
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()
__SCREAMING_SNAKE_CASE			:   List[str]         =     logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
__SCREAMING_SNAKE_CASE			:   List[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     UpperCAmelCase__							(       __magic_name__						:     Optional[int]							,  __magic_name__						:     int							,  __magic_name__						:     Optional[int]  ):


					'''simple docstring'''

					lowerCAmelCase		:					Any  	=     state_dict.pop(__magic_name__  )
					lowerCAmelCase		:					str  	=     val
def     UpperCAmelCase__							(       __magic_name__						:     str  ):


					'''simple docstring'''

					lowerCAmelCase		:					Dict  	=     OrderedDict()
					for key, value in state_dict.items():
										if "backbone.0.body" in key:
															lowerCAmelCase		:					List[Any]  	=     key.replace('''backbone.0.body'''							,  '''backbone.conv_encoder.model'''  )
															lowerCAmelCase		:					Any  	=     value
										else:
															lowerCAmelCase		:					List[str]  	=     value

					return new_state_dict
def     UpperCAmelCase__							(       __magic_name__						:     List[str]							,  __magic_name__						:     str=False  ):


					'''simple docstring'''

					lowerCAmelCase		:					str  	=     ''''''
					if is_panoptic:
										lowerCAmelCase		:					Optional[int]  	=     '''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		:					Optional[int]  	=     state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''  )
										lowerCAmelCase		:					Union[str, Any]  	=     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		:					Tuple  	=     in_proj_weight[:2_56, :]
										lowerCAmelCase		:					Optional[int]  	=     in_proj_bias[:2_56]
										lowerCAmelCase		:					List[str]  	=     in_proj_weight[2_56:5_12, :]
										lowerCAmelCase		:					str  	=     in_proj_bias[2_56:5_12]
										lowerCAmelCase		:					Optional[Any]  	=     in_proj_weight[-2_56:, :]
										lowerCAmelCase		:					Dict  	=     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
										lowerCAmelCase		:					Optional[int]  	=     state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'''  )
										lowerCAmelCase		:					Optional[Any]  	=     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		:					Tuple  	=     in_proj_weight[:2_56, :]
										lowerCAmelCase		:					Any  	=     in_proj_bias[:2_56]
										lowerCAmelCase		:					int  	=     in_proj_weight[2_56:5_12, :]
										lowerCAmelCase		:					Tuple  	=     in_proj_bias[2_56:5_12]
										lowerCAmelCase		:					int  	=     in_proj_weight[-2_56:, :]
										lowerCAmelCase		:					List[str]  	=     in_proj_bias[-2_56:]
										# read in weights + bias of input projection layer of cross-attention
										lowerCAmelCase		:					str  	=     state_dict.pop(
										    f'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'''  )
										lowerCAmelCase		:					Tuple  	=     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		:					Optional[int]  	=     in_proj_weight_cross_attn[:2_56, :]
										lowerCAmelCase		:					Any  	=     in_proj_bias_cross_attn[:2_56]
										lowerCAmelCase		:					str  	=     in_proj_weight_cross_attn[2_56:5_12, :]
										lowerCAmelCase		:					Dict  	=     in_proj_bias_cross_attn[2_56:5_12]
										lowerCAmelCase		:					Any  	=     in_proj_weight_cross_attn[-2_56:, :]
										lowerCAmelCase		:					str  	=     in_proj_bias_cross_attn[-2_56:]
def     UpperCAmelCase__							(       ):


					'''simple docstring'''

					lowerCAmelCase		:					Dict  	=     '''http://images.cocodataset.org/val2017/000000039769.jpg'''
					lowerCAmelCase		:					Optional[int]  	=     Image.open(requests.get(__magic_name__							,  stream=__magic_name__  ).raw  )

					return im
@torch.no_grad()
def     UpperCAmelCase__							(       __magic_name__						:     Tuple							,  __magic_name__						:     Optional[int]  ):


					'''simple docstring'''

					lowerCAmelCase		:					Dict  	=     DetrConfig()
					# set backbone and dilation attributes
					if "resnet101" in model_name:
										lowerCAmelCase		:					Union[str, Any]  	=     '''resnet101'''
					if "dc5" in model_name:
										lowerCAmelCase		:					Any  	=     True
					lowerCAmelCase		:					List[Any]  	=     '''panoptic''' in model_name
					if is_panoptic:
										lowerCAmelCase		:					List[str]  	=     2_50
					else:
										lowerCAmelCase		:					Dict  	=     91
										lowerCAmelCase		:					List[Any]  	=     '''huggingface/label-files'''
										lowerCAmelCase		:					List[str]  	=     '''coco-detection-id2label.json'''
										lowerCAmelCase		:					Optional[Any]  	=     json.load(open(hf_hub_download(__magic_name__							,  __magic_name__							,  repo_type='''dataset'''  )							,  '''r'''  )  )
										lowerCAmelCase		:					int  	=     {int(__magic_name__  ): v for k, v in idalabel.items()}
										lowerCAmelCase		:					int  	=     idalabel
										lowerCAmelCase		:					str  	=     {v: k for k, v in idalabel.items()}

					# load image processor
					lowerCAmelCase		:					Any  	=     '''coco_panoptic''' if is_panoptic else '''coco_detection'''
					lowerCAmelCase		:					Dict  	=     DetrImageProcessor(format=__magic_name__  )

					# prepare image
					lowerCAmelCase		:					Optional[Any]  	=     prepare_img()
					lowerCAmelCase		:					Optional[int]  	=     image_processor(images=__magic_name__							,  return_tensors='''pt'''  )
					lowerCAmelCase		:					Union[str, Any]  	=     encoding['''pixel_values''']

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

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


if __name__ == "__main__":
		__SCREAMING_SNAKE_CASE			:   Optional[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.'
		)
		__SCREAMING_SNAKE_CASE			:   Union[str, Any]         =     parser.parse_args()
		convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)


import json
import os
from dataclasses import dataclass

import numpy as np
import pyarrow as pa

import datasets
from utils import get_duration


__SCREAMING_SNAKE_CASE			:   List[Any]         =     10_00_00_00_00_00
__SCREAMING_SNAKE_CASE			:   Optional[Any]         =     1_00_00

__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE			:   Any         =     os.path.split(__file__)
__SCREAMING_SNAKE_CASE			:   List[str]         =     os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))
def     UpperCAmelCase__							(       __magic_name__						:     int							,  __magic_name__						:     int  ):


					'''simple docstring'''

					lowerCAmelCase		:					Optional[int]  	=     pa.Table.from_pydict({'''col''': [0] * chunk_size}  )
					lowerCAmelCase		:					int  	=     pa.concat_tables([table] * (num_examples // chunk_size)  )
					return datasets.Dataset(__magic_name__							,  fingerprint='''table_100B'''  )


@dataclass
class 		__magic_name__   :
		_lowerCAmelCase		=	42
		_lowerCAmelCase		=	42
		_lowerCAmelCase		=	42
		_lowerCAmelCase		=	42





		def       _A							(							self    :	Optional[int]				):
							lowerCAmelCase		:					Union[str, Any]  	=     np.random.default_rng(self.seed				)
							lowerCAmelCase		:					Tuple  	=     rng.integers(low=self.low						,  high=self.high						,  size=self.size				).tolist()





		def __iter__(							self    :	Dict				):
							return iter(self._sampled_values				)





		def __len__(							self    :	Tuple				):
							return self.size



@get_duration
def     UpperCAmelCase__							(       __magic_name__						:     datasets.Dataset  ):


					'''simple docstring'''

					lowerCAmelCase		:					Union[str, Any]  	=     dataset[0]
@get_duration
def     UpperCAmelCase__							(       __magic_name__						:     datasets.Dataset  ):


					'''simple docstring'''

					lowerCAmelCase		:					Any  	=     dataset[-1]
@get_duration
def     UpperCAmelCase__							(       __magic_name__						:     datasets.Dataset  ):


					'''simple docstring'''

					lowerCAmelCase		:					int  	=     dataset[range(len(__magic_name__  ) // 2							,  len(__magic_name__  ) // 2 + 10_24  )]
@get_duration
def     UpperCAmelCase__							(       __magic_name__						:     datasets.Dataset  ):


					'''simple docstring'''

					lowerCAmelCase		:					List[Any]  	=     dataset[RandIter(0							,  len(__magic_name__  )							,  10_24							,  seed=42  )]
def     UpperCAmelCase__							(       ):


					'''simple docstring'''

					lowerCAmelCase		:					Dict  	=     {'''num examples''': SPEED_TEST_N_EXAMPLES}
					lowerCAmelCase		:					Tuple  	=     (get_first_row, get_last_row, get_batch_of_aaaa_rows, get_batch_of_aaaa_random_rows)
					print('''generating dataset'''  )
					lowerCAmelCase		:					Dict  	=     generate_100B_dataset(num_examples=__magic_name__							,  chunk_size=__magic_name__  )
					print('''Functions'''  )
					for func in functions:
										print(func.__name__  )
										lowerCAmelCase		:					List[Any]  	=     func(__magic_name__  )

					with open(__magic_name__							,  '''wb'''  ) as f:
										f.write(json.dumps(__magic_name__  ).encode('''utf-8'''  )  )


if __name__ == "__main__":  # useful to run the profiler
		benchmark_table_100B()
