
"""simple docstring"""


import argparse
import json
import os

import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile

from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_transformers.convert_switch_transformers_original_flax_checkpoint_to_pytorch import (
    rename_keys,
)
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
from transformers.utils.hub import convert_file_size_to_int






def    lowerCAmelCase_   (		snake_case_		:			Union[str, Any]							,    snake_case_		:			Dict			)   ->Tuple:
			if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
						# expert layer
						lowerCamelCase__	:     Tuple              =flax_key_tuple[:-1] + ('weight',)
						lowerCamelCase__	:     int              =torch.permute(lowerCamelCase_							,    (0, 2, 1)			)
			elif flax_key_tuple[-1] == "kernel" and ".".join(lowerCamelCase_			):
						# linear layer
						lowerCamelCase__	:     Any              =flax_key_tuple[:-1] + ('weight',)
						lowerCamelCase__	:     int              =flax_tensor.T
			elif flax_key_tuple[-1] in ["scale", "embedding"]:
						lowerCamelCase__	:     List[str]              =flax_key_tuple[:-1] + ('weight',)

			return flax_key_tuple, flax_tensor






def    lowerCAmelCase_   (		snake_case_		:			Optional[Any]							,    snake_case_		:			str							,    snake_case_		:			int			)   ->List[str]:
			if "metadata" in layer:
						lowerCamelCase__	:     Any              =layer.split('metadata'			)
						lowerCamelCase__	:     Union[str, Any]              =''.join(split_layer[0]			)[:-1]
						lowerCamelCase__	:     List[str]              =[tuple(('metadata' + split_layer[1]).split('/'			)			)]
			elif "kvstore" in layer:
						lowerCamelCase__	:     str              =layer.split('kvstore'			)
						lowerCamelCase__	:     Union[str, Any]              =''.join(split_layer[0]			)[:-1]
						lowerCamelCase__	:     Optional[Any]              =[tuple(('kvstore' + split_layer[1]).split('/'			)			)]

			else:
						lowerCamelCase__	:     int              =layer.split('/'			)
						lowerCamelCase__	:     Tuple              ='/'.join(split_layer[:-1]			)
						lowerCamelCase__	:     Optional[Any]              =(split_layer[-1],)

			if "kvstore/path" in layer:
						lowerCamelCase__	:     str              =f"""{switch_checkpoint_path}/{checkpoint_info[layer]}"""
			elif "kvstore/driver" in layer:
						lowerCamelCase__	:     List[str]              ='file'
			else:
						lowerCamelCase__	:     Optional[int]              =checkpoint_info[layer]

			return curr_real_layer_name, split_layer, content






def    lowerCAmelCase_   (		snake_case_		:			Any							,    snake_case_		:			int			)   ->Dict:
			lowerCamelCase__	:     Tuple              =rename_keys(lowerCamelCase_			)
			lowerCamelCase__	:     List[str]              ={}
			for k, v in current_block.items():
						lowerCamelCase__	:     Dict              =v
			lowerCamelCase__	:     Any              =new_current_block
			torch.save(lowerCamelCase_							,    lowerCamelCase_			)






def    lowerCAmelCase_   (		snake_case_		:			Tuple							,    snake_case_		:			Optional[int]							,    snake_case_		:			Tuple							,    snake_case_		:			Optional[int]							,    snake_case_		:			Tuple = WEIGHTS_NAME			)   ->Dict:
			lowerCamelCase__	:     Optional[Any]              =convert_file_size_to_int(lowerCamelCase_			)
			lowerCamelCase__	:     Any              =[]
			lowerCamelCase__	:     Optional[Any]              ={}
			lowerCamelCase__	:     Any              =0
			lowerCamelCase__	:     str              =0

			os.makedirs(lowerCamelCase_							,    exist_ok=lowerCamelCase_			)
			with gfile.GFile(switch_checkpoint_path + '/checkpoint'							,    'rb'			) as fp:
						lowerCamelCase__	:     Union[str, Any]              =serialization.msgpack_restore(fp.read()			)['optimizer']['target']
						lowerCamelCase__	:     Optional[int]              =flatten_dict(lowerCamelCase_							,    sep='/'			)

			lowerCamelCase__	:     str              ={}
			for layer in checkpoint_info.keys():
						lowerCamelCase__	:     List[Any]              =get_key_and_tensorstore_dict(
						    lowerCamelCase_							,    lowerCamelCase_							,    lowerCamelCase_			)
						if curr_real_layer_name in all_layers:
									lowerCamelCase__	:     List[Any]              =content
						else:
									lowerCamelCase__	:     Dict              ={split_layer[-1]: content}

			for key in all_layers.keys():
						# open tensorstore file
						lowerCamelCase__	:     Dict              =ts.open(unflatten_dict(all_layers[key]			)			).result().read().result()
						lowerCamelCase__	:     Tuple              =torch.tensor(lowerCamelCase_			)
						lowerCamelCase__	:     Dict              =raw_weights.numel() * dtype_byte_size(raw_weights.dtype			)

						# use the renaming pattern from the small conversion scripts
						lowerCamelCase__	:     str              =rename_base_flax_keys(tuple(key.split('/'			)			)							,    lowerCamelCase_			)
						lowerCamelCase__	:     Any              ='/'.join(lowerCamelCase_			)

						# If this weight is going to tip up over the maximal size, we split.
						if current_block_size + weight_size > max_shard_size:
									lowerCamelCase__	:     List[str]              =os.path.join(
									    lowerCamelCase_							,    weights_name.replace('.bin'							,    f"""-{len(lowerCamelCase_			)+1:05d}-of-???.bin"""			)			)
									rename_and_save_block(lowerCamelCase_							,    lowerCamelCase_			)
									sharded_state_dicts.append(current_block.keys()			)
									del current_block
									lowerCamelCase__	:     Any              ={}
									lowerCamelCase__	:     str              =0

						lowerCamelCase__	:     List[str]              =raw_weights.to(getattr(lowerCamelCase_							,    lowerCamelCase_			)			)
						current_block_size += weight_size
						total_size += weight_size

			# Add the last block
			lowerCamelCase__	:     List[Any]              =os.path.join(lowerCamelCase_							,    weights_name.replace('.bin'							,    f"""-{len(lowerCamelCase_			)+1:05d}-of-???.bin"""			)			)
			rename_and_save_block(lowerCamelCase_							,    lowerCamelCase_			)
			sharded_state_dicts.append(current_block.keys()			)

			# If we only have one shard, we return it
			if len(lowerCamelCase_			) == 1:
						return {weights_name: sharded_state_dicts[0]}, None

			# Otherwise, let's build the index
			lowerCamelCase__	:     int              ={}
			lowerCamelCase__	:     Optional[Any]              ={}
			for idx, shard in enumerate(lowerCamelCase_			):
						lowerCamelCase__	:     str              =weights_name.replace(
						    '.bin'							,    f"""-{idx+1:05d}-of-{len(lowerCamelCase_			):05d}.bin"""			)  # len(sharded_state_dicts):05d}
						lowerCamelCase__	:     List[str]              =os.path.join(lowerCamelCase_							,    weights_name.replace('.bin'							,    f"""-{idx+1:05d}-of-???.bin"""			)			)
						os.rename(lowerCamelCase_							,    os.path.join(lowerCamelCase_							,    lowerCamelCase_			)			)
						lowerCamelCase__	:     int              =shard
						for key in shard:
									lowerCamelCase__	:     Optional[int]              =shard_file

    # Add the metadata
			lowerCamelCase__	:     Any              ={'total_size': total_size}
			lowerCamelCase__	:     Tuple              ={'metadata': metadata, 'weight_map': weight_map}

			with open(os.path.join(lowerCamelCase_							,    lowerCamelCase_			)							,    'w'							,    encoding='utf-8'			) as f:
						lowerCamelCase__	:     Tuple              =json.dumps(lowerCamelCase_							,    indent=2							,    sort_keys=lowerCamelCase_			) + '\n'
						f.write(lowerCamelCase_			)

			return metadata, index


if __name__ == "__main__":
							lowerCAmelCase								=						argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    """--switch_t5x_checkpoint_path""",
							    default="""/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128/checkpoint_634600""",
							    type=str,
							    required=False,
							    help="""Path to a directory containing a folder per layer. Follows the original Google format.""",
							)
							parser.add_argument("""--max_shard_size""", default="""10GB""", required=False, help="""Max shard size""")
							parser.add_argument("""--dtype""", default="""bfloat16""", type=str, required=False, help="""dtype of the saved model""")
							parser.add_argument(
							    """--pytorch_dump_folder_path""",
							    default="""/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128-converted""",
							    type=str,
							    required=False,
							    help="""Path to the output pytorch model.""",
							)
							lowerCAmelCase								=						parser.parse_args()
							shard_on_the_fly(
							    args.switch_tax_checkpoint_path,
							    args.pytorch_dump_folder_path,
							    args.max_shard_size,
							    args.dtype,
							)






def    lowerCAmelCase_   (		)   ->str:
			from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

			lowerCamelCase__	:     Optional[Any]              =SwitchTransformersConfig.from_pretrained('google/switch-base-8'			)
			config.save_pretrained('/home/arthur_huggingface_co/transformers/switch_converted'			)
			lowerCamelCase__	:     str              =SwitchTransformersForConditionalGeneration.from_pretrained(
			    '/home/arthur_huggingface_co/transformers/switch_converted'							,    device_map='auto'			)

			lowerCamelCase__	:     str              =TaTokenizer.from_pretrained('t5-small'			)
			lowerCamelCase__	:     Tuple              ='A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.'

			lowerCamelCase__	:     Optional[int]              =tokenizer(lowerCamelCase_							,    return_tensors='pt'			).input_ids
			lowerCamelCase__	:     Tuple              =model.generate(lowerCamelCase_							,    decoder_start_token_id=0			)
			print(tokenizer.decode(out[0]			)			)



import gc
import unittest

import numpy as np
import torch
from torch.backends.cuda import sdp_kernel

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)
from diffusers.utils import randn_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_a, require_torch_gpu

from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()






class _lowerCamelCase(						_a, unittest.TestCase   ):
       lowercase_ :	Union[str, Any]     =			ConsistencyModelPipeline
       lowercase_ :	Tuple     =			UNCONDITIONAL_IMAGE_GENERATION_PARAMS
       lowercase_ :	List[str]     =			UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS

       # Override required_optional_params to remove num_images_per_prompt
       lowercase_ :	List[str]     =			frozenset(
           [
               """num_inference_steps""",
               """generator""",
               """latents""",
               """output_type""",
               """return_dict""",
               """callback""",
               """callback_steps""",
           ]   )
       @property
       def 			UpperCamelCase     (					self)       ->   Tuple:




           """simple docstring"""

           _lowercase       :							Tuple      		=		UNetaDModel.from_pretrained(
               'diffusers/consistency-models-test',      subfolder='test_unet',      )
           return unet
       @property
       def 			UpperCamelCase     (					self)       ->   List[Any]:




           """simple docstring"""

           _lowercase       :							Tuple      		=		UNetaDModel.from_pretrained(
               'diffusers/consistency-models-test',      subfolder='test_unet_class_cond',      )
           return unet
       def 			UpperCamelCase     (					self,      lowerCamelCase=False)       ->   Dict:




           """simple docstring"""

           if class_cond:
               _lowercase       :							Union[str, Any]      		=		self.dummy_cond_unet
           else:
               _lowercase       :							Union[str, Any]      		=		self.dummy_uncond_unet

           # Default to CM multistep sampler
           _lowercase       :							List[str]      		=		CMStochasticIterativeScheduler(
               num_train_timesteps=40,      sigma_min=0.0_0_2,      sigma_max=8_0.0,      )

           _lowercase       :							Optional[Any]      		=		{
               'unet': unet,
               'scheduler': scheduler,
           }

           return components
       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase=0)       ->   Tuple:




           """simple docstring"""

           if str(lowerCamelCase).startswith('mps'):
               _lowercase       :							str      		=		torch.manual_seed(lowerCamelCase)
           else:
               _lowercase       :							int      		=		torch.Generator(device=lowerCamelCase).manual_seed(lowerCamelCase)

           _lowercase       :							Tuple      		=		{
               'batch_size': 1,
               'num_inference_steps': None,
               'timesteps': [22, 0],
               'generator': generator,
               'output_type': 'np',
           }

           return inputs
       def 			UpperCamelCase     (					self)       ->   Any:




           """simple docstring"""

           _lowercase       :							Optional[int]      		=		'cpu'  # ensure determinism for the device-dependent torch.Generator
           _lowercase       :							Optional[int]      		=		self.get_dummy_components()
           _lowercase       :							str      		=		ConsistencyModelPipeline(**lowerCamelCase)
           _lowercase       :							Dict      		=		pipe.to(lowerCamelCase)
           pipe.set_progress_bar_config(disable=lowerCamelCase)

           _lowercase       :							Tuple      		=		self.get_dummy_inputs(lowerCamelCase)
           _lowercase       :							Optional[int]      		=		pipe(**lowerCamelCase).images
           assert image.shape == (1, 32, 32, 3)

           _lowercase       :							int      		=		image[0, -3:, -3:, -1]
           _lowercase       :							Dict      		=		np.array([0.3_5_7_2, 0.6_2_7_3, 0.4_0_3_1, 0.3_9_6_1, 0.4_3_2_1, 0.5_7_3_0, 0.5_2_6_6, 0.4_7_8_0, 0.5_0_0_4])

           assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-3
       def 			UpperCamelCase     (					self)       ->   Any:




           """simple docstring"""

           _lowercase       :							Union[str, Any]      		=		'cpu'  # ensure determinism for the device-dependent torch.Generator
           _lowercase       :							Dict      		=		self.get_dummy_components(class_cond=lowerCamelCase)
           _lowercase       :							Any      		=		ConsistencyModelPipeline(**lowerCamelCase)
           _lowercase       :							str      		=		pipe.to(lowerCamelCase)
           pipe.set_progress_bar_config(disable=lowerCamelCase)

           _lowercase       :							Any      		=		self.get_dummy_inputs(lowerCamelCase)
           _lowercase       :							Any      		=		0
           _lowercase       :							List[str]      		=		pipe(**lowerCamelCase).images
           assert image.shape == (1, 32, 32, 3)

           _lowercase       :							Any      		=		image[0, -3:, -3:, -1]
           _lowercase       :							Union[str, Any]      		=		np.array([0.3_5_7_2, 0.6_2_7_3, 0.4_0_3_1, 0.3_9_6_1, 0.4_3_2_1, 0.5_7_3_0, 0.5_2_6_6, 0.4_7_8_0, 0.5_0_0_4])

           assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-3
       def 			UpperCamelCase     (					self)       ->   Tuple:




           """simple docstring"""

           _lowercase       :							Optional[int]      		=		'cpu'  # ensure determinism for the device-dependent torch.Generator
           _lowercase       :							Any      		=		self.get_dummy_components()
           _lowercase       :							Optional[Any]      		=		ConsistencyModelPipeline(**lowerCamelCase)
           _lowercase       :							List[str]      		=		pipe.to(lowerCamelCase)
           pipe.set_progress_bar_config(disable=lowerCamelCase)

           _lowercase       :							Optional[Any]      		=		self.get_dummy_inputs(lowerCamelCase)
           _lowercase       :							Union[str, Any]      		=		1
           _lowercase       :							Tuple      		=		None
           _lowercase       :							Tuple      		=		pipe(**lowerCamelCase).images
           assert image.shape == (1, 32, 32, 3)

           _lowercase       :							str      		=		image[0, -3:, -3:, -1]
           _lowercase       :							List[str]      		=		np.array([0.5_0_0_4, 0.5_0_0_4, 0.4_9_9_4, 0.5_0_0_8, 0.4_9_7_6, 0.5_0_1_8, 0.4_9_9_0, 0.4_9_8_2, 0.4_9_8_7])

           assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-3




       def 			UpperCamelCase     (					self)       ->   str:




           """simple docstring"""

           _lowercase       :							Union[str, Any]      		=		'cpu'  # ensure determinism for the device-dependent torch.Generator
           _lowercase       :							Dict      		=		self.get_dummy_components(class_cond=lowerCamelCase)
           _lowercase       :							Dict      		=		ConsistencyModelPipeline(**lowerCamelCase)
           _lowercase       :							Optional[Any]      		=		pipe.to(lowerCamelCase)
           pipe.set_progress_bar_config(disable=lowerCamelCase)

           _lowercase       :							Tuple      		=		self.get_dummy_inputs(lowerCamelCase)
           _lowercase       :							Tuple      		=		1
           _lowercase       :							int      		=		None
           _lowercase       :							Tuple      		=		0
           _lowercase       :							Dict      		=		pipe(**lowerCamelCase).images
           assert image.shape == (1, 32, 32, 3)

           _lowercase       :							List[str]      		=		image[0, -3:, -3:, -1]
           _lowercase       :							Any      		=		np.array([0.5_0_0_4, 0.5_0_0_4, 0.4_9_9_4, 0.5_0_0_8, 0.4_9_7_6, 0.5_0_1_8, 0.4_9_9_0, 0.4_9_8_2, 0.4_9_8_7])

           assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-3






@slow
@require_torch_gpu
class _lowerCamelCase(						unittest.TestCase   ):
       def 			UpperCamelCase     (					self)       ->   Union[str, Any]:




           """simple docstring"""

           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()
       def 			UpperCamelCase     (					self,      lowerCamelCase=0,      lowerCamelCase=False,      lowerCamelCase="cpu",      lowerCamelCase=torch.floataa,      lowerCamelCase=(1, 3, 64, 64))       ->   Optional[Any]:




           """simple docstring"""

           _lowercase       :							List[Any]      		=		torch.manual_seed(lowerCamelCase)

           _lowercase       :							str      		=		{
               'num_inference_steps': None,
               'timesteps': [22, 0],
               'class_labels': 0,
               'generator': generator,
               'output_type': 'np',
           }

           if get_fixed_latents:
               _lowercase       :							Optional[Any]      		=		self.get_fixed_latents(seed=lowerCamelCase,      device=lowerCamelCase,      dtype=lowerCamelCase,      shape=lowerCamelCase)
               _lowercase       :							Tuple      		=		latents

           return inputs
       def 			UpperCamelCase     (					self,      lowerCamelCase=0,      lowerCamelCase="cpu",      lowerCamelCase=torch.floataa,      lowerCamelCase=(1, 3, 64, 64))       ->   Any:




           """simple docstring"""

           if type(lowerCamelCase) == str:
               _lowercase       :							Union[str, Any]      		=		torch.device(lowerCamelCase)
           _lowercase       :							int      		=		torch.Generator(device=lowerCamelCase).manual_seed(lowerCamelCase)
           _lowercase       :							List[str]      		=		randn_tensor(lowerCamelCase,      generator=lowerCamelCase,      device=lowerCamelCase,      dtype=lowerCamelCase)
           return latents
       def 			UpperCamelCase     (					self)       ->   str:




           """simple docstring"""

           _lowercase       :							Tuple      		=		UNetaDModel.from_pretrained('diffusers/consistency_models',      subfolder='diffusers_cd_imagenet64_l2')
           _lowercase       :							Optional[int]      		=		CMStochasticIterativeScheduler(
               num_train_timesteps=40,      sigma_min=0.0_0_2,      sigma_max=8_0.0,      )
           _lowercase       :							Any      		=		ConsistencyModelPipeline(unet=lowerCamelCase,      scheduler=lowerCamelCase)
           pipe.to(torch_device=lowerCamelCase)
           pipe.set_progress_bar_config(disable=lowerCamelCase)

           _lowercase       :							str      		=		self.get_inputs()
           _lowercase       :							Optional[int]      		=		pipe(**lowerCamelCase).images
           assert image.shape == (1, 64, 64, 3)

           _lowercase       :							str      		=		image[0, -3:, -3:, -1]

           _lowercase       :							Optional[Any]      		=		np.array([0.0_8_8_8, 0.0_8_8_1, 0.0_6_6_6, 0.0_4_7_9, 0.0_2_9_2, 0.0_1_9_5, 0.0_2_0_1, 0.0_1_6_3, 0.0_2_5_4])

           assert np.abs(image_slice.flatten() - expected_slice).max() < 2E-2
       def 			UpperCamelCase     (					self)       ->   str:




           """simple docstring"""

           _lowercase       :							List[str]      		=		UNetaDModel.from_pretrained('diffusers/consistency_models',      subfolder='diffusers_cd_imagenet64_l2')
           _lowercase       :							List[str]      		=		CMStochasticIterativeScheduler(
               num_train_timesteps=40,      sigma_min=0.0_0_2,      sigma_max=8_0.0,      )
           _lowercase       :							Union[str, Any]      		=		ConsistencyModelPipeline(unet=lowerCamelCase,      scheduler=lowerCamelCase)
           pipe.to(torch_device=lowerCamelCase)
           pipe.set_progress_bar_config(disable=lowerCamelCase)

           _lowercase       :							List[Any]      		=		self.get_inputs()
           _lowercase       :							int      		=		1
           _lowercase       :							Optional[Any]      		=		None
           _lowercase       :							str      		=		pipe(**lowerCamelCase).images
           assert image.shape == (1, 64, 64, 3)

           _lowercase       :							List[Any]      		=		image[0, -3:, -3:, -1]

           _lowercase       :							List[str]      		=		np.array([0.0_3_4_0, 0.0_1_5_2, 0.0_0_6_3, 0.0_2_6_7, 0.0_2_2_1, 0.0_1_0_7, 0.0_4_1_6, 0.0_1_8_6, 0.0_2_1_7])

           assert np.abs(image_slice.flatten() - expected_slice).max() < 2E-2
       @require_torch_a
       def 			UpperCamelCase     (					self)       ->   Union[str, Any]:




           """simple docstring"""

           _lowercase       :							str      		=		UNetaDModel.from_pretrained('diffusers/consistency_models',      subfolder='diffusers_cd_imagenet64_l2')
           _lowercase       :							Optional[int]      		=		CMStochasticIterativeScheduler(
               num_train_timesteps=40,      sigma_min=0.0_0_2,      sigma_max=8_0.0,      )
           _lowercase       :							Optional[int]      		=		ConsistencyModelPipeline(unet=lowerCamelCase,      scheduler=lowerCamelCase)
           pipe.to(torch_device=lowerCamelCase,      torch_dtype=torch.floataa)
           pipe.set_progress_bar_config(disable=lowerCamelCase)

           _lowercase       :							Any      		=		self.get_inputs(get_fixed_latents=lowerCamelCase,      device=lowerCamelCase)
           # Ensure usage of flash attention in torch 2.0
           with sdp_kernel(enable_flash=lowerCamelCase,      enable_math=lowerCamelCase,      enable_mem_efficient=lowerCamelCase):
               _lowercase       :							Dict      		=		pipe(**lowerCamelCase).images
           assert image.shape == (1, 64, 64, 3)

           _lowercase       :							Any      		=		image[0, -3:, -3:, -1]

           _lowercase       :							Union[str, Any]      		=		np.array([0.1_8_7_5, 0.1_4_2_8, 0.1_2_8_9, 0.2_1_5_1, 0.2_0_9_2, 0.1_4_7_7, 0.1_8_7_7, 0.1_6_4_1, 0.1_3_5_3])

           assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-3




       @require_torch_a
       def 			UpperCamelCase     (					self)       ->   Union[str, Any]:




           """simple docstring"""

           _lowercase       :							Dict      		=		UNetaDModel.from_pretrained('diffusers/consistency_models',      subfolder='diffusers_cd_imagenet64_l2')
           _lowercase       :							Optional[Any]      		=		CMStochasticIterativeScheduler(
               num_train_timesteps=40,      sigma_min=0.0_0_2,      sigma_max=8_0.0,      )
           _lowercase       :							int      		=		ConsistencyModelPipeline(unet=lowerCamelCase,      scheduler=lowerCamelCase)
           pipe.to(torch_device=lowerCamelCase,      torch_dtype=torch.floataa)
           pipe.set_progress_bar_config(disable=lowerCamelCase)

           _lowercase       :							List[Any]      		=		self.get_inputs(get_fixed_latents=lowerCamelCase,      device=lowerCamelCase)
           _lowercase       :							int      		=		1
           _lowercase       :							str      		=		None
           # Ensure usage of flash attention in torch 2.0
           with sdp_kernel(enable_flash=lowerCamelCase,      enable_math=lowerCamelCase,      enable_mem_efficient=lowerCamelCase):
               _lowercase       :							Union[str, Any]      		=		pipe(**lowerCamelCase).images
           assert image.shape == (1, 64, 64, 3)

           _lowercase       :							Any      		=		image[0, -3:, -3:, -1]

           _lowercase       :							int      		=		np.array([0.1_6_6_3, 0.1_9_4_8, 0.2_2_7_5, 0.1_6_8_0, 0.1_2_0_4, 0.1_2_4_5, 0.1_8_5_8, 0.1_3_3_8, 0.2_0_9_5])

           assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-3


