"""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

_UpperCamelCase     :		Tuple											=					get_logger()

_UpperCamelCase     :		Optional[dict]											=					None


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


      def __init__(     self	,    a=None	,    a=None	,    **a							) ->      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__    :	Union[str, Any]     					=			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__    :	List[str]     					=			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[Any]     					=			str(jax.devices()[0]							)
             lowercase__    :	str     					=			jnp_array_kwargs


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

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


      def 	_UpperCAmelCase (     self	,    a							) ->      List[Any]:
             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 	_UpperCAmelCase (     self	,    a							) ->      Optional[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__    :	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__    :	Optional[int]     					=			{'dtype': jnp.intaa}
                    else:
                           lowercase__    :	List[str]     					=			{'dtype': jnp.intaa}
             elif isinstance(a	,    (np.number, np.ndarray)							) and np.issubdtype(value.dtype	,    np.floating							):
                    lowercase__    :	Union[str, Any]     					=			{'dtype': jnp.floataa}
             elif config.PIL_AVAILABLE and "PIL" in sys.modules:
                    import PIL.Image

                    if isinstance(a	,    PIL.Image.Image							):
                           lowercase__    :	Optional[Any]     					=			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__    :	List[str]     					=			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 	_UpperCAmelCase (     self	,    a							) ->      Any:
             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__    :	Tuple     					=			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 	_UpperCAmelCase (     self	,    a							) ->      Any:
             return map_nested(self._recursive_tensorize	,    a	,    map_list=a							)


      def 	_UpperCAmelCase (     self	,    a							) ->      Mapping:
             lowercase__    :	Optional[int]     					=			self.numpy_arrow_extractor().extract_row(a							)
             lowercase__    :	List[Any]     					=			self.python_features_decoder.decode_row(a							)
             return self.recursive_tensorize(a							)


      def 	_UpperCAmelCase (     self	,    a							) ->      "jax.Array":
             lowercase__    :	Union[str, Any]     					=			self.numpy_arrow_extractor().extract_column(a							)
             lowercase__    :	List[str]     					=			self.python_features_decoder.decode_column(a	,    pa_table.column_names[0]							)
             lowercase__    :	Union[str, Any]     					=			self.recursive_tensorize(a							)
             lowercase__    :	str     					=			self._consolidate(a							)
             return column


      def 	_UpperCAmelCase (     self	,    a							) ->      Mapping:
             lowercase__    :	List[Any]     					=			self.numpy_arrow_extractor().extract_batch(a							)
             lowercase__    :	str     					=			self.python_features_decoder.decode_batch(a							)
             lowercase__    :	List[str]     					=			self.recursive_tensorize(a							)
             for column_name in batch:
                    lowercase__    :	List[Any]     					=			self._consolidate(batch[column_name]							)
             return batch


"""simple docstring"""





import unittest

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device


if is_torch_available():
       import torch

       from transformers import AutoModelForImageClassification

if is_vision_available():
       from transformers import AutoImageProcessor


@require_torch
@require_vision
class   UpperCAmelCase_			(					unittest.TestCase):


      @slow
      def 	_UpperCAmelCase (     self							) ->      str:
             lowercase__    :	Optional[Any]     					=			AutoImageProcessor.from_pretrained('microsoft/dit-base-finetuned-rvlcdip'							)
             lowercase__    :	Union[str, Any]     					=			AutoModelForImageClassification.from_pretrained('microsoft/dit-base-finetuned-rvlcdip'							)
             model.to(a							)

             from datasets import load_dataset

             lowercase__    :	str     					=			load_dataset('nielsr/rvlcdip-demo'							)

             lowercase__    :	Tuple     					=			dataset['train'][0]['image'].convert('RGB'							)

             lowercase__    :	int     					=			image_processor(a	,    return_tensors='pt'							).to(a							)

             # forward pass
             with torch.no_grad():
                    lowercase__    :	List[str]     					=			model(**a							)
                    lowercase__    :	List[Any]     					=			outputs.logits

             lowercase__    :	Union[str, Any]     					=			torch.Size((1, 1_6)							)
             self.assertEqual(logits.shape	,    a							)

             lowercase__    :	Tuple     					=			torch.tensor(
                 [-0.4_158, -0.4_092, -0.4_347]	,    device=a	,    dtype=torch.float	,    )
             self.assertTrue(torch.allclose(logits[0, :3]	,    a	,    atol=1e-4							)							)

