





"""simple docstring"""



import argparse
import json
from collections import OrderedDict
from pathlib import Path

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

from transformers import (
    ConditionalDetrConfig,
    ConditionalDetrForObjectDetection,
    ConditionalDetrForSegmentation,
    ConditionalDetrImageProcessor,
)
from transformers.utils import logging


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

# here we list all keys to be renamed (original name on the left, our name on the right)
a_ =			[]
for i in range(6):
       # 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}.cross_attn.out_proj.weight''',
               f'''decoder.layers.{i}.encoder_attn.out_proj.weight''',
           )
       )
       rename_keys.append(
           (
               f'''transformer.decoder.layers.{i}.cross_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'''))

       # q, k, v projections in self/cross-attention in decoder for conditional DETR
       rename_keys.append(
           (f'''transformer.decoder.layers.{i}.sa_qcontent_proj.weight''', f'''decoder.layers.{i}.sa_qcontent_proj.weight''')
       )
       rename_keys.append(
           (f'''transformer.decoder.layers.{i}.sa_kcontent_proj.weight''', f'''decoder.layers.{i}.sa_kcontent_proj.weight''')
       )
       rename_keys.append(
           (f'''transformer.decoder.layers.{i}.sa_qpos_proj.weight''', f'''decoder.layers.{i}.sa_qpos_proj.weight''')
       )
       rename_keys.append(
           (f'''transformer.decoder.layers.{i}.sa_kpos_proj.weight''', f'''decoder.layers.{i}.sa_kpos_proj.weight''')
       )
       rename_keys.append((f'''transformer.decoder.layers.{i}.sa_v_proj.weight''', f'''decoder.layers.{i}.sa_v_proj.weight'''))
       rename_keys.append(
           (f'''transformer.decoder.layers.{i}.ca_qcontent_proj.weight''', f'''decoder.layers.{i}.ca_qcontent_proj.weight''')
       )
       # rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.weight", f"decoder.layers.{i}.ca_qpos_proj.weight"))
       rename_keys.append(
           (f'''transformer.decoder.layers.{i}.ca_kcontent_proj.weight''', f'''decoder.layers.{i}.ca_kcontent_proj.weight''')
       )
       rename_keys.append(
           (f'''transformer.decoder.layers.{i}.ca_kpos_proj.weight''', f'''decoder.layers.{i}.ca_kpos_proj.weight''')
       )
       rename_keys.append((f'''transformer.decoder.layers.{i}.ca_v_proj.weight''', f'''decoder.layers.{i}.ca_v_proj.weight'''))
       rename_keys.append(
           (f'''transformer.decoder.layers.{i}.ca_qpos_sine_proj.weight''', f'''decoder.layers.{i}.ca_qpos_sine_proj.weight''')
       )

       rename_keys.append(
           (f'''transformer.decoder.layers.{i}.sa_qcontent_proj.bias''', f'''decoder.layers.{i}.sa_qcontent_proj.bias''')
       )
       rename_keys.append(
           (f'''transformer.decoder.layers.{i}.sa_kcontent_proj.bias''', f'''decoder.layers.{i}.sa_kcontent_proj.bias''')
       )
       rename_keys.append((f'''transformer.decoder.layers.{i}.sa_qpos_proj.bias''', f'''decoder.layers.{i}.sa_qpos_proj.bias'''))
       rename_keys.append((f'''transformer.decoder.layers.{i}.sa_kpos_proj.bias''', f'''decoder.layers.{i}.sa_kpos_proj.bias'''))
       rename_keys.append((f'''transformer.decoder.layers.{i}.sa_v_proj.bias''', f'''decoder.layers.{i}.sa_v_proj.bias'''))
       rename_keys.append(
           (f'''transformer.decoder.layers.{i}.ca_qcontent_proj.bias''', f'''decoder.layers.{i}.ca_qcontent_proj.bias''')
       )
       # rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.bias", f"decoder.layers.{i}.ca_qpos_proj.bias"))
       rename_keys.append(
           (f'''transformer.decoder.layers.{i}.ca_kcontent_proj.bias''', f'''decoder.layers.{i}.ca_kcontent_proj.bias''')
       )
       rename_keys.append((f'''transformer.decoder.layers.{i}.ca_kpos_proj.bias''', f'''decoder.layers.{i}.ca_kpos_proj.bias'''))
       rename_keys.append((f'''transformer.decoder.layers.{i}.ca_v_proj.bias''', f'''decoder.layers.{i}.ca_v_proj.bias'''))
       rename_keys.append(
           (f'''transformer.decoder.layers.{i}.ca_qpos_sine_proj.bias''', f'''decoder.layers.{i}.ca_qpos_sine_proj.bias''')
       )

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
# for conditional DETR, also convert reference point head and query scale MLP
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"""),
        ("""transformer.decoder.ref_point_head.layers.0.weight""", """decoder.ref_point_head.layers.0.weight"""),
        ("""transformer.decoder.ref_point_head.layers.0.bias""", """decoder.ref_point_head.layers.0.bias"""),
        ("""transformer.decoder.ref_point_head.layers.1.weight""", """decoder.ref_point_head.layers.1.weight"""),
        ("""transformer.decoder.ref_point_head.layers.1.bias""", """decoder.ref_point_head.layers.1.bias"""),
        ("""transformer.decoder.query_scale.layers.0.weight""", """decoder.query_scale.layers.0.weight"""),
        ("""transformer.decoder.query_scale.layers.0.bias""", """decoder.query_scale.layers.0.bias"""),
        ("""transformer.decoder.query_scale.layers.1.weight""", """decoder.query_scale.layers.1.weight"""),
        ("""transformer.decoder.query_scale.layers.1.bias""", """decoder.query_scale.layers.1.bias"""),
        ("""transformer.decoder.layers.0.ca_qpos_proj.weight""", """decoder.layers.0.ca_qpos_proj.weight"""),
        ("""transformer.decoder.layers.0.ca_qpos_proj.bias""", """decoder.layers.0.ca_qpos_proj.bias"""),
    ]
)







def 		__lowercase     (   snake_case_  :  Dict				,snake_case_  :  Any				,snake_case_  :  Optional[int]						)   ->Dict:



    '''simple docstring'''







    __A    :						Union[str, Any]						 =     state_dict.pop(snake_case_						)
    __A    :						List[Any]						 =     val







def 		__lowercase     (   snake_case_  :  int						)   ->List[Any]:



    '''simple docstring'''







    __A    :						str						 =     OrderedDict()
    for key, value in state_dict.items():
        if "backbone.0.body" in key:
            __A    :						int						 =     key.replace('''backbone.0.body'''				,'''backbone.conv_encoder.model'''						)
            __A    :						Union[str, Any]						 =     value
        else:
            __A    :						Optional[Any]						 =     value

    return new_state_dict







def 		__lowercase     (   snake_case_  :  List[Any]				,snake_case_  :  Dict=False						)   ->List[str]:



    '''simple docstring'''







    __A    :						Tuple						 =     ''''''
    if is_panoptic:
        __A    :						List[str]						 =     '''conditional_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)
        __A    :						List[str]						 =     state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""						)
        __A    :						List[Any]						 =     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
        __A    :						Dict						 =     in_proj_weight[:256, :]
        __A    :						Union[str, Any]						 =     in_proj_bias[:256]
        __A    :						List[Any]						 =     in_proj_weight[256:512, :]
        __A    :						List[str]						 =     in_proj_bias[256:512]
        __A    :						Optional[int]						 =     in_proj_weight[-256:, :]
        __A    :						Union[str, Any]						 =     in_proj_bias[-256:]







def 		__lowercase     (   )   ->Dict:



    '''simple docstring'''







    __A    :						Dict						 =     '''http://images.cocodataset.org/val2017/000000039769.jpg'''
    __A    :						List[str]						 =     Image.open(requests.get(snake_case_				,stream=snake_case_						).raw						)

    return im







@torch.no_grad()
def 		__lowercase     (   snake_case_  :  Optional[int]				,snake_case_  :  Dict						)   ->Optional[int]:



    '''simple docstring'''







    __A    :						Optional[int]						 =     ConditionalDetrConfig()
    # set backbone and dilation attributes
    if "resnet101" in model_name:
        __A    :						List[str]						 =     '''resnet101'''
    if "dc5" in model_name:
        __A    :						int						 =     True
    __A    :						Optional[Any]						 =     '''panoptic''' in model_name
    if is_panoptic:
        __A    :						List[Any]						 =     250
    else:
        __A    :						Union[str, Any]						 =     91
        __A    :						Tuple						 =     '''huggingface/label-files'''
        __A    :						Tuple						 =     '''coco-detection-id2label.json'''
        __A    :						Optional[int]						 =     json.load(open(hf_hub_download(snake_case_				,snake_case_				,repo_type='''dataset'''						)				,'''r'''						)						)
        __A    :						Dict						 =     {int(snake_case_						): v for k, v in idalabel.items()}
        __A    :						List[str]						 =     idalabel
        __A    :						Union[str, Any]						 =     {v: k for k, v in idalabel.items()}

    # load image processor
    __A    :						List[str]						 =     '''coco_panoptic''' if is_panoptic else '''coco_detection'''
    __A    :						Any						 =     ConditionalDetrImageProcessor(format=snake_case_						)

    # prepare image
    __A    :						str						 =     prepare_img()
    __A    :						Union[str, Any]						 =     image_processor(images=snake_case_				,return_tensors='''pt'''						)
    __A    :						Tuple						 =     encoding['''pixel_values''']

    logger.info(F"""Converting model {model_name}..."""						)

    # load original model from torch hub
    __A    :						str						 =     torch.hub.load('''DeppMeng/ConditionalDETR'''				,snake_case_				,pretrained=snake_case_						).eval()
    __A    :						List[Any]						 =     conditional_detr.state_dict()
    # rename keys
    for src, dest in rename_keys:
        if is_panoptic:
            __A    :						Dict						 =     '''conditional_detr.''' + src
        rename_key(snake_case_				,snake_case_				,snake_case_						)
    __A    :						Optional[int]						 =     rename_backbone_keys(snake_case_						)
    # query, key and value matrices need special treatment
    read_in_q_k_v(snake_case_				,is_panoptic=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
    __A    :						Dict						 =     '''conditional_detr.model.''' if is_panoptic else '''model.'''
    for key in state_dict.copy().keys():
        if is_panoptic:
            if (
                key.startswith('''conditional_detr'''						)
                and not key.startswith('''class_labels_classifier'''						)
                and not key.startswith('''bbox_predictor'''						)
            ):
                __A    :						str						 =     state_dict.pop(snake_case_						)
                __A    :						List[str]						 =     val
            elif "class_labels_classifier" in key or "bbox_predictor" in key:
                __A    :						Tuple						 =     state_dict.pop(snake_case_						)
                __A    :						str						 =     val
            elif key.startswith('''bbox_attention'''						) or key.startswith('''mask_head'''						):
                continue
            else:
                __A    :						List[Any]						 =     state_dict.pop(snake_case_						)
                __A    :						Union[str, Any]						 =     val
        else:
            if not key.startswith('''class_labels_classifier'''						) and not key.startswith('''bbox_predictor'''						):
                __A    :						Dict						 =     state_dict.pop(snake_case_						)
                __A    :						Tuple						 =     val
    # finally, create HuggingFace model and load state dict
    __A    :						int						 =     ConditionalDetrForSegmentation(snake_case_						) if is_panoptic else ConditionalDetrForObjectDetection(snake_case_						)
    model.load_state_dict(snake_case_						)
    model.eval()
    model.push_to_hub(repo_id=snake_case_				,organization='''DepuMeng'''				,commit_message='''Add model'''						)
    # verify our conversion
    __A    :						str						 =     conditional_detr(snake_case_						)
    __A    :						List[Any]						 =     model(snake_case_						)
    assert torch.allclose(outputs.logits				,original_outputs['''pred_logits''']				,atol=1e-4						)
    assert torch.allclose(outputs.pred_boxes				,original_outputs['''pred_boxes''']				,atol=1e-4						)
    if is_panoptic:
        assert torch.allclose(outputs.pred_masks				,original_outputs['''pred_masks''']				,atol=1e-4						)

    # 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_						)


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

       parser.add_argument(
           """--model_name""",
           default="""conditional_detr_resnet50""",
           type=str,
           help="""Name of the CONDITIONAL_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."""
       )
       a_ =			parser.parse_args()
       convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)





'''simple docstring'''





from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow

from ..bert.test_tokenization_bert import BertTokenizationTest




@require_tokenizers
class        __A      ( UpperCamelCase__	):
    a__       :						Optional[Any]          =  DistilBertTokenizer
    a__       :						Any          =  DistilBertTokenizerFast
    a__       :						str          =  True



    @slow
    def 					_lowercase						(self    :    int ):
         UpperCAmelCase_           =   DistilBertTokenizer.from_pretrained("distilbert-base-uncased" )

         UpperCAmelCase_           =   tokenizer.encode("sequence builders"   ,			add_special_tokens=__a )
         UpperCAmelCase_           =   tokenizer.encode("multi-sequence build"   ,			add_special_tokens=__a )

         UpperCAmelCase_           =   tokenizer.build_inputs_with_special_tokens(__a )
         UpperCAmelCase_           =   tokenizer.build_inputs_with_special_tokens(__a   ,			__a )

         assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
         assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
             tokenizer.sep_token_id
         ]




