





import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DeformableDetrConfig, DeformableDetrForObjectDetection, DeformableDetrImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE_      :			int		  =      logging.get_logger(__name__)



def    SCREAMING_SNAKE_CASE							(			snake_case       )		->						Tuple:
		if "backbone.0.body" in orig_key:
				__lowercase					=    orig_key.replace('backbone.0.body'	,      'backbone.conv_encoder.model'       )
		if "transformer" in orig_key:
				__lowercase					=    orig_key.replace('transformer.'	,      ''       )
		if "norm1" in orig_key:
				if "encoder" in orig_key:
						__lowercase					=    orig_key.replace('norm1'	,      'self_attn_layer_norm'       )
				else:
						__lowercase					=    orig_key.replace('norm1'	,      'encoder_attn_layer_norm'       )
		if "norm2" in orig_key:
				if "encoder" in orig_key:
						__lowercase					=    orig_key.replace('norm2'	,      'final_layer_norm'       )
				else:
						__lowercase					=    orig_key.replace('norm2'	,      'self_attn_layer_norm'       )
		if "norm3" in orig_key:
				__lowercase					=    orig_key.replace('norm3'	,      'final_layer_norm'       )
		if "linear1" in orig_key:
				__lowercase					=    orig_key.replace('linear1'	,      'fc1'       )
		if "linear2" in orig_key:
				__lowercase					=    orig_key.replace('linear2'	,      'fc2'       )
		if "query_embed" in orig_key:
				__lowercase					=    orig_key.replace('query_embed'	,      'query_position_embeddings'       )
		if "cross_attn" in orig_key:
				__lowercase					=    orig_key.replace('cross_attn'	,      'encoder_attn'       )

		return orig_key



def    SCREAMING_SNAKE_CASE							(			snake_case       )		->						Union[str, Any]:
		# transformer decoder self-attention layers
		for i in range(6       ):
				# read in weights + bias of input projection layer of self-attention
				__lowercase					=    state_dict.pop(F"decoder.layers.{i}.self_attn.in_proj_weight"       )
				__lowercase					=    state_dict.pop(F"decoder.layers.{i}.self_attn.in_proj_bias"       )
				# next, add query, keys and values (in that order) to the state dict
				__lowercase					=    in_proj_weight[:256, :]
				__lowercase					=    in_proj_bias[:256]
				__lowercase					=    in_proj_weight[256:512, :]
				__lowercase					=    in_proj_bias[256:512]
				__lowercase					=    in_proj_weight[-256:, :]
				__lowercase					=    in_proj_bias[-256:]



def    SCREAMING_SNAKE_CASE							(			)		->						Optional[Any]:
		__lowercase					=    'http://images.cocodataset.org/val2017/000000039769.jpg'
		__lowercase					=    Image.open(requests.get(snake_case	,      stream=snake_case       ).raw       )

		return im



@torch.no_grad()
def    SCREAMING_SNAKE_CASE							(			snake_case	,      snake_case	,      snake_case	,      snake_case	,      snake_case	,      snake_case	,      snake_case	,      )		->						Tuple:
		__lowercase					=    DeformableDetrConfig()
		# set config attributes
		if single_scale:
				__lowercase					=    1
		__lowercase					=    dilation
		__lowercase					=    with_box_refine
		__lowercase					=    two_stage
		# set labels
		__lowercase					=    91
		__lowercase					=    'huggingface/label-files'
		__lowercase					=    'coco-detection-id2label.json'
		__lowercase					=    json.load(open(cached_download(hf_hub_url(snake_case	,      snake_case	,      repo_type='dataset'       )       )	,      'r'       )       )
		__lowercase					=    {int(snake_case       ): v for k, v in idalabel.items()}
		__lowercase					=    idalabel
		__lowercase					=    {v: k for k, v in idalabel.items()}

		# load image processor
		__lowercase					=    DeformableDetrImageProcessor(format='coco_detection'       )

		# prepare image
		__lowercase					=    prepare_img()
		__lowercase					=    image_processor(images=snake_case	,      return_tensors='pt'       )
		__lowercase					=    encoding['pixel_values']

		logger.info('Converting model...'       )

		# load original state dict
		__lowercase					=    torch.load(snake_case	,      map_location='cpu'       )['model']
		# rename keys
		for key in state_dict.copy().keys():
				__lowercase					=    state_dict.pop(snake_case       )
				__lowercase					=    val
		# query, key and value matrices need special treatment
		read_in_q_k_v(snake_case       )
		# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
		__lowercase					=    'model.'
		for key in state_dict.copy().keys():
				if not key.startswith('class_embed'       ) and not key.startswith('bbox_embed'       ):
						__lowercase					=    state_dict.pop(snake_case       )
						__lowercase					=    val
    # finally, create HuggingFace model and load state dict
		__lowercase					=    DeformableDetrForObjectDetection(snake_case       )
		model.load_state_dict(snake_case       )
		model.eval()

		__lowercase					=    'cuda' if torch.cuda.is_available() else 'cpu'
		model.to(snake_case       )
		# verify our conversion
		__lowercase					=    model(pixel_values.to(snake_case       )       )

		__lowercase					=    torch.tensor(
		    [[-9.6645, -4.3449, -5.8705], [-9.7035, -3.8504, -5.0724], [-10.5634, -5.3379, -7.5116]]       )
		__lowercase					=    torch.tensor([[0.8693, 0.2289, 0.2492], [0.3150, 0.5489, 0.5845], [0.5563, 0.7580, 0.8518]]       )

		if single_scale:
				__lowercase					=    torch.tensor(
				    [[-9.9051, -4.2541, -6.4852], [-9.6947, -4.0854, -6.8033], [-10.0665, -5.8470, -7.7003]]       )
				__lowercase					=    torch.tensor([[0.7292, 0.4991, 0.5532], [0.7959, 0.2426, 0.4236], [0.7582, 0.3518, 0.4451]]       )

		if single_scale and dilation:
				__lowercase					=    torch.tensor(
				    [[-8.9652, -4.1074, -5.6635], [-9.0596, -4.9447, -6.6075], [-10.1178, -4.5275, -6.2671]]       )
				__lowercase					=    torch.tensor([[0.7665, 0.4130, 0.4769], [0.8364, 0.1841, 0.3391], [0.6261, 0.3895, 0.7978]]       )

		if with_box_refine:
				__lowercase					=    torch.tensor(
				    [[-8.8895, -5.4187, -6.8153], [-8.4706, -6.1668, -7.6184], [-9.0042, -5.5359, -6.9141]]       )
				__lowercase					=    torch.tensor([[0.7828, 0.2208, 0.4323], [0.0892, 0.5996, 0.1319], [0.5524, 0.6389, 0.8914]]       )

		if with_box_refine and two_stage:
				__lowercase					=    torch.tensor(
				    [[-6.7108, -4.3213, -6.3777], [-8.9014, -6.1799, -6.7240], [-6.9315, -4.4735, -6.2298]]       )
				__lowercase					=    torch.tensor([[0.2583, 0.5499, 0.4683], [0.7652, 0.9068, 0.4882], [0.5490, 0.2763, 0.0564]]       )

		print('Logits:'	,      outputs.logits[0, :3, :3]       )

		assert torch.allclose(outputs.logits[0, :3, :3]	,      expected_logits.to(snake_case       )	,      atol=1E-4       )
		assert torch.allclose(outputs.pred_boxes[0, :3, :3]	,      expected_boxes.to(snake_case       )	,      atol=1E-4       )

		print('Everything ok!'       )

		# Save model and image processor
		logger.info(F"Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."       )
		Path(snake_case       ).mkdir(exist_ok=snake_case       )
		model.save_pretrained(snake_case       )
		image_processor.save_pretrained(snake_case       )

		# Push to hub
		if push_to_hub:
				__lowercase					=    'deformable-detr'
				model_name += "-single-scale" if single_scale else ""
				model_name += "-dc5" if dilation else ""
				model_name += "-with-box-refine" if with_box_refine else ""
				model_name += "-two-stage" if two_stage else ""
				print('Pushing model to hub...'       )
				model.push_to_hub(repo_path_or_name=snake_case	,      organization='nielsr'	,      commit_message='Add model'       )


if __name__ == "__main__":
		SCREAMING_SNAKE_CASE_      :			List[str]		  =      argparse.ArgumentParser()

		parser.add_argument(
		    '''--checkpoint_path''',
		    type=str,
		    default='''/home/niels/checkpoints/deformable_detr/r50_deformable_detr-checkpoint.pth''',
		    help='''Path to Pytorch checkpoint (.pth file) you\'d like to convert.''',
		)
		parser.add_argument('''--single_scale''', action='''store_true''', help='''Whether to set config.num_features_levels = 1.''')
		parser.add_argument('''--dilation''', action='''store_true''', help='''Whether to set config.dilation=True.''')
		parser.add_argument('''--with_box_refine''', action='''store_true''', help='''Whether to set config.with_box_refine=True.''')
		parser.add_argument('''--two_stage''', action='''store_true''', help='''Whether to set config.two_stage=True.''')
		parser.add_argument(
		    '''--pytorch_dump_folder_path''',
		    default=None,
		    type=str,
		    required=True,
		    help='''Path to the folder to output PyTorch model.''',
		)
		parser.add_argument(
		    '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
		)
		SCREAMING_SNAKE_CASE_      :			List[str]		  =      parser.parse_args()
		convert_deformable_detr_checkpoint(
		    args.checkpoint_path,
		    args.single_scale,
		    args.dilation,
		    args.with_box_refine,
		    args.two_stage,
		    args.pytorch_dump_folder_path,
		    args.push_to_hub,
		)











import importlib
from typing import List, Optional, Union

from seqeval.metrics import accuracy_score, classification_report

import datasets


SCREAMING_SNAKE_CASE_      :			Any		  =      '''\
@inproceedings{ramshaw-marcus-1995-text,
    title = "Text Chunking using Transformation-Based Learning",
    author = "Ramshaw, Lance  and
      Marcus, Mitch",
    booktitle = "Third Workshop on Very Large Corpora",
    year = "1995",
    url = "https://www.aclweb.org/anthology/W95-0107",
}
@misc{seqeval,
  title={{seqeval}: A Python framework for sequence labeling evaluation},
  url={https://github.com/chakki-works/seqeval},
  note={Software available from https://github.com/chakki-works/seqeval},
  author={Hiroki Nakayama},
  year={2018},
}
'''

SCREAMING_SNAKE_CASE_      :			Tuple		  =      '''\
seqeval is a Python framework for sequence labeling evaluation.
seqeval can evaluate the performance of chunking tasks such as named-entity recognition, part-of-speech tagging, semantic role labeling and so on.

This is well-tested by using the Perl script conlleval, which can be used for
measuring the performance of a system that has processed the CoNLL-2000 shared task data.

seqeval supports following formats:
IOB1
IOB2
IOE1
IOE2
IOBES

See the [README.md] file at https://github.com/chakki-works/seqeval for more information.
'''

SCREAMING_SNAKE_CASE_      :			Dict		  =      '''
Produces labelling scores along with its sufficient statistics
from a source against one or more references.

Args:
    predictions: List of List of predicted labels (Estimated targets as returned by a tagger)
    references: List of List of reference labels (Ground truth (correct) target values)
    suffix: True if the IOB prefix is after type, False otherwise. default: False
    scheme: Specify target tagging scheme. Should be one of ["IOB1", "IOB2", "IOE1", "IOE2", "IOBES", "BILOU"].
        default: None
    mode: Whether to count correct entity labels with incorrect I/B tags as true positives or not.
        If you want to only count exact matches, pass mode="strict". default: None.
    sample_weight: Array-like of shape (n_samples,), weights for individual samples. default: None
    zero_division: Which value to substitute as a metric value when encountering zero division. Should be on of 0, 1,
        "warn". "warn" acts as 0, but the warning is raised.

Returns:
    \'scores\': dict. Summary of the scores for overall and per type
        Overall:
            \'accuracy\': accuracy,
            \'precision\': precision,
            \'recall\': recall,
            \'f1\': F1 score, also known as balanced F-score or F-measure,
        Per type:
            \'precision\': precision,
            \'recall\': recall,
            \'f1\': F1 score, also known as balanced F-score or F-measure
Examples:

    >>> predictions = [[\'O\', \'O\', \'B-MISC\', \'I-MISC\', \'I-MISC\', \'I-MISC\', \'O\'], [\'B-PER\', \'I-PER\', \'O\']]
    >>> references = [[\'O\', \'O\', \'O\', \'B-MISC\', \'I-MISC\', \'I-MISC\', \'O\'], [\'B-PER\', \'I-PER\', \'O\']]
    >>> seqeval = datasets.load_metric("seqeval")
    >>> results = seqeval.compute(predictions=predictions, references=references)
    >>> print(list(results.keys()))
    [\'MISC\', \'PER\', \'overall_precision\', \'overall_recall\', \'overall_f1\', \'overall_accuracy\']
    >>> print(results["overall_f1"])
    0.5
    >>> print(results["PER"]["f1"])
    1.0
'''

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







	'''simple docstring'''




	def       UpperCAmelCase (	self       :    Any      )					->   str:

			'''simple docstring'''




			return datasets.MetricInfo(
			    description=_DESCRIPTION				,			citation=_CITATION				,			homepage='https://github.com/chakki-works/seqeval'				,			inputs_description=_KWARGS_DESCRIPTION				,			features=datasets.Features(
			        {
			            'predictions': datasets.Sequence(datasets.Value('string'				,			id='label'      )				,			id='sequence'      ),
			            'references': datasets.Sequence(datasets.Value('string'				,			id='label'      )				,			id='sequence'      ),
			        }      )				,			codebase_urls=['https://github.com/chakki-works/seqeval']				,			reference_urls=['https://github.com/chakki-works/seqeval']				,			)






	def       UpperCAmelCase (	self       :    Optional[Any]				,			__lowerCamelCase       :    int				,			__lowerCamelCase       :    str				,			__lowerCamelCase       :    bool = False				,			__lowerCamelCase       :    Optional[str] = None				,			__lowerCamelCase       :    Optional[str] = None				,			__lowerCamelCase       :    Optional[List[int]] = None				,			__lowerCamelCase       :    Union[str, int] = "warn"				,			)					->   Tuple:

			'''simple docstring'''




			if scheme is not None:
					try:
							__lowercase					=    importlib.import_module('seqeval.scheme'      )
							__lowercase					=    getattr(__lowerCamelCase				,			__lowerCamelCase      )
					except AttributeError:
							raise ValueError(F"Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {scheme}"      )
			__lowercase					=    classification_report(
			    y_true=__lowerCamelCase				,			y_pred=__lowerCamelCase				,			suffix=__lowerCamelCase				,			output_dict=__lowerCamelCase				,			scheme=__lowerCamelCase				,			mode=__lowerCamelCase				,			sample_weight=__lowerCamelCase				,			zero_division=__lowerCamelCase				,			)
			report.pop('macro avg'      )
			report.pop('weighted avg'      )
			__lowercase					=    report.pop('micro avg'      )

			__lowercase					=    {
			    type_name: {
			        'precision': score['precision'],
			        'recall': score['recall'],
			        'f1': score['f1-score'],
			        'number': score['support'],
			    }
			    for type_name, score in report.items()
			}
			__lowercase					=    overall_score['precision']
			__lowercase					=    overall_score['recall']
			__lowercase					=    overall_score['f1-score']
			__lowercase					=    accuracy_score(y_true=__lowerCamelCase				,			y_pred=__lowerCamelCase      )

			return scores




