

import argparse

import torch

from transformers import (
    SpeechTaConfig,
    SpeechTaFeatureExtractor,
    SpeechTaForSpeechToSpeech,
    SpeechTaForSpeechToText,
    SpeechTaForTextToSpeech,
    SpeechTaProcessor,
    SpeechTaTokenizer,
    logging,
)
from transformers.tokenization_utils import AddedToken


logging.set_verbosity_info()
_a:    Union[str, Any] 							=			logging.get_logger("""transformers.models.speecht5""")

_a:    Optional[int] 							=			{
    """speech_encoder_prenet.layer_norm""": """speecht5.encoder.prenet.feature_projection.layer_norm""",
    """speech_encoder_prenet.post_extract_proj""": """speecht5.encoder.prenet.feature_projection.projection""",
    """speech_encoder_prenet.pos_conv.0""": """speecht5.encoder.prenet.pos_conv_embed.conv""",
    """speech_encoder_prenet.mask_emb""": """speecht5.encoder.prenet.masked_spec_embed""",
}
_a:    List[Any] 							=			{
    """text_encoder_prenet.encoder_prenet.0""": """speecht5.encoder.prenet.embed_tokens""",
    """text_encoder_prenet.encoder_prenet.1.alpha""": """speecht5.encoder.prenet.encode_positions.alpha""",
}
_a:    List[str] 							=			{
    """speech_decoder_prenet.decoder_prenet.0.0.prenet.0.0""": """speecht5.decoder.prenet.layers.0""",
    """speech_decoder_prenet.decoder_prenet.0.0.prenet.1.0""": """speecht5.decoder.prenet.layers.1""",
    """speech_decoder_prenet.decoder_prenet.0.1""": """speecht5.decoder.prenet.final_layer""",
    """speech_decoder_prenet.decoder_prenet.1.alpha""": """speecht5.decoder.prenet.encode_positions.alpha""",
    """speech_decoder_prenet.spkembs_layer.0""": """speecht5.decoder.prenet.speaker_embeds_layer""",
}
_a:    str 							=			{
    """speech_decoder_postnet.feat_out""": """speech_decoder_postnet.feat_out""",
    """speech_decoder_postnet.prob_out""": """speech_decoder_postnet.prob_out""",
    """speech_decoder_postnet.postnet.postnet.0.0""": """speech_decoder_postnet.layers.0.conv""",
    """speech_decoder_postnet.postnet.postnet.0.1""": """speech_decoder_postnet.layers.0.batch_norm""",
    """speech_decoder_postnet.postnet.postnet.1.0""": """speech_decoder_postnet.layers.1.conv""",
    """speech_decoder_postnet.postnet.postnet.1.1""": """speech_decoder_postnet.layers.1.batch_norm""",
    """speech_decoder_postnet.postnet.postnet.2.0""": """speech_decoder_postnet.layers.2.conv""",
    """speech_decoder_postnet.postnet.postnet.2.1""": """speech_decoder_postnet.layers.2.batch_norm""",
    """speech_decoder_postnet.postnet.postnet.3.0""": """speech_decoder_postnet.layers.3.conv""",
    """speech_decoder_postnet.postnet.postnet.3.1""": """speech_decoder_postnet.layers.3.batch_norm""",
    """speech_decoder_postnet.postnet.postnet.4.0""": """speech_decoder_postnet.layers.4.conv""",
    """speech_decoder_postnet.postnet.postnet.4.1""": """speech_decoder_postnet.layers.4.batch_norm""",
}
_a:    Union[str, Any] 							=			{
    """text_decoder_prenet.embed_tokens""": """speecht5.decoder.prenet.embed_tokens""",
}
_a:    List[Any] 							=			{
    """text_decoder_postnet.output_projection""": """text_decoder_postnet.lm_head""",
}
_a:    List[str] 							=			{
    """encoder.layers.*.self_attn.k_proj""": """speecht5.encoder.wrapped_encoder.layers.*.attention.k_proj""",
    """encoder.layers.*.self_attn.v_proj""": """speecht5.encoder.wrapped_encoder.layers.*.attention.v_proj""",
    """encoder.layers.*.self_attn.q_proj""": """speecht5.encoder.wrapped_encoder.layers.*.attention.q_proj""",
    """encoder.layers.*.self_attn.out_proj""": """speecht5.encoder.wrapped_encoder.layers.*.attention.out_proj""",
    """encoder.layers.*.self_attn_layer_norm""": """speecht5.encoder.wrapped_encoder.layers.*.layer_norm""",
    """encoder.layers.*.fc1""": """speecht5.encoder.wrapped_encoder.layers.*.feed_forward.intermediate_dense""",
    """encoder.layers.*.fc2""": """speecht5.encoder.wrapped_encoder.layers.*.feed_forward.output_dense""",
    """encoder.layers.*.final_layer_norm""": """speecht5.encoder.wrapped_encoder.layers.*.final_layer_norm""",
    """encoder.layer_norm""": """speecht5.encoder.wrapped_encoder.layer_norm""",
    """encoder.pos_emb.pe_k""": """speecht5.encoder.wrapped_encoder.embed_positions.pe_k""",
}
_a:    List[Any] 							=			{
    """decoder.layers.*.self_attn.k_proj""": """speecht5.decoder.wrapped_decoder.layers.*.self_attn.k_proj""",
    """decoder.layers.*.self_attn.v_proj""": """speecht5.decoder.wrapped_decoder.layers.*.self_attn.v_proj""",
    """decoder.layers.*.self_attn.q_proj""": """speecht5.decoder.wrapped_decoder.layers.*.self_attn.q_proj""",
    """decoder.layers.*.self_attn.out_proj""": """speecht5.decoder.wrapped_decoder.layers.*.self_attn.out_proj""",
    """decoder.layers.*.self_attn_layer_norm""": """speecht5.decoder.wrapped_decoder.layers.*.self_attn_layer_norm""",
    """decoder.layers.*.encoder_attn.k_proj""": """speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.k_proj""",
    """decoder.layers.*.encoder_attn.v_proj""": """speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.v_proj""",
    """decoder.layers.*.encoder_attn.q_proj""": """speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.q_proj""",
    """decoder.layers.*.encoder_attn.out_proj""": """speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.out_proj""",
    """decoder.layers.*.encoder_attn_layer_norm""": """speecht5.decoder.wrapped_decoder.layers.*.encoder_attn_layer_norm""",
    """decoder.layers.*.fc1""": """speecht5.decoder.wrapped_decoder.layers.*.feed_forward.intermediate_dense""",
    """decoder.layers.*.fc2""": """speecht5.decoder.wrapped_decoder.layers.*.feed_forward.output_dense""",
    """decoder.layers.*.final_layer_norm""": """speecht5.decoder.wrapped_decoder.layers.*.final_layer_norm""",
}
_a:    Any 							=			{
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
_a:    Union[str, Any] 							=			{
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
_a:    List[Any] 							=			{
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
_a:    str 							=			[]
_a:    Dict 							=			[
    """encoder.version""",
    """encoder.layers.*.norm_k.weight""",
    """encoder.layers.*.norm_k.bias""",
    """decoder.version""",
    """decoder.layers.*.norm_k.weight""",
    """decoder.layers.*.norm_k.bias""",
    """decoder.pos_emb.pe_k""",
    """speech_encoder_prenet.embed_positions._float_tensor""",
    """text_decoder_prenet.embed_positions._float_tensor""",
]
_a:    List[str] 							=			IGNORE_KEYS + [
    """encoder.proj""",
    """text_encoder_prenet.*""",
    """speech_decoder_prenet.*""",
    """speech_decoder_postnet.*""",
]
_a:    Any 							=			IGNORE_KEYS + [
    """encoder.proj""",
    """speech_encoder_prenet.*""",
    """text_decoder_prenet.*""",
    """text_decoder_postnet.*""",
]
_a:    str 							=			IGNORE_KEYS + [
    """encoder.proj""",
    """text_encoder_prenet.*""",
    """text_decoder_prenet.*""",
    """text_decoder_postnet.*""",
]



def       __lowerCAmelCase      (						A       ,     A       ,     A       ,     A       ,     A    ):
	for attribute in key.split("."    ):
		UpperCAmelCase_       				=			getattr(A       ,     A    )

	if weight_type is not None:
		UpperCAmelCase_       				=			getattr(A       ,     A    ).shape
	else:
		UpperCAmelCase_       				=			hf_pointer.shape

	if hf_shape != value.shape:
		raise ValueError(
		    F"Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"
		    F" {value.shape} for {full_name}"    )

	if weight_type == "weight":
		UpperCAmelCase_       				=			value
	elif weight_type == "weight_g":
		UpperCAmelCase_       				=			value
	elif weight_type == "weight_v":
		UpperCAmelCase_       				=			value
	elif weight_type == "bias":
		UpperCAmelCase_       				=			value
	elif weight_type == "running_mean":
		UpperCAmelCase_       				=			value
	elif weight_type == "running_var":
		UpperCAmelCase_       				=			value
	elif weight_type == "num_batches_tracked":
		UpperCAmelCase_       				=			value
	else:
		UpperCAmelCase_       				=			value

	logger.info(F"{key + ('.' + weight_type if weight_type is not None else '')} was initialized from {full_name}."    )



def       __lowerCAmelCase      (						A       ,     A    ):
	for key in ignore_keys:
		if key.endswith(".*"    ):
			if name.startswith(key[:-1]    ):
				return True
		elif ".*." in key:
			UpperCAmelCase_				,   UpperCAmelCase_       				=			key.split(".*."    )
			if prefix in name and suffix in name:
				return True
		elif key in name:
			return True
	return False



def       __lowerCAmelCase      (						A       ,     A       ,     A    ):
	UpperCAmelCase_       				=			[]

	if task == "s2t":
		UpperCAmelCase_       				=			hf_model.speechta.encoder.prenet.feature_encoder
		UpperCAmelCase_       				=			MAPPING_S2T
		UpperCAmelCase_       				=			IGNORE_KEYS_S2T
	elif task == "t2s":
		UpperCAmelCase_       				=			None
		UpperCAmelCase_       				=			MAPPING_T2S
		UpperCAmelCase_       				=			IGNORE_KEYS_T2S
	elif task == "s2s":
		UpperCAmelCase_       				=			hf_model.speechta.encoder.prenet.feature_encoder
		UpperCAmelCase_       				=			MAPPING_S2S
		UpperCAmelCase_       				=			IGNORE_KEYS_S2S
	else:
		raise ValueError(F"Unsupported task: {task}"    )

	for name, value in fairseq_dict.items():
		if should_ignore(A       ,     A    ):
			logger.info(F"{name} was ignored"    )
			continue

		UpperCAmelCase_       				=			False
		if "conv_layers" in name:
			load_conv_layer(
			    A       ,     A       ,     A       ,     A       ,     hf_model.config.feat_extract_norm == "group"       ,     )
			UpperCAmelCase_       				=			True
		else:
			for key, mapped_key in MAPPING.items():
				# mapped_key = "speecht5." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key

				if "*" in key:
					UpperCAmelCase_				,   UpperCAmelCase_       				=			key.split(".*."    )
					if prefix in name and suffix in name:
						UpperCAmelCase_       				=			suffix

                # if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
				if key in name:
					UpperCAmelCase_       				=			True
					if "*" in mapped_key:
						UpperCAmelCase_       				=			name.split(A    )[0].split("."    )[-2]
						UpperCAmelCase_       				=			mapped_key.replace("*"       ,     A    )
					if "weight_g" in name:
						UpperCAmelCase_       				=			"weight_g"
					elif "weight_v" in name:
						UpperCAmelCase_       				=			"weight_v"
					elif "bias" in name:
						UpperCAmelCase_       				=			"bias"
					elif "weight" in name:
						UpperCAmelCase_       				=			"weight"
					elif "running_mean" in name:
						UpperCAmelCase_       				=			"running_mean"
					elif "running_var" in name:
						UpperCAmelCase_       				=			"running_var"
					elif "num_batches_tracked" in name:
						UpperCAmelCase_       				=			"num_batches_tracked"
					else:
						UpperCAmelCase_       				=			None
					set_recursively(A       ,     A       ,     A       ,     A       ,     A    )
				continue
		if not is_used:
			unused_weights.append(A    )

	logger.warning(F"Unused weights: {unused_weights}"    )



def       __lowerCAmelCase      (						A       ,     A       ,     A       ,     A       ,     A    ):
	UpperCAmelCase_       				=			full_name.split("conv_layers."    )[-1]
	UpperCAmelCase_       				=			name.split("."    )
	UpperCAmelCase_       				=			int(items[0]    )
	UpperCAmelCase_       				=			int(items[1]    )

	if type_id == 0:
		if "bias" in name:
			if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
				raise ValueError(
				    F"{full_name} has size {value.shape}, but"
				    F" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."    )
			UpperCAmelCase_       				=			value
			logger.info(F"Feat extract conv layer {layer_id} was initialized from {full_name}."    )
		elif "weight" in name:
			if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
				raise ValueError(
				    F"{full_name} has size {value.shape}, but"
				    F" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."    )
			UpperCAmelCase_       				=			value
			logger.info(F"Feat extract conv layer {layer_id} was initialized from {full_name}."    )
	elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
		if "bias" in name:
			if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
				raise ValueError(
				    F"{full_name} has size {value.shape}, but"
				    F" {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found."    )
			UpperCAmelCase_       				=			value
			logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."    )
		elif "weight" in name:
			if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
				raise ValueError(
				    F"{full_name} has size {value.shape}, but"
				    F" {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found."    )
			UpperCAmelCase_       				=			value
			logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."    )
	else:
		unused_weights.append(A    )



@torch.no_grad()
def       __lowerCAmelCase      (						A       ,     A       ,     A       ,     A=None       ,     A=None       ,     A=None       ,     ):
	if config_path is not None:
		UpperCAmelCase_       				=			SpeechTaConfig.from_pretrained(A    )
	else:
		UpperCAmelCase_       				=			SpeechTaConfig()

	if task == "s2t":
		UpperCAmelCase_       				=			config.max_text_positions
		UpperCAmelCase_       				=			SpeechTaForSpeechToText(A    )
	elif task == "t2s":
		UpperCAmelCase_       				=			1876
		UpperCAmelCase_       				=			600
		UpperCAmelCase_       				=			config.max_speech_positions
		UpperCAmelCase_       				=			SpeechTaForTextToSpeech(A    )
	elif task == "s2s":
		UpperCAmelCase_       				=			1876
		UpperCAmelCase_       				=			config.max_speech_positions
		UpperCAmelCase_       				=			SpeechTaForSpeechToSpeech(A    )
	else:
		raise ValueError(F"Unknown task name: {task}"    )

	if vocab_path:
		UpperCAmelCase_       				=			SpeechTaTokenizer(A       ,     model_max_length=config.max_text_positions    )

		# Mask token behaves like a normal word, i.e. include the space before it
		UpperCAmelCase_       				=			AddedToken("<mask>"       ,     lstrip=A       ,     rstrip=A    )
		UpperCAmelCase_       				=			mask_token
		tokenizer.add_special_tokens({"mask_token": mask_token}    )
		tokenizer.add_tokens(["<ctc_blank>"]    )

	UpperCAmelCase_       				=			SpeechTaFeatureExtractor()
	UpperCAmelCase_       				=			SpeechTaProcessor(tokenizer=A       ,     feature_extractor=A    )
	processor.save_pretrained(A    )

	UpperCAmelCase_       				=			torch.load(A    )
	recursively_load_weights(fairseq_checkpoint["model"]       ,     A       ,     A    )

	model.save_pretrained(A    )

	if repo_id:
		print("Pushing to the hub..."    )
		processor.push_to_hub(A    )
		model.push_to_hub(A    )


if __name__ == "__main__":
					_a:    Any 							=			argparse.ArgumentParser()
					parser.add_argument(
					    """--task""",
					    default="""s2t""",
					    type=str,
					    help="""Type of the SpeechT5 model you'd like to convert. Should be one of 's2t', 't2s', 's2s'.""",
					)
					parser.add_argument("""--checkpoint_path""", required=True, default=None, type=str, help="""Path to fairseq checkpoint""")
					parser.add_argument("""--vocab_path""", default=None, type=str, help="""Path to SentencePiece model""")
					parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
					parser.add_argument(
					    """--pytorch_dump_folder_path""", required=True, default=None, type=str, help="""Path to the output PyTorch model."""
					)
					parser.add_argument(
					    """--push_to_hub""", default=None, type=str, help="""Where to upload the converted model on the 🤗 hub."""
					)

					_a:    Dict 							=			parser.parse_args()
					convert_speechta_checkpoint(
					    args.task,
					    args.checkpoint_path,
					    args.pytorch_dump_folder_path,
					    args.config_path,
					    args.vocab_path,
					    args.push_to_hub,
					)


import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest

class 						__UpperCamelCase    (					lowercase						):
	SCREAMING_SNAKE_CASE__    				=     (KDPMaDiscreteScheduler,)
	SCREAMING_SNAKE_CASE__    				=     10




	def       __A					(		self	:						Union[str, Any]		,	**lowerCAmelCase	:						Optional[Any]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			{
		    "num_train_timesteps": 1_100,
		    "beta_start": 0.0_001,
		    "beta_end": 0.02,
		    "beta_schedule": "linear",
		}

		config.update(**lowerCAmelCase							)
		return config




	def       __A					(		self	:						List[Any]							):


		'''simple docstring'''







		for timesteps in [10, 50, 100, 1_000]:
			self.check_over_configs(num_train_timesteps=lowerCAmelCase							)




	def       __A					(		self	:						Optional[Any]							):


		'''simple docstring'''







		for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]		,	[0.0_002, 0.002, 0.02]							):
			self.check_over_configs(beta_start=lowerCAmelCase		,	beta_end=lowerCAmelCase							)




	def       __A					(		self	:						List[str]							):


		'''simple docstring'''







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




	def       __A					(		self	:						List[str]							):


		'''simple docstring'''







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




	def       __A					(		self	:						List[Any]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			self.scheduler_classes[0]
		UpperCAmelCase_       				=			self.get_scheduler_config(prediction_type="v_prediction"							)
		UpperCAmelCase_       				=			scheduler_class(**lowerCAmelCase							)

		scheduler.set_timesteps(self.num_inference_steps							)

		UpperCAmelCase_       				=			self.dummy_model()
		UpperCAmelCase_       				=			self.dummy_sample_deter * scheduler.init_noise_sigma
		UpperCAmelCase_       				=			sample.to(lowerCAmelCase							)

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

			UpperCAmelCase_       				=			model(lowerCAmelCase		,	lowerCAmelCase							)

			UpperCAmelCase_       				=			scheduler.step(lowerCAmelCase		,	lowerCAmelCase		,	lowerCAmelCase							)
			UpperCAmelCase_       				=			output.prev_sample

		UpperCAmelCase_       				=			torch.sum(torch.abs(lowerCAmelCase							)							)
		UpperCAmelCase_       				=			torch.mean(torch.abs(lowerCAmelCase							)							)

		if torch_device in ["cpu", "mps"]:
			assert abs(result_sum.item() - 4.6_934e-07							) < 1e-2
			assert abs(result_mean.item() - 6.1_112e-10							) < 1e-3
		else:
			# CUDA
			assert abs(result_sum.item() - 4.693_428_650_170_972e-07							) < 1e-2
			assert abs(result_mean.item() - 0.0_002							) < 1e-3




	def       __A					(		self	:						int							):


		'''simple docstring'''







		if torch_device == "mps":
			return
		UpperCAmelCase_       				=			self.scheduler_classes[0]
		UpperCAmelCase_       				=			self.get_scheduler_config()
		UpperCAmelCase_       				=			scheduler_class(**lowerCAmelCase							)

		scheduler.set_timesteps(self.num_inference_steps							)

		UpperCAmelCase_       				=			self.dummy_model()
		UpperCAmelCase_       				=			self.dummy_sample_deter * scheduler.init_noise_sigma
		UpperCAmelCase_       				=			sample.to(lowerCAmelCase							)

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

			UpperCAmelCase_       				=			model(lowerCAmelCase		,	lowerCAmelCase							)

			UpperCAmelCase_       				=			scheduler.step(lowerCAmelCase		,	lowerCAmelCase		,	lowerCAmelCase							)
			UpperCAmelCase_       				=			output.prev_sample

		UpperCAmelCase_       				=			torch.sum(torch.abs(lowerCAmelCase							)							)
		UpperCAmelCase_       				=			torch.mean(torch.abs(lowerCAmelCase							)							)

		if torch_device in ["cpu", "mps"]:
			assert abs(result_sum.item() - 20.4_125							) < 1e-2
			assert abs(result_mean.item() - 0.0_266							) < 1e-3
		else:
			# CUDA
			assert abs(result_sum.item() - 20.4_125							) < 1e-2
			assert abs(result_mean.item() - 0.0_266							) < 1e-3




	def       __A					(		self	:						Dict							):


		'''simple docstring'''







		if torch_device == "mps":
			return
		UpperCAmelCase_       				=			self.scheduler_classes[0]
		UpperCAmelCase_       				=			self.get_scheduler_config()
		UpperCAmelCase_       				=			scheduler_class(**lowerCAmelCase							)

		scheduler.set_timesteps(self.num_inference_steps		,	device=lowerCAmelCase							)

		UpperCAmelCase_       				=			self.dummy_model()
		UpperCAmelCase_       				=			self.dummy_sample_deter.to(lowerCAmelCase							) * scheduler.init_noise_sigma

		for t in scheduler.timesteps:
			UpperCAmelCase_       				=			scheduler.scale_model_input(lowerCAmelCase		,	lowerCAmelCase							)

			UpperCAmelCase_       				=			model(lowerCAmelCase		,	lowerCAmelCase							)

			UpperCAmelCase_       				=			scheduler.step(lowerCAmelCase		,	lowerCAmelCase		,	lowerCAmelCase							)
			UpperCAmelCase_       				=			output.prev_sample

		UpperCAmelCase_       				=			torch.sum(torch.abs(lowerCAmelCase							)							)
		UpperCAmelCase_       				=			torch.mean(torch.abs(lowerCAmelCase							)							)

		if str(lowerCAmelCase							).startswith("cpu"							):
			# The following sum varies between 148 and 156 on mps. Why?
			assert abs(result_sum.item() - 20.4_125							) < 1e-2
			assert abs(result_mean.item() - 0.0_266							) < 1e-3
		else:
			# CUDA
			assert abs(result_sum.item() - 20.4_125							) < 1e-2
			assert abs(result_mean.item() - 0.0_266							) < 1e-3