






import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image

from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor
from transformers.utils import logging


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



def        _lowerCamelCase(      ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_ :						str    				= DetaConfig(
				    num_queries=900     ,     encoder_ffn_dim=2048     ,     decoder_ffn_dim=2048     ,     num_feature_levels=5     ,     assign_first_stage=lowerCAmelCase__     ,     with_box_refine=lowerCAmelCase__     ,     two_stage=lowerCAmelCase__     ,     )

				# set labels
				SCREAMING_SNAKE_CASE_ :						List[str]    				= 91
				SCREAMING_SNAKE_CASE_ :						str    				= "huggingface/label-files"
				SCREAMING_SNAKE_CASE_ :						Optional[int]    				= "coco-detection-id2label.json"
				SCREAMING_SNAKE_CASE_ :						Any    				= json.load(open(cached_download(hf_hub_url(lowerCAmelCase__     ,     lowerCAmelCase__     ,     repo_type='dataset'   )   )     ,     'r'   )   )
				SCREAMING_SNAKE_CASE_ :						List[str]    				= {int(lowerCAmelCase__   ): v for k, v in idalabel.items()}
				SCREAMING_SNAKE_CASE_ :						str    				= idalabel
				SCREAMING_SNAKE_CASE_ :						Any    				= {v: k for k, v in idalabel.items()}

				return config



def        _lowerCamelCase(      lowerCAmelCase__					:		str   ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_ :						Dict    				= []

				# stem
				# fmt: off
				rename_keys.append(('backbone.0.body.conv1.weight', 'model.backbone.model.embedder.embedder.convolution.weight')   )
				rename_keys.append(('backbone.0.body.bn1.weight', 'model.backbone.model.embedder.embedder.normalization.weight')   )
				rename_keys.append(('backbone.0.body.bn1.bias', 'model.backbone.model.embedder.embedder.normalization.bias')   )
				rename_keys.append(('backbone.0.body.bn1.running_mean', 'model.backbone.model.embedder.embedder.normalization.running_mean')   )
				rename_keys.append(('backbone.0.body.bn1.running_var', 'model.backbone.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'''model.backbone.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'''model.backbone.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'''model.backbone.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'''model.backbone.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'''model.backbone.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'''model.backbone.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'''model.backbone.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'''model.backbone.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'''model.backbone.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'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var''',
																    )   )
    # transformer encoder
				for i in range(config.encoder_layers   ):
								rename_keys.append((F'''transformer.encoder.layers.{i}.self_attn.sampling_offsets.weight''', F'''model.encoder.layers.{i}.self_attn.sampling_offsets.weight''')   )
								rename_keys.append((F'''transformer.encoder.layers.{i}.self_attn.sampling_offsets.bias''', F'''model.encoder.layers.{i}.self_attn.sampling_offsets.bias''')   )
								rename_keys.append((F'''transformer.encoder.layers.{i}.self_attn.attention_weights.weight''', F'''model.encoder.layers.{i}.self_attn.attention_weights.weight''')   )
								rename_keys.append((F'''transformer.encoder.layers.{i}.self_attn.attention_weights.bias''', F'''model.encoder.layers.{i}.self_attn.attention_weights.bias''')   )
								rename_keys.append((F'''transformer.encoder.layers.{i}.self_attn.value_proj.weight''', F'''model.encoder.layers.{i}.self_attn.value_proj.weight''')   )
								rename_keys.append((F'''transformer.encoder.layers.{i}.self_attn.value_proj.bias''', F'''model.encoder.layers.{i}.self_attn.value_proj.bias''')   )
								rename_keys.append((F'''transformer.encoder.layers.{i}.self_attn.output_proj.weight''', F'''model.encoder.layers.{i}.self_attn.output_proj.weight''')   )
								rename_keys.append((F'''transformer.encoder.layers.{i}.self_attn.output_proj.bias''', F'''model.encoder.layers.{i}.self_attn.output_proj.bias''')   )
								rename_keys.append((F'''transformer.encoder.layers.{i}.norm1.weight''', F'''model.encoder.layers.{i}.self_attn_layer_norm.weight''')   )
								rename_keys.append((F'''transformer.encoder.layers.{i}.norm1.bias''', F'''model.encoder.layers.{i}.self_attn_layer_norm.bias''')   )
								rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.weight''', F'''model.encoder.layers.{i}.fc1.weight''')   )
								rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.bias''', F'''model.encoder.layers.{i}.fc1.bias''')   )
								rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.weight''', F'''model.encoder.layers.{i}.fc2.weight''')   )
								rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.bias''', F'''model.encoder.layers.{i}.fc2.bias''')   )
								rename_keys.append((F'''transformer.encoder.layers.{i}.norm2.weight''', F'''model.encoder.layers.{i}.final_layer_norm.weight''')   )
								rename_keys.append((F'''transformer.encoder.layers.{i}.norm2.bias''', F'''model.encoder.layers.{i}.final_layer_norm.bias''')   )

				# transformer decoder
				for i in range(config.decoder_layers   ):
								rename_keys.append((F'''transformer.decoder.layers.{i}.cross_attn.sampling_offsets.weight''', F'''model.decoder.layers.{i}.encoder_attn.sampling_offsets.weight''')   )
								rename_keys.append((F'''transformer.decoder.layers.{i}.cross_attn.sampling_offsets.bias''', F'''model.decoder.layers.{i}.encoder_attn.sampling_offsets.bias''')   )
								rename_keys.append((F'''transformer.decoder.layers.{i}.cross_attn.attention_weights.weight''', F'''model.decoder.layers.{i}.encoder_attn.attention_weights.weight''')   )
								rename_keys.append((F'''transformer.decoder.layers.{i}.cross_attn.attention_weights.bias''', F'''model.decoder.layers.{i}.encoder_attn.attention_weights.bias''')   )
								rename_keys.append((F'''transformer.decoder.layers.{i}.cross_attn.value_proj.weight''', F'''model.decoder.layers.{i}.encoder_attn.value_proj.weight''')   )
								rename_keys.append((F'''transformer.decoder.layers.{i}.cross_attn.value_proj.bias''', F'''model.decoder.layers.{i}.encoder_attn.value_proj.bias''')   )
								rename_keys.append((F'''transformer.decoder.layers.{i}.cross_attn.output_proj.weight''', F'''model.decoder.layers.{i}.encoder_attn.output_proj.weight''')   )
								rename_keys.append((F'''transformer.decoder.layers.{i}.cross_attn.output_proj.bias''', F'''model.decoder.layers.{i}.encoder_attn.output_proj.bias''')   )
								rename_keys.append((F'''transformer.decoder.layers.{i}.norm1.weight''', F'''model.decoder.layers.{i}.encoder_attn_layer_norm.weight''')   )
								rename_keys.append((F'''transformer.decoder.layers.{i}.norm1.bias''', F'''model.decoder.layers.{i}.encoder_attn_layer_norm.bias''')   )
								rename_keys.append((F'''transformer.decoder.layers.{i}.self_attn.out_proj.weight''', F'''model.decoder.layers.{i}.self_attn.out_proj.weight''')   )
								rename_keys.append((F'''transformer.decoder.layers.{i}.self_attn.out_proj.bias''', F'''model.decoder.layers.{i}.self_attn.out_proj.bias''')   )
								rename_keys.append((F'''transformer.decoder.layers.{i}.norm2.weight''', F'''model.decoder.layers.{i}.self_attn_layer_norm.weight''')   )
								rename_keys.append((F'''transformer.decoder.layers.{i}.norm2.bias''', F'''model.decoder.layers.{i}.self_attn_layer_norm.bias''')   )
								rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.weight''', F'''model.decoder.layers.{i}.fc1.weight''')   )
								rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.bias''', F'''model.decoder.layers.{i}.fc1.bias''')   )
								rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.weight''', F'''model.decoder.layers.{i}.fc2.weight''')   )
								rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.bias''', F'''model.decoder.layers.{i}.fc2.bias''')   )
								rename_keys.append((F'''transformer.decoder.layers.{i}.norm3.weight''', F'''model.decoder.layers.{i}.final_layer_norm.weight''')   )
								rename_keys.append((F'''transformer.decoder.layers.{i}.norm3.bias''', F'''model.decoder.layers.{i}.final_layer_norm.bias''')   )

				# fmt: on

				return rename_keys



def        _lowerCamelCase(      lowerCAmelCase__					:		str     ,     lowerCAmelCase__					:		Union[str, Any]     ,     lowerCAmelCase__					:		int   ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= dct.pop(lowerCAmelCase__   )
				SCREAMING_SNAKE_CASE_ :						Optional[int]    				= val



def        _lowerCamelCase(      lowerCAmelCase__					:		str     ,     lowerCAmelCase__					:		Any   ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_ :						Any    				= config.d_model
				for i in range(config.decoder_layers   ):
								# read in weights + bias of input projection layer of self-attention
								SCREAMING_SNAKE_CASE_ :						int    				= state_dict.pop(F'''transformer.decoder.layers.{i}.self_attn.in_proj_weight'''   )
								SCREAMING_SNAKE_CASE_ :						Any    				= state_dict.pop(F'''transformer.decoder.layers.{i}.self_attn.in_proj_bias'''   )
								# next, add query, keys and values (in that order) to the state dict
								SCREAMING_SNAKE_CASE_ :						Any    				= in_proj_weight[:hidden_size, :]
								SCREAMING_SNAKE_CASE_ :						Dict    				= in_proj_bias[:hidden_size]
								SCREAMING_SNAKE_CASE_ :						Tuple    				= in_proj_weight[
								    hidden_size : hidden_size * 2, :
								]
								SCREAMING_SNAKE_CASE_ :						Dict    				= in_proj_bias[hidden_size : hidden_size * 2]
								SCREAMING_SNAKE_CASE_ :						str    				= in_proj_weight[-hidden_size:, :]
								SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= in_proj_bias[-hidden_size:]



def        _lowerCamelCase(      ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= "http://images.cocodataset.org/val2017/000000039769.jpg"
				SCREAMING_SNAKE_CASE_ :						Any    				= Image.open(requests.get(lowerCAmelCase__     ,     stream=lowerCAmelCase__   ).raw   )

				return im



@torch.no_grad()
def        _lowerCamelCase(      lowerCAmelCase__					:		Optional[int]     ,     lowerCAmelCase__					:		List[str]     ,     lowerCAmelCase__					:		Dict   ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_ :						Dict    				= get_deta_config()

				# load original state dict
				if model_name == "deta-resnet-50":
								SCREAMING_SNAKE_CASE_ :						Optional[int]    				= "adet_checkpoint0011.pth"
				elif model_name == "deta-resnet-50-24-epochs":
								SCREAMING_SNAKE_CASE_ :						str    				= "adet_2x_checkpoint0023.pth"
				else:
								raise ValueError(F'''Model name {model_name} not supported'''   )
				SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= hf_hub_download(repo_id='nielsr/deta-checkpoints'     ,     filename=lowerCAmelCase__   )
				SCREAMING_SNAKE_CASE_ :						Tuple    				= torch.load(lowerCAmelCase__     ,     map_location='cpu'   )["model"]

				# rename keys
				SCREAMING_SNAKE_CASE_ :						Any    				= create_rename_keys(lowerCAmelCase__   )
				for src, dest in rename_keys:
								rename_key(lowerCAmelCase__     ,     lowerCAmelCase__     ,     lowerCAmelCase__   )
				read_in_decoder_q_k_v(lowerCAmelCase__     ,     lowerCAmelCase__   )

				# fix some prefixes
				for key in state_dict.copy().keys():
								if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
												SCREAMING_SNAKE_CASE_ :						List[Any]    				= state_dict.pop(lowerCAmelCase__   )
												SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= val
								if "input_proj" in key:
												SCREAMING_SNAKE_CASE_ :						int    				= state_dict.pop(lowerCAmelCase__   )
												SCREAMING_SNAKE_CASE_ :						Tuple    				= val
								if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
												SCREAMING_SNAKE_CASE_ :						List[str]    				= state_dict.pop(lowerCAmelCase__   )
												SCREAMING_SNAKE_CASE_ :						Tuple    				= val

    # finally, create HuggingFace model and load state dict
				SCREAMING_SNAKE_CASE_ :						Tuple    				= DetaForObjectDetection(lowerCAmelCase__   )
				model.load_state_dict(lowerCAmelCase__   )
				model.eval()

				SCREAMING_SNAKE_CASE_ :						Any    				= "cuda" if torch.cuda.is_available() else "cpu"
				model.to(lowerCAmelCase__   )

				# load image processor
				SCREAMING_SNAKE_CASE_ :						Optional[int]    				= DetaImageProcessor(format='coco_detection'   )

				# verify our conversion on image
				SCREAMING_SNAKE_CASE_ :						Tuple    				= prepare_img()
				SCREAMING_SNAKE_CASE_ :						List[Any]    				= processor(images=lowerCAmelCase__     ,     return_tensors='pt'   )
				SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= encoding["pixel_values"]
				SCREAMING_SNAKE_CASE_ :						Optional[int]    				= model(pixel_values.to(lowerCAmelCase__   )   )

				# verify logits
				if model_name == "deta-resnet-50":
								SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= torch.tensor(
								    [[-7.3_978, -2.5_406, -4.1_668], [-8.2_684, -3.9_933, -3.8_096], [-7.0_515, -3.7_973, -5.8_516]]   )
								SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= torch.tensor([[0.5_043, 0.4_973, 0.9_998], [0.2_542, 0.5_489, 0.4_748], [0.5_490, 0.2_765, 0.0_570]]   )
				elif model_name == "deta-resnet-50-24-epochs":
								SCREAMING_SNAKE_CASE_ :						str    				= torch.tensor(
								    [[-7.1_688, -2.4_857, -4.8_669], [-7.8_630, -3.8_154, -4.2_674], [-7.2_730, -4.1_865, -5.5_323]]   )
								SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= torch.tensor([[0.5_021, 0.4_971, 0.9_994], [0.2_546, 0.5_486, 0.4_731], [0.1_686, 0.1_986, 0.2_142]]   )

				assert torch.allclose(outputs.logits[0, :3, :3]     ,     expected_logits.to(lowerCAmelCase__   )     ,     atol=1E-4   )
				assert torch.allclose(outputs.pred_boxes[0, :3, :3]     ,     expected_boxes.to(lowerCAmelCase__   )     ,     atol=1E-4   )
				print('Everything ok!'   )

				if pytorch_dump_folder_path:
								# Save model and processor
								logger.info(F'''Saving PyTorch model and processor to {pytorch_dump_folder_path}...'''   )
								Path(lowerCAmelCase__   ).mkdir(exist_ok=lowerCAmelCase__   )
								model.save_pretrained(lowerCAmelCase__   )
								processor.save_pretrained(lowerCAmelCase__   )

				# Push to hub
				if push_to_hub:
								print('Pushing model and processor to hub...'   )
								model.push_to_hub(F'''jozhang97/{model_name}'''   )
								processor.push_to_hub(F'''jozhang97/{model_name}'''   )


if __name__ == "__main__":
				A										=      argparse.ArgumentParser()

				parser.add_argument(
				    '--model_name',
				    type=str,
				    default='deta-resnet-50',
				    choices=['deta-resnet-50', 'deta-resnet-50-24-epochs'],
				    help='Name of the 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 or not to push the converted model to the 🤗 hub.'
				)
				A										=      parser.parse_args()
				convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)



'''simple docstring'''




#
# Sort large text files in a minimum amount of memory
#
import argparse
import os





class _snake_case :
							"""simple docstring"""





							__a				       =  '''block_{0}.dat'''







							def __init__( self      ,					__A						)  ->int:



											"""simple docstring"""







											__lowerCamelCase		:   Dict           =       filename
											__lowerCamelCase		:   Union[str, Any]           =       []







							def 		__SCREAMING_SNAKE_CASE( self      ,					__A      ,					__A						)  ->int:



											"""simple docstring"""







											__lowerCamelCase		:   Optional[int]           =       self.BLOCK_FILENAME_FORMAT.format(__A						)
											with open(__A      ,					"w"						) as file:
															file.write(__A						)
											self.block_filenames.append(__A						)







							def 		__SCREAMING_SNAKE_CASE( self						)  ->int:



											"""simple docstring"""







											return self.block_filenames







							def 		__SCREAMING_SNAKE_CASE( self      ,					__A      ,					__A=None						)  ->Dict:



											"""simple docstring"""







											__lowerCamelCase		:   Optional[int]           =       0
											with open(self.filename						) as file:
															while True:
																			__lowerCamelCase		:   Union[str, Any]           =       file.readlines(__A						)

																			if lines == []:
																							break

																			if sort_key is None:
																							lines.sort()
																			else:
																							lines.sort(key=__A						)

																			self.write_block("".join(__A						)      ,					__A						)
																			i += 1







							def 		__SCREAMING_SNAKE_CASE( self						)  ->List[Any]:



											"""simple docstring"""







											map(os.remove      ,					self.block_filenames						)





class _snake_case :
							"""simple docstring"""





							def 		__SCREAMING_SNAKE_CASE( self      ,					__A						)  ->Optional[int]:



											"""simple docstring"""







											__lowerCamelCase		:   Union[str, Any]           =       -1
											__lowerCamelCase		:   Optional[Any]           =       None

											for i in range(len(__A						)						):
															if min_str is None or choices[i] < min_str:
																			__lowerCamelCase		:   Tuple           =       i

											return min_index





class _snake_case :
							"""simple docstring"""





							def __init__( self      ,					__A						)  ->List[Any]:



											"""simple docstring"""







											__lowerCamelCase		:   Tuple           =       files
											__lowerCamelCase		:   Tuple           =       set()
											__lowerCamelCase		:   Optional[Any]           =       len(__A						)
											__lowerCamelCase		:   List[str]           =       {i: None for i in range(self.num_buffers						)}







							def 		__SCREAMING_SNAKE_CASE( self						)  ->Any:



											"""simple docstring"""







											return {
											    i: self.buffers[i] for i in range(self.num_buffers						) if i not in self.empty
											}







							def 		__SCREAMING_SNAKE_CASE( self						)  ->Dict:



											"""simple docstring"""







											for i in range(self.num_buffers						):
															if self.buffers[i] is None and i not in self.empty:
																			__lowerCamelCase		:   Tuple           =       self.files[i].readline()

																			if self.buffers[i] == "":
																							self.empty.add(__A						)
																							self.files[i].close()

											if len(self.empty						) == self.num_buffers:
															return False

											return True







							def 		__SCREAMING_SNAKE_CASE( self      ,					__A						)  ->Any:



											"""simple docstring"""







											__lowerCamelCase		:   Tuple           =       self.buffers[index]
											__lowerCamelCase		:   List[Any]           =       None

											return value





class _snake_case :
							"""simple docstring"""





							def __init__( self      ,					__A						)  ->List[str]:



											"""simple docstring"""







											__lowerCamelCase		:   List[Any]           =       merge_strategy







							def 		__SCREAMING_SNAKE_CASE( self      ,					__A      ,					__A      ,					__A						)  ->Optional[int]:



											"""simple docstring"""







											__lowerCamelCase		:   List[str]           =       FilesArray(self.get_file_handles(__A      ,					__A						)						)
											with open(__A      ,					"w"      ,					__A						) as outfile:
															while buffers.refresh():
																			__lowerCamelCase		:   Optional[Any]           =       self.merge_strategy.select(buffers.get_dict()						)
																			outfile.write(buffers.unshift(__A						)						)







							def 		__SCREAMING_SNAKE_CASE( self      ,					__A      ,					__A						)  ->str:



											"""simple docstring"""







											__lowerCamelCase		:   Union[str, Any]           =       {}

											for i in range(len(__A						)						):
															__lowerCamelCase		:   Tuple           =       open(filenames[i]      ,					"r"      ,					__A						)  # noqa: UP015

											return files





class _snake_case :
							"""simple docstring"""





							def __init__( self      ,					__A						)  ->str:



											"""simple docstring"""







											__lowerCamelCase		:   Tuple           =       block_size







							def 		__SCREAMING_SNAKE_CASE( self      ,					__A      ,					__A=None						)  ->List[str]:



											"""simple docstring"""







											__lowerCamelCase		:   Optional[int]           =       self.get_number_blocks(__A      ,					self.block_size						)
											__lowerCamelCase		:   Optional[int]           =       FileSplitter(__A						)
											splitter.split(self.block_size      ,					__A						)

											__lowerCamelCase		:   List[str]           =       FileMerger(NWayMerge()						)
											__lowerCamelCase		:   str           =       self.block_size / (num_blocks + 1)
											merger.merge(splitter.get_block_filenames()      ,					filename + ".out"      ,					__A						)

											splitter.cleanup()







							def 		__SCREAMING_SNAKE_CASE( self      ,					__A      ,					__A						)  ->List[str]:



											"""simple docstring"""







											return (os.stat(__A						).st_size / block_size) + 1





def 				a_							(    lowercase     )      ->List[Any]:






				'''simple docstring'''

				if string[-1].lower() == "k":
								return int(string[:-1]     ) * 1024
				elif string[-1].lower() == "m":
								return int(string[:-1]     ) * 1024 * 1024
				elif string[-1].lower() == "g":
								return int(string[:-1]     ) * 1024 * 1024 * 1024
				else:
								return int(lowercase     )





def 				a_							(    )      ->Optional[Any]:






				'''simple docstring'''

				__lowerCamelCase		:   List[str]           =       argparse.ArgumentParser()
				parser.add_argument(
				    "-m"				,    "--mem"				,    help="amount of memory to use for sorting"				,    default="100M"     )
				parser.add_argument(
				    "filename"				,    metavar="<filename>"				,    nargs=1				,    help="name of file to sort"     )
				__lowerCamelCase		:   List[str]           =       parser.parse_args()

				__lowerCamelCase		:   Tuple           =       ExternalSort(parse_memory(args.mem     )     )
				sorter.sort(args.filename[0]     )


if __name__ == "__main__":
				main()





