



"""simple docstring"""
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        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__=True, SCREAMING_SNAKE_CASE__="pt"	)			->      Any:
		a_     :      Dict 						=     {"add_prefix_space": True} if isinstance(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	) and not line.startswith(" "	) else {}
		a_     :      Dict 						=     padding_side
		return tokenizer(
		    [line], max_length=SCREAMING_SNAKE_CASE__, padding="max_length" if pad_to_max_length else None, truncation=SCREAMING_SNAKE_CASE__, return_tensors=SCREAMING_SNAKE_CASE__, add_special_tokens=SCREAMING_SNAKE_CASE__, **SCREAMING_SNAKE_CASE__, )



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__=None, )			->      Optional[int]:
		a_     :      Optional[int] 						=     input_ids.ne(SCREAMING_SNAKE_CASE__	).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 			snake_case_						(						a_				):






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






							def __len__(   self      ):
									return len(self.src_lens      )






							def __getitem__(   self			,     a_      ):
									a_     :      str 						=     index + 1  # linecache starts at 1
									a_     :      List[str] 						=     self.prefix + linecache.getline(str(self.src_file      )			,     a_      ).rstrip("\n"      )
									a_     :      Tuple 						=     linecache.getline(str(self.tgt_file      )			,     a_      ).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			,     a_      ):
											source_line += self.tokenizer.eos_token
											tgt_line += self.tokenizer.eos_token

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

									a_     :      Any 						=     encode_line(a_			,     a_			,     self.max_source_length			,     "right"      )
									a_     :      Union[str, Any] 						=     encode_line(a_			,     a_			,     self.max_target_length			,     "right"      )

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






							@staticmethod
							def snake_case_  (   a_      ):
									return [len(a_      ) for x in Path(a_      ).open().readlines()]






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

SCREAMING_SNAKE_CASE_				= getLogger(__name__)



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__	)			->      Optional[int]:
		return list(itertools.chain.from_iterable(SCREAMING_SNAKE_CASE__	)	)



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__	)			->      None:
		a_     :      Any 						=     get_git_info()
		save_json(SCREAMING_SNAKE_CASE__, os.path.join(SCREAMING_SNAKE_CASE__, "git_log.json"	)	)



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__=4, **SCREAMING_SNAKE_CASE__	)			->      List[str]:
		with open(SCREAMING_SNAKE_CASE__, "w"	) as f:
				json.dump(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, indent=SCREAMING_SNAKE_CASE__, **SCREAMING_SNAKE_CASE__	)



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__	)			->      str:
		with open(SCREAMING_SNAKE_CASE__	) as f:
				return json.load(SCREAMING_SNAKE_CASE__	)



def        lowerCAmelCase_						(							)			->      Dict:
		a_     :      int 						=     git.Repo(search_parent_directories=SCREAMING_SNAKE_CASE__	)
		a_     :      Dict 						=     {
		    "repo_id": str(SCREAMING_SNAKE_CASE__	),
		    "repo_sha": str(repo.head.object.hexsha	),
		    "repo_branch": str(repo.active_branch	),
		    "hostname": str(socket.gethostname()	),
		}
		return repo_infos



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)			->      List:
		return list(map(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)	)



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)			->      Union[str, Any]:
		with open(SCREAMING_SNAKE_CASE__, "wb"	) as f:
				return pickle.dump(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__	)			->      Optional[int]:

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

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

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

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

		return white_space_fix(remove_articles(remove_punc(lower(SCREAMING_SNAKE_CASE__	)	)	)	)



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)			->      List[str]:
		a_     :      Dict 						=     normalize_answer(SCREAMING_SNAKE_CASE__	).split()
		a_     :      Any 						=     normalize_answer(SCREAMING_SNAKE_CASE__	).split()
		a_     :      int 						=     Counter(SCREAMING_SNAKE_CASE__	) & Counter(SCREAMING_SNAKE_CASE__	)
		a_     :      Dict 						=     sum(common.values()	)
		if num_same == 0:
				return 0
		a_     :      int 						=     1.0 * num_same / len(SCREAMING_SNAKE_CASE__	)
		a_     :      Union[str, Any] 						=     1.0 * num_same / len(SCREAMING_SNAKE_CASE__	)
		a_     :      List[Any] 						=     (2 * precision * recall) / (precision + recall)
		return fa



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)			->      Optional[Any]:
		return normalize_answer(SCREAMING_SNAKE_CASE__	) == normalize_answer(SCREAMING_SNAKE_CASE__	)



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)			->      Dict:
		assert len(SCREAMING_SNAKE_CASE__	) == len(SCREAMING_SNAKE_CASE__	)
		a_     :      List[str] 						=     0
		for hypo, pred in zip(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	):
				em += exact_match_score(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)
		if len(SCREAMING_SNAKE_CASE__	) > 0:
				em /= len(SCREAMING_SNAKE_CASE__	)
		return {"em": em}



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__	)			->      Any:
		return model_prefix.startswith("rag"	)



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)			->      int:
		a_     :      int 						=     {p: p for p in extra_params}
		# T5 models don't have `dropout` param, they have `dropout_rate` instead
		a_     :      List[str] 						=     "dropout_rate"
		for p in extra_params:
				if getattr(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	):
						if not hasattr(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	) and not hasattr(SCREAMING_SNAKE_CASE__, equivalent_param[p]	):
								logger.info("config doesn't have a `{}` attribute".format(SCREAMING_SNAKE_CASE__	)	)
								delattr(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)
								continue
						a_     :      Optional[Any] 						=     p if hasattr(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	) else equivalent_param[p]
						setattr(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, getattr(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)	)
						delattr(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)
		return hparams, config




"""simple docstring"""
import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()




class 			snake_case_						(						unittest.TestCase				):






							@property
							def snake_case_  (   self      ):
									torch.manual_seed(0      )
									a_     :      Any 						=     UNetaDModel(
									    block_out_channels=(3_2, 6_4)			,     layers_per_block=2			,     sample_size=3_2			,     in_channels=3			,     out_channels=3			,     down_block_types=("DownBlock2D", "AttnDownBlock2D")			,     up_block_types=("AttnUpBlock2D", "UpBlock2D")			,     )
									return model






							def snake_case_  (   self      ):
									a_     :      Any 						=     self.dummy_uncond_unet
									a_     :      Any 						=     PNDMScheduler()

									a_     :      List[Any] 						=     PNDMPipeline(unet=a_			,     scheduler=a_      )
									pndm.to(a_      )
									pndm.set_progress_bar_config(disable=a_      )

									a_     :      Tuple 						=     torch.manual_seed(0      )
									a_     :      Union[str, Any] 						=     pndm(generator=a_			,     num_inference_steps=2_0			,     output_type="numpy"      ).images

									a_     :      int 						=     torch.manual_seed(0      )
									a_     :      Optional[int] 						=     pndm(generator=a_			,     num_inference_steps=2_0			,     output_type="numpy"			,     return_dict=a_      )[0]

									a_     :      List[Any] 						=     image[0, -3:, -3:, -1]
									a_     :      Union[str, Any] 						=     image_from_tuple[0, -3:, -3:, -1]

									assert image.shape == (1, 3_2, 3_2, 3)
									a_     :      List[Any] 						=     np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0]      )

									assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-2
									assert np.abs(image_from_tuple_slice.flatten() - expected_slice      ).max() < 1e-2




@slow
@require_torch
class 			snake_case_						(						unittest.TestCase				):






							def snake_case_  (   self      ):
									a_     :      Optional[Any] 						=     "google/ddpm-cifar10-32"

									a_     :      Optional[int] 						=     UNetaDModel.from_pretrained(a_      )
									a_     :      Union[str, Any] 						=     PNDMScheduler()

									a_     :      List[Any] 						=     PNDMPipeline(unet=a_			,     scheduler=a_      )
									pndm.to(a_      )
									pndm.set_progress_bar_config(disable=a_      )
									a_     :      Optional[int] 						=     torch.manual_seed(0      )
									a_     :      List[Any] 						=     pndm(generator=a_			,     output_type="numpy"      ).images

									a_     :      str 						=     image[0, -3:, -3:, -1]

									assert image.shape == (1, 3_2, 3_2, 3)
									a_     :      Optional[Any] 						=     np.array([0.1_564, 0.14_645, 0.1_406, 0.14_715, 0.12_425, 0.14_045, 0.13_115, 0.12_175, 0.125]      )

									assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-2