





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 			lowercase				(							__lowerCAmelCase		,			unittest.TestCase     ):



						"""simple docstring"""

						_UpperCamelCase  :	str								=	RobertaTokenizer
						_UpperCamelCase  :	Any								=	RobertaTokenizerFast
						_UpperCamelCase  :	Any								=	True
						_UpperCamelCase  :	Optional[int]								=	{"cls_token": "<s>"}







						def 		__UpperCAmelCase       (    self			:		Optional[Any]					):





										'''simple docstring'''







										super().setUp()

										# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
										_snake_case :			Optional[int]  						=   [
										    """l""",
										    """o""",
										    """w""",
										    """e""",
										    """r""",
										    """s""",
										    """t""",
										    """i""",
										    """d""",
										    """n""",
										    """\u0120""",
										    """\u0120l""",
										    """\u0120n""",
										    """\u0120lo""",
										    """\u0120low""",
										    """er""",
										    """\u0120lowest""",
										    """\u0120newer""",
										    """\u0120wider""",
										    """<unk>""",
										]
										_snake_case :			List[str]  						=   dict(zip(_UpperCamelCase ,				range(len(_UpperCamelCase					)					)					)					)
										_snake_case :			List[Any]  						=   ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
										_snake_case :			Union[str, Any]  						=   {"""unk_token""": """<unk>"""}

										_snake_case :			Dict  						=   os.path.join(self.tmpdirname ,				VOCAB_FILES_NAMES['vocab_file']					)
										_snake_case :			Tuple  						=   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(_UpperCamelCase					) + '\n'					)
										with open(self.merges_file ,				'w' ,				encoding='utf-8'					) as fp:
														fp.write('\n'.join(_UpperCamelCase					)					)







						def 		__UpperCAmelCase       (    self			:		int ,				**lowerCamelCase_			:		Optional[Any]					):





										'''simple docstring'''







										kwargs.update(self.special_tokens_map					)
										return self.tokenizer_class.from_pretrained(self.tmpdirname ,				**_UpperCamelCase					)







						def 		__UpperCAmelCase       (    self			:		int ,				**lowerCamelCase_			:		Tuple					):





										'''simple docstring'''







										kwargs.update(self.special_tokens_map					)
										return RobertaTokenizerFast.from_pretrained(self.tmpdirname ,				**_UpperCamelCase					)







						def 		__UpperCAmelCase       (    self			:		Any ,				lowerCamelCase_			:		str					):





										'''simple docstring'''







										_snake_case :			Tuple  						=   """lower newer"""
										_snake_case :			Dict  						=   """lower newer"""
										return input_text, output_text







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





										'''simple docstring'''







										_snake_case :			Optional[int]  						=   self.tokenizer_class(self.vocab_file ,				self.merges_file ,				**self.special_tokens_map					)
										_snake_case :			List[Any]  						=   """lower newer"""
										_snake_case :			str  						=   ["""l""", """o""", """w""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
										_snake_case :			Any  						=   tokenizer.tokenize(_UpperCamelCase					)  # , add_prefix_space=True)
										self.assertListEqual(_UpperCamelCase ,				_UpperCamelCase					)

										_snake_case :			Optional[Any]  						=   tokens + [tokenizer.unk_token]
										_snake_case :			Dict  						=   [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
										self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCamelCase					) ,				_UpperCamelCase					)







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





										'''simple docstring'''







										_snake_case :			Optional[int]  						=   self.get_tokenizer()

										self.assertListEqual(tokenizer.encode('Hello world!' ,				add_special_tokens=_UpperCamelCase					) ,				[0, 3_14_14, 2_32, 3_28, 2]					)
										self.assertListEqual(
										    tokenizer.encode('Hello world! cécé herlolip 418' ,				add_special_tokens=_UpperCamelCase					) ,				[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2] ,				)







						@slow
						def 		__UpperCAmelCase       (    self			:		List[Any]					):





										'''simple docstring'''







										_snake_case :			int  						=   self.tokenizer_class.from_pretrained('roberta-base'					)

										_snake_case :			Optional[Any]  						=   tokenizer.encode('sequence builders' ,				add_special_tokens=_UpperCamelCase					)
										_snake_case :			Optional[int]  						=   tokenizer.encode('multi-sequence build' ,				add_special_tokens=_UpperCamelCase					)

										_snake_case :			Dict  						=   tokenizer.encode(
										    'sequence builders' ,				add_special_tokens=_UpperCamelCase ,				add_prefix_space=_UpperCamelCase					)
										_snake_case :			Union[str, Any]  						=   tokenizer.encode(
										    'sequence builders' ,				'multi-sequence build' ,				add_special_tokens=_UpperCamelCase ,				add_prefix_space=_UpperCamelCase					)

										_snake_case :			List[str]  						=   tokenizer.build_inputs_with_special_tokens(_UpperCamelCase					)
										_snake_case :			Dict  						=   tokenizer.build_inputs_with_special_tokens(_UpperCamelCase ,				_UpperCamelCase					)

										assert encoded_sentence == encoded_text_from_decode
										assert encoded_pair == encoded_pair_from_decode







						def 		__UpperCAmelCase       (    self			:		Any					):





										'''simple docstring'''







										_snake_case :			str  						=   self.get_tokenizer()

										_snake_case :			List[Any]  						=   """Encode this sequence."""
										_snake_case :			Any  						=   tokenizer.byte_encoder[""" """.encode('utf-8'					)[0]]

										# Testing encoder arguments
										_snake_case :			Any  						=   tokenizer.encode(_UpperCamelCase ,				add_special_tokens=_UpperCamelCase ,				add_prefix_space=_UpperCamelCase					)
										_snake_case :			int  						=   tokenizer.convert_ids_to_tokens(encoded[0]					)[0]
										self.assertNotEqual(_UpperCamelCase ,				_UpperCamelCase					)

										_snake_case :			List[str]  						=   tokenizer.encode(_UpperCamelCase ,				add_special_tokens=_UpperCamelCase ,				add_prefix_space=_UpperCamelCase					)
										_snake_case :			int  						=   tokenizer.convert_ids_to_tokens(encoded[0]					)[0]
										self.assertEqual(_UpperCamelCase ,				_UpperCamelCase					)

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

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

										_snake_case :			str  						=   """Encode <mask> sequence"""
										_snake_case :			Union[str, Any]  						=   """Encode <mask>sequence"""

										_snake_case :			str  						=   tokenizer.encode(_UpperCamelCase					)
										_snake_case :			Optional[int]  						=   encoded.index(_UpperCamelCase					)
										_snake_case :			Optional[Any]  						=   tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]					)[0]
										self.assertEqual(_UpperCamelCase ,				_UpperCamelCase					)

										_snake_case :			Any  						=   tokenizer.encode(_UpperCamelCase					)
										_snake_case :			Any  						=   encoded.index(_UpperCamelCase					)
										_snake_case :			Dict  						=   tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]					)[0]
										self.assertNotEqual(_UpperCamelCase ,				_UpperCamelCase					)







						def 		__UpperCAmelCase       (    self			:		Tuple					):





										'''simple docstring'''







										pass







						def 		__UpperCAmelCase       (    self			:		Optional[int]					):





										'''simple docstring'''







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

																		# 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']					) ,				)

																		_snake_case :			Any  						=   tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']					)
																		_snake_case :			Optional[Any]  						=   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(
																		    _UpperCamelCase ,				['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']					)
																		self.assertSequenceEqual(
																		    _UpperCamelCase ,				['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']					)







						def 		__UpperCAmelCase       (    self			:		Tuple					):





										'''simple docstring'''







										for trim_offsets, add_prefix_space in itertools.product([True, False] ,				repeat=2					):
														_snake_case :			int  						=   self.rust_tokenizer_class.from_pretrained(
														    self.tmpdirname ,				use_fast=_UpperCamelCase ,				add_prefix_space=_UpperCamelCase ,				trim_offsets=_UpperCamelCase					)

														_snake_case :			List[str]  						=   json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()					)
														_snake_case :			Optional[int]  						=   json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()					)

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

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







						def 		__UpperCAmelCase       (    self			:		str					):





										'''simple docstring'''







										for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
														with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''					):
																		_snake_case :			int  						=   """hello"""  # `hello` is a token in the vocabulary of `pretrained_name`
																		_snake_case :			str  						=   f'''{text_of_1_token} {text_of_1_token}'''

																		_snake_case :			int  						=   self.rust_tokenizer_class.from_pretrained(
																		    _UpperCamelCase ,				use_fast=_UpperCamelCase ,				add_prefix_space=_UpperCamelCase ,				trim_offsets=_UpperCamelCase					)
																		_snake_case :			List[Any]  						=   tokenizer_r(_UpperCamelCase ,				return_offsets_mapping=_UpperCamelCase ,				add_special_tokens=_UpperCamelCase					)
																		self.assertEqual(encoding.offset_mapping[0] ,				(0, len(_UpperCamelCase					))					)
																		self.assertEqual(
																		    encoding.offset_mapping[1] ,				(len(_UpperCamelCase					) + 1, len(_UpperCamelCase					) + 1 + len(_UpperCamelCase					)) ,				)

																		_snake_case :			Optional[Any]  						=   self.rust_tokenizer_class.from_pretrained(
																		    _UpperCamelCase ,				use_fast=_UpperCamelCase ,				add_prefix_space=_UpperCamelCase ,				trim_offsets=_UpperCamelCase					)
																		_snake_case :			Optional[Any]  						=   tokenizer_r(_UpperCamelCase ,				return_offsets_mapping=_UpperCamelCase ,				add_special_tokens=_UpperCamelCase					)
																		self.assertEqual(encoding.offset_mapping[0] ,				(0, len(_UpperCamelCase					))					)
																		self.assertEqual(
																		    encoding.offset_mapping[1] ,				(len(_UpperCamelCase					) + 1, len(_UpperCamelCase					) + 1 + len(_UpperCamelCase					)) ,				)

																		_snake_case :			Optional[Any]  						=   self.rust_tokenizer_class.from_pretrained(
																		    _UpperCamelCase ,				use_fast=_UpperCamelCase ,				add_prefix_space=_UpperCamelCase ,				trim_offsets=_UpperCamelCase					)
																		_snake_case :			Optional[int]  						=   tokenizer_r(_UpperCamelCase ,				return_offsets_mapping=_UpperCamelCase ,				add_special_tokens=_UpperCamelCase					)
																		self.assertEqual(encoding.offset_mapping[0] ,				(0, len(_UpperCamelCase					))					)
																		self.assertEqual(
																		    encoding.offset_mapping[1] ,				(len(_UpperCamelCase					), len(_UpperCamelCase					) + 1 + len(_UpperCamelCase					)) ,				)

																		_snake_case :			Union[str, Any]  						=   self.rust_tokenizer_class.from_pretrained(
																		    _UpperCamelCase ,				use_fast=_UpperCamelCase ,				add_prefix_space=_UpperCamelCase ,				trim_offsets=_UpperCamelCase					)
																		_snake_case :			Optional[int]  						=   tokenizer_r(_UpperCamelCase ,				return_offsets_mapping=_UpperCamelCase ,				add_special_tokens=_UpperCamelCase					)
																		self.assertEqual(encoding.offset_mapping[0] ,				(0, len(_UpperCamelCase					))					)
																		self.assertEqual(
																		    encoding.offset_mapping[1] ,				(len(_UpperCamelCase					), len(_UpperCamelCase					) + 1 + len(_UpperCamelCase					)) ,				)

																		_snake_case :			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)),
																		# )

																		_snake_case :			Union[str, Any]  						=   self.rust_tokenizer_class.from_pretrained(
																		    _UpperCamelCase ,				use_fast=_UpperCamelCase ,				add_prefix_space=_UpperCamelCase ,				trim_offsets=_UpperCamelCase					)
																		_snake_case :			List[str]  						=   tokenizer_r(_UpperCamelCase ,				return_offsets_mapping=_UpperCamelCase ,				add_special_tokens=_UpperCamelCase					)
																		self.assertEqual(encoding.offset_mapping[0] ,				(1, 1 + len(_UpperCamelCase					))					)
																		self.assertEqual(
																		    encoding.offset_mapping[1] ,				(1 + len(_UpperCamelCase					) + 1, 1 + len(_UpperCamelCase					) + 1 + len(_UpperCamelCase					)) ,				)

																		_snake_case :			Union[str, Any]  						=   self.rust_tokenizer_class.from_pretrained(
																		    _UpperCamelCase ,				use_fast=_UpperCamelCase ,				add_prefix_space=_UpperCamelCase ,				trim_offsets=_UpperCamelCase					)
																		_snake_case :			int  						=   tokenizer_r(_UpperCamelCase ,				return_offsets_mapping=_UpperCamelCase ,				add_special_tokens=_UpperCamelCase					)
																		self.assertEqual(encoding.offset_mapping[0] ,				(0, 1 + len(_UpperCamelCase					))					)
																		self.assertEqual(
																		    encoding.offset_mapping[1] ,				(1 + len(_UpperCamelCase					), 1 + len(_UpperCamelCase					) + 1 + len(_UpperCamelCase					)) ,				)

																		_snake_case :			Dict  						=   self.rust_tokenizer_class.from_pretrained(
																		    _UpperCamelCase ,				use_fast=_UpperCamelCase ,				add_prefix_space=_UpperCamelCase ,				trim_offsets=_UpperCamelCase					)
																		_snake_case :			Tuple  						=   tokenizer_r(_UpperCamelCase ,				return_offsets_mapping=_UpperCamelCase ,				add_special_tokens=_UpperCamelCase					)
																		self.assertEqual(encoding.offset_mapping[0] ,				(0, 1 + len(_UpperCamelCase					))					)
																		self.assertEqual(
																		    encoding.offset_mapping[1] ,				(1 + len(_UpperCamelCase					), 1 + len(_UpperCamelCase					) + 1 + len(_UpperCamelCase					)) ,				)













import torch

from transformers import AutoModel


class 			lowercase				(							torch.nn.Module     ):



						"""simple docstring"""

						def __init__(    self			:		Tuple ,				lowerCamelCase_			:		Dict="sayef/fsner-bert-base-uncased"					):





										'''simple docstring'''







										super(lowerCamelCase_ ,				self					).__init__()

										_snake_case :			Optional[Any]  						=   AutoModel.from_pretrained(lowerCamelCase_ ,				return_dict=lowerCamelCase_					)
										_snake_case :			str  						=   torch.nn.CosineSimilarity(3 ,				1e-08					)
										_snake_case :			str  						=   torch.nn.Softmax(dim=1					)







						def 		__UpperCAmelCase       (    self			:		int ,				**lowerCamelCase_			:		List[str]					):





										'''simple docstring'''







										return self.bert(**lowerCamelCase_					).last_hidden_state







						def 		__UpperCAmelCase       (    self			:		List[Any] ,				lowerCamelCase_			:		Union[str, Any]					):





										'''simple docstring'''







										return token_embeddings.sum(2 ,				keepdim=lowerCamelCase_					)







						def 		__UpperCAmelCase       (    self			:		Dict ,				lowerCamelCase_			:		Dict ,				lowerCamelCase_			:		Tuple ,				lowerCamelCase_			:		Optional[Any]=1					):





										'''simple docstring'''







										return self.softmax(T * self.cos(lowerCamelCase_ ,				lowerCamelCase_					)					)







						def 		__UpperCAmelCase       (    self			:		Optional[Any] ,				lowerCamelCase_			:		Any ,				lowerCamelCase_			:		Union[str, Any]					):





										'''simple docstring'''







										_snake_case :			int  						=   W_supports['sizes'].tolist()
										_snake_case :			int  						=   W_supports['start_token_id'].item()
										_snake_case :			List[str]  						=   W_supports['end_token_id'].item()

										del W_supports["sizes"]
										del W_supports["start_token_id"]
										del W_supports["end_token_id"]

										_snake_case :			Optional[int]  						=   self.BERT(**lowerCamelCase_					)
										_snake_case :			Optional[Any]  						=   self.BERT(**lowerCamelCase_					)

										_snake_case :			Optional[int]  						=   None
										_snake_case :			Optional[int]  						=   None

										_snake_case :			List[str]  						=   W_supports['input_ids'] == start_token_id
										_snake_case :			Union[str, Any]  						=   W_supports['input_ids'] == end_token_id

										for i, size in enumerate(lowerCamelCase_					):
														if i == 0:
																		_snake_case :			str  						=   0
														else:
																		_snake_case :			Union[str, Any]  						=   support_sizes[i - 1]

														_snake_case :			Tuple  						=   S[s : s + size][start_token_masks[s : s + size]]
														_snake_case :			Optional[int]  						=   S[s : s + size][end_token_masks[s : s + size]]

														_snake_case :			Tuple  						=   torch.matmul(q[i] ,				s_start.T					).sum(1					).softmax(0					)
														_snake_case :			Union[str, Any]  						=   torch.matmul(q[i] ,				s_end.T					).sum(1					).softmax(0					)

														if p_starts is not None:
																		_snake_case :			Optional[Any]  						=   torch.vstack((p_starts, p_start)					)
																		_snake_case :			List[str]  						=   torch.vstack((p_ends, p_end)					)
														else:
																		_snake_case :			Union[str, Any]  						=   p_start
																		_snake_case :			Any  						=   p_end

										return p_starts, p_ends






