





'''simple docstring'''







import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter

from transformers import HfArgumentParser





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      Optional[Any]=None,      SCREAMING_SNAKE_CASE__   :      List[str]=None )			->						Optional[int]:
						return field(default_factory=lambda: default,      metadata=SCREAMING_SNAKE_CASE__ )


@dataclass
class        __a   :
							__a       : str          =							field(
							    metadata={"help": "The csv file to plot."}							,		)
							__a       : bool          =							field(
							    default=lowerCamelCase							,		metadata={"help": "Whether to plot along batch size or sequence length. Defaults to sequence length."}							,		)
							__a       : bool          =							field(
							    default=lowerCamelCase							,		metadata={"help": "Whether the csv file has time results or memory results. Defaults to memory results."}							,		)
							__a       : bool          =							field(
							    default=lowerCamelCase							,		metadata={"help": "Disable logarithmic scale when plotting"}							,		)
							__a       : bool          =							field(
							    default=lowerCamelCase							,		metadata={
							        "help": "Whether the csv file has training results or inference results. Defaults to inference results."
							    }							,		)
							__a       : Optional[str]          =							field(
							    default=lowerCamelCase							,		metadata={"help": "Filename under which the plot will be saved. If unused no plot is saved."}							,		)
							__a       : Optional[List[str]]          =							list_field(
							    default=lowerCamelCase							,		metadata={"help": "List of model names that are used instead of the ones in the csv file."}  )





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      int )			->						Optional[Any]:
						try:
												int(SCREAMING_SNAKE_CASE__ )
												return True
						except ValueError:
												return False





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      Union[str, Any] )			->						int:
						try:
												float(SCREAMING_SNAKE_CASE__ )
												return True
						except ValueError:
												return False


class        __a   :

							def __init__(				self				:  Optional[int]							,  __magic_name__				:  int )       ->							Optional[int]:







													"""simple docstring"""
													UpperCAmelCase_      :   Optional[Any]      =	args
													UpperCAmelCase_      :   List[Any]      =	defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}} )

													with open(self.args.csv_file							,  newline='''''' ) as csv_file:
																			UpperCAmelCase_      :   Optional[int]      =	csv.DictReader(__magic_name__ )
																			for row in reader:
																									UpperCAmelCase_      :   Tuple      =	row['''model''']
																									self.result_dict[model_name]["bsz"].append(int(row['''batch_size'''] ) )
																									self.result_dict[model_name]["seq_len"].append(int(row['''sequence_length'''] ) )
																									if can_convert_to_int(row['''result'''] ):
																															# value is not None
																															UpperCAmelCase_      :   int      =	int(row['''result'''] )
																									elif can_convert_to_float(row['''result'''] ):
																															# value is not None
																															UpperCAmelCase_      :   Any      =	float(row['''result'''] )



							def      UpperCAmelCase__						(				self				:  Optional[Any] )       ->							Tuple:







													"""simple docstring"""
													UpperCAmelCase_    ,							UpperCAmelCase_      :   Optional[Any]      =	plt.subplots()
													UpperCAmelCase_      :   Dict      =	'''Time usage''' if self.args.is_time else '''Memory usage'''
													UpperCAmelCase_      :   Dict      =	title_str + ''' for training''' if self.args.is_train else title_str + ''' for inference'''

													if not self.args.no_log_scale:
																			# set logarithm scales
																			ax.set_xscale('''log''' )
																			ax.set_yscale('''log''' )

													for axis in [ax.xaxis, ax.yaxis]:
																			axis.set_major_formatter(ScalarFormatter() )

													for model_name_idx, model_name in enumerate(self.result_dict.keys() ):
																			UpperCAmelCase_      :   List[str]      =	sorted(set(self.result_dict[model_name]['''bsz'''] ) )
																			UpperCAmelCase_      :   int      =	sorted(set(self.result_dict[model_name]['''seq_len'''] ) )
																			UpperCAmelCase_      :   List[str]      =	self.result_dict[model_name]['''result''']

																			((UpperCAmelCase_)    ,							(UpperCAmelCase_))      :   int      =	(
																			    (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
																			)

																			UpperCAmelCase_      :   Any      =	(
																			    model_name if self.args.short_model_names is None else self.args.short_model_names[model_name_idx]
																			)

																			for inner_loop_value in inner_loop_array:
																									if self.args.plot_along_batch:
																															UpperCAmelCase_      :   str      =	np.asarray(
																															    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]							,  dtype=__magic_name__							,  )
																									else:
																															UpperCAmelCase_      :   Optional[Any]      =	np.asarray(
																															    [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]							,  dtype=np.floataa							,  )

																									((UpperCAmelCase_)    ,							(UpperCAmelCase_))      :   Any      =	(
																									    ('''batch_size''', '''len''') if self.args.plot_along_batch else ('''in #tokens''', '''bsz''')
																									)

																									UpperCAmelCase_      :   Tuple      =	np.asarray(__magic_name__							,  __magic_name__ )[: len(__magic_name__ )]
																									plt.scatter(
																									    __magic_name__							,  __magic_name__							,  label=F"""{label_model_name} - {inner_loop_label}: {inner_loop_value}""" )
																									plt.plot(__magic_name__							,  __magic_name__							,  '''--''' )

																			title_str += F""" {label_model_name} vs."""

													UpperCAmelCase_      :   int      =	title_str[:-4]
													UpperCAmelCase_      :   List[str]      =	'''Time in s''' if self.args.is_time else '''Memory in MB'''

													# plot
													plt.title(__magic_name__ )
													plt.xlabel(__magic_name__ )
													plt.ylabel(__magic_name__ )
													plt.legend()

													if self.args.figure_png_file is not None:
																			plt.savefig(self.args.figure_png_file )
													else:
																			plt.show()





def 							lowerCamelCase_   (	)			->						str:
						UpperCAmelCase_      :   int      =	HfArgumentParser(SCREAMING_SNAKE_CASE__ )
						UpperCAmelCase_      :   Dict      =	parser.parse_args_into_dataclasses()[0]
						UpperCAmelCase_      :   List[Any]      =	Plot(args=SCREAMING_SNAKE_CASE__ )
						plot.plot()


if __name__ == "__main__":
					main()






"""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()
__UpperCamelCase       :    Union[str, Any]         =		logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
__UpperCamelCase       :    Optional[Any]         =		[]
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     (	_snake_case		:       List[str],				_snake_case		:       Tuple,				_snake_case		:       Any       ):
   _lowercase							     =       state_dict.pop(_snake_case       )
   _lowercase							     =       val


def   __UpperCAmelCase     (	_snake_case		:       Optional[int]       ):
   _lowercase							     =       OrderedDict()
   for key, value in state_dict.items():
      if "backbone.0.body" in key:
         _lowercase							     =       key.replace("backbone.0.body",				"backbone.conv_encoder.model"       )
         _lowercase							     =       value
      else:
         _lowercase							     =       value

   return new_state_dict


def   __UpperCAmelCase     (	_snake_case		:       Optional[Any],				_snake_case		:       int=False       ):
   _lowercase							     =       ""
   if is_panoptic:
      _lowercase							     =       "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)
      _lowercase							     =       state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""       )
      _lowercase							     =       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
      _lowercase							     =       in_proj_weight[:2_5_6, :]
      _lowercase							     =       in_proj_bias[:2_5_6]
      _lowercase							     =       in_proj_weight[2_5_6:5_1_2, :]
      _lowercase							     =       in_proj_bias[2_5_6:5_1_2]
      _lowercase							     =       in_proj_weight[-2_5_6:, :]
      _lowercase							     =       in_proj_bias[-2_5_6:]
   # 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
      _lowercase							     =       state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""       )
      _lowercase							     =       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
      _lowercase							     =       in_proj_weight[:2_5_6, :]
      _lowercase							     =       in_proj_bias[:2_5_6]
      _lowercase							     =       in_proj_weight[2_5_6:5_1_2, :]
      _lowercase							     =       in_proj_bias[2_5_6:5_1_2]
      _lowercase							     =       in_proj_weight[-2_5_6:, :]
      _lowercase							     =       in_proj_bias[-2_5_6:]
      # read in weights + bias of input projection layer of cross-attention
      _lowercase							     =       state_dict.pop(
          f"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""       )
      _lowercase							     =       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
      _lowercase							     =       in_proj_weight_cross_attn[:2_5_6, :]
      _lowercase							     =       in_proj_bias_cross_attn[:2_5_6]
      _lowercase							     =       in_proj_weight_cross_attn[2_5_6:5_1_2, :]
      _lowercase							     =       in_proj_bias_cross_attn[2_5_6:5_1_2]
      _lowercase							     =       in_proj_weight_cross_attn[-2_5_6:, :]
      _lowercase							     =       in_proj_bias_cross_attn[-2_5_6:]


def   __UpperCAmelCase     (	):
   _lowercase							     =       "http://images.cocodataset.org/val2017/000000039769.jpg"
   _lowercase							     =       Image.open(requests.get(_snake_case,				stream=_snake_case       ).raw       )

   return im


@torch.no_grad()
def   __UpperCAmelCase     (	_snake_case		:       Tuple,				_snake_case		:       str       ):
   _lowercase							     =       DetrConfig()
   # set backbone and dilation attributes
   if "resnet101" in model_name:
      _lowercase							     =       "resnet101"
   if "dc5" in model_name:
      _lowercase							     =       True
   _lowercase							     =       "panoptic" in model_name
   if is_panoptic:
      _lowercase							     =       2_5_0
   else:
      _lowercase							     =       9_1
      _lowercase							     =       "huggingface/label-files"
      _lowercase							     =       "coco-detection-id2label.json"
      _lowercase							     =       json.load(open(hf_hub_download(_snake_case,				_snake_case,				repo_type="dataset"       ),				"r"       )       )
      _lowercase							     =       {int(_snake_case       ): v for k, v in idalabel.items()}
      _lowercase							     =       idalabel
      _lowercase							     =       {v: k for k, v in idalabel.items()}

   # load image processor
   _lowercase							     =       "coco_panoptic" if is_panoptic else "coco_detection"
   _lowercase							     =       DetrImageProcessor(format=_snake_case       )

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

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

   # load original model from torch hub
   _lowercase							     =       torch.hub.load("facebookresearch/detr",				_snake_case,				pretrained=_snake_case       ).eval()
   _lowercase							     =       detr.state_dict()
   # rename keys
   for src, dest in rename_keys:
      if is_panoptic:
         _lowercase							     =       "detr." + src
      rename_key(_snake_case,				_snake_case,				_snake_case       )
   _lowercase							     =       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
   _lowercase							     =       "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"       )
         ):
            _lowercase							     =       state_dict.pop(_snake_case       )
            _lowercase							     =       val
         elif "class_labels_classifier" in key or "bbox_predictor" in key:
            _lowercase							     =       state_dict.pop(_snake_case       )
            _lowercase							     =       val
         elif key.startswith("bbox_attention"       ) or key.startswith("mask_head"       ):
            continue
         else:
            _lowercase							     =       state_dict.pop(_snake_case       )
            _lowercase							     =       val
      else:
         if not key.startswith("class_labels_classifier"       ) and not key.startswith("bbox_predictor"       ):
            _lowercase							     =       state_dict.pop(_snake_case       )
            _lowercase							     =       val
    # finally, create HuggingFace model and load state dict
   _lowercase							     =       DetrForSegmentation(_snake_case       ) if is_panoptic else DetrForObjectDetection(_snake_case       )
   model.load_state_dict(_snake_case       )
   model.eval()
   # verify our conversion
   _lowercase							     =       detr(_snake_case       )
   _lowercase							     =       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__":
  __UpperCamelCase       :    int         =		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       :    str         =		parser.parse_args()
  convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)