


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 						a__			(				A__,      A__,      A__,      A__,      A__=True,      A__="pt"				):
			SCREAMING_SNAKE_CASE_		:     Tuple					=					{'add_prefix_space': True} if isinstance(A__,      A__				) and not line.startswith(' '				) else {}
			SCREAMING_SNAKE_CASE_		:     Dict					=					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 						a__			(				A__,      A__,      A__=None,      ):
			SCREAMING_SNAKE_CASE_		:     int					=					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 	__lowercase    (__SCREAMING_SNAKE_CASE  ):





							"""simple docstring"""







							def __init__(      self			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__="train"			,						lowerCAmelCase__=None			,						lowerCAmelCase__=None			,						lowerCAmelCase__=None			,						lowerCAmelCase__=""			,						):





										"""simple docstring"""




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






							def __len__(      self       ):





										"""simple docstring"""




										return len(self.src_lens       )






							def __getitem__(      self			,						lowerCAmelCase__       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					index + 1  # linecache starts at 1
										SCREAMING_SNAKE_CASE_		:     List[str]					=					self.prefix + linecache.getline(str(self.src_file       )			,						lowerCAmelCase__       ).rstrip('\n'       )
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					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
										SCREAMING_SNAKE_CASE_		:     Any					=					(
										    self.tokenizer.question_encoder if isinstance(self.tokenizer			,						lowerCAmelCase__       ) else self.tokenizer
										)
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					self.tokenizer.generator if isinstance(self.tokenizer			,						lowerCAmelCase__       ) else self.tokenizer

										SCREAMING_SNAKE_CASE_		:     Dict					=					encode_line(lowerCAmelCase__			,						lowerCAmelCase__			,						self.max_source_length			,						'right'       )
										SCREAMING_SNAKE_CASE_		:     Tuple					=					encode_line(lowerCAmelCase__			,						lowerCAmelCase__			,						self.max_target_length			,						'right'       )

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






							@staticmethod
							def 					UpperCamelCase__     (      lowerCAmelCase__       ):





										"""simple docstring"""




										return [len(lowerCAmelCase__       ) for x in Path(lowerCAmelCase__       ).open().readlines()]






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     int					=					torch.stack([x['input_ids'] for x in batch]       )
										SCREAMING_SNAKE_CASE_		:     int					=					torch.stack([x['attention_mask'] for x in batch]       )
										SCREAMING_SNAKE_CASE_		:     List[str]					=					torch.stack([x['decoder_input_ids'] for x in batch]       )
										SCREAMING_SNAKE_CASE_		:     Dict					=					(
										    self.tokenizer.generator.pad_token_id
										    if isinstance(self.tokenizer			,						lowerCAmelCase__       )
										    else self.tokenizer.pad_token_id
										)
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					(
										    self.tokenizer.question_encoder.pad_token_id
										    if isinstance(self.tokenizer			,						lowerCAmelCase__       )
										    else self.tokenizer.pad_token_id
										)
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					trim_batch(lowerCAmelCase__			,						lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		:     str					=					trim_batch(lowerCAmelCase__			,						lowerCAmelCase__			,						attention_mask=lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     Tuple					=					{
										    'input_ids': source_ids,
										    'attention_mask': source_mask,
										    'decoder_input_ids': y,
										}
										return batch







lowerCAmelCase__			:      Tuple								=getLogger(__name__)






def 						a__			(				A__				):
			return list(itertools.chain.from_iterable(A__				)				)






def 						a__			(				A__				):
			SCREAMING_SNAKE_CASE_		:     Optional[int]					=					get_git_info()
			save_json(A__,      os.path.join(A__,      'git_log.json'				)				)






def 						a__			(				A__,      A__,      A__=4,      **A__				):
			with open(A__,      'w'				) as f:
						json.dump(A__,      A__,      indent=A__,      **A__				)






def 						a__			(				A__				):
			with open(A__				) as f:
						return json.load(A__				)






def 						a__			(				):
			SCREAMING_SNAKE_CASE_		:     List[str]					=					git.Repo(search_parent_directories=A__				)
			SCREAMING_SNAKE_CASE_		:     Any					=					{
			    '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 						a__			(				A__,      A__				):
			return list(map(A__,      A__				)				)






def 						a__			(				A__,      A__				):
			with open(A__,      'wb'				) as f:
						return pickle.dump(A__,      A__				)






def 						a__			(				A__				):

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

			def white_space_fix(A__				):
						return " ".join(text.split()				)

			def remove_punc(A__				):
						SCREAMING_SNAKE_CASE_		:     Optional[int]					=					set(string.punctuation				)
						return "".join(ch for ch in text if ch not in exclude				)

			def lower(A__				):
						return text.lower()

			return white_space_fix(remove_articles(remove_punc(lower(A__				)				)				)				)






def 						a__			(				A__,      A__				):
			SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					normalize_answer(A__				).split()
			SCREAMING_SNAKE_CASE_		:     List[str]					=					normalize_answer(A__				).split()
			SCREAMING_SNAKE_CASE_		:     Optional[int]					=					Counter(A__				) & Counter(A__				)
			SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					sum(common.values()				)
			if num_same == 0:
						return 0
			SCREAMING_SNAKE_CASE_		:     List[Any]					=					1.0 * num_same / len(A__				)
			SCREAMING_SNAKE_CASE_		:     int					=					1.0 * num_same / len(A__				)
			SCREAMING_SNAKE_CASE_		:     List[str]					=					(2 * precision * recall) / (precision + recall)
			return fa






def 						a__			(				A__,      A__				):
			return normalize_answer(A__				) == normalize_answer(A__				)






def 						a__			(				A__,      A__				):
			assert len(A__				) == len(A__				)
			SCREAMING_SNAKE_CASE_		:     Any					=					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 						a__			(				A__				):
			return model_prefix.startswith('rag'				)






def 						a__			(				A__,      A__,      A__				):
			SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					{p: p for p in extra_params}
			# T5 models don't have `dropout` param, they have `dropout_rate` instead
			SCREAMING_SNAKE_CASE_		:     List[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
									SCREAMING_SNAKE_CASE_		:     Any					=					p if hasattr(A__,      A__				) else equivalent_param[p]
									setattr(A__,      A__,      getattr(A__,      A__				)				)
									delattr(A__,      A__				)
			return hparams, config












'''simple docstring'''




from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






class 				__snake_case     (					__magic_name__							):





	def __init__(		self      ,       UpperCamelCase_      ,       UpperCamelCase_		)      ->		int:
		super().__init__()

		# make sure scheduler can always be converted to DDIM
		snake_case__	       =   DDIMScheduler.from_config(scheduler.config		)

		self.register_modules(unet=UpperCamelCase_      ,       scheduler=UpperCamelCase_		)





	@torch.no_grad()
	def __call__(		self      ,       UpperCamelCase_ = 1      ,       UpperCamelCase_ = None      ,       UpperCamelCase_ = 0.0      ,       UpperCamelCase_ = 50      ,       UpperCamelCase_ = None      ,       UpperCamelCase_ = "pil"      ,       UpperCamelCase_ = True      ,       )      ->		Union[ImagePipelineOutput, Tuple]:

		# Sample gaussian noise to begin loop
		if isinstance(self.unet.config.sample_size      ,       UpperCamelCase_		):
			snake_case__	       =   (
			    batch_size,
			    self.unet.config.in_channels,
			    self.unet.config.sample_size,
			    self.unet.config.sample_size,
			)
		else:
			snake_case__	       =   (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

		if isinstance(UpperCamelCase_      ,       UpperCamelCase_		) and len(UpperCamelCase_		) != batch_size:
			raise ValueError(
			    F'''You have passed a list of generators of length {len(UpperCamelCase_		)}, but requested an effective batch'''
			    F''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''		)

		snake_case__	       =   randn_tensor(UpperCamelCase_      ,       generator=UpperCamelCase_      ,       device=self.device      ,       dtype=self.unet.dtype		)

		# set step values
		self.scheduler.set_timesteps(UpperCamelCase_		)

		for t in self.progress_bar(self.scheduler.timesteps		):
			# 1. predict noise model_output
			snake_case__	       =   self.unet(UpperCamelCase_      ,       UpperCamelCase_		).sample

			# 2. predict previous mean of image x_t-1 and add variance depending on eta
			# eta corresponds to η in paper and should be between [0, 1]
			# do x_t -> x_t-1
			snake_case__	       =   self.scheduler.step(
			    UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       eta=UpperCamelCase_      ,       use_clipped_model_output=UpperCamelCase_      ,       generator=UpperCamelCase_		).prev_sample

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

		if not return_dict:
			return (image,)

		return ImagePipelineOutput(images=UpperCamelCase_		)



