


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()
a : Tuple		=				logging.get_logger(__name__)


def 		lowercase_  (     _UpperCamelCase  ):


			'''simple docstring'''







			if "resnet-50" in model_name:
						__lowercase 					=   ResNetConfig.from_pretrained('''microsoft/resnet-50'''  )
			elif "resnet-101" in model_name:
						__lowercase 					=   ResNetConfig.from_pretrained('''microsoft/resnet-101'''  )
			else:
						raise ValueError('''Model name should include either resnet50 or resnet101'''  )

			__lowercase 					=   DetrConfig(use_timm_backbone=_UpperCamelCase       ,			backbone_config=_UpperCamelCase  )

			# set label attributes
			__lowercase 					=   '''panoptic''' in model_name
			if is_panoptic:
						__lowercase 					=   2_50
			else:
						__lowercase 					=   91
						__lowercase 					=   '''huggingface/label-files'''
						__lowercase 					=   '''coco-detection-id2label.json'''
						__lowercase 					=   json.load(open(hf_hub_download(_UpperCamelCase       ,			_UpperCamelCase       ,			repo_type='''dataset'''  )       ,			'''r'''  )  )
						__lowercase 					=   {int(_UpperCamelCase  ): v for k, v in idalabel.items()}
						__lowercase 					=   idalabel
						__lowercase 					=   {v: k for k, v in idalabel.items()}

			return config, is_panoptic


def 		lowercase_  (     _UpperCamelCase  ):


			'''simple docstring'''







			__lowercase 					=   []

			# 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_  (     _UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase  ):


			'''simple docstring'''







			__lowercase 					=   state_dict.pop(_UpperCamelCase  )
			__lowercase 					=   val


def 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase=False  ):


			'''simple docstring'''







			__lowercase 					=   ''''''
			if is_panoptic:
						__lowercase 					=   '''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)
						__lowercase 					=   state_dict.pop(F'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'  )
						__lowercase 					=   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
						__lowercase 					=   in_proj_weight[:2_56, :]
						__lowercase 					=   in_proj_bias[:2_56]
						__lowercase 					=   in_proj_weight[2_56:5_12, :]
						__lowercase 					=   in_proj_bias[2_56:5_12]
						__lowercase 					=   in_proj_weight[-2_56:, :]
						__lowercase 					=   in_proj_bias[-2_56:]
			# 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
						__lowercase 					=   state_dict.pop(F'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'  )
						__lowercase 					=   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
						__lowercase 					=   in_proj_weight[:2_56, :]
						__lowercase 					=   in_proj_bias[:2_56]
						__lowercase 					=   in_proj_weight[2_56:5_12, :]
						__lowercase 					=   in_proj_bias[2_56:5_12]
						__lowercase 					=   in_proj_weight[-2_56:, :]
						__lowercase 					=   in_proj_bias[-2_56:]
						# read in weights + bias of input projection layer of cross-attention
						__lowercase 					=   state_dict.pop(
						    F'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'  )
						__lowercase 					=   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
						__lowercase 					=   in_proj_weight_cross_attn[:2_56, :]
						__lowercase 					=   in_proj_bias_cross_attn[:2_56]
						__lowercase 					=   in_proj_weight_cross_attn[2_56:5_12, :]
						__lowercase 					=   in_proj_bias_cross_attn[2_56:5_12]
						__lowercase 					=   in_proj_weight_cross_attn[-2_56:, :]
						__lowercase 					=   in_proj_bias_cross_attn[-2_56:]


def 		lowercase_  (     ):


			'''simple docstring'''







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

			return im


@torch.no_grad()
def 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase=None       ,			_UpperCamelCase=False  ):


			'''simple docstring'''







			__lowercase					, __lowercase 					=   get_detr_config(_UpperCamelCase  )

			# load original model from torch hub
			__lowercase 					=   {
			    '''detr-resnet-50''': '''detr_resnet50''',
			    '''detr-resnet-101''': '''detr_resnet101''',
			}
			logger.info(F'Converting model {model_name}...'  )
			__lowercase 					=   torch.hub.load('''facebookresearch/detr'''       ,			model_name_to_original_name[model_name]       ,			pretrained=_UpperCamelCase  ).eval()
			__lowercase 					=   detr.state_dict()
			# rename keys
			for src, dest in create_rename_keys(_UpperCamelCase  ):
						if is_panoptic:
									__lowercase 					=   '''detr.''' + src
						rename_key(_UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase  )
			# query, key and value matrices need special treatment
			read_in_q_k_v(_UpperCamelCase       ,			is_panoptic=_UpperCamelCase  )
			# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
			__lowercase 					=   '''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'''  )
									):
												__lowercase 					=   state_dict.pop(_UpperCamelCase  )
												__lowercase 					=   val
									elif "class_labels_classifier" in key or "bbox_predictor" in key:
												__lowercase 					=   state_dict.pop(_UpperCamelCase  )
												__lowercase 					=   val
									elif key.startswith('''bbox_attention'''  ) or key.startswith('''mask_head'''  ):
												continue
									else:
												__lowercase 					=   state_dict.pop(_UpperCamelCase  )
												__lowercase 					=   val
						else:
									if not key.startswith('''class_labels_classifier'''  ) and not key.startswith('''bbox_predictor'''  ):
												__lowercase 					=   state_dict.pop(_UpperCamelCase  )
												__lowercase 					=   val

    # finally, create HuggingFace model and load state dict
			__lowercase 					=   DetrForSegmentation(_UpperCamelCase  ) if is_panoptic else DetrForObjectDetection(_UpperCamelCase  )
			model.load_state_dict(_UpperCamelCase  )
			model.eval()

			# verify our conversion on an image
			__lowercase 					=   '''coco_panoptic''' if is_panoptic else '''coco_detection'''
			__lowercase 					=   DetrImageProcessor(format=_UpperCamelCase  )

			__lowercase 					=   processor(images=prepare_img()       ,			return_tensors='''pt'''  )
			__lowercase 					=   encoding['''pixel_values''']

			__lowercase 					=   detr(_UpperCamelCase  )
			__lowercase 					=   model(_UpperCamelCase  )

			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(_UpperCamelCase  ).mkdir(exist_ok=_UpperCamelCase  )
						model.save_pretrained(_UpperCamelCase  )
						processor.save_pretrained(_UpperCamelCase  )

			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__":
	a : Dict		=				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.''')
	a : Any		=				parser.parse_args()
	convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






import unittest

from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax

from .test_modeling_common_flax import FlaxModelTesterMixin


if is_flax_available():
	import jax
@require_flax
class 	lowerCamelCase_       (				lowerCAmelCase__     ,     unittest.TestCase				):



					'''simple docstring'''







					__UpperCAmelCase            =				FlaxAutoencoderKL






					@property
					def A      (				self						)   ->			Optional[int]:







								'''simple docstring'''

								__lowercase 					=   4
								__lowercase 					=   3
								__lowercase 					=   (3_2, 3_2)

								__lowercase 					=   jax.random.PRNGKey(0						)
								__lowercase 					=   jax.random.uniform(snake_case_ ,       ((batch_size, num_channels) + sizes)						)

								return {"sample": image, "prng_key": prng_key}






					def A      (				self						)   ->			Any:







								'''simple docstring'''

								__lowercase 					=   {
								    '''block_out_channels''': [3_2, 6_4],
								    '''in_channels''': 3,
								    '''out_channels''': 3,
								    '''down_block_types''': ['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''],
								    '''up_block_types''': ['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''],
								    '''latent_channels''': 4,
								}
								__lowercase 					=   self.dummy_input
								return init_dict, inputs_dict


