

'''simple docstring'''





import argparse
import json
import os
import pickle
import shutil

import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset

from transformers import (
    BertConfig,
    BertForMaskedLM,
    BertTokenizer,
    DistilBertConfig,
    DistilBertForMaskedLM,
    DistilBertTokenizer,
    GPTaConfig,
    GPTaLMHeadModel,
    GPTaTokenizer,
    RobertaConfig,
    RobertaForMaskedLM,
    RobertaTokenizer,
)
from utils import git_log, init_gpu_params, logger, set_seed


A_	= {
    "distilbert": (DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer),
    "roberta": (RobertaConfig, RobertaForMaskedLM, RobertaTokenizer),
    "bert": (BertConfig, BertForMaskedLM, BertTokenizer),
    "gpt2": (GPTaConfig, GPTaLMHeadModel, GPTaTokenizer),
}







def   _UpperCamelCase	(		__UpperCamelCase	)       ->			int:
					assert (args.mlm and args.alpha_mlm > 0.0) or (not args.mlm and args.alpha_mlm == 0.0)
					assert (args.alpha_mlm > 0.0 and args.alpha_clm == 0.0) or (args.alpha_mlm == 0.0 and args.alpha_clm > 0.0)
					if args.mlm:
										assert os.path.isfile(args.token_counts	)
										assert (args.student_type in ["roberta", "distilbert"]) and (args.teacher_type in ["roberta", "bert"])
					else:
										assert (args.student_type in ["gpt2"]) and (args.teacher_type in ["gpt2"])

					assert args.teacher_type == args.student_type or (
					    args.student_type == "distilbert" and args.teacher_type == "bert"
					)
					assert os.path.isfile(args.student_config	)
					if args.student_pretrained_weights is not None:
										assert os.path.isfile(args.student_pretrained_weights	)

					if args.freeze_token_type_embds:
										assert args.student_type in ["roberta"]

					assert args.alpha_ce >= 0.0
					assert args.alpha_mlm >= 0.0
					assert args.alpha_clm >= 0.0
					assert args.alpha_mse >= 0.0
					assert args.alpha_cos >= 0.0
					assert args.alpha_ce + args.alpha_mlm + args.alpha_clm + args.alpha_mse + args.alpha_cos > 0.0







def   _UpperCamelCase	(		__UpperCamelCase    ,__UpperCamelCase	)       ->			List[Any]:
					if args.student_type == "roberta":
										lowerCamelCase_  				=      False
					elif args.student_type == "gpt2":
										lowerCamelCase_  				=      False







def   _UpperCamelCase	(		__UpperCamelCase    ,__UpperCamelCase	)       ->			Any:
					if args.student_type == "roberta":
										lowerCamelCase_  				=      False







def   _UpperCamelCase	(		)       ->			Any:
					lowerCamelCase_  				=      argparse.ArgumentParser(description='Training'	)
					parser.add_argument('--force'    ,action='store_true'    ,help='Overwrite dump_path if it already exists.'	)

					parser.add_argument(
					    '--dump_path'    ,type=__UpperCamelCase    ,required=__UpperCamelCase    ,help='The output directory (log, checkpoints, parameters, etc.)'	)
					parser.add_argument(
					    '--data_file'    ,type=__UpperCamelCase    ,required=__UpperCamelCase    ,help='The binarized file (tokenized + tokens_to_ids) and grouped by sequence.'    ,)

					parser.add_argument(
					    '--student_type'    ,type=__UpperCamelCase    ,choices=['distilbert', 'roberta', 'gpt2']    ,required=__UpperCamelCase    ,help='The student type (DistilBERT, RoBERTa).'    ,)
					parser.add_argument('--student_config'    ,type=__UpperCamelCase    ,required=__UpperCamelCase    ,help='Path to the student configuration.'	)
					parser.add_argument(
					    '--student_pretrained_weights'    ,default=__UpperCamelCase    ,type=__UpperCamelCase    ,help='Load student initialization checkpoint.'	)

					parser.add_argument(
					    '--teacher_type'    ,choices=['bert', 'roberta', 'gpt2']    ,required=__UpperCamelCase    ,help='Teacher type (BERT, RoBERTa).'	)
					parser.add_argument('--teacher_name'    ,type=__UpperCamelCase    ,required=__UpperCamelCase    ,help='The teacher model.'	)

					parser.add_argument('--temperature'    ,default=2.0    ,type=__UpperCamelCase    ,help='Temperature for the softmax temperature.'	)
					parser.add_argument(
					    '--alpha_ce'    ,default=0.5    ,type=__UpperCamelCase    ,help='Linear weight for the distillation loss. Must be >=0.'	)
					parser.add_argument(
					    '--alpha_mlm'    ,default=0.0    ,type=__UpperCamelCase    ,help='Linear weight for the MLM loss. Must be >=0. Should be used in conjunction with `mlm` flag.'    ,)
					parser.add_argument('--alpha_clm'    ,default=0.5    ,type=__UpperCamelCase    ,help='Linear weight for the CLM loss. Must be >=0.'	)
					parser.add_argument('--alpha_mse'    ,default=0.0    ,type=__UpperCamelCase    ,help='Linear weight of the MSE loss. Must be >=0.'	)
					parser.add_argument(
					    '--alpha_cos'    ,default=0.0    ,type=__UpperCamelCase    ,help='Linear weight of the cosine embedding loss. Must be >=0.'	)

					parser.add_argument(
					    '--mlm'    ,action='store_true'    ,help='The LM step: MLM or CLM. If `mlm` is True, the MLM is used over CLM.'	)
					parser.add_argument(
					    '--mlm_mask_prop'    ,default=0.15    ,type=__UpperCamelCase    ,help='Proportion of tokens for which we need to make a prediction.'    ,)
					parser.add_argument('--word_mask'    ,default=0.8    ,type=__UpperCamelCase    ,help='Proportion of tokens to mask out.'	)
					parser.add_argument('--word_keep'    ,default=0.1    ,type=__UpperCamelCase    ,help='Proportion of tokens to keep.'	)
					parser.add_argument('--word_rand'    ,default=0.1    ,type=__UpperCamelCase    ,help='Proportion of tokens to randomly replace.'	)
					parser.add_argument(
					    '--mlm_smoothing'    ,default=0.7    ,type=__UpperCamelCase    ,help='Smoothing parameter to emphasize more rare tokens (see XLM, similar to word2vec).'    ,)
					parser.add_argument('--token_counts'    ,type=__UpperCamelCase    ,help='The token counts in the data_file for MLM.'	)

					parser.add_argument(
					    '--restrict_ce_to_mask'    ,action='store_true'    ,help='If true, compute the distillation loss only the [MLM] prediction distribution.'    ,)
					parser.add_argument(
					    '--freeze_pos_embs'    ,action='store_true'    ,help='Freeze positional embeddings during distillation. For student_type in [\'roberta\', \'gpt2\'] only.'    ,)
					parser.add_argument(
					    '--freeze_token_type_embds'    ,action='store_true'    ,help='Freeze token type embeddings during distillation if existent. For student_type in [\'roberta\'] only.'    ,)

					parser.add_argument('--n_epoch'    ,type=__UpperCamelCase    ,default=3    ,help='Number of pass on the whole dataset.'	)
					parser.add_argument('--batch_size'    ,type=__UpperCamelCase    ,default=5    ,help='Batch size (for each process).'	)
					parser.add_argument(
					    '--group_by_size'    ,action='store_false'    ,help='If true, group sequences that have similar length into the same batch. Default is true.'    ,)

					parser.add_argument(
					    '--gradient_accumulation_steps'    ,type=__UpperCamelCase    ,default=50    ,help='Gradient accumulation for larger training batches.'    ,)
					parser.add_argument('--warmup_prop'    ,default=0.05    ,type=__UpperCamelCase    ,help='Linear warmup proportion.'	)
					parser.add_argument('--weight_decay'    ,default=0.0    ,type=__UpperCamelCase    ,help='Weight decay if we apply some.'	)
					parser.add_argument('--learning_rate'    ,default=5e-4    ,type=__UpperCamelCase    ,help='The initial learning rate for Adam.'	)
					parser.add_argument('--adam_epsilon'    ,default=1e-6    ,type=__UpperCamelCase    ,help='Epsilon for Adam optimizer.'	)
					parser.add_argument('--max_grad_norm'    ,default=5.0    ,type=__UpperCamelCase    ,help='Max gradient norm.'	)
					parser.add_argument('--initializer_range'    ,default=0.02    ,type=__UpperCamelCase    ,help='Random initialization range.'	)

					parser.add_argument(
					    '--fp16'    ,action='store_true'    ,help='Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit'    ,)
					parser.add_argument(
					    '--fp16_opt_level'    ,type=__UpperCamelCase    ,default='O1'    ,help=(
					        'For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\'].'
					        'See details at https://nvidia.github.io/apex/amp.html'
					    )    ,)
					parser.add_argument('--n_gpu'    ,type=__UpperCamelCase    ,default=1    ,help='Number of GPUs in the node.'	)
					parser.add_argument('--local_rank'    ,type=__UpperCamelCase    ,default=-1    ,help='Distributed training - Local rank'	)
					parser.add_argument('--seed'    ,type=__UpperCamelCase    ,default=56    ,help='Random seed'	)

					parser.add_argument('--log_interval'    ,type=__UpperCamelCase    ,default=5_00    ,help='Tensorboard logging interval.'	)
					parser.add_argument('--checkpoint_interval'    ,type=__UpperCamelCase    ,default=40_00    ,help='Checkpoint interval.'	)
					lowerCamelCase_  				=      parser.parse_args()
					sanity_checks(__UpperCamelCase	)

					# ARGS #
					init_gpu_params(__UpperCamelCase	)
					set_seed(__UpperCamelCase	)
					if args.is_master:
										if os.path.exists(args.dump_path	):
															if not args.force:
																				raise ValueError(
																				    f'''Serialization dir {args.dump_path} already exists, but you have not precised wheter to overwrite'''
																				    ' itUse `--force` if you want to overwrite it'	)
															else:
																				shutil.rmtree(args.dump_path	)

										if not os.path.exists(args.dump_path	):
															os.makedirs(args.dump_path	)
										logger.info(f'''Experiment will be dumped and logged in {args.dump_path}'''	)

										# SAVE PARAMS #
										logger.info(f'''Param: {args}'''	)
										with open(os.path.join(args.dump_path    ,'parameters.json'	)    ,'w'	) as f:
															json.dump(vars(__UpperCamelCase	)    ,__UpperCamelCase    ,indent=4	)
										git_log(args.dump_path	)

					lowerCamelCase_				,lowerCamelCase_				,lowerCamelCase_  				=      MODEL_CLASSES[args.student_type]
					lowerCamelCase_				,lowerCamelCase_				,lowerCamelCase_  				=      MODEL_CLASSES[args.teacher_type]

					# TOKENIZER #
					lowerCamelCase_  				=      teacher_tokenizer_class.from_pretrained(args.teacher_name	)
					lowerCamelCase_  				=      {}
					for tok_name, tok_symbol in tokenizer.special_tokens_map.items():
										lowerCamelCase_  				=      tokenizer.all_special_tokens.index(__UpperCamelCase	)
										lowerCamelCase_  				=      tokenizer.all_special_ids[idx]
					logger.info(f'''Special tokens {special_tok_ids}'''	)
					lowerCamelCase_  				=      special_tok_ids
					lowerCamelCase_  				=      tokenizer.max_model_input_sizes[args.teacher_name]

					# DATA LOADER #
					logger.info(f'''Loading data from {args.data_file}'''	)
					with open(args.data_file    ,'rb'	) as fp:
										lowerCamelCase_  				=      pickle.load(__UpperCamelCase	)

					if args.mlm:
										logger.info(f'''Loading token counts from {args.token_counts} (already pre-computed)'''	)
										with open(args.token_counts    ,'rb'	) as fp:
															lowerCamelCase_  				=      pickle.load(__UpperCamelCase	)

										lowerCamelCase_  				=      np.maximum(__UpperCamelCase    ,1	) ** -args.mlm_smoothing
										for idx in special_tok_ids.values():
															lowerCamelCase_  				=      0.0  # do not predict special tokens
										lowerCamelCase_  				=      torch.from_numpy(__UpperCamelCase	)
					else:
										lowerCamelCase_  				=      None

					lowerCamelCase_  				=      LmSeqsDataset(params=__UpperCamelCase    ,data=__UpperCamelCase	)
					logger.info('Data loader created.'	)

					# STUDENT #
					logger.info(f'''Loading student config from {args.student_config}'''	)
					lowerCamelCase_  				=      student_config_class.from_pretrained(args.student_config	)
					lowerCamelCase_  				=      True

					if args.student_pretrained_weights is not None:
										logger.info(f'''Loading pretrained weights from {args.student_pretrained_weights}'''	)
										lowerCamelCase_  				=      student_model_class.from_pretrained(args.student_pretrained_weights    ,config=__UpperCamelCase	)
					else:
										lowerCamelCase_  				=      student_model_class(__UpperCamelCase	)

					if args.n_gpu > 0:
										student.to(f'''cuda:{args.local_rank}'''	)
					logger.info('Student loaded.'	)

					# TEACHER #
					lowerCamelCase_  				=      teacher_model_class.from_pretrained(args.teacher_name    ,output_hidden_states=__UpperCamelCase	)
					if args.n_gpu > 0:
										teacher.to(f'''cuda:{args.local_rank}'''	)
					logger.info(f'''Teacher loaded from {args.teacher_name}.'''	)

					# FREEZING #
					if args.freeze_pos_embs:
										freeze_pos_embeddings(__UpperCamelCase    ,__UpperCamelCase	)
					if args.freeze_token_type_embds:
										freeze_token_type_embeddings(__UpperCamelCase    ,__UpperCamelCase	)

					# SANITY CHECKS #
					assert student.config.vocab_size == teacher.config.vocab_size
					assert student.config.hidden_size == teacher.config.hidden_size
					assert student.config.max_position_embeddings == teacher.config.max_position_embeddings
					if args.mlm:
										assert token_probs.size(0	) == stu_architecture_config.vocab_size

					# DISTILLER #
					torch.cuda.empty_cache()
					lowerCamelCase_  				=      Distiller(
					    params=__UpperCamelCase    ,dataset=__UpperCamelCase    ,token_probs=__UpperCamelCase    ,student=__UpperCamelCase    ,teacher=__UpperCamelCase	)
					distiller.train()
					logger.info('Let\'s go get some drinks.'	)


if __name__ == "__main__":
				main()











"""simple docstring"""
import argparse
import json
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, ResNetConfig
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase__    						=			logging.get_logger(__name__)





def 				lowercase__		(					lowerCamelCase       ):
				# initialize config
				if "resnet-50" in model_name:
								_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    ResNetConfig.from_pretrained('microsoft/resnet-50'       )
				elif "resnet-101" in model_name:
								_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    ResNetConfig.from_pretrained('microsoft/resnet-101'       )
				else:
								raise ValueError('Model name should include either resnet50 or resnet101'       )

				_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    DetrConfig(use_timm_backbone=lowerCamelCase,						backbone_config=lowerCamelCase       )

				# set label attributes
				_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    'panoptic' in model_name
				if is_panoptic:
								_SCREAMING_SNAKE_CASE   :		List[str]       		=    250
				else:
								_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    91
								_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    'huggingface/label-files'
								_SCREAMING_SNAKE_CASE   :		Dict       		=    'coco-detection-id2label.json'
								_SCREAMING_SNAKE_CASE   :		List[str]       		=    json.load(open(hf_hub_download(lowerCamelCase,						lowerCamelCase,						repo_type='dataset'       ),						'r'       )       )
								_SCREAMING_SNAKE_CASE   :		Dict       		=    {int(lowerCamelCase       ): v for k, v in idalabel.items()}
								_SCREAMING_SNAKE_CASE   :		List[Any]       		=    idalabel
								_SCREAMING_SNAKE_CASE   :		Tuple       		=    {v: k for k, v in idalabel.items()}

				return config, is_panoptic





def 				lowercase__		(					lowerCamelCase       ):
				# here we list all keys to be renamed (original name on the left, our name on the right)
				_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    []

				# stem
				# fmt: off
				rename_keys.append(('backbone.0.body.conv1.weight', 'backbone.conv_encoder.model.embedder.embedder.convolution.weight')       )
				rename_keys.append(('backbone.0.body.bn1.weight', 'backbone.conv_encoder.model.embedder.embedder.normalization.weight')       )
				rename_keys.append(('backbone.0.body.bn1.bias', 'backbone.conv_encoder.model.embedder.embedder.normalization.bias')       )
				rename_keys.append(('backbone.0.body.bn1.running_mean', 'backbone.conv_encoder.model.embedder.embedder.normalization.running_mean')       )
				rename_keys.append(('backbone.0.body.bn1.running_var', 'backbone.conv_encoder.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"""backbone.conv_encoder.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"""backbone.conv_encoder.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"""backbone.conv_encoder.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"""backbone.conv_encoder.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"""backbone.conv_encoder.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"""backbone.conv_encoder.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"""backbone.conv_encoder.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"""backbone.conv_encoder.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"""backbone.conv_encoder.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"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var""",
																    )       )
    # fmt: on

				for i in range(config.encoder_layers       ):
								# 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'),
				    ]       )

				return rename_keys





def 				lowercase__		(					lowerCamelCase,						lowerCamelCase,						lowerCamelCase       ):
				_SCREAMING_SNAKE_CASE   :		Dict       		=    state_dict.pop(lowerCamelCase       )
				_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    val





def 				lowercase__		(					lowerCamelCase,						lowerCamelCase=False       ):
				_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    ''
				if is_panoptic:
								_SCREAMING_SNAKE_CASE   :		List[Any]       		=    '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)
								_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""       )
								_SCREAMING_SNAKE_CASE   :		Dict       		=    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
								_SCREAMING_SNAKE_CASE   :		Any       		=    in_proj_weight[:256, :]
								_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    in_proj_bias[:256]
								_SCREAMING_SNAKE_CASE   :		Tuple       		=    in_proj_weight[256:512, :]
								_SCREAMING_SNAKE_CASE   :		Tuple       		=    in_proj_bias[256:512]
								_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    in_proj_weight[-256:, :]
								_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    in_proj_bias[-256:]
				# 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
								_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""       )
								_SCREAMING_SNAKE_CASE   :		int       		=    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
								_SCREAMING_SNAKE_CASE   :		Tuple       		=    in_proj_weight[:256, :]
								_SCREAMING_SNAKE_CASE   :		str       		=    in_proj_bias[:256]
								_SCREAMING_SNAKE_CASE   :		str       		=    in_proj_weight[256:512, :]
								_SCREAMING_SNAKE_CASE   :		int       		=    in_proj_bias[256:512]
								_SCREAMING_SNAKE_CASE   :		List[Any]       		=    in_proj_weight[-256:, :]
								_SCREAMING_SNAKE_CASE   :		List[str]       		=    in_proj_bias[-256:]
								# read in weights + bias of input projection layer of cross-attention
								_SCREAMING_SNAKE_CASE   :		str       		=    state_dict.pop(
								    f"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""       )
								_SCREAMING_SNAKE_CASE   :		List[Any]       		=    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
								_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    in_proj_weight_cross_attn[:256, :]
								_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    in_proj_bias_cross_attn[:256]
								_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    in_proj_weight_cross_attn[256:512, :]
								_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    in_proj_bias_cross_attn[256:512]
								_SCREAMING_SNAKE_CASE   :		int       		=    in_proj_weight_cross_attn[-256:, :]
								_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    in_proj_bias_cross_attn[-256:]





def 				lowercase__		(					):
				_SCREAMING_SNAKE_CASE   :		List[Any]       		=    'http://images.cocodataset.org/val2017/000000039769.jpg'
				_SCREAMING_SNAKE_CASE   :		Dict       		=    Image.open(requests.get(lowerCamelCase,						stream=lowerCamelCase       ).raw       )

				return im





@torch.no_grad()
def 				lowercase__		(					lowerCamelCase,						lowerCamelCase=None,						lowerCamelCase=False       ):
				_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE   :		str       		=    get_detr_config(lowerCamelCase       )

				# load original model from torch hub
				_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    {
				    'detr-resnet-50': 'detr_resnet50',
				    'detr-resnet-101': 'detr_resnet101',
				}
				logger.info(f"""Converting model {model_name}..."""       )
				_SCREAMING_SNAKE_CASE   :		int       		=    torch.hub.load('facebookresearch/detr',						model_name_to_original_name[model_name],						pretrained=lowerCamelCase       ).eval()
				_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    detr.state_dict()
				# rename keys
				for src, dest in create_rename_keys(lowerCamelCase       ):
								if is_panoptic:
												_SCREAMING_SNAKE_CASE   :		int       		=    'detr.' + src
								rename_key(lowerCamelCase,						lowerCamelCase,						lowerCamelCase       )
				# query, key and value matrices need special treatment
				read_in_q_k_v(lowerCamelCase,						is_panoptic=lowerCamelCase       )
				# 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   :		str       		=    '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'       )
												):
																_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    state_dict.pop(lowerCamelCase       )
																_SCREAMING_SNAKE_CASE   :		int       		=    val
												elif "class_labels_classifier" in key or "bbox_predictor" in key:
																_SCREAMING_SNAKE_CASE   :		Tuple       		=    state_dict.pop(lowerCamelCase       )
																_SCREAMING_SNAKE_CASE   :		Dict       		=    val
												elif key.startswith('bbox_attention'       ) or key.startswith('mask_head'       ):
																continue
												else:
																_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    state_dict.pop(lowerCamelCase       )
																_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    val
								else:
												if not key.startswith('class_labels_classifier'       ) and not key.startswith('bbox_predictor'       ):
																_SCREAMING_SNAKE_CASE   :		List[str]       		=    state_dict.pop(lowerCamelCase       )
																_SCREAMING_SNAKE_CASE   :		int       		=    val

    # finally, create HuggingFace model and load state dict
				_SCREAMING_SNAKE_CASE   :		Dict       		=    DetrForSegmentation(lowerCamelCase       ) if is_panoptic else DetrForObjectDetection(lowerCamelCase       )
				model.load_state_dict(lowerCamelCase       )
				model.eval()

				# verify our conversion on an image
				_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    'coco_panoptic' if is_panoptic else 'coco_detection'
				_SCREAMING_SNAKE_CASE   :		int       		=    DetrImageProcessor(format=lowerCamelCase       )

				_SCREAMING_SNAKE_CASE   :		Any       		=    processor(images=prepare_img(),						return_tensors='pt'       )
				_SCREAMING_SNAKE_CASE   :		Tuple       		=    encoding['pixel_values']

				_SCREAMING_SNAKE_CASE   :		List[str]       		=    detr(lowerCamelCase       )
				_SCREAMING_SNAKE_CASE   :		int       		=    model(lowerCamelCase       )

				assert torch.allclose(outputs.logits,						original_outputs['pred_logits'],						atol=1E-3       )
				assert torch.allclose(outputs.pred_boxes,						original_outputs['pred_boxes'],						atol=1E-3       )
				if is_panoptic:
								assert torch.allclose(outputs.pred_masks,						original_outputs['pred_masks'],						atol=1E-4       )
				print('Looks ok!'       )

				if pytorch_dump_folder_path is not None:
								# 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       )
								processor.save_pretrained(lowerCamelCase       )

				if push_to_hub:
								# Upload model and image processor to the hub
								logger.info('Uploading PyTorch model and image processor to the hub...'       )
								model.push_to_hub(f"""nielsr/{model_name}"""       )
								processor.push_to_hub(f"""nielsr/{model_name}"""       )


if __name__ == "__main__":
			lowerCAmelCase__    						=			argparse.ArgumentParser()

			parser.add_argument(
			    '--model_name',
			    default='detr-resnet-50',
			    type=str,
			    choices=['detr-resnet-50', 'detr-resnet-101'],
			    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.'
			)
			parser.add_argument('--push_to_hub', action='store_true', help='Whether to push the model to the hub or not.')
			lowerCAmelCase__    						=			parser.parse_args()
			convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)

