

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     :       Tuple        =       {
    "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     :       Optional[Any]        =       ["GPT2TokenizerFast"]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase     :       Union[str, Any]        =       [
					    "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     :       Any        =       [
					    "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     :       int        =       ["TFGPT2Tokenizer"]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase     :       Optional[int]        =       ["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     :       Dict        =       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


from .balanced_parentheses import balanced_parentheses
from .stack import Stack







def  UpperCAmelCase_   (_lowerCAmelCase :		str		):
			return {"+": 1, "-": 1, "*": 2, "/": 2, "^": 3}.get(_lowerCAmelCase     ,			-1		)







def  UpperCAmelCase_   (_lowerCAmelCase :		str		):
			if not balanced_parentheses(_lowerCAmelCase		):
						raise ValueError("Mismatched parentheses"		)
			__UpperCamelCase    :    Stack[str]								=      Stack()
			__UpperCamelCase    :    int								=      []
			for char in expression_str:
						if char.isalpha() or char.isdigit():
									postfix.append(_lowerCAmelCase		)
						elif char == "(":
									stack.push(_lowerCAmelCase		)
						elif char == ")":
									while not stack.is_empty() and stack.peek() != "(":
												postfix.append(stack.pop()		)
									stack.pop()
						else:
									while not stack.is_empty() and precedence(_lowerCAmelCase		) <= precedence(stack.peek()		):
												postfix.append(stack.pop()		)
									stack.push(_lowerCAmelCase		)
			while not stack.is_empty():
						postfix.append(stack.pop()		)
			return " ".join(_lowerCAmelCase		)


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

					testmod()
					lowercase     :       int        =       "a+b*(c^d-e)^(f+g*h)-i"

					print("Infix to Postfix Notation demonstration:\n")
					print("Infix notation: " + expression)
					print("Postfix notation: " + infix_to_postfix(expression))