


"""simple docstring"""







import subprocess
import sys

from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch


class       SCREAMING_SNAKE_CASE__				(		lowerCAmelCase_				):
       """simple docstring"""


       @require_torch
       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     int								=							"\nfrom transformers import BertConfig, BertModel, BertTokenizer, pipeline\n        "

         lowerCAmelCase     :     Optional[int]								=							"\nmname = \"hf-internal-testing/tiny-random-bert\"\nBertConfig.from_pretrained(mname)\nBertModel.from_pretrained(mname)\nBertTokenizer.from_pretrained(mname)\npipe = pipeline(task=\"fill-mask\", model=mname)\nprint(\"success\")\n        "

         lowerCAmelCase     :     int								=							"\nimport socket\ndef offline_socket(*args, **kwargs): raise RuntimeError(\"Offline mode is enabled, we shouldn\'t access internet\")\nsocket.socket = offline_socket\n        "

         # Force fetching the files so that we can use the cache
         lowerCAmelCase     :     str								=							"hf-internal-testing/tiny-random-bert"
         BertConfig.from_pretrained(__snake_case						)
         BertModel.from_pretrained(__snake_case						)
         BertTokenizer.from_pretrained(__snake_case						)
         pipeline(task="fill-mask"		,     model=__snake_case						)

         # baseline - just load from_pretrained with normal network
         lowerCAmelCase     :     List[Any]								=							[sys.executable, "-c", "\n".join([load, run, mock]						)]

         # should succeed
         lowerCAmelCase     :     Any								=							self.get_env()
         # should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
         lowerCAmelCase     :     Optional[Any]								=							"1"
         lowerCAmelCase     :     Dict								=							subprocess.run(__snake_case		,     env=__snake_case		,     check=__snake_case		,     capture_output=__snake_case						)
         self.assertEqual(result.returncode		,     0		,     result.stderr						)
         self.assertIn("success"		,     result.stdout.decode()						)

       @require_torch
       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     Any								=							"\nfrom transformers import BertConfig, BertModel, BertTokenizer, pipeline\n        "

         lowerCAmelCase     :     List[str]								=							"\nmname = \"hf-internal-testing/tiny-random-bert\"\nBertConfig.from_pretrained(mname)\nBertModel.from_pretrained(mname)\nBertTokenizer.from_pretrained(mname)\npipe = pipeline(task=\"fill-mask\", model=mname)\nprint(\"success\")\n        "

         lowerCAmelCase     :     str								=							"\nimport socket\ndef offline_socket(*args, **kwargs): raise socket.error(\"Faking flaky internet\")\nsocket.socket = offline_socket\n        "

         # Force fetching the files so that we can use the cache
         lowerCAmelCase     :     Optional[Any]								=							"hf-internal-testing/tiny-random-bert"
         BertConfig.from_pretrained(__snake_case						)
         BertModel.from_pretrained(__snake_case						)
         BertTokenizer.from_pretrained(__snake_case						)
         pipeline(task="fill-mask"		,     model=__snake_case						)

         # baseline - just load from_pretrained with normal network
         lowerCAmelCase     :     str								=							[sys.executable, "-c", "\n".join([load, run, mock]						)]

         # should succeed
         lowerCAmelCase     :     List[Any]								=							self.get_env()
         lowerCAmelCase     :     str								=							subprocess.run(__snake_case		,     env=__snake_case		,     check=__snake_case		,     capture_output=__snake_case						)
         self.assertEqual(result.returncode		,     0		,     result.stderr						)
         self.assertIn("success"		,     result.stdout.decode()						)

       @require_torch
       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     List[Any]								=							"\nfrom transformers import BertConfig, BertModel, BertTokenizer\n        "

         lowerCAmelCase     :     Dict								=							"\nmname = \"hf-internal-testing/tiny-random-bert-sharded\"\nBertConfig.from_pretrained(mname)\nBertModel.from_pretrained(mname)\nprint(\"success\")\n        "

         lowerCAmelCase     :     Optional[int]								=							"\nimport socket\ndef offline_socket(*args, **kwargs): raise ValueError(\"Offline mode is enabled\")\nsocket.socket = offline_socket\n        "

         # baseline - just load from_pretrained with normal network
         lowerCAmelCase     :     str								=							[sys.executable, "-c", "\n".join([load, run]						)]

         # should succeed
         lowerCAmelCase     :     List[Any]								=							self.get_env()
         lowerCAmelCase     :     Optional[int]								=							subprocess.run(__snake_case		,     env=__snake_case		,     check=__snake_case		,     capture_output=__snake_case						)
         self.assertEqual(result.returncode		,     0		,     result.stderr						)
         self.assertIn("success"		,     result.stdout.decode()						)

         # next emulate no network
         lowerCAmelCase     :     str								=							[sys.executable, "-c", "\n".join([load, mock, run]						)]

         # Doesn't fail anymore since the model is in the cache due to other tests, so commenting this.
         # env["TRANSFORMERS_OFFLINE"] = "0"
         # result = subprocess.run(cmd, env=env, check=False, capture_output=True)
         # self.assertEqual(result.returncode, 1, result.stderr)

         # should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
         lowerCAmelCase     :     int								=							"1"
         lowerCAmelCase     :     int								=							subprocess.run(__snake_case		,     env=__snake_case		,     check=__snake_case		,     capture_output=__snake_case						)
         self.assertEqual(result.returncode		,     0		,     result.stderr						)
         self.assertIn("success"		,     result.stdout.decode()						)

       @require_torch
       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     Optional[Any]								=							"\nfrom transformers import pipeline\n        "
         lowerCAmelCase     :     Any								=							"\nmname = \"hf-internal-testing/tiny-random-bert\"\npipe = pipeline(model=mname)\n        "

         lowerCAmelCase     :     List[str]								=							"\nimport socket\ndef offline_socket(*args, **kwargs): raise socket.error(\"Offline mode is enabled\")\nsocket.socket = offline_socket\n        "
         lowerCAmelCase     :     List[Any]								=							self.get_env()
         lowerCAmelCase     :     Dict								=							"1"
         lowerCAmelCase     :     Dict								=							[sys.executable, "-c", "\n".join([load, mock, run]						)]
         lowerCAmelCase     :     Optional[Any]								=							subprocess.run(__snake_case		,     env=__snake_case		,     check=__snake_case		,     capture_output=__snake_case						)
         self.assertEqual(result.returncode		,     1		,     result.stderr						)
         self.assertIn(
             "You cannot infer task automatically within `pipeline` when using offline mode"		,     result.stderr.decode().replace("\n"		,     ""						)		,     )







       @require_torch
       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     int								=							"\nfrom transformers import AutoModel\n        "
         lowerCAmelCase     :     List[Any]								=							"\nmname = \"hf-internal-testing/test_dynamic_model\"\nAutoModel.from_pretrained(mname, trust_remote_code=True)\nprint(\"success\")\n        "

         # baseline - just load from_pretrained with normal network
         lowerCAmelCase     :     str								=							[sys.executable, "-c", "\n".join([load, run]						)]

         # should succeed
         lowerCAmelCase     :     List[Any]								=							self.get_env()
         lowerCAmelCase     :     str								=							subprocess.run(__snake_case		,     env=__snake_case		,     check=__snake_case		,     capture_output=__snake_case						)
         self.assertEqual(result.returncode		,     0		,     result.stderr						)
         self.assertIn("success"		,     result.stdout.decode()						)

         # should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
         lowerCAmelCase     :     Dict								=							"1"
         lowerCAmelCase     :     Any								=							subprocess.run(__snake_case		,     env=__snake_case		,     check=__snake_case		,     capture_output=__snake_case						)
         self.assertEqual(result.returncode		,     0		,     result.stderr						)
         self.assertIn("success"		,     result.stdout.decode()						)








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






def        UpperCamelCase_(							_snake_case   :  Union[List, PIL.Image.Image, torch.Tensor]	):




			"""simple docstring"""






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

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

						__a        =[np.array(i.resize((w, h)       ,     resample=PIL_INTERPOLATION['lanczos']	)	)[None, :] for i in image]
						__a        =np.concatenate(_snake_case       ,     axis=0	)
						__a        =np.array(_snake_case	).astype(np.floataa	) / 255.0
						__a        =image.transpose(0       ,     3       ,     1       ,     2	)
						__a        =2.0 * image - 1.0
						__a        =torch.from_numpy(_snake_case	)
			elif isinstance(image[0]       ,     torch.Tensor	):
						__a        =torch.cat(_snake_case       ,     dim=0	)
			return image






def        UpperCamelCase_(							_snake_case   :  Union[List, PIL.Image.Image, torch.Tensor]	):




			"""simple docstring"""






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

			if isinstance(mask[0]       ,     PIL.Image.Image	):
						__a      ,    __a        =mask[0].size
						__a      ,    __a        =(x - x % 32 for x in (w, h))  # resize to integer multiple of 32
						__a        =[np.array(m.convert('L'	).resize((w, h)       ,     resample=PIL_INTERPOLATION['nearest']	)	)[None, :] for m in mask]
						__a        =np.concatenate(_snake_case       ,     axis=0	)
						__a        =mask.astype(np.floataa	) / 255.0
						__a        =0
						__a        =1
						__a        =torch.from_numpy(_snake_case	)
			elif isinstance(mask[0]       ,     torch.Tensor	):
						__a        =torch.cat(_snake_case       ,     dim=0	)
			return mask






class 	__magic_name__    (			lowerCAmelCase_      ):
			SCREAMING_SNAKE_CASE							   =						42
			SCREAMING_SNAKE_CASE							   =						42





			def __init__(		self					,  __snake_case					,  __snake_case					)   -> Union[str, Any]:




						'''simple docstring'''
						super().__init__()
						self.register_modules(unet=__snake_case					,  scheduler=__snake_case					)





			@torch.no_grad()
			def __call__(		self					,  __snake_case					,  __snake_case					,  __snake_case = 250					,  __snake_case = 0.0					,  __snake_case = 10					,  __snake_case = 10					,  __snake_case = None					,  __snake_case = "pil"					,  __snake_case = True					,  )   -> Union[ImagePipelineOutput, Tuple]:




						'''simple docstring'''
						__a        =image

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

						__a        =original_image.shape[0]

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

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

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

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

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

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

						if not return_dict:
									return (image,)

						return ImagePipelineOutput(images=__snake_case					)






