# 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		:str   	=  get_logger()

__snake_case		:Optional[dict]   	=  None

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



       def __init__(	self							:  str  ,	__SCREAMING_SNAKE_CASE							:  int=None  ,	__SCREAMING_SNAKE_CASE							:  List[Any]=None  ,	**__SCREAMING_SNAKE_CASE							:  Any):


           '''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`.''')
           __a		 =       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:
               __a		 =       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])}.')
               __a		 =       str(jax.devices()[0])
           __a		 =       jnp_array_kwargs



       @staticmethod
       def 	_lowerCamelCase    (	):


           '''simple docstring'''

           import jax

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



       def 	_lowerCamelCase    (	self							:  Union[str, Any]  ,	__SCREAMING_SNAKE_CASE							:  Tuple):


           '''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							:  str  ,	__SCREAMING_SNAKE_CASE							:  str):


           '''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()

           __a		 =       {}

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

               if isinstance(__SCREAMING_SNAKE_CASE  ,	PIL.Image.Image):
                   __a		 =       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:
               __a		 =       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							:  str):


           '''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):
               __a		 =       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							:  Tuple  ,	__SCREAMING_SNAKE_CASE							:  dict):


           '''simple docstring'''

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



       def 	_lowerCamelCase    (	self							:  str  ,	__SCREAMING_SNAKE_CASE							:  pa.Table):


           '''simple docstring'''

           __a		 =       self.numpy_arrow_extractor().extract_row(__SCREAMING_SNAKE_CASE)
           __a		 =       self.python_features_decoder.decode_row(__SCREAMING_SNAKE_CASE)
           return self.recursive_tensorize(__SCREAMING_SNAKE_CASE)



       def 	_lowerCamelCase    (	self							:  Tuple  ,	__SCREAMING_SNAKE_CASE							:  pa.Table):


           '''simple docstring'''

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





       def 	_lowerCamelCase    (	self							:  Union[str, Any]  ,	__SCREAMING_SNAKE_CASE							:  pa.Table):


           '''simple docstring'''

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










import random

import torch
from huggingface_hub import HfApi

from diffusers import UNetaDModel


__snake_case		:List[str]   	=  HfApi()

__snake_case		:str   	=  {}
# fmt: off
__snake_case		:Optional[Any]   	=  torch.tensor([
    -0.7_5_1_5, -1.6_8_8_3, 0.2_4_2_0, 0.0_3_0_0, 0.6_3_4_7, 1.3_4_3_3, -1.1_7_4_3, -3.7_4_6_7,
    1.2_3_4_2, -2.2_4_8_5, 0.4_6_3_6, 0.8_0_7_6, -0.7_9_9_1, 0.3_9_6_9, 0.8_4_9_8, 0.9_1_8_9,
    -1.8_8_8_7, -3.3_5_2_2, 0.7_6_3_9, 0.2_0_4_0, 0.6_2_7_1, -2.7_1_4_8, -1.6_3_1_6, 3.0_8_3_9,
    0.3_1_8_6, 0.2_7_2_1, -0.9_7_5_9, -1.2_4_6_1, 2.6_2_5_7, 1.3_5_5_7
])
__snake_case		:Union[str, Any]   	=  torch.tensor([
    -2.3_6_3_9, -2.5_3_4_4, 0.0_0_5_4, -0.6_6_7_4, 1.5_9_9_0, 1.0_1_5_8, 0.3_1_2_4, -2.1_4_3_6,
    1.8_7_9_5, -2.5_4_2_9, -0.1_5_6_6, -0.3_9_7_3, 1.2_4_9_0, 2.6_4_4_7, 1.2_2_8_3, -0.5_2_0_8,
    -2.8_1_5_4, -3.5_1_1_9, 2.3_8_3_8, 1.2_0_3_3, 1.7_2_0_1, -2.1_2_5_6, -1.4_5_7_6, 2.7_9_4_8,
    2.4_2_0_4, -0.9_7_5_2, -1.2_5_4_6, 0.8_0_2_7, 3.2_7_5_8, 3.1_3_6_5
])
__snake_case		:str   	=  torch.tensor([
    -0.6_5_3_1, -0.6_8_9_1, -0.3_1_7_2, -0.5_3_7_5, -0.9_1_4_0, -0.5_3_6_7, -0.1_1_7_5, -0.7_8_6_9,
    -0.3_8_0_8, -0.4_5_1_3, -0.2_0_9_8, -0.0_0_8_3, 0.3_1_8_3, 0.5_1_4_0, 0.2_2_4_7, -0.1_3_0_4,
    -0.1_3_0_2, -0.2_8_0_2, -0.2_0_8_4, -0.2_0_2_5, -0.4_9_6_7, -0.4_8_7_3, -0.0_8_6_1, 0.6_9_2_5,
    0.0_2_5_0, 0.1_2_9_0, -0.1_5_4_3, 0.6_3_1_6, 1.0_4_6_0, 1.4_9_4_3
])
__snake_case		:List[Any]   	=  torch.tensor([
    0.0_9_1_1, 0.1_1_0_7, 0.0_1_8_2, 0.0_4_3_5, -0.0_8_0_5, -0.0_6_0_8, 0.0_3_8_1, 0.2_1_7_2,
    -0.0_2_8_0, 0.1_3_2_7, -0.0_2_9_9, -0.0_2_5_5, -0.0_0_5_0, -0.1_1_7_0, -0.1_0_4_6, 0.0_3_0_9,
    0.1_3_6_7, 0.1_7_2_8, -0.0_5_3_3, -0.0_7_4_8, -0.0_5_3_4, 0.1_6_2_4, 0.0_3_8_4, -0.1_8_0_5,
    -0.0_7_0_7, 0.0_6_4_2, 0.0_2_2_0, -0.0_1_3_4, -0.1_3_3_3, -0.1_5_0_5
])
__snake_case		:Any   	=  torch.tensor([
    0.1_3_2_1, 0.1_3_3_7, 0.0_4_4_0, 0.0_6_2_2, -0.0_5_9_1, -0.0_3_7_0, 0.0_5_0_3, 0.2_1_3_3,
    -0.0_1_7_7, 0.1_4_1_5, -0.0_1_1_6, -0.0_1_1_2, 0.0_0_4_4, -0.0_9_8_0, -0.0_7_8_9, 0.0_3_9_5,
    0.1_5_0_2, 0.1_7_8_5, -0.0_4_8_8, -0.0_5_1_4, -0.0_4_0_4, 0.1_5_3_9, 0.0_4_5_4, -0.1_5_5_9,
    -0.0_6_6_5, 0.0_6_5_9, 0.0_3_8_3, -0.0_0_0_5, -0.1_2_6_6, -0.1_3_8_6
])
__snake_case		:List[str]   	=  torch.tensor([
    0.1_1_5_4, 0.1_2_1_8, 0.0_3_0_7, 0.0_5_2_6, -0.0_7_1_1, -0.0_5_4_1, 0.0_3_6_6, 0.2_0_7_8,
    -0.0_2_6_7, 0.1_3_1_7, -0.0_2_2_6, -0.0_1_9_3, -0.0_0_1_4, -0.1_0_5_5, -0.0_9_0_2, 0.0_3_3_0,
    0.1_3_9_1, 0.1_7_0_9, -0.0_5_6_2, -0.0_6_9_3, -0.0_5_6_0, 0.1_4_8_2, 0.0_3_8_1, -0.1_6_8_3,
    -0.0_6_8_1, 0.0_6_6_1, 0.0_3_3_1, -0.0_0_4_6, -0.1_2_6_8, -0.1_4_3_1
])
__snake_case		:Optional[int]   	=  torch.tensor([
    0.1_1_9_2, 0.1_2_4_0, 0.0_4_1_4, 0.0_6_0_6, -0.0_5_5_7, -0.0_4_1_2, 0.0_4_3_0, 0.2_0_4_2,
    -0.0_2_0_0, 0.1_3_8_5, -0.0_1_1_5, -0.0_1_3_2, 0.0_0_1_7, -0.0_9_6_5, -0.0_8_0_2, 0.0_3_9_8,
    0.1_4_3_3, 0.1_7_4_7, -0.0_4_5_8, -0.0_5_3_3, -0.0_4_0_7, 0.1_5_4_5, 0.0_4_1_9, -0.1_5_7_4,
    -0.0_6_4_5, 0.0_6_2_6, 0.0_3_4_1, -0.0_0_1_0, -0.1_1_9_9, -0.1_3_9_0
])
__snake_case		:Tuple   	=  torch.tensor([
    0.1_0_7_5, 0.1_0_7_4, 0.0_2_0_5, 0.0_4_3_1, -0.0_7_7_4, -0.0_6_0_7, 0.0_2_9_8, 0.2_0_4_2,
    -0.0_3_2_0, 0.1_2_6_7, -0.0_2_8_1, -0.0_2_5_0, -0.0_0_6_4, -0.1_0_9_1, -0.0_9_4_6, 0.0_2_9_0,
    0.1_3_2_8, 0.1_6_5_0, -0.0_5_8_0, -0.0_7_3_8, -0.0_5_8_6, 0.1_4_4_0, 0.0_3_3_7, -0.1_7_4_6,
    -0.0_7_1_2, 0.0_6_0_5, 0.0_2_5_0, -0.0_0_9_9, -0.1_3_1_6, -0.1_4_7_3
])
__snake_case		:List[Any]   	=  torch.tensor([
    -1.4_5_7_2, -2.0_4_8_1, -0.0_4_1_4, -0.6_0_0_5, 1.4_1_3_6, 0.5_8_4_8, 0.4_0_2_8, -2.7_3_3_0,
    1.2_2_1_2, -2.1_2_2_8, 0.2_1_5_5, 0.4_0_3_9, 0.7_6_6_2, 2.0_5_3_5, 0.7_4_7_7, -0.3_2_4_3,
    -2.1_7_5_8, -2.7_6_4_8, 1.6_9_4_7, 0.7_0_2_6, 1.2_3_3_8, -1.6_0_7_8, -0.8_6_8_2, 2.2_8_1_0,
    1.8_5_7_4, -0.5_7_1_8, -0.5_5_8_6, -0.0_1_8_6, 2.3_4_1_5, 2.1_2_5_1])
__snake_case		:Optional[Any]   	=  torch.tensor([
    -1.3_6_9_0, -1.9_7_2_0, -0.4_0_9_0, -0.6_9_6_6, 1.4_6_6_0, 0.9_9_3_8, -0.1_3_8_5, -2.7_3_2_4,
    0.7_7_3_6, -1.8_9_1_7, 0.2_9_2_3, 0.4_2_9_3, 0.1_6_9_3, 1.4_1_1_2, 1.1_8_8_7, -0.3_1_8_1,
    -2.2_1_6_0, -2.6_3_8_1, 1.3_1_7_0, 0.8_1_6_3, 0.9_2_4_0, -1.6_5_4_4, -0.6_0_9_9, 2.5_2_5_9,
    1.6_4_3_0, -0.9_0_9_0, -0.9_3_9_2, -0.0_1_2_6, 2.4_2_6_8, 2.3_2_6_6
])
__snake_case		:Optional[Any]   	=  torch.tensor([
    -1.3_5_2_5, -1.9_6_2_8, -0.3_9_5_6, -0.6_8_6_0, 1.4_6_6_4, 1.0_0_1_4, -0.1_2_5_9, -2.7_2_1_2,
    0.7_7_7_2, -1.8_8_1_1, 0.2_9_9_6, 0.4_3_8_8, 0.1_7_0_4, 1.4_0_2_9, 1.1_7_0_1, -0.3_0_2_7,
    -2.2_0_5_3, -2.6_2_8_7, 1.3_3_5_0, 0.8_1_3_1, 0.9_2_7_4, -1.6_2_9_2, -0.6_0_9_8, 2.5_1_3_1,
    1.6_5_0_5, -0.8_9_5_8, -0.9_2_9_8, -0.0_1_5_1, 2.4_2_5_7, 2.3_3_5_5
])
__snake_case		:List[str]   	=  torch.tensor([
    -2.0_5_8_5, -2.7_8_9_7, -0.2_8_5_0, -0.8_9_4_0, 1.9_0_5_2, 0.5_7_0_2, 0.6_3_4_5, -3.8_9_5_9,
    1.5_9_3_2, -3.2_3_1_9, 0.1_9_7_4, 0.0_2_8_7, 1.7_5_6_6, 2.6_5_4_3, 0.8_3_8_7, -0.5_3_5_1,
    -3.2_7_3_6, -4.3_3_7_5, 2.9_0_2_9, 1.6_3_9_0, 1.4_6_4_0, -2.1_7_0_1, -1.9_0_1_3, 2.9_3_4_1,
    3.4_9_8_1, -0.6_2_5_5, -1.1_6_4_4, -0.1_5_9_1, 3.7_0_9_7, 3.2_0_6_6
])
__snake_case		:Any   	=  torch.tensor([
    -2.3_1_3_9, -2.5_5_9_4, -0.0_1_9_7, -0.6_7_8_5, 1.7_0_0_1, 1.1_6_0_6, 0.3_0_7_5, -2.1_7_4_0,
    1.8_0_7_1, -2.5_6_3_0, -0.0_9_2_6, -0.3_8_1_1, 1.2_1_1_6, 2.6_2_4_6, 1.2_7_3_1, -0.5_3_9_8,
    -2.8_1_5_3, -3.6_1_4_0, 2.3_8_9_3, 1.3_2_6_2, 1.6_2_5_8, -2.1_8_5_6, -1.3_2_6_7, 2.8_3_9_5,
    2.3_7_7_9, -1.0_6_2_3, -1.2_4_6_8, 0.8_9_5_9, 3.3_3_6_7, 3.2_2_4_3
])
__snake_case		:List[str]   	=  torch.tensor([
    -2.0_6_2_8, -2.7_6_6_7, -0.2_0_8_9, -0.8_2_6_3, 2.0_5_3_9, 0.5_9_9_2, 0.6_4_9_5, -3.8_3_3_6,
    1.6_0_2_5, -3.2_8_1_7, 0.1_7_2_1, -0.0_6_3_3, 1.7_5_1_6, 2.7_0_3_9, 0.8_1_0_0, -0.5_9_0_8,
    -3.2_1_1_3, -4.4_3_4_3, 2.9_2_5_7, 1.3_6_3_2, 1.5_5_6_2, -2.1_4_8_9, -1.9_8_9_4, 3.0_5_6_0,
    3.3_3_9_6, -0.7_3_2_8, -1.0_4_1_7, 0.0_3_8_3, 3.7_0_9_3, 3.2_3_4_3
])
__snake_case		:Union[str, Any]   	=  torch.tensor([
    -1.4_5_7_4, -2.0_5_6_9, -0.0_4_7_3, -0.6_1_1_7, 1.4_0_1_8, 0.5_7_6_9, 0.4_1_2_9, -2.7_3_4_4,
    1.2_2_4_1, -2.1_3_9_7, 0.2_0_0_0, 0.3_9_3_7, 0.7_6_1_6, 2.0_4_5_3, 0.7_3_2_4, -0.3_3_9_1,
    -2.1_7_4_6, -2.7_7_4_4, 1.6_9_6_3, 0.6_9_2_1, 1.2_1_8_7, -1.6_1_7_2, -0.8_8_7_7, 2.2_4_3_9,
    1.8_4_7_1, -0.5_8_3_9, -0.5_6_0_5, -0.0_4_6_4, 2.3_2_5_0, 2.1_2_1_9
])
# fmt: on

__snake_case		:List[Any]   	=  api.list_models(filter='''diffusers''')
for mod in models:
 if "google" in mod.author or mod.modelId == "CompVis/ldm-celebahq-256":
  __snake_case		:List[str]   	=  '''/home/patrick/google_checkpoints/''' + mod.modelId.split('''/''')[-1]

  print(f'Started running {mod.modelId}!!!')

  if mod.modelId.startswith('''CompVis'''):
   __snake_case		:Optional[int]   	=  UNetaDModel.from_pretrained(local_checkpoint, subfolder='''unet''')
  else:
   __snake_case		:str   	=  UNetaDModel.from_pretrained(local_checkpoint)

  torch.manual_seed(0)
  random.seed(0)

  __snake_case		:List[Any]   	=  torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)
  __snake_case		:List[Any]   	=  torch.tensor([10] * noise.shape[0])
  with torch.no_grad():
   __snake_case		:Any   	=  model(noise, time_step).sample

  assert torch.allclose(
      logits[0, 0, 0, :30], results['''_'''.join('''_'''.join(mod.modelId.split('''/''')).split('''-'''))], atol=1E-3
  )
  print(f'{mod.modelId} has passed successfully!!!')





