


'''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      a      (			lowerCamelCase__						,				lowerCamelCase__						,				lowerCamelCase__						,				lowerCamelCase__						,				lowerCamelCase__=True						,				lowerCamelCase__="pt"   ):
			'''simple docstring'''





			A_     :						Dict     =					{"add_prefix_space": True} if isinstance(_lowerCamelCase						,				_lowerCamelCase   ) and not line.startswith(""" """   ) else {}
			A_     :						List[Any]     =					padding_side
			return tokenizer(
			    [line]						,				max_length=_lowerCamelCase						,				padding="""max_length""" if pad_to_max_length else None						,				truncation=_lowerCamelCase						,				return_tensors=_lowerCamelCase						,				add_special_tokens=_lowerCamelCase						,				**_lowerCamelCase						,				)



def      a      (			lowerCamelCase__						,				lowerCamelCase__						,				lowerCamelCase__=None						,				):
			'''simple docstring'''





			A_     :						str     =					input_ids.ne(_lowerCamelCase   ).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							(      a__			):


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


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


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

				# Pad source and target to the right
				A_     :						Tuple     =					(
				    self.tokenizer.question_encoder if isinstance(self.tokenizer	,				lowercase							) else self.tokenizer
				)
				A_     :						Union[str, Any]     =					self.tokenizer.generator if isinstance(self.tokenizer	,				lowercase							) else self.tokenizer

				A_     :						str     =					encode_line(lowercase	,				lowercase	,				self.max_source_length	,				"""right"""							)
				A_     :						int     =					encode_line(lowercase	,				lowercase	,				self.max_target_length	,				"""right"""							)

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


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


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






lowerCamelCase							:Any       = getLogger(__name__)



def      a      (			lowerCamelCase__   ):
			'''simple docstring'''





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



def      a      (			lowerCamelCase__   ):
			'''simple docstring'''





			A_     :						List[str]     =					get_git_info()
			save_json(_lowerCamelCase						,				os.path.join(_lowerCamelCase						,				"""git_log.json"""   )   )



def      a      (			lowerCamelCase__						,				lowerCamelCase__						,				lowerCamelCase__=4						,				**lowerCamelCase__   ):
			'''simple docstring'''





			with open(_lowerCamelCase						,				"""w"""   ) as f:
						json.dump(_lowerCamelCase						,				_lowerCamelCase						,				indent=_lowerCamelCase						,				**_lowerCamelCase   )



def      a      (			lowerCamelCase__   ):
			'''simple docstring'''





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



def      a      (			):
			'''simple docstring'''





			A_     :						Optional[Any]     =					git.Repo(search_parent_directories=_lowerCamelCase   )
			A_     :						Optional[int]     =					{
			    "repo_id": str(_lowerCamelCase   ),
			    "repo_sha": str(repo.head.object.hexsha   ),
			    "repo_branch": str(repo.active_branch   ),
			    "hostname": str(socket.gethostname()   ),
			}
			return repo_infos



def      a      (			lowerCamelCase__						,				lowerCamelCase__   ):
			'''simple docstring'''





			return list(map(_lowerCamelCase						,				_lowerCamelCase   )   )



def      a      (			lowerCamelCase__						,				lowerCamelCase__   ):
			'''simple docstring'''





			with open(_lowerCamelCase						,				"""wb"""   ) as f:
						return pickle.dump(_lowerCamelCase						,				_lowerCamelCase   )



def      a      (			lowerCamelCase__   ):
			'''simple docstring'''





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

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

			def remove_punc(lowerCamelCase__   ):
						A_     :						Optional[Any]     =					set(string.punctuation   )
						return "".join(ch for ch in text if ch not in exclude   )

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

			return white_space_fix(remove_articles(remove_punc(lower(_lowerCamelCase   )   )   )   )



def      a      (			lowerCamelCase__						,				lowerCamelCase__   ):
			'''simple docstring'''





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



def      a      (			lowerCamelCase__						,				lowerCamelCase__   ):
			'''simple docstring'''





			return normalize_answer(_lowerCamelCase   ) == normalize_answer(_lowerCamelCase   )



def      a      (			lowerCamelCase__						,				lowerCamelCase__   ):
			'''simple docstring'''





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



def      a      (			lowerCamelCase__   ):
			'''simple docstring'''





			return model_prefix.startswith("""rag"""   )



def      a      (			lowerCamelCase__						,				lowerCamelCase__						,				lowerCamelCase__   ):
			'''simple docstring'''





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




'''simple docstring'''




import unittest
from typing import Tuple

import torch

from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch







@require_torch
class      _lowerCAmelCase							:


	@property
	def    _a     (self							):
				return self.get_dummy_input()


	@property
	def    _a     (self							):
				if self.block_type == "down":
							return (4, 32, 16, 16)
				elif self.block_type == "mid":
							return (4, 32, 32, 32)
				elif self.block_type == "up":
							return (4, 32, 64, 64)

				raise ValueError(F'\'{self.block_type}\' is not a supported block_type. Set it to \'up\', \'mid\', or \'down\'.'							)


	def    _a     (self	,				lowercase=True	,				lowercase=False	,				lowercase=False	,				lowercase=False	,				):
				A_     :						List[str]     =					4
				A_     :						int     =					32
				A_     :						Optional[int]     =					(32, 32)

				A_     :						Optional[Any]     =					torch.manual_seed(0							)
				A_     :						int     =					torch.device(lowercase							)
				A_     :						int     =					(batch_size, num_channels) + sizes
				A_     :						Optional[int]     =					randn_tensor(lowercase	,				generator=lowercase	,				device=lowercase							)
				A_     :						Union[str, Any]     =					{"""hidden_states""": hidden_states}

				if include_temb:
							A_     :						str     =					128
							A_     :						List[Any]     =					randn_tensor((batch_size, temb_channels)	,				generator=lowercase	,				device=lowercase							)

				if include_res_hidden_states_tuple:
							A_     :						List[str]     =					torch.manual_seed(1							)
							A_     :						int     =					(randn_tensor(lowercase	,				generator=lowercase	,				device=lowercase							),)

				if include_encoder_hidden_states:
							A_     :						List[str]     =					floats_tensor((batch_size, 32, 32)							).to(lowercase							)

				if include_skip_sample:
							A_     :						Dict     =					randn_tensor(((batch_size, 3) + sizes)	,				generator=lowercase	,				device=lowercase							)

				return dummy_input


	def    _a     (self							):
				A_     :						Tuple     =					{
				    """in_channels""": 32,
				    """out_channels""": 32,
				    """temb_channels""": 128,
				}
				if self.block_type == "up":
							A_     :						Any     =					32

				if self.block_type == "mid":
							init_dict.pop("""out_channels"""							)

				A_     :						Optional[int]     =					self.dummy_input
				return init_dict, inputs_dict


	def    _a     (self	,				lowercase							):
				A_,   A_     :						Optional[Any]     =					self.prepare_init_args_and_inputs_for_common()
				A_     :						int     =					self.block_class(**lowercase							)
				unet_block.to(lowercase							)
				unet_block.eval()

				with torch.no_grad():
							A_     :						List[str]     =					unet_block(**lowercase							)

				if isinstance(lowercase	,				lowercase							):
							A_     :						Union[str, Any]     =					output[0]

				self.assertEqual(output.shape	,				self.output_shape							)

				A_     :						int     =					output[0, -1, -3:, -3:]
				A_     :						List[Any]     =					torch.tensor(lowercase							).to(lowercase							)
				assert torch_all_close(output_slice.flatten()	,				lowercase	,				atol=5E-3							)


	@unittest.skipIf(torch_device == """mps"""	,				"""Training is not supported in mps"""							)
	def    _a     (self							):
				A_,   A_     :						Tuple     =					self.prepare_init_args_and_inputs_for_common()
				A_     :						List[str]     =					self.block_class(**lowercase							)
				model.to(lowercase							)
				model.train()
				A_     :						Any     =					model(**lowercase							)

				if isinstance(lowercase	,				lowercase							):
							A_     :						str     =					output[0]

				A_     :						Union[str, Any]     =					torch.device(lowercase							)
				A_     :						Tuple     =					randn_tensor(output.shape	,				device=lowercase							)
				A_     :						List[str]     =					torch.nn.functional.mse_loss(lowercase	,				lowercase							)
				loss.backward()