



import functools
import logging
import os
import sys
import threading
from logging import (
    CRITICAL,  # NOQA
    DEBUG,  # NOQA
    ERROR,  # NOQA
    FATAL,  # NOQA
    INFO,  # NOQA
    NOTSET,  # NOQA
    WARN,  # NOQA
    WARNING,  # NOQA
)
from typing import Optional

import huggingface_hub.utils as hf_hub_utils
from tqdm import auto as tqdm_lib


_snake_case		=  threading.Lock()
_snake_case		=  None

_snake_case		=  {
    "debug": logging.DEBUG,
    "info": logging.INFO,
    "warning": logging.WARNING,
    "error": logging.ERROR,
    "critical": logging.CRITICAL,
}

_snake_case		=  logging.WARNING

_snake_case		=  True






def 				A				(					):





							'''simple docstring'''




							_lowerCAmelCase     :							Tuple    =    os.getenv("TRANSFORMERS_VERBOSITY"				, _lowerCamelCase  )
							if env_level_str:
														if env_level_str in log_levels:
																					return log_levels[env_level_str]
														else:
																					logging.getLogger().warning(
																					    F"Unknown option TRANSFORMERS_VERBOSITY={env_level_str}, "
																					    F"has to be one of: { ', '.join(log_levels.keys()  ) }"  )
							return _default_log_level






def 				A				(					):





							'''simple docstring'''




							return __name__.split("."  )[0]






def 				A				(					):





							'''simple docstring'''




							return logging.getLogger(_get_library_name()  )






def 				A				(					):





							'''simple docstring'''




							global _default_handler

							with _lock:
														if _default_handler:
																					# This library has already configured the library root logger.
																					return
														_lowerCAmelCase     :							Union[str, Any]    =    logging.StreamHandler()  # Set sys.stderr as stream.
														_lowerCAmelCase     :							Tuple    =    sys.stderr.flush

														# Apply our default configuration to the library root logger.
														_lowerCAmelCase     :							Tuple    =    _get_library_root_logger()
														library_root_logger.addHandler(_default_handler  )
														library_root_logger.setLevel(_get_default_logging_level()  )
														_lowerCAmelCase     :							List[Any]    =    False






def 				A				(					):





							'''simple docstring'''




							global _default_handler

							with _lock:
														if not _default_handler:
																					return

														_lowerCAmelCase     :							Tuple    =    _get_library_root_logger()
														library_root_logger.removeHandler(_default_handler  )
														library_root_logger.setLevel(logging.NOTSET  )
														_lowerCAmelCase     :							List[str]    =    None






def 				A				(					):





							'''simple docstring'''




							return log_levels






def 				A				(					_lowerCamelCase = None  ):





							'''simple docstring'''




							if name is None:
														_lowerCAmelCase     :							Union[str, Any]    =    _get_library_name()

							_configure_library_root_logger()
							return logging.getLogger(_lowerCamelCase  )






def 				A				(					):





							'''simple docstring'''




							_configure_library_root_logger()
							return _get_library_root_logger().getEffectiveLevel()






def 				A				(					_lowerCamelCase  ):





							'''simple docstring'''




							_configure_library_root_logger()
							_get_library_root_logger().setLevel(_lowerCamelCase  )






def 				A				(					):





							'''simple docstring'''




							return set_verbosity(_lowerCamelCase  )






def 				A				(					):





							'''simple docstring'''




							return set_verbosity(_lowerCamelCase  )






def 				A				(					):





							'''simple docstring'''




							return set_verbosity(_lowerCamelCase  )






def 				A				(					):





							'''simple docstring'''




							return set_verbosity(_lowerCamelCase  )






def 				A				(					):





							'''simple docstring'''




							_configure_library_root_logger()

							assert _default_handler is not None
							_get_library_root_logger().removeHandler(_default_handler  )






def 				A				(					):





							'''simple docstring'''




							_configure_library_root_logger()

							assert _default_handler is not None
							_get_library_root_logger().addHandler(_default_handler  )






def 				A				(					_lowerCamelCase  ):





							'''simple docstring'''




							_configure_library_root_logger()

							assert handler is not None
							_get_library_root_logger().addHandler(_lowerCamelCase  )






def 				A				(					_lowerCamelCase  ):





							'''simple docstring'''




							_configure_library_root_logger()

							assert handler is not None and handler not in _get_library_root_logger().handlers
							_get_library_root_logger().removeHandler(_lowerCamelCase  )






def 				A				(					):





							'''simple docstring'''




							_configure_library_root_logger()
							_lowerCAmelCase     :							Union[str, Any]    =    False






def 				A				(					):





							'''simple docstring'''




							_configure_library_root_logger()
							_lowerCAmelCase     :							int    =    True






def 				A				(					):





							'''simple docstring'''




							_lowerCAmelCase     :							str    =    _get_library_root_logger().handlers

							for handler in handlers:
														_lowerCAmelCase     :							Union[str, Any]    =    logging.Formatter("[%(levelname)s|%(filename)s:%(lineno)s] %(asctime)s >> %(message)s"  )
														handler.setFormatter(_lowerCamelCase  )






def 				A				(					):





							'''simple docstring'''




							_lowerCAmelCase     :							int    =    _get_library_root_logger().handlers

							for handler in handlers:
														handler.setFormatter(_lowerCamelCase  )






def 				A				(					self				, *_lowerCamelCase				, **_lowerCamelCase  ):





							'''simple docstring'''




							_lowerCAmelCase     :							Union[str, Any]    =    os.getenv("TRANSFORMERS_NO_ADVISORY_WARNINGS"				, _lowerCamelCase  )
							if no_advisory_warnings:
														return
							self.warning(*_lowerCamelCase				, **_lowerCamelCase  )


_snake_case		=  warning_advice






@functools.lru_cache(_lowerCamelCase  )
def 				A				(					self				, *_lowerCamelCase				, **_lowerCamelCase  ):





							'''simple docstring'''




							self.warning(*_lowerCamelCase				, **_lowerCamelCase  )


_snake_case		=  warning_once



class   UpperCAmelCase_    :







							def __init__(   self,			*__a,			**__a):  # pylint: disable=unused-argument

														'''simple docstring'''

														_lowerCAmelCase     :							Union[str, Any]    =    args[0] if args else None







							def __iter__(   self):

														'''simple docstring'''

														return iter(self._iterator)







							def __getattr__(   self,			__a):

														'''simple docstring'''

														def empty_fn(*__a,			**__a):  # pylint: disable=unused-argument
																					return

														return empty_fn







							def __enter__(   self):

														'''simple docstring'''

														return self







							def __exit__(   self,			__a,			__a,			__a):

														'''simple docstring'''

														return



class   UpperCAmelCase_    :







							def __call__(   self,			*__a,			**__a):

														'''simple docstring'''

														if _tqdm_active:
																					return tqdm_lib.tqdm(*__a,			**__a)
														else:
																					return EmptyTqdm(*__a,			**__a)







							def    snake_case__      (   self,			*__a,			**__a):

														'''simple docstring'''

														_lowerCAmelCase     :							List[str]    =    None
														if _tqdm_active:
																					return tqdm_lib.tqdm.set_lock(*__a,			**__a)







							def    snake_case__      (   self):

														'''simple docstring'''

														if _tqdm_active:
																					return tqdm_lib.tqdm.get_lock()



_snake_case		=  _tqdm_cls()






def 				A				(					):





							'''simple docstring'''




							global _tqdm_active
							return bool(_tqdm_active  )






def 				A				(					):





							'''simple docstring'''




							global _tqdm_active
							_lowerCAmelCase     :							int    =    True
							hf_hub_utils.enable_progress_bars()






def 				A				(					):





							'''simple docstring'''




							global _tqdm_active
							_lowerCAmelCase     :							Optional[Any]    =    False
							hf_hub_utils.disable_progress_bars()





import math
from typing import Dict, Iterable, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    is_torch_available,
    is_torch_tensor,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_torch_available():
						import torch

if is_vision_available():
						import PIL


_snake_case		=  logging.get_logger(__name__)






def 				A				(					_lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase  ):





							'''simple docstring'''




							def constraint_to_multiple_of(_lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase=0				, _lowerCamelCase=None  ):
														_lowerCAmelCase     :							Tuple    =    round(val / multiple  ) * multiple

														if max_val is not None and x > max_val:
																					_lowerCAmelCase     :							Optional[int]    =    math.floor(val / multiple  ) * multiple

														if x < min_val:
																					_lowerCAmelCase     :							List[str]    =    math.ceil(val / multiple  ) * multiple

														return x

							_lowerCAmelCase     :							Union[str, Any]    =    (output_size, output_size) if isinstance(_lowerCamelCase				, _lowerCamelCase  ) else output_size

							_lowerCAmelCase   ,    _lowerCAmelCase     :							Optional[Any]    =    get_image_size(_lowerCamelCase  )
							_lowerCAmelCase   ,    _lowerCAmelCase     :							Any    =    output_size

							# determine new height and width
							_lowerCAmelCase     :							List[Any]    =    output_height / input_height
							_lowerCAmelCase     :							Any    =    output_width / input_width

							if keep_aspect_ratio:
														# scale as little as possible
														if abs(1 - scale_width  ) < abs(1 - scale_height  ):
																					# fit width
																					_lowerCAmelCase     :							Union[str, Any]    =    scale_width
														else:
																					# fit height
																					_lowerCAmelCase     :							Union[str, Any]    =    scale_height

							_lowerCAmelCase     :							List[str]    =    constraint_to_multiple_of(scale_height * input_height				, multiple=_lowerCamelCase  )
							_lowerCAmelCase     :							Dict    =    constraint_to_multiple_of(scale_width * input_width				, multiple=_lowerCamelCase  )

							return (new_height, new_width)



class   UpperCAmelCase_    (       a):
							lowerCamelCase__   			=		['pixel_values']







							def __init__(   self,			__a = True,			__a = None,			__a = PILImageResampling.BILINEAR,			__a = False,			__a = 1,			__a = True,			__a = 1 / 255,			__a = True,			__a = None,			__a = None,			**__a,			):

														'''simple docstring'''

														super().__init__(**__a)
														_lowerCAmelCase     :							Any    =    size if size is not None else {"height": 384, "width": 384}
														_lowerCAmelCase     :							Optional[int]    =    get_size_dict(__a)
														_lowerCAmelCase     :							Optional[Any]    =    do_resize
														_lowerCAmelCase     :							Dict    =    size
														_lowerCAmelCase     :							Any    =    keep_aspect_ratio
														_lowerCAmelCase     :							str    =    ensure_multiple_of
														_lowerCAmelCase     :							str    =    resample
														_lowerCAmelCase     :							Dict    =    do_rescale
														_lowerCAmelCase     :							Optional[int]    =    rescale_factor
														_lowerCAmelCase     :							Dict    =    do_normalize
														_lowerCAmelCase     :							Dict    =    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
														_lowerCAmelCase     :							int    =    image_std if image_std is not None else IMAGENET_STANDARD_STD







							def    snake_case__      (   self,			__a,			__a,			__a = False,			__a = 1,			__a = PILImageResampling.BICUBIC,			__a = None,			**__a,			):

														'''simple docstring'''

														_lowerCAmelCase     :							List[Any]    =    get_size_dict(__a)
														if "height" not in size or "width" not in size:
																					raise ValueError(f"The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}")
														_lowerCAmelCase     :							List[Any]    =    get_resize_output_image_size(
														    __a,			output_size=(size["height"], size["width"]),			keep_aspect_ratio=__a,			multiple=__a,			)
														return resize(__a,			size=__a,			resample=__a,			data_format=__a,			**__a)







							def    snake_case__      (   self,			__a,			__a,			__a = None,			**__a,			):

														'''simple docstring'''

														return rescale(__a,			scale=__a,			data_format=__a,			**__a)







							def    snake_case__      (   self,			__a,			__a,			__a,			__a = None,			**__a,			):

														'''simple docstring'''

														return normalize(__a,			mean=__a,			std=__a,			data_format=__a,			**__a)







							def    snake_case__      (   self,			__a,			__a = None,			__a = None,			__a = None,			__a = None,			__a = None,			__a = None,			__a = None,			__a = None,			__a = None,			__a = None,			__a = None,			__a = ChannelDimension.FIRST,			**__a,			):

														'''simple docstring'''

														_lowerCAmelCase     :							int    =    do_resize if do_resize is not None else self.do_resize
														_lowerCAmelCase     :							List[Any]    =    size if size is not None else self.size
														_lowerCAmelCase     :							str    =    get_size_dict(__a)
														_lowerCAmelCase     :							Dict    =    keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
														_lowerCAmelCase     :							Any    =    ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
														_lowerCAmelCase     :							int    =    resample if resample is not None else self.resample
														_lowerCAmelCase     :							Union[str, Any]    =    do_rescale if do_rescale is not None else self.do_rescale
														_lowerCAmelCase     :							Tuple    =    rescale_factor if rescale_factor is not None else self.rescale_factor
														_lowerCAmelCase     :							List[str]    =    do_normalize if do_normalize is not None else self.do_normalize
														_lowerCAmelCase     :							Dict    =    image_mean if image_mean is not None else self.image_mean
														_lowerCAmelCase     :							List[str]    =    image_std if image_std is not None else self.image_std

														_lowerCAmelCase     :							Optional[Any]    =    make_list_of_images(__a)

														if not valid_images(__a):
																					raise ValueError(
																					    "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
																					    "torch.Tensor, tf.Tensor or jax.ndarray.")

														if do_resize and size is None or resample is None:
																					raise ValueError("Size and resample must be specified if do_resize is True.")

														if do_rescale and rescale_factor is None:
																					raise ValueError("Rescale factor must be specified if do_rescale is True.")

														if do_normalize and (image_mean is None or image_std is None):
																					raise ValueError("Image mean and std must be specified if do_normalize is True.")

														# All transformations expect numpy arrays.
														_lowerCAmelCase     :							List[Any]    =    [to_numpy_array(__a) for image in images]

														if do_resize:
																					_lowerCAmelCase     :							Any    =    [self.resize(image=__a,			size=__a,			resample=__a) for image in images]

														if do_rescale:
																					_lowerCAmelCase     :							List[str]    =    [self.rescale(image=__a,			scale=__a) for image in images]

														if do_normalize:
																					_lowerCAmelCase     :							Dict    =    [self.normalize(image=__a,			mean=__a,			std=__a) for image in images]

														_lowerCAmelCase     :							List[str]    =    [to_channel_dimension_format(__a,			__a) for image in images]

														_lowerCAmelCase     :							Optional[Any]    =    {"pixel_values": images}
														return BatchFeature(data=__a,			tensor_type=__a)







							def    snake_case__      (   self,			__a,			__a = None):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[Any]    =    outputs.logits

														# Resize logits and compute semantic segmentation maps
														if target_sizes is not None:
																					if len(__a) != len(__a):
																												raise ValueError(
																												    "Make sure that you pass in as many target sizes as the batch dimension of the logits")

																					if is_torch_tensor(__a):
																												_lowerCAmelCase     :							List[Any]    =    target_sizes.numpy()

																					_lowerCAmelCase     :							Dict    =    []

																					for idx in range(len(__a)):
																												_lowerCAmelCase     :							int    =    torch.nn.functional.interpolate(
																												    logits[idx].unsqueeze(dim=0),			size=target_sizes[idx],			mode="bilinear",			align_corners=__a)
																												_lowerCAmelCase     :							int    =    resized_logits[0].argmax(dim=0)
																												semantic_segmentation.append(__a)
														else:
																					_lowerCAmelCase     :							Dict    =    logits.argmax(dim=1)
																					_lowerCAmelCase     :							str    =    [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0])]

														return semantic_segmentation
