

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_			(				_a							):


							"""simple docstring"""





							def __init__( self					,						_A					,						_A					,						_A = None					,						_A = None					,						_A = False					,						**_A					,						):
												super().__init__(features=_A					,						cache_dir=_A					,						keep_in_memory=_A					,						**_A							)
												__lowerCAmelCase					=							Sql(
												    cache_dir=_A					,						features=_A					,						sql=_A					,						con=_A					,						**_A					,						)




							def        A__  ( self							):
												__lowerCAmelCase					=							None
												__lowerCAmelCase					=							None
												__lowerCAmelCase					=							None
												__lowerCAmelCase					=							None

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

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





class     snake_case_			:


							"""simple docstring"""





							def __init__( self					,						_A					,						_A					,						_A					,						_A = None					,						_A = None					,						**_A					,						):
												if num_proc is not None and num_proc <= 0:
																	raise ValueError(F"""num_proc {num_proc} must be an integer > 0."""							)

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




							def        A__  ( self							):
												__lowerCAmelCase					=							self.to_sql_kwargs.pop('sql'					,						_A							)
												__lowerCAmelCase					=							self.to_sql_kwargs.pop('con'					,						_A							)
												__lowerCAmelCase					=							self.to_sql_kwargs.pop('index'					,						_A							)

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




							def        A__  ( self					,						_A							):
												__lowerCAmelCase,	__lowerCAmelCase,	__lowerCAmelCase					=							args
												__lowerCAmelCase					=							{**to_sql_kwargs, 'if_exists': 'append'} if offset > 0 else to_sql_kwargs
												__lowerCAmelCase					=							query_table(
												    table=self.dataset.data					,						key=slice(_A					,						offset + self.batch_size							)					,						indices=self.dataset._indices					,						)
												__lowerCAmelCase					=							batch.to_pandas()
												__lowerCAmelCase					=							df.to_sql(self.name					,						self.con					,						index=_A					,						**_A							)
												return num_rows or len(_A							)




							def        A__  ( self					,						_A					,						**_A							):
												__lowerCAmelCase					=							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:
																	__lowerCAmelCase,	__lowerCAmelCase					=							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					,						_A					,						_A							)]					,						)					,						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_			:


									"""simple docstring"""





									@staticmethod
									def        A__  ( *_A					,						**_A							):
														pass


def        __lowercase    (  UpperCAmelCase__      ):

					"""simple docstring"""






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





@is_pipeline_test
@require_vision
@require_timm
@require_torch
class     snake_case_			(				unittest.TestCase							):


							"""simple docstring"""





							__UpperCAmelCase       =MODEL_FOR_DEPTH_ESTIMATION_MAPPING




							def        A__  ( self					,						_A					,						_A					,						_A							):
												__lowerCAmelCase					=							DepthEstimationPipeline(model=_A					,						image_processor=_A							)
												return depth_estimator, [
												    "./tests/fixtures/tests_samples/COCO/000000039769.png",
												    "./tests/fixtures/tests_samples/COCO/000000039769.png",
												]




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

												__lowerCAmelCase					=							datasets.load_dataset('hf-internal-testing/fixtures_image_utils'					,						'image'					,						split='test'							)
												__lowerCAmelCase					=							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							)},
												    ]					,						_A					,						)




							@require_tf
							@unittest.skip('Depth estimation is not implemented in TF'							)
							def        A__  ( self							):
												pass




							@slow
							@require_torch
							def        A__  ( self							):
												__lowerCAmelCase					=							'Intel/dpt-large'
												__lowerCAmelCase					=							pipeline('depth-estimation'					,						model=_A							)
												__lowerCAmelCase					=							depth_estimator('http://images.cocodataset.org/val2017/000000039769.jpg'							)
												__lowerCAmelCase					=							hashimage(outputs['depth']							)

												# This seems flaky.
												# self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
												self.assertEqual(nested_simplify(outputs['predicted_depth'].max().item()							)					,						29.304							)
												self.assertEqual(nested_simplify(outputs['predicted_depth'].min().item()							)					,						2.662							)




							@require_torch
							def        A__  ( self							):
												# This is highly irregular to have no small tests.
												self.skipTest('There is not hf-internal-testing tiny model for either GLPN nor DPT'							)






