


import os
import unittest

from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin







class  snake_case_     (lowerCamelCase_			,		unittest.TestCase   ):
					UpperCAmelCase__      :					str					   =    TransfoXLTokenizer
					UpperCAmelCase__      :					Tuple					   =    False
					UpperCAmelCase__      :					int					   =    False
					def 				lowerCamelCase__(   self     :Any   )      ->					Dict:
												super().setUp()

												a__    =  [
												    '<unk>',
												    '[CLS]',
												    '[SEP]',
												    'want',
												    'unwanted',
												    'wa',
												    'un',
												    'running',
												    ',',
												    'low',
												    'l',
												]
												a__    =  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 				lowerCamelCase__(   self     :str    ,**__snake_case     :Optional[Any]   )      ->					Union[str, Any]:
												a__    =  True
												return TransfoXLTokenizer.from_pretrained(self.tmpdirname    ,**__snake_case   )
					def 				lowerCamelCase__(   self     :str    ,__snake_case     :Union[str, Any]   )      ->					Union[str, Any]:
												a__    =  '<unk> UNwanted , running'
												a__    =  '<unk> unwanted, running'
												return input_text, output_text
					def 				lowerCamelCase__(   self     :Union[str, Any]   )      ->					Union[str, Any]:
												a__    =  TransfoXLTokenizer(vocab_file=self.vocab_file    ,lower_case=__snake_case   )

												a__    =  tokenizer.tokenize('<unk> UNwanted , running'   )
												self.assertListEqual(__snake_case    ,['<unk>', 'unwanted', ',', 'running']   )

												self.assertListEqual(tokenizer.convert_tokens_to_ids(__snake_case   )    ,[0, 4, 8, 7]   )
					def 				lowerCamelCase__(   self     :Optional[Any]   )      ->					List[Any]:
												a__    =  TransfoXLTokenizer(lower_case=__snake_case   )

												self.assertListEqual(
												    tokenizer.tokenize(' \tHeLLo ! how  \n Are yoU ?  '   )    ,['hello', '!', 'how', 'are', 'you', '?']   )
					def 				lowerCamelCase__(   self     :Any   )      ->					Optional[int]:
												a__    =  TransfoXLTokenizer(lower_case=__snake_case   )

												self.assertListEqual(
												    tokenizer.tokenize(' \tHeLLo ! how  \n Are yoU ?  '   )    ,['HeLLo', '!', 'how', 'Are', 'yoU', '?']   )
					def 				lowerCamelCase__(   self     :Union[str, Any]   )      ->					List[Any]:
												a__    =  TransfoXLTokenizer(lower_case=__snake_case   )
												a__    =  'Hello (bracket) and side-scrolled [and] Henry\'s $5,000 with 3.34 m. What\'s up!?'
												a__    =  [
												    'Hello',
												    '(',
												    'bracket',
												    ')',
												    'and',
												    'side',
												    '@-@',
												    'scrolled',
												    '[',
												    'and',
												    ']',
												    'Henry',
												    '\'s',
												    '$',
												    '5',
												    '@,@',
												    '000',
												    'with',
												    '3',
												    '@.@',
												    '34',
												    'm',
												    '.',
												    'What',
												    '\'s',
												    'up',
												    '!',
												    '?',
												]

												self.assertListEqual(tokenizer.tokenize(__snake_case   )    ,__snake_case   )

												self.assertEqual(tokenizer.convert_tokens_to_string(__snake_case   )    ,__snake_case   )



					def 				lowerCamelCase__(   self     :List[Any]   )      ->					List[Any]:
												a__    =  self.get_tokenizer()
												a__    =  len(__snake_case   )

												tokenizer.add_tokens(['new1', 'new2']   )
												tokenizer.move_added_token('new1'    ,1   )

												# Check that moved token is not copied (duplicate)
												self.assertEqual(len(__snake_case   )    ,original_len + 2   )
												# Check that token is moved to specified id
												self.assertEqual(tokenizer.encode('new1'   )    ,[1]   )
												self.assertEqual(tokenizer.decode([1]   )    ,'new1'   )








from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


snake_case							: Dict      =   logging.get_logger(__name__)







class  snake_case_     (lowerCamelCase_   ):
					UpperCAmelCase__      :					Dict					   =    ['''pixel_values''']
					def __init__(   self     :Optional[Any]    ,__snake_case     :bool = True    ,__snake_case     :int = 32    ,__snake_case     :Union[str, Any]=PILImageResampling.BILINEAR    ,__snake_case     :bool = True    ,**__snake_case     :Tuple    ,)      ->					None:
												a__    =  do_resize
												a__    =  do_rescale
												a__    =  size_divisor
												a__    =  resample
												super().__init__(**__snake_case   )
					def 				lowerCamelCase__(   self     :Union[str, Any]    ,__snake_case     :np.ndarray    ,__snake_case     :int    ,__snake_case     :Tuple    ,__snake_case     :Optional[ChannelDimension] = None    ,**__snake_case     :List[Any]   )      ->					np.ndarray:
												a__   ,      a__    =  get_image_size(__snake_case   )
												# Rounds the height and width down to the closest multiple of size_divisor
												a__    =  height // size_divisor * size_divisor
												a__    =  width // size_divisor * size_divisor
												a__    =  resize(__snake_case    ,(new_h, new_w)    ,resample=__snake_case    ,data_format=__snake_case    ,**__snake_case   )
												return image
					def 				lowerCamelCase__(   self     :List[str]    ,__snake_case     :np.ndarray    ,__snake_case     :float    ,__snake_case     :Optional[ChannelDimension] = None    ,**__snake_case     :str   )      ->					np.ndarray:
												return rescale(image=__snake_case    ,scale=__snake_case    ,data_format=__snake_case    ,**__snake_case   )



					def 				lowerCamelCase__(   self     :Tuple    ,__snake_case     :Union["PIL.Image.Image", TensorType, List["PIL.Image.Image"], List[TensorType]]    ,__snake_case     :Optional[bool] = None    ,__snake_case     :Optional[int] = None    ,__snake_case     :Union[str, Any]=None    ,__snake_case     :Optional[bool] = None    ,__snake_case     :Optional[Union[TensorType, str]] = None    ,__snake_case     :ChannelDimension = ChannelDimension.FIRST    ,**__snake_case     :List[Any]    ,)      ->					BatchFeature:
												a__    =  do_resize if do_resize is not None else self.do_resize
												a__    =  do_rescale if do_rescale is not None else self.do_rescale
												a__    =  size_divisor if size_divisor is not None else self.size_divisor
												a__    =  resample if resample is not None else self.resample

												if do_resize and size_divisor is None:
																			raise ValueError('size_divisor is required for resizing'   )

												a__    =  make_list_of_images(__snake_case   )

												if not valid_images(__snake_case   ):
																			raise ValueError('Invalid image(s)'   )

												# All transformations expect numpy arrays.
												a__    =  [to_numpy_array(__snake_case   ) for img in images]

												if do_resize:
																			a__    =  [self.resize(__snake_case    ,size_divisor=__snake_case    ,resample=__snake_case   ) for image in images]

												if do_rescale:
																			a__    =  [self.rescale(__snake_case    ,scale=1 / 2_55   ) for image in images]

												a__    =  [to_channel_dimension_format(__snake_case    ,__snake_case   ) for image in images]

												a__    =  {'pixel_values': images}
												return BatchFeature(data=__snake_case    ,tensor_type=__snake_case   )




