

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    (  UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__=True		,					UpperCAmelCase__="pt"      ):

					"""simple docstring"""






					__lowerCAmelCase					=							{'add_prefix_space': True} if isinstance(UpperCAmelCase__		,					UpperCAmelCase__      ) and not line.startswith(' '      ) else {}
					__lowerCAmelCase					=							padding_side
					return tokenizer(
					    [line]		,					max_length=UpperCAmelCase__		,					padding='max_length' if pad_to_max_length else None		,					truncation=UpperCAmelCase__		,					return_tensors=UpperCAmelCase__		,					add_special_tokens=UpperCAmelCase__		,					**UpperCAmelCase__		,					)


def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__=None		,					):

					"""simple docstring"""






					__lowerCAmelCase					=							input_ids.ne(UpperCAmelCase__      ).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							):


							"""simple docstring"""





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




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




							def __getitem__( self					,						_A							):
												__lowerCAmelCase					=							index + 1  # linecache starts at 1
												__lowerCAmelCase					=							self.prefix + linecache.getline(str(self.src_file							)					,						_A							).rstrip('\n'							)
												__lowerCAmelCase					=							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
												__lowerCAmelCase					=							(
												    self.tokenizer.question_encoder if isinstance(self.tokenizer					,						_A							) else self.tokenizer
												)
												__lowerCAmelCase					=							self.tokenizer.generator if isinstance(self.tokenizer					,						_A							) else self.tokenizer

												__lowerCAmelCase					=							encode_line(_A					,						_A					,						self.max_source_length					,						'right'							)
												__lowerCAmelCase					=							encode_line(_A					,						_A					,						self.max_target_length					,						'right'							)

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




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




							def        A__  ( self					,						_A							):
												__lowerCAmelCase					=							torch.stack([x['input_ids'] for x in batch]							)
												__lowerCAmelCase					=							torch.stack([x['attention_mask'] for x in batch]							)
												__lowerCAmelCase					=							torch.stack([x['decoder_input_ids'] for x in batch]							)
												__lowerCAmelCase					=							(
												    self.tokenizer.generator.pad_token_id
												    if isinstance(self.tokenizer					,						_A							)
												    else self.tokenizer.pad_token_id
												)
												__lowerCAmelCase					=							(
												    self.tokenizer.question_encoder.pad_token_id
												    if isinstance(self.tokenizer					,						_A							)
												    else self.tokenizer.pad_token_id
												)
												__lowerCAmelCase					=							trim_batch(_A					,						_A							)
												__lowerCAmelCase,	__lowerCAmelCase					=							trim_batch(_A					,						_A					,						attention_mask=_A							)
												__lowerCAmelCase					=							{
												    'input_ids': source_ids,
												    'attention_mask': source_mask,
												    'decoder_input_ids': y,
												}
												return batch
lowerCamelCase      				=     getLogger(__name__)


def        __lowercase    (  UpperCAmelCase__      ):

					"""simple docstring"""






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


def        __lowercase    (  UpperCAmelCase__      ):

					"""simple docstring"""






					__lowerCAmelCase					=							get_git_info()
					save_json(UpperCAmelCase__		,					os.path.join(UpperCAmelCase__		,					'git_log.json'      )      )


def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__=4		,					**UpperCAmelCase__      ):

					"""simple docstring"""






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


def        __lowercase    (  UpperCAmelCase__      ):

					"""simple docstring"""






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


def        __lowercase    (  ):

					"""simple docstring"""






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


def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






					return list(map(UpperCAmelCase__		,					UpperCAmelCase__      )      )


def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






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


def        __lowercase    (  UpperCAmelCase__      ):

					"""simple docstring"""






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

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

					def remove_punc(UpperCAmelCase__      ):
										__lowerCAmelCase					=							set(string.punctuation      )
										return "".join(ch for ch in text if ch not in exclude      )

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

					return white_space_fix(remove_articles(remove_punc(lower(UpperCAmelCase__      )      )      )      )


def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






					__lowerCAmelCase					=							normalize_answer(UpperCAmelCase__      ).split()
					__lowerCAmelCase					=							normalize_answer(UpperCAmelCase__      ).split()
					__lowerCAmelCase					=							Counter(UpperCAmelCase__      ) & Counter(UpperCAmelCase__      )
					__lowerCAmelCase					=							sum(common.values()      )
					if num_same == 0:
										return 0
					__lowerCAmelCase					=							1.0 * num_same / len(UpperCAmelCase__      )
					__lowerCAmelCase					=							1.0 * num_same / len(UpperCAmelCase__      )
					__lowerCAmelCase					=							(2 * precision * recall) / (precision + recall)
					return fa


def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






					return normalize_answer(UpperCAmelCase__      ) == normalize_answer(UpperCAmelCase__      )


def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






					assert len(UpperCAmelCase__      ) == len(UpperCAmelCase__      )
					__lowerCAmelCase					=							0
					for hypo, pred in zip(UpperCAmelCase__		,					UpperCAmelCase__      ):
										em += exact_match_score(UpperCAmelCase__		,					UpperCAmelCase__      )
					if len(UpperCAmelCase__      ) > 0:
										em /= len(UpperCAmelCase__      )
					return {"em": em}


def        __lowercase    (  UpperCAmelCase__      ):

					"""simple docstring"""






					return model_prefix.startswith('rag'      )


def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






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









from dataclasses import dataclass
from typing import Dict, Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .attention_processor import AttentionProcessor, AttnProcessor
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, DiagonalGaussianDistribution, Encoder





@dataclass
class     snake_case_			(				_a							):


							"""simple docstring"""





							__UpperCAmelCase       =42





class     snake_case_			(				_a			, _a							):


							"""simple docstring"""





							__UpperCAmelCase       =True




							@register_to_config
							def __init__( self					,						_A = 3					,						_A = 3					,						_A = ("DownEncoderBlock2D",)					,						_A = ("UpDecoderBlock2D",)					,						_A = (6_4,)					,						_A = 1					,						_A = "silu"					,						_A = 4					,						_A = 3_2					,						_A = 3_2					,						_A = 0.1_8215					,						):
												super().__init__()

												# pass init params to Encoder
												__lowerCAmelCase					=							Encoder(
												    in_channels=_A					,						out_channels=_A					,						down_block_types=_A					,						block_out_channels=_A					,						layers_per_block=_A					,						act_fn=_A					,						norm_num_groups=_A					,						double_z=_A					,						)

												# pass init params to Decoder
												__lowerCAmelCase					=							Decoder(
												    in_channels=_A					,						out_channels=_A					,						up_block_types=_A					,						block_out_channels=_A					,						layers_per_block=_A					,						norm_num_groups=_A					,						act_fn=_A					,						)

												__lowerCAmelCase					=							nn.Convad(2 * latent_channels					,						2 * latent_channels					,						1							)
												__lowerCAmelCase					=							nn.Convad(_A					,						_A					,						1							)

												__lowerCAmelCase					=							False
												__lowerCAmelCase					=							False

												# only relevant if vae tiling is enabled
												__lowerCAmelCase					=							self.config.sample_size
												__lowerCAmelCase					=							(
												    self.config.sample_size[0]
												    if isinstance(self.config.sample_size					,						(list, tuple)							)
												    else self.config.sample_size
												)
												__lowerCAmelCase					=							int(sample_size / (2 ** (len(self.config.block_out_channels							) - 1))							)
												__lowerCAmelCase					=							0.25




							def        A__  ( self					,						_A					,						_A=False							):
												if isinstance(_A					,						(Encoder, Decoder)							):
																	__lowerCAmelCase					=							value




							def        A__  ( self					,						_A = True							):
												__lowerCAmelCase					=							use_tiling




							def        A__  ( self							):
												self.enable_tiling(_A							)




							def        A__  ( self							):
												__lowerCAmelCase					=							True




							def        A__  ( self							):
												__lowerCAmelCase					=							False




							@property
							# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
							def        A__  ( self							):
												__lowerCAmelCase					=							{}

												def fn_recursive_add_processors(_A					,						_A					,						_A							):
																	if hasattr(_A					,						'set_processor'							):
																						__lowerCAmelCase					=							module.processor

																	for sub_name, child in module.named_children():
																						fn_recursive_add_processors(F"""{name}.{sub_name}"""					,						_A					,						_A							)

																	return processors

												for name, module in self.named_children():
																	fn_recursive_add_processors(_A					,						_A					,						_A							)

												return processors




							def        A__  ( self					,						_A							):
												__lowerCAmelCase					=							len(self.attn_processors.keys()							)

												if isinstance(_A					,						_A							) and len(_A							) != count:
																	raise ValueError(
																	    F"""A dict of processors was passed, but the number of processors {len(_A							)} does not match the"""
																	    F""" number of attention layers: {count}. Please make sure to pass {count} processor classes."""							)

												def fn_recursive_attn_processor(_A					,						_A					,						_A							):
																	if hasattr(_A					,						'set_processor'							):
																						if not isinstance(_A					,						_A							):
																											module.set_processor(_A							)
																						else:
																											module.set_processor(processor.pop(F"""{name}.processor"""							)							)

																	for sub_name, child in module.named_children():
																						fn_recursive_attn_processor(F"""{name}.{sub_name}"""					,						_A					,						_A							)

												for name, module in self.named_children():
																	fn_recursive_attn_processor(_A					,						_A					,						_A							)




							def        A__  ( self							):
												self.set_attn_processor(AttnProcessor()							)




							@apply_forward_hook
							def        A__  ( self					,						_A					,						_A = True							):
												if self.use_tiling and (x.shape[-1] > self.tile_sample_min_size or x.shape[-2] > self.tile_sample_min_size):
																	return self.tiled_encode(_A					,						return_dict=_A							)

												if self.use_slicing and x.shape[0] > 1:
																	__lowerCAmelCase					=							[self.encoder(_A							) for x_slice in x.split(1							)]
																	__lowerCAmelCase					=							torch.cat(_A							)
												else:
																	__lowerCAmelCase					=							self.encoder(_A							)

												__lowerCAmelCase					=							self.quant_conv(_A							)
												__lowerCAmelCase					=							DiagonalGaussianDistribution(_A							)

												if not return_dict:
																	return (posterior,)

												return AutoencoderKLOutput(latent_dist=_A							)




							def        A__  ( self					,						_A					,						_A = True							):
												if self.use_tiling and (z.shape[-1] > self.tile_latent_min_size or z.shape[-2] > self.tile_latent_min_size):
																	return self.tiled_decode(_A					,						return_dict=_A							)

												__lowerCAmelCase					=							self.post_quant_conv(_A							)
												__lowerCAmelCase					=							self.decoder(_A							)

												if not return_dict:
																	return (dec,)

												return DecoderOutput(sample=_A							)




							@apply_forward_hook
							def        A__  ( self					,						_A					,						_A = True							):
												if self.use_slicing and z.shape[0] > 1:
																	__lowerCAmelCase					=							[self._decode(_A							).sample for z_slice in z.split(1							)]
																	__lowerCAmelCase					=							torch.cat(_A							)
												else:
																	__lowerCAmelCase					=							self._decode(_A							).sample

												if not return_dict:
																	return (decoded,)

												return DecoderOutput(sample=_A							)




							def        A__  ( self					,						_A					,						_A					,						_A							):
												__lowerCAmelCase					=							min(a.shape[2]					,						b.shape[2]					,						_A							)
												for y in range(_A							):
																	__lowerCAmelCase					=							a[:, :, -blend_extent + y, :] * (1 - y / blend_extent) + b[:, :, y, :] * (y / blend_extent)
												return b




							def        A__  ( self					,						_A					,						_A					,						_A							):
												__lowerCAmelCase					=							min(a.shape[3]					,						b.shape[3]					,						_A							)
												for x in range(_A							):
																	__lowerCAmelCase					=							a[:, :, :, -blend_extent + x] * (1 - x / blend_extent) + b[:, :, :, x] * (x / blend_extent)
												return b




							def        A__  ( self					,						_A					,						_A = True							):
												__lowerCAmelCase					=							int(self.tile_sample_min_size * (1 - self.tile_overlap_factor)							)
												__lowerCAmelCase					=							int(self.tile_latent_min_size * self.tile_overlap_factor							)
												__lowerCAmelCase					=							self.tile_latent_min_size - blend_extent

												# Split the image into 512x512 tiles and encode them separately.
												__lowerCAmelCase					=							[]
												for i in range(0					,						x.shape[2]					,						_A							):
																	__lowerCAmelCase					=							[]
																	for j in range(0					,						x.shape[3]					,						_A							):
																						__lowerCAmelCase					=							x[:, :, i : i + self.tile_sample_min_size, j : j + self.tile_sample_min_size]
																						__lowerCAmelCase					=							self.encoder(_A							)
																						__lowerCAmelCase					=							self.quant_conv(_A							)
																						row.append(_A							)
																	rows.append(_A							)
												__lowerCAmelCase					=							[]
												for i, row in enumerate(_A							):
																	__lowerCAmelCase					=							[]
																	for j, tile in enumerate(_A							):
																						# blend the above tile and the left tile
																						# to the current tile and add the current tile to the result row
																						if i > 0:
																											__lowerCAmelCase					=							self.blend_v(rows[i - 1][j]					,						_A					,						_A							)
																						if j > 0:
																											__lowerCAmelCase					=							self.blend_h(row[j - 1]					,						_A					,						_A							)
																						result_row.append(tile[:, :, :row_limit, :row_limit]							)
																	result_rows.append(torch.cat(_A					,						dim=3							)							)

												__lowerCAmelCase					=							torch.cat(_A					,						dim=2							)
												__lowerCAmelCase					=							DiagonalGaussianDistribution(_A							)

												if not return_dict:
																	return (posterior,)

												return AutoencoderKLOutput(latent_dist=_A							)




							def        A__  ( self					,						_A					,						_A = True							):
												__lowerCAmelCase					=							int(self.tile_latent_min_size * (1 - self.tile_overlap_factor)							)
												__lowerCAmelCase					=							int(self.tile_sample_min_size * self.tile_overlap_factor							)
												__lowerCAmelCase					=							self.tile_sample_min_size - blend_extent

												# Split z into overlapping 64x64 tiles and decode them separately.
												# The tiles have an overlap to avoid seams between tiles.
												__lowerCAmelCase					=							[]
												for i in range(0					,						z.shape[2]					,						_A							):
																	__lowerCAmelCase					=							[]
																	for j in range(0					,						z.shape[3]					,						_A							):
																						__lowerCAmelCase					=							z[:, :, i : i + self.tile_latent_min_size, j : j + self.tile_latent_min_size]
																						__lowerCAmelCase					=							self.post_quant_conv(_A							)
																						__lowerCAmelCase					=							self.decoder(_A							)
																						row.append(_A							)
																	rows.append(_A							)
												__lowerCAmelCase					=							[]
												for i, row in enumerate(_A							):
																	__lowerCAmelCase					=							[]
																	for j, tile in enumerate(_A							):
																						# blend the above tile and the left tile
																						# to the current tile and add the current tile to the result row
																						if i > 0:
																											__lowerCAmelCase					=							self.blend_v(rows[i - 1][j]					,						_A					,						_A							)
																						if j > 0:
																											__lowerCAmelCase					=							self.blend_h(row[j - 1]					,						_A					,						_A							)
																						result_row.append(tile[:, :, :row_limit, :row_limit]							)
																	result_rows.append(torch.cat(_A					,						dim=3							)							)

												__lowerCAmelCase					=							torch.cat(_A					,						dim=2							)
												if not return_dict:
																	return (dec,)

												return DecoderOutput(sample=_A							)




							def        A__  ( self					,						_A					,						_A = False					,						_A = True					,						_A = None					,						):
												__lowerCAmelCase					=							sample
												__lowerCAmelCase					=							self.encode(_A							).latent_dist
												if sample_posterior:
																	__lowerCAmelCase					=							posterior.sample(generator=_A							)
												else:
																	__lowerCAmelCase					=							posterior.mode()
												__lowerCAmelCase					=							self.decode(_A							).sample

												if not return_dict:
																	return (dec,)

												return DecoderOutput(sample=_A							)






