


"""simple docstring"""







import argparse
import shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import (
    SeqaSeqDataset,
    calculate_bleu,
    calculate_rouge,
    chunks,
    lmap,
    load_json,
    parse_numeric_n_bool_cl_kwargs,
    save_json,
    use_task_specific_params,
    write_txt_file,
)


__lowercase    =				getLogger(__name__)


def 					lowercase      (    A_  ,       A_  ,       A_  ,       A_ = 8  ,       A_ = 1_024  ,       A_="val"  ,       A_=None  ,       A_=False  ,       A_="summarization"  ,       A_=None  ,       A_=1  ,       A_ = None  ,       A_=""  ,       **A_  ,       )->				Dict:
  '''simple docstring'''







  a						:		Union[str, Any]											=      str(A_	)
  assert local_rank is not None
  torch.distributed.init_process_group(backend="nccl"  ,       rank=A_	)

  a						:		Any											=      Path(A_	)
  a						:		List[str]											=      save_dir.joinpath(F'''rank_{local_rank}_output.json'''	)
  torch.cuda.set_device(A_	)
  a						:		Union[str, Any]											=      AutoModelForSeqaSeqLM.from_pretrained(A_	).cuda()
  if fpaa:
    a						:		str											=      model.half()
  # determine if we need to increase num_beams
  use_task_specific_params(A_  ,       A_	)  # update config with task specific params
  a						:		List[Any]											=      generate_kwargs.pop("num_beams"  ,       model.config.num_beams	)  # AttributeError risk?
  if num_return_sequences > num_beams:
    a						:		int											=      num_return_sequences

  a						:		Union[str, Any]											=      AutoTokenizer.from_pretrained(A_	)
  logger.info(F'''Inferred tokenizer type: {tokenizer.__class__}'''	)  # if this is wrong, check config.model_type.

  if max_source_length is None:
    a						:		Optional[Any]											=      tokenizer.model_max_length
  if prefix is None:
    a						:		List[str]											=      prefix or getattr(model.config  ,       "prefix"  ,       ""	) or ""
  a						:		List[str]											=      SeqaSeqDataset(
      A_  ,       A_  ,       A_  ,       max_target_length=1_024  ,       type_path=A_  ,       n_obs=A_  ,       prefix=A_  ,       **A_  ,       )
  # I set shuffle=True for a more accurate progress bar.
  # If all the longest samples are first, the prog bar estimate is too high at the beginning.
  a						:		Dict											=      ds.make_sortish_sampler(A_  ,       distributed=A_  ,       add_extra_examples=A_  ,       shuffle=A_	)
  a						:		Optional[Any]											=      DataLoader(A_  ,       sampler=A_  ,       batch_size=A_  ,       collate_fn=ds.collate_fn	)
  a						:		List[Any]											=      []
  for batch in tqdm(A_	):
    a						:		Union[str, Any]											=      model.generate(
        input_ids=batch["input_ids"].to(model.device	)  ,       attention_mask=batch["attention_mask"].to(model.device	)  ,       num_return_sequences=A_  ,       num_beams=A_  ,       **A_  ,       )
    a						:		Tuple											=      tokenizer.batch_decode(A_  ,       skip_special_tokens=A_  ,       clean_up_tokenization_spaces=A_	)
    a						:		Optional[Any]											=      batch["ids"]
    if num_return_sequences > 1:
      a						:		str											=      chunks(A_  ,       A_	)  # batch size chunks, each of size num_return_seq
    for i, pred in enumerate(A_	):
      results.append({"pred": pred, "id": ids[i].item()}	)
  save_json(A_  ,       A_	)
  return results, sampler.num_replicas


def 					lowercase      (    )->				Dict:
  '''simple docstring'''







  a						:		List[str]											=      argparse.ArgumentParser(
      epilog="Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate"	)
  parser.add_argument("--data_dir"  ,       type=A_  ,       help="like cnn_dm/test.source"	)
  parser.add_argument(
      "--model_name"  ,       type=A_  ,       help="like facebook/bart-large-cnn,t5-base, etc."  ,       default="sshleifer/distilbart-xsum-12-3"  ,       )
  parser.add_argument("--save_dir"  ,       type=A_  ,       help="where to save"  ,       default="tmp_gen"	)
  parser.add_argument("--max_source_length"  ,       type=A_  ,       default=A_	)
  parser.add_argument(
      "--type_path"  ,       type=A_  ,       default="test"  ,       help="which subset to evaluate typically train/val/test"	)
  parser.add_argument("--task"  ,       type=A_  ,       default="summarization"  ,       help="used for task_specific_params + metrics"	)
  parser.add_argument("--bs"  ,       type=A_  ,       default=8  ,       required=A_  ,       help="batch size"	)
  parser.add_argument(
      "--local_rank"  ,       type=A_  ,       default=-1  ,       required=A_  ,       help="should be passed by distributed.launch"	)

  parser.add_argument(
      "--n_obs"  ,       type=A_  ,       default=A_  ,       required=A_  ,       help="How many observations. Defaults to all."	)
  parser.add_argument(
      "--num_return_sequences"  ,       type=A_  ,       default=1  ,       required=A_  ,       help="How many sequences to return"	)
  parser.add_argument(
      "--sync_timeout"  ,       type=A_  ,       default=600  ,       required=A_  ,       help="How long should master process wait for other processes to finish."  ,       )
  parser.add_argument("--src_lang"  ,       type=A_  ,       default=A_  ,       required=A_	)
  parser.add_argument("--tgt_lang"  ,       type=A_  ,       default=A_  ,       required=A_	)
  parser.add_argument(
      "--prefix"  ,       type=A_  ,       required=A_  ,       default=A_  ,       help="will be added to the begininng of src examples"	)
  parser.add_argument("--fp16"  ,       action="store_true"	)
  parser.add_argument("--debug"  ,       action="store_true"	)
  a						:		Dict											=      time.time()
  a							,		a						:		List[str]											=      parser.parse_known_args()
  a						:		Optional[Any]											=      parse_numeric_n_bool_cl_kwargs(A_	)
  if generate_kwargs and args.local_rank <= 0:
    print(F'''parsed the following generate kwargs: {generate_kwargs}'''	)
  a						:		List[Any]											=      Path(args.save_dir + "_tmp"	)
  Path(A_	).mkdir(exist_ok=A_	)  # this handles locking.
  a						:		List[str]											=      list(json_save_dir.glob("rank_*.json"	)	)
  if intermediate_files:
    raise ValueError(F'''Found files at {json_save_dir} please move or remove them.'''	)
    # In theory, a node could finish and save before another node hits this. If this happens, we can address later.
  a						:		Any											=      {}
  if args.src_lang is not None:
    a						:		int											=      args.src_lang
  if args.tgt_lang is not None:
    a						:		Optional[Any]											=      args.tgt_lang

  Path(args.save_dir	).mkdir(exist_ok=A_	)
  a							,		a						:		Optional[Any]											=      eval_data_dir(
      args.data_dir  ,       A_  ,       args.model_name  ,       type_path=args.type_path  ,       bs=args.bs  ,       fpaa=args.fpaa  ,       task=args.task  ,       local_rank=args.local_rank  ,       n_obs=args.n_obs  ,       max_source_length=args.max_source_length  ,       num_return_sequences=args.num_return_sequences  ,       prefix=args.prefix  ,       dataset_kwargs=A_  ,       **A_  ,       )

  if args.local_rank <= 0:
    a						:		Optional[int]											=      Path(args.save_dir	)
    save_dir.mkdir(exist_ok=A_	)
    a						:		Union[str, Any]											=      gather_results_from_each_node(A_  ,       A_  ,       args.sync_timeout	)
    a						:		Union[str, Any]											=      combine_partial_results(A_	)
    if args.num_return_sequences > 1:
      a						:		List[Any]											=      save_dir.joinpath("pseudolabel_results.json"	)
      print(F'''Saving aggregated results at {save_path}, intermediate in {json_save_dir}/'''	)
      save_json(A_  ,       A_	)
      return
    a						:		Any											=      Path(args.data_dir	).joinpath(args.type_path + ".target"	)
    with open(A_	) as f:
      a						:		Optional[Any]											=      [x.rstrip() for x in f.readlines()][: len(A_	)]

    # Calculate metrics, save metrics,  and save _generations.txt
    a						:		Optional[Any]											=      "translation" in args.task
    a						:		Optional[int]											=      calculate_bleu if calc_bleu else calculate_rouge
    a						:		List[str]											=      "bleu" if calc_bleu else "rouge"
    a						:		Dict											=      score_fn(A_  ,       A_	)
    a						:		List[Any]											=      len(A_	)
    a						:		Union[str, Any]											=      time.time() - start_time
    a						:		Optional[Any]											=      round(runtime / metrics["n_obs"]  ,       4	)
    a						:		str											=      num_replicas
    # TODO(@stas00): add whatever metadata to metrics
    a						:		str											=      save_dir.joinpath(F'''{args.type_path}_{metric_name}.json'''	)
    save_json(A_  ,       A_  ,       indent=A_	)
    print(A_	)
    write_txt_file(A_  ,       save_dir.joinpath(F'''{args.type_path}_generations.txt'''	)	)
    if args.debug:
      write_txt_file(A_  ,       save_dir.joinpath(F'''{args.type_path}.target'''	)	)
    else:
      shutil.rmtree(A_	)


def 					lowercase      (    A_	)->				List:
  '''simple docstring'''







  a						:		int											=      []
  for partial_result in partial_results:
    records.extend(A_	)
  a						:		List[str]											=      sorted(A_  ,       key=lambda A_	: x["id"]	)
  a						:		List[str]											=      [x["pred"] for x in records]
  return preds


def 					lowercase      (    A_  ,       A_  ,       A_	)->				List[Dict[str, List]]:
  '''simple docstring'''







  a						:		Optional[int]											=      time.time()
  logger.info("waiting for all nodes to finish"	)
  a						:		Optional[Any]											=      None
  while (time.time() - start_wait) < timeout:
    a						:		Optional[int]											=      list(save_dir.glob("rank_*.json"	)	)
    if len(A_	) < num_replicas:
      continue
    try:
      # make sure all json files are fully saved
      a						:		Optional[Any]											=      lmap(A_  ,       A_	)
      return json_data
    except JSONDecodeError:
      continue
  else:
    raise TimeoutError("Rank 0 gave up on waiting for other processes"	)
  # Unreachable


if __name__ == "__main__":
 # Usage for MT:
 run_generate()










"""simple docstring"""







import argparse
import json

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import ConvNextConfig, SegformerImageProcessor, UperNetConfig, UperNetForSemanticSegmentation


def 					lowercase      (    A_	)->				Optional[Any]:
  '''simple docstring'''







  a						:		Optional[Any]											=      384
  if "tiny" in model_name:
    a						:		List[str]											=      [3, 3, 9, 3]
    a						:		Optional[Any]											=      [96, 192, 384, 768]
  if "small" in model_name:
    a						:		Tuple											=      [3, 3, 27, 3]
    a						:		str											=      [96, 192, 384, 768]
  if "base" in model_name:
    a						:		Union[str, Any]											=      [3, 3, 27, 3]
    a						:		Dict											=      [128, 256, 512, 1_024]
    a						:		Any											=      512
  if "large" in model_name:
    a						:		Optional[Any]											=      [3, 3, 27, 3]
    a						:		str											=      [192, 384, 768, 1_536]
    a						:		Dict											=      768
  if "xlarge" in model_name:
    a						:		str											=      [3, 3, 27, 3]
    a						:		List[Any]											=      [256, 512, 1_024, 2_048]
    a						:		List[Any]											=      1_024

  # set label information
  a						:		int											=      150
  a						:		str											=      "huggingface/label-files"
  a						:		Tuple											=      "ade20k-id2label.json"
  a						:		Dict											=      json.load(open(hf_hub_download(A_  ,       A_  ,       repo_type="dataset"	)  ,       "r"	)	)
  a						:		int											=      {int(A_	): v for k, v in idalabel.items()}
  a						:		List[Any]											=      {v: k for k, v in idalabel.items()}

  a						:		Optional[int]											=      ConvNextConfig(
      depths=A_  ,       hidden_sizes=A_  ,       out_features=["stage1", "stage2", "stage3", "stage4"]	)
  a						:		Tuple											=      UperNetConfig(
      backbone_config=A_  ,       auxiliary_in_channels=A_  ,       num_labels=A_  ,       idalabel=A_  ,       labelaid=A_  ,       )

  return config


def 					lowercase      (    A_	)->				Tuple:
  '''simple docstring'''







  a						:		int											=      []

  # fmt: off
  # stem
  rename_keys.append(("backbone.downsample_layers.0.0.weight", "backbone.embeddings.patch_embeddings.weight")	)
  rename_keys.append(("backbone.downsample_layers.0.0.bias", "backbone.embeddings.patch_embeddings.bias")	)
  rename_keys.append(("backbone.downsample_layers.0.1.weight", "backbone.embeddings.layernorm.weight")	)
  rename_keys.append(("backbone.downsample_layers.0.1.bias", "backbone.embeddings.layernorm.bias")	)
  # stages
  for i in range(len(config.backbone_config.depths	)	):
    for j in range(config.backbone_config.depths[i]	):
      rename_keys.append((F'''backbone.stages.{i}.{j}.gamma''', F'''backbone.encoder.stages.{i}.layers.{j}.layer_scale_parameter''')	)
      rename_keys.append((F'''backbone.stages.{i}.{j}.depthwise_conv.weight''', F'''backbone.encoder.stages.{i}.layers.{j}.dwconv.weight''')	)
      rename_keys.append((F'''backbone.stages.{i}.{j}.depthwise_conv.bias''', F'''backbone.encoder.stages.{i}.layers.{j}.dwconv.bias''')	)
      rename_keys.append((F'''backbone.stages.{i}.{j}.norm.weight''', F'''backbone.encoder.stages.{i}.layers.{j}.layernorm.weight''')	)
      rename_keys.append((F'''backbone.stages.{i}.{j}.norm.bias''', F'''backbone.encoder.stages.{i}.layers.{j}.layernorm.bias''')	)
      rename_keys.append((F'''backbone.stages.{i}.{j}.pointwise_conv1.weight''', F'''backbone.encoder.stages.{i}.layers.{j}.pwconv1.weight''')	)
      rename_keys.append((F'''backbone.stages.{i}.{j}.pointwise_conv1.bias''', F'''backbone.encoder.stages.{i}.layers.{j}.pwconv1.bias''')	)
      rename_keys.append((F'''backbone.stages.{i}.{j}.pointwise_conv2.weight''', F'''backbone.encoder.stages.{i}.layers.{j}.pwconv2.weight''')	)
      rename_keys.append((F'''backbone.stages.{i}.{j}.pointwise_conv2.bias''', F'''backbone.encoder.stages.{i}.layers.{j}.pwconv2.bias''')	)
    if i > 0:
      rename_keys.append((F'''backbone.downsample_layers.{i}.0.weight''', F'''backbone.encoder.stages.{i}.downsampling_layer.0.weight''')	)
      rename_keys.append((F'''backbone.downsample_layers.{i}.0.bias''', F'''backbone.encoder.stages.{i}.downsampling_layer.0.bias''')	)
      rename_keys.append((F'''backbone.downsample_layers.{i}.1.weight''', F'''backbone.encoder.stages.{i}.downsampling_layer.1.weight''')	)
      rename_keys.append((F'''backbone.downsample_layers.{i}.1.bias''', F'''backbone.encoder.stages.{i}.downsampling_layer.1.bias''')	)

    rename_keys.append((F'''backbone.norm{i}.weight''', F'''backbone.hidden_states_norms.stage{i+1}.weight''')	)
    rename_keys.append((F'''backbone.norm{i}.bias''', F'''backbone.hidden_states_norms.stage{i+1}.bias''')	)

  # decode head
  rename_keys.extend(
      [
          ("decode_head.conv_seg.weight", "decode_head.classifier.weight"),
          ("decode_head.conv_seg.bias", "decode_head.classifier.bias"),
          ("auxiliary_head.conv_seg.weight", "auxiliary_head.classifier.weight"),
          ("auxiliary_head.conv_seg.bias", "auxiliary_head.classifier.bias"),
      ]	)
  # fmt: on

  return rename_keys


def 					lowercase      (    A_  ,       A_  ,       A_	)->				str:
  '''simple docstring'''







  a						:		str											=      dct.pop(A_	)
  a						:		str											=      val


def 					lowercase      (    A_  ,       A_  ,       A_	)->				int:
  '''simple docstring'''







  a						:		Any											=      {
      "upernet-convnext-tiny": "https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_tiny_fp16_512x512_160k_ade20k/upernet_convnext_tiny_fp16_512x512_160k_ade20k_20220227_124553-cad485de.pth",
      "upernet-convnext-small": "https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_small_fp16_512x512_160k_ade20k/upernet_convnext_small_fp16_512x512_160k_ade20k_20220227_131208-1b1e394f.pth",
      "upernet-convnext-base": "https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_base_fp16_512x512_160k_ade20k/upernet_convnext_base_fp16_512x512_160k_ade20k_20220227_181227-02a24fc6.pth",
      "upernet-convnext-large": "https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_large_fp16_640x640_160k_ade20k/upernet_convnext_large_fp16_640x640_160k_ade20k_20220226_040532-e57aa54d.pth",
      "upernet-convnext-xlarge": "https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_xlarge_fp16_640x640_160k_ade20k/upernet_convnext_xlarge_fp16_640x640_160k_ade20k_20220226_080344-95fc38c2.pth",
  }
  a						:		Tuple											=      model_name_to_url[model_name]
  a						:		int											=      torch.hub.load_state_dict_from_url(A_  ,       map_location="cpu"	)["state_dict"]

  a						:		Optional[Any]											=      get_upernet_config(A_	)
  a						:		int											=      UperNetForSemanticSegmentation(A_	)
  model.eval()

  # replace "bn" => "batch_norm"
  for key in state_dict.copy().keys():
    a						:		Tuple											=      state_dict.pop(A_	)
    if "bn" in key:
      a						:		Optional[int]											=      key.replace("bn"  ,       "batch_norm"	)
    a						:		Any											=      val

  # rename keys
  a						:		Any											=      create_rename_keys(A_	)
  for src, dest in rename_keys:
    rename_key(A_  ,       A_  ,       A_	)

  model.load_state_dict(A_	)

  # verify on image
  a						:		Dict											=      "https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg"
  a						:		Any											=      Image.open(requests.get(A_  ,       stream=A_	).raw	).convert("RGB"	)

  a						:		Union[str, Any]											=      SegformerImageProcessor()
  a						:		Dict											=      processor(A_  ,       return_tensors="pt"	).pixel_values

  with torch.no_grad():
    a						:		Any											=      model(A_	)

  if model_name == "upernet-convnext-tiny":
    a						:		List[str]											=      torch.tensor(
        [[-8.8_1_1_0, -8.8_1_1_0, -8.6_5_2_1], [-8.8_1_1_0, -8.8_1_1_0, -8.6_5_2_1], [-8.7_7_4_6, -8.7_7_4_6, -8.6_1_3_0]]	)
  elif model_name == "upernet-convnext-small":
    a						:		int											=      torch.tensor(
        [[-8.8_2_3_6, -8.8_2_3_6, -8.6_7_7_1], [-8.8_2_3_6, -8.8_2_3_6, -8.6_7_7_1], [-8.7_6_3_8, -8.7_6_3_8, -8.6_2_4_0]]	)
  elif model_name == "upernet-convnext-base":
    a						:		Union[str, Any]											=      torch.tensor(
        [[-8.8_5_5_8, -8.8_5_5_8, -8.6_9_0_5], [-8.8_5_5_8, -8.8_5_5_8, -8.6_9_0_5], [-8.7_6_6_9, -8.7_6_6_9, -8.6_0_2_1]]	)
  elif model_name == "upernet-convnext-large":
    a						:		Union[str, Any]											=      torch.tensor(
        [[-8.6_6_6_0, -8.6_6_6_0, -8.6_2_1_0], [-8.6_6_6_0, -8.6_6_6_0, -8.6_2_1_0], [-8.6_3_1_0, -8.6_3_1_0, -8.5_9_6_4]]	)
  elif model_name == "upernet-convnext-xlarge":
    a						:		str											=      torch.tensor(
        [[-8.4_9_8_0, -8.4_9_8_0, -8.3_9_7_7], [-8.4_9_8_0, -8.4_9_8_0, -8.3_9_7_7], [-8.4_3_7_9, -8.4_3_7_9, -8.3_4_1_2]]	)
  print("Logits:"  ,       outputs.logits[0, 0, :3, :3]	)
  assert torch.allclose(outputs.logits[0, 0, :3, :3]  ,       A_  ,       atol=1e-4	)
  print("Looks ok!"	)

  if pytorch_dump_folder_path is not None:
    print(F'''Saving model {model_name} to {pytorch_dump_folder_path}'''	)
    model.save_pretrained(A_	)
    print(F'''Saving processor to {pytorch_dump_folder_path}'''	)
    processor.save_pretrained(A_	)

  if push_to_hub:
    print(F'''Pushing model and processor for {model_name} to hub'''	)
    model.push_to_hub(F'''openmmlab/{model_name}'''	)
    processor.push_to_hub(F'''openmmlab/{model_name}'''	)


if __name__ == "__main__":
 __lowercase    =				argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     """--model_name""",
     default="""upernet-convnext-tiny""",
     type=str,
     choices=[f'''upernet-convnext-{size}''' for size in ["""tiny""", """small""", """base""", """large""", """xlarge"""]],
     help="""Name of the ConvNext UperNet model you'd like to convert.""",
 )
 parser.add_argument(
     """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
 )
 parser.add_argument(
     """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
 )

 __lowercase    =				parser.parse_args()
 convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






