
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   UpperCAmelCase		(						A_ ):



   def __init__(self		:					Tuple    ,						snake_case__		:					Union[str, "sqlalchemy.sql.Selectable"]    ,						snake_case__		:					Union[str, "sqlalchemy.engine.Connection", "sqlalchemy.engine.Engine", "sqlite3.Connection"]    ,						snake_case__		:					Optional[Features] = None    ,						snake_case__		:					str = None    ,						snake_case__		:					bool = False    ,						**snake_case__		:					Dict    ,						)		->	Optional[int]:


          '''simple docstring'''


          super().__init__(features=snake_case__    ,						cache_dir=snake_case__    ,						keep_in_memory=snake_case__    ,						**snake_case__ )
          snake_case       :   Tuple							=      Sql(
              cache_dir=snake_case__    ,						features=snake_case__    ,						sql=snake_case__    ,						con=snake_case__    ,						**snake_case__    ,						)



   def 				_SCREAMING_SNAKE_CASE   (self		:					Any )		->	Optional[int]:


          '''simple docstring'''


          snake_case       :   Optional[int]							=      None
          snake_case       :   int							=      None
          snake_case       :   List[str]							=      None
          snake_case       :   int							=      None

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

          # Build dataset for splits
          snake_case       :   Optional[int]							=      self.builder.as_dataset(
              split="train"    ,						verification_mode=snake_case__    ,						in_memory=self.keep_in_memory )
          return dataset






class   UpperCAmelCase		:



   def __init__(self		:					Union[str, Any]    ,						snake_case__		:					Dataset    ,						snake_case__		:					str    ,						snake_case__		:					Union[str, "sqlalchemy.engine.Connection", "sqlalchemy.engine.Engine", "sqlite3.Connection"]    ,						snake_case__		:					Optional[int] = None    ,						snake_case__		:					Optional[int] = None    ,						**snake_case__		:					Dict    ,						)		->	Optional[Any]:


          '''simple docstring'''


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

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



   def 				_SCREAMING_SNAKE_CASE   (self		:					List[Any] )		->	int:


          '''simple docstring'''


          snake_case       :   int							=      self.to_sql_kwargs.pop("sql"    ,						snake_case__ )
          snake_case       :   Tuple							=      self.to_sql_kwargs.pop("con"    ,						snake_case__ )
          snake_case       :   Tuple							=      self.to_sql_kwargs.pop("index"    ,						snake_case__ )

          snake_case       :   Union[str, Any]							=      self._write(index=snake_case__    ,						**self.to_sql_kwargs )
          return written



   def 				_SCREAMING_SNAKE_CASE   (self		:					str    ,						snake_case__		:					Tuple )		->	Optional[Any]:


          '''simple docstring'''


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



   def 				_SCREAMING_SNAKE_CASE   (self		:					List[Any]    ,						snake_case__		:					str    ,						**snake_case__		:					Union[str, Any] )		->	int:


          '''simple docstring'''


          snake_case       :   List[str]							=      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:
                 snake_case				,					snake_case       :   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    ,						snake_case__    ,						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 hashlib
import unittest

from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image
else:







    class __snake_case			:







       @staticmethod
       def lowerCamelCase						(      *_snake_case		:						Optional[int]					,						**_snake_case		:						int):

            """simple docstring"""


            pass


def    A						(__A   :    Image       )   ->      str:







     """simple docstring"""


     UpperCAmelCase_          =							hashlib.mda(image.tobytes()       )
     return m.hexdigest()







@is_pipeline_test
@require_vision
@require_timm
@require_torch
class __snake_case			(			unittest.TestCase		):
   UpperCAmelCase__  :    Tuple          =  MODEL_FOR_DEPTH_ESTIMATION_MAPPING







   def lowerCamelCase						(      self		:						Tuple					,						_snake_case		:						Optional[Any]					,						_snake_case		:						Optional[Any]					,						_snake_case		:						List[str]):

        """simple docstring"""


        UpperCAmelCase_          =							DepthEstimationPipeline(model=_snake_case					,						image_processor=_snake_case)
        return depth_estimator, [
            "./tests/fixtures/tests_samples/COCO/000000039769.png",
            "./tests/fixtures/tests_samples/COCO/000000039769.png",
        ]







   def lowerCamelCase						(      self		:						str					,						_snake_case		:						Optional[int]					,						_snake_case		:						List[str]):

        """simple docstring"""


        UpperCAmelCase_          =							depth_estimator('''./tests/fixtures/tests_samples/COCO/000000039769.png''')
        self.assertEqual({'''predicted_depth''': ANY(torch.Tensor), '''depth''': ANY(Image.Image)}					,						_snake_case)
        import datasets

        UpperCAmelCase_          =							datasets.load_dataset('''hf-internal-testing/fixtures_image_utils'''					,						'''image'''					,						split='''test''')
        UpperCAmelCase_          =							depth_estimator(
            [
                Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''),
                '''http://images.cocodataset.org/val2017/000000039769.jpg''',
                # RGBA
                dataset[0]['''file'''],
                # LA
                dataset[1]['''file'''],
                # L
                dataset[2]['''file'''],
            ])
        self.assertEqual(
            [
                {'''predicted_depth''': ANY(torch.Tensor), '''depth''': ANY(Image.Image)},
                {'''predicted_depth''': ANY(torch.Tensor), '''depth''': ANY(Image.Image)},
                {'''predicted_depth''': ANY(torch.Tensor), '''depth''': ANY(Image.Image)},
                {'''predicted_depth''': ANY(torch.Tensor), '''depth''': ANY(Image.Image)},
                {'''predicted_depth''': ANY(torch.Tensor), '''depth''': ANY(Image.Image)},
            ]					,						_snake_case					,						)







   @require_tf
   @unittest.skip('''Depth estimation is not implemented in TF''')
   def lowerCamelCase						(      self		:						Union[str, Any]):

        """simple docstring"""


        pass







   @slow
   @require_torch
   def lowerCamelCase						(      self		:						List[str]):

        """simple docstring"""


        UpperCAmelCase_          =							'''Intel/dpt-large'''
        UpperCAmelCase_          =							pipeline('''depth-estimation'''					,						model=_snake_case)
        UpperCAmelCase_          =							depth_estimator('''http://images.cocodataset.org/val2017/000000039769.jpg''')
        UpperCAmelCase_          =							hashimage(outputs['''depth'''])

        # This seems flaky.
        # self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
        self.assertEqual(nested_simplify(outputs['''predicted_depth'''].max().item())					,						2_9.3_0_4)
        self.assertEqual(nested_simplify(outputs['''predicted_depth'''].min().item())					,						2.6_6_2)







   @require_torch
   def lowerCamelCase						(      self		:						Optional[Any]):

        """simple docstring"""


        self.skipTest('''There is not hf-internal-testing tiny model for either GLPN nor DPT''')






