
'''simple docstring'''
import logging
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union

import librosa
import torch
from datasets import DatasetDict, load_dataset
from packaging import version
from torch import nn

from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaForPreTraining,
    is_apex_available,
    trainer_utils,
)
from transformers.models.wavaveca.modeling_wavaveca import _compute_mask_indices


if is_apex_available():
  from apex import amp

if version.parse(version.parse(torch.__version__).base_version) >= version.parse("1.6"):
  a_											=						True
  from torch.cuda.amp import autocast


a_											=						logging.getLogger(__name__)






@dataclass
class    UpperCAmelCase_				:
 UpperCAmelCase_		    =						field(
     metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}					)
 UpperCAmelCase_		    =						field(
     default=snake_case__    ,						metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}    ,						)
 UpperCAmelCase_		    =						field(
     default=snake_case__    ,						metadata={"""help""": """Whether to freeze the feature extractor layers of the model."""}					)
 UpperCAmelCase_		    =						field(
     default=snake_case__    ,						metadata={"""help""": """Whether to log verbose messages or not."""}    ,						)
 UpperCAmelCase_		    =						field(
     default=2.0    ,						metadata={"""help""": """Maximum temperature for gumbel softmax."""}					)
 UpperCAmelCase_		    =						field(
     default=0.5    ,						metadata={"""help""": """Minimum temperature for gumbel softmax."""}					)
 UpperCAmelCase_		    =						field(
     default=0.9_9_9_9_9_5    ,						metadata={"""help""": """Decay of gumbel temperature during training."""}					)


def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",		datefmt="%m/%d/%Y %H:%M:%S",		handlers=[logging.StreamHandler(sys.stdout       )],		)
    snake_case_ :    Any     			=						logging.WARNING
    if model_args.verbose_logging:
        snake_case_ :    str     			=						logging.DEBUG
    elif trainer_utils.is_main_process(training_args.local_rank       ):
        snake_case_ :    List[str]     			=						logging.INFO
    logger.setLevel(__SCREAMING_SNAKE_CASE       )






@dataclass
class    UpperCAmelCase_				:
 UpperCAmelCase_		    =						field(
     default=snake_case__    ,						metadata={"""help""": """The name of the dataset to use (via the datasets library)."""}					)
 UpperCAmelCase_		    =						field(
     default=snake_case__    ,						metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""}					)
 UpperCAmelCase_		    =						field(
     default="""train"""    ,						metadata={
         """help""": """The name of the training data set split to use (via the datasets library). Defaults to 'train'"""
     }    ,						)
 UpperCAmelCase_		    =						field(
     default="""validation"""    ,						metadata={
         """help""": (
             """The name of the validation data set split to use (via the datasets library). Defaults to 'validation'"""
         )
     }    ,						)
 UpperCAmelCase_		    =						field(
     default="""file"""    ,						metadata={"""help""": """Column in the dataset that contains speech file path. Defaults to 'file'"""}    ,						)
 UpperCAmelCase_		    =						field(
     default=snake_case__    ,						metadata={"""help""": """Overwrite the cached preprocessed datasets or not."""}					)
 UpperCAmelCase_		    =						field(
     default=1    ,						metadata={
         """help""": """The percentage of the train set used as validation set in case there's no validation split"""
     }    ,						)
 UpperCAmelCase_		    =						field(
     default=snake_case__    ,						metadata={"""help""": """The number of processes to use for the preprocessing."""}    ,						)
 UpperCAmelCase_		    =						field(
     default=2_0.0    ,						metadata={"""help""": """Filter audio files that are longer than `max_duration_in_seconds` seconds"""}					)






@dataclass
class    UpperCAmelCase_				:
 UpperCAmelCase_		    =						42
 UpperCAmelCase_		    =						42
 UpperCAmelCase_		    =						"longest"
 UpperCAmelCase_		    =						None
 UpperCAmelCase_		    =						None





 def __call__(    self ,       lowercase_):
     # reformat list to dict and set to pytorch format
     snake_case_ :    List[Any]     			=						self.feature_extractor.pad(
         lowercase_ ,       max_length=self.max_length ,       padding=self.padding ,       pad_to_multiple_of=self.pad_to_multiple_of ,       return_tensors="pt" ,       )
     snake_case_ :    Optional[int]     			=						self.model._get_feat_extract_output_lengths(batch["input_values"].shape[-1])

     snake_case_ :    Tuple     			=						batch["input_values"].shape[0]

     # make sure that no loss is computed on padded inputs
     if batch["attention_mask"] is not None:
         # compute real output lengths according to convolution formula
         snake_case_ :    Tuple     			=						self.model._get_feat_extract_output_lengths(batch["attention_mask"].sum(-1)).to(
             torch.long)

         snake_case_ :    Union[str, Any]     			=						torch.zeros(
             (batch_size, mask_indices_seq_length) ,       dtype=torch.long ,       device=batch["input_values"].device)

         # these two operations makes sure that all values
         # before the output lengths indices are attended to
         snake_case_ :    Optional[Any]     			=						1
         snake_case_ :    Dict     			=						attention_mask.flip([-1]).cumsum(-1).flip([-1]).bool()

     # sample randomly masked indices
     snake_case_ :    str     			=						_compute_mask_indices(
         (batch_size, mask_indices_seq_length) ,       self.model.config.mask_time_prob ,       self.model.config.mask_time_length ,       attention_mask=lowercase_ ,       min_masks=2 ,       )

     return batch






class    UpperCAmelCase_				(	snake_case__					):





 def __init__(    self ,       *lowercase_ ,       lowercase_=1 ,       lowercase_=0 ,       lowercase_=1.0 ,       **lowercase_):
     super().__init__(*lowercase_ ,       **lowercase_)
     snake_case_ :    Union[str, Any]     			=						0
     snake_case_ :    int     			=						max_gumbel_temp
     snake_case_ :    Union[str, Any]     			=						min_gumbel_temp
     snake_case_ :    List[str]     			=						gumbel_temp_decay





 def        snake_case__		(    self ,       lowercase_ ,       lowercase_):
     model.train()
     snake_case_ :    Dict     			=						self._prepare_inputs(lowercase_)

     if self.use_amp:
         with autocast():
             snake_case_ :    Dict     			=						self.compute_loss(lowercase_ ,       lowercase_)
     else:
         snake_case_ :    Tuple     			=						self.compute_loss(lowercase_ ,       lowercase_)

     if self.args.n_gpu > 1 or self.deepspeed:
         if model.module.config.ctc_loss_reduction == "mean":
             snake_case_ :    Any     			=						loss.mean()
         elif model.module.config.ctc_loss_reduction == "sum":
             snake_case_ :    List[Any]     			=						loss.sum() / (inputs["mask_time_indices"]).sum()
         else:
             raise ValueError(F'{model.config.ctc_loss_reduction} is not valid. Choose one of [\'mean\', \'sum\']')

     if self.args.gradient_accumulation_steps > 1:
         snake_case_ :    int     			=						loss / self.args.gradient_accumulation_steps

     if self.use_amp:
         self.scaler.scale(lowercase_).backward()
     elif self.use_apex:
         with amp.scale_loss(lowercase_ ,       self.optimizer) as scaled_loss:
             scaled_loss.backward()
     elif self.deepspeed:
         self.deepspeed.backward(lowercase_)
     else:
         loss.backward()

     self.num_update_step += 1
     # make sure gumbel softmax temperature is decayed
     if self.args.n_gpu > 1 or self.deepspeed:
         model.module.set_gumbel_temperature(
             max(self.max_gumbel_temp * self.gumbel_temp_decay**self.num_update_step ,       self.min_gumbel_temp))
     else:
         model.set_gumbel_temperature(
             max(self.max_gumbel_temp * self.gumbel_temp_decay**self.num_update_step ,       self.min_gumbel_temp))

     return loss.detach()


def 						UpperCamelCase_     (	):
    """simple docstring"""






    snake_case_ :    str     			=						HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)       )

    snake_case_			, snake_case_			, snake_case_ :    str     			=						parser.parse_args_into_dataclasses()
    configure_logger(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )

    # Downloading and loading a dataset from the hub.
    snake_case_ :    Optional[Any]     			=						load_dataset(data_args.dataset_name,		data_args.dataset_config_name,		cache_dir=model_args.cache_dir       )

    if "validation" not in datasets.keys():
        # make sure only "validation" and "train" keys remain"
        snake_case_ :    Optional[int]     			=						DatasetDict()
        snake_case_ :    Optional[Any]     			=						load_dataset(
            data_args.dataset_name,		data_args.dataset_config_name,		split=f'{data_args.train_split_name}[:{data_args.validation_split_percentage}%]',		cache_dir=model_args.cache_dir,		)
        snake_case_ :    Tuple     			=						load_dataset(
            data_args.dataset_name,		data_args.dataset_config_name,		split=f'{data_args.train_split_name}[{data_args.validation_split_percentage}%:]',		cache_dir=model_args.cache_dir,		)
    else:
        # make sure only "validation" and "train" keys remain"
        snake_case_ :    Tuple     			=						DatasetDict()
        snake_case_ :    Dict     			=						load_dataset(
            data_args.dataset_name,		data_args.dataset_config_name,		split="validation",		cache_dir=model_args.cache_dir,		)
        snake_case_ :    Union[str, Any]     			=						load_dataset(
            data_args.dataset_name,		data_args.dataset_config_name,		split=f'{data_args.train_split_name}',		cache_dir=model_args.cache_dir,		)

    # only normalized-inputs-training is supported
    snake_case_ :    List[str]     			=						WavaVecaFeatureExtractor.from_pretrained(
        model_args.model_name_or_path,		cache_dir=model_args.cache_dir,		do_normalize=__SCREAMING_SNAKE_CASE       )

    def prepare_dataset(__SCREAMING_SNAKE_CASE       ):
        # check that all files have the correct sampling rate
        snake_case_			, snake_case_ :    Optional[Any]     			=						librosa.load(batch[data_args.speech_file_column],		sr=feature_extractor.sampling_rate       )
        return batch

    # load audio files into numpy arrays
    snake_case_ :    Optional[int]     			=						datasets.map(
        __SCREAMING_SNAKE_CASE,		num_proc=data_args.preprocessing_num_workers,		remove_columns=datasets["train"].column_names       )

    # filter audio files that are too long
    snake_case_ :    Any     			=						vectorized_datasets.filter(
        lambda __SCREAMING_SNAKE_CASE       : len(data["speech"]       ) < int(data_args.max_duration_in_seconds * feature_extractor.sampling_rate       )       )

    def normalize(__SCREAMING_SNAKE_CASE       ):
        return feature_extractor(batch["speech"],		sampling_rate=feature_extractor.sampling_rate       )

    # normalize and transform to `BatchFeatures`
    snake_case_ :    Optional[Any]     			=						vectorized_datasets.map(
        __SCREAMING_SNAKE_CASE,		batched=__SCREAMING_SNAKE_CASE,		num_proc=data_args.preprocessing_num_workers,		load_from_cache_file=not data_args.overwrite_cache,		remove_columns=vectorized_datasets["train"].column_names,		)

    # pretraining is only supported for "newer" stable layer norm architecture
    # apply_spec_augment has to be True, mask_feature_prob has to be 0.0
    snake_case_ :    Optional[int]     			=						WavaVecaConfig.from_pretrained(
        model_args.model_name_or_path,		cache_dir=model_args.cache_dir,		gradient_checkpointing=training_args.gradient_checkpointing,		)

    if not config.do_stable_layer_norm or config.feat_extract_norm != "layer":
        raise ValueError(
            "PreTraining is only supported for ``config.do_stable_layer_norm=True`` and"
            " ``config.feat_extract_norm='layer'"       )

    snake_case_ :    int     			=						WavaVecaForPreTraining(__SCREAMING_SNAKE_CASE       )

    snake_case_ :    Dict     			=						DataCollatorForWavaVecaPretraining(model=__SCREAMING_SNAKE_CASE,		feature_extractor=__SCREAMING_SNAKE_CASE       )

    snake_case_ :    str     			=						WavaVecaPreTrainer(
        model=__SCREAMING_SNAKE_CASE,		data_collator=__SCREAMING_SNAKE_CASE,		args=__SCREAMING_SNAKE_CASE,		train_dataset=vectorized_datasets["train"],		eval_dataset=vectorized_datasets["validation"],		tokenizer=__SCREAMING_SNAKE_CASE,		max_gumbel_temp=model_args.max_gumbel_temperature,		min_gumbel_temp=model_args.min_gumbel_temperature,		gumbel_temp_decay=model_args.gumbel_temperature_decay,		)
    trainer.train()


if __name__ == "__main__":
  main()






'''simple docstring'''
import gc
import random
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionInstructPixaPixPipeline,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()






class    UpperCAmelCase_				(	snake_case__    ,						snake_case__    ,						snake_case__    ,						unittest.TestCase					):
 UpperCAmelCase_		    =						StableDiffusionInstructPixaPixPipeline
 UpperCAmelCase_		    =						TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""height""", """width""", """cross_attention_kwargs"""}
 UpperCAmelCase_		    =						TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
 UpperCAmelCase_		    =						IMAGE_TO_IMAGE_IMAGE_PARAMS
 UpperCAmelCase_		    =						IMAGE_TO_IMAGE_IMAGE_PARAMS





 def        snake_case__		(    self):
     torch.manual_seed(0)
     snake_case_ :    List[str]     			=						UNetaDConditionModel(
         block_out_channels=(32, 64) ,       layers_per_block=2 ,       sample_size=32 ,       in_channels=8 ,       out_channels=4 ,       down_block_types=("DownBlock2D", "CrossAttnDownBlock2D") ,       up_block_types=("CrossAttnUpBlock2D", "UpBlock2D") ,       cross_attention_dim=32 ,       )
     snake_case_ :    Any     			=						PNDMScheduler(skip_prk_steps=lowercase_)
     torch.manual_seed(0)
     snake_case_ :    Tuple     			=						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 ,       )
     torch.manual_seed(0)
     snake_case_ :    Union[str, Any]     			=						CLIPTextConfig(
         bos_token_id=0 ,       eos_token_id=2 ,       hidden_size=32 ,       intermediate_size=37 ,       layer_norm_eps=1E-05 ,       num_attention_heads=4 ,       num_hidden_layers=5 ,       pad_token_id=1 ,       vocab_size=10_00 ,       )
     snake_case_ :    Union[str, Any]     			=						CLIPTextModel(lowercase_)
     snake_case_ :    Optional[Any]     			=						CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

     snake_case_ :    str     			=						{
         "unet": unet,
         "scheduler": scheduler,
         "vae": vae,
         "text_encoder": text_encoder,
         "tokenizer": tokenizer,
         "safety_checker": None,
         "feature_extractor": None,
     }
     return components





 def        snake_case__		(    self ,       lowercase_ ,       lowercase_=0):
     snake_case_ :    int     			=						floats_tensor((1, 3, 32, 32) ,       rng=random.Random(lowercase_)).to(lowercase_)
     snake_case_ :    Any     			=						image.cpu().permute(0 ,       2 ,       3 ,       1)[0]
     snake_case_ :    str     			=						Image.fromarray(np.uinta(lowercase_)).convert("RGB")
     if str(lowercase_).startswith("mps"):
         snake_case_ :    Union[str, Any]     			=						torch.manual_seed(lowercase_)
     else:
         snake_case_ :    Any     			=						torch.Generator(device=lowercase_).manual_seed(lowercase_)
     snake_case_ :    List[str]     			=						{
         "prompt": "A painting of a squirrel eating a burger",
         "image": image,
         "generator": generator,
         "num_inference_steps": 2,
         "guidance_scale": 6.0,
         "image_guidance_scale": 1,
         "output_type": "numpy",
     }
     return inputs





 def        snake_case__		(    self):
     snake_case_ :    Union[str, Any]     			=						"cpu"  # ensure determinism for the device-dependent torch.Generator
     snake_case_ :    Tuple     			=						self.get_dummy_components()
     snake_case_ :    Any     			=						StableDiffusionInstructPixaPixPipeline(**lowercase_)
     snake_case_ :    Union[str, Any]     			=						sd_pipe.to(lowercase_)
     sd_pipe.set_progress_bar_config(disable=lowercase_)

     snake_case_ :    Optional[Any]     			=						self.get_dummy_inputs(lowercase_)
     snake_case_ :    List[Any]     			=						sd_pipe(**lowercase_).images
     snake_case_ :    List[Any]     			=						image[0, -3:, -3:, -1]
     assert image.shape == (1, 32, 32, 3)
     snake_case_ :    Optional[int]     			=						np.array([0.7_526, 0.3_750, 0.4_547, 0.6_117, 0.5_866, 0.5_016, 0.4_327, 0.5_642, 0.4_815])

     assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-3





 def        snake_case__		(    self):
     snake_case_ :    Optional[int]     			=						"cpu"  # ensure determinism for the device-dependent torch.Generator
     snake_case_ :    Union[str, Any]     			=						self.get_dummy_components()
     snake_case_ :    str     			=						StableDiffusionInstructPixaPixPipeline(**lowercase_)
     snake_case_ :    List[str]     			=						sd_pipe.to(lowercase_)
     sd_pipe.set_progress_bar_config(disable=lowercase_)

     snake_case_ :    List[Any]     			=						self.get_dummy_inputs(lowercase_)
     snake_case_ :    List[Any]     			=						"french fries"
     snake_case_ :    Union[str, Any]     			=						sd_pipe(**lowercase_ ,       negative_prompt=lowercase_)
     snake_case_ :    Optional[int]     			=						output.images
     snake_case_ :    Optional[Any]     			=						image[0, -3:, -3:, -1]

     assert image.shape == (1, 32, 32, 3)
     snake_case_ :    Tuple     			=						np.array([0.7_511, 0.3_642, 0.4_553, 0.6_236, 0.5_797, 0.5_013, 0.4_343, 0.5_611, 0.4_831])

     assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-3





 def        snake_case__		(    self):
     snake_case_ :    List[str]     			=						"cpu"  # ensure determinism for the device-dependent torch.Generator
     snake_case_ :    Union[str, Any]     			=						self.get_dummy_components()
     snake_case_ :    Tuple     			=						StableDiffusionInstructPixaPixPipeline(**lowercase_)
     snake_case_ :    Tuple     			=						sd_pipe.to(lowercase_)
     sd_pipe.set_progress_bar_config(disable=lowercase_)

     snake_case_ :    List[Any]     			=						self.get_dummy_inputs(lowercase_)
     snake_case_ :    str     			=						[inputs["prompt"]] * 2

     snake_case_ :    Optional[Any]     			=						np.array(inputs["image"]).astype(np.floataa) / 255.0
     snake_case_ :    List[str]     			=						torch.from_numpy(lowercase_).unsqueeze(0).to(lowercase_)
     snake_case_ :    Any     			=						image / 2 + 0.5
     snake_case_ :    str     			=						image.permute(0 ,       3 ,       1 ,       2)
     snake_case_ :    Dict     			=						image.repeat(2 ,       1 ,       1 ,       1)

     snake_case_ :    List[str]     			=						sd_pipe(**lowercase_).images
     snake_case_ :    Dict     			=						image[-1, -3:, -3:, -1]

     assert image.shape == (2, 32, 32, 3)
     snake_case_ :    List[Any]     			=						np.array([0.5_812, 0.5_748, 0.5_222, 0.5_908, 0.5_695, 0.7_174, 0.6_804, 0.5_523, 0.5_579])

     assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-3





 def        snake_case__		(    self):
     snake_case_ :    List[str]     			=						"cpu"  # ensure determinism for the device-dependent torch.Generator
     snake_case_ :    Tuple     			=						self.get_dummy_components()
     snake_case_ :    Tuple     			=						EulerAncestralDiscreteScheduler(
         beta_start=0.00_085 ,       beta_end=0.012 ,       beta_schedule="scaled_linear")
     snake_case_ :    Dict     			=						StableDiffusionInstructPixaPixPipeline(**lowercase_)
     snake_case_ :    Union[str, Any]     			=						sd_pipe.to(lowercase_)
     sd_pipe.set_progress_bar_config(disable=lowercase_)

     snake_case_ :    Tuple     			=						self.get_dummy_inputs(lowercase_)
     snake_case_ :    Optional[int]     			=						sd_pipe(**lowercase_).images
     snake_case_ :    Union[str, Any]     			=						image[0, -3:, -3:, -1]

     snake_case_ :    Optional[Any]     			=						[round(lowercase_ ,       4) for x in image_slice.flatten().tolist()]
     print(",".join([str(lowercase_) for x in slice]))

     assert image.shape == (1, 32, 32, 3)
     snake_case_ :    Tuple     			=						np.array([0.7_417, 0.3_842, 0.4_732, 0.5_776, 0.5_891, 0.5_139, 0.4_052, 0.5_673, 0.4_986])

     assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-3





 def        snake_case__		(    self):
     super().test_inference_batch_single_identical(expected_max_diff=3E-3)





 def        snake_case__		(    self):
     snake_case_ :    Optional[Any]     			=						self.get_dummy_components()
     snake_case_ :    Union[str, Any]     			=						StableDiffusionInstructPixaPixPipeline(**lowercase_)
     snake_case_ :    List[str]     			=						VaeImageProcessor(do_resize=lowercase_ ,       do_normalize=lowercase_)
     snake_case_ :    List[Any]     			=						pipe.to(lowercase_)
     pipe.set_progress_bar_config(disable=lowercase_)

     snake_case_ :    str     			=						pipe(**self.get_dummy_inputs_by_type(lowercase_ ,       input_image_type="pt"))[0]

     snake_case_ :    str     			=						components["vae"]
     snake_case_ :    str     			=						self.get_dummy_inputs_by_type(lowercase_ ,       input_image_type="pt")

     for image_param in self.image_latents_params:
         if image_param in inputs.keys():
             snake_case_ :    str     			=						vae.encode(inputs[image_param]).latent_dist.mode()

     snake_case_ :    List[str]     			=						pipe(**lowercase_)[0]

     snake_case_ :    Any     			=						np.abs(out - out_latents_inputs).max()
     self.assertLess(lowercase_ ,       1E-4 ,       "passing latents as image input generate different result from passing image")






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





 def        snake_case__		(    self):
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()





 def        snake_case__		(    self ,       lowercase_=0):
     snake_case_ :    int     			=						torch.manual_seed(lowercase_)
     snake_case_ :    Union[str, Any]     			=						load_image(
         "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg")
     snake_case_ :    Tuple     			=						{
         "prompt": "turn him into a cyborg",
         "image": image,
         "generator": generator,
         "num_inference_steps": 3,
         "guidance_scale": 7.5,
         "image_guidance_scale": 1.0,
         "output_type": "numpy",
     }
     return inputs





 def        snake_case__		(    self):
     snake_case_ :    str     			=						StableDiffusionInstructPixaPixPipeline.from_pretrained(
         "timbrooks/instruct-pix2pix" ,       safety_checker=lowercase_)
     pipe.to(lowercase_)
     pipe.set_progress_bar_config(disable=lowercase_)
     pipe.enable_attention_slicing()

     snake_case_ :    int     			=						self.get_inputs()
     snake_case_ :    Dict     			=						pipe(**lowercase_).images
     snake_case_ :    str     			=						image[0, -3:, -3:, -1].flatten()

     assert image.shape == (1, 5_12, 5_12, 3)
     snake_case_ :    Dict     			=						np.array([0.5_902, 0.6_015, 0.6_027, 0.5_983, 0.6_092, 0.6_061, 0.5_765, 0.5_785, 0.5_555])

     assert np.abs(expected_slice - image_slice).max() < 1E-3





 def        snake_case__		(    self):
     snake_case_ :    int     			=						StableDiffusionInstructPixaPixPipeline.from_pretrained(
         "timbrooks/instruct-pix2pix" ,       safety_checker=lowercase_)
     snake_case_ :    Dict     			=						LMSDiscreteScheduler.from_config(pipe.scheduler.config)
     pipe.to(lowercase_)
     pipe.set_progress_bar_config(disable=lowercase_)
     pipe.enable_attention_slicing()

     snake_case_ :    int     			=						self.get_inputs()
     snake_case_ :    Dict     			=						pipe(**lowercase_).images
     snake_case_ :    str     			=						image[0, -3:, -3:, -1].flatten()

     assert image.shape == (1, 5_12, 5_12, 3)
     snake_case_ :    str     			=						np.array([0.6_578, 0.6_817, 0.6_972, 0.6_761, 0.6_856, 0.6_916, 0.6_428, 0.6_516, 0.6_301])

     assert np.abs(expected_slice - image_slice).max() < 1E-3





 def        snake_case__		(    self):
     snake_case_ :    Optional[int]     			=						StableDiffusionInstructPixaPixPipeline.from_pretrained(
         "timbrooks/instruct-pix2pix" ,       safety_checker=lowercase_)
     snake_case_ :    List[Any]     			=						DDIMScheduler.from_config(pipe.scheduler.config)
     pipe.to(lowercase_)
     pipe.set_progress_bar_config(disable=lowercase_)
     pipe.enable_attention_slicing()

     snake_case_ :    str     			=						self.get_inputs()
     snake_case_ :    Optional[Any]     			=						pipe(**lowercase_).images
     snake_case_ :    Dict     			=						image[0, -3:, -3:, -1].flatten()

     assert image.shape == (1, 5_12, 5_12, 3)
     snake_case_ :    List[str]     			=						np.array([0.3_828, 0.3_834, 0.3_818, 0.3_792, 0.3_865, 0.3_752, 0.3_792, 0.3_847, 0.3_753])

     assert np.abs(expected_slice - image_slice).max() < 1E-3





 def        snake_case__		(    self):
     snake_case_ :    Union[str, Any]     			=						0

     def callback_fn(lowercase_ ,       lowercase_ ,       lowercase_) -> None:
         snake_case_ :    Optional[Any]     			=						True
         nonlocal number_of_steps
         number_of_steps += 1
         if step == 1:
             snake_case_ :    Optional[Any]     			=						latents.detach().cpu().numpy()
             assert latents.shape == (1, 4, 64, 64)
             snake_case_ :    Optional[int]     			=						latents[0, -3:, -3:, -1]
             snake_case_ :    Optional[int]     			=						np.array([-0.2_463, -0.4_644, -0.9_756, 1.5_176, 1.4_414, 0.7_866, 0.9_897, 0.8_521, 0.7_983])

             assert np.abs(latents_slice.flatten() - expected_slice).max() < 5E-2
         elif step == 2:
             snake_case_ :    List[str]     			=						latents.detach().cpu().numpy()
             assert latents.shape == (1, 4, 64, 64)
             snake_case_ :    Union[str, Any]     			=						latents[0, -3:, -3:, -1]
             snake_case_ :    Any     			=						np.array([-0.2_644, -0.4_626, -0.9_653, 1.5_176, 1.4_551, 0.7_686, 0.9_805, 0.8_452, 0.8_115])

             assert np.abs(latents_slice.flatten() - expected_slice).max() < 5E-2

     snake_case_ :    Optional[int]     			=						False

     snake_case_ :    Union[str, Any]     			=						StableDiffusionInstructPixaPixPipeline.from_pretrained(
         "timbrooks/instruct-pix2pix" ,       safety_checker=lowercase_ ,       torch_dtype=torch.floataa)
     snake_case_ :    Dict     			=						pipe.to(lowercase_)
     pipe.set_progress_bar_config(disable=lowercase_)
     pipe.enable_attention_slicing()

     snake_case_ :    Any     			=						self.get_inputs()
     pipe(**lowercase_ ,       callback=lowercase_ ,       callback_steps=1)
     assert callback_fn.has_been_called
     assert number_of_steps == 3





 def        snake_case__		(    self):
     torch.cuda.empty_cache()
     torch.cuda.reset_max_memory_allocated()
     torch.cuda.reset_peak_memory_stats()

     snake_case_ :    int     			=						StableDiffusionInstructPixaPixPipeline.from_pretrained(
         "timbrooks/instruct-pix2pix" ,       safety_checker=lowercase_ ,       torch_dtype=torch.floataa)
     snake_case_ :    str     			=						pipe.to(lowercase_)
     pipe.set_progress_bar_config(disable=lowercase_)
     pipe.enable_attention_slicing(1)
     pipe.enable_sequential_cpu_offload()

     snake_case_ :    Dict     			=						self.get_inputs()
     snake_case_ :    int     			=						pipe(**lowercase_)

     snake_case_ :    Dict     			=						torch.cuda.max_memory_allocated()
     # make sure that less than 2.2 GB is allocated
     assert mem_bytes < 2.2 * 10**9





 def        snake_case__		(    self):
     snake_case_ :    List[Any]     			=						self.get_inputs()
     # resize to resolution that is divisible by 8 but not 16 or 32
     snake_case_ :    Union[str, Any]     			=						inputs["image"].resize((5_04, 5_04))

     snake_case_ :    str     			=						"timbrooks/instruct-pix2pix"
     snake_case_ :    Any     			=						StableDiffusionInstructPixaPixPipeline.from_pretrained(
         lowercase_ ,       safety_checker=lowercase_ ,       )
     pipe.to(lowercase_)
     pipe.set_progress_bar_config(disable=lowercase_)
     pipe.enable_attention_slicing()

     snake_case_ :    Any     			=						pipe(**lowercase_)
     snake_case_ :    int     			=						output.images[0]

     snake_case_ :    Tuple     			=						image[2_55:2_58, 3_83:3_86, -1]

     assert image.shape == (5_04, 5_04, 3)
     snake_case_ :    Optional[int]     			=						np.array([0.2_726, 0.2_529, 0.2_664, 0.2_655, 0.2_641, 0.2_642, 0.2_591, 0.2_649, 0.2_590])

     assert np.abs(image_slice.flatten() - expected_slice).max() < 5E-3




