




import json
import os
import shutil
import tempfile
import unittest

from transformers import BatchEncoding, CanineTokenizer
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.tokenization_utils import AddedToken
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin




class 		__A   (      UpperCamelCase__       ,    unittest.TestCase      ):
						UpperCamelCase      =       CanineTokenizer
						UpperCamelCase      =       False

						def      A__    (		self :Tuple			):
										'''simple docstring'''

										super().setUp()
										__magic_name__						:						Optional[int]										=CanineTokenizer()
										tokenizer.save_pretrained(self.tmpdirname			)

						@cached_property
						def      A__    (		self :Optional[Any]			):
										'''simple docstring'''

										return CanineTokenizer.from_pretrained("""google/canine-s"""			)

						def      A__    (		self :Optional[int]     ,			**__snake_case :Any			):
										'''simple docstring'''

										__magic_name__						:						Any										=self.tokenizer_class.from_pretrained(self.tmpdirname     ,			**__snake_case			)
										__magic_name__						:						Optional[int]										=10_24
										return tokenizer

						@require_torch
						def      A__    (		self :int			):
										'''simple docstring'''

										__magic_name__						:						str										=self.canine_tokenizer
										__magic_name__						:						Any										=["""Life is like a box of chocolates.""", """You never know what you're gonna get."""]
										# fmt: off
										__magic_name__						:						Optional[int]										=[5_73_44, 76, 1_05, 1_02, 1_01, 32, 1_05, 1_15, 32, 1_08, 1_05, 1_07, 1_01, 32, 97, 32, 98, 1_11, 1_20, 32, 1_11, 1_02, 32, 99, 1_04, 1_11, 99, 1_11, 1_08, 97, 1_16, 1_01, 1_15, 46, 5_73_45, 0, 0, 0, 0]
										# fmt: on
										__magic_name__						:						Dict										=tokenizer(__snake_case     ,			padding=__snake_case     ,			return_tensors="""pt"""			)
										self.assertIsInstance(__snake_case     ,			__snake_case			)

										__magic_name__						:						Optional[int]										=list(batch.input_ids.numpy()[0]			)

										self.assertListEqual(__snake_case     ,			__snake_case			)

										self.assertEqual((2, 39)     ,			batch.input_ids.shape			)
										self.assertEqual((2, 39)     ,			batch.attention_mask.shape			)

						@require_torch
						def      A__    (		self :Union[str, Any]			):
										'''simple docstring'''

										__magic_name__						:						Union[str, Any]										=self.canine_tokenizer
										__magic_name__						:						Optional[Any]										=["""Once there was a man.""", """He wrote a test in HuggingFace Tranformers."""]
										__magic_name__						:						int										=tokenizer(__snake_case     ,			padding=__snake_case     ,			return_tensors="""pt"""			)
										# check if input_ids, attention_mask and token_type_ids are returned
										self.assertIn("""input_ids"""     ,			__snake_case			)
										self.assertIn("""attention_mask"""     ,			__snake_case			)
										self.assertIn("""token_type_ids"""     ,			__snake_case			)

						@require_torch
						def      A__    (		self :int			):
										'''simple docstring'''

										__magic_name__						:						Dict										=self.canine_tokenizer
										__magic_name__						:						List[Any]										=[
										    """What's the weater?""",
										    """It's about 25 degrees.""",
										]
										__magic_name__						:						Any										=tokenizer(
										    text_target=__snake_case     ,			max_length=32     ,			padding="""max_length"""     ,			truncation=__snake_case     ,			return_tensors="""pt"""			)
										self.assertEqual(32     ,			targets["""input_ids"""].shape[1]			)

						def      A__    (		self :Any			):
										'''simple docstring'''

										__magic_name__						:						Optional[Any]										=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
										__magic_name__						:						Tuple										=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__						:						Any										=tempfile.mkdtemp()

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

																		__magic_name__						:						Optional[Any]										=tokenizer.__class__.from_pretrained(__snake_case			)
																		__magic_name__						:						str										=after_tokenizer.encode(__snake_case     ,			add_special_tokens=__snake_case			)
																		self.assertListEqual(__snake_case     ,			__snake_case			)

																		shutil.rmtree(__snake_case			)

										__magic_name__						:						int										=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
																		__magic_name__						:						str										=tempfile.mkdtemp()

																		__magic_name__						:						Optional[int]										=""" He is very happy, UNwant\u00E9d,running"""

																		__magic_name__						:						Optional[Any]										=tokenizer.additional_special_tokens

																		# We can add a new special token for Canine as follows:
																		__magic_name__						:						Optional[int]										=chr(0xE_0_0_7			)
																		additional_special_tokens.append(__snake_case			)
																		tokenizer.add_special_tokens({"""additional_special_tokens""": additional_special_tokens}			)
																		__magic_name__						:						List[Any]										=tokenizer.encode(__snake_case     ,			add_special_tokens=__snake_case			)
																		tokenizer.save_pretrained(__snake_case			)

																		__magic_name__						:						Optional[Any]										=tokenizer.__class__.from_pretrained(__snake_case			)
																		__magic_name__						:						List[Any]										=after_tokenizer.encode(__snake_case     ,			add_special_tokens=__snake_case			)
																		self.assertListEqual(__snake_case     ,			__snake_case			)
																		self.assertIn(__snake_case     ,			after_tokenizer.additional_special_tokens			)
																		self.assertEqual(after_tokenizer.model_max_length     ,			42			)

																		__magic_name__						:						Optional[int]										=tokenizer.__class__.from_pretrained(__snake_case     ,			model_max_length=43			)
																		self.assertEqual(tokenizer.model_max_length     ,			43			)

																		shutil.rmtree(__snake_case			)

						def      A__    (		self :Optional[Any]			):
										'''simple docstring'''

										__magic_name__						:						Optional[int]										=self.get_tokenizers(do_lower_case=__snake_case			)
										for tokenizer in tokenizers:
														with self.subTest(f"{tokenizer.__class__.__name__}"			):
																		__magic_name__					,			__magic_name__						:						List[str]										=self.get_clean_sequence(__snake_case			)

																		# a special token for Canine can be defined as follows:
																		__magic_name__						:						Tuple										=0xE_0_0_5
																		__magic_name__						:						Tuple										=chr(__snake_case			)

																		tokenizer.add_special_tokens({"""cls_token""": special_token}			)
																		__magic_name__						:						Optional[int]										=tokenizer.encode(__snake_case     ,			add_special_tokens=__snake_case			)
																		self.assertEqual(len(__snake_case			)     ,			1			)

																		__magic_name__						:						Any										=tokenizer.decode(ids + encoded_special_token     ,			clean_up_tokenization_spaces=__snake_case			)
																		__magic_name__						:						Union[str, Any]										=tokenizer.encode(__snake_case     ,			add_special_tokens=__snake_case			)

																		__magic_name__						:						Optional[int]										=tokenizer.encode(__snake_case     ,			add_special_tokens=__snake_case			)
																		__magic_name__						:						Union[str, Any]										=tokenizer.encode(__snake_case     ,			add_special_tokens=__snake_case			)
																		self.assertEqual(__snake_case     ,			input_encoded + special_token_id			)

																		__magic_name__						:						List[str]										=tokenizer.decode(__snake_case     ,			skip_special_tokens=__snake_case			)
																		self.assertTrue(special_token not in decoded			)

						def      A__    (		self :Dict			):
										'''simple docstring'''

										__magic_name__						:						Dict										=self.get_tokenizers(do_lower_case=__snake_case			)
										for tokenizer in tokenizers:
														with self.subTest(f"{tokenizer.__class__.__name__}"			):
																		__magic_name__						:						Tuple										=chr(0xE_0_0_5			)
																		__magic_name__						:						Union[str, Any]										=chr(0xE_0_0_6			)

																		# `add_tokens` method stores special tokens only in `tokenizer.unique_no_split_tokens`. (in tokenization_utils.py)
																		tokenizer.add_tokens([SPECIAL_TOKEN_1]     ,			special_tokens=__snake_case			)
																		# `add_special_tokens` method stores special tokens in `tokenizer.additional_special_tokens`,
																		# which also occur in `tokenizer.all_special_tokens`. (in tokenization_utils_base.py)
																		tokenizer.add_special_tokens({"""additional_special_tokens""": [SPECIAL_TOKEN_2]}			)

																		__magic_name__						:						List[Any]										=tokenizer.tokenize(__snake_case			)
																		__magic_name__						:						Union[str, Any]										=tokenizer.tokenize(__snake_case			)

																		self.assertEqual(len(__snake_case			)     ,			1			)
																		self.assertEqual(len(__snake_case			)     ,			1			)
																		self.assertEqual(token_a[0]     ,			__snake_case			)
																		self.assertEqual(token_a[0]     ,			__snake_case			)

						@require_tokenizers
						def      A__    (		self :int			):
										'''simple docstring'''

										__magic_name__						:						Dict										=self.get_tokenizers(do_lower_case=__snake_case			)
										for tokenizer in tokenizers:
														with self.subTest(f"{tokenizer.__class__.__name__}"			):
																		# a special token for Canine can be defined as follows:
																		__magic_name__						:						Dict										=0xE_0_0_6
																		__magic_name__						:						Tuple										=chr(__snake_case			)

																		__magic_name__						:						str										=AddedToken(__snake_case     ,			lstrip=__snake_case			)
																		tokenizer.add_special_tokens({"""additional_special_tokens""": [new_token]}			)

																		with tempfile.TemporaryDirectory() as tmp_dir_name:
																						tokenizer.save_pretrained(__snake_case			)
																						tokenizer.from_pretrained(__snake_case			)

						def      A__    (		self :int			):
										'''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(__snake_case			)

																		with open(os.path.join(__snake_case     ,			"""special_tokens_map.json"""			)     ,			encoding="""utf-8"""			) as json_file:
																						__magic_name__						:						List[Any]										=json.load(__snake_case			)

																		with open(os.path.join(__snake_case     ,			"""tokenizer_config.json"""			)     ,			encoding="""utf-8"""			) as json_file:
																						__magic_name__						:						str										=json.load(__snake_case			)

																		# a special token for Canine can be defined as follows:
																		__magic_name__						:						int										=0xE_0_0_6
																		__magic_name__						:						List[str]										=chr(__snake_case			)

																		__magic_name__						:						Union[str, Any]										=[new_token_a]
																		__magic_name__						:						List[Any]										=[new_token_a]

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

																		# 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__						:						Union[str, Any]										=tokenizer_class.from_pretrained(__snake_case     ,			extra_ids=0			)
																		self.assertIn(__snake_case     ,			tokenizer_without_change_in_init.additional_special_tokens			)
																		# self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
																		self.assertEqual(
																		    [new_token_a]     ,			tokenizer_without_change_in_init.convert_ids_to_tokens(
																		        tokenizer_without_change_in_init.convert_tokens_to_ids([new_token_a]			)			)     ,			)

																		__magic_name__						:						str										=0xE_0_0_7
																		__magic_name__						:						Optional[int]										=chr(__snake_case			)
																		# Now we test that we can change the value of additional_special_tokens in the from_pretrained
																		__magic_name__						:						List[Any]										=[AddedToken(__snake_case     ,			lstrip=__snake_case			)]
																		__magic_name__						:						str										=tokenizer_class.from_pretrained(
																		    __snake_case     ,			additional_special_tokens=__snake_case     ,			extra_ids=0			)

																		self.assertIn(__snake_case     ,			tokenizer.additional_special_tokens			)
																		# self.assertIn(new_token_2,tokenizer.get_vocab()) # ByT5Tokenization no vocab
																		self.assertEqual(
																		    [new_token_a]     ,			tokenizer.convert_ids_to_tokens(tokenizer.convert_tokens_to_ids([new_token_a]			)			)			)

						@require_tokenizers
						def      A__    (		self :str			):
										'''simple docstring'''

										__magic_name__						:						List[str]										=self.get_tokenizers(do_lower_case=__snake_case			)
										for tokenizer in tokenizers:
														with self.subTest(f"{tokenizer.__class__.__name__}"			):
																		__magic_name__						:						Dict										="""hello world"""
																		if self.space_between_special_tokens:
																						__magic_name__						:						Dict										="""[CLS] hello world [SEP]"""
																		else:
																						__magic_name__						:						int										=input
																		__magic_name__						:						Any										=tokenizer.encode(__snake_case     ,			add_special_tokens=__snake_case			)
																		__magic_name__						:						List[Any]										=tokenizer.decode(__snake_case     ,			spaces_between_special_tokens=self.space_between_special_tokens			)
																		self.assertIn(__snake_case     ,			[output, output.lower()]			)

						def      A__    (		self :List[str]			):
										'''simple docstring'''

										__magic_name__						:						str										=self.get_tokenizers()
										for tokenizer in tokenizers:
														with self.subTest(f"{tokenizer.__class__.__name__}"			):
																		__magic_name__						:						str										=[
																		    """bos_token""",
																		    """eos_token""",
																		    """unk_token""",
																		    """sep_token""",
																		    """pad_token""",
																		    """cls_token""",
																		    """mask_token""",
																		]

										__magic_name__						:						Union[str, Any]										="""a"""
										__magic_name__						:						int										=ord(__snake_case			)

										for attr in attributes_list:
														setattr(__snake_case     ,			attr + """_id"""     ,			__snake_case			)
														self.assertEqual(getattr(__snake_case     ,			__snake_case			)     ,			__snake_case			)
														self.assertEqual(getattr(__snake_case     ,			attr + """_id"""			)     ,			__snake_case			)

														setattr(__snake_case     ,			attr + """_id"""     ,			__snake_case			)
														self.assertEqual(getattr(__snake_case     ,			__snake_case			)     ,			__snake_case			)
														self.assertEqual(getattr(__snake_case     ,			attr + """_id"""			)     ,			__snake_case			)

										setattr(__snake_case     ,			"""additional_special_tokens_ids"""     ,			[]			)
										self.assertListEqual(getattr(__snake_case     ,			"""additional_special_tokens"""			)     ,			[]			)
										self.assertListEqual(getattr(__snake_case     ,			"""additional_special_tokens_ids"""			)     ,			[]			)

										__magic_name__						:						Optional[int]										=0xE_0_0_6
										__magic_name__						:						Any										=chr(__snake_case			)
										setattr(__snake_case     ,			"""additional_special_tokens_ids"""     ,			[additional_special_token_id]			)
										self.assertListEqual(getattr(__snake_case     ,			"""additional_special_tokens"""			)     ,			[additional_special_token]			)
										self.assertListEqual(getattr(__snake_case     ,			"""additional_special_tokens_ids"""			)     ,			[additional_special_token_id]			)

						def      A__    (		self :int			):
										'''simple docstring'''

										pass

						def      A__    (		self :str			):
										'''simple docstring'''

										pass

						def      A__    (		self :str			):
										'''simple docstring'''

										pass

						def      A__    (		self :Tuple			):
										'''simple docstring'''

										pass

						def      A__    (		self :Tuple			):
										'''simple docstring'''

										pass

						def      A__    (		self :Any			):
										'''simple docstring'''

										pass

						def      A__    (		self :Optional[int]			):
										'''simple docstring'''

										pass


						def      A__    (		self :int			):
										'''simple docstring'''

										pass










import os
from typing import Dict, List, Union

import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs

from .tokenization_gpta import GPTaTokenizer




class 		__A   (      tf.keras.layers.Layer      ):

						def __init__(		self :Optional[int]     ,			__snake_case :Dict[str, int]     ,			__snake_case :List[str]     ,			__snake_case :int = None     ,			__snake_case :int = None			):
										'''simple docstring'''

										super().__init__()
										__magic_name__						:						Optional[int]										=pad_token_id
										__magic_name__						:						List[Any]										=max_length
										__magic_name__						:						Dict										=vocab
										__magic_name__						:						int										=merges
										__magic_name__						:						Optional[int]										=BytePairTokenizer(__snake_case     ,			__snake_case     ,			sequence_length=__snake_case			)

						@classmethod
						def      A__    (		cls :List[Any]     ,			__snake_case :GPTaTokenizer     ,			*__snake_case :int     ,			**__snake_case :Any			):
										'''simple docstring'''

										__magic_name__						:						List[Any]										=[""" """.join(__snake_case			) for m in tokenizer.bpe_ranks.keys()]
										__magic_name__						:						str										=tokenizer.get_vocab()
										return cls(__snake_case     ,			__snake_case     ,			*__snake_case     ,			**__snake_case			)

						@classmethod
						def      A__    (		cls :Dict     ,			__snake_case :Union[str, os.PathLike]     ,			*__snake_case :Union[str, Any]     ,			**__snake_case :int			):
										'''simple docstring'''

										__magic_name__						:						Dict										=GPTaTokenizer.from_pretrained(__snake_case     ,			*__snake_case     ,			**__snake_case			)
										return cls.from_tokenizer(__snake_case     ,			*__snake_case     ,			**__snake_case			)

						@classmethod
						def      A__    (		cls :Optional[Any]     ,			__snake_case :List[Any]			):
										'''simple docstring'''

										return cls(**__snake_case			)

						def      A__    (		self :Union[str, Any]			):
										'''simple docstring'''

										return {
										    "vocab": self.vocab,
										    "merges": self.merges,
										    "max_length": self.max_length,
										    "pad_token_id": self.pad_token_id,
										}


						def      A__    (		self :List[Any]     ,			__snake_case :Dict     ,			__snake_case :int = None			):
										'''simple docstring'''

										__magic_name__						:						Optional[Any]										=self.tf_tokenizer(__snake_case			)
										__magic_name__						:						Tuple										=tf.ones_like(__snake_case			)

										if self.pad_token_id is not None:
														# pad the tokens up to max length
														__magic_name__						:						Tuple										=max_length if max_length is not None else self.max_length

														if max_length is not None:
																		__magic_name__					,			__magic_name__						:						Tuple										=pad_model_inputs(
																		    __snake_case     ,			max_seq_length=__snake_case     ,			pad_value=self.pad_token_id			)

										return {"attention_mask": attention_mask, "input_ids": input_ids}




