
import argparse
import json
from pathlib import Path

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

from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE							:int											=       logging.get_logger(__name__)


def 		UpperCAmelCase    (			)	->			Any:







				"""simple docstring"""



				__A         =				DetaConfig(
				    num_queries=9_0_0	,     encoder_ffn_dim=2_0_4_8	,     decoder_ffn_dim=2_0_4_8	,     num_feature_levels=5	,     assign_first_stage=a_	,     with_box_refine=a_	,     two_stage=a_	,     )

				# set labels
				__A         =				9_1
				__A         =				"huggingface/label-files"
				__A         =				"coco-detection-id2label.json"
				__A         =				json.load(open(cached_download(hf_hub_url(a_	,     a_	,     repo_type="dataset"		)		)	,     "r"		)		)
				__A         =				{int(a_		): v for k, v in idalabel.items()}
				__A         =				idalabel
				__A         =				{v: k for k, v in idalabel.items()}

				return config


def 		UpperCAmelCase    (			a_		)	->			Dict:







				"""simple docstring"""



				__A         =				[]

				# stem
				# fmt: off
				rename_keys.append(("backbone.0.body.conv1.weight", "model.backbone.model.embedder.embedder.convolution.weight")		)
				rename_keys.append(("backbone.0.body.bn1.weight", "model.backbone.model.embedder.embedder.normalization.weight")		)
				rename_keys.append(("backbone.0.body.bn1.bias", "model.backbone.model.embedder.embedder.normalization.bias")		)
				rename_keys.append(("backbone.0.body.bn1.running_mean", "model.backbone.model.embedder.embedder.normalization.running_mean")		)
				rename_keys.append(("backbone.0.body.bn1.running_var", "model.backbone.model.embedder.embedder.normalization.running_var")		)
				# stages
				for stage_idx in range(len(config.backbone_config.depths		)		):
								for layer_idx in range(config.backbone_config.depths[stage_idx]		):
												# shortcut
												if layer_idx == 0:
																rename_keys.append(
																    (
																        F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight''',
																        F'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight''',
																    )		)
																rename_keys.append(
																    (
																        F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight''',
																        F'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight''',
																    )		)
																rename_keys.append(
																    (
																        F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias''',
																        F'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias''',
																    )		)
																rename_keys.append(
																    (
																        F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean''',
																        F'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean''',
																    )		)
																rename_keys.append(
																    (
																        F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var''',
																        F'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var''',
																    )		)
												# 3 convs
												for i in range(3		):
																rename_keys.append(
																    (
																        F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight''',
																        F'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight''',
																    )		)
																rename_keys.append(
																    (
																        F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight''',
																        F'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight''',
																    )		)
																rename_keys.append(
																    (
																        F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias''',
																        F'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias''',
																    )		)
																rename_keys.append(
																    (
																        F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean''',
																        F'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean''',
																    )		)
																rename_keys.append(
																    (
																        F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var''',
																        F'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var''',
																    )		)
    # transformer encoder
				for i in range(config.encoder_layers		):
								rename_keys.append((F'''transformer.encoder.layers.{i}.self_attn.sampling_offsets.weight''', F'''model.encoder.layers.{i}.self_attn.sampling_offsets.weight''')		)
								rename_keys.append((F'''transformer.encoder.layers.{i}.self_attn.sampling_offsets.bias''', F'''model.encoder.layers.{i}.self_attn.sampling_offsets.bias''')		)
								rename_keys.append((F'''transformer.encoder.layers.{i}.self_attn.attention_weights.weight''', F'''model.encoder.layers.{i}.self_attn.attention_weights.weight''')		)
								rename_keys.append((F'''transformer.encoder.layers.{i}.self_attn.attention_weights.bias''', F'''model.encoder.layers.{i}.self_attn.attention_weights.bias''')		)
								rename_keys.append((F'''transformer.encoder.layers.{i}.self_attn.value_proj.weight''', F'''model.encoder.layers.{i}.self_attn.value_proj.weight''')		)
								rename_keys.append((F'''transformer.encoder.layers.{i}.self_attn.value_proj.bias''', F'''model.encoder.layers.{i}.self_attn.value_proj.bias''')		)
								rename_keys.append((F'''transformer.encoder.layers.{i}.self_attn.output_proj.weight''', F'''model.encoder.layers.{i}.self_attn.output_proj.weight''')		)
								rename_keys.append((F'''transformer.encoder.layers.{i}.self_attn.output_proj.bias''', F'''model.encoder.layers.{i}.self_attn.output_proj.bias''')		)
								rename_keys.append((F'''transformer.encoder.layers.{i}.norm1.weight''', F'''model.encoder.layers.{i}.self_attn_layer_norm.weight''')		)
								rename_keys.append((F'''transformer.encoder.layers.{i}.norm1.bias''', F'''model.encoder.layers.{i}.self_attn_layer_norm.bias''')		)
								rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.weight''', F'''model.encoder.layers.{i}.fc1.weight''')		)
								rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.bias''', F'''model.encoder.layers.{i}.fc1.bias''')		)
								rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.weight''', F'''model.encoder.layers.{i}.fc2.weight''')		)
								rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.bias''', F'''model.encoder.layers.{i}.fc2.bias''')		)
								rename_keys.append((F'''transformer.encoder.layers.{i}.norm2.weight''', F'''model.encoder.layers.{i}.final_layer_norm.weight''')		)
								rename_keys.append((F'''transformer.encoder.layers.{i}.norm2.bias''', F'''model.encoder.layers.{i}.final_layer_norm.bias''')		)

				# transformer decoder
				for i in range(config.decoder_layers		):
								rename_keys.append((F'''transformer.decoder.layers.{i}.cross_attn.sampling_offsets.weight''', F'''model.decoder.layers.{i}.encoder_attn.sampling_offsets.weight''')		)
								rename_keys.append((F'''transformer.decoder.layers.{i}.cross_attn.sampling_offsets.bias''', F'''model.decoder.layers.{i}.encoder_attn.sampling_offsets.bias''')		)
								rename_keys.append((F'''transformer.decoder.layers.{i}.cross_attn.attention_weights.weight''', F'''model.decoder.layers.{i}.encoder_attn.attention_weights.weight''')		)
								rename_keys.append((F'''transformer.decoder.layers.{i}.cross_attn.attention_weights.bias''', F'''model.decoder.layers.{i}.encoder_attn.attention_weights.bias''')		)
								rename_keys.append((F'''transformer.decoder.layers.{i}.cross_attn.value_proj.weight''', F'''model.decoder.layers.{i}.encoder_attn.value_proj.weight''')		)
								rename_keys.append((F'''transformer.decoder.layers.{i}.cross_attn.value_proj.bias''', F'''model.decoder.layers.{i}.encoder_attn.value_proj.bias''')		)
								rename_keys.append((F'''transformer.decoder.layers.{i}.cross_attn.output_proj.weight''', F'''model.decoder.layers.{i}.encoder_attn.output_proj.weight''')		)
								rename_keys.append((F'''transformer.decoder.layers.{i}.cross_attn.output_proj.bias''', F'''model.decoder.layers.{i}.encoder_attn.output_proj.bias''')		)
								rename_keys.append((F'''transformer.decoder.layers.{i}.norm1.weight''', F'''model.decoder.layers.{i}.encoder_attn_layer_norm.weight''')		)
								rename_keys.append((F'''transformer.decoder.layers.{i}.norm1.bias''', F'''model.decoder.layers.{i}.encoder_attn_layer_norm.bias''')		)
								rename_keys.append((F'''transformer.decoder.layers.{i}.self_attn.out_proj.weight''', F'''model.decoder.layers.{i}.self_attn.out_proj.weight''')		)
								rename_keys.append((F'''transformer.decoder.layers.{i}.self_attn.out_proj.bias''', F'''model.decoder.layers.{i}.self_attn.out_proj.bias''')		)
								rename_keys.append((F'''transformer.decoder.layers.{i}.norm2.weight''', F'''model.decoder.layers.{i}.self_attn_layer_norm.weight''')		)
								rename_keys.append((F'''transformer.decoder.layers.{i}.norm2.bias''', F'''model.decoder.layers.{i}.self_attn_layer_norm.bias''')		)
								rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.weight''', F'''model.decoder.layers.{i}.fc1.weight''')		)
								rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.bias''', F'''model.decoder.layers.{i}.fc1.bias''')		)
								rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.weight''', F'''model.decoder.layers.{i}.fc2.weight''')		)
								rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.bias''', F'''model.decoder.layers.{i}.fc2.bias''')		)
								rename_keys.append((F'''transformer.decoder.layers.{i}.norm3.weight''', F'''model.decoder.layers.{i}.final_layer_norm.weight''')		)
								rename_keys.append((F'''transformer.decoder.layers.{i}.norm3.bias''', F'''model.decoder.layers.{i}.final_layer_norm.bias''')		)

				# fmt: on

				return rename_keys


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







				"""simple docstring"""



				__A         =				dct.pop(a_		)
				__A         =				val


def 		UpperCAmelCase    (			a_	,     a_		)	->			List[Any]:







				"""simple docstring"""



				__A         =				config.d_model
				for i in range(config.decoder_layers		):
								# read in weights + bias of input projection layer of self-attention
								__A         =				state_dict.pop(F'''transformer.decoder.layers.{i}.self_attn.in_proj_weight'''		)
								__A         =				state_dict.pop(F'''transformer.decoder.layers.{i}.self_attn.in_proj_bias'''		)
								# next, add query, keys and values (in that order) to the state dict
								__A         =				in_proj_weight[:hidden_size, :]
								__A         =				in_proj_bias[:hidden_size]
								__A         =				in_proj_weight[
								    hidden_size : hidden_size * 2, :
								]
								__A         =				in_proj_bias[hidden_size : hidden_size * 2]
								__A         =				in_proj_weight[-hidden_size:, :]
								__A         =				in_proj_bias[-hidden_size:]


def 		UpperCAmelCase    (			)	->			str:







				"""simple docstring"""



				__A         =				"http://images.cocodataset.org/val2017/000000039769.jpg"
				__A         =				Image.open(requests.get(a_	,     stream=a_		).raw		)

				return im


@torch.no_grad()
def 		UpperCAmelCase    (			a_	,     a_	,     a_		)	->			List[Any]:







				"""simple docstring"""



				__A         =				get_deta_config()

				# load original state dict
				if model_name == "deta-resnet-50":
								__A         =				"adet_checkpoint0011.pth"
				elif model_name == "deta-resnet-50-24-epochs":
								__A         =				"adet_2x_checkpoint0023.pth"
				else:
								raise ValueError(F'''Model name {model_name} not supported'''		)
				__A         =				hf_hub_download(repo_id="nielsr/deta-checkpoints"	,     filename=a_		)
				__A         =				torch.load(a_	,     map_location="cpu"		)["model"]

				# rename keys
				__A         =				create_rename_keys(a_		)
				for src, dest in rename_keys:
								rename_key(a_	,     a_	,     a_		)
				read_in_decoder_q_k_v(a_	,     a_		)

				# fix some prefixes
				for key in state_dict.copy().keys():
								if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
												__A         =				state_dict.pop(a_		)
												__A         =				val
								if "input_proj" in key:
												__A         =				state_dict.pop(a_		)
												__A         =				val
								if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
												__A         =				state_dict.pop(a_		)
												__A         =				val

    # finally, create HuggingFace model and load state dict
				__A         =				DetaForObjectDetection(a_		)
				model.load_state_dict(a_		)
				model.eval()

				__A         =				"cuda" if torch.cuda.is_available() else "cpu"
				model.to(a_		)

				# load image processor
				__A         =				DetaImageProcessor(format="coco_detection"		)

				# verify our conversion on image
				__A         =				prepare_img()
				__A         =				processor(images=a_	,     return_tensors="pt"		)
				__A         =				encoding["pixel_values"]
				__A         =				model(pixel_values.to(a_		)		)

				# verify logits
				if model_name == "deta-resnet-50":
								__A         =				torch.tensor(
								    [[-7.3_978, -2.5_406, -4.1_668], [-8.2_684, -3.9_933, -3.8_096], [-7.0_515, -3.7_973, -5.8_516]]		)
								__A         =				torch.tensor([[0.5_043, 0.4_973, 0.9_998], [0.2_542, 0.5_489, 0.4_748], [0.5_490, 0.2_765, 0.0_570]]		)
				elif model_name == "deta-resnet-50-24-epochs":
								__A         =				torch.tensor(
								    [[-7.1_688, -2.4_857, -4.8_669], [-7.8_630, -3.8_154, -4.2_674], [-7.2_730, -4.1_865, -5.5_323]]		)
								__A         =				torch.tensor([[0.5_021, 0.4_971, 0.9_994], [0.2_546, 0.5_486, 0.4_731], [0.1_686, 0.1_986, 0.2_142]]		)

				assert torch.allclose(outputs.logits[0, :3, :3]	,     expected_logits.to(a_		)	,     atol=1E-4		)
				assert torch.allclose(outputs.pred_boxes[0, :3, :3]	,     expected_boxes.to(a_		)	,     atol=1E-4		)
				print("Everything ok!"		)

				if pytorch_dump_folder_path:
								# Save model and processor
								logger.info(F'''Saving PyTorch model and processor to {pytorch_dump_folder_path}...'''		)
								Path(a_		).mkdir(exist_ok=a_		)
								model.save_pretrained(a_		)
								processor.save_pretrained(a_		)

				# Push to hub
				if push_to_hub:
								print("Pushing model and processor to hub..."		)
								model.push_to_hub(F'''jozhang97/{model_name}'''		)
								processor.push_to_hub(F'''jozhang97/{model_name}'''		)


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

					parser.add_argument(
					    '--model_name',
					    type=str,
					    default='deta-resnet-50',
					    choices=['deta-resnet-50', 'deta-resnet-50-24-epochs'],
					    help='Name of the 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.',
					)
					parser.add_argument(
					    '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
					)
					SCREAMING_SNAKE_CASE							:Optional[Any]											=       parser.parse_args()
					convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)


from typing import Any

import numpy as np





class 							UpperCAmelCase   :




	'''simple docstring'''





	def __init__( self      :				Dict							,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"				)

					__A         =				tableau
					__A    ,							__A         =				tableau.shape

					# Number of decision variables x1, x2, x3...
					__A         =				n_vars

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

					# 2 if there are >= or == constraints (nonstandard), 1 otherwise (std)
					__A         =				(self.n_art_vars > 0) + 1

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

					# Objectives for each stage
					__A         =				["max"]

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

					__A         =				[""]

					# Index of current pivot row and column
					__A         =				None
					__A         =				None

					# Does objective row only contain (non)-negative values?
					__A         =				False






	@staticmethod
	def        UpperCamelCase_						( *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
					__A         =				["x", "s", "a"]
					__A         =				[]
					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        UpperCamelCase_						( self      :				List[Any]							,A      :				np.ndarray				):
					__A         =				self.objectives[-1]

					# Find entries of highest magnitude in objective rows
					__A         =				(objective == "min") - (objective == "max")
					__A         =				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:
									__A         =				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
					__A         =				slice(self.n_stages							,self.n_rows				)

					# RHS
					__A         =				tableau[s, -1]

					# Elements of pivot column within slice
					__A         =				tableau[s, col_idx]

					# Array filled with nans
					__A         =				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
					__A         =				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
					__A         =				np.nanargmin(A				) + self.n_stages
					return row_idx, col_idx






	def        UpperCamelCase_						( self      :				List[Any]							,A      :				np.ndarray							,A      :				int							,A      :				int				):
					__A         =				tableau[row_idx].copy()

					__A         =				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
					__A         =				piv_row
					return tableau






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

					if not self.objectives:
									return tableau

					# Slice containing ids for artificial columns
					__A         =				slice(-self.n_art_vars - 1							,-1				)

					# Delete the artificial variable columns
					__A         =				np.delete(A							,A							,axis=1				)

					# Delete the objective row of the first stage
					__A         =				np.delete(A							,0							,axis=0				)

					__A         =				1
					self.n_rows -= 1
					__A         =				0
					__A         =				False
					return tableau






	def        UpperCamelCase_						( self      :				Dict				):
					# Stop simplex algorithm from cycling.
					for _ in range(1_00				):
									# Completion of each stage removes an objective. If both stages
									# are complete, then no objectives are left
									if not self.objectives:
													__A         =				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				)

									__A    ,							__A         =				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
													__A         =				self.change_stage(self.tableau				)
									else:
													__A         =				self.pivot(self.tableau							,A							,A				)
					return {}







	def        UpperCamelCase_						( self      :				Dict							,A      :				np.ndarray							,A      :				list[str]				):
					__A         =				{"P": abs(tableau[0, -1]				)}

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

									# First entry in the nonzero ids
									__A         =				nonzero[0][0]
									__A         =				tableau[nonzero_rowidx, i]

									# If there is only one nonzero value in column, which is one
									if n_nonzero == nonzero_val == 1:
													__A         =				tableau[nonzero_rowidx, -1]
													__A         =				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()
