
# 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_   	=	get_logger()

UpperCamelCase_   	=	None

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







							def __init__(						self  :  Any      ,				snake_case__  :  Tuple=None      ,				snake_case__  :  List[str]=None      ,				**snake_case__  :  Optional[Any] ):
								"""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`." )
								__lowerCAmelCase         =							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:
									__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       UpperCAmelCase__					(						):
								"""simple docstring"""







								import jax

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







							def       UpperCAmelCase__					(						self  :  List[Any]      ,				snake_case__  :  Optional[Any] ):
								"""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  :  Any      ,				snake_case__  :  str ):
								"""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()

								__lowerCAmelCase         =							{}

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







							def       UpperCAmelCase__					(						self  :  Dict      ,				snake_case__  :  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 ):
									__lowerCAmelCase         =							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]      ,				snake_case__  :  dict ):
								"""simple docstring"""







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







							def       UpperCAmelCase__					(						self  :  Tuple      ,				snake_case__  :  pa.Table ):
								"""simple docstring"""







								__lowerCAmelCase         =							self.numpy_arrow_extractor().extract_row(snake_case__ )
								__lowerCAmelCase         =							self.python_features_decoder.decode_row(snake_case__ )
								return self.recursive_tensorize(snake_case__ )







							def       UpperCAmelCase__					(						self  :  int      ,				snake_case__  :  pa.Table ):
								"""simple docstring"""







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







							def       UpperCAmelCase__					(						self  :  Optional[int]      ,				snake_case__  :  pa.Table ):
								"""simple docstring"""







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








import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
					import torch

					from transformers.generation import DisjunctiveConstraint

@require_torch
class       a		(   unittest.TestCase					):







							def       UpperCAmelCase__					(						self  :  Optional[int] ):
								"""simple docstring"""







								__lowerCAmelCase         =							[[1, 2, 4], [1, 2, 3, 4]]
								__lowerCAmelCase         =							DisjunctiveConstraint(snake_case__ )
								self.assertTrue(isinstance(dc.token_ids      ,				snake_case__ ) )

								with self.assertRaises(snake_case__ ):
									DisjunctiveConstraint(torch.LongTensor([[1, 2, 4], [1, 2, 3]] ) )

								with self.assertRaises(snake_case__ ):
									DisjunctiveConstraint([torch.LongTensor([1, 2, 4] ), torch.LongTensor([1, 2, 3, 4, 5] )] )







							def       UpperCAmelCase__					(						self  :  Tuple ):
								"""simple docstring"""







								__lowerCAmelCase         =							[[1, 2], [1, 2, 3, 4]]

								with self.assertRaises(snake_case__ ):
									DisjunctiveConstraint(snake_case__ )  # fails here







							def       UpperCAmelCase__					(						self  :  Union[str, Any] ):
								"""simple docstring"""







								__lowerCAmelCase         =							[[1, 2, 3], [1, 2, 4]]

								__lowerCAmelCase         =							DisjunctiveConstraint(snake_case__ )

								__lowerCAmelCase   ,						__lowerCAmelCase   ,						__lowerCAmelCase         =							dc.update(1 )
								__lowerCAmelCase         =							stepped is True and completed is False and reset is False
								self.assertTrue(snake_case__ )
								self.assertTrue(not dc.completed )
								self.assertTrue(dc.current_seq == [1] )

								__lowerCAmelCase   ,						__lowerCAmelCase   ,						__lowerCAmelCase         =							dc.update(2 )
								__lowerCAmelCase         =							stepped is True and completed is False and reset is False
								self.assertTrue(snake_case__ )
								self.assertTrue(not dc.completed )
								self.assertTrue(dc.current_seq == [1, 2] )

								__lowerCAmelCase   ,						__lowerCAmelCase   ,						__lowerCAmelCase         =							dc.update(3 )
								__lowerCAmelCase         =							stepped is True and completed is True and reset is False
								self.assertTrue(snake_case__ )
								self.assertTrue(dc.completed )  # Completed!
								self.assertTrue(dc.current_seq == [1, 2, 3] )







							def       UpperCAmelCase__					(						self  :  Optional[Any] ):
								"""simple docstring"""







								__lowerCAmelCase         =							[[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]

								__lowerCAmelCase         =							DisjunctiveConstraint(snake_case__ )

								__lowerCAmelCase   ,						__lowerCAmelCase   ,						__lowerCAmelCase         =							dc.update(1 )
								self.assertTrue(not dc.completed )
								self.assertTrue(dc.current_seq == [1] )

								__lowerCAmelCase   ,						__lowerCAmelCase   ,						__lowerCAmelCase         =							dc.update(2 )
								self.assertTrue(not dc.completed )
								self.assertTrue(dc.current_seq == [1, 2] )

								__lowerCAmelCase   ,						__lowerCAmelCase   ,						__lowerCAmelCase         =							dc.update(4 )
								self.assertTrue(not dc.completed )
								self.assertTrue(dc.current_seq == [1, 2, 4] )

								__lowerCAmelCase   ,						__lowerCAmelCase   ,						__lowerCAmelCase         =							dc.update(5 )
								self.assertTrue(dc.completed )  # Completed!
								self.assertTrue(dc.current_seq == [1, 2, 4, 5] )

								dc.reset()

								__lowerCAmelCase   ,						__lowerCAmelCase   ,						__lowerCAmelCase         =							dc.update(1 )
								self.assertTrue(not dc.completed )
								self.assertTrue(dc.remaining() == 3 )
								self.assertTrue(dc.current_seq == [1] )

								__lowerCAmelCase   ,						__lowerCAmelCase   ,						__lowerCAmelCase         =							dc.update(2 )
								self.assertTrue(not dc.completed )
								self.assertTrue(dc.remaining() == 2 )
								self.assertTrue(dc.current_seq == [1, 2] )

								__lowerCAmelCase   ,						__lowerCAmelCase   ,						__lowerCAmelCase         =							dc.update(5 )
								self.assertTrue(dc.completed )  # Completed!
								self.assertTrue(dc.remaining() == 0 )
								self.assertTrue(dc.current_seq == [1, 2, 5] )






