
"""simple docstring"""




# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path


lowerCAmelCase__							=				Path(__file__).resolve().parents[3] / 'src'
sys.path.insert(1, str(git_repo_path))

import dataclasses  # noqa
import io  # noqa
import itertools  # noqa
import json  # noqa
import os  # noqa
import unittest  # noqa
from copy import deepcopy  # noqa

from parameterized import parameterized  # noqa
from transformers import TrainingArguments, is_torch_available  # noqa
from transformers.deepspeed import is_deepspeed_available  # noqa
from transformers.file_utils import WEIGHTS_NAME  # noqa
from transformers.testing_utils import (  # noqa
    CaptureLogger,
    ExtendSysPath,
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    mockenv_context,
    require_deepspeed,
    require_torch_gpu,
    require_torch_multi_gpu,
    slow,
)
from transformers.trainer_utils import set_seed  # noqa


set_seed(42)

lowerCAmelCase__							=				{'base': 'patrickvonplaten/wav2vec2_tiny_random', 'robust': 'patrickvonplaten/wav2vec2_tiny_random_robust'}

lowerCAmelCase__							=				'zero2'
lowerCAmelCase__							=				'zero3'
lowerCAmelCase__							=				[ZEROa, ZEROa]



def        _lowerCamelCase ( __a,							__a,							__a    ):
		# customize the test name generator function as we want both params to appear in the sub-test
		# name, as by default it shows only the first param
		SCREAMING_SNAKE_CASE_						 = parameterized.to_safe_name('''_'''.join(str(__a    ) for x in param.args    )    )
		return F'{func.__name__}_{param_based_name}'


# Cartesian-product of zero stages with models to test
lowerCAmelCase__							=				list(itertools.product(stages, models.keys()))





@slow
@require_deepspeed
@require_torch_gpu
class  snake_case     (							__lowercase   ):

							@parameterized.expand(SCREAMING_SNAKE_CASE_       ,       name_func=SCREAMING_SNAKE_CASE_    )
							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									self.run_and_check(
									    stage=SCREAMING_SNAKE_CASE_       ,       model=SCREAMING_SNAKE_CASE_       ,       distributed=SCREAMING_SNAKE_CASE_       ,       fpaa=SCREAMING_SNAKE_CASE_       ,       )

							@require_torch_multi_gpu
							@parameterized.expand(SCREAMING_SNAKE_CASE_       ,       name_func=SCREAMING_SNAKE_CASE_    )
							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									self.run_and_check(
									    stage=SCREAMING_SNAKE_CASE_       ,       model=SCREAMING_SNAKE_CASE_       ,       distributed=SCREAMING_SNAKE_CASE_       ,       fpaa=SCREAMING_SNAKE_CASE_       ,       )

							@parameterized.expand(SCREAMING_SNAKE_CASE_       ,       name_func=SCREAMING_SNAKE_CASE_    )
							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									self.run_and_check(
									    stage=SCREAMING_SNAKE_CASE_       ,       model=SCREAMING_SNAKE_CASE_       ,       distributed=SCREAMING_SNAKE_CASE_       ,       fpaa=SCREAMING_SNAKE_CASE_       ,       )

							@require_torch_multi_gpu
							@parameterized.expand(SCREAMING_SNAKE_CASE_       ,       name_func=SCREAMING_SNAKE_CASE_    )
							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									self.run_and_check(
									    stage=SCREAMING_SNAKE_CASE_       ,       model=SCREAMING_SNAKE_CASE_       ,       distributed=SCREAMING_SNAKE_CASE_       ,       fpaa=SCREAMING_SNAKE_CASE_       ,       )

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									pass

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_ = 10       ,       SCREAMING_SNAKE_CASE_ = True       ,       SCREAMING_SNAKE_CASE_ = True       ,       SCREAMING_SNAKE_CASE_ = True       ,       ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = models[model]

									SCREAMING_SNAKE_CASE_						 = self.run_trainer(
									    stage=SCREAMING_SNAKE_CASE_       ,       model_name=SCREAMING_SNAKE_CASE_       ,       eval_steps=SCREAMING_SNAKE_CASE_       ,       num_train_epochs=1       ,       distributed=SCREAMING_SNAKE_CASE_       ,       fpaa=SCREAMING_SNAKE_CASE_       ,       )

									self.do_checks(SCREAMING_SNAKE_CASE_    )

									return output_dir

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_ = 10       ,       SCREAMING_SNAKE_CASE_ = 1       ,       SCREAMING_SNAKE_CASE_ = True       ,       SCREAMING_SNAKE_CASE_ = True       ,       ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.get_auto_remove_tmp_dir('''./xxx'''       ,       after=SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = f'\n            --model_name_or_path {model_name}\n            --dataset_name hf-internal-testing/librispeech_asr_dummy\n            --dataset_config_name clean\n            --train_split_name validation\n            --validation_split_name validation\n            --output_dir {output_dir}\n            --num_train_epochs {str(SCREAMING_SNAKE_CASE_    )}\n            --per_device_train_batch_size 2\n            --per_device_eval_batch_size 2\n            --evaluation_strategy steps\n            --learning_rate 5e-4\n            --warmup_steps 8\n            --orthography timit\n            --preprocessing_num_workers 1\n            --group_by_length\n            --freeze_feature_extractor\n            --report_to none\n            --save_steps 0\n            --eval_steps {eval_steps}\n            --report_to none\n        '.split()

									if fpaa:
											args.extend(['''--fp16''']    )

									# currently ds_config_wav2vec2_zero.json requires "zero_optimization.find_unused_parameters": true,
									# hence the separate config files
									SCREAMING_SNAKE_CASE_						 = f'--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json'.split()
									SCREAMING_SNAKE_CASE_						 = [f'{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py']
									SCREAMING_SNAKE_CASE_						 = self.get_launcher(SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_						 = launcher + script + args + ds_args
									# keep for quick debug
									# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
									execute_subprocess_async(SCREAMING_SNAKE_CASE_       ,       env=self.get_env()    )

									return output_dir







							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_=False    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = min(2       ,       get_gpu_count()    ) if distributed else 1
									return f'deepspeed --num_nodes 1 --num_gpus {num_gpus}'.split()

"""simple docstring"""




import warnings
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch

from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


lowerCAmelCase__							=				logging.get_logger(__name__)  # pylint: disable=invalid-name



def        _lowerCamelCase ( __a    ):
		warnings.warn(
		    '''The preprocess method is deprecated and will be removed in a future version. Please'''
		    ''' use VaeImageProcessor.preprocess instead''',							__a,							)
		if isinstance(__a,							torch.Tensor    ):
				return image
		elif isinstance(__a,							PIL.Image.Image    ):
				SCREAMING_SNAKE_CASE_						 = [image]

		if isinstance(image[0],							PIL.Image.Image    ):
				SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = image[0].size
				SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = (x - x % 8 for x in (w, h))  # resize to integer multiple of 8

				SCREAMING_SNAKE_CASE_						 = [np.array(i.resize((w, h),							resample=PIL_INTERPOLATION['''lanczos''']    )    )[None, :] for i in image]
				SCREAMING_SNAKE_CASE_						 = np.concatenate(__a,							axis=0    )
				SCREAMING_SNAKE_CASE_						 = np.array(__a    ).astype(np.floataa    ) / 2_5_5.0
				SCREAMING_SNAKE_CASE_						 = image.transpose(0,							3,							1,							2    )
				SCREAMING_SNAKE_CASE_						 = 2.0 * image - 1.0
				SCREAMING_SNAKE_CASE_						 = torch.from_numpy(__a    )
		elif isinstance(image[0],							torch.Tensor    ):
				SCREAMING_SNAKE_CASE_						 = torch.cat(__a,							dim=0    )
		return image



def        _lowerCamelCase ( __a    ):
		if isinstance(__a,							torch.Tensor    ):
				return mask
		elif isinstance(__a,							PIL.Image.Image    ):
				SCREAMING_SNAKE_CASE_						 = [mask]

		if isinstance(mask[0],							PIL.Image.Image    ):
				SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = mask[0].size
				SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
				SCREAMING_SNAKE_CASE_						 = [np.array(m.convert('''L'''    ).resize((w, h),							resample=PIL_INTERPOLATION['''nearest''']    )    )[None, :] for m in mask]
				SCREAMING_SNAKE_CASE_						 = np.concatenate(__a,							axis=0    )
				SCREAMING_SNAKE_CASE_						 = mask.astype(np.floataa    ) / 2_5_5.0
				SCREAMING_SNAKE_CASE_						 = 0
				SCREAMING_SNAKE_CASE_						 = 1
				SCREAMING_SNAKE_CASE_						 = torch.from_numpy(__a    )
		elif isinstance(mask[0],							torch.Tensor    ):
				SCREAMING_SNAKE_CASE_						 = torch.cat(__a,							dim=0    )
		return mask





class  snake_case     (							__lowercase   ):
							UpperCAmelCase__						     =      42
							UpperCAmelCase__						     =      42

							def __init__(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									super().__init__()
									self.register_modules(unet=SCREAMING_SNAKE_CASE_       ,       scheduler=SCREAMING_SNAKE_CASE_    )







							@torch.no_grad()
							def __call__(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_ = 2_50       ,       SCREAMING_SNAKE_CASE_ = 0.0       ,       SCREAMING_SNAKE_CASE_ = 10       ,       SCREAMING_SNAKE_CASE_ = 10       ,       SCREAMING_SNAKE_CASE_ = None       ,       SCREAMING_SNAKE_CASE_ = "pil"       ,       SCREAMING_SNAKE_CASE_ = True       ,       ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = image

									SCREAMING_SNAKE_CASE_						 = _preprocess_image(SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = original_image.to(device=self.device       ,       dtype=self.unet.dtype    )
									SCREAMING_SNAKE_CASE_						 = _preprocess_mask(SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = mask_image.to(device=self.device       ,       dtype=self.unet.dtype    )

									SCREAMING_SNAKE_CASE_						 = original_image.shape[0]

									# sample gaussian noise to begin the loop
									if isinstance(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ) and len(SCREAMING_SNAKE_CASE_    ) != batch_size:
											raise ValueError(
											    f'You have passed a list of generators of length {len(SCREAMING_SNAKE_CASE_    )}, but requested an effective batch'
											    f' size of {batch_size}. Make sure the batch size matches the length of the generators.'    )

									SCREAMING_SNAKE_CASE_						 = original_image.shape
									SCREAMING_SNAKE_CASE_						 = randn_tensor(SCREAMING_SNAKE_CASE_       ,       generator=SCREAMING_SNAKE_CASE_       ,       device=self.device       ,       dtype=self.unet.dtype    )

									# set step values
									self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       self.device    )
									SCREAMING_SNAKE_CASE_						 = eta

									SCREAMING_SNAKE_CASE_						 = self.scheduler.timesteps[0] + 1
									SCREAMING_SNAKE_CASE_						 = generator[0] if isinstance(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ) else generator
									for i, t in enumerate(self.progress_bar(self.scheduler.timesteps    )    ):
											if t < t_last:
													# predict the noise residual
													SCREAMING_SNAKE_CASE_						 = self.unet(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ).sample
													# compute previous image: x_t -> x_t-1
													SCREAMING_SNAKE_CASE_						 = self.scheduler.step(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ).prev_sample

											else:
													# compute the reverse: x_t-1 -> x_t
													SCREAMING_SNAKE_CASE_						 = self.scheduler.undo_step(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
											SCREAMING_SNAKE_CASE_						 = t

									SCREAMING_SNAKE_CASE_						 = (image / 2 + 0.5).clamp(0       ,       1    )
									SCREAMING_SNAKE_CASE_						 = image.cpu().permute(0       ,       2       ,       3       ,       1    ).numpy()
									if output_type == "pil":
											SCREAMING_SNAKE_CASE_						 = self.numpy_to_pil(SCREAMING_SNAKE_CASE_    )

									if not return_dict:
											return (image,)

									return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE_    )