

'''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       ( lowerCAmelCase__				,     unittest.TestCase				):
       lowerCAmelCase_				=	HerbertTokenizer
       lowerCAmelCase_				=	HerbertTokenizerFast
       lowerCAmelCase_				=	True







       def      snake_case				(							self     :	Tuple ):







            """simple docstring"""







            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:
                 __lowercase     						=f_data.read().replace('\n\n'  , '\n' ).strip()

            __lowercase     						=[
                '<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>',
            ]
            __lowercase     						=dict(zip(__lowerCAmelCase  , range(len(__lowerCAmelCase ) ) ) )
            __lowercase     						=['l o 123', 'lo w 1456', 'e r</w> 1789', '']

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







       def      snake_case				(							self     :	Union[str, Any]  , __lowercase     :	Union[str, Any] ):







            """simple docstring"""







            __lowercase     						='lower newer'
            __lowercase     						='lower newer'
            return input_text, output_text







       def      snake_case				(							self     :	List[Any] ):







            """simple docstring"""







            __lowercase     						=self.tokenizer_class(vocab_file=self.vocab_file  , merges_file=self.merges_file )

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

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







       def      snake_case				(							self     :	Union[str, Any] ):







            """simple docstring"""







            if not self.test_rust_tokenizer:
                 return

            __lowercase     						=self.get_tokenizer()
            __lowercase     						=self.get_rust_tokenizer()

            __lowercase     						='lower,newer'

            __lowercase     						=tokenizer.tokenize(__lowerCAmelCase )
            __lowercase     						=rust_tokenizer.tokenize(__lowerCAmelCase )
            self.assertListEqual(__lowerCAmelCase  , __lowerCAmelCase )

            __lowercase     						=tokenizer.encode(__lowerCAmelCase  , add_special_tokens=__lowerCAmelCase )
            __lowercase     						=rust_tokenizer.encode(__lowerCAmelCase  , add_special_tokens=__lowerCAmelCase )
            self.assertListEqual(__lowerCAmelCase  , __lowerCAmelCase )

            __lowercase     						=self.get_rust_tokenizer()
            __lowercase     						=tokenizer.encode(__lowerCAmelCase )
            __lowercase     						=rust_tokenizer.encode(__lowerCAmelCase )
            self.assertListEqual(__lowerCAmelCase  , __lowerCAmelCase )







       @slow
       def      snake_case				(							self     :	Tuple ):







            """simple docstring"""







            __lowercase     						=self.tokenizer_class.from_pretrained('allegro/herbert-base-cased' )

            __lowercase     						=tokenizer.encode('konstruowanie sekwencji'  , add_special_tokens=__lowerCAmelCase )
            __lowercase     						=tokenizer.encode('konstruowanie wielu sekwencji'  , add_special_tokens=__lowerCAmelCase )

            __lowercase     						=tokenizer.build_inputs_with_special_tokens(__lowerCAmelCase )
            __lowercase     						=tokenizer.build_inputs_with_special_tokens(__lowerCAmelCase  , __lowerCAmelCase )

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





'''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


UpperCAmelCase							=					logging.get_logger(__name__)


class 					lowerCAmelCase       ( A				):
       lowerCAmelCase_				=	["pixel_values"]







       def __init__(							self     :	List[Any]  , __lowercase     :	bool = True  , __lowercase     :	Optional[Dict[str, int]] = None  , __lowercase     :	PILImageResampling = PILImageResampling.BILINEAR  , __lowercase     :	bool = True  , __lowercase     :	Union[int, float] = 1 / 255  , __lowercase     :	bool = True  , __lowercase     :	Optional[Union[float, List[float]]] = None  , __lowercase     :	Optional[Union[float, List[float]]] = None  , **__lowercase     :	List[Any]  , ):







            """simple docstring"""







            super().__init__(**__lowercase )
            __lowercase     						=size if size is not None else {'height': 224, 'width': 224}
            __lowercase     						=get_size_dict(__lowercase )
            __lowercase     						=do_resize
            __lowercase     						=do_rescale
            __lowercase     						=do_normalize
            __lowercase     						=size
            __lowercase     						=resample
            __lowercase     						=rescale_factor
            __lowercase     						=image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
            __lowercase     						=image_std if image_std is not None else IMAGENET_STANDARD_STD







       def      snake_case				(							self     :	Dict  , __lowercase     :	np.ndarray  , __lowercase     :	Dict[str, int]  , __lowercase     :	PILImageResampling = PILImageResampling.BILINEAR  , __lowercase     :	Optional[Union[str, ChannelDimension]] = None  , **__lowercase     :	Union[str, Any]  , ):







            """simple docstring"""







            __lowercase     						=get_size_dict(__lowercase )
            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(
                __lowercase  , size=(size['height'], size['width'])  , resample=__lowercase  , data_format=__lowercase  , **__lowercase )







       def      snake_case				(							self     :	str  , __lowercase     :	np.ndarray  , __lowercase     :	float  , __lowercase     :	Optional[Union[str, ChannelDimension]] = None  , **__lowercase     :	Optional[Any] ):







            """simple docstring"""







            return rescale(__lowercase  , scale=__lowercase  , data_format=__lowercase  , **__lowercase )







       def      snake_case				(							self     :	Any  , __lowercase     :	np.ndarray  , __lowercase     :	Union[float, List[float]]  , __lowercase     :	Union[float, List[float]]  , __lowercase     :	Optional[Union[str, ChannelDimension]] = None  , **__lowercase     :	str  , ):







            """simple docstring"""







            return normalize(__lowercase  , mean=__lowercase  , std=__lowercase  , data_format=__lowercase  , **__lowercase )







       def      snake_case				(							self     :	str  , __lowercase     :	ImageInput  , __lowercase     :	Optional[bool] = None  , __lowercase     :	Dict[str, int] = None  , __lowercase     :	PILImageResampling = None  , __lowercase     :	Optional[bool] = None  , __lowercase     :	Optional[float] = None  , __lowercase     :	Optional[bool] = None  , __lowercase     :	Optional[Union[float, List[float]]] = None  , __lowercase     :	Optional[Union[float, List[float]]] = None  , __lowercase     :	Optional[Union[str, TensorType]] = None  , __lowercase     :	Union[str, ChannelDimension] = ChannelDimension.FIRST  , **__lowercase     :	Tuple  , ):







            """simple docstring"""







            __lowercase     						=do_resize if do_resize is not None else self.do_resize
            __lowercase     						=do_rescale if do_rescale is not None else self.do_rescale
            __lowercase     						=do_normalize if do_normalize is not None else self.do_normalize
            __lowercase     						=resample if resample is not None else self.resample
            __lowercase     						=rescale_factor if rescale_factor is not None else self.rescale_factor
            __lowercase     						=image_mean if image_mean is not None else self.image_mean
            __lowercase     						=image_std if image_std is not None else self.image_std

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

            __lowercase     						=make_list_of_images(__lowercase )

            if not valid_images(__lowercase ):
                 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.
            __lowercase     						=[to_numpy_array(__lowercase ) for image in images]

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

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

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

            __lowercase     						=[to_channel_dimension_format(__lowercase  , __lowercase ) for image in images]

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


