

'''simple docstring'''
import argparse
import os

import align
import numpy as np
import requests
import tensorflow as tf
import torch
from PIL import Image
from tokenizer import Tokenizer

from transformers import (
    AlignConfig,
    AlignModel,
    AlignProcessor,
    BertConfig,
    BertTokenizer,
    EfficientNetConfig,
    EfficientNetImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
_lowerCamelCase	 =			logging.get_logger(__name__)


def       a__    (  _SCREAMING_SNAKE_CASE					:    List[str]							)			-> Union[str, Any]:


	"""simple docstring"""





	UpperCAmelCase_			:	List[str]      =   tf.image.resize(_SCREAMING_SNAKE_CASE     ,							(3_46, 3_46)							)
	UpperCAmelCase_			:	List[str]      =   tf.image.crop_to_bounding_box(_SCREAMING_SNAKE_CASE     ,							(3_46 - 2_89) // 2     ,							(3_46 - 2_89) // 2     ,							2_89     ,							2_89							)
	return image


def       a__    (  )			-> Dict:


	"""simple docstring"""





	UpperCAmelCase_			:	str      =   EfficientNetConfig.from_pretrained("google/efficientnet-b7"							)
	UpperCAmelCase_			:	str      =   2_89
	UpperCAmelCase_			:	Dict      =   6_40
	UpperCAmelCase_			:	List[Any]      =   {"0": "LABEL_0", "1": "LABEL_1"}
	UpperCAmelCase_			:	List[Any]      =   {"LABEL_0": 0, "LABEL_1": 1}
	UpperCAmelCase_			:	List[str]      =   []

	UpperCAmelCase_			:	Dict      =   BertConfig()
	UpperCAmelCase_			:	Dict      =   AlignConfig.from_text_vision_configs(
	    text_config=_SCREAMING_SNAKE_CASE     ,							vision_config=_SCREAMING_SNAKE_CASE     ,							projection_dim=6_40							)
	return config


def       a__    (  )			-> Dict:


	"""simple docstring"""





	UpperCAmelCase_			:	List[str]      =   "http://images.cocodataset.org/val2017/000000039769.jpg"
	UpperCAmelCase_			:	Any      =   Image.open(requests.get(_SCREAMING_SNAKE_CASE     ,							stream=_SCREAMING_SNAKE_CASE							).raw							)
	return im


def       a__    (  )			-> Tuple:


	"""simple docstring"""





	UpperCAmelCase_			:	Any      =   EfficientNetImageProcessor(
	    do_center_crop=_SCREAMING_SNAKE_CASE     ,							rescale_factor=1 / 127.5     ,							rescale_offset=_SCREAMING_SNAKE_CASE     ,							do_normalize=_SCREAMING_SNAKE_CASE     ,							include_top=_SCREAMING_SNAKE_CASE     ,							resample=Image.BILINEAR     ,							)
	UpperCAmelCase_			:	int      =   BertTokenizer.from_pretrained("bert-base-uncased"							)
	UpperCAmelCase_			:	Optional[Any]      =   64
	UpperCAmelCase_			:	Any      =   AlignProcessor(image_processor=_SCREAMING_SNAKE_CASE     ,							tokenizer=_SCREAMING_SNAKE_CASE							)
	return processor


def       a__    (  _SCREAMING_SNAKE_CASE					:    List[Any]							)			-> Dict:


	"""simple docstring"""





	UpperCAmelCase_			:	Tuple      =   [v.split("_"							)[0].split("block"							)[1] for v in original_param_names if v.startswith("block"							)]
	UpperCAmelCase_			:	List[str]      =   list(set(_SCREAMING_SNAKE_CASE							)							)
	UpperCAmelCase_			:	Union[str, Any]      =   sorted(_SCREAMING_SNAKE_CASE							)
	UpperCAmelCase_			:	Tuple      =   len(_SCREAMING_SNAKE_CASE							)
	UpperCAmelCase_			:	List[str]      =   {b: str(_SCREAMING_SNAKE_CASE							) for b, i in zip(_SCREAMING_SNAKE_CASE     ,							range(_SCREAMING_SNAKE_CASE							)							)}

	UpperCAmelCase_			:	List[str]      =   []
	rename_keys.append(("stem_conv/kernel:0", "embeddings.convolution.weight")							)
	rename_keys.append(("stem_bn/gamma:0", "embeddings.batchnorm.weight")							)
	rename_keys.append(("stem_bn/beta:0", "embeddings.batchnorm.bias")							)
	rename_keys.append(("stem_bn/moving_mean:0", "embeddings.batchnorm.running_mean")							)
	rename_keys.append(("stem_bn/moving_variance:0", "embeddings.batchnorm.running_var")							)

	for b in block_names:
		UpperCAmelCase_			:	Union[str, Any]      =   block_name_mapping[b]
		rename_keys.append((F'''block{b}_expand_conv/kernel:0''', F'''encoder.blocks.{hf_b}.expansion.expand_conv.weight''')							)
		rename_keys.append((F'''block{b}_expand_bn/gamma:0''', F'''encoder.blocks.{hf_b}.expansion.expand_bn.weight''')							)
		rename_keys.append((F'''block{b}_expand_bn/beta:0''', F'''encoder.blocks.{hf_b}.expansion.expand_bn.bias''')							)
		rename_keys.append(
		    (F'''block{b}_expand_bn/moving_mean:0''', F'''encoder.blocks.{hf_b}.expansion.expand_bn.running_mean''')							)
		rename_keys.append(
		    (F'''block{b}_expand_bn/moving_variance:0''', F'''encoder.blocks.{hf_b}.expansion.expand_bn.running_var''')							)
		rename_keys.append(
		    (F'''block{b}_dwconv/depthwise_kernel:0''', F'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_conv.weight''')							)
		rename_keys.append((F'''block{b}_bn/gamma:0''', F'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.weight''')							)
		rename_keys.append((F'''block{b}_bn/beta:0''', F'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.bias''')							)
		rename_keys.append(
		    (F'''block{b}_bn/moving_mean:0''', F'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_mean''')							)
		rename_keys.append(
		    (F'''block{b}_bn/moving_variance:0''', F'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_var''')							)

		rename_keys.append((F'''block{b}_se_reduce/kernel:0''', F'''encoder.blocks.{hf_b}.squeeze_excite.reduce.weight''')							)
		rename_keys.append((F'''block{b}_se_reduce/bias:0''', F'''encoder.blocks.{hf_b}.squeeze_excite.reduce.bias''')							)
		rename_keys.append((F'''block{b}_se_expand/kernel:0''', F'''encoder.blocks.{hf_b}.squeeze_excite.expand.weight''')							)
		rename_keys.append((F'''block{b}_se_expand/bias:0''', F'''encoder.blocks.{hf_b}.squeeze_excite.expand.bias''')							)
		rename_keys.append(
		    (F'''block{b}_project_conv/kernel:0''', F'''encoder.blocks.{hf_b}.projection.project_conv.weight''')							)
		rename_keys.append((F'''block{b}_project_bn/gamma:0''', F'''encoder.blocks.{hf_b}.projection.project_bn.weight''')							)
		rename_keys.append((F'''block{b}_project_bn/beta:0''', F'''encoder.blocks.{hf_b}.projection.project_bn.bias''')							)
		rename_keys.append(
		    (F'''block{b}_project_bn/moving_mean:0''', F'''encoder.blocks.{hf_b}.projection.project_bn.running_mean''')							)
		rename_keys.append(
		    (F'''block{b}_project_bn/moving_variance:0''', F'''encoder.blocks.{hf_b}.projection.project_bn.running_var''')							)

	UpperCAmelCase_			:	Dict      =   {}
	for item in rename_keys:
		if item[0] in original_param_names:
			UpperCAmelCase_			:	Optional[int]      =   "vision_model." + item[1]

    # BERT text encoder
	UpperCAmelCase_			:	Tuple      =   []
	UpperCAmelCase_			:	Any      =   "tf_bert_model/bert"
	UpperCAmelCase_			:	Dict      =   "text_model"
	for i in range(12							):
		rename_keys.append(
		    (
		        F'''{old}/encoder/layer_._{i}/attention/self/query/kernel:0''',
		        F'''{new}.encoder.layer.{i}.attention.self.query.weight''',
		    )							)
		rename_keys.append(
		    (
		        F'''{old}/encoder/layer_._{i}/attention/self/query/bias:0''',
		        F'''{new}.encoder.layer.{i}.attention.self.query.bias''',
		    )							)
		rename_keys.append(
		    (
		        F'''{old}/encoder/layer_._{i}/attention/self/key/kernel:0''',
		        F'''{new}.encoder.layer.{i}.attention.self.key.weight''',
		    )							)
		rename_keys.append(
		    (
		        F'''{old}/encoder/layer_._{i}/attention/self/key/bias:0''',
		        F'''{new}.encoder.layer.{i}.attention.self.key.bias''',
		    )							)
		rename_keys.append(
		    (
		        F'''{old}/encoder/layer_._{i}/attention/self/value/kernel:0''',
		        F'''{new}.encoder.layer.{i}.attention.self.value.weight''',
		    )							)
		rename_keys.append(
		    (
		        F'''{old}/encoder/layer_._{i}/attention/self/value/bias:0''',
		        F'''{new}.encoder.layer.{i}.attention.self.value.bias''',
		    )							)
		rename_keys.append(
		    (
		        F'''{old}/encoder/layer_._{i}/attention/output/dense/kernel:0''',
		        F'''{new}.encoder.layer.{i}.attention.output.dense.weight''',
		    )							)
		rename_keys.append(
		    (
		        F'''{old}/encoder/layer_._{i}/attention/output/dense/bias:0''',
		        F'''{new}.encoder.layer.{i}.attention.output.dense.bias''',
		    )							)
		rename_keys.append(
		    (
		        F'''{old}/encoder/layer_._{i}/attention/output/LayerNorm/gamma:0''',
		        F'''{new}.encoder.layer.{i}.attention.output.LayerNorm.weight''',
		    )							)
		rename_keys.append(
		    (
		        F'''{old}/encoder/layer_._{i}/attention/output/LayerNorm/beta:0''',
		        F'''{new}.encoder.layer.{i}.attention.output.LayerNorm.bias''',
		    )							)
		rename_keys.append(
		    (
		        F'''{old}/encoder/layer_._{i}/intermediate/dense/kernel:0''',
		        F'''{new}.encoder.layer.{i}.intermediate.dense.weight''',
		    )							)
		rename_keys.append(
		    (
		        F'''{old}/encoder/layer_._{i}/intermediate/dense/bias:0''',
		        F'''{new}.encoder.layer.{i}.intermediate.dense.bias''',
		    )							)
		rename_keys.append(
		    (F'''{old}/encoder/layer_._{i}/output/dense/kernel:0''', F'''{new}.encoder.layer.{i}.output.dense.weight''')							)
		rename_keys.append(
		    (F'''{old}/encoder/layer_._{i}/output/dense/bias:0''', F'''{new}.encoder.layer.{i}.output.dense.bias''')							)
		rename_keys.append(
		    (F'''{old}/encoder/layer_._{i}/output/LayerNorm/gamma:0''', F'''{new}.encoder.layer.{i}.output.LayerNorm.weight''')							)
		rename_keys.append(
		    (F'''{old}/encoder/layer_._{i}/output/LayerNorm/beta:0''', F'''{new}.encoder.layer.{i}.output.LayerNorm.bias''')							)

	rename_keys.append((F'''{old}/embeddings/word_embeddings/weight:0''', F'''{new}.embeddings.word_embeddings.weight''')							)
	rename_keys.append(
	    (F'''{old}/embeddings/position_embeddings/embeddings:0''', F'''{new}.embeddings.position_embeddings.weight''')							)
	rename_keys.append(
	    (F'''{old}/embeddings/token_type_embeddings/embeddings:0''', F'''{new}.embeddings.token_type_embeddings.weight''')							)
	rename_keys.append((F'''{old}/embeddings/LayerNorm/gamma:0''', F'''{new}.embeddings.LayerNorm.weight''')							)
	rename_keys.append((F'''{old}/embeddings/LayerNorm/beta:0''', F'''{new}.embeddings.LayerNorm.bias''')							)

	rename_keys.append((F'''{old}/pooler/dense/kernel:0''', F'''{new}.pooler.dense.weight''')							)
	rename_keys.append((F'''{old}/pooler/dense/bias:0''', F'''{new}.pooler.dense.bias''')							)
	rename_keys.append(("dense/kernel:0", "text_projection.weight")							)
	rename_keys.append(("dense/bias:0", "text_projection.bias")							)
	rename_keys.append(("dense/bias:0", "text_projection.bias")							)
	rename_keys.append(("temperature:0", "temperature")							)

	for item in rename_keys:
		if item[0] in original_param_names:
			UpperCAmelCase_			:	int      =   item[1]
	return key_mapping


def       a__    (  _SCREAMING_SNAKE_CASE					:    Dict     ,							_SCREAMING_SNAKE_CASE					:    str     ,							_SCREAMING_SNAKE_CASE					:    Dict							)			-> Any:


	"""simple docstring"""





	list(hf_params.keys()							)

	for key, value in tf_params.items():
		if key not in key_mapping:
			continue

		UpperCAmelCase_			:	Optional[Any]      =   key_mapping[key]
		if "_conv" in key and "kernel" in key:
			UpperCAmelCase_			:	str      =   torch.from_numpy(_SCREAMING_SNAKE_CASE							).permute(3     ,							2     ,							0     ,							1							)
		elif "embeddings" in key:
			UpperCAmelCase_			:	List[str]      =   torch.from_numpy(_SCREAMING_SNAKE_CASE							)
		elif "depthwise_kernel" in key:
			UpperCAmelCase_			:	List[str]      =   torch.from_numpy(_SCREAMING_SNAKE_CASE							).permute(2     ,							3     ,							0     ,							1							)
		elif "kernel" in key:
			UpperCAmelCase_			:	Tuple      =   torch.from_numpy(np.transpose(_SCREAMING_SNAKE_CASE							)							)
		elif "temperature" in key:
			UpperCAmelCase_			:	List[str]      =   value
		elif "bn/gamma" or "bn/beta" in key:
			UpperCAmelCase_			:	List[Any]      =   torch.from_numpy(np.transpose(_SCREAMING_SNAKE_CASE							)							).squeeze()
		else:
			UpperCAmelCase_			:	Optional[int]      =   torch.from_numpy(_SCREAMING_SNAKE_CASE							)

		# Replace HF parameters with original TF model parameters
		hf_params[hf_key].copy_(_SCREAMING_SNAKE_CASE							)


@torch.no_grad()
def       a__    (  _SCREAMING_SNAKE_CASE					:    Dict     ,							_SCREAMING_SNAKE_CASE					:    Any     ,							_SCREAMING_SNAKE_CASE					:    Union[str, Any]     ,							_SCREAMING_SNAKE_CASE					:    str							)			-> List[str]:


	"""simple docstring"""





	UpperCAmelCase_			:	Optional[Any]      =   64
	UpperCAmelCase_			:	Any      =   Tokenizer(_SCREAMING_SNAKE_CASE							)
	UpperCAmelCase_			:	List[str]      =   align.Align("efficientnet-b7"     ,							"bert-base"     ,							6_40     ,							_SCREAMING_SNAKE_CASE     ,							tok.get_vocab_size()							)
	original_model.compile()
	original_model.load_weights(_SCREAMING_SNAKE_CASE							)

	UpperCAmelCase_			:	Optional[int]      =   original_model.trainable_variables
	UpperCAmelCase_			:	List[Any]      =   original_model.non_trainable_variables
	UpperCAmelCase_			:	List[str]      =   {param.name: param.numpy() for param in tf_params}
	for param in tf_non_train_params:
		UpperCAmelCase_			:	Optional[Any]      =   param.numpy()
	UpperCAmelCase_			:	Optional[Any]      =   list(tf_params.keys()							)

	# Load HuggingFace model
	UpperCAmelCase_			:	Optional[Any]      =   get_align_config()
	UpperCAmelCase_			:	Optional[Any]      =   AlignModel(_SCREAMING_SNAKE_CASE							).eval()
	UpperCAmelCase_			:	Any      =   hf_model.state_dict()

	# Create src-to-dst parameter name mapping dictionary
	print("Converting parameters..."							)
	UpperCAmelCase_			:	Any      =   rename_keys(_SCREAMING_SNAKE_CASE							)
	replace_params(_SCREAMING_SNAKE_CASE     ,							_SCREAMING_SNAKE_CASE     ,							_SCREAMING_SNAKE_CASE							)

	# Initialize processor
	UpperCAmelCase_			:	Any      =   get_processor()
	UpperCAmelCase_			:	Optional[int]      =   processor(
	    images=prepare_img()     ,							text="A picture of a cat"     ,							padding="max_length"     ,							max_length=64     ,							return_tensors="pt"							)

	# HF model inference
	hf_model.eval()
	with torch.no_grad():
		UpperCAmelCase_			:	str      =   hf_model(**_SCREAMING_SNAKE_CASE							)

	UpperCAmelCase_			:	List[str]      =   outputs.image_embeds.detach().numpy()
	UpperCAmelCase_			:	List[Any]      =   outputs.text_embeds.detach().numpy()

	# Original model inference
	UpperCAmelCase_			:	List[str]      =   False
	UpperCAmelCase_			:	str      =   EfficientNetImageProcessor(
	    do_center_crop=_SCREAMING_SNAKE_CASE     ,							do_rescale=_SCREAMING_SNAKE_CASE     ,							do_normalize=_SCREAMING_SNAKE_CASE     ,							include_top=_SCREAMING_SNAKE_CASE     ,							resample=Image.BILINEAR     ,							)
	UpperCAmelCase_			:	Dict      =   tf_image_processor(images=prepare_img()     ,							return_tensors="tf"     ,							data_format="channels_last"							)["pixel_values"]
	UpperCAmelCase_			:	Optional[int]      =   tok(tf.constant(["A picture of a cat"]							)							)

	UpperCAmelCase_			:	Optional[Any]      =   original_model.image_encoder(_SCREAMING_SNAKE_CASE     ,							training=_SCREAMING_SNAKE_CASE							)
	UpperCAmelCase_			:	Dict      =   original_model.text_encoder(_SCREAMING_SNAKE_CASE     ,							training=_SCREAMING_SNAKE_CASE							)

	UpperCAmelCase_			:	str      =   tf.nn.la_normalize(_SCREAMING_SNAKE_CASE     ,							axis=-1							)
	UpperCAmelCase_			:	Tuple      =   tf.nn.la_normalize(_SCREAMING_SNAKE_CASE     ,							axis=-1							)

	# Check whether original and HF model outputs match  -> np.allclose
	if not np.allclose(_SCREAMING_SNAKE_CASE     ,							_SCREAMING_SNAKE_CASE     ,							atol=1E-3							):
		raise ValueError("The predicted image features are not the same."							)
	if not np.allclose(_SCREAMING_SNAKE_CASE     ,							_SCREAMING_SNAKE_CASE     ,							atol=1E-3							):
		raise ValueError("The predicted text features are not the same."							)
	print("Model outputs match!"							)

	if save_model:
		# Create folder to save model
		if not os.path.isdir(_SCREAMING_SNAKE_CASE							):
			os.mkdir(_SCREAMING_SNAKE_CASE							)
		# Save converted model and image processor
		hf_model.save_pretrained(_SCREAMING_SNAKE_CASE							)
		processor.save_pretrained(_SCREAMING_SNAKE_CASE							)

	if push_to_hub:
		# Push model and image processor to hub
		print("Pushing converted ALIGN to the hub..."							)
		processor.push_to_hub("align-base"							)
		hf_model.push_to_hub("align-base"							)


if __name__ == "__main__":
					_lowerCamelCase	 =			argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--checkpoint_path""",
					    default="""./weights/model-weights""",
					    type=str,
					    help="""Path to the pretrained TF ALIGN checkpoint.""",
					)
					parser.add_argument(
					    """--pytorch_dump_folder_path""",
					    default="""hf_model""",
					    type=str,
					    help="""Path to the output PyTorch model directory.""",
					)
					parser.add_argument("""--save_model""", action="""store_true""", help="""Save model to local""")
					parser.add_argument("""--push_to_hub""", action="""store_true""", help="""Push model and image processor to the hub""")

					_lowerCamelCase	 =			parser.parse_args()
					convert_align_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)







'''simple docstring'''
import torch

from diffusers import KDPMaAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest
class  _snake_case						(__SCREAMING_SNAKE_CASE):
			__A   :     Optional[int]          =(KDPMaAncestralDiscreteScheduler,)
			__A   :     List[Any]          =10






			def 						UpperCamelCase__				(   self       ,**_snake_case			):
				UpperCAmelCase_			:	List[Any]      =   {
				    "num_train_timesteps": 11_00,
				    "beta_start": 0.0001,
				    "beta_end": 0.02,
				    "beta_schedule": "linear",
				}

				config.update(**_snake_case			)
				return config






			def 						UpperCamelCase__				(   self			):
				for timesteps in [10, 50, 1_00, 10_00]:
					self.check_over_configs(num_train_timesteps=_snake_case			)






			def 						UpperCamelCase__				(   self			):
				for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]       ,[0.0002, 0.002, 0.02]			):
					self.check_over_configs(beta_start=_snake_case       ,beta_end=_snake_case			)






			def 						UpperCamelCase__				(   self			):
				for schedule in ["linear", "scaled_linear"]:
					self.check_over_configs(beta_schedule=_snake_case			)






			def 						UpperCamelCase__				(   self			):
				if torch_device == "mps":
					return
				UpperCAmelCase_			:	str      =   self.scheduler_classes[0]
				UpperCAmelCase_			:	str      =   self.get_scheduler_config()
				UpperCAmelCase_			:	str      =   scheduler_class(**_snake_case			)

				scheduler.set_timesteps(self.num_inference_steps			)

				UpperCAmelCase_			:	Dict      =   torch.manual_seed(0			)

				UpperCAmelCase_			:	str      =   self.dummy_model()
				UpperCAmelCase_			:	str      =   self.dummy_sample_deter * scheduler.init_noise_sigma
				UpperCAmelCase_			:	Optional[Any]      =   sample.to(_snake_case			)

				for i, t in enumerate(scheduler.timesteps			):
					UpperCAmelCase_			:	Tuple      =   scheduler.scale_model_input(_snake_case       ,_snake_case			)

					UpperCAmelCase_			:	Union[str, Any]      =   model(_snake_case       ,_snake_case			)

					UpperCAmelCase_			:	Optional[Any]      =   scheduler.step(_snake_case       ,_snake_case       ,_snake_case       ,generator=_snake_case			)
					UpperCAmelCase_			:	Optional[Any]      =   output.prev_sample

				UpperCAmelCase_			:	Dict      =   torch.sum(torch.abs(_snake_case			)			)
				UpperCAmelCase_			:	List[Any]      =   torch.mean(torch.abs(_snake_case			)			)

				assert abs(result_sum.item() - 13849.3877			) < 1E-2
				assert abs(result_mean.item() - 18.0331			) < 5E-3






			def 						UpperCamelCase__				(   self			):
				for prediction_type in ["epsilon", "v_prediction"]:
					self.check_over_configs(prediction_type=_snake_case			)






			def 						UpperCamelCase__				(   self			):
				if torch_device == "mps":
					return
				UpperCAmelCase_			:	Any      =   self.scheduler_classes[0]
				UpperCAmelCase_			:	Any      =   self.get_scheduler_config(prediction_type="v_prediction"			)
				UpperCAmelCase_			:	Dict      =   scheduler_class(**_snake_case			)

				scheduler.set_timesteps(self.num_inference_steps			)

				UpperCAmelCase_			:	Union[str, Any]      =   self.dummy_model()
				UpperCAmelCase_			:	List[str]      =   self.dummy_sample_deter * scheduler.init_noise_sigma
				UpperCAmelCase_			:	int      =   sample.to(_snake_case			)

				UpperCAmelCase_			:	str      =   torch.manual_seed(0			)

				for i, t in enumerate(scheduler.timesteps			):
					UpperCAmelCase_			:	Dict      =   scheduler.scale_model_input(_snake_case       ,_snake_case			)

					UpperCAmelCase_			:	str      =   model(_snake_case       ,_snake_case			)

					UpperCAmelCase_			:	Optional[Any]      =   scheduler.step(_snake_case       ,_snake_case       ,_snake_case       ,generator=_snake_case			)
					UpperCAmelCase_			:	Any      =   output.prev_sample

				UpperCAmelCase_			:	Any      =   torch.sum(torch.abs(_snake_case			)			)
				UpperCAmelCase_			:	Tuple      =   torch.mean(torch.abs(_snake_case			)			)

				assert abs(result_sum.item() - 328.9970			) < 1E-2
				assert abs(result_mean.item() - 0.4284			) < 1E-3






			def 						UpperCamelCase__				(   self			):
				if torch_device == "mps":
					return
				UpperCAmelCase_			:	Optional[Any]      =   self.scheduler_classes[0]
				UpperCAmelCase_			:	Union[str, Any]      =   self.get_scheduler_config()
				UpperCAmelCase_			:	str      =   scheduler_class(**_snake_case			)

				scheduler.set_timesteps(self.num_inference_steps       ,device=_snake_case			)
				UpperCAmelCase_			:	Dict      =   torch.manual_seed(0			)

				UpperCAmelCase_			:	List[str]      =   self.dummy_model()
				UpperCAmelCase_			:	str      =   self.dummy_sample_deter.to(_snake_case			) * scheduler.init_noise_sigma

				for t in scheduler.timesteps:
					UpperCAmelCase_			:	Union[str, Any]      =   scheduler.scale_model_input(_snake_case       ,_snake_case			)

					UpperCAmelCase_			:	Tuple      =   model(_snake_case       ,_snake_case			)

					UpperCAmelCase_			:	int      =   scheduler.step(_snake_case       ,_snake_case       ,_snake_case       ,generator=_snake_case			)
					UpperCAmelCase_			:	List[str]      =   output.prev_sample

				UpperCAmelCase_			:	Any      =   torch.sum(torch.abs(_snake_case			)			)
				UpperCAmelCase_			:	Optional[int]      =   torch.mean(torch.abs(_snake_case			)			)

				assert abs(result_sum.item() - 13849.3818			) < 1E-1
				assert abs(result_mean.item() - 18.0331			) < 1E-3




