




'''simple docstring'''


import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
				import jax
				import jaxlib

_snake_case : str  				=						get_logger()

_snake_case : List[Any]  				=						None

class      A       (						TensorFormatter[Mapping, 'jax.Array', Mapping]       ):







							def __init__(    self			:    Any				,     lowerCAmelCase_			:    Union[str, Any]=None				,     lowerCAmelCase_			:    Tuple=None				,     **lowerCAmelCase_			:    Dict   )     ->				str:


														"""simple docstring"""


														super().__init__(features=__a   )
														import jax
														from jaxlib.xla_client import Device

														if isinstance(__a				,     __a   ):
																					raise ValueError(
																					    F'Expected {device} to be a `str` not {type(__a   )}, as `jaxlib.xla_extension.Device` '
																					    '''is not serializable neither with `pickle` nor with `dill`. Instead you can surround '''
																					    '''the device with `str()` to get its string identifier that will be internally mapped '''
																					    '''to the actual `jaxlib.xla_extension.Device`.'''   )
														_a      	=							device if isinstance(__a				,     __a   ) else str(jax.devices()[0]   )
														# using global variable since `jaxlib.xla_extension.Device` is not serializable neither
														# with `pickle` nor with `dill`, so we need to use a global variable instead
														global DEVICE_MAPPING
														if DEVICE_MAPPING is None:
																					_a      	=							self._map_devices_to_str()
														if self.device not in list(DEVICE_MAPPING.keys()   ):
																					logger.warning(
																					    F'Device with string identifier {self.device} not listed among the available '
																					    F'devices: {list(DEVICE_MAPPING.keys()   )}, so falling back to the default '
																					    F'device: {str(jax.devices()[0]   )}.'   )
																					_a      	=							str(jax.devices()[0]   )
														_a      	=							jnp_array_kwargs







							@staticmethod
							def   __lowerCAmelCase     (    )     ->				Dict[str, "jaxlib.xla_extension.Device"]:


														"""simple docstring"""


														import jax

														return {str(__a   ): device for device in jax.devices()}







							def   __lowerCAmelCase     (    self			:    int				,     lowerCAmelCase_			:    str   )     ->				Any:


														"""simple docstring"""


														import jax
														import jax.numpy as jnp

														if isinstance(__a				,     __a   ) and column:
																					if all(
																					    isinstance(__a				,     jax.Array   ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column   ):
																												return jnp.stack(__a				,     axis=0   )
														return column







							def   __lowerCAmelCase     (    self			:    Tuple				,     lowerCAmelCase_			:    Any   )     ->				Dict:


														"""simple docstring"""


														import jax
														import jax.numpy as jnp

														if isinstance(__a				,     (str, bytes, type(__a   ))   ):
																					return value
														elif isinstance(__a				,     (np.character, np.ndarray)   ) and np.issubdtype(value.dtype				,     np.character   ):
																					return value.tolist()

														_a      	=							{}

														if isinstance(__a				,     (np.number, np.ndarray)   ) and np.issubdtype(value.dtype				,     np.integer   ):
																					# the default int precision depends on the jax config
																					# see https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html#double-64bit-precision
																					if jax.config.jax_enable_xaa:
																												_a      	=							{'''dtype''': jnp.intaa}
																					else:
																												_a      	=							{'''dtype''': jnp.intaa}
														elif isinstance(__a				,     (np.number, np.ndarray)   ) and np.issubdtype(value.dtype				,     np.floating   ):
																					_a      	=							{'''dtype''': jnp.floataa}
														elif config.PIL_AVAILABLE and "PIL" in sys.modules:
																					import PIL.Image

																					if isinstance(__a				,     PIL.Image.Image   ):
																												_a      	=							np.asarray(__a   )

        # using global variable since `jaxlib.xla_extension.Device` is not serializable neither
        # with `pickle` nor with `dill`, so we need to use a global variable instead
														global DEVICE_MAPPING
														if DEVICE_MAPPING is None:
																					_a      	=							self._map_devices_to_str()

														with jax.default_device(DEVICE_MAPPING[self.device]   ):
																					# calling jnp.array on a np.ndarray does copy the data
																					# see https://github.com/google/jax/issues/4486
																					return jnp.array(__a				,     **{**default_dtype, **self.jnp_array_kwargs}   )







							def   __lowerCAmelCase     (    self			:    Dict				,     lowerCAmelCase_			:    List[str]   )     ->				str:


														"""simple docstring"""


														import jax

														# support for torch, tf, jax etc.
														if config.TORCH_AVAILABLE and "torch" in sys.modules:
																					import torch

																					if isinstance(__a				,     torch.Tensor   ):
																												return self._tensorize(data_struct.detach().cpu().numpy()[()]   )
														if hasattr(__a				,     '''__array__'''   ) and not isinstance(__a				,     jax.Array   ):
																					_a      	=							data_struct.__array__()
														# support for nested types like struct of list of struct
														if isinstance(__a				,     np.ndarray   ):
																					if data_struct.dtype == object:  # jax arrays cannot be instantied from an array of objects
																												return self._consolidate([self.recursive_tensorize(__a   ) for substruct in data_struct]   )
														elif isinstance(__a				,     (list, tuple)   ):
																					return self._consolidate([self.recursive_tensorize(__a   ) for substruct in data_struct]   )
														return self._tensorize(__a   )







							def   __lowerCAmelCase     (    self			:    Optional[Any]				,     lowerCAmelCase_			:    Dict   )     ->				Optional[int]:


														"""simple docstring"""


														return map_nested(self._recursive_tensorize				,     __a				,     map_list=__a   )







							def   __lowerCAmelCase     (    self			:    List[Any]				,     lowerCAmelCase_			:    Union[str, Any]   )     ->				Mapping:


														"""simple docstring"""


														_a      	=							self.numpy_arrow_extractor().extract_row(__a   )
														_a      	=							self.python_features_decoder.decode_row(__a   )
														return self.recursive_tensorize(__a   )







							def   __lowerCAmelCase     (    self			:    Optional[Any]				,     lowerCAmelCase_			:    Dict   )     ->				"jax.Array":


														"""simple docstring"""


														_a      	=							self.numpy_arrow_extractor().extract_column(__a   )
														_a      	=							self.python_features_decoder.decode_column(__a				,     pa_table.column_names[0]   )
														_a      	=							self.recursive_tensorize(__a   )
														_a      	=							self._consolidate(__a   )
														return column







							def   __lowerCAmelCase     (    self			:    Union[str, Any]				,     lowerCAmelCase_			:    Union[str, Any]   )     ->				Mapping:


														"""simple docstring"""


														_a      	=							self.numpy_arrow_extractor().extract_batch(__a   )
														_a      	=							self.python_features_decoder.decode_batch(__a   )
														_a      	=							self.recursive_tensorize(__a   )
														for column_name in batch:
																					_a      	=							self._consolidate(batch[column_name]   )
														return batch



"""simple docstring"""





import fire

from utils import calculate_rouge, save_json



def  lowerCamelCase__						(     __snake_case,	__snake_case,	__snake_case=None,	**__snake_case      )				->						Optional[int]:


					"""simple docstring"""





					_UpperCamelCase									=    [x.strip() for x in open(__snake_case      ).readlines()]
					_UpperCamelCase									=    [x.strip() for x in open(__snake_case      ).readlines()][: len(__snake_case      )]
					_UpperCamelCase									=    calculate_rouge(__snake_case,	__snake_case,	**__snake_case      )
					if save_path is not None:
										save_json(__snake_case,	__snake_case,	indent=__snake_case      )
					return metrics  # these print nicely


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






