






'''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     A    (      SCREAMING_SNAKE_CASE__,   unittest.TestCase						):






 """simple docstring"""





 __a			:						List[str]      =						CanineTokenizer
 __a			:						Optional[int]      =						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			,     **__lowerCAmelCase ):
       UpperCamelCase_				:   int					    =							self.tokenizer_class.from_pretrained(self.tmpdirname			,     **__lowerCAmelCase )
       UpperCamelCase_				:   Union[str, Any]					    =							10_24
       return tokenizer
 @require_torch
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   Optional[Any]					    =							self.canine_tokenizer
       UpperCamelCase_				:   Optional[Any]					    =							["""Life is like a box of chocolates.""", """You never know what you're gonna get."""]
       # fmt: off
       UpperCamelCase_				:   Dict					    =							[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_				:   str					    =							tokenizer(__lowerCAmelCase			,     padding=__lowerCAmelCase			,     return_tensors="""pt""" )
       self.assertIsInstance(__lowerCAmelCase			,     __lowerCAmelCase )

       UpperCamelCase_				:   List[Any]					    =							list(batch.input_ids.numpy()[0] )

       self.assertListEqual(__lowerCAmelCase			,     __lowerCAmelCase )

       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[str]					    =							["""Once there was a man.""", """He wrote a test in HuggingFace Tranformers."""]
       UpperCamelCase_				:   Tuple					    =							tokenizer(__lowerCAmelCase			,     padding=__lowerCAmelCase			,     return_tensors="""pt""" )
       # check if input_ids, attention_mask and token_type_ids are returned
       self.assertIn("""input_ids"""			,     __lowerCAmelCase )
       self.assertIn("""attention_mask"""			,     __lowerCAmelCase )
       self.assertIn("""token_type_ids"""			,     __lowerCAmelCase )
 @require_torch
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   List[Any]					    =							self.canine_tokenizer
       UpperCamelCase_				:   Optional[int]					    =							[
           """What's the weater?""",
           """It's about 25 degrees.""",
       ]
       UpperCamelCase_				:   Optional[int]					    =							tokenizer(
           text_target=__lowerCAmelCase			,     max_length=32			,     padding="""max_length"""			,     truncation=__lowerCAmelCase			,     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_				:   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_				:   List[str]					    =							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_				:   str					    =							tempfile.mkdtemp()

                   UpperCamelCase_				:   Dict					    =							""" He is very happy, UNwant\u00E9d,running"""
                   UpperCamelCase_				:   Optional[int]					    =							tokenizer.encode(__lowerCAmelCase			,     add_special_tokens=__lowerCAmelCase )
                   tokenizer.save_pretrained(__lowerCAmelCase )

                   UpperCamelCase_				:   List[str]					    =							tokenizer.__class__.from_pretrained(__lowerCAmelCase )
                   UpperCamelCase_				:   List[Any]					    =							after_tokenizer.encode(__lowerCAmelCase			,     add_special_tokens=__lowerCAmelCase )
                   self.assertListEqual(__lowerCAmelCase			,     __lowerCAmelCase )

                   shutil.rmtree(__lowerCAmelCase )

       UpperCamelCase_				:   List[str]					    =							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_				:   Dict					    =							tempfile.mkdtemp()

                   UpperCamelCase_				:   List[Any]					    =							""" He is very happy, UNwant\u00E9d,running"""

                   UpperCamelCase_				:   Optional[Any]					    =							tokenizer.additional_special_tokens

                   # We can add a new special token for Canine as follows:
                   UpperCamelCase_				:   List[Any]					    =							chr(0Xe_007 )
                   additional_special_tokens.append(__lowerCAmelCase )
                   tokenizer.add_special_tokens({"""additional_special_tokens""": additional_special_tokens} )
                   UpperCamelCase_				:   List[str]					    =							tokenizer.encode(__lowerCAmelCase			,     add_special_tokens=__lowerCAmelCase )
                   tokenizer.save_pretrained(__lowerCAmelCase )

                   UpperCamelCase_				:   List[str]					    =							tokenizer.__class__.from_pretrained(__lowerCAmelCase )
                   UpperCamelCase_				:   List[str]					    =							after_tokenizer.encode(__lowerCAmelCase			,     add_special_tokens=__lowerCAmelCase )
                   self.assertListEqual(__lowerCAmelCase			,     __lowerCAmelCase )
                   self.assertIn(__lowerCAmelCase			,     after_tokenizer.additional_special_tokens )
                   self.assertEqual(after_tokenizer.model_max_length			,     42 )

                   UpperCamelCase_				:   str					    =							tokenizer.__class__.from_pretrained(__lowerCAmelCase			,     model_max_length=43 )
                   self.assertEqual(tokenizer.model_max_length			,     43 )

                   shutil.rmtree(__lowerCAmelCase )
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   Tuple					    =							self.get_tokenizers(do_lower_case=__lowerCAmelCase )
       for tokenizer in tokenizers:
             with self.subTest(F"{tokenizer.__class__.__name__}" ):
                   UpperCamelCase_    ,			UpperCamelCase_				:   Union[str, Any]					    =							self.get_clean_sequence(__lowerCAmelCase )

                   # a special token for Canine can be defined as follows:
                   UpperCamelCase_				:   Union[str, Any]					    =							0Xe_005
                   UpperCamelCase_				:   List[Any]					    =							chr(__lowerCAmelCase )

                   tokenizer.add_special_tokens({"""cls_token""": special_token} )
                   UpperCamelCase_				:   List[str]					    =							tokenizer.encode(__lowerCAmelCase			,     add_special_tokens=__lowerCAmelCase )
                   self.assertEqual(len(__lowerCAmelCase )			,     1 )

                   UpperCamelCase_				:   Optional[int]					    =							tokenizer.decode(ids + encoded_special_token			,     clean_up_tokenization_spaces=__lowerCAmelCase )
                   UpperCamelCase_				:   List[str]					    =							tokenizer.encode(__lowerCAmelCase			,     add_special_tokens=__lowerCAmelCase )

                   UpperCamelCase_				:   Optional[Any]					    =							tokenizer.encode(__lowerCAmelCase			,     add_special_tokens=__lowerCAmelCase )
                   UpperCamelCase_				:   Optional[Any]					    =							tokenizer.encode(__lowerCAmelCase			,     add_special_tokens=__lowerCAmelCase )
                   self.assertEqual(__lowerCAmelCase			,     input_encoded + special_token_id )

                   UpperCamelCase_				:   Any					    =							tokenizer.decode(__lowerCAmelCase			,     skip_special_tokens=__lowerCAmelCase )
                   self.assertTrue(special_token not in decoded )
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   int					    =							self.get_tokenizers(do_lower_case=__lowerCAmelCase )
       for tokenizer in tokenizers:
             with self.subTest(F"{tokenizer.__class__.__name__}" ):
                   UpperCamelCase_				:   Union[str, Any]					    =							chr(0Xe_005 )
                   UpperCamelCase_				:   Union[str, Any]					    =							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=__lowerCAmelCase )
                   # `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_				:   Tuple					    =							tokenizer.tokenize(__lowerCAmelCase )
                   UpperCamelCase_				:   int					    =							tokenizer.tokenize(__lowerCAmelCase )

                   self.assertEqual(len(__lowerCAmelCase )			,     1 )
                   self.assertEqual(len(__lowerCAmelCase )			,     1 )
                   self.assertEqual(token_a[0]			,     __lowerCAmelCase )
                   self.assertEqual(token_a[0]			,     __lowerCAmelCase )
 @require_tokenizers
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   Optional[int]					    =							self.get_tokenizers(do_lower_case=__lowerCAmelCase )
       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_				:   List[str]					    =							chr(__lowerCAmelCase )

                   UpperCamelCase_				:   Dict					    =							AddedToken(__lowerCAmelCase			,     lstrip=__lowerCAmelCase )
                   tokenizer.add_special_tokens({"""additional_special_tokens""": [new_token]} )

                   with tempfile.TemporaryDirectory() as tmp_dir_name:
                         tokenizer.save_pretrained(__lowerCAmelCase )
                         tokenizer.from_pretrained(__lowerCAmelCase )
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   Union[str, Any]					    =							[]
       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(__lowerCAmelCase )

                   with open(os.path.join(__lowerCAmelCase			,     """special_tokens_map.json""" )			,     encoding="""utf-8""" ) as json_file:
                         UpperCamelCase_				:   str					    =							json.load(__lowerCAmelCase )

                   with open(os.path.join(__lowerCAmelCase			,     """tokenizer_config.json""" )			,     encoding="""utf-8""" ) as json_file:
                         UpperCamelCase_				:   Any					    =							json.load(__lowerCAmelCase )

                   # a special token for Canine can be defined as follows:
                   UpperCamelCase_				:   Any					    =							0Xe_006
                   UpperCamelCase_				:   str					    =							chr(__lowerCAmelCase )

                   UpperCamelCase_				:   Tuple					    =							[new_token_a]
                   UpperCamelCase_				:   int					    =							[new_token_a]

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

                   # 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_				:   Tuple					    =							tokenizer_class.from_pretrained(__lowerCAmelCase			,     extra_ids=0 )
                   self.assertIn(__lowerCAmelCase			,     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_				:   Tuple					    =							0Xe_007
                   UpperCamelCase_				:   List[Any]					    =							chr(__lowerCAmelCase )
                   # Now we test that we can change the value of additional_special_tokens in the from_pretrained
                   UpperCamelCase_				:   Optional[Any]					    =							[AddedToken(__lowerCAmelCase			,     lstrip=__lowerCAmelCase )]
                   UpperCamelCase_				:   Optional[int]					    =							tokenizer_class.from_pretrained(
                       __lowerCAmelCase			,     additional_special_tokens=__lowerCAmelCase			,     extra_ids=0 )

                   self.assertIn(__lowerCAmelCase			,     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_				:   Optional[int]					    =							self.get_tokenizers(do_lower_case=__lowerCAmelCase )
       for tokenizer in tokenizers:
             with self.subTest(F"{tokenizer.__class__.__name__}" ):
                   UpperCamelCase_				:   Tuple					    =							"""hello world"""
                   if self.space_between_special_tokens:
                         UpperCamelCase_				:   Any					    =							"""[CLS] hello world [SEP]"""
                   else:
                         UpperCamelCase_				:   List[Any]					    =							input
                   UpperCamelCase_				:   Dict					    =							tokenizer.encode(__lowerCAmelCase			,     add_special_tokens=__lowerCAmelCase )
                   UpperCamelCase_				:   Optional[Any]					    =							tokenizer.decode(__lowerCAmelCase			,     spaces_between_special_tokens=self.space_between_special_tokens )
                   self.assertIn(__lowerCAmelCase			,     [output, output.lower()] )
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   Optional[Any]					    =							self.get_tokenizers()
       for tokenizer in tokenizers:
             with self.subTest(F"{tokenizer.__class__.__name__}" ):
                   UpperCamelCase_				:   Union[str, Any]					    =							[
                       """bos_token""",
                       """eos_token""",
                       """unk_token""",
                       """sep_token""",
                       """pad_token""",
                       """cls_token""",
                       """mask_token""",
                   ]

       UpperCamelCase_				:   str					    =							"""a"""
       UpperCamelCase_				:   Tuple					    =							ord(__lowerCAmelCase )

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

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

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

       UpperCamelCase_				:   Union[str, Any]					    =							0Xe_006
       UpperCamelCase_				:   Union[str, Any]					    =							chr(__lowerCAmelCase )
       setattr(__lowerCAmelCase			,     """additional_special_tokens_ids"""			,     [additional_special_token_id] )
       self.assertListEqual(getattr(__lowerCAmelCase			,     """additional_special_tokens""" )			,     [additional_special_token] )
       self.assertListEqual(getattr(__lowerCAmelCase			,     """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














'''simple docstring'''

import inspect
import os
import unittest
from dataclasses import dataclass

import torch

from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda, require_multi_gpu
from accelerate.utils import KwargsHandler




@dataclass
class     A    (      SCREAMING_SNAKE_CASE__						):






 """simple docstring"""





 __a			:						int      =						0
 __a			:						bool      =						False
 __a			:						float      =						3.0






class     A    (      unittest.TestCase						):






 """simple docstring"""





 def   _UpperCAmelCase       (							self ):
       # If no defaults are changed, `to_kwargs` returns an empty dict.
       self.assertDictEqual(MockClass().to_kwargs()			,     {} )
       self.assertDictEqual(MockClass(a=2 ).to_kwargs()			,     {"""a""": 2} )
       self.assertDictEqual(MockClass(a=2			,     b=__lowerCAmelCase ).to_kwargs()			,     {"""a""": 2, """b""": True} )
       self.assertDictEqual(MockClass(a=2			,     c=2.25 ).to_kwargs()			,     {"""a""": 2, """c""": 2.25} )
 @require_cuda
 def   _UpperCAmelCase       (							self ):
       # If no defaults are changed, `to_kwargs` returns an empty dict.
       UpperCamelCase_				:   int					    =							GradScalerKwargs(init_scale=10_24			,     growth_factor=2 )
       AcceleratorState._reset_state()
       UpperCamelCase_				:   Dict					    =							Accelerator(mixed_precision="""fp16"""			,     kwargs_handlers=[scaler_handler] )
       print(accelerator.use_fpaa )
       UpperCamelCase_				:   Any					    =							accelerator.scaler

       # Check the kwargs have been applied
       self.assertEqual(scaler._init_scale			,     10_24.0 )
       self.assertEqual(scaler._growth_factor			,     2.0 )

       # Check the other values are at the default
       self.assertEqual(scaler._backoff_factor			,     0.5 )
       self.assertEqual(scaler._growth_interval			,     20_00 )
       self.assertEqual(scaler._enabled			,     __lowerCAmelCase )





 @require_multi_gpu
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   Optional[int]					    =							["""torchrun""", F"--nproc_per_node={torch.cuda.device_count()}", inspect.getfile(self.__class__ )]
       execute_subprocess_async(__lowerCAmelCase			,     env=os.environ.copy() )


if __name__ == "__main__":
      UpperCamelCase							=DistributedDataParallelKwargs(bucket_cap_mb=15, find_unused_parameters=True)
      UpperCamelCase							=Accelerator(kwargs_handlers=[ddp_scaler])
      UpperCamelCase							=torch.nn.Linear(100, 200)
      UpperCamelCase							=accelerator.prepare(model)

      # Check the values changed in kwargs
      UpperCamelCase							=""
      UpperCamelCase							=model.bucket_bytes_cap // (1024 * 1024)
      if observed_bucket_cap_map != 15:
            error_msg += f"Kwargs badly passed, should have `15` but found {observed_bucket_cap_map}.\n"
      if model.find_unused_parameters is not True:
            error_msg += f"Kwargs badly passed, should have `True` but found {model.find_unused_parameters}.\n"

      # Check the values of the defaults
      if model.dim != 0:
            error_msg += f"Default value not respected, should have `0` but found {model.dim}.\n"
      if model.broadcast_buffers is not True:
            error_msg += f"Default value not respected, should have `True` but found {model.broadcast_buffers}.\n"
      if model.gradient_as_bucket_view is not False:
            error_msg += f"Default value not respected, should have `False` but found {model.gradient_as_bucket_view}.\n"

      # Raise error at the end to make sure we don't stop at the first failure.
      if len(error_msg) > 0:
            raise ValueError(error_msg)






