F'Unknown aggregation: {aggregation_op}'     )







def      snake_case__							(  lowerCAmelCase_,       lowerCAmelCase_     ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE 		=sql_query['agg']
     if aggregation_op_index >= 3:
          SCREAMING_SNAKE_CASE 		=_Aggregation(UpperCamelCase__     )
     else:
          SCREAMING_SNAKE_CASE 		=_Aggregation.NONE

     SCREAMING_SNAKE_CASE 		=sql_query['sel']
     SCREAMING_SNAKE_CASE 		=[
         _Condition(UpperCamelCase__,       _Operator(UpperCamelCase__     ),       UpperCamelCase__     )
         for column, operator, cmp_value in zip(
             sql_query['conds']['column_index'],       sql_query['conds']['operator_index'],       sql_query['conds']['condition']     )
     ]

     SCREAMING_SNAKE_CASE 		=[]
     for row in range(len(table['rows']     )     ):
          if _respect_conditions(UpperCamelCase__,       table['rows'][row],       UpperCamelCase__     ):
               indices.append((row, target_column)     )

     if not indices:
          return [], aggregation_op

     if len(UpperCamelCase__     ) == 1:
          return indices, aggregation_op

     # Parsing of MIN/MAX.
     if aggregation_op_index in (1, 2):
          SCREAMING_SNAKE_CASE 		={2: min, 1: max}
          SCREAMING_SNAKE_CASE 		=[(table['rows'][i][j], index) for index, (i, j) in enumerate(UpperCamelCase__     )]
          SCREAMING_SNAKE_CASE 		=functools.reduce(operators[sql_query['agg']],       UpperCamelCase__     )

          SCREAMING_SNAKE_CASE 		=[indices[reduced[1]]]
          return ret, _Aggregation.NONE

     return indices, aggregation_op







def      snake_case__							(  lowerCAmelCase_,       lowerCAmelCase_,       lowerCAmelCase_     ):





     """simple docstring"""







     if float_answer is not None:
          return [str(UpperCamelCase__     )]
     return [str(table['real_rows'][r][c]     ) for r, c in answer_coordinates]







def      snake_case__							(  lowerCAmelCase_,       lowerCAmelCase_     ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE		,    SCREAMING_SNAKE_CASE 		=_get_answer_coordinates(UpperCamelCase__,       UpperCamelCase__     )
     SCREAMING_SNAKE_CASE 		=_get_float_answer(UpperCamelCase__,       UpperCamelCase__,       UpperCamelCase__     )
     SCREAMING_SNAKE_CASE 		=_get_answer_text(UpperCamelCase__,       UpperCamelCase__,       UpperCamelCase__     )
     # keep the original data the same with TaPas
     if len(UpperCamelCase__     ) == 0:
          SCREAMING_SNAKE_CASE 		=[EMPTY_ANSWER]
     return answer_text






"""simple docstring"""






import unittest

import numpy as np
import torch

from diffusers import DDPMPipeline, DDPMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device


enable_full_determinism()




class    __snake_case(					unittest.TestCase       ):




    @property
    def   A   (   self			):





      '''simple docstring'''



      torch.manual_seed(0			)
      _SCREAMING_SNAKE_CASE        =			UNetaDModel(
          block_out_channels=(32, 64)    ,							layers_per_block=2    ,							sample_size=32    ,							in_channels=3    ,							out_channels=3    ,							down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')    ,							up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')    ,							)
      return model




    def   A   (   self			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			'''cpu'''
      _SCREAMING_SNAKE_CASE        =			self.dummy_uncond_unet
      _SCREAMING_SNAKE_CASE        =			DDPMScheduler()

      _SCREAMING_SNAKE_CASE        =			DDPMPipeline(unet=A_    ,							scheduler=A_			)
      ddpm.to(A_			)
      ddpm.set_progress_bar_config(disable=A_			)

      _SCREAMING_SNAKE_CASE        =			torch.Generator(device=A_			).manual_seed(0			)
      _SCREAMING_SNAKE_CASE        =			ddpm(generator=A_    ,							num_inference_steps=2    ,							output_type='''numpy'''			).images

      _SCREAMING_SNAKE_CASE        =			torch.Generator(device=A_			).manual_seed(0			)
      _SCREAMING_SNAKE_CASE        =			ddpm(generator=A_    ,							num_inference_steps=2    ,							output_type='''numpy'''    ,							return_dict=A_			)[0]

      _SCREAMING_SNAKE_CASE        =			image[0, -3:, -3:, -1]
      _SCREAMING_SNAKE_CASE        =			image_from_tuple[0, -3:, -3:, -1]

      assert image.shape == (1, 32, 32, 3)
      _SCREAMING_SNAKE_CASE        =			np.array(
          [9.956e-01, 5.785e-01, 4.675e-01, 9.930e-01, 0.0, 1.000, 1.199e-03, 2.648e-04, 5.101e-04]			)

      assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-2
      assert np.abs(image_from_tuple_slice.flatten() - expected_slice			).max() < 1e-2




    def   A   (   self			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			self.dummy_uncond_unet
      _SCREAMING_SNAKE_CASE        =			DDPMScheduler(prediction_type='''sample'''			)

      _SCREAMING_SNAKE_CASE        =			DDPMPipeline(unet=A_    ,							scheduler=A_			)
      ddpm.to(A_			)
      ddpm.set_progress_bar_config(disable=A_			)

      _SCREAMING_SNAKE_CASE        =			torch.manual_seed(0			)
      _SCREAMING_SNAKE_CASE        =			ddpm(generator=A_    ,							num_inference_steps=2    ,							output_type='''numpy'''			).images

      _SCREAMING_SNAKE_CASE        =			torch.manual_seed(0			)
      _SCREAMING_SNAKE_CASE        =			ddpm(generator=A_    ,							num_inference_steps=2    ,							output_type='''numpy'''			)[0]

      _SCREAMING_SNAKE_CASE        =			image[0, -3:, -3:, -1]
      _SCREAMING_SNAKE_CASE        =			image_eps[0, -3:, -3:, -1]

      assert image.shape == (1, 32, 32, 3)
      _SCREAMING_SNAKE_CASE        =			1e-2 if torch_device != '''mps''' else 3e-2
      assert np.abs(image_slice.flatten() - image_eps_slice.flatten()			).max() < tolerance




@slow
@require_torch_gpu
class    __snake_case(					unittest.TestCase       ):




    def   A   (   self			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			'''google/ddpm-cifar10-32'''

      _SCREAMING_SNAKE_CASE        =			UNetaDModel.from_pretrained(A_			)
      _SCREAMING_SNAKE_CASE        =			DDPMScheduler.from_pretrained(A_			)

      _SCREAMING_SNAKE_CASE        =			DDPMPipeline(unet=A_    ,							scheduler=A_			)
      ddpm.to(A_			)
      ddpm.set_progress_bar_config(disable=A_			)

      _SCREAMING_SNAKE_CASE        =			torch.manual_seed(0			)
      _SCREAMING_SNAKE_CASE        =			ddpm(generator=A_    ,							output_type='''numpy'''			).images

      _SCREAMING_SNAKE_CASE        =			image[0, -3:, -3:, -1]

      assert image.shape == (1, 32, 32, 3)
      _SCREAMING_SNAKE_CASE        =			np.array([0.4_200, 0.3_588, 0.1_939, 0.3_847, 0.3_382, 0.2_647, 0.4_155, 0.3_582, 0.3_385]			)
      assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-2
