





'''simple docstring'''






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


if is_vision_available():
      import PIL


A__							:  List[Any]    =logging.get_logger(__name__)
def   UpperCamelCase__ (						lowerCAmelCase    ):





      """simple docstring"""


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

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

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

      raise ValueError(f"Could not make batched video from {videos}"    )





class 					UpperCAmelCase	(     snake_case_  ):
      _lowercase:     str			=  ['''pixel_values''']




      def __init__(     self     :					Any     ,     __snake_case     :					bool = True     ,     __snake_case     :					Dict[str, int] = None     ,     __snake_case     :					PILImageResampling = PILImageResampling.BILINEAR     ,     __snake_case     :					bool = True     ,     __snake_case     :					Dict[str, int] = None     ,     __snake_case     :					bool = True     ,     __snake_case     :					Union[int, float] = 1 / 2_55     ,     __snake_case     :					bool = True     ,     __snake_case     :					Optional[Union[float, List[float]]] = None     ,     __snake_case     :					Optional[Union[float, List[float]]] = None     ,     **__snake_case     :					str     ,     )							-> None:
            super().__init__(**__snake_case    )
            _lowerCAmelCase					=				size if size is not None else {"""shortest_edge""": 2_24}
            _lowerCAmelCase					=				get_size_dict(__snake_case     ,     default_to_square=__snake_case    )
            _lowerCAmelCase					=				crop_size if crop_size is not None else {"""height""": 2_24, """width""": 2_24}
            _lowerCAmelCase					=				get_size_dict(__snake_case     ,     param_name="""crop_size"""    )

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




      def 							lowercase__       (     self     :					List[Any]     ,     __snake_case     :					np.ndarray     ,     __snake_case     :					Dict[str, int]     ,     __snake_case     :					PILImageResampling = PILImageResampling.BILINEAR     ,     __snake_case     :					Optional[Union[str, ChannelDimension]] = None     ,     **__snake_case     :					Dict     ,     )							-> np.ndarray:
            _lowerCAmelCase					=				get_size_dict(__snake_case     ,     default_to_square=__snake_case    )
            if "shortest_edge" in size:
                  _lowerCAmelCase					=				get_resize_output_image_size(__snake_case     ,     size["""shortest_edge"""]     ,     default_to_square=__snake_case    )
            elif "height" in size and "width" in size:
                  _lowerCAmelCase					=				(size["""height"""], size["""width"""])
            else:
                  raise ValueError(f"Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"    )
            return resize(__snake_case     ,     size=__snake_case     ,     resample=__snake_case     ,     data_format=__snake_case     ,     **__snake_case    )




      def 							lowercase__       (     self     :					Dict     ,     __snake_case     :					np.ndarray     ,     __snake_case     :					Dict[str, int]     ,     __snake_case     :					Optional[Union[str, ChannelDimension]] = None     ,     **__snake_case     :					Optional[int]     ,     )							-> np.ndarray:
            _lowerCAmelCase					=				get_size_dict(__snake_case    )
            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(__snake_case     ,     size=(size["""height"""], size["""width"""])     ,     data_format=__snake_case     ,     **__snake_case    )




      def 							lowercase__       (     self     :					List[Any]     ,     __snake_case     :					np.ndarray     ,     __snake_case     :					Union[int, float]     ,     __snake_case     :					Optional[Union[str, ChannelDimension]] = None     ,     **__snake_case     :					int     ,     )							-> List[str]:
            return rescale(__snake_case     ,     scale=__snake_case     ,     data_format=__snake_case     ,     **__snake_case    )




      def 							lowercase__       (     self     :					List[Any]     ,     __snake_case     :					np.ndarray     ,     __snake_case     :					Union[float, List[float]]     ,     __snake_case     :					Union[float, List[float]]     ,     __snake_case     :					Optional[Union[str, ChannelDimension]] = None     ,     **__snake_case     :					Tuple     ,     )							-> np.ndarray:
            return normalize(__snake_case     ,     mean=__snake_case     ,     std=__snake_case     ,     data_format=__snake_case     ,     **__snake_case    )




      def 							lowercase__       (     self     :					Optional[Any]     ,     __snake_case     :					ImageInput     ,     __snake_case     :					bool = None     ,     __snake_case     :					Dict[str, int] = None     ,     __snake_case     :					PILImageResampling = None     ,     __snake_case     :					bool = None     ,     __snake_case     :					Dict[str, int] = None     ,     __snake_case     :					bool = None     ,     __snake_case     :					float = None     ,     __snake_case     :					bool = None     ,     __snake_case     :					Optional[Union[float, List[float]]] = None     ,     __snake_case     :					Optional[Union[float, List[float]]] = None     ,     __snake_case     :					Optional[ChannelDimension] = ChannelDimension.FIRST     ,     )							-> np.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_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.
            _lowerCAmelCase					=				to_numpy_array(__snake_case    )

            if do_resize:
                  _lowerCAmelCase					=				self.resize(image=__snake_case     ,     size=__snake_case     ,     resample=__snake_case    )

            if do_center_crop:
                  _lowerCAmelCase					=				self.center_crop(__snake_case     ,     size=__snake_case    )

            if do_rescale:
                  _lowerCAmelCase					=				self.rescale(image=__snake_case     ,     scale=__snake_case    )

            if do_normalize:
                  _lowerCAmelCase					=				self.normalize(image=__snake_case     ,     mean=__snake_case     ,     std=__snake_case    )

            _lowerCAmelCase					=				to_channel_dimension_format(__snake_case     ,     __snake_case    )
            return image




      def 							lowercase__       (     self     :					Dict     ,     __snake_case     :					ImageInput     ,     __snake_case     :					bool = None     ,     __snake_case     :					Dict[str, int] = None     ,     __snake_case     :					PILImageResampling = None     ,     __snake_case     :					bool = None     ,     __snake_case     :					Dict[str, int] = None     ,     __snake_case     :					bool = None     ,     __snake_case     :					float = None     ,     __snake_case     :					bool = None     ,     __snake_case     :					Optional[Union[float, List[float]]] = None     ,     __snake_case     :					Optional[Union[float, List[float]]] = None     ,     __snake_case     :					Optional[Union[str, TensorType]] = None     ,     __snake_case     :					ChannelDimension = ChannelDimension.FIRST     ,     **__snake_case     :					Optional[Any]     ,     )							-> PIL.Image.Image:
            _lowerCAmelCase					=				do_resize if do_resize is not None else self.do_resize
            _lowerCAmelCase					=				resample if resample is not None else self.resample
            _lowerCAmelCase					=				do_center_crop if do_center_crop is not None else self.do_center_crop
            _lowerCAmelCase					=				do_rescale if do_rescale is not None else self.do_rescale
            _lowerCAmelCase					=				rescale_factor if rescale_factor is not None else self.rescale_factor
            _lowerCAmelCase					=				do_normalize if do_normalize is not None else self.do_normalize
            _lowerCAmelCase					=				image_mean if image_mean is not None else self.image_mean
            _lowerCAmelCase					=				image_std if image_std is not None else self.image_std

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

            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."""    )

            _lowerCAmelCase					=				make_batched(__snake_case    )

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

            _lowerCAmelCase					=				{"""pixel_values""": videos}
            return BatchFeature(data=__snake_case     ,     tensor_type=__snake_case    )













'''simple docstring'''






import os
import unittest

from transformers import BatchEncoding
from transformers.models.bert.tokenization_bert import (
    BasicTokenizer,
    WordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.models.prophetnet.tokenization_prophetnet import VOCAB_FILES_NAMES, ProphetNetTokenizer
from transformers.testing_utils import require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin




class 					UpperCAmelCase	(     snake_case_				,						unittest.TestCase  ):
      _lowercase:     Optional[int]			=  ProphetNetTokenizer
      _lowercase:     Union[str, Any]			=  False




      def 							lowercase__       (     self     :					Dict    )							-> Optional[int]:
            super().setUp()

            _lowerCAmelCase					=				[
                """[UNK]""",
                """[CLS]""",
                """[SEP]""",
                """[PAD]""",
                """[MASK]""",
                """want""",
                """##want""",
                """##ed""",
                """wa""",
                """un""",
                """runn""",
                """##ing""",
                """,""",
                """low""",
                """lowest""",
            ]
            _lowerCAmelCase					=				os.path.join(self.tmpdirname     ,     VOCAB_FILES_NAMES["""vocab_file"""]    )
            with open(self.vocab_file     ,     """w"""     ,     encoding="""utf-8"""    ) as vocab_writer:
                  vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]    )    )




      def 							lowercase__       (     self     :					Tuple     ,     __snake_case     :					str    )							-> List[str]:
            _lowerCAmelCase					=				"""UNwant\u00E9d,running"""
            _lowerCAmelCase					=				"""unwanted, running"""
            return input_text, output_text




      def 							lowercase__       (     self     :					Union[str, Any]    )							-> Optional[int]:
            _lowerCAmelCase					=				self.tokenizer_class(self.vocab_file    )

            _lowerCAmelCase					=				tokenizer.tokenize("""UNwant\u00E9d,running"""    )
            self.assertListEqual(__snake_case     ,     ["""un""", """##want""", """##ed""", """,""", """runn""", """##ing"""]    )
            self.assertListEqual(tokenizer.convert_tokens_to_ids(__snake_case    )     ,     [9, 6, 7, 12, 10, 11]    )




      def 							lowercase__       (     self     :					List[Any]    )							-> List[str]:
            _lowerCAmelCase					=				BasicTokenizer()

            self.assertListEqual(tokenizer.tokenize("""ah\u535A\u63A8zz"""    )     ,     ["""ah""", """\u535A""", """\u63A8""", """zz"""]    )




      def 							lowercase__       (     self     :					Optional[Any]    )							-> List[Any]:
            _lowerCAmelCase					=				BasicTokenizer(do_lower_case=__snake_case    )

            self.assertListEqual(
                tokenizer.tokenize(""" \tHeLLo!how  \n Are yoU?  """    )     ,     ["""hello""", """!""", """how""", """are""", """you""", """?"""]    )
            self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""    )     ,     ["""hello"""]    )




      def 							lowercase__       (     self     :					Dict    )							-> int:
            _lowerCAmelCase					=				BasicTokenizer(do_lower_case=__snake_case     ,     strip_accents=__snake_case    )

            self.assertListEqual(
                tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """    )     ,     ["""hällo""", """!""", """how""", """are""", """you""", """?"""]    )
            self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""    )     ,     ["""h\u00E9llo"""]    )




      def 							lowercase__       (     self     :					Dict    )							-> int:
            _lowerCAmelCase					=				BasicTokenizer(do_lower_case=__snake_case     ,     strip_accents=__snake_case    )

            self.assertListEqual(
                tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """    )     ,     ["""hallo""", """!""", """how""", """are""", """you""", """?"""]    )
            self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""    )     ,     ["""hello"""]    )




      def 							lowercase__       (     self     :					Dict    )							-> Optional[Any]:
            _lowerCAmelCase					=				BasicTokenizer(do_lower_case=__snake_case    )

            self.assertListEqual(
                tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """    )     ,     ["""hallo""", """!""", """how""", """are""", """you""", """?"""]    )
            self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""    )     ,     ["""hello"""]    )




      def 							lowercase__       (     self     :					str    )							-> List[str]:
            _lowerCAmelCase					=				BasicTokenizer(do_lower_case=__snake_case    )

            self.assertListEqual(
                tokenizer.tokenize(""" \tHeLLo!how  \n Are yoU?  """    )     ,     ["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?"""]    )




      def 							lowercase__       (     self     :					Union[str, Any]    )							-> str:
            _lowerCAmelCase					=				BasicTokenizer(do_lower_case=__snake_case     ,     strip_accents=__snake_case    )

            self.assertListEqual(
                tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """    )     ,     ["""HäLLo""", """!""", """how""", """Are""", """yoU""", """?"""]    )




      def 							lowercase__       (     self     :					Tuple    )							-> str:
            _lowerCAmelCase					=				BasicTokenizer(do_lower_case=__snake_case     ,     strip_accents=__snake_case    )

            self.assertListEqual(
                tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """    )     ,     ["""HaLLo""", """!""", """how""", """Are""", """yoU""", """?"""]    )




      def 							lowercase__       (     self     :					Dict    )							-> Optional[int]:
            _lowerCAmelCase					=				BasicTokenizer(do_lower_case=__snake_case     ,     never_split=["""[UNK]"""]    )

            self.assertListEqual(
                tokenizer.tokenize(""" \tHeLLo!how  \n Are yoU? [UNK]"""    )     ,     ["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?""", """[UNK]"""]    )




      def 							lowercase__       (     self     :					Any    )							-> Dict:
            _lowerCAmelCase					=				["""[UNK]""", """[CLS]""", """[SEP]""", """want""", """##want""", """##ed""", """wa""", """un""", """runn""", """##ing"""]

            _lowerCAmelCase					=				{}
            for i, token in enumerate(__snake_case    ):
                  _lowerCAmelCase					=				i
            _lowerCAmelCase					=				WordpieceTokenizer(vocab=__snake_case     ,     unk_token="""[UNK]"""    )

            self.assertListEqual(tokenizer.tokenize(""""""    )     ,     []    )

            self.assertListEqual(tokenizer.tokenize("""unwanted running"""    )     ,     ["""un""", """##want""", """##ed""", """runn""", """##ing"""]    )

            self.assertListEqual(tokenizer.tokenize("""unwantedX running"""    )     ,     ["""[UNK]""", """runn""", """##ing"""]    )




      @require_torch
      def 							lowercase__       (     self     :					Optional[int]    )							-> Optional[Any]:
            _lowerCAmelCase					=				self.tokenizer_class.from_pretrained("""microsoft/prophetnet-large-uncased"""    )

            _lowerCAmelCase					=				["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
            _lowerCAmelCase					=				[10_37, 21_46, 2_04_23, 20_05, 76_80, 78_49, 39_89, 10_12, 1_02]
            _lowerCAmelCase					=				tokenizer(__snake_case     ,     padding=__snake_case     ,     return_tensors="""pt"""    )
            self.assertIsInstance(__snake_case     ,     __snake_case    )
            _lowerCAmelCase					=				list(batch.input_ids.numpy()[0]    )
            self.assertListEqual(__snake_case     ,     __snake_case    )

            self.assertEqual((2, 9)     ,     batch.input_ids.shape    )
            self.assertEqual((2, 9)     ,     batch.attention_mask.shape    )




      def 							lowercase__       (     self     :					List[Any]    )							-> Any:
            self.assertTrue(_is_whitespace(""" """    )    )
            self.assertTrue(_is_whitespace("""\t"""    )    )
            self.assertTrue(_is_whitespace("""\r"""    )    )
            self.assertTrue(_is_whitespace("""\n"""    )    )
            self.assertTrue(_is_whitespace("""\u00A0"""    )    )

            self.assertFalse(_is_whitespace("""A"""    )    )
            self.assertFalse(_is_whitespace("""-"""    )    )




      def 							lowercase__       (     self     :					str    )							-> Union[str, Any]:
            self.assertTrue(_is_control("""\u0005"""    )    )

            self.assertFalse(_is_control("""A"""    )    )
            self.assertFalse(_is_control(""" """    )    )
            self.assertFalse(_is_control("""\t"""    )    )
            self.assertFalse(_is_control("""\r"""    )    )




      def 							lowercase__       (     self     :					int    )							-> int:
            self.assertTrue(_is_punctuation("""-"""    )    )
            self.assertTrue(_is_punctuation("""$"""    )    )
            self.assertTrue(_is_punctuation("""`"""    )    )
            self.assertTrue(_is_punctuation("""."""    )    )

            self.assertFalse(_is_punctuation("""A"""    )    )
            self.assertFalse(_is_punctuation(""" """    )    )




      @slow
      def 							lowercase__       (     self     :					int    )							-> Optional[int]:
            _lowerCAmelCase					=				self.tokenizer_class.from_pretrained("""microsoft/prophetnet-large-uncased"""    )

            _lowerCAmelCase					=				tokenizer.encode("""sequence builders"""     ,     add_special_tokens=__snake_case    )
            _lowerCAmelCase					=				tokenizer.encode("""multi-sequence build"""     ,     add_special_tokens=__snake_case    )

            _lowerCAmelCase					=				tokenizer.build_inputs_with_special_tokens(__snake_case    )
            _lowerCAmelCase					=				tokenizer.build_inputs_with_special_tokens(__snake_case     ,     __snake_case    )

            assert encoded_sentence == text + [1_02]
            assert encoded_pair == text + [1_02] + text_a + [1_02]






