
import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging


__snake_case								=  logging.get_logger(__name__)


__snake_case								=  {"vocab_file": "vocab.json", "merges_file": "merges.txt"}

# See all LED models at https://huggingface.co/models?filter=LED
__snake_case								=  {
    "vocab_file": {
        "allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json",
    },
    "merges_file": {
        "allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json",
    },
}

__snake_case								=  {
    "allenai/led-base-16384": 1_63_84,
}






@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def 							_lowercase							(     )		->		int:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__													= (
					    list(range(ord('!'		)	,	ord('~'		) + 1		)		) + list(range(ord('¡'		)	,	ord('¬'		) + 1		)		) + list(range(ord('®'		)	,	ord('ÿ'		) + 1		)		)
					)
					SCREAMING_SNAKE_CASE__													= bs[:]
					SCREAMING_SNAKE_CASE__													= 0
					for b in range(2**8		):
										if b not in bs:
															bs.append(__lowerCamelCase		)
															cs.append(2**8 + n		)
															n += 1
					SCREAMING_SNAKE_CASE__													= [chr(__lowerCamelCase		) for n in cs]
					return dict(zip(__lowerCamelCase	,	__lowerCamelCase		)		)






def 							_lowercase							(     UpperCamelCase_		)		->		Dict:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__													= set()
					SCREAMING_SNAKE_CASE__													= word[0]
					for char in word[1:]:
										pairs.add((prev_char, char)		)
										SCREAMING_SNAKE_CASE__													= char
					return pairs




class 						lowercase__  (						_UpperCAmelCase  ):
	A__    :   Dict				       =VOCAB_FILES_NAMES
	A__    :   Optional[int]				       =PRETRAINED_VOCAB_FILES_MAP
	A__    :   List[Any]				       =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	A__    :   Union[str, Any]				       =["""input_ids""", """attention_mask"""]
	def __init__(			self							:   Optional[Any]       ,   UpperCAmelCase_							:   Optional[int]       ,   UpperCAmelCase_							:   Any       ,   UpperCAmelCase_							:   int="replace"       ,   UpperCAmelCase_							:   Optional[Any]="<s>"       ,   UpperCAmelCase_							:   str="</s>"       ,   UpperCAmelCase_							:   Any="</s>"       ,   UpperCAmelCase_							:   Dict="<s>"       ,   UpperCAmelCase_							:   List[str]="<unk>"       ,   UpperCAmelCase_							:   Union[str, Any]="<pad>"       ,   UpperCAmelCase_							:   Dict="<mask>"       ,   UpperCAmelCase_							:   Dict=False       ,   **UpperCAmelCase_							:   int       ,   ):
						SCREAMING_SNAKE_CASE__													= AddedToken(UpperCAmelCase_       ,   lstrip=UpperCAmelCase_       ,   rstrip=UpperCAmelCase_							) if isinstance(UpperCAmelCase_       ,   UpperCAmelCase_							) else bos_token
						SCREAMING_SNAKE_CASE__													= AddedToken(UpperCAmelCase_       ,   lstrip=UpperCAmelCase_       ,   rstrip=UpperCAmelCase_							) if isinstance(UpperCAmelCase_       ,   UpperCAmelCase_							) else eos_token
						SCREAMING_SNAKE_CASE__													= AddedToken(UpperCAmelCase_       ,   lstrip=UpperCAmelCase_       ,   rstrip=UpperCAmelCase_							) if isinstance(UpperCAmelCase_       ,   UpperCAmelCase_							) else sep_token
						SCREAMING_SNAKE_CASE__													= AddedToken(UpperCAmelCase_       ,   lstrip=UpperCAmelCase_       ,   rstrip=UpperCAmelCase_							) if isinstance(UpperCAmelCase_       ,   UpperCAmelCase_							) else cls_token
						SCREAMING_SNAKE_CASE__													= AddedToken(UpperCAmelCase_       ,   lstrip=UpperCAmelCase_       ,   rstrip=UpperCAmelCase_							) if isinstance(UpperCAmelCase_       ,   UpperCAmelCase_							) else unk_token
						SCREAMING_SNAKE_CASE__													= AddedToken(UpperCAmelCase_       ,   lstrip=UpperCAmelCase_       ,   rstrip=UpperCAmelCase_							) if isinstance(UpperCAmelCase_       ,   UpperCAmelCase_							) else pad_token

						# Mask token behave like a normal word, i.e. include the space before it
						SCREAMING_SNAKE_CASE__													= AddedToken(UpperCAmelCase_       ,   lstrip=UpperCAmelCase_       ,   rstrip=UpperCAmelCase_							) if isinstance(UpperCAmelCase_       ,   UpperCAmelCase_							) else mask_token

						super().__init__(
						    errors=UpperCAmelCase_       ,   bos_token=UpperCAmelCase_       ,   eos_token=UpperCAmelCase_       ,   unk_token=UpperCAmelCase_       ,   sep_token=UpperCAmelCase_       ,   cls_token=UpperCAmelCase_       ,   pad_token=UpperCAmelCase_       ,   mask_token=UpperCAmelCase_       ,   add_prefix_space=UpperCAmelCase_       ,   **UpperCAmelCase_       ,   )

						with open(UpperCAmelCase_       ,   encoding='utf-8'							) as vocab_handle:
											SCREAMING_SNAKE_CASE__													= json.load(UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= {v: k for k, v in self.encoder.items()}
						SCREAMING_SNAKE_CASE__													= errors  # how to handle errors in decoding
						SCREAMING_SNAKE_CASE__													= bytes_to_unicode()
						SCREAMING_SNAKE_CASE__													= {v: k for k, v in self.byte_encoder.items()}
						with open(UpperCAmelCase_       ,   encoding='utf-8'							) as merges_handle:
											SCREAMING_SNAKE_CASE__													= merges_handle.read().split('\n'							)[1:-1]
						SCREAMING_SNAKE_CASE__													= [tuple(merge.split()							) for merge in bpe_merges]
						SCREAMING_SNAKE_CASE__													= dict(zip(UpperCAmelCase_       ,   range(len(UpperCAmelCase_							)							)							)							)
						SCREAMING_SNAKE_CASE__													= {}
						SCREAMING_SNAKE_CASE__													= add_prefix_space

						# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
						SCREAMING_SNAKE_CASE__													= re.compile(r'\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'							)
	@property
	# Copied from transformers.models.bart.tokenization_bart.BartTokenizer.vocab_size
	def      A_				(			self							:   int							):
						return len(self.encoder							)
	def      A_				(			self							:   str							):
						return dict(self.encoder       ,   **self.added_tokens_encoder							)
	def      A_				(			self							:   Any       ,   UpperCAmelCase_							:   Dict							):







						if token in self.cache:
											return self.cache[token]
						SCREAMING_SNAKE_CASE__													= tuple(UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= get_pairs(UpperCAmelCase_							)

						if not pairs:
											return token

						while True:
											SCREAMING_SNAKE_CASE__													= min(UpperCAmelCase_       ,   key=lambda UpperCAmelCase_							: self.bpe_ranks.get(UpperCAmelCase_       ,   float('inf'							)							)							)
											if bigram not in self.bpe_ranks:
																break
											SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__													= bigram
											SCREAMING_SNAKE_CASE__													= []
											SCREAMING_SNAKE_CASE__													= 0
											while i < len(UpperCAmelCase_							):
																try:
																					SCREAMING_SNAKE_CASE__													= word.index(UpperCAmelCase_       ,   UpperCAmelCase_							)
																except ValueError:
																					new_word.extend(word[i:]							)
																					break
																else:
																					new_word.extend(word[i:j]							)
																					SCREAMING_SNAKE_CASE__													= j

																if word[i] == first and i < len(UpperCAmelCase_							) - 1 and word[i + 1] == second:
																					new_word.append(first + second							)
																					i += 2
																else:
																					new_word.append(word[i]							)
																					i += 1
											SCREAMING_SNAKE_CASE__													= tuple(UpperCAmelCase_							)
											SCREAMING_SNAKE_CASE__													= new_word
											if len(UpperCAmelCase_							) == 1:
																break
											else:
																SCREAMING_SNAKE_CASE__													= get_pairs(UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= ' '.join(UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= word
						return word
	def      A_				(			self							:   Optional[int]       ,   UpperCAmelCase_							:   str							):
						SCREAMING_SNAKE_CASE__													= []
						for token in re.findall(self.pat       ,   UpperCAmelCase_							):
											SCREAMING_SNAKE_CASE__													= ''.join(
											    self.byte_encoder[b] for b in token.encode('utf-8'							)							)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
											bpe_tokens.extend(bpe_token for bpe_token in self.bpe(UpperCAmelCase_							).split(' '							)							)
						return bpe_tokens
	def      A_				(			self							:   Optional[Any]       ,   UpperCAmelCase_							:   Any							):
						return self.encoder.get(UpperCAmelCase_       ,   self.encoder.get(self.unk_token							)							)
	def      A_				(			self							:   Any       ,   UpperCAmelCase_							:   Tuple							):
						return self.decoder.get(UpperCAmelCase_							)
	def      A_				(			self							:   Optional[Any]       ,   UpperCAmelCase_							:   Optional[Any]							):
						SCREAMING_SNAKE_CASE__													= ''.join(UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= bytearray([self.byte_decoder[c] for c in text]							).decode('utf-8'       ,   errors=self.errors							)
						return text
	def      A_				(			self							:   Union[str, Any]       ,   UpperCAmelCase_							:   str       ,   UpperCAmelCase_							:   Optional[str] = None							):







						if not os.path.isdir(UpperCAmelCase_							):
											logger.error(F'Vocabulary path ({save_directory}) should be a directory'							)
											return
						SCREAMING_SNAKE_CASE__													= os.path.join(
						    UpperCAmelCase_       ,   (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']							)
						SCREAMING_SNAKE_CASE__													= os.path.join(
						    UpperCAmelCase_       ,   (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']							)

						with open(UpperCAmelCase_       ,   'w'       ,   encoding='utf-8'							) as f:
											f.write(json.dumps(self.encoder       ,   indent=2       ,   sort_keys=UpperCAmelCase_       ,   ensure_ascii=UpperCAmelCase_							) + '\n'							)

						SCREAMING_SNAKE_CASE__													= 0
						with open(UpperCAmelCase_       ,   'w'       ,   encoding='utf-8'							) as writer:
											writer.write('#version: 0.2\n'							)
											for bpe_tokens, token_index in sorted(self.bpe_ranks.items()       ,   key=lambda UpperCAmelCase_							: kv[1]							):
																if index != token_index:
																					logger.warning(
																					    F'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
																					    ' Please check that the tokenizer is not corrupted!'							)
																					SCREAMING_SNAKE_CASE__													= token_index
																writer.write(' '.join(UpperCAmelCase_							) + '\n'							)
																index += 1

						return vocab_file, merge_file
	def      A_				(			self							:   Optional[Any]       ,   UpperCAmelCase_							:   List[int]       ,   UpperCAmelCase_							:   Optional[List[int]] = None							):







						if token_ids_a is None:
											return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
						SCREAMING_SNAKE_CASE__													= [self.cls_token_id]
						SCREAMING_SNAKE_CASE__													= [self.sep_token_id]
						return cls + token_ids_a + sep + sep + token_ids_a + sep
	def      A_				(			self							:   List[str]       ,   UpperCAmelCase_							:   List[int]       ,   UpperCAmelCase_							:   Optional[List[int]] = None       ,   UpperCAmelCase_							:   bool = False							):







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

						if token_ids_a is None:
											return [1] + ([0] * len(UpperCAmelCase_							)) + [1]
						return [1] + ([0] * len(UpperCAmelCase_							)) + [1, 1] + ([0] * len(UpperCAmelCase_							)) + [1]
	def      A_				(			self							:   Optional[int]       ,   UpperCAmelCase_							:   List[int]       ,   UpperCAmelCase_							:   Optional[List[int]] = None							):
						SCREAMING_SNAKE_CASE__													= [self.sep_token_id]
						SCREAMING_SNAKE_CASE__													= [self.cls_token_id]

						if token_ids_a is None:
											return len(cls + token_ids_a + sep							) * [0]
						return len(cls + token_ids_a + sep + sep + token_ids_a + sep							) * [0]
	def      A_				(			self							:   List[str]       ,   UpperCAmelCase_							:   Optional[Any]       ,   UpperCAmelCase_							:   Union[str, Any]=False       ,   **UpperCAmelCase_							:   Optional[int]							):
						SCREAMING_SNAKE_CASE__													= kwargs.pop('add_prefix_space'       ,   self.add_prefix_space							)
						if (is_split_into_words or add_prefix_space) and (len(UpperCAmelCase_							) > 0 and not text[0].isspace()):
											SCREAMING_SNAKE_CASE__													= ' ' + text
						return (text, kwargs)





	def      A_				(			self							:   Optional[Any]       ,   UpperCAmelCase_							:   Union[Dict[str, EncodedInput], BatchEncoding]       ,   UpperCAmelCase_							:   Optional[int] = None       ,   UpperCAmelCase_							:   PaddingStrategy = PaddingStrategy.DO_NOT_PAD       ,   UpperCAmelCase_							:   Optional[int] = None       ,   UpperCAmelCase_							:   Optional[bool] = None       ,   ):
						SCREAMING_SNAKE_CASE__													= super()._pad(
						    encoded_inputs=UpperCAmelCase_       ,   max_length=UpperCAmelCase_       ,   padding_strategy=UpperCAmelCase_       ,   pad_to_multiple_of=UpperCAmelCase_       ,   return_attention_mask=UpperCAmelCase_       ,   )

						# Load from model defaults
						if return_attention_mask is None:
											SCREAMING_SNAKE_CASE__													= 'attention_mask' in self.model_input_names

						if return_attention_mask and "global_attention_mask" in encoded_inputs:
											SCREAMING_SNAKE_CASE__													= encoded_inputs[self.model_input_names[0]]
											# `global_attention_mask` need to have the same length as other (sequential) inputs.
											SCREAMING_SNAKE_CASE__													= len(encoded_inputs['global_attention_mask']							) != len(UpperCAmelCase_							)

											if needs_to_be_padded:
																SCREAMING_SNAKE_CASE__													= len(UpperCAmelCase_							) - len(encoded_inputs['global_attention_mask']							)

																if self.padding_side == "right":
																					# Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
																					SCREAMING_SNAKE_CASE__													= (
																					    encoded_inputs['global_attention_mask'] + [-1] * difference
																					)
																elif self.padding_side == "left":
																					SCREAMING_SNAKE_CASE__													= [-1] * difference + encoded_inputs[
																					    'global_attention_mask'
																					]
																else:
																					raise ValueError('Invalid padding strategy:' + str(self.padding_side							)							)

						return encoded_inputs











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 		__lowerCAmelCase	(	__magic_name__      ,       unittest.TestCase ):
							UpperCamelCase__					     =  GPTaTokenizer
							UpperCamelCase__					     =  GPTaTokenizerFast
							UpperCamelCase__					     =  True
							UpperCamelCase__					     =  {'''add_prefix_space''': True}
							UpperCamelCase__					     =  False
							def       lowerCamelCase__							(						self						:Optional[Any]				):







								'''simple docstring'''







								super().setUp()

								# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
								a										=							[
								    """l""",
								    """o""",
								    """w""",
								    """e""",
								    """r""",
								    """s""",
								    """t""",
								    """i""",
								    """d""",
								    """n""",
								    """\u0120""",
								    """\u0120l""",
								    """\u0120n""",
								    """\u0120lo""",
								    """\u0120low""",
								    """er""",
								    """\u0120lowest""",
								    """\u0120newer""",
								    """\u0120wider""",
								    """<unk>""",
								    """<|endoftext|>""",
								]
								a										=							dict(zip(__magic_name__		,      range(len(__magic_name__				)				)				)				)
								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(__magic_name__				) + """\n"""				)
								with open(self.merges_file		,      """w"""		,      encoding="""utf-8"""				) as fp:
									fp.write("""\n""".join(__magic_name__				)				)
							def       lowerCamelCase__							(						self						:Dict		,      **__magic_name__						:List[str]				):







								'''simple docstring'''







								kwargs.update(self.special_tokens_map				)
								return GPTaTokenizer.from_pretrained(self.tmpdirname		,      **__magic_name__				)
							def       lowerCamelCase__							(						self						:List[str]		,      **__magic_name__						:Optional[Any]				):







								'''simple docstring'''







								kwargs.update(self.special_tokens_map				)
								return GPTaTokenizerFast.from_pretrained(self.tmpdirname		,      **__magic_name__				)
							def       lowerCamelCase__							(						self						:Dict		,      __magic_name__						:List[str]				):







								'''simple docstring'''







								a										=							"""lower newer"""
								a										=							"""lower newer"""
								return input_text, output_text
							def       lowerCamelCase__							(						self						:int				):







								'''simple docstring'''







								a										=							GPTaTokenizer(self.vocab_file		,      self.merges_file		,      **self.special_tokens_map				)
								a										=							"""lower newer"""
								a										=							["""\u0120low""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
								a										=							tokenizer.tokenize(__magic_name__		,      add_prefix_space=__magic_name__				)
								self.assertListEqual(__magic_name__		,      __magic_name__				)

								a										=							tokens + [tokenizer.unk_token]
								a										=							[14, 15, 10, 9, 3, 2, 15, 19]
								self.assertListEqual(tokenizer.convert_tokens_to_ids(__magic_name__				)		,      __magic_name__				)
							def       lowerCamelCase__							(						self						:int				):







								'''simple docstring'''







								if not self.test_rust_tokenizer:
									return

								a										=							self.get_tokenizer()
								a										=							self.get_rust_tokenizer(add_prefix_space=__magic_name__				)

								a										=							"""lower newer"""

								# Testing tokenization
								a										=							tokenizer.tokenize(__magic_name__		,      add_prefix_space=__magic_name__				)
								a										=							rust_tokenizer.tokenize(__magic_name__				)
								self.assertListEqual(__magic_name__		,      __magic_name__				)

								# Testing conversion to ids without special tokens
								a										=							tokenizer.encode(__magic_name__		,      add_special_tokens=__magic_name__		,      add_prefix_space=__magic_name__				)
								a										=							rust_tokenizer.encode(__magic_name__		,      add_special_tokens=__magic_name__				)
								self.assertListEqual(__magic_name__		,      __magic_name__				)

								# Testing conversion to ids with special tokens
								a										=							self.get_rust_tokenizer(add_prefix_space=__magic_name__				)
								a										=							tokenizer.encode(__magic_name__		,      add_prefix_space=__magic_name__				)
								a										=							rust_tokenizer.encode(__magic_name__				)
								self.assertListEqual(__magic_name__		,      __magic_name__				)

								# Testing the unknown token
								a										=							tokens + [rust_tokenizer.unk_token]
								a										=							[14, 15, 10, 9, 3, 2, 15, 19]
								self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(__magic_name__				)		,      __magic_name__				)
							def       lowerCamelCase__							(						self						:Optional[int]		,      *__magic_name__						:Tuple		,      **__magic_name__						:str				):







								'''simple docstring'''







								pass
							def       lowerCamelCase__							(						self						:Optional[Any]		,      __magic_name__						:Union[str, Any]=15				):







								'''simple docstring'''







								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(__magic_name__		,      **__magic_name__				)

										# Simple input
										a										=							"""This is a simple input"""
										a										=							["""This is a simple input 1""", """This is a simple input 2"""]
										a										=							("""This is a simple input""", """This is a pair""")
										a										=							[
										    ("""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(__magic_name__		,      tokenizer_r.encode		,      __magic_name__		,      max_length=__magic_name__		,      padding="""max_length"""				)

										# Simple input
										self.assertRaises(__magic_name__		,      tokenizer_r.encode_plus		,      __magic_name__		,      max_length=__magic_name__		,      padding="""max_length"""				)

										# Simple input
										self.assertRaises(
										    __magic_name__		,      tokenizer_r.batch_encode_plus		,      __magic_name__		,      max_length=__magic_name__		,      padding="""max_length"""		,      )

										# Pair input
										self.assertRaises(__magic_name__		,      tokenizer_r.encode		,      __magic_name__		,      max_length=__magic_name__		,      padding="""max_length"""				)

										# Pair input
										self.assertRaises(__magic_name__		,      tokenizer_r.encode_plus		,      __magic_name__		,      max_length=__magic_name__		,      padding="""max_length"""				)

										# Pair input
										self.assertRaises(
										    __magic_name__		,      tokenizer_r.batch_encode_plus		,      __magic_name__		,      max_length=__magic_name__		,      padding="""max_length"""		,      )
							def       lowerCamelCase__							(						self						:str				):







								'''simple docstring'''







								a										=							GPTaTokenizer.from_pretrained(self.tmpdirname		,      pad_token="""<pad>"""				)

								# Simple input
								a										=							"""This is a simple input"""
								a										=							["""This is a simple input looooooooong""", """This is a simple input"""]
								a										=							("""This is a simple input""", """This is a pair""")
								a										=							[
								    ("""This is a simple input loooooong""", """This is a simple input"""),
								    ("""This is a simple pair loooooong""", """This is a simple pair"""),
								]

								a										=							tokenizer.pad_token_id

								a										=							tokenizer(__magic_name__		,      padding="""max_length"""		,      max_length=30		,      return_tensors="""np"""				)
								a										=							tokenizer(__magic_name__		,      padding=__magic_name__		,      truncate=__magic_name__		,      return_tensors="""np"""				)
								a										=							tokenizer(*__magic_name__		,      padding="""max_length"""		,      max_length=60		,      return_tensors="""np"""				)
								a										=							tokenizer(__magic_name__		,      padding=__magic_name__		,      truncate=__magic_name__		,      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       lowerCamelCase__							(						self						:Union[str, Any]				):







								'''simple docstring'''







								a										=							"""$$$"""
								a										=							GPTaTokenizer.from_pretrained(self.tmpdirname		,      bos_token=__magic_name__		,      add_bos_token=__magic_name__				)

								a										=							"""This is a simple input"""
								a										=							["""This is a simple input 1""", """This is a simple input 2"""]

								a										=							tokenizer.bos_token_id

								a										=							tokenizer(__magic_name__				)
								a										=							tokenizer(__magic_name__				)

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

								a										=							tokenizer.decode(out_s.input_ids				)
								a										=							tokenizer.batch_decode(out_sa.input_ids				)

								self.assertEqual(decode_s.split()[0]		,      __magic_name__				)
								self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa				)				)
							def       lowerCamelCase__							(						self						:Optional[int]				):







								'''simple docstring'''







								pass





							def       lowerCamelCase__							(						self						:Any				):







								'''simple docstring'''







								a										=							[self.get_tokenizer(do_lower_case=__magic_name__		,      add_bos_token=__magic_name__				)]
								for tokenizer in tokenizers:
									with self.subTest(F'{tokenizer.__class__.__name__}'				):
										a										=							"""Encode this."""
										a										=							"""This one too please."""
										a										=							tokenizer.encode(__magic_name__		,      add_special_tokens=__magic_name__				)
										encoded_sequence += tokenizer.encode(__magic_name__		,      add_special_tokens=__magic_name__				)
										a										=							tokenizer.encode_plus(
										    __magic_name__		,      __magic_name__		,      add_special_tokens=__magic_name__		,      return_special_tokens_mask=__magic_name__		,      )
										a										=							encoded_sequence_dict["""input_ids"""]
										a										=							encoded_sequence_dict["""special_tokens_mask"""]
										self.assertEqual(len(__magic_name__				)		,      len(__magic_name__				)				)

										a										=							[
										    (x if not special_tokens_mask[i] else None) for i, x in enumerate(__magic_name__				)
										]
										a										=							[x for x in filtered_sequence if x is not None]
										self.assertEqual(__magic_name__		,      __magic_name__				)







@require_tokenizers
class 		__lowerCAmelCase	(	unittest.TestCase ):
							def       lowerCamelCase__							(						self						:Optional[int]				):







								'''simple docstring'''







								a										=							AutoTokenizer.from_pretrained("""facebook/opt-350m"""		,      from_slow=__magic_name__				)
								a										=							"""A photo of a cat"""

								a										=							tokenizer.encode(
								    __magic_name__		,      )
								self.assertEqual(__magic_name__		,      [2, 250, 1345, 9, 10, 4758]				)
								tokenizer.save_pretrained("""test_opt"""				)

								a										=							AutoTokenizer.from_pretrained("""./test_opt"""				)
								a										=							tokenizer.encode(
								    __magic_name__		,      )
								self.assertEqual(__magic_name__		,      [2, 250, 1345, 9, 10, 4758]				)
							def       lowerCamelCase__							(						self						:Union[str, Any]				):







								'''simple docstring'''







								a										=							AutoTokenizer.from_pretrained("""facebook/opt-350m"""		,      use_slow=__magic_name__				)
								a										=							"""A photo of a cat"""

								a										=							tokenizer.encode(
								    __magic_name__		,      )
								# Same as above
								self.assertEqual(__magic_name__		,      [2, 250, 1345, 9, 10, 4758]				)





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







								'''simple docstring'''







								a										=							AutoTokenizer.from_pretrained("""facebook/opt-350m"""		,      from_slow=__magic_name__				)

								a										=							"""bos"""
								a										=							tokenizer.get_vocab()["""bos"""]

								a										=							"""A photo of a cat"""
								a										=							tokenizer.encode(
								    __magic_name__		,      )
								# We changed the bos token
								self.assertEqual(__magic_name__		,      [3_1957, 250, 1345, 9, 10, 4758]				)
								tokenizer.save_pretrained("""./tok"""				)
								a										=							AutoTokenizer.from_pretrained("""./tok"""				)
								self.assertTrue(tokenizer.is_fast				)
								a										=							tokenizer.encode(
								    __magic_name__		,      )
								self.assertEqual(__magic_name__		,      [3_1957, 250, 1345, 9, 10, 4758]				)

