



'''simple docstring'''





import json
from typing import Iterator, List, Union

from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.processors import TemplateProcessing







class        UpperCAmelCase_			(			_a      ):
    """simple docstring"""







    def __init__(       self   :	Any					,	snake_case_   :	str = "▁"					,	snake_case_   :	bool = True					,	snake_case_   :	Union[str, AddedToken] = "<unk>"					,	snake_case_   :	Union[str, AddedToken] = "</s>"					,	snake_case_   :	Union[str, AddedToken] = "<pad>"					,	):
          snake_case__    :       Any       		= {
              """pad""": {"""id""": 0, """token""": pad_token},
              """eos""": {"""id""": 1, """token""": eos_token},
              """unk""": {"""id""": 2, """token""": unk_token},
          }

          snake_case__    :       Dict       		= [None] * len(self.special_tokens		)
          for token_dict in self.special_tokens.values():
                snake_case__    :       Any       		= token_dict["""token"""]

          snake_case__    :       Optional[Any]       		= Tokenizer(Unigram()		)

          snake_case__    :       Tuple       		= normalizers.Sequence(
              [
                  normalizers.Nmt(),
                  normalizers.NFKC(),
                  normalizers.Replace(Regex(""" {2,}"""		)					,	""" """		),
                  normalizers.Lowercase(),
              ]		)
          snake_case__    :       Dict       		= pre_tokenizers.Sequence(
              [
                  pre_tokenizers.Metaspace(replacement=snake_case_					,	add_prefix_space=snake_case_		),
                  pre_tokenizers.Digits(individual_digits=snake_case_		),
                  pre_tokenizers.Punctuation(),
              ]		)
          snake_case__    :       Union[str, Any]       		= decoders.Metaspace(replacement=snake_case_					,	add_prefix_space=snake_case_		)

          snake_case__    :       int       		= TemplateProcessing(
              single=f"$A {self.special_tokens['eos']['token']}"					,	special_tokens=[(self.special_tokens["""eos"""]["""token"""], self.special_tokens["""eos"""]["""id"""])]					,	)

          snake_case__    :       Optional[Any]       		= {
              """model""": """SentencePieceUnigram""",
              """replacement""": replacement,
              """add_prefix_space""": add_prefix_space,
          }

          super().__init__(snake_case_					,	snake_case_		)




    def lowerCamelCase			(       self   :	Dict					,	snake_case_   :	Union[str, List[str]]					,	snake_case_   :	int = 8_000					,	snake_case_   :	bool = True					,	):
          snake_case__    :       List[Any]       		= trainers.UnigramTrainer(
              vocab_size=snake_case_					,	special_tokens=self.special_tokens_list					,	show_progress=snake_case_					,	)

          if isinstance(snake_case_					,	snake_case_		):
                snake_case__    :       Optional[int]       		= [files]
          self._tokenizer.train(snake_case_					,	trainer=snake_case_		)

          self.add_unk_id()




    def lowerCamelCase			(       self   :	str					,	snake_case_   :	Union[Iterator[str], Iterator[Iterator[str]]]					,	snake_case_   :	int = 8_000					,	snake_case_   :	bool = True					,	):
          snake_case__    :       List[str]       		= trainers.UnigramTrainer(
              vocab_size=snake_case_					,	special_tokens=self.special_tokens_list					,	show_progress=snake_case_					,	)

          self._tokenizer.train_from_iterator(snake_case_					,	trainer=snake_case_		)

          self.add_unk_id()




    def lowerCamelCase			(       self   :	Any		):
          snake_case__    :       Optional[int]       		= json.loads(self._tokenizer.to_str()		)

          snake_case__    :       Union[str, Any]       		= self.special_tokens["""unk"""]["""id"""]

          snake_case__    :       List[Any]       		= Tokenizer.from_str(json.dumps(snake_case_		)		)





'''simple docstring'''





# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# 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.

# this script dumps information about the environment

import os
import sys

import transformers


__a  			=						"3"

print("Python version:", sys.version)
print("transformers version:", transformers.__version__)

try:
       import torch

       print("Torch version:", torch.__version__)
       print("Cuda available:", torch.cuda.is_available())
       print("Cuda version:", torch.version.cuda)
       print("CuDNN version:", torch.backends.cudnn.version())
       print("Number of GPUs available:", torch.cuda.device_count())
       print("NCCL version:", torch.cuda.nccl.version())
except ImportError:
       print("Torch version:", None)

try:
       import deepspeed

       print("DeepSpeed version:", deepspeed.__version__)
except ImportError:
       print("DeepSpeed version:", None)

try:
       import tensorflow as tf

       print("TensorFlow version:", tf.__version__)
       print("TF GPUs available:", bool(tf.config.list_physical_devices("GPU")))
       print("Number of TF GPUs available:", len(tf.config.list_physical_devices("GPU")))
except ImportError:
       print("TensorFlow version:", None)
