





import json
import os
import unittest

from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin



class 							_UpperCamelCase					(	lowerCAmelCase_  ,						unittest.TestCase     ):
       _UpperCamelCase				:					Tuple         =   DebertaTokenizer
       _UpperCamelCase				:					str         =   True
       _UpperCamelCase				:					Tuple         =   DebertaTokenizerFast

       def lowercase (		self: Optional[int]		)       -> Dict:




              """simple docstring"""


              super().setUp()

              # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
              UpperCamelCase_          =  [
                  "l",
                  "o",
                  "w",
                  "e",
                  "r",
                  "s",
                  "t",
                  "i",
                  "d",
                  "n",
                  "\u0120",
                  "\u0120l",
                  "\u0120n",
                  "\u0120lo",
                  "\u0120low",
                  "er",
                  "\u0120lowest",
                  "\u0120newer",
                  "\u0120wider",
                  "[UNK]",
              ]
              UpperCamelCase_          =  dict(zip(_SCREAMING_SNAKE_CASE					,   range(len(_SCREAMING_SNAKE_CASE		)		)		)		)
              UpperCamelCase_          =  ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
              UpperCamelCase_          =  {"unk_token": "[UNK]"}

              UpperCamelCase_          =  os.path.join(self.tmpdirname					,   VOCAB_FILES_NAMES["vocab_file"]		)
              UpperCamelCase_          =  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(_SCREAMING_SNAKE_CASE		) + "\n"		)
              with open(self.merges_file					,   "w"					,   encoding="utf-8"		) as fp:
                     fp.write("\n".join(_SCREAMING_SNAKE_CASE		)		)

       def lowercase (		self: Optional[int]					,   **_SCREAMING_SNAKE_CASE: Dict		)       -> Union[str, Any]:




              """simple docstring"""


              kwargs.update(self.special_tokens_map		)
              return self.tokenizer_class.from_pretrained(self.tmpdirname					,   **_SCREAMING_SNAKE_CASE		)

       def lowercase (		self: Any					,   _SCREAMING_SNAKE_CASE: Any		)       -> List[str]:




              """simple docstring"""


              UpperCamelCase_          =  "lower newer"
              UpperCamelCase_          =  "lower newer"
              return input_text, output_text

       def lowercase (		self: List[str]		)       -> Optional[Any]:




              """simple docstring"""


              UpperCamelCase_          =  self.get_tokenizer()
              UpperCamelCase_          =  "lower newer"
              UpperCamelCase_          =  ["l", "o", "w", "er", "\u0120", "n", "e", "w", "er"]
              UpperCamelCase_          =  tokenizer.tokenize(_SCREAMING_SNAKE_CASE		)
              self.assertListEqual(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		)

              UpperCamelCase_          =  tokens + [tokenizer.unk_token]
              UpperCamelCase_          =  [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
              self.assertListEqual(tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE		)					,   _SCREAMING_SNAKE_CASE		)

       def lowercase (		self: Union[str, Any]		)       -> str:




              """simple docstring"""


              UpperCamelCase_          =  self.get_tokenizer()
              UpperCamelCase_          =  tokenizer("Hello"					,   "World"		)
              UpperCamelCase_          =  [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
              self.assertListEqual(tokd["token_type_ids"]					,   _SCREAMING_SNAKE_CASE		)

       @slow
       def lowercase (		self: int		)       -> Dict:




              """simple docstring"""


              UpperCamelCase_          =  self.tokenizer_class.from_pretrained("microsoft/deberta-base"		)

              UpperCamelCase_          =  tokenizer.encode("sequence builders"					,   add_special_tokens=_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_          =  tokenizer.encode("multi-sequence build"					,   add_special_tokens=_SCREAMING_SNAKE_CASE		)

              UpperCamelCase_          =  tokenizer.encode(
                  "sequence builders"					,   add_special_tokens=_SCREAMING_SNAKE_CASE					,   add_prefix_space=_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_          =  tokenizer.encode(
                  "sequence builders"					,   "multi-sequence build"					,   add_special_tokens=_SCREAMING_SNAKE_CASE					,   add_prefix_space=_SCREAMING_SNAKE_CASE		)

              UpperCamelCase_          =  tokenizer.build_inputs_with_special_tokens(_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_          =  tokenizer.build_inputs_with_special_tokens(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		)

              assert encoded_sentence == encoded_text_from_decode
              assert encoded_pair == encoded_pair_from_decode



       @slow
       def lowercase (		self: List[str]		)       -> List[Any]:




              """simple docstring"""


              UpperCamelCase_          =  [self.tokenizer_class]
              if self.test_rust_tokenizer:
                     tokenizer_classes.append(self.rust_tokenizer_class		)

              for tokenizer_class in tokenizer_classes:
                     UpperCamelCase_          =  tokenizer_class.from_pretrained("microsoft/deberta-base"		)

                     UpperCamelCase_          =  [
                         "ALBERT: A Lite BERT for Self-supervised Learning of Language Representations",
                         "ALBERT incorporates two parameter reduction techniques",
                         "The first one is a factorized embedding parameterization. By decomposing the large vocabulary"
                         " embedding matrix into two small matrices, we separate the size of the hidden layers from the size of"
                         " vocabulary embedding.",
                     ]

                     UpperCamelCase_          =  tokenizer(_SCREAMING_SNAKE_CASE					,   padding=_SCREAMING_SNAKE_CASE		)
                     UpperCamelCase_          =  [tokenizer.decode(_SCREAMING_SNAKE_CASE					,   skip_special_tokens=_SCREAMING_SNAKE_CASE		) for seq in encoding["input_ids"]]

                     # fmt: off
                     UpperCamelCase_          =  {
                         "input_ids": [
                             [1, 2118, 11126, 565, 35, 83, 25191, 163, 18854, 13, 12156, 12, 16101, 25376, 13807, 9, 22205, 27893, 1635, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                             [1, 2118, 11126, 565, 24536, 80, 43797, 4878, 7373, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                             [1, 133, 78, 65, 16, 10, 3724, 1538, 33183, 11303, 43797, 1938, 4, 870, 24165, 29105, 5, 739, 32644, 33183, 11303, 36173, 88, 80, 650, 7821, 45940, 6, 52, 2559, 5, 1836, 9, 5, 7397, 13171, 31, 5, 1836, 9, 32644, 33183, 11303, 4, 2]
                         ],
                         "token_type_ids": [
                             [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                             [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                             [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
                         ],
                         "attention_mask": [
                             [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                             [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                             [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
                         ]
                     }
                     # fmt: on

                     UpperCamelCase_          =  [
                         "ALBERT: A Lite BERT for Self-supervised Learning of Language Representations",
                         "ALBERT incorporates two parameter reduction techniques",
                         "The first one is a factorized embedding parameterization. By decomposing the large vocabulary"
                         " embedding matrix into two small matrices, we separate the size of the hidden layers from the size of"
                         " vocabulary embedding.",
                     ]

                     self.assertDictEqual(encoding.data					,   _SCREAMING_SNAKE_CASE		)

                     for expected, decoded in zip(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		):
                            self.assertEqual(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		)








import os
import sys
import tempfile

import torch

from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment




def  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_=()					,		UpperCamelCase_=None					,		UpperCamelCase_="no"					,		UpperCamelCase_="29500"  )     ->		Optional[Any]:
       UpperCamelCase_          =  False
       UpperCamelCase_          =  False
       if any(key.startswith("KAGGLE"  ) for key in os.environ.keys()  ):
              UpperCamelCase_          =  True
       elif "IPython" in sys.modules:
              UpperCamelCase_          =  "google.colab" in str(sys.modules["IPython"].get_ipython()  )

       try:
              UpperCamelCase_          =  PrecisionType(mixed_precision.lower()  )
       except ValueError:
              raise ValueError(
                  F'''Unknown mixed_precision mode: {args.mixed_precision.lower()}. Choose between {PrecisionType.list()}.'''  )

       if (in_colab or in_kaggle) and (os.environ.get("TPU_NAME"					,		UpperCamelCase_  ) is not None):
              # TPU launch
              import torch_xla.distributed.xla_multiprocessing as xmp

              if len(AcceleratorState._shared_state  ) > 0:
                     raise ValueError(
                         "To train on TPU in Colab or Kaggle Kernel, the `Accelerator` should only be initialized inside "
                         "your training function. Restart your notebook and make sure no cells initializes an "
                         "`Accelerator`."  )
              if num_processes is None:
                     UpperCamelCase_          =  8

              UpperCamelCase_          =  PrepareForLaunch(UpperCamelCase_					,		distributed_type="TPU"  )
              print(F'''Launching a training on {num_processes} TPU cores.'''  )
              xmp.spawn(UpperCamelCase_					,		args=UpperCamelCase_					,		nprocs=UpperCamelCase_					,		start_method="fork"  )
       elif in_colab:
              # No need for a distributed launch otherwise as it's either CPU or one GPU.
              if torch.cuda.is_available():
                     print("Launching training on one GPU."  )
              else:
                     print("Launching training on one CPU."  )
              function(*UpperCamelCase_  )
       else:
              if num_processes is None:
                     raise ValueError(
                         "You have to specify the number of GPUs you would like to use, add `num_processes=...` to your call."  )

              if num_processes > 1:
                     # Multi-GPU launch
                     from torch.multiprocessing import start_processes
                     from torch.multiprocessing.spawn import ProcessRaisedException

                     if len(AcceleratorState._shared_state  ) > 0:
                            raise ValueError(
                                "To launch a multi-GPU training from your notebook, the `Accelerator` should only be initialized "
                                "inside your training function. Restart your notebook and make sure no cells initializes an "
                                "`Accelerator`."  )

                     if torch.cuda.is_initialized():
                            raise ValueError(
                                "To launch a multi-GPU training from your notebook, you need to avoid running any instruction "
                                "using `torch.cuda` in any cell. Restart your notebook and make sure no cells use any CUDA "
                                "function."  )

                     # torch.distributed will expect a few environment variable to be here. We set the ones common to each
                     # process here (the other ones will be set be the launcher).
                     with patch_environment(
                         world_size=UpperCamelCase_					,		master_addr="127.0.01"					,		master_port=UpperCamelCase_					,		mixed_precision=UpperCamelCase_  ):
                            UpperCamelCase_          =  PrepareForLaunch(UpperCamelCase_					,		distributed_type="MULTI_GPU"  )
                            print(F'''Launching training on {num_processes} GPUs.'''  )
                            try:
                                   start_processes(UpperCamelCase_					,		args=UpperCamelCase_					,		nprocs=UpperCamelCase_					,		start_method="fork"  )
                            except ProcessRaisedException as e:
                                   if "Cannot re-initialize CUDA in forked subprocess" in e.args[0]:
                                          raise RuntimeError(
                                              "CUDA has been initialized before the `notebook_launcher` could create a forked subprocess. "
                                              "This likely stems from an outside import causing issues once the `notebook_launcher()` is called. "
                                              "Please review your imports and test them when running the `notebook_launcher()` to identify "
                                              "which one is problematic."  ) from e

              else:
                     # No need for a distributed launch otherwise as it's either CPU, GPU or MPS.
                     if is_mps_available():
                            UpperCamelCase_          =  "1"
                            print("Launching training on MPS."  )
                     elif torch.cuda.is_available():
                            print("Launching training on one GPU."  )
                     else:
                            print("Launching training on CPU."  )
                     function(*UpperCamelCase_  )







def  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_=()					,		UpperCamelCase_=2  )     ->		Optional[Any]:
       from torch.multiprocessing import start_processes

       with tempfile.NamedTemporaryFile() as tmp_file:
              # torch.distributed will expect a few environment variable to be here. We set the ones common to each
              # process here (the other ones will be set be the launcher).
              with patch_environment(
                  world_size=UpperCamelCase_					,		master_addr="127.0.01"					,		master_port="29500"					,		accelerate_mixed_precision="no"					,		accelerate_debug_rdv_file=tmp_file.name					,		accelerate_use_cpu="yes"					,		):
                     UpperCamelCase_          =  PrepareForLaunch(UpperCamelCase_					,		debug=UpperCamelCase_  )
                     start_processes(UpperCamelCase_					,		args=UpperCamelCase_					,		nprocs=UpperCamelCase_					,		start_method="fork"  )

