



import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors

@require_tokenizers
class  __snake_case      (   SCREAMING_SNAKE_CASE			,unittest.TestCase):







							'''simple docstring'''






							UpperCamelCase__				:      str         =					MvpTokenizer
							UpperCamelCase__				:      Tuple         =					MvpTokenizerFast
							UpperCamelCase__				:      Union[str, Any]         =					True
							UpperCamelCase__				:      Dict         =					filter_roberta_detectors


							def _a      (							self		):
											super().setUp()
											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 _a      (							self  ,  **a_		):
											kwargs.update(self.special_tokens_map		)
											return self.tokenizer_class.from_pretrained(self.tmpdirname  ,  **a_		)


							def _a      (							self  ,  **a_		):
											kwargs.update(self.special_tokens_map		)
											return self.rust_tokenizer_class.from_pretrained(self.tmpdirname  ,  **a_		)


							def _a      (							self  ,  a_		):
											return "lower newer", "lower newer"


							@cached_property
							def _a      (							self		):
											return MvpTokenizer.from_pretrained("""RUCAIBox/mvp"""		)


							@cached_property
							def _a      (							self		):
											return MvpTokenizerFast.from_pretrained("""RUCAIBox/mvp"""		)


							@require_torch
							def _a      (							self		):
											a__				      =		["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
											a__				      =		[0, 250, 251, 17_818, 13, 39_186, 1_938, 4, 2]

											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
															a__				      =		tokenizer(a_  ,  max_length=len(a_		)  ,  padding=a_  ,  return_tensors="""pt"""		)
															self.assertIsInstance(a_  ,  a_		)

															self.assertEqual((2, 9)  ,  batch.input_ids.shape		)
															self.assertEqual((2, 9)  ,  batch.attention_mask.shape		)
															a__				      =		batch.input_ids.tolist()[0]
															self.assertListEqual(a_  ,  a_		)
															# Test that special tokens are reset


							@require_torch
							def _a      (							self		):
											a__				      =		["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
															a__				      =		tokenizer(a_  ,  padding=a_  ,  return_tensors="""pt"""		)
															# check if input_ids are returned and no labels
															self.assertIn("""input_ids"""  ,  a_		)
															self.assertIn("""attention_mask"""  ,  a_		)
															self.assertNotIn("""labels"""  ,  a_		)
															self.assertNotIn("""decoder_attention_mask"""  ,  a_		)


							@require_torch
							def _a      (							self		):
											a__				      =		[
											    """Summary of the text.""",
											    """Another summary.""",
											]
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
															a__				      =		tokenizer(text_target=a_  ,  max_length=32  ,  padding="""max_length"""  ,  return_tensors="""pt"""		)
															self.assertEqual(32  ,  targets["""input_ids"""].shape[1]		)


							@require_torch
							def _a      (							self		):
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
															a__				      =		tokenizer(
															    ["""I am a small frog""" * 1_024, """I am a small frog"""]  ,  padding=a_  ,  truncation=a_  ,  return_tensors="""pt"""		)
															self.assertIsInstance(a_  ,  a_		)
															self.assertEqual(batch.input_ids.shape  ,  (2, 1_024)		)


							@require_torch
							def _a      (							self		):
											a__				      =		["""A long paragraph for summarization."""]
											a__				      =		[
											    """Summary of the text.""",
											]
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
															a__				      =		tokenizer(a_  ,  text_target=a_  ,  return_tensors="""pt"""		)
															a__				      =		inputs["""input_ids"""]
															a__				      =		inputs["""labels"""]
															self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()		)
															self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()		)
															self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()		)
															self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()		)


							def _a      (							self		):
											pass







							def _a      (							self		):
											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, 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>"""]		)






import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


UpperCAmelCase							 =					logging.get_logger(__name__)

UpperCAmelCase							 =					{"""vocab_file""": """spiece.model"""}

UpperCAmelCase							 =					{
    """vocab_file""": {
        """bert_for_seq_generation""": (
            """https://huggingface.co/google/bert_for_seq_generation_L-24_bbc_encoder/resolve/main/spiece.model"""
        ),
    }
}

UpperCAmelCase							 =					{"""bert_for_seq_generation""": 512}

class  __snake_case      (   SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							UpperCamelCase__				:      Optional[Any]         =					VOCAB_FILES_NAMES
							UpperCamelCase__				:      Any         =					PRETRAINED_VOCAB_FILES_MAP
							UpperCamelCase__				:      List[str]         =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							UpperCamelCase__				:      List[int]         =					[]
							UpperCamelCase__				:      Optional[int]         =					["""input_ids""", """attention_mask"""]


							def __init__(							self  ,  a_  ,  a_="<s>"  ,  a_="</s>"  ,  a_="<unk>"  ,  a_="<pad>"  ,  a_="<::::>"  ,  a_ = None  ,  **a_  ,  ):
											a__				      =		{} if sp_model_kwargs is None else sp_model_kwargs

											# Add extra_ids to the special token list
											super().__init__(
											    bos_token=a_  ,  eos_token=a_  ,  unk_token=a_  ,  pad_token=a_  ,  sep_token=a_  ,  sp_model_kwargs=self.sp_model_kwargs  ,  **a_  ,  )

											a__				      =		vocab_file

											a__				      =		spm.SentencePieceProcessor(**self.sp_model_kwargs		)
											self.sp_model.Load(a_		)


							@property
							def _a      (							self		):
											return self.sp_model.get_piece_size()


							def _a      (							self		):
											a__				      =		{self.convert_ids_to_tokens(a_		): i for i in range(self.vocab_size		)}
											vocab.update(self.added_tokens_encoder		)
											return vocab


							def __getstate__(							self		):
											a__				      =		self.__dict__.copy()
											a__				      =		None
											return state


							def __setstate__(							self  ,  a_		):
											a__				      =		d

											# for backward compatibility
											if not hasattr(self  ,  """sp_model_kwargs"""		):
															a__				      =		{}

											a__				      =		spm.SentencePieceProcessor(**self.sp_model_kwargs		)
											self.sp_model.Load(self.vocab_file		)


							def _a      (							self  ,  a_		):
											return self.sp_model.encode(a_  ,  out_type=a_		)


							def _a      (							self  ,  a_		):
											return self.sp_model.piece_to_id(a_		)


							def _a      (							self  ,  a_		):
											a__				      =		self.sp_model.IdToPiece(a_		)
											return token


							def _a      (							self  ,  a_		):
											a__				      =		[]
											a__				      =		""""""
											for token in tokens:
															# make sure that special tokens are not decoded using sentencepiece model
															if token in self.all_special_tokens:
																			out_string += self.sp_model.decode(a_		) + token
																			a__				      =		[]
															else:
																			current_sub_tokens.append(a_		)
											out_string += self.sp_model.decode(a_		)
											return out_string.strip()







							def _a      (							self  ,  a_  ,  a_ = None		):
											if not os.path.isdir(a_		):
															logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''		)
															return
											a__				      =		os.path.join(
											    a_  ,  (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]		)

											if os.path.abspath(self.vocab_file		) != os.path.abspath(a_		) and os.path.isfile(self.vocab_file		):
															copyfile(self.vocab_file  ,  a_		)
											elif not os.path.isfile(self.vocab_file		):
															with open(a_  ,  """wb"""		) as fi:
																			a__				      =		self.sp_model.serialized_model_proto()
																			fi.write(a_		)

											return (out_vocab_file,)

