


'''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    __magic_name__				(						TensorFormatter[Mapping, "jax.Array", Mapping]							):






       def __init__(     self    ,				snake_case=None    ,				snake_case=None    ,				**snake_case)		->    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  :  int				       =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  :  int				       =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  :  Tuple				       =str(jax.devices()[0])
          _UpperCAmelCase  :  Any				       =jnp_array_kwargs






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




          '''simple docstring'''
          import jax

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






       def      lowerCAmelCase						(     self    ,				snake_case)		->    Union[str, 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      lowerCAmelCase						(     self    ,				snake_case)		->    Dict:




          '''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  :  Any				       ={}

          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  :  List[str]				       ={'dtype': jnp.intaa}
             else:
                _UpperCAmelCase  :  int				       ={'dtype': jnp.intaa}
          elif isinstance(snake_case    ,				(np.number, np.ndarray)) and np.issubdtype(value.dtype    ,				np.floating):
             _UpperCAmelCase  :  List[Any]				       ={'dtype': jnp.floataa}
          elif config.PIL_AVAILABLE and "PIL" in sys.modules:
             import PIL.Image

             if isinstance(snake_case    ,				PIL.Image.Image):
                _UpperCAmelCase  :  int				       =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      lowerCAmelCase						(     self    ,				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):
             _UpperCAmelCase  :  Optional[Any]				       =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      lowerCAmelCase						(     self    ,				snake_case)		->    Any:




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






       def      lowerCAmelCase						(     self    ,				snake_case)		->    Mapping:




          '''simple docstring'''
          _UpperCAmelCase  :  Any				       =self.numpy_arrow_extractor().extract_row(snake_case)
          _UpperCAmelCase  :  Optional[Any]				       =self.python_features_decoder.decode_row(snake_case)
          return self.recursive_tensorize(snake_case)






       def      lowerCAmelCase						(     self    ,				snake_case)		->    "jax.Array":




          '''simple docstring'''
          _UpperCAmelCase  :  str				       =self.numpy_arrow_extractor().extract_column(snake_case)
          _UpperCAmelCase  :  Tuple				       =self.python_features_decoder.decode_column(snake_case    ,				pa_table.column_names[0])
          _UpperCAmelCase  :  str				       =self.recursive_tensorize(snake_case)
          _UpperCAmelCase  :  str				       =self._consolidate(snake_case)
          return column






       def      lowerCAmelCase						(     self    ,				snake_case)		->    Mapping:




          '''simple docstring'''
          _UpperCAmelCase  :  Any				       =self.numpy_arrow_extractor().extract_batch(snake_case)
          _UpperCAmelCase  :  List[Any]				       =self.python_features_decoder.decode_batch(snake_case)
          _UpperCAmelCase  :  str				       =self.recursive_tensorize(snake_case)
          for column_name in batch:
             _UpperCAmelCase  :  Dict				       =self._consolidate(batch[column_name])
          return batch





'''simple docstring'''






import unittest

from diffusers.pipelines.pipeline_utils import is_safetensors_compatible






class    __magic_name__				(						unittest.TestCase							):






       def      lowerCAmelCase						(     self)		->    Union[str, Any]:




          '''simple docstring'''
          _UpperCAmelCase  :  Any				       =[
              'safety_checker/pytorch_model.bin',
              'safety_checker/model.safetensors',
              'vae/diffusion_pytorch_model.bin',
              'vae/diffusion_pytorch_model.safetensors',
              'text_encoder/pytorch_model.bin',
              'text_encoder/model.safetensors',
              'unet/diffusion_pytorch_model.bin',
              'unet/diffusion_pytorch_model.safetensors',
          ]
          self.assertTrue(is_safetensors_compatible(snake_case))






       def      lowerCAmelCase						(     self)		->    List[str]:




          '''simple docstring'''
          _UpperCAmelCase  :  Optional[int]				       =[
              'unet/diffusion_pytorch_model.bin',
              'unet/diffusion_pytorch_model.safetensors',
          ]
          self.assertTrue(is_safetensors_compatible(snake_case))






       def      lowerCAmelCase						(     self)		->    Any:




          '''simple docstring'''
          _UpperCAmelCase  :  Union[str, Any]				       =[
              'safety_checker/pytorch_model.bin',
              'safety_checker/model.safetensors',
              'vae/diffusion_pytorch_model.bin',
              'vae/diffusion_pytorch_model.safetensors',
              'text_encoder/pytorch_model.bin',
              'text_encoder/model.safetensors',
              'unet/diffusion_pytorch_model.bin',
              # Removed: 'unet/diffusion_pytorch_model.safetensors',
          ]
          self.assertFalse(is_safetensors_compatible(snake_case))






       def      lowerCAmelCase						(     self)		->    Dict:




          '''simple docstring'''
          _UpperCAmelCase  :  int				       =[
              'text_encoder/pytorch_model.bin',
              'text_encoder/model.safetensors',
          ]
          self.assertTrue(is_safetensors_compatible(snake_case))






       def      lowerCAmelCase						(     self)		->    Any:




          '''simple docstring'''
          _UpperCAmelCase  :  Any				       =[
              'safety_checker/pytorch_model.bin',
              'safety_checker/model.safetensors',
              'vae/diffusion_pytorch_model.bin',
              'vae/diffusion_pytorch_model.safetensors',
              'text_encoder/pytorch_model.bin',
              # Removed: 'text_encoder/model.safetensors',
              'unet/diffusion_pytorch_model.bin',
              'unet/diffusion_pytorch_model.safetensors',
          ]
          self.assertFalse(is_safetensors_compatible(snake_case))






       def      lowerCAmelCase						(     self)		->    List[Any]:




          '''simple docstring'''
          _UpperCAmelCase  :  Union[str, Any]				       =[
              'safety_checker/pytorch_model.fp16.bin',
              'safety_checker/model.fp16.safetensors',
              'vae/diffusion_pytorch_model.fp16.bin',
              'vae/diffusion_pytorch_model.fp16.safetensors',
              'text_encoder/pytorch_model.fp16.bin',
              'text_encoder/model.fp16.safetensors',
              'unet/diffusion_pytorch_model.fp16.bin',
              'unet/diffusion_pytorch_model.fp16.safetensors',
          ]
          _UpperCAmelCase  :  str				       ='fp16'
          self.assertTrue(is_safetensors_compatible(snake_case    ,				variant=snake_case))






       def      lowerCAmelCase						(     self)		->    Union[str, Any]:




          '''simple docstring'''
          _UpperCAmelCase  :  Tuple				       =[
              'unet/diffusion_pytorch_model.fp16.bin',
              'unet/diffusion_pytorch_model.fp16.safetensors',
          ]
          _UpperCAmelCase  :  int				       ='fp16'
          self.assertTrue(is_safetensors_compatible(snake_case    ,				variant=snake_case))






       def      lowerCAmelCase						(     self)		->    Optional[int]:




          '''simple docstring'''
          # pass variant but use the non-variant filenames
          _UpperCAmelCase  :  Optional[int]				       =[
              'unet/diffusion_pytorch_model.bin',
              'unet/diffusion_pytorch_model.safetensors',
          ]
          _UpperCAmelCase  :  Optional[Any]				       ='fp16'
          self.assertTrue(is_safetensors_compatible(snake_case    ,				variant=snake_case))






       def      lowerCAmelCase						(     self)		->    int:




          '''simple docstring'''
          _UpperCAmelCase  :  str				       =[
              'safety_checker/pytorch_model.fp16.bin',
              'safety_checker/model.fp16.safetensors',
              'vae/diffusion_pytorch_model.fp16.bin',
              'vae/diffusion_pytorch_model.fp16.safetensors',
              'text_encoder/pytorch_model.fp16.bin',
              'text_encoder/model.fp16.safetensors',
              'unet/diffusion_pytorch_model.fp16.bin',
              # Removed: 'unet/diffusion_pytorch_model.fp16.safetensors',
          ]
          _UpperCAmelCase  :  List[str]				       ='fp16'
          self.assertFalse(is_safetensors_compatible(snake_case    ,				variant=snake_case))






       def      lowerCAmelCase						(     self)		->    Union[str, Any]:




          '''simple docstring'''
          _UpperCAmelCase  :  Dict				       =[
              'text_encoder/pytorch_model.fp16.bin',
              'text_encoder/model.fp16.safetensors',
          ]
          _UpperCAmelCase  :  Optional[Any]				       ='fp16'
          self.assertTrue(is_safetensors_compatible(snake_case    ,				variant=snake_case))






       def      lowerCAmelCase						(     self)		->    Any:




          '''simple docstring'''
          # pass variant but use the non-variant filenames
          _UpperCAmelCase  :  Optional[int]				       =[
              'text_encoder/pytorch_model.bin',
              'text_encoder/model.safetensors',
          ]
          _UpperCAmelCase  :  List[Any]				       ='fp16'
          self.assertTrue(is_safetensors_compatible(snake_case    ,				variant=snake_case))






       def      lowerCAmelCase						(     self)		->    Union[str, Any]:




          '''simple docstring'''
          _UpperCAmelCase  :  str				       =[
              'safety_checker/pytorch_model.fp16.bin',
              'safety_checker/model.fp16.safetensors',
              'vae/diffusion_pytorch_model.fp16.bin',
              'vae/diffusion_pytorch_model.fp16.safetensors',
              'text_encoder/pytorch_model.fp16.bin',
              # 'text_encoder/model.fp16.safetensors',
              'unet/diffusion_pytorch_model.fp16.bin',
              'unet/diffusion_pytorch_model.fp16.safetensors',
          ]
          _UpperCAmelCase  :  Tuple				       ='fp16'
          self.assertFalse(is_safetensors_compatible(snake_case    ,				variant=snake_case))

