import multiprocessing
from typing import TYPE_CHECKING, Optional, Union

from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream


if TYPE_CHECKING:
    import sqlitea

    import sqlalchemy



class 							a				(     __lowerCAmelCase			):







 """simple docstring"""






 def __init__(   self						,							lowerCAmelCase_						,							lowerCAmelCase_						,							lowerCAmelCase_ = None						,							lowerCAmelCase_ = None						,							lowerCAmelCase_ = False						,							**lowerCAmelCase_						,							)    ->   Tuple:
        super().__init__(features=__SCREAMING_SNAKE_CASE						,							cache_dir=__SCREAMING_SNAKE_CASE						,							keep_in_memory=__SCREAMING_SNAKE_CASE						,							**__SCREAMING_SNAKE_CASE				)
        _A	  =    Sql(
            cache_dir=__SCREAMING_SNAKE_CASE						,							features=__SCREAMING_SNAKE_CASE						,							sql=__SCREAMING_SNAKE_CASE						,							con=__SCREAMING_SNAKE_CASE						,							**__SCREAMING_SNAKE_CASE						,							)



 def 	UpperCAmelCase				(   self				)    ->   int:
        _A	  =    None
        _A	  =    None
        _A	  =    None
        _A	  =    None

        self.builder.download_and_prepare(
            download_config=__SCREAMING_SNAKE_CASE						,							download_mode=__SCREAMING_SNAKE_CASE						,							verification_mode=__SCREAMING_SNAKE_CASE						,							base_path=__SCREAMING_SNAKE_CASE						,							)

        # Build dataset for splits
        _A	  =    self.builder.as_dataset(
            split="""train"""						,							verification_mode=__SCREAMING_SNAKE_CASE						,							in_memory=self.keep_in_memory				)
        return dataset



class 							a				:







 """simple docstring"""






 def __init__(   self						,							lowerCAmelCase_						,							lowerCAmelCase_						,							lowerCAmelCase_						,							lowerCAmelCase_ = None						,							lowerCAmelCase_ = None						,							**lowerCAmelCase_						,							)    ->   Tuple:




        if num_proc is not None and num_proc <= 0:
               raise ValueError(F'''num_proc {num_proc} must be an integer > 0.'''				)

        _A	  =    dataset
        _A	  =    name
        _A	  =    con
        _A	  =    batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
        _A	  =    num_proc
        _A	  =    to_sql_kwargs



 def 	UpperCAmelCase				(   self				)    ->   int:
        _A	  =    self.to_sql_kwargs.pop("""sql"""						,							__SCREAMING_SNAKE_CASE				)
        _A	  =    self.to_sql_kwargs.pop("""con"""						,							__SCREAMING_SNAKE_CASE				)
        _A	  =    self.to_sql_kwargs.pop("""index"""						,							__SCREAMING_SNAKE_CASE				)

        _A	  =    self._write(index=__SCREAMING_SNAKE_CASE						,							**self.to_sql_kwargs				)
        return written



 def 	UpperCAmelCase				(   self						,							lowerCAmelCase_				)    ->   Optional[int]:
        _A  ,    _A  ,    _A	  =    args
        _A	  =    {**to_sql_kwargs, """if_exists""": """append"""} if offset > 0 else to_sql_kwargs
        _A	  =    query_table(
            table=self.dataset.data						,							key=slice(__SCREAMING_SNAKE_CASE						,							offset + self.batch_size				)						,							indices=self.dataset._indices						,							)
        _A	  =    batch.to_pandas()
        _A	  =    df.to_sql(self.name						,							self.con						,							index=__SCREAMING_SNAKE_CASE						,							**__SCREAMING_SNAKE_CASE				)
        return num_rows or len(__SCREAMING_SNAKE_CASE				)



 def 	UpperCAmelCase				(   self						,							lowerCAmelCase_						,							**lowerCAmelCase_				)    ->   int:
        _A	  =    0

        if self.num_proc is None or self.num_proc == 1:
               for offset in logging.tqdm(
                   range(0						,							len(self.dataset				)						,							self.batch_size				)						,							unit="""ba"""						,							disable=not logging.is_progress_bar_enabled()						,							desc="""Creating SQL from Arrow format"""						,							):
                      written += self._batch_sql((offset, index, to_sql_kwargs)				)
        else:
               _A  ,    _A	  =    len(self.dataset				), self.batch_size
               with multiprocessing.Pool(self.num_proc				) as pool:
                      for num_rows in logging.tqdm(
                          pool.imap(
                              self._batch_sql						,							[(offset, index, to_sql_kwargs) for offset in range(0						,							__SCREAMING_SNAKE_CASE						,							__SCREAMING_SNAKE_CASE				)]						,							)						,							total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size						,							unit="""ba"""						,							disable=not logging.is_progress_bar_enabled()						,							desc="""Creating SQL from Arrow format"""						,							):
                             written += num_rows

        return written







'''simple docstring'''



import random
import unittest

import torch

from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin







@skip_mps
class 		lowerCAmelCase__  ( a					,   a					,   unittest.TestCase   ):



 """simple docstring"""



 lowerCAmelCase__       = IFInpaintingPipeline
 lowerCAmelCase__       = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"width", "height"}
 lowerCAmelCase__       = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
 lowerCAmelCase__       = PipelineTesterMixin.required_optional_params - {"latents"}
 def  UpperCAmelCase__						(						self   :			Optional[int]     )    ->					Dict:


      """simple docstring"""




      return self._get_dummy_components()
 def  UpperCAmelCase__						(						self   :			str   ,				__SCREAMING_SNAKE_CASE   :			List[Any]   ,				__SCREAMING_SNAKE_CASE   :			Dict=0     )    ->					Any:


      """simple docstring"""




      if str(__SCREAMING_SNAKE_CASE     ).startswith("""mps"""     ):
           __SCREAMING_SNAKE_CASE      = torch.manual_seed(__SCREAMING_SNAKE_CASE     )
      else:
           __SCREAMING_SNAKE_CASE      = torch.Generator(device=__SCREAMING_SNAKE_CASE     ).manual_seed(__SCREAMING_SNAKE_CASE     )

      __SCREAMING_SNAKE_CASE      = floats_tensor((1, 3, 32, 32)   ,				rng=random.Random(__SCREAMING_SNAKE_CASE     )     ).to(__SCREAMING_SNAKE_CASE     )
      __SCREAMING_SNAKE_CASE      = floats_tensor((1, 3, 32, 32)   ,				rng=random.Random(__SCREAMING_SNAKE_CASE     )     ).to(__SCREAMING_SNAKE_CASE     )

      __SCREAMING_SNAKE_CASE      = {
          """prompt""": """A painting of a squirrel eating a burger""",
          """image""": image,
          """mask_image""": mask_image,
          """generator""": generator,
          """num_inference_steps""": 2,
          """output_type""": """numpy""",
      }

      return inputs
 @unittest.skipIf(
     torch_device != """cuda""" or not is_xformers_available()   ,				reason="""XFormers attention is only available with CUDA and `xformers` installed"""   ,				)
 def  UpperCAmelCase__						(						self   :			str     )    ->					Optional[int]:


      """simple docstring"""




      self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3     )
 def  UpperCAmelCase__						(						self   :			str     )    ->					List[str]:


      """simple docstring"""




      self._test_save_load_optional_components()
 @unittest.skipIf(torch_device != """cuda"""   ,				reason="""float16 requires CUDA"""     )
 def  UpperCAmelCase__						(						self   :			Dict     )    ->					int:


      """simple docstring"""




      super().test_save_load_floataa(expected_max_diff=1E-1     )
 def  UpperCAmelCase__						(						self   :			List[Any]     )    ->					Optional[int]:


      """simple docstring"""




      self._test_attention_slicing_forward_pass(expected_max_diff=1E-2     )
 def  UpperCAmelCase__						(						self   :			Union[str, Any]     )    ->					List[Any]:


      """simple docstring"""




      self._test_save_load_local()


 def  UpperCAmelCase__						(						self   :			Any     )    ->					List[Any]:


      """simple docstring"""




      self._test_inference_batch_single_identical(
          expected_max_diff=1E-2   ,				)






