





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						):







       """simple docstring"""





       SCREAMING_SNAKE_CASE_   :			Any  			=	CanineTokenizer
       SCREAMING_SNAKE_CASE_   :			Optional[Any]  			=	False







       def  __UpperCAmelCase			(     self						:						Union[str, Any]	)    ->       Optional[int]:
         super().setUp()
         _lowercase						    =			CanineTokenizer()
         tokenizer.save_pretrained(self.tmpdirname	)







       @cached_property
       def  __UpperCAmelCase			(     self						:						List[str]	)    ->       int:
         return CanineTokenizer.from_pretrained('google/canine-s'	)







       def  __UpperCAmelCase			(     self						:						Any    ,**__A						:						List[Any]	)    ->       CanineTokenizer:
         _lowercase						    =			self.tokenizer_class.from_pretrained(self.tmpdirname    ,**__A	)
         _lowercase						    =			1024
         return tokenizer







       @require_torch
       def  __UpperCAmelCase			(     self						:						Any	)    ->       Optional[Any]:
         _lowercase						    =			self.canine_tokenizer
         _lowercase						    =			['Life is like a box of chocolates.', 'You never know what you\'re gonna get.']
         # fmt: off
         _lowercase						    =			[5_7344, 76, 105, 102, 101, 32, 105, 115, 32, 108, 105, 107, 101, 32, 97, 32, 98, 111, 120, 32, 111, 102, 32, 99, 104, 111, 99, 111, 108, 97, 116, 101, 115, 46, 5_7345, 0, 0, 0, 0]
         # fmt: on
         _lowercase						    =			tokenizer(__A    ,padding=__A    ,return_tensors='pt'	)
         self.assertIsInstance(__A    ,__A	)

         _lowercase						    =			list(batch.input_ids.numpy()[0]	)

         self.assertListEqual(__A    ,__A	)

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







       @require_torch
       def  __UpperCAmelCase			(     self						:						Union[str, Any]	)    ->       Optional[int]:
         _lowercase						    =			self.canine_tokenizer
         _lowercase						    =			['Once there was a man.', 'He wrote a test in HuggingFace Tranformers.']
         _lowercase						    =			tokenizer(__A    ,padding=__A    ,return_tensors='pt'	)
         # check if input_ids, attention_mask and token_type_ids are returned
         self.assertIn('input_ids'    ,__A	)
         self.assertIn('attention_mask'    ,__A	)
         self.assertIn('token_type_ids'    ,__A	)







       @require_torch
       def  __UpperCAmelCase			(     self						:						Optional[int]	)    ->       Tuple:
         _lowercase						    =			self.canine_tokenizer
         _lowercase						    =			[
             'What\'s the weater?',
             'It\'s about 25 degrees.',
         ]
         _lowercase						    =			tokenizer(
             text_target=__A    ,max_length=32    ,padding='max_length'    ,truncation=__A    ,return_tensors='pt'	)
         self.assertEqual(32    ,targets['input_ids'].shape[1]	)







       def  __UpperCAmelCase			(     self						:						Optional[Any]	)    ->       List[Any]:
         # safety check on max_len default value so we are sure the test works
         _lowercase						    =			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
         _lowercase						    =			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
             _lowercase						    =			tempfile.mkdtemp()

             _lowercase						    =			' He is very happy, UNwant\u00E9d,running'
             _lowercase						    =			tokenizer.encode(__A    ,add_special_tokens=__A	)
             tokenizer.save_pretrained(__A	)

             _lowercase						    =			tokenizer.__class__.from_pretrained(__A	)
             _lowercase						    =			after_tokenizer.encode(__A    ,add_special_tokens=__A	)
             self.assertListEqual(__A    ,__A	)

             shutil.rmtree(__A	)

         _lowercase						    =			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
             _lowercase						    =			tempfile.mkdtemp()

             _lowercase						    =			' He is very happy, UNwant\u00E9d,running'

             _lowercase						    =			tokenizer.additional_special_tokens

             # We can add a new special token for Canine as follows:
             _lowercase						    =			chr(0xE007	)
             additional_special_tokens.append(__A	)
             tokenizer.add_special_tokens({'additional_special_tokens': additional_special_tokens}	)
             _lowercase						    =			tokenizer.encode(__A    ,add_special_tokens=__A	)
             tokenizer.save_pretrained(__A	)

             _lowercase						    =			tokenizer.__class__.from_pretrained(__A	)
             _lowercase						    =			after_tokenizer.encode(__A    ,add_special_tokens=__A	)
             self.assertListEqual(__A    ,__A	)
             self.assertIn(__A    ,after_tokenizer.additional_special_tokens	)
             self.assertEqual(after_tokenizer.model_max_length    ,42	)

             _lowercase						    =			tokenizer.__class__.from_pretrained(__A    ,model_max_length=43	)
             self.assertEqual(tokenizer.model_max_length    ,43	)

             shutil.rmtree(__A	)







       def  __UpperCAmelCase			(     self						:						Union[str, Any]	)    ->       int:
         _lowercase						    =			self.get_tokenizers(do_lower_case=__A	)
         for tokenizer in tokenizers:
           with self.subTest(F"""{tokenizer.__class__.__name__}"""	):
             _lowercase    ,		_lowercase						    =			self.get_clean_sequence(__A	)

             # a special token for Canine can be defined as follows:
             _lowercase						    =			0xE005
             _lowercase						    =			chr(__A	)

             tokenizer.add_special_tokens({'cls_token': special_token}	)
             _lowercase						    =			tokenizer.encode(__A    ,add_special_tokens=__A	)
             self.assertEqual(len(__A	)    ,1	)

             _lowercase						    =			tokenizer.decode(ids + encoded_special_token    ,clean_up_tokenization_spaces=__A	)
             _lowercase						    =			tokenizer.encode(__A    ,add_special_tokens=__A	)

             _lowercase						    =			tokenizer.encode(__A    ,add_special_tokens=__A	)
             _lowercase						    =			tokenizer.encode(__A    ,add_special_tokens=__A	)
             self.assertEqual(__A    ,input_encoded + special_token_id	)

             _lowercase						    =			tokenizer.decode(__A    ,skip_special_tokens=__A	)
             self.assertTrue(special_token not in decoded	)







       def  __UpperCAmelCase			(     self						:						List[str]	)    ->       Dict:
         _lowercase						    =			self.get_tokenizers(do_lower_case=__A	)
         for tokenizer in tokenizers:
           with self.subTest(F"""{tokenizer.__class__.__name__}"""	):
             _lowercase						    =			chr(0xE005	)
             _lowercase						    =			chr(0xE006	)

             # `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=__A	)
             # `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]}	)

             _lowercase						    =			tokenizer.tokenize(__A	)
             _lowercase						    =			tokenizer.tokenize(__A	)

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







       @require_tokenizers
       def  __UpperCAmelCase			(     self						:						str	)    ->       List[str]:
         _lowercase						    =			self.get_tokenizers(do_lower_case=__A	)
         for tokenizer in tokenizers:
           with self.subTest(F"""{tokenizer.__class__.__name__}"""	):
             # a special token for Canine can be defined as follows:
             _lowercase						    =			0xE006
             _lowercase						    =			chr(__A	)

             _lowercase						    =			AddedToken(__A    ,lstrip=__A	)
             tokenizer.add_special_tokens({'additional_special_tokens': [new_token]}	)

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







       def  __UpperCAmelCase			(     self						:						Optional[Any]	)    ->       Optional[Any]:
         _lowercase						    =			[]
         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(__A	)

             with open(os.path.join(__A    ,'special_tokens_map.json'	)    ,encoding='utf-8'	) as json_file:
               _lowercase						    =			json.load(__A	)

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

             # a special token for Canine can be defined as follows:
             _lowercase						    =			0xE006
             _lowercase						    =			chr(__A	)

             _lowercase						    =			[new_token_a]
             _lowercase						    =			[new_token_a]

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

             # 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
             _lowercase						    =			tokenizer_class.from_pretrained(__A    ,extra_ids=0	)
             self.assertIn(__A    ,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]	)	)    ,)

             _lowercase						    =			0xE007
             _lowercase						    =			chr(__A	)
             # Now we test that we can change the value of additional_special_tokens in the from_pretrained
             _lowercase						    =			[AddedToken(__A    ,lstrip=__A	)]
             _lowercase						    =			tokenizer_class.from_pretrained(
                 __A    ,additional_special_tokens=__A    ,extra_ids=0	)

             self.assertIn(__A    ,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						:						str	)    ->       Optional[int]:
         _lowercase						    =			self.get_tokenizers(do_lower_case=__A	)
         for tokenizer in tokenizers:
           with self.subTest(F"""{tokenizer.__class__.__name__}"""	):
             _lowercase						    =			'hello world'
             if self.space_between_special_tokens:
               _lowercase						    =			'[CLS] hello world [SEP]'
             else:
               _lowercase						    =			input
             _lowercase						    =			tokenizer.encode(__A    ,add_special_tokens=__A	)
             _lowercase						    =			tokenizer.decode(__A    ,spaces_between_special_tokens=self.space_between_special_tokens	)
             self.assertIn(__A    ,[output, output.lower()]	)







       def  __UpperCAmelCase			(     self						:						List[str]	)    ->       int:
         _lowercase						    =			self.get_tokenizers()
         for tokenizer in tokenizers:
           with self.subTest(F"""{tokenizer.__class__.__name__}"""	):
             _lowercase						    =			[
                 'bos_token',
                 'eos_token',
                 'unk_token',
                 'sep_token',
                 'pad_token',
                 'cls_token',
                 'mask_token',
             ]

         _lowercase						    =			'a'
         _lowercase						    =			ord(__A	)

         for attr in attributes_list:
           setattr(__A    ,attr + '_id'    ,__A	)
           self.assertEqual(getattr(__A    ,__A	)    ,__A	)
           self.assertEqual(getattr(__A    ,attr + '_id'	)    ,__A	)

           setattr(__A    ,attr + '_id'    ,__A	)
           self.assertEqual(getattr(__A    ,__A	)    ,__A	)
           self.assertEqual(getattr(__A    ,attr + '_id'	)    ,__A	)

         setattr(__A    ,'additional_special_tokens_ids'    ,[]	)
         self.assertListEqual(getattr(__A    ,'additional_special_tokens'	)    ,[]	)
         self.assertListEqual(getattr(__A    ,'additional_special_tokens_ids'	)    ,[]	)

         _lowercase						    =			0xE006
         _lowercase						    =			chr(__A	)
         setattr(__A    ,'additional_special_tokens_ids'    ,[additional_special_token_id]	)
         self.assertListEqual(getattr(__A    ,'additional_special_tokens'	)    ,[additional_special_token]	)
         self.assertListEqual(getattr(__A    ,'additional_special_tokens_ids'	)    ,[additional_special_token_id]	)







       def  __UpperCAmelCase			(     self						:						Any	)    ->       Any:
         pass







       def  __UpperCAmelCase			(     self						:						Any	)    ->       Tuple:
         pass







       def  __UpperCAmelCase			(     self						:						List[str]	)    ->       List[str]:
         pass







       def  __UpperCAmelCase			(     self						:						int	)    ->       str:
         pass







       def  __UpperCAmelCase			(     self						:						Tuple	)    ->       int:
         pass







       def  __UpperCAmelCase			(     self						:						str	)    ->       Tuple:
         pass







       def  __UpperCAmelCase			(     self						:						Dict	)    ->       List[Any]:
         pass







       def  __UpperCAmelCase			(     self						:						Union[str, Any]	)    ->       Dict:
         pass

"""simple docstring"""




# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# full vocab, merges file, and thus also resulting in a larger model due to a large vocab size.
# This gives ~3MB in total for all files.
#
# If you want a 50 times smaller than this see `fsmt-make-super-tiny-model.py`, which is slightly more complicated
#
#
# It will be used then as "stas/tiny-wmt19-en-de"

# Build
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration

_A     :			Dict  						=			"""facebook/wmt19-en-de"""
_A     :			str  						=			FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
_A     :			List[Any]  						=			FSMTConfig.from_pretrained(mname)
config.update(
    dict(
        d_model=4,
        encoder_layers=1,
        decoder_layers=1,
        encoder_ffn_dim=4,
        decoder_ffn_dim=4,
        encoder_attention_heads=1,
        decoder_attention_heads=1,
    )
)

_A     :			int  						=			FSMTForConditionalGeneration(config)
print(F"num of params {tiny_model.num_parameters()}")

# Test
_A     :			Dict  						=			tokenizer(["""Making tiny model"""], return_tensors="""pt""")
_A     :			List[Any]  						=			tiny_model(**batch)

print("""test output:""", len(outputs.logits[0]))

# Save
_A     :			List[Any]  						=			"""tiny-wmt19-en-de"""
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(F"Generated {mname_tiny}")

# Upload
# transformers-cli upload tiny-wmt19-en-de


