
from __future__ import annotations

from collections.abc import Iterable
from dataclasses import dataclass
from typing import Any

class __snake_case	:
				_a	:		int=       ("""_front""", """_back""", """_len""")

				@dataclass
				class __snake_case	:
								_a	:		Any=       None
								_a	:		Deque._Node | None=       None
								_a	:		Deque._Node | None=       None






				class __snake_case	:
								_a	:		Dict=       ("""_cur""",)
								def __init__(						self	,snake_case   ):



															'''simple docstring'''







															lowercase							:  str 			=   cur
								def __iter__(						self   ):



															'''simple docstring'''







															return self
								def    _SCREAMING_SNAKE_CASE (						self   ):



															'''simple docstring'''







															if self._cur is None:
																						# finished iterating
																						raise StopIteration
															lowercase							:  Tuple 			=   self._cur.val
															lowercase							:  int 			=   self._cur.next_node

															return val






				def __init__(						self	,snake_case = None   ):



											'''simple docstring'''







											lowercase							:  List[str] 			=   None
											lowercase							:  List[Any] 			=   None
											lowercase							:  Optional[int] 			=   0

											if iterable is not None:
																		# append every value to the deque
																		for val in iterable:
																									self.append(snake_case   )
				def    _SCREAMING_SNAKE_CASE (						self	,snake_case   ):



											'''simple docstring'''







											lowercase							:  List[str] 			=   self._Node(snake_case	,snake_case	,snake_case   )
											if self.is_empty():
																		# front = back
																		lowercase							:  Tuple 			=   node
																		lowercase							:  List[str] 			=   1
											else:
																		# connect nodes
																		lowercase							:  Optional[Any] 			=   node
																		lowercase							:  Union[str, Any] 			=   self._back
																		lowercase							:  str 			=   node  # assign new back to the new node

																		self._len += 1

																		# make sure there were no errors
																		assert not self.is_empty(), "Error on appending value."
				def    _SCREAMING_SNAKE_CASE (						self	,snake_case   ):



											'''simple docstring'''







											lowercase							:  List[Any] 			=   self._Node(snake_case	,snake_case	,snake_case   )
											if self.is_empty():
																		# front = back
																		lowercase							:  Dict 			=   node
																		lowercase							:  int 			=   1
											else:
																		# connect nodes
																		lowercase							:  Tuple 			=   self._front
																		lowercase							:  Union[str, Any] 			=   node
																		lowercase							:  Optional[int] 			=   node  # assign new front to the new node

																		self._len += 1

																		# make sure there were no errors
																		assert not self.is_empty(), "Error on appending value."
				def    _SCREAMING_SNAKE_CASE (						self	,snake_case   ):



											'''simple docstring'''







											for val in iterable:
																		self.append(snake_case   )
				def    _SCREAMING_SNAKE_CASE (						self	,snake_case   ):



											'''simple docstring'''







											for val in iterable:
																		self.appendleft(snake_case   )
				def    _SCREAMING_SNAKE_CASE (						self   ):



											'''simple docstring'''







											assert not self.is_empty(), "Deque is empty."

											lowercase							:  Optional[Any] 			=   self._back
											lowercase							:  int 			=   self._back.prev_node  # set new back
											# drop the last node - python will deallocate memory automatically
											lowercase							:  Any 			=   None

											self._len -= 1

											return topop.val
				def    _SCREAMING_SNAKE_CASE (						self   ):



											'''simple docstring'''







											assert not self.is_empty(), "Deque is empty."

											lowercase							:  int 			=   self._front
											lowercase							:  List[str] 			=   self._front.next_node  # set new front and drop the first node
											lowercase							:  int 			=   None

											self._len -= 1

											return topop.val
				def    _SCREAMING_SNAKE_CASE (						self   ):



											'''simple docstring'''







											return self._front is None
				def __len__(						self   ):



											'''simple docstring'''







											return self._len
				def __eq__(						self	,snake_case   ):



											'''simple docstring'''







											if not isinstance(snake_case	,snake_case   ):
																		return NotImplemented

											lowercase							:  int 			=   self._front
											lowercase							:  Optional[int] 			=   other._front

											# if the length of the dequeues are not the same, they are not equal
											if len(self   ) != len(snake_case   ):
																		return False

											while me is not None and oth is not None:
																		# compare every value
																		if me.val != oth.val:
																									return False
																		lowercase							:  str 			=   me.next_node
																		lowercase							:  Dict 			=   oth.next_node

											return True
				def __iter__(						self   ):



											'''simple docstring'''







											return Deque._Iterator(self._front   )
				def __repr__(						self   ):



											'''simple docstring'''







											lowercase							:  List[str] 			=   []
											lowercase							:  Union[str, Any] 			=   self._front
											while aux is not None:
																		# append the values in a list to display
																		values_list.append(aux.val   )
																		lowercase							:  Optional[int] 			=   aux.next_node

											return f"[{', '.join(repr(snake_case   ) for val in values_list   )}]"


if __name__ == "__main__":
		import doctest

		doctest.testmod()


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,
)


lowercase						:      Any							=							{
    """configuration_gpt2""": ["""GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPT2Config""", """GPT2OnnxConfig"""],
    """tokenization_gpt2""": ["""GPT2Tokenizer"""],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase						:      List[Any]							=							["""GPT2TokenizerFast"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase						:      str							=							[
      """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:
  lowercase						:      Tuple							=							[
      """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:
  lowercase						:      List[str]							=							["""TFGPT2Tokenizer"""]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase						:      str							=							["""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

  lowercase						:      Union[str, Any]							=							_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
