
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 DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
A  :			Any     				=				logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
A  :			str     				=				[]
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}.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'),
    ]
)


def 					__lowerCAmelCase    (    a__		,					a__		,					a__						) ->						List[str]:
				__a    							=					state_dict.pop(a__						)
				__a    							=					val


def 					__lowerCAmelCase    (    a__						) ->						List[Any]:
				__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 					__lowerCAmelCase    (    a__		,					a__=False						) ->						str:
				__a    							=					''''''
				if is_panoptic:
								__a    							=					'''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:]
				# 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
								__a    							=					state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""						)
								__a    							=					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
								__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:]
								# read in weights + bias of input projection layer of cross-attention
								__a    							=					state_dict.pop(
								    F"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""						)
								__a    							=					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
								__a    							=					in_proj_weight_cross_attn[:256, :]
								__a    							=					in_proj_bias_cross_attn[:256]
								__a    							=					in_proj_weight_cross_attn[256:512, :]
								__a    							=					in_proj_bias_cross_attn[256:512]
								__a    							=					in_proj_weight_cross_attn[-256:, :]
								__a    							=					in_proj_bias_cross_attn[-256:]


def 					__lowerCAmelCase    (    ) ->						Union[str, Any]:
				__a    							=					'''http://images.cocodataset.org/val2017/000000039769.jpg'''
				__a    							=					Image.open(requests.get(a__		,					stream=a__						).raw						)

				return im


@torch.no_grad()
def 					__lowerCAmelCase    (    a__		,					a__						) ->						Tuple:
				__a    							=					DetrConfig()
				# 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(a__		,					a__		,					repo_type='''dataset'''						)		,					'''r'''						)						)
								__a    							=					{int(a__						): v for k, v in idalabel.items()}
								__a    							=					idalabel
								__a    							=					{v: k for k, v in idalabel.items()}

				# load image processor
				__a    							=					'''coco_panoptic''' if is_panoptic else '''coco_detection'''
				__a    							=					DetrImageProcessor(format=a__						)

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

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

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


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

					parser.add_argument(
					    '--model_name', default='detr_resnet50', type=str, 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.'
					)
					A  :			int     				=				parser.parse_args()
					convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)

import importlib
import importlib.metadata
import os
import warnings
from distutils.util import strtobool
from functools import lru_cache

import torch
from packaging import version
from packaging.version import parse

from .environment import parse_flag_from_env
from .versions import compare_versions, is_torch_version


try:
					import torch_xla.core.xla_model as xm  # noqa: F401

					A  :			List[str]     				=				True
except ImportError:
					A  :			List[Any]     				=				False


# Cache this result has it's a C FFI call which can be pretty time-consuming
A  :			str     				=				torch.distributed.is_available()


def 					__lowerCAmelCase    (    a__						) ->						List[str]:
				# Check we're not importing a "pkg_name" directory somewhere but the actual library by trying to grab the version
				__a    							=					importlib.util.find_spec(a__						) is not None
				if package_exists:
								try:
												__a    							=					importlib.metadata.metadata(a__						)
												return True
								except importlib.metadata.PackageNotFoundError:
												return False


def 					__lowerCAmelCase    (    ) ->						bool:
				return _torch_distributed_available


def 					__lowerCAmelCase    (    ) ->						Tuple:
				try:
								pass
				except ImportError:
								print(
								    '''Intel(R) oneCCL Bindings for PyTorch* is required to run DDP on Intel(R) GPUs, but it is not'''
								    ''' detected. If you see \"ValueError: Invalid backend: \'ccl\'\" error, please install Intel(R) oneCCL'''
								    ''' Bindings for PyTorch*.'''						)
				return (
				    importlib.util.find_spec('''torch_ccl'''						) is not None
				    or importlib.util.find_spec('''oneccl_bindings_for_pytorch'''						) is not None
				)


def 					__lowerCAmelCase    (    ) ->						Union[str, Any]:
				return importlib.metadata.version('''oneccl_bind_pt'''						)


def 					__lowerCAmelCase    (    ) ->						Dict:
				return _is_package_available('''transformer_engine'''						)


@lru_cache
def 					__lowerCAmelCase    (    a__=True						) ->						Union[str, Any]:
				# Due to bugs on the amp series GPUs, we disable torch-xla on them
				if torch.cuda.is_available():
								return False
				if _tpu_available and check_device:
								try:
												# Will raise a RuntimeError if no XLA configuration is found
												__a    							=					xm.xla_device()
												return True
								except RuntimeError:
												return False
				return _tpu_available


def 					__lowerCAmelCase    (    ) ->						Any:
				return _is_package_available('''deepspeed'''						)


def 					__lowerCAmelCase    (    a__=False						) ->						Optional[Any]:
				if is_tpu_available():
								return not ignore_tpu
				if torch.cuda.is_available():
								return torch.cuda.is_bfaa_supported()
				if is_npu_available():
								return False
				return True


def 					__lowerCAmelCase    (    ) ->						List[str]:
				__a    							=					_is_package_available('''bitsandbytes'''						)
				if package_exists:
								__a    							=					version.parse(importlib.metadata.version('''bitsandbytes'''						)						)
								return compare_versions(a__		,					'''>='''		,					'''0.39.0'''						)
				return False


def 					__lowerCAmelCase    (    ) ->						Any:
				__a    							=					_is_package_available('''bitsandbytes'''						)
				if package_exists:
								__a    							=					version.parse(importlib.metadata.version('''bitsandbytes'''						)						)
								return compare_versions(a__		,					'''>='''		,					'''0.37.2'''						)
				return False


def 					__lowerCAmelCase    (    ) ->						int:
				return _is_package_available('''bitsandbytes'''						)


def 					__lowerCAmelCase    (    ) ->						str:
				if strtobool(os.environ.get('''ACCELERATE_USE_MEGATRON_LM'''		,					'''False'''						)						) == 1:
								__a    							=					importlib.util.find_spec('''megatron'''						) is not None
								if package_exists:
												try:
																__a    							=					parse(importlib.metadata.version('''megatron-lm'''						)						)
																return compare_versions(a__		,					'''>='''		,					'''2.2.0'''						)
												except Exception as e:
																warnings.warn(F"""Parse Megatron version failed. Exception:{e}"""						)
																return False


def 					__lowerCAmelCase    (    ) ->						List[str]:
				return _is_package_available('''safetensors'''						)


def 					__lowerCAmelCase    (    ) ->						Optional[Any]:
				return _is_package_available('''transformers'''						)


def 					__lowerCAmelCase    (    ) ->						int:
				return _is_package_available('''datasets'''						)


def 					__lowerCAmelCase    (    ) ->						Optional[int]:
				return _is_package_available('''aim'''						)


def 					__lowerCAmelCase    (    ) ->						List[Any]:
				return _is_package_available('''tensorboard'''						) or _is_package_available('''tensorboardX'''						)


def 					__lowerCAmelCase    (    ) ->						List[Any]:
				return _is_package_available('''wandb'''						)


def 					__lowerCAmelCase    (    ) ->						Optional[int]:
				return _is_package_available('''comet_ml'''						)


def 					__lowerCAmelCase    (    ) ->						str:
				return _is_package_available('''boto3'''						)


def 					__lowerCAmelCase    (    ) ->						Optional[Any]:
				if _is_package_available('''rich'''						):
								if "ACCELERATE_DISABLE_RICH" in os.environ:
												warnings.warn(
												    '''`ACCELERATE_DISABLE_RICH` is deprecated and will be removed in v0.22.0 and deactivated by default. Please use `ACCELERATE_ENABLE_RICH` if you wish to use `rich`.'''						)
												return not parse_flag_from_env('''ACCELERATE_DISABLE_RICH'''		,					a__						)
								return parse_flag_from_env('''ACCELERATE_ENABLE_RICH'''		,					a__						)
				return False


def 					__lowerCAmelCase    (    ) ->						Any:
				return _is_package_available('''sagemaker'''						)


def 					__lowerCAmelCase    (    ) ->						int:
				return _is_package_available('''tqdm'''						)


def 					__lowerCAmelCase    (    ) ->						Union[str, Any]:
				return _is_package_available('''mlflow'''						)


def 					__lowerCAmelCase    (    ) ->						Optional[Any]:
				return is_torch_version('''>='''		,					'''1.12'''						) and torch.backends.mps.is_available() and torch.backends.mps.is_built()


def 					__lowerCAmelCase    (    ) ->						str:
				def get_major_and_minor_from_version(a__						):
								return str(version.parse(a__						).major						) + "." + str(version.parse(a__						).minor						)

				__a    							=					importlib.metadata.version('''torch'''						)
				if importlib.util.find_spec('''intel_extension_for_pytorch'''						) is None:
								return False
				__a    							=					'''N/A'''
				try:
								__a    							=					importlib.metadata.version('''intel_extension_for_pytorch'''						)
				except importlib.metadata.PackageNotFoundError:
								return False
				__a    							=					get_major_and_minor_from_version(_torch_version						)
				__a    							=					get_major_and_minor_from_version(_ipex_version						)
				if torch_major_and_minor != ipex_major_and_minor:
								warnings.warn(
								    F"""Intel Extension for PyTorch {ipex_major_and_minor} needs to work with PyTorch {ipex_major_and_minor}.*,"""
								    F""" but PyTorch {_torch_version} is found. Please switch to the matching version and run again."""						)
								return False
				return True


@lru_cache
def 					__lowerCAmelCase    (    a__=False						) ->						List[Any]:
				if importlib.util.find_spec('''torch'''						) is None or importlib.util.find_spec('''torch_npu'''						) is None:
								return False

				import torch
				import torch_npu  # noqa: F401

				if check_device:
								try:
												# Will raise a RuntimeError if no NPU is found
												__a    							=					torch.npu.device_count()
												return torch.npu.is_available()
								except RuntimeError:
												return False
				return hasattr(a__		,					'''npu'''						) and torch.npu.is_available()






@lru_cache
def 					__lowerCAmelCase    (    a__=False						) ->						Union[str, Any]:
				if not parse_flag_from_env('''ACCELERATE_USE_XPU'''		,					default=a__						):
								return False
				if is_ipex_available():
								import torch

								if is_torch_version('''<='''		,					'''1.12'''						):
												return False
				else:
								return False

				import intel_extension_for_pytorch  # noqa: F401

				if check_device:
								try:
												# Will raise a RuntimeError if no XPU  is found
												__a    							=					torch.xpu.device_count()
												return torch.xpu.is_available()
								except RuntimeError:
												return False
				return hasattr(a__		,					'''xpu'''						) and torch.xpu.is_available()