






'''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[str]  	=  None






class 							lowercase__     (							TensorFormatter[Mapping, """jax.Array""", Mapping]       ):





	def __init__(			self :    str			,lowerCamelCase__ :    Dict=None			,lowerCamelCase__ :    Tuple=None			,**lowerCamelCase__ :    Optional[int]			):







							'''simple docstring'''
							super().__init__(features=_snake_case			)
							import jax
							from jaxlib.xla_client import Device

							if isinstance(_snake_case			,_snake_case			):
													raise ValueError(
													    F'Expected {device} to be a `str` not {type(_snake_case			)}, 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`.'			)
							_UpperCamelCase :		List[str]			    =       device if isinstance(_snake_case			,_snake_case			) 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:
													_UpperCamelCase :		List[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]			)}.'			)
													_UpperCamelCase :		List[str]			    =       str(jax.devices()[0]			)
							_UpperCamelCase :		Optional[int]			    =       jnp_array_kwargs





	@staticmethod
	def      UpperCamelCase_  (			):







							'''simple docstring'''
							import jax

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





	def      UpperCamelCase_  (			self :    int			,lowerCamelCase__ :    Optional[int]			):







							'''simple docstring'''
							import jax
							import jax.numpy as jnp

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





	def      UpperCamelCase_  (			self :    Optional[int]			,lowerCamelCase__ :    Tuple			):







							'''simple docstring'''
							import jax
							import jax.numpy as jnp

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

							_UpperCamelCase :		int			    =       {}

							if isinstance(_snake_case			,(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:
																			_UpperCamelCase :		Optional[Any]			    =       {"dtype": jnp.intaa}
													else:
																			_UpperCamelCase :		Dict			    =       {"dtype": jnp.intaa}
							elif isinstance(_snake_case			,(np.number, np.ndarray)			) and np.issubdtype(value.dtype			,np.floating			):
													_UpperCamelCase :		str			    =       {"dtype": jnp.floataa}
							elif config.PIL_AVAILABLE and "PIL" in sys.modules:
													import PIL.Image

													if isinstance(_snake_case			,PIL.Image.Image			):
																			_UpperCamelCase :		Any			    =       np.asarray(_snake_case			)

        # 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:
													_UpperCamelCase :		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(_snake_case			,**{**default_dtype, **self.jnp_array_kwargs}			)





	def      UpperCamelCase_  (			self :    List[Any]			,lowerCamelCase__ :    Union[str, Any]			):







							'''simple docstring'''
							import jax

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

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





	def      UpperCamelCase_  (			self :    List[str]			,lowerCamelCase__ :    List[str]			):







							'''simple docstring'''
							return map_nested(self._recursive_tensorize			,_snake_case			,map_list=_snake_case			)





	def      UpperCamelCase_  (			self :    Dict			,lowerCamelCase__ :    Optional[Any]			):







							'''simple docstring'''
							_UpperCamelCase :		Union[str, Any]			    =       self.numpy_arrow_extractor().extract_row(_snake_case			)
							_UpperCamelCase :		Union[str, Any]			    =       self.python_features_decoder.decode_row(_snake_case			)
							return self.recursive_tensorize(_snake_case			)





	def      UpperCamelCase_  (			self :    Optional[int]			,lowerCamelCase__ :    Optional[int]			):







							'''simple docstring'''
							_UpperCamelCase :		List[str]			    =       self.numpy_arrow_extractor().extract_column(_snake_case			)
							_UpperCamelCase :		str			    =       self.python_features_decoder.decode_column(_snake_case			,pa_table.column_names[0]			)
							_UpperCamelCase :		Optional[int]			    =       self.recursive_tensorize(_snake_case			)
							_UpperCamelCase :		int			    =       self._consolidate(_snake_case			)
							return column





	def      UpperCamelCase_  (			self :    str			,lowerCamelCase__ :    Union[str, Any]			):







							'''simple docstring'''
							_UpperCamelCase :		Optional[Any]			    =       self.numpy_arrow_extractor().extract_batch(_snake_case			)
							_UpperCamelCase :		Optional[Any]			    =       self.python_features_decoder.decode_batch(_snake_case			)
							_UpperCamelCase :		List[Any]			    =       self.recursive_tensorize(_snake_case			)
							for column_name in batch:
													_UpperCamelCase :		Tuple			    =       self._consolidate(batch[column_name]			)
							return batch







'''simple docstring'''
import tempfile
import unittest

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
    is_torch_available,
    require_optimum,
    require_torch,
    slow,
)


if is_torch_available():
					import torch






@require_torch
@require_optimum
@slow
class 							lowercase__     (							unittest.TestCase       ):





	def      UpperCamelCase_  (			self :    str			):







							'''simple docstring'''
							_UpperCamelCase :		Any			    =       'hf-internal-testing/tiny-random-t5'
							_UpperCamelCase :		str			    =       AutoTokenizer.from_pretrained(lowerCamelCase__			)
							_UpperCamelCase :		int			    =       AutoModelForSeqaSeqLM.from_pretrained(lowerCamelCase__			)

							_UpperCamelCase :		List[str]			    =       tokenizer('This is me'			,return_tensors='pt'			)

							_UpperCamelCase :		str			    =       model.to_bettertransformer()

							self.assertTrue(any('BetterTransformer' in mod.__class__.__name__ for _, mod in model.named_modules()			)			)

							_UpperCamelCase :		Optional[Any]			    =       model.generate(**lowerCamelCase__			)

							_UpperCamelCase :		Union[str, Any]			    =       model.reverse_bettertransformer()

							self.assertFalse(any('BetterTransformer' in mod.__class__.__name__ for _, mod in model.named_modules()			)			)

							with tempfile.TemporaryDirectory() as tmpdirname:
													model.save_pretrained(lowerCamelCase__			)

													_UpperCamelCase :		List[Any]			    =       AutoModelForSeqaSeqLM.from_pretrained(lowerCamelCase__			)

													self.assertFalse(
													    any('BetterTransformer' in mod.__class__.__name__ for _, mod in model_reloaded.named_modules()			)			)

													_UpperCamelCase :		Optional[Any]			    =       model_reloaded.generate(**lowerCamelCase__			)
													self.assertTrue(torch.allclose(lowerCamelCase__			,lowerCamelCase__			)			)





	def      UpperCamelCase_  (			self :    Any			):







							'''simple docstring'''
							_UpperCamelCase :		List[Any]			    =       'hf-internal-testing/tiny-random-t5'
							_UpperCamelCase :		List[Any]			    =       AutoModelForSeqaSeqLM.from_pretrained(lowerCamelCase__			)

							_UpperCamelCase :		List[str]			    =       model.to_bettertransformer()

							with tempfile.TemporaryDirectory() as tmpdirname:
													with self.assertRaises(lowerCamelCase__			):
																			model.save_pretrained(lowerCamelCase__			)

													_UpperCamelCase :		str			    =       model.reverse_bettertransformer()
													model.save_pretrained(lowerCamelCase__			)


