



'''simple docstring'''




import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple

from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


if is_torch_available():
						lowerCamelCase							:List[Any]       = '''pt'''
elif is_tf_available():
						lowerCamelCase							:int       = '''tf'''
else:
						lowerCamelCase							:Dict       = '''jax'''







class      _lowerCAmelCase							(      __UpperCAmelCase				,      unittest.TestCase			):
	__SCREAMING_SNAKE_CASE       :		str		      = PerceiverTokenizer
	__SCREAMING_SNAKE_CASE       :		List[str]		      = False


	def    _a     (self							):
				super().setUp()
				A_     :						int     =					PerceiverTokenizer()
				tokenizer.save_pretrained(self.tmpdirname							)


	@cached_property
	def    _a     (self							):
				return PerceiverTokenizer.from_pretrained("""deepmind/language-perceiver"""							)


	def    _a     (self	,				**lowercase							):
				return self.tokenizer_class.from_pretrained(self.tmpdirname	,				**lowercase							)


	def    _a     (self	,				lowercase	,				lowercase=False	,				lowercase=20	,				lowercase=5							):
				# XXX The default common tokenizer tests assume that every ID is decodable on its own.
				# This assumption is invalid for Perceiver because single bytes might not be
				# valid utf-8 (byte 128 for instance).
				# Here we're overriding the smallest possible method to provide
				# a clean sequence without making the same assumption.

				A_     :						Any     =					[]
				for i in range(len(lowercase							)							):
							try:
										A_     :						List[str]     =					tokenizer.decode([i]	,				clean_up_tokenization_spaces=lowercase							)
							except UnicodeDecodeError:
										pass
							toks.append((i, tok)							)

				A_     :						Optional[Any]     =					list(filter(lambda lowercase							: re.match(R"""^[ a-zA-Z]+$"""	,				t[1]							)	,				lowercase							)							)
				A_     :						List[str]     =					list(filter(lambda lowercase							: [t[0]] == tokenizer.encode(t[1]	,				add_special_tokens=lowercase							)	,				lowercase							)							)
				if max_length is not None and len(lowercase							) > max_length:
							A_     :						Dict     =					toks[:max_length]
				if min_length is not None and len(lowercase							) < min_length and len(lowercase							) > 0:
							while len(lowercase							) < min_length:
										A_     :						Dict     =					toks + toks
        # toks_str = [t[1] for t in toks]
				A_     :						Optional[Any]     =					[t[0] for t in toks]

				# Ensure consistency
				A_     :						List[str]     =					tokenizer.decode(lowercase	,				clean_up_tokenization_spaces=lowercase							)
				if " " not in output_txt and len(lowercase							) > 1:
							A_     :						Optional[Any]     =					(
							    tokenizer.decode([toks_ids[0]]	,				clean_up_tokenization_spaces=lowercase							)
							    + """ """
							    + tokenizer.decode(toks_ids[1:]	,				clean_up_tokenization_spaces=lowercase							)
							)
				if with_prefix_space:
							A_     :						int     =					""" """ + output_txt
				A_     :						Union[str, Any]     =					tokenizer.encode(lowercase	,				add_special_tokens=lowercase							)
				return output_txt, output_ids


	def    _a     (self							):
				A_     :						Optional[Any]     =					self.perceiver_tokenizer
				A_     :						Any     =					"""Unicode €."""
				A_     :						Tuple     =					tokenizer(lowercase							)
				A_     :						Any     =					[4, 91, 116, 111, 105, 117, 106, 107, 38, 232, 136, 178, 52, 5]
				self.assertEqual(encoded["""input_ids"""]	,				lowercase							)

				# decoding
				A_     :						Optional[Any]     =					tokenizer.decode(lowercase							)
				self.assertEqual(lowercase	,				"""[CLS]Unicode €.[SEP]"""							)

				A_     :						List[Any]     =					tokenizer("""e è é ê ë"""							)
				A_     :						Any     =					[4, 107, 38, 201, 174, 38, 201, 175, 38, 201, 176, 38, 201, 177, 5]
				self.assertEqual(encoded["""input_ids"""]	,				lowercase							)
				# decoding
				A_     :						Tuple     =					tokenizer.decode(lowercase							)
				self.assertEqual(lowercase	,				"""[CLS]e è é ê ë[SEP]"""							)

				# encode/decode, but with `encode` instead of `__call__`
				self.assertEqual(tokenizer.decode(tokenizer.encode("""e è é ê ë"""							)							)	,				"""[CLS]e è é ê ë[SEP]"""							)


	def    _a     (self							):
				A_     :						str     =					self.perceiver_tokenizer
				A_     :						Optional[Any]     =					["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
				# fmt: off
				A_     :						Optional[int]     =					[4, 71, 38, 114, 117, 116, 109, 38, 118, 103, 120, 103, 109, 120, 103, 118, 110, 38, 108, 117, 120, 38, 121, 123, 115, 115, 103, 120, 111, 128, 103, 122, 111, 117, 116, 52, 5, 0]
				# fmt: on
				A_     :						List[Any]     =					tokenizer(lowercase	,				padding=lowercase	,				return_tensors=lowercase							)
				self.assertIsInstance(lowercase	,				lowercase							)

				if FRAMEWORK != "jax":
							A_     :						Optional[Any]     =					list(batch.input_ids.numpy()[0]							)
				else:
							A_     :						str     =					list(batch.input_ids.tolist()[0]							)

				self.assertListEqual(lowercase	,				lowercase							)

				self.assertEqual((2, 38)	,				batch.input_ids.shape							)
				self.assertEqual((2, 38)	,				batch.attention_mask.shape							)


	def    _a     (self							):
				A_     :						Dict     =					self.perceiver_tokenizer
				A_     :						Dict     =					["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
				A_     :						List[Any]     =					tokenizer(lowercase	,				padding=lowercase	,				return_tensors=lowercase							)
				# check if input_ids are returned and no decoder_input_ids
				self.assertIn("""input_ids"""	,				lowercase							)
				self.assertIn("""attention_mask"""	,				lowercase							)
				self.assertNotIn("""decoder_input_ids"""	,				lowercase							)
				self.assertNotIn("""decoder_attention_mask"""	,				lowercase							)


	def    _a     (self							):
				A_     :						Dict     =					self.perceiver_tokenizer
				A_     :						Union[str, Any]     =					[
				    """Summary of the text.""",
				    """Another summary.""",
				]
				A_     :						Dict     =					tokenizer(
				    text_target=lowercase	,				max_length=32	,				padding="""max_length"""	,				truncation=lowercase	,				return_tensors=lowercase							)
				self.assertEqual(32	,				targets["""input_ids"""].shape[1]							)


	def    _a     (self							):
				# safety check on max_len default value so we are sure the test works
				A_     :						int     =					self.get_tokenizers()
				for tokenizer in tokenizers:
							with self.subTest(F'{tokenizer.__class__.__name__}'							):
										self.assertNotEqual(tokenizer.model_max_length	,				42							)

        # Now let's start the test
				A_     :						List[Any]     =					self.get_tokenizers()
				for tokenizer in tokenizers:
							with self.subTest(F'{tokenizer.__class__.__name__}'							):
										# Isolate this from the other tests because we save additional tokens/etc
										A_     :						str     =					tempfile.mkdtemp()

										A_     :						Union[str, Any]     =					""" He is very happy, UNwant\u00E9d,running"""
										A_     :						List[str]     =					tokenizer.encode(lowercase	,				add_special_tokens=lowercase							)
										tokenizer.save_pretrained(lowercase							)

										A_     :						Any     =					tokenizer.__class__.from_pretrained(lowercase							)
										A_     :						Any     =					after_tokenizer.encode(lowercase	,				add_special_tokens=lowercase							)
										self.assertListEqual(lowercase	,				lowercase							)

										shutil.rmtree(lowercase							)

				A_     :						List[Any]     =					self.get_tokenizers(model_max_length=42							)
				for tokenizer in tokenizers:
							with self.subTest(F'{tokenizer.__class__.__name__}'							):
										# Isolate this from the other tests because we save additional tokens/etc
										A_     :						Optional[Any]     =					tempfile.mkdtemp()

										A_     :						List[Any]     =					""" He is very happy, UNwant\u00E9d,running"""
										tokenizer.add_tokens(["""bim""", """bambam"""]							)
										A_     :						int     =					tokenizer.additional_special_tokens
										additional_special_tokens.append("""new_additional_special_token"""							)
										tokenizer.add_special_tokens({"""additional_special_tokens""": additional_special_tokens}							)
										A_     :						str     =					tokenizer.encode(lowercase	,				add_special_tokens=lowercase							)
										tokenizer.save_pretrained(lowercase							)

										A_     :						int     =					tokenizer.__class__.from_pretrained(lowercase							)
										A_     :						List[Any]     =					after_tokenizer.encode(lowercase	,				add_special_tokens=lowercase							)
										self.assertListEqual(lowercase	,				lowercase							)
										self.assertIn("""new_additional_special_token"""	,				after_tokenizer.additional_special_tokens							)
										self.assertEqual(after_tokenizer.model_max_length	,				42							)

										A_     :						Union[str, Any]     =					tokenizer.__class__.from_pretrained(lowercase	,				model_max_length=43							)
										self.assertEqual(tokenizer.model_max_length	,				43							)

										shutil.rmtree(lowercase							)


	def    _a     (self							):
				A_     :						Dict     =					[]
				if self.test_slow_tokenizer:
							tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())							)

				if self.test_rust_tokenizer:
							tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())							)

				for tokenizer_class, tokenizer_utils in tokenizer_list:
							with tempfile.TemporaryDirectory() as tmp_dir:
										tokenizer_utils.save_pretrained(lowercase							)

										with open(os.path.join(lowercase	,				"""special_tokens_map.json"""							)	,				encoding="""utf-8"""							) as json_file:
													A_     :						Optional[int]     =					json.load(lowercase							)

										with open(os.path.join(lowercase	,				"""tokenizer_config.json"""							)	,				encoding="""utf-8"""							) as json_file:
													A_     :						int     =					json.load(lowercase							)

										A_     :						Optional[int]     =					[F'<extra_id_{i}>' for i in range(125							)]

										A_     :						Tuple     =					added_tokens_extra_ids + [
										    """an_additional_special_token"""
										]
										A_     :						Tuple     =					added_tokens_extra_ids + [
										    """an_additional_special_token"""
										]

										with open(os.path.join(lowercase	,				"""special_tokens_map.json"""							)	,				"""w"""	,				encoding="""utf-8"""							) as outfile:
													json.dump(lowercase	,				lowercase							)
										with open(os.path.join(lowercase	,				"""tokenizer_config.json"""							)	,				"""w"""	,				encoding="""utf-8"""							) as outfile:
													json.dump(lowercase	,				lowercase							)

										# the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
										# into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
										# "special_tokens_map.json" files
										A_     :						List[str]     =					tokenizer_class.from_pretrained(
										    lowercase	,				)
										self.assertIn(
										    """an_additional_special_token"""	,				tokenizer_without_change_in_init.additional_special_tokens							)
										self.assertEqual(
										    ["""an_additional_special_token"""]	,				tokenizer_without_change_in_init.convert_ids_to_tokens(
										        tokenizer_without_change_in_init.convert_tokens_to_ids(["""an_additional_special_token"""]							)							)	,				)

										# Now we test that we can change the value of additional_special_tokens in the from_pretrained
										A_     :						List[str]     =					added_tokens_extra_ids + [AddedToken("""a_new_additional_special_token"""	,				lstrip=lowercase							)]
										A_     :						str     =					tokenizer_class.from_pretrained(
										    lowercase	,				additional_special_tokens=lowercase	,				)

										self.assertIn("""a_new_additional_special_token"""	,				tokenizer.additional_special_tokens							)
										self.assertEqual(
										    ["""a_new_additional_special_token"""]	,				tokenizer.convert_ids_to_tokens(
										        tokenizer.convert_tokens_to_ids(["""a_new_additional_special_token"""]							)							)	,				)


	def    _a     (self							):
				A_     :						List[Any]     =					self.perceiver_tokenizer
				self.assertEqual(tokenizer.decode([178]							)	,				"""�"""							)


	def    _a     (self							):
				pass


	def    _a     (self							):
				pass


	def    _a     (self							):
				pass


	def    _a     (self							):
				pass


	def    _a     (self							):
				# The default common tokenizer tests uses invalid tokens for Perceiver that can only accept one-character
				# strings and special added tokens as tokens
				A_     :						Tuple     =					self.get_tokenizers(fast=lowercase	,				do_lower_case=lowercase							)
				for tokenizer in tokenizers:
							with self.subTest(F'{tokenizer.__class__.__name__}'							):
										A_     :						Optional[int]     =					["""[CLS]""", """t""", """h""", """i""", """s""", """ """, """i""", """s""", """ """, """a""", """ """, """t""", """e""", """s""", """t""", """[SEP]"""]
										A_     :						int     =					tokenizer.convert_tokens_to_string(lowercase							)

										self.assertIsInstance(lowercase	,				lowercase							)




'''simple docstring'''




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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging


lowerCamelCase							:Union[str, Any]       = logging.get_logger(__name__)

lowerCamelCase							:Union[str, Any]       = {'''vocab_file''': '''spiece.model'''}

lowerCamelCase							:Tuple       = {
    '''vocab_file''': {
        '''TsinghuaAI/CPM-Generate''': '''https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model''',
    }
}







class      _lowerCAmelCase							(      __UpperCAmelCase			):


	def __init__(self	,				lowercase	,				lowercase=False	,				lowercase=True	,				lowercase=False	,				lowercase="<s>"	,				lowercase="</s>"	,				lowercase="<unk>"	,				lowercase="<sep>"	,				lowercase="<pad>"	,				lowercase="<cls>"	,				lowercase="<mask>"	,				lowercase=["<eop>", "<eod>"]	,				lowercase = None	,				**lowercase	,				):
				A_     :						Optional[int]     =					AddedToken(lowercase	,				lstrip=lowercase	,				rstrip=lowercase							) if isinstance(lowercase	,				lowercase							) else mask_token

				A_     :						str     =					{} if sp_model_kwargs is None else sp_model_kwargs

				super().__init__(
				    do_lower_case=lowercase	,				remove_space=lowercase	,				keep_accents=lowercase	,				bos_token=lowercase	,				eos_token=lowercase	,				unk_token=lowercase	,				sep_token=lowercase	,				pad_token=lowercase	,				cls_token=lowercase	,				mask_token=lowercase	,				additional_special_tokens=lowercase	,				sp_model_kwargs=self.sp_model_kwargs	,				**lowercase	,				)

				A_     :						List[str]     =					3

				A_     :						List[str]     =					do_lower_case
				A_     :						Tuple     =					remove_space
				A_     :						Tuple     =					keep_accents
				A_     :						Tuple     =					vocab_file

				A_     :						int     =					spm.SentencePieceProcessor(**self.sp_model_kwargs							)
				self.sp_model.Load(lowercase							)

				try:
							import jieba
				except ModuleNotFoundError as error:
							raise error.__class__(
							    """You need to install jieba to use CpmTokenizer or CpmTokenizerFast. """
							    """See https://pypi.org/project/jieba/ for installation."""							)
				A_     :						Tuple     =					jieba
				A_     :						int     =					str.maketrans(""" \n"""	,				"""\u2582\u2583"""							)


	@property
	# Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.vocab_size
	def    _a     (self							):
				return len(self.sp_model							)


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


	def __getstate__(self							):
				A_     :						Optional[Any]     =					self.__dict__.copy()
				A_     :						Tuple     =					None
				return state


	def __setstate__(self	,				lowercase							):
				A_     :						Tuple     =					d

				# for backward compatibility
				if not hasattr(self	,				"""sp_model_kwargs"""							):
							A_     :						List[Any]     =					{}

				A_     :						int     =					spm.SentencePieceProcessor(**self.sp_model_kwargs							)
				self.sp_model.Load(self.vocab_file							)


	def    _a     (self	,				lowercase							):
				if self.remove_space:
							A_     :						Union[str, Any]     =					""" """.join(inputs.strip().split()							)
				else:
							A_     :						Optional[int]     =					inputs
				A_     :						List[str]     =					outputs.replace("""``"""	,				"""\""""							).replace("""''"""	,				"""\""""							)

				if not self.keep_accents:
							A_     :						str     =					unicodedata.normalize("""NFKD"""	,				lowercase							)
							A_     :						Union[str, Any]     =					"""""".join([c for c in outputs if not unicodedata.combining(lowercase							)]							)
				if self.do_lower_case:
							A_     :						int     =					outputs.lower()

				return outputs


	def    _a     (self	,				lowercase							):
				A_     :						Optional[int]     =					self.preprocess_text(lowercase							)
				A_     :						Dict     =					self.sp_model.encode(lowercase	,				out_type=lowercase							)
				A_     :						List[Any]     =					[]
				for piece in pieces:
							if len(lowercase							) > 1 and piece[-1] == str(""","""							) and piece[-2].isdigit():
										A_     :						Union[str, Any]     =					self.sp_model.EncodeAsPieces(piece[:-1].replace(lowercase	,				""""""							)							)
										if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
													if len(cur_pieces[0]							) == 1:
																A_     :						Dict     =					cur_pieces[1:]
													else:
																A_     :						str     =					cur_pieces[0][1:]
										cur_pieces.append(piece[-1]							)
										new_pieces.extend(lowercase							)
							else:
										new_pieces.append(lowercase							)

				return new_pieces


	def    _a     (self	,				lowercase							):
				return self.sp_model.PieceToId(lowercase							)


	def    _a     (self	,				lowercase							):
				return self.sp_model.IdToPiece(lowercase							)


	def    _a     (self	,				lowercase							):
				A_     :						int     =					"""""".join(lowercase							).replace(lowercase	,				""" """							).strip()
				return out_string


	def    _a     (self	,				lowercase	,				lowercase = None							):
				A_     :						List[str]     =					[self.sep_token_id]
				A_     :						Any     =					[self.cls_token_id]
				if token_ids_a is None:
							return token_ids_a + sep + cls
				return token_ids_a + sep + token_ids_a + sep + cls


	def    _a     (self	,				lowercase	,				lowercase = None	,				lowercase = False							):

				if already_has_special_tokens:
							return super().get_special_tokens_mask(
							    token_ids_a=lowercase	,				token_ids_a=lowercase	,				already_has_special_tokens=lowercase							)

				if token_ids_a is not None:
							return ([0] * len(lowercase							)) + [1] + ([0] * len(lowercase							)) + [1, 1]
				return ([0] * len(lowercase							)) + [1, 1]


	def    _a     (self	,				lowercase	,				lowercase = None							):
				A_     :						str     =					[self.sep_token_id]
				A_     :						str     =					[2]

				if token_ids_a is None:
							return len(token_ids_a + sep							) * [0] + cls_segment_id
				return len(token_ids_a + sep							) * [0] + len(token_ids_a + sep							) * [1] + cls_segment_id


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

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

				return (out_vocab_file,)


	def    _a     (self	,				*lowercase	,				**lowercase							):
				A_     :						Any     =					super()._decode(*lowercase	,				**lowercase							)
				A_     :						int     =					text.replace(""" """	,				""""""							).replace("""\u2582"""	,				""" """							).replace("""\u2583"""	,				"""\n"""							)
				return text