"""simple docstring"""

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase    	=    {
    '''configuration_roberta''': ['''ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RobertaConfig''', '''RobertaOnnxConfig'''],
    '''tokenization_roberta''': ['''RobertaTokenizer'''],
}

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

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCAmelCase    	=    [
		    '''ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''RobertaForCausalLM''',
		    '''RobertaForMaskedLM''',
		    '''RobertaForMultipleChoice''',
		    '''RobertaForQuestionAnswering''',
		    '''RobertaForSequenceClassification''',
		    '''RobertaForTokenClassification''',
		    '''RobertaModel''',
		    '''RobertaPreTrainedModel''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCAmelCase    	=    [
		    '''TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''TFRobertaForCausalLM''',
		    '''TFRobertaForMaskedLM''',
		    '''TFRobertaForMultipleChoice''',
		    '''TFRobertaForQuestionAnswering''',
		    '''TFRobertaForSequenceClassification''',
		    '''TFRobertaForTokenClassification''',
		    '''TFRobertaMainLayer''',
		    '''TFRobertaModel''',
		    '''TFRobertaPreTrainedModel''',
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCAmelCase    	=    [
		    '''FlaxRobertaForCausalLM''',
		    '''FlaxRobertaForMaskedLM''',
		    '''FlaxRobertaForMultipleChoice''',
		    '''FlaxRobertaForQuestionAnswering''',
		    '''FlaxRobertaForSequenceClassification''',
		    '''FlaxRobertaForTokenClassification''',
		    '''FlaxRobertaModel''',
		    '''FlaxRobertaPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
		from .tokenization_roberta import RobertaTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_roberta_fast import RobertaTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_roberta import (
				    ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
				    RobertaForCausalLM,
				    RobertaForMaskedLM,
				    RobertaForMultipleChoice,
				    RobertaForQuestionAnswering,
				    RobertaForSequenceClassification,
				    RobertaForTokenClassification,
				    RobertaModel,
				    RobertaPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_roberta import (
				    TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFRobertaForCausalLM,
				    TFRobertaForMaskedLM,
				    TFRobertaForMultipleChoice,
				    TFRobertaForQuestionAnswering,
				    TFRobertaForSequenceClassification,
				    TFRobertaForTokenClassification,
				    TFRobertaMainLayer,
				    TFRobertaModel,
				    TFRobertaPreTrainedModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_roberta import (
				    FlaxRobertaForCausalLM,
				    FlaxRobertaForMaskedLM,
				    FlaxRobertaForMultipleChoice,
				    FlaxRobertaForQuestionAnswering,
				    FlaxRobertaForSequenceClassification,
				    FlaxRobertaForTokenClassification,
				    FlaxRobertaModel,
				    FlaxRobertaPreTrainedModel,
				)

else:
		import sys

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



import fire

from utils import calculate_rouge, save_json
def 							__SCREAMING_SNAKE_CASE     (   a__    :   Any			,a__    :   Tuple			,a__    :   Any=None			,**a__    :   Dict       )					->				Optional[Any]:
	__A :			int		    =			[x.strip() for x in open(a__       ).readlines()]
	__A :			List[str]		    =			[x.strip() for x in open(a__       ).readlines()][: len(a__       )]
	__A :			List[Any]		    =			calculate_rouge(a__			,a__			,**a__       )
	if save_path is not None:
		save_json(a__			,a__			,indent=a__       )
	return metrics  # these print nicely


if __name__ == "__main__":
			fire.Fire(calculate_rouge_path)





