






'''simple docstring'''





from argparse import ArgumentParser

from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand


__A							:							Any  							=		logging.get_logger(__name__)  # pylint: disable=invalid-name






def    lowerCamelCase_     (    lowercase__):
 if not path:
  return "pipe"

 for ext in PipelineDataFormat.SUPPORTED_FORMATS:
  if path.endswith(lowercase__):
   return ext

 raise Exception(
     F'''Unable to determine file format from file extension {path}. '''
     F'''Please provide the format through --format {PipelineDataFormat.SUPPORTED_FORMATS}''')






def    lowerCamelCase_     (    lowercase__):
 lowerCamelCase__  =     pipeline(
     task=args.task				,     model=args.model if args.model else None				,     config=args.config				,     tokenizer=args.tokenizer				,     device=args.device				,     )
 lowerCamelCase__  =     try_infer_format_from_ext(args.input) if args.format == "infer" else args.format
 lowerCamelCase__  =     PipelineDataFormat.from_str(
     format=lowercase__				,     output_path=args.output				,     input_path=args.input				,     column=args.column if args.column else nlp.default_input_names				,     overwrite=args.overwrite				,     )
 return RunCommand(lowercase__				,     lowercase__)
class 				lowercase       (							_lowerCamelCase      ):


    '''simple docstring'''

    def __init__(					self     :     Tuple				,     __lowerCamelCase     :     Pipeline				,     __lowerCamelCase     :     PipelineDataFormat     )							->							Dict:

     '''simple docstring'''




     lowerCamelCase__  =     nlp
     lowerCamelCase__  =     reader

    @staticmethod
    def    a__				(					__lowerCamelCase     :     ArgumentParser     )							->							str:

     '''simple docstring'''




     lowerCamelCase__  =     parser.add_parser("run"				,     help="Run a pipeline through the CLI"     )
     run_parser.add_argument("--task"				,     choices=get_supported_tasks()				,     help="Task to run"     )
     run_parser.add_argument("--input"				,     type=__lowerCamelCase				,     help="Path to the file to use for inference"     )
     run_parser.add_argument("--output"				,     type=__lowerCamelCase				,     help="Path to the file that will be used post to write results."     )
     run_parser.add_argument("--model"				,     type=__lowerCamelCase				,     help="Name or path to the model to instantiate."     )
     run_parser.add_argument("--config"				,     type=__lowerCamelCase				,     help="Name or path to the model's config to instantiate."     )
     run_parser.add_argument(
         "--tokenizer"				,     type=__lowerCamelCase				,     help="Name of the tokenizer to use. (default: same as the model name)"     )
     run_parser.add_argument(
         "--column"				,     type=__lowerCamelCase				,     help="Name of the column to use as input. (For multi columns input as QA use column1,columns2)"				,     )
     run_parser.add_argument(
         "--format"				,     type=__lowerCamelCase				,     default="infer"				,     choices=PipelineDataFormat.SUPPORTED_FORMATS				,     help="Input format to read from"				,     )
     run_parser.add_argument(
         "--device"				,     type=__lowerCamelCase				,     default=-1				,     help="Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)"				,     )
     run_parser.add_argument("--overwrite"				,     action="store_true"				,     help="Allow overwriting the output file."     )
     run_parser.set_defaults(func=__lowerCamelCase     )




    def    a__				(					self     :     Dict     )							->							Any:

     '''simple docstring'''




     lowerCamelCase__  ,	lowerCamelCase__  =     self._nlp, []

     for entry in self._reader:
      lowerCamelCase__  =     nlp(**__lowerCamelCase     ) if self._reader.is_multi_columns else nlp(__lowerCamelCase     )
      if isinstance(__lowerCamelCase				,     __lowerCamelCase     ):
       outputs.append(__lowerCamelCase     )
      else:
       outputs += output

        # Saving data
     if self._nlp.binary_output:
      lowerCamelCase__  =     self._reader.save_binary(__lowerCamelCase     )
      logger.warning(f'''Current pipeline requires output to be in binary format, saving at {binary_path}'''     )
     else:
      self._reader.save(__lowerCamelCase     )









'''simple docstring'''





# Lint as: python3
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

__A							:							Any  							=		get_logger()

__A							:							Optional[dict]  							=		None
class 				lowercase       (							TensorFormatter[Mapping, "jax.Array", Mapping]      ):


    '''simple docstring'''

    def __init__(					self     :     str				,     __lowerCamelCase     :     Optional[Any]=None				,     __lowerCamelCase     :     Dict=None				,     **__lowerCamelCase     :     str     )							->							List[Any]:

     '''simple docstring'''




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

     if isinstance(__lowerCamelCase				,     __lowerCamelCase     ):
      raise ValueError(
          f'''Expected {device} to be a `str` not {type(__lowerCamelCase     )}, 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`."     )
     lowerCamelCase__  =     device if isinstance(__lowerCamelCase				,     __lowerCamelCase     ) 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:
      lowerCamelCase__  =     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]     )}.'''     )
      lowerCamelCase__  =     str(jax.devices()[0]     )
     lowerCamelCase__  =     jnp_array_kwargs

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

     '''simple docstring'''




     import jax

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

    def    a__				(					self     :     Optional[Any]				,     __lowerCamelCase     :     Optional[int]     )							->							List[str]:

     '''simple docstring'''




     import jax
     import jax.numpy as jnp

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

    def    a__				(					self     :     List[Any]				,     __lowerCamelCase     :     List[Any]     )							->							Optional[int]:

     '''simple docstring'''




     import jax
     import jax.numpy as jnp

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

     lowerCamelCase__  =     {}

     if isinstance(__lowerCamelCase				,     (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:
       lowerCamelCase__  =     {"dtype": jnp.intaa}
      else:
       lowerCamelCase__  =     {"dtype": jnp.intaa}
     elif isinstance(__lowerCamelCase				,     (np.number, np.ndarray)     ) and np.issubdtype(value.dtype				,     np.floating     ):
      lowerCamelCase__  =     {"dtype": jnp.floataa}
     elif config.PIL_AVAILABLE and "PIL" in sys.modules:
      import PIL.Image

      if isinstance(__lowerCamelCase				,     PIL.Image.Image     ):
       lowerCamelCase__  =     np.asarray(__lowerCamelCase     )

        # 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:
      lowerCamelCase__  =     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(__lowerCamelCase				,     **{**default_dtype, **self.jnp_array_kwargs}     )

    def    a__				(					self     :     List[str]				,     __lowerCamelCase     :     Optional[Any]     )							->							Dict:

     '''simple docstring'''




     import jax

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

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

    def    a__				(					self     :     Any				,     __lowerCamelCase     :     dict     )							->							int:

     '''simple docstring'''




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

    def    a__				(					self     :     Union[str, Any]				,     __lowerCamelCase     :     pa.Table     )							->							Mapping:

     '''simple docstring'''




     lowerCamelCase__  =     self.numpy_arrow_extractor().extract_row(__lowerCamelCase     )
     lowerCamelCase__  =     self.python_features_decoder.decode_row(__lowerCamelCase     )
     return self.recursive_tensorize(__lowerCamelCase     )

    def    a__				(					self     :     List[Any]				,     __lowerCamelCase     :     pa.Table     )							->							"jax.Array":

     '''simple docstring'''




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




    def    a__				(					self     :     List[str]				,     __lowerCamelCase     :     pa.Table     )							->							Mapping:

     '''simple docstring'''




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

