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


UpperCamelCase  							=						datasets.logging.get_logger(__name__)


UpperCamelCase  							=						'''\
@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",
}

'''

UpperCamelCase  							=						'''\
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.
'''

UpperCamelCase  							=						'''
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 			lowercase_				(			_lowerCamelCase							:       List[Any]		,	_lowerCamelCase							:       int		,	_lowerCamelCase							:       Dict=False		,	_lowerCamelCase							:       List[Any]=False		,	_lowerCamelCase							:       Optional[int]=True		,	_lowerCamelCase							:       Union[str, Any]=False		,	_lowerCamelCase							:       Optional[int]="dummy_doc"):
    lowercase__ :   Dict    					=				{doc: key_lines}
    lowercase__ :   Optional[int]    					=				{doc: sys_lines}

    lowercase__ :   Tuple    					=				{}

    lowercase__ :   Union[str, Any]    					=				0
    lowercase__ :   str    					=				0
    lowercase__ :   Tuple    					=				0
    lowercase__ :   Optional[int]    					=				0
    lowercase__ :   str    					=				0
    lowercase__ :   str    					=				0

    lowercase__					,   lowercase__ :   Union[str, Any]    					=				reader.get_doc_mentions(_lowerCamelCase		,	key_doc_lines[doc]		,	_lowerCamelCase)
    key_singletons_num += singletons_num

    if NP_only or min_span:
        lowercase__ :   int    					=				reader.set_annotated_parse_trees(_lowerCamelCase		,	key_doc_lines[doc]		,	_lowerCamelCase		,	_lowerCamelCase)

    lowercase__					,   lowercase__ :   Union[str, Any]    					=				reader.get_doc_mentions(_lowerCamelCase		,	sys_doc_lines[doc]		,	_lowerCamelCase)
    sys_singletons_num += singletons_num

    if NP_only or min_span:
        lowercase__ :   Optional[int]    					=				reader.set_annotated_parse_trees(_lowerCamelCase		,	key_doc_lines[doc]		,	_lowerCamelCase		,	_lowerCamelCase)

    if remove_nested:
        lowercase__					,   lowercase__ :   List[str]    					=				reader.remove_nested_coref_mentions(_lowerCamelCase		,	_lowerCamelCase)
        key_nested_coref_num += nested_mentions
        key_removed_nested_clusters += removed_clusters

        lowercase__					,   lowercase__ :   List[Any]    					=				reader.remove_nested_coref_mentions(_lowerCamelCase		,	_lowerCamelCase)
        sys_nested_coref_num += nested_mentions
        sys_removed_nested_clusters += removed_clusters

    lowercase__ :   List[str]    					=				reader.get_mention_assignments(_lowerCamelCase		,	_lowerCamelCase)
    lowercase__ :   Tuple    					=				reader.get_mention_assignments(_lowerCamelCase		,	_lowerCamelCase)

    lowercase__ :   str    					=				(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 			lowercase_				(			_lowerCamelCase							:       List[str]		,	_lowerCamelCase							:       str		,	_lowerCamelCase							:       List[str]		,	_lowerCamelCase							:       Union[str, Any]		,	_lowerCamelCase							:       str		,	_lowerCamelCase							:       Union[str, Any]		,	_lowerCamelCase							:       Tuple):
    lowercase__ :   Tuple    					=				get_coref_infos(_lowerCamelCase		,	_lowerCamelCase		,	_lowerCamelCase		,	_lowerCamelCase		,	_lowerCamelCase		,	_lowerCamelCase)

    lowercase__ :   List[Any]    					=				{}
    lowercase__ :   List[str]    					=				0
    lowercase__ :   List[Any]    					=				0

    for name, metric in metrics:
        lowercase__					,   lowercase__					,   lowercase__ :   Dict    					=				evaluator.evaluate_documents(_lowerCamelCase		,	_lowerCamelCase		,	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:
        lowercase__ :   str    					=				(conll / 3) * 100
        logger.info(f'''CoNLL score: {conll:.2f}''')
        output_scores.update({"conll_score": conll})

    return output_scores

def 			lowercase_				(			_lowerCamelCase							:       Dict):
    lowercase__ :   List[str]    					=				False
    for line in key_lines:
        if not line.startswith("#"):
            if len(line.split()) > 6:
                lowercase__ :   Union[str, Any]    					=				line.split()[5]
                if not parse_col == "-":
                    lowercase__ :   str    					=				True
                    break
                else:
                    break
    return has_gold_parse



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






   def       __UpperCamelCase     (      self				:     Optional[int]  )					->						Optional[int]:
       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       __UpperCamelCase     (      self				:     Tuple	,						lowercase_				:     Tuple	,						lowercase_				:     Any	,						lowercase_				:     Optional[Any]=True	,						lowercase_				:     Optional[Any]=False	,						lowercase_				:     Tuple=False	,						lowercase_				:     Any=False  )					->						Dict:
       lowercase__ :   int    					=				[
           ("mentions", evaluator.mentions),
           ("muc", evaluator.muc),
           ("bcub", evaluator.b_cubed),
           ("ceafe", evaluator.ceafe),
           ("lea", evaluator.lea),
       ]

       if min_span:
           lowercase__ :   Optional[int]    					=				util.check_gold_parse_annotation(lowercase_  )
           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"

       lowercase__ :   Any    					=				evaluate(
           key_lines=lowercase_	,						sys_lines=lowercase_	,						metrics=lowercase_	,						NP_only=lowercase_	,						remove_nested=lowercase_	,						keep_singletons=lowercase_	,						min_span=lowercase_	,						)

       return score







"""simple docstring"""




from typing import List, Optional, Tuple, Union

import torch
from torch import nn
from torch.nn import CrossEntropyLoss

from ... import AutoBackbone
from ...modeling_outputs import SemanticSegmenterOutput
from ...modeling_utils import PreTrainedModel
from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, replace_return_docstrings
from ...utils.backbone_utils import BackboneMixin
from .configuration_upernet import UperNetConfig


__UpperCamelCase						=      [
    '''openmmlab/upernet-convnext-tiny''',
    # See all UperNet models at https://huggingface.co/models?filter=upernet
]

# General docstring
__UpperCamelCase						=      '''UperNetConfig'''
class 					lowerCAmelCase	(					nn.Module    ):




		'''simple docstring'''







		def __init__(	self						,       lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__ = 0						,       lowerCAmelCase__ = False						,       lowerCAmelCase__ = 1						,       )     ->							None:
								super().__init__()
								SCREAMING_SNAKE_CASE													=					nn.Convad(
								    in_channels=lowerCAmelCase__						,       out_channels=lowerCAmelCase__						,       kernel_size=lowerCAmelCase__						,       padding=lowerCAmelCase__						,       bias=lowerCAmelCase__						,       dilation=lowerCAmelCase__						,       )
								SCREAMING_SNAKE_CASE													=					nn.BatchNormad(lowerCAmelCase__				)
								SCREAMING_SNAKE_CASE													=					nn.ReLU()







		def 							__A						(	self						,       lowerCAmelCase__				)     ->							torch.Tensor:
								SCREAMING_SNAKE_CASE													=					self.conv(lowerCAmelCase__				)
								SCREAMING_SNAKE_CASE													=					self.batch_norm(lowerCAmelCase__				)
								SCREAMING_SNAKE_CASE													=					self.activation(lowerCAmelCase__				)

								return output





class 					lowerCAmelCase	(					nn.Module    ):




		'''simple docstring'''







		def __init__(	self						,       lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__				)     ->							None:
								super().__init__()
								SCREAMING_SNAKE_CASE													=					[
								    nn.AdaptiveAvgPoolad(lowerCAmelCase__				),
								    UperNetConvModule(lowerCAmelCase__						,       lowerCAmelCase__						,       kernel_size=1				),
								]
								for i, layer in enumerate(self.layers				):
														self.add_module(str(lowerCAmelCase__				)						,       lowerCAmelCase__				)







		def 							__A						(	self						,       lowerCAmelCase__				)     ->							torch.Tensor:
								SCREAMING_SNAKE_CASE													=					input
								for layer in self.layers:
														SCREAMING_SNAKE_CASE													=					layer(lowerCAmelCase__				)
								return hidden_state





class 					lowerCAmelCase	(					nn.Module    ):




		'''simple docstring'''







		def __init__(	self						,       lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__				)     ->							None:
								super().__init__()
								SCREAMING_SNAKE_CASE													=					pool_scales
								SCREAMING_SNAKE_CASE													=					align_corners
								SCREAMING_SNAKE_CASE													=					in_channels
								SCREAMING_SNAKE_CASE													=					channels
								SCREAMING_SNAKE_CASE													=					[]
								for i, pool_scale in enumerate(lowerCAmelCase__				):
														SCREAMING_SNAKE_CASE													=					UperNetPyramidPoolingBlock(pool_scale=lowerCAmelCase__						,       in_channels=lowerCAmelCase__						,       channels=lowerCAmelCase__				)
														self.blocks.append(lowerCAmelCase__				)
														self.add_module(str(lowerCAmelCase__				)						,       lowerCAmelCase__				)







		def 							__A						(	self						,       lowerCAmelCase__				)     ->							List[torch.Tensor]:
								SCREAMING_SNAKE_CASE													=					[]
								for ppm in self.blocks:
														SCREAMING_SNAKE_CASE													=					ppm(lowerCAmelCase__				)
														SCREAMING_SNAKE_CASE													=					nn.functional.interpolate(
														    lowerCAmelCase__						,       size=x.size()[2:]						,       mode='bilinear'						,       align_corners=self.align_corners				)
														ppm_outs.append(lowerCAmelCase__				)
								return ppm_outs





class 					lowerCAmelCase	(					nn.Module    ):




		'''simple docstring'''







		def __init__(	self						,       lowerCAmelCase__						,       lowerCAmelCase__				)     ->							List[str]:
								super().__init__()

								SCREAMING_SNAKE_CASE													=					config
								SCREAMING_SNAKE_CASE													=					config.pool_scales  # e.g. (1, 2, 3, 6)
								SCREAMING_SNAKE_CASE													=					in_channels
								SCREAMING_SNAKE_CASE													=					config.hidden_size
								SCREAMING_SNAKE_CASE													=					False
								SCREAMING_SNAKE_CASE													=					nn.Convad(self.channels						,       config.num_labels						,       kernel_size=1				)

								# PSP Module
								SCREAMING_SNAKE_CASE													=					UperNetPyramidPoolingModule(
								    self.pool_scales						,       self.in_channels[-1]						,       self.channels						,       align_corners=self.align_corners						,       )
								SCREAMING_SNAKE_CASE													=					UperNetConvModule(
								    self.in_channels[-1] + len(self.pool_scales				) * self.channels						,       self.channels						,       kernel_size=3						,       padding=1						,       )
								# FPN Module
								SCREAMING_SNAKE_CASE													=					nn.ModuleList()
								SCREAMING_SNAKE_CASE													=					nn.ModuleList()
								for in_channels in self.in_channels[:-1]:  # skip the top layer
														SCREAMING_SNAKE_CASE													=					UperNetConvModule(lowerCAmelCase__						,       self.channels						,       kernel_size=1				)
														SCREAMING_SNAKE_CASE													=					UperNetConvModule(self.channels						,       self.channels						,       kernel_size=3						,       padding=1				)
														self.lateral_convs.append(lowerCAmelCase__				)
														self.fpn_convs.append(lowerCAmelCase__				)

								SCREAMING_SNAKE_CASE													=					UperNetConvModule(
								    len(self.in_channels				) * self.channels						,       self.channels						,       kernel_size=3						,       padding=1						,       )







		def 							__A						(	self				)     ->							int:
								self.apply(self._init_weights				)







		def 							__A						(	self						,       lowerCAmelCase__				)     ->							Tuple:
								if isinstance(lowerCAmelCase__						,       nn.Convad				):
														module.weight.data.normal_(mean=0.0						,       std=self.config.initializer_range				)
														if module.bias is not None:
																				module.bias.data.zero_()







		def 							__A						(	self						,       lowerCAmelCase__				)     ->							Optional[int]:
								SCREAMING_SNAKE_CASE													=					inputs[-1]
								SCREAMING_SNAKE_CASE													=					[x]
								psp_outs.extend(self.psp_modules(lowerCAmelCase__				)				)
								SCREAMING_SNAKE_CASE													=					torch.cat(lowerCAmelCase__						,       dim=1				)
								SCREAMING_SNAKE_CASE													=					self.bottleneck(lowerCAmelCase__				)

								return output







		def 							__A						(	self						,       lowerCAmelCase__				)     ->							torch.Tensor:
								# build laterals
								SCREAMING_SNAKE_CASE													=					[lateral_conv(encoder_hidden_states[i]				) for i, lateral_conv in enumerate(self.lateral_convs				)]

								laterals.append(self.psp_forward(lowerCAmelCase__				)				)

								# build top-down path
								SCREAMING_SNAKE_CASE													=					len(lowerCAmelCase__				)
								for i in range(used_backbone_levels - 1						,       0						,       -1				):
														SCREAMING_SNAKE_CASE													=					laterals[i - 1].shape[2:]
														SCREAMING_SNAKE_CASE													=					laterals[i - 1] + nn.functional.interpolate(
														    laterals[i]						,       size=lowerCAmelCase__						,       mode='bilinear'						,       align_corners=self.align_corners				)

								# build outputs
								SCREAMING_SNAKE_CASE													=					[self.fpn_convs[i](laterals[i]				) for i in range(used_backbone_levels - 1				)]
								# append psp feature
								fpn_outs.append(laterals[-1]				)

								for i in range(used_backbone_levels - 1						,       0						,       -1				):
														SCREAMING_SNAKE_CASE													=					nn.functional.interpolate(
														    fpn_outs[i]						,       size=fpn_outs[0].shape[2:]						,       mode='bilinear'						,       align_corners=self.align_corners				)
								SCREAMING_SNAKE_CASE													=					torch.cat(lowerCAmelCase__						,       dim=1				)
								SCREAMING_SNAKE_CASE													=					self.fpn_bottleneck(lowerCAmelCase__				)
								SCREAMING_SNAKE_CASE													=					self.classifier(lowerCAmelCase__				)

								return output





class 					lowerCAmelCase	(					nn.Module    ):




		'''simple docstring'''







		def __init__(	self						,       lowerCAmelCase__						,       lowerCAmelCase__ = 2						,       lowerCAmelCase__ = 3						,       lowerCAmelCase__ = 1				)     ->							None:
								super().__init__()

								SCREAMING_SNAKE_CASE													=					config
								SCREAMING_SNAKE_CASE													=					config.auxiliary_in_channels
								SCREAMING_SNAKE_CASE													=					config.auxiliary_channels
								SCREAMING_SNAKE_CASE													=					config.auxiliary_num_convs
								SCREAMING_SNAKE_CASE													=					config.auxiliary_concat_input
								SCREAMING_SNAKE_CASE													=					in_index

								SCREAMING_SNAKE_CASE													=					(kernel_size // 2) * dilation
								SCREAMING_SNAKE_CASE													=					[]
								convs.append(
								    UperNetConvModule(
								        self.in_channels						,       self.channels						,       kernel_size=lowerCAmelCase__						,       padding=lowerCAmelCase__						,       dilation=lowerCAmelCase__				)				)
								for i in range(self.num_convs - 1				):
														convs.append(
														    UperNetConvModule(
														        self.channels						,       self.channels						,       kernel_size=lowerCAmelCase__						,       padding=lowerCAmelCase__						,       dilation=lowerCAmelCase__				)				)
								if self.num_convs == 0:
														SCREAMING_SNAKE_CASE													=					nn.Identity()
								else:
														SCREAMING_SNAKE_CASE													=					nn.Sequential(*lowerCAmelCase__				)
								if self.concat_input:
														SCREAMING_SNAKE_CASE													=					UperNetConvModule(
														    self.in_channels + self.channels						,       self.channels						,       kernel_size=lowerCAmelCase__						,       padding=kernel_size // 2				)

								SCREAMING_SNAKE_CASE													=					nn.Convad(self.channels						,       config.num_labels						,       kernel_size=1				)







		def 							__A						(	self				)     ->							Dict:
								self.apply(self._init_weights				)







		def 							__A						(	self						,       lowerCAmelCase__				)     ->							Dict:
								if isinstance(lowerCAmelCase__						,       nn.Convad				):
														module.weight.data.normal_(mean=0.0						,       std=self.config.initializer_range				)
														if module.bias is not None:
																				module.bias.data.zero_()







		def 							__A						(	self						,       lowerCAmelCase__				)     ->							torch.Tensor:
								# just take the relevant feature maps
								SCREAMING_SNAKE_CASE													=					encoder_hidden_states[self.in_index]
								SCREAMING_SNAKE_CASE													=					self.convs(lowerCAmelCase__				)
								if self.concat_input:
														SCREAMING_SNAKE_CASE													=					self.conv_cat(torch.cat([hidden_states, output]						,       dim=1				)				)
								SCREAMING_SNAKE_CASE													=					self.classifier(lowerCAmelCase__				)
								return output





class 					lowerCAmelCase	(					lowerCamelCase_    ):




		'''simple docstring'''







		SCREAMING_SNAKE_CASE_						:     int								=     UperNetConfig
		SCREAMING_SNAKE_CASE_						:     Optional[Any]								=     """pixel_values"""
		SCREAMING_SNAKE_CASE_						:     Optional[int]								=     True







		def 							__A						(	self						,       lowerCAmelCase__				)     ->							List[str]:
								if isinstance(lowerCAmelCase__						,       lowerCAmelCase__				):
														module.backbone.init_weights()
														module.decode_head.init_weights()
														module.auxiliary_head.init_weights()







		def 							__A						(	self				)     ->							int:
								self.backbone.init_weights()
								self.decode_head.init_weights()
								self.auxiliary_head.init_weights()







		def 							__A						(	self						,       lowerCAmelCase__						,       lowerCAmelCase__=False				)     ->							str:
								if isinstance(lowerCAmelCase__						,       lowerCAmelCase__				):
														SCREAMING_SNAKE_CASE													=					value





__UpperCamelCase						=      R'''
    Parameters:
    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
    it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
    behavior.
        config ([`UperNetConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
'''

__UpperCamelCase						=      R'''
    Args:
        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using
            [`AutoImageProcessor`]. See [`SegformerImageProcessor.__call__`] for details.
        output_attentions (`bool`, *optional*):
            Whether or not to return the attentions tensors of all attention layers in case the backbone has them. See
            `attentions` under returned tensors for more detail.
        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers of the backbone. See `hidden_states` under
            returned tensors for more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
'''
@add_start_docstrings(
    """UperNet framework leveraging any vision backbone e.g. for ADE20k, CityScapes."""	,    lowerCamelCase_	,    )
class 					lowerCAmelCase	(					lowerCamelCase_    ):




		'''simple docstring'''







		def __init__(	self						,       lowerCAmelCase__				)     ->							Optional[int]:
								super().__init__(lowerCAmelCase__				)

								SCREAMING_SNAKE_CASE													=					AutoBackbone.from_config(config.backbone_config				)

								# Semantic segmentation head(s)
								SCREAMING_SNAKE_CASE													=					UperNetHead(lowerCAmelCase__						,       in_channels=self.backbone.channels				)
								SCREAMING_SNAKE_CASE													=					UperNetFCNHead(lowerCAmelCase__				) if config.use_auxiliary_head else None

								# Initialize weights and apply final processing
								self.post_init()







		@add_start_docstrings_to_model_forward(UPERNET_INPUTS_DOCSTRING.format('batch_size, sequence_length'				)				)
		@replace_return_docstrings(output_type=lowerCAmelCase__						,       config_class=_CONFIG_FOR_DOC				)
		def 							__A						(	self						,       lowerCAmelCase__ = None						,       lowerCAmelCase__ = None						,       lowerCAmelCase__ = None						,       lowerCAmelCase__ = None						,       lowerCAmelCase__ = None						,       )     ->							Union[tuple, SemanticSegmenterOutput]:
								SCREAMING_SNAKE_CASE													=					return_dict if return_dict is not None else self.config.use_return_dict
								SCREAMING_SNAKE_CASE													=					(
								    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
								)
								SCREAMING_SNAKE_CASE													=					output_attentions if output_attentions is not None else self.config.output_attentions

								SCREAMING_SNAKE_CASE													=					self.backbone.forward_with_filtered_kwargs(
								    lowerCAmelCase__						,       output_hidden_states=lowerCAmelCase__						,       output_attentions=lowerCAmelCase__				)
								SCREAMING_SNAKE_CASE													=					outputs.feature_maps

								SCREAMING_SNAKE_CASE													=					self.decode_head(lowerCAmelCase__				)
								SCREAMING_SNAKE_CASE													=					nn.functional.interpolate(lowerCAmelCase__						,       size=pixel_values.shape[2:]						,       mode='bilinear'						,       align_corners=lowerCAmelCase__				)

								SCREAMING_SNAKE_CASE													=					None
								if self.auxiliary_head is not None:
														SCREAMING_SNAKE_CASE													=					self.auxiliary_head(lowerCAmelCase__				)
														SCREAMING_SNAKE_CASE													=					nn.functional.interpolate(
														    lowerCAmelCase__						,       size=pixel_values.shape[2:]						,       mode='bilinear'						,       align_corners=lowerCAmelCase__				)

								SCREAMING_SNAKE_CASE													=					None
								if labels is not None:
														if self.config.num_labels == 1:
																				raise ValueError('The number of labels should be greater than one'				)
														else:
																				# compute weighted loss
																				SCREAMING_SNAKE_CASE													=					CrossEntropyLoss(ignore_index=self.config.loss_ignore_index				)
																				SCREAMING_SNAKE_CASE													=					loss_fct(lowerCAmelCase__						,       lowerCAmelCase__				)
																				SCREAMING_SNAKE_CASE													=					loss_fct(lowerCAmelCase__						,       lowerCAmelCase__				)
																				SCREAMING_SNAKE_CASE													=					main_loss + self.config.auxiliary_loss_weight * auxiliary_loss

								if not return_dict:
														if output_hidden_states:
																				SCREAMING_SNAKE_CASE													=					(logits,) + outputs[1:]
														else:
																				SCREAMING_SNAKE_CASE													=					(logits,) + outputs[2:]
														return ((loss,) + output) if loss is not None else output

								return SemanticSegmenterOutput(
								    loss=lowerCAmelCase__						,       logits=lowerCAmelCase__						,       hidden_states=outputs.hidden_states						,       attentions=outputs.attentions						,       )


