






from typing import List, Union

import numpy as np

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
	from PIL import Image

	from ..image_utils import load_image

if is_torch_available():
	import torch

	from ..models.auto.modeling_auto import MODEL_FOR_DEPTH_ESTIMATION_MAPPING

__lowerCamelCase      :    Optional[int]   		=   logging.get_logger(__name__)



@add_end_docstrings(UpperCamelCase_		)
class 							SCREAMING_SNAKE_CASE__		(       UpperCamelCase_		):





					"""simple docstring"""






					def __init__(      self  :						str							,	*__A  :						str							,	**__A  :						Optional[Any] ):
						super().__init__(*__A							,	**__A )
						requires_backends(self							,	"vision" )
						self.check_model_type(__A )



					def __call__(      self  :						List[Any]							,	__A  :						Union[str, List[str], "Image.Image", List["Image.Image"]]							,	**__A  :						Optional[int] ):
						return super().__call__(__A							,	**__A )



					def  _lowercase  (      self  :						List[str]							,	**__A  :						Optional[Any] ):
						return {}, {}, {}



					def  _lowercase  (      self  :						Dict							,	__A  :						Tuple ):
						snake_case__				:   Dict     =						load_image(__A )
						snake_case__				:   Tuple     =						image.size
						snake_case__				:   Any     =						self.image_processor(images=__A							,	return_tensors=self.framework )
						return model_inputs



					def  _lowercase  (      self  :						int							,	__A  :						Dict ):
						snake_case__				:   Optional[Any]     =						self.model(**__A )
						return model_outputs






					def  _lowercase  (      self  :						Union[str, Any]							,	__A  :						Tuple ):
						snake_case__				:   str     =						model_outputs.predicted_depth
						snake_case__				:   Optional[Any]     =						torch.nn.functional.interpolate(
						    predicted_depth.unsqueeze(1 )							,	size=self.image_size[::-1]							,	mode="bicubic"							,	align_corners=__A )
						snake_case__				:   List[Any]     =						prediction.squeeze().cpu().numpy()
						snake_case__				:   str     =						(output * 2_5_5 / np.max(__A )).astype("uint8" )
						snake_case__				:   Any     =						Image.fromarray(__A )
						snake_case__				:   Dict     =						{}
						snake_case__				:   Tuple     =						predicted_depth
						snake_case__				:   Dict     =						depth
						return output_dict











# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position

__lowerCamelCase      :    Union[str, Any]   		=   """2.13.1"""

import platform

import pyarrow
from packaging import version


if version.parse(platform.python_version()) < version.parse("""3.7"""):
	raise ImportWarning(
	    """To use `datasets`, Python>=3.7 is required, and the current version of Python doesn't match this condition."""
	)

if version.parse(pyarrow.__version__).major < 8:
	raise ImportWarning(
	    """To use `datasets`, the module `pyarrow>=8.0.0` is required, and the current version of `pyarrow` doesn't match this condition.\n"""
	    """If you are running this in a Google Colab, you should probably just restart the runtime to use the right version of `pyarrow`."""
	)

del platform
del pyarrow
del version

from .arrow_dataset import Dataset
from .arrow_reader import ReadInstruction
from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
from .combine import concatenate_datasets, interleave_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .download import *
from .features import *
from .fingerprint import disable_caching, enable_caching, is_caching_enabled, set_caching_enabled
from .info import DatasetInfo, MetricInfo
from .inspect import (
    get_dataset_config_info,
    get_dataset_config_names,
    get_dataset_infos,
    get_dataset_split_names,
    inspect_dataset,
    inspect_metric,
    list_datasets,
    list_metrics,
)
from .iterable_dataset import IterableDataset
from .load import load_dataset, load_dataset_builder, load_from_disk, load_metric
from .metric import Metric
from .splits import (
    NamedSplit,
    NamedSplitAll,
    Split,
    SplitBase,
    SplitDict,
    SplitGenerator,
    SplitInfo,
    SubSplitInfo,
    percent,
)
from .tasks import *
from .utils import *
from .utils import logging


# deprecated modules
from datasets import arrow_dataset as _arrow_dataset  # isort:skip
from datasets import utils as _utils  # isort:skip
from datasets.utils import download_manager as _deprecated_download_manager  # isort:skip

__lowerCamelCase      :    List[Any]   		=   concatenate_datasets
__lowerCamelCase      :    List[str]   		=   DownloadConfig
__lowerCamelCase      :    Union[str, Any]   		=   DownloadManager
__lowerCamelCase      :    str   		=   DownloadMode
__lowerCamelCase      :    Union[str, Any]   		=   DownloadConfig
__lowerCamelCase      :    List[str]   		=   DownloadMode
__lowerCamelCase      :    Dict   		=   DownloadManager

del _arrow_dataset, _utils, _deprecated_download_manager



