
"""simple docstring"""




import copy
import tempfile
import unittest

from huggingface_hub import HfFolder, delete_repo
from parameterized import parameterized
from requests.exceptions import HTTPError

from transformers import AutoConfig, GenerationConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test





class  snake_case     (							unittest.TestCase   ):

							@parameterized.expand([(None,), ('''foo.json''',)]    )
							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = GenerationConfig(
									    do_sample=SCREAMING_SNAKE_CASE_       ,       temperature=0.7       ,       length_penalty=1.0       ,       bad_words_ids=[[1, 2, 3], [4, 5]]       ,       )
									with tempfile.TemporaryDirectory() as tmp_dir:
											config.save_pretrained(SCREAMING_SNAKE_CASE_       ,       config_name=SCREAMING_SNAKE_CASE_    )
											SCREAMING_SNAKE_CASE_						 = GenerationConfig.from_pretrained(SCREAMING_SNAKE_CASE_       ,       config_name=SCREAMING_SNAKE_CASE_    )

									# Checks parameters that were specified
									self.assertEqual(loaded_config.do_sample       ,       SCREAMING_SNAKE_CASE_    )
									self.assertEqual(loaded_config.temperature       ,       0.7    )
									self.assertEqual(loaded_config.length_penalty       ,       1.0    )
									self.assertEqual(loaded_config.bad_words_ids       ,       [[1, 2, 3], [4, 5]]    )

									# Checks parameters that were not specified (defaults)
									self.assertEqual(loaded_config.top_k       ,       50    )
									self.assertEqual(loaded_config.max_length       ,       20    )
									self.assertEqual(loaded_config.max_time       ,       SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = AutoConfig.from_pretrained('''gpt2'''    )
									SCREAMING_SNAKE_CASE_						 = GenerationConfig.from_model_config(SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = GenerationConfig()

									# The generation config has loaded a few non-default parameters from the model config
									self.assertNotEqual(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )

									# One of those parameters is eos_token_id -- check if it matches
									self.assertNotEqual(generation_config_from_model.eos_token_id       ,       default_generation_config.eos_token_id    )
									self.assertEqual(generation_config_from_model.eos_token_id       ,       model_config.eos_token_id    )

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = GenerationConfig()
									SCREAMING_SNAKE_CASE_						 = {
									    '''max_new_tokens''': 10_24,
									    '''foo''': '''bar''',
									}
									SCREAMING_SNAKE_CASE_						 = copy.deepcopy(SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = generation_config.update(**SCREAMING_SNAKE_CASE_    )

									# update_kwargs was not modified (no side effects)
									self.assertEqual(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )

									# update_kwargs was used to update the config on valid attributes
									self.assertEqual(generation_config.max_new_tokens       ,       10_24    )

									# `.update()` returns a dictionary of unused kwargs
									self.assertEqual(SCREAMING_SNAKE_CASE_       ,       {'''foo''': '''bar'''}    )

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = GenerationConfig()
									SCREAMING_SNAKE_CASE_						 = '''bar'''

									with tempfile.TemporaryDirectory('''test-generation-config'''    ) as tmp_dir:
											generation_config.save_pretrained(SCREAMING_SNAKE_CASE_    )

											SCREAMING_SNAKE_CASE_						 = GenerationConfig.from_pretrained(SCREAMING_SNAKE_CASE_    )
									# update_kwargs was used to update the config on valid attributes
									self.assertEqual(new_config.foo       ,       '''bar'''    )

									SCREAMING_SNAKE_CASE_						 = GenerationConfig.from_model_config(SCREAMING_SNAKE_CASE_    )
									assert not hasattr(SCREAMING_SNAKE_CASE_       ,       '''foo'''    )  # no new kwargs should be initialized if from config







							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = GenerationConfig()
									self.assertEqual(default_config.temperature       ,       1.0    )
									self.assertEqual(default_config.do_sample       ,       SCREAMING_SNAKE_CASE_    )
									self.assertEqual(default_config.num_beams       ,       1    )

									SCREAMING_SNAKE_CASE_						 = GenerationConfig(
									    do_sample=SCREAMING_SNAKE_CASE_       ,       temperature=0.7       ,       length_penalty=1.0       ,       bad_words_ids=[[1, 2, 3], [4, 5]]       ,       )
									self.assertEqual(config.temperature       ,       0.7    )
									self.assertEqual(config.do_sample       ,       SCREAMING_SNAKE_CASE_    )
									self.assertEqual(config.num_beams       ,       1    )

									with tempfile.TemporaryDirectory() as tmp_dir:
											config.save_pretrained(SCREAMING_SNAKE_CASE_    )
											SCREAMING_SNAKE_CASE_						 = GenerationConfig.from_pretrained(SCREAMING_SNAKE_CASE_       ,       temperature=1.0    )

									self.assertEqual(loaded_config.temperature       ,       1.0    )
									self.assertEqual(loaded_config.do_sample       ,       SCREAMING_SNAKE_CASE_    )
									self.assertEqual(loaded_config.num_beams       ,       1    )  # default value







@is_staging_test
class  snake_case     (							unittest.TestCase   ):

							@classmethod
							def _lowercase					(cls    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = TOKEN
									HfFolder.save_token(SCREAMING_SNAKE_CASE_    )

							@classmethod
							def _lowercase					(cls    ):

									"""simple docstring"""







									try:
											delete_repo(token=cls._token       ,       repo_id='''test-generation-config'''    )
									except HTTPError:
											pass

									try:
											delete_repo(token=cls._token       ,       repo_id='''valid_org/test-generation-config-org'''    )
									except HTTPError:
											pass

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = GenerationConfig(
									    do_sample=SCREAMING_SNAKE_CASE_       ,       temperature=0.7       ,       length_penalty=1.0       ,       )
									config.push_to_hub('''test-generation-config'''       ,       use_auth_token=self._token    )

									SCREAMING_SNAKE_CASE_						 = GenerationConfig.from_pretrained(f'{USER}/test-generation-config'    )
									for k, v in config.to_dict().items():
											if k != "transformers_version":
													self.assertEqual(SCREAMING_SNAKE_CASE_       ,       getattr(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )    )

        # Reset repo
									delete_repo(token=self._token       ,       repo_id='''test-generation-config'''    )

									# Push to hub via save_pretrained
									with tempfile.TemporaryDirectory() as tmp_dir:
											config.save_pretrained(
											    SCREAMING_SNAKE_CASE_       ,       repo_id='''test-generation-config'''       ,       push_to_hub=SCREAMING_SNAKE_CASE_       ,       use_auth_token=self._token    )

									SCREAMING_SNAKE_CASE_						 = GenerationConfig.from_pretrained(f'{USER}/test-generation-config'    )
									for k, v in config.to_dict().items():
											if k != "transformers_version":
													self.assertEqual(SCREAMING_SNAKE_CASE_       ,       getattr(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )    )







							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = GenerationConfig(
									    do_sample=SCREAMING_SNAKE_CASE_       ,       temperature=0.7       ,       length_penalty=1.0       ,       )
									config.push_to_hub('''valid_org/test-generation-config-org'''       ,       use_auth_token=self._token    )

									SCREAMING_SNAKE_CASE_						 = GenerationConfig.from_pretrained('''valid_org/test-generation-config-org'''    )
									for k, v in config.to_dict().items():
											if k != "transformers_version":
													self.assertEqual(SCREAMING_SNAKE_CASE_       ,       getattr(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )    )

        # Reset repo
									delete_repo(token=self._token       ,       repo_id='''valid_org/test-generation-config-org'''    )

									# Push to hub via save_pretrained
									with tempfile.TemporaryDirectory() as tmp_dir:
											config.save_pretrained(
											    SCREAMING_SNAKE_CASE_       ,       repo_id='''valid_org/test-generation-config-org'''       ,       push_to_hub=SCREAMING_SNAKE_CASE_       ,       use_auth_token=self._token    )

									SCREAMING_SNAKE_CASE_						 = GenerationConfig.from_pretrained('''valid_org/test-generation-config-org'''    )
									for k, v in config.to_dict().items():
											if k != "transformers_version":
													self.assertEqual(SCREAMING_SNAKE_CASE_       ,       getattr(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )    )

"""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_    )