






"""simple docstring"""
from sklearn.metrics import fa_score, matthews_corrcoef

import datasets

from .record_evaluation import evaluate as evaluate_record


lowerCAmelCase__    						=			'\\n@article{wang2019superglue,\n  title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},\n  author={Wang, Alex and Pruksachatkun, Yada and Nangia, Nikita and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R},\n  journal={arXiv preprint arXiv:1905.00537},\n  year={2019}\n}\n'

lowerCAmelCase__    						=			'\\nSuperGLUE (https://super.gluebenchmark.com/) is a new benchmark styled after\nGLUE with a new set of more difficult language understanding tasks, improved\nresources, and a new public leaderboard.\n'

lowerCAmelCase__    						=			'\nCompute SuperGLUE evaluation metric associated to each SuperGLUE dataset.\nArgs:\n    predictions: list of predictions to score. Depending on the SuperGlUE subset:\n        - for \'record\': list of question-answer dictionaries with the following keys:\n            - \'idx\': index of the question as specified by the dataset\n            - \'prediction_text\': the predicted answer text\n        - for \'multirc\': list of question-answer dictionaries with the following keys:\n            - \'idx\': index of the question-answer pair as specified by the dataset\n            - \'prediction\': the predicted answer label\n        - otherwise: list of predicted labels\n    references: list of reference labels. Depending on the SuperGLUE subset:\n        - for \'record\': list of question-answers dictionaries with the following keys:\n            - \'idx\': index of the question as specified by the dataset\n            - \'answers\': list of possible answers\n        - otherwise: list of reference labels\nReturns: depending on the SuperGLUE subset:\n    - for \'record\':\n        - \'exact_match\': Exact match between answer and gold answer\n        - \'f1\': F1 score\n    - for \'multirc\':\n        - \'exact_match\': Exact match between answer and gold answer\n        - \'f1_m\': Per-question macro-F1 score\n        - \'f1_a\': Average F1 score over all answers\n    - for \'axb\':\n        \'matthews_correlation\': Matthew Correlation\n    - for \'cb\':\n        - \'accuracy\': Accuracy\n        - \'f1\': F1 score\n    - for all others:\n        - \'accuracy\': Accuracy\nExamples:\n\n    >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'copa\')  # any of ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]\n    >>> predictions = [0, 1]\n    >>> references = [0, 1]\n    >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'accuracy\': 1.0}\n\n    >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'cb\')\n    >>> predictions = [0, 1]\n    >>> references = [0, 1]\n    >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'accuracy\': 1.0, \'f1\': 1.0}\n\n    >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'record\')\n    >>> predictions = [{\'idx\': {\'passage\': 0, \'query\': 0}, \'prediction_text\': \'answer\'}]\n    >>> references = [{\'idx\': {\'passage\': 0, \'query\': 0}, \'answers\': [\'answer\', \'another_answer\']}]\n    >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'exact_match\': 1.0, \'f1\': 1.0}\n\n    >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'multirc\')\n    >>> predictions = [{\'idx\': {\'answer\': 0, \'paragraph\': 0, \'question\': 0}, \'prediction\': 0}, {\'idx\': {\'answer\': 1, \'paragraph\': 2, \'question\': 3}, \'prediction\': 1}]\n    >>> references = [0, 1]\n    >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'exact_match\': 1.0, \'f1_m\': 1.0, \'f1_a\': 1.0}\n\n    >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'axb\')\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'matthews_correlation\': 1.0}\n'





def 				lowercase__		(					lowerCamelCase,						lowerCamelCase       ):
				return float((preds == labels).mean()       )





def 				lowercase__		(					lowerCamelCase,						lowerCamelCase,						lowerCamelCase="binary"       ):
				_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    simple_accuracy(lowerCamelCase,						lowerCamelCase       )
				_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    float(fa_score(y_true=lowerCamelCase,						y_pred=lowerCamelCase,						average=lowerCamelCase       )       )
				return {
				    "accuracy": acc,
				    "f1": fa,
				}





def 				lowercase__		(					lowerCamelCase,						lowerCamelCase       ):
				_SCREAMING_SNAKE_CASE   :		int       		=    {}
				for id_pred, label in zip(lowerCamelCase,						lowerCamelCase       ):
								_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    f"""{id_pred["idx"]["paragraph"]}-{id_pred["idx"]["question"]}"""
								_SCREAMING_SNAKE_CASE   :		Any       		=    id_pred['prediction']
								if question_id in question_map:
												question_map[question_id].append((pred, label)       )
								else:
												_SCREAMING_SNAKE_CASE   :		List[Any]       		=    [(pred, label)]
				_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE   :		List[Any]       		=    [], []
				for question, preds_labels in question_map.items():
								_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE   :		Dict       		=    zip(*lowerCamelCase       )
								_SCREAMING_SNAKE_CASE   :		str       		=    fa_score(y_true=lowerCamelCase,						y_pred=lowerCamelCase,						average='macro'       )
								fas.append(lowerCamelCase       )
								_SCREAMING_SNAKE_CASE   :		int       		=    int(sum(pred == label for pred, label in preds_labels       ) == len(lowerCamelCase       )       )
								ems.append(lowerCamelCase       )
				_SCREAMING_SNAKE_CASE   :		str       		=    float(sum(lowerCamelCase       ) / len(lowerCamelCase       )       )
				_SCREAMING_SNAKE_CASE   :		Tuple       		=    sum(lowerCamelCase       ) / len(lowerCamelCase       )
				_SCREAMING_SNAKE_CASE   :		List[Any]       		=    float(fa_score(y_true=lowerCamelCase,						y_pred=[id_pred['prediction'] for id_pred in ids_preds]       )       )
				return {"exact_match": em, "f1_m": fa_m, "f1_a": fa_a}






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







						def 			A      (    self							)		->							List[Any]:
										if self.config_name not in [
										    "boolq",
										    "cb",
										    "copa",
										    "multirc",
										    "record",
										    "rte",
										    "wic",
										    "wsc",
										    "wsc.fixed",
										    "axb",
										    "axg",
										]:
														raise KeyError(
														    'You should supply a configuration name selected in '
														    '["boolq", "cb", "copa", "multirc", "record", "rte", "wic", "wsc", "wsc.fixed", "axb", "axg",]'							)
										return datasets.MetricInfo(
										    description=_DESCRIPTION   ,				citation=_CITATION   ,				inputs_description=_KWARGS_DESCRIPTION   ,				features=datasets.Features(self._get_feature_types()							)   ,				codebase_urls=[]   ,				reference_urls=[]   ,				format='numpy' if not self.config_name == 'record' and not self.config_name == 'multirc' else None   ,				)







						def 			A      (    self							)		->							str:
										if self.config_name == "record":
														return {
														    "predictions": {
														        "idx": {
														            "passage": datasets.Value('int64'							),
														            "query": datasets.Value('int64'							),
														        },
														        "prediction_text": datasets.Value('string'							),
														    },
														    "references": {
														        "idx": {
														            "passage": datasets.Value('int64'							),
														            "query": datasets.Value('int64'							),
														        },
														        "answers": datasets.Sequence(datasets.Value('string'							)							),
														    },
														}
										elif self.config_name == "multirc":
														return {
														    "predictions": {
														        "idx": {
														            "answer": datasets.Value('int64'							),
														            "paragraph": datasets.Value('int64'							),
														            "question": datasets.Value('int64'							),
														        },
														        "prediction": datasets.Value('int64'							),
														    },
														    "references": datasets.Value('int64'							),
														}
										else:
														return {
														    "predictions": datasets.Value('int64'							),
														    "references": datasets.Value('int64'							),
														}







						def 			A      (    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_							)		->							List[str]:
										if self.config_name == "axb":
														return {"matthews_correlation": matthews_corrcoef(lowerCAmelCase_   ,				lowerCAmelCase_							)}
										elif self.config_name == "cb":
														return acc_and_fa(lowerCAmelCase_   ,				lowerCAmelCase_   ,				fa_avg='macro'							)
										elif self.config_name == "record":
														_SCREAMING_SNAKE_CASE   :		str       		=    [
														    {
														        'qas': [
														            {'id': ref['idx']['query'], 'answers': [{'text': ans} for ans in ref['answers']]}
														            for ref in references
														        ]
														    }
														]
														_SCREAMING_SNAKE_CASE   :		Dict       		=    {pred['idx']['query']: pred['prediction_text'] for pred in predictions}
														return evaluate_record(lowerCAmelCase_   ,				lowerCAmelCase_							)[0]
										elif self.config_name == "multirc":
														return evaluate_multirc(lowerCAmelCase_   ,				lowerCAmelCase_							)
										elif self.config_name in ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]:
														return {"accuracy": simple_accuracy(lowerCAmelCase_   ,				lowerCAmelCase_							)}
										else:
														raise KeyError(
														    'You should supply a configuration name selected in '
														    '["boolq", "cb", "copa", "multirc", "record", "rte", "wic", "wsc", "wsc.fixed", "axb", "axg",]'							)









"""simple docstring"""
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 				lowercase__		(					lowerCamelCase       ):
				# initialize config
				if "resnet-50" in model_name:
								_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    ResNetConfig.from_pretrained('microsoft/resnet-50'       )
				elif "resnet-101" in model_name:
								_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    ResNetConfig.from_pretrained('microsoft/resnet-101'       )
				else:
								raise ValueError('Model name should include either resnet50 or resnet101'       )

				_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    DetrConfig(use_timm_backbone=lowerCamelCase,						backbone_config=lowerCamelCase       )

				# set label attributes
				_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    'panoptic' in model_name
				if is_panoptic:
								_SCREAMING_SNAKE_CASE   :		List[str]       		=    250
				else:
								_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    91
								_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    'huggingface/label-files'
								_SCREAMING_SNAKE_CASE   :		Dict       		=    'coco-detection-id2label.json'
								_SCREAMING_SNAKE_CASE   :		List[str]       		=    json.load(open(hf_hub_download(lowerCamelCase,						lowerCamelCase,						repo_type='dataset'       ),						'r'       )       )
								_SCREAMING_SNAKE_CASE   :		Dict       		=    {int(lowerCamelCase       ): v for k, v in idalabel.items()}
								_SCREAMING_SNAKE_CASE   :		List[Any]       		=    idalabel
								_SCREAMING_SNAKE_CASE   :		Tuple       		=    {v: k for k, v in idalabel.items()}

				return config, is_panoptic





def 				lowercase__		(					lowerCamelCase       ):
				# here we list all keys to be renamed (original name on the left, our name on the right)
				_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    []

				# 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 				lowercase__		(					lowerCamelCase,						lowerCamelCase,						lowerCamelCase       ):
				_SCREAMING_SNAKE_CASE   :		Dict       		=    state_dict.pop(lowerCamelCase       )
				_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    val





def 				lowercase__		(					lowerCamelCase,						lowerCamelCase=False       ):
				_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    ''
				if is_panoptic:
								_SCREAMING_SNAKE_CASE   :		List[Any]       		=    '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)
								_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""       )
								_SCREAMING_SNAKE_CASE   :		Dict       		=    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
								_SCREAMING_SNAKE_CASE   :		Any       		=    in_proj_weight[:256, :]
								_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    in_proj_bias[:256]
								_SCREAMING_SNAKE_CASE   :		Tuple       		=    in_proj_weight[256:512, :]
								_SCREAMING_SNAKE_CASE   :		Tuple       		=    in_proj_bias[256:512]
								_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    in_proj_weight[-256:, :]
								_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    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
								_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""       )
								_SCREAMING_SNAKE_CASE   :		int       		=    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
								_SCREAMING_SNAKE_CASE   :		Tuple       		=    in_proj_weight[:256, :]
								_SCREAMING_SNAKE_CASE   :		str       		=    in_proj_bias[:256]
								_SCREAMING_SNAKE_CASE   :		str       		=    in_proj_weight[256:512, :]
								_SCREAMING_SNAKE_CASE   :		int       		=    in_proj_bias[256:512]
								_SCREAMING_SNAKE_CASE   :		List[Any]       		=    in_proj_weight[-256:, :]
								_SCREAMING_SNAKE_CASE   :		List[str]       		=    in_proj_bias[-256:]
								# read in weights + bias of input projection layer of cross-attention
								_SCREAMING_SNAKE_CASE   :		str       		=    state_dict.pop(
								    f"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""       )
								_SCREAMING_SNAKE_CASE   :		List[Any]       		=    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
								_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    in_proj_weight_cross_attn[:256, :]
								_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    in_proj_bias_cross_attn[:256]
								_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    in_proj_weight_cross_attn[256:512, :]
								_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    in_proj_bias_cross_attn[256:512]
								_SCREAMING_SNAKE_CASE   :		int       		=    in_proj_weight_cross_attn[-256:, :]
								_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    in_proj_bias_cross_attn[-256:]





def 				lowercase__		(					):
				_SCREAMING_SNAKE_CASE   :		List[Any]       		=    'http://images.cocodataset.org/val2017/000000039769.jpg'
				_SCREAMING_SNAKE_CASE   :		Dict       		=    Image.open(requests.get(lowerCamelCase,						stream=lowerCamelCase       ).raw       )

				return im





@torch.no_grad()
def 				lowercase__		(					lowerCamelCase,						lowerCamelCase=None,						lowerCamelCase=False       ):
				_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE   :		str       		=    get_detr_config(lowerCamelCase       )

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

    # finally, create HuggingFace model and load state dict
				_SCREAMING_SNAKE_CASE   :		Dict       		=    DetrForSegmentation(lowerCamelCase       ) if is_panoptic else DetrForObjectDetection(lowerCamelCase       )
				model.load_state_dict(lowerCamelCase       )
				model.eval()

				# verify our conversion on an image
				_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    'coco_panoptic' if is_panoptic else 'coco_detection'
				_SCREAMING_SNAKE_CASE   :		int       		=    DetrImageProcessor(format=lowerCamelCase       )

				_SCREAMING_SNAKE_CASE   :		Any       		=    processor(images=prepare_img(),						return_tensors='pt'       )
				_SCREAMING_SNAKE_CASE   :		Tuple       		=    encoding['pixel_values']

				_SCREAMING_SNAKE_CASE   :		List[str]       		=    detr(lowerCamelCase       )
				_SCREAMING_SNAKE_CASE   :		int       		=    model(lowerCamelCase       )

				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(lowerCamelCase       ).mkdir(exist_ok=lowerCamelCase       )
								model.save_pretrained(lowerCamelCase       )
								processor.save_pretrained(lowerCamelCase       )

				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)

