





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()
__UpperCAmelCase						=      logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
__UpperCAmelCase						=      []
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 			__UpperCamelCase				(			lowercase__	:       Tuple		,    lowercase__	:       Optional[int]		,    lowercase__	:       Optional[int]		)	->      Optional[int]:
    '''simple docstring'''





    lowerCAmelCase_					:	Union[str, Any]						=   state_dict.pop(lowercase__		)
    lowerCAmelCase_					:	Union[str, Any]						=   val
def 			__UpperCamelCase				(			lowercase__	:       List[str]		)	->      str:
    '''simple docstring'''





    lowerCAmelCase_					:	Tuple						=   OrderedDict()
    for key, value in state_dict.items():
        if "backbone.0.body" in key:
            lowerCAmelCase_					:	Dict						=   key.replace("""backbone.0.body"""		,    """backbone.conv_encoder.model"""		)
            lowerCAmelCase_					:	Any						=   value
        else:
            lowerCAmelCase_					:	Optional[int]						=   value

    return new_state_dict
def 			__UpperCamelCase				(			lowercase__	:       Tuple		,    lowercase__	:       int=False		)	->      List[str]:
    '''simple docstring'''





    lowerCAmelCase_					:	Optional[Any]						=   """"""
    if is_panoptic:
        lowerCAmelCase_					:	Optional[Any]						=   """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)
        lowerCAmelCase_					:	Union[str, Any]						=   state_dict.pop(f'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'		)
        lowerCAmelCase_					:	Union[str, 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
        lowerCAmelCase_					:	str						=   in_proj_weight[:256, :]
        lowerCAmelCase_					:	Union[str, Any]						=   in_proj_bias[:256]
        lowerCAmelCase_					:	Optional[Any]						=   in_proj_weight[256:512, :]
        lowerCAmelCase_					:	List[str]						=   in_proj_bias[256:512]
        lowerCAmelCase_					:	List[str]						=   in_proj_weight[-256:, :]
        lowerCAmelCase_					:	Tuple						=   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
        lowerCAmelCase_					:	List[Any]						=   state_dict.pop(f'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'		)
        lowerCAmelCase_					:	Optional[Any]						=   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
        lowerCAmelCase_					:	Optional[int]						=   in_proj_weight[:256, :]
        lowerCAmelCase_					:	Tuple						=   in_proj_bias[:256]
        lowerCAmelCase_					:	List[str]						=   in_proj_weight[256:512, :]
        lowerCAmelCase_					:	Optional[int]						=   in_proj_bias[256:512]
        lowerCAmelCase_					:	Union[str, Any]						=   in_proj_weight[-256:, :]
        lowerCAmelCase_					:	int						=   in_proj_bias[-256:]
        # read in weights + bias of input projection layer of cross-attention
        lowerCAmelCase_					:	Tuple						=   state_dict.pop(
            f'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'		)
        lowerCAmelCase_					:	str						=   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
        lowerCAmelCase_					:	Optional[Any]						=   in_proj_weight_cross_attn[:256, :]
        lowerCAmelCase_					:	Optional[int]						=   in_proj_bias_cross_attn[:256]
        lowerCAmelCase_					:	str						=   in_proj_weight_cross_attn[256:512, :]
        lowerCAmelCase_					:	List[str]						=   in_proj_bias_cross_attn[256:512]
        lowerCAmelCase_					:	Optional[Any]						=   in_proj_weight_cross_attn[-256:, :]
        lowerCAmelCase_					:	str						=   in_proj_bias_cross_attn[-256:]
def 			__UpperCamelCase				(			)	->      Optional[int]:
    '''simple docstring'''





    lowerCAmelCase_					:	Optional[Any]						=   """http://images.cocodataset.org/val2017/000000039769.jpg"""
    lowerCAmelCase_					:	List[str]						=   Image.open(requests.get(lowercase__		,    stream=lowercase__		).raw		)

    return im
@torch.no_grad()
def 			__UpperCamelCase				(			lowercase__	:       Optional[int]		,    lowercase__	:       str		)	->      Optional[Any]:
    '''simple docstring'''





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

    # load image processor
    lowerCAmelCase_					:	Any						=   """coco_panoptic""" if is_panoptic else """coco_detection"""
    lowerCAmelCase_					:	List[str]						=   DetrImageProcessor(format=lowercase__		)

    # prepare image
    lowerCAmelCase_					:	Union[str, Any]						=   prepare_img()
    lowerCAmelCase_					:	Dict						=   image_processor(images=lowercase__		,    return_tensors="""pt"""		)
    lowerCAmelCase_					:	Any						=   encoding["""pixel_values"""]

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

    # load original model from torch hub
    lowerCAmelCase_					:	Dict						=   torch.hub.load("""facebookresearch/detr"""		,    lowercase__		,    pretrained=lowercase__		).eval()
    lowerCAmelCase_					:	str						=   detr.state_dict()
    # rename keys
    for src, dest in rename_keys:
        if is_panoptic:
            lowerCAmelCase_					:	Any						=   """detr.""" + src
        rename_key(lowercase__		,    lowercase__		,    lowercase__		)
    lowerCAmelCase_					:	int						=   rename_backbone_keys(lowercase__		)
    # query, key and value matrices need special treatment
    read_in_q_k_v(lowercase__		,    is_panoptic=lowercase__		)
    # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
    lowerCAmelCase_					:	Tuple						=   """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"""		)
            ):
                lowerCAmelCase_					:	Union[str, Any]						=   state_dict.pop(lowercase__		)
                lowerCAmelCase_					:	int						=   val
            elif "class_labels_classifier" in key or "bbox_predictor" in key:
                lowerCAmelCase_					:	Optional[int]						=   state_dict.pop(lowercase__		)
                lowerCAmelCase_					:	Tuple						=   val
            elif key.startswith("""bbox_attention"""		) or key.startswith("""mask_head"""		):
                continue
            else:
                lowerCAmelCase_					:	Dict						=   state_dict.pop(lowercase__		)
                lowerCAmelCase_					:	Optional[Any]						=   val
        else:
            if not key.startswith("""class_labels_classifier"""		) and not key.startswith("""bbox_predictor"""		):
                lowerCAmelCase_					:	str						=   state_dict.pop(lowercase__		)
                lowerCAmelCase_					:	List[str]						=   val
    # finally, create HuggingFace model and load state dict
    lowerCAmelCase_					:	Optional[int]						=   DetrForSegmentation(lowercase__		) if is_panoptic else DetrForObjectDetection(lowercase__		)
    model.load_state_dict(lowercase__		)
    model.eval()
    # verify our conversion
    lowerCAmelCase_					:	Dict						=   detr(lowercase__		)
    lowerCAmelCase_					:	Optional[int]						=   model(lowercase__		)
    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(lowercase__		).mkdir(exist_ok=lowercase__		)
    model.save_pretrained(lowercase__		)
    image_processor.save_pretrained(lowercase__		)


if __name__ == "__main__":
  __UpperCAmelCase						=      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.'
  )
  __UpperCAmelCase						=      parser.parse_args()
  convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)









import unittest

from transformers import BlenderbotTokenizer, BlenderbotTokenizerFast
from transformers.utils import cached_property

class 		__a							(	unittest.TestCase      ):



       @cached_property
       def  A							(     self	:   Optional[int]			):
           return BlenderbotTokenizer.from_pretrained("""facebook/blenderbot-3B"""			)



       @cached_property
       def  A							(     self	:   Optional[int]			):
           return BlenderbotTokenizerFast.from_pretrained("""facebook/blenderbot-3B"""			)



       def  A							(     self	:   Optional[Any]			):
           lowerCAmelCase_					:	List[str]						=   self.tokenizer_ab
           lowerCAmelCase_					:	Any						=   """ I am a small frog."""
           lowerCAmelCase_					:	Union[str, Any]						=   tok([src_text]					,			padding=UpperCAmelCase					,			truncation=UpperCAmelCase			)["""input_ids"""]
           lowerCAmelCase_					:	int						=   tok.batch_decode(UpperCAmelCase					,			skip_special_tokens=UpperCAmelCase					,			clean_up_tokenization_spaces=UpperCAmelCase			)[0]
           assert src_text == decoded



       def  A							(     self	:   List[Any]			):
           lowerCAmelCase_					:	Dict						=   self.rust_tokenizer_ab
           lowerCAmelCase_					:	str						=   """ I am a small frog."""
           lowerCAmelCase_					:	Tuple						=   tok([src_text]					,			padding=UpperCAmelCase					,			truncation=UpperCAmelCase			)["""input_ids"""]
           lowerCAmelCase_					:	int						=   tok.batch_decode(UpperCAmelCase					,			skip_special_tokens=UpperCAmelCase					,			clean_up_tokenization_spaces=UpperCAmelCase			)[0]
           assert src_text == decoded



       def  A							(     self	:   str			):
           assert self.tokenizer_ab.add_prefix_space
           assert self.tokenizer_ab([""" Sam""", """Sam"""]			).input_ids == [[55_02, 2], [55_02, 2]]



       def  A							(     self	:   List[str]			):
           assert self.rust_tokenizer_ab.add_prefix_space
           assert self.rust_tokenizer_ab([""" Sam""", """Sam"""]			).input_ids == [[55_02, 2], [55_02, 2]]


