






"""simple docstring"""







import unittest

from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


_UpperCamelCase					      =  get_tests_dir("""fixtures/test_sentencepiece.model""")







@require_sentencepiece
class  lowerCamelCase__ (	snake_case   ,					unittest.TestCase			):
    SCREAMING_SNAKE_CASE		=      XLMProphetNetTokenizer
    SCREAMING_SNAKE_CASE		=      False
    SCREAMING_SNAKE_CASE		=      True






    def     _UpperCamelCase							(			self ):
          super().setUp()

          # We have a SentencePiece fixture for testing
          UpperCAmelCase          =	XLMProphetNetTokenizer(A     ,keep_accents=A )
          tokenizer.save_pretrained(self.tmpdirname )






    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	"""[PAD]"""
          UpperCAmelCase          =	0

          self.assertEqual(self.get_tokenizer()._convert_token_to_id(A )     ,A )
          self.assertEqual(self.get_tokenizer()._convert_id_to_token(A )     ,A )






    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	list(self.get_tokenizer().get_vocab().keys() )

          self.assertEqual(vocab_keys[0]     ,"""[PAD]""" )
          self.assertEqual(vocab_keys[1]     ,"""[CLS]""" )
          self.assertEqual(vocab_keys[-1]     ,"""j""" )
          self.assertEqual(len(A )     ,1_012 )






    def     _UpperCamelCase							(			self ):
          self.assertEqual(self.get_tokenizer().vocab_size     ,1_012 )






    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	XLMProphetNetTokenizer(A     ,keep_accents=A )

          UpperCAmelCase          =	tokenizer.tokenize("""This is a test""" )
          self.assertListEqual(A     ,["""▁This""", """▁is""", """▁a""", """▁t""", """est"""] )

          self.assertListEqual(
              tokenizer.convert_tokens_to_ids(A )     ,[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]     ,)

          UpperCAmelCase          =	tokenizer.tokenize("""I was born in 92000, and this is falsé.""" )
          self.assertListEqual(
              A     ,[
                  SPIECE_UNDERLINE + """I""",
                  SPIECE_UNDERLINE + """was""",
                  SPIECE_UNDERLINE + """b""",
                  """or""",
                  """n""",
                  SPIECE_UNDERLINE + """in""",
                  SPIECE_UNDERLINE + """""",
                  """9""",
                  """2""",
                  """0""",
                  """0""",
                  """0""",
                  """,""",
                  SPIECE_UNDERLINE + """and""",
                  SPIECE_UNDERLINE + """this""",
                  SPIECE_UNDERLINE + """is""",
                  SPIECE_UNDERLINE + """f""",
                  """al""",
                  """s""",
                  """é""",
                  """.""",
              ]     ,)
          UpperCAmelCase          =	tokenizer.convert_tokens_to_ids(A )
          self.assertListEqual(
              A     ,[
                  value + tokenizer.fairseq_offset
                  for value in [8, 21, 84, 55, 24, 19, 7, -9, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, -9, 4]
              ]     ,)

          UpperCAmelCase          =	tokenizer.convert_ids_to_tokens(A )
          self.assertListEqual(
              A     ,[
                  SPIECE_UNDERLINE + """I""",
                  SPIECE_UNDERLINE + """was""",
                  SPIECE_UNDERLINE + """b""",
                  """or""",
                  """n""",
                  SPIECE_UNDERLINE + """in""",
                  SPIECE_UNDERLINE + """""",
                  """[UNK]""",
                  """2""",
                  """0""",
                  """0""",
                  """0""",
                  """,""",
                  SPIECE_UNDERLINE + """and""",
                  SPIECE_UNDERLINE + """this""",
                  SPIECE_UNDERLINE + """is""",
                  SPIECE_UNDERLINE + """f""",
                  """al""",
                  """s""",
                  """[UNK]""",
                  """.""",
              ]     ,)






    @cached_property
    def     _UpperCamelCase							(			self ):
          return XLMProphetNetTokenizer.from_pretrained("""microsoft/xprophetnet-large-wiki100-cased""" )






    @slow
    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	"""Hello World!"""
          UpperCAmelCase          =	[35_389, 6_672, 49, 2]
          self.assertListEqual(A     ,self.big_tokenizer.encode(A ) )






    @slow
    def     _UpperCamelCase							(			self ):
          # fmt: off
          UpperCAmelCase          =	{"""input_ids""": [[11_073, 82_783, 18, 26, 82_783, 549, 51_540, 248, 17_209, 1_301, 217, 20, 215_186, 1_325, 147, 17_209, 1_301, 217, 20, 56_370, 53, 122_020, 20, 16_477, 27, 87_355, 4_548, 20, 4_728, 78_392, 17, 159_969, 18, 26, 24_491, 629, 15, 538, 22_704, 5_439, 15, 2_788, 24_491, 9_885, 15, 43_534, 605, 15, 814, 18_403, 33_200, 29, 15, 43_534, 24_458, 12_410, 111, 24_966, 83_669, 9_637, 144_068, 26, 850, 22_346, 27, 147, 24_966, 83_669, 83_490, 26, 39_113, 735, 27, 689, 656, 2_800, 1_339, 4_600, 53, 122_020, 115_785, 34, 816, 1_339, 46_887, 18, 147, 53_905, 1_951, 42_238, 41_170, 17_732, 834, 436, 15, 27_523, 98_733, 217, 147, 5_542, 4_981, 930, 17_347, 16, 2], [20_091, 629, 94, 82_786, 58, 490, 20, 1_528, 84, 53_905, 344, 80_592, 110_128, 18_822, 5_267, 1_306, 62, 152_537, 308, 7_997, 401, 124_427, 549, 35_442, 225, 109, 15_055, 25_748, 147, 7_119, 43_712, 34, 767, 135_366, 18, 16, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [592, 63_784, 119_466, 17, 147_808, 88_214, 18, 656, 81, 32, 3_296, 10_280, 16, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
          # fmt: on

          self.tokenizer_integration_test_util(
              expected_encoding=A     ,model_name="""microsoft/xprophetnet-large-wiki100-cased"""     ,revision="""1acad1643ddd54a44df6a1b797ada8373685d90e"""     ,)











"""simple docstring"""







from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

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,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
  import PIL

_UpperCamelCase					      =  logging.get_logger(__name__)

def _a			(		_snake_case							):







      """simple docstring"""






      if isinstance(_snake_case				,	(list, tuple)							) and isinstance(videos[0]				,	(list, tuple)							) and is_valid_image(videos[0][0]							):
            return videos

      elif isinstance(_snake_case				,	(list, tuple)							) and is_valid_image(videos[0]							):
            return [videos]

      elif is_valid_image(_snake_case							):
            return [[videos]]

      raise ValueError(F'''Could not make batched video from {videos}'''							)







class  lowerCamelCase__ (	snake_case			):
    SCREAMING_SNAKE_CASE		=      ['''pixel_values''']






    def __init__(			self     ,A = True     ,A = None     ,A = PILImageResampling.BILINEAR     ,A = True     ,A = None     ,A = True     ,A = 1 / 255     ,A = True     ,A = True     ,A = None     ,A = None     ,**A     ,):
          super().__init__(**A )
          UpperCAmelCase          =	size if size is not None else {"""shortest_edge""": 256}
          UpperCAmelCase          =	get_size_dict(A     ,default_to_square=A )
          UpperCAmelCase          =	crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
          UpperCAmelCase          =	get_size_dict(A     ,param_name="""crop_size""" )

          UpperCAmelCase          =	do_resize
          UpperCAmelCase          =	size
          UpperCAmelCase          =	do_center_crop
          UpperCAmelCase          =	crop_size
          UpperCAmelCase          =	resample
          UpperCAmelCase          =	do_rescale
          UpperCAmelCase          =	rescale_factor
          UpperCAmelCase          =	offset
          UpperCAmelCase          =	do_normalize
          UpperCAmelCase          =	image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
          UpperCAmelCase          =	image_std if image_std is not None else IMAGENET_STANDARD_STD






    def     _UpperCamelCase							(			self     ,A     ,A     ,A = PILImageResampling.BILINEAR     ,A = None     ,**A     ,):
          UpperCAmelCase          =	get_size_dict(A     ,default_to_square=A )
          if "shortest_edge" in size:
                UpperCAmelCase          =	get_resize_output_image_size(A     ,size["""shortest_edge"""]     ,default_to_square=A )
          elif "height" in size and "width" in size:
                UpperCAmelCase          =	(size["""height"""], size["""width"""])
          else:
                raise ValueError(F'''Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}''' )
          return resize(A     ,size=A     ,resample=A     ,data_format=A     ,**A )






    def     _UpperCamelCase							(			self     ,A     ,A     ,A = None     ,**A     ,):
          UpperCAmelCase          =	get_size_dict(A )
          if "height" not in size or "width" not in size:
                raise ValueError(F'''Size must have \'height\' and \'width\' as keys. Got {size.keys()}''' )
          return center_crop(A     ,size=(size["""height"""], size["""width"""])     ,data_format=A     ,**A )






    def     _UpperCamelCase							(			self     ,A     ,A     ,A = True     ,A = None     ,**A     ,):
          UpperCAmelCase          =	image.astype(np.floataa )
          if offset:
                UpperCAmelCase          =	image - (scale / 2)
          return rescale(A     ,scale=A     ,data_format=A     ,**A )






    def     _UpperCamelCase							(			self     ,A     ,A     ,A     ,A = None     ,**A     ,):
          return normalize(A     ,mean=A     ,std=A     ,data_format=A     ,**A )






    def     _UpperCamelCase							(			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     ,):
          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_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.""" )

          if offset and not do_rescale:
                raise ValueError("""For offset, do_rescale must also be set to True.""" )

          # All transformations expect numpy arrays.
          UpperCAmelCase          =	to_numpy_array(A )

          if do_resize:
                UpperCAmelCase          =	self.resize(image=A     ,size=A     ,resample=A )

          if do_center_crop:
                UpperCAmelCase          =	self.center_crop(A     ,size=A )

          if do_rescale:
                UpperCAmelCase          =	self.rescale(image=A     ,scale=A     ,offset=A )

          if do_normalize:
                UpperCAmelCase          =	self.normalize(image=A     ,mean=A     ,std=A )

          UpperCAmelCase          =	to_channel_dimension_format(A     ,A )
          return image






    def     _UpperCamelCase							(			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 = None     ,A = ChannelDimension.FIRST     ,**A     ,):
          UpperCAmelCase          =	do_resize if do_resize is not None else self.do_resize
          UpperCAmelCase          =	resample if resample is not None else self.resample
          UpperCAmelCase          =	do_center_crop if do_center_crop is not None else self.do_center_crop
          UpperCAmelCase          =	do_rescale if do_rescale is not None else self.do_rescale
          UpperCAmelCase          =	rescale_factor if rescale_factor is not None else self.rescale_factor
          UpperCAmelCase          =	offset if offset is not None else self.offset
          UpperCAmelCase          =	do_normalize if do_normalize is not None else self.do_normalize
          UpperCAmelCase          =	image_mean if image_mean is not None else self.image_mean
          UpperCAmelCase          =	image_std if image_std is not None else self.image_std

          UpperCAmelCase          =	size if size is not None else self.size
          UpperCAmelCase          =	get_size_dict(A     ,default_to_square=A )
          UpperCAmelCase          =	crop_size if crop_size is not None else self.crop_size
          UpperCAmelCase          =	get_size_dict(A     ,param_name="""crop_size""" )

          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.""" )

          UpperCAmelCase          =	make_batched(A )

          UpperCAmelCase          =	[
              [
                  self._preprocess_image(
                      image=A     ,do_resize=A     ,size=A     ,resample=A     ,do_center_crop=A     ,crop_size=A     ,do_rescale=A     ,rescale_factor=A     ,offset=A     ,do_normalize=A     ,image_mean=A     ,image_std=A     ,data_format=A     ,)
                  for img in video
              ]
              for video in videos
          ]

          UpperCAmelCase          =	{"""pixel_values""": videos}
          return BatchFeature(data=A     ,tensor_type=A )



