





import unittest

import numpy as np
from datasets import load_dataset

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_image_inputs


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image

   from transformers import BeitImageProcessor




class        _a    (    unittest.TestCase):

      """simple docstring"""


      def __init__(		self:			Dict	,    __lowerCamelCase:			Any	,    __lowerCamelCase:			Optional[int]=7	,    __lowerCamelCase:			Any=3	,    __lowerCamelCase:			List[str]=18	,    __lowerCamelCase:			List[Any]=30	,    __lowerCamelCase:			Tuple=400	,    __lowerCamelCase:			List[str]=True	,    __lowerCamelCase:			Any=None	,    __lowerCamelCase:			int=True	,    __lowerCamelCase:			Any=None	,    __lowerCamelCase:			Dict=True	,    __lowerCamelCase:			List[Any]=[0.5, 0.5, 0.5]	,    __lowerCamelCase:			Optional[Any]=[0.5, 0.5, 0.5]	,    __lowerCamelCase:			int=False	,    ):
          '''simple docstring'''


          UpperCamelCase__:	Optional[int]					=     size if size is not None else {"height": 20, "width": 20}
          UpperCamelCase__:	List[Any]					=     crop_size if crop_size is not None else {"height": 18, "width": 18}
          UpperCamelCase__:	Optional[int]					=     parent
          UpperCamelCase__:	int					=     batch_size
          UpperCamelCase__:	int					=     num_channels
          UpperCamelCase__:	str					=     image_size
          UpperCamelCase__:	Any					=     min_resolution
          UpperCamelCase__:	Union[str, Any]					=     max_resolution
          UpperCamelCase__:	Optional[Any]					=     do_resize
          UpperCamelCase__:	Any					=     size
          UpperCamelCase__:	str					=     do_center_crop
          UpperCamelCase__:	Any					=     crop_size
          UpperCamelCase__:	Any					=     do_normalize
          UpperCamelCase__:	int					=     image_mean
          UpperCamelCase__:	Tuple					=     image_std
          UpperCamelCase__:	int					=     do_reduce_labels






      def 			UpperCAmelCase_       (		self:			Tuple					):
          '''simple docstring'''


          return {
              "do_resize": self.do_resize,
              "size": self.size,
              "do_center_crop": self.do_center_crop,
              "crop_size": self.crop_size,
              "do_normalize": self.do_normalize,
              "image_mean": self.image_mean,
              "image_std": self.image_std,
              "do_reduce_labels": self.do_reduce_labels,
          }



def 				lowerCAmelCase_ (							):
    UpperCamelCase__:	Dict					=     load_dataset("hf-internal-testing/fixtures_ade20k"    ,split="test")

    UpperCamelCase__:	Optional[Any]					=     Image.open(dataset[0]["file"])
    UpperCamelCase__:	str					=     Image.open(dataset[1]["file"])

    return image, map



def 				lowerCAmelCase_ (							):
    UpperCamelCase__:	Dict					=     load_dataset("hf-internal-testing/fixtures_ade20k"    ,split="test")

    UpperCamelCase__:	int					=     Image.open(ds[0]["file"])
    UpperCamelCase__:	int					=     Image.open(ds[1]["file"])
    UpperCamelCase__:	List[str]					=     Image.open(ds[2]["file"])
    UpperCamelCase__:	List[Any]					=     Image.open(ds[3]["file"])

    return [imagea, imagea], [mapa, mapa]




@require_torch
@require_vision
class        _a    (    UpperCamelCase__						,			unittest.TestCase):

      """simple docstring"""


      UpperCamelCase__  		=       BeitImageProcessor if is_vision_available() else None






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


          UpperCamelCase__:	str					=     BeitImageProcessingTester(self					)






      @property
      def 			UpperCAmelCase_       (		self:			Any					):
          '''simple docstring'''


          return self.image_processor_tester.prepare_image_processor_dict()






      def 			UpperCAmelCase_       (		self:			Dict					):
          '''simple docstring'''


          UpperCamelCase__:	int					=     self.image_processing_class(**self.image_processor_dict					)
          self.assertTrue(hasattr(__lowerCamelCase	,    "do_resize"					)					)
          self.assertTrue(hasattr(__lowerCamelCase	,    "size"					)					)
          self.assertTrue(hasattr(__lowerCamelCase	,    "do_center_crop"					)					)
          self.assertTrue(hasattr(__lowerCamelCase	,    "center_crop"					)					)
          self.assertTrue(hasattr(__lowerCamelCase	,    "do_normalize"					)					)
          self.assertTrue(hasattr(__lowerCamelCase	,    "image_mean"					)					)
          self.assertTrue(hasattr(__lowerCamelCase	,    "image_std"					)					)






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


          UpperCamelCase__:	Tuple					=     self.image_processing_class.from_dict(self.image_processor_dict					)
          self.assertEqual(image_processor.size	,    {"height": 20, "width": 20}					)
          self.assertEqual(image_processor.crop_size	,    {"height": 18, "width": 18}					)
          self.assertEqual(image_processor.do_reduce_labels	,    __lowerCamelCase					)

          UpperCamelCase__:	int					=     self.image_processing_class.from_dict(
              self.image_processor_dict	,    size=42	,    crop_size=84	,    reduce_labels=__lowerCamelCase					)
          self.assertEqual(image_processor.size	,    {"height": 42, "width": 42}					)
          self.assertEqual(image_processor.crop_size	,    {"height": 84, "width": 84}					)
          self.assertEqual(image_processor.do_reduce_labels	,    __lowerCamelCase					)






      def 			UpperCAmelCase_       (		self:			Tuple					):
          '''simple docstring'''


          pass






      def 			UpperCAmelCase_       (		self:			Dict					):
          '''simple docstring'''


          UpperCamelCase__:	Tuple					=     self.image_processing_class(**self.image_processor_dict					)
          # create random PIL images
          UpperCamelCase__:	Optional[Any]					=     prepare_image_inputs(self.image_processor_tester	,    equal_resolution=__lowerCamelCase					)
          for image in image_inputs:
              self.assertIsInstance(__lowerCamelCase	,    Image.Image					)

          # Test not batched input
          UpperCamelCase__:	List[Any]					=     image_processing(image_inputs[0]	,    return_tensors="pt"					).pixel_values
          self.assertEqual(
              encoded_images.shape	,    (
                  1,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size["height"],
                  self.image_processor_tester.crop_size["width"],
              )	,    )

          # Test batched
          UpperCamelCase__:	str					=     image_processing(__lowerCamelCase	,    return_tensors="pt"					).pixel_values
          self.assertEqual(
              encoded_images.shape	,    (
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size["height"],
                  self.image_processor_tester.crop_size["width"],
              )	,    )






      def 			UpperCAmelCase_       (		self:			Dict					):
          '''simple docstring'''


          UpperCamelCase__:	Optional[Any]					=     self.image_processing_class(**self.image_processor_dict					)
          # create random numpy tensors
          UpperCamelCase__:	List[str]					=     prepare_image_inputs(self.image_processor_tester	,    equal_resolution=__lowerCamelCase	,    numpify=__lowerCamelCase					)
          for image in image_inputs:
              self.assertIsInstance(__lowerCamelCase	,    np.ndarray					)

          # Test not batched input
          UpperCamelCase__:	Union[str, Any]					=     image_processing(image_inputs[0]	,    return_tensors="pt"					).pixel_values
          self.assertEqual(
              encoded_images.shape	,    (
                  1,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size["height"],
                  self.image_processor_tester.crop_size["width"],
              )	,    )

          # Test batched
          UpperCamelCase__:	Dict					=     image_processing(__lowerCamelCase	,    return_tensors="pt"					).pixel_values
          self.assertEqual(
              encoded_images.shape	,    (
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size["height"],
                  self.image_processor_tester.crop_size["width"],
              )	,    )






      def 			UpperCAmelCase_       (		self:			Union[str, Any]					):
          '''simple docstring'''


          UpperCamelCase__:	int					=     self.image_processing_class(**self.image_processor_dict					)
          # create random PyTorch tensors
          UpperCamelCase__:	int					=     prepare_image_inputs(self.image_processor_tester	,    equal_resolution=__lowerCamelCase	,    torchify=__lowerCamelCase					)
          for image in image_inputs:
              self.assertIsInstance(__lowerCamelCase	,    torch.Tensor					)

          # Test not batched input
          UpperCamelCase__:	Dict					=     image_processing(image_inputs[0]	,    return_tensors="pt"					).pixel_values
          self.assertEqual(
              encoded_images.shape	,    (
                  1,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size["height"],
                  self.image_processor_tester.crop_size["width"],
              )	,    )

          # Test batched
          UpperCamelCase__:	str					=     image_processing(__lowerCamelCase	,    return_tensors="pt"					).pixel_values
          self.assertEqual(
              encoded_images.shape	,    (
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size["height"],
                  self.image_processor_tester.crop_size["width"],
              )	,    )






      def 			UpperCAmelCase_       (		self:			Tuple					):
          '''simple docstring'''


          UpperCamelCase__:	Union[str, Any]					=     self.image_processing_class(**self.image_processor_dict					)
          # create random PyTorch tensors
          UpperCamelCase__:	Dict					=     prepare_image_inputs(self.image_processor_tester	,    equal_resolution=__lowerCamelCase	,    torchify=__lowerCamelCase					)
          UpperCamelCase__:	str					=     []
          for image in image_inputs:
              self.assertIsInstance(__lowerCamelCase	,    torch.Tensor					)
              maps.append(torch.zeros(image.shape[-2:]					).long()					)

          # Test not batched input
          UpperCamelCase__:	Dict					=     image_processing(image_inputs[0]	,    maps[0]	,    return_tensors="pt"					)
          self.assertEqual(
              encoding["pixel_values"].shape	,    (
                  1,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size["height"],
                  self.image_processor_tester.crop_size["width"],
              )	,    )
          self.assertEqual(
              encoding["labels"].shape	,    (
                  1,
                  self.image_processor_tester.crop_size["height"],
                  self.image_processor_tester.crop_size["width"],
              )	,    )
          self.assertEqual(encoding["labels"].dtype	,    torch.long					)
          self.assertTrue(encoding["labels"].min().item() >= 0					)
          self.assertTrue(encoding["labels"].max().item() <= 255					)

          # Test batched
          UpperCamelCase__:	Any					=     image_processing(__lowerCamelCase	,    __lowerCamelCase	,    return_tensors="pt"					)
          self.assertEqual(
              encoding["pixel_values"].shape	,    (
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size["height"],
                  self.image_processor_tester.crop_size["width"],
              )	,    )
          self.assertEqual(
              encoding["labels"].shape	,    (
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.crop_size["height"],
                  self.image_processor_tester.crop_size["width"],
              )	,    )
          self.assertEqual(encoding["labels"].dtype	,    torch.long					)
          self.assertTrue(encoding["labels"].min().item() >= 0					)
          self.assertTrue(encoding["labels"].max().item() <= 255					)

          # Test not batched input (PIL images)
          UpperCamelCase__				, UpperCamelCase__:	str					=     prepare_semantic_single_inputs()

          UpperCamelCase__:	Any					=     image_processing(__lowerCamelCase	,    __lowerCamelCase	,    return_tensors="pt"					)
          self.assertEqual(
              encoding["pixel_values"].shape	,    (
                  1,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size["height"],
                  self.image_processor_tester.crop_size["width"],
              )	,    )
          self.assertEqual(
              encoding["labels"].shape	,    (
                  1,
                  self.image_processor_tester.crop_size["height"],
                  self.image_processor_tester.crop_size["width"],
              )	,    )
          self.assertEqual(encoding["labels"].dtype	,    torch.long					)
          self.assertTrue(encoding["labels"].min().item() >= 0					)
          self.assertTrue(encoding["labels"].max().item() <= 255					)

          # Test batched input (PIL images)
          UpperCamelCase__				, UpperCamelCase__:	List[str]					=     prepare_semantic_batch_inputs()

          UpperCamelCase__:	Optional[int]					=     image_processing(__lowerCamelCase	,    __lowerCamelCase	,    return_tensors="pt"					)
          self.assertEqual(
              encoding["pixel_values"].shape	,    (
                  2,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size["height"],
                  self.image_processor_tester.crop_size["width"],
              )	,    )
          self.assertEqual(
              encoding["labels"].shape	,    (
                  2,
                  self.image_processor_tester.crop_size["height"],
                  self.image_processor_tester.crop_size["width"],
              )	,    )
          self.assertEqual(encoding["labels"].dtype	,    torch.long					)
          self.assertTrue(encoding["labels"].min().item() >= 0					)
          self.assertTrue(encoding["labels"].max().item() <= 255					)






      def 			UpperCAmelCase_       (		self:			Any					):
          '''simple docstring'''


          UpperCamelCase__:	Dict					=     self.image_processing_class(**self.image_processor_dict					)

          # ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
          UpperCamelCase__				, UpperCamelCase__:	Any					=     prepare_semantic_single_inputs()
          UpperCamelCase__:	int					=     image_processing(__lowerCamelCase	,    __lowerCamelCase	,    return_tensors="pt"					)
          self.assertTrue(encoding["labels"].min().item() >= 0					)
          self.assertTrue(encoding["labels"].max().item() <= 150					)

          UpperCamelCase__:	List[Any]					=     True
          UpperCamelCase__:	List[str]					=     image_processing(__lowerCamelCase	,    __lowerCamelCase	,    return_tensors="pt"					)
          self.assertTrue(encoding["labels"].min().item() >= 0					)
          self.assertTrue(encoding["labels"].max().item() <= 255					)








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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


A__:    Union[str, Any]  	=	logging.get_logger(__name__)

A__:    int  	=	{'''vocab_file''': '''sentencepiece.model'''}

A__:    Dict  	=	{
    '''vocab_file''': {
        '''google/rembert''': '''https://huggingface.co/google/rembert/resolve/main/sentencepiece.model''',
    },
}

A__:    Any  	=	{
    '''google/rembert''': 256,
}




class        _a    (    UpperCamelCase__):

      """simple docstring"""


      UpperCamelCase__  		=       VOCAB_FILES_NAMES
      UpperCamelCase__  		=       PRETRAINED_VOCAB_FILES_MAP
      UpperCamelCase__  		=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES






      def __init__(		self:			str	,    __lowerCamelCase:			Optional[Any]	,    __lowerCamelCase:			List[str]=False	,    __lowerCamelCase:			Optional[Any]=True	,    __lowerCamelCase:			Dict=True	,    __lowerCamelCase:			Tuple="[CLS]"	,    __lowerCamelCase:			List[str]="[SEP]"	,    __lowerCamelCase:			List[str]="[UNK]"	,    __lowerCamelCase:			Union[str, Any]="[SEP]"	,    __lowerCamelCase:			List[Any]="[PAD]"	,    __lowerCamelCase:			Optional[int]="[CLS]"	,    __lowerCamelCase:			str="[MASK]"	,    **__lowerCamelCase:			Union[str, Any]	,    ):
          '''simple docstring'''


          super().__init__(
              do_lower_case=__lowerCamelCase	,    remove_space=__lowerCamelCase	,    keep_accents=__lowerCamelCase	,    bos_token=__lowerCamelCase	,    eos_token=__lowerCamelCase	,    unk_token=__lowerCamelCase	,    sep_token=__lowerCamelCase	,    pad_token=__lowerCamelCase	,    cls_token=__lowerCamelCase	,    mask_token=__lowerCamelCase	,    **__lowerCamelCase	,    )

          UpperCamelCase__:	Optional[int]					=     do_lower_case
          UpperCamelCase__:	Optional[int]					=     remove_space
          UpperCamelCase__:	str					=     keep_accents
          UpperCamelCase__:	Union[str, Any]					=     vocab_file

          UpperCamelCase__:	Any					=     spm.SentencePieceProcessor()
          self.sp_model.Load(__lowerCamelCase					)






      @property
      def 			UpperCAmelCase_       (		self:			Any					):
          '''simple docstring'''


          return len(self.sp_model					)






      def 			UpperCAmelCase_       (		self:			Optional[Any]					):
          '''simple docstring'''


          UpperCamelCase__:	List[Any]					=     {self.convert_ids_to_tokens(__lowerCamelCase					): i for i in range(self.vocab_size					)}
          vocab.update(self.added_tokens_encoder					)
          return vocab






      def __getstate__(		self:			Optional[int]					):
          '''simple docstring'''


          UpperCamelCase__:	Any					=     self.__dict__.copy()
          UpperCamelCase__:	str					=     None
          return state






      def __setstate__(		self:			Optional[int]	,    __lowerCamelCase:			Union[str, Any]					):
          '''simple docstring'''


          UpperCamelCase__:	int					=     d
          UpperCamelCase__:	List[Any]					=     spm.SentencePieceProcessor()
          self.sp_model.Load(self.vocab_file					)






      def 			UpperCAmelCase_       (		self:			Tuple	,    __lowerCamelCase:			str	,    __lowerCamelCase:			Optional[Any]=False					):
          '''simple docstring'''


          UpperCamelCase__:	Union[str, Any]					=     self.sp_model.EncodeAsPieces(__lowerCamelCase					)
          return pieces






      def 			UpperCAmelCase_       (		self:			Tuple	,    __lowerCamelCase:			Union[str, Any]					):
          '''simple docstring'''


          return self.sp_model.PieceToId(__lowerCamelCase					)






      def 			UpperCAmelCase_       (		self:			Tuple	,    __lowerCamelCase:			Optional[int]					):
          '''simple docstring'''


          return self.sp_model.IdToPiece(__lowerCamelCase					)






      def 			UpperCAmelCase_       (		self:			Optional[Any]	,    __lowerCamelCase:			Any					):
          '''simple docstring'''


          UpperCamelCase__:	Optional[Any]					=     self.sp_model.decode_pieces(__lowerCamelCase					)
          return out_string






      def 			UpperCAmelCase_       (		self:			Any	,    __lowerCamelCase:			List[int]	,    __lowerCamelCase:			Optional[List[int]] = None					):
          '''simple docstring'''


          UpperCamelCase__:	int					=     [self.sep_token_id]
          UpperCamelCase__:	int					=     [self.cls_token_id]
          if token_ids_a is None:
              return cls + token_ids_a + sep
          return cls + token_ids_a + sep + token_ids_a + sep






      def 			UpperCAmelCase_       (		self:			List[str]	,    __lowerCamelCase:			List[int]	,    __lowerCamelCase:			Optional[List[int]] = None	,    __lowerCamelCase:			bool = False					):
          '''simple docstring'''


          if already_has_special_tokens:
              if token_ids_a is not None:
                  raise ValueError(
                      "You should not supply a second sequence if the provided sequence of "
                      "ids is already formatted with special tokens for the model."					)
              return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

          if token_ids_a is not None:
              return [1] + ([0] * len(__lowerCamelCase					)) + [1] + ([0] * len(__lowerCamelCase					)) + [1]
          return [1] + ([0] * len(__lowerCamelCase					)) + [1]






      def 			UpperCAmelCase_       (		self:			Any	,    __lowerCamelCase:			List[int]	,    __lowerCamelCase:			Optional[List[int]] = None					):
          '''simple docstring'''


          UpperCamelCase__:	Dict					=     [self.sep_token_id]
          UpperCamelCase__:	Dict					=     [self.cls_token_id]

          if token_ids_a is None:
              return len(cls + token_ids_a + sep					) * [0]
          return len(cls + token_ids_a + sep					) * [0] + len(token_ids_a + sep					) * [1]






      def 			UpperCAmelCase_       (		self:			Optional[Any]	,    __lowerCamelCase:			str	,    __lowerCamelCase:			Optional[str] = None					):
          '''simple docstring'''


          if not os.path.isdir(__lowerCamelCase					):
              logger.error("Vocabulary path ({}) should be a directory".format(__lowerCamelCase					)					)
              return
          UpperCamelCase__:	Dict					=     os.path.join(
              __lowerCamelCase	,    (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]					)

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

          return (out_vocab_file,)

