import tempfile
import unittest

from make_student import create_student_by_copying_alternating_layers

from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch


A     :  List[Any]					     =       "sshleifer/bart-tiny-random"
A     :  Union[str, Any]					     =       "patrickvonplaten/t5-tiny-random"


@require_torch
class   lowerCamelCase					(unittest.TestCase					):
   """simple docstring"""







   @cached_property
   def    __A     (							self :       List[str]   )  ->	List[str]:
         return AutoConfig.from_pretrained(__magic_name__   )







   def    __A     (							self :       Optional[int]   )  ->	str:
         SCREAMING_SNAKE_CASE_     , *SCREAMING_SNAKE_CASE_          =  create_student_by_copying_alternating_layers(__magic_name__  ,     tempfile.mkdtemp()  ,     e=1  ,     d=1   )
         self.assertEqual(student.config.num_hidden_layers  ,     1   )







   def    __A     (							self :       List[Any]   )  ->	str:
         SCREAMING_SNAKE_CASE_     , *SCREAMING_SNAKE_CASE_          =  create_student_by_copying_alternating_layers(__magic_name__  ,     tempfile.mkdtemp()  ,     e=1  ,     d=__magic_name__   )







   def    __A     (							self :       Optional[int]   )  ->	Dict:
         SCREAMING_SNAKE_CASE_     , *SCREAMING_SNAKE_CASE_          =  create_student_by_copying_alternating_layers(__magic_name__  ,     tempfile.mkdtemp()  ,     e=1  ,     d=__magic_name__   )
         self.assertEqual(student.config.encoder_layers  ,     1   )
         self.assertEqual(student.config.decoder_layers  ,     self.teacher_config.encoder_layers   )







   def    __A     (							self :       int   )  ->	Tuple:
         SCREAMING_SNAKE_CASE_     , *SCREAMING_SNAKE_CASE_          =  create_student_by_copying_alternating_layers(__magic_name__  ,     tempfile.mkdtemp()  ,     e=1  ,     d=1   )
         self.assertEqual(student.config.encoder_layers  ,     1   )
         self.assertEqual(student.config.decoder_layers  ,     1   )







   def    __A     (							self :       Union[str, Any]   )  ->	Optional[int]:
         with self.assertRaises(__magic_name__   ):
               create_student_by_copying_alternating_layers(__magic_name__  ,     tempfile.mkdtemp()  ,     e=__magic_name__  ,     d=__magic_name__   )



# 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     :  Optional[Any]					     =       get_logger()

A     :  Optional[dict]					     =       None


class   lowerCamelCase					(TensorFormatter[Mapping, '''jax.Array''', Mapping]					):
  """simple docstring"""







  def __init__(							self :       int  ,     __magic_name__ :       int=None  ,     __magic_name__ :       Union[str, Any]=None  ,     **__magic_name__ :       str   )  ->	Tuple:
        super().__init__(features=__magic_name__   )
        import jax
        from jaxlib.xla_client import Device

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







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

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







  def    __A     (							self :       Optional[int]  ,     __magic_name__ :       Union[str, Any]   )  ->	List[str]:
        import jax
        import jax.numpy as jnp

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







  def    __A     (							self :       Tuple  ,     __magic_name__ :       int   )  ->	Optional[Any]:
        import jax
        import jax.numpy as jnp

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

        SCREAMING_SNAKE_CASE_          =  {}

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

              if isinstance(__magic_name__  ,     PIL.Image.Image   ):
                    SCREAMING_SNAKE_CASE_          =  np.asarray(__magic_name__   )

        # 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:
              SCREAMING_SNAKE_CASE_          =  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(__magic_name__  ,     **{**default_dtype, **self.jnp_array_kwargs}   )







  def    __A     (							self :       Optional[int]  ,     __magic_name__ :       Optional[Any]   )  ->	Union[str, Any]:
        import jax

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

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







  def    __A     (							self :       int  ,     __magic_name__ :       dict   )  ->	Any:
        return map_nested(self._recursive_tensorize  ,     __magic_name__  ,     map_list=__magic_name__   )







  def    __A     (							self :       Optional[Any]  ,     __magic_name__ :       pa.Table   )  ->	Mapping:
        SCREAMING_SNAKE_CASE_          =  self.numpy_arrow_extractor().extract_row(__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  self.python_features_decoder.decode_row(__magic_name__   )
        return self.recursive_tensorize(__magic_name__   )







  def    __A     (							self :       Dict  ,     __magic_name__ :       pa.Table   )  ->	"jax.Array":
        SCREAMING_SNAKE_CASE_          =  self.numpy_arrow_extractor().extract_column(__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  self.python_features_decoder.decode_column(__magic_name__  ,     pa_table.column_names[0]   )
        SCREAMING_SNAKE_CASE_          =  self.recursive_tensorize(__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  self._consolidate(__magic_name__   )
        return column







  def    __A     (							self :       Dict  ,     __magic_name__ :       pa.Table   )  ->	Mapping:
        SCREAMING_SNAKE_CASE_          =  self.numpy_arrow_extractor().extract_batch(__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  self.python_features_decoder.decode_batch(__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  self.recursive_tensorize(__magic_name__   )
        for column_name in batch:
              SCREAMING_SNAKE_CASE_          =  self._consolidate(batch[column_name]   )
        return batch


