



from typing import TYPE_CHECKING

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


__lowerCamelCase     :							str  							=				{"configuration_xglm": ["XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XGLMConfig"]}

try:
		if not is_sentencepiece_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCamelCase     :							List[str]  							=				["XGLMTokenizer"]

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCamelCase     :							Dict  							=				["XGLMTokenizerFast"]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCamelCase     :							Union[str, Any]  							=				[
		    "XGLM_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "XGLMForCausalLM",
		    "XGLMModel",
		    "XGLMPreTrainedModel",
		]


try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCamelCase     :							int  							=				[
		    "FlaxXGLMForCausalLM",
		    "FlaxXGLMModel",
		    "FlaxXGLMPreTrainedModel",
		]


try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCamelCase     :							int  							=				[
		    "TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "TFXGLMForCausalLM",
		    "TFXGLMModel",
		    "TFXGLMPreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig

		try:
				if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_xglm import XGLMTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_xglm_fast import XGLMTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_xglm import (
				    TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFXGLMForCausalLM,
				    TFXGLMModel,
				    TFXGLMPreTrainedModel,
				)


else:
		import sys

		__lowerCamelCase     :							Dict  							=				_LazyModule(__name__, globals()["__file__"], _import_structure)








import fire

from utils import calculate_rouge, save_json

def    lowerCamelCase_(lowerCamelCase_    ,   lowerCamelCase_    ,   lowerCamelCase_=None    ,   **lowerCamelCase_    ) ->       Optional[int]:
						UpperCAmelCase         =     [x.strip() for x in open(lowerCamelCase_    ).readlines()]
						UpperCAmelCase         =     [x.strip() for x in open(lowerCamelCase_    ).readlines()][: len(lowerCamelCase_    )]
						UpperCAmelCase         =     calculate_rouge(lowerCamelCase_    ,   lowerCamelCase_    ,   **lowerCamelCase_    )
						if save_path is not None:
												save_json(lowerCamelCase_    ,   lowerCamelCase_    ,   indent=lowerCamelCase_    )
						return metrics  # these print nicely


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



