


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()
snake_case			:      Dict 							=   logging.get_logger(__name__)


def 				SCREAMING_SNAKE_CASE (     UpperCAmelCase__							):

				"""simple docstring"""






				if "backbone.0.body" in orig_key:
								_SCREAMING_SNAKE_CASE      = orig_key.replace('backbone.0.body'   ,'backbone.conv_encoder.model'							)
				if "transformer" in orig_key:
								_SCREAMING_SNAKE_CASE      = orig_key.replace('transformer.'   ,''							)
				if "norm1" in orig_key:
								if "encoder" in orig_key:
												_SCREAMING_SNAKE_CASE      = orig_key.replace('norm1'   ,'self_attn_layer_norm'							)
								else:
												_SCREAMING_SNAKE_CASE      = orig_key.replace('norm1'   ,'encoder_attn_layer_norm'							)
				if "norm2" in orig_key:
								if "encoder" in orig_key:
												_SCREAMING_SNAKE_CASE      = orig_key.replace('norm2'   ,'final_layer_norm'							)
								else:
												_SCREAMING_SNAKE_CASE      = orig_key.replace('norm2'   ,'self_attn_layer_norm'							)
				if "norm3" in orig_key:
								_SCREAMING_SNAKE_CASE      = orig_key.replace('norm3'   ,'final_layer_norm'							)
				if "linear1" in orig_key:
								_SCREAMING_SNAKE_CASE      = orig_key.replace('linear1'   ,'fc1'							)
				if "linear2" in orig_key:
								_SCREAMING_SNAKE_CASE      = orig_key.replace('linear2'   ,'fc2'							)
				if "query_embed" in orig_key:
								_SCREAMING_SNAKE_CASE      = orig_key.replace('query_embed'   ,'query_position_embeddings'							)
				if "cross_attn" in orig_key:
								_SCREAMING_SNAKE_CASE      = orig_key.replace('cross_attn'   ,'encoder_attn'							)

				return orig_key


def 				SCREAMING_SNAKE_CASE (     UpperCAmelCase__							):

				"""simple docstring"""






				for i in range(6							):
								# read in weights + bias of input projection layer of self-attention
								_SCREAMING_SNAKE_CASE      = state_dict.pop(f'''decoder.layers.{i}.self_attn.in_proj_weight'''							)
								_SCREAMING_SNAKE_CASE      = 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
								_SCREAMING_SNAKE_CASE      = in_proj_weight[:256, :]
								_SCREAMING_SNAKE_CASE      = in_proj_bias[:256]
								_SCREAMING_SNAKE_CASE      = in_proj_weight[256:512, :]
								_SCREAMING_SNAKE_CASE      = in_proj_bias[256:512]
								_SCREAMING_SNAKE_CASE      = in_proj_weight[-256:, :]
								_SCREAMING_SNAKE_CASE      = in_proj_bias[-256:]


def 				SCREAMING_SNAKE_CASE (     ):

				"""simple docstring"""






				_SCREAMING_SNAKE_CASE      = 'http://images.cocodataset.org/val2017/000000039769.jpg'
				_SCREAMING_SNAKE_CASE      = Image.open(requests.get(UpperCAmelCase__   ,stream=UpperCAmelCase__							).raw							)

				return im


@torch.no_grad()
def 				SCREAMING_SNAKE_CASE (     UpperCAmelCase__   ,UpperCAmelCase__   ,UpperCAmelCase__   ,UpperCAmelCase__   ,UpperCAmelCase__   ,UpperCAmelCase__   ,UpperCAmelCase__   ,):

				"""simple docstring"""






				_SCREAMING_SNAKE_CASE      = DeformableDetrConfig()
				# set config attributes
				if single_scale:
								_SCREAMING_SNAKE_CASE      = 1
				_SCREAMING_SNAKE_CASE      = dilation
				_SCREAMING_SNAKE_CASE      = with_box_refine
				_SCREAMING_SNAKE_CASE      = two_stage
				# set labels
				_SCREAMING_SNAKE_CASE      = 91
				_SCREAMING_SNAKE_CASE      = 'huggingface/label-files'
				_SCREAMING_SNAKE_CASE      = 'coco-detection-id2label.json'
				_SCREAMING_SNAKE_CASE      = json.load(open(cached_download(hf_hub_url(UpperCAmelCase__   ,UpperCAmelCase__   ,repo_type='dataset'							)							)   ,'r'							)							)
				_SCREAMING_SNAKE_CASE      = {int(UpperCAmelCase__							): v for k, v in idalabel.items()}
				_SCREAMING_SNAKE_CASE      = idalabel
				_SCREAMING_SNAKE_CASE      = {v: k for k, v in idalabel.items()}

				# load image processor
				_SCREAMING_SNAKE_CASE      = DeformableDetrImageProcessor(format='coco_detection'							)

				# prepare image
				_SCREAMING_SNAKE_CASE      = prepare_img()
				_SCREAMING_SNAKE_CASE      = image_processor(images=UpperCAmelCase__   ,return_tensors='pt'							)
				_SCREAMING_SNAKE_CASE      = encoding['pixel_values']

				logger.info('Converting model...'							)

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

				_SCREAMING_SNAKE_CASE      = 'cuda' if torch.cuda.is_available() else 'cpu'
				model.to(UpperCAmelCase__							)
				# verify our conversion
				_SCREAMING_SNAKE_CASE      = model(pixel_values.to(UpperCAmelCase__							)							)

				_SCREAMING_SNAKE_CASE      = 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], [-10.5634, -5.3_3_7_9, -7.5_1_1_6]]							)
				_SCREAMING_SNAKE_CASE      = 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:
								_SCREAMING_SNAKE_CASE      = 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], [-10.0665, -5.8_4_7_0, -7.7_0_0_3]]							)
								_SCREAMING_SNAKE_CASE      = 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:
								_SCREAMING_SNAKE_CASE      = 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], [-10.1178, -4.5_2_7_5, -6.2_6_7_1]]							)
								_SCREAMING_SNAKE_CASE      = 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:
								_SCREAMING_SNAKE_CASE      = 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]]							)
								_SCREAMING_SNAKE_CASE      = 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:
								_SCREAMING_SNAKE_CASE      = 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]]							)
								_SCREAMING_SNAKE_CASE      = 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(UpperCAmelCase__							)   ,atol=1e-4							)
				assert torch.allclose(outputs.pred_boxes[0, :3, :3]   ,expected_boxes.to(UpperCAmelCase__							)   ,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(UpperCAmelCase__							).mkdir(exist_ok=UpperCAmelCase__							)
				model.save_pretrained(UpperCAmelCase__							)
				image_processor.save_pretrained(UpperCAmelCase__							)

				# Push to hub
				if push_to_hub:
								_SCREAMING_SNAKE_CASE      = '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=UpperCAmelCase__   ,organization='nielsr'   ,commit_message='Add model'							)


if __name__ == "__main__":
						snake_case			:      Dict 							=   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.'
						)
						snake_case			:      List[Any] 							=   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,
						)




import sys
from dataclasses import dataclass
from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Union

import pandas as pd
import pyarrow as pa

import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast


if TYPE_CHECKING:
							import sqlitea

							import sqlalchemy


snake_case			:      List[str] 							=   datasets.utils.logging.get_logger(__name__)



@dataclass
class    __lowercase  (       datasets.BuilderConfig							):

							"""simple docstring"""


							SCREAMING_SNAKE_CASE   :					Union[str, "sqlalchemy.sql.Selectable"]        =						None
							SCREAMING_SNAKE_CASE   :					Union[str, "sqlalchemy.engine.Connection", "sqlalchemy.engine.Engine", "sqlite3.Connection"]        =						None
							SCREAMING_SNAKE_CASE   :					Optional[Union[str, List[str]]]        =						None
							SCREAMING_SNAKE_CASE   :					bool        =						True
							SCREAMING_SNAKE_CASE   :					Optional[Union[List, Tuple, Dict]]        =						None
							SCREAMING_SNAKE_CASE   :					Optional[Union[List, Dict]]        =						None
							SCREAMING_SNAKE_CASE   :					Optional[List[str]]        =						None
							SCREAMING_SNAKE_CASE   :					Optional[int]        =						1_00_00
							SCREAMING_SNAKE_CASE   :					Optional[datasets.Features]        =						None





							def 		__magic_name__			(    self	)->	int:
											if self.sql is None:
															raise ValueError('sql must be specified'	)
											if self.con is None:
															raise ValueError('con must be specified'	)





							def 		__magic_name__			(    self				, A_				, A_ = None				, )->	str:
											_SCREAMING_SNAKE_CASE      = config_kwargs.copy()
											# We need to stringify the Selectable object to make its hash deterministic

											# The process of stringifying is explained here: http://docs.sqlalchemy.org/en/latest/faq/sqlexpressions.html
											_SCREAMING_SNAKE_CASE      = config_kwargs['sql']
											if not isinstance(A_				, A_	):
															if datasets.config.SQLALCHEMY_AVAILABLE and "sqlalchemy" in sys.modules:
																			import sqlalchemy

																			if isinstance(A_				, sqlalchemy.sql.Selectable	):
																							_SCREAMING_SNAKE_CASE      = sqlalchemy.create_engine(config_kwargs['con'].split('://'	)[0] + '://'	)
																							_SCREAMING_SNAKE_CASE      = str(sql.compile(dialect=engine.dialect	)	)
																							_SCREAMING_SNAKE_CASE      = sql_str
																			else:
																							raise TypeError(
																							    F'''Supported types for \'sql\' are string and sqlalchemy.sql.Selectable but got {type(A_	)}: {sql}'''	)
															else:
																			raise TypeError(
																			    F'''Supported types for \'sql\' are string and sqlalchemy.sql.Selectable but got {type(A_	)}: {sql}'''	)
											_SCREAMING_SNAKE_CASE      = config_kwargs['con']
											if not isinstance(A_				, A_	):
															_SCREAMING_SNAKE_CASE      = id(A_	)
															logger.info(
															    F'''SQL connection \'con\' of type {type(A_	)} couldn\'t be hashed properly. To enable hashing, specify \'con\' as URI string instead.'''	)

											return super().create_config_id(A_				, custom_features=A_	)





							@property
							def 		__magic_name__			(    self	)->	Dict:
											_SCREAMING_SNAKE_CASE      = {
											    'index_col': self.index_col,
											    'columns': self.columns,
											    'params': self.params,
											    'coerce_float': self.coerce_float,
											    'parse_dates': self.parse_dates,
											}
											return pd_read_sql_kwargs







class    __lowercase  (       datasets.ArrowBasedBuilder							):

							"""simple docstring"""


							SCREAMING_SNAKE_CASE   :					Any        =						SqlConfig





							def 		__magic_name__			(    self	)->	Dict:
											return datasets.DatasetInfo(features=self.config.features	)





							def 		__magic_name__			(    self				, A_	)->	Optional[Any]:
											return [datasets.SplitGenerator(name=datasets.Split.TRAIN				, gen_kwargs={}	)]





							def 		__magic_name__			(    self				, A_	)->	pa.Table:
											if self.config.features is not None:
															_SCREAMING_SNAKE_CASE      = self.config.features.arrow_schema
															if all(not require_storage_cast(A_	) for feature in self.config.features.values()	):
																			# cheaper cast
																			_SCREAMING_SNAKE_CASE      = pa.Table.from_arrays([pa_table[field.name] for field in schema]				, schema=A_	)
															else:
																			# more expensive cast; allows str <-> int/float or str to Audio for example
																			_SCREAMING_SNAKE_CASE      = table_cast(A_				, A_	)
											return pa_table





							def 		__magic_name__			(    self	)->	Optional[int]:
											_SCREAMING_SNAKE_CASE      = self.config.chunksize
											_SCREAMING_SNAKE_CASE      = pd.read_sql(
											    self.config.sql				, self.config.con				, chunksize=A_				, **self.config.pd_read_sql_kwargs	)
											_SCREAMING_SNAKE_CASE      = [sql_reader] if chunksize is None else sql_reader
											for chunk_idx, df in enumerate(A_	):
															_SCREAMING_SNAKE_CASE      = pa.Table.from_pandas(A_	)
															yield chunk_idx, self._cast_table(A_	)
