





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 UpperCAmelCase_				( unittest.TestCase ):


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       logging.get_logger()

       # the current default level is logging.WARNING
       A_          =       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(__snake_case )


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       logging.get_verbosity()

       A_          =       logging.get_logger('''transformers.models.bart.tokenization_bart''' )
       A_          =       '''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(__snake_case ) as cl:
                 logger.warning(__snake_case )
            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(__snake_case ) as cl:
            logger.warning(__snake_case )
       self.assertEqual(cl.out ,'''''' )

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

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


  @mockenv(TRANSFORMERS_VERBOSITY='''error''' )
  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       transformers.utils.logging._reset_library_root_logger()
       # this action activates the env var
       A_          =       logging.get_logger('''transformers.models.bart.tokenization_bart''' )

       A_          =       os.getenv('''TRANSFORMERS_VERBOSITY''' ,__snake_case )
       A_          =       logging.log_levels[env_level_str]

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

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


  @mockenv(TRANSFORMERS_VERBOSITY='''super-error''' )
  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       transformers.utils.logging._reset_library_root_logger()
       A_          =       logging.logging.getLogger()
       with CaptureLogger(__snake_case ) 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 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       transformers.utils.logging._reset_library_root_logger()

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

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

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




def      UpperCAmelCase_    (    )	->				Optional[int]:
     '''simple docstring'''


     disable_progress_bar()
     assert are_progress_bars_disabled()

     enable_progress_bar()
     assert not are_progress_bars_disabled()










from typing import Dict, List, Optional, 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_vision_available, logging


if is_vision_available():
      import PIL


a__      :     Dict         =		logging.get_logger(__name__)





class UpperCAmelCase_				( _SCREAMING_SNAKE_CASE ):
  __lowerCAmelCase   :						Optional[int]							=   ["""pixel_values"""]


  def __init__(							self ,__snake_case = True ,__snake_case = None ,__snake_case = None ,__snake_case = PILImageResampling.BILINEAR ,__snake_case = True ,__snake_case = 1 / 2_5_5 ,__snake_case = True ,__snake_case = None ,__snake_case = None ,**__snake_case ,):



       """simple docstring"""





       super().__init__(**__snake_case )
       A_          =       size if size is not None else {'''shortest_edge''': 3_8_4}
       A_          =       get_size_dict(__snake_case ,default_to_square=__snake_case )

       A_          =       do_resize
       A_          =       size
       # Default value set here for backwards compatibility where the value in config is None
       A_          =       crop_pct if crop_pct is not None else 2_2_4 / 2_5_6
       A_          =       resample
       A_          =       do_rescale
       A_          =       rescale_factor
       A_          =       do_normalize
       A_          =       image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
       A_          =       image_std if image_std is not None else IMAGENET_STANDARD_STD


  def 						__UpperCAmelCase     (							self ,__snake_case ,__snake_case ,__snake_case ,__snake_case = PILImageResampling.BICUBIC ,__snake_case = None ,**__snake_case ,):



       """simple docstring"""





       A_          =       get_size_dict(__snake_case ,default_to_square=__snake_case )
       if "shortest_edge" not in size:
            raise ValueError(f'Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}' )
       A_          =       size['''shortest_edge''']

       if shortest_edge < 3_8_4:
            # maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
            A_          =       int(shortest_edge / crop_pct )
            A_          =       get_resize_output_image_size(__snake_case ,size=__snake_case ,default_to_square=__snake_case )
            A_          =       resize(image=__snake_case ,size=__snake_case ,resample=__snake_case ,data_format=__snake_case ,**__snake_case )
            # then crop to (shortest_edge, shortest_edge)
            return center_crop(image=__snake_case ,size=(shortest_edge, shortest_edge) ,data_format=__snake_case ,**__snake_case )
       else:
            # warping (no cropping) when evaluated at 384 or larger
            return resize(
                __snake_case ,size=(shortest_edge, shortest_edge) ,resample=__snake_case ,data_format=__snake_case ,**__snake_case )


  def 						__UpperCAmelCase     (							self ,__snake_case ,__snake_case ,__snake_case = None ,**__snake_case ,):



       """simple docstring"""





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


  def 						__UpperCAmelCase     (							self ,__snake_case ,__snake_case ,__snake_case ,__snake_case = None ,**__snake_case ,):



       """simple docstring"""





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




  def 						__UpperCAmelCase     (							self ,__snake_case ,__snake_case = None ,__snake_case = None ,__snake_case = None ,__snake_case = None ,__snake_case = None ,__snake_case = None ,__snake_case = None ,__snake_case = None ,__snake_case = None ,__snake_case = None ,__snake_case = ChannelDimension.FIRST ,**__snake_case ,):



       """simple docstring"""





       A_          =       do_resize if do_resize is not None else self.do_resize
       A_          =       crop_pct if crop_pct is not None else self.crop_pct
       A_          =       resample if resample is not None else self.resample
       A_          =       do_rescale if do_rescale is not None else self.do_rescale
       A_          =       rescale_factor if rescale_factor is not None else self.rescale_factor
       A_          =       do_normalize if do_normalize is not None else self.do_normalize
       A_          =       image_mean if image_mean is not None else self.image_mean
       A_          =       image_std if image_std is not None else self.image_std

       A_          =       size if size is not None else self.size
       A_          =       get_size_dict(__snake_case ,default_to_square=__snake_case )

       A_          =       make_list_of_images(__snake_case )

       if not valid_images(__snake_case ):
            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_resize and size["shortest_edge"] < 3_8_4 and crop_pct is None:
            raise ValueError('''crop_pct must be specified if size < 384.''' )

       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.
       A_          =       [to_numpy_array(__snake_case ) for image in images]

       if do_resize:
            A_          =       [self.resize(image=__snake_case ,size=__snake_case ,crop_pct=__snake_case ,resample=__snake_case ) for image in images]

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

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

       A_          =       [to_channel_dimension_format(__snake_case ,__snake_case ) for image in images]

       A_          =       {'''pixel_values''': images}
       return BatchFeature(data=__snake_case ,tensor_type=__snake_case )



