



import gc
import unittest

import numpy as np
import torch

from diffusers import RePaintPipeline, RePaintScheduler, UNetaDModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_image,
    load_numpy,
    nightly,
    require_torch_gpu,
    skip_mps,
    torch_device,
)

from ..pipeline_params import IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()






class 				SCREAMING_SNAKE_CASE     (							lowerCamelCase__     , unittest.TestCase   ):
							__lowerCamelCase		:		Optional[int]      =RePaintPipeline
							__lowerCamelCase		:		Union[str, Any]      =IMAGE_INPAINTING_PARAMS - {'width', 'height', 'guidance_scale'}
							__lowerCamelCase		:		int      =PipelineTesterMixin.required_optional_params - {
							    'latents',
							    'num_images_per_prompt',
							    'callback',
							    'callback_steps',
							}
							__lowerCamelCase		:		List[Any]      =IMAGE_INPAINTING_BATCH_PARAMS
							__lowerCamelCase		:		List[Any]      =False





							def   UpperCamelCase_   (				self						:						Any		):

											'''simple docstring'''
											torch.manual_seed(0		)
											torch.manual_seed(0		)
											__a					       =						UNetaDModel(
											    block_out_channels=(32, 64) ,			layers_per_block=2 ,			sample_size=32 ,			in_channels=3 ,			out_channels=3 ,			down_block_types=("""DownBlock2D""", """AttnDownBlock2D""") ,			up_block_types=("""AttnUpBlock2D""", """UpBlock2D""") ,			)
											__a					       =						RePaintScheduler()
											__a					       =						{"""unet""": unet, """scheduler""": scheduler}
											return components





							def   UpperCamelCase_   (				self						:						Optional[int] ,			__lowercase						:						Dict ,			__lowercase						:						List[str]=0		):

											'''simple docstring'''
											if str(__lowercase		).startswith("""mps"""		):
															__a					       =						torch.manual_seed(__lowercase		)
											else:
															__a					       =						torch.Generator(device=__lowercase		).manual_seed(__lowercase		)
											__a					       =						np.random.RandomState(__lowercase		).standard_normal((1, 3, 32, 32)		)
											__a					       =						torch.from_numpy(__lowercase		).to(device=__lowercase ,			dtype=torch.floataa		)
											__a					       =						(image > 0).to(device=__lowercase ,			dtype=torch.floataa		)
											__a					       =						{
											    """image""": image,
											    """mask_image""": mask,
											    """generator""": generator,
											    """num_inference_steps""": 5,
											    """eta""": 0.0,
											    """jump_length""": 2,
											    """jump_n_sample""": 2,
											    """output_type""": """numpy""",
											}
											return inputs





							def   UpperCamelCase_   (				self						:						List[Any]		):

											'''simple docstring'''
											__a					       =						"""cpu"""  # ensure determinism for the device-dependent torch.Generator
											__a					       =						self.get_dummy_components()
											__a					       =						RePaintPipeline(**__lowercase		)
											__a					       =						sd_pipe.to(__lowercase		)
											sd_pipe.set_progress_bar_config(disable=__lowercase		)

											__a					       =						self.get_dummy_inputs(__lowercase		)
											__a					       =						sd_pipe(**__lowercase		).images
											__a					       =						image[0, -3:, -3:, -1]

											assert image.shape == (1, 32, 32, 3)
											__a					       =						np.array([1.0000, 0.5426, 0.5497, 0.2200, 1.0000, 1.0000, 0.5623, 1.0000, 0.6274]		)

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





							@skip_mps
							def   UpperCamelCase_   (				self						:						int		):

											'''simple docstring'''
											return super().test_save_load_local()





							@unittest.skip("""non-deterministic pipeline"""		)
							def   UpperCamelCase_   (				self						:						Union[str, Any]		):

											'''simple docstring'''
											return super().test_inference_batch_single_identical()





							@skip_mps
							def   UpperCamelCase_   (				self						:						Tuple		):

											'''simple docstring'''
											return super().test_dict_tuple_outputs_equivalent()





							@skip_mps
							def   UpperCamelCase_   (				self						:						str		):

											'''simple docstring'''
											return super().test_save_load_optional_components()







							@skip_mps
							def   UpperCamelCase_   (				self						:						Optional[Any]		):

											'''simple docstring'''
											return super().test_attention_slicing_forward_pass()






@nightly
@require_torch_gpu
class 				SCREAMING_SNAKE_CASE     (							unittest.TestCase   ):





							def   UpperCamelCase_   (				self						:						Any		):

											'''simple docstring'''
											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()







							def   UpperCamelCase_   (				self						:						List[Any]		):

											'''simple docstring'''
											__a					       =						load_image(
											    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"""
											    """repaint/celeba_hq_256.png"""		)
											__a					       =						load_image(
											    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png"""		)
											__a					       =						load_numpy(
											    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"""
											    """repaint/celeba_hq_256_result.npy"""		)

											__a					       =						"""google/ddpm-ema-celebahq-256"""
											__a					       =						UNetaDModel.from_pretrained(__lowercase		)
											__a					       =						RePaintScheduler.from_pretrained(__lowercase		)

											__a					       =						RePaintPipeline(unet=__lowercase ,			scheduler=__lowercase		).to(__lowercase		)
											repaint.set_progress_bar_config(disable=__lowercase		)
											repaint.enable_attention_slicing()

											__a					       =						torch.manual_seed(0		)
											__a					       =						repaint(
											    __lowercase ,			__lowercase ,			num_inference_steps=250 ,			eta=0.0 ,			jump_length=10 ,			jump_n_sample=10 ,			generator=__lowercase ,			output_type="""np""" ,			)
											__a					       =						output.images[0]

											assert image.shape == (256, 256, 3)
											assert np.abs(expected_image - image		).mean() < 1E-2





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

import transformers
from transformers import (
    AutoConfig,
    AutoModelForQuestionAnswering,
    AutoTokenizer,
    DataCollatorWithPadding,
    HfArgumentParser,
    SquadDataset,
    Trainer,
    TrainingArguments,
)
from transformers import SquadDataTrainingArguments as DataTrainingArguments
from transformers.trainer_utils import is_main_process


lowerCamelCase__     =	logging.getLogger(__name__)






@dataclass
class 				SCREAMING_SNAKE_CASE     :
							__lowerCamelCase		:		str      =field(
							    metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}   )
							__lowerCamelCase		:		Optional[str]      =field(
							    default=lowerCamelCase__     , metadata={'help': 'Pretrained config name or path if not the same as model_name'}   )
							__lowerCamelCase		:		Optional[str]      =field(
							    default=lowerCamelCase__     , metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}   )
							__lowerCamelCase		:		bool      =field(default=lowerCamelCase__     , metadata={'help': 'Set this flag to use fast tokenization.'}   )
							# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
							# or just modify its tokenizer_config.json.
							__lowerCamelCase		:		Optional[str]      =field(
							    default=lowerCamelCase__     , metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}     , )



def 					lowerCAmelCase__  (							):
				"""simple docstring"""







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

				if (
				    os.path.exists(training_args.output_dir      )
				    and os.listdir(training_args.output_dir      )
				    and training_args.do_train
				    and not training_args.overwrite_output_dir
				):
								raise ValueError(
								    f"Output directory ({training_args.output_dir}) already exists and is not empty. Use"
								    """ --overwrite_output_dir to overcome."""      )

				# Setup logging
				logging.basicConfig(
				    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""			,       datefmt="""%m/%d/%Y %H:%M:%S"""			,       level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN			,       )
				logger.warning(
				    """Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s"""			,       training_args.local_rank			,       training_args.device			,       training_args.n_gpu			,       bool(training_args.local_rank != -1      )			,       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"""			,       _SCREAMING_SNAKE_CASE      )

				# Prepare Question-Answering task
				# Load pretrained model and tokenizer
				#
				# Distributed training:
				# The .from_pretrained methods guarantee that only one local process can concurrently
				# download model & vocab.

				__a					       =						AutoConfig.from_pretrained(
				    model_args.config_name if model_args.config_name else model_args.model_name_or_path			,       cache_dir=model_args.cache_dir			,       )
				__a					       =						AutoTokenizer.from_pretrained(
				    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path			,       cache_dir=model_args.cache_dir			,       use_fast=_SCREAMING_SNAKE_CASE			,       )
				__a					       =						AutoModelForQuestionAnswering.from_pretrained(
				    model_args.model_name_or_path			,       from_tf=bool(""".ckpt""" in model_args.model_name_or_path      )			,       config=_SCREAMING_SNAKE_CASE			,       cache_dir=model_args.cache_dir			,       )

				# Get datasets
				__a					       =						hasattr(model.config			,       """lang2id"""      )
				__a					       =						(
				    SquadDataset(
				        _SCREAMING_SNAKE_CASE			,       tokenizer=_SCREAMING_SNAKE_CASE			,       is_language_sensitive=_SCREAMING_SNAKE_CASE			,       cache_dir=model_args.cache_dir      )
				    if training_args.do_train
				    else None
				)
				__a					       =						(
				    SquadDataset(
				        _SCREAMING_SNAKE_CASE			,       tokenizer=_SCREAMING_SNAKE_CASE			,       mode="""dev"""			,       is_language_sensitive=_SCREAMING_SNAKE_CASE			,       cache_dir=model_args.cache_dir			,       )
				    if training_args.do_eval
				    else None
				)

				# Data collator
				__a					       =						DataCollatorWithPadding(_SCREAMING_SNAKE_CASE			,       pad_to_multiple_of=8      ) if training_args.fpaa else None

				# Initialize our Trainer
				__a					       =						Trainer(
				    model=_SCREAMING_SNAKE_CASE			,       args=_SCREAMING_SNAKE_CASE			,       train_dataset=_SCREAMING_SNAKE_CASE			,       eval_dataset=_SCREAMING_SNAKE_CASE			,       data_collator=_SCREAMING_SNAKE_CASE			,       )

				# Training
				if training_args.do_train:
								trainer.train(
								    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path      ) else None      )
								trainer.save_model()
								# For convenience, we also re-save the tokenizer to the same directory,
								# so that you can share your model easily on huggingface.co/models =)
								if trainer.is_world_master():
												tokenizer.save_pretrained(training_args.output_dir      )

def 					lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE    :     Optional[Any]      ):
				"""simple docstring"""







				main()


if __name__ == "__main__":
						main()
