



import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


__A   			=get_tests_dir("fixtures/test_sentencepiece.model")

@require_sentencepiece
@require_tokenizers
class 		UpperCAmelCase__			(			__UpperCamelCase						,unittest.TestCase   ):




			'''simple docstring'''






			UpperCamelCase									=       XGLMTokenizer
			UpperCamelCase									=       XGLMTokenizerFast
			UpperCamelCase									=       True
			UpperCamelCase									=       True




			def  snake_case__     (   self							: Any ):
					'''simple docstring'''





					super().setUp()

					# We have a SentencePiece fixture for testing
					__UpperCAmelCase    :     Any													=	XGLMTokenizer(a_       ,       keep_accents=a_ )
					tokenizer.save_pretrained(self.tmpdirname )




			def  snake_case__     (   self							: List[Any] ):
					'''simple docstring'''





					__UpperCAmelCase    :     str													=	'''<pad>'''
					__UpperCAmelCase    :     Optional[Any]													=	1

					self.assertEqual(self.get_tokenizer()._convert_token_to_id(a_ )       ,       a_ )
					self.assertEqual(self.get_tokenizer()._convert_id_to_token(a_ )       ,       a_ )




			def  snake_case__     (   self							: Union[str, Any] ):
					'''simple docstring'''





					__UpperCAmelCase    :     Any													=	list(self.get_tokenizer().get_vocab().keys() )

					self.assertEqual(vocab_keys[0]       ,       '''<s>''' )
					self.assertEqual(vocab_keys[1]       ,       '''<pad>''' )
					self.assertEqual(len(a_ )       ,       10_08 )




			def  snake_case__     (   self							: Dict ):
					'''simple docstring'''





					self.assertEqual(self.get_tokenizer().vocab_size       ,       10_08 )




			def  snake_case__     (   self							: List[Any] ):
					'''simple docstring'''





					__UpperCAmelCase    :     List[Any]													=	XGLMTokenizer(a_       ,       keep_accents=a_ )

					__UpperCAmelCase    :     Dict													=	tokenizer.tokenize('''This is a test''' )
					self.assertListEqual(a_       ,       ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''] )

					self.assertListEqual(
					    tokenizer.convert_tokens_to_ids(a_ )       ,       [value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]]       ,       )

					__UpperCAmelCase    :     List[str]													=	tokenizer.tokenize('''I was born in 92000, and this is falsé.''' )
					self.assertListEqual(
					    a_       ,       [
					        SPIECE_UNDERLINE + '''I''',
					        SPIECE_UNDERLINE + '''was''',
					        SPIECE_UNDERLINE + '''b''',
					        '''or''',
					        '''n''',
					        SPIECE_UNDERLINE + '''in''',
					        SPIECE_UNDERLINE + '''''',
					        '''9''',
					        '''2''',
					        '''0''',
					        '''0''',
					        '''0''',
					        ''',''',
					        SPIECE_UNDERLINE + '''and''',
					        SPIECE_UNDERLINE + '''this''',
					        SPIECE_UNDERLINE + '''is''',
					        SPIECE_UNDERLINE + '''f''',
					        '''al''',
					        '''s''',
					        '''é''',
					        '''.''',
					    ]       ,       )
					__UpperCAmelCase    :     Optional[int]													=	tokenizer.convert_tokens_to_ids(a_ )
					self.assertListEqual(
					    a_       ,       [
					        value + tokenizer.fairseq_offset
					        for value in [8, 21, 84, 55, 24, 19, 7, 2, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 2, 4]
					    ]       ,       )

					__UpperCAmelCase    :     int													=	tokenizer.convert_ids_to_tokens(a_ )
					self.assertListEqual(
					    a_       ,       [
					        SPIECE_UNDERLINE + '''I''',
					        SPIECE_UNDERLINE + '''was''',
					        SPIECE_UNDERLINE + '''b''',
					        '''or''',
					        '''n''',
					        SPIECE_UNDERLINE + '''in''',
					        SPIECE_UNDERLINE + '''''',
					        '''<unk>''',
					        '''2''',
					        '''0''',
					        '''0''',
					        '''0''',
					        ''',''',
					        SPIECE_UNDERLINE + '''and''',
					        SPIECE_UNDERLINE + '''this''',
					        SPIECE_UNDERLINE + '''is''',
					        SPIECE_UNDERLINE + '''f''',
					        '''al''',
					        '''s''',
					        '''<unk>''',
					        '''.''',
					    ]       ,       )




			@cached_property
			def  snake_case__     (   self							: Union[str, Any] ):
					'''simple docstring'''





					return XGLMTokenizer.from_pretrained('''facebook/xglm-564M''' )




			def  snake_case__     (   self							: List[Any] ):
					'''simple docstring'''





					with tempfile.NamedTemporaryFile() as f:
							shutil.copyfile(a_       ,       f.name )
							__UpperCAmelCase    :     Tuple													=	XGLMTokenizer(f.name       ,       keep_accents=a_ )
							__UpperCAmelCase    :     str													=	pickle.dumps(a_ )
					pickle.loads(a_ )




			def  snake_case__     (   self							: Any ):
					'''simple docstring'''





					if not self.test_rust_tokenizer:
							return

					__UpperCAmelCase    :     Dict													=	self.get_tokenizer()
					__UpperCAmelCase    :     str													=	self.get_rust_tokenizer()

					__UpperCAmelCase    :     List[Any]													=	'''I was born in 92000, and this is falsé.'''

					__UpperCAmelCase    :     Any													=	tokenizer.tokenize(a_ )
					__UpperCAmelCase    :     Tuple													=	rust_tokenizer.tokenize(a_ )
					self.assertListEqual(a_       ,       a_ )

					__UpperCAmelCase    :     Dict													=	tokenizer.encode(a_       ,       add_special_tokens=a_ )
					__UpperCAmelCase    :     List[str]													=	rust_tokenizer.encode(a_       ,       add_special_tokens=a_ )
					self.assertListEqual(a_       ,       a_ )

					__UpperCAmelCase    :     int													=	self.get_rust_tokenizer()
					__UpperCAmelCase    :     Optional[int]													=	tokenizer.encode(a_ )
					__UpperCAmelCase    :     Optional[int]													=	rust_tokenizer.encode(a_ )
					self.assertListEqual(a_       ,       a_ )




			@slow
			def  snake_case__     (   self							: Any ):
					'''simple docstring'''





					__UpperCAmelCase    :     Optional[int]													=	'''Hello World!'''
					__UpperCAmelCase    :     Any													=	[2, 3_12_27, 44_47, 35]

					self.assertListEqual(a_       ,       self.big_tokenizer.encode(a_ ) )




			@slow
			def  snake_case__     (   self							: List[str] ):
					'''simple docstring'''





					__UpperCAmelCase    :     Optional[int]													=	(
					    '''This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'''
					    ''' add words that should not exsist and be tokenized to unk, such as saoneuhaoesuth'''
					)
					# fmt: off
					__UpperCAmelCase    :     Dict													=	[2, 10_18, 67, 11, 19_88, 26_17, 56_31, 2_78, 11, 34_07, 48, 7_16_30, 2_80_85, 4, 32_34, 1_57, 13, 6, 5, 6, 4, 35_26, 7_68, 15, 6_59, 57, 2_98, 39_83, 8_64, 1_29, 21, 6, 5, 1_36_75, 3_77, 6_52, 75_80, 1_03_41, 1_55, 28_17, 4_22, 16_66, 7, 16_74, 53, 1_13, 20_22_77, 1_78_92, 33, 60, 87, 4, 32_34, 1_57, 61, 26_67, 5_23_76, 19, 88, 23, 7_35]
					# fmt: on

					self.assertListEqual(a_       ,       self.big_tokenizer.encode(a_ ) )




			@slow
			def  snake_case__     (   self							: int ):
					'''simple docstring'''





					__UpperCAmelCase    :     Dict													=	{
					    '''input_ids''': [[2, 10_88_25, 11_63, 15, 8_80_10, 4_73, 1_58_98, 1_57, 1_36_72, 18_57, 3_12, 8, 23_80_21, 11_63, 53, 1_36_72, 18_57, 3_12, 8, 5_32_83, 18_23_96, 8, 1_85_66, 16, 3_67_33, 41_01, 8, 2_30, 24_40_17, 12_25_53, 7, 15, 13_25_97, 4, 2_93, 1_25_11, 76_10, 4, 34_14, 13_25_97, 9, 4, 3_23_61, 3_62, 4, 7_34, 2_85_12, 3_25_69, 18, 4, 3_23_61, 2_60_96, 1_49_82, 73, 1_87_15, 2_14_33, 23_52_61, 15, 4_92, 1_24_27, 16, 53, 1_87_15, 2_14_33, 6_54_54, 15, 2_36_59, 5_63, 16, 2_78, 5_97, 28_43, 5_95, 79_31, 18_23_96, 6_41_86, 22, 8_86, 5_95, 13_29_81, 53, 2_55_40, 34_49, 4_39_82, 3_99_01, 59_51, 8_78, 3_30, 4, 2_76_94, 8_02_69, 3_12, 53, 65_17, 1_17_80, 6_11, 2_04_08, 5], [2, 6, 13_25_97, 67, 4_28_97, 33, 5_92, 8, 16_37_29, 2_55_40, 3_61, 13_69_97, 10_95_14, 17_32_30, 7, 5_01, 60, 10_29_13, 1_96, 56_31, 2_35, 6_32_43, 4_73, 6, 23_17_57, 74, 52_77, 79_05, 53, 30_95, 3_73_17, 22, 4_54, 18_38_74, 5], [2, 2_68, 3_12_98, 4_65_30, 6, 13_29_35, 4_38_31, 7, 5_97, 32, 24, 36_88, 98_65, 5]],
					    '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]
					}  # noqa: E501
					# fmt: on

					self.tokenizer_integration_test_util(
					    expected_encoding=a_       ,       model_name='''facebook/xglm-564M'''       ,       padding=a_       ,       )










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 (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


__A   			=logging.get_logger(__name__)


if is_vision_available():
				import PIL

class 		UpperCAmelCase__			(			__UpperCamelCase   ):




			'''simple docstring'''






			UpperCamelCase									=       ["""pixel_values"""]




			def __init__(   self							: Tuple       ,       a_							: bool = True       ,       a_							: Dict[str, int] = None       ,       a_							: PILImageResampling = PILImageResampling.BICUBIC       ,       a_							: bool = True       ,       a_							: Dict[str, int] = None       ,       a_							: bool = True       ,       a_							: Union[int, float] = 1 / 2_55       ,       a_							: bool = True       ,       a_							: Optional[Union[float, List[float]]] = None       ,       a_							: Optional[Union[float, List[float]]] = None       ,       a_							: bool = True       ,       **a_							: str       ,       ):
					'''simple docstring'''





					super().__init__(**a_ )
					__UpperCAmelCase    :     List[Any]													=	size if size is not None else {'''shortest_edge''': 2_24}
					__UpperCAmelCase    :     List[str]													=	get_size_dict(a_       ,       default_to_square=a_ )
					__UpperCAmelCase    :     int													=	crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
					__UpperCAmelCase    :     Optional[int]													=	get_size_dict(a_       ,       default_to_square=a_       ,       param_name='''crop_size''' )

					__UpperCAmelCase    :     int													=	do_resize
					__UpperCAmelCase    :     Union[str, Any]													=	size
					__UpperCAmelCase    :     Union[str, Any]													=	resample
					__UpperCAmelCase    :     Any													=	do_center_crop
					__UpperCAmelCase    :     Any													=	crop_size
					__UpperCAmelCase    :     Any													=	do_rescale
					__UpperCAmelCase    :     Dict													=	rescale_factor
					__UpperCAmelCase    :     Union[str, Any]													=	do_normalize
					__UpperCAmelCase    :     Any													=	image_mean if image_mean is not None else OPENAI_CLIP_MEAN
					__UpperCAmelCase    :     int													=	image_std if image_std is not None else OPENAI_CLIP_STD
					__UpperCAmelCase    :     List[str]													=	do_convert_rgb




			def  snake_case__     (   self							: Optional[Any]       ,       a_							: np.ndarray       ,       a_							: Dict[str, int]       ,       a_							: PILImageResampling = PILImageResampling.BICUBIC       ,       a_							: Optional[Union[str, ChannelDimension]] = None       ,       **a_							: Optional[int]       ,       ):
					'''simple docstring'''





					__UpperCAmelCase    :     Dict													=	get_size_dict(a_       ,       default_to_square=a_ )
					if "shortest_edge" not in size:
							raise ValueError(F'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}' )
					__UpperCAmelCase    :     Optional[int]													=	get_resize_output_image_size(a_       ,       size=size['''shortest_edge''']       ,       default_to_square=a_ )
					return resize(a_       ,       size=a_       ,       resample=a_       ,       data_format=a_       ,       **a_ )




			def  snake_case__     (   self							: Union[str, Any]       ,       a_							: np.ndarray       ,       a_							: Dict[str, int]       ,       a_							: Optional[Union[str, ChannelDimension]] = None       ,       **a_							: Dict       ,       ):
					'''simple docstring'''





					__UpperCAmelCase    :     Optional[Any]													=	get_size_dict(a_ )
					if "height" not in size or "width" not in size:
							raise ValueError(F'The `size` parameter must contain the keys (height, width). Got {size.keys()}' )
					return center_crop(a_       ,       size=(size['''height'''], size['''width'''])       ,       data_format=a_       ,       **a_ )




			def  snake_case__     (   self							: Union[str, Any]       ,       a_							: np.ndarray       ,       a_							: Union[int, float]       ,       a_							: Optional[Union[str, ChannelDimension]] = None       ,       **a_							: List[str]       ,       ):
					'''simple docstring'''





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




			def  snake_case__     (   self							: Optional[Any]       ,       a_							: np.ndarray       ,       a_							: Union[float, List[float]]       ,       a_							: Union[float, List[float]]       ,       a_							: Optional[Union[str, ChannelDimension]] = None       ,       **a_							: Dict       ,       ):
					'''simple docstring'''





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




			def  snake_case__     (   self							: Any       ,       a_							: ImageInput       ,       a_							: bool = None       ,       a_							: Dict[str, int] = None       ,       a_							: PILImageResampling = None       ,       a_							: bool = None       ,       a_							: int = None       ,       a_							: bool = None       ,       a_							: float = None       ,       a_							: bool = None       ,       a_							: Optional[Union[float, List[float]]] = None       ,       a_							: Optional[Union[float, List[float]]] = None       ,       a_							: bool = None       ,       a_							: Optional[Union[str, TensorType]] = None       ,       a_							: Optional[ChannelDimension] = ChannelDimension.FIRST       ,       **a_							: Dict       ,       ):
					'''simple docstring'''





					__UpperCAmelCase    :     Tuple													=	do_resize if do_resize is not None else self.do_resize
					__UpperCAmelCase    :     Optional[Any]													=	size if size is not None else self.size
					__UpperCAmelCase    :     Dict													=	get_size_dict(a_       ,       param_name='''size'''       ,       default_to_square=a_ )
					__UpperCAmelCase    :     int													=	resample if resample is not None else self.resample
					__UpperCAmelCase    :     Optional[Any]													=	do_center_crop if do_center_crop is not None else self.do_center_crop
					__UpperCAmelCase    :     Any													=	crop_size if crop_size is not None else self.crop_size
					__UpperCAmelCase    :     Dict													=	get_size_dict(a_       ,       param_name='''crop_size'''       ,       default_to_square=a_ )
					__UpperCAmelCase    :     int													=	do_rescale if do_rescale is not None else self.do_rescale
					__UpperCAmelCase    :     Optional[int]													=	rescale_factor if rescale_factor is not None else self.rescale_factor
					__UpperCAmelCase    :     List[str]													=	do_normalize if do_normalize is not None else self.do_normalize
					__UpperCAmelCase    :     Optional[int]													=	image_mean if image_mean is not None else self.image_mean
					__UpperCAmelCase    :     Tuple													=	image_std if image_std is not None else self.image_std
					__UpperCAmelCase    :     List[str]													=	do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

					__UpperCAmelCase    :     List[str]													=	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_center_crop and crop_size is None:
							raise ValueError('''Crop size must be specified if do_center_crop is True.''' )

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

					if do_normalize and (image_mean is None or image_std is None):
							raise ValueError('''Image mean and std must be specified if do_normalize is True.''' )

					# PIL RGBA images are converted to RGB
					if do_convert_rgb:
							__UpperCAmelCase    :     Optional[Any]													=	[convert_to_rgb(a_ ) for image in images]

					# All transformations expect numpy arrays.
					__UpperCAmelCase    :     int													=	[to_numpy_array(a_ ) for image in images]

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

					if do_center_crop:
							__UpperCAmelCase    :     List[str]													=	[self.center_crop(image=a_       ,       size=a_ ) for image in images]

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

					if do_normalize:
							__UpperCAmelCase    :     Optional[int]													=	[self.normalize(image=a_       ,       mean=a_       ,       std=a_ ) for image in images]

					__UpperCAmelCase    :     Optional[int]													=	[to_channel_dimension_format(a_       ,       a_ ) for image in images]

					__UpperCAmelCase    :     Union[str, Any]													=	{'''pixel_values''': images}
					return BatchFeature(data=a_       ,       tensor_type=a_ )





