



'''simple docstring'''







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







def  SCREAMING_SNAKE_CASE__ (				):
		SCREAMING_SNAKE_CASE_						:Optional[int]										=      DetaConfig(
		    num_queries=900 ,		encoder_ffn_dim=2048 ,		decoder_ffn_dim=2048 ,		num_feature_levels=5 ,		assign_first_stage=SCREAMING_SNAKE_CASE ,		with_box_refine=SCREAMING_SNAKE_CASE ,		two_stage=SCREAMING_SNAKE_CASE ,		)

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

		return config







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE     ):
		SCREAMING_SNAKE_CASE_						:Tuple										=      []

		# 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  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     ):
		SCREAMING_SNAKE_CASE_						:Tuple										=      dct.pop(SCREAMING_SNAKE_CASE     )
		SCREAMING_SNAKE_CASE_						:str										=      val







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     ):
		# transformer decoder self-attention layers
		SCREAMING_SNAKE_CASE_						:List[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_						:Dict										=      state_dict.pop(F'transformer.decoder.layers.{i}.self_attn.in_proj_weight'     )
				SCREAMING_SNAKE_CASE_						:int										=      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_						:Optional[Any]										=      in_proj_weight[:hidden_size, :]
				SCREAMING_SNAKE_CASE_						:List[Any]										=      in_proj_bias[:hidden_size]
				SCREAMING_SNAKE_CASE_						:List[str]										=      in_proj_weight[
				    hidden_size : hidden_size * 2, :
				]
				SCREAMING_SNAKE_CASE_						:Any										=      in_proj_bias[hidden_size : hidden_size * 2]
				SCREAMING_SNAKE_CASE_						:List[Any]										=      in_proj_weight[-hidden_size:, :]
				SCREAMING_SNAKE_CASE_						:Optional[int]										=      in_proj_bias[-hidden_size:]







def  SCREAMING_SNAKE_CASE__ (				):
		SCREAMING_SNAKE_CASE_						:Optional[int]										=      'http://images.cocodataset.org/val2017/000000039769.jpg'
		SCREAMING_SNAKE_CASE_						:str										=      Image.open(requests.get(SCREAMING_SNAKE_CASE ,		stream=SCREAMING_SNAKE_CASE     ).raw     )

		return im







@torch.no_grad()
def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     ):
		SCREAMING_SNAKE_CASE_						:Tuple										=      get_deta_config()

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

		# rename keys
		SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      create_rename_keys(SCREAMING_SNAKE_CASE     )
		for src, dest in rename_keys:
				rename_key(SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     )
		read_in_decoder_q_k_v(SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     )

		# 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(SCREAMING_SNAKE_CASE     )
						SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      val
				if "input_proj" in key:
						SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      state_dict.pop(SCREAMING_SNAKE_CASE     )
						SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      val
				if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
						SCREAMING_SNAKE_CASE_						:Optional[Any]										=      state_dict.pop(SCREAMING_SNAKE_CASE     )
						SCREAMING_SNAKE_CASE_						:List[Any]										=      val

    # finally, create HuggingFace model and load state dict
		SCREAMING_SNAKE_CASE_						:List[Any]										=      DetaForObjectDetection(SCREAMING_SNAKE_CASE     )
		model.load_state_dict(SCREAMING_SNAKE_CASE     )
		model.eval()

		SCREAMING_SNAKE_CASE_						:Any										=      'cuda' if torch.cuda.is_available() else 'cpu'
		model.to(SCREAMING_SNAKE_CASE     )

		# load image processor
		SCREAMING_SNAKE_CASE_						:str										=      DetaImageProcessor(format='coco_detection'     )

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

		# verify logits
		if model_name == "deta-resnet-50":
				SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      torch.tensor(
				    [[-7.3_9_7_8, -2.5_4_0_6, -4.1_6_6_8], [-8.2_6_8_4, -3.9_9_3_3, -3.8_0_9_6], [-7.0_5_1_5, -3.7_9_7_3, -5.8_5_1_6]]     )
				SCREAMING_SNAKE_CASE_						:Dict										=      torch.tensor([[0.5_0_4_3, 0.4_9_7_3, 0.9_9_9_8], [0.2_5_4_2, 0.5_4_8_9, 0.4_7_4_8], [0.5_4_9_0, 0.2_7_6_5, 0.0_5_7_0]]     )
		elif model_name == "deta-resnet-50-24-epochs":
				SCREAMING_SNAKE_CASE_						:int										=      torch.tensor(
				    [[-7.1_6_8_8, -2.4_8_5_7, -4.8_6_6_9], [-7.8_6_3_0, -3.8_1_5_4, -4.2_6_7_4], [-7.2_7_3_0, -4.1_8_6_5, -5.5_3_2_3]]     )
				SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      torch.tensor([[0.5_0_2_1, 0.4_9_7_1, 0.9_9_9_4], [0.2_5_4_6, 0.5_4_8_6, 0.4_7_3_1], [0.1_6_8_6, 0.1_9_8_6, 0.2_1_4_2]]     )

		assert torch.allclose(outputs.logits[0, :3, :3] ,		expected_logits.to(SCREAMING_SNAKE_CASE     ) ,		atol=1E-4     )
		assert torch.allclose(outputs.pred_boxes[0, :3, :3] ,		expected_boxes.to(SCREAMING_SNAKE_CASE     ) ,		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(SCREAMING_SNAKE_CASE     ).mkdir(exist_ok=SCREAMING_SNAKE_CASE     )
				model.save_pretrained(SCREAMING_SNAKE_CASE     )
				processor.save_pretrained(SCREAMING_SNAKE_CASE     )

		# 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__":
							SCREAMING_SNAKE_CASE__     : Union[str, Any]				=   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."""
							)
							SCREAMING_SNAKE_CASE__     : List[Any]				=   parser.parse_args()
							convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





import json
import os
import subprocess
import sys
import warnings
from argparse import ArgumentParser
from contextlib import AbstractContextManager
from typing import Dict, List, Optional

import requests

from ..utils import logging
from . import BaseTransformersCLICommand


_lowerCamelCase         =	logging.get_logger(__name__)  # pylint: disable=invalid-name


_lowerCamelCase         =	"""lfs-multipart-upload"""

class    _SCREAMING_SNAKE_CASE						(UpperCamelCase			):





     @staticmethod
     def 						__snake_case     (							UpperCamelCase			:				ArgumentParser					)->Any:
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						parser.add_parser(
          "lfs-enable-largefiles"  ,	help=(
              "Deprecated: use `huggingface-cli` instead. Configure your repository to enable upload of files > 5GB."
          )  ,	)
      enable_parser.add_argument("path"  ,	type=UpperCamelCase  ,	help="Local path to repository you want to configure."					)
      enable_parser.set_defaults(func=lambda UpperCamelCase					: LfsEnableCommand(UpperCamelCase					)					)

      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						parser.add_parser(
          UpperCamelCase  ,	help=(
              "Deprecated: use `huggingface-cli` instead. "
              "Command will get called by git-lfs, do not call it directly."
          )  ,	)
      upload_parser.set_defaults(func=lambda UpperCamelCase					: LfsUploadCommand(UpperCamelCase					)					)






class    _SCREAMING_SNAKE_CASE						:





     def __init__(							self			:				Union[str, Any]  ,	UpperCamelCase			:				List[Any]					)->List[str]:
      __SCREAMING_SNAKE_CASE							:     str							=						args






     def 						__snake_case     (							self			:				Union[str, Any]					)->List[Any]:
      warnings.warn(
          "Managing repositories through transformers-cli is deprecated. Please use `huggingface-cli` instead."					)
      __SCREAMING_SNAKE_CASE							:     List[Any]							=						os.path.abspath(self.args.path					)
      if not os.path.isdir(UpperCamelCase					):
       print("This does not look like a valid git repo."					)
       exit(1					)
      subprocess.run(
          "git config lfs.customtransfer.multipart.path transformers-cli".split()  ,	check=UpperCamelCase  ,	cwd=UpperCamelCase					)
      subprocess.run(
          F"""git config lfs.customtransfer.multipart.args {LFS_MULTIPART_UPLOAD_COMMAND}""".split()  ,	check=UpperCamelCase  ,	cwd=UpperCamelCase  ,	)
      print("Local repo set up for largefiles"					)






def 						_lowerCAmelCase    (					__lowerCamelCase :  Dict  ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE							:     List[str]							=						json.dumps(__lowerCamelCase  ) + "\n"
 sys.stdout.write(__lowerCamelCase  )
 sys.stdout.flush()






def 						_lowerCAmelCase    (					):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE							:     Optional[int]							=						json.loads(sys.stdin.readline().strip()  )

 if "terminate" in (msg.get("type"  ), msg.get("event"  )):
  # terminate message received
  return None

 if msg.get("event"  ) not in ("download", "upload"):
  logger.critical("Received unexpected message"  )
  sys.exit(1  )

 return msg

class    _SCREAMING_SNAKE_CASE						(UpperCamelCase			):





     def __init__(							self			:				str  ,	UpperCamelCase			:				str  ,	UpperCamelCase			:				int  ,	UpperCamelCase			:				int					)->int:
      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						filepath
      __SCREAMING_SNAKE_CASE							:     List[str]							=						seek_from
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						read_limit
      __SCREAMING_SNAKE_CASE							:     int							=						0





     def __enter__(							self			:				Optional[Any]					)->int:
      __SCREAMING_SNAKE_CASE							:     int							=						open(self.filepath  ,	"rb"					)
      self.f.seek(self.seek_from					)
      return self





     def __len__(							self			:				int					)->Dict:
      __SCREAMING_SNAKE_CASE							:     str							=						os.fstat(self.f.fileno()					).st_size
      return min(self.read_limit  ,	total_length - self.seek_from					)





     def 						__snake_case     (							self			:				Any  ,	UpperCamelCase			:				List[str]=-1					)->Optional[Any]:
      if self.n_seen >= self.read_limit:
       return b""
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						self.read_limit - self.n_seen
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						self.f.read(remaining_amount if n < 0 else min(UpperCamelCase  ,	UpperCamelCase					)					)
      self.n_seen += len(UpperCamelCase					)
      return data





     def __iter__(							self			:				Optional[Any]					)->Any:
      yield self.read(n=4 * 1_0_2_4 * 1_0_2_4					)






     def __exit__(							self			:				Tuple  ,	*UpperCamelCase			:				int					)->str:
      self.f.close()






class    _SCREAMING_SNAKE_CASE						:





     def __init__(							self			:				List[str]  ,	UpperCamelCase			:				Optional[int]					)->Optional[int]:
      __SCREAMING_SNAKE_CASE							:     Any							=						args






     def 						__snake_case     (							self			:				int					)->int:
      # Immediately after invoking a custom transfer process, git-lfs
      # sends initiation data to the process over stdin.
      # This tells the process useful information about the configuration.
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						json.loads(sys.stdin.readline().strip()					)
      if not (init_msg.get("event"					) == "init" and init_msg.get("operation"					) == "upload"):
       write_msg({"error": {"code": 3_2, "message": "Wrong lfs init operation"}}					)
       sys.exit(1					)

      # The transfer process should use the information it needs from the
      # initiation structure, and also perform any one-off setup tasks it
      # needs to do. It should then respond on stdout with a simple empty
      # confirmation structure, as follows:
      write_msg({}					)

      # After the initiation exchange, git-lfs will send any number of
      # transfer requests to the stdin of the transfer process, in a serial sequence.
      while True:
       __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						read_msg()
       if msg is None:
        # When all transfers have been processed, git-lfs will send
        # a terminate event to the stdin of the transfer process.
        # On receiving this message the transfer process should
        # clean up and terminate. No response is expected.
        sys.exit(0					)

       __SCREAMING_SNAKE_CASE							:     Dict							=						msg["oid"]
       __SCREAMING_SNAKE_CASE							:     List[str]							=						msg["path"]
       __SCREAMING_SNAKE_CASE							:     Tuple							=						msg["action"]["href"]
       __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						msg["action"]["header"]
       __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						int(header.pop("chunk_size"					)					)
       __SCREAMING_SNAKE_CASE							:     List[str]							=						list(header.values()					)

       __SCREAMING_SNAKE_CASE							:     List[str]							=						[]
       for i, presigned_url in enumerate(UpperCamelCase					):
        with FileSlice(UpperCamelCase  ,	seek_from=i * chunk_size  ,	read_limit=UpperCamelCase					) as data:
         __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						requests.put(UpperCamelCase  ,	data=UpperCamelCase					)
         r.raise_for_status()
         parts.append(
             {
                 "etag": r.headers.get("etag"					),
                 "partNumber": i + 1,
             }					)
         # In order to support progress reporting while data is uploading / downloading,
         # the transfer process should post messages to stdout
         write_msg(
             {
                 "event": "progress",
                 "oid": oid,
                 "bytesSoFar": (i + 1) * chunk_size,
                 "bytesSinceLast": chunk_size,
             }					)
         # Not precise but that's ok.

       __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						requests.post(
           UpperCamelCase  ,	json={
               "oid": oid,
               "parts": parts,
           }  ,	)
       r.raise_for_status()

       write_msg({"event": "complete", "oid": oid}					)






