'''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

__lowercase          =  get_logger()

__lowercase          =  None






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


			'''simple docstring'''






			def __init__(   self       ,    __lowerCAmelCase=None       ,    __lowerCAmelCase=None       ,    **__lowerCAmelCase):




					"""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`.""")
					lowerCAmelCase   					=  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:
							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_       (   ):




					"""simple docstring"""


					import jax

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



			def 							a_       (   self       ,    __lowerCAmelCase):




					"""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 							a_       (   self       ,    __lowerCAmelCase):




					"""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()

					lowerCAmelCase   					=  {}

					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:
									lowerCAmelCase   					=  {"""dtype""": jnp.intaa}
							else:
									lowerCAmelCase   					=  {"""dtype""": jnp.intaa}
					elif isinstance(_a       ,    (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(_a       ,    PIL.Image.Image):
									lowerCAmelCase   					=  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:
							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(_a       ,    **{**default_dtype, **self.jnp_array_kwargs})



			def 							a_       (   self       ,    __lowerCAmelCase):




					"""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):
							lowerCAmelCase   					=  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 							a_       (   self       ,    __lowerCAmelCase):




					"""simple docstring"""


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



			def 							a_       (   self       ,    __lowerCAmelCase):




					"""simple docstring"""


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



			def 							a_       (   self       ,    __lowerCAmelCase):




					"""simple docstring"""


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







			def 							a_       (   self       ,    __lowerCAmelCase):




					"""simple docstring"""


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










from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
  import numpy as np
  import tensorflow as tf

  from transformers import TFCamembertModel







@require_tf
@require_sentencepiece
@require_tokenizers
class 						lowercase							(   unittest.TestCase		):

     @slow
     def 					a__	(  self				)						->							Any:
           _A							:     Tuple			=						TFCamembertModel.from_pretrained("""jplu/tf-camembert-base"""				)

           _A							:     List[Any]			=						tf.convert_to_tensor(
               [[5, 121, 11, 660, 16, 730, 2_5543, 110, 83, 6]]		,     dtype=tf.intaa		,     )  # J'aime le camembert !"

           _A							:     List[str]			=						model(_a				)["""last_hidden_state"""]
           _A							:     Union[str, Any]			=						tf.TensorShape((1, 10, 768)				)
           self.assertEqual(output.shape		,     _a				)
           # compare the actual values for a slice.
           _A							:     List[Any]			=						tf.convert_to_tensor(
               [[[-0.0254, 0.0235, 0.1027], [0.0606, -0.1811, -0.0418], [-0.1561, -0.1127, 0.2687]]]		,     dtype=tf.floataa		,     )
           # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
           # camembert.eval()
           # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

           self.assertTrue(np.allclose(output[:, :3, :3].numpy()		,     expected_slice.numpy()		,     atol=1e-4				)				)

