


import itertools
import json
import os
import unittest

from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class 	snake_case_       (__lowercase     ,						unittest.TestCase				):
					UpperCAmelCase__ :   List[Any]   				=      RobertaTokenizer
					UpperCAmelCase__ :   str   				=      RobertaTokenizerFast
					UpperCAmelCase__ :   List[Any]   				=      True
					UpperCAmelCase__ :   Union[str, Any]   				=      {'''cls_token''': '''<s>'''}
					def 			lowerCamelCase__(				self       :Tuple				) ->   Optional[int]:
												super().setUp()

												# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
												a__					=					[
												    'l',
												    'o',
												    'w',
												    'e',
												    'r',
												    's',
												    't',
												    'i',
												    'd',
												    'n',
												    '\u0120',
												    '\u0120l',
												    '\u0120n',
												    '\u0120lo',
												    '\u0120low',
												    'er',
												    '\u0120lowest',
												    '\u0120newer',
												    '\u0120wider',
												    '<unk>',
												]
												a__					=					dict(zip(_a      ,range(len(_a				)				)				)				)
												a__					=					['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
												a__					=					{'unk_token': '<unk>'}

												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'      ,encoding='utf-8'				) as fp:
																			fp.write(json.dumps(_a				) + '\n'				)
												with open(self.merges_file      ,'w'      ,encoding='utf-8'				) as fp:
																			fp.write('\n'.join(_a				)				)
					def 			lowerCamelCase__(				self       :Optional[Any]      ,**__snake_case       :Optional[Any]				) ->   Any:
												kwargs.update(self.special_tokens_map				)
												return self.tokenizer_class.from_pretrained(self.tmpdirname      ,**_a				)
					def 			lowerCamelCase__(				self       :Union[str, Any]      ,**__snake_case       :List[Any]				) ->   List[Any]:
												kwargs.update(self.special_tokens_map				)
												return RobertaTokenizerFast.from_pretrained(self.tmpdirname      ,**_a				)
					def 			lowerCamelCase__(				self       :Tuple      ,__snake_case       :Union[str, Any]				) ->   Dict:
												a__					=					'lower newer'
												a__					=					'lower newer'
												return input_text, output_text
					def 			lowerCamelCase__(				self       :List[Any]				) ->   Optional[Any]:
												a__					=					self.tokenizer_class(self.vocab_file      ,self.merges_file      ,**self.special_tokens_map				)
												a__					=					'lower newer'
												a__					=					['l', 'o', 'w', 'er', '\u0120', 'n', 'e', 'w', 'er']
												a__					=					tokenizer.tokenize(_a				)  # , add_prefix_space=True)
												self.assertListEqual(_a      ,_a				)

												a__					=					tokens + [tokenizer.unk_token]
												a__					=					[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
												self.assertListEqual(tokenizer.convert_tokens_to_ids(_a				)      ,_a				)
					def 			lowerCamelCase__(				self       :List[str]				) ->   int:
												a__					=					self.get_tokenizer()

												self.assertListEqual(tokenizer.encode('Hello world!'      ,add_special_tokens=_a				)      ,[0, 3_14_14, 2_32, 3_28, 2]				)
												self.assertListEqual(
												    tokenizer.encode('Hello world! cécé herlolip 418'      ,add_special_tokens=_a				)      ,[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]      ,)
					@slow
					def 			lowerCamelCase__(				self       :Optional[int]				) ->   int:
												a__					=					self.tokenizer_class.from_pretrained('roberta-base'				)

												a__					=					tokenizer.encode('sequence builders'      ,add_special_tokens=_a				)
												a__					=					tokenizer.encode('multi-sequence build'      ,add_special_tokens=_a				)

												a__					=					tokenizer.encode(
												    'sequence builders'      ,add_special_tokens=_a      ,add_prefix_space=_a				)
												a__					=					tokenizer.encode(
												    'sequence builders'      ,'multi-sequence build'      ,add_special_tokens=_a      ,add_prefix_space=_a				)

												a__					=					tokenizer.build_inputs_with_special_tokens(_a				)
												a__					=					tokenizer.build_inputs_with_special_tokens(_a      ,_a				)

												assert encoded_sentence == encoded_text_from_decode
												assert encoded_pair == encoded_pair_from_decode
					def 			lowerCamelCase__(				self       :int				) ->   Optional[Any]:
												a__					=					self.get_tokenizer()

												a__					=					'Encode this sequence.'
												a__					=					tokenizer.byte_encoder[' '.encode('utf-8'				)[0]]

												# Testing encoder arguments
												a__					=					tokenizer.encode(_a      ,add_special_tokens=_a      ,add_prefix_space=_a				)
												a__					=					tokenizer.convert_ids_to_tokens(encoded[0]				)[0]
												self.assertNotEqual(_a      ,_a				)

												a__					=					tokenizer.encode(_a      ,add_special_tokens=_a      ,add_prefix_space=_a				)
												a__					=					tokenizer.convert_ids_to_tokens(encoded[0]				)[0]
												self.assertEqual(_a      ,_a				)

												tokenizer.add_special_tokens({'bos_token': '<s>'}				)
												a__					=					tokenizer.encode(_a      ,add_special_tokens=_a				)
												a__					=					tokenizer.convert_ids_to_tokens(encoded[1]				)[0]
												self.assertNotEqual(_a      ,_a				)

												# Testing spaces after special tokens
												a__					=					'<mask>'
												tokenizer.add_special_tokens(
												    {'mask_token': AddedToken(_a      ,lstrip=_a      ,rstrip=_a				)}				)  # mask token has a left space
												a__					=					tokenizer.convert_tokens_to_ids(_a				)

												a__					=					'Encode <mask> sequence'
												a__					=					'Encode <mask>sequence'

												a__					=					tokenizer.encode(_a				)
												a__					=					encoded.index(_a				)
												a__					=					tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]				)[0]
												self.assertEqual(_a      ,_a				)

												a__					=					tokenizer.encode(_a				)
												a__					=					encoded.index(_a				)
												a__					=					tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]				)[0]
												self.assertNotEqual(_a      ,_a				)
					def 			lowerCamelCase__(				self       :Optional[int]				) ->   Any:
												pass
					def 			lowerCamelCase__(				self       :str				) ->   Optional[int]:
												for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																			with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'				):
																										a__					=					self.rust_tokenizer_class.from_pretrained(_a      ,**_a				)
																										a__					=					self.tokenizer_class.from_pretrained(_a      ,**_a				)
																										a__					=					'A, <mask> AllenNLP sentence.'
																										a__					=					tokenizer_r.encode_plus(_a      ,add_special_tokens=_a      ,return_token_type_ids=_a				)
																										a__					=					tokenizer_p.encode_plus(_a      ,add_special_tokens=_a      ,return_token_type_ids=_a				)

																										# token_type_ids should put 0 everywhere
																										self.assertEqual(sum(tokens_r['token_type_ids']				)      ,sum(tokens_p['token_type_ids']				)				)

																										# attention_mask should put 1 everywhere, so sum over length should be 1
																										self.assertEqual(
																										    sum(tokens_r['attention_mask']				) / len(tokens_r['attention_mask']				)      ,sum(tokens_p['attention_mask']				) / len(tokens_p['attention_mask']				)      ,)

																										a__					=					tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']				)
																										a__					=					tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids']				)

																										# Rust correctly handles the space before the mask while python doesnt
																										self.assertSequenceEqual(tokens_p['input_ids']      ,[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]				)
																										self.assertSequenceEqual(tokens_r['input_ids']      ,[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]				)

																										self.assertSequenceEqual(
																										    _a      ,['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']				)
																										self.assertSequenceEqual(
																										    _a      ,['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']				)
					def 			lowerCamelCase__(				self       :Optional[int]				) ->   Any:
												for trim_offsets, add_prefix_space in itertools.product([True, False]      ,repeat=2				):
																			a__					=					self.rust_tokenizer_class.from_pretrained(
																			    self.tmpdirname      ,use_fast=_a      ,add_prefix_space=_a      ,trim_offsets=_a				)

																			a__					=					json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()				)
																			a__					=					json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()				)

																			self.assertEqual(pre_tokenizer_state['add_prefix_space']      ,_a				)

																			self.assertEqual(post_processor_state['add_prefix_space']      ,_a				)
																			self.assertEqual(post_processor_state['trim_offsets']      ,_a				)



					def 			lowerCamelCase__(				self       :Union[str, Any]				) ->   Optional[int]:
												# Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space` and
												# `trim_offsets`
												for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																			with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'				):
																										a__					=					'hello'  # `hello` is a token in the vocabulary of `pretrained_name`
																										a__					=					F'{text_of_1_token} {text_of_1_token}'

																										a__					=					self.rust_tokenizer_class.from_pretrained(
																										    _a      ,use_fast=_a      ,add_prefix_space=_a      ,trim_offsets=_a				)
																										a__					=					tokenizer_r(_a      ,return_offsets_mapping=_a      ,add_special_tokens=_a				)
																										self.assertEqual(encoding.offset_mapping[0]      ,(0, len(_a				))				)
																										self.assertEqual(
																										    encoding.offset_mapping[1]      ,(len(_a				) + 1, len(_a				) + 1 + len(_a				))      ,)

																										a__					=					self.rust_tokenizer_class.from_pretrained(
																										    _a      ,use_fast=_a      ,add_prefix_space=_a      ,trim_offsets=_a				)
																										a__					=					tokenizer_r(_a      ,return_offsets_mapping=_a      ,add_special_tokens=_a				)
																										self.assertEqual(encoding.offset_mapping[0]      ,(0, len(_a				))				)
																										self.assertEqual(
																										    encoding.offset_mapping[1]      ,(len(_a				) + 1, len(_a				) + 1 + len(_a				))      ,)

																										a__					=					self.rust_tokenizer_class.from_pretrained(
																										    _a      ,use_fast=_a      ,add_prefix_space=_a      ,trim_offsets=_a				)
																										a__					=					tokenizer_r(_a      ,return_offsets_mapping=_a      ,add_special_tokens=_a				)
																										self.assertEqual(encoding.offset_mapping[0]      ,(0, len(_a				))				)
																										self.assertEqual(
																										    encoding.offset_mapping[1]      ,(len(_a				), len(_a				) + 1 + len(_a				))      ,)

																										a__					=					self.rust_tokenizer_class.from_pretrained(
																										    _a      ,use_fast=_a      ,add_prefix_space=_a      ,trim_offsets=_a				)
																										a__					=					tokenizer_r(_a      ,return_offsets_mapping=_a      ,add_special_tokens=_a				)
																										self.assertEqual(encoding.offset_mapping[0]      ,(0, len(_a				))				)
																										self.assertEqual(
																										    encoding.offset_mapping[1]      ,(len(_a				), len(_a				) + 1 + len(_a				))      ,)

																										a__					=					F' {text}'

																										# tokenizer_r = self.rust_tokenizer_class.from_pretrained(
																										#     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
																										# )
																										# encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
																										# self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
																										# self.assertEqual(
																										#     encoding.offset_mapping[1],
																										#     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
																										# )

																										a__					=					self.rust_tokenizer_class.from_pretrained(
																										    _a      ,use_fast=_a      ,add_prefix_space=_a      ,trim_offsets=_a				)
																										a__					=					tokenizer_r(_a      ,return_offsets_mapping=_a      ,add_special_tokens=_a				)
																										self.assertEqual(encoding.offset_mapping[0]      ,(1, 1 + len(_a				))				)
																										self.assertEqual(
																										    encoding.offset_mapping[1]      ,(1 + len(_a				) + 1, 1 + len(_a				) + 1 + len(_a				))      ,)

																										a__					=					self.rust_tokenizer_class.from_pretrained(
																										    _a      ,use_fast=_a      ,add_prefix_space=_a      ,trim_offsets=_a				)
																										a__					=					tokenizer_r(_a      ,return_offsets_mapping=_a      ,add_special_tokens=_a				)
																										self.assertEqual(encoding.offset_mapping[0]      ,(0, 1 + len(_a				))				)
																										self.assertEqual(
																										    encoding.offset_mapping[1]      ,(1 + len(_a				), 1 + len(_a				) + 1 + len(_a				))      ,)

																										a__					=					self.rust_tokenizer_class.from_pretrained(
																										    _a      ,use_fast=_a      ,add_prefix_space=_a      ,trim_offsets=_a				)
																										a__					=					tokenizer_r(_a      ,return_offsets_mapping=_a      ,add_special_tokens=_a				)
																										self.assertEqual(encoding.offset_mapping[0]      ,(0, 1 + len(_a				))				)
																										self.assertEqual(
																										    encoding.offset_mapping[1]      ,(1 + len(_a				), 1 + len(_a				) + 1 + len(_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 rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
						import PIL


snake_case							:	str					      =				logging.get_logger(__name__)






def 							__lowercase       (			__lowerCAmelCase							:    Union[str, Any] ,   __lowerCAmelCase							:    Optional[Any]       ):
							a__					=					b.T
							a__					=					np.sum(np.square(__lowerCAmelCase       ) ,   axis=1       )
							a__					=					np.sum(np.square(__lowerCAmelCase       ) ,   axis=0       )
							a__					=					np.matmul(__lowerCAmelCase ,   __lowerCAmelCase       )
							a__					=					aa[:, None] - 2 * ab + ba[None, :]
							return d






def 							__lowercase       (			__lowerCAmelCase							:    List[Any] ,   __lowerCAmelCase							:    str       ):
							a__					=					x.reshape(-1 ,   3       )
							a__					=					squared_euclidean_distance(__lowerCAmelCase ,   __lowerCAmelCase       )
							return np.argmin(__lowerCAmelCase ,   axis=1       )







class 	snake_case_       (lowerCamelCase_				):
					UpperCAmelCase__ :   Optional[Any]   				=      ['''pixel_values''']
					def __init__(				self       :Dict      ,__snake_case       :Optional[Union[List[List[int]], np.ndarray]] = None      ,__snake_case       :bool = True      ,__snake_case       :Dict[str, int] = None      ,__snake_case       :PILImageResampling = PILImageResampling.BILINEAR      ,__snake_case       :bool = True      ,__snake_case       :bool = True      ,**__snake_case       :Optional[int]      ,) ->   None:
												super().__init__(**__snake_case				)
												a__					=					size if size is not None else {'height': 2_56, 'width': 2_56}
												a__					=					get_size_dict(__snake_case				)
												a__					=					np.array(__snake_case				) if clusters is not None else None
												a__					=					do_resize
												a__					=					size
												a__					=					resample
												a__					=					do_normalize
												a__					=					do_color_quantize
					def 			lowerCamelCase__(				self       :Union[str, Any]      ,__snake_case       :np.ndarray      ,__snake_case       :Dict[str, int]      ,__snake_case       :PILImageResampling = PILImageResampling.BILINEAR      ,__snake_case       :Optional[Union[str, ChannelDimension]] = None      ,**__snake_case       :Any      ,) ->   np.ndarray:
												a__					=					get_size_dict(__snake_case				)
												if "height" not in size or "width" not in size:
																			raise ValueError(F'Size dictionary must contain both height and width keys. Got {size.keys()}'				)
												return resize(
												    __snake_case      ,size=(size['height'], size['width'])      ,resample=__snake_case      ,data_format=__snake_case      ,**__snake_case				)
					def 			lowerCamelCase__(				self       :List[Any]      ,__snake_case       :np.ndarray      ,__snake_case       :Optional[Union[str, ChannelDimension]] = None      ,) ->   np.ndarray:
												a__					=					rescale(image=__snake_case      ,scale=1 / 1_27.5      ,data_format=__snake_case				)
												a__					=					image - 1
												return image



					def 			lowerCamelCase__(				self       :Optional[Any]      ,__snake_case       :ImageInput      ,__snake_case       :bool = None      ,__snake_case       :Dict[str, int] = None      ,__snake_case       :PILImageResampling = None      ,__snake_case       :bool = None      ,__snake_case       :Optional[bool] = None      ,__snake_case       :Optional[Union[List[List[int]], np.ndarray]] = None      ,__snake_case       :Optional[Union[str, TensorType]] = None      ,__snake_case       :Optional[Union[str, ChannelDimension]] = ChannelDimension.FIRST      ,**__snake_case       :List[str]      ,) ->   PIL.Image.Image:
												a__					=					do_resize if do_resize is not None else self.do_resize
												a__					=					size if size is not None else self.size
												a__					=					get_size_dict(__snake_case				)
												a__					=					resample if resample is not None else self.resample
												a__					=					do_normalize if do_normalize is not None else self.do_normalize
												a__					=					do_color_quantize if do_color_quantize is not None else self.do_color_quantize
												a__					=					clusters if clusters is not None else self.clusters
												a__					=					np.array(__snake_case				)

												a__					=					make_list_of_images(__snake_case				)

												if not valid_images(__snake_case				):
																			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 or resample is None:
																			raise ValueError('Size and resample must be specified if do_resize is True.'				)

												if do_color_quantize and clusters is None:
																			raise ValueError('Clusters must be specified if do_color_quantize is True.'				)

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

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

												if do_normalize:
																			a__					=					[self.normalize(image=__snake_case				) for image in images]

												if do_color_quantize:
																			a__					=					[to_channel_dimension_format(__snake_case      ,ChannelDimension.LAST				) for image in images]
																			# color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
																			a__					=					np.array(__snake_case				)
																			a__					=					color_quantize(__snake_case      ,__snake_case				).reshape(images.shape[:-1]				)

																			# flatten to (batch_size, height*width)
																			a__					=					images.shape[0]
																			a__					=					images.reshape(__snake_case      ,-1				)

																			# We need to convert back to a list of images to keep consistent behaviour across processors.
																			a__					=					list(__snake_case				)
												else:
																			a__					=					[to_channel_dimension_format(__snake_case      ,__snake_case				) for image in images]

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




