




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


__lowerCamelCase   :						Union[str, Any]				=			logging.get_logger(__name__)  # pylint: disable=invalid-name


class    lowerCamelCase    (		_lowerCamelCase						):






			'''simple docstring'''





			def __init__( self			:   Any       ,						lowerCamelCase_			:   CLIPSegForImageSegmentation       ,						lowerCamelCase_			:   CLIPSegProcessor       ,						lowerCamelCase_			:   AutoencoderKL       ,						lowerCamelCase_			:   CLIPTextModel       ,						lowerCamelCase_			:   CLIPTokenizer       ,						lowerCamelCase_			:   UNetaDConditionModel       ,						lowerCamelCase_			:   Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]       ,						lowerCamelCase_			:   StableDiffusionSafetyChecker       ,						lowerCamelCase_			:   CLIPImageProcessor       ,						)						->   Tuple:
								super().__init__()

								if hasattr(scheduler.config       ,						'''steps_offset'''  ) and scheduler.config.steps_offset != 1:
													__magic_name__							:	Tuple    							=							(
													    F'''The configuration file of this scheduler: {scheduler} is outdated.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_  )
													__magic_name__							:	Dict    							=							dict(scheduler.config  )
													__magic_name__							:	List[str]    							=							1
													__magic_name__							:	Any    							=							FrozenDict(lowerCamelCase_  )

								if hasattr(scheduler.config       ,						'''skip_prk_steps'''  ) and scheduler.config.skip_prk_steps is False:
													__magic_name__							:	Tuple    							=							(
													    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_  )
													__magic_name__							:	Optional[int]    							=							dict(scheduler.config  )
													__magic_name__							:	Tuple    							=							True
													__magic_name__							:	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 							UpperCAmelCase__       ( self			:   Union[str, Any]       ,						lowerCamelCase_			:   Optional[Union[str, int]] = "auto"  )						->   Union[str, Any]:
								if slice_size == "auto":
													# half the attention head size is usually a good trade-off between
													# speed and memory
													__magic_name__							:	Any    							=							self.unet.config.attention_head_dim // 2
								self.unet.set_attention_slice(lowerCamelCase_  )

			def 							UpperCAmelCase__       ( self			:   List[Any]  )						->   List[Any]:
								self.enable_attention_slicing(lowerCamelCase_  )

			def 							UpperCAmelCase__       ( self			:   str  )						->   List[Any]:
								if is_accelerate_available():
													from accelerate import cpu_offload
								else:
													raise ImportError('''Please install accelerate via `pip install accelerate`'''  )

								__magic_name__							:	Optional[Any]    							=							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 							UpperCAmelCase__       ( self			:   Optional[Any]  )						->   int:
								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			:   Any       ,						lowerCamelCase_			:   Union[str, List[str]]       ,						lowerCamelCase_			:   Union[torch.FloatTensor, PIL.Image.Image]       ,						lowerCamelCase_			:   str       ,						lowerCamelCase_			:   int = 512       ,						lowerCamelCase_			:   int = 512       ,						lowerCamelCase_			:   int = 50       ,						lowerCamelCase_			:   float = 7.5       ,						lowerCamelCase_			:   Optional[Union[str, List[str]]] = None       ,						lowerCamelCase_			:   Optional[int] = 1       ,						lowerCamelCase_			:   float = 0.0       ,						lowerCamelCase_			:   Optional[torch.Generator] = None       ,						lowerCamelCase_			:   Optional[torch.FloatTensor] = None       ,						lowerCamelCase_			:   Optional[str] = "pil"       ,						lowerCamelCase_			:   bool = True       ,						lowerCamelCase_			:   Optional[Callable[[int, int, torch.FloatTensor], None]] = None       ,						lowerCamelCase_			:   int = 1       ,						**lowerCamelCase_			:   Optional[int]       ,						)						->   str:
								__magic_name__							:	Union[str, Any]    							=							self.segmentation_processor(
								    text=[text]       ,						images=[image]       ,						padding='''max_length'''       ,						return_tensors='''pt'''  ).to(self.device  )
								__magic_name__							:	Tuple    							=							self.segmentation_model(**lowerCamelCase_  )
								__magic_name__							:	List[str]    							=							torch.sigmoid(outputs.logits  ).cpu().detach().unsqueeze(-1  ).numpy()
								__magic_name__							:	Dict    							=							self.numpy_to_pil(lowerCamelCase_  )[0].resize(image.size  )

								# Run inpainting pipeline with the generated mask
								__magic_name__							:	Dict    							=							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_       ,						)









import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List

import git
import torch
from torch.utils.data import Dataset

from transformers import BartTokenizer, RagTokenizer, TaTokenizer







def 		lowercase__   (		__A:	str							,__A:	Optional[Any]							,__A:	List[Any]							,__A:	str							,__A:	List[str]=True							,__A:	List[Any]="pt"				):





					'''simple docstring'''






					__magic_name__							:	Optional[Any]    							=							{'''add_prefix_space''': True} if isinstance(__A							,__A				) and not line.startswith(''' '''				) else {}
					__magic_name__							:	List[str]    							=							padding_side
					return tokenizer(
					    [line]							,max_length=__A							,padding='''max_length''' if pad_to_max_length else None							,truncation=__A							,return_tensors=__A							,add_special_tokens=__A							,**__A							,)







def 		lowercase__   (		__A:	int							,__A:	int							,__A:	Optional[Any]=None							,):





					'''simple docstring'''






					__magic_name__							:	List[Any]    							=							input_ids.ne(__A				).any(dim=0				)
					if attention_mask is None:
										return input_ids[:, keep_column_mask]
					else:
										return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])


class    lowerCamelCase    (		_lowerCamelCase						):






			'''simple docstring'''





			def __init__( self			:   List[str]       ,						lowerCamelCase_			:   Union[str, Any]       ,						lowerCamelCase_			:   str       ,						lowerCamelCase_			:   Optional[int]       ,						lowerCamelCase_			:   Optional[Any]       ,						lowerCamelCase_			:   Any="train"       ,						lowerCamelCase_			:   Optional[int]=None       ,						lowerCamelCase_			:   List[Any]=None       ,						lowerCamelCase_			:   Optional[int]=None       ,						lowerCamelCase_			:   List[Any]=""       ,						)						->   Union[str, Any]:
								super().__init__()
								__magic_name__							:	Any    							=							Path(lowerCamelCase_  ).joinpath(type_path + '''.source'''  )
								__magic_name__							:	Tuple    							=							Path(lowerCamelCase_  ).joinpath(type_path + '''.target'''  )
								__magic_name__							:	str    							=							self.get_char_lens(self.src_file  )
								__magic_name__							:	Dict    							=							max_source_length
								__magic_name__							:	Any    							=							max_target_length
								assert min(self.src_lens  ) > 0, F'''found empty line in {self.src_file}'''
								__magic_name__							:	Union[str, Any]    							=							tokenizer
								__magic_name__							:	str    							=							prefix
								if n_obs is not None:
													__magic_name__							:	Optional[int]    							=							self.src_lens[:n_obs]
								__magic_name__							:	Optional[Any]    							=							src_lang
								__magic_name__							:	Tuple    							=							tgt_lang

			def __len__( self			:   Optional[int]  )						->   List[Any]:
								return len(self.src_lens  )

			def __getitem__( self			:   Optional[int]       ,						lowerCamelCase_			:   Optional[int]  )						->   Dict[str, torch.Tensor]:
								__magic_name__							:	Optional[int]    							=							index + 1  # linecache starts at 1
								__magic_name__							:	Optional[int]    							=							self.prefix + linecache.getline(str(self.src_file  )       ,						lowerCamelCase_  ).rstrip('''\n'''  )
								__magic_name__							:	Dict    							=							linecache.getline(str(self.tgt_file  )       ,						lowerCamelCase_  ).rstrip('''\n'''  )
								assert source_line, F'''empty source line for index {index}'''
								assert tgt_line, F'''empty tgt line for index {index}'''

								# Need to add eos token manually for T5
								if isinstance(self.tokenizer       ,						lowerCamelCase_  ):
													source_line += self.tokenizer.eos_token
													tgt_line += self.tokenizer.eos_token

								# Pad source and target to the right
								__magic_name__							:	int    							=							(
								    self.tokenizer.question_encoder if isinstance(self.tokenizer       ,						lowerCamelCase_  ) else self.tokenizer
								)
								__magic_name__							:	Optional[Any]    							=							self.tokenizer.generator if isinstance(self.tokenizer       ,						lowerCamelCase_  ) else self.tokenizer

								__magic_name__							:	Tuple    							=							encode_line(lowerCamelCase_       ,						lowerCamelCase_       ,						self.max_source_length       ,						'''right'''  )
								__magic_name__							:	int    							=							encode_line(lowerCamelCase_       ,						lowerCamelCase_       ,						self.max_target_length       ,						'''right'''  )

								__magic_name__							:	Optional[Any]    							=							source_inputs['''input_ids'''].squeeze()
								__magic_name__							:	List[str]    							=							target_inputs['''input_ids'''].squeeze()
								__magic_name__							:	Optional[int]    							=							source_inputs['''attention_mask'''].squeeze()
								return {
								    "input_ids": source_ids,
								    "attention_mask": src_mask,
								    "decoder_input_ids": target_ids,
								}

			@staticmethod
			def 							UpperCAmelCase__       ( lowerCamelCase_			:   List[str]  )						->   Dict:
								return [len(lowerCamelCase_  ) for x in Path(lowerCamelCase_  ).open().readlines()]


			def 							UpperCAmelCase__       ( self			:   Dict       ,						lowerCamelCase_			:   Optional[Any]  )						->   Dict[str, torch.Tensor]:
								__magic_name__							:	str    							=							torch.stack([x['''input_ids'''] for x in batch]  )
								__magic_name__							:	Any    							=							torch.stack([x['''attention_mask'''] for x in batch]  )
								__magic_name__							:	Tuple    							=							torch.stack([x['''decoder_input_ids'''] for x in batch]  )
								__magic_name__							:	Tuple    							=							(
								    self.tokenizer.generator.pad_token_id
								    if isinstance(self.tokenizer       ,						lowerCamelCase_  )
								    else self.tokenizer.pad_token_id
								)
								__magic_name__							:	Optional[Any]    							=							(
								    self.tokenizer.question_encoder.pad_token_id
								    if isinstance(self.tokenizer       ,						lowerCamelCase_  )
								    else self.tokenizer.pad_token_id
								)
								__magic_name__							:	Dict    							=							trim_batch(lowerCamelCase_       ,						lowerCamelCase_  )
								__magic_name__							,    __magic_name__							:	Optional[int]    							=							trim_batch(lowerCamelCase_       ,						lowerCamelCase_       ,						attention_mask=lowerCamelCase_  )
								__magic_name__							:	Optional[Any]    							=							{
								    '''input_ids''': source_ids,
								    '''attention_mask''': source_mask,
								    '''decoder_input_ids''': y,
								}
								return batch






__lowerCamelCase   :						Dict				=			getLogger(__name__)







def 		lowercase__   (		__A:	List[List]				):





					'''simple docstring'''






					return list(itertools.chain.from_iterable(__A				)				)







def 		lowercase__   (		__A:	str				):





					'''simple docstring'''






					__magic_name__							:	Any    							=							get_git_info()
					save_json(__A							,os.path.join(__A							,'''git_log.json'''				)				)







def 		lowercase__   (		__A:	Optional[int]							,__A:	Union[str, Any]							,__A:	Union[str, Any]=4							,**__A:	Optional[Any]				):





					'''simple docstring'''






					with open(__A							,'''w'''				) as f:
										json.dump(__A							,__A							,indent=__A							,**__A				)







def 		lowercase__   (		__A:	List[Any]				):





					'''simple docstring'''






					with open(__A				) as f:
										return json.load(__A				)







def 		lowercase__   (		):





					'''simple docstring'''






					__magic_name__							:	int    							=							git.Repo(search_parent_directories=__A				)
					__magic_name__							:	str    							=							{
					    '''repo_id''': str(__A				),
					    '''repo_sha''': str(repo.head.object.hexsha				),
					    '''repo_branch''': str(repo.active_branch				),
					    '''hostname''': str(socket.gethostname()				),
					}
					return repo_infos







def 		lowercase__   (		__A:	Callable							,__A:	Iterable				):





					'''simple docstring'''






					return list(map(__A							,__A				)				)







def 		lowercase__   (		__A:	int							,__A:	Dict				):





					'''simple docstring'''






					with open(__A							,'''wb'''				) as f:
										return pickle.dump(__A							,__A				)







def 		lowercase__   (		__A:	Any				):





					'''simple docstring'''






					def remove_articles(__A:	Any				):
										return re.sub(r'''\b(a|an|the)\b'''							,''' '''							,__A				)

					def white_space_fix(__A:	Optional[int]				):
										return " ".join(text.split()				)

					def remove_punc(__A:	Any				):
										__magic_name__							:	Union[str, Any]    							=							set(string.punctuation				)
										return "".join(ch for ch in text if ch not in exclude				)

					def lower(__A:	Optional[int]				):
										return text.lower()

					return white_space_fix(remove_articles(remove_punc(lower(__A				)				)				)				)







def 		lowercase__   (		__A:	int							,__A:	Optional[Any]				):





					'''simple docstring'''






					__magic_name__							:	Union[str, Any]    							=							normalize_answer(__A				).split()
					__magic_name__							:	Optional[int]    							=							normalize_answer(__A				).split()
					__magic_name__							:	Dict    							=							Counter(__A				) & Counter(__A				)
					__magic_name__							:	Tuple    							=							sum(common.values()				)
					if num_same == 0:
										return 0
					__magic_name__							:	int    							=							1.0 * num_same / len(__A				)
					__magic_name__							:	Tuple    							=							1.0 * num_same / len(__A				)
					__magic_name__							:	str    							=							(2 * precision * recall) / (precision + recall)
					return fa







def 		lowercase__   (		__A:	List[Any]							,__A:	Dict				):





					'''simple docstring'''






					return normalize_answer(__A				) == normalize_answer(__A				)







def 		lowercase__   (		__A:	List[str]							,__A:	List[str]				):





					'''simple docstring'''






					assert len(__A				) == len(__A				)
					__magic_name__							:	Optional[int]    							=							0
					for hypo, pred in zip(__A							,__A				):
										em += exact_match_score(__A							,__A				)
					if len(__A				) > 0:
										em /= len(__A				)
					return {"em": em}







def 		lowercase__   (		__A:	int				):





					'''simple docstring'''






					return model_prefix.startswith('''rag'''				)







def 		lowercase__   (		__A:	Optional[Any]							,__A:	List[str]							,__A:	Dict				):





					'''simple docstring'''






					__magic_name__							:	Tuple    							=							{p: p for p in extra_params}
					# T5 models don't have `dropout` param, they have `dropout_rate` instead
					__magic_name__							:	Any    							=							'''dropout_rate'''
					for p in extra_params:
										if getattr(__A							,__A							,__A				):
															if not hasattr(__A							,__A				) and not hasattr(__A							,equivalent_param[p]				):
																				logger.info('''config doesn\'t have a `{}` attribute'''.format(__A				)				)
																				delattr(__A							,__A				)
																				continue
															__magic_name__							:	str    							=							p if hasattr(__A							,__A				) else equivalent_param[p]
															setattr(__A							,__A							,getattr(__A							,__A				)				)
															delattr(__A							,__A				)
					return hparams, config



