






"""simple docstring"""





import unittest

import numpy as np

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

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 DPTImageProcessor






class      a						(					unittest.TestCase		):






    def __init__(       self     :							Dict       ,						__SCREAMING_SNAKE_CASE     :							Union[str, Any]       ,						__SCREAMING_SNAKE_CASE     :							List[Any]=7       ,						__SCREAMING_SNAKE_CASE     :							int=3       ,						__SCREAMING_SNAKE_CASE     :							Optional[Any]=18       ,						__SCREAMING_SNAKE_CASE     :							Union[str, Any]=30       ,						__SCREAMING_SNAKE_CASE     :							str=400       ,						__SCREAMING_SNAKE_CASE     :							Dict=True       ,						__SCREAMING_SNAKE_CASE     :							List[Any]=None       ,						__SCREAMING_SNAKE_CASE     :							str=True       ,						__SCREAMING_SNAKE_CASE     :							Optional[int]=[0.5, 0.5, 0.5]       ,						__SCREAMING_SNAKE_CASE     :							Optional[int]=[0.5, 0.5, 0.5]       ,						)     ->		Dict:
      lowerCamelCase_         =						size if size is not None else {"height": 18, "width": 18}
      lowerCamelCase_         =						parent
      lowerCamelCase_         =						batch_size
      lowerCamelCase_         =						num_channels
      lowerCamelCase_         =						image_size
      lowerCamelCase_         =						min_resolution
      lowerCamelCase_         =						max_resolution
      lowerCamelCase_         =						do_resize
      lowerCamelCase_         =						size
      lowerCamelCase_         =						do_normalize
      lowerCamelCase_         =						image_mean
      lowerCamelCase_         =						image_std






    def    UpperCamelCase       (       self     :							Union[str, Any]					)     ->		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,
      }






@require_torch
@require_vision
class      a						(					a_  ,      unittest.TestCase		):
    SCREAMING_SNAKE_CASE						:     Dict						= DPTImageProcessor if is_vision_available() else None






    def    UpperCamelCase       (       self     :							Optional[int]					)     ->		Tuple:
      lowerCamelCase_         =						DPTImageProcessingTester(self					)






    @property
    def    UpperCamelCase       (       self     :							List[Any]					)     ->		int:
      return self.image_processor_tester.prepare_image_processor_dict()






    def    UpperCamelCase       (       self     :							List[Any]					)     ->		List[str]:
      lowerCamelCase_         =						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       ,						'size'					)					)






    def    UpperCamelCase       (       self     :							Union[str, Any]					)     ->		Optional[Any]:
      lowerCamelCase_         =						self.image_processing_class.from_dict(self.image_processor_dict					)
      self.assertEqual(image_processor.size       ,						{'height': 18, 'width': 18}					)

      lowerCamelCase_         =						self.image_processing_class.from_dict(self.image_processor_dict       ,						size=42					)
      self.assertEqual(image_processor.size       ,						{'height': 42, 'width': 42}					)






    def    UpperCamelCase       (       self     :							List[str]					)     ->		Union[str, Any]:
      # Initialize image_processing
      lowerCamelCase_         =						self.image_processing_class(**self.image_processor_dict					)
      # create random PIL images
      lowerCamelCase_         =						prepare_image_inputs(self.image_processor_tester       ,						equal_resolution=_a					)
      for image in image_inputs:
        self.assertIsInstance(_a       ,						Image.Image					)

      # Test not batched input
      lowerCamelCase_         =						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.size['height'],
              self.image_processor_tester.size['width'],
          )       ,						)

      # Test batched
      lowerCamelCase_         =						image_processing(_a       ,						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.size['height'],
              self.image_processor_tester.size['width'],
          )       ,						)






    def    UpperCamelCase       (       self     :							Optional[Any]					)     ->		Optional[int]:
      # Initialize image_processing
      lowerCamelCase_         =						self.image_processing_class(**self.image_processor_dict					)
      # create random numpy tensors
      lowerCamelCase_         =						prepare_image_inputs(self.image_processor_tester       ,						equal_resolution=_a       ,						numpify=_a					)
      for image in image_inputs:
        self.assertIsInstance(_a       ,						np.ndarray					)

      # Test not batched input
      lowerCamelCase_         =						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.size['height'],
              self.image_processor_tester.size['width'],
          )       ,						)

      # Test batched
      lowerCamelCase_         =						image_processing(_a       ,						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.size['height'],
              self.image_processor_tester.size['width'],
          )       ,						)






    def    UpperCamelCase       (       self     :							int					)     ->		str:
      # Initialize image_processing
      lowerCamelCase_         =						self.image_processing_class(**self.image_processor_dict					)
      # create random PyTorch tensors
      lowerCamelCase_         =						prepare_image_inputs(self.image_processor_tester       ,						equal_resolution=_a       ,						torchify=_a					)
      for image in image_inputs:
        self.assertIsInstance(_a       ,						torch.Tensor					)

      # Test not batched input
      lowerCamelCase_         =						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.size['height'],
              self.image_processor_tester.size['width'],
          )       ,						)

      # Test batched
      lowerCamelCase_         =						image_processing(_a       ,						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.size['height'],
              self.image_processor_tester.size['width'],
          )       ,						)






"""simple docstring"""




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

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


if is_sentencepiece_available():
   from .tokenization_fnet import FNetTokenizer
else:
   _A			:       Union[str, Any]   		=       None

_A			:       str   		=       logging.get_logger(__name__)
_A			:       Tuple   		=       {"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

_A			:       Optional[Any]   		=       {
    """vocab_file""": {
        """google/fnet-base""": """https://huggingface.co/google/fnet-base/resolve/main/spiece.model""",
        """google/fnet-large""": """https://huggingface.co/google/fnet-large/resolve/main/spiece.model""",
    },
    """tokenizer_file""": {
        """google/fnet-base""": """https://huggingface.co/google/fnet-base/resolve/main/tokenizer.json""",
        """google/fnet-large""": """https://huggingface.co/google/fnet-large/resolve/main/tokenizer.json""",
    },
}

_A			:       Optional[Any]   		=       {
    """google/fnet-base""": 5_12,
    """google/fnet-large""": 5_12,
}

_A			:       List[str]   		=       """▁"""

class 		a__						(      a_						):
   __lowerCAmelCase            =				VOCAB_FILES_NAMES
   __lowerCAmelCase            =				PRETRAINED_VOCAB_FILES_MAP
   __lowerCAmelCase            =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __lowerCAmelCase            =				["""input_ids""", """token_type_ids"""]
   __lowerCAmelCase            =				FNetTokenizer
   def __init__(			self	,    _a=None	,    _a=None	,    _a=False	,    _a=True	,    _a=True	,    _a="<unk>"	,    _a="[SEP]"	,    _a="<pad>"	,    _a="[CLS]"	,    _a="[MASK]"	,    **_a	,    ):
          # Mask token behave like a normal word, i.e. include the space before it and
          # is included in the raw text, there should be a match in a non-normalized sentence.
          lowercase    :				int       		=							(
              AddedToken(_a	,    lstrip=_a	,    rstrip=_a	,    normalized=_a				)
              if isinstance(_a	,    _a				)
              else mask_token
          )

          super().__init__(
              _a	,    tokenizer_file=_a	,    do_lower_case=_a	,    remove_space=_a	,    keep_accents=_a	,    unk_token=_a	,    sep_token=_a	,    pad_token=_a	,    cls_token=_a	,    mask_token=_a	,    **_a	,    )

          lowercase    :				Dict       		=							do_lower_case
          lowercase    :				Union[str, Any]       		=							remove_space
          lowercase    :				Any       		=							keep_accents
          lowercase    :				List[Any]       		=							vocab_file
          lowercase    :				Union[str, Any]       		=							False if not self.vocab_file else True
   def 			__magic_name__			(			self	,    _a	,    _a = None				):
          lowercase    :				Optional[Any]       		=							[self.sep_token_id]
          lowercase    :				str       		=							[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 			__magic_name__			(			self	,    _a	,    _a = None				):
          lowercase    :				Any       		=							[self.sep_token_id]
          lowercase    :				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 			__magic_name__			(			self	,    _a	,    _a = None				):
          if not os.path.isdir(_a				):
                 logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""				)
                 return
          lowercase    :				Optional[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,)


