
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    _snake_case (						UpperCAmelCase_				):


 def __init__(       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_ = None       ,	SCREAMING_SNAKE_CASE_ = None       ,	SCREAMING_SNAKE_CASE_ = False       ,	**SCREAMING_SNAKE_CASE_       ,	):


   '''simple docstring'''




   super().__init__(features=SCREAMING_SNAKE_CASE_       ,	cache_dir=SCREAMING_SNAKE_CASE_       ,	keep_in_memory=SCREAMING_SNAKE_CASE_       ,	**SCREAMING_SNAKE_CASE_)
   lowercase__	: Any     		=   Sql(
       cache_dir=SCREAMING_SNAKE_CASE_       ,	features=SCREAMING_SNAKE_CASE_       ,	sql=SCREAMING_SNAKE_CASE_       ,	con=SCREAMING_SNAKE_CASE_       ,	**SCREAMING_SNAKE_CASE_       ,	)



 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Any     		=   None
   lowercase__	: Optional[Any]     		=   None
   lowercase__	: Optional[int]     		=   None
   lowercase__	: int     		=   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
   lowercase__	: str     		=   self.builder.as_dataset(
       split="""train"""       ,	verification_mode=SCREAMING_SNAKE_CASE_       ,	in_memory=self.keep_in_memory)
   return dataset



class    _snake_case :


 def __init__(       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_ = None       ,	SCREAMING_SNAKE_CASE_ = None       ,	**SCREAMING_SNAKE_CASE_       ,	):


   '''simple docstring'''




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

   lowercase__	: Union[str, Any]     		=   dataset
   lowercase__	: int     		=   name
   lowercase__	: Optional[int]     		=   con
   lowercase__	: str     		=   batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
   lowercase__	: List[Any]     		=   num_proc
   lowercase__	: Dict     		=   to_sql_kwargs


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Dict     		=   self.to_sql_kwargs.pop("""sql"""       ,	SCREAMING_SNAKE_CASE_)
   lowercase__	: Union[str, Any]     		=   self.to_sql_kwargs.pop("""con"""       ,	SCREAMING_SNAKE_CASE_)
   lowercase__	: List[Any]     		=   self.to_sql_kwargs.pop("""index"""       ,	SCREAMING_SNAKE_CASE_)

   lowercase__	: List[Any]     		=   self._write(index=SCREAMING_SNAKE_CASE_       ,	**self.to_sql_kwargs)
   return written


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__		,     lowercase__		,     lowercase__	: Optional[int]     		=   args
   lowercase__	: List[Any]     		=   {**to_sql_kwargs, """if_exists""": """append"""} if offset > 0 else to_sql_kwargs
   lowercase__	: Tuple     		=   query_table(
       table=self.dataset.data       ,	key=slice(SCREAMING_SNAKE_CASE_       ,	offset + self.batch_size)       ,	indices=self.dataset._indices       ,	)
   lowercase__	: Dict     		=   batch.to_pandas()
   lowercase__	: Dict     		=   df.to_sql(self.name       ,	self.con       ,	index=SCREAMING_SNAKE_CASE_       ,	**SCREAMING_SNAKE_CASE_)
   return num_rows or len(SCREAMING_SNAKE_CASE_)



 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	**SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: Optional[int]     		=   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:
     lowercase__		,     lowercase__	: List[str]     		=   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








import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image

     from transformers import ViTImageProcessor



class    _snake_case (						unittest.TestCase				):


 def __init__(       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_=13       ,	SCREAMING_SNAKE_CASE_=3       ,	SCREAMING_SNAKE_CASE_=2_24       ,	SCREAMING_SNAKE_CASE_=30       ,	SCREAMING_SNAKE_CASE_=4_00       ,	SCREAMING_SNAKE_CASE_=True       ,	SCREAMING_SNAKE_CASE_=None       ,	SCREAMING_SNAKE_CASE_=True       ,	SCREAMING_SNAKE_CASE_=[0.5, 0.5, 0.5]       ,	SCREAMING_SNAKE_CASE_=[0.5, 0.5, 0.5]       ,	):


   '''simple docstring'''




   lowercase__	: List[str]     		=   size if size is not None else {"""height""": 18, """width""": 18}
   lowercase__	: int     		=   parent
   lowercase__	: Union[str, Any]     		=   batch_size
   lowercase__	: List[str]     		=   num_channels
   lowercase__	: str     		=   image_size
   lowercase__	: int     		=   min_resolution
   lowercase__	: Dict     		=   max_resolution
   lowercase__	: Tuple     		=   do_resize
   lowercase__	: Union[str, Any]     		=   size
   lowercase__	: Any     		=   do_normalize
   lowercase__	: Tuple     		=   image_mean
   lowercase__	: str     		=   image_std



 def   lowercase__  (       self):


   '''simple docstring'''




   return {
       "image_mean": self.image_mean,
       "image_std": self.image_std,
       "do_normalize": self.do_normalize,
       "do_resize": self.do_resize,
       "size": self.size,
   }



@require_torch
@require_vision
class    _snake_case (						UpperCAmelCase_	,    unittest.TestCase				):
 __lowerCAmelCase	:  Optional[Any]	   =      ViTImageProcessor if is_vision_available() else None


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: str     		=   EfficientFormerImageProcessorTester(self)


 @property
 def   lowercase__  (       self):


   '''simple docstring'''




   return self.image_proc_tester.prepare_image_processor_dict()


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Any     		=   self.image_processing_class(**self.image_processor_dict)
   self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_       ,	"""image_mean"""))
   self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_       ,	"""image_std"""))
   self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_       ,	"""do_normalize"""))
   self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_       ,	"""do_resize"""))
   self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_       ,	"""size"""))


 def   lowercase__  (       self):


   '''simple docstring'''




   pass


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: str     		=   self.image_processing_class(**self.image_processor_dict)
   # create random PIL images
   lowercase__	: List[Any]     		=   prepare_image_inputs(self.image_proc_tester       ,	equal_resolution=SCREAMING_SNAKE_CASE_)
   for image in image_inputs:
     self.assertIsInstance(SCREAMING_SNAKE_CASE_       ,	Image.Image)

   # Test not batched input
   lowercase__	: int     		=   image_processor(image_inputs[0]       ,	return_tensors="""pt""").pixel_values
   self.assertEqual(
       encoded_images.shape       ,	(
           1,
           self.image_proc_tester.num_channels,
           self.image_proc_tester.size["""height"""],
           self.image_proc_tester.size["""width"""],
       )       ,	)

   # Test batched
   lowercase__	: str     		=   image_processor(SCREAMING_SNAKE_CASE_       ,	return_tensors="""pt""").pixel_values
   self.assertEqual(
       encoded_images.shape       ,	(
           self.image_proc_tester.batch_size,
           self.image_proc_tester.num_channels,
           self.image_proc_tester.size["""height"""],
           self.image_proc_tester.size["""width"""],
       )       ,	)


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Tuple     		=   self.image_processing_class(**self.image_processor_dict)
   # create random numpy tensors
   lowercase__	: str     		=   prepare_image_inputs(self.image_proc_tester       ,	equal_resolution=SCREAMING_SNAKE_CASE_       ,	numpify=SCREAMING_SNAKE_CASE_)
   for image in image_inputs:
     self.assertIsInstance(SCREAMING_SNAKE_CASE_       ,	np.ndarray)

   # Test not batched input
   lowercase__	: Optional[int]     		=   image_processor(image_inputs[0]       ,	return_tensors="""pt""").pixel_values
   self.assertEqual(
       encoded_images.shape       ,	(
           1,
           self.image_proc_tester.num_channels,
           self.image_proc_tester.size["""height"""],
           self.image_proc_tester.size["""width"""],
       )       ,	)

   # Test batched
   lowercase__	: Dict     		=   image_processor(SCREAMING_SNAKE_CASE_       ,	return_tensors="""pt""").pixel_values
   self.assertEqual(
       encoded_images.shape       ,	(
           self.image_proc_tester.batch_size,
           self.image_proc_tester.num_channels,
           self.image_proc_tester.size["""height"""],
           self.image_proc_tester.size["""width"""],
       )       ,	)



 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: List[str]     		=   self.image_processing_class(**self.image_processor_dict)
   # create random PyTorch tensors
   lowercase__	: Dict     		=   prepare_image_inputs(self.image_proc_tester       ,	equal_resolution=SCREAMING_SNAKE_CASE_       ,	torchify=SCREAMING_SNAKE_CASE_)
   for image in image_inputs:
     self.assertIsInstance(SCREAMING_SNAKE_CASE_       ,	torch.Tensor)

   # Test not batched input
   lowercase__	: int     		=   image_processor(image_inputs[0]       ,	return_tensors="""pt""").pixel_values
   self.assertEqual(
       encoded_images.shape       ,	(
           1,
           self.image_proc_tester.num_channels,
           self.image_proc_tester.size["""height"""],
           self.image_proc_tester.size["""width"""],
       )       ,	)

   # Test batched
   lowercase__	: Any     		=   image_processor(SCREAMING_SNAKE_CASE_       ,	return_tensors="""pt""").pixel_values
   self.assertEqual(
       encoded_images.shape       ,	(
           self.image_proc_tester.batch_size,
           self.image_proc_tester.num_channels,
           self.image_proc_tester.size["""height"""],
           self.image_proc_tester.size["""width"""],
       )       ,	)






