



"""simple docstring"""
import argparse
import json
import os
import pickle
import shutil

import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset

from transformers import (
    BertConfig,
    BertForMaskedLM,
    BertTokenizer,
    DistilBertConfig,
    DistilBertForMaskedLM,
    DistilBertTokenizer,
    GPTaConfig,
    GPTaLMHeadModel,
    GPTaTokenizer,
    RobertaConfig,
    RobertaForMaskedLM,
    RobertaTokenizer,
)
from utils import git_log, init_gpu_params, logger, set_seed


__a      =				{
    "distilbert": (DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer),
    "roberta": (RobertaConfig, RobertaForMaskedLM, RobertaTokenizer),
    "bert": (BertConfig, BertForMaskedLM, BertTokenizer),
    "gpt2": (GPTaConfig, GPTaLMHeadModel, GPTaTokenizer),
}
def    A_  (			_lowercase	):







						'''simple docstring'''


						assert (args.mlm and args.alpha_mlm > 0.0) or (not args.mlm and args.alpha_mlm == 0.0)
						assert (args.alpha_mlm > 0.0 and args.alpha_clm == 0.0) or (args.alpha_mlm == 0.0 and args.alpha_clm > 0.0)
						if args.mlm:
												assert os.path.isfile(args.token_counts	)
												assert (args.student_type in ["roberta", "distilbert"]) and (args.teacher_type in ["roberta", "bert"])
						else:
												assert (args.student_type in ["gpt2"]) and (args.teacher_type in ["gpt2"])

						assert args.teacher_type == args.student_type or (
						    args.student_type == "distilbert" and args.teacher_type == "bert"
						)
						assert os.path.isfile(args.student_config	)
						if args.student_pretrained_weights is not None:
												assert os.path.isfile(args.student_pretrained_weights	)

						if args.freeze_token_type_embds:
												assert args.student_type in ["roberta"]

						assert args.alpha_ce >= 0.0
						assert args.alpha_mlm >= 0.0
						assert args.alpha_clm >= 0.0
						assert args.alpha_mse >= 0.0
						assert args.alpha_cos >= 0.0
						assert args.alpha_ce + args.alpha_mlm + args.alpha_clm + args.alpha_mse + args.alpha_cos > 0.0
def    A_  (			_lowercase,					_lowercase	):







						'''simple docstring'''


						if args.student_type == "roberta":
												snake_case_ :Tuple						=			False
						elif args.student_type == "gpt2":
												snake_case_ :Union[str, Any]						=			False
def    A_  (			_lowercase,					_lowercase	):







						'''simple docstring'''


						if args.student_type == "roberta":
												snake_case_ :List[str]						=			False
def    A_  (			):







						'''simple docstring'''


						snake_case_ :Union[str, Any]						=			argparse.ArgumentParser(description="""Training"""	)
						parser.add_argument("""--force""",					action="""store_true""",					help="""Overwrite dump_path if it already exists."""	)

						parser.add_argument(
						    """--dump_path""",					type=_lowercase,					required=_lowercase,					help="""The output directory (log, checkpoints, parameters, etc.)"""	)
						parser.add_argument(
						    """--data_file""",					type=_lowercase,					required=_lowercase,					help="""The binarized file (tokenized + tokens_to_ids) and grouped by sequence.""",					)

						parser.add_argument(
						    """--student_type""",					type=_lowercase,					choices=["""distilbert""", """roberta""", """gpt2"""],					required=_lowercase,					help="""The student type (DistilBERT, RoBERTa).""",					)
						parser.add_argument("""--student_config""",					type=_lowercase,					required=_lowercase,					help="""Path to the student configuration."""	)
						parser.add_argument(
						    """--student_pretrained_weights""",					default=_lowercase,					type=_lowercase,					help="""Load student initialization checkpoint."""	)

						parser.add_argument(
						    """--teacher_type""",					choices=["""bert""", """roberta""", """gpt2"""],					required=_lowercase,					help="""Teacher type (BERT, RoBERTa)."""	)
						parser.add_argument("""--teacher_name""",					type=_lowercase,					required=_lowercase,					help="""The teacher model."""	)

						parser.add_argument("""--temperature""",					default=2.0,					type=_lowercase,					help="""Temperature for the softmax temperature."""	)
						parser.add_argument(
						    """--alpha_ce""",					default=0.5,					type=_lowercase,					help="""Linear weight for the distillation loss. Must be >=0."""	)
						parser.add_argument(
						    """--alpha_mlm""",					default=0.0,					type=_lowercase,					help="""Linear weight for the MLM loss. Must be >=0. Should be used in conjunction with `mlm` flag.""",					)
						parser.add_argument("""--alpha_clm""",					default=0.5,					type=_lowercase,					help="""Linear weight for the CLM loss. Must be >=0."""	)
						parser.add_argument("""--alpha_mse""",					default=0.0,					type=_lowercase,					help="""Linear weight of the MSE loss. Must be >=0."""	)
						parser.add_argument(
						    """--alpha_cos""",					default=0.0,					type=_lowercase,					help="""Linear weight of the cosine embedding loss. Must be >=0."""	)

						parser.add_argument(
						    """--mlm""",					action="""store_true""",					help="""The LM step: MLM or CLM. If `mlm` is True, the MLM is used over CLM."""	)
						parser.add_argument(
						    """--mlm_mask_prop""",					default=0.15,					type=_lowercase,					help="""Proportion of tokens for which we need to make a prediction.""",					)
						parser.add_argument("""--word_mask""",					default=0.8,					type=_lowercase,					help="""Proportion of tokens to mask out."""	)
						parser.add_argument("""--word_keep""",					default=0.1,					type=_lowercase,					help="""Proportion of tokens to keep."""	)
						parser.add_argument("""--word_rand""",					default=0.1,					type=_lowercase,					help="""Proportion of tokens to randomly replace."""	)
						parser.add_argument(
						    """--mlm_smoothing""",					default=0.7,					type=_lowercase,					help="""Smoothing parameter to emphasize more rare tokens (see XLM, similar to word2vec).""",					)
						parser.add_argument("""--token_counts""",					type=_lowercase,					help="""The token counts in the data_file for MLM."""	)

						parser.add_argument(
						    """--restrict_ce_to_mask""",					action="""store_true""",					help="""If true, compute the distillation loss only the [MLM] prediction distribution.""",					)
						parser.add_argument(
						    """--freeze_pos_embs""",					action="""store_true""",					help="""Freeze positional embeddings during distillation. For student_type in ['roberta', 'gpt2'] only.""",					)
						parser.add_argument(
						    """--freeze_token_type_embds""",					action="""store_true""",					help="""Freeze token type embeddings during distillation if existent. For student_type in ['roberta'] only.""",					)

						parser.add_argument("""--n_epoch""",					type=_lowercase,					default=3,					help="""Number of pass on the whole dataset."""	)
						parser.add_argument("""--batch_size""",					type=_lowercase,					default=5,					help="""Batch size (for each process)."""	)
						parser.add_argument(
						    """--group_by_size""",					action="""store_false""",					help="""If true, group sequences that have similar length into the same batch. Default is true.""",					)

						parser.add_argument(
						    """--gradient_accumulation_steps""",					type=_lowercase,					default=50,					help="""Gradient accumulation for larger training batches.""",					)
						parser.add_argument("""--warmup_prop""",					default=0.05,					type=_lowercase,					help="""Linear warmup proportion."""	)
						parser.add_argument("""--weight_decay""",					default=0.0,					type=_lowercase,					help="""Weight decay if we apply some."""	)
						parser.add_argument("""--learning_rate""",					default=5e-4,					type=_lowercase,					help="""The initial learning rate for Adam."""	)
						parser.add_argument("""--adam_epsilon""",					default=1e-6,					type=_lowercase,					help="""Epsilon for Adam optimizer."""	)
						parser.add_argument("""--max_grad_norm""",					default=5.0,					type=_lowercase,					help="""Max gradient norm."""	)
						parser.add_argument("""--initializer_range""",					default=0.02,					type=_lowercase,					help="""Random initialization range."""	)

						parser.add_argument(
						    """--fp16""",					action="""store_true""",					help="""Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit""",					)
						parser.add_argument(
						    """--fp16_opt_level""",					type=_lowercase,					default="""O1""",					help=(
						        """For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."""
						        """See details at https://nvidia.github.io/apex/amp.html"""
						    ),					)
						parser.add_argument("""--n_gpu""",					type=_lowercase,					default=1,					help="""Number of GPUs in the node."""	)
						parser.add_argument("""--local_rank""",					type=_lowercase,					default=-1,					help="""Distributed training - Local rank"""	)
						parser.add_argument("""--seed""",					type=_lowercase,					default=56,					help="""Random seed"""	)

						parser.add_argument("""--log_interval""",					type=_lowercase,					default=500,					help="""Tensorboard logging interval."""	)
						parser.add_argument("""--checkpoint_interval""",					type=_lowercase,					default=4000,					help="""Checkpoint interval."""	)
						snake_case_ :Tuple						=			parser.parse_args()
						sanity_checks(_lowercase	)

						# ARGS #
						init_gpu_params(_lowercase	)
						set_seed(_lowercase	)
						if args.is_master:
												if os.path.exists(args.dump_path	):
																		if not args.force:
																								raise ValueError(
																								    f"""Serialization dir {args.dump_path} already exists, but you have not precised wheter to overwrite"""
																								    """ itUse `--force` if you want to overwrite it"""	)
																		else:
																								shutil.rmtree(args.dump_path	)

												if not os.path.exists(args.dump_path	):
																		os.makedirs(args.dump_path	)
												logger.info(f"""Experiment will be dumped and logged in {args.dump_path}"""	)

												# SAVE PARAMS #
												logger.info(f"""Param: {args}"""	)
												with open(os.path.join(args.dump_path,					"""parameters.json"""	),					"""w"""	) as f:
																		json.dump(vars(_lowercase	),					_lowercase,					indent=4	)
												git_log(args.dump_path	)

						snake_case_,       snake_case_,       snake_case_ :Any						=			MODEL_CLASSES[args.student_type]
						snake_case_,       snake_case_,       snake_case_ :int						=			MODEL_CLASSES[args.teacher_type]

						# TOKENIZER #
						snake_case_ :Any						=			teacher_tokenizer_class.from_pretrained(args.teacher_name	)
						snake_case_ :Optional[Any]						=			{}
						for tok_name, tok_symbol in tokenizer.special_tokens_map.items():
												snake_case_ :Union[str, Any]						=			tokenizer.all_special_tokens.index(_lowercase	)
												snake_case_ :Union[str, Any]						=			tokenizer.all_special_ids[idx]
						logger.info(f"""Special tokens {special_tok_ids}"""	)
						snake_case_ :str						=			special_tok_ids
						snake_case_ :Any						=			tokenizer.max_model_input_sizes[args.teacher_name]

						# DATA LOADER #
						logger.info(f"""Loading data from {args.data_file}"""	)
						with open(args.data_file,					"""rb"""	) as fp:
												snake_case_ :str						=			pickle.load(_lowercase	)

						if args.mlm:
												logger.info(f"""Loading token counts from {args.token_counts} (already pre-computed)"""	)
												with open(args.token_counts,					"""rb"""	) as fp:
																		snake_case_ :Optional[Any]						=			pickle.load(_lowercase	)

												snake_case_ :Tuple						=			np.maximum(_lowercase,					1	) ** -args.mlm_smoothing
												for idx in special_tok_ids.values():
																		snake_case_ :Optional[int]						=			0.0  # do not predict special tokens
												snake_case_ :int						=			torch.from_numpy(_lowercase	)
						else:
												snake_case_ :List[str]						=			None

						snake_case_ :Optional[int]						=			LmSeqsDataset(params=_lowercase,					data=_lowercase	)
						logger.info("""Data loader created."""	)

						# STUDENT #
						logger.info(f"""Loading student config from {args.student_config}"""	)
						snake_case_ :List[Any]						=			student_config_class.from_pretrained(args.student_config	)
						snake_case_ :Union[str, Any]						=			True

						if args.student_pretrained_weights is not None:
												logger.info(f"""Loading pretrained weights from {args.student_pretrained_weights}"""	)
												snake_case_ :List[str]						=			student_model_class.from_pretrained(args.student_pretrained_weights,					config=_lowercase	)
						else:
												snake_case_ :Optional[int]						=			student_model_class(_lowercase	)

						if args.n_gpu > 0:
												student.to(f"""cuda:{args.local_rank}"""	)
						logger.info("""Student loaded."""	)

						# TEACHER #
						snake_case_ :Dict						=			teacher_model_class.from_pretrained(args.teacher_name,					output_hidden_states=_lowercase	)
						if args.n_gpu > 0:
												teacher.to(f"""cuda:{args.local_rank}"""	)
						logger.info(f"""Teacher loaded from {args.teacher_name}."""	)

						# FREEZING #
						if args.freeze_pos_embs:
												freeze_pos_embeddings(_lowercase,					_lowercase	)
						if args.freeze_token_type_embds:
												freeze_token_type_embeddings(_lowercase,					_lowercase	)

						# SANITY CHECKS #
						assert student.config.vocab_size == teacher.config.vocab_size
						assert student.config.hidden_size == teacher.config.hidden_size
						assert student.config.max_position_embeddings == teacher.config.max_position_embeddings
						if args.mlm:
												assert token_probs.size(0	) == stu_architecture_config.vocab_size

						# DISTILLER #
						torch.cuda.empty_cache()
						snake_case_ :Optional[int]						=			Distiller(
						    params=_lowercase,					dataset=_lowercase,					token_probs=_lowercase,					student=_lowercase,					teacher=_lowercase	)
						distiller.train()
						logger.info("""Let's go get some drinks."""	)


if __name__ == "__main__":
						main()





"""simple docstring"""
import gc
import math
import unittest

import torch

from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


__a      =				logging.get_logger(__name__)

enable_full_determinism()
class 				lowerCamelCase  (			_lowerCAmelCase    ,						_lowerCAmelCase    ,						unittest.TestCase				):






					'''simple docstring'''





					_A		:			List[Any]						=     UNetaDModel
					_A		:			Union[str, Any]						=     """sample"""






					@property
					def    lowerCAmelCase_				(       self:							str						)   ->	Tuple:
											snake_case_ :List[str]						=			4
											snake_case_ :Tuple						=			3
											snake_case_ :Optional[Any]						=			(32, 32)

											snake_case_ :str						=			floats_tensor((batch_size, num_channels) + sizes						).to(snake_case						)
											snake_case_ :Union[str, Any]						=			torch.tensor([10]						).to(snake_case						)

											return {"sample": noise, "timestep": time_step}






					@property
					def    lowerCAmelCase_				(       self:							List[str]						)   ->	Dict:
											return (3, 32, 32)






					@property
					def    lowerCAmelCase_				(       self:							Optional[int]						)   ->	Optional[int]:
											return (3, 32, 32)






					def    lowerCAmelCase_				(       self:							Optional[int]						)   ->	Dict:
											snake_case_ :Any						=			{
											    """block_out_channels""": (32, 64),
											    """down_block_types""": ("""DownBlock2D""", """AttnDownBlock2D"""),
											    """up_block_types""": ("""AttnUpBlock2D""", """UpBlock2D"""),
											    """attention_head_dim""": 3,
											    """out_channels""": 3,
											    """in_channels""": 3,
											    """layers_per_block""": 2,
											    """sample_size""": 32,
											}
											snake_case_ :Tuple						=			self.dummy_input
											return init_dict, inputs_dict

class 				lowerCamelCase  (			_lowerCAmelCase    ,						_lowerCAmelCase    ,						unittest.TestCase				):






					'''simple docstring'''





					_A		:			List[str]						=     UNetaDModel
					_A		:			Union[str, Any]						=     """sample"""






					@property
					def    lowerCAmelCase_				(       self:							str						)   ->	str:
											snake_case_ :List[str]						=			4
											snake_case_ :Optional[int]						=			4
											snake_case_ :int						=			(32, 32)

											snake_case_ :Any						=			floats_tensor((batch_size, num_channels) + sizes						).to(snake_case						)
											snake_case_ :List[Any]						=			torch.tensor([10]						).to(snake_case						)

											return {"sample": noise, "timestep": time_step}






					@property
					def    lowerCAmelCase_				(       self:							Optional[int]						)   ->	Optional[int]:
											return (4, 32, 32)






					@property
					def    lowerCAmelCase_				(       self:							List[Any]						)   ->	int:
											return (4, 32, 32)






					def    lowerCAmelCase_				(       self:							Union[str, Any]						)   ->	List[Any]:
											snake_case_ :Dict						=			{
											    """sample_size""": 32,
											    """in_channels""": 4,
											    """out_channels""": 4,
											    """layers_per_block""": 2,
											    """block_out_channels""": (32, 64),
											    """attention_head_dim""": 32,
											    """down_block_types""": ("""DownBlock2D""", """DownBlock2D"""),
											    """up_block_types""": ("""UpBlock2D""", """UpBlock2D"""),
											}
											snake_case_ :List[str]						=			self.dummy_input
											return init_dict, inputs_dict






					def    lowerCAmelCase_				(       self:							Optional[int]						)   ->	Optional[Any]:
											snake_case_,       snake_case_ :List[str]						=			UNetaDModel.from_pretrained("""fusing/unet-ldm-dummy-update"""						,     output_loading_info=snake_case						)

											self.assertIsNotNone(snake_case						)
											self.assertEqual(len(loading_info["""missing_keys"""]						)						,     0						)

											model.to(snake_case						)
											snake_case_ :List[str]						=			model(**self.dummy_input						).sample

											assert image is not None, "Make sure output is not None"






					@unittest.skipIf(torch_device != """cuda"""						,     """This test is supposed to run on GPU"""						)
					def    lowerCAmelCase_				(       self:							Tuple						)   ->	Dict:
											snake_case_,       snake_case_ :Union[str, Any]						=			UNetaDModel.from_pretrained("""fusing/unet-ldm-dummy-update"""						,     output_loading_info=snake_case						)
											model.to(snake_case						)
											snake_case_ :Union[str, Any]						=			model(**self.dummy_input						).sample

											assert image is not None, "Make sure output is not None"






					@unittest.skipIf(torch_device != """cuda"""						,     """This test is supposed to run on GPU"""						)
					def    lowerCAmelCase_				(       self:							str						)   ->	Any:
											# by defautl model loading will use accelerate as `low_cpu_mem_usage=True`
											snake_case_,       snake_case_ :List[str]						=			UNetaDModel.from_pretrained("""fusing/unet-ldm-dummy-update"""						,     output_loading_info=snake_case						)
											model_accelerate.to(snake_case						)
											model_accelerate.eval()

											snake_case_ :List[Any]						=			torch.randn(
											    1						,     model_accelerate.config.in_channels						,     model_accelerate.config.sample_size						,     model_accelerate.config.sample_size						,     generator=torch.manual_seed(0						)						,     )
											snake_case_ :int						=			noise.to(snake_case						)
											snake_case_ :str						=			torch.tensor([10] * noise.shape[0]						).to(snake_case						)

											snake_case_ :Optional[int]						=			model_accelerate(snake_case						,     snake_case						)["""sample"""]

											# two models don't need to stay in the device at the same time
											del model_accelerate
											torch.cuda.empty_cache()
											gc.collect()

											snake_case_,       snake_case_ :str						=			UNetaDModel.from_pretrained(
											    """fusing/unet-ldm-dummy-update"""						,     output_loading_info=snake_case						,     low_cpu_mem_usage=snake_case						)
											model_normal_load.to(snake_case						)
											model_normal_load.eval()
											snake_case_ :int						=			model_normal_load(snake_case						,     snake_case						)["""sample"""]

											assert torch_all_close(snake_case						,     snake_case						,     rtol=1E-3						)






					def    lowerCAmelCase_				(       self:							Tuple						)   ->	Any:
											snake_case_ :Tuple						=			UNetaDModel.from_pretrained("""fusing/unet-ldm-dummy-update"""						)
											model.eval()
											model.to(snake_case						)

											snake_case_ :Optional[int]						=			torch.randn(
											    1						,     model.config.in_channels						,     model.config.sample_size						,     model.config.sample_size						,     generator=torch.manual_seed(0						)						,     )
											snake_case_ :int						=			noise.to(snake_case						)
											snake_case_ :List[Any]						=			torch.tensor([10] * noise.shape[0]						).to(snake_case						)

											with torch.no_grad():
																	snake_case_ :Union[str, Any]						=			model(snake_case						,     snake_case						).sample

											snake_case_ :Optional[int]						=			output[0, -1, -3:, -3:].flatten().cpu()
											# fmt: off
											snake_case_ :Dict						=			torch.tensor([-1_3.3_2_5_8, -2_0.1_1_0_0, -1_5.9_8_7_3, -1_7.6_6_1_7, -2_3.0_5_9_6, -1_7.9_4_1_9, -1_3.3_6_7_5, -1_6.1_8_8_9, -1_2.3_8_0_0]						)
											# fmt: on

											self.assertTrue(torch_all_close(snake_case						,     snake_case						,     rtol=1E-3						)						)

class 				lowerCamelCase  (			_lowerCAmelCase    ,						_lowerCAmelCase    ,						unittest.TestCase				):






					'''simple docstring'''





					_A		:			List[Any]						=     UNetaDModel
					_A		:			List[Any]						=     """sample"""






					@property
					def    lowerCAmelCase_				(       self:							Union[str, Any]						,     snake_case:							int=(32, 32)						)   ->	Tuple:
											snake_case_ :Union[str, Any]						=			4
											snake_case_ :Any						=			3

											snake_case_ :int						=			floats_tensor((batch_size, num_channels) + sizes						).to(snake_case						)
											snake_case_ :Any						=			torch.tensor(batch_size * [10]						).to(dtype=torch.intaa						,     device=snake_case						)

											return {"sample": noise, "timestep": time_step}






					@property
					def    lowerCAmelCase_				(       self:							Union[str, Any]						)   ->	Any:
											return (3, 32, 32)






					@property
					def    lowerCAmelCase_				(       self:							int						)   ->	Tuple:
											return (3, 32, 32)






					def    lowerCAmelCase_				(       self:							List[str]						)   ->	Tuple:
											snake_case_ :List[Any]						=			{
											    """block_out_channels""": [32, 64, 64, 64],
											    """in_channels""": 3,
											    """layers_per_block""": 1,
											    """out_channels""": 3,
											    """time_embedding_type""": """fourier""",
											    """norm_eps""": 1E-6,
											    """mid_block_scale_factor""": math.sqrt(2.0						),
											    """norm_num_groups""": None,
											    """down_block_types""": [
											        """SkipDownBlock2D""",
											        """AttnSkipDownBlock2D""",
											        """SkipDownBlock2D""",
											        """SkipDownBlock2D""",
											    ],
											    """up_block_types""": [
											        """SkipUpBlock2D""",
											        """SkipUpBlock2D""",
											        """AttnSkipUpBlock2D""",
											        """SkipUpBlock2D""",
											    ],
											}
											snake_case_ :int						=			self.dummy_input
											return init_dict, inputs_dict






					@slow
					def    lowerCAmelCase_				(       self:							Optional[Any]						)   ->	List[Any]:
											snake_case_,       snake_case_ :List[Any]						=			UNetaDModel.from_pretrained("""google/ncsnpp-celebahq-256"""						,     output_loading_info=snake_case						)
											self.assertIsNotNone(snake_case						)
											self.assertEqual(len(loading_info["""missing_keys"""]						)						,     0						)

											model.to(snake_case						)
											snake_case_ :Any						=			self.dummy_input
											snake_case_ :int						=			floats_tensor((4, 3) + (256, 256)						).to(snake_case						)
											snake_case_ :int						=			noise
											snake_case_ :int						=			model(**snake_case						)

											assert image is not None, "Make sure output is not None"






					@slow
					def    lowerCAmelCase_				(       self:							str						)   ->	Dict:
											snake_case_ :Dict						=			UNetaDModel.from_pretrained("""google/ncsnpp-celebahq-256"""						)
											model.to(snake_case						)

											snake_case_ :List[str]						=			4
											snake_case_ :Optional[int]						=			3
											snake_case_ :List[str]						=			(256, 256)

											snake_case_ :Tuple						=			torch.ones((batch_size, num_channels) + sizes						).to(snake_case						)
											snake_case_ :str						=			torch.tensor(batch_size * [1E-4]						).to(snake_case						)

											with torch.no_grad():
																	snake_case_ :Dict						=			model(snake_case						,     snake_case						).sample

											snake_case_ :int						=			output[0, -3:, -3:, -1].flatten().cpu()
											# fmt: off
											snake_case_ :Optional[Any]						=			torch.tensor([-4_8_4_2.8_6_9_1, -6_4_9_9.6_6_3_1, -3_8_0_0.1_9_5_3, -7_9_7_8.2_6_8_6, -1_0_9_8_0.7_1_2_9, -2_0_0_2_8.8_5_3_5, 8_1_4_8.2_8_2_2, 2_3_4_2.2_9_0_5, 5_6_7.7_6_0_8]						)
											# fmt: on

											self.assertTrue(torch_all_close(snake_case						,     snake_case						,     rtol=1E-2						)						)






					def    lowerCAmelCase_				(       self:							List[str]						)   ->	List[Any]:
											snake_case_ :Optional[Any]						=			UNetaDModel.from_pretrained("""fusing/ncsnpp-ffhq-ve-dummy-update"""						)
											model.to(snake_case						)

											snake_case_ :Optional[int]						=			4
											snake_case_ :Optional[Any]						=			3
											snake_case_ :Optional[Any]						=			(32, 32)

											snake_case_ :Dict						=			torch.ones((batch_size, num_channels) + sizes						).to(snake_case						)
											snake_case_ :Any						=			torch.tensor(batch_size * [1E-4]						).to(snake_case						)

											with torch.no_grad():
																	snake_case_ :str						=			model(snake_case						,     snake_case						).sample

											snake_case_ :int						=			output[0, -3:, -3:, -1].flatten().cpu()
											# fmt: off
											snake_case_ :int						=			torch.tensor([-0.0_3_2_5, -0.0_9_0_0, -0.0_8_6_9, -0.0_3_3_2, -0.0_7_2_5, -0.0_2_7_0, -0.0_1_0_1, 0.0_2_2_7, 0.0_2_5_6]						)
											# fmt: on

											self.assertTrue(torch_all_close(snake_case						,     snake_case						,     rtol=1E-2						)						)






					def    lowerCAmelCase_				(       self:							Dict						)   ->	Optional[Any]:
											# not required for this model
											pass
