

"""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:
							__UpperCamelCase    : int				 =						None

__UpperCamelCase    : Any				 =						logging.get_logger(__name__)
__UpperCamelCase    : Tuple				 =						{'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

__UpperCamelCase    : List[str]				 =						{
    '''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''',
    },
}

__UpperCamelCase    : List[Any]				 =						{
    '''google/fnet-base''': 5_1_2,
    '''google/fnet-large''': 5_1_2,
}

__UpperCamelCase    : List[Any]				 =						'''▁'''







class 							SCREAMING_SNAKE_CASE   (						a_			):






					"""simple docstring"""



					lowercase__												=			VOCAB_FILES_NAMES
					lowercase__												=			PRETRAINED_VOCAB_FILES_MAP
					lowercase__												=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					lowercase__												=			["input_ids", "token_type_ids"]
					lowercase__												=			FNetTokenizer







					def __init__(			self     :							Dict  ,lowercase_     :							List[Any]=None  ,lowercase_     :							List[str]=None  ,lowercase_     :							Dict=False  ,lowercase_     :							Optional[Any]=True  ,lowercase_     :							Optional[int]=True  ,lowercase_     :							str="<unk>"  ,lowercase_     :							int="[SEP]"  ,lowercase_     :							List[Any]="<pad>"  ,lowercase_     :							Tuple="[CLS]"  ,lowercase_     :							int="[MASK]"  ,**lowercase_     :							Dict  ,):
						# 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.
						lowerCAmelCase__      :		Optional[Any]    							=      (
						    AddedToken(lowercase_  ,lstrip=lowercase_  ,rstrip=lowercase_  ,normalized=lowercase_						)
						    if isinstance(lowercase_  ,lowercase_						)
						    else mask_token
						)

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

						lowerCAmelCase__      :		str    							=      do_lower_case
						lowerCAmelCase__      :		Dict    							=      remove_space
						lowerCAmelCase__      :		Tuple    							=      keep_accents
						lowerCAmelCase__      :		List[Any]    							=      vocab_file
						lowerCAmelCase__      :		List[str]    							=      False if not self.vocab_file else True







					def 			__lowerCAmelCase					(			self     :							Tuple  ,lowercase_     :							List[int]  ,lowercase_     :							Optional[List[int]] = None						):
						lowerCAmelCase__      :		Tuple    							=      [self.sep_token_id]
						lowerCAmelCase__      :		Union[str, Any]    							=      [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 			__lowerCAmelCase					(			self     :							Union[str, Any]  ,lowercase_     :							List[int]  ,lowercase_     :							Optional[List[int]] = None						):
						lowerCAmelCase__      :		str    							=      [self.sep_token_id]
						lowerCAmelCase__      :		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 			__lowerCAmelCase					(			self     :							Union[str, Any]  ,lowercase_     :							str  ,lowercase_     :							Optional[str] = None						):
						if not os.path.isdir(lowercase_						):
							logger.error(F'Vocabulary path ({save_directory}) should be a directory'						)
							return
						lowerCAmelCase__      :		Optional[int]    							=      os.path.join(
						    lowercase_  ,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']						)

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

						return (out_vocab_file,)







"""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_image_inputs


if is_torch_available():
							import torch

if is_vision_available():
							from PIL import Image

							from transformers import LevitImageProcessor







class 							SCREAMING_SNAKE_CASE   (						unittest.TestCase			):






					"""simple docstring"""



					def __init__(			self     :							Any  ,lowercase_     :							Dict  ,lowercase_     :							List[str]=7  ,lowercase_     :							Tuple=3  ,lowercase_     :							List[str]=1_8  ,lowercase_     :							Optional[Any]=3_0  ,lowercase_     :							List[Any]=4_0_0  ,lowercase_     :							List[Any]=True  ,lowercase_     :							Any=None  ,lowercase_     :							Optional[Any]=True  ,lowercase_     :							str=None  ,lowercase_     :							List[Any]=True  ,lowercase_     :							Dict=[0.5, 0.5, 0.5]  ,lowercase_     :							Dict=[0.5, 0.5, 0.5]  ,):
						lowerCAmelCase__      :		Any    							=      size if size is not None else {'''shortest_edge''': 1_8}
						lowerCAmelCase__      :		Union[str, Any]    							=      crop_size if crop_size is not None else {'''height''': 1_8, '''width''': 1_8}
						lowerCAmelCase__      :		Dict    							=      parent
						lowerCAmelCase__      :		Dict    							=      batch_size
						lowerCAmelCase__      :		List[str]    							=      num_channels
						lowerCAmelCase__      :		Any    							=      image_size
						lowerCAmelCase__      :		Union[str, Any]    							=      min_resolution
						lowerCAmelCase__      :		Dict    							=      max_resolution
						lowerCAmelCase__      :		List[str]    							=      do_resize
						lowerCAmelCase__      :		Optional[Any]    							=      size
						lowerCAmelCase__      :		Tuple    							=      do_center_crop
						lowerCAmelCase__      :		Optional[int]    							=      crop_size
						lowerCAmelCase__      :		List[str]    							=      do_normalize
						lowerCAmelCase__      :		Tuple    							=      image_mean
						lowerCAmelCase__      :		int    							=      image_std







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







@require_torch
@require_vision
class 							SCREAMING_SNAKE_CASE   (						a_       ,				unittest.TestCase			):






					"""simple docstring"""



					lowercase__												=			LevitImageProcessor if is_vision_available() else None







					def 			__lowerCAmelCase					(			self     :							List[str]						):
						lowerCAmelCase__      :		Optional[Any]    							=      LevitImageProcessingTester(self						)







					@property
					def 			__lowerCAmelCase					(			self     :							Any						):
						return self.image_processor_tester.prepare_image_processor_dict()







					def 			__lowerCAmelCase					(			self     :							str						):
						lowerCAmelCase__      :		Optional[int]    							=      self.image_processing_class(**self.image_processor_dict						)
						self.assertTrue(hasattr(lowercase_  ,'''image_mean'''						)						)
						self.assertTrue(hasattr(lowercase_  ,'''image_std'''						)						)
						self.assertTrue(hasattr(lowercase_  ,'''do_normalize'''						)						)
						self.assertTrue(hasattr(lowercase_  ,'''do_resize'''						)						)
						self.assertTrue(hasattr(lowercase_  ,'''do_center_crop'''						)						)
						self.assertTrue(hasattr(lowercase_  ,'''size'''						)						)







					def 			__lowerCAmelCase					(			self     :							Any						):
						lowerCAmelCase__      :		int    							=      self.image_processing_class.from_dict(self.image_processor_dict						)
						self.assertEqual(image_processor.size  ,{'''shortest_edge''': 1_8}						)
						self.assertEqual(image_processor.crop_size  ,{'''height''': 1_8, '''width''': 1_8}						)

						lowerCAmelCase__      :		List[str]    							=      self.image_processing_class.from_dict(self.image_processor_dict  ,size=4_2  ,crop_size=8_4						)
						self.assertEqual(image_processor.size  ,{'''shortest_edge''': 4_2}						)
						self.assertEqual(image_processor.crop_size  ,{'''height''': 8_4, '''width''': 8_4}						)







					def 			__lowerCAmelCase					(			self     :							Union[str, Any]						):
						pass







					def 			__lowerCAmelCase					(			self     :							Optional[int]						):
						# Initialize image_processing
						lowerCAmelCase__      :		List[str]    							=      self.image_processing_class(**self.image_processor_dict						)
						# create random PIL images
						lowerCAmelCase__      :		Union[str, Any]    							=      prepare_image_inputs(self.image_processor_tester  ,equal_resolution=lowercase_						)
						for image in image_inputs:
							self.assertIsInstance(lowercase_  ,Image.Image						)

						# Test not batched input
						lowerCAmelCase__      :		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
						lowerCAmelCase__      :		Optional[int]    							=      image_processing(lowercase_  ,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 			__lowerCAmelCase					(			self     :							Union[str, Any]						):
						# Initialize image_processing
						lowerCAmelCase__      :		str    							=      self.image_processing_class(**self.image_processor_dict						)
						# create random numpy tensors
						lowerCAmelCase__      :		Optional[Any]    							=      prepare_image_inputs(self.image_processor_tester  ,equal_resolution=lowercase_  ,numpify=lowercase_						)
						for image in image_inputs:
							self.assertIsInstance(lowercase_  ,np.ndarray						)

						# Test not batched input
						lowerCAmelCase__      :		Tuple    							=      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
						lowerCAmelCase__      :		Union[str, Any]    							=      image_processing(lowercase_  ,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 			__lowerCAmelCase					(			self     :							int						):
						# Initialize image_processing
						lowerCAmelCase__      :		Tuple    							=      self.image_processing_class(**self.image_processor_dict						)
						# create random PyTorch tensors
						lowerCAmelCase__      :		Dict    							=      prepare_image_inputs(self.image_processor_tester  ,equal_resolution=lowercase_  ,torchify=lowercase_						)
						for image in image_inputs:
							self.assertIsInstance(lowercase_  ,torch.Tensor						)

						# Test not batched input
						lowerCAmelCase__      :		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
						lowerCAmelCase__      :		Optional[int]    							=      image_processing(lowercase_  ,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'''],
						    )  ,)




