






'''simple docstring'''




from typing import TYPE_CHECKING

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


lowerCamelCase_							      =      {
    'configuration_gpt2': ['GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPT2Config', 'GPT2OnnxConfig'],
    'tokenization_gpt2': ['GPT2Tokenizer'],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase_							      =      ['GPT2TokenizerFast']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase_							      =      [
        'GPT2_PRETRAINED_MODEL_ARCHIVE_LIST',
        'GPT2DoubleHeadsModel',
        'GPT2ForQuestionAnswering',
        'GPT2ForSequenceClassification',
        'GPT2ForTokenClassification',
        'GPT2LMHeadModel',
        'GPT2Model',
        'GPT2PreTrainedModel',
        'load_tf_weights_in_gpt2',
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase_							      =      [
        'TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST',
        'TFGPT2DoubleHeadsModel',
        'TFGPT2ForSequenceClassification',
        'TFGPT2LMHeadModel',
        'TFGPT2MainLayer',
        'TFGPT2Model',
        'TFGPT2PreTrainedModel',
    ]

try:
    if not is_keras_nlp_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase_							      =      ['TFGPT2Tokenizer']

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase_							      =      ['FlaxGPT2LMHeadModel', 'FlaxGPT2Model', 'FlaxGPT2PreTrainedModel']

if TYPE_CHECKING:
    from .configuration_gpta import GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTaConfig, GPTaOnnxConfig
    from .tokenization_gpta import GPTaTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_gpta_fast import GPTaTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_gpta import (
            GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
            GPTaDoubleHeadsModel,
            GPTaForQuestionAnswering,
            GPTaForSequenceClassification,
            GPTaForTokenClassification,
            GPTaLMHeadModel,
            GPTaModel,
            GPTaPreTrainedModel,
            load_tf_weights_in_gpta,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_gpta import (
            TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFGPTaDoubleHeadsModel,
            TFGPTaForSequenceClassification,
            TFGPTaLMHeadModel,
            TFGPTaMainLayer,
            TFGPTaModel,
            TFGPTaPreTrainedModel,
        )

    try:
        if not is_keras_nlp_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_gpta_tf import TFGPTaTokenizer

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel, FlaxGPTaPreTrainedModel

else:
    import sys

    lowerCamelCase_							      =      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)














'''simple docstring'''




from __future__ import annotations

from collections.abc import Iterator
from typing import Any

class      _UpperCAmelCase    :




   """simple docstring"""

   def __init__(	self				:			Any   ,   __UpperCAmelCase				:			Any	):





         '''simple docstring'''





         _A      =			data
         _A      =			None

   def __str__(	self				:			str	):





         '''simple docstring'''





         return f'''{self.data}'''





class      _UpperCAmelCase    :




   """simple docstring"""

   def __init__(	self				:			Optional[int]	):





         '''simple docstring'''





         _A      =			None
         _A      =			None

   def __iter__(	self				:			List[Any]	):





         '''simple docstring'''





         _A      =			self.front
         while node:
               yield node.data
               _A      =			node.next

   def __len__(	self				:			Union[str, Any]	):





         '''simple docstring'''





         return len(tuple(iter(self	)	)	)

   def __str__(	self				:			int	):





         '''simple docstring'''





         return " <- ".join(str(__UpperCAmelCase	) for item in self	)

   def 		lowerCAmelCase				(	self				:			List[Any]	):





         '''simple docstring'''





         return len(self	) == 0

   def 		lowerCAmelCase				(	self				:			int   ,   __UpperCAmelCase				:			Any	):





         '''simple docstring'''





         _A      =			Node(__UpperCAmelCase	)
         if self.is_empty():
               _A      =			_A      =			node
         else:
               assert isinstance(self.rear   ,   __UpperCAmelCase	)
               _A      =			node
               _A      =			node

   def 		lowerCAmelCase				(	self				:			str	):





         '''simple docstring'''





         if self.is_empty():
               raise IndexError("dequeue from empty queue"	)
         assert isinstance(self.front   ,   __UpperCAmelCase	)
         _A      =			self.front
         _A      =			self.front.next
         if self.front is None:
               _A      =			None
         return node.data

   def 		lowerCAmelCase				(	self				:			int	):





         '''simple docstring'''





         _A      =			_A      =			None


if __name__ == "__main__":
    from doctest import testmod

    testmod()





