
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()
_lowerCamelCase					   =logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
_lowerCamelCase					   =[]
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     _a					(						lowerCamelCase,	lowerCamelCase,	lowerCamelCase							):
 lowerCamelCase		:	Any							=      state_dict.pop(lowerCamelCase							)
 lowerCamelCase		:	List[Any]							=      val





def     _a					(						lowerCamelCase							):
 lowerCamelCase		:	Union[str, Any]							=      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		:	List[str]							=      value
  else:
   lowerCamelCase		:	Tuple							=      value

 return new_state_dict





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





def     _a					(						):
 lowerCamelCase		:	str							=      """http://images.cocodataset.org/val2017/000000039769.jpg"""
 lowerCamelCase		:	Optional[int]							=      Image.open(requests.get(lowerCamelCase,	stream=lowerCamelCase							).raw							)

 return im





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

 # load image processor
 lowerCamelCase		:	int							=      """coco_panoptic""" if is_panoptic else """coco_detection"""
 lowerCamelCase		:	Optional[int]							=      ConditionalDetrImageProcessor(format=lowerCamelCase							)

 # prepare image
 lowerCamelCase		:	str							=      prepare_img()
 lowerCamelCase		:	List[Any]							=      image_processor(images=lowerCamelCase,	return_tensors="""pt"""							)
 lowerCamelCase		:	int							=      encoding["""pixel_values"""]

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

 # load original model from torch hub
 lowerCamelCase		:	Optional[int]							=      torch.hub.load("""DeppMeng/ConditionalDETR""",	lowerCamelCase,	pretrained=lowerCamelCase							).eval()
 lowerCamelCase		:	Optional[int]							=      conditional_detr.state_dict()
 # rename keys
 for src, dest in rename_keys:
  if is_panoptic:
   lowerCamelCase		:	Dict							=      """conditional_detr.""" + src
  rename_key(lowerCamelCase,	lowerCamelCase,	lowerCamelCase							)
 lowerCamelCase		:	str							=      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
 lowerCamelCase		:	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"""							)
   ):
    lowerCamelCase		:	List[str]							=      state_dict.pop(lowerCamelCase							)
    lowerCamelCase		:	Optional[Any]							=      val
   elif "class_labels_classifier" in key or "bbox_predictor" in key:
    lowerCamelCase		:	Any							=      state_dict.pop(lowerCamelCase							)
    lowerCamelCase		:	str							=      val
   elif key.startswith("""bbox_attention"""							) or key.startswith("""mask_head"""							):
    continue
   else:
    lowerCamelCase		:	str							=      state_dict.pop(lowerCamelCase							)
    lowerCamelCase		:	str							=      val
  else:
   if not key.startswith("""class_labels_classifier"""							) and not key.startswith("""bbox_predictor"""							):
    lowerCamelCase		:	int							=      state_dict.pop(lowerCamelCase							)
    lowerCamelCase		:	Union[str, Any]							=      val
    # finally, create HuggingFace model and load state dict
 lowerCamelCase		:	str							=      ConditionalDetrForSegmentation(lowerCamelCase							) if is_panoptic else ConditionalDetrForObjectDetection(lowerCamelCase							)
 model.load_state_dict(lowerCamelCase							)
 model.eval()
 model.push_to_hub(repo_id=lowerCamelCase,	organization="""DepuMeng""",	commit_message="""Add model"""							)
 # verify our conversion
 lowerCamelCase		:	Dict							=      conditional_detr(lowerCamelCase							)
 lowerCamelCase		:	Union[str, Any]							=      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__":
      _lowerCamelCase					   =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."""
      )
      _lowerCamelCase					   =parser.parse_args()
      convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)






from __future__ import annotations

import collections
import pprint
from pathlib import Path





def     _a					(						lowerCamelCase							):
 return "".join(sorted(lowerCamelCase							)							)





def     _a					(						lowerCamelCase							):
 return word_by_signature[signature(lowerCamelCase							)]


_lowerCamelCase					   =Path(__file__).parent.joinpath("""words.txt""").read_text(encoding="""utf-8""")
_lowerCamelCase					   =sorted({word.strip().lower() for word in data.splitlines()})

_lowerCamelCase					   =collections.defaultdict(list)
for word in word_list:
      word_by_signature[signature(word)].append(word)

if __name__ == "__main__":
      _lowerCamelCase					   ={word: anagram(word) for word in word_list if len(anagram(word)) > 1}

      with open("""anagrams.txt""", """w""") as file:
            file.write("""all_anagrams = \n """)
            file.write(pprint.pformat(all_anagrams))




