






import unittest

from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


snake_case    				=		"""▁"""

snake_case    				=		get_tests_dir("""fixtures/test_sentencepiece.model""")


@require_sentencepiece
class  SCREAMING_SNAKE_CASE			(			lowerCAmelCase      ,		unittest.TestCase					):
     '''simple docstring'''


     UpperCamelCase_							:		int			=	BertGenerationTokenizer
     UpperCamelCase_							:		Optional[int]			=	False
     UpperCamelCase_							:		Tuple			=	True


     def 				_A       (				self       :	List[Any]      ):
       super().setUp()

       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    BertGenerationTokenizer(UpperCAmelCase_					, keep_accents=UpperCAmelCase_      )
       tokenizer.save_pretrained(self.tmpdirname      )


     def 				_A       (				self       :	Dict      ):
       SCREAMING_SNAKE_CASE       :      Any							=    "<s>"
       SCREAMING_SNAKE_CASE       :      List[str]							=    1

       self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCAmelCase_      )					, UpperCAmelCase_      )
       self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCAmelCase_      )					, UpperCAmelCase_      )


     def 				_A       (				self       :	Union[str, Any]      ):
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    list(self.get_tokenizer().get_vocab().keys()      )

       self.assertEqual(vocab_keys[0]					, "<unk>"      )
       self.assertEqual(vocab_keys[1]					, "<s>"      )
       self.assertEqual(vocab_keys[-1]					, "<pad>"      )
       self.assertEqual(len(UpperCAmelCase_      )					, 1002      )


     def 				_A       (				self       :	Tuple      ):
       self.assertEqual(self.get_tokenizer().vocab_size					, 1000      )


     def 				_A       (				self       :	Any      ):
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    BertGenerationTokenizer(UpperCAmelCase_					, keep_accents=UpperCAmelCase_      )

       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    tokenizer.tokenize("This is a test"      )
       self.assertListEqual(UpperCAmelCase_					, ["▁This", "▁is", "▁a", "▁t", "est"]      )

       self.assertListEqual(
           tokenizer.convert_tokens_to_ids(UpperCAmelCase_      )					, [285, 46, 10, 170, 382]					, )

       SCREAMING_SNAKE_CASE       :      str							=    tokenizer.tokenize("I was born in 92000, and this is falsé."      )
       self.assertListEqual(
           UpperCAmelCase_					, [
               SPIECE_UNDERLINE + "I",
               SPIECE_UNDERLINE + "was",
               SPIECE_UNDERLINE + "b",
               "or",
               "n",
               SPIECE_UNDERLINE + "in",
               SPIECE_UNDERLINE + "",
               "9",
               "2",
               "0",
               "0",
               "0",
               ",",
               SPIECE_UNDERLINE + "and",
               SPIECE_UNDERLINE + "this",
               SPIECE_UNDERLINE + "is",
               SPIECE_UNDERLINE + "f",
               "al",
               "s",
               "é",
               ".",
           ]					, )
       SCREAMING_SNAKE_CASE       :      str							=    tokenizer.convert_tokens_to_ids(UpperCAmelCase_      )
       self.assertListEqual(
           UpperCAmelCase_					, [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4]					, )

       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    tokenizer.convert_ids_to_tokens(UpperCAmelCase_      )
       self.assertListEqual(
           UpperCAmelCase_					, [
               SPIECE_UNDERLINE + "I",
               SPIECE_UNDERLINE + "was",
               SPIECE_UNDERLINE + "b",
               "or",
               "n",
               SPIECE_UNDERLINE + "in",
               SPIECE_UNDERLINE + "",
               "<unk>",
               "2",
               "0",
               "0",
               "0",
               ",",
               SPIECE_UNDERLINE + "and",
               SPIECE_UNDERLINE + "this",
               SPIECE_UNDERLINE + "is",
               SPIECE_UNDERLINE + "f",
               "al",
               "s",
               "<unk>",
               ".",
           ]					, )


     @cached_property
     def 				_A       (				self       :	Optional[int]      ):
       return BertGenerationTokenizer.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder"      )


     @slow
     def 				_A       (				self       :	Dict      ):
       SCREAMING_SNAKE_CASE       :      List[Any]							=    "Hello World!"
       SCREAMING_SNAKE_CASE       :      str							=    [1_8536, 2260, 101]

       self.assertListEqual(UpperCAmelCase_					, self.big_tokenizer.encode(UpperCAmelCase_      )      )


     @slow
     def 				_A       (				self       :	Any      ):
       SCREAMING_SNAKE_CASE       :      int							=    (
           "This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"
           " add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"
       )
       SCREAMING_SNAKE_CASE       :      str							=    [
           871,
           419,
           358,
           946,
           991,
           2521,
           452,
           358,
           1357,
           387,
           7751,
           3536,
           112,
           985,
           456,
           126,
           865,
           938,
           5400,
           5734,
           458,
           1368,
           467,
           786,
           2462,
           5246,
           1159,
           633,
           865,
           4519,
           457,
           582,
           852,
           2557,
           427,
           916,
           508,
           405,
           3_4324,
           497,
           391,
           408,
           1_1342,
           1244,
           385,
           100,
           938,
           985,
           456,
           574,
           362,
           1_2597,
           3200,
           3129,
           1172,
       ]

       self.assertListEqual(UpperCAmelCase_					, self.big_tokenizer.encode(UpperCAmelCase_      )      )


     @require_torch
     @slow
     def 				_A       (				self       :	int      ):
       import torch

       from transformers import BertGenerationConfig, BertGenerationEncoder

       # Build sequence
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    list(self.big_tokenizer.get_vocab().keys()      )[:10]
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    " ".join(UpperCAmelCase_      )
       SCREAMING_SNAKE_CASE       :      str							=    self.big_tokenizer.encode_plus(UpperCAmelCase_					, return_tensors="pt"					, return_token_type_ids=UpperCAmelCase_      )
       SCREAMING_SNAKE_CASE       :      str							=    self.big_tokenizer.batch_encode_plus(
           [sequence + " " + sequence]					, return_tensors="pt"					, return_token_type_ids=UpperCAmelCase_      )

       SCREAMING_SNAKE_CASE       :      int							=    BertGenerationConfig()
       SCREAMING_SNAKE_CASE       :      Tuple							=    BertGenerationEncoder(UpperCAmelCase_      )

       assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

       with torch.no_grad():
         model(**UpperCAmelCase_      )
         model(**UpperCAmelCase_      )






     @slow
     def 				_A       (				self       :	Any      ):
       # fmt: off
       SCREAMING_SNAKE_CASE       :      str							=    {"input_ids": [[3_9286, 458, 3_6335, 2001, 456, 1_3073, 1_3266, 455, 113, 7746, 1741, 1_1157, 391, 1_3073, 1_3266, 455, 113, 3967, 3_5412, 113, 4936, 109, 3870, 2377, 113, 3_0084, 4_5720, 458, 134, 1_7496, 112, 503, 1_1672, 113, 118, 112, 5665, 1_3347, 3_8687, 112, 1496, 3_1389, 112, 3268, 4_7264, 134, 962, 112, 1_6377, 8035, 2_3130, 430, 1_2169, 1_5518, 2_8592, 458, 146, 4_1697, 109, 391, 1_2169, 1_5518, 1_6689, 458, 146, 4_1358, 109, 452, 726, 4034, 111, 763, 3_5412, 5082, 388, 1903, 111, 9051, 391, 2870, 4_8918, 1900, 1123, 550, 998, 112, 9586, 1_5985, 455, 391, 410, 2_2955, 3_7636, 114], [448, 1_7496, 419, 3663, 385, 763, 113, 2_7533, 2870, 3283, 1_3043, 1639, 2_4713, 523, 656, 2_4013, 1_8550, 2521, 517, 2_7014, 2_1244, 420, 1212, 1465, 391, 927, 4833, 388, 578, 1_1786, 114, 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], [484, 2169, 7687, 2_1932, 1_8146, 726, 363, 1_7032, 3391, 114, 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, 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, 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, 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, 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], [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, 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]]}  # noqa: E501
       # fmt: on

       self.tokenizer_integration_test_util(
           expected_encoding=UpperCAmelCase_					, model_name="google/bert_for_seq_generation_L-24_bbc_encoder"					, revision="c817d1fd1be2ffa69431227a1fe320544943d4db"					, )














# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from pathlib import Path

import torch

from ...utils import is_npu_available, is_xpu_available
from .config_args import ClusterConfig, default_json_config_file
from .config_utils import SubcommandHelpFormatter


snake_case    				=		"""Create a default config file for Accelerate with only a few flags set."""


def   lowerCamelCase__			(     lowercase="no"						,     lowercase = default_json_config_file						,     lowercase = False				):

  """simple docstring"""






  SCREAMING_SNAKE_CASE       :      Tuple							=    Path(lowercase				)
  path.parent.mkdir(parents=lowercase						,     exist_ok=lowercase				)
  if path.exists():
    print(
        F'''Configuration already exists at {save_location}, will not override. Run `accelerate config` manually or pass a different `save_location`.'''				)
    return False
  SCREAMING_SNAKE_CASE       :      int							=    mixed_precision.lower()
  if mixed_precision not in ["no", "fp16", "bf16", "fp8"]:
    raise ValueError(
        F'''`mixed_precision` should be one of \'no\', \'fp16\', \'bf16\', or \'fp8\'. Received {mixed_precision}'''				)
  SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    {
      "compute_environment": "LOCAL_MACHINE",
      "mixed_precision": mixed_precision,
  }
  if torch.cuda.is_available():
    SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    torch.cuda.device_count()
    SCREAMING_SNAKE_CASE       :      int							=    num_gpus
    SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    False
    if num_gpus > 1:
      SCREAMING_SNAKE_CASE       :      Tuple							=    "MULTI_GPU"
    else:
      SCREAMING_SNAKE_CASE       :      Optional[Any]							=    "NO"
  elif is_xpu_available() and use_xpu:
    SCREAMING_SNAKE_CASE       :      List[str]							=    torch.xpu.device_count()
    SCREAMING_SNAKE_CASE       :      str							=    num_xpus
    SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    False
    if num_xpus > 1:
      SCREAMING_SNAKE_CASE       :      Any							=    "MULTI_XPU"
    else:
      SCREAMING_SNAKE_CASE       :      str							=    "NO"
  elif is_npu_available():
    SCREAMING_SNAKE_CASE       :      List[Any]							=    torch.npu.device_count()
    SCREAMING_SNAKE_CASE       :      Optional[Any]							=    num_npus
    SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    False
    if num_npus > 1:
      SCREAMING_SNAKE_CASE       :      str							=    "MULTI_NPU"
    else:
      SCREAMING_SNAKE_CASE       :      int							=    "NO"
  else:
    SCREAMING_SNAKE_CASE       :      Dict							=    0
    SCREAMING_SNAKE_CASE       :      List[Any]							=    True
    SCREAMING_SNAKE_CASE       :      Dict							=    1
    SCREAMING_SNAKE_CASE       :      int							=    "NO"
  SCREAMING_SNAKE_CASE       :      Dict							=    ClusterConfig(**lowercase				)
  config.to_json_file(lowercase				)
  return path


def   lowerCamelCase__			(     lowercase						,     lowercase				):

  """simple docstring"""






  SCREAMING_SNAKE_CASE       :      Dict							=    parser.add_parser("default"						,     parents=lowercase						,     help=lowercase						,     formatter_class=lowercase				)
  parser.add_argument(
      "--config_file"						,     default=lowercase						,     help=(
          "The path to use to store the config file. Will default to a file named default_config.yaml in the cache "
          "location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have "
          "such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed "
          "with 'huggingface'."
      )						,     dest="save_location"						,     )

  parser.add_argument(
      "--mixed_precision"						,     choices=["no", "fp16", "bf16"]						,     type=lowercase						,     help="Whether or not to use mixed precision training. "
      "Choose between FP16 and BF16 (bfloat16) training. "
      "BF16 training is only supported on Nvidia Ampere GPUs and PyTorch 1.10 or later."						,     default="no"						,     )
  parser.set_defaults(func=lowercase				)
  return parser


def   lowerCamelCase__			(     lowercase				):

  """simple docstring"""






  SCREAMING_SNAKE_CASE       :      Optional[int]							=    write_basic_config(args.mixed_precision						,     args.save_location				)
  if config_file:
    print(F'''accelerate configuration saved at {config_file}'''				)






