


'''simple docstring'''



import sys

from transformers import AutoTokenizer


A__	:      Any				=						sys.argv[1]
A__	:      Optional[int]				=						sys.argv[2]
A__	:      Dict				=						int(sys.argv[3])

A__	:      str				=						0

A__	:      Any				=						AutoTokenizer.from_pretrained(model_name_or_path)
max_len -= tokenizer.num_special_tokens_to_add()

with open(dataset, """rt""") as f_p:
      for line in f_p:
            A__	:      List[str]				=						line.rstrip()

            if not line:
                  print(line)
                  A__	:      Dict				=						0
                  continue

            A__	:      Dict				=						line.split()[0]

            A__	:      Tuple				=						len(tokenizer.tokenize(token))

            # Token contains strange control characters like \x96 or \x95
            # Just filter out the complete line
            if current_subwords_len == 0:
                  continue

            if (subword_len_counter + current_subwords_len) > max_len:
                  print("""""")
                  print(line)
                  A__	:      int				=						current_subwords_len
                  continue

            subword_len_counter += current_subwords_len

            print(line)










'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


A__	:      List[str]				=						{
    """configuration_flaubert""": ["""FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FlaubertConfig""", """FlaubertOnnxConfig"""],
    """tokenization_flaubert""": ["""FlaubertTokenizer"""],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__	:      int				=						[
          """FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """FlaubertForMultipleChoice""",
          """FlaubertForQuestionAnswering""",
          """FlaubertForQuestionAnsweringSimple""",
          """FlaubertForSequenceClassification""",
          """FlaubertForTokenClassification""",
          """FlaubertModel""",
          """FlaubertWithLMHeadModel""",
          """FlaubertPreTrainedModel""",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__	:      int				=						[
          """TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """TFFlaubertForMultipleChoice""",
          """TFFlaubertForQuestionAnsweringSimple""",
          """TFFlaubertForSequenceClassification""",
          """TFFlaubertForTokenClassification""",
          """TFFlaubertModel""",
          """TFFlaubertPreTrainedModel""",
          """TFFlaubertWithLMHeadModel""",
      ]


if TYPE_CHECKING:
      from .configuration_flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig, FlaubertOnnxConfig
      from .tokenization_flaubert import FlaubertTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flaubert import (
                FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                FlaubertForMultipleChoice,
                FlaubertForQuestionAnswering,
                FlaubertForQuestionAnsweringSimple,
                FlaubertForSequenceClassification,
                FlaubertForTokenClassification,
                FlaubertModel,
                FlaubertPreTrainedModel,
                FlaubertWithLMHeadModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_flaubert import (
                TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFFlaubertForMultipleChoice,
                TFFlaubertForQuestionAnsweringSimple,
                TFFlaubertForSequenceClassification,
                TFFlaubertForTokenClassification,
                TFFlaubertModel,
                TFFlaubertPreTrainedModel,
                TFFlaubertWithLMHeadModel,
            )

else:
      import sys

      A__	:      Tuple				=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






