

"""simple docstring"""




import os
from pickle import UnpicklingError
from typing import Dict, Tuple

import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict, unflatten_dict

import transformers

from .utils import logging


A_							:			Dict											= logging.get_logger(__name__)

def 	A       (     snake_case__		,				snake_case__		,				snake_case__		,				snake_case__=False    )     ->   List[Any]:




			'''simple docstring'''


			try:
						import torch  # noqa: F401
			except ImportError:
						logger.error(
						    """Loading a PyTorch model in Flax, requires both PyTorch and Flax to be installed. Please see"""
						    """ https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation"""
						    """ instructions."""    )
						raise

			if not is_sharded:
						SCREAMING_SNAKE_CASE__								=      os.path.abspath(SCREAMING_SNAKE_CASE_    )
						logger.info(f"""Loading PyTorch weights from {pt_path}"""    )

						SCREAMING_SNAKE_CASE__								=      torch.load(SCREAMING_SNAKE_CASE_		,				map_location="""cpu"""    )
						logger.info(f"""PyTorch checkpoint contains {sum(t.numel() for t in pt_state_dict.values()    ):,} parameters."""    )

						SCREAMING_SNAKE_CASE__								=      convert_pytorch_state_dict_to_flax(SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_    )
			else:
						# model is sharded and pytorch_checkpoint_path already contains the list of .pt shard files
						SCREAMING_SNAKE_CASE__								=      convert_pytorch_sharded_state_dict_to_flax(SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_    )
			return flax_state_dict

def 	A       (     snake_case__		,				snake_case__		,				snake_case__		,				snake_case__		,				)     ->   (Tuple[str], np.ndarray):




			'''simple docstring'''


			def is_key_or_prefix_key_in_dict(snake_case__    ) -> bool:
						return len(set(SCREAMING_SNAKE_CASE_    ) & {key, (model_prefix,) + key}    ) > 0

			# layer norm
			SCREAMING_SNAKE_CASE__								=      pt_tuple_key[:-1] + ("scale",)
			if pt_tuple_key[-1] in ["weight", "gamma"] and is_key_or_prefix_key_in_dict(SCREAMING_SNAKE_CASE_    ):
						return renamed_pt_tuple_key, pt_tensor

			# batch norm layer mean
			SCREAMING_SNAKE_CASE__								=      pt_tuple_key[:-1] + ("mean",)
			if pt_tuple_key[-1] == "running_mean" and not is_key_or_prefix_key_in_dict(SCREAMING_SNAKE_CASE_    ):
						return renamed_pt_tuple_key, pt_tensor

			# batch norm layer var
			SCREAMING_SNAKE_CASE__								=      pt_tuple_key[:-1] + ("var",)
			if pt_tuple_key[-1] == "running_var" and not is_key_or_prefix_key_in_dict(SCREAMING_SNAKE_CASE_    ):
						return renamed_pt_tuple_key, pt_tensor

			# embedding
			SCREAMING_SNAKE_CASE__								=      pt_tuple_key[:-1] + ("embedding",)
			if pt_tuple_key[-1] == "weight" and is_key_or_prefix_key_in_dict(SCREAMING_SNAKE_CASE_    ):
						return renamed_pt_tuple_key, pt_tensor

			# conv layer
			SCREAMING_SNAKE_CASE__								=      pt_tuple_key[:-1] + ("kernel",)
			if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4 and not is_key_or_prefix_key_in_dict(SCREAMING_SNAKE_CASE_    ):
						SCREAMING_SNAKE_CASE__								=      pt_tensor.transpose(2		,				3		,				1		,				0    )
						return renamed_pt_tuple_key, pt_tensor

			# linear layer
			SCREAMING_SNAKE_CASE__								=      pt_tuple_key[:-1] + ("kernel",)
			if pt_tuple_key[-1] == "weight" and not is_key_or_prefix_key_in_dict(SCREAMING_SNAKE_CASE_    ):
						SCREAMING_SNAKE_CASE__								=      pt_tensor.T
						return renamed_pt_tuple_key, pt_tensor

			# old PyTorch layer norm weight
			SCREAMING_SNAKE_CASE__								=      pt_tuple_key[:-1] + ("weight",)
			if pt_tuple_key[-1] == "gamma":
						return renamed_pt_tuple_key, pt_tensor

			# old PyTorch layer norm bias
			SCREAMING_SNAKE_CASE__								=      pt_tuple_key[:-1] + ("bias",)
			if pt_tuple_key[-1] == "beta":
						return renamed_pt_tuple_key, pt_tensor

			# New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
			SCREAMING_SNAKE_CASE__								=      None
			if pt_tuple_key[-3::2] == ("parametrizations", "original0"):
						SCREAMING_SNAKE_CASE__								=      pt_tuple_key[-2] + "_g"
			elif pt_tuple_key[-3::2] == ("parametrizations", "original1"):
						SCREAMING_SNAKE_CASE__								=      pt_tuple_key[-2] + "_v"
			if name is not None:
						SCREAMING_SNAKE_CASE__								=      pt_tuple_key[:-3] + (name,)
						return renamed_pt_tuple_key, pt_tensor

			return pt_tuple_key, pt_tensor

def 	A       (     snake_case__		,				snake_case__    )     ->   List[str]:




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__								=      {k: v.numpy() for k, v in pt_state_dict.items()}

			SCREAMING_SNAKE_CASE__								=      flax_model.base_model_prefix

			# use params dict if the model contains batch norm layers
			if "params" in flax_model.params:
						SCREAMING_SNAKE_CASE__								=      flax_model.params["params"]
			else:
						SCREAMING_SNAKE_CASE__								=      flax_model.params
			SCREAMING_SNAKE_CASE__								=      flatten_dict(SCREAMING_SNAKE_CASE_    )

			# add batch_stats keys,values to dict
			if "batch_stats" in flax_model.params:
						SCREAMING_SNAKE_CASE__								=      flatten_dict(flax_model.params["""batch_stats"""]    )
						random_flax_state_dict.update(SCREAMING_SNAKE_CASE_    )

			SCREAMING_SNAKE_CASE__								=      {}

			SCREAMING_SNAKE_CASE__								=      (model_prefix not in flax_model_params) and (
			    model_prefix in {k.split("""."""    )[0] for k in pt_state_dict.keys()}
			)
			SCREAMING_SNAKE_CASE__								=      (model_prefix in flax_model_params) and (
			    model_prefix not in {k.split("""."""    )[0] for k in pt_state_dict.keys()}
			)

			# Need to change some parameters name to match Flax names
			for pt_key, pt_tensor in pt_state_dict.items():
						SCREAMING_SNAKE_CASE__								=      tuple(pt_key.split("""."""    )    )

						# remove base model prefix if necessary
						SCREAMING_SNAKE_CASE__								=      pt_tuple_key[0] == model_prefix
						if load_model_with_head_into_base_model and has_base_model_prefix:
									SCREAMING_SNAKE_CASE__								=      pt_tuple_key[1:]

						# Correctly rename weight parameters
						SCREAMING_SNAKE_CASE__								=      rename_key_and_reshape_tensor(
						    SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_    )

						# add model prefix if necessary
						SCREAMING_SNAKE_CASE__								=      (model_prefix,) + flax_key in random_flax_state_dict
						if load_base_model_into_model_with_head and require_base_model_prefix:
									SCREAMING_SNAKE_CASE__								=      (model_prefix,) + flax_key

						if flax_key in random_flax_state_dict:
									if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
												raise ValueError(
												    f"""PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape """
												    f"""{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}."""    )

        # add batch stats if the model contains batchnorm layers
						if "batch_stats" in flax_model.params:
									if "mean" in flax_key[-1] or "var" in flax_key[-1]:
												SCREAMING_SNAKE_CASE__								=      jnp.asarray(SCREAMING_SNAKE_CASE_    )
												continue
									# remove num_batches_tracked key
									if "num_batches_tracked" in flax_key[-1]:
												flax_state_dict.pop(SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_    )
												continue

									# also add unexpected weight so that warning is thrown
									SCREAMING_SNAKE_CASE__								=      jnp.asarray(SCREAMING_SNAKE_CASE_    )

						else:
									# also add unexpected weight so that warning is thrown
									SCREAMING_SNAKE_CASE__								=      jnp.asarray(SCREAMING_SNAKE_CASE_    )

			return unflatten_dict(SCREAMING_SNAKE_CASE_    )

def 	A       (     snake_case__		,				snake_case__    )     ->   Any:




			'''simple docstring'''


			import torch

			# Load the index
			SCREAMING_SNAKE_CASE__								=      {}
			for shard_file in shard_filenames:
						# load using msgpack utils
						SCREAMING_SNAKE_CASE__								=      torch.load(SCREAMING_SNAKE_CASE_    )
						SCREAMING_SNAKE_CASE__								=      {k: v.numpy() for k, v in pt_state_dict.items()}

						SCREAMING_SNAKE_CASE__								=      flax_model.base_model_prefix

						# use params dict if the model contains batch norm layers and then add batch_stats keys,values to dict
						if "batch_stats" in flax_model.params:
									SCREAMING_SNAKE_CASE__								=      flax_model.params["params"]

									SCREAMING_SNAKE_CASE__								=      flatten_dict(SCREAMING_SNAKE_CASE_    )
									random_flax_state_dict.update(flatten_dict(flax_model.params["""batch_stats"""]    )    )
						else:
									SCREAMING_SNAKE_CASE__								=      flax_model.params
									SCREAMING_SNAKE_CASE__								=      flatten_dict(SCREAMING_SNAKE_CASE_    )

						SCREAMING_SNAKE_CASE__								=      (model_prefix not in flax_model_params) and (
						    model_prefix in {k.split("""."""    )[0] for k in pt_state_dict.keys()}
						)
						SCREAMING_SNAKE_CASE__								=      (model_prefix in flax_model_params) and (
						    model_prefix not in {k.split("""."""    )[0] for k in pt_state_dict.keys()}
						)
						# Need to change some parameters name to match Flax names
						for pt_key, pt_tensor in pt_state_dict.items():
									SCREAMING_SNAKE_CASE__								=      tuple(pt_key.split("""."""    )    )

									# remove base model prefix if necessary
									SCREAMING_SNAKE_CASE__								=      pt_tuple_key[0] == model_prefix
									if load_model_with_head_into_base_model and has_base_model_prefix:
												SCREAMING_SNAKE_CASE__								=      pt_tuple_key[1:]

									# Correctly rename weight parameters
									SCREAMING_SNAKE_CASE__								=      rename_key_and_reshape_tensor(
									    SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_    )
									# add model prefix if necessary
									SCREAMING_SNAKE_CASE__								=      (model_prefix,) + flax_key in random_flax_state_dict
									if load_base_model_into_model_with_head and require_base_model_prefix:
												SCREAMING_SNAKE_CASE__								=      (model_prefix,) + flax_key

									if flax_key in random_flax_state_dict:
												if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
															raise ValueError(
															    f"""PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape """
															    f"""{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}."""    )

            # add batch stats if the model contains batchnorm layers
									if "batch_stats" in flax_model.params:
												if "mean" in flax_key[-1]:
															SCREAMING_SNAKE_CASE__								=      jnp.asarray(SCREAMING_SNAKE_CASE_    )
															continue
												if "var" in flax_key[-1]:
															SCREAMING_SNAKE_CASE__								=      jnp.asarray(SCREAMING_SNAKE_CASE_    )
															continue
												# remove num_batches_tracked key
												if "num_batches_tracked" in flax_key[-1]:
															flax_state_dict.pop(SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_    )
															continue

												# also add unexpected weight so that warning is thrown
												SCREAMING_SNAKE_CASE__								=      jnp.asarray(SCREAMING_SNAKE_CASE_    )

									else:
												# also add unexpected weight so that warning is thrown
												SCREAMING_SNAKE_CASE__								=      jnp.asarray(SCREAMING_SNAKE_CASE_    )
			return unflatten_dict(SCREAMING_SNAKE_CASE_    )

def 	A       (     snake_case__		,				snake_case__    )     ->   str:




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__								=      os.path.abspath(SCREAMING_SNAKE_CASE_    )
			logger.info(f"""Loading Flax weights from {flax_checkpoint_path}"""    )

			# import correct flax class
			SCREAMING_SNAKE_CASE__								=      getattr(SCREAMING_SNAKE_CASE_		,				"""Flax""" + model.__class__.__name__    )

			# load flax weight dict
			with open(SCREAMING_SNAKE_CASE_		,				"""rb"""    ) as state_f:
						try:
									SCREAMING_SNAKE_CASE__								=      from_bytes(SCREAMING_SNAKE_CASE_		,				state_f.read()    )
						except UnpicklingError:
									raise EnvironmentError(f"""Unable to convert {flax_checkpoint_path} to Flax deserializable object. """    )

			return load_flax_weights_in_pytorch_model(SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_    )




def 	A       (     snake_case__		,				snake_case__    )     ->   Tuple:




			'''simple docstring'''


			try:
						import torch  # noqa: F401
			except ImportError:
						logger.error(
						    """Loading a Flax weights in PyTorch, requires both PyTorch and Flax to be installed. Please see"""
						    """ https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation"""
						    """ instructions."""    )
						raise

			# check if we have bf16 weights
			SCREAMING_SNAKE_CASE__								=      flatten_dict(jax.tree_util.tree_map(lambda snake_case__    : x.dtype == jnp.bfloataa		,				SCREAMING_SNAKE_CASE_    )    ).values()
			if any(SCREAMING_SNAKE_CASE_    ):
						# convert all weights to fp32 if the are bf16 since torch.from_numpy can-not handle bf16
						# and bf16 is not fully supported in PT yet.
						logger.warning(
						    """Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` """
						    """before loading those in PyTorch model."""    )
						SCREAMING_SNAKE_CASE__								=      jax.tree_util.tree_map(
						    lambda snake_case__    : params.astype(np.floataa    ) if params.dtype == jnp.bfloataa else params		,				SCREAMING_SNAKE_CASE_    )

			SCREAMING_SNAKE_CASE__								=      flatten_dict(SCREAMING_SNAKE_CASE_    )
			SCREAMING_SNAKE_CASE__								=      pt_model.state_dict()

			SCREAMING_SNAKE_CASE__								=      (pt_model.base_model_prefix in flax_state) and (
			    pt_model.base_model_prefix not in {k.split("""."""    )[0] for k in pt_model_dict.keys()}
			)
			SCREAMING_SNAKE_CASE__								=      (pt_model.base_model_prefix not in flax_state) and (
			    pt_model.base_model_prefix in {k.split("""."""    )[0] for k in pt_model_dict.keys()}
			)

			# keep track of unexpected & missing keys
			SCREAMING_SNAKE_CASE__								=      []
			SCREAMING_SNAKE_CASE__								=      set(pt_model_dict.keys()    )

			for flax_key_tuple, flax_tensor in flax_state_dict.items():
						SCREAMING_SNAKE_CASE__								=      flax_key_tuple[0] == pt_model.base_model_prefix
						SCREAMING_SNAKE_CASE__								=      ".".join((pt_model.base_model_prefix,) + flax_key_tuple    ) in pt_model_dict

						# adapt flax_key to prepare for loading from/to base model only
						if load_model_with_head_into_base_model and has_base_model_prefix:
									SCREAMING_SNAKE_CASE__								=      flax_key_tuple[1:]
						elif load_base_model_into_model_with_head and require_base_model_prefix:
									SCREAMING_SNAKE_CASE__								=      (pt_model.base_model_prefix,) + flax_key_tuple

						# rename flax weights to PyTorch format
						if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 4 and ".".join(SCREAMING_SNAKE_CASE_    ) not in pt_model_dict:
									# conv layer
									SCREAMING_SNAKE_CASE__								=      flax_key_tuple[:-1] + ("weight",)
									SCREAMING_SNAKE_CASE__								=      jnp.transpose(SCREAMING_SNAKE_CASE_		,				(3, 2, 0, 1)    )
						elif flax_key_tuple[-1] == "kernel" and ".".join(SCREAMING_SNAKE_CASE_    ) not in pt_model_dict:
									# linear layer
									SCREAMING_SNAKE_CASE__								=      flax_key_tuple[:-1] + ("weight",)
									SCREAMING_SNAKE_CASE__								=      flax_tensor.T
						elif flax_key_tuple[-1] in ["scale", "embedding"]:
									SCREAMING_SNAKE_CASE__								=      flax_key_tuple[:-1] + ("weight",)

						# adding batch stats from flax batch norm to pt
						elif "mean" in flax_key_tuple[-1]:
									SCREAMING_SNAKE_CASE__								=      flax_key_tuple[:-1] + ("running_mean",)
						elif "var" in flax_key_tuple[-1]:
									SCREAMING_SNAKE_CASE__								=      flax_key_tuple[:-1] + ("running_var",)

						if "batch_stats" in flax_state:
									SCREAMING_SNAKE_CASE__								=      ".".join(flax_key_tuple[1:]    )  # Remove the params/batch_stats header
						else:
									SCREAMING_SNAKE_CASE__								=      ".".join(SCREAMING_SNAKE_CASE_    )

						# We also need to look at `pt_model_dict` and see if there are keys requiring further transformation.
						SCREAMING_SNAKE_CASE__								=      {}
						# New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
						for key in pt_model_dict:
									SCREAMING_SNAKE_CASE__								=      key.split("""."""    )
									SCREAMING_SNAKE_CASE__								=      None
									if key_components[-3::2] == ["parametrizations", "original0"]:
												SCREAMING_SNAKE_CASE__								=      key_components[-2] + "_g"
									elif key_components[-3::2] == ["parametrizations", "original1"]:
												SCREAMING_SNAKE_CASE__								=      key_components[-2] + "_v"
									if name is not None:
												SCREAMING_SNAKE_CASE__								=      key_components[:-3] + [name]
												SCREAMING_SNAKE_CASE__								=      ".".join(SCREAMING_SNAKE_CASE_    )
												SCREAMING_SNAKE_CASE__								=      key

						if flax_key in special_pt_names:
									SCREAMING_SNAKE_CASE__								=      special_pt_names[flax_key]

						if flax_key in pt_model_dict:
									if flax_tensor.shape != pt_model_dict[flax_key].shape:
												raise ValueError(
												    f"""Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected """
												    f"""to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}."""    )
									else:
												# add weight to pytorch dict
												SCREAMING_SNAKE_CASE__								=      np.asarray(SCREAMING_SNAKE_CASE_    ) if not isinstance(SCREAMING_SNAKE_CASE_		,				np.ndarray    ) else flax_tensor
												SCREAMING_SNAKE_CASE__								=      torch.from_numpy(SCREAMING_SNAKE_CASE_    )
												# remove from missing keys
												missing_keys.remove(SCREAMING_SNAKE_CASE_    )
						else:
									# weight is not expected by PyTorch model
									unexpected_keys.append(SCREAMING_SNAKE_CASE_    )

			pt_model.load_state_dict(SCREAMING_SNAKE_CASE_    )

			# re-transform missing_keys to list
			SCREAMING_SNAKE_CASE__								=      list(SCREAMING_SNAKE_CASE_    )

			if len(SCREAMING_SNAKE_CASE_    ) > 0:
						logger.warning(
						    """Some weights of the Flax model were not used when initializing the PyTorch model"""
						    f""" {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing"""
						    f""" {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture"""
						    """ (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This"""
						    f""" IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect"""
						    """ to be exactly identical (e.g. initializing a BertForSequenceClassification model from a"""
						    """ FlaxBertForSequenceClassification model)."""    )
			else:
						logger.warning(f"""All Flax model weights were used when initializing {pt_model.__class__.__name__}.\n"""    )
			if len(SCREAMING_SNAKE_CASE_    ) > 0:
						logger.warning(
						    f"""Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly"""
						    f""" initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to"""
						    """ use it for predictions and inference."""    )
			else:
						logger.warning(
						    f"""All the weights of {pt_model.__class__.__name__} were initialized from the Flax model.\n"""
						    """If your task is similar to the task the model of the checkpoint was trained on, """
						    f"""you can already use {pt_model.__class__.__name__} for predictions without further training."""    )

			return pt_model





"""simple docstring"""




import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()





class    lowerCamelCase						(unittest.TestCase			):

			@property
			def 					SCREAMING_SNAKE_CASE				(					self				:						Dict       )   ->    Any:
						torch.manual_seed(0       )
						SCREAMING_SNAKE_CASE__								=      UNetaDModel(
						    block_out_channels=(3_2, 6_4)    ,				layers_per_block=2    ,				sample_size=3_2    ,				in_channels=3    ,				out_channels=3    ,				down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")    ,				up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")    ,				)
						return model





			def 					SCREAMING_SNAKE_CASE				(					self				:						Any       )   ->    Tuple:
						SCREAMING_SNAKE_CASE__								=      self.dummy_uncond_unet
						SCREAMING_SNAKE_CASE__								=      KarrasVeScheduler()

						SCREAMING_SNAKE_CASE__								=      KarrasVePipeline(unet=__UpperCAmelCase    ,				scheduler=__UpperCAmelCase       )
						pipe.to(__UpperCAmelCase       )
						pipe.set_progress_bar_config(disable=__UpperCAmelCase       )

						SCREAMING_SNAKE_CASE__								=      torch.manual_seed(0       )
						SCREAMING_SNAKE_CASE__								=      pipe(num_inference_steps=2    ,				generator=__UpperCAmelCase    ,				output_type="""numpy"""       ).images

						SCREAMING_SNAKE_CASE__								=      torch.manual_seed(0       )
						SCREAMING_SNAKE_CASE__								=      pipe(num_inference_steps=2    ,				generator=__UpperCAmelCase    ,				output_type="""numpy"""    ,				return_dict=__UpperCAmelCase       )[0]

						SCREAMING_SNAKE_CASE__								=      image[0, -3:, -3:, -1]
						SCREAMING_SNAKE_CASE__								=      image_from_tuple[0, -3:, -3:, -1]

						assert image.shape == (1, 3_2, 3_2, 3)
						SCREAMING_SNAKE_CASE__								=      np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]       )

						assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-2
						assert np.abs(image_from_tuple_slice.flatten() - expected_slice       ).max() < 1e-2





@slow
@require_torch
class    lowerCamelCase						(unittest.TestCase			):

			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[Any]       )   ->    Optional[int]:
						SCREAMING_SNAKE_CASE__								=      """google/ncsnpp-celebahq-256"""
						SCREAMING_SNAKE_CASE__								=      UNetaDModel.from_pretrained(__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      KarrasVeScheduler()

						SCREAMING_SNAKE_CASE__								=      KarrasVePipeline(unet=__UpperCAmelCase    ,				scheduler=__UpperCAmelCase       )
						pipe.to(__UpperCAmelCase       )
						pipe.set_progress_bar_config(disable=__UpperCAmelCase       )

						SCREAMING_SNAKE_CASE__								=      torch.manual_seed(0       )
						SCREAMING_SNAKE_CASE__								=      pipe(num_inference_steps=2_0    ,				generator=__UpperCAmelCase    ,				output_type="""numpy"""       ).images

						SCREAMING_SNAKE_CASE__								=      image[0, -3:, -3:, -1]
						assert image.shape == (1, 2_5_6, 2_5_6, 3)
						SCREAMING_SNAKE_CASE__								=      np.array([0.578, 0.5_811, 0.5_924, 0.5_809, 0.587, 0.5_886, 0.5_861, 0.5_802, 0.586]       )

						assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-2


