





import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

from datasets import Dataset, load_dataset

import transformers
from transformers import (
    CONFIG_MAPPING,
    MODEL_FOR_MASKED_LM_MAPPING,
    AutoConfig,
    AutoModelForMaskedLM,
    AutoTokenizer,
    DataCollatorForWholeWordMask,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process


_SCREAMING_SNAKE_CASE 		=						logging.getLogger(__name__)
_SCREAMING_SNAKE_CASE 		=						list(MODEL_FOR_MASKED_LM_MAPPING.keys())
_SCREAMING_SNAKE_CASE 		=						tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class SCREAMING_SNAKE_CASE_  :
						__magic_name__:     Optional[str]  =    field(
						    default=snake_case_    ,			metadata={
						        "help": (
						            "The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."
						        )
						    }    ,			)
						__magic_name__:     Optional[str]  =    field(
						    default=snake_case_    ,			metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(snake_case_   )}    ,			)
						__magic_name__:     Optional[str]  =    field(
						    default=snake_case_    ,			metadata={
						        "help": (
						            "Override some existing default config settings when a model is trained from scratch. Example: "
						            "n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"
						        )
						    }    ,			)
						__magic_name__:     Optional[str]  =    field(
						    default=snake_case_    ,			metadata={"help": "Pretrained config name or path if not the same as model_name"}   )
						__magic_name__:     Optional[str]  =    field(
						    default=snake_case_    ,			metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}   )
						__magic_name__:     Optional[str]  =    field(
						    default=snake_case_    ,			metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}    ,			)
						__magic_name__:     bool  =    field(
						    default=snake_case_    ,			metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."}    ,			)
						__magic_name__:     str  =    field(
						    default="main"    ,			metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}    ,			)
						__magic_name__:     bool  =    field(
						    default=snake_case_    ,			metadata={
						        "help": (
						            "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
						            "with private models)."
						        )
						    }    ,			)






						def UpperCAmelCase_			( self : str		)							->      str:







							"""simple docstring"""


							if self.config_overrides is not None and (self.config_name is not None or self.model_name_or_path is not None):
								raise ValueError(
								    '--config_overrides can\'t be used in combination with --config_name or --model_name_or_path'		)
@dataclass
class SCREAMING_SNAKE_CASE_  :
						__magic_name__:     Optional[str]  =    field(
						    default=snake_case_    ,			metadata={"help": "The name of the dataset to use (via the datasets library)."}   )
						__magic_name__:     Optional[str]  =    field(
						    default=snake_case_    ,			metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}   )
						__magic_name__:     Optional[str]  =    field(default=snake_case_    ,			metadata={"help": "The input training data file (a text file)."}   )
						__magic_name__:     Optional[str]  =    field(
						    default=snake_case_    ,			metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."}    ,			)
						__magic_name__:     Optional[str]  =    field(
						    default=snake_case_    ,			metadata={"help": "An optional input train ref data file for whole word masking in Chinese."}    ,			)
						__magic_name__:     Optional[str]  =    field(
						    default=snake_case_    ,			metadata={"help": "An optional input validation ref data file for whole word masking in Chinese."}    ,			)
						__magic_name__:     bool  =    field(
						    default=snake_case_    ,			metadata={"help": "Overwrite the cached training and evaluation sets"}   )
						__magic_name__:     Optional[int]  =    field(
						    default=5    ,			metadata={
						        "help": "The percentage of the train set used as validation set in case there's no validation split"
						    }    ,			)
						__magic_name__:     Optional[int]  =    field(
						    default=snake_case_    ,			metadata={
						        "help": (
						            "The maximum total input sequence length after tokenization. Sequences longer "
						            "than this will be truncated. Default to the max input length of the model."
						        )
						    }    ,			)
						__magic_name__:     Optional[int]  =    field(
						    default=snake_case_    ,			metadata={"help": "The number of processes to use for the preprocessing."}    ,			)
						__magic_name__:     float  =    field(
						    default=0.1_5    ,			metadata={"help": "Ratio of tokens to mask for masked language modeling loss"}   )
						__magic_name__:     bool  =    field(
						    default=snake_case_    ,			metadata={
						        "help": (
						            "Whether to pad all samples to `max_seq_length`. "
						            "If False, will pad the samples dynamically when batching to the maximum length in the batch."
						        )
						    }    ,			)






						def UpperCAmelCase_			( self : int		)							->      Optional[int]:







							"""simple docstring"""


							if self.train_file is not None:
								snake_case_						:     Optional[int]     =		self.train_file.split('.'		)[-1]
								assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file."
							if self.validation_file is not None:
								snake_case_						:     Optional[Any]     =		self.validation_file.split('.'		)[-1]
								assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."


def 							SCREAMING_SNAKE_CASE__  (      __a				,	__a				):
	with open(__a				,	'r'				,	encoding='utf-8'				) as f:
		snake_case_						:     Dict     =		[json.loads(__a				) for line in f.read().splitlines() if (len(__a				) > 0 and not line.isspace())]
	assert len(__a				) == len(__a				)

	snake_case_						:     Tuple     =		{c: dataset[c] for c in dataset.column_names}
	snake_case_						:     int     =		refs
	return Dataset.from_dict(__a				)


def 							SCREAMING_SNAKE_CASE__  (      ):
	# See all possible arguments in src/transformers/training_args.py
	# or by passing the --help flag to this script.
	# We now keep distinct sets of args, for a cleaner separation of concerns.

	snake_case_						:     Optional[Any]     =		HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)				)
	if len(sys.argv				) == 2 and sys.argv[1].endswith('.json'				):
		# If we pass only one argument to the script and it's the path to a json file,
		# let's parse it to get our arguments.
		snake_case_						:     int     =		parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]				)				)
	else:
		snake_case_						:     Any     =		parser.parse_args_into_dataclasses()

	# Detecting last checkpoint.
	snake_case_						:     Optional[int]     =		None
	if os.path.isdir(training_args.output_dir				) and training_args.do_train and not training_args.overwrite_output_dir:
		snake_case_						:     Union[str, Any]     =		get_last_checkpoint(training_args.output_dir				)
		if last_checkpoint is None and len(os.listdir(training_args.output_dir				)				) > 0:
			raise ValueError(
			    f"""Output directory ({training_args.output_dir}) already exists and is not empty. """
			    'Use --overwrite_output_dir to overcome.'				)
		elif last_checkpoint is not None:
			logger.info(
			    f"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
			    'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.'				)

    # Setup logging
	logging.basicConfig(
	    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'				,	datefmt='%m/%d/%Y %H:%M:%S'				,	handlers=[logging.StreamHandler(sys.stdout				)]				,	)
	logger.setLevel(logging.INFO if is_main_process(training_args.local_rank				) else logging.WARN				)

	# Log on each process the small summary:
	logger.warning(
	    f"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
	    + f"""distributed training: {bool(training_args.local_rank != -1				)}, 16-bits training: {training_args.fpaa}"""				)
	# Set the verbosity to info of the Transformers logger (on main process only):
	if is_main_process(training_args.local_rank				):
		transformers.utils.logging.set_verbosity_info()
		transformers.utils.logging.enable_default_handler()
		transformers.utils.logging.enable_explicit_format()
	logger.info('Training/evaluation parameters %s'				,	__a				)

	# Set seed before initializing model.
	set_seed(training_args.seed				)

	# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
	# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
	# (the dataset will be downloaded automatically from the datasets Hub).
	#
	# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
	# 'text' is found. You can easily tweak this behavior (see below).
	#
	# In distributed training, the load_dataset function guarantee that only one local process can concurrently
	# download the dataset.
	if data_args.dataset_name is not None:
		# Downloading and loading a dataset from the hub.
		snake_case_						:     List[str]     =		load_dataset(data_args.dataset_name				,	data_args.dataset_config_name				)
		if "validation" not in datasets.keys():
			snake_case_						:     Optional[int]     =		load_dataset(
			    data_args.dataset_name				,	data_args.dataset_config_name				,	split=f"""train[:{data_args.validation_split_percentage}%]"""				,	)
			snake_case_						:     Any     =		load_dataset(
			    data_args.dataset_name				,	data_args.dataset_config_name				,	split=f"""train[{data_args.validation_split_percentage}%:]"""				,	)
	else:
		snake_case_						:     Any     =		{}
		if data_args.train_file is not None:
			snake_case_						:     str     =		data_args.train_file
		if data_args.validation_file is not None:
			snake_case_						:     List[Any]     =		data_args.validation_file
		snake_case_						:     Dict     =		data_args.train_file.split('.'				)[-1]
		if extension == "txt":
			snake_case_						:     Dict     =		'text'
		snake_case_						:     Optional[int]     =		load_dataset(__a				,	data_files=__a				)
	# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
	# https://huggingface.co/docs/datasets/loading_datasets.html.

	# Load pretrained model and tokenizer
	#
	# Distributed training:
	# The .from_pretrained methods guarantee that only one local process can concurrently
	# download model & vocab.
	snake_case_						:     Tuple     =		{
	    'cache_dir': model_args.cache_dir,
	    'revision': model_args.model_revision,
	    'use_auth_token': True if model_args.use_auth_token else None,
	}
	if model_args.config_name:
		snake_case_						:     str     =		AutoConfig.from_pretrained(model_args.config_name				,	**__a				)
	elif model_args.model_name_or_path:
		snake_case_						:     Dict     =		AutoConfig.from_pretrained(model_args.model_name_or_path				,	**__a				)
	else:
		snake_case_						:     List[str]     =		CONFIG_MAPPING[model_args.model_type]()
		logger.warning('You are instantiating a new config instance from scratch.'				)
		if model_args.config_overrides is not None:
			logger.info(f"""Overriding config: {model_args.config_overrides}"""				)
			config.update_from_string(model_args.config_overrides				)
			logger.info(f"""New config: {config}"""				)

	snake_case_						:     Optional[int]     =		{
	    'cache_dir': model_args.cache_dir,
	    'use_fast': model_args.use_fast_tokenizer,
	    'revision': model_args.model_revision,
	    'use_auth_token': True if model_args.use_auth_token else None,
	}
	if model_args.tokenizer_name:
		snake_case_						:     List[str]     =		AutoTokenizer.from_pretrained(model_args.tokenizer_name				,	**__a				)
	elif model_args.model_name_or_path:
		snake_case_						:     List[Any]     =		AutoTokenizer.from_pretrained(model_args.model_name_or_path				,	**__a				)
	else:
		raise ValueError(
		    'You are instantiating a new tokenizer from scratch. This is not supported by this script.'
		    'You can do it from another script, save it, and load it from here, using --tokenizer_name.'				)

	if model_args.model_name_or_path:
		snake_case_						:     Dict     =		AutoModelForMaskedLM.from_pretrained(
		    model_args.model_name_or_path				,	from_tf=bool('.ckpt' in model_args.model_name_or_path				)				,	config=__a				,	cache_dir=model_args.cache_dir				,	revision=model_args.model_revision				,	use_auth_token=True if model_args.use_auth_token else None				,	)
	else:
		logger.info('Training new model from scratch'				)
		snake_case_						:     List[str]     =		AutoModelForMaskedLM.from_config(__a				)

	model.resize_token_embeddings(len(__a				)				)

	# Preprocessing the datasets.
	# First we tokenize all the texts.
	if training_args.do_train:
		snake_case_						:     Union[str, Any]     =		datasets['train'].column_names
	else:
		snake_case_						:     Tuple     =		datasets['validation'].column_names
	snake_case_						:     Union[str, Any]     =		'text' if 'text' in column_names else column_names[0]

	snake_case_						:     str     =		'max_length' if data_args.pad_to_max_length else False

	def tokenize_function(__a				):
		# Remove empty lines
		snake_case_						:     str     =		[line for line in examples['text'] if len(__a				) > 0 and not line.isspace()]
		return tokenizer(examples['text']				,	padding=__a				,	truncation=__a				,	max_length=data_args.max_seq_length				)

	snake_case_						:     Union[str, Any]     =		datasets.map(
	    __a				,	batched=__a				,	num_proc=data_args.preprocessing_num_workers				,	remove_columns=[text_column_name]				,	load_from_cache_file=not data_args.overwrite_cache				,	)

	# Add the chinese references if provided
	if data_args.train_ref_file is not None:
		snake_case_						:     str     =		add_chinese_references(tokenized_datasets['train']				,	data_args.train_ref_file				)
	if data_args.validation_ref_file is not None:
		snake_case_						:     Optional[Any]     =		add_chinese_references(
		    tokenized_datasets['validation']				,	data_args.validation_ref_file				)
	# If we have ref files, need to avoid it removed by trainer
	snake_case_						:     str     =		data_args.train_ref_file or data_args.validation_ref_file
	if has_ref:
		snake_case_						:     Tuple     =		False

	# Data collator
	# This one will take care of randomly masking the tokens.
	snake_case_						:     Dict     =		DataCollatorForWholeWordMask(tokenizer=__a				,	mlm_probability=data_args.mlm_probability				)

	# Initialize our Trainer
	snake_case_						:     List[str]     =		Trainer(
	    model=__a				,	args=__a				,	train_dataset=tokenized_datasets['train'] if training_args.do_train else None				,	eval_dataset=tokenized_datasets['validation'] if training_args.do_eval else None				,	tokenizer=__a				,	data_collator=__a				,	)

	# Training
	if training_args.do_train:
		if last_checkpoint is not None:
			snake_case_						:     Tuple     =		last_checkpoint
		elif model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path				):
			snake_case_						:     Tuple     =		model_args.model_name_or_path
		else:
			snake_case_						:     str     =		None
		snake_case_						:     Optional[Any]     =		trainer.train(resume_from_checkpoint=__a				)
		trainer.save_model()  # Saves the tokenizer too for easy upload

		snake_case_						:     Dict     =		os.path.join(training_args.output_dir				,	'train_results.txt'				)
		if trainer.is_world_process_zero():
			with open(__a				,	'w'				) as writer:
				logger.info('***** Train results *****'				)
				for key, value in sorted(train_result.metrics.items()				):
					logger.info(f"""  {key} = {value}"""				)
					writer.write(f"""{key} = {value}\n"""				)

            # Need to save the state, since Trainer.save_model saves only the tokenizer with the model
			trainer.state.save_to_json(os.path.join(training_args.output_dir				,	'trainer_state.json'				)				)

    # Evaluation
	snake_case_						:     Dict     =		{}
	if training_args.do_eval:
		logger.info('*** Evaluate ***'				)

		snake_case_						:     int     =		trainer.evaluate()

		snake_case_						:     int     =		math.exp(eval_output['eval_loss']				)
		snake_case_						:     Optional[int]     =		perplexity

		snake_case_						:     int     =		os.path.join(training_args.output_dir				,	'eval_results_mlm_wwm.txt'				)
		if trainer.is_world_process_zero():
			with open(__a				,	'w'				) as writer:
				logger.info('***** Eval results *****'				)
				for key, value in sorted(results.items()				):
					logger.info(f"""  {key} = {value}"""				)
					writer.write(f"""{key} = {value}\n"""				)

	return results


def 							SCREAMING_SNAKE_CASE__  (      __a				):
	# For xla_spawn (TPUs)
	main()


if __name__ == "__main__":
						main()








import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, XLMRobertaTokenizer

from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()
class SCREAMING_SNAKE_CASE_  (   snake_case_    ,			snake_case_    ,			snake_case_    ,			unittest.TestCase   ):
						__magic_name__:     int	 =    AltDiffusionPipeline
						__magic_name__:     Any	 =    TEXT_TO_IMAGE_PARAMS
						__magic_name__:     Any	 =    TEXT_TO_IMAGE_BATCH_PARAMS
						__magic_name__:     Any	 =    TEXT_TO_IMAGE_IMAGE_PARAMS
						__magic_name__:     Union[str, Any]	 =    TEXT_TO_IMAGE_IMAGE_PARAMS






						def UpperCAmelCase_			( self : List[Any]		)							->      int:







							"""simple docstring"""


							torch.manual_seed(0		)
							snake_case_						:     List[str]     =		UNetaDConditionModel(
							    block_out_channels=(32, 64)						,				layers_per_block=2						,				sample_size=32						,				in_channels=4						,				out_channels=4						,				down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')						,				up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')						,				cross_attention_dim=32						,				)
							snake_case_						:     Optional[Any]     =		DDIMScheduler(
							    beta_start=0.0_0_0_8_5						,				beta_end=0.0_1_2						,				beta_schedule='scaled_linear'						,				clip_sample=_A						,				set_alpha_to_one=_A						,				)
							torch.manual_seed(0		)
							snake_case_						:     Union[str, Any]     =		AutoencoderKL(
							    block_out_channels=[32, 64]						,				in_channels=3						,				out_channels=3						,				down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']						,				up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']						,				latent_channels=4						,				)

							# TODO: address the non-deterministic text encoder (fails for save-load tests)
							# torch.manual_seed(0)
							# text_encoder_config = RobertaSeriesConfig(
							#     hidden_size=32,
							#     project_dim=32,
							#     intermediate_size=37,
							#     layer_norm_eps=1e-05,
							#     num_attention_heads=4,
							#     num_hidden_layers=5,
							#     vocab_size=5002,
							# )
							# text_encoder = RobertaSeriesModelWithTransformation(text_encoder_config)

							torch.manual_seed(0		)
							snake_case_						:     Optional[Any]     =		CLIPTextConfig(
							    bos_token_id=0						,				eos_token_id=2						,				hidden_size=32						,				projection_dim=32						,				intermediate_size=37						,				layer_norm_eps=1E-05						,				num_attention_heads=4						,				num_hidden_layers=5						,				pad_token_id=1						,				vocab_size=5002						,				)
							snake_case_						:     Any     =		CLIPTextModel(_A		)

							snake_case_						:     Any     =		XLMRobertaTokenizer.from_pretrained('hf-internal-testing/tiny-xlm-roberta'		)
							snake_case_						:     Dict     =		77

							snake_case_						:     List[Any]     =		{
							    'unet': unet,
							    'scheduler': scheduler,
							    'vae': vae,
							    'text_encoder': text_encoder,
							    'tokenizer': tokenizer,
							    'safety_checker': None,
							    'feature_extractor': None,
							}
							return components






						def UpperCAmelCase_			( self : int						,				_A : Optional[int]						,				_A : int=0		)							->      Dict:







							"""simple docstring"""


							if str(_A		).startswith('mps'		):
								snake_case_						:     Union[str, Any]     =		torch.manual_seed(_A		)
							else:
								snake_case_						:     Union[str, Any]     =		torch.Generator(device=_A		).manual_seed(_A		)
							snake_case_						:     Any     =		{
							    'prompt': 'A painting of a squirrel eating a burger',
							    'generator': generator,
							    'num_inference_steps': 2,
							    'guidance_scale': 6.0,
							    'output_type': 'numpy',
							}
							return inputs






						def UpperCAmelCase_			( self : Optional[Any]		)							->      Any:







							"""simple docstring"""


							super().test_attention_slicing_forward_pass(expected_max_diff=3E-3		)






						def UpperCAmelCase_			( self : List[Any]		)							->      Dict:







							"""simple docstring"""


							super().test_inference_batch_single_identical(expected_max_diff=3E-3		)






						def UpperCAmelCase_			( self : Dict		)							->      Any:







							"""simple docstring"""


							snake_case_						:     Tuple     =		'cpu'  # ensure determinism for the device-dependent torch.Generator

							snake_case_						:     Any     =		self.get_dummy_components()
							torch.manual_seed(0		)
							snake_case_						:     Any     =		RobertaSeriesConfig(
							    hidden_size=32						,				project_dim=32						,				intermediate_size=37						,				layer_norm_eps=1E-05						,				num_attention_heads=4						,				num_hidden_layers=5						,				vocab_size=5002						,				)
							# TODO: remove after fixing the non-deterministic text encoder
							snake_case_						:     Optional[Any]     =		RobertaSeriesModelWithTransformation(_A		)
							snake_case_						:     Optional[Any]     =		text_encoder

							snake_case_						:     Optional[Any]     =		AltDiffusionPipeline(**_A		)
							snake_case_						:     List[Any]     =		alt_pipe.to(_A		)
							alt_pipe.set_progress_bar_config(disable=_A		)

							snake_case_						:     Optional[Any]     =		self.get_dummy_inputs(_A		)
							snake_case_						:     int     =		'A photo of an astronaut'
							snake_case_						:     Tuple     =		alt_pipe(**_A		)
							snake_case_						:     Any     =		output.images
							snake_case_						:     Optional[int]     =		image[0, -3:, -3:, -1]

							assert image.shape == (1, 64, 64, 3)
							snake_case_						:     Any     =		np.array(
							    [0.5_7_4_8_1_6_2, 0.6_0_4_4_7_1_4_5, 0.4_8_8_2_1_2_1_7, 0.5_0_1_0_0_6_3_6, 0.5_4_3_1_1_8_5, 0.4_5_7_6_3_6_8_3, 0.4_9_6_5_7_6_9_6, 0.4_8_1_3_2_7_3_3, 0.4_7_5_7_3_0_9_3]		)

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






						def UpperCAmelCase_			( self : Optional[int]		)							->      Optional[int]:







							"""simple docstring"""


							snake_case_						:     Optional[Any]     =		'cpu'  # ensure determinism for the device-dependent torch.Generator

							snake_case_						:     Any     =		self.get_dummy_components()
							snake_case_						:     List[str]     =		PNDMScheduler(skip_prk_steps=_A		)
							torch.manual_seed(0		)
							snake_case_						:     Optional[int]     =		RobertaSeriesConfig(
							    hidden_size=32						,				project_dim=32						,				intermediate_size=37						,				layer_norm_eps=1E-05						,				num_attention_heads=4						,				num_hidden_layers=5						,				vocab_size=5002						,				)
							# TODO: remove after fixing the non-deterministic text encoder
							snake_case_						:     Tuple     =		RobertaSeriesModelWithTransformation(_A		)
							snake_case_						:     Any     =		text_encoder
							snake_case_						:     Tuple     =		AltDiffusionPipeline(**_A		)
							snake_case_						:     Dict     =		alt_pipe.to(_A		)
							alt_pipe.set_progress_bar_config(disable=_A		)

							snake_case_						:     Dict     =		self.get_dummy_inputs(_A		)
							snake_case_						:     Tuple     =		alt_pipe(**_A		)
							snake_case_						:     int     =		output.images
							snake_case_						:     List[Any]     =		image[0, -3:, -3:, -1]

							assert image.shape == (1, 64, 64, 3)
							snake_case_						:     Optional[int]     =		np.array(
							    [0.5_1_6_0_5_0_9_3, 0.5_7_0_7_2_4_1, 0.4_7_3_6_5_5_0_7, 0.5_0_5_7_8_8_8_6, 0.5_6_3_3_8_7_7, 0.4_6_4_2_5_0_3, 0.5_1_8_2_0_8_1, 0.4_8_7_6_3_4_8_4, 0.4_9_0_8_4_2_3_7]		)

							assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-2
@slow
@require_torch_gpu
class SCREAMING_SNAKE_CASE_  (   unittest.TestCase   ):






						def UpperCAmelCase_			( self : List[Any]		)							->      Tuple:







							"""simple docstring"""


							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()






						def UpperCAmelCase_			( self : int		)							->      List[str]:







							"""simple docstring"""


							snake_case_						:     Optional[int]     =		AltDiffusionPipeline.from_pretrained('BAAI/AltDiffusion'						,				safety_checker=_A		)
							snake_case_						:     Optional[int]     =		alt_pipe.to(_A		)
							alt_pipe.set_progress_bar_config(disable=_A		)

							snake_case_						:     str     =		'A painting of a squirrel eating a burger'
							snake_case_						:     Tuple     =		torch.manual_seed(0		)
							snake_case_						:     str     =		alt_pipe([prompt]						,				generator=_A						,				guidance_scale=6.0						,				num_inference_steps=20						,				output_type='np'		)

							snake_case_						:     Any     =		output.images

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

							assert image.shape == (1, 512, 512, 3)
							snake_case_						:     Union[str, Any]     =		np.array([0.1_0_1_0, 0.0_8_0_0, 0.0_7_9_4, 0.0_8_8_5, 0.0_8_4_3, 0.0_7_6_2, 0.0_7_6_9, 0.0_7_2_9, 0.0_5_8_6]		)

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






						def UpperCAmelCase_			( self : Optional[int]		)							->      Union[str, Any]:







							"""simple docstring"""


							snake_case_						:     Optional[Any]     =		DDIMScheduler.from_pretrained('BAAI/AltDiffusion'						,				subfolder='scheduler'		)

							snake_case_						:     Union[str, Any]     =		AltDiffusionPipeline.from_pretrained('BAAI/AltDiffusion'						,				scheduler=_A						,				safety_checker=_A		)
							snake_case_						:     List[str]     =		alt_pipe.to(_A		)
							alt_pipe.set_progress_bar_config(disable=_A		)

							snake_case_						:     List[Any]     =		'A painting of a squirrel eating a burger'
							snake_case_						:     int     =		torch.manual_seed(0		)

							snake_case_						:     List[Any]     =		alt_pipe([prompt]						,				generator=_A						,				num_inference_steps=2						,				output_type='numpy'		)
							snake_case_						:     Any     =		output.images

							snake_case_						:     Dict     =		image[0, -3:, -3:, -1]

							assert image.shape == (1, 512, 512, 3)
							snake_case_						:     List[Any]     =		np.array([0.4_0_1_9, 0.4_0_5_2, 0.3_8_1_0, 0.4_1_1_9, 0.3_9_1_6, 0.3_9_8_2, 0.4_6_5_1, 0.4_1_9_5, 0.5_3_2_3]		)

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

