

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 				a  :
						_lowercase    						=			field(
						    default=UpperCAmelCase							, metadata={
						        "help": (
						            "The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."
						        )
						    }							, )
						_lowercase    						=			field(
						    default=UpperCAmelCase							, metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(UpperCAmelCase				)}							, )
						_lowercase    						=			field(
						    default=UpperCAmelCase							, 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"
						        )
						    }							, )
						_lowercase    						=			field(
						    default=UpperCAmelCase							, metadata={"help": "Pretrained config name or path if not the same as model_name"}				)
						_lowercase    						=			field(
						    default=UpperCAmelCase							, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}				)
						_lowercase    						=			field(
						    default=UpperCAmelCase							, metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}							, )
						_lowercase    						=			field(
						    default=UpperCAmelCase							, metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."}							, )
						_lowercase    						=			field(
						    default="main"							, metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}							, )
						_lowercase    						=			field(
						    default=UpperCAmelCase							, metadata={
						        "help": (
						            "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
						            "with private models)."
						        )
						    }							, )


						def        _UpperCAmelCase				(       self	):







										'''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 				a  :
						_lowercase    						=			field(
						    default=UpperCAmelCase							, metadata={"help": "The name of the dataset to use (via the datasets library)."}				)
						_lowercase    						=			field(
						    default=UpperCAmelCase							, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}				)
						_lowercase    						=			field(default=UpperCAmelCase							, metadata={"help": "The input training data file (a text file)."}				)
						_lowercase    						=			field(
						    default=UpperCAmelCase							, metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."}							, )
						_lowercase    						=			field(
						    default=UpperCAmelCase							, metadata={"help": "An optional input train ref data file for whole word masking in Chinese."}							, )
						_lowercase    						=			field(
						    default=UpperCAmelCase							, metadata={"help": "An optional input validation ref data file for whole word masking in Chinese."}							, )
						_lowercase    						=			field(
						    default=UpperCAmelCase							, metadata={"help": "Overwrite the cached training and evaluation sets"}				)
						_lowercase    						=			field(
						    default=5							, metadata={
						        "help": "The percentage of the train set used as validation set in case there's no validation split"
						    }							, )
						_lowercase    						=			field(
						    default=UpperCAmelCase							, 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."
						        )
						    }							, )
						_lowercase    						=			field(
						    default=UpperCAmelCase							, metadata={"help": "The number of processes to use for the preprocessing."}							, )
						_lowercase    						=			field(
						    default=0.15							, metadata={"help": "Ratio of tokens to mask for masked language modeling loss"}				)
						_lowercase    						=			field(
						    default=UpperCAmelCase							, 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	):







										'''simple docstring'''


										if self.train_file is not None:
														_UpperCAmelCase       :		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:
														_UpperCAmelCase       :		Tuple 		=    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							(				lowerCAmelCase:  List[str]  ,      lowerCAmelCase:  Optional[Any] )   ->       Union[str, Any]:
				with open(lowerCAmelCase  ,      "r"  ,      encoding="utf-8" ) as f:
								_UpperCAmelCase       :		Any 		=    [json.loads(lowerCAmelCase ) for line in f.read().splitlines() if (len(lowerCAmelCase ) > 0 and not line.isspace())]
				assert len(lowerCAmelCase ) == len(lowerCAmelCase )

				_UpperCAmelCase       :		str 		=    {c: dataset[c] for c in dataset.column_names}
				_UpperCAmelCase       :		Tuple 		=    refs
				return Dataset.from_dict(lowerCAmelCase )

def 							__SCREAMING_SNAKE_CASE							(				)   ->       Any:
				# 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.

				_UpperCAmelCase       :		List[str] 		=    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.
								_UpperCAmelCase		,							_UpperCAmelCase		,							_UpperCAmelCase       :		List[str] 		=    parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
				else:
								_UpperCAmelCase		,							_UpperCAmelCase		,							_UpperCAmelCase       :		str 		=    parser.parse_args_into_dataclasses()

				# Detecting last checkpoint.
				_UpperCAmelCase       :		List[Any] 		=    None
				if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
								_UpperCAmelCase       :		List[str] 		=    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"  ,      lowerCAmelCase )

				# 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.
								_UpperCAmelCase       :		int 		=    load_dataset(data_args.dataset_name  ,      data_args.dataset_config_name )
								if "validation" not in datasets.keys():
												_UpperCAmelCase       :		str 		=    load_dataset(
												    data_args.dataset_name  ,      data_args.dataset_config_name  ,      split=F'train[:{data_args.validation_split_percentage}%]'  ,      )
												_UpperCAmelCase       :		Union[str, Any] 		=    load_dataset(
												    data_args.dataset_name  ,      data_args.dataset_config_name  ,      split=F'train[{data_args.validation_split_percentage}%:]'  ,      )
				else:
								_UpperCAmelCase       :		Dict 		=    {}
								if data_args.train_file is not None:
												_UpperCAmelCase       :		List[Any] 		=    data_args.train_file
								if data_args.validation_file is not None:
												_UpperCAmelCase       :		Optional[int] 		=    data_args.validation_file
								_UpperCAmelCase       :		Optional[int] 		=    data_args.train_file.split("." )[-1]
								if extension == "txt":
												_UpperCAmelCase       :		Optional[int] 		=    "text"
								_UpperCAmelCase       :		Any 		=    load_dataset(lowerCAmelCase  ,      data_files=lowerCAmelCase )
				# 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.
				_UpperCAmelCase       :		Optional[int] 		=    {
				    "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:
								_UpperCAmelCase       :		Union[str, Any] 		=    AutoConfig.from_pretrained(model_args.config_name  ,      **lowerCAmelCase )
				elif model_args.model_name_or_path:
								_UpperCAmelCase       :		List[Any] 		=    AutoConfig.from_pretrained(model_args.model_name_or_path  ,      **lowerCAmelCase )
				else:
								_UpperCAmelCase       :		Optional[Any] 		=    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}' )

				_UpperCAmelCase       :		List[str] 		=    {
				    "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:
								_UpperCAmelCase       :		List[str] 		=    AutoTokenizer.from_pretrained(model_args.tokenizer_name  ,      **lowerCAmelCase )
				elif model_args.model_name_or_path:
								_UpperCAmelCase       :		Any 		=    AutoTokenizer.from_pretrained(model_args.model_name_or_path  ,      **lowerCAmelCase )
				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:
								_UpperCAmelCase       :		Union[str, Any] 		=    AutoModelForMaskedLM.from_pretrained(
								    model_args.model_name_or_path  ,      from_tf=bool(".ckpt" in model_args.model_name_or_path )  ,      config=lowerCAmelCase  ,      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" )
								_UpperCAmelCase       :		List[Any] 		=    AutoModelForMaskedLM.from_config(lowerCAmelCase )

				model.resize_token_embeddings(len(lowerCAmelCase ) )

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

				_UpperCAmelCase       :		int 		=    "max_length" if data_args.pad_to_max_length else False

				def tokenize_function(lowerCAmelCase:  List[Any] ):
								# Remove empty lines
								_UpperCAmelCase       :		int 		=    [line for line in examples["text"] if len(lowerCAmelCase ) > 0 and not line.isspace()]
								return tokenizer(examples["text"]  ,      padding=lowerCAmelCase  ,      truncation=lowerCAmelCase  ,      max_length=data_args.max_seq_length )

				_UpperCAmelCase       :		Optional[int] 		=    datasets.map(
				    lowerCAmelCase  ,      batched=lowerCAmelCase  ,      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:
								_UpperCAmelCase       :		Any 		=    add_chinese_references(tokenized_datasets["train"]  ,      data_args.train_ref_file )
				if data_args.validation_ref_file is not None:
								_UpperCAmelCase       :		Any 		=    add_chinese_references(
								    tokenized_datasets["validation"]  ,      data_args.validation_ref_file )
				# If we have ref files, need to avoid it removed by trainer
				_UpperCAmelCase       :		Optional[Any] 		=    data_args.train_ref_file or data_args.validation_ref_file
				if has_ref:
								_UpperCAmelCase       :		Tuple 		=    False

				# Data collator
				# This one will take care of randomly masking the tokens.
				_UpperCAmelCase       :		List[Any] 		=    DataCollatorForWholeWordMask(tokenizer=lowerCAmelCase  ,      mlm_probability=data_args.mlm_probability )

				# Initialize our Trainer
				_UpperCAmelCase       :		str 		=    Trainer(
				    model=lowerCAmelCase  ,      args=lowerCAmelCase  ,      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=lowerCAmelCase  ,      data_collator=lowerCAmelCase  ,      )

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

								_UpperCAmelCase       :		List[str] 		=    os.path.join(training_args.output_dir  ,      "train_results.txt" )
								if trainer.is_world_process_zero():
												with open(lowerCAmelCase  ,      "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
				_UpperCAmelCase       :		Optional[Any] 		=    {}
				if training_args.do_eval:
								logger.info("*** Evaluate ***" )

								_UpperCAmelCase       :		Any 		=    trainer.evaluate()

								_UpperCAmelCase       :		Optional[int] 		=    math.exp(eval_output["eval_loss"] )
								_UpperCAmelCase       :		Dict 		=    perplexity

								_UpperCAmelCase       :		List[Any] 		=    os.path.join(training_args.output_dir  ,      "eval_results_mlm_wwm.txt" )
								if trainer.is_world_process_zero():
												with open(lowerCAmelCase  ,      "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							(				lowerCAmelCase:  str )   ->       str:
				# For xla_spawn (TPUs)
				main()


if __name__ == "__main__":
					main()





import gc
import unittest

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

from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings
from diffusers.utils import load_numpy, slow, torch_device
from diffusers.utils.testing_utils import require_torch_gpu


SCREAMING_SNAKE_CASE_        =      False

class 				a  (	unittest.TestCase				):


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


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


						@property
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										return 12


						@property
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										return 12


						@property
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										return 32


						@property
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										torch.manual_seed(0	)
										_UpperCAmelCase       :		Optional[int] 		=    VQModel(
										    block_out_channels=[32, 64]						,					in_channels=3						,					out_channels=3						,					down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]						,					up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]						,					latent_channels=3						,					num_vq_embeddings=self.num_embed						,					vq_embed_dim=3						,					)
										return model


						@property
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		List[str] 		=    CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"	)
										return tokenizer


						@property
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										torch.manual_seed(0	)
										_UpperCAmelCase       :		int 		=    CLIPTextConfig(
										    bos_token_id=0						,					eos_token_id=2						,					hidden_size=self.text_embedder_hidden_size						,					intermediate_size=37						,					layer_norm_eps=1e-05						,					num_attention_heads=4						,					num_hidden_layers=5						,					pad_token_id=1						,					vocab_size=1000						,					)
										return CLIPTextModel(A_	)


						@property
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										torch.manual_seed(0	)

										_UpperCAmelCase       :		Optional[int] 		=    12
										_UpperCAmelCase       :		Union[str, Any] 		=    12

										_UpperCAmelCase       :		str 		=    {
										    "attention_bias": True,
										    "cross_attention_dim": 32,
										    "attention_head_dim": height * width,
										    "num_attention_heads": 1,
										    "num_vector_embeds": self.num_embed,
										    "num_embeds_ada_norm": self.num_embeds_ada_norm,
										    "norm_num_groups": 32,
										    "sample_size": width,
										    "activation_fn": "geglu-approximate",
										}

										_UpperCAmelCase       :		Any 		=    TransformeraDModel(**A_	)
										return model


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Dict 		=    "cpu"

										_UpperCAmelCase       :		List[str] 		=    self.dummy_vqvae
										_UpperCAmelCase       :		Any 		=    self.dummy_text_encoder
										_UpperCAmelCase       :		Optional[int] 		=    self.dummy_tokenizer
										_UpperCAmelCase       :		List[Any] 		=    self.dummy_transformer
										_UpperCAmelCase       :		Union[str, Any] 		=    VQDiffusionScheduler(self.num_embed	)
										_UpperCAmelCase       :		Union[str, Any] 		=    LearnedClassifierFreeSamplingEmbeddings(learnable=A_	)

										_UpperCAmelCase       :		Optional[Any] 		=    VQDiffusionPipeline(
										    vqvae=A_						,					text_encoder=A_						,					tokenizer=A_						,					transformer=A_						,					scheduler=A_						,					learned_classifier_free_sampling_embeddings=A_						,					)
										_UpperCAmelCase       :		Optional[int] 		=    pipe.to(A_	)
										pipe.set_progress_bar_config(disable=A_	)

										_UpperCAmelCase       :		Union[str, Any] 		=    "teddy bear playing in the pool"

										_UpperCAmelCase       :		Union[str, Any] 		=    torch.Generator(device=A_	).manual_seed(0	)
										_UpperCAmelCase       :		List[Any] 		=    pipe([prompt]						,					generator=A_						,					num_inference_steps=2						,					output_type="np"	)
										_UpperCAmelCase       :		List[Any] 		=    output.images

										_UpperCAmelCase       :		Dict 		=    torch.Generator(device=A_	).manual_seed(0	)
										_UpperCAmelCase       :		Union[str, Any] 		=    pipe(
										    [prompt]						,					generator=A_						,					output_type="np"						,					return_dict=A_						,					num_inference_steps=2	)[0]

										_UpperCAmelCase       :		Optional[Any] 		=    image[0, -3:, -3:, -1]
										_UpperCAmelCase       :		int 		=    image_from_tuple[0, -3:, -3:, -1]

										assert image.shape == (1, 24, 24, 3)

										_UpperCAmelCase       :		List[str] 		=    np.array([0.65_51, 0.61_68, 0.50_08, 0.56_76, 0.56_59, 0.42_95, 0.60_73, 0.55_99, 0.49_92]	)

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





						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Optional[int] 		=    "cpu"

										_UpperCAmelCase       :		Tuple 		=    self.dummy_vqvae
										_UpperCAmelCase       :		Optional[Any] 		=    self.dummy_text_encoder
										_UpperCAmelCase       :		List[str] 		=    self.dummy_tokenizer
										_UpperCAmelCase       :		List[Any] 		=    self.dummy_transformer
										_UpperCAmelCase       :		Dict 		=    VQDiffusionScheduler(self.num_embed	)
										_UpperCAmelCase       :		Dict 		=    LearnedClassifierFreeSamplingEmbeddings(
										    learnable=A_						,					hidden_size=self.text_embedder_hidden_size						,					length=tokenizer.model_max_length	)

										_UpperCAmelCase       :		Optional[int] 		=    VQDiffusionPipeline(
										    vqvae=A_						,					text_encoder=A_						,					tokenizer=A_						,					transformer=A_						,					scheduler=A_						,					learned_classifier_free_sampling_embeddings=A_						,					)
										_UpperCAmelCase       :		Union[str, Any] 		=    pipe.to(A_	)
										pipe.set_progress_bar_config(disable=A_	)

										_UpperCAmelCase       :		int 		=    "teddy bear playing in the pool"

										_UpperCAmelCase       :		Dict 		=    torch.Generator(device=A_	).manual_seed(0	)
										_UpperCAmelCase       :		int 		=    pipe([prompt]						,					generator=A_						,					num_inference_steps=2						,					output_type="np"	)
										_UpperCAmelCase       :		Union[str, Any] 		=    output.images

										_UpperCAmelCase       :		Optional[Any] 		=    torch.Generator(device=A_	).manual_seed(0	)
										_UpperCAmelCase       :		int 		=    pipe(
										    [prompt]						,					generator=A_						,					output_type="np"						,					return_dict=A_						,					num_inference_steps=2	)[0]

										_UpperCAmelCase       :		Any 		=    image[0, -3:, -3:, -1]
										_UpperCAmelCase       :		Optional[int] 		=    image_from_tuple[0, -3:, -3:, -1]

										assert image.shape == (1, 24, 24, 3)

										_UpperCAmelCase       :		Union[str, Any] 		=    np.array([0.66_93, 0.60_75, 0.49_59, 0.57_01, 0.55_83, 0.43_33, 0.61_71, 0.56_84, 0.49_88]	)

										assert np.abs(image_slice.flatten() - expected_slice	).max() < 2.0
										assert np.abs(image_from_tuple_slice.flatten() - expected_slice	).max() < 1e-2







@slow
@require_torch_gpu
class 				a  (	unittest.TestCase				):


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


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





						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Optional[Any] 		=    load_numpy(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
										    "/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy"	)

										_UpperCAmelCase       :		Dict 		=    VQDiffusionPipeline.from_pretrained("microsoft/vq-diffusion-ithq"	)
										_UpperCAmelCase       :		Optional[int] 		=    pipeline.to(A_	)
										pipeline.set_progress_bar_config(disable=A_	)

										# requires GPU generator for gumbel softmax
										# don't use GPU generator in tests though
										_UpperCAmelCase       :		Optional[int] 		=    torch.Generator(device=A_	).manual_seed(0	)
										_UpperCAmelCase       :		Optional[Any] 		=    pipeline(
										    "teddy bear playing in the pool"						,					num_images_per_prompt=1						,					generator=A_						,					output_type="np"						,					)

										_UpperCAmelCase       :		str 		=    output.images[0]

										assert image.shape == (256, 256, 3)
										assert np.abs(expected_image - image	).max() < 2.0


