




'''simple docstring'''

from typing import List, Optional

from tokenizers import ByteLevelBPETokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer


a       :						int								=		logging.get_logger(__name__)

a       :						List[Any]								=		{
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
}

a       :						Dict								=		{
    '''vocab_file''': {
        '''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json'''
    },
    '''merges_file''': {
        '''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt'''
    },
    '''tokenizer_config_file''': {
        '''facebook/blenderbot_small-90M''': (
            '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json'''
        )
    },
}

a       :						List[Any]								=		{
    '''facebook/blenderbot_small-90M''': 512,
}



class 		SCREAMING_SNAKE_CASE__  (			_UpperCamelCase	):
			__SCREAMING_SNAKE_CASE    =			VOCAB_FILES_NAMES
			__SCREAMING_SNAKE_CASE    =			PRETRAINED_VOCAB_FILES_MAP
			__SCREAMING_SNAKE_CASE    =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			__SCREAMING_SNAKE_CASE    =			BlenderbotSmallTokenizer





			def __init__(      self						:    str      ,		a_						:    int=None      ,		a_						:    str=None      ,		a_						:    str="<|endoftext|>"      ,		a_						:    Any="<|endoftext|>"      ,		a_						:    str="<|endoftext|>"      ,		a_						:    Any=False      ,		a_						:    Union[str, Any]=True      ,		**a_						:    str      ,		):



						"""simple docstring"""




						super().__init__(
						    ByteLevelBPETokenizer(
						        vocab=a_      ,		merges=a_      ,		add_prefix_space=a_      ,		trim_offsets=a_      ,		)      ,		bos_token=a_      ,		eos_token=a_      ,		unk_token=a_      ,		**a_      ,		)
						__snake_case			=		add_prefix_space





			def 							A (      self						:    Tuple      ,		a_						:    int      ,		a_						:    Any=None							):



						"""simple docstring"""




						__snake_case			=		[self.bos_token_id] + token_ids_a + [self.eos_token_id]
						if token_ids_a is None:
									return output

						return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]






			def 							A (      self						:    List[str]      ,		a_						:    List[int]      ,		a_						:    Optional[List[int]] = None							):



						"""simple docstring"""




						__snake_case			=		[self.sep_token_id]
						__snake_case			=		[self.cls_token_id]

						if token_ids_a is None:
									return len(cls + token_ids_a + sep							) * [0]
						return len(cls + token_ids_a + sep + sep + token_ids_a + sep							) * [0]












from typing import TYPE_CHECKING

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


_A					:				Any				=			{"""configuration_xglm""": ["""XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XGLMConfig"""]}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _A					:				Optional[Any]				=			["""XGLMTokenizer"""]

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

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _A					:				str				=			[
      """XGLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """XGLMForCausalLM""",
      """XGLMModel""",
      """XGLMPreTrainedModel""",
  ]


try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _A					:				Tuple				=			[
      """FlaxXGLMForCausalLM""",
      """FlaxXGLMModel""",
      """FlaxXGLMPreTrainedModel""",
  ]


try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _A					:				List[Any]				=			[
      """TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """TFXGLMForCausalLM""",
      """TFXGLMModel""",
      """TFXGLMPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig

  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_xglm import XGLMTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_xglm_fast import XGLMTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_xglm import (
        TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFXGLMForCausalLM,
        TFXGLMModel,
        TFXGLMPreTrainedModel,
    )


else:
  import sys

  _A					:				List[Any]				=			_LazyModule(__name__, globals()["""__file__"""], _import_structure)
