




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

_UpperCAmelCase	:						Optional[Any]			=     get_logger()

_UpperCAmelCase	:						List[Any]			=     None




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






       def __init__(   self:					int       ,					_lowerCAmelCase:					List[Any]=None       ,					_lowerCAmelCase:					int=None       ,					**_lowerCAmelCase:					List[str]				):
         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`."				)
         lowercase    :int      	=						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:
           lowercase    :Any      	=						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]				)}."				)
           lowercase    :List[str]      	=						str(jax.devices()[0]				)
         lowercase    :int      	=						jnp_array_kwargs






       @staticmethod
       def 					SCREAMING_SNAKE_CASE    (   ):
         import jax

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






       def 					SCREAMING_SNAKE_CASE    (   self:					Any       ,					_lowerCAmelCase:					int				):
         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 					SCREAMING_SNAKE_CASE    (   self:					Union[str, Any]       ,					_lowerCAmelCase:					List[Any]				):
         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()

         lowercase    :Optional[Any]      	=						{}

         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:
             lowercase    :List[str]      	=						{'dtype': jnp.intaa}
           else:
             lowercase    :Tuple      	=						{'dtype': jnp.intaa}
         elif isinstance(_A       ,					(np.number, np.ndarray)				) and np.issubdtype(value.dtype       ,					np.floating				):
           lowercase    :Any      	=						{'dtype': jnp.floataa}
         elif config.PIL_AVAILABLE and "PIL" in sys.modules:
           import PIL.Image

           if isinstance(_A       ,					PIL.Image.Image				):
             lowercase    :int      	=						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:
           lowercase    :Optional[Any]      	=						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 					SCREAMING_SNAKE_CASE    (   self:					List[Any]       ,					_lowerCAmelCase:					List[str]				):
         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				):
           lowercase    :Optional[Any]      	=						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 					SCREAMING_SNAKE_CASE    (   self:					Dict       ,					_lowerCAmelCase:					str				):
         return map_nested(self._recursive_tensorize       ,					_A       ,					map_list=_A				)






       def 					SCREAMING_SNAKE_CASE    (   self:					Any       ,					_lowerCAmelCase:					str				):
         lowercase    :Union[str, Any]      	=						self.numpy_arrow_extractor().extract_row(_A				)
         lowercase    :int      	=						self.python_features_decoder.decode_row(_A				)
         return self.recursive_tensorize(_A				)






       def 					SCREAMING_SNAKE_CASE    (   self:					int       ,					_lowerCAmelCase:					Any				):
         lowercase    :Dict      	=						self.numpy_arrow_extractor().extract_column(_A				)
         lowercase    :List[Any]      	=						self.python_features_decoder.decode_column(_A       ,					pa_table.column_names[0]				)
         lowercase    :Optional[Any]      	=						self.recursive_tensorize(_A				)
         lowercase    :Optional[Any]      	=						self._consolidate(_A				)
         return column






       def 					SCREAMING_SNAKE_CASE    (   self:					Any       ,					_lowerCAmelCase:					Union[str, Any]				):
         lowercase    :Tuple      	=						self.numpy_arrow_extractor().extract_batch(_A				)
         lowercase    :Any      	=						self.python_features_decoder.decode_batch(_A				)
         lowercase    :str      	=						self.recursive_tensorize(_A				)
         for column_name in batch:
           lowercase    :Optional[Any]      	=						self._consolidate(batch[column_name]				)
         return batch






import inspect
import os
import unittest

import torch

import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
    execute_subprocess_async,
    require_cpu,
    require_huggingface_suite,
    require_multi_gpu,
    require_single_gpu,
)
from accelerate.utils import patch_environment




@require_huggingface_suite
class    __lowerCAmelCase       (							unittest.TestCase):






       def 					SCREAMING_SNAKE_CASE    (   self:					List[str]				):
         lowercase    :List[str]      	=						inspect.getfile(accelerate.test_utils				)
         lowercase    :List[Any]      	=						os.path.sep.join(
             mod_file.split(os.path.sep				)[:-1] + ["scripts", "external_deps", "test_metrics.py"]				)

         from accelerate.test_utils.scripts.external_deps import test_metrics  # noqa: F401

         lowercase    :str      	=						test_metrics






       @require_cpu
       def 					SCREAMING_SNAKE_CASE    (   self:					Tuple				):
         debug_launcher(self.test_metrics.main       ,					num_processes=1				)






       @require_cpu
       def 					SCREAMING_SNAKE_CASE    (   self:					int				):
         debug_launcher(self.test_metrics.main				)






       @require_single_gpu
       def 					SCREAMING_SNAKE_CASE    (   self:					List[Any]				):
         self.test_metrics.main()






       @require_multi_gpu
       def 					SCREAMING_SNAKE_CASE    (   self:					Optional[Any]				):
         print(F"Found {torch.cuda.device_count()} devices."				)
         lowercase    :str      	=						["torchrun", F"--nproc_per_node={torch.cuda.device_count()}", self.test_file_path]
         with patch_environment(omp_num_threads=1				):
           execute_subprocess_async(_lowerCAmelCase       ,					env=os.environ.copy()				)



