


"""simple docstring"""


import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional

import datasets
import evaluate
import numpy as np
from datasets import load_dataset

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    default_data_collator,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version


# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.31.0")

require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/text-classification/requirements.txt")

a_       							=    logging.getLogger(__name__)
@dataclass
class snake_case      :
		__UpperCamelCase           =			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           =			field(
		    default=_UpperCamelCase     ,				metadata={'help': 'Overwrite the cached preprocessed datasets or not.'})
		__UpperCamelCase           =			field(
		    default=_UpperCamelCase     ,				metadata={
		        'help': (
		            'Whether to pad all samples to `max_seq_length`. '
		            'If False, will pad the samples dynamically when batching to the maximum length in the batch.'
		        )
		    }     ,				)
		__UpperCamelCase           =			field(
		    default=_UpperCamelCase     ,				metadata={
		        'help': (
		            'For debugging purposes or quicker training, truncate the number of training examples to this '
		            'value if set.'
		        )
		    }     ,				)
		__UpperCamelCase           =			field(
		    default=_UpperCamelCase     ,				metadata={
		        'help': (
		            'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
		            'value if set.'
		        )
		    }     ,				)
		__UpperCamelCase           =			field(
		    default=_UpperCamelCase     ,				metadata={
		        'help': (
		            'For debugging purposes or quicker training, truncate the number of prediction examples to this '
		            'value if set.'
		        )
		    }     ,				)





@dataclass
class snake_case      :
		__UpperCamelCase           =			field(
		    default=_UpperCamelCase     ,				metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'})
		__UpperCamelCase           =			field(
		    default=_UpperCamelCase     ,				metadata={'help': 'Evaluation language. Also train language if `train_language` is set to None.'})
		__UpperCamelCase           =			field(
		    default=_UpperCamelCase     ,				metadata={'help': 'Train language if it is different from the evaluation language.'})
		__UpperCamelCase           =			field(
		    default=_UpperCamelCase     ,				metadata={'help': 'Pretrained config name or path if not the same as model_name'})
		__UpperCamelCase           =			field(
		    default=_UpperCamelCase     ,				metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'})
		__UpperCamelCase           =			field(
		    default=_UpperCamelCase     ,				metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}     ,				)
		__UpperCamelCase           =			field(
		    default=_UpperCamelCase     ,				metadata={'help': 'arg to indicate if tokenizer should do lower case in AutoTokenizer.from_pretrained()'}     ,				)
		__UpperCamelCase           =			field(
		    default=_UpperCamelCase     ,				metadata={'help': 'Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'}     ,				)
		__UpperCamelCase           =			field(
		    default='main'     ,				metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'}     ,				)
		__UpperCamelCase           =			field(
		    default=_UpperCamelCase     ,				metadata={
		        'help': (
		            'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
		            'with private models).'
		        )
		    }     ,				)
		__UpperCamelCase           =			field(
		    default=_UpperCamelCase     ,				metadata={'help': 'Will enable to load a pretrained model whose head dimensions are different.'}     ,				)






def      a__ (				)	->	Optional[int]:
				# 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.

				_A					  =			HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)   )
				_A  ,		_A  ,		_A					  =			parser.parse_args_into_dataclasses()

				# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
				# information sent is the one passed as arguments along with your Python/PyTorch versions.
				send_example_telemetry("run_xnli"     ,			__lowercase   )

				# Setup logging
				logging.basicConfig(
				    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"     ,			datefmt="%m/%d/%Y %H:%M:%S"     ,			handlers=[logging.StreamHandler(sys.stdout   )]     ,			)

				if training_args.should_log:
								# The default of training_args.log_level is passive, so we set log level at info here to have that default.
								transformers.utils.logging.set_verbosity_info()

				_A					  =			training_args.get_process_log_level()
				logger.setLevel(__lowercase   )
				datasets.utils.logging.set_verbosity(__lowercase   )
				transformers.utils.logging.set_verbosity(__lowercase   )
				transformers.utils.logging.enable_default_handler()
				transformers.utils.logging.enable_explicit_format()

				# Log on each process the small summary:
				logger.warning(
				    f"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
				    + f"""distributed training: {bool(training_args.local_rank != -1   )}, 16-bits training: {training_args.fpaa}"""   )
				logger.info(f"""Training/evaluation parameters {training_args}"""   )

				# Detecting last checkpoint.
				_A					  =			None
				if os.path.isdir(training_args.output_dir   ) and training_args.do_train and not training_args.overwrite_output_dir:
								_A					  =			get_last_checkpoint(training_args.output_dir   )
								if last_checkpoint is None and len(os.listdir(training_args.output_dir   )   ) > 0:
												raise ValueError(
												    f"""Output directory ({training_args.output_dir}) already exists and is not empty. """
												    "Use --overwrite_output_dir to overcome."   )
								elif last_checkpoint is not None:
												logger.info(
												    f"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
												    "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."   )

    # Set seed before initializing model.
				set_seed(training_args.seed   )

				# In distributed training, the load_dataset function guarantees that only one local process can concurrently
				# download the dataset.
				# Downloading and loading xnli dataset from the hub.
				if training_args.do_train:
								if model_args.train_language is None:
												_A					  =			load_dataset(
												    "xnli"     ,			model_args.language     ,			split="train"     ,			cache_dir=model_args.cache_dir     ,			use_auth_token=True if model_args.use_auth_token else None     ,			)
								else:
												_A					  =			load_dataset(
												    "xnli"     ,			model_args.train_language     ,			split="train"     ,			cache_dir=model_args.cache_dir     ,			use_auth_token=True if model_args.use_auth_token else None     ,			)
								_A					  =			train_dataset.features["label"].names

				if training_args.do_eval:
								_A					  =			load_dataset(
								    "xnli"     ,			model_args.language     ,			split="validation"     ,			cache_dir=model_args.cache_dir     ,			use_auth_token=True if model_args.use_auth_token else None     ,			)
								_A					  =			eval_dataset.features["label"].names

				if training_args.do_predict:
								_A					  =			load_dataset(
								    "xnli"     ,			model_args.language     ,			split="test"     ,			cache_dir=model_args.cache_dir     ,			use_auth_token=True if model_args.use_auth_token else None     ,			)
								_A					  =			predict_dataset.features["label"].names

				# Labels
				_A					  =			len(__lowercase   )

				# Load pretrained model and tokenizer
				# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
				# download model & vocab.
				_A					  =			AutoConfig.from_pretrained(
				    model_args.config_name if model_args.config_name else model_args.model_name_or_path     ,			num_labels=__lowercase     ,			idalabel={str(__lowercase   ): label for i, label in enumerate(__lowercase   )}     ,			labelaid={label: i for i, label in enumerate(__lowercase   )}     ,			finetuning_task="xnli"     ,			cache_dir=model_args.cache_dir     ,			revision=model_args.model_revision     ,			use_auth_token=True if model_args.use_auth_token else None     ,			)
				_A					  =			AutoTokenizer.from_pretrained(
				    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path     ,			do_lower_case=model_args.do_lower_case     ,			cache_dir=model_args.cache_dir     ,			use_fast=model_args.use_fast_tokenizer     ,			revision=model_args.model_revision     ,			use_auth_token=True if model_args.use_auth_token else None     ,			)
				_A					  =			AutoModelForSequenceClassification.from_pretrained(
				    model_args.model_name_or_path     ,			from_tf=bool(".ckpt" in model_args.model_name_or_path   )     ,			config=__lowercase     ,			cache_dir=model_args.cache_dir     ,			revision=model_args.model_revision     ,			use_auth_token=True if model_args.use_auth_token else None     ,			ignore_mismatched_sizes=model_args.ignore_mismatched_sizes     ,			)

				# Preprocessing the datasets
				# Padding strategy
				if data_args.pad_to_max_length:
								_A					  =			"max_length"
				else:
								# We will pad later, dynamically at batch creation, to the max sequence length in each batch
								_A					  =			False

				def preprocess_function(__lowercase   ):
								# Tokenize the texts
								return tokenizer(
								    examples["premise"]     ,			examples["hypothesis"]     ,			padding=__lowercase     ,			max_length=data_args.max_seq_length     ,			truncation=__lowercase     ,			)

				if training_args.do_train:
								if data_args.max_train_samples is not None:
												_A					  =			min(len(__lowercase   )     ,			data_args.max_train_samples   )
												_A					  =			train_dataset.select(range(__lowercase   )   )
								with training_args.main_process_first(desc="train dataset map pre-processing"   ):
												_A					  =			train_dataset.map(
												    __lowercase     ,			batched=__lowercase     ,			load_from_cache_file=not data_args.overwrite_cache     ,			desc="Running tokenizer on train dataset"     ,			)
								# Log a few random samples from the training set:
								for index in random.sample(range(len(__lowercase   )   )     ,			3   ):
												logger.info(f"""Sample {index} of the training set: {train_dataset[index]}."""   )

				if training_args.do_eval:
								if data_args.max_eval_samples is not None:
												_A					  =			min(len(__lowercase   )     ,			data_args.max_eval_samples   )
												_A					  =			eval_dataset.select(range(__lowercase   )   )
								with training_args.main_process_first(desc="validation dataset map pre-processing"   ):
												_A					  =			eval_dataset.map(
												    __lowercase     ,			batched=__lowercase     ,			load_from_cache_file=not data_args.overwrite_cache     ,			desc="Running tokenizer on validation dataset"     ,			)

				if training_args.do_predict:
								if data_args.max_predict_samples is not None:
												_A					  =			min(len(__lowercase   )     ,			data_args.max_predict_samples   )
												_A					  =			predict_dataset.select(range(__lowercase   )   )
								with training_args.main_process_first(desc="prediction dataset map pre-processing"   ):
												_A					  =			predict_dataset.map(
												    __lowercase     ,			batched=__lowercase     ,			load_from_cache_file=not data_args.overwrite_cache     ,			desc="Running tokenizer on prediction dataset"     ,			)

    # Get the metric function
				_A					  =			evaluate.load("xnli"   )

				# You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
				# predictions and label_ids field) and has to return a dictionary string to float.
				def compute_metrics(__lowercase   ):
								_A					  =			p.predictions[0] if isinstance(p.predictions     ,			__lowercase   ) else p.predictions
								_A					  =			np.argmax(__lowercase     ,			axis=1   )
								return metric.compute(predictions=__lowercase     ,			references=p.label_ids   )

				# Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
				if data_args.pad_to_max_length:
								_A					  =			default_data_collator
				elif training_args.fpaa:
								_A					  =			DataCollatorWithPadding(__lowercase     ,			pad_to_multiple_of=8   )
				else:
								_A					  =			None

				# Initialize our Trainer
				_A					  =			Trainer(
				    model=__lowercase     ,			args=__lowercase     ,			train_dataset=train_dataset if training_args.do_train else None     ,			eval_dataset=eval_dataset if training_args.do_eval else None     ,			compute_metrics=__lowercase     ,			tokenizer=__lowercase     ,			data_collator=__lowercase     ,			)

				# Training
				if training_args.do_train:
								_A					  =			None
								if training_args.resume_from_checkpoint is not None:
												_A					  =			training_args.resume_from_checkpoint
								elif last_checkpoint is not None:
												_A					  =			last_checkpoint
								_A					  =			trainer.train(resume_from_checkpoint=__lowercase   )
								_A					  =			train_result.metrics
								_A					  =			(
								    data_args.max_train_samples if data_args.max_train_samples is not None else len(__lowercase   )
								)
								_A					  =			min(__lowercase     ,			len(__lowercase   )   )

								trainer.save_model()  # Saves the tokenizer too for easy upload

								trainer.log_metrics("train"     ,			__lowercase   )
								trainer.save_metrics("train"     ,			__lowercase   )
								trainer.save_state()

				# Evaluation
				if training_args.do_eval:
								logger.info("*** Evaluate ***"   )
								_A					  =			trainer.evaluate(eval_dataset=__lowercase   )

								_A					  =			data_args.max_eval_samples if data_args.max_eval_samples is not None else len(__lowercase   )
								_A					  =			min(__lowercase     ,			len(__lowercase   )   )

								trainer.log_metrics("eval"     ,			__lowercase   )
								trainer.save_metrics("eval"     ,			__lowercase   )

				# Prediction
				if training_args.do_predict:
								logger.info("*** Predict ***"   )
								_A  ,		_A  ,		_A					  =			trainer.predict(__lowercase     ,			metric_key_prefix="predict"   )

								_A					  =			(
								    data_args.max_predict_samples if data_args.max_predict_samples is not None else len(__lowercase   )
								)
								_A					  =			min(__lowercase     ,			len(__lowercase   )   )

								trainer.log_metrics("predict"     ,			__lowercase   )
								trainer.save_metrics("predict"     ,			__lowercase   )

								_A					  =			np.argmax(__lowercase     ,			axis=1   )
								_A					  =			os.path.join(training_args.output_dir     ,			"predictions.txt"   )
								if trainer.is_world_process_zero():
												with open(__lowercase     ,			"w"   ) as writer:
																writer.write("index\tprediction\n"   )
																for index, item in enumerate(__lowercase   ):
																				_A					  =			label_list[item]
																				writer.write(f"""{index}\t{item}\n"""   )


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()
a_       							=    logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
a_       							=    []
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      a__ (				__lowercase     ,			__lowercase     ,			__lowercase   )	->	List[str]:
				_A					  =			state_dict.pop(__lowercase   )
				_A					  =			val






def      a__ (				__lowercase   )	->	List[str]:
				_A					  =			OrderedDict()
				for key, value in state_dict.items():
								if "backbone.0.body" in key:
												_A					  =			key.replace("backbone.0.body"     ,			"backbone.conv_encoder.model"   )
												_A					  =			value
								else:
												_A					  =			value

				return new_state_dict






def      a__ (				__lowercase     ,			__lowercase=False   )	->	Any:
				_A					  =			""
				if is_panoptic:
								_A					  =			"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)
								_A					  =			state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""   )
								_A					  =			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
								_A					  =			in_proj_weight[:256, :]
								_A					  =			in_proj_bias[:256]
								_A					  =			in_proj_weight[256:512, :]
								_A					  =			in_proj_bias[256:512]
								_A					  =			in_proj_weight[-256:, :]
								_A					  =			in_proj_bias[-256:]






def      a__ (				)	->	int:
				_A					  =			"http://images.cocodataset.org/val2017/000000039769.jpg"
				_A					  =			Image.open(requests.get(__lowercase     ,			stream=__lowercase   ).raw   )

				return im






@torch.no_grad()
def      a__ (				__lowercase     ,			__lowercase   )	->	Any:
				_A					  =			ConditionalDetrConfig()
				# set backbone and dilation attributes
				if "resnet101" in model_name:
								_A					  =			"resnet101"
				if "dc5" in model_name:
								_A					  =			True
				_A					  =			"panoptic" in model_name
				if is_panoptic:
								_A					  =			250
				else:
								_A					  =			91
								_A					  =			"huggingface/label-files"
								_A					  =			"coco-detection-id2label.json"
								_A					  =			json.load(open(hf_hub_download(__lowercase     ,			__lowercase     ,			repo_type="dataset"   )     ,			"r"   )   )
								_A					  =			{int(__lowercase   ): v for k, v in idalabel.items()}
								_A					  =			idalabel
								_A					  =			{v: k for k, v in idalabel.items()}

				# load image processor
				_A					  =			"coco_panoptic" if is_panoptic else "coco_detection"
				_A					  =			ConditionalDetrImageProcessor(format=__lowercase   )

				# prepare image
				_A					  =			prepare_img()
				_A					  =			image_processor(images=__lowercase     ,			return_tensors="pt"   )
				_A					  =			encoding["pixel_values"]

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

				# load original model from torch hub
				_A					  =			torch.hub.load("DeppMeng/ConditionalDETR"     ,			__lowercase     ,			pretrained=__lowercase   ).eval()
				_A					  =			conditional_detr.state_dict()
				# rename keys
				for src, dest in rename_keys:
								if is_panoptic:
												_A					  =			"conditional_detr." + src
								rename_key(__lowercase     ,			__lowercase     ,			__lowercase   )
				_A					  =			rename_backbone_keys(__lowercase   )
				# query, key and value matrices need special treatment
				read_in_q_k_v(__lowercase     ,			is_panoptic=__lowercase   )
				# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
				_A					  =			"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"   )
												):
																_A					  =			state_dict.pop(__lowercase   )
																_A					  =			val
												elif "class_labels_classifier" in key or "bbox_predictor" in key:
																_A					  =			state_dict.pop(__lowercase   )
																_A					  =			val
												elif key.startswith("bbox_attention"   ) or key.startswith("mask_head"   ):
																continue
												else:
																_A					  =			state_dict.pop(__lowercase   )
																_A					  =			val
								else:
												if not key.startswith("class_labels_classifier"   ) and not key.startswith("bbox_predictor"   ):
																_A					  =			state_dict.pop(__lowercase   )
																_A					  =			val
    # finally, create HuggingFace model and load state dict
				_A					  =			ConditionalDetrForSegmentation(__lowercase   ) if is_panoptic else ConditionalDetrForObjectDetection(__lowercase   )
				model.load_state_dict(__lowercase   )
				model.eval()
				model.push_to_hub(repo_id=__lowercase     ,			organization="DepuMeng"     ,			commit_message="Add model"   )
				# verify our conversion
				_A					  =			conditional_detr(__lowercase   )
				_A					  =			model(__lowercase   )
				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(__lowercase   ).mkdir(exist_ok=__lowercase   )
				model.save_pretrained(__lowercase   )
				image_processor.save_pretrained(__lowercase   )


if __name__ == "__main__":
		a_       							=    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."
		)
		a_       							=    parser.parse_args()
		convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)