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()
lowerCamelCase_				:		Dict       						=       logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
lowerCamelCase_				:		Union[str, 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}.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(	__lowerCamelCase      ,				__lowerCamelCase      ,				__lowerCamelCase						):
					__a							=   state_dict.pop(__lowerCamelCase						)
					__a							=   val
def 							lowerCAmelCase(	__lowerCamelCase						):
					__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(	__lowerCamelCase      ,				__lowerCamelCase=False						):
					__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(	):
					__a							=   'http://images.cocodataset.org/val2017/000000039769.jpg'
					__a							=   Image.open(requests.get(__lowerCamelCase      ,				stream=__lowerCamelCase						).raw						)

					return im
@torch.no_grad()
def 							lowerCAmelCase(	__lowerCamelCase      ,				__lowerCamelCase						):
					__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(__lowerCamelCase      ,				__lowerCamelCase      ,				repo_type='dataset'						)      ,				'r'						)						)
										__a							=   {int(__lowerCamelCase						): 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=__lowerCamelCase						)

					# prepare image
					__a							=   prepare_img()
					__a							=   image_processor(images=__lowerCamelCase      ,				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'      ,				__lowerCamelCase      ,				pretrained=__lowerCamelCase						).eval()
					__a							=   detr.state_dict()
					# rename keys
					for src, dest in rename_keys:
										if is_panoptic:
															__a							=   'detr.' + src
										rename_key(__lowerCamelCase      ,				__lowerCamelCase      ,				__lowerCamelCase						)
					__a							=   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
					__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(__lowerCamelCase						)
																				__a							=   val
															elif "class_labels_classifier" in key or "bbox_predictor" in key:
																				__a							=   state_dict.pop(__lowerCamelCase						)
																				__a							=   val
															elif key.startswith('bbox_attention'						) or key.startswith('mask_head'						):
																				continue
															else:
																				__a							=   state_dict.pop(__lowerCamelCase						)
																				__a							=   val
										else:
															if not key.startswith('class_labels_classifier'						) and not key.startswith('bbox_predictor'						):
																				__a							=   state_dict.pop(__lowerCamelCase						)
																				__a							=   val
    # finally, create HuggingFace model and load state dict
					__a							=   DetrForSegmentation(__lowerCamelCase						) if is_panoptic else DetrForObjectDetection(__lowerCamelCase						)
					model.load_state_dict(__lowerCamelCase						)
					model.eval()
					# verify our conversion
					__a							=   detr(__lowerCamelCase						)
					__a							=   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__":
	lowerCamelCase_				:		Union[str, Any]       						=       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."""
	)
	lowerCamelCase_				:		Optional[int]       						=       parser.parse_args()
	convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)





from typing import Any

import numpy as np
def 							lowerCAmelCase(	__lowerCamelCase						):
					assert np.shape(__lowerCamelCase						)[0] == np.shape(__lowerCamelCase						)[1]

					# If matrix not symmetric, exit right away.
					if np.allclose(__lowerCamelCase      ,				matrix.T						) is False:
										return False

					# Get eigenvalues and eignevectors for a symmetric matrix.
					__a					,      __a							=   np.linalg.eigh(__lowerCamelCase						)

					# Check sign of all eigenvalues.
					# np.all returns a value of type np.bool_
					return bool(np.all(eigen_values > 0						)						)
def 							lowerCAmelCase(	__lowerCamelCase						):
					__a							=   np.random.randn(__lowerCamelCase      ,				__lowerCamelCase						)
					__a							=   np.dot(__lowerCamelCase      ,				random_matrix.T						)
					assert _is_matrix_spd(__lowerCamelCase						)
					return spd_matrix
def 							lowerCAmelCase(	__lowerCamelCase      ,				__lowerCamelCase      ,				__lowerCamelCase = 1000      ,				__lowerCamelCase = 1e-8      ,				):
					assert np.shape(__lowerCamelCase						)[0] == np.shape(__lowerCamelCase						)[1]
					assert np.shape(__lowerCamelCase						)[0] == np.shape(__lowerCamelCase						)[0]
					assert _is_matrix_spd(__lowerCamelCase						)

					# Initialize solution guess, residual, search direction.
					__a							=   np.zeros((np.shape(__lowerCamelCase						)[0], 1)						)
					__a							=   np.copy(__lowerCamelCase						)
					__a							=   np.copy(__lowerCamelCase						)

					# Set initial errors in solution guess and residual.
					__a							=   1e9
					__a							=   1e9
					__a							=   1e9

					# Set iteration counter to threshold number of iterations.
					__a							=   0

					while error > tol:
										# Save this value so we only calculate the matrix-vector product once.
										__a							=   np.dot(__lowerCamelCase      ,				__lowerCamelCase						)

										# The main algorithm.

										# Update search direction magnitude.
										__a							=   np.dot(ra.T      ,				__lowerCamelCase						) / np.dot(pa.T      ,				__lowerCamelCase						)
										# Update solution guess.
										__a							=   xa + alpha * pa
										# Calculate new residual.
										__a							=   ra - alpha * w
										# Calculate new Krylov subspace scale.
										__a							=   np.dot(r.T      ,				__lowerCamelCase						) / np.dot(ra.T      ,				__lowerCamelCase						)
										# Calculate new A conjuage search direction.
										__a							=   r + beta * pa

										# Calculate errors.
										__a							=   np.linalg.norm(r - ra						)
										__a							=   np.linalg.norm(x - xa						)
										__a							=   np.maximum(__lowerCamelCase      ,				__lowerCamelCase						)

										# Update variables.
										__a							=   np.copy(__lowerCamelCase						)
										__a							=   np.copy(__lowerCamelCase						)
										__a							=   np.copy(__lowerCamelCase						)

										# Update number of iterations.
										iterations += 1
										if iterations > max_iterations:
															break

					return x
def 							lowerCAmelCase(	):
					__a							=   3
					__a							=   _create_spd_matrix(__lowerCamelCase						)
					__a							=   np.random.randn(__lowerCamelCase      ,				1						)
					__a							=   np.dot(__lowerCamelCase      ,				__lowerCamelCase						)

					# Numpy solution.
					__a							=   np.linalg.solve(__lowerCamelCase      ,				__lowerCamelCase						)

					# Our implementation.
					__a							=   conjugate_gradient(__lowerCamelCase      ,				__lowerCamelCase						)

					# Ensure both solutions are close to x_true (and therefore one another).
					assert np.linalg.norm(x_numpy - x_true						) <= 1e-6
					assert np.linalg.norm(x_conjugate_gradient - x_true						) <= 1e-6


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	test_conjugate_gradient()




