
"""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 DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor
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}.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    lowercase	(  lowerCAmelCase__  ,lowerCAmelCase__  ,lowerCAmelCase__	):
			lowerCamelCase_ 		=     state_dict.pop(lowerCAmelCase__	)
			lowerCamelCase_ 		=     val






def    lowercase	(  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    lowercase	(  lowerCAmelCase__  ,lowerCAmelCase__=False	):
			lowerCamelCase_ 		=     ''''''
			if is_panoptic:
						lowerCamelCase_ 		=     '''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[:256, :]
						lowerCamelCase_ 		=     in_proj_bias[:256]
						lowerCamelCase_ 		=     in_proj_weight[256:512, :]
						lowerCamelCase_ 		=     in_proj_bias[256:512]
						lowerCamelCase_ 		=     in_proj_weight[-256:, :]
						lowerCamelCase_ 		=     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
						lowerCamelCase_ 		=     state_dict.pop(f"{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"	)
						lowerCamelCase_ 		=     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
						lowerCamelCase_ 		=     in_proj_weight[:256, :]
						lowerCamelCase_ 		=     in_proj_bias[:256]
						lowerCamelCase_ 		=     in_proj_weight[256:512, :]
						lowerCamelCase_ 		=     in_proj_bias[256:512]
						lowerCamelCase_ 		=     in_proj_weight[-256:, :]
						lowerCamelCase_ 		=     in_proj_bias[-256:]
						# read in weights + bias of input projection layer of cross-attention
						lowerCamelCase_ 		=     state_dict.pop(
						    f"{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"	)
						lowerCamelCase_ 		=     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
						lowerCamelCase_ 		=     in_proj_weight_cross_attn[:256, :]
						lowerCamelCase_ 		=     in_proj_bias_cross_attn[:256]
						lowerCamelCase_ 		=     in_proj_weight_cross_attn[256:512, :]
						lowerCamelCase_ 		=     in_proj_bias_cross_attn[256:512]
						lowerCamelCase_ 		=     in_proj_weight_cross_attn[-256:, :]
						lowerCamelCase_ 		=     in_proj_bias_cross_attn[-256:]






def    lowercase	(  ):
			lowerCamelCase_ 		=     '''http://images.cocodataset.org/val2017/000000039769.jpg'''
			lowerCamelCase_ 		=     Image.open(requests.get(lowerCAmelCase__  ,stream=lowerCAmelCase__	).raw	)

			return im






@torch.no_grad()
def    lowercase	(  lowerCAmelCase__  ,lowerCAmelCase__	):
			lowerCamelCase_ 		=     DetrConfig()
			# 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_ 		=     250
			else:
						lowerCamelCase_ 		=     91
						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_ 		=     DetrImageProcessor(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('''facebookresearch/detr'''  ,lowerCAmelCase__  ,pretrained=lowerCAmelCase__	).eval()
			lowerCamelCase_ 		=     detr.state_dict()
			# rename keys
			for src, dest in rename_keys:
						if is_panoptic:
									lowerCamelCase_ 		=     '''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_ 		=     '''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'''	)
									):
												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_ 		=     DetrForSegmentation(lowerCAmelCase__	) if is_panoptic else DetrForObjectDetection(lowerCAmelCase__	)
			model.load_state_dict(lowerCAmelCase__	)
			model.eval()
			# verify our conversion
			lowerCamelCase_ 		=     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__":
						A_						       =				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_						       =				parser.parse_args()
						convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)





"""simple docstring"""


import warnings
from collections.abc import Callable
from typing import Any

import numpy
from matplotlib import pyplot

A_						       =				0.25 + 0.0J
A_						       =				-0.4 + 0.6J
A_						       =				-0.1 + 0.651J
A_						       =				-2.0
A_						       =				56
A_						       =				2.0
A_						       =				666






def    lowercase	(  lowerCAmelCase__  ,lowerCAmelCase__	):
			return numpy.exp(lowerCAmelCase__	) + c_parameter






def    lowercase	(  lowerCAmelCase__  ,lowerCAmelCase__	):
			return z_values * z_values + c_parameter






def    lowercase	(  lowerCAmelCase__  ,lowerCAmelCase__	):
			lowerCamelCase_ 		=     numpy.linspace(-window_size  ,lowerCAmelCase__  ,lowerCAmelCase__	)
			lowerCamelCase_ 		=     x.reshape((nb_pixels, 1)	)
			lowerCamelCase_ 		=     numpy.linspace(-window_size  ,lowerCAmelCase__  ,lowerCAmelCase__	)
			lowerCamelCase_ 		=     y.reshape((1, nb_pixels)	)
			return x + 1.0J * y






def    lowercase	(  lowerCAmelCase__  ,lowerCAmelCase__  ,lowerCAmelCase__  ,lowerCAmelCase__  ,lowerCAmelCase__ = None  ,):
			lowerCamelCase_ 		=     z_a.astype('''complex64'''	)
			for _ in range(lowerCAmelCase__	):
						lowerCamelCase_ 		=     eval_function(lowerCAmelCase__  ,lowerCAmelCase__	)
						if infinity is not None:
									numpy.nan_to_num(lowerCAmelCase__  ,copy=lowerCAmelCase__  ,nan=lowerCAmelCase__	)
									lowerCamelCase_ 		=     infinity
			return z_n






def    lowercase	(  lowerCAmelCase__  ,lowerCAmelCase__  ,lowerCAmelCase__  ,lowerCAmelCase__  ,):
			lowerCamelCase_ 		=     (abs(lowerCAmelCase__	)).transpose()
			lowerCamelCase_ 		=     abs_z_final[::-1, :]
			pyplot.matshow(abs_z_final < escape_radius	)
			pyplot.title(f"Julia set of ${function_label}$, $c={function_params}$"	)
			pyplot.show()






def    lowercase	(  ):
			warnings.filterwarnings(
			    '''ignore'''  ,category=lowerCAmelCase__  ,message='''overflow encountered in multiply'''	)
			warnings.filterwarnings(
			    '''ignore'''  ,category=lowerCAmelCase__  ,message='''invalid value encountered in multiply'''  ,)
			warnings.filterwarnings(
			    '''ignore'''  ,category=lowerCAmelCase__  ,message='''overflow encountered in absolute'''	)
			warnings.filterwarnings(
			    '''ignore'''  ,category=lowerCAmelCase__  ,message='''overflow encountered in exp'''	)


if __name__ == "__main__":
						A_						       =				prepare_grid(window_size, nb_pixels)

						ignore_overflow_warnings()  # See file header for explanations

						A_						       =				24
						A_						       =				2 * abs(c_cauliflower) + 1
						A_						       =				iterate_function(
						    eval_quadratic_polynomial,
						    c_cauliflower,
						    nb_iterations,
						    z_a,
						    infinity=1.1 * escape_radius,
						)
						show_results("""z^2+c""", c_cauliflower, escape_radius, z_final)

						A_						       =				64
						A_						       =				2 * abs(c_polynomial_a) + 1
						A_						       =				iterate_function(
						    eval_quadratic_polynomial,
						    c_polynomial_a,
						    nb_iterations,
						    z_a,
						    infinity=1.1 * escape_radius,
						)
						show_results("""z^2+c""", c_polynomial_a, escape_radius, z_final)

						A_						       =				161
						A_						       =				2 * abs(c_polynomial_a) + 1
						A_						       =				iterate_function(
						    eval_quadratic_polynomial,
						    c_polynomial_a,
						    nb_iterations,
						    z_a,
						    infinity=1.1 * escape_radius,
						)
						show_results("""z^2+c""", c_polynomial_a, escape_radius, z_final)

						A_						       =				12
						A_						       =				10000.0
						A_						       =				iterate_function(
						    eval_exponential,
						    c_exponential,
						    nb_iterations,
						    z_a + 2,
						    infinity=1.0e10,
						)
						show_results("""e^z+c""", c_exponential, escape_radius, z_final)



