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    :      Dict	 =						{
    """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    :      Tuple	 =						["""GPT2TokenizerFast"""]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_lowerCamelCase    :      Optional[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:
							_lowerCamelCase    :      Optional[int]	 =						[
							    """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    :      Optional[Any]	 =						["""TFGPT2Tokenizer"""]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_lowerCamelCase    :      Any	 =						["""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    :      Any	 =						_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)












"""simple docstring"""







from collections import Counter
from functools import total_ordering

from data_structures.heap.heap import Heap


@total_ordering
class     __UpperCAmelCase:




						"""simple docstring"""




						def __init__(   self       ,  snake_case__       ,  snake_case__			):
												'''simple docstring'''
												lowercase__				:      Optional[Any]=       word
												lowercase__				:      Any=       count




						def __eq__(   self       ,  snake_case__			):
												'''simple docstring'''
												if not isinstance(snake_case__       ,  snake_case__			):
																		return NotImplemented
												return self.count == other.count




						def __lt__(   self       ,  snake_case__			):
												'''simple docstring'''
												if not isinstance(snake_case__       ,  snake_case__			):
																		return NotImplemented
												return self.count < other.count





def 						lowercase__(A					,		A						)				->list[str]:




						"""simple docstring"""



						lowercase__				:      Heap[WordCount]=       Heap()
						lowercase__				:      str=       Counter(A						)
						heap.build_max_heap(
						    [WordCount(A					,		A						) for word, count in count_by_word.items()]						)
						return [heap.extract_max().word for _ in range(min(A					,		len(A						)						)						)]


if __name__ == "__main__":
			import doctest

			doctest.testmod()






