






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():
					SCREAMING_SNAKE_CASE__   :      Dict				=		"""pt"""
elif is_tf_available():
					SCREAMING_SNAKE_CASE__   :      List[str]				=		"""tf"""
else:
					SCREAMING_SNAKE_CASE__   :      List[Any]				=		"""jax"""
class     lowerCamelCase_					(   lowerCamelCase      ,					unittest.TestCase			):
				a__       							=	PerceiverTokenizer
				a__       							=	False







				def       A						(    self  ):
								"""simple docstring"""
								super().setUp()
								__magic_name__		:str   =   PerceiverTokenizer()
								tokenizer.save_pretrained(self.tmpdirname  )







				@cached_property
				def       A						(    self  ):
								"""simple docstring"""
								return PerceiverTokenizer.from_pretrained('''deepmind/language-perceiver'''  )







				def       A						(    self							,       **__lowerCAmelCase  ):
								"""simple docstring"""
								return self.tokenizer_class.from_pretrained(self.tmpdirname							,       **__lowerCAmelCase  )







				def       A						(    self							,       __lowerCAmelCase							,       __lowerCAmelCase=False							,       __lowerCAmelCase=2_0							,       __lowerCAmelCase=5  ):
								"""simple docstring"""
								# 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.

								__magic_name__		:List[str]   =   []
								for i in range(len(__lowerCAmelCase  )  ):
												try:
																__magic_name__		:Optional[int]   =   tokenizer.decode([i]							,       clean_up_tokenization_spaces=__lowerCAmelCase  )
												except UnicodeDecodeError:
																pass
												toks.append((i, tok)  )

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

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







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:Union[str, Any]   =   self.perceiver_tokenizer
								__magic_name__		:List[Any]   =   '''Unicode €.'''
								__magic_name__		:Tuple   =   tokenizer(__lowerCAmelCase  )
								__magic_name__		:Optional[int]   =   [4, 9_1, 1_1_6, 1_1_1, 1_0_5, 1_1_7, 1_0_6, 1_0_7, 3_8, 2_3_2, 1_3_6, 1_7_8, 5_2, 5]
								self.assertEqual(encoded['''input_ids''']							,       __lowerCAmelCase  )

								# decoding
								__magic_name__		:List[str]   =   tokenizer.decode(__lowerCAmelCase  )
								self.assertEqual(__lowerCAmelCase							,       '''[CLS]Unicode €.[SEP]'''  )

								__magic_name__		:int   =   tokenizer('''e è é ê ë'''  )
								__magic_name__		:List[Any]   =   [4, 1_0_7, 3_8, 2_0_1, 1_7_4, 3_8, 2_0_1, 1_7_5, 3_8, 2_0_1, 1_7_6, 3_8, 2_0_1, 1_7_7, 5]
								self.assertEqual(encoded['''input_ids''']							,       __lowerCAmelCase  )
								# decoding
								__magic_name__		:Tuple   =   tokenizer.decode(__lowerCAmelCase  )
								self.assertEqual(__lowerCAmelCase							,       '''[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  ):
								"""simple docstring"""
								__magic_name__		:Tuple   =   self.perceiver_tokenizer
								__magic_name__		:Tuple   =   ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
								# fmt: off
								__magic_name__		:Union[str, Any]   =   [4, 7_1, 3_8, 1_1_4, 1_1_7, 1_1_6, 1_0_9, 3_8, 1_1_8, 1_0_3, 1_2_0, 1_0_3, 1_0_9, 1_2_0, 1_0_3, 1_1_8, 1_1_0, 3_8, 1_0_8, 1_1_7, 1_2_0, 3_8, 1_2_1, 1_2_3, 1_1_5, 1_1_5, 1_0_3, 1_2_0, 1_1_1, 1_2_8, 1_0_3, 1_2_2, 1_1_1, 1_1_7, 1_1_6, 5_2, 5, 0]
								# fmt: on
								__magic_name__		:List[str]   =   tokenizer(__lowerCAmelCase							,       padding=__lowerCAmelCase							,       return_tensors=__lowerCAmelCase  )
								self.assertIsInstance(__lowerCAmelCase							,       __lowerCAmelCase  )

								if FRAMEWORK != "jax":
												__magic_name__		:List[Any]   =   list(batch.input_ids.numpy()[0]  )
								else:
												__magic_name__		:Dict   =   list(batch.input_ids.tolist()[0]  )

								self.assertListEqual(__lowerCAmelCase							,       __lowerCAmelCase  )

								self.assertEqual((2, 3_8)							,       batch.input_ids.shape  )
								self.assertEqual((2, 3_8)							,       batch.attention_mask.shape  )







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:Optional[Any]   =   self.perceiver_tokenizer
								__magic_name__		:Dict   =   ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
								__magic_name__		:Any   =   tokenizer(__lowerCAmelCase							,       padding=__lowerCAmelCase							,       return_tensors=__lowerCAmelCase  )
								# check if input_ids are returned and no decoder_input_ids
								self.assertIn('''input_ids'''							,       __lowerCAmelCase  )
								self.assertIn('''attention_mask'''							,       __lowerCAmelCase  )
								self.assertNotIn('''decoder_input_ids'''							,       __lowerCAmelCase  )
								self.assertNotIn('''decoder_attention_mask'''							,       __lowerCAmelCase  )







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:str   =   self.perceiver_tokenizer
								__magic_name__		:Optional[Any]   =   [
								    '''Summary of the text.''',
								    '''Another summary.''',
								]
								__magic_name__		:int   =   tokenizer(
								    text_target=__lowerCAmelCase							,       max_length=3_2							,       padding='''max_length'''							,       truncation=__lowerCAmelCase							,       return_tensors=__lowerCAmelCase  )
								self.assertEqual(3_2							,       targets['''input_ids'''].shape[1]  )







				def       A						(    self  ):
								"""simple docstring"""
								# safety check on max_len default value so we are sure the test works
								__magic_name__		:List[str]   =   self.get_tokenizers()
								for tokenizer in tokenizers:
												with self.subTest(F'''{tokenizer.__class__.__name__}'''  ):
																self.assertNotEqual(tokenizer.model_max_length							,       4_2  )

        # Now let's start the test
								__magic_name__		:Dict   =   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
																__magic_name__		:Union[str, Any]   =   tempfile.mkdtemp()

																__magic_name__		:Union[str, Any]   =   ''' He is very happy, UNwant\u00E9d,running'''
																__magic_name__		:Optional[Any]   =   tokenizer.encode(__lowerCAmelCase							,       add_special_tokens=__lowerCAmelCase  )
																tokenizer.save_pretrained(__lowerCAmelCase  )

																__magic_name__		:int   =   tokenizer.__class__.from_pretrained(__lowerCAmelCase  )
																__magic_name__		:Union[str, Any]   =   after_tokenizer.encode(__lowerCAmelCase							,       add_special_tokens=__lowerCAmelCase  )
																self.assertListEqual(__lowerCAmelCase							,       __lowerCAmelCase  )

																shutil.rmtree(__lowerCAmelCase  )

								__magic_name__		:int   =   self.get_tokenizers(model_max_length=4_2  )
								for tokenizer in tokenizers:
												with self.subTest(F'''{tokenizer.__class__.__name__}'''  ):
																# Isolate this from the other tests because we save additional tokens/etc
																__magic_name__		:Union[str, Any]   =   tempfile.mkdtemp()

																__magic_name__		:Tuple   =   ''' He is very happy, UNwant\u00E9d,running'''
																tokenizer.add_tokens(['''bim''', '''bambam''']  )
																__magic_name__		:Optional[Any]   =   tokenizer.additional_special_tokens
																additional_special_tokens.append('''new_additional_special_token'''  )
																tokenizer.add_special_tokens({'''additional_special_tokens''': additional_special_tokens}  )
																__magic_name__		:List[Any]   =   tokenizer.encode(__lowerCAmelCase							,       add_special_tokens=__lowerCAmelCase  )
																tokenizer.save_pretrained(__lowerCAmelCase  )

																__magic_name__		:int   =   tokenizer.__class__.from_pretrained(__lowerCAmelCase  )
																__magic_name__		:Union[str, Any]   =   after_tokenizer.encode(__lowerCAmelCase							,       add_special_tokens=__lowerCAmelCase  )
																self.assertListEqual(__lowerCAmelCase							,       __lowerCAmelCase  )
																self.assertIn('''new_additional_special_token'''							,       after_tokenizer.additional_special_tokens  )
																self.assertEqual(after_tokenizer.model_max_length							,       4_2  )

																__magic_name__		:Optional[Any]   =   tokenizer.__class__.from_pretrained(__lowerCAmelCase							,       model_max_length=4_3  )
																self.assertEqual(tokenizer.model_max_length							,       4_3  )

																shutil.rmtree(__lowerCAmelCase  )







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:str   =   []
								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(__lowerCAmelCase  )

																with open(os.path.join(__lowerCAmelCase							,       '''special_tokens_map.json'''  )							,       encoding='''utf-8'''  ) as json_file:
																				__magic_name__		:Union[str, Any]   =   json.load(__lowerCAmelCase  )

																with open(os.path.join(__lowerCAmelCase							,       '''tokenizer_config.json'''  )							,       encoding='''utf-8'''  ) as json_file:
																				__magic_name__		:Tuple   =   json.load(__lowerCAmelCase  )

																__magic_name__		:str   =   [F'''<extra_id_{i}>''' for i in range(1_2_5  )]

																__magic_name__		:List[str]   =   added_tokens_extra_ids + [
																    '''an_additional_special_token'''
																]
																__magic_name__		:Tuple   =   added_tokens_extra_ids + [
																    '''an_additional_special_token'''
																]

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

																# 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
																__magic_name__		:Optional[int]   =   tokenizer_class.from_pretrained(
																    __lowerCAmelCase							,       )
																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
																__magic_name__		:Optional[Any]   =   added_tokens_extra_ids + [AddedToken('''a_new_additional_special_token'''							,       lstrip=__lowerCAmelCase  )]
																__magic_name__		:Any   =   tokenizer_class.from_pretrained(
																    __lowerCAmelCase							,       additional_special_tokens=__lowerCAmelCase							,       )

																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  ):
								"""simple docstring"""
								__magic_name__		:List[str]   =   self.perceiver_tokenizer
								self.assertEqual(tokenizer.decode([1_7_8]  )							,       '''�'''  )







				def       A						(    self  ):
								"""simple docstring"""
								pass







				def       A						(    self  ):
								"""simple docstring"""
								pass







				def       A						(    self  ):
								"""simple docstring"""
								pass







				def       A						(    self  ):
								"""simple docstring"""
								pass







				def       A						(    self  ):
								"""simple docstring"""
								# The default common tokenizer tests uses invalid tokens for Perceiver that can only accept one-character
								# strings and special added tokens as tokens
								__magic_name__		:List[Any]   =   self.get_tokenizers(fast=__lowerCAmelCase							,       do_lower_case=__lowerCAmelCase  )
								for tokenizer in tokenizers:
												with self.subTest(F'''{tokenizer.__class__.__name__}'''  ):
																__magic_name__		:Any   =   ['''[CLS]''', '''t''', '''h''', '''i''', '''s''', ''' ''', '''i''', '''s''', ''' ''', '''a''', ''' ''', '''t''', '''e''', '''s''', '''t''', '''[SEP]''']
																__magic_name__		:Union[str, Any]   =   tokenizer.convert_tokens_to_string(__lowerCAmelCase  )

																self.assertIsInstance(__lowerCAmelCase							,       __lowerCAmelCase  )














import numpy as np
import torch
from torch.utils.data import Dataset

from utils import logger
class     lowerCamelCase_					(   lowerCamelCase			):







				def __init__(    self							,       __lowerCAmelCase							,       __lowerCAmelCase  ):
								"""simple docstring"""
								__magic_name__		:Optional[int]   =   params

								__magic_name__		:Any   =   np.array(__lowerCAmelCase  )
								__magic_name__		:Optional[Any]   =   np.array([len(__lowerCAmelCase  ) for t in data]  )

								self.check()
								self.remove_long_sequences()
								self.remove_empty_sequences()
								self.remove_unknown_sequences()
								self.check()
								self.print_statistics()







				def __getitem__(    self							,       __lowerCAmelCase  ):
								"""simple docstring"""
								return (self.token_ids[index], self.lengths[index])







				def __len__(    self  ):
								"""simple docstring"""
								return len(self.lengths  )







				def       A						(    self  ):
								"""simple docstring"""
								assert len(self.token_ids  ) == len(self.lengths  )
								assert all(self.lengths[i] == len(self.token_ids[i]  ) for i in range(len(self.lengths  )  )  )







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:Any   =   self.params.max_model_input_size
								__magic_name__		:int   =   self.lengths > max_len
								logger.info(F'''Splitting {sum(__lowerCAmelCase  )} too long sequences.'''  )

								def divide_chunks(__lowerCAmelCase							,       __lowerCAmelCase  ):
												return [l[i : i + n] for i in range(0							,       len(__lowerCAmelCase  )							,       __lowerCAmelCase  )]

								__magic_name__		:Optional[int]   =   []
								__magic_name__		:List[Any]   =   []
								if self.params.mlm:
												__magic_name__  ,					__magic_name__		:Optional[Any]   =   self.params.special_tok_ids['''cls_token'''], self.params.special_tok_ids['''sep_token''']
								else:
												__magic_name__  ,					__magic_name__		:Tuple   =   self.params.special_tok_ids['''bos_token'''], self.params.special_tok_ids['''eos_token''']

								for seq_, len_ in zip(self.token_ids							,       self.lengths  ):
												assert (seq_[0] == cls_id) and (seq_[-1] == sep_id), seq_
												if len_ <= max_len:
																new_tok_ids.append(seq_  )
																new_lengths.append(len_  )
												else:
																__magic_name__		:int   =   []
																for sub_s in divide_chunks(seq_							,       max_len - 2  ):
																				if sub_s[0] != cls_id:
																								__magic_name__		:List[Any]   =   np.insert(__lowerCAmelCase							,       0							,       __lowerCAmelCase  )
																				if sub_s[-1] != sep_id:
																								__magic_name__		:Union[str, Any]   =   np.insert(__lowerCAmelCase							,       len(__lowerCAmelCase  )							,       __lowerCAmelCase  )
																				assert len(__lowerCAmelCase  ) <= max_len
																				assert (sub_s[0] == cls_id) and (sub_s[-1] == sep_id), sub_s
																				sub_seqs.append(__lowerCAmelCase  )

																new_tok_ids.extend(__lowerCAmelCase  )
																new_lengths.extend([len(__lowerCAmelCase  ) for l in sub_seqs]  )

								__magic_name__		:Tuple   =   np.array(__lowerCAmelCase  )
								__magic_name__		:Optional[int]   =   np.array(__lowerCAmelCase  )







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:Optional[Any]   =   len(self  )
								__magic_name__		:int   =   self.lengths > 1_1
								__magic_name__		:List[str]   =   self.token_ids[indices]
								__magic_name__		:Union[str, Any]   =   self.lengths[indices]
								__magic_name__		:List[str]   =   len(self  )
								logger.info(F'''Remove {init_size - new_size} too short (<=11 tokens) sequences.'''  )







				def       A						(    self  ):
								"""simple docstring"""
								if "unk_token" not in self.params.special_tok_ids:
												return
								else:
												__magic_name__		:Tuple   =   self.params.special_tok_ids['''unk_token''']
								__magic_name__		:Dict   =   len(self  )
								__magic_name__		:Tuple   =   np.array([np.count_nonzero(a == unk_token_id  ) for a in self.token_ids]  )
								__magic_name__		:int   =   (unk_occs / self.lengths) < 0.5
								__magic_name__		:str   =   self.token_ids[indices]
								__magic_name__		:str   =   self.lengths[indices]
								__magic_name__		:Any   =   len(self  )
								logger.info(F'''Remove {init_size - new_size} sequences with a high level of unknown tokens (50%).'''  )







				def       A						(    self  ):
								"""simple docstring"""
								if not self.params.is_master:
												return
								logger.info(F'''{len(self  )} sequences'''  )
								# data_len = sum(self.lengths)
								# nb_unique_tokens = len(Counter(list(chain(*self.token_ids))))
								# logger.info(f'{data_len} tokens ({nb_unique_tokens} unique)')

								# unk_idx = self.params.special_tok_ids['unk_token']
								# nb_unknown = sum([(t==unk_idx).sum() for t in self.token_ids])
								# logger.info(f'{nb_unknown} unknown tokens (covering {100*nb_unknown/data_len:.2f}% of the data)')







				def       A						(    self							,       __lowerCAmelCase  ):
								"""simple docstring"""
								__magic_name__		:Optional[Any]   =   [t[0] for t in batch]
								__magic_name__		:List[Any]   =   [t[1] for t in batch]
								assert len(__lowerCAmelCase  ) == len(__lowerCAmelCase  )

								# Max for paddings
								__magic_name__		:Tuple   =   max(__lowerCAmelCase  )

								# Pad token ids
								if self.params.mlm:
												__magic_name__		:Any   =   self.params.special_tok_ids['''pad_token''']
								else:
												__magic_name__		:str   =   self.params.special_tok_ids['''unk_token''']
								__magic_name__		:Any   =   [list(t.astype(__lowerCAmelCase  )  ) + [pad_idx] * (max_seq_len_ - len(__lowerCAmelCase  )) for t in token_ids]
								assert len(tk_  ) == len(__lowerCAmelCase  )
								assert all(len(__lowerCAmelCase  ) == max_seq_len_ for t in tk_  )

								__magic_name__		:Optional[int]   =   torch.tensor(tk_  )  # (bs, max_seq_len_)
								__magic_name__		:Optional[int]   =   torch.tensor(__lowerCAmelCase  )  # (bs)
								return tk_t, lg_t






