





'''simple docstring'''
import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, parse_numeric_n_bool_cl_kwargs, use_task_specific_params


a__							:							Any			=getLogger(__name__)


a__							:							List[Any]			='''cuda''' if torch.cuda.is_available() else '''cpu'''
def     lowercase__       (       __lowercase						:  List[str]    ,  __lowercase						:  str    ,  __lowercase						:  str    ,  __lowercase						:  int = 8    ,  __lowercase						:  str = DEFAULT_DEVICE    ,  __lowercase						:  str=False    ,  __lowercase						:  Union[str, Any]="summarization"    ,  __lowercase						:  Union[str, Any]=None    ,  **__lowercase						:  Any    ,  )			->				Dict:



       """simple docstring"""






       __UpperCamelCase	=  Path(__lowercase					).open('w'    ,  encoding='utf-8'					)
       __UpperCamelCase	=  str(__lowercase					)
       __UpperCamelCase	=  AutoModelForSeqaSeqLM.from_pretrained(__lowercase					).to(__lowercase					)
       if fpaa:
              __UpperCamelCase	=  model.half()

       __UpperCamelCase	=  AutoTokenizer.from_pretrained(__lowercase					)
       logger.info(F'''Inferred tokenizer type: {tokenizer.__class__}'''					)  # if this is wrong, check config.model_type.

       __UpperCamelCase	=  time.time()
       # update config with task specific params
       use_task_specific_params(__lowercase    ,  __lowercase					)
       if prefix is None:
              __UpperCamelCase	=  prefix or getattr(model.config    ,  'prefix'    ,  ''					) or ''
       for examples_chunk in tqdm(list(chunks(__lowercase    ,  __lowercase					)					)					):
              __UpperCamelCase	=  [prefix + text for text in examples_chunk]
              __UpperCamelCase	=  tokenizer(__lowercase    ,  return_tensors='pt'    ,  truncation=__lowercase    ,  padding='longest'					).to(__lowercase					)
              __UpperCamelCase	=  model.generate(
                  input_ids=batch.input_ids    ,  attention_mask=batch.attention_mask    ,  **__lowercase    ,  )
              __UpperCamelCase	=  tokenizer.batch_decode(__lowercase    ,  skip_special_tokens=__lowercase    ,  clean_up_tokenization_spaces=__lowercase					)
              for hypothesis in dec:
                     fout.write(hypothesis + '\n'					)
                     fout.flush()
       fout.close()
       __UpperCamelCase	=  int(time.time() - start_time					)  # seconds
       __UpperCamelCase	=  len(__lowercase					)
       return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs    ,  4					)}
def     lowercase__       (       )			->				Any:



       """simple docstring"""






       return datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'					)
def     lowercase__       (       __lowercase						:  str=True					)			->				Any:



       """simple docstring"""






       __UpperCamelCase	=  argparse.ArgumentParser()
       parser.add_argument('model_name'    ,  type=__lowercase    ,  help='like facebook/bart-large-cnn,t5-base, etc.'					)
       parser.add_argument('input_path'    ,  type=__lowercase    ,  help='like cnn_dm/test.source'					)
       parser.add_argument('save_path'    ,  type=__lowercase    ,  help='where to save summaries'					)
       parser.add_argument('--reference_path'    ,  type=__lowercase    ,  required=__lowercase    ,  help='like cnn_dm/test.target'					)
       parser.add_argument('--score_path'    ,  type=__lowercase    ,  required=__lowercase    ,  default='metrics.json'    ,  help='where to save metrics'					)
       parser.add_argument('--device'    ,  type=__lowercase    ,  required=__lowercase    ,  default=__lowercase    ,  help='cuda, cuda:1, cpu etc.'					)
       parser.add_argument(
           '--prefix'    ,  type=__lowercase    ,  required=__lowercase    ,  default=__lowercase    ,  help='will be added to the begininng of src examples'					)
       parser.add_argument('--task'    ,  type=__lowercase    ,  default='summarization'    ,  help='used for task_specific_params + metrics'					)
       parser.add_argument('--bs'    ,  type=__lowercase    ,  default=8    ,  required=__lowercase    ,  help='batch size'					)
       parser.add_argument(
           '--n_obs'    ,  type=__lowercase    ,  default=-1    ,  required=__lowercase    ,  help='How many observations. Defaults to all.'					)
       parser.add_argument('--fp16'    ,  action='store_true'					)
       parser.add_argument('--dump-args'    ,  action='store_true'    ,  help='print the custom hparams with the results'					)
       parser.add_argument(
           '--info'    ,  nargs='?'    ,  type=__lowercase    ,  const=datetime_now()    ,  help=(
               'use in conjunction w/ --dump-args to print with the results whatever other info you\'d like, e.g.'
               ' lang=en-ru. If no value is passed, the current datetime string will be used.'
           )    ,  )
       # Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate
       __UpperCamelCase	,						__UpperCamelCase	=  parser.parse_known_args()
       __UpperCamelCase	=  parse_numeric_n_bool_cl_kwargs(__lowercase					)
       if parsed_args and verbose:
              print(F'''parsed the following generate kwargs: {parsed_args}'''					)
       with open(args.input_path					) as f:
              __UpperCamelCase	=  [' ' + x.rstrip() if 't5' in args.model_name else x.rstrip() for x in f.readlines()]
       if args.n_obs > 0:
              __UpperCamelCase	=  examples[: args.n_obs]
       Path(args.save_path					).parent.mkdir(exist_ok=__lowercase					)
       if args.reference_path is None and Path(args.score_path					).exists():
              warnings.warn(F'''score_path {args.score_path} will be overwritten unless you type ctrl-c.'''					)
       __UpperCamelCase	=  generate_summaries_or_translations(
           __lowercase    ,  args.save_path    ,  args.model_name    ,  batch_size=args.bs    ,  device=args.device    ,  fpaa=args.fpaa    ,  task=args.task    ,  prefix=args.prefix    ,  **__lowercase    ,  )

       if args.reference_path is None:
              return {}

       # Compute scores
       __UpperCamelCase	=  calculate_bleu if 'translation' in args.task else calculate_rouge
       __UpperCamelCase	=  [x.rstrip() for x in open(args.save_path					).readlines()]
       __UpperCamelCase	=  [x.rstrip() for x in open(args.reference_path					).readlines()][: len(__lowercase					)]
       __UpperCamelCase	=  score_fn(__lowercase    ,  __lowercase					)
       scores.update(__lowercase					)

       if args.dump_args:
              scores.update(__lowercase					)
       if args.info:
              __UpperCamelCase	=  args.info

       if verbose:
              print(__lowercase					)

       if args.score_path is not None:
              json.dump(__lowercase    ,  open(args.score_path    ,  'w'					)					)

       return scores


if __name__ == "__main__":
    # Usage for MT:
    # python run_eval.py MODEL_NAME $DATA_DIR/test.source $save_dir/test_translations.txt --reference_path $DATA_DIR/test.target --score_path $save_dir/test_bleu.json  --task translation $@
    run_generate(verbose=True)







'''simple docstring'''
import argparse
import json
import os

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

from transformers import ConvNextImageProcessor, ConvNextVaConfig, ConvNextVaForImageClassification
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
a__							:							Union[str, Any]			=logging.get_logger(__name__)
def     lowercase__       (       __lowercase						:  Union[str, Any]					)			->				Union[str, Any]:



       """simple docstring"""






       __UpperCamelCase	=  ConvNextVaConfig()

       if "atto" in checkpoint_url:
              __UpperCamelCase	=  [2, 2, 6, 2]
              __UpperCamelCase	=  [40, 80, 160, 320]
       if "femto" in checkpoint_url:
              __UpperCamelCase	=  [2, 2, 6, 2]
              __UpperCamelCase	=  [48, 96, 192, 384]
       if "pico" in checkpoint_url:
              __UpperCamelCase	=  [2, 2, 6, 2]
              __UpperCamelCase	=  [64, 128, 256, 512]
       if "nano" in checkpoint_url:
              __UpperCamelCase	=  [2, 2, 8, 2]
              __UpperCamelCase	=  [80, 160, 320, 640]
       if "tiny" in checkpoint_url:
              __UpperCamelCase	=  [3, 3, 9, 3]
              __UpperCamelCase	=  [96, 192, 384, 768]
       if "base" in checkpoint_url:
              __UpperCamelCase	=  [3, 3, 27, 3]
              __UpperCamelCase	=  [128, 256, 512, 1024]
       if "large" in checkpoint_url:
              __UpperCamelCase	=  [3, 3, 27, 3]
              __UpperCamelCase	=  [192, 384, 768, 1536]
       if "huge" in checkpoint_url:
              __UpperCamelCase	=  [3, 3, 27, 3]
              __UpperCamelCase	=  [352, 704, 1408, 2816]

       __UpperCamelCase	=  1000
       __UpperCamelCase	=  'imagenet-1k-id2label.json'
       __UpperCamelCase	=  (1, 1000)

       __UpperCamelCase	=  'huggingface/label-files'
       __UpperCamelCase	=  num_labels
       __UpperCamelCase	=  json.load(open(hf_hub_download(__lowercase    ,  __lowercase    ,  repo_type='dataset'					)    ,  'r'					)					)
       __UpperCamelCase	=  {int(__lowercase					): v for k, v in idalabel.items()}

       __UpperCamelCase	=  idalabel
       __UpperCamelCase	=  {v: k for k, v in idalabel.items()}
       __UpperCamelCase	=  hidden_sizes
       __UpperCamelCase	=  depths

       return config, expected_shape
def     lowercase__       (       __lowercase						:  Tuple					)			->				Optional[Any]:



       """simple docstring"""






       if "downsample_layers.0.0" in name:
              __UpperCamelCase	=  name.replace('downsample_layers.0.0'    ,  'embeddings.patch_embeddings'					)
       if "downsample_layers.0.1" in name:
              __UpperCamelCase	=  name.replace('downsample_layers.0.1'    ,  'embeddings.norm'					)  # we rename to layernorm later on
       if "downsample_layers.1.0" in name:
              __UpperCamelCase	=  name.replace('downsample_layers.1.0'    ,  'stages.1.downsampling_layer.0'					)
       if "downsample_layers.1.1" in name:
              __UpperCamelCase	=  name.replace('downsample_layers.1.1'    ,  'stages.1.downsampling_layer.1'					)
       if "downsample_layers.2.0" in name:
              __UpperCamelCase	=  name.replace('downsample_layers.2.0'    ,  'stages.2.downsampling_layer.0'					)
       if "downsample_layers.2.1" in name:
              __UpperCamelCase	=  name.replace('downsample_layers.2.1'    ,  'stages.2.downsampling_layer.1'					)
       if "downsample_layers.3.0" in name:
              __UpperCamelCase	=  name.replace('downsample_layers.3.0'    ,  'stages.3.downsampling_layer.0'					)
       if "downsample_layers.3.1" in name:
              __UpperCamelCase	=  name.replace('downsample_layers.3.1'    ,  'stages.3.downsampling_layer.1'					)
       if "stages" in name and "downsampling_layer" not in name:
              # stages.0.0. for instance should be renamed to stages.0.layers.0.
              __UpperCamelCase	=  name[: len('stages.0'					)] + '.layers' + name[len('stages.0'					) :]
       if "gamma" in name:
              __UpperCamelCase	=  name.replace('gamma'    ,  'weight'					)
       if "beta" in name:
              __UpperCamelCase	=  name.replace('beta'    ,  'bias'					)
       if "stages" in name:
              __UpperCamelCase	=  name.replace('stages'    ,  'encoder.stages'					)
       if "norm" in name:
              __UpperCamelCase	=  name.replace('norm'    ,  'layernorm'					)
       if "head" in name:
              __UpperCamelCase	=  name.replace('head'    ,  'classifier'					)

       return name
def     lowercase__       (       )			->				Any:



       """simple docstring"""






       __UpperCamelCase	=  'http://images.cocodataset.org/val2017/000000039769.jpg'
       __UpperCamelCase	=  Image.open(requests.get(__lowercase    ,  stream=__lowercase					).raw					)
       return im
def     lowercase__       (       __lowercase						:  Optional[Any]					)			->				Dict:



       """simple docstring"""






       if "224" in checkpoint_url:
              __UpperCamelCase	=  224
              __UpperCamelCase	=  224 / 256
       elif "384" in checkpoint_url:
              __UpperCamelCase	=  384
              __UpperCamelCase	=  None
       else:
              __UpperCamelCase	=  512
              __UpperCamelCase	=  None

       return ConvNextImageProcessor(
           size=__lowercase    ,  crop_pct=__lowercase    ,  image_mean=[0.4_8_5, 0.4_5_6, 0.4_0_6]    ,  image_std=[0.2_2_9, 0.2_2_4, 0.2_2_5]    ,  resample=PILImageResampling.BICUBIC    ,  )
@torch.no_grad()
def     lowercase__       (       __lowercase						:  int    ,  __lowercase						:  Dict    ,  __lowercase						:  Dict    ,  __lowercase						:  Dict					)			->				Optional[int]:



       """simple docstring"""






       print('Downloading original model from checkpoint...'					)
       # define ConvNeXTV2 configuration based on URL
       __UpperCamelCase	,						__UpperCamelCase	=  get_convnextva_config(__lowercase					)
       # load original state_dict from URL
       __UpperCamelCase	=  torch.hub.load_state_dict_from_url(__lowercase					)['model']

       print('Converting model parameters...'					)
       # rename keys
       for key in state_dict.copy().keys():
              __UpperCamelCase	=  state_dict.pop(__lowercase					)
              __UpperCamelCase	=  val
       # add prefix to all keys expect classifier head
       for key in state_dict.copy().keys():
              __UpperCamelCase	=  state_dict.pop(__lowercase					)
              if not key.startswith('classifier'					):
                     __UpperCamelCase	=  'convnextv2.' + key
              __UpperCamelCase	=  val

       # load HuggingFace model
       __UpperCamelCase	=  ConvNextVaForImageClassification(__lowercase					)
       model.load_state_dict(__lowercase					)
       model.eval()

       # Check outputs on an image, prepared by ConvNextImageProcessor
       __UpperCamelCase	=  convert_preprocessor(__lowercase					)
       __UpperCamelCase	=  preprocessor(images=prepare_img()    ,  return_tensors='pt'					)
       __UpperCamelCase	=  model(**__lowercase					).logits

       # note: the logits below were obtained without center cropping
       if checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_atto_1k_224_ema.pt":
              __UpperCamelCase	=  torch.tensor([-0.3_9_3_0, 0.1_7_4_7, -0.5_2_4_6, 0.4_1_7_7, 0.4_2_9_5]					)
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_femto_1k_224_ema.pt":
              __UpperCamelCase	=  torch.tensor([-0.1_7_2_7, -0.5_3_4_1, -0.7_8_1_8, -0.4_7_4_5, -0.6_5_6_6]					)
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_pico_1k_224_ema.pt":
              __UpperCamelCase	=  torch.tensor([-0.0_3_3_3, 0.1_5_6_3, -0.9_1_3_7, 0.1_0_5_4, 0.0_3_8_1]					)
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_nano_1k_224_ema.pt":
              __UpperCamelCase	=  torch.tensor([-0.1_7_4_4, -0.1_5_5_5, -0.0_7_1_3, 0.0_9_5_0, -0.1_4_3_1]					)
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_tiny_1k_224_ema.pt":
              __UpperCamelCase	=  torch.tensor([0.9_9_9_6, 0.1_9_6_6, -0.4_3_8_6, -0.3_4_7_2, 0.6_6_6_1]					)
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_base_1k_224_ema.pt":
              __UpperCamelCase	=  torch.tensor([-0.2_5_5_3, -0.6_7_0_8, -0.1_3_5_9, 0.2_5_1_8, -0.2_4_8_8]					)
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_large_1k_224_ema.pt":
              __UpperCamelCase	=  torch.tensor([-0.0_6_7_3, -0.5_6_2_7, -0.3_7_5_3, -0.2_7_2_2, 0.0_1_7_8]					)
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_huge_1k_224_ema.pt":
              __UpperCamelCase	=  torch.tensor([-0.6_3_7_7, -0.7_4_5_8, -0.2_1_5_0, 0.1_1_8_4, -0.0_5_9_7]					)
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_224_ema.pt":
              __UpperCamelCase	=  torch.tensor([1.0_7_9_9, 0.2_3_2_2, -0.8_8_6_0, 1.0_2_1_9, 0.6_2_3_1]					)
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_384_ema.pt":
              __UpperCamelCase	=  torch.tensor([0.3_7_6_6, 0.4_9_1_7, -1.1_4_2_6, 0.9_9_4_2, 0.6_0_2_4]					)
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_224_ema.pt":
              __UpperCamelCase	=  torch.tensor([0.4_2_2_0, -0.6_9_1_9, -0.4_3_1_7, -0.2_8_8_1, -0.6_6_0_9]					)
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_384_ema.pt":
              __UpperCamelCase	=  torch.tensor([0.1_0_8_2, -0.8_2_8_6, -0.5_0_9_5, 0.4_6_8_1, -0.8_0_8_5]					)
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_224_ema.pt":
              __UpperCamelCase	=  torch.tensor([-0.2_4_1_9, -0.6_2_2_1, 0.2_1_7_6, -0.0_9_8_0, -0.7_5_2_7]					)
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_384_ema.pt":
              __UpperCamelCase	=  torch.tensor([0.0_3_9_1, -0.4_3_7_1, 0.3_7_8_6, 0.1_2_5_1, -0.2_7_8_4]					)
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_224_ema.pt":
              __UpperCamelCase	=  torch.tensor([-0.0_5_0_4, 0.5_6_3_6, -0.1_7_2_9, -0.6_5_0_7, -0.3_9_4_9]					)
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_384_ema.pt":
              __UpperCamelCase	=  torch.tensor([0.3_5_6_0, 0.9_4_8_6, 0.3_1_4_9, -0.2_6_6_7, -0.5_1_3_8]					)
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_384_ema.pt":
              __UpperCamelCase	=  torch.tensor([-0.2_4_6_9, -0.4_5_5_0, -0.5_8_5_3, -0.0_8_1_0, 0.0_3_0_9]					)
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_512_ema.pt":
              __UpperCamelCase	=  torch.tensor([-0.3_0_9_0, 0.0_8_0_2, -0.0_6_8_2, -0.1_9_7_9, -0.2_8_2_6]					)
       else:
              raise ValueError(F'''Unknown URL: {checkpoint_url}'''					)

       assert torch.allclose(logits[0, :5]    ,  __lowercase    ,  atol=1e-3					)
       assert logits.shape == expected_shape
       print('Model outputs match the original results!'					)

       if save_model:
              print('Saving model to local...'					)
              # Create folder to save model
              if not os.path.isdir(__lowercase					):
                     os.mkdir(__lowercase					)

              model.save_pretrained(__lowercase					)
              preprocessor.save_pretrained(__lowercase					)

       __UpperCamelCase	=  'convnextv2'
       if "atto" in checkpoint_url:
              model_name += "-atto"
       if "femto" in checkpoint_url:
              model_name += "-femto"
       if "pico" in checkpoint_url:
              model_name += "-pico"
       if "nano" in checkpoint_url:
              model_name += "-nano"
       elif "tiny" in checkpoint_url:
              model_name += "-tiny"
       elif "base" in checkpoint_url:
              model_name += "-base"
       elif "large" in checkpoint_url:
              model_name += "-large"
       elif "huge" in checkpoint_url:
              model_name += "-huge"
       if "22k" in checkpoint_url and "1k" not in checkpoint_url:
              model_name += "-22k"
       elif "22k" in checkpoint_url and "1k" in checkpoint_url:
              model_name += "-22k-1k"
       elif "1k" in checkpoint_url:
              model_name += "-1k"
       if "224" in checkpoint_url:
              model_name += "-224"
       elif "384" in checkpoint_url:
              model_name += "-384"
       elif "512" in checkpoint_url:
              model_name += "-512"

       if push_to_hub:
              print(F'''Pushing {model_name} to the hub...'''					)
              model.push_to_hub(__lowercase					)
              preprocessor.push_to_hub(__lowercase					)


if __name__ == "__main__":
    a__							:							str			=argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '''--checkpoint_url''',
        default='''https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_atto_1k_224_ema.pt''',
        type=str,
        help='''URL of the original ConvNeXTV2 checkpoint you\'d like to convert.''',
    )
    parser.add_argument(
        '''--pytorch_dump_folder_path''',
        default='''model''',
        type=str,
        help='''Path to the output PyTorch model directory.''',
    )
    parser.add_argument('''--save_model''', action='''store_true''', help='''Save model to local''')
    parser.add_argument('''--push_to_hub''', action='''store_true''', help='''Push model and image preprocessor to the hub''')

    a__							:							Optional[Any]			=parser.parse_args()
    convert_convnextva_checkpoint(
        args.checkpoint_url, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub
    )
