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       lowerCamelCase_	(  snake_case_		,      unittest.TestCase					):






    '''simple docstring'''






    a__      :		Union[str, Any]         = RobertaTokenizer
    a__      :		Optional[Any]         = RobertaTokenizerFast
    a__      :		List[str]         = True
    a__      :		Tuple         = {"""cls_token""": """<s>"""}







    def      UpperCamelCase__  (      self)			->      Any:
        super().setUp()

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

        __UpperCamelCase	:Dict         =						os.path.join(self.tmpdirname					,					VOCAB_FILES_NAMES['''vocab_file'''])
        __UpperCamelCase	:Dict         =						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      UpperCamelCase__  (      self					,					**__lowercase)			->      str:
        kwargs.update(self.special_tokens_map)
        return self.tokenizer_class.from_pretrained(self.tmpdirname					,					**A_)







    def      UpperCamelCase__  (      self					,					**__lowercase)			->      Union[str, Any]:
        kwargs.update(self.special_tokens_map)
        return RobertaTokenizerFast.from_pretrained(self.tmpdirname					,					**A_)







    def      UpperCamelCase__  (      self					,					__lowercase)			->      Union[str, Any]:
        __UpperCamelCase	:Any         =						'''lower newer'''
        __UpperCamelCase	:Dict         =						'''lower newer'''
        return input_text, output_text







    def      UpperCamelCase__  (      self)			->      Union[str, Any]:
        __UpperCamelCase	:Union[str, Any]         =						self.tokenizer_class(self.vocab_file					,					self.merges_file					,					**self.special_tokens_map)
        __UpperCamelCase	:List[Any]         =						'''lower newer'''
        __UpperCamelCase	:List[Any]         =						['''l''', '''o''', '''w''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
        __UpperCamelCase	:Union[str, Any]         =						tokenizer.tokenize(A_)  # , add_prefix_space=True)
        self.assertListEqual(A_					,					A_)

        __UpperCamelCase	:Optional[Any]         =						tokens + [tokenizer.unk_token]
        __UpperCamelCase	:List[str]         =						[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(A_)					,					A_)







    def      UpperCamelCase__  (      self)			->      List[str]:
        __UpperCamelCase	:List[str]         =						self.get_tokenizer()

        self.assertListEqual(tokenizer.encode('''Hello world!'''					,					add_special_tokens=A_)					,					[0, 31_414, 232, 328, 2])
        self.assertListEqual(
            tokenizer.encode('''Hello world! cécé herlolip 418'''					,					add_special_tokens=A_)					,					[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]					,					)







    @slow
    def      UpperCamelCase__  (      self)			->      Optional[int]:
        __UpperCamelCase	:List[str]         =						self.tokenizer_class.from_pretrained('''roberta-base''')

        __UpperCamelCase	:str         =						tokenizer.encode('''sequence builders'''					,					add_special_tokens=A_)
        __UpperCamelCase	:List[str]         =						tokenizer.encode('''multi-sequence build'''					,					add_special_tokens=A_)

        __UpperCamelCase	:List[str]         =						tokenizer.encode(
            '''sequence builders'''					,					add_special_tokens=A_					,					add_prefix_space=A_)
        __UpperCamelCase	:Dict         =						tokenizer.encode(
            '''sequence builders'''					,					'''multi-sequence build'''					,					add_special_tokens=A_					,					add_prefix_space=A_)

        __UpperCamelCase	:List[str]         =						tokenizer.build_inputs_with_special_tokens(A_)
        __UpperCamelCase	:Any         =						tokenizer.build_inputs_with_special_tokens(A_					,					A_)

        assert encoded_sentence == encoded_text_from_decode
        assert encoded_pair == encoded_pair_from_decode







    def      UpperCamelCase__  (      self)			->      str:
        __UpperCamelCase	:str         =						self.get_tokenizer()

        __UpperCamelCase	:Tuple         =						'''Encode this sequence.'''
        __UpperCamelCase	:str         =						tokenizer.byte_encoder[''' '''.encode('''utf-8''')[0]]

        # Testing encoder arguments
        __UpperCamelCase	:List[str]         =						tokenizer.encode(A_					,					add_special_tokens=A_					,					add_prefix_space=A_)
        __UpperCamelCase	:Tuple         =						tokenizer.convert_ids_to_tokens(encoded[0])[0]
        self.assertNotEqual(A_					,					A_)

        __UpperCamelCase	:int         =						tokenizer.encode(A_					,					add_special_tokens=A_					,					add_prefix_space=A_)
        __UpperCamelCase	:Any         =						tokenizer.convert_ids_to_tokens(encoded[0])[0]
        self.assertEqual(A_					,					A_)

        tokenizer.add_special_tokens({'''bos_token''': '''<s>'''})
        __UpperCamelCase	:int         =						tokenizer.encode(A_					,					add_special_tokens=A_)
        __UpperCamelCase	:Union[str, Any]         =						tokenizer.convert_ids_to_tokens(encoded[1])[0]
        self.assertNotEqual(A_					,					A_)

        # Testing spaces after special tokens
        __UpperCamelCase	:Optional[Any]         =						'''<mask>'''
        tokenizer.add_special_tokens(
            {'''mask_token''': AddedToken(A_					,					lstrip=A_					,					rstrip=A_)})  # mask token has a left space
        __UpperCamelCase	:str         =						tokenizer.convert_tokens_to_ids(A_)

        __UpperCamelCase	:int         =						'''Encode <mask> sequence'''
        __UpperCamelCase	:Optional[int]         =						'''Encode <mask>sequence'''

        __UpperCamelCase	:Any         =						tokenizer.encode(A_)
        __UpperCamelCase	:List[str]         =						encoded.index(A_)
        __UpperCamelCase	:Union[str, Any]         =						tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1])[0]
        self.assertEqual(A_					,					A_)

        __UpperCamelCase	:int         =						tokenizer.encode(A_)
        __UpperCamelCase	:Optional[Any]         =						encoded.index(A_)
        __UpperCamelCase	:Optional[int]         =						tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1])[0]
        self.assertNotEqual(A_					,					A_)







    def      UpperCamelCase__  (      self)			->      Any:
        pass







    def      UpperCamelCase__  (      self)			->      int:






        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
            with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""):
                __UpperCamelCase	:Optional[Any]         =						self.rust_tokenizer_class.from_pretrained(A_					,					**A_)
                __UpperCamelCase	:Union[str, Any]         =						self.tokenizer_class.from_pretrained(A_					,					**A_)
                __UpperCamelCase	:Optional[int]         =						'''A, <mask> AllenNLP sentence.'''
                __UpperCamelCase	:int         =						tokenizer_r.encode_plus(A_					,					add_special_tokens=A_					,					return_token_type_ids=A_)
                __UpperCamelCase	:Optional[Any]         =						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'''])					,					)

                __UpperCamelCase	:List[str]         =						tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids'''])
                __UpperCamelCase	:Optional[int]         =						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, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2])
                self.assertSequenceEqual(tokens_r['''input_ids''']					,					[0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 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      UpperCamelCase__  (      self)			->      Optional[Any]:






        for trim_offsets, add_prefix_space in itertools.product([True, False]					,					repeat=2):
            __UpperCamelCase	:Optional[Any]         =						self.rust_tokenizer_class.from_pretrained(
                self.tmpdirname					,					use_fast=A_					,					add_prefix_space=A_					,					trim_offsets=A_)

            __UpperCamelCase	:Optional[int]         =						json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__())
            __UpperCamelCase	:Any         =						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      UpperCamelCase__  (      self)			->      Any:






        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
            with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""):
                __UpperCamelCase	:Tuple         =						'''hello'''  # `hello` is a token in the vocabulary of `pretrained_name`
                __UpperCamelCase	:Optional[Any]         =						f"""{text_of_1_token} {text_of_1_token}"""

                __UpperCamelCase	:Optional[Any]         =						self.rust_tokenizer_class.from_pretrained(
                    A_					,					use_fast=A_					,					add_prefix_space=A_					,					trim_offsets=A_)
                __UpperCamelCase	:Optional[int]         =						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_))					,					)

                __UpperCamelCase	:int         =						self.rust_tokenizer_class.from_pretrained(
                    A_					,					use_fast=A_					,					add_prefix_space=A_					,					trim_offsets=A_)
                __UpperCamelCase	:str         =						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_))					,					)

                __UpperCamelCase	:int         =						self.rust_tokenizer_class.from_pretrained(
                    A_					,					use_fast=A_					,					add_prefix_space=A_					,					trim_offsets=A_)
                __UpperCamelCase	:List[Any]         =						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_))					,					)

                __UpperCamelCase	:str         =						self.rust_tokenizer_class.from_pretrained(
                    A_					,					use_fast=A_					,					add_prefix_space=A_					,					trim_offsets=A_)
                __UpperCamelCase	:Tuple         =						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_))					,					)

                __UpperCamelCase	:Union[str, Any]         =						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)),
                # )

                __UpperCamelCase	:str         =						self.rust_tokenizer_class.from_pretrained(
                    A_					,					use_fast=A_					,					add_prefix_space=A_					,					trim_offsets=A_)
                __UpperCamelCase	:str         =						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_))					,					)

                __UpperCamelCase	:Optional[int]         =						self.rust_tokenizer_class.from_pretrained(
                    A_					,					use_fast=A_					,					add_prefix_space=A_					,					trim_offsets=A_)
                __UpperCamelCase	:Optional[int]         =						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_))					,					)

                __UpperCamelCase	:Union[str, Any]         =						self.rust_tokenizer_class.from_pretrained(
                    A_					,					use_fast=A_					,					add_prefix_space=A_					,					trim_offsets=A_)
                __UpperCamelCase	:Tuple         =						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_))					,					)







'''simple docstring'''
import unittest

import numpy as np

from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
						import torch

if is_vision_available():
						from PIL import Image

						from transformers import DonutImageProcessor


class 			SCREAMING_SNAKE_CASE__      (     unittest.TestCase):







				def __init__(       self						,				A_						,				A_=7						,				A_=3						,				A_=18						,				A_=30						,				A_=400						,				A_=True						,				A_=None						,				A_=True						,				A_=False						,				A_=True						,				A_=True						,				A_=[0.5, 0.5, 0.5]						,				A_=[0.5, 0.5, 0.5]						,				)->		Dict:







											'''simple docstring'''






											UpperCamelCase   			=     parent
											UpperCamelCase   			=     batch_size
											UpperCamelCase   			=     num_channels
											UpperCamelCase   			=     image_size
											UpperCamelCase   			=     min_resolution
											UpperCamelCase   			=     max_resolution
											UpperCamelCase   			=     do_resize
											UpperCamelCase   			=     size if size is not None else {'height': 18, 'width': 20}
											UpperCamelCase   			=     do_thumbnail
											UpperCamelCase   			=     do_align_axis
											UpperCamelCase   			=     do_pad
											UpperCamelCase   			=     do_normalize
											UpperCamelCase   			=     image_mean
											UpperCamelCase   			=     image_std







				def 						UpperCAmelCase_    (       self     )->		List[Any]:







											'''simple docstring'''






											return {
											    "do_resize": self.do_resize,
											    "size": self.size,
											    "do_thumbnail": self.do_thumbnail,
											    "do_align_long_axis": self.do_align_axis,
											    "do_pad": self.do_pad,
											    "do_normalize": self.do_normalize,
											    "image_mean": self.image_mean,
											    "image_std": self.image_std,
											}


@require_torch
@require_vision
class 			SCREAMING_SNAKE_CASE__      (     snake_case_							,       unittest.TestCase):
				lowerCAmelCase_     			=							DonutImageProcessor if is_vision_available() else None







				def 						UpperCAmelCase_    (       self     )->		str:







											'''simple docstring'''






											UpperCamelCase   			=     DonutImageProcessingTester(self     )







				@property
				def 						UpperCAmelCase_    (       self     )->		str:







											'''simple docstring'''






											return self.image_processor_tester.prepare_image_processor_dict()







				def 						UpperCAmelCase_    (       self     )->		Optional[int]:







											'''simple docstring'''






											UpperCamelCase   			=     self.image_processing_class(**self.image_processor_dict     )
											self.assertTrue(hasattr(A_						,				'do_resize'     )     )
											self.assertTrue(hasattr(A_						,				'size'     )     )
											self.assertTrue(hasattr(A_						,				'do_thumbnail'     )     )
											self.assertTrue(hasattr(A_						,				'do_align_long_axis'     )     )
											self.assertTrue(hasattr(A_						,				'do_pad'     )     )
											self.assertTrue(hasattr(A_						,				'do_normalize'     )     )
											self.assertTrue(hasattr(A_						,				'image_mean'     )     )
											self.assertTrue(hasattr(A_						,				'image_std'     )     )







				def 						UpperCAmelCase_    (       self     )->		Optional[int]:







											'''simple docstring'''






											UpperCamelCase   			=     self.image_processing_class.from_dict(self.image_processor_dict     )
											self.assertEqual(image_processor.size						,				{'height': 18, 'width': 20}     )

											UpperCamelCase   			=     self.image_processing_class.from_dict(self.image_processor_dict						,				size=42     )
											self.assertEqual(image_processor.size						,				{'height': 42, 'width': 42}     )

											# Previous config had dimensions in (width, height) order
											UpperCamelCase   			=     self.image_processing_class.from_dict(self.image_processor_dict						,				size=(42, 84)     )
											self.assertEqual(image_processor.size						,				{'height': 84, 'width': 42}     )







				def 						UpperCAmelCase_    (       self     )->		Tuple:







											'''simple docstring'''






											pass







				@is_flaky()
				def 						UpperCAmelCase_    (       self     )->		Any:







											'''simple docstring'''






											UpperCamelCase   			=     self.image_processing_class(**self.image_processor_dict     )
											# create random PIL images
											UpperCamelCase   			=     prepare_image_inputs(self.image_processor_tester						,				equal_resolution=A_     )
											for image in image_inputs:
																		self.assertIsInstance(A_						,				Image.Image     )

											# Test not batched input
											UpperCamelCase   			=     image_processing(image_inputs[0]						,				return_tensors='pt'     ).pixel_values
											self.assertEqual(
											    encoded_images.shape						,				(
											        1,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.size['height'],
											        self.image_processor_tester.size['width'],
											    )						,				)

											# Test batched
											UpperCamelCase   			=     image_processing(A_						,				return_tensors='pt'     ).pixel_values
											self.assertEqual(
											    encoded_images.shape						,				(
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.size['height'],
											        self.image_processor_tester.size['width'],
											    )						,				)







				@is_flaky()
				def 						UpperCAmelCase_    (       self     )->		Optional[int]:







											'''simple docstring'''






											UpperCamelCase   			=     self.image_processing_class(**self.image_processor_dict     )
											# create random numpy tensors
											UpperCamelCase   			=     prepare_image_inputs(self.image_processor_tester						,				equal_resolution=A_						,				numpify=A_     )
											for image in image_inputs:
																		self.assertIsInstance(A_						,				np.ndarray     )

											# Test not batched input
											UpperCamelCase   			=     image_processing(image_inputs[0]						,				return_tensors='pt'     ).pixel_values
											self.assertEqual(
											    encoded_images.shape						,				(
											        1,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.size['height'],
											        self.image_processor_tester.size['width'],
											    )						,				)

											# Test batched
											UpperCamelCase   			=     image_processing(A_						,				return_tensors='pt'     ).pixel_values
											self.assertEqual(
											    encoded_images.shape						,				(
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.size['height'],
											        self.image_processor_tester.size['width'],
											    )						,				)







				@is_flaky()
				def 						UpperCAmelCase_    (       self     )->		Dict:







											'''simple docstring'''






											UpperCamelCase   			=     self.image_processing_class(**self.image_processor_dict     )
											# create random PyTorch tensors
											UpperCamelCase   			=     prepare_image_inputs(self.image_processor_tester						,				equal_resolution=A_						,				torchify=A_     )
											for image in image_inputs:
																		self.assertIsInstance(A_						,				torch.Tensor     )

											# Test not batched input
											UpperCamelCase   			=     image_processing(image_inputs[0]						,				return_tensors='pt'     ).pixel_values
											self.assertEqual(
											    encoded_images.shape						,				(
											        1,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.size['height'],
											        self.image_processor_tester.size['width'],
											    )						,				)

											# Test batched
											UpperCamelCase   			=     image_processing(A_						,				return_tensors='pt'     ).pixel_values
											self.assertEqual(
											    encoded_images.shape						,				(
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.size['height'],
											        self.image_processor_tester.size['width'],
											    )						,				)




