


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

import numpy as np

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


if is_torch_available():
	import torch


lowerCAmelCase           =		logging.get_logger(__name__)





class       A    (   A_ ):
							UpperCamelCase_   :							List[str]       =['''pixel_values''']


							def __init__(self      ,				lowerCAmelCase = True      ,				lowerCAmelCase = None      ,				lowerCAmelCase = PILImageResampling.BILINEAR      ,				lowerCAmelCase = True      ,				lowerCAmelCase = None      ,				lowerCAmelCase = True      ,				lowerCAmelCase = 1 / 2_5_5      ,				lowerCAmelCase = True      ,				lowerCAmelCase = None      ,				lowerCAmelCase = None      ,				**lowerCAmelCase      ,				):
									super().__init__(**lowerCAmelCase					)
									__lowercase=			size if size is not None else {'shortest_edge': 2_5_6}
									__lowercase=			get_size_dict(lowerCAmelCase      ,				default_to_square=lowerCAmelCase					)
									__lowercase=			crop_size if crop_size is not None else {'height': 2_2_4, 'width': 2_2_4}
									__lowercase=			get_size_dict(lowerCAmelCase      ,				param_name='crop_size'					)
									__lowercase=			do_resize
									__lowercase=			size
									__lowercase=			resample
									__lowercase=			do_center_crop
									__lowercase=			crop_size
									__lowercase=			do_rescale
									__lowercase=			rescale_factor
									__lowercase=			do_normalize
									__lowercase=			image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
									__lowercase=			image_std if image_std is not None else IMAGENET_STANDARD_STD


							def 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase      ,				lowerCAmelCase = PILImageResampling.BICUBIC      ,				lowerCAmelCase = None      ,				**lowerCAmelCase      ,				):
									__lowercase=			get_size_dict(lowerCAmelCase      ,				default_to_square=lowerCAmelCase					)
									if "shortest_edge" not in size:
											raise ValueError(f'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'					)
									__lowercase=			get_resize_output_image_size(lowerCAmelCase      ,				size=size['shortest_edge']      ,				default_to_square=lowerCAmelCase					)
									return resize(lowerCAmelCase      ,				size=lowerCAmelCase      ,				resample=lowerCAmelCase      ,				data_format=lowerCAmelCase      ,				**lowerCAmelCase					)


							def 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase      ,				lowerCAmelCase = None      ,				**lowerCAmelCase      ,				):
									__lowercase=			get_size_dict(lowerCAmelCase					)
									if "height" not in size or "width" not in size:
											raise ValueError(f'The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}'					)
									return center_crop(lowerCAmelCase      ,				size=(size['height'], size['width'])      ,				data_format=lowerCAmelCase      ,				**lowerCAmelCase					)


							def 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase      ,				lowerCAmelCase = None      ,				**lowerCAmelCase					):
									return rescale(lowerCAmelCase      ,				scale=lowerCAmelCase      ,				data_format=lowerCAmelCase      ,				**lowerCAmelCase					)


							def 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase      ,				lowerCAmelCase      ,				lowerCAmelCase = None      ,				**lowerCAmelCase      ,				):
									return normalize(lowerCAmelCase      ,				mean=lowerCAmelCase      ,				std=lowerCAmelCase      ,				data_format=lowerCAmelCase      ,				**lowerCAmelCase					)


							def 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase = None      ,				lowerCAmelCase = None      ,				lowerCAmelCase = None      ,				lowerCAmelCase = None      ,				lowerCAmelCase = None      ,				lowerCAmelCase = None      ,				lowerCAmelCase = None      ,				lowerCAmelCase = None      ,				lowerCAmelCase = None      ,				lowerCAmelCase = None      ,				lowerCAmelCase = None      ,				lowerCAmelCase = ChannelDimension.FIRST      ,				**lowerCAmelCase      ,				):
									__lowercase=			do_resize if do_resize is not None else self.do_resize
									__lowercase=			size if size is not None else self.size
									__lowercase=			get_size_dict(lowerCAmelCase      ,				default_to_square=lowerCAmelCase					)
									__lowercase=			resample if resample is not None else self.resample
									__lowercase=			do_center_crop if do_center_crop is not None else self.do_center_crop
									__lowercase=			crop_size if crop_size is not None else self.crop_size
									__lowercase=			get_size_dict(lowerCAmelCase      ,				param_name='crop_size'					)
									__lowercase=			do_rescale if do_rescale is not None else self.do_rescale
									__lowercase=			rescale_factor if rescale_factor is not None else self.rescale_factor
									__lowercase=			do_normalize if do_normalize is not None else self.do_normalize
									__lowercase=			image_mean if image_mean is not None else self.image_mean
									__lowercase=			image_std if image_std is not None else self.image_std

									__lowercase=			make_list_of_images(lowerCAmelCase					)

									if not valid_images(lowerCAmelCase					):
											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:
											raise ValueError('Size must be specified if do_resize is True.'					)

									if do_center_crop and crop_size is None:
											raise ValueError('Crop size must be specified if do_center_crop 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.
									__lowercase=			[to_numpy_array(lowerCAmelCase					) for image in images]

									if do_resize:
											__lowercase=			[self.resize(image=lowerCAmelCase      ,				size=lowerCAmelCase      ,				resample=lowerCAmelCase					) for image in images]

									if do_center_crop:
											__lowercase=			[self.center_crop(image=lowerCAmelCase      ,				size=lowerCAmelCase					) for image in images]

									if do_rescale:
											__lowercase=			[self.rescale(image=lowerCAmelCase      ,				scale=lowerCAmelCase					) for image in images]

									if do_normalize:
											__lowercase=			[self.normalize(image=lowerCAmelCase      ,				mean=lowerCAmelCase      ,				std=lowerCAmelCase					) for image in images]

									__lowercase=			[to_channel_dimension_format(lowerCAmelCase      ,				lowerCAmelCase					) for image in images]

									__lowercase=			{'pixel_values': images}
									return BatchFeature(data=lowerCAmelCase      ,				tensor_type=lowerCAmelCase					)







							def 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase = None					):
									__lowercase=			outputs.logits

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

											if is_torch_tensor(lowerCAmelCase					):
													__lowercase=			target_sizes.numpy()

											__lowercase=			[]

											for idx in range(len(lowerCAmelCase					)					):
													__lowercase=			torch.nn.functional.interpolate(
													    logits[idx].unsqueeze(dim=0					)      ,				size=target_sizes[idx]      ,				mode='bilinear'      ,				align_corners=lowerCAmelCase					)
													__lowercase=			resized_logits[0].argmax(dim=0					)
													semantic_segmentation.append(lowerCAmelCase					)
									else:
											__lowercase=			logits.argmax(dim=1					)
											__lowercase=			[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]					)]

									return semantic_segmentation










import os
import unittest

from huggingface_hub.utils import are_progress_bars_disabled

import transformers.models.bart.tokenization_bart
from transformers import logging
from transformers.testing_utils import CaptureLogger, mockenv, mockenv_context
from transformers.utils.logging import disable_progress_bar, enable_progress_bar





class       A    (   unittest.TestCase ):


							def 			_A       (self					):
									__lowercase=			logging.get_logger()

									# the current default level is logging.WARNING
									__lowercase=			logging.get_verbosity()

									logging.set_verbosity_error()
									self.assertEqual(logger.getEffectiveLevel()      ,				logging.get_verbosity()					)

									logging.set_verbosity_warning()
									self.assertEqual(logger.getEffectiveLevel()      ,				logging.get_verbosity()					)

									logging.set_verbosity_info()
									self.assertEqual(logger.getEffectiveLevel()      ,				logging.get_verbosity()					)

									logging.set_verbosity_debug()
									self.assertEqual(logger.getEffectiveLevel()      ,				logging.get_verbosity()					)

									# restore to the original level
									logging.set_verbosity(lowerCAmelCase					)


							def 			_A       (self					):
									__lowercase=			logging.get_verbosity()

									__lowercase=			logging.get_logger('transformers.models.bart.tokenization_bart'					)
									__lowercase=			'Testing 1, 2, 3'

									# should be able to log warnings (if default settings weren't overridden by `pytest --log-level-all`)
									if level_origin <= logging.WARNING:
											with CaptureLogger(lowerCAmelCase					) as cl:
													logger.warning(lowerCAmelCase					)
											self.assertEqual(cl.out      ,				msg + '\n'					)

									# this is setting the level for all of `transformers.*` loggers
									logging.set_verbosity_error()

									# should not be able to log warnings
									with CaptureLogger(lowerCAmelCase					) as cl:
											logger.warning(lowerCAmelCase					)
									self.assertEqual(cl.out      ,				''					)

									# should be able to log warnings again
									logging.set_verbosity_warning()
									with CaptureLogger(lowerCAmelCase					) as cl:
											logger.warning(lowerCAmelCase					)
									self.assertEqual(cl.out      ,				msg + '\n'					)

									# restore to the original level
									logging.set_verbosity(lowerCAmelCase					)


							@mockenv(TRANSFORMERS_VERBOSITY='error'					)
							def 			_A       (self					):
									# reset for the env var to take effect, next time some logger call is made
									transformers.utils.logging._reset_library_root_logger()
									# this action activates the env var
									__lowercase=			logging.get_logger('transformers.models.bart.tokenization_bart'					)

									__lowercase=			os.getenv('TRANSFORMERS_VERBOSITY'      ,				lowerCAmelCase					)
									__lowercase=			logging.log_levels[env_level_str]

									__lowercase=			logging.get_verbosity()
									self.assertEqual(
									    lowerCAmelCase      ,				lowerCAmelCase      ,				f'TRANSFORMERS_VERBOSITY={env_level_str}/{env_level}, but internal verbosity is {current_level}'      ,				)

									# restore to the original level
									__lowercase=			''
									transformers.utils.logging._reset_library_root_logger()


							@mockenv(TRANSFORMERS_VERBOSITY='super-error'					)
							def 			_A       (self					):
									# reset for the env var to take effect, next time some logger call is made
									transformers.utils.logging._reset_library_root_logger()
									__lowercase=			logging.logging.getLogger()
									with CaptureLogger(lowerCAmelCase					) as cl:
											# this action activates the env var
											logging.get_logger('transformers.models.bart.tokenization_bart'					)
									self.assertIn('Unknown option TRANSFORMERS_VERBOSITY=super-error'      ,				cl.out					)

									# no need to restore as nothing was changed







							def 			_A       (self					):
									# testing `logger.warning_advice()`
									transformers.utils.logging._reset_library_root_logger()

									__lowercase=			logging.get_logger('transformers.models.bart.tokenization_bart'					)
									__lowercase=			'Testing 1, 2, 3'

									with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS='1'					):
											# nothing should be logged as env var disables this method
											with CaptureLogger(lowerCAmelCase					) as cl:
													logger.warning_advice(lowerCAmelCase					)
											self.assertEqual(cl.out      ,				''					)

									with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS=''					):
											# should log normally as TRANSFORMERS_NO_ADVISORY_WARNINGS is unset
											with CaptureLogger(lowerCAmelCase					) as cl:
													logger.warning_advice(lowerCAmelCase					)
											self.assertEqual(cl.out      ,				msg + '\n'					)







def        _lowerCamelCase(	)						->	Optional[int]:

		'''simple docstring'''







		disable_progress_bar()
		assert are_progress_bars_disabled()

		enable_progress_bar()
		assert not are_progress_bars_disabled()






