




'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


A							:				Tuple			     =	{
    """configuration_openai""": ["""OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """OpenAIGPTConfig"""],
    """tokenization_openai""": ["""OpenAIGPTTokenizer"""],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  A							:				Any			     =	["""OpenAIGPTTokenizerFast"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  A							:				Any			     =	[
      """OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """OpenAIGPTDoubleHeadsModel""",
      """OpenAIGPTForSequenceClassification""",
      """OpenAIGPTLMHeadModel""",
      """OpenAIGPTModel""",
      """OpenAIGPTPreTrainedModel""",
      """load_tf_weights_in_openai_gpt""",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  A							:				Optional[Any]			     =	[
      """TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """TFOpenAIGPTDoubleHeadsModel""",
      """TFOpenAIGPTForSequenceClassification""",
      """TFOpenAIGPTLMHeadModel""",
      """TFOpenAIGPTMainLayer""",
      """TFOpenAIGPTModel""",
      """TFOpenAIGPTPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_openai import OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenAIGPTConfig
  from .tokenization_openai import OpenAIGPTTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_openai_fast import OpenAIGPTTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_openai import (
        OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
        OpenAIGPTDoubleHeadsModel,
        OpenAIGPTForSequenceClassification,
        OpenAIGPTLMHeadModel,
        OpenAIGPTModel,
        OpenAIGPTPreTrainedModel,
        load_tf_weights_in_openai_gpt,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_openai import (
        TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFOpenAIGPTDoubleHeadsModel,
        TFOpenAIGPTForSequenceClassification,
        TFOpenAIGPTLMHeadModel,
        TFOpenAIGPTMainLayer,
        TFOpenAIGPTModel,
        TFOpenAIGPTPreTrainedModel,
    )

else:
  import sys

  A							:				int			     =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)










'''simple docstring'''
import unittest
import warnings
from dataclasses import dataclass

from transformers.convert_slow_tokenizer import SpmConverter
from transformers.testing_utils import get_tests_dir






@dataclass
class 	SCREAMING_SNAKE_CASE:
       snake_case_			:					str






class 	SCREAMING_SNAKE_CASE(    unittest.TestCase					):

       def    snake_case__      (   self					)   -> Optional[int]:

           """simple docstring"""



           __lowercase          =						get_tests_dir("""fixtures/test_sentencepiece.model"""					)
           __lowercase          =						get_tests_dir("""fixtures/test_sentencepiece_with_bytefallback.model"""					)

           __lowercase          =						FakeOriginalTokenizer(vocab_file=lowerCamelCase__					)

           with warnings.catch_warnings(record=lowerCamelCase__					) as w:
               __lowercase          =						SpmConverter(lowerCamelCase__					)
           self.assertEqual(len(lowerCamelCase__					) ,		0					)

           __lowercase          =						FakeOriginalTokenizer(vocab_file=lowerCamelCase__					)

           with warnings.catch_warnings(record=lowerCamelCase__					) as w:
               __lowercase          =						SpmConverter(lowerCamelCase__					)
           self.assertEqual(len(lowerCamelCase__					) ,		1					)

           self.assertIn(
               """The sentencepiece tokenizer that you are converting to a fast tokenizer uses the byte fallback option"""
               """ which is not implemented in the fast tokenizers.""" ,		str(w[0].message					) ,		)




