

import coval  # From: git+https://github.com/ns-moosavi/coval.git # noqa: F401
from coval.conll import reader, util
from coval.eval import evaluator

import datasets


__magic_name__								=datasets.logging.get_logger(__name__)


__magic_name__								='''\
@InProceedings{moosavi2019minimum,
    author = { Nafise Sadat Moosavi, Leo Born, Massimo Poesio and Michael Strube},
    title = {Using Automatically Extracted Minimum Spans to Disentangle Coreference Evaluation from Boundary Detection},
    year = {2019},
    booktitle = {Proceedings of the 57th Annual Meeting of
        the Association for Computational Linguistics (Volume 1: Long Papers)},
    publisher = {Association for Computational Linguistics},
    address = {Florence, Italy},
}

@inproceedings{10.3115/1072399.1072405,
author = {Vilain, Marc and Burger, John and Aberdeen, John and Connolly, Dennis and Hirschman, Lynette},
title = {A Model-Theoretic Coreference Scoring Scheme},
year = {1995},
isbn = {1558604022},
publisher = {Association for Computational Linguistics},
address = {USA},
url = {https://doi.org/10.3115/1072399.1072405},
doi = {10.3115/1072399.1072405},
booktitle = {Proceedings of the 6th Conference on Message Understanding},
pages = {45–52},
numpages = {8},
location = {Columbia, Maryland},
series = {MUC6 ’95}
}

@INPROCEEDINGS{Bagga98algorithmsfor,
    author = {Amit Bagga and Breck Baldwin},
    title = {Algorithms for Scoring Coreference Chains},
    booktitle = {In The First International Conference on Language Resources and Evaluation Workshop on Linguistics Coreference},
    year = {1998},
    pages = {563--566}
}

@INPROCEEDINGS{Luo05oncoreference,
    author = {Xiaoqiang Luo},
    title = {On coreference resolution performance metrics},
    booktitle = {In Proc. of HLT/EMNLP},
    year = {2005},
    pages = {25--32},
    publisher = {URL}
}

@inproceedings{moosavi-strube-2016-coreference,
    title = "Which Coreference Evaluation Metric Do You Trust? A Proposal for a Link-based Entity Aware Metric",
    author = "Moosavi, Nafise Sadat  and
      Strube, Michael",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1060",
    doi = "10.18653/v1/P16-1060",
    pages = "632--642",
}

'''

__magic_name__								='''\
CoVal is a coreference evaluation tool for the CoNLL and ARRAU datasets which
implements of the common evaluation metrics including MUC [Vilain et al, 1995],
B-cubed [Bagga and Baldwin, 1998], CEAFe [Luo et al., 2005],
LEA [Moosavi and Strube, 2016] and the averaged CoNLL score
(the average of the F1 values of MUC, B-cubed and CEAFe)
[Denis and Baldridge, 2009a; Pradhan et al., 2011].

This wrapper of CoVal currently only work with CoNLL line format:
The CoNLL format has one word per line with all the annotation for this word in column separated by spaces:
Column	Type	Description
1	Document ID	This is a variation on the document filename
2	Part number	Some files are divided into multiple parts numbered as 000, 001, 002, ... etc.
3	Word number
4	Word itself	This is the token as segmented/tokenized in the Treebank. Initially the *_skel file contain the placeholder [WORD] which gets replaced by the actual token from the Treebank which is part of the OntoNotes release.
5	Part-of-Speech
6	Parse bit	This is the bracketed structure broken before the first open parenthesis in the parse, and the word/part-of-speech leaf replaced with a *. The full parse can be created by substituting the asterix with the "([pos] [word])" string (or leaf) and concatenating the items in the rows of that column.
7	Predicate lemma	The predicate lemma is mentioned for the rows for which we have semantic role information. All other rows are marked with a "-"
8	Predicate Frameset ID	This is the PropBank frameset ID of the predicate in Column 7.
9	Word sense	This is the word sense of the word in Column 3.
10	Speaker/Author	This is the speaker or author name where available. Mostly in Broadcast Conversation and Web Log data.
11	Named Entities	These columns identifies the spans representing various named entities.
12:N	Predicate Arguments	There is one column each of predicate argument structure information for the predicate mentioned in Column 7.
N	Coreference	Coreference chain information encoded in a parenthesis structure.
More informations on the format can be found here (section "*_conll File Format"): http://www.conll.cemantix.org/2012/data.html

Details on the evaluation on CoNLL can be found here: https://github.com/ns-moosavi/coval/blob/master/conll/README.md

CoVal code was written by @ns-moosavi.
Some parts are borrowed from https://github.com/clarkkev/deep-coref/blob/master/evaluation.py
The test suite is taken from https://github.com/conll/reference-coreference-scorers/
Mention evaluation and the test suite are added by @andreasvc.
Parsing CoNLL files is developed by Leo Born.
'''

__magic_name__								='''
Calculates coreference evaluation metrics.
Args:
    predictions: list of sentences. Each sentence is a list of word predictions to score in the CoNLL format.
        Each prediction is a word with its annotations as a string made of columns joined with spaces.
        Only columns 4, 5, 6 and the last column are used (word, POS, Pars and coreference annotation)
        See the details on the format in the description of the metric.
    references: list of sentences. Each sentence is a list of word reference to score in the CoNLL format.
        Each reference is a word with its annotations as a string made of columns joined with spaces.
        Only columns 4, 5, 6 and the last column are used (word, POS, Pars and coreference annotation)
        See the details on the format in the description of the metric.
    keep_singletons: After extracting all mentions of key or system files,
        mentions whose corresponding coreference chain is of size one,
        are considered as singletons. The default evaluation mode will include
        singletons in evaluations if they are included in the key or the system files.
        By setting \'keep_singletons=False\', all singletons in the key and system files
        will be excluded from the evaluation.
    NP_only: Most of the recent coreference resolvers only resolve NP mentions and
        leave out the resolution of VPs. By setting the \'NP_only\' option, the scorer will only evaluate the resolution of NPs.
    min_span: By setting \'min_span\', the scorer reports the results based on automatically detected minimum spans.
        Minimum spans are determined using the MINA algorithm.

Returns:
    \'mentions\': mentions
    \'muc\': MUC metric [Vilain et al, 1995]
    \'bcub\': B-cubed [Bagga and Baldwin, 1998]
    \'ceafe\': CEAFe [Luo et al., 2005]
    \'lea\': LEA [Moosavi and Strube, 2016]
    \'conll_score\': averaged CoNLL score (the average of the F1 values of MUC, B-cubed and CEAFe)

Examples:

    >>> coval = datasets.load_metric(\'coval\')
    >>> words = [\'bc/cctv/00/cctv_0005   0   0       Thank   VBP  (TOP(S(VP*    thank  01   1    Xu_li  *           (V*)        *       -\',
    ... \'bc/cctv/00/cctv_0005   0   1         you   PRP        (NP*)      -    -   -    Xu_li  *        (ARG1*)   (ARG0*)   (116)\',
    ... \'bc/cctv/00/cctv_0005   0   2    everyone    NN        (NP*)      -    -   -    Xu_li  *    (ARGM-DIS*)        *    (116)\',
    ... \'bc/cctv/00/cctv_0005   0   3         for    IN        (PP*       -    -   -    Xu_li  *        (ARG2*         *       -\',
    ... \'bc/cctv/00/cctv_0005   0   4    watching   VBG   (S(VP*))))   watch  01   1    Xu_li  *             *)      (V*)      -\',
    ... \'bc/cctv/00/cctv_0005   0   5           .     .          *))      -    -   -    Xu_li  *             *         *       -\']
    >>> references = [words]
    >>> predictions = [words]
    >>> results = coval.compute(predictions=predictions, references=references)
    >>> print(results) # doctest:+ELLIPSIS
    {\'mentions/recall\': 1.0,[...] \'conll_score\': 100.0}
'''







def  __UpperCamelCase    (						A ,							A ,							A=False ,							A=False ,							A=True ,							A=False ,							A="dummy_doc"							):
     UpperCamelCase__										=      {doc: key_lines}
     UpperCamelCase__										=      {doc: sys_lines}

     UpperCamelCase__										=      {}

     UpperCamelCase__										=      0
     UpperCamelCase__										=      0
     UpperCamelCase__										=      0
     UpperCamelCase__										=      0
     UpperCamelCase__										=      0
     UpperCamelCase__										=      0

     UpperCamelCase__						,       UpperCamelCase__										=      reader.get_doc_mentions(A ,							key_doc_lines[doc] ,							A							)
     key_singletons_num += singletons_num

     if NP_only or min_span:
          UpperCamelCase__										=      reader.set_annotated_parse_trees(A ,							key_doc_lines[doc] ,							A ,							A							)

     UpperCamelCase__						,       UpperCamelCase__										=      reader.get_doc_mentions(A ,							sys_doc_lines[doc] ,							A							)
     sys_singletons_num += singletons_num

     if NP_only or min_span:
          UpperCamelCase__										=      reader.set_annotated_parse_trees(A ,							key_doc_lines[doc] ,							A ,							A							)

     if remove_nested:
          UpperCamelCase__						,       UpperCamelCase__										=      reader.remove_nested_coref_mentions(A ,							A							)
          key_nested_coref_num += nested_mentions
          key_removed_nested_clusters += removed_clusters

          UpperCamelCase__						,       UpperCamelCase__										=      reader.remove_nested_coref_mentions(A ,							A							)
          sys_nested_coref_num += nested_mentions
          sys_removed_nested_clusters += removed_clusters

     UpperCamelCase__										=      reader.get_mention_assignments(A ,							A							)
     UpperCamelCase__										=      reader.get_mention_assignments(A ,							A							)

     UpperCamelCase__										=      (key_clusters, sys_clusters, key_mention_sys_cluster, sys_mention_key_cluster)

     if remove_nested:
          logger.info(
              '''Number of removed nested coreferring mentions in the key '''
              f"annotation: {key_nested_coref_num}; and system annotation: {sys_nested_coref_num}"							)
          logger.info(
              '''Number of resulting singleton clusters in the key '''
              f"annotation: {key_removed_nested_clusters}; and system annotation: {sys_removed_nested_clusters}"							)

     if not keep_singletons:
          logger.info(
              f"{key_singletons_num:d} and {sys_singletons_num:d} singletons are removed from the key and system "
              '''files, respectively'''							)

     return doc_coref_infos







def  __UpperCamelCase    (						A ,							A ,							A ,							A ,							A ,							A ,							A							):
     UpperCamelCase__										=      get_coref_infos(A ,							A ,							A ,							A ,							A ,							A							)

     UpperCamelCase__										=      {}
     UpperCamelCase__										=      0
     UpperCamelCase__										=      0

     for name, metric in metrics:
          UpperCamelCase__						,       UpperCamelCase__						,       UpperCamelCase__										=      evaluator.evaluate_documents(A ,							A ,							beta=1							)
          if name in ["muc", "bcub", "ceafe"]:
               conll += fa
               conll_subparts_num += 1
          output_scores.update({f"{name}/recall": recall, f"{name}/precision": precision, f"{name}/f1": fa}							)

          logger.info(
              name.ljust(10							) ,							f"Recall: {recall * 100:.2f}" ,							f" Precision: {precision * 100:.2f}" ,							f" F1: {fa * 100:.2f}" ,							)

     if conll_subparts_num == 3:
          UpperCamelCase__										=      (conll / 3) * 100
          logger.info(f"CoNLL score: {conll:.2f}"							)
          output_scores.update({'''conll_score''': conll}							)

     return output_scores







def  __UpperCamelCase    (						A							):
     UpperCamelCase__										=      False
     for line in key_lines:
          if not line.startswith('''#'''							):
               if len(line.split()							) > 6:
                    UpperCamelCase__										=      line.split()[5]
                    if not parse_col == "-":
                         UpperCamelCase__										=      True
                         break
                    else:
                         break
     return has_gold_parse



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION	, _KWARGS_DESCRIPTION							)
class     _A   (    datasets.Metric							):






  def 		_a					(self	)		->      Tuple:





       '''simple docstring'''





       return datasets.MetricInfo(
           description=_DESCRIPTION   ,				citation=_CITATION   ,				inputs_description=_KWARGS_DESCRIPTION   ,				features=datasets.Features(
               {
                   '''predictions''': datasets.Sequence(datasets.Value('''string'''	)	),
                   '''references''': datasets.Sequence(datasets.Value('''string'''	)	),
               }	)   ,				codebase_urls=['''https://github.com/ns-moosavi/coval''']   ,				reference_urls=[
               '''https://github.com/ns-moosavi/coval''',
               '''https://www.aclweb.org/anthology/P16-1060''',
               '''http://www.conll.cemantix.org/2012/data.html''',
           ]   ,				)






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_=True   ,				SCREAMING_SNAKE_CASE_=False   ,				SCREAMING_SNAKE_CASE_=False   ,				SCREAMING_SNAKE_CASE_=False	)		->      Union[str, Any]:





       '''simple docstring'''





       UpperCamelCase__										=      [
           ('''mentions''', evaluator.mentions),
           ('''muc''', evaluator.muc),
           ('''bcub''', evaluator.b_cubed),
           ('''ceafe''', evaluator.ceafe),
           ('''lea''', evaluator.lea),
       ]

       if min_span:
            UpperCamelCase__										=      util.check_gold_parse_annotation(SCREAMING_SNAKE_CASE_	)
            if not has_gold_parse:
                 raise NotImplementedError('''References should have gold parse annotation to use \'min_span\'.'''	)
                 # util.parse_key_file(key_file)
                 # key_file = key_file + ".parsed"

       UpperCamelCase__										=      evaluate(
           key_lines=SCREAMING_SNAKE_CASE_   ,				sys_lines=SCREAMING_SNAKE_CASE_   ,				metrics=SCREAMING_SNAKE_CASE_   ,				NP_only=SCREAMING_SNAKE_CASE_   ,				remove_nested=SCREAMING_SNAKE_CASE_   ,				keep_singletons=SCREAMING_SNAKE_CASE_   ,				min_span=SCREAMING_SNAKE_CASE_   ,				)

       return score




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   (    __UpperCamelCase							):
  SCREAMING_SNAKE_CASE_	:	"DiagonalGaussianDistribution"



class     _A   (    __UpperCamelCase	, __UpperCamelCase							):
  SCREAMING_SNAKE_CASE_	:	List[str]						=True






  @register_to_config
  def __init__(self   ,				SCREAMING_SNAKE_CASE_ = 3   ,				SCREAMING_SNAKE_CASE_ = 3   ,				SCREAMING_SNAKE_CASE_ = ("DownEncoderBlock2D",)   ,				SCREAMING_SNAKE_CASE_ = ("UpDecoderBlock2D",)   ,				SCREAMING_SNAKE_CASE_ = (64,)   ,				SCREAMING_SNAKE_CASE_ = 1   ,				SCREAMING_SNAKE_CASE_ = "silu"   ,				SCREAMING_SNAKE_CASE_ = 4   ,				SCREAMING_SNAKE_CASE_ = 32   ,				SCREAMING_SNAKE_CASE_ = 32   ,				SCREAMING_SNAKE_CASE_ = 0.18215   ,				)		->      Optional[int]:





       '''simple docstring'''





       super().__init__()

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

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

       UpperCamelCase__										=      nn.Convad(2 * latent_channels   ,				2 * latent_channels   ,				1	)
       UpperCamelCase__										=      nn.Convad(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				1	)

       UpperCamelCase__										=      False
       UpperCamelCase__										=      False

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






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_=False	)		->      Dict:





       '''simple docstring'''





       if isinstance(SCREAMING_SNAKE_CASE_   ,				(Encoder, Decoder)	):
            UpperCamelCase__										=      value






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_ = True	)		->      Union[str, Any]:





       '''simple docstring'''





       UpperCamelCase__										=      use_tiling






  def 		_a					(self	)		->      int:





       '''simple docstring'''





       self.enable_tiling(SCREAMING_SNAKE_CASE_	)






  def 		_a					(self	)		->      Any:





       '''simple docstring'''





       UpperCamelCase__										=      True






  def 		_a					(self	)		->      List[Any]:





       '''simple docstring'''





       UpperCamelCase__										=      False






  @property
  # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
  def 		_a					(self	)		->      Dict[str, AttentionProcessor]:





       '''simple docstring'''





       UpperCamelCase__										=      {}

       def fn_recursive_add_processors(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	):
            if hasattr(SCREAMING_SNAKE_CASE_   ,				'''set_processor'''	):
                 UpperCamelCase__										=      module.processor

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

            return processors

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

       return processors






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_	)		->      List[Any]:





       '''simple docstring'''





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

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

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

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






  def 		_a					(self	)		->      Tuple:





       '''simple docstring'''





       self.set_attn_processor(AttnProcessor()	)






  @apply_forward_hook
  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_ = True	)		->      AutoencoderKLOutput:





       '''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(SCREAMING_SNAKE_CASE_   ,				return_dict=SCREAMING_SNAKE_CASE_	)

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

       UpperCamelCase__										=      self.quant_conv(SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      DiagonalGaussianDistribution(SCREAMING_SNAKE_CASE_	)

       if not return_dict:
            return (posterior,)

       return AutoencoderKLOutput(latent_dist=SCREAMING_SNAKE_CASE_	)






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_ = True	)		->      Union[DecoderOutput, torch.FloatTensor]:





       '''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(SCREAMING_SNAKE_CASE_   ,				return_dict=SCREAMING_SNAKE_CASE_	)

       UpperCamelCase__										=      self.post_quant_conv(SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      self.decoder(SCREAMING_SNAKE_CASE_	)

       if not return_dict:
            return (dec,)

       return DecoderOutput(sample=SCREAMING_SNAKE_CASE_	)






  @apply_forward_hook
  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_ = True	)		->      Union[DecoderOutput, torch.FloatTensor]:





       '''simple docstring'''





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

       if not return_dict:
            return (decoded,)

       return DecoderOutput(sample=SCREAMING_SNAKE_CASE_	)






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)		->      Union[str, Any]:





       '''simple docstring'''





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






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)		->      List[Any]:





       '''simple docstring'''





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






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_ = True	)		->      AutoencoderKLOutput:





       '''simple docstring'''





       UpperCamelCase__										=      int(self.tile_sample_min_size * (1 - self.tile_overlap_factor)	)
       UpperCamelCase__										=      int(self.tile_latent_min_size * self.tile_overlap_factor	)
       UpperCamelCase__										=      self.tile_latent_min_size - blend_extent

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

       UpperCamelCase__										=      torch.cat(SCREAMING_SNAKE_CASE_   ,				dim=2	)
       UpperCamelCase__										=      DiagonalGaussianDistribution(SCREAMING_SNAKE_CASE_	)

       if not return_dict:
            return (posterior,)

       return AutoencoderKLOutput(latent_dist=SCREAMING_SNAKE_CASE_	)






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_ = True	)		->      Union[DecoderOutput, torch.FloatTensor]:





       '''simple docstring'''





       UpperCamelCase__										=      int(self.tile_latent_min_size * (1 - self.tile_overlap_factor)	)
       UpperCamelCase__										=      int(self.tile_sample_min_size * self.tile_overlap_factor	)
       UpperCamelCase__										=      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.
       UpperCamelCase__										=      []
       for i in range(0   ,				z.shape[2]   ,				SCREAMING_SNAKE_CASE_	):
            UpperCamelCase__										=      []
            for j in range(0   ,				z.shape[3]   ,				SCREAMING_SNAKE_CASE_	):
                 UpperCamelCase__										=      z[:, :, i : i + self.tile_latent_min_size, j : j + self.tile_latent_min_size]
                 UpperCamelCase__										=      self.post_quant_conv(SCREAMING_SNAKE_CASE_	)
                 UpperCamelCase__										=      self.decoder(SCREAMING_SNAKE_CASE_	)
                 row.append(SCREAMING_SNAKE_CASE_	)
            rows.append(SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      []
       for i, row in enumerate(SCREAMING_SNAKE_CASE_	):
            UpperCamelCase__										=      []
            for j, tile in enumerate(SCREAMING_SNAKE_CASE_	):
                 # blend the above tile and the left tile
                 # to the current tile and add the current tile to the result row
                 if i > 0:
                      UpperCamelCase__										=      self.blend_v(rows[i - 1][j]   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)
                 if j > 0:
                      UpperCamelCase__										=      self.blend_h(row[j - 1]   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)
                 result_row.append(tile[:, :, :row_limit, :row_limit]	)
            result_rows.append(torch.cat(SCREAMING_SNAKE_CASE_   ,				dim=3	)	)

       UpperCamelCase__										=      torch.cat(SCREAMING_SNAKE_CASE_   ,				dim=2	)
       if not return_dict:
            return (dec,)

       return DecoderOutput(sample=SCREAMING_SNAKE_CASE_	)






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_ = False   ,				SCREAMING_SNAKE_CASE_ = True   ,				SCREAMING_SNAKE_CASE_ = None   ,				)		->      Union[DecoderOutput, torch.FloatTensor]:





       '''simple docstring'''





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

       if not return_dict:
            return (dec,)

       return DecoderOutput(sample=SCREAMING_SNAKE_CASE_	)



