'''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 							snake_case__				(     _A:   Dict ,  _A:   List[str] ,  _A:   str ,  _A:   Any ,  _A:   Tuple=True ,  _A:   Union[str, Any]="pt"					)       ->      int:

		'''simple docstring'''


		lowerCAmelCase     =    {"""add_prefix_space""": True} if isinstance(_A ,  _A					) and not line.startswith(""" """					) else {}
		lowerCAmelCase     =    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 							snake_case__				(     _A:   List[str] ,  _A:   List[Any] ,  _A:   Optional[int]=None ,  )       ->      Optional[Any]:

		'''simple docstring'''


		lowerCAmelCase     =    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 							a__(   lowerCAmelCase__      ):


			'''simple docstring'''






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




					"""simple docstring"""


					super().__init__()
					lowerCAmelCase     =    Path(__lowerCAmelCase).joinpath(type_path + """.source""")
					lowerCAmelCase     =    Path(__lowerCAmelCase).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):




					"""simple docstring"""


					return len(self.src_lens)



			def __getitem__(  self			,       __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase     =    index + 1  # linecache starts at 1
					lowerCAmelCase     =    self.prefix + linecache.getline(str(self.src_file)			,       __lowerCAmelCase).rstrip("""\n""")
					lowerCAmelCase     =    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
					lowerCAmelCase     =    (
					    self.tokenizer.question_encoder if isinstance(self.tokenizer			,       __lowerCAmelCase) else self.tokenizer
					)
					lowerCAmelCase     =    self.tokenizer.generator if isinstance(self.tokenizer			,       __lowerCAmelCase) else self.tokenizer

					lowerCAmelCase     =    encode_line(__lowerCAmelCase			,       __lowerCAmelCase			,       self.max_source_length			,       """right""")
					lowerCAmelCase     =    encode_line(__lowerCAmelCase			,       __lowerCAmelCase			,       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_			(  __lowerCAmelCase):




					"""simple docstring"""


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







			def 				a_			(  self			,       __lowerCAmelCase):




					"""simple docstring"""


					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			,       __lowerCAmelCase)
					    else self.tokenizer.pad_token_id
					)
					lowerCAmelCase     =    (
					    self.tokenizer.question_encoder.pad_token_id
					    if isinstance(self.tokenizer			,       __lowerCAmelCase)
					    else self.tokenizer.pad_token_id
					)
					lowerCAmelCase     =    trim_batch(__lowerCAmelCase			,       __lowerCAmelCase)
					lowerCAmelCase   ,	lowerCAmelCase     =    trim_batch(__lowerCAmelCase			,       __lowerCAmelCase			,       attention_mask=__lowerCAmelCase)
					lowerCAmelCase     =    {
					    """input_ids""": source_ids,
					    """attention_mask""": source_mask,
					    """decoder_input_ids""": y,
					}
					return batch







__lowercase        =    getLogger(__name__)






def 							snake_case__				(     _A:   List[List]					)       ->      List[str]:

		'''simple docstring'''


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






def 							snake_case__				(     _A:   str					)       ->      None:

		'''simple docstring'''


		lowerCAmelCase     =    get_git_info()
		save_json(_A ,  os.path.join(_A ,  """git_log.json"""					)					)






def 							snake_case__				(     _A:   Tuple ,  _A:   Optional[Any] ,  _A:   Optional[Any]=4 ,  **_A:   List[Any]					)       ->      Tuple:

		'''simple docstring'''


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






def 							snake_case__				(     _A:   Dict					)       ->      Tuple:

		'''simple docstring'''


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






def 							snake_case__				(     )       ->      Union[str, Any]:

		'''simple docstring'''


		lowerCAmelCase     =    git.Repo(search_parent_directories=_A					)
		lowerCAmelCase     =    {
		    """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 							snake_case__				(     _A:   Callable ,  _A:   Iterable					)       ->      List:

		'''simple docstring'''


		return list(map(_A ,  _A					)					)






def 							snake_case__				(     _A:   Optional[int] ,  _A:   Tuple					)       ->      Optional[int]:

		'''simple docstring'''


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






def 							snake_case__				(     _A:   int					)       ->      List[str]:

		'''simple docstring'''


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

		def white_space_fix(_A:   List[str]					):
				return " ".join(text.split()					)

		def remove_punc(_A:   List[Any]					):
				lowerCAmelCase     =    set(string.punctuation					)
				return "".join(ch for ch in text if ch not in exclude					)

		def lower(_A:   List[Any]					):
				return text.lower()

		return white_space_fix(remove_articles(remove_punc(lower(_A					)					)					)					)






def 							snake_case__				(     _A:   Optional[Any] ,  _A:   List[Any]					)       ->      Optional[int]:

		'''simple docstring'''


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






def 							snake_case__				(     _A:   Tuple ,  _A:   int					)       ->      int:

		'''simple docstring'''


		return normalize_answer(_A					) == normalize_answer(_A					)






def 							snake_case__				(     _A:   List[str] ,  _A:   List[str]					)       ->      Dict:

		'''simple docstring'''


		assert len(_A					) == len(_A					)
		lowerCAmelCase     =    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 							snake_case__				(     _A:   str					)       ->      Optional[Any]:

		'''simple docstring'''


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






def 							snake_case__				(     _A:   List[Any] ,  _A:   Any ,  _A:   Dict					)       ->      Optional[int]:

		'''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(_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
						lowerCAmelCase     =    p if hasattr(_A ,  _A					) else equivalent_param[p]
						setattr(_A ,  _A ,  getattr(_A ,  _A					)					)
						delattr(_A ,  _A					)
		return hparams, config





'''simple docstring'''




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 							a__(   lowerCAmelCase__      ):


			'''simple docstring'''






			UpperCAmelCase_     :	"DiagonalGaussianDistribution"







class 							a__(   lowerCAmelCase__					,		lowerCAmelCase__      ):


			'''simple docstring'''






			UpperCAmelCase_     :	List[Any]						=			True



			@register_to_config
			def __init__(  self			,       __lowerCAmelCase = 3			,       __lowerCAmelCase = 3			,       __lowerCAmelCase = ("DownEncoderBlock2D",)			,       __lowerCAmelCase = ("UpDecoderBlock2D",)			,       __lowerCAmelCase = (64,)			,       __lowerCAmelCase = 1			,       __lowerCAmelCase = "silu"			,       __lowerCAmelCase = 4			,       __lowerCAmelCase = 32			,       __lowerCAmelCase = 32			,       __lowerCAmelCase = 0.18215			,       ):




					"""simple docstring"""


					super().__init__()

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

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

					lowerCAmelCase     =    nn.Convad(2 * latent_channels			,       2 * latent_channels			,       1)
					lowerCAmelCase     =    nn.Convad(__lowerCAmelCase			,       __lowerCAmelCase			,       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			,       __lowerCAmelCase			,       __lowerCAmelCase=False):




					"""simple docstring"""


					if isinstance(__lowerCAmelCase			,       (Encoder, Decoder)):
							lowerCAmelCase     =    value



			def 				a_			(  self			,       __lowerCAmelCase = True):




					"""simple docstring"""


					lowerCAmelCase     =    use_tiling



			def 				a_			(  self):




					"""simple docstring"""


					self.enable_tiling(__lowerCAmelCase)



			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    True



			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    False



			@property
			# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    {}

					def fn_recursive_add_processors(__lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase):
							if hasattr(__lowerCAmelCase			,       """set_processor"""):
									lowerCAmelCase     =    module.processor

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

							return processors

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

					return processors



			def 				a_			(  self			,       __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase     =    len(self.attn_processors.keys())

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

					def fn_recursive_attn_processor(__lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase):
							if hasattr(__lowerCAmelCase			,       """set_processor"""):
									if not isinstance(__lowerCAmelCase			,       __lowerCAmelCase):
											module.set_processor(__lowerCAmelCase)
									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}"			,       __lowerCAmelCase			,       __lowerCAmelCase)

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



			def 				a_			(  self):




					"""simple docstring"""


					self.set_attn_processor(AttnProcessor())



			@apply_forward_hook
			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase = True):




					"""simple docstring"""


					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(__lowerCAmelCase			,       return_dict=__lowerCAmelCase)

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

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

					if not return_dict:
							return (posterior,)

					return AutoencoderKLOutput(latent_dist=__lowerCAmelCase)



			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase = True):




					"""simple docstring"""


					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(__lowerCAmelCase			,       return_dict=__lowerCAmelCase)

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

					if not return_dict:
							return (dec,)

					return DecoderOutput(sample=__lowerCAmelCase)



			@apply_forward_hook
			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase = True):




					"""simple docstring"""


					if self.use_slicing and z.shape[0] > 1:
							lowerCAmelCase     =    [self._decode(__lowerCAmelCase).sample for z_slice in z.split(1)]
							lowerCAmelCase     =    torch.cat(__lowerCAmelCase)
					else:
							lowerCAmelCase     =    self._decode(__lowerCAmelCase).sample

					if not return_dict:
							return (decoded,)

					return DecoderOutput(sample=__lowerCAmelCase)



			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase):




					"""simple docstring"""


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



			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase):




					"""simple docstring"""


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



			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase = True):




					"""simple docstring"""


					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]			,       __lowerCAmelCase):
							lowerCAmelCase     =    []
							for j in range(0			,       x.shape[3]			,       __lowerCAmelCase):
									lowerCAmelCase     =    x[:, :, i : i + self.tile_sample_min_size, j : j + self.tile_sample_min_size]
									lowerCAmelCase     =    self.encoder(__lowerCAmelCase)
									lowerCAmelCase     =    self.quant_conv(__lowerCAmelCase)
									row.append(__lowerCAmelCase)
							rows.append(__lowerCAmelCase)
					lowerCAmelCase     =    []
					for i, row in enumerate(__lowerCAmelCase):
							lowerCAmelCase     =    []
							for j, tile in enumerate(__lowerCAmelCase):
									# 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]			,       __lowerCAmelCase			,       __lowerCAmelCase)
									if j > 0:
											lowerCAmelCase     =    self.blend_h(row[j - 1]			,       __lowerCAmelCase			,       __lowerCAmelCase)
									result_row.append(tile[:, :, :row_limit, :row_limit])
							result_rows.append(torch.cat(__lowerCAmelCase			,       dim=3))

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

					if not return_dict:
							return (posterior,)

					return AutoencoderKLOutput(latent_dist=__lowerCAmelCase)



			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase = True):




					"""simple docstring"""


					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]			,       __lowerCAmelCase):
							lowerCAmelCase     =    []
							for j in range(0			,       z.shape[3]			,       __lowerCAmelCase):
									lowerCAmelCase     =    z[:, :, i : i + self.tile_latent_min_size, j : j + self.tile_latent_min_size]
									lowerCAmelCase     =    self.post_quant_conv(__lowerCAmelCase)
									lowerCAmelCase     =    self.decoder(__lowerCAmelCase)
									row.append(__lowerCAmelCase)
							rows.append(__lowerCAmelCase)
					lowerCAmelCase     =    []
					for i, row in enumerate(__lowerCAmelCase):
							lowerCAmelCase     =    []
							for j, tile in enumerate(__lowerCAmelCase):
									# 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]			,       __lowerCAmelCase			,       __lowerCAmelCase)
									if j > 0:
											lowerCAmelCase     =    self.blend_h(row[j - 1]			,       __lowerCAmelCase			,       __lowerCAmelCase)
									result_row.append(tile[:, :, :row_limit, :row_limit])
							result_rows.append(torch.cat(__lowerCAmelCase			,       dim=3))

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

					return DecoderOutput(sample=__lowerCAmelCase)







			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase = False			,       __lowerCAmelCase = True			,       __lowerCAmelCase = None			,       ):




					"""simple docstring"""


					lowerCAmelCase     =    sample
					lowerCAmelCase     =    self.encode(__lowerCAmelCase).latent_dist
					if sample_posterior:
							lowerCAmelCase     =    posterior.sample(generator=__lowerCAmelCase)
					else:
							lowerCAmelCase     =    posterior.mode()
					lowerCAmelCase     =    self.decode(__lowerCAmelCase).sample

					if not return_dict:
							return (dec,)

					return DecoderOutput(sample=__lowerCAmelCase)




