


import re
from pathlib import Path
from unittest import TestCase

import pytest
@pytest.mark.integration
class 						lowerCamelCase_							(					lowerCAmelCase__					):



					'''simple docstring'''







					def A   (					self			,							snake_case_    ) ->      List[Any]:







								'''simple docstring'''

								with open(snake_case_			,							encoding='''utf-8'''    ) as input_file:
											__lowercase			       = re.compile(r'''(?!.*\b(?:encoding|rb|w|wb|w+|wb+|ab|ab+)\b)(?<=\s)(open)\((.*)\)'''    )
											__lowercase			       = input_file.read()
											__lowercase			       = regexp.search(snake_case_    )

								return match






					def A   (					self			,							snake_case_    ) ->      Any:







								'''simple docstring'''

								with open(snake_case_			,							encoding='''utf-8'''    ) as input_file:
											__lowercase			       = re.compile(r'''#[^\r\n]*print\(|\"[^\r\n]*print\(|\"\"\".*?print\(.*?\"\"\"|(print\()'''			,							re.DOTALL    )
											__lowercase			       = input_file.read()
											# use `re.finditer` to handle the case where the ignored groups would be matched first by `re.search`
											__lowercase			       = regexp.finditer(snake_case_    )

								__lowercase			       = [match for match in matches if match is not None and match.group(1    ) is not None]
								return matches[0] if matches else None






					def A   (					self    ) ->      str:







								'''simple docstring'''

								__lowercase			       = Path('''./datasets'''    )
								__lowercase			       = list(dataset_paths.absolute().glob('''**/*.py'''    )    )

								for dataset in dataset_files:
											if self._no_encoding_on_file_open(str(snake_case_    )    ):
														raise AssertionError(F'open(...) must use utf-8 encoding in {dataset}'    )






					def A   (					self    ) ->      List[str]:







								'''simple docstring'''

								__lowercase			       = Path('''./datasets'''    )
								__lowercase			       = list(dataset_paths.absolute().glob('''**/*.py'''    )    )

								for dataset in dataset_files:
											if self._no_print_statements(str(snake_case_    )    ):
														raise AssertionError(F'print statement found in {dataset}. Use datasets.logger/logging instead.'    )






from typing import TYPE_CHECKING

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


a			:			Optional[Any]												=					{
    '''configuration_llama''': ['''LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LlamaConfig'''],
}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a			:			Optional[int]												=					['''LlamaTokenizer''']

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a			:			Optional[int]												=					['''LlamaTokenizerFast''']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a			:			Any												=					[
	    '''LlamaForCausalLM''',
	    '''LlamaModel''',
	    '''LlamaPreTrainedModel''',
	    '''LlamaForSequenceClassification''',
	]


if TYPE_CHECKING:
	from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig

	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_llama import LlamaTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_llama_fast import LlamaTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel


else:
	import sys

	a			:			Dict												=					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


