
# 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

_snake_case      = get_logger()

_snake_case      = None
class       _lowerCAmelCase  (		TensorFormatter[Mapping, "jax.Array", Mapping]							):

 """simple docstring"""

 def __init__(						self			:			Union[str, Any]					, SCREAMING_SNAKE_CASE__			:			Union[str, Any]=None					, SCREAMING_SNAKE_CASE__			:			List[str]=None					, **SCREAMING_SNAKE_CASE__			:			Dict   ):






      """simple docstring"""



      super().__init__(features=SCREAMING_SNAKE_CASE__   )
      import jax
      from jaxlib.xla_client import Device

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

 @staticmethod
 def      __lowerCAmelCase  (						):






      """simple docstring"""



      import jax

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

 def      __lowerCAmelCase  (						self			:			int					, SCREAMING_SNAKE_CASE__			:			Optional[Any]   ):






      """simple docstring"""



      import jax
      import jax.numpy as jnp

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

 def      __lowerCAmelCase  (						self			:			List[Any]					, SCREAMING_SNAKE_CASE__			:			Any   ):






      """simple docstring"""



      import jax
      import jax.numpy as jnp

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

      UpperCamelCase						    =					{}

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

           if isinstance(SCREAMING_SNAKE_CASE__					, PIL.Image.Image   ):
                UpperCamelCase						    =					np.asarray(SCREAMING_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						    =					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(SCREAMING_SNAKE_CASE__					, **{**default_dtype, **self.jnp_array_kwargs}   )

 def      __lowerCAmelCase  (						self			:			Tuple					, SCREAMING_SNAKE_CASE__			:			Dict   ):






      """simple docstring"""



      import jax

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

           if isinstance(SCREAMING_SNAKE_CASE__					, torch.Tensor   ):
                return self._tensorize(data_struct.detach().cpu().numpy()[()]   )
      if hasattr(SCREAMING_SNAKE_CASE__					, '__array__'   ) and not isinstance(SCREAMING_SNAKE_CASE__					, jax.Array   ):
           UpperCamelCase						    =					data_struct.__array__()
      # support for nested types like struct of list of struct
      if isinstance(SCREAMING_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(SCREAMING_SNAKE_CASE__   ) for substruct in data_struct]   )
      elif isinstance(SCREAMING_SNAKE_CASE__					, (list, tuple)   ):
           return self._consolidate([self.recursive_tensorize(SCREAMING_SNAKE_CASE__   ) for substruct in data_struct]   )
      return self._tensorize(SCREAMING_SNAKE_CASE__   )

 def      __lowerCAmelCase  (						self			:			str					, SCREAMING_SNAKE_CASE__			:			dict   ):






      """simple docstring"""



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

 def      __lowerCAmelCase  (						self			:			Optional[Any]					, SCREAMING_SNAKE_CASE__			:			pa.Table   ):






      """simple docstring"""



      UpperCamelCase						    =					self.numpy_arrow_extractor().extract_row(SCREAMING_SNAKE_CASE__   )
      UpperCamelCase						    =					self.python_features_decoder.decode_row(SCREAMING_SNAKE_CASE__   )
      return self.recursive_tensorize(SCREAMING_SNAKE_CASE__   )

 def      __lowerCAmelCase  (						self			:			Optional[int]					, SCREAMING_SNAKE_CASE__			:			pa.Table   ):






      """simple docstring"""



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






 def      __lowerCAmelCase  (						self			:			str					, SCREAMING_SNAKE_CASE__			:			pa.Table   ):






      """simple docstring"""



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




from __future__ import annotations

from collections import Counter
from random import random
class       _lowerCAmelCase  :

 """simple docstring"""

 def __init__(						self			:			Union[str, Any]   ):






      """simple docstring"""



      UpperCamelCase						    =					{}

 def      __lowerCAmelCase  (						self			:			Tuple					, SCREAMING_SNAKE_CASE__			:			str   ):






      """simple docstring"""



      UpperCamelCase						    =					{}

 def      __lowerCAmelCase  (						self			:			Optional[Any]					, SCREAMING_SNAKE_CASE__			:			str					, SCREAMING_SNAKE_CASE__			:			str					, SCREAMING_SNAKE_CASE__			:			float   ):






      """simple docstring"""



      if nodea not in self.connections:
           self.add_node(SCREAMING_SNAKE_CASE__   )
      if nodea not in self.connections:
           self.add_node(SCREAMING_SNAKE_CASE__   )
      UpperCamelCase						    =					probability

 def      __lowerCAmelCase  (						self			:			str   ):






      """simple docstring"""



      return list(self.connections   )






 def      __lowerCAmelCase  (						self			:			Any					, SCREAMING_SNAKE_CASE__			:			str   ):






      """simple docstring"""



      UpperCamelCase						    =					0
      UpperCamelCase						    =					random()

      for dest in self.connections[node]:
           current_probability += self.connections[node][dest]
           if current_probability > random_value:
                return dest
      return ""




def  __lowerCamelCase					( _lowercase						,     _lowercase						,     _lowercase			)       ->      dict[str, int]:
     UpperCamelCase						    =					MarkovChainGraphUndirectedUnweighted()

     for nodea, nodea, probability in transitions:
          graph.add_transition_probability(_lowercase						,     _lowercase						,     _lowercase			)

     UpperCamelCase						    =					Counter(graph.get_nodes()			)
     UpperCamelCase						    =					start

     for _ in range(_lowercase			):
          UpperCamelCase						    =					graph.transition(_lowercase			)
          visited[node] += 1

     return visited


if __name__ == "__main__":
 import doctest

 doctest.testmod()


