






from typing import Callable, List, Optional, Union

import PIL
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPSegForImageSegmentation,
    CLIPSegProcessor,
    CLIPTextModel,
    CLIPTokenizer,
)

from diffusers import DiffusionPipeline
from diffusers.configuration_utils import FrozenDict
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionInpaintPipeline
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import deprecate, is_accelerate_available, logging


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

class 				lowerCAmelCase_	(		__snake_case			):


				def __init__(					self ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						):
											super().__init__()

											if hasattr(scheduler.config ,						'steps_offset'      ) and scheduler.config.steps_offset != 1:
																		_lowercase							:   Dict 							=   (
																		    F"""The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"""
																		    F""" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure """
																		    'to update the config accordingly as leaving `steps_offset` might led to incorrect results'
																		    ' in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,'
																		    ' it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`'
																		    ' file'
																		)
																		deprecate('steps_offset!=1' ,						'1.0.0' ,						_lowerCAmelCase ,						standard_warn=_lowerCAmelCase      )
																		_lowercase							:   List[str] 							=   dict(scheduler.config      )
																		_lowercase							:   Any 							=   1
																		_lowercase							:   List[Any] 							=   FrozenDict(_lowerCAmelCase      )

											if hasattr(scheduler.config ,						'skip_prk_steps'      ) and scheduler.config.skip_prk_steps is False:
																		_lowercase							:   Any 							=   (
																		    F"""The configuration file of this scheduler: {scheduler} has not set the configuration"""
																		    ' `skip_prk_steps`. `skip_prk_steps` should be set to True in the configuration file. Please make'
																		    ' sure to update the config accordingly as not setting `skip_prk_steps` in the config might lead to'
																		    ' incorrect results in future versions. If you have downloaded this checkpoint from the Hugging Face'
																		    ' Hub, it would be very nice if you could open a Pull request for the'
																		    ' `scheduler/scheduler_config.json` file'
																		)
																		deprecate('skip_prk_steps not set' ,						'1.0.0' ,						_lowerCAmelCase ,						standard_warn=_lowerCAmelCase      )
																		_lowercase							:   str 							=   dict(scheduler.config      )
																		_lowercase							:   int 							=   True
																		_lowercase							:   Union[str, Any] 							=   FrozenDict(_lowerCAmelCase      )

											if safety_checker is None:
																		logger.warning(
																		    F"""You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"""
																		    ' that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered'
																		    ' results in services or applications open to the public. Both the diffusers team and Hugging Face'
																		    ' strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling'
																		    ' it only for use-cases that involve analyzing network behavior or auditing its results. For more'
																		    ' information, please have a look at https://github.com/huggingface/diffusers/pull/254 .'      )

											self.register_modules(
											    segmentation_model=_lowerCAmelCase ,						segmentation_processor=_lowerCAmelCase ,						vae=_lowerCAmelCase ,						text_encoder=_lowerCAmelCase ,						tokenizer=_lowerCAmelCase ,						unet=_lowerCAmelCase ,						scheduler=_lowerCAmelCase ,						safety_checker=_lowerCAmelCase ,						feature_extractor=_lowerCAmelCase ,						)


				def 						__a					(					self ,						_lowerCAmelCase = "auto"      ):
											if slice_size == "auto":
																		# half the attention head size is usually a good trade-off between
																		# speed and memory
																		_lowercase							:   str 							=   self.unet.config.attention_head_dim // 2
											self.unet.set_attention_slice(_lowerCAmelCase      )


				def 						__a					(					self      ):
											self.enable_attention_slicing(_lowerCAmelCase      )


				def 						__a					(					self      ):
											if is_accelerate_available():
																		from accelerate import cpu_offload
											else:
																		raise ImportError('Please install accelerate via `pip install accelerate`'      )

											_lowercase							:   Tuple 							=   torch.device('cuda'      )

											for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae, self.safety_checker]:
																		if cpu_offloaded_model is not None:
																									cpu_offload(_lowerCAmelCase ,						_lowerCAmelCase      )


				@property
				# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
				def 						__a					(					self      ):
											if self.device != torch.device('meta'      ) or not hasattr(self.unet ,						'_hf_hook'      ):
																		return self.device
											for module in self.unet.modules():
																		if (
																		    hasattr(_lowerCAmelCase ,						'_hf_hook'      )
																		    and hasattr(module._hf_hook ,						'execution_device'      )
																		    and module._hf_hook.execution_device is not None
																		):
																									return torch.device(module._hf_hook.execution_device      )
											return self.device


				@torch.no_grad()
				def __call__(					self ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase = 5_1_2 ,						_lowerCAmelCase = 5_1_2 ,						_lowerCAmelCase = 5_0 ,						_lowerCAmelCase = 7.5 ,						_lowerCAmelCase = None ,						_lowerCAmelCase = 1 ,						_lowerCAmelCase = 0.0 ,						_lowerCAmelCase = None ,						_lowerCAmelCase = None ,						_lowerCAmelCase = "pil" ,						_lowerCAmelCase = True ,						_lowerCAmelCase = None ,						_lowerCAmelCase = 1 ,						**_lowerCAmelCase ,						):
											_lowercase							:   str 							=   self.segmentation_processor(
											    text=[text] ,						images=[image] ,						padding='max_length' ,						return_tensors='pt'      ).to(self.device      )
											_lowercase							:   Any 							=   self.segmentation_model(**_lowerCAmelCase      )
											_lowercase							:   str 							=   torch.sigmoid(outputs.logits      ).cpu().detach().unsqueeze(-1      ).numpy()
											_lowercase							:   Optional[int] 							=   self.numpy_to_pil(_lowerCAmelCase      )[0].resize(image.size      )

											# Run inpainting pipeline with the generated mask
											_lowercase							:   str 							=   StableDiffusionInpaintPipeline(
											    vae=self.vae ,						text_encoder=self.text_encoder ,						tokenizer=self.tokenizer ,						unet=self.unet ,						scheduler=self.scheduler ,						safety_checker=self.safety_checker ,						feature_extractor=self.feature_extractor ,						)
											return inpainting_pipeline(
											    prompt=_lowerCAmelCase ,						image=_lowerCAmelCase ,						mask_image=_lowerCAmelCase ,						height=_lowerCAmelCase ,						width=_lowerCAmelCase ,						num_inference_steps=_lowerCAmelCase ,						guidance_scale=_lowerCAmelCase ,						negative_prompt=_lowerCAmelCase ,						num_images_per_prompt=_lowerCAmelCase ,						eta=_lowerCAmelCase ,						generator=_lowerCAmelCase ,						latents=_lowerCAmelCase ,						output_type=_lowerCAmelCase ,						return_dict=_lowerCAmelCase ,						callback=_lowerCAmelCase ,						callback_steps=_lowerCAmelCase ,						)












"""simple docstring"""





import unittest

from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers.models.esm.modeling_esmfold import EsmForProteinFolding



class       __A		:

					def __init__(			self  :      str     ,		__snake_case  :      int     ,		__snake_case  :      Dict=1_3     ,		__snake_case  :      Dict=7     ,		__snake_case  :      str=False     ,		__snake_case  :      Dict=True     ,		__snake_case  :      Tuple=False     ,		__snake_case  :      List[Any]=False     ,		__snake_case  :      Dict=1_9     ,		__snake_case  :      Dict=3_2     ,		__snake_case  :      Union[str, Any]=5     ,		__snake_case  :      List[Any]=4     ,		__snake_case  :      List[Any]=3_7     ,		__snake_case  :      Union[str, Any]="gelu"     ,		__snake_case  :      List[str]=0.1     ,		__snake_case  :      Union[str, Any]=0.1     ,		__snake_case  :      List[str]=5_1_2     ,		__snake_case  :      List[str]=1_6     ,		__snake_case  :      Tuple=2     ,		__snake_case  :      Optional[Any]=0.02     ,		__snake_case  :      str=3     ,		__snake_case  :      List[str]=4     ,		__snake_case  :      Optional[int]=None     ,		)			->							Optional[int]:
											__magic_name__:	Any							=       parent
											__magic_name__:	Optional[Any]							=       batch_size
											__magic_name__:	Optional[Any]							=       seq_length
											__magic_name__:	Optional[Any]							=       is_training
											__magic_name__:	str							=       use_input_mask
											__magic_name__:	Optional[Any]							=       use_token_type_ids
											__magic_name__:	List[str]							=       use_labels
											__magic_name__:	List[str]							=       vocab_size
											__magic_name__:	Union[str, Any]							=       hidden_size
											__magic_name__:	List[Any]							=       num_hidden_layers
											__magic_name__:	Dict							=       num_attention_heads
											__magic_name__:	int							=       intermediate_size
											__magic_name__:	List[Any]							=       hidden_act
											__magic_name__:	int							=       hidden_dropout_prob
											__magic_name__:	List[str]							=       attention_probs_dropout_prob
											__magic_name__:	Tuple							=       max_position_embeddings
											__magic_name__:	Any							=       type_vocab_size
											__magic_name__:	Union[str, Any]							=       type_sequence_label_size
											__magic_name__:	List[str]							=       initializer_range
											__magic_name__:	Optional[Any]							=       num_labels
											__magic_name__:	List[str]							=       num_choices
											__magic_name__:	Tuple							=       scope

					def   lowerCamelCase__   (			self  :      Optional[int]					)			->							Any:
											__magic_name__:	Union[str, Any]							=       ids_tensor([self.batch_size, self.seq_length]     ,		self.vocab_size					)

											__magic_name__:	Dict							=       None
											if self.use_input_mask:
																	__magic_name__:	Optional[int]							=       random_attention_mask([self.batch_size, self.seq_length]					)

											__magic_name__:	str							=       None
											__magic_name__:	Optional[Any]							=       None
											__magic_name__:	int							=       None
											if self.use_labels:
																	__magic_name__:	List[Any]							=       ids_tensor([self.batch_size]     ,		self.type_sequence_label_size					)
																	__magic_name__:	str							=       ids_tensor([self.batch_size, self.seq_length]     ,		self.num_labels					)
																	__magic_name__:	Dict							=       ids_tensor([self.batch_size]     ,		self.num_choices					)

											__magic_name__:	Dict							=       self.get_config()

											return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels

					def   lowerCamelCase__   (			self  :      int					)			->							Any:
											__magic_name__:	List[Any]							=       EsmConfig(
											    vocab_size=3_3     ,		hidden_size=self.hidden_size     ,		pad_token_id=1     ,		num_hidden_layers=self.num_hidden_layers     ,		num_attention_heads=self.num_attention_heads     ,		intermediate_size=self.intermediate_size     ,		hidden_act=self.hidden_act     ,		hidden_dropout_prob=self.hidden_dropout_prob     ,		attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,		max_position_embeddings=self.max_position_embeddings     ,		type_vocab_size=self.type_vocab_size     ,		initializer_range=self.initializer_range     ,		is_folding_model=__snake_case     ,		esmfold_config={"""trunk""": {"""num_blocks""": 2}, """fp16_esm""": False}     ,		)
											return config

					def   lowerCamelCase__   (			self  :      Optional[int]     ,		__snake_case  :      Optional[Any]     ,		__snake_case  :      Optional[Any]     ,		__snake_case  :      Any     ,		__snake_case  :      List[Any]     ,		__snake_case  :      Any     ,		__snake_case  :      Union[str, Any]					)			->							Optional[Any]:
											__magic_name__:	str							=       EsmForProteinFolding(config=__snake_case					).float()
											model.to(__snake_case					)
											model.eval()
											__magic_name__:	str							=       model(__snake_case     ,		attention_mask=__snake_case					)
											__magic_name__:	Any							=       model(__snake_case					)
											__magic_name__:	Dict							=       model(__snake_case					)

											self.parent.assertEqual(result.positions.shape     ,		(8, self.batch_size, self.seq_length, 1_4, 3)					)
											self.parent.assertEqual(result.angles.shape     ,		(8, self.batch_size, self.seq_length, 7, 2)					)






					def   lowerCamelCase__   (			self  :      Any					)			->							int:
											__magic_name__:	str							=       self.prepare_config_and_inputs()
											(
											    (
											    __magic_name__
											),			(
											    __magic_name__
											),			(
											    __magic_name__
											),			(
											    __magic_name__
											),			(
											    __magic_name__
											),			(
											    __magic_name__
											),			
											):	Union[str, Any]							=       config_and_inputs
											__magic_name__:	Any							=       {"""input_ids""": input_ids, """attention_mask""": input_mask}
											return config, inputs_dict



@require_torch
class       __A		(   SCREAMING_SNAKE_CASE_     ,SCREAMING_SNAKE_CASE_     ,unittest.TestCase			):
					UpperCAmelCase__								=				False

					UpperCAmelCase__								=				(EsmForProteinFolding,) if is_torch_available() else ()
					UpperCAmelCase__								=				()
					UpperCAmelCase__								=				{} if is_torch_available() else {}
					UpperCAmelCase__								=				False

					def   lowerCamelCase__   (			self  :      List[Any]					)			->							Optional[Any]:
											__magic_name__:	Optional[int]							=       EsmFoldModelTester(self					)
											__magic_name__:	Optional[Any]							=       ConfigTester(self     ,		config_class=__snake_case     ,		hidden_size=3_7					)

					def   lowerCamelCase__   (			self  :      Optional[int]					)			->							Any:
											self.config_tester.run_common_tests()

					def   lowerCamelCase__   (			self  :      List[str]					)			->							str:
											__magic_name__:	Tuple							=       self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*__snake_case					)

					@unittest.skip("""Does not support attention outputs"""					)
					def   lowerCamelCase__   (			self  :      List[str]					)			->							str:
											pass

					@unittest.skip
					def   lowerCamelCase__   (			self  :      List[Any]					)			->							Tuple:
											pass

					@unittest.skip("""Esm does not support embedding resizing"""					)
					def   lowerCamelCase__   (			self  :      List[Any]					)			->							Optional[int]:
											pass

					@unittest.skip("""Esm does not support embedding resizing"""					)
					def   lowerCamelCase__   (			self  :      str					)			->							str:
											pass

					@unittest.skip("""ESMFold does not support passing input embeds!"""					)
					def   lowerCamelCase__   (			self  :      Optional[int]					)			->							List[Any]:
											pass

					@unittest.skip("""ESMFold does not support head pruning."""					)
					def   lowerCamelCase__   (			self  :      Any					)			->							List[str]:
											pass

					@unittest.skip("""ESMFold does not support head pruning."""					)
					def   lowerCamelCase__   (			self  :      Union[str, Any]					)			->							List[Any]:
											pass

					@unittest.skip("""ESMFold does not support head pruning."""					)
					def   lowerCamelCase__   (			self  :      int					)			->							Tuple:
											pass

					@unittest.skip("""ESMFold does not support head pruning."""					)
					def   lowerCamelCase__   (			self  :      Optional[int]					)			->							Optional[Any]:
											pass

					@unittest.skip("""ESMFold does not support head pruning."""					)
					def   lowerCamelCase__   (			self  :      Optional[Any]					)			->							Optional[Any]:
											pass

					@unittest.skip("""ESMFold does not output hidden states in the normal way."""					)
					def   lowerCamelCase__   (			self  :      Union[str, Any]					)			->							List[Any]:
											pass

					@unittest.skip("""ESMfold does not output hidden states in the normal way."""					)
					def   lowerCamelCase__   (			self  :      Any					)			->							int:
											pass

					@unittest.skip("""ESMFold only has one output format."""					)
					def   lowerCamelCase__   (			self  :      Union[str, Any]					)			->							str:
											pass

					@unittest.skip("""This test doesn't work for ESMFold and doesn't test core functionality"""					)
					def   lowerCamelCase__   (			self  :      List[Any]					)			->							Dict:
											pass

					@unittest.skip("""ESMFold does not support input chunking."""					)
					def   lowerCamelCase__   (			self  :      int					)			->							Any:
											pass

					@unittest.skip("""ESMFold doesn't respect you and it certainly doesn't respect your initialization arguments."""					)
					def   lowerCamelCase__   (			self  :      int					)			->							Union[str, Any]:
											pass

					@unittest.skip("""ESMFold doesn't support torchscript compilation."""					)
					def   lowerCamelCase__   (			self  :      Any					)			->							Optional[Any]:
											pass

					@unittest.skip("""ESMFold doesn't support torchscript compilation."""					)
					def   lowerCamelCase__   (			self  :      Optional[int]					)			->							int:
											pass

					@unittest.skip("""ESMFold doesn't support torchscript compilation."""					)
					def   lowerCamelCase__   (			self  :      Union[str, Any]					)			->							List[str]:
											pass

					@unittest.skip("""ESMFold doesn't support data parallel."""					)
					def   lowerCamelCase__   (			self  :      List[str]					)			->							str:
											pass






					@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""					)
					def   lowerCamelCase__   (			self  :      Union[str, Any]					)			->							Dict:
											pass



@require_torch
class       __A		(   SCREAMING_SNAKE_CASE_			):

					@slow
					def   lowerCamelCase__   (			self  :      Optional[Any]					)			->							List[Any]:
											__magic_name__:	Any							=       EsmForProteinFolding.from_pretrained("""facebook/esmfold_v1"""					).float()
											model.eval()
											__magic_name__:	Optional[int]							=       torch.tensor([[0, 6, 4, 1_3, 5, 4, 1_6, 1_2, 1_1, 7, 2]]					)
											__magic_name__:	Dict							=       model(__snake_case					)["""positions"""]
											__magic_name__:	Tuple							=       torch.tensor([2.5828, 0.7993, -10.9334]     ,		dtype=torch.floataa					)
											self.assertTrue(torch.allclose(position_outputs[0, 0, 0, 0]     ,		__snake_case     ,		atol=1E-4					)					)



