



import argparse
from collections import defaultdict







def snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )		->							Optional[int]:
		UpperCAmelCase_						=				f'''{file}_{class_name}_{test_name}'''
		done_test[_id] += 1

		with open(__SCREAMING_SNAKE_CASE			,     "r" ) as f:
				UpperCAmelCase_						=				f.readlines()

		UpperCAmelCase_						=				f'''class {class_name}('''
		UpperCAmelCase_						=				f'''{4 * ' '}def {test_name}('''
		UpperCAmelCase_						=				f'''{8 * ' '}{correct_line.split()[0]}'''
		UpperCAmelCase_						=				f'''{16 * ' '}{correct_line.split()[0]}'''
		UpperCAmelCase_						=				False
		UpperCAmelCase_						=				False
		UpperCAmelCase_						=				False
		UpperCAmelCase_						=				False
		UpperCAmelCase_						=				0
		UpperCAmelCase_						=				0

		UpperCAmelCase_						=				[]
		for line in lines:
				if line.startswith(__SCREAMING_SNAKE_CASE ):
						UpperCAmelCase_						=				True
				elif in_class and line.startswith(__SCREAMING_SNAKE_CASE ):
						UpperCAmelCase_						=				True
				elif in_class and in_func and (line.startswith(__SCREAMING_SNAKE_CASE ) or line.startswith(__SCREAMING_SNAKE_CASE )):
						UpperCAmelCase_						=				len(line.split(correct_line.split()[0] )[0] )
						count += 1

						if count == done_test[_id]:
								UpperCAmelCase_						=				True

				if in_class and in_func and in_line:
						if ")" not in line:
								continue
						else:
								UpperCAmelCase_						=				True

				if in_class and in_func and in_line and insert_line:
						new_lines.append(f'''{spaces * ' '}{correct_line}''' )
						UpperCAmelCase_						=				UpperCAmelCase_						=				UpperCAmelCase_						=				UpperCAmelCase_						=				False
				else:
						new_lines.append(__SCREAMING_SNAKE_CASE )

		with open(__SCREAMING_SNAKE_CASE			,     "w" ) as f:
				for line in new_lines:
						f.write(__SCREAMING_SNAKE_CASE )







def snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE=None )		->							Dict:
		if fail is not None:
				with open(__SCREAMING_SNAKE_CASE			,     "r" ) as f:
						UpperCAmelCase_						=				{l.strip() for l in f.readlines()}
		else:
				UpperCAmelCase_						=				None

		with open(__SCREAMING_SNAKE_CASE			,     "r" ) as f:
				UpperCAmelCase_						=				f.readlines()

		UpperCAmelCase_						=				defaultdict(__SCREAMING_SNAKE_CASE )
		for line in correct_lines:
				UpperCAmelCase_					,						UpperCAmelCase_					,						UpperCAmelCase_					,						UpperCAmelCase_						=				line.split(";" )
				if test_failures is None or "::".join([file, class_name, test_name] ) in test_failures:
						overwrite_file(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )


if __name__ == "__main__":
			SCREAMING_SNAKE_CASE     				=						argparse.ArgumentParser()
			parser.add_argument("--correct_filename", help="filename of tests with expected result")
			parser.add_argument("--fail_filename", help="filename of test failures", type=str, default=None)
			SCREAMING_SNAKE_CASE     				=						parser.parse_args()

			main(args.correct_filename, args.fail_filename)






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


SCREAMING_SNAKE_CASE     				=						{
    "configuration_gpt_bigcode": ["GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTBigCodeConfig"],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			SCREAMING_SNAKE_CASE     				=						[
			    "GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "GPTBigCodeForSequenceClassification",
			    "GPTBigCodeForTokenClassification",
			    "GPTBigCodeForCausalLM",
			    "GPTBigCodeModel",
			    "GPTBigCodePreTrainedModel",
			]

if TYPE_CHECKING:
			from .configuration_gpt_bigcode import GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTBigCodeConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_gpt_bigcode import (
						    GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST,
						    GPTBigCodeForCausalLM,
						    GPTBigCodeForSequenceClassification,
						    GPTBigCodeForTokenClassification,
						    GPTBigCodeModel,
						    GPTBigCodePreTrainedModel,
						)


else:
			import sys

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

