

from __future__ import annotations

def 	lowerCamelCase__	(   _a):
	SCREAMING_SNAKE_CASE				: Tuple			       =    {1}
	SCREAMING_SNAKE_CASE				: Optional[Any]			       =    [1]
	for _ in range(2							,     depth + 1):
		SCREAMING_SNAKE_CASE				: Tuple			       =    [*previous_coefficients, 0]
		SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    [0, *previous_coefficients]
		SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    []
		for x, y in zip(_a							,     _a):
			coefficients.add(x + y)
			previous_coefficients.append(x + y)
	return coefficients

def 	lowerCamelCase__	(   _a):
	SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    set()
	for number in unique_coefficients:
		SCREAMING_SNAKE_CASE				: List[Any]			       =    2
		SCREAMING_SNAKE_CASE				: List[Any]			       =    number
		while divisor**2 <= copy_number:
			SCREAMING_SNAKE_CASE				: Optional[Any]			       =    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 	lowerCamelCase__	(   _a = 51):
	SCREAMING_SNAKE_CASE				: List[str]			       =    get_pascal_triangle_unique_coefficients(_a)
	SCREAMING_SNAKE_CASE				: List[Any]			       =    get_squarefrees(_a)
	return sum(_a)


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


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


a_         =      {
    'configuration_codegen': ['CODEGEN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CodeGenConfig', 'CodeGenOnnxConfig'],
    'tokenization_codegen': ['CodeGenTokenizer'],
}

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

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a_         =      [
			    'CODEGEN_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'CodeGenForCausalLM',
			    'CodeGenModel',
			    'CodeGenPreTrainedModel',
			]

if TYPE_CHECKING:
			from .configuration_codegen import CODEGEN_PRETRAINED_CONFIG_ARCHIVE_MAP, CodeGenConfig, CodeGenOnnxConfig
			from .tokenization_codegen import CodeGenTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_codegen_fast import CodeGenTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_codegen import (
						    CODEGEN_PRETRAINED_MODEL_ARCHIVE_LIST,
						    CodeGenForCausalLM,
						    CodeGenModel,
						    CodeGenPreTrainedModel,
						)

else:
			import sys

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