




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


_lowerCamelCase      			=						datasets.logging.get_logger(__name__)


_lowerCamelCase      			=						'\\n@InProceedings{moosavi2019minimum,\n    author = { Nafise Sadat Moosavi, Leo Born, Massimo Poesio and Michael Strube},\n    title = {Using Automatically Extracted Minimum Spans to Disentangle Coreference Evaluation from Boundary Detection},\n    year = {2019},\n    booktitle = {Proceedings of the 57th Annual Meeting of\n        the Association for Computational Linguistics (Volume 1: Long Papers)},\n    publisher = {Association for Computational Linguistics},\n    address = {Florence, Italy},\n}\n\n@inproceedings{10.3115/1072399.1072405,\nauthor = {Vilain, Marc and Burger, John and Aberdeen, John and Connolly, Dennis and Hirschman, Lynette},\ntitle = {A Model-Theoretic Coreference Scoring Scheme},\nyear = {1995},\nisbn = {1558604022},\npublisher = {Association for Computational Linguistics},\naddress = {USA},\nurl = {https://doi.org/10.3115/1072399.1072405},\ndoi = {10.3115/1072399.1072405},\nbooktitle = {Proceedings of the 6th Conference on Message Understanding},\npages = {45–52},\nnumpages = {8},\nlocation = {Columbia, Maryland},\nseries = {MUC6 ’95}\n}\n\n@INPROCEEDINGS{Bagga98algorithmsfor,\n    author = {Amit Bagga and Breck Baldwin},\n    title = {Algorithms for Scoring Coreference Chains},\n    booktitle = {In The First International Conference on Language Resources and Evaluation Workshop on Linguistics Coreference},\n    year = {1998},\n    pages = {563--566}\n}\n\n@INPROCEEDINGS{Luo05oncoreference,\n    author = {Xiaoqiang Luo},\n    title = {On coreference resolution performance metrics},\n    booktitle = {In Proc. of HLT/EMNLP},\n    year = {2005},\n    pages = {25--32},\n    publisher = {URL}\n}\n\n@inproceedings{moosavi-strube-2016-coreference,\n    title = "Which Coreference Evaluation Metric Do You Trust? A Proposal for a Link-based Entity Aware Metric",\n    author = "Moosavi, Nafise Sadat  and\n      Strube, Michael",\n    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",\n    month = aug,\n    year = "2016",\n    address = "Berlin, Germany",\n    publisher = "Association for Computational Linguistics",\n    url = "https://www.aclweb.org/anthology/P16-1060",\n    doi = "10.18653/v1/P16-1060",\n    pages = "632--642",\n}\n\n'

_lowerCamelCase      			=						'\\nCoVal is a coreference evaluation tool for the CoNLL and ARRAU datasets which\nimplements of the common evaluation metrics including MUC [Vilain et al, 1995],\nB-cubed [Bagga and Baldwin, 1998], CEAFe [Luo et al., 2005],\nLEA [Moosavi and Strube, 2016] and the averaged CoNLL score\n(the average of the F1 values of MUC, B-cubed and CEAFe)\n[Denis and Baldridge, 2009a; Pradhan et al., 2011].\n\nThis wrapper of CoVal currently only work with CoNLL line format:\nThe CoNLL format has one word per line with all the annotation for this word in column separated by spaces:\nColumn	Type	Description\n1	Document ID	This is a variation on the document filename\n2	Part number	Some files are divided into multiple parts numbered as 000, 001, 002, ... etc.\n3	Word number\n4	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.\n5	Part-of-Speech\n6	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.\n7	Predicate lemma	The predicate lemma is mentioned for the rows for which we have semantic role information. All other rows are marked with a "-"\n8	Predicate Frameset ID	This is the PropBank frameset ID of the predicate in Column 7.\n9	Word sense	This is the word sense of the word in Column 3.\n10	Speaker/Author	This is the speaker or author name where available. Mostly in Broadcast Conversation and Web Log data.\n11	Named Entities	These columns identifies the spans representing various named entities.\n12:N	Predicate Arguments	There is one column each of predicate argument structure information for the predicate mentioned in Column 7.\nN	Coreference	Coreference chain information encoded in a parenthesis structure.\nMore informations on the format can be found here (section "*_conll File Format"): http://www.conll.cemantix.org/2012/data.html\n\nDetails on the evaluation on CoNLL can be found here: https://github.com/ns-moosavi/coval/blob/master/conll/README.md\n\nCoVal code was written by @ns-moosavi.\nSome parts are borrowed from https://github.com/clarkkev/deep-coref/blob/master/evaluation.py\nThe test suite is taken from https://github.com/conll/reference-coreference-scorers/\nMention evaluation and the test suite are added by @andreasvc.\nParsing CoNLL files is developed by Leo Born.\n'

_lowerCamelCase      			=						'\nCalculates coreference evaluation metrics.\nArgs:\n    predictions: list of sentences. Each sentence is a list of word predictions to score in the CoNLL format.\n        Each prediction is a word with its annotations as a string made of columns joined with spaces.\n        Only columns 4, 5, 6 and the last column are used (word, POS, Pars and coreference annotation)\n        See the details on the format in the description of the metric.\n    references: list of sentences. Each sentence is a list of word reference to score in the CoNLL format.\n        Each reference is a word with its annotations as a string made of columns joined with spaces.\n        Only columns 4, 5, 6 and the last column are used (word, POS, Pars and coreference annotation)\n        See the details on the format in the description of the metric.\n    keep_singletons: After extracting all mentions of key or system files,\n        mentions whose corresponding coreference chain is of size one,\n        are considered as singletons. The default evaluation mode will include\n        singletons in evaluations if they are included in the key or the system files.\n        By setting \'keep_singletons=False\', all singletons in the key and system files\n        will be excluded from the evaluation.\n    NP_only: Most of the recent coreference resolvers only resolve NP mentions and\n        leave out the resolution of VPs. By setting the \'NP_only\' option, the scorer will only evaluate the resolution of NPs.\n    min_span: By setting \'min_span\', the scorer reports the results based on automatically detected minimum spans.\n        Minimum spans are determined using the MINA algorithm.\n\nReturns:\n    \'mentions\': mentions\n    \'muc\': MUC metric [Vilain et al, 1995]\n    \'bcub\': B-cubed [Bagga and Baldwin, 1998]\n    \'ceafe\': CEAFe [Luo et al., 2005]\n    \'lea\': LEA [Moosavi and Strube, 2016]\n    \'conll_score\': averaged CoNLL score (the average of the F1 values of MUC, B-cubed and CEAFe)\n\nExamples:\n\n    >>> coval = datasets.load_metric(\'coval\')\n    >>> words = [\'bc/cctv/00/cctv_0005   0   0       Thank   VBP  (TOP(S(VP*    thank  01   1    Xu_li  *           (V*)        *       -\',\n    ... \'bc/cctv/00/cctv_0005   0   1         you   PRP        (NP*)      -    -   -    Xu_li  *        (ARG1*)   (ARG0*)   (116)\',\n    ... \'bc/cctv/00/cctv_0005   0   2    everyone    NN        (NP*)      -    -   -    Xu_li  *    (ARGM-DIS*)        *    (116)\',\n    ... \'bc/cctv/00/cctv_0005   0   3         for    IN        (PP*       -    -   -    Xu_li  *        (ARG2*         *       -\',\n    ... \'bc/cctv/00/cctv_0005   0   4    watching   VBG   (S(VP*))))   watch  01   1    Xu_li  *             *)      (V*)      -\',\n    ... \'bc/cctv/00/cctv_0005   0   5           .     .          *))      -    -   -    Xu_li  *             *         *       -\']\n    >>> references = [words]\n    >>> predictions = [words]\n    >>> results = coval.compute(predictions=predictions, references=references)\n    >>> print(results) # doctest:+ELLIPSIS\n    {\'mentions/recall\': 1.0,[...] \'conll_score\': 100.0}\n'




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Union[str, Any]	,					__UpperCamelCase      :						List[Any]	,					__UpperCamelCase      :						List[str]=False	,					__UpperCamelCase      :						List[str]=False	,					__UpperCamelCase      :						List[Any]=True	,					__UpperCamelCase      :						Tuple=False	,					__UpperCamelCase      :						str="dummy_doc"				)  ->							Dict:
    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(__UpperCamelCase	,					key_doc_lines[doc]	,					__UpperCamelCase				)
    key_singletons_num += singletons_num

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

    UpperCAmelCase_			,							UpperCAmelCase_										=					reader.get_doc_mentions(__UpperCamelCase	,					sys_doc_lines[doc]	,					__UpperCamelCase				)
    sys_singletons_num += singletons_num

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

    if remove_nested:
        UpperCAmelCase_			,							UpperCAmelCase_										=					reader.remove_nested_coref_mentions(__UpperCamelCase	,					__UpperCamelCase				)
        key_nested_coref_num += nested_mentions
        key_removed_nested_clusters += removed_clusters

        UpperCAmelCase_			,							UpperCAmelCase_										=					reader.remove_nested_coref_mentions(__UpperCamelCase	,					__UpperCamelCase				)
        sys_nested_coref_num += nested_mentions
        sys_removed_nested_clusters += removed_clusters

    UpperCAmelCase_										=					reader.get_mention_assignments(__UpperCamelCase	,					__UpperCamelCase				)
    UpperCAmelCase_										=					reader.get_mention_assignments(__UpperCamelCase	,					__UpperCamelCase				)

    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  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Optional[Any]	,					__UpperCamelCase      :						Optional[Any]	,					__UpperCamelCase      :						int	,					__UpperCamelCase      :						Optional[int]	,					__UpperCamelCase      :						Any	,					__UpperCamelCase      :						Any	,					__UpperCamelCase      :						Dict				)  ->							Optional[int]:
    UpperCAmelCase_										=					get_coref_infos(__UpperCamelCase	,					__UpperCamelCase	,					__UpperCamelCase	,					__UpperCamelCase	,					__UpperCamelCase	,					__UpperCamelCase				)

    UpperCAmelCase_										=					{}
    UpperCAmelCase_										=					0
    UpperCAmelCase_										=					0

    for name, metric in metrics:
        UpperCAmelCase_			,							UpperCAmelCase_			,							UpperCAmelCase_										=					evaluator.evaluate_documents(__UpperCamelCase	,					__UpperCamelCase	,					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  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Dict				)  ->							int:
    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      ):

     '''simple docstring'''





     def   lowerCamelCase_       (     self       :      str    ):
         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   lowerCamelCase_       (     self       :      List[Any] ,   __snake_case       :      str ,   __snake_case       :      Any ,   __snake_case       :      Dict=True ,   __snake_case       :      List[str]=False ,   __snake_case       :      str=False ,   __snake_case       :      str=False    ):
         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(__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=__snake_case ,   sys_lines=__snake_case ,   metrics=__snake_case ,   NP_only=__snake_case ,   remove_nested=__snake_case ,   keep_singletons=__snake_case ,   min_span=__snake_case ,   )

         return score











import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging


logging.set_verbosity_info()
_lowerCamelCase      			=						logging.get_logger(__name__)




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Tuple				)  ->							Dict:
    # initialize config
    if "resnet-50" in model_name:
        UpperCAmelCase_										=					ResNetConfig.from_pretrained('''microsoft/resnet-50'''				)
    elif "resnet-101" in model_name:
        UpperCAmelCase_										=					ResNetConfig.from_pretrained('''microsoft/resnet-101'''				)
    else:
        raise ValueError('''Model name should include either resnet50 or resnet101'''				)

    UpperCAmelCase_										=					DetrConfig(use_timm_backbone=__UpperCamelCase	,					backbone_config=__UpperCamelCase				)

    # set label attributes
    UpperCAmelCase_										=					'''panoptic''' in model_name
    if is_panoptic:
        UpperCAmelCase_										=					250
    else:
        UpperCAmelCase_										=					91
        UpperCAmelCase_										=					'''huggingface/label-files'''
        UpperCAmelCase_										=					'''coco-detection-id2label.json'''
        UpperCAmelCase_										=					json.load(open(hf_hub_download(__UpperCamelCase	,					__UpperCamelCase	,					repo_type='''dataset'''				)	,					'''r'''				)				)
        UpperCAmelCase_										=					{int(__UpperCamelCase				): v for k, v in idalabel.items()}
        UpperCAmelCase_										=					idalabel
        UpperCAmelCase_										=					{v: k for k, v in idalabel.items()}

    return config, is_panoptic




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						List[str]				)  ->							Union[str, Any]:
    # here we list all keys to be renamed (original name on the left, our name on the right)
    UpperCAmelCase_										=					[]

    # stem
    # fmt: off
    rename_keys.append(('''backbone.0.body.conv1.weight''', '''backbone.conv_encoder.model.embedder.embedder.convolution.weight''')				)
    rename_keys.append(('''backbone.0.body.bn1.weight''', '''backbone.conv_encoder.model.embedder.embedder.normalization.weight''')				)
    rename_keys.append(('''backbone.0.body.bn1.bias''', '''backbone.conv_encoder.model.embedder.embedder.normalization.bias''')				)
    rename_keys.append(('''backbone.0.body.bn1.running_mean''', '''backbone.conv_encoder.model.embedder.embedder.normalization.running_mean''')				)
    rename_keys.append(('''backbone.0.body.bn1.running_var''', '''backbone.conv_encoder.model.embedder.embedder.normalization.running_var''')				)
    # stages
    for stage_idx in range(len(config.backbone_config.depths				)				):
        for layer_idx in range(config.backbone_config.depths[stage_idx]				):
            # shortcut
            if layer_idx == 0:
                rename_keys.append(
                    (
                        f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight',
                        f'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight',
                    )				)
                rename_keys.append(
                    (
                        f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight',
                        f'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight',
                    )				)
                rename_keys.append(
                    (
                        f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias',
                        f'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias',
                    )				)
                rename_keys.append(
                    (
                        f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean',
                        f'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean',
                    )				)
                rename_keys.append(
                    (
                        f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var',
                        f'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var',
                    )				)
            # 3 convs
            for i in range(3				):
                rename_keys.append(
                    (
                        f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight',
                        f'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight',
                    )				)
                rename_keys.append(
                    (
                        f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight',
                        f'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight',
                    )				)
                rename_keys.append(
                    (
                        f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias',
                        f'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias',
                    )				)
                rename_keys.append(
                    (
                        f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean',
                        f'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean',
                    )				)
                rename_keys.append(
                    (
                        f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var',
                        f'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var',
                    )				)
    # fmt: on

    for i in range(config.encoder_layers				):
        # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
        rename_keys.append(
            (
                f'transformer.encoder.layers.{i}.self_attn.out_proj.weight',
                f'encoder.layers.{i}.self_attn.out_proj.weight',
            )				)
        rename_keys.append(
            (f'transformer.encoder.layers.{i}.self_attn.out_proj.bias', f'encoder.layers.{i}.self_attn.out_proj.bias')				)
        rename_keys.append((f'transformer.encoder.layers.{i}.linear1.weight', f'encoder.layers.{i}.fc1.weight')				)
        rename_keys.append((f'transformer.encoder.layers.{i}.linear1.bias', f'encoder.layers.{i}.fc1.bias')				)
        rename_keys.append((f'transformer.encoder.layers.{i}.linear2.weight', f'encoder.layers.{i}.fc2.weight')				)
        rename_keys.append((f'transformer.encoder.layers.{i}.linear2.bias', f'encoder.layers.{i}.fc2.bias')				)
        rename_keys.append(
            (f'transformer.encoder.layers.{i}.norm1.weight', f'encoder.layers.{i}.self_attn_layer_norm.weight')				)
        rename_keys.append(
            (f'transformer.encoder.layers.{i}.norm1.bias', f'encoder.layers.{i}.self_attn_layer_norm.bias')				)
        rename_keys.append(
            (f'transformer.encoder.layers.{i}.norm2.weight', f'encoder.layers.{i}.final_layer_norm.weight')				)
        rename_keys.append((f'transformer.encoder.layers.{i}.norm2.bias', f'encoder.layers.{i}.final_layer_norm.bias')				)
        # decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
        rename_keys.append(
            (
                f'transformer.decoder.layers.{i}.self_attn.out_proj.weight',
                f'decoder.layers.{i}.self_attn.out_proj.weight',
            )				)
        rename_keys.append(
            (f'transformer.decoder.layers.{i}.self_attn.out_proj.bias', f'decoder.layers.{i}.self_attn.out_proj.bias')				)
        rename_keys.append(
            (
                f'transformer.decoder.layers.{i}.multihead_attn.out_proj.weight',
                f'decoder.layers.{i}.encoder_attn.out_proj.weight',
            )				)
        rename_keys.append(
            (
                f'transformer.decoder.layers.{i}.multihead_attn.out_proj.bias',
                f'decoder.layers.{i}.encoder_attn.out_proj.bias',
            )				)
        rename_keys.append((f'transformer.decoder.layers.{i}.linear1.weight', f'decoder.layers.{i}.fc1.weight')				)
        rename_keys.append((f'transformer.decoder.layers.{i}.linear1.bias', f'decoder.layers.{i}.fc1.bias')				)
        rename_keys.append((f'transformer.decoder.layers.{i}.linear2.weight', f'decoder.layers.{i}.fc2.weight')				)
        rename_keys.append((f'transformer.decoder.layers.{i}.linear2.bias', f'decoder.layers.{i}.fc2.bias')				)
        rename_keys.append(
            (f'transformer.decoder.layers.{i}.norm1.weight', f'decoder.layers.{i}.self_attn_layer_norm.weight')				)
        rename_keys.append(
            (f'transformer.decoder.layers.{i}.norm1.bias', f'decoder.layers.{i}.self_attn_layer_norm.bias')				)
        rename_keys.append(
            (f'transformer.decoder.layers.{i}.norm2.weight', f'decoder.layers.{i}.encoder_attn_layer_norm.weight')				)
        rename_keys.append(
            (f'transformer.decoder.layers.{i}.norm2.bias', f'decoder.layers.{i}.encoder_attn_layer_norm.bias')				)
        rename_keys.append(
            (f'transformer.decoder.layers.{i}.norm3.weight', f'decoder.layers.{i}.final_layer_norm.weight')				)
        rename_keys.append((f'transformer.decoder.layers.{i}.norm3.bias', f'decoder.layers.{i}.final_layer_norm.bias')				)

    # convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
    rename_keys.extend(
        [
            ('''input_proj.weight''', '''input_projection.weight'''),
            ('''input_proj.bias''', '''input_projection.bias'''),
            ('''query_embed.weight''', '''query_position_embeddings.weight'''),
            ('''transformer.decoder.norm.weight''', '''decoder.layernorm.weight'''),
            ('''transformer.decoder.norm.bias''', '''decoder.layernorm.bias'''),
            ('''class_embed.weight''', '''class_labels_classifier.weight'''),
            ('''class_embed.bias''', '''class_labels_classifier.bias'''),
            ('''bbox_embed.layers.0.weight''', '''bbox_predictor.layers.0.weight'''),
            ('''bbox_embed.layers.0.bias''', '''bbox_predictor.layers.0.bias'''),
            ('''bbox_embed.layers.1.weight''', '''bbox_predictor.layers.1.weight'''),
            ('''bbox_embed.layers.1.bias''', '''bbox_predictor.layers.1.bias'''),
            ('''bbox_embed.layers.2.weight''', '''bbox_predictor.layers.2.weight'''),
            ('''bbox_embed.layers.2.bias''', '''bbox_predictor.layers.2.bias'''),
        ]				)

    return rename_keys




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Optional[Any]	,					__UpperCamelCase      :						int	,					__UpperCamelCase      :						Optional[int]				)  ->							Union[str, Any]:
    UpperCAmelCase_										=					state_dict.pop(__UpperCamelCase				)
    UpperCAmelCase_										=					val




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						str	,					__UpperCamelCase      :						List[Any]=False				)  ->							Dict:
    UpperCAmelCase_										=					''''''
    if is_panoptic:
        UpperCAmelCase_										=					'''detr.'''

    # first: transformer encoder
    for i in range(6				):
        # read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
        UpperCAmelCase_										=					state_dict.pop(f'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'				)
        UpperCAmelCase_										=					state_dict.pop(f'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias'				)
        # next, add query, keys and values (in that order) to the state dict
        UpperCAmelCase_										=					in_proj_weight[:256, :]
        UpperCAmelCase_										=					in_proj_bias[:256]
        UpperCAmelCase_										=					in_proj_weight[256:512, :]
        UpperCAmelCase_										=					in_proj_bias[256:512]
        UpperCAmelCase_										=					in_proj_weight[-256:, :]
        UpperCAmelCase_										=					in_proj_bias[-256:]
    # next: transformer decoder (which is a bit more complex because it also includes cross-attention)
    for i in range(6				):
        # read in weights + bias of input projection layer of self-attention
        UpperCAmelCase_										=					state_dict.pop(f'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'				)
        UpperCAmelCase_										=					state_dict.pop(f'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias'				)
        # next, add query, keys and values (in that order) to the state dict
        UpperCAmelCase_										=					in_proj_weight[:256, :]
        UpperCAmelCase_										=					in_proj_bias[:256]
        UpperCAmelCase_										=					in_proj_weight[256:512, :]
        UpperCAmelCase_										=					in_proj_bias[256:512]
        UpperCAmelCase_										=					in_proj_weight[-256:, :]
        UpperCAmelCase_										=					in_proj_bias[-256:]
        # read in weights + bias of input projection layer of cross-attention
        UpperCAmelCase_										=					state_dict.pop(
            f'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'				)
        UpperCAmelCase_										=					state_dict.pop(f'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias'				)
        # next, add query, keys and values (in that order) of cross-attention to the state dict
        UpperCAmelCase_										=					in_proj_weight_cross_attn[:256, :]
        UpperCAmelCase_										=					in_proj_bias_cross_attn[:256]
        UpperCAmelCase_										=					in_proj_weight_cross_attn[256:512, :]
        UpperCAmelCase_										=					in_proj_bias_cross_attn[256:512]
        UpperCAmelCase_										=					in_proj_weight_cross_attn[-256:, :]
        UpperCAmelCase_										=					in_proj_bias_cross_attn[-256:]




def  SCREAMING_SNAKE_CASE     (	)  ->							int:
    UpperCAmelCase_										=					'''http://images.cocodataset.org/val2017/000000039769.jpg'''
    UpperCAmelCase_										=					Image.open(requests.get(__UpperCamelCase	,					stream=__UpperCamelCase				).raw				)

    return im




@torch.no_grad()
def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Tuple	,					__UpperCamelCase      :						Any=None	,					__UpperCamelCase      :						Optional[Any]=False				)  ->							Optional[Any]:
    UpperCAmelCase_			,							UpperCAmelCase_										=					get_detr_config(__UpperCamelCase				)

    # load original model from torch hub
    UpperCAmelCase_										=					{
        '''detr-resnet-50''': '''detr_resnet50''',
        '''detr-resnet-101''': '''detr_resnet101''',
    }
    logger.info(f'Converting model {model_name}...'				)
    UpperCAmelCase_										=					torch.hub.load('''facebookresearch/detr'''	,					model_name_to_original_name[model_name]	,					pretrained=__UpperCamelCase				).eval()
    UpperCAmelCase_										=					detr.state_dict()
    # rename keys
    for src, dest in create_rename_keys(__UpperCamelCase				):
        if is_panoptic:
            UpperCAmelCase_										=					'''detr.''' + src
        rename_key(__UpperCamelCase	,					__UpperCamelCase	,					__UpperCamelCase				)
    # query, key and value matrices need special treatment
    read_in_q_k_v(__UpperCamelCase	,					is_panoptic=__UpperCamelCase				)
    # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
    UpperCAmelCase_										=					'''detr.model.''' if is_panoptic else '''model.'''
    for key in state_dict.copy().keys():
        if is_panoptic:
            if (
                key.startswith('''detr'''				)
                and not key.startswith('''class_labels_classifier'''				)
                and not key.startswith('''bbox_predictor'''				)
            ):
                UpperCAmelCase_										=					state_dict.pop(__UpperCamelCase				)
                UpperCAmelCase_										=					val
            elif "class_labels_classifier" in key or "bbox_predictor" in key:
                UpperCAmelCase_										=					state_dict.pop(__UpperCamelCase				)
                UpperCAmelCase_										=					val
            elif key.startswith('''bbox_attention'''				) or key.startswith('''mask_head'''				):
                continue
            else:
                UpperCAmelCase_										=					state_dict.pop(__UpperCamelCase				)
                UpperCAmelCase_										=					val
        else:
            if not key.startswith('''class_labels_classifier'''				) and not key.startswith('''bbox_predictor'''				):
                UpperCAmelCase_										=					state_dict.pop(__UpperCamelCase				)
                UpperCAmelCase_										=					val

    # finally, create HuggingFace model and load state dict
    UpperCAmelCase_										=					DetrForSegmentation(__UpperCamelCase				) if is_panoptic else DetrForObjectDetection(__UpperCamelCase				)
    model.load_state_dict(__UpperCamelCase				)
    model.eval()

    # verify our conversion on an image
    UpperCAmelCase_										=					'''coco_panoptic''' if is_panoptic else '''coco_detection'''
    UpperCAmelCase_										=					DetrImageProcessor(format=__UpperCamelCase				)

    UpperCAmelCase_										=					processor(images=prepare_img()	,					return_tensors='''pt'''				)
    UpperCAmelCase_										=					encoding['''pixel_values''']

    UpperCAmelCase_										=					detr(__UpperCamelCase				)
    UpperCAmelCase_										=					model(__UpperCamelCase				)

    assert torch.allclose(outputs.logits	,					original_outputs['''pred_logits''']	,					atol=1e-3				)
    assert torch.allclose(outputs.pred_boxes	,					original_outputs['''pred_boxes''']	,					atol=1e-3				)
    if is_panoptic:
        assert torch.allclose(outputs.pred_masks	,					original_outputs['''pred_masks''']	,					atol=1e-4				)
    print('''Looks ok!'''				)

    if pytorch_dump_folder_path is not None:
        # Save model and image processor
        logger.info(f'Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'				)
        Path(__UpperCamelCase				).mkdir(exist_ok=__UpperCamelCase				)
        model.save_pretrained(__UpperCamelCase				)
        processor.save_pretrained(__UpperCamelCase				)

    if push_to_hub:
        # Upload model and image processor to the hub
        logger.info('''Uploading PyTorch model and image processor to the hub...'''				)
        model.push_to_hub(f'nielsr/{model_name}'				)
        processor.push_to_hub(f'nielsr/{model_name}'				)


if __name__ == "__main__":
 _lowerCamelCase      			=						argparse.ArgumentParser()

 parser.add_argument(
     '--model_name',
     default='detr-resnet-50',
     type=str,
     choices=['detr-resnet-50', 'detr-resnet-101'],
     help='Name of the DETR model you\'d like to convert.',
 )
 parser.add_argument(
     '--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
 )
 parser.add_argument('--push_to_hub', action='store_true', help='Whether to push the model to the hub or not.')
 _lowerCamelCase      			=						parser.parse_args()
 convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





