





'''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 DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor
from transformers.utils import logging


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

# here we list all keys to be renamed (original name on the left, our name on the right)
lowercase_         =			[]
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}.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"""),
    ]
)





def lowerCamelCase						(					__lowerCamelCase ,	__lowerCamelCase ,	__lowerCamelCase   )						->Optional[Any]:
    _SCREAMING_SNAKE_CASE     =      state_dict.pop(__lowerCamelCase   )
    _SCREAMING_SNAKE_CASE     =      val





def lowerCamelCase						(					__lowerCamelCase   )						->Union[str, Any]:
    _SCREAMING_SNAKE_CASE     =      OrderedDict()
    for key, value in state_dict.items():
        if "backbone.0.body" in key:
            _SCREAMING_SNAKE_CASE     =      key.replace("""backbone.0.body""" ,	"""backbone.conv_encoder.model"""   )
            _SCREAMING_SNAKE_CASE     =      value
        else:
            _SCREAMING_SNAKE_CASE     =      value

    return new_state_dict





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





def lowerCamelCase						(					)						->Optional[Any]:
    _SCREAMING_SNAKE_CASE     =      """http://images.cocodataset.org/val2017/000000039769.jpg"""
    _SCREAMING_SNAKE_CASE     =      Image.open(requests.get(__lowerCamelCase ,	stream=__lowerCamelCase   ).raw   )

    return im





@torch.no_grad()
def lowerCamelCase						(					__lowerCamelCase ,	__lowerCamelCase   )						->Optional[int]:
    _SCREAMING_SNAKE_CASE     =      DetrConfig()
    # set backbone and dilation attributes
    if "resnet101" in model_name:
        _SCREAMING_SNAKE_CASE     =      """resnet101"""
    if "dc5" in model_name:
        _SCREAMING_SNAKE_CASE     =      True
    _SCREAMING_SNAKE_CASE     =      """panoptic""" in model_name
    if is_panoptic:
        _SCREAMING_SNAKE_CASE     =      250
    else:
        _SCREAMING_SNAKE_CASE     =      91
        _SCREAMING_SNAKE_CASE     =      """huggingface/label-files"""
        _SCREAMING_SNAKE_CASE     =      """coco-detection-id2label.json"""
        _SCREAMING_SNAKE_CASE     =      json.load(open(hf_hub_download(__lowerCamelCase ,	__lowerCamelCase ,	repo_type="""dataset"""   ) ,	"""r"""   )   )
        _SCREAMING_SNAKE_CASE     =      {int(__lowerCamelCase   ): v for k, v in idalabel.items()}
        _SCREAMING_SNAKE_CASE     =      idalabel
        _SCREAMING_SNAKE_CASE     =      {v: k for k, v in idalabel.items()}

    # load image processor
    _SCREAMING_SNAKE_CASE     =      """coco_panoptic""" if is_panoptic else """coco_detection"""
    _SCREAMING_SNAKE_CASE     =      DetrImageProcessor(format=__lowerCamelCase   )

    # prepare image
    _SCREAMING_SNAKE_CASE     =      prepare_img()
    _SCREAMING_SNAKE_CASE     =      image_processor(images=__lowerCamelCase ,	return_tensors="""pt"""   )
    _SCREAMING_SNAKE_CASE     =      encoding["""pixel_values"""]

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

    # load original model from torch hub
    _SCREAMING_SNAKE_CASE     =      torch.hub.load("""facebookresearch/detr""" ,	__lowerCamelCase ,	pretrained=__lowerCamelCase   ).eval()
    _SCREAMING_SNAKE_CASE     =      detr.state_dict()
    # rename keys
    for src, dest in rename_keys:
        if is_panoptic:
            _SCREAMING_SNAKE_CASE     =      """detr.""" + src
        rename_key(__lowerCamelCase ,	__lowerCamelCase ,	__lowerCamelCase   )
    _SCREAMING_SNAKE_CASE     =      rename_backbone_keys(__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     =      """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     =      state_dict.pop(__lowerCamelCase   )
                _SCREAMING_SNAKE_CASE     =      val
            elif "class_labels_classifier" in key or "bbox_predictor" in key:
                _SCREAMING_SNAKE_CASE     =      state_dict.pop(__lowerCamelCase   )
                _SCREAMING_SNAKE_CASE     =      val
            elif key.startswith("""bbox_attention"""   ) or key.startswith("""mask_head"""   ):
                continue
            else:
                _SCREAMING_SNAKE_CASE     =      state_dict.pop(__lowerCamelCase   )
                _SCREAMING_SNAKE_CASE     =      val
        else:
            if not key.startswith("""class_labels_classifier"""   ) and not key.startswith("""bbox_predictor"""   ):
                _SCREAMING_SNAKE_CASE     =      state_dict.pop(__lowerCamelCase   )
                _SCREAMING_SNAKE_CASE     =      val
    # finally, create HuggingFace model and load state dict
    _SCREAMING_SNAKE_CASE     =      DetrForSegmentation(__lowerCamelCase   ) if is_panoptic else DetrForObjectDetection(__lowerCamelCase   )
    model.load_state_dict(__lowerCamelCase   )
    model.eval()
    # verify our conversion
    _SCREAMING_SNAKE_CASE     =      detr(__lowerCamelCase   )
    _SCREAMING_SNAKE_CASE     =      model(__lowerCamelCase   )
    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(__lowerCamelCase   ).mkdir(exist_ok=__lowerCamelCase   )
    model.save_pretrained(__lowerCamelCase   )
    image_processor.save_pretrained(__lowerCamelCase   )


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

      parser.add_argument(
          """--model_name""", default="""detr_resnet50""", type=str, 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."""
      )
      lowercase_         =			parser.parse_args()
      convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)










'''simple docstring'''

from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowercase_         =			logging.get_logger(__name__)

lowercase_         =			{
    """google/rembert""": """https://huggingface.co/google/rembert/resolve/main/config.json""",
    # See all RemBERT models at https://huggingface.co/models?filter=rembert
}


class  a_					(    snake_case_	):

    '''simple docstring'''
    UpperCamelCase			   =			'''rembert'''


    def __init__(		self			,		A=25_0300			,		A=1152			,		A=32			,		A=18			,		A=256			,		A=1664			,		A=4608			,		A="gelu"			,		A=0.0			,		A=0.0			,		A=0.1			,		A=512			,		A=2			,		A=0.02			,		A=1e-12			,		A=True			,		A=0			,		A=312			,		A=313			,		**A			,		)		->       Dict:
        super().__init__(pad_token_id=A			,		bos_token_id=A			,		eos_token_id=A			,		**A	)

        _SCREAMING_SNAKE_CASE     =      vocab_size
        _SCREAMING_SNAKE_CASE     =      input_embedding_size
        _SCREAMING_SNAKE_CASE     =      output_embedding_size
        _SCREAMING_SNAKE_CASE     =      max_position_embeddings
        _SCREAMING_SNAKE_CASE     =      hidden_size
        _SCREAMING_SNAKE_CASE     =      num_hidden_layers
        _SCREAMING_SNAKE_CASE     =      num_attention_heads
        _SCREAMING_SNAKE_CASE     =      intermediate_size
        _SCREAMING_SNAKE_CASE     =      hidden_act
        _SCREAMING_SNAKE_CASE     =      hidden_dropout_prob
        _SCREAMING_SNAKE_CASE     =      attention_probs_dropout_prob
        _SCREAMING_SNAKE_CASE     =      classifier_dropout_prob
        _SCREAMING_SNAKE_CASE     =      initializer_range
        _SCREAMING_SNAKE_CASE     =      type_vocab_size
        _SCREAMING_SNAKE_CASE     =      layer_norm_eps
        _SCREAMING_SNAKE_CASE     =      use_cache
        _SCREAMING_SNAKE_CASE     =      False




class  a_					(    snake_case_	):

    '''simple docstring'''


    @property
    def       snake_case_(		self	)		->       Mapping[str, Mapping[int, str]]:
        if self.task == "multiple-choice":
            _SCREAMING_SNAKE_CASE     =      {0: """batch""", 1: """choice""", 2: """sequence"""}
        else:
            _SCREAMING_SNAKE_CASE     =      {0: """batch""", 1: """sequence"""}
        return OrderedDict(
            [
                ("""input_ids""", dynamic_axis),
                ("""attention_mask""", dynamic_axis),
                ("""token_type_ids""", dynamic_axis),
            ]	)





    @property
    def       snake_case_(		self	)		->       float:
        return 1e-4



