'''simple docstring'''




import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_video_inputs


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import VivitImageProcessor

class 	lowerCamelCase_	(unittest.TestCase    ):
      '''simple docstring'''

      def __init__(       self						:				int						,					A						:				Dict						,					A						:				Optional[int]=7						,					A						:				Tuple=3						,					A						:				Optional[Any]=10						,					A						:				int=18						,					A						:				Dict=30						,					A						:				List[str]=400						,					A						:				int=True						,					A						:				Optional[Any]=None						,					A						:				Optional[Any]=True						,					A						:				List[Any]=[0.5, 0.5, 0.5]						,					A						:				List[str]=[0.5, 0.5, 0.5]						,					A						:				Optional[int]=None						,					):
             _UpperCAmelCase     :  Dict							      =    size if size is not None else {"shortest_edge": 18}
             _UpperCAmelCase     :  Optional[Any]							      =    crop_size if crop_size is not None else {"height": 18, "width": 18}

             _UpperCAmelCase     :  Tuple							      =    parent
             _UpperCAmelCase     :  Any							      =    batch_size
             _UpperCAmelCase     :  Optional[int]							      =    num_channels
             _UpperCAmelCase     :  Optional[Any]							      =    num_frames
             _UpperCAmelCase     :  Any							      =    image_size
             _UpperCAmelCase     :  Dict							      =    min_resolution
             _UpperCAmelCase     :  Any							      =    max_resolution
             _UpperCAmelCase     :  Optional[int]							      =    do_resize
             _UpperCAmelCase     :  str							      =    size
             _UpperCAmelCase     :  List[Any]							      =    do_normalize
             _UpperCAmelCase     :  Any							      =    image_mean
             _UpperCAmelCase     :  Tuple							      =    image_std
             _UpperCAmelCase     :  Any							      =    crop_size





      def      _A	(       self						:				List[Any]       ):
             return {
                 "image_mean": self.image_mean,
                 "image_std": self.image_std,
                 "do_normalize": self.do_normalize,
                 "do_resize": self.do_resize,
                 "size": self.size,
                 "crop_size": self.crop_size,
             }

@require_torch
@require_vision
class 	lowerCamelCase_	(snake_case__							, unittest.TestCase    ):
      '''simple docstring'''

      __UpperCamelCase:  Dict						=						VivitImageProcessor if is_vision_available() else None




      def      _A	(       self						:				int       ):
             _UpperCAmelCase     :  Tuple							      =    VivitImageProcessingTester(self       )




      @property
      def      _A	(       self						:				Optional[Any]       ):
             return self.image_processor_tester.prepare_image_processor_dict()




      def      _A	(       self						:				Union[str, Any]       ):
             _UpperCAmelCase     :  str							      =    self.image_processing_class(**self.image_processor_dict       )
             self.assertTrue(hasattr(A						,					"image_mean"       )       )
             self.assertTrue(hasattr(A						,					"image_std"       )       )
             self.assertTrue(hasattr(A						,					"do_normalize"       )       )
             self.assertTrue(hasattr(A						,					"do_resize"       )       )
             self.assertTrue(hasattr(A						,					"do_center_crop"       )       )
             self.assertTrue(hasattr(A						,					"size"       )       )




      def      _A	(       self						:				List[Any]       ):
             _UpperCAmelCase     :  Union[str, Any]							      =    self.image_processing_class.from_dict(self.image_processor_dict       )
             self.assertEqual(image_processor.size						,					{"shortest_edge": 18}       )
             self.assertEqual(image_processor.crop_size						,					{"height": 18, "width": 18}       )

             _UpperCAmelCase     :  Optional[int]							      =    self.image_processing_class.from_dict(self.image_processor_dict						,					size=42						,					crop_size=84       )
             self.assertEqual(image_processor.size						,					{"shortest_edge": 42}       )
             self.assertEqual(image_processor.crop_size						,					{"height": 84, "width": 84}       )




      def      _A	(       self						:				Tuple       ):
             # Initialize image_processing
             _UpperCAmelCase     :  Optional[Any]							      =    self.image_processing_class(**self.image_processor_dict       )
             # create random PIL videos
             _UpperCAmelCase     :  Any							      =    prepare_video_inputs(self.image_processor_tester						,					equal_resolution=A       )
             for video in video_inputs:
                    self.assertIsInstance(A						,					A       )
                    self.assertIsInstance(video[0]						,					Image.Image       )

             # Test not batched input
             _UpperCAmelCase     :  str							      =    image_processing(video_inputs[0]						,					return_tensors="pt"       ).pixel_values
             self.assertEqual(
                 encoded_videos.shape						,					(
                     1,
                     self.image_processor_tester.num_frames,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.crop_size["height"],
                     self.image_processor_tester.crop_size["width"],
                 )						,					)

             # Test batched
             _UpperCAmelCase     :  List[Any]							      =    image_processing(A						,					return_tensors="pt"       ).pixel_values
             self.assertEqual(
                 encoded_videos.shape						,					(
                     self.image_processor_tester.batch_size,
                     self.image_processor_tester.num_frames,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.crop_size["height"],
                     self.image_processor_tester.crop_size["width"],
                 )						,					)




      def      _A	(       self						:				List[Any]       ):
             # Initialize image_processing
             _UpperCAmelCase     :  List[str]							      =    self.image_processing_class(**self.image_processor_dict       )
             # create random numpy tensors
             _UpperCAmelCase     :  List[Any]							      =    prepare_video_inputs(self.image_processor_tester						,					equal_resolution=A						,					numpify=A       )
             for video in video_inputs:
                    self.assertIsInstance(A						,					A       )
                    self.assertIsInstance(video[0]						,					np.ndarray       )

             # Test not batched input
             _UpperCAmelCase     :  Tuple							      =    image_processing(video_inputs[0]						,					return_tensors="pt"       ).pixel_values
             self.assertEqual(
                 encoded_videos.shape						,					(
                     1,
                     self.image_processor_tester.num_frames,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.crop_size["height"],
                     self.image_processor_tester.crop_size["width"],
                 )						,					)

             # Test batched
             _UpperCAmelCase     :  Optional[int]							      =    image_processing(A						,					return_tensors="pt"       ).pixel_values
             self.assertEqual(
                 encoded_videos.shape						,					(
                     self.image_processor_tester.batch_size,
                     self.image_processor_tester.num_frames,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.crop_size["height"],
                     self.image_processor_tester.crop_size["width"],
                 )						,					)





      def      _A	(       self						:				List[Any]       ):
             # Initialize image_processing
             _UpperCAmelCase     :  Optional[Any]							      =    self.image_processing_class(**self.image_processor_dict       )
             # create random PyTorch tensors
             _UpperCAmelCase     :  Optional[int]							      =    prepare_video_inputs(self.image_processor_tester						,					equal_resolution=A						,					torchify=A       )
             for video in video_inputs:
                    self.assertIsInstance(A						,					A       )
                    self.assertIsInstance(video[0]						,					torch.Tensor       )

             # Test not batched input
             _UpperCAmelCase     :  Optional[Any]							      =    image_processing(video_inputs[0]						,					return_tensors="pt"       ).pixel_values
             self.assertEqual(
                 encoded_videos.shape						,					(
                     1,
                     self.image_processor_tester.num_frames,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.crop_size["height"],
                     self.image_processor_tester.crop_size["width"],
                 )						,					)

             # Test batched
             _UpperCAmelCase     :  List[Any]							      =    image_processing(A						,					return_tensors="pt"       ).pixel_values
             self.assertEqual(
                 encoded_videos.shape						,					(
                     self.image_processor_tester.batch_size,
                     self.image_processor_tester.num_frames,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.crop_size["height"],
                     self.image_processor_tester.crop_size["width"],
                 )						,					)


'''simple docstring'''




import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
  from .tokenization_pegasus import PegasusTokenizer
else:
  __SCREAMING_SNAKE_CASE       :     Optional[Any]     =  None


__SCREAMING_SNAKE_CASE       :     Dict     =  logging.get_logger(__name__)


__SCREAMING_SNAKE_CASE       :     Union[str, Any]     =  """▁"""

__SCREAMING_SNAKE_CASE       :     str     =  {"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

__SCREAMING_SNAKE_CASE       :     int     =  {
    """vocab_file""": {"""google/pegasus-xsum""": """https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"""},
    """tokenizer_file""": {
        """google/pegasus-xsum""": """https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json"""
    },
}

__SCREAMING_SNAKE_CASE       :     str     =  {
    """google/pegasus-xsum""": 512,
}

class 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

      __UpperCamelCase:  Optional[int]						=						VOCAB_FILES_NAMES
      __UpperCamelCase:  Dict						=						PRETRAINED_VOCAB_FILES_MAP
      __UpperCamelCase:  List[Any]						=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      __UpperCamelCase:  Optional[int]						=						PegasusTokenizer
      __UpperCamelCase:  Optional[Any]						=						["input_ids", "attention_mask"]




      def __init__(       self						:				Dict						,					A						:				List[str]=None						,					A						:				Union[str, Any]=None						,					A						:				Optional[int]="<pad>"						,					A						:				Tuple="</s>"						,					A						:				Union[str, Any]="<unk>"						,					A						:				Union[str, Any]="<mask_2>"						,					A						:				Dict="<mask_1>"						,					A						:				Union[str, Any]=None						,					A						:				int=103						,					**A						:				Optional[Any]						,					):
             _UpperCAmelCase     :  Dict							      =    offset

             if additional_special_tokens is not None:
                    if not isinstance(A						,					A       ):
                           raise TypeError(
                               F"""additional_special_tokens should be of type {type(A       )}, but is"""
                               F""" {type(A       )}"""       )

                    _UpperCAmelCase     :  Optional[int]							      =    (
                        ([mask_token_sent] + additional_special_tokens)
                        if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
                        else additional_special_tokens
                    )
                    # fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
                    additional_special_tokens_extended += [
                        F"""<unk_{i}>""" for i in range(len(A       )						,					self.offset - 1       )
                    ]

                    if len(set(A       )       ) != len(A       ):
                           raise ValueError(
                               "Please make sure that the provided additional_special_tokens do not contain an incorrectly"
                               F""" shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}."""       )
                    _UpperCAmelCase     :  Any							      =    additional_special_tokens_extended
             else:
                    _UpperCAmelCase     :  Dict							      =    [mask_token_sent] if mask_token_sent is not None else []
                    additional_special_tokens += [F"""<unk_{i}>""" for i in range(2						,					self.offset       )]

             super().__init__(
                 A						,					tokenizer_file=A						,					pad_token=A						,					eos_token=A						,					unk_token=A						,					mask_token=A						,					mask_token_sent=A						,					offset=A						,					additional_special_tokens=A						,					**A						,					)
             _UpperCAmelCase     :  Optional[Any]							      =    vocab_file
             _UpperCAmelCase     :  Optional[Any]							      =    False if not self.vocab_file else True




      def      _A	(       self						:				List[str]						,					A						:				Optional[Any]       ):
             _UpperCAmelCase     :  Any							      =    set(self.all_special_ids       )  # call it once instead of inside list comp
             all_special_ids.remove(self.unk_token_id       )  # <unk> is only sometimes special

             if all_special_ids != set(range(len(self.additional_special_tokens       ) + 3       )       ):
                    raise ValueError(
                        "There should be 3 special tokens: mask_token, pad_token, and eos_token +"
                        F""" {len(self.additional_special_tokens       )} additional_special_tokens, but got {all_special_ids}"""       )

             return [1 if x in all_special_ids else 0 for x in seq]




      def      _A	(       self						:				str						,					A						:				List						,					A						:				Optional[List] = None						,					A						:				bool = False       ):
             if already_has_special_tokens:
                    return self._special_token_mask(A       )
             elif token_ids_a is None:
                    return self._special_token_mask(A       ) + [1]
             else:
                    return self._special_token_mask(token_ids_a + token_ids_a       ) + [1]




      def      _A	(       self						:				Optional[int]						,					A						:				Union[str, Any]						,					A						:				int=None       ):
             if token_ids_a is None:
                    return token_ids_a + [self.eos_token_id]
             # We don't expect to process pairs, but leave the pair logic for API consistency
             return token_ids_a + token_ids_a + [self.eos_token_id]





      def      _A	(       self						:				Union[str, Any]						,					A						:				str						,					A						:				Optional[str] = None       ):
             if not self.can_save_slow_tokenizer:
                    raise ValueError(
                        "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
                        "tokenizer."       )

             if not os.path.isdir(A       ):
                    logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""       )
                    return
             _UpperCAmelCase     :  List[Any]							      =    os.path.join(
                 A						,					(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]       )

             if os.path.abspath(self.vocab_file       ) != os.path.abspath(A       ):
                    copyfile(self.vocab_file						,					A       )

             return (out_vocab_file,)

