

'''simple docstring'''
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 					_snake_case		(__SCREAMING_SNAKE_CASE			,			unittest.TestCase):
			__A  :	List[Any]        =CanineTokenizer
			__A  :	Any        =False






			def 		UpperCamelCase__							(  self     ):
				super().setUp()
				UpperCAmelCase_     :      Dict						=  CanineTokenizer()
				tokenizer.save_pretrained(self.tmpdirname     )






			@cached_property
			def 		UpperCamelCase__							(  self     ):
				return CanineTokenizer.from_pretrained("google/canine-s"     )






			def 		UpperCamelCase__							(  self			,**_snake_case     ):
				UpperCAmelCase_     :      Optional[int]						=  self.tokenizer_class.from_pretrained(self.tmpdirname			,**_snake_case     )
				UpperCAmelCase_     :      int						=  10_24
				return tokenizer






			@require_torch
			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_     :      Dict						=  self.canine_tokenizer
				UpperCAmelCase_     :      str						=  ["Life is like a box of chocolates.", "You never know what you're gonna get."]
				# fmt: off
				UpperCAmelCase_     :      Any						=  [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
				UpperCAmelCase_     :      List[Any]						=  tokenizer(_snake_case			,padding=_snake_case			,return_tensors="pt"     )
				self.assertIsInstance(_snake_case			,_snake_case     )

				UpperCAmelCase_     :      str						=  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 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_     :      Tuple						=  self.canine_tokenizer
				UpperCAmelCase_     :      List[Any]						=  ["Once there was a man.", "He wrote a test in HuggingFace Tranformers."]
				UpperCAmelCase_     :      Tuple						=  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 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_     :      Optional[int]						=  self.canine_tokenizer
				UpperCAmelCase_     :      Tuple						=  [
				    "What's the weater?",
				    "It's about 25 degrees.",
				]
				UpperCAmelCase_     :      Union[str, 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 		UpperCamelCase__							(  self     ):
				# safety check on max_len default value so we are sure the test works
				UpperCAmelCase_     :      List[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
				UpperCAmelCase_     :      Optional[int]						=  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
						UpperCAmelCase_     :      List[str]						=  tempfile.mkdtemp()

						UpperCAmelCase_     :      Dict						=  " He is very happy, UNwant\u00E9d,running"
						UpperCAmelCase_     :      int						=  tokenizer.encode(_snake_case			,add_special_tokens=_snake_case     )
						tokenizer.save_pretrained(_snake_case     )

						UpperCAmelCase_     :      Optional[int]						=  tokenizer.__class__.from_pretrained(_snake_case     )
						UpperCAmelCase_     :      Optional[Any]						=  after_tokenizer.encode(_snake_case			,add_special_tokens=_snake_case     )
						self.assertListEqual(_snake_case			,_snake_case     )

						shutil.rmtree(_snake_case     )

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

						UpperCAmelCase_     :      List[str]						=  " He is very happy, UNwant\u00E9d,running"

						UpperCAmelCase_     :      Union[str, Any]						=  tokenizer.additional_special_tokens

						# We can add a new special token for Canine as follows:
						UpperCAmelCase_     :      Any						=  chr(0xE_007     )
						additional_special_tokens.append(_snake_case     )
						tokenizer.add_special_tokens({"additional_special_tokens": additional_special_tokens}     )
						UpperCAmelCase_     :      int						=  tokenizer.encode(_snake_case			,add_special_tokens=_snake_case     )
						tokenizer.save_pretrained(_snake_case     )

						UpperCAmelCase_     :      Optional[Any]						=  tokenizer.__class__.from_pretrained(_snake_case     )
						UpperCAmelCase_     :      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     )

						UpperCAmelCase_     :      List[str]						=  tokenizer.__class__.from_pretrained(_snake_case			,model_max_length=43     )
						self.assertEqual(tokenizer.model_max_length			,43     )

						shutil.rmtree(_snake_case     )






			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_     :      Dict						=  self.get_tokenizers(do_lower_case=_snake_case     )
				for tokenizer in tokenizers:
					with self.subTest(f'''{tokenizer.__class__.__name__}'''     ):
						UpperCAmelCase_					,					UpperCAmelCase_     :      Any						=  self.get_clean_sequence(_snake_case     )

						# a special token for Canine can be defined as follows:
						UpperCAmelCase_     :      int						=  0xE_005
						UpperCAmelCase_     :      int						=  chr(_snake_case     )

						tokenizer.add_special_tokens({"cls_token": special_token}     )
						UpperCAmelCase_     :      int						=  tokenizer.encode(_snake_case			,add_special_tokens=_snake_case     )
						self.assertEqual(len(_snake_case     )			,1     )

						UpperCAmelCase_     :      Optional[Any]						=  tokenizer.decode(ids + encoded_special_token			,clean_up_tokenization_spaces=_snake_case     )
						UpperCAmelCase_     :      List[Any]						=  tokenizer.encode(_snake_case			,add_special_tokens=_snake_case     )

						UpperCAmelCase_     :      str						=  tokenizer.encode(_snake_case			,add_special_tokens=_snake_case     )
						UpperCAmelCase_     :      Optional[int]						=  tokenizer.encode(_snake_case			,add_special_tokens=_snake_case     )
						self.assertEqual(_snake_case			,input_encoded + special_token_id     )

						UpperCAmelCase_     :      Optional[Any]						=  tokenizer.decode(_snake_case			,skip_special_tokens=_snake_case     )
						self.assertTrue(special_token not in decoded     )






			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_     :      int						=  self.get_tokenizers(do_lower_case=_snake_case     )
				for tokenizer in tokenizers:
					with self.subTest(f'''{tokenizer.__class__.__name__}'''     ):
						UpperCAmelCase_     :      str						=  chr(0xE_005     )
						UpperCAmelCase_     :      List[str]						=  chr(0xE_006     )

						# `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]}     )

						UpperCAmelCase_     :      Optional[int]						=  tokenizer.tokenize(_snake_case     )
						UpperCAmelCase_     :      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 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_     :      Optional[int]						=  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:
						UpperCAmelCase_     :      Union[str, Any]						=  0xE_006
						UpperCAmelCase_     :      Any						=  chr(_snake_case     )

						UpperCAmelCase_     :      Tuple						=  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 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_     :      List[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:
							UpperCAmelCase_     :      Any						=  json.load(_snake_case     )

						with open(os.path.join(_snake_case			,"tokenizer_config.json"     )			,encoding="utf-8"     ) as json_file:
							UpperCAmelCase_     :      Dict						=  json.load(_snake_case     )

						# a special token for Canine can be defined as follows:
						UpperCAmelCase_     :      Tuple						=  0xE_006
						UpperCAmelCase_     :      str						=  chr(_snake_case     )

						UpperCAmelCase_     :      Any						=  [new_token_a]
						UpperCAmelCase_     :      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
						UpperCAmelCase_     :      List[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]     )     )			,)

						UpperCAmelCase_     :      List[str]						=  0xE_007
						UpperCAmelCase_     :      Any						=  chr(_snake_case     )
						# Now we test that we can change the value of additional_special_tokens in the from_pretrained
						UpperCAmelCase_     :      List[Any]						=  [AddedToken(_snake_case			,lstrip=_snake_case     )]
						UpperCAmelCase_     :      List[Any]						=  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 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_     :      int						=  self.get_tokenizers(do_lower_case=_snake_case     )
				for tokenizer in tokenizers:
					with self.subTest(f'''{tokenizer.__class__.__name__}'''     ):
						UpperCAmelCase_     :      Tuple						=  "hello world"
						if self.space_between_special_tokens:
							UpperCAmelCase_     :      str						=  "[CLS] hello world [SEP]"
						else:
							UpperCAmelCase_     :      List[str]						=  input
						UpperCAmelCase_     :      str						=  tokenizer.encode(_snake_case			,add_special_tokens=_snake_case     )
						UpperCAmelCase_     :      List[Any]						=  tokenizer.decode(_snake_case			,spaces_between_special_tokens=self.space_between_special_tokens     )
						self.assertIn(_snake_case			,[output, output.lower()]     )






			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_     :      Dict						=  self.get_tokenizers()
				for tokenizer in tokenizers:
					with self.subTest(f'''{tokenizer.__class__.__name__}'''     ):
						UpperCAmelCase_     :      Optional[int]						=  [
						    "bos_token",
						    "eos_token",
						    "unk_token",
						    "sep_token",
						    "pad_token",
						    "cls_token",
						    "mask_token",
						]

				UpperCAmelCase_     :      Tuple						=  "a"
				UpperCAmelCase_     :      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"     )			,[]     )

				UpperCAmelCase_     :      Optional[int]						=  0xE_006
				UpperCAmelCase_     :      List[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 		UpperCamelCase__							(  self     ):
				pass






			def 		UpperCamelCase__							(  self     ):
				pass






			def 		UpperCamelCase__							(  self     ):
				pass






			def 		UpperCamelCase__							(  self     ):
				pass






			def 		UpperCamelCase__							(  self     ):
				pass






			def 		UpperCamelCase__							(  self     ):
				pass






			def 		UpperCamelCase__							(  self     ):
				pass






			def 		UpperCamelCase__							(  self     ):
				pass











from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments

def      UpperCAmelCase_    (    )	->				Optional[Any]:
     '''simple docstring'''


     A_          =       HfArgumentParser(_UpperCAmelCase				)
     A_          =       parser.parse_args_into_dataclasses()[0]
     A_          =       TensorFlowBenchmark(args=_UpperCAmelCase				)
     try:
          A_          =       parser.parse_args_into_dataclasses()[0]
     except ValueError as e:
          A_          =       '''Arg --no_{0} is no longer used, please use --no-{0} instead.'''
          A_          =       ''' '''.join(str(_UpperCAmelCase				).split(''' '''				)[:-1]				)
          A_          =       ''''''
          A_          =       eval(str(_UpperCAmelCase				).split(''' '''				)[-1]				)
          A_          =       []
          for arg in depreciated_args:
               # arg[2:] removes '--'
               if arg[2:] in TensorFlowBenchmark.deprecated_args:
                    # arg[5:] removes '--no_'
                    full_error_msg += arg_error_msg.format(arg[5:]				)
               else:
                    wrong_args.append(_UpperCAmelCase				)
          if len(_UpperCAmelCase				) > 0:
               A_          =       full_error_msg + begin_error_msg + str(_UpperCAmelCase				)
          raise ValueError(_UpperCAmelCase				)
     benchmark.run()


if __name__ == "__main__":
      main()



