



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,
)


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

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

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase							=       [
	    """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:
	UpperCAmelCase							=       [
	    """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:
	UpperCAmelCase							=       ["""TFGPT2Tokenizer"""]

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

	UpperCAmelCase							=       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






from __future__ import annotations

def       A_   (						__a  :					int		):

				"""simple docstring"""
				a__					   =		{1}
				a__					   =		[1]
				for _ in range(2			,		depth + 1		):
								a__					   =		[*previous_coefficients, 0]
								a__					   =		[0, *previous_coefficients]
								a__					   =		[]
								for x, y in zip(__a			,		__a		):
												coefficients.add(x + y		)
												previous_coefficients.append(x + y		)
				return coefficients

def       A_   (						__a  :					set[int]		):

				"""simple docstring"""
				a__					   =		set()
				for number in unique_coefficients:
								a__					   =		2
								a__					   =		number
								while divisor**2 <= copy_number:
												a__					   =		0
												while copy_number % divisor == 0:
																copy_number //= divisor
																multiplicity += 1
												if multiplicity >= 2:
																non_squarefrees.add(__a		)
																break
												divisor += 1

				return unique_coefficients.difference(__a		)

def       A_   (						__a  :					int = 51		):

				"""simple docstring"""
				a__					   =		get_pascal_triangle_unique_coefficients(__a		)
				a__					   =		get_squarefrees(__a		)
				return sum(__a		)


if __name__ == "__main__":
	print(f"""{solution() = }""")

