
"""simple docstring"""


from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


_lowercase											=			logging.get_logger(__name__)







class 			lowerCAmelCase_			( _lowercase      ):



      '''simple docstring'''


      _lowerCamelCase:      Any =      ['''pixel_values''']


      def __init__(	self       : Any					,A_       : bool = True					,A_       : Optional[Dict[str, int]] = None					,A_       : PILImageResampling = PILImageResampling.BILINEAR					,A_       : bool = True					,A_       : Union[int, float] = 1 / 255					,A_       : bool = True					,A_       : Optional[Union[float, List[float]]] = None					,A_       : Optional[Union[float, List[float]]] = None					,**A_       : Dict					,)       ->				None:
       super().__init__(**A_ )
       A  				=				size if size is not None else {'height': 224, 'width': 224}
       A  				=				get_size_dict(A_ )
       A  				=				do_resize
       A  				=				do_rescale
       A  				=				do_normalize
       A  				=				size
       A  				=				resample
       A  				=				rescale_factor
       A  				=				image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
       A  				=				image_std if image_std is not None else IMAGENET_STANDARD_STD


      def   _SCREAMING_SNAKE_CASE (	self       : Optional[Any]					,A_       : np.ndarray					,A_       : Dict[str, int]					,A_       : PILImageResampling = PILImageResampling.BILINEAR					,A_       : Optional[Union[str, ChannelDimension]] = None					,**A_       : Optional[Any]					,)       ->				np.ndarray:
       A  				=				get_size_dict(A_ )
       if "height" not in size or "width" not in size:
        raise ValueError(F'The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}' )
       return resize(
           A_					,size=(size['height'], size['width'])					,resample=A_					,data_format=A_					,**A_ )


      def   _SCREAMING_SNAKE_CASE (	self       : int					,A_       : np.ndarray					,A_       : float					,A_       : Optional[Union[str, ChannelDimension]] = None					,**A_       : List[str] )       ->				np.ndarray:
       return rescale(A_					,scale=A_					,data_format=A_					,**A_ )


      def   _SCREAMING_SNAKE_CASE (	self       : int					,A_       : np.ndarray					,A_       : Union[float, List[float]]					,A_       : Union[float, List[float]]					,A_       : Optional[Union[str, ChannelDimension]] = None					,**A_       : List[str]					,)       ->				np.ndarray:
       return normalize(A_					,mean=A_					,std=A_					,data_format=A_					,**A_ )




      def   _SCREAMING_SNAKE_CASE (	self       : Optional[Any]					,A_       : ImageInput					,A_       : Optional[bool] = None					,A_       : Dict[str, int] = None					,A_       : PILImageResampling = None					,A_       : Optional[bool] = None					,A_       : Optional[float] = None					,A_       : Optional[bool] = None					,A_       : Optional[Union[float, List[float]]] = None					,A_       : Optional[Union[float, List[float]]] = None					,A_       : Optional[Union[str, TensorType]] = None					,A_       : Union[str, ChannelDimension] = ChannelDimension.FIRST					,**A_       : Union[str, Any]					,)       ->				str:
       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  				=				do_normalize if do_normalize is not None else self.do_normalize
       A  				=				resample if resample is not None else self.resample
       A  				=				rescale_factor if rescale_factor is not None else self.rescale_factor
       A  				=				image_mean if image_mean is not None else self.image_mean
       A  				=				image_std if image_std is not None else self.image_std

       A  				=				size if size is not None else self.size
       A  				=				get_size_dict(A_ )

       A  				=				make_list_of_images(A_ )

       if not valid_images(A_ ):
        raise ValueError(
            'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
            'torch.Tensor, tf.Tensor or jax.ndarray.' )

       if do_resize and size is None:
        raise ValueError('Size must be specified if do_resize is True.' )

       if do_rescale and rescale_factor is None:
        raise ValueError('Rescale factor must be specified if do_rescale is True.' )

       # All transformations expect numpy arrays.
       A  				=				[to_numpy_array(A_ ) for image in images]

       if do_resize:
        A  				=				[self.resize(image=A_					,size=A_					,resample=A_ ) for image in images]

       if do_rescale:
        A  				=				[self.rescale(image=A_					,scale=A_ ) for image in images]

       if do_normalize:
        A  				=				[self.normalize(image=A_					,mean=A_					,std=A_ ) for image in images]

       A  				=				[to_channel_dimension_format(A_					,A_ ) for image in images]

       A  				=				{'pixel_values': images}
       return BatchFeature(data=A_					,tensor_type=A_ )

"""simple docstring"""


import json
import os
import unittest

from transformers import HerbertTokenizer, HerbertTokenizerFast
from transformers.models.herbert.tokenization_herbert import VOCAB_FILES_NAMES
from transformers.testing_utils import get_tests_dir, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class 			lowerCAmelCase_			( _lowercase						,   unittest.TestCase      ):



						'''simple docstring'''


						_lowerCamelCase:      List[str] =      HerbertTokenizer
						_lowerCamelCase:      Any =      HerbertTokenizerFast
						_lowerCamelCase:      Optional[int] =      True


						def   _SCREAMING_SNAKE_CASE (	self       : Dict )       ->				Tuple:
							super().setUp()

							# Use a simpler test file without japanese/chinese characters
							with open(F'{get_tests_dir()}/fixtures/sample_text_no_unicode.txt'					,encoding='utf-8' ) as f_data:
								A  				=				f_data.read().replace('\n\n'					,'\n' ).strip()

							A  				=				[
							    '<s>',
							    '</s>',
							    'l',
							    'o',
							    'w',
							    'e',
							    'r',
							    's',
							    't',
							    'i',
							    'd',
							    'n',
							    'w</w>',
							    'r</w>',
							    't</w>',
							    'lo',
							    'low',
							    'er</w>',
							    'low</w>',
							    'lowest</w>',
							    'newer</w>',
							    'wider</w>',
							    ',</w>',
							    '<unk>',
							]
							A  				=				dict(zip(A_					,range(len(A_ ) ) ) )
							A  				=				['l o 123', 'lo w 1456', 'e r</w> 1789', '']

							A  				=				os.path.join(self.tmpdirname					,VOCAB_FILES_NAMES['vocab_file'] )
							A  				=				os.path.join(self.tmpdirname					,VOCAB_FILES_NAMES['merges_file'] )
							with open(self.vocab_file					,'w' ) as fp:
								fp.write(json.dumps(A_ ) )
							with open(self.merges_file					,'w' ) as fp:
								fp.write('\n'.join(A_ ) )


						def   _SCREAMING_SNAKE_CASE (	self       : Tuple					,A_       : List[str] )       ->				int:
							A  				=				'lower newer'
							A  				=				'lower newer'
							return input_text, output_text


						def   _SCREAMING_SNAKE_CASE (	self       : List[Any] )       ->				int:
							A  				=				self.tokenizer_class(vocab_file=self.vocab_file					,merges_file=self.merges_file )

							A  				=				'lower'
							A  				=				['low', 'er</w>']
							A  				=				tokenizer.tokenize(A_ )
							self.assertListEqual(A_					,A_ )

							A  				=				tokens + ['<unk>']
							A  				=				[16, 17, 23]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(A_ )					,A_ )


						def   _SCREAMING_SNAKE_CASE (	self       : Dict )       ->				Optional[int]:
							if not self.test_rust_tokenizer:
								return

							A  				=				self.get_tokenizer()
							A  				=				self.get_rust_tokenizer()

							A  				=				'lower,newer'

							A  				=				tokenizer.tokenize(A_ )
							A  				=				rust_tokenizer.tokenize(A_ )
							self.assertListEqual(A_					,A_ )

							A  				=				tokenizer.encode(A_					,add_special_tokens=A_ )
							A  				=				rust_tokenizer.encode(A_					,add_special_tokens=A_ )
							self.assertListEqual(A_					,A_ )

							A  				=				self.get_rust_tokenizer()
							A  				=				tokenizer.encode(A_ )
							A  				=				rust_tokenizer.encode(A_ )
							self.assertListEqual(A_					,A_ )




						@slow
						def   _SCREAMING_SNAKE_CASE (	self       : str )       ->				List[Any]:
							A  				=				self.tokenizer_class.from_pretrained('allegro/herbert-base-cased' )

							A  				=				tokenizer.encode('konstruowanie sekwencji'					,add_special_tokens=A_ )
							A  				=				tokenizer.encode('konstruowanie wielu sekwencji'					,add_special_tokens=A_ )

							A  				=				tokenizer.build_inputs_with_special_tokens(A_ )
							A  				=				tokenizer.build_inputs_with_special_tokens(A_					,A_ )

							assert encoded_sentence == [0] + text + [2]
							assert encoded_pair == [0] + text + [2] + text_a + [2]