'''simple docstring'''




import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DeformableDetrConfig, DeformableDetrForObjectDetection, DeformableDetrImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
__SCREAMING_SNAKE_CASE :  Tuple  =						logging.get_logger(__name__)

def 			UpperCamelCase_							(      _UpperCAmelCase						:   Tuple					)					->					List[Any]:


       """simple docstring"""







       if "backbone.0.body" in orig_key:
              _UpperCAmelCase  :						Union[str, Any]							   =					orig_key.replace("backbone.0.body"    ,			"backbone.conv_encoder.model"					)
       if "transformer" in orig_key:
              _UpperCAmelCase  :						Dict							   =					orig_key.replace("transformer."    ,			""					)
       if "norm1" in orig_key:
              if "encoder" in orig_key:
                     _UpperCAmelCase  :						Any							   =					orig_key.replace("norm1"    ,			"self_attn_layer_norm"					)
              else:
                     _UpperCAmelCase  :						int							   =					orig_key.replace("norm1"    ,			"encoder_attn_layer_norm"					)
       if "norm2" in orig_key:
              if "encoder" in orig_key:
                     _UpperCAmelCase  :						List[Any]							   =					orig_key.replace("norm2"    ,			"final_layer_norm"					)
              else:
                     _UpperCAmelCase  :						Union[str, Any]							   =					orig_key.replace("norm2"    ,			"self_attn_layer_norm"					)
       if "norm3" in orig_key:
              _UpperCAmelCase  :						Union[str, Any]							   =					orig_key.replace("norm3"    ,			"final_layer_norm"					)
       if "linear1" in orig_key:
              _UpperCAmelCase  :						Union[str, Any]							   =					orig_key.replace("linear1"    ,			"fc1"					)
       if "linear2" in orig_key:
              _UpperCAmelCase  :						int							   =					orig_key.replace("linear2"    ,			"fc2"					)
       if "query_embed" in orig_key:
              _UpperCAmelCase  :						Union[str, Any]							   =					orig_key.replace("query_embed"    ,			"query_position_embeddings"					)
       if "cross_attn" in orig_key:
              _UpperCAmelCase  :						List[str]							   =					orig_key.replace("cross_attn"    ,			"encoder_attn"					)

       return orig_key

def 			UpperCamelCase_							(      _UpperCAmelCase						:   str					)					->					Tuple:


       """simple docstring"""







       for i in range(6					):
              # read in weights + bias of input projection layer of self-attention
              _UpperCAmelCase  :						int							   =					state_dict.pop(F"""decoder.layers.{i}.self_attn.in_proj_weight"""					)
              _UpperCAmelCase  :						Optional[int]							   =					state_dict.pop(F"""decoder.layers.{i}.self_attn.in_proj_bias"""					)
              # next, add query, keys and values (in that order) to the state dict
              _UpperCAmelCase  :						List[str]							   =					in_proj_weight[:256, :]
              _UpperCAmelCase  :						Union[str, Any]							   =					in_proj_bias[:256]
              _UpperCAmelCase  :						int							   =					in_proj_weight[256:512, :]
              _UpperCAmelCase  :						Tuple							   =					in_proj_bias[256:512]
              _UpperCAmelCase  :						Optional[int]							   =					in_proj_weight[-256:, :]
              _UpperCAmelCase  :						str							   =					in_proj_bias[-256:]

def 			UpperCamelCase_							(      )					->					Union[str, Any]:


       """simple docstring"""







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

       return im

@torch.no_grad()
def 			UpperCamelCase_							(      _UpperCAmelCase						:   List[str]    ,			_UpperCAmelCase						:   Union[str, Any]    ,			_UpperCAmelCase						:   List[Any]    ,			_UpperCAmelCase						:   List[Any]    ,			_UpperCAmelCase						:   Tuple    ,			_UpperCAmelCase						:   Union[str, Any]    ,			_UpperCAmelCase						:   Union[str, Any]    ,			)					->					Optional[Any]:


       """simple docstring"""







       _UpperCAmelCase  :						List[Any]							   =					DeformableDetrConfig()
       # set config attributes
       if single_scale:
              _UpperCAmelCase  :						Tuple							   =					1
       _UpperCAmelCase  :						List[Any]							   =					dilation
       _UpperCAmelCase  :						List[Any]							   =					with_box_refine
       _UpperCAmelCase  :						str							   =					two_stage
       # set labels
       _UpperCAmelCase  :						List[Any]							   =					91
       _UpperCAmelCase  :						Dict							   =					"huggingface/label-files"
       _UpperCAmelCase  :						Any							   =					"coco-detection-id2label.json"
       _UpperCAmelCase  :						Optional[int]							   =					json.load(open(cached_download(hf_hub_url(__lowerCAmelCase    ,			__lowerCAmelCase    ,			repo_type="dataset"					)					)    ,			"r"					)					)
       _UpperCAmelCase  :						List[Any]							   =					{int(__lowerCAmelCase					): v for k, v in idalabel.items()}
       _UpperCAmelCase  :						Any							   =					idalabel
       _UpperCAmelCase  :						int							   =					{v: k for k, v in idalabel.items()}

       # load image processor
       _UpperCAmelCase  :						Any							   =					DeformableDetrImageProcessor(format="coco_detection"					)

       # prepare image
       _UpperCAmelCase  :						Tuple							   =					prepare_img()
       _UpperCAmelCase  :						Optional[Any]							   =					image_processor(images=__lowerCAmelCase    ,			return_tensors="pt"					)
       _UpperCAmelCase  :						str							   =					encoding["pixel_values"]

       logger.info("Converting model..."					)

       # load original state dict
       _UpperCAmelCase  :						Any							   =					torch.load(__lowerCAmelCase    ,			map_location="cpu"					)["model"]
       # rename keys
       for key in state_dict.copy().keys():
              _UpperCAmelCase  :						Any							   =					state_dict.pop(__lowerCAmelCase					)
              _UpperCAmelCase  :						List[Any]							   =					val
       # query, key and value matrices need special treatment
       read_in_q_k_v(__lowerCAmelCase					)
       # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
       _UpperCAmelCase  :						Tuple							   =					"model."
       for key in state_dict.copy().keys():
              if not key.startswith("class_embed"					) and not key.startswith("bbox_embed"					):
                     _UpperCAmelCase  :						Optional[int]							   =					state_dict.pop(__lowerCAmelCase					)
                     _UpperCAmelCase  :						Optional[int]							   =					val
    # finally, create HuggingFace model and load state dict
       _UpperCAmelCase  :						Optional[int]							   =					DeformableDetrForObjectDetection(__lowerCAmelCase					)
       model.load_state_dict(__lowerCAmelCase					)
       model.eval()

       _UpperCAmelCase  :						str							   =					"cuda" if torch.cuda.is_available() else "cpu"
       model.to(__lowerCAmelCase					)
       # verify our conversion
       _UpperCAmelCase  :						Tuple							   =					model(pixel_values.to(__lowerCAmelCase					)					)

       _UpperCAmelCase  :						List[str]							   =					torch.tensor(
           [[-9.6_6_4_5, -4.3_4_4_9, -5.8_7_0_5], [-9.7_0_3_5, -3.8_5_0_4, -5.0_7_2_4], [-10.5_634, -5.3_3_7_9, -7.5_1_1_6]]					)
       _UpperCAmelCase  :						int							   =					torch.tensor([[0.8_6_9_3, 0.2_2_8_9, 0.2_4_9_2], [0.3_1_5_0, 0.5_4_8_9, 0.5_8_4_5], [0.5_5_6_3, 0.7_5_8_0, 0.8_5_1_8]]					)

       if single_scale:
              _UpperCAmelCase  :						Union[str, Any]							   =					torch.tensor(
                  [[-9.9_0_5_1, -4.2_5_4_1, -6.4_8_5_2], [-9.6_9_4_7, -4.0_8_5_4, -6.8_0_3_3], [-10.0_665, -5.8_4_7_0, -7.7_0_0_3]]					)
              _UpperCAmelCase  :						Dict							   =					torch.tensor([[0.7_2_9_2, 0.4_9_9_1, 0.5_5_3_2], [0.7_9_5_9, 0.2_4_2_6, 0.4_2_3_6], [0.7_5_8_2, 0.3_5_1_8, 0.4_4_5_1]]					)

       if single_scale and dilation:
              _UpperCAmelCase  :						Any							   =					torch.tensor(
                  [[-8.9_6_5_2, -4.1_0_7_4, -5.6_6_3_5], [-9.0_5_9_6, -4.9_4_4_7, -6.6_0_7_5], [-10.1_178, -4.5_2_7_5, -6.2_6_7_1]]					)
              _UpperCAmelCase  :						Optional[int]							   =					torch.tensor([[0.7_6_6_5, 0.4_1_3_0, 0.4_7_6_9], [0.8_3_6_4, 0.1_8_4_1, 0.3_3_9_1], [0.6_2_6_1, 0.3_8_9_5, 0.7_9_7_8]]					)

       if with_box_refine:
              _UpperCAmelCase  :						Any							   =					torch.tensor(
                  [[-8.8_8_9_5, -5.4_1_8_7, -6.8_1_5_3], [-8.4_7_0_6, -6.1_6_6_8, -7.6_1_8_4], [-9.0_0_4_2, -5.5_3_5_9, -6.9_1_4_1]]					)
              _UpperCAmelCase  :						Tuple							   =					torch.tensor([[0.7_8_2_8, 0.2_2_0_8, 0.4_3_2_3], [0.0_8_9_2, 0.5_9_9_6, 0.1_3_1_9], [0.5_5_2_4, 0.6_3_8_9, 0.8_9_1_4]]					)

       if with_box_refine and two_stage:
              _UpperCAmelCase  :						str							   =					torch.tensor(
                  [[-6.7_1_0_8, -4.3_2_1_3, -6.3_7_7_7], [-8.9_0_1_4, -6.1_7_9_9, -6.7_2_4_0], [-6.9_3_1_5, -4.4_7_3_5, -6.2_2_9_8]]					)
              _UpperCAmelCase  :						Optional[int]							   =					torch.tensor([[0.2_5_8_3, 0.5_4_9_9, 0.4_6_8_3], [0.7_6_5_2, 0.9_0_6_8, 0.4_8_8_2], [0.5_4_9_0, 0.2_7_6_3, 0.0_5_6_4]]					)

       print("Logits:"    ,			outputs.logits[0, :3, :3]					)

       assert torch.allclose(outputs.logits[0, :3, :3]    ,			expected_logits.to(__lowerCAmelCase					)    ,			atol=1e-4					)
       assert torch.allclose(outputs.pred_boxes[0, :3, :3]    ,			expected_boxes.to(__lowerCAmelCase					)    ,			atol=1e-4					)

       print("Everything ok!"					)

       # 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					)

       # Push to hub
       if push_to_hub:
              _UpperCAmelCase  :						List[str]							   =					"deformable-detr"
              model_name += "-single-scale" if single_scale else ""
              model_name += "-dc5" if dilation else ""
              model_name += "-with-box-refine" if with_box_refine else ""
              model_name += "-two-stage" if two_stage else ""
              print("Pushing model to hub..."					)
              model.push_to_hub(repo_path_or_name=__lowerCAmelCase    ,			organization="nielsr"    ,			commit_message="Add model"					)


if __name__ == "__main__":
  __SCREAMING_SNAKE_CASE :  Optional[int]  =						argparse.ArgumentParser()

  parser.add_argument(
      """--checkpoint_path""",
      type=str,
      default="""/home/niels/checkpoints/deformable_detr/r50_deformable_detr-checkpoint.pth""",
      help="""Path to Pytorch checkpoint (.pth file) you\'d like to convert.""",
  )
  parser.add_argument("""--single_scale""", action="""store_true""", help="""Whether to set config.num_features_levels = 1.""")
  parser.add_argument("""--dilation""", action="""store_true""", help="""Whether to set config.dilation=True.""")
  parser.add_argument("""--with_box_refine""", action="""store_true""", help="""Whether to set config.with_box_refine=True.""")
  parser.add_argument("""--two_stage""", action="""store_true""", help="""Whether to set config.two_stage=True.""")
  parser.add_argument(
      """--pytorch_dump_folder_path""",
      default=None,
      type=str,
      required=True,
      help="""Path to the folder to output PyTorch model.""",
  )
  parser.add_argument(
      """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
  )
  __SCREAMING_SNAKE_CASE :  Dict  =						parser.parse_args()
  convert_deformable_detr_checkpoint(
      args.checkpoint_path,
      args.single_scale,
      args.dilation,
      args.with_box_refine,
      args.two_stage,
      args.pytorch_dump_folder_path,
      args.push_to_hub,
  )


'''simple docstring'''




from typing import Any

import numpy as np

class 			lowerCamelCase_			:
      '''simple docstring'''

      def __init__( self		:       Optional[int]		,      A		:       np.ndarray		,      A		:       int ):
             # Check if RHS is negative
             if np.any(tableau[:, -1]		,      where=tableau[:, -1] < 0 ):
                    raise ValueError("RHS must be > 0" )

             _UpperCAmelCase  :						Union[str, Any]							   =					tableau
             _UpperCAmelCase   ,						_UpperCAmelCase  :						str							   =					tableau.shape

             # Number of decision variables x1, x2, x3...
             _UpperCAmelCase  :						Any							   =					n_vars

             # Number of artificial variables to be minimised
             _UpperCAmelCase  :						List[str]							   =					len(np.where(tableau[self.n_vars : -1] == -1 )[0] )

             # 2 if there are >= or == constraints (nonstandard), 1 otherwise (std)
             _UpperCAmelCase  :						List[Any]							   =					(self.n_art_vars > 0) + 1

             # Number of slack variables added to make inequalities into equalities
             _UpperCAmelCase  :						int							   =					self.n_rows - self.n_stages

             # Objectives for each stage
             _UpperCAmelCase  :						Optional[Any]							   =					["max"]

             # In two stage simplex, first minimise then maximise
             if self.n_art_vars:
                    self.objectives.append("min" )

             _UpperCAmelCase  :						Any							   =					[""]

             # Index of current pivot row and column
             _UpperCAmelCase  :						Optional[Any]							   =					None
             _UpperCAmelCase  :						Dict							   =					None

             # Does objective row only contain (non)-negative values?
             _UpperCAmelCase  :						List[str]							   =					False




      @staticmethod
      def   _A			( *A		:       int ):
             if len(A ) != 3:
                    raise ValueError("Must provide n_vars, n_slack, and n_art_vars" )

             if not all(x >= 0 and isinstance(A		,      A ) for x in args ):
                    raise ValueError("All arguments must be non-negative integers" )

             # decision | slack | artificial
             _UpperCAmelCase  :						Optional[Any]							   =					["x", "s", "a"]
             _UpperCAmelCase  :						int							   =					[]
             for i in range(3 ):
                    for j in range(args[i] ):
                           titles.append(string_starts[i] + str(j + 1 ) )
             titles.append("RHS" )
             return titles




      def   _A			( self		:       List[str]		,      A		:       np.ndarray ):
             _UpperCAmelCase  :						str							   =					self.objectives[-1]

             # Find entries of highest magnitude in objective rows
             _UpperCAmelCase  :						List[str]							   =					(objective == "min") - (objective == "max")
             _UpperCAmelCase  :						Optional[Any]							   =					np.argmax(sign * tableau[0, : self.n_vars] )

             # Choice is only valid if below 0 for maximise, and above for minimise
             if sign * self.tableau[0, col_idx] <= 0:
                    _UpperCAmelCase  :						Dict							   =					True
                    return 0, 0

             # Pivot row is chosen as having the lowest quotient when elements of
             # the pivot column divide the right-hand side

             # Slice excluding the objective rows
             _UpperCAmelCase  :						Any							   =					slice(self.n_stages		,      self.n_rows )

             # RHS
             _UpperCAmelCase  :						Optional[Any]							   =					tableau[s, -1]

             # Elements of pivot column within slice
             _UpperCAmelCase  :						Optional[Any]							   =					tableau[s, col_idx]

             # Array filled with nans
             _UpperCAmelCase  :						List[Any]							   =					np.full(self.n_rows - self.n_stages		,      np.nan )

             # If element in pivot column is greater than zeron_stages, return
             # quotient or nan otherwise
             _UpperCAmelCase  :						str							   =					np.divide(A		,      A		,      out=A		,      where=divisor > 0 )

             # Arg of minimum quotient excluding the nan values. n_stages is added
             # to compensate for earlier exclusion of objective columns
             _UpperCAmelCase  :						Optional[Any]							   =					np.nanargmin(A ) + self.n_stages
             return row_idx, col_idx




      def   _A			( self		:       List[Any]		,      A		:       np.ndarray		,      A		:       int		,      A		:       int ):
             _UpperCAmelCase  :						Union[str, Any]							   =					tableau[row_idx].copy()

             _UpperCAmelCase  :						Optional[Any]							   =					piv_row[col_idx]

             # Entry becomes 1
             piv_row *= 1 / piv_val

             # Variable in pivot column becomes basic, ie the only non-zero entry
             for idx, coeff in enumerate(tableau[:, col_idx] ):
                    tableau[idx] += -coeff * piv_row
             _UpperCAmelCase  :						Optional[Any]							   =					piv_row
             return tableau




      def   _A			( self		:       Optional[Any]		,      A		:       np.ndarray ):
             self.objectives.pop()

             if not self.objectives:
                    return tableau

             # Slice containing ids for artificial columns
             _UpperCAmelCase  :						Union[str, Any]							   =					slice(-self.n_art_vars - 1		,      -1 )

             # Delete the artificial variable columns
             _UpperCAmelCase  :						List[Any]							   =					np.delete(A		,      A		,      axis=1 )

             # Delete the objective row of the first stage
             _UpperCAmelCase  :						Union[str, Any]							   =					np.delete(A		,      0		,      axis=0 )

             _UpperCAmelCase  :						Any							   =					1
             self.n_rows -= 1
             _UpperCAmelCase  :						Dict							   =					0
             _UpperCAmelCase  :						List[str]							   =					False
             return tableau




      def   _A			( self		:       Optional[Any] ):
             # Stop simplex algorithm from cycling.
             for _ in range(100 ):
                    # Completion of each stage removes an objective. If both stages
                    # are complete, then no objectives are left
                    if not self.objectives:
                           _UpperCAmelCase  :						Any							   =					self.generate_col_titles(
                               self.n_vars		,      self.n_slack		,      self.n_art_vars )

                           # Find the values of each variable at optimal solution
                           return self.interpret_tableau(self.tableau		,      self.col_titles )

                    _UpperCAmelCase   ,						_UpperCAmelCase  :						List[str]							   =					self.find_pivot(self.tableau )

                    # If there are no more negative values in objective row
                    if self.stop_iter:
                           # Delete artificial variable columns and rows. Update attributes
                           _UpperCAmelCase  :						int							   =					self.change_stage(self.tableau )
                    else:
                           _UpperCAmelCase  :						Optional[Any]							   =					self.pivot(self.tableau		,      A		,      A )
             return {}





      def   _A			( self		:       List[Any]		,      A		:       np.ndarray		,      A		:       list[str] ):
             _UpperCAmelCase  :						str							   =					{"P": abs(tableau[0, -1] )}

             for i in range(self.n_vars ):
                    # Gives ids of nonzero entries in the ith column
                    _UpperCAmelCase  :						Dict							   =					np.nonzero(tableau[:, i] )
                    _UpperCAmelCase  :						Dict							   =					len(nonzero[0] )

                    # First entry in the nonzero ids
                    _UpperCAmelCase  :						str							   =					nonzero[0][0]
                    _UpperCAmelCase  :						str							   =					tableau[nonzero_rowidx, i]

                    # If there is only one nonzero value in column, which is one
                    if n_nonzero == nonzero_val == 1:
                           _UpperCAmelCase  :						Optional[Any]							   =					tableau[nonzero_rowidx, -1]
                           _UpperCAmelCase  :						Any							   =					rhs_val

        # Check for basic variables
             for title in col_titles:
                    # Don't add RHS or slack variables to output dict
                    if title[0] not in "R-s-a":
                           output_dict.setdefault(A		,      0 )
             return output_dict


if __name__ == "__main__":
  import doctest

  doctest.testmod()

