'''simple docstring'''

import jax
import jax.numpy as jnp
from bigbird_flax import FlaxBigBirdForNaturalQuestions
from datasets import load_from_disk

from transformers import BigBirdTokenizerFast


lowercase_  		=			{0: '''null''', 1: '''short''', 2: '''long''', 3: '''yes''', 4: '''no'''}
lowercase_  		=			set(''''''.join(['''‘''', '''’''', '''´''', '''`''', '''.''', ''',''', '''-''', '''"''']))



def 				UpperCamelCase__      (      a__      ,   a__=0      ,   a__=None   ):


						'''simple docstring'''






						return [" ".join(x.split(' '   )[begin:end]   ) for x in answers if len(x.split(' '   )   ) > 1]



def 				UpperCamelCase__      (      a__      ,   a__=False   ):


						'''simple docstring'''






						if make_sub_answers:
												# if answers are longer than one word, make sure a predictions is correct if it coresponds to the complete 1: or :-1 sub word
												# *e.g.* if the correct answer contains a prefix such as "the", or "a"
												_lowerCAmelCase					    =(
												    given_answers + get_sub_answers(a__      ,   begin=1   ) + get_sub_answers(a__      ,   end=-1   )
												)
						_lowerCAmelCase					    =[]
						for answer in given_answers:
												_lowerCAmelCase					    =answer.replace('_'      ,   ' '   ).lower()
												_lowerCAmelCase					    =''.join(c if c not in PUNCTUATION_SET_TO_EXCLUDE else ' ' for c in alias   )
												answers.append(' '.join(alias.split()   ).strip()   )
						return set(a__   )



def 				UpperCamelCase__      (      a__      ,   a__      ,   a__=1      ,   a__=1_0_0   ):


						'''simple docstring'''






						_lowerCAmelCase    ,					_lowerCAmelCase					    =jax.lax.top_k(a__      ,   a__   )
						_lowerCAmelCase    ,					_lowerCAmelCase					    =jax.lax.top_k(a__      ,   a__   )

						_lowerCAmelCase					    =best_end_idx[:, None] - best_start_idx[None, :]
						_lowerCAmelCase					    =jnp.logical_or(widths < 0      ,   widths > max_size   )
						_lowerCAmelCase					    =(best_end_scores[:, None] + best_start_scores[None, :]) - (1E8 * mask)
						_lowerCAmelCase					    =jnp.argmax(a__   ).item()

						return best_start_idx[best_score % top_k], best_end_idx[best_score // top_k]



def 				UpperCamelCase__      (      a__   ):


						'''simple docstring'''






						_lowerCAmelCase					    =sample['question']['text']
						_lowerCAmelCase					    =sample['document']['tokens']['token']
						_lowerCAmelCase					    =sample['document']['tokens']['is_html']
						_lowerCAmelCase					    =sample['annotations']['long_answer']
						_lowerCAmelCase					    =sample['annotations']['short_answers']

						_lowerCAmelCase					    =' '.join([context[i] for i in range(len(a__   )   ) if not is_html[i]]   )

						# 0 - No ; 1 - Yes
						for answer in sample["annotations"]["yes_no_answer"]:
												if answer == 0 or answer == 1:
																		return {
																		    "question": question,
																		    "context": context_string,
																		    "short": [],
																		    "long": [],
																		    "category": "no" if answer == 0 else "yes",
																		}

						_lowerCAmelCase					    =[]
						for s in short_answers:
												short_targets.extend(s['text']   )
						_lowerCAmelCase					    =list(set(a__   )   )

						_lowerCAmelCase					    =[]
						for s in long_answers:
												if s["start_token"] == -1:
																		continue
												_lowerCAmelCase					    =context[s['start_token'] : s['end_token']]
												_lowerCAmelCase					    =is_html[s['start_token'] : s['end_token']]
												_lowerCAmelCase					    =' '.join([answer[i] for i in range(len(a__   )   ) if not html[i]]   )
												if new_answer not in long_targets:
																		long_targets.append(a__   )

						_lowerCAmelCase					    ='long_short' if len(short_targets + long_targets   ) > 0 else 'null'

						return {
						    "question": question,
						    "context": context_string,
						    "short": short_targets,
						    "long": long_targets,
						    "category": category,
						}



def 				UpperCamelCase__      (      ):


						'''simple docstring'''






						_lowerCAmelCase					    =load_from_disk('natural-questions-validation'   )
						_lowerCAmelCase					    =dataset.map(a__   ).remove_columns(['annotations', 'document', 'id']   )
						print(a__   )

						_lowerCAmelCase					    =dataset.filter(lambda a__   : (len(x['question']   ) + len(x['context']   )) < 4 * 4_0_9_6   )
						_lowerCAmelCase					    =short_validation_dataset.filter(lambda a__   : x["category"] != "null"   )
						short_validation_dataset

						_lowerCAmelCase					    ='vasudevgupta/flax-bigbird-natural-questions'
						_lowerCAmelCase					    =FlaxBigBirdForNaturalQuestions.from_pretrained(a__   )
						_lowerCAmelCase					    =BigBirdTokenizerFast.from_pretrained(a__   )

						@jax.jit
						def forward(*a__      ,   **a__   ):
												_lowerCAmelCase    ,					_lowerCAmelCase    ,					_lowerCAmelCase					    =model(*a__      ,   **a__   )
												return start_logits, end_logits, jnp.argmax(a__      ,   axis=-1   )

						def evaluate(a__   ):
												# encode question and context so that they are separated by a tokenizer.sep_token and cut at max_length
												_lowerCAmelCase					    =tokenizer(
												    example['question']      ,   example['context']      ,   return_tensors='np'      ,   max_length=4_0_9_6      ,   padding='max_length'      ,   truncation=a__      ,   )

												_lowerCAmelCase    ,					_lowerCAmelCase    ,					_lowerCAmelCase					    =forward(**a__   )

												_lowerCAmelCase					    =CATEGORY_MAPPING[category.item()]

												_lowerCAmelCase					    =example['long'] + example['short']
												if example["category"] in ["yes", "no", "null"]:
																		_lowerCAmelCase					    =[example['category']]
												_lowerCAmelCase					    =example['category'] != 'null'
												# Now target can be: "yes", "no", "null", "list of long & short answers"

												if predicted_category in ["yes", "no", "null"]:
																		_lowerCAmelCase					    =[predicted_category]
																		_lowerCAmelCase					    =example['output'] == example['targets']
																		_lowerCAmelCase					    =predicted_category != 'null'
																		return example

												_lowerCAmelCase					    =3_8 if predicted_category == 'short' else 1_0_2_4
												_lowerCAmelCase    ,					_lowerCAmelCase					    =get_best_valid_start_end_idx(
												    start_scores[0]      ,   end_scores[0]      ,   top_k=8      ,   max_size=a__   )

												_lowerCAmelCase					    =inputs['input_ids'][0].tolist()
												_lowerCAmelCase					    =[tokenizer.decode(input_ids[start_score : end_score + 1]   )]

												_lowerCAmelCase					    =expand_to_aliases(example['targets']      ,   make_sub_answers=a__   )
												_lowerCAmelCase					    =expand_to_aliases(example['output']   )

												# some preprocessing to both prediction and answer
												_lowerCAmelCase					    ={''.join(a.split()   ) for a in answers}
												_lowerCAmelCase					    ={''.join(p.split()   ) for p in predictions}
												_lowerCAmelCase					    ={s for s in predictions if s not in ['``', '\'\'', '`', '\'']}

												# if there is a common element, it's a exact match
												_lowerCAmelCase					    =len(list(answers & predictions   )   ) > 0
												_lowerCAmelCase					    =predicted_category != 'null' and len(a__   ) > 0

												return example

						_lowerCAmelCase					    =short_validation_dataset.map(a__   )

						_lowerCAmelCase					    =len(a__   )
						_lowerCAmelCase					    =len(short_validation_dataset.filter(lambda a__   : x["match"] == 1   )   )
						print('EM score:'      ,   (matched / total) * 1_0_0      ,   '%'   )


if __name__ == "__main__":
							main()






'''simple docstring'''

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()
lowercase_  		=			logging.get_logger(__name__)



def 				UpperCamelCase__      (      a__   ):


						'''simple docstring'''






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

						return orig_key



def 				UpperCamelCase__      (      a__   ):


						'''simple docstring'''






						for i in range(6   ):
												# read in weights + bias of input projection layer of self-attention
												_lowerCAmelCase					    =state_dict.pop(F'''decoder.layers.{i}.self_attn.in_proj_weight'''   )
												_lowerCAmelCase					    =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
												_lowerCAmelCase					    =in_proj_weight[:2_5_6, :]
												_lowerCAmelCase					    =in_proj_bias[:2_5_6]
												_lowerCAmelCase					    =in_proj_weight[2_5_6:5_1_2, :]
												_lowerCAmelCase					    =in_proj_bias[2_5_6:5_1_2]
												_lowerCAmelCase					    =in_proj_weight[-2_5_6:, :]
												_lowerCAmelCase					    =in_proj_bias[-2_5_6:]



def 				UpperCamelCase__      (      ):


						'''simple docstring'''






						_lowerCAmelCase					    ='http://images.cocodataset.org/val2017/000000039769.jpg'
						_lowerCAmelCase					    =Image.open(requests.get(a__      ,   stream=a__   ).raw   )

						return im



@torch.no_grad()
def 				UpperCamelCase__      (      a__      ,   a__      ,   a__      ,   a__      ,   a__      ,   a__      ,   a__      ,   ):


						'''simple docstring'''






						_lowerCAmelCase					    =DeformableDetrConfig()
						# set config attributes
						if single_scale:
												_lowerCAmelCase					    =1
						_lowerCAmelCase					    =dilation
						_lowerCAmelCase					    =with_box_refine
						_lowerCAmelCase					    =two_stage
						# set labels
						_lowerCAmelCase					    =9_1
						_lowerCAmelCase					    ='huggingface/label-files'
						_lowerCAmelCase					    ='coco-detection-id2label.json'
						_lowerCAmelCase					    =json.load(open(cached_download(hf_hub_url(a__      ,   a__      ,   repo_type='dataset'   )   )      ,   'r'   )   )
						_lowerCAmelCase					    ={int(a__   ): v for k, v in idalabel.items()}
						_lowerCAmelCase					    =idalabel
						_lowerCAmelCase					    ={v: k for k, v in idalabel.items()}

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

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

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

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

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

						_lowerCAmelCase					    =torch.tensor(
						    [[-9.6_645, -4.3_449, -5.8_705], [-9.7_035, -3.8_504, -5.0_724], [-10.5_634, -5.3_379, -7.5_116]]   )
						_lowerCAmelCase					    =torch.tensor([[0.8_693, 0.2_289, 0.2_492], [0.3_150, 0.5_489, 0.5_845], [0.5_563, 0.7_580, 0.8_518]]   )

						if single_scale:
												_lowerCAmelCase					    =torch.tensor(
												    [[-9.9_051, -4.2_541, -6.4_852], [-9.6_947, -4.0_854, -6.8_033], [-10.0_665, -5.8_470, -7.7_003]]   )
												_lowerCAmelCase					    =torch.tensor([[0.7_292, 0.4_991, 0.5_532], [0.7_959, 0.2_426, 0.4_236], [0.7_582, 0.3_518, 0.4_451]]   )

						if single_scale and dilation:
												_lowerCAmelCase					    =torch.tensor(
												    [[-8.9_652, -4.1_074, -5.6_635], [-9.0_596, -4.9_447, -6.6_075], [-10.1_178, -4.5_275, -6.2_671]]   )
												_lowerCAmelCase					    =torch.tensor([[0.7_665, 0.4_130, 0.4_769], [0.8_364, 0.1_841, 0.3_391], [0.6_261, 0.3_895, 0.7_978]]   )

						if with_box_refine:
												_lowerCAmelCase					    =torch.tensor(
												    [[-8.8_895, -5.4_187, -6.8_153], [-8.4_706, -6.1_668, -7.6_184], [-9.0_042, -5.5_359, -6.9_141]]   )
												_lowerCAmelCase					    =torch.tensor([[0.7_828, 0.2_208, 0.4_323], [0.0_892, 0.5_996, 0.1_319], [0.5_524, 0.6_389, 0.8_914]]   )

						if with_box_refine and two_stage:
												_lowerCAmelCase					    =torch.tensor(
												    [[-6.7_108, -4.3_213, -6.3_777], [-8.9_014, -6.1_799, -6.7_240], [-6.9_315, -4.4_735, -6.2_298]]   )
												_lowerCAmelCase					    =torch.tensor([[0.2_583, 0.5_499, 0.4_683], [0.7_652, 0.9_068, 0.4_882], [0.5_490, 0.2_763, 0.0_564]]   )

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

						assert torch.allclose(outputs.logits[0, :3, :3]      ,   expected_logits.to(a__   )      ,   atol=1E-4   )
						assert torch.allclose(outputs.pred_boxes[0, :3, :3]      ,   expected_boxes.to(a__   )      ,   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(a__   ).mkdir(exist_ok=a__   )
						model.save_pretrained(a__   )
						image_processor.save_pretrained(a__   )

						# Push to hub
						if push_to_hub:
												_lowerCAmelCase					    ='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=a__      ,   organization='nielsr'      ,   commit_message='Add model'   )


if __name__ == "__main__":
							lowercase_  		=			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.'''
							)
							lowercase_  		=			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,
							)





