


'''simple docstring'''

import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import datasets
import numpy as np
import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    PreTrainedTokenizer,
    TFAutoModelForSequenceClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()

def 							__magic_name__(						lowerCamelCase,				lowerCamelCase,				lowerCamelCase,				lowerCamelCase,				lowerCamelCase,				lowerCamelCase = None,				):
						__lowerCAmelCase							       =				{}

						if train_file is not None:
												__lowerCAmelCase							       =				[train_file]
						if eval_file is not None:
												__lowerCAmelCase							       =				[eval_file]
						if test_file is not None:
												__lowerCAmelCase							       =				[test_file]

						__lowerCAmelCase							       =				datasets.load_dataset('''csv''',				data_files=lowerCamelCase)
						__lowerCAmelCase							       =				list(ds[list(files.keys())[0]].features.keys())
						__lowerCAmelCase							       =				features_name.pop(lowerCamelCase)
						__lowerCAmelCase							       =				list(set(ds[list(files.keys())[0]][label_name]))
						__lowerCAmelCase							       =				{label: i for i, label in enumerate(lowerCamelCase)}
						__lowerCAmelCase							       =				tokenizer.model_input_names
						__lowerCAmelCase							       =				{}

						if len(lowerCamelCase) == 1:
												for k in files.keys():
																		__lowerCAmelCase							       =				ds[k].map(
																		    lambda lowerCamelCase: tokenizer.batch_encode_plus(
																		        example[features_name[0]],				truncation=lowerCamelCase,				max_length=lowerCamelCase,				padding='''max_length'''),				batched=lowerCamelCase,				)
						elif len(lowerCamelCase) == 2:
												for k in files.keys():
																		__lowerCAmelCase							       =				ds[k].map(
																		    lambda lowerCamelCase: tokenizer.batch_encode_plus(
																		        (example[features_name[0]], example[features_name[1]]),				truncation=lowerCamelCase,				max_length=lowerCamelCase,				padding='''max_length''',				),				batched=lowerCamelCase,				)

						def gen_train():
												for ex in transformed_ds[datasets.Split.TRAIN]:
																		__lowerCAmelCase							       =				{k: v for k, v in ex.items() if k in input_names}
																		__lowerCAmelCase							       =				labelaid[ex[label_name]]
																		yield (d, label)

						def gen_val():
												for ex in transformed_ds[datasets.Split.VALIDATION]:
																		__lowerCAmelCase							       =				{k: v for k, v in ex.items() if k in input_names}
																		__lowerCAmelCase							       =				labelaid[ex[label_name]]
																		yield (d, label)

						def gen_test():
												for ex in transformed_ds[datasets.Split.TEST]:
																		__lowerCAmelCase							       =				{k: v for k, v in ex.items() if k in input_names}
																		__lowerCAmelCase							       =				labelaid[ex[label_name]]
																		yield (d, label)

						__lowerCAmelCase							       =				(
						    tf.data.Dataset.from_generator(
						        lowerCamelCase,				({k: tf.intaa for k in input_names}, tf.intaa),				({k: tf.TensorShape([None]) for k in input_names}, tf.TensorShape([])),				)
						    if datasets.Split.TRAIN in transformed_ds
						    else None
						)

						if train_ds is not None:
												__lowerCAmelCase							       =				train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN])))

						__lowerCAmelCase							       =				(
						    tf.data.Dataset.from_generator(
						        lowerCamelCase,				({k: tf.intaa for k in input_names}, tf.intaa),				({k: tf.TensorShape([None]) for k in input_names}, tf.TensorShape([])),				)
						    if datasets.Split.VALIDATION in transformed_ds
						    else None
						)

						if val_ds is not None:
												__lowerCAmelCase							       =				val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION])))

						__lowerCAmelCase							       =				(
						    tf.data.Dataset.from_generator(
						        lowerCamelCase,				({k: tf.intaa for k in input_names}, tf.intaa),				({k: tf.TensorShape([None]) for k in input_names}, tf.TensorShape([])),				)
						    if datasets.Split.TEST in transformed_ds
						    else None
						)

						if test_ds is not None:
												__lowerCAmelCase							       =				test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST])))

						return train_ds, val_ds, test_ds, labelaid


_UpperCAmelCase				: List[Any]							    =				logging.getLogger(__name__)


@dataclass
class       a__				:







				"""simple docstring"""



				__UpperCamelCase    :		int	=	field(metadata={'help': 'Which column contains the label'}      )
				__UpperCamelCase    :		str	=	field(default=__A							,						metadata={'help': 'The path of the training file'}      )
				__UpperCamelCase    :		Optional[str]	=	field(default=__A							,						metadata={'help': 'The path of the development file'}      )
				__UpperCamelCase    :		Optional[str]	=	field(default=__A							,						metadata={'help': 'The path of the test file'}      )
				__UpperCamelCase    :		int	=	field(
				    default=128							,						metadata={
				        'help': (
				            'The maximum total input sequence length after tokenization. Sequences longer '
				            'than this will be truncated, sequences shorter will be padded.'
				        )
				    }							,						)
				__UpperCamelCase    :		bool	=	field(
				    default=__A							,						metadata={'help': 'Overwrite the cached training and evaluation sets'}      )


@dataclass
class       a__				:







				"""simple docstring"""



				__UpperCamelCase    :		str	=	field(
				    metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}      )
				__UpperCamelCase    :		Optional[str]	=	field(
				    default=__A							,						metadata={'help': 'Pretrained config name or path if not the same as model_name'}      )
				__UpperCamelCase    :		Optional[str]	=	field(
				    default=__A							,						metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}      )
				__UpperCamelCase    :		bool	=	field(default=__A							,						metadata={'help': 'Set this flag to use fast tokenization.'}      )
				# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
				# or just modify its tokenizer_config.json.
				__UpperCamelCase    :		Optional[str]	=	field(
				    default=__A							,						metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}							,						)

def 							__magic_name__(						):
						# See all possible arguments in src/transformers/training_args.py
						# or by passing the --help flag to this script.
						# We now keep distinct sets of args, for a cleaner separation of concerns.
						__lowerCAmelCase							       =				HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments))
						__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase							       =				parser.parse_args_into_dataclasses()

						if (
						    os.path.exists(training_args.output_dir)
						    and os.listdir(training_args.output_dir)
						    and training_args.do_train
						    and not training_args.overwrite_output_dir
						):
												raise ValueError(
												    F"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
												    ''' --overwrite_output_dir to overcome.''')

						# Setup logging
						logging.basicConfig(
						    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''',				datefmt='''%m/%d/%Y %H:%M:%S''',				level=logging.INFO,				)
						logger.info(
						    F"""n_replicas: {training_args.n_replicas}, distributed training: {bool(training_args.n_replicas > 1)}, """
						    F"""16-bits training: {training_args.fpaa}""")
						logger.info(F"""Training/evaluation parameters {training_args}""")

						# Load pretrained model and tokenizer
						#
						# Distributed training:
						# The .from_pretrained methods guarantee that only one local process can concurrently
						# download model & vocab.

						__lowerCAmelCase							       =				AutoTokenizer.from_pretrained(
						    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,				cache_dir=model_args.cache_dir,				)

						__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase							       =				get_tfds(
						    train_file=data_args.train_file,				eval_file=data_args.dev_file,				test_file=data_args.test_file,				tokenizer=lowerCamelCase,				label_column_id=data_args.label_column_id,				max_seq_length=data_args.max_seq_length,				)

						__lowerCAmelCase							       =				AutoConfig.from_pretrained(
						    model_args.config_name if model_args.config_name else model_args.model_name_or_path,				num_labels=len(lowerCamelCase),				labelaid=lowerCamelCase,				idalabel={id: label for label, id in labelaid.items()},				finetuning_task='''text-classification''',				cache_dir=model_args.cache_dir,				)

						with training_args.strategy.scope():
												__lowerCAmelCase							       =				TFAutoModelForSequenceClassification.from_pretrained(
												    model_args.model_name_or_path,				from_pt=bool('''.bin''' in model_args.model_name_or_path),				config=lowerCamelCase,				cache_dir=model_args.cache_dir,				)

						def compute_metrics(lowerCamelCase) -> Dict:
												__lowerCAmelCase							       =				np.argmax(p.predictions,				axis=1)

												return {"acc": (preds == p.label_ids).mean()}

						# Initialize our Trainer
						__lowerCAmelCase							       =				TFTrainer(
						    model=lowerCamelCase,				args=lowerCamelCase,				train_dataset=lowerCamelCase,				eval_dataset=lowerCamelCase,				compute_metrics=lowerCamelCase,				)

						# Training
						if training_args.do_train:
												trainer.train()
												trainer.save_model()
												tokenizer.save_pretrained(training_args.output_dir)

						# Evaluation
						__lowerCAmelCase							       =				{}
						if training_args.do_eval:
												logger.info('''*** Evaluate ***''')
												__lowerCAmelCase							       =				trainer.evaluate()
												__lowerCAmelCase							       =				os.path.join(training_args.output_dir,				'''eval_results.txt''')

												with open(lowerCamelCase,				'''w''') as writer:
																		logger.info('''***** Eval results *****''')

																		for key, value in result.items():
																								logger.info(F"""  {key} = {value}""")
																								writer.write(F"""{key} = {value}\n""")

																		results.update(lowerCamelCase)

						return results


if __name__ == "__main__":
	main()





'''simple docstring'''

import argparse
import json
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    ConditionalDetrConfig,
    ConditionalDetrForObjectDetection,
    ConditionalDetrForSegmentation,
    ConditionalDetrImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
_UpperCAmelCase				: Optional[int]							    =				logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
_UpperCAmelCase				: Any							    =				[]
for i in range(6):
	# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
	rename_keys.append(
	    (f"""transformer.encoder.layers.{i}.self_attn.out_proj.weight""", f"""encoder.layers.{i}.self_attn.out_proj.weight""")
	)
	rename_keys.append(
	    (f"""transformer.encoder.layers.{i}.self_attn.out_proj.bias""", f"""encoder.layers.{i}.self_attn.out_proj.bias""")
	)
	rename_keys.append((f"""transformer.encoder.layers.{i}.linear1.weight""", f"""encoder.layers.{i}.fc1.weight"""))
	rename_keys.append((f"""transformer.encoder.layers.{i}.linear1.bias""", f"""encoder.layers.{i}.fc1.bias"""))
	rename_keys.append((f"""transformer.encoder.layers.{i}.linear2.weight""", f"""encoder.layers.{i}.fc2.weight"""))
	rename_keys.append((f"""transformer.encoder.layers.{i}.linear2.bias""", f"""encoder.layers.{i}.fc2.bias"""))
	rename_keys.append(
	    (f"""transformer.encoder.layers.{i}.norm1.weight""", f"""encoder.layers.{i}.self_attn_layer_norm.weight""")
	)
	rename_keys.append((f"""transformer.encoder.layers.{i}.norm1.bias""", f"""encoder.layers.{i}.self_attn_layer_norm.bias"""))
	rename_keys.append((f"""transformer.encoder.layers.{i}.norm2.weight""", f"""encoder.layers.{i}.final_layer_norm.weight"""))
	rename_keys.append((f"""transformer.encoder.layers.{i}.norm2.bias""", f"""encoder.layers.{i}.final_layer_norm.bias"""))
	# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
	rename_keys.append(
	    (f"""transformer.decoder.layers.{i}.self_attn.out_proj.weight""", f"""decoder.layers.{i}.self_attn.out_proj.weight""")
	)
	rename_keys.append(
	    (f"""transformer.decoder.layers.{i}.self_attn.out_proj.bias""", f"""decoder.layers.{i}.self_attn.out_proj.bias""")
	)
	rename_keys.append(
	    (
	        f"""transformer.decoder.layers.{i}.cross_attn.out_proj.weight""",
	        f"""decoder.layers.{i}.encoder_attn.out_proj.weight""",
	    )
	)
	rename_keys.append(
	    (
	        f"""transformer.decoder.layers.{i}.cross_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"""))

	# q, k, v projections in self/cross-attention in decoder for conditional DETR
	rename_keys.append(
	    (f"""transformer.decoder.layers.{i}.sa_qcontent_proj.weight""", f"""decoder.layers.{i}.sa_qcontent_proj.weight""")
	)
	rename_keys.append(
	    (f"""transformer.decoder.layers.{i}.sa_kcontent_proj.weight""", f"""decoder.layers.{i}.sa_kcontent_proj.weight""")
	)
	rename_keys.append(
	    (f"""transformer.decoder.layers.{i}.sa_qpos_proj.weight""", f"""decoder.layers.{i}.sa_qpos_proj.weight""")
	)
	rename_keys.append(
	    (f"""transformer.decoder.layers.{i}.sa_kpos_proj.weight""", f"""decoder.layers.{i}.sa_kpos_proj.weight""")
	)
	rename_keys.append((f"""transformer.decoder.layers.{i}.sa_v_proj.weight""", f"""decoder.layers.{i}.sa_v_proj.weight"""))
	rename_keys.append(
	    (f"""transformer.decoder.layers.{i}.ca_qcontent_proj.weight""", f"""decoder.layers.{i}.ca_qcontent_proj.weight""")
	)
	# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.weight", f"decoder.layers.{i}.ca_qpos_proj.weight"))
	rename_keys.append(
	    (f"""transformer.decoder.layers.{i}.ca_kcontent_proj.weight""", f"""decoder.layers.{i}.ca_kcontent_proj.weight""")
	)
	rename_keys.append(
	    (f"""transformer.decoder.layers.{i}.ca_kpos_proj.weight""", f"""decoder.layers.{i}.ca_kpos_proj.weight""")
	)
	rename_keys.append((f"""transformer.decoder.layers.{i}.ca_v_proj.weight""", f"""decoder.layers.{i}.ca_v_proj.weight"""))
	rename_keys.append(
	    (f"""transformer.decoder.layers.{i}.ca_qpos_sine_proj.weight""", f"""decoder.layers.{i}.ca_qpos_sine_proj.weight""")
	)

	rename_keys.append(
	    (f"""transformer.decoder.layers.{i}.sa_qcontent_proj.bias""", f"""decoder.layers.{i}.sa_qcontent_proj.bias""")
	)
	rename_keys.append(
	    (f"""transformer.decoder.layers.{i}.sa_kcontent_proj.bias""", f"""decoder.layers.{i}.sa_kcontent_proj.bias""")
	)
	rename_keys.append((f"""transformer.decoder.layers.{i}.sa_qpos_proj.bias""", f"""decoder.layers.{i}.sa_qpos_proj.bias"""))
	rename_keys.append((f"""transformer.decoder.layers.{i}.sa_kpos_proj.bias""", f"""decoder.layers.{i}.sa_kpos_proj.bias"""))
	rename_keys.append((f"""transformer.decoder.layers.{i}.sa_v_proj.bias""", f"""decoder.layers.{i}.sa_v_proj.bias"""))
	rename_keys.append(
	    (f"""transformer.decoder.layers.{i}.ca_qcontent_proj.bias""", f"""decoder.layers.{i}.ca_qcontent_proj.bias""")
	)
	# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.bias", f"decoder.layers.{i}.ca_qpos_proj.bias"))
	rename_keys.append(
	    (f"""transformer.decoder.layers.{i}.ca_kcontent_proj.bias""", f"""decoder.layers.{i}.ca_kcontent_proj.bias""")
	)
	rename_keys.append((f"""transformer.decoder.layers.{i}.ca_kpos_proj.bias""", f"""decoder.layers.{i}.ca_kpos_proj.bias"""))
	rename_keys.append((f"""transformer.decoder.layers.{i}.ca_v_proj.bias""", f"""decoder.layers.{i}.ca_v_proj.bias"""))
	rename_keys.append(
	    (f"""transformer.decoder.layers.{i}.ca_qpos_sine_proj.bias""", f"""decoder.layers.{i}.ca_qpos_sine_proj.bias""")
	)

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
# for conditional DETR, also convert reference point head and query scale MLP
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"""),
        ("""transformer.decoder.ref_point_head.layers.0.weight""", """decoder.ref_point_head.layers.0.weight"""),
        ("""transformer.decoder.ref_point_head.layers.0.bias""", """decoder.ref_point_head.layers.0.bias"""),
        ("""transformer.decoder.ref_point_head.layers.1.weight""", """decoder.ref_point_head.layers.1.weight"""),
        ("""transformer.decoder.ref_point_head.layers.1.bias""", """decoder.ref_point_head.layers.1.bias"""),
        ("""transformer.decoder.query_scale.layers.0.weight""", """decoder.query_scale.layers.0.weight"""),
        ("""transformer.decoder.query_scale.layers.0.bias""", """decoder.query_scale.layers.0.bias"""),
        ("""transformer.decoder.query_scale.layers.1.weight""", """decoder.query_scale.layers.1.weight"""),
        ("""transformer.decoder.query_scale.layers.1.bias""", """decoder.query_scale.layers.1.bias"""),
        ("""transformer.decoder.layers.0.ca_qpos_proj.weight""", """decoder.layers.0.ca_qpos_proj.weight"""),
        ("""transformer.decoder.layers.0.ca_qpos_proj.bias""", """decoder.layers.0.ca_qpos_proj.bias"""),
    ]
)

def 							__magic_name__(						lowerCamelCase,				lowerCamelCase,				lowerCamelCase):
						__lowerCAmelCase							       =				state_dict.pop(lowerCamelCase)
						__lowerCAmelCase							       =				val

def 							__magic_name__(						lowerCamelCase):
						__lowerCAmelCase							       =				OrderedDict()
						for key, value in state_dict.items():
												if "backbone.0.body" in key:
																		__lowerCAmelCase							       =				key.replace('''backbone.0.body''',				'''backbone.conv_encoder.model''')
																		__lowerCAmelCase							       =				value
												else:
																		__lowerCAmelCase							       =				value

						return new_state_dict

def 							__magic_name__(						lowerCamelCase,				lowerCamelCase=False):
						__lowerCAmelCase							       =				''''''
						if is_panoptic:
												__lowerCAmelCase							       =				'''conditional_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)
												__lowerCAmelCase							       =				state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight""")
												__lowerCAmelCase							       =				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
												__lowerCAmelCase							       =				in_proj_weight[:2_5_6, :]
												__lowerCAmelCase							       =				in_proj_bias[:2_5_6]
												__lowerCAmelCase							       =				in_proj_weight[2_5_6:5_1_2, :]
												__lowerCAmelCase							       =				in_proj_bias[2_5_6:5_1_2]
												__lowerCAmelCase							       =				in_proj_weight[-2_5_6:, :]
												__lowerCAmelCase							       =				in_proj_bias[-2_5_6:]

def 							__magic_name__(						):
						__lowerCAmelCase							       =				'''http://images.cocodataset.org/val2017/000000039769.jpg'''
						__lowerCAmelCase							       =				Image.open(requests.get(lowerCamelCase,				stream=lowerCamelCase).raw)

						return im

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

						# load image processor
						__lowerCAmelCase							       =				'''coco_panoptic''' if is_panoptic else '''coco_detection'''
						__lowerCAmelCase							       =				ConditionalDetrImageProcessor(format=lowerCamelCase)

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

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

						# load original model from torch hub
						__lowerCAmelCase							       =				torch.hub.load('''DeppMeng/ConditionalDETR''',				lowerCamelCase,				pretrained=lowerCamelCase).eval()
						__lowerCAmelCase							       =				conditional_detr.state_dict()
						# rename keys
						for src, dest in rename_keys:
												if is_panoptic:
																		__lowerCAmelCase							       =				'''conditional_detr.''' + src
												rename_key(lowerCamelCase,				lowerCamelCase,				lowerCamelCase)
						__lowerCAmelCase							       =				rename_backbone_keys(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
						__lowerCAmelCase							       =				'''conditional_detr.model.''' if is_panoptic else '''model.'''
						for key in state_dict.copy().keys():
												if is_panoptic:
																		if (
																		    key.startswith('''conditional_detr''')
																		    and not key.startswith('''class_labels_classifier''')
																		    and not key.startswith('''bbox_predictor''')
																		):
																								__lowerCAmelCase							       =				state_dict.pop(lowerCamelCase)
																								__lowerCAmelCase							       =				val
																		elif "class_labels_classifier" in key or "bbox_predictor" in key:
																								__lowerCAmelCase							       =				state_dict.pop(lowerCamelCase)
																								__lowerCAmelCase							       =				val
																		elif key.startswith('''bbox_attention''') or key.startswith('''mask_head'''):
																								continue
																		else:
																								__lowerCAmelCase							       =				state_dict.pop(lowerCamelCase)
																								__lowerCAmelCase							       =				val
												else:
																		if not key.startswith('''class_labels_classifier''') and not key.startswith('''bbox_predictor'''):
																								__lowerCAmelCase							       =				state_dict.pop(lowerCamelCase)
																								__lowerCAmelCase							       =				val
    # finally, create HuggingFace model and load state dict
						__lowerCAmelCase							       =				ConditionalDetrForSegmentation(lowerCamelCase) if is_panoptic else ConditionalDetrForObjectDetection(lowerCamelCase)
						model.load_state_dict(lowerCamelCase)
						model.eval()
						model.push_to_hub(repo_id=lowerCamelCase,				organization='''DepuMeng''',				commit_message='''Add model''')
						# verify our conversion
						__lowerCAmelCase							       =				conditional_detr(lowerCamelCase)
						__lowerCAmelCase							       =				model(lowerCamelCase)
						assert torch.allclose(outputs.logits,				original_outputs['''pred_logits'''],				atol=1E-4)
						assert torch.allclose(outputs.pred_boxes,				original_outputs['''pred_boxes'''],				atol=1E-4)
						if is_panoptic:
												assert torch.allclose(outputs.pred_masks,				original_outputs['''pred_masks'''],				atol=1E-4)

						# Save model and image processor
						logger.info(F"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}...""")
						Path(lowerCamelCase).mkdir(exist_ok=lowerCamelCase)
						model.save_pretrained(lowerCamelCase)
						image_processor.save_pretrained(lowerCamelCase)


if __name__ == "__main__":
	_UpperCAmelCase				: str							    =				argparse.ArgumentParser()

	parser.add_argument(
	    """--model_name""",
	    default="""conditional_detr_resnet50""",
	    type=str,
	    help="""Name of the CONDITIONAL_DETR model you'd like to convert.""",
	)
	parser.add_argument(
	    """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
	)
	_UpperCAmelCase				: Any							    =				parser.parse_args()
	convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)

