"""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()
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}.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      __UpperCAmelCase       (      lowercase ,lowercase ,lowercase		):







  """simple docstring"""
  _UpperCAmelCase    			=   state_dict.pop(lowercase		)
  _UpperCAmelCase    			=   val






def      __UpperCAmelCase       (      lowercase		):







  """simple docstring"""
  _UpperCAmelCase    			=   OrderedDict()
  for key, value in state_dict.items():
    if "backbone.0.body" in key:
      _UpperCAmelCase    			=   key.replace("""backbone.0.body""" ,"""backbone.conv_encoder.model"""		)
      _UpperCAmelCase    			=   value
    else:
      _UpperCAmelCase    			=   value

  return new_state_dict






def      __UpperCAmelCase       (      lowercase ,lowercase=False		):







  """simple docstring"""
  _UpperCAmelCase    			=   """"""
  if is_panoptic:
    _UpperCAmelCase    			=   """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)
    _UpperCAmelCase    			=   state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''		)
    _UpperCAmelCase    			=   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
    _UpperCAmelCase    			=   in_proj_weight[:2_56, :]
    _UpperCAmelCase    			=   in_proj_bias[:2_56]
    _UpperCAmelCase    			=   in_proj_weight[2_56:5_12, :]
    _UpperCAmelCase    			=   in_proj_bias[2_56:5_12]
    _UpperCAmelCase    			=   in_proj_weight[-2_56:, :]
    _UpperCAmelCase    			=   in_proj_bias[-2_56:]






def      __UpperCAmelCase       (      ):







  """simple docstring"""
  _UpperCAmelCase    			=   """http://images.cocodataset.org/val2017/000000039769.jpg"""
  _UpperCAmelCase    			=   Image.open(requests.get(lowercase ,stream=lowercase		).raw		)

  return im






@torch.no_grad()
def      __UpperCAmelCase       (      lowercase ,lowercase		):







  """simple docstring"""
  _UpperCAmelCase    			=   ConditionalDetrConfig()
  # set backbone and dilation attributes
  if "resnet101" in model_name:
    _UpperCAmelCase    			=   """resnet101"""
  if "dc5" in model_name:
    _UpperCAmelCase    			=   True
  _UpperCAmelCase    			=   """panoptic""" in model_name
  if is_panoptic:
    _UpperCAmelCase    			=   2_50
  else:
    _UpperCAmelCase    			=   91
    _UpperCAmelCase    			=   """huggingface/label-files"""
    _UpperCAmelCase    			=   """coco-detection-id2label.json"""
    _UpperCAmelCase    			=   json.load(open(hf_hub_download(lowercase ,lowercase ,repo_type="""dataset"""		) ,"""r"""		)		)
    _UpperCAmelCase    			=   {int(lowercase		): v for k, v in idalabel.items()}
    _UpperCAmelCase    			=   idalabel
    _UpperCAmelCase    			=   {v: k for k, v in idalabel.items()}

  # load image processor
  _UpperCAmelCase    			=   """coco_panoptic""" if is_panoptic else """coco_detection"""
  _UpperCAmelCase    			=   ConditionalDetrImageProcessor(format=lowercase		)

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

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

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

"""simple docstring"""


from __future__ import annotations

import typing
from collections import Counter






def      __UpperCAmelCase       (      lowercase		):







  """simple docstring"""
  _UpperCAmelCase    			=   Counter()
  for base in range(1 ,max_perimeter + 1		):
    for perpendicular in range(lowercase ,max_perimeter + 1		):
      _UpperCAmelCase    			=   (base * base + perpendicular * perpendicular) ** 0.5
      if hypotenuse == int(lowercase		):
        _UpperCAmelCase    			=   int(base + perpendicular + hypotenuse		)
        if perimeter > max_perimeter:
          continue
        triplets[perimeter] += 1
  return triplets






def      __UpperCAmelCase       (      lowercase = 10_00		):







  """simple docstring"""
  _UpperCAmelCase    			=   pythagorean_triple(lowercase		)
  return triplets.most_common(1		)[0][0]


if __name__ == "__main__":
    print(F'''Perimeter {solution()} has maximum solutions''')
