






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()
lowerCamelCase					    =	logging.get_logger(__name__)
def 							UpperCAmelCase   ( _lowercase   ):


    '''simple docstring'''



    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 							UpperCAmelCase   ( _lowercase   ):


    '''simple docstring'''



    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 							UpperCAmelCase   ( ):


    '''simple docstring'''



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

    return im
@torch.no_grad()
def 							UpperCAmelCase   ( _lowercase,   _lowercase,   _lowercase,   _lowercase,   _lowercase,   _lowercase,   _lowercase,   ):


    '''simple docstring'''



    _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(UpperCAmelCase__,   UpperCAmelCase__,   repo_type='dataset'   )   ),   'r'   )   )
    _lowercase     			=						{int(UpperCAmelCase__   ): 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=UpperCAmelCase__,   return_tensors='pt'   )
    _lowercase     			=						encoding['pixel_values']

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

    # load original state dict
    _lowercase     			=						torch.load(UpperCAmelCase__,   map_location='cpu'   )['model']
    # rename keys
    for key in state_dict.copy().keys():
        _lowercase     			=						state_dict.pop(UpperCAmelCase__   )
        _lowercase     			=						val
    # query, key and value matrices need special treatment
    read_in_q_k_v(UpperCAmelCase__   )
    # 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(UpperCAmelCase__   )
            _lowercase     			=						val
             # finally, create HuggingFace model and load state dict
    _lowercase     			=						DeformableDetrForObjectDetection(UpperCAmelCase__   )
    model.load_state_dict(UpperCAmelCase__   )
    model.eval()

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

    _lowercase     			=						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]]   )
    _lowercase     			=						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:
        _lowercase     			=						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]]   )
        _lowercase     			=						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:
        _lowercase     			=						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]]   )
        _lowercase     			=						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:
        _lowercase     			=						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]]   )
        _lowercase     			=						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:
        _lowercase     			=						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]]   )
        _lowercase     			=						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(UpperCAmelCase__   ),   atol=1e-4   )
    assert torch.allclose(outputs.pred_boxes[0, :3, :3],   expected_boxes.to(UpperCAmelCase__   ),   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(UpperCAmelCase__   ).mkdir(exist_ok=UpperCAmelCase__   )
    model.save_pretrained(UpperCAmelCase__   )
    image_processor.save_pretrained(UpperCAmelCase__   )

    # 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=UpperCAmelCase__,   organization='nielsr',   commit_message='Add model'   )


if __name__ == "__main__":
   lowerCamelCase					    =	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.'''
   )
   lowerCamelCase					    =	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 functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


A_		:  str										=  logging.get_logger(__name__)

A_		:  Dict										=  {
    '''facebook/hubert-base-ls960''': '''https://huggingface.co/facebook/hubert-base-ls960/resolve/main/config.json''',
    # See all Hubert models at https://huggingface.co/models?filter=hubert
}



class 		_lowercase		(							UpperCAmelCase__						):
   _UpperCAmelCase  						=      '''hubert'''

   def __init__(   self      :							Optional[Any]				, __lowerCAmelCase      :							str=32				, __lowerCAmelCase      :							int=768				, __lowerCAmelCase      :							Optional[int]=12				, __lowerCAmelCase      :							List[Any]=12				, __lowerCAmelCase      :							Optional[Any]=3072				, __lowerCAmelCase      :							Tuple="gelu"				, __lowerCAmelCase      :							Dict=0.1				, __lowerCAmelCase      :							List[Any]=0.1				, __lowerCAmelCase      :							str=0.1				, __lowerCAmelCase      :							Union[str, Any]=True				, __lowerCAmelCase      :							Tuple=0.0				, __lowerCAmelCase      :							Optional[int]=0.1				, __lowerCAmelCase      :							List[str]=0.1				, __lowerCAmelCase      :							Dict=0.0_2				, __lowerCAmelCase      :							Any=1E-5				, __lowerCAmelCase      :							Optional[Any]="group"				, __lowerCAmelCase      :							int="gelu"				, __lowerCAmelCase      :							Dict=(512, 512, 512, 512, 512, 512, 512)				, __lowerCAmelCase      :							Tuple=(5, 2, 2, 2, 2, 2, 2)				, __lowerCAmelCase      :							Tuple=(10, 3, 3, 3, 3, 2, 2)				, __lowerCAmelCase      :							Tuple=False				, __lowerCAmelCase      :							Any=128				, __lowerCAmelCase      :							Tuple=16				, __lowerCAmelCase      :							Tuple=False				, __lowerCAmelCase      :							int=True				, __lowerCAmelCase      :							List[str]=0.0_5				, __lowerCAmelCase      :							List[str]=10				, __lowerCAmelCase      :							int=2				, __lowerCAmelCase      :							List[Any]=0.0				, __lowerCAmelCase      :							Dict=10				, __lowerCAmelCase      :							List[str]=0				, __lowerCAmelCase      :							Union[str, Any]="sum"				, __lowerCAmelCase      :							Dict=False				, __lowerCAmelCase      :							List[str]=False				, __lowerCAmelCase      :							int=256				, __lowerCAmelCase      :							List[str]=0				, __lowerCAmelCase      :							str=1				, __lowerCAmelCase      :							Any=2				, **__lowerCAmelCase      :							Tuple				, )							->					Tuple:


    """simple docstring"""






    super().__init__(**__lowerCAmelCase				, pad_token_id=__lowerCAmelCase				, bos_token_id=__lowerCAmelCase				, eos_token_id=__lowerCAmelCase				)
    a							=		hidden_size
    a							=		feat_extract_norm
    a							=		feat_extract_activation
    a							=		list(__lowerCAmelCase				)
    a							=		list(__lowerCAmelCase				)
    a							=		list(__lowerCAmelCase				)
    a							=		conv_bias
    a							=		num_conv_pos_embeddings
    a							=		num_conv_pos_embedding_groups
    a							=		len(self.conv_dim				)
    a							=		num_hidden_layers
    a							=		intermediate_size
    a							=		hidden_act
    a							=		num_attention_heads
    a							=		hidden_dropout
    a							=		attention_dropout
    a							=		activation_dropout
    a							=		feat_proj_layer_norm
    a							=		feat_proj_dropout
    a							=		final_dropout
    a							=		layerdrop
    a							=		layer_norm_eps
    a							=		initializer_range
    a							=		vocab_size
    a							=		do_stable_layer_norm
    a							=		use_weighted_layer_sum
    a							=		classifier_proj_size

    if (
        (len(self.conv_stride				) != self.num_feat_extract_layers)
        or (len(self.conv_kernel				) != self.num_feat_extract_layers)
        or (len(self.conv_dim				) != self.num_feat_extract_layers)
    ):
     raise ValueError(
         "Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="
         " `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="
         f""" {len(self.conv_dim				)}`, `len(config.conv_stride) = {len(self.conv_stride				)}`,"""
         f""" `len(config.conv_kernel) = {len(self.conv_kernel				)}`."""				)

    # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
    a							=		apply_spec_augment
    a							=		mask_time_prob
    a							=		mask_time_length
    a							=		mask_time_min_masks
    a							=		mask_feature_prob
    a							=		mask_feature_length
    a							=		mask_feature_min_masks

    # ctc loss
    a							=		ctc_loss_reduction
    a							=		ctc_zero_infinity




   @property
   def 		A				(   self      :							Tuple				)							->					Optional[Any]:


    """simple docstring"""






    return functools.reduce(operator.mul				, self.conv_stride				, 1				)

