





import json
import os
import unittest

from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class 				a			(      a_   ,unittest.TestCase			):
				SCREAMING_SNAKE_CASE__     :   Dict		      =     GPTaTokenizer
				SCREAMING_SNAKE_CASE__     :   Any		      =     GPTaTokenizerFast
				SCREAMING_SNAKE_CASE__     :   List[str]		      =     True
				SCREAMING_SNAKE_CASE__     :   Dict		      =     {"add_prefix_space": True}
				SCREAMING_SNAKE_CASE__     :   Union[str, Any]		      =     False






				def 			snake_case_    (  self					):

										"""simple docstring"""




										super().setUp()

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

										__SCREAMING_SNAKE_CASE:				Optional[int]           =   os.path.join(self.tmpdirname			, VOCAB_FILES_NAMES['''vocab_file''']					)
										__SCREAMING_SNAKE_CASE:				str           =   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(_lowerCAmelCase					) + '''\n'''					)
										with open(self.merges_file			, '''w'''			, encoding='''utf-8'''					) as fp:
																fp.write('''\n'''.join(_lowerCAmelCase					)					)






				def 			snake_case_    (  self			, **_lowerCAmelCase					):

										"""simple docstring"""




										kwargs.update(self.special_tokens_map					)
										return GPTaTokenizer.from_pretrained(self.tmpdirname			, **_lowerCAmelCase					)






				def 			snake_case_    (  self			, **_lowerCAmelCase					):

										"""simple docstring"""




										kwargs.update(self.special_tokens_map					)
										return GPTaTokenizerFast.from_pretrained(self.tmpdirname			, **_lowerCAmelCase					)






				def 			snake_case_    (  self			, _lowerCAmelCase					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Optional[int]           =   '''lower newer'''
										__SCREAMING_SNAKE_CASE:				str           =   '''lower newer'''
										return input_text, output_text






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Tuple           =   GPTaTokenizer(self.vocab_file			, self.merges_file			, **self.special_tokens_map					)
										__SCREAMING_SNAKE_CASE:				Tuple           =   '''lower newer'''
										__SCREAMING_SNAKE_CASE:				int           =   ['''\u0120low''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
										__SCREAMING_SNAKE_CASE:				Optional[int]           =   tokenizer.tokenize(_lowerCAmelCase			, add_prefix_space=_lowerCAmelCase					)
										self.assertListEqual(_lowerCAmelCase			, _lowerCAmelCase					)

										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   tokens + [tokenizer.unk_token]
										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   [14, 15, 10, 9, 3, 2, 15, 19]
										self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCAmelCase					)			, _lowerCAmelCase					)






				def 			snake_case_    (  self					):

										"""simple docstring"""




										if not self.test_rust_tokenizer:
																return

										__SCREAMING_SNAKE_CASE:				List[Any]           =   self.get_tokenizer()
										__SCREAMING_SNAKE_CASE:				List[str]           =   self.get_rust_tokenizer(add_prefix_space=_lowerCAmelCase					)

										__SCREAMING_SNAKE_CASE:				int           =   '''lower newer'''

										# Testing tokenization
										__SCREAMING_SNAKE_CASE:				Any           =   tokenizer.tokenize(_lowerCAmelCase			, add_prefix_space=_lowerCAmelCase					)
										__SCREAMING_SNAKE_CASE:				List[str]           =   rust_tokenizer.tokenize(_lowerCAmelCase					)
										self.assertListEqual(_lowerCAmelCase			, _lowerCAmelCase					)

										# Testing conversion to ids without special tokens
										__SCREAMING_SNAKE_CASE:				str           =   tokenizer.encode(_lowerCAmelCase			, add_special_tokens=_lowerCAmelCase			, add_prefix_space=_lowerCAmelCase					)
										__SCREAMING_SNAKE_CASE:				int           =   rust_tokenizer.encode(_lowerCAmelCase			, add_special_tokens=_lowerCAmelCase					)
										self.assertListEqual(_lowerCAmelCase			, _lowerCAmelCase					)

										# Testing conversion to ids with special tokens
										__SCREAMING_SNAKE_CASE:				Dict           =   self.get_rust_tokenizer(add_prefix_space=_lowerCAmelCase					)
										__SCREAMING_SNAKE_CASE:				Dict           =   tokenizer.encode(_lowerCAmelCase			, add_prefix_space=_lowerCAmelCase					)
										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   rust_tokenizer.encode(_lowerCAmelCase					)
										self.assertListEqual(_lowerCAmelCase			, _lowerCAmelCase					)

										# Testing the unknown token
										__SCREAMING_SNAKE_CASE:				str           =   tokens + [rust_tokenizer.unk_token]
										__SCREAMING_SNAKE_CASE:				List[Any]           =   [14, 15, 10, 9, 3, 2, 15, 19]
										self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(_lowerCAmelCase					)			, _lowerCAmelCase					)






				def 			snake_case_    (  self			, *_lowerCAmelCase			, **_lowerCAmelCase					):

										"""simple docstring"""




										pass






				def 			snake_case_    (  self			, _lowerCAmelCase=15					):

										"""simple docstring"""




										for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""					):
																						__SCREAMING_SNAKE_CASE:				Optional[Any]           =   self.rust_tokenizer_class.from_pretrained(_lowerCAmelCase			, **_lowerCAmelCase					)

																						# Simple input
																						__SCREAMING_SNAKE_CASE:				Dict           =   '''This is a simple input'''
																						__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   ['''This is a simple input 1''', '''This is a simple input 2''']
																						__SCREAMING_SNAKE_CASE:				Dict           =   ('''This is a simple input''', '''This is a pair''')
																						__SCREAMING_SNAKE_CASE:				Dict           =   [
																						    ('''This is a simple input 1''', '''This is a simple input 2'''),
																						    ('''This is a simple pair 1''', '''This is a simple pair 2'''),
																						]

																						# Simple input tests
																						self.assertRaises(_lowerCAmelCase			, tokenizer_r.encode			, _lowerCAmelCase			, max_length=_lowerCAmelCase			, padding='''max_length'''					)

																						# Simple input
																						self.assertRaises(_lowerCAmelCase			, tokenizer_r.encode_plus			, _lowerCAmelCase			, max_length=_lowerCAmelCase			, padding='''max_length'''					)

																						# Simple input
																						self.assertRaises(
																						    _lowerCAmelCase			, tokenizer_r.batch_encode_plus			, _lowerCAmelCase			, max_length=_lowerCAmelCase			, padding='''max_length'''			, )

																						# Pair input
																						self.assertRaises(_lowerCAmelCase			, tokenizer_r.encode			, _lowerCAmelCase			, max_length=_lowerCAmelCase			, padding='''max_length'''					)

																						# Pair input
																						self.assertRaises(_lowerCAmelCase			, tokenizer_r.encode_plus			, _lowerCAmelCase			, max_length=_lowerCAmelCase			, padding='''max_length'''					)

																						# Pair input
																						self.assertRaises(
																						    _lowerCAmelCase			, tokenizer_r.batch_encode_plus			, _lowerCAmelCase			, max_length=_lowerCAmelCase			, padding='''max_length'''			, )






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				List[str]           =   GPTaTokenizer.from_pretrained(self.tmpdirname			, pad_token='''<pad>'''					)

										# Simple input
										__SCREAMING_SNAKE_CASE:				List[str]           =   '''This is a simple input'''
										__SCREAMING_SNAKE_CASE:				int           =   ['''This is a simple input looooooooong''', '''This is a simple input''']
										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   ('''This is a simple input''', '''This is a pair''')
										__SCREAMING_SNAKE_CASE:				Any           =   [
										    ('''This is a simple input loooooong''', '''This is a simple input'''),
										    ('''This is a simple pair loooooong''', '''This is a simple pair'''),
										]

										__SCREAMING_SNAKE_CASE:				List[str]           =   tokenizer.pad_token_id

										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   tokenizer(_lowerCAmelCase			, padding='''max_length'''			, max_length=30			, return_tensors='''np'''					)
										__SCREAMING_SNAKE_CASE:				int           =   tokenizer(_lowerCAmelCase			, padding=_lowerCAmelCase			, truncate=_lowerCAmelCase			, return_tensors='''np'''					)
										__SCREAMING_SNAKE_CASE:				List[Any]           =   tokenizer(*_lowerCAmelCase			, padding='''max_length'''			, max_length=60			, return_tensors='''np'''					)
										__SCREAMING_SNAKE_CASE:				List[str]           =   tokenizer(_lowerCAmelCase			, padding=_lowerCAmelCase			, truncate=_lowerCAmelCase			, return_tensors='''np'''					)

										# s
										# test single string max_length padding
										self.assertEqual(out_s['''input_ids'''].shape[-1]			, 30					)
										self.assertTrue(pad_token_id in out_s['''input_ids''']					)
										self.assertTrue(0 in out_s['''attention_mask''']					)

										# s2
										# test automatic padding
										self.assertEqual(out_sa['''input_ids'''].shape[-1]			, 33					)
										# long slice doesn't have padding
										self.assertFalse(pad_token_id in out_sa['''input_ids'''][0]					)
										self.assertFalse(0 in out_sa['''attention_mask'''][0]					)
										# short slice does have padding
										self.assertTrue(pad_token_id in out_sa['''input_ids'''][1]					)
										self.assertTrue(0 in out_sa['''attention_mask'''][1]					)

										# p
										# test single pair max_length padding
										self.assertEqual(out_p['''input_ids'''].shape[-1]			, 60					)
										self.assertTrue(pad_token_id in out_p['''input_ids''']					)
										self.assertTrue(0 in out_p['''attention_mask''']					)

										# p2
										# test automatic padding pair
										self.assertEqual(out_pa['''input_ids'''].shape[-1]			, 52					)
										# long slice pair doesn't have padding
										self.assertFalse(pad_token_id in out_pa['''input_ids'''][0]					)
										self.assertFalse(0 in out_pa['''attention_mask'''][0]					)
										# short slice pair does have padding
										self.assertTrue(pad_token_id in out_pa['''input_ids'''][1]					)
										self.assertTrue(0 in out_pa['''attention_mask'''][1]					)






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				int           =   '''$$$'''
										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   GPTaTokenizer.from_pretrained(self.tmpdirname			, bos_token=_lowerCAmelCase			, add_bos_token=_lowerCAmelCase					)

										__SCREAMING_SNAKE_CASE:				List[Any]           =   '''This is a simple input'''
										__SCREAMING_SNAKE_CASE:				int           =   ['''This is a simple input 1''', '''This is a simple input 2''']

										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   tokenizer.bos_token_id

										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   tokenizer(_lowerCAmelCase					)
										__SCREAMING_SNAKE_CASE:				List[str]           =   tokenizer(_lowerCAmelCase					)

										self.assertEqual(out_s.input_ids[0]			, _lowerCAmelCase					)
										self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids					)					)

										__SCREAMING_SNAKE_CASE:				Tuple           =   tokenizer.decode(out_s.input_ids					)
										__SCREAMING_SNAKE_CASE:				Dict           =   tokenizer.batch_decode(out_sa.input_ids					)

										self.assertEqual(decode_s.split()[0]			, _lowerCAmelCase					)
										self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa					)					)






				def 			snake_case_    (  self					):

										"""simple docstring"""




										pass






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   [self.get_tokenizer(do_lower_case=_lowerCAmelCase			, add_bos_token=_lowerCAmelCase					)]
										for tokenizer in tokenizers:
																with self.subTest(f"""{tokenizer.__class__.__name__}"""					):
																						__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   '''Encode this.'''
																						__SCREAMING_SNAKE_CASE:				Optional[Any]           =   '''This one too please.'''
																						__SCREAMING_SNAKE_CASE:				Optional[Any]           =   tokenizer.encode(_lowerCAmelCase			, add_special_tokens=_lowerCAmelCase					)
																						encoded_sequence += tokenizer.encode(_lowerCAmelCase			, add_special_tokens=_lowerCAmelCase					)
																						__SCREAMING_SNAKE_CASE:				int           =   tokenizer.encode_plus(
																						    _lowerCAmelCase			, _lowerCAmelCase			, add_special_tokens=_lowerCAmelCase			, return_special_tokens_mask=_lowerCAmelCase			, )
																						__SCREAMING_SNAKE_CASE:				Any           =   encoded_sequence_dict['''input_ids''']
																						__SCREAMING_SNAKE_CASE:				str           =   encoded_sequence_dict['''special_tokens_mask''']
																						self.assertEqual(len(_lowerCAmelCase					)			, len(_lowerCAmelCase					)					)

																						__SCREAMING_SNAKE_CASE:				List[Any]           =   [
																						    (x if not special_tokens_mask[i] else None) for i, x in enumerate(_lowerCAmelCase					)
																						]
																						__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   [x for x in filtered_sequence if x is not None]
																						self.assertEqual(_lowerCAmelCase			, _lowerCAmelCase					)





@require_tokenizers
class 				a			(      unittest.TestCase			):






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				int           =   AutoTokenizer.from_pretrained('''facebook/opt-350m'''			, from_slow=_lowerCAmelCase					)
										__SCREAMING_SNAKE_CASE:				Optional[int]           =   '''A photo of a cat'''

										__SCREAMING_SNAKE_CASE:				List[Any]           =   tokenizer.encode(
										    _lowerCAmelCase			, )
										self.assertEqual(_lowerCAmelCase			, [2, 250, 1345, 9, 10, 4758]					)
										tokenizer.save_pretrained('''test_opt'''					)

										__SCREAMING_SNAKE_CASE:				int           =   AutoTokenizer.from_pretrained('''./test_opt'''					)
										__SCREAMING_SNAKE_CASE:				Dict           =   tokenizer.encode(
										    _lowerCAmelCase			, )
										self.assertEqual(_lowerCAmelCase			, [2, 250, 1345, 9, 10, 4758]					)






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				List[Any]           =   AutoTokenizer.from_pretrained('''facebook/opt-350m'''			, use_slow=_lowerCAmelCase					)
										__SCREAMING_SNAKE_CASE:				List[Any]           =   '''A photo of a cat'''

										__SCREAMING_SNAKE_CASE:				List[str]           =   tokenizer.encode(
										    _lowerCAmelCase			, )
										# Same as above
										self.assertEqual(_lowerCAmelCase			, [2, 250, 1345, 9, 10, 4758]					)






				@unittest.skip('''This test is failing because of a bug in the fast tokenizer'''					)
				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				str           =   AutoTokenizer.from_pretrained('''facebook/opt-350m'''			, from_slow=_lowerCAmelCase					)

										__SCREAMING_SNAKE_CASE:				Tuple           =   '''bos'''
										__SCREAMING_SNAKE_CASE:				str           =   tokenizer.get_vocab()['''bos''']

										__SCREAMING_SNAKE_CASE:				List[Any]           =   '''A photo of a cat'''
										__SCREAMING_SNAKE_CASE:				int           =   tokenizer.encode(
										    _lowerCAmelCase			, )
										# We changed the bos token
										self.assertEqual(_lowerCAmelCase			, [31957, 250, 1345, 9, 10, 4758]					)
										tokenizer.save_pretrained('''./tok'''					)
										__SCREAMING_SNAKE_CASE:				str           =   AutoTokenizer.from_pretrained('''./tok'''					)
										self.assertTrue(tokenizer.is_fast					)
										__SCREAMING_SNAKE_CASE:				int           =   tokenizer.encode(
										    _lowerCAmelCase			, )
										self.assertEqual(_lowerCAmelCase			, [31957, 250, 1345, 9, 10, 4758]					)







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

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
		from .tokenization_pegasus import PegasusTokenizer
else:
		UpperCAmelCase   :    Union[str, Any]     =		None


UpperCAmelCase   :    Any     =		logging.get_logger(__name__)


UpperCAmelCase   :    Optional[Any]     =		"▁"

UpperCAmelCase   :    str     =		{"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

UpperCAmelCase   :    Dict     =		{
    "vocab_file": {"google/pegasus-xsum": "https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"},
    "tokenizer_file": {
        "google/pegasus-xsum": "https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json"
    },
}

UpperCAmelCase   :    List[Any]     =		{
    "google/pegasus-xsum": 5_12,
}







class        __lowercase       (							a_  ):



		"""simple docstring"""

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







		def __init__( self		,      A=None		,      A=None		,      A="<pad>"		,      A="</s>"		,      A="<unk>"		,      A="<mask_2>"		,      A="<mask_1>"		,      A=None		,      A=1_03		,      **A		,      )  -> Any:







					'''simple docstring'''







					lowerCamelCase    		=  offset

					if additional_special_tokens is not None:
								if not isinstance(A		,      A	):
											raise TypeError(
											    F'additional_special_tokens should be of type {type(A	)}, but is'
											    F' {type(A	)}'	)

								lowerCamelCase    		=  (
								    ([mask_token_sent] + additional_special_tokens)
								    if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
								    else additional_special_tokens
								)
								# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
								additional_special_tokens_extended += [
								    F'<unk_{i}>' for i in range(len(A	)		,      self.offset - 1	)
								]

								if len(set(A	)	) != len(A	):
											raise ValueError(
											    """Please make sure that the provided additional_special_tokens do not contain an incorrectly"""
											    F' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.'	)
								lowerCamelCase    		=  additional_special_tokens_extended
					else:
								lowerCamelCase    		=  [mask_token_sent] if mask_token_sent is not None else []
								additional_special_tokens += [F'<unk_{i}>' for i in range(2		,      self.offset	)]

					super().__init__(
					    A		,      tokenizer_file=A		,      pad_token=A		,      eos_token=A		,      unk_token=A		,      mask_token=A		,      mask_token_sent=A		,      offset=A		,      additional_special_tokens=A		,      **A		,      )
					lowerCamelCase    		=  vocab_file
					lowerCamelCase    		=  False if not self.vocab_file else True







		def 					__A   ( self		,      A	)  -> Dict:







					'''simple docstring'''







					lowerCamelCase    		=  set(self.all_special_ids	)  # call it once instead of inside list comp
					all_special_ids.remove(self.unk_token_id	)  # <unk> is only sometimes special

					if all_special_ids != set(range(len(self.additional_special_tokens	) + 3	)	):
								raise ValueError(
								    """There should be 3 special tokens: mask_token, pad_token, and eos_token +"""
								    F' {len(self.additional_special_tokens	)} additional_special_tokens, but got {all_special_ids}'	)

					return [1 if x in all_special_ids else 0 for x in seq]







		def 					__A   ( self		,      A		,      A = None		,      A = False	)  -> List[int]:







					'''simple docstring'''







					if already_has_special_tokens:
								return self._special_token_mask(A	)
					elif token_ids_a is None:
								return self._special_token_mask(A	) + [1]
					else:
								return self._special_token_mask(token_ids_a + token_ids_a	) + [1]







		def 					__A   ( self		,      A		,      A=None	)  -> List[int]:







					'''simple docstring'''







					if token_ids_a is None:
								return token_ids_a + [self.eos_token_id]
					# We don't expect to process pairs, but leave the pair logic for API consistency
					return token_ids_a + token_ids_a + [self.eos_token_id]







		def 					__A   ( self		,      A		,      A = None	)  -> Tuple[str]:







					'''simple docstring'''







					if not self.can_save_slow_tokenizer:
								raise ValueError(
								    """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
								    """tokenizer."""	)

					if not os.path.isdir(A	):
								logger.error(F'Vocabulary path ({save_directory}) should be a directory'	)
								return
					lowerCamelCase    		=  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	):
								copyfile(self.vocab_file		,      A	)

					return (out_vocab_file,)

