
'''simple docstring'''





import argparse
import json
from pathlib import Path

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

from transformers import ConvNextConfig, ConvNextForImageClassification, ConvNextImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
A							     = logging.get_logger(__name__)
def 	snake_case_							(				_a  :				Tuple      ):
      snake_case_    							=				ConvNextConfig()

      if "tiny" in checkpoint_url:
            snake_case_    							=				[3, 3, 9, 3]
            snake_case_    							=				[96, 192, 384, 768]
      if "small" in checkpoint_url:
            snake_case_    							=				[3, 3, 27, 3]
            snake_case_    							=				[96, 192, 384, 768]
      if "base" in checkpoint_url:
            snake_case_    							=				[3, 3, 27, 3]
            snake_case_    							=				[128, 256, 512, 1_024]
      if "large" in checkpoint_url:
            snake_case_    							=				[3, 3, 27, 3]
            snake_case_    							=				[192, 384, 768, 1_536]
      if "xlarge" in checkpoint_url:
            snake_case_    							=				[3, 3, 27, 3]
            snake_case_    							=				[256, 512, 1_024, 2_048]

      if "1k" in checkpoint_url:
            snake_case_    							=				1_000
            snake_case_    							=				"imagenet-1k-id2label.json"
            snake_case_    							=				(1, 1_000)
      else:
            snake_case_    							=				21_841
            snake_case_    							=				"imagenet-22k-id2label.json"
            snake_case_    							=				(1, 21_841)

      snake_case_    							=				"huggingface/label-files"
      snake_case_    							=				num_labels
      snake_case_    							=				json.load(open(hf_hub_download(_a			,       _a			,       repo_type="dataset"      )			,       "r"      )      )
      snake_case_    							=				{int(_a      ): v for k, v in idalabel.items()}
      if "1k" not in checkpoint_url:
            # this dataset contains 21843 labels but the model only has 21841
            # we delete the classes as mentioned in https://github.com/google-research/big_transfer/issues/18
            del idalabel[9_205]
            del idalabel[15_027]
      snake_case_    							=				idalabel
      snake_case_    							=				{v: k for k, v in idalabel.items()}
      snake_case_    							=				hidden_sizes
      snake_case_    							=				depths

      return config, expected_shape
def 	snake_case_							(				_a  :				List[str]      ):
      if "downsample_layers.0.0" in name:
            snake_case_    							=				name.replace("downsample_layers.0.0"			,       "embeddings.patch_embeddings"      )
      if "downsample_layers.0.1" in name:
            snake_case_    							=				name.replace("downsample_layers.0.1"			,       "embeddings.norm"      )  # we rename to layernorm later on
      if "downsample_layers.1.0" in name:
            snake_case_    							=				name.replace("downsample_layers.1.0"			,       "stages.1.downsampling_layer.0"      )
      if "downsample_layers.1.1" in name:
            snake_case_    							=				name.replace("downsample_layers.1.1"			,       "stages.1.downsampling_layer.1"      )
      if "downsample_layers.2.0" in name:
            snake_case_    							=				name.replace("downsample_layers.2.0"			,       "stages.2.downsampling_layer.0"      )
      if "downsample_layers.2.1" in name:
            snake_case_    							=				name.replace("downsample_layers.2.1"			,       "stages.2.downsampling_layer.1"      )
      if "downsample_layers.3.0" in name:
            snake_case_    							=				name.replace("downsample_layers.3.0"			,       "stages.3.downsampling_layer.0"      )
      if "downsample_layers.3.1" in name:
            snake_case_    							=				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.
            snake_case_    							=				name[: len("stages.0"      )] + ".layers" + name[len("stages.0"      ) :]
      if "stages" in name:
            snake_case_    							=				name.replace("stages"			,       "encoder.stages"      )
      if "norm" in name:
            snake_case_    							=				name.replace("norm"			,       "layernorm"      )
      if "gamma" in name:
            snake_case_    							=				name.replace("gamma"			,       "layer_scale_parameter"      )
      if "head" in name:
            snake_case_    							=				name.replace("head"			,       "classifier"      )

      return name
def 	snake_case_							(				):
      snake_case_    							=				"http://images.cocodataset.org/val2017/000000039769.jpg"
      snake_case_    							=				Image.open(requests.get(_a			,       stream=_a      ).raw      )
      return im
@torch.no_grad()
def 	snake_case_							(				_a  :				Any			,       _a  :				List[Any]      ):
      snake_case_							,						snake_case_    							=				get_convnext_config(_a      )
      # load original state_dict from URL
      snake_case_    							=				torch.hub.load_state_dict_from_url(_a      )["model"]
      # rename keys
      for key in state_dict.copy().keys():
            snake_case_    							=				state_dict.pop(_a      )
            snake_case_    							=				val
      # add prefix to all keys expect classifier head
      for key in state_dict.copy().keys():
            snake_case_    							=				state_dict.pop(_a      )
            if not key.startswith("classifier"      ):
                  snake_case_    							=				"convnext." + key
            snake_case_    							=				val

      # load HuggingFace model
      snake_case_    							=				ConvNextForImageClassification(_a      )
      model.load_state_dict(_a      )
      model.eval()

      # Check outputs on an image, prepared by ConvNextImageProcessor
      snake_case_    							=				224 if "224" in checkpoint_url else 384
      snake_case_    							=				ConvNextImageProcessor(size=_a      )
      snake_case_    							=				image_processor(images=prepare_img()			,       return_tensors="pt"      ).pixel_values

      snake_case_    							=				model(_a      ).logits

      # note: the logits below were obtained without center cropping
      if checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_tiny_1k_224_ema.pth":
            snake_case_    							=				torch.tensor([-0.1_2_1_0, -0.6_6_0_5, 0.1_9_1_8]      )
      elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_small_1k_224_ema.pth":
            snake_case_    							=				torch.tensor([-0.4_4_7_3, -0.1_8_4_7, -0.6_3_6_5]      )
      elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_224_ema.pth":
            snake_case_    							=				torch.tensor([0.4_5_2_5, 0.7_5_3_9, 0.0_3_0_8]      )
      elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_384.pth":
            snake_case_    							=				torch.tensor([0.3_5_6_1, 0.6_3_5_0, -0.0_3_8_4]      )
      elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_224_ema.pth":
            snake_case_    							=				torch.tensor([0.4_1_7_4, -0.0_9_8_9, 0.1_4_8_9]      )
      elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_384.pth":
            snake_case_    							=				torch.tensor([0.2_5_1_3, -0.1_3_4_9, -0.1_6_1_3]      )
      elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_224.pth":
            snake_case_    							=				torch.tensor([1.2_9_8_0, 0.3_6_3_1, -0.1_1_9_8]      )
      elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_224.pth":
            snake_case_    							=				torch.tensor([1.2_9_6_3, 0.1_2_2_7, 0.1_7_2_3]      )
      elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_224.pth":
            snake_case_    							=				torch.tensor([1.7_9_5_6, 0.8_3_9_0, 0.2_8_2_0]      )
      elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_224.pth":
            snake_case_    							=				torch.tensor([-0.2_8_2_2, -0.0_5_0_2, -0.0_8_7_8]      )
      elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_384.pth":
            snake_case_    							=				torch.tensor([-0.5_6_7_2, -0.0_7_3_0, -0.4_3_4_8]      )
      elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_224.pth":
            snake_case_    							=				torch.tensor([0.2_6_8_1, 0.2_3_6_5, 0.6_2_4_6]      )
      elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_384.pth":
            snake_case_    							=				torch.tensor([-0.2_6_4_2, 0.3_9_3_1, 0.5_1_1_6]      )
      elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_224_ema.pth":
            snake_case_    							=				torch.tensor([-0.6_6_7_7, -0.1_8_7_3, -0.8_3_7_9]      )
      elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_384_ema.pth":
            snake_case_    							=				torch.tensor([-0.7_7_4_9, -0.2_9_6_7, -0.6_4_4_4]      )
      else:
            raise ValueError(F'''Unknown URL: {checkpoint_url}'''      )

      assert torch.allclose(logits[0, :3]			,       _a			,       atol=1E-3      )
      assert logits.shape == expected_shape

      Path(_a      ).mkdir(exist_ok=_a      )
      print(F'''Saving model to {pytorch_dump_folder_path}'''      )
      model.save_pretrained(_a      )
      print(F'''Saving image processor to {pytorch_dump_folder_path}'''      )
      image_processor.save_pretrained(_a      )

      print("Pushing model to the hub..."      )
      snake_case_    							=				"convnext"
      if "tiny" in checkpoint_url:
            model_name += "-tiny"
      elif "small" in checkpoint_url:
            model_name += "-small"
      elif "base" in checkpoint_url:
            model_name += "-base"
      elif "xlarge" in checkpoint_url:
            model_name += "-xlarge"
      elif "large" in checkpoint_url:
            model_name += "-large"
      if "224" in checkpoint_url:
            model_name += "-224"
      elif "384" in checkpoint_url:
            model_name += "-384"
      if "22k" in checkpoint_url and "1k" not in checkpoint_url:
            model_name += "-22k"
      if "22k" in checkpoint_url and "1k" in checkpoint_url:
            model_name += "-22k-1k"

      model.push_to_hub(
          repo_path_or_name=Path(_a			,       _a      )			,       organization="nielsr"			,       commit_message="Add model"			,       )


if __name__ == "__main__":
     A							     = argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         "--checkpoint_url",
         default="https://dl.fbaipublicfiles.com/convnext/convnext_tiny_1k_224_ema.pth",
         type=str,
         help="URL of the original ConvNeXT checkpoint you'd like to convert.",
     )
     parser.add_argument(
         "--pytorch_dump_folder_path",
         default=None,
         type=str,
         required=True,
         help="Path to the output PyTorch model directory.",
     )

     A							     = parser.parse_args()
     convert_convnext_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)



import argparse
import logging
import os
import sys
from collections import namedtuple

import torch
from modeling_bertabs import BertAbs, build_predictor
from torch.utils.data import DataLoader, SequentialSampler
from tqdm import tqdm

from transformers import BertTokenizer

from .utils_summarization import (
    CNNDMDataset,
    build_mask,
    compute_token_type_ids,
    encode_for_summarization,
    truncate_or_pad,
)


a__				:      Any    		=     logging.getLogger(__name__)
logging.basicConfig(stream=sys.stdout, level=logging.INFO)


a__				:      Optional[int]    		=     namedtuple('''Batch''', ['''document_names''', '''batch_size''', '''src''', '''segs''', '''mask_src''', '''tgt_str'''])







def 				UpperCAmelCase_(    a__   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				List[str]				      = BertTokenizer.from_pretrained('''bert-base-uncased''' ,						do_lower_case=a__   )
    SCREAMING_SNAKE_CASE				:				List[Any]				      = BertAbs.from_pretrained('''remi/bertabs-finetuned-extractive-abstractive-summarization'''   )
    model.to(args.device   )
    model.eval()

    SCREAMING_SNAKE_CASE				:				int				      = {
        '''BOS''': tokenizer.vocab['''[unused0]'''],
        '''EOS''': tokenizer.vocab['''[unused1]'''],
        '''PAD''': tokenizer.vocab['''[PAD]'''],
    }

    if args.compute_rouge:
        SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = []
        SCREAMING_SNAKE_CASE				:				Dict				      = []

        import nltk
        import rouge

        nltk.download('''punkt'''   )
        SCREAMING_SNAKE_CASE				:				Tuple				      = rouge.Rouge(
            metrics=['''rouge-n''', '''rouge-l'''] ,						max_n=2 ,						limit_length=a__ ,						length_limit=args.beam_size ,						length_limit_type='''words''' ,						apply_avg=a__ ,						apply_best=a__ ,						alpha=0.5 ,						weight_factor=1.2 ,						stemming=a__ ,						)

    # these (unused) arguments are defined to keep the compatibility
    # with the legacy code and will be deleted in a next iteration.
    SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = ''''''
    SCREAMING_SNAKE_CASE				:				Tuple				      = ''''''

    SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = build_data_iterator(a__ ,						a__   )
    SCREAMING_SNAKE_CASE				:				str				      = build_predictor(a__ ,						a__ ,						a__ ,						a__   )

    logger.info('''***** Running evaluation *****'''   )
    logger.info('''  Number examples = %d''' ,						len(data_iterator.dataset   )   )
    logger.info('''  Batch size = %d''' ,						args.batch_size   )
    logger.info(''''''   )
    logger.info('''***** Beam Search parameters *****'''   )
    logger.info('''  Beam size = %d''' ,						args.beam_size   )
    logger.info('''  Minimum length = %d''' ,						args.min_length   )
    logger.info('''  Maximum length = %d''' ,						args.max_length   )
    logger.info('''  Alpha (length penalty) = %.2f''' ,						args.alpha   )
    logger.info('''  Trigrams %s be blocked''' ,						('''will''' if args.block_trigram else '''will NOT''')   )

    for batch in tqdm(a__   ):
        SCREAMING_SNAKE_CASE				:				Tuple				      = predictor.translate_batch(a__   )
        SCREAMING_SNAKE_CASE				:				Optional[Any]				      = predictor.from_batch(a__   )
        SCREAMING_SNAKE_CASE				:				List[str]				      = [format_summary(a__   ) for t in translations]
        save_summaries(a__ ,						args.summaries_output_dir ,						batch.document_names   )

        if args.compute_rouge:
            reference_summaries += batch.tgt_str
            generated_summaries += summaries

    if args.compute_rouge:
        SCREAMING_SNAKE_CASE				:				List[Any]				      = rouge_evaluator.get_scores(a__ ,						a__   )
        SCREAMING_SNAKE_CASE				:				Dict				      = format_rouge_scores(a__   )
        save_rouge_scores(a__   )
        print(a__   )







def 				UpperCAmelCase_(    a__ ,						a__ ,						a__   ):



    """simple docstring"""



    for summary, document_name in zip(a__ ,						a__   ):
        # Prepare the summary file's name
        if "." in document_name:
            SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = '''.'''.join(document_name.split('''.'''   )[:-1]   )
            SCREAMING_SNAKE_CASE				:				int				      = document_name.split('''.'''   )[-1]
            SCREAMING_SNAKE_CASE				:				Tuple				      = bare_document_name + '''_summary.''' + extension
        else:
            SCREAMING_SNAKE_CASE				:				Optional[int]				      = document_name + '''_summary'''

        SCREAMING_SNAKE_CASE				:				List[Any]				      = os.path.join(a__ ,						a__   )
        with open(a__ ,						'''w'''   ) as output:
            output.write(a__   )







def 				UpperCAmelCase_(    a__   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE				:				Any				      = translation
    SCREAMING_SNAKE_CASE				:				int				      = (
        raw_summary.replace('''[unused0]''' ,						''''''   )
        .replace('''[unused3]''' ,						''''''   )
        .replace('''[PAD]''' ,						''''''   )
        .replace('''[unused1]''' ,						''''''   )
        .replace(r''' +''' ,						''' '''   )
        .replace(''' [unused2] ''' ,						'''. '''   )
        .replace('''[unused2]''' ,						''''''   )
        .strip()
    )

    return summary







def 				UpperCAmelCase_(    a__   ):



    """simple docstring"""



    return """\n
****** ROUGE SCORES ******

** ROUGE 1
F1        >> {:.3f}
Precision >> {:.3f}
Recall    >> {:.3f}

** ROUGE 2
F1        >> {:.3f}
Precision >> {:.3f}
Recall    >> {:.3f}

** ROUGE L
F1        >> {:.3f}
Precision >> {:.3f}
Recall    >> {:.3f}""".format(
        scores['''rouge-1''']['''f'''] ,						scores['''rouge-1''']['''p'''] ,						scores['''rouge-1''']['''r'''] ,						scores['''rouge-2''']['''f'''] ,						scores['''rouge-2''']['''p'''] ,						scores['''rouge-2''']['''r'''] ,						scores['''rouge-l''']['''f'''] ,						scores['''rouge-l''']['''p'''] ,						scores['''rouge-l''']['''r'''] ,						)







def 				UpperCAmelCase_(    a__   ):



    """simple docstring"""



    with open('''rouge_scores.txt''' ,						'''w'''   ) as output:
        output.write(a__   )







def 				UpperCAmelCase_(    a__ ,						a__   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				List[str]				      = load_and_cache_examples(a__ ,						a__   )
    SCREAMING_SNAKE_CASE				:				List[str]				      = SequentialSampler(a__   )

    def collate_fn(a__   ):
        return collate(a__ ,						a__ ,						block_size=512 ,						device=args.device   )

    SCREAMING_SNAKE_CASE				:				Dict				      = DataLoader(
        a__ ,						sampler=a__ ,						batch_size=args.batch_size ,						collate_fn=a__ ,						)

    return iterator







def 				UpperCAmelCase_(    a__ ,						a__   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				Optional[int]				      = CNNDMDataset(args.documents_dir   )
    return dataset







def 				UpperCAmelCase_(    a__ ,						a__ ,						a__ ,						a__   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				str				      = [x for x in data if not len(x[1]   ) == 0]  # remove empty_files
    SCREAMING_SNAKE_CASE				:				int				      = [name for name, _, _ in data]
    SCREAMING_SNAKE_CASE				:				List[str]				      = [''' '''.join(a__   ) for _, _, summary_list in data]

    SCREAMING_SNAKE_CASE				:				Dict				      = [encode_for_summarization(a__ ,						a__ ,						a__   ) for _, story, summary in data]
    SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = torch.tensor(
        [truncate_or_pad(a__ ,						a__ ,						tokenizer.pad_token_id   ) for story, _ in encoded_text]   )
    SCREAMING_SNAKE_CASE				:				List[Any]				      = compute_token_type_ids(a__ ,						tokenizer.cls_token_id   )
    SCREAMING_SNAKE_CASE				:				int				      = build_mask(a__ ,						tokenizer.pad_token_id   )

    SCREAMING_SNAKE_CASE				:				Optional[int]				      = Batch(
        document_names=a__ ,						batch_size=len(a__   ) ,						src=encoded_stories.to(a__   ) ,						segs=encoder_token_type_ids.to(a__   ) ,						mask_src=encoder_mask.to(a__   ) ,						tgt_str=a__ ,						)

    return batch







def 				UpperCAmelCase_(    a__ ,						a__   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				Optional[Any]				      = summary_tokens.to('''cpu'''   ).numpy()
    SCREAMING_SNAKE_CASE				:				str				      = tokenizer.decode(a__   )
    SCREAMING_SNAKE_CASE				:				int				      = summary.split('''.'''   )
    SCREAMING_SNAKE_CASE				:				Dict				      = [s + '''.''' for s in sentences]
    return sentences







def 				UpperCAmelCase_(    ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				str				      = argparse.ArgumentParser()
    parser.add_argument(
        '''--documents_dir''' ,						default=a__ ,						type=a__ ,						required=a__ ,						help='''The folder where the documents to summarize are located.''' ,						)
    parser.add_argument(
        '''--summaries_output_dir''' ,						default=a__ ,						type=a__ ,						required=a__ ,						help='''The folder in wich the summaries should be written. Defaults to the folder where the documents are''' ,						)
    parser.add_argument(
        '''--compute_rouge''' ,						default=a__ ,						type=a__ ,						required=a__ ,						help='''Compute the ROUGE metrics during evaluation. Only available for the CNN/DailyMail dataset.''' ,						)
    # EVALUATION options
    parser.add_argument(
        '''--no_cuda''' ,						default=a__ ,						type=a__ ,						help='''Whether to force the execution on CPU.''' ,						)
    parser.add_argument(
        '''--batch_size''' ,						default=4 ,						type=a__ ,						help='''Batch size per GPU/CPU for training.''' ,						)
    # BEAM SEARCH arguments
    parser.add_argument(
        '''--min_length''' ,						default=50 ,						type=a__ ,						help='''Minimum number of tokens for the summaries.''' ,						)
    parser.add_argument(
        '''--max_length''' ,						default=200 ,						type=a__ ,						help='''Maixmum number of tokens for the summaries.''' ,						)
    parser.add_argument(
        '''--beam_size''' ,						default=5 ,						type=a__ ,						help='''The number of beams to start with for each example.''' ,						)
    parser.add_argument(
        '''--alpha''' ,						default=0.95 ,						type=a__ ,						help='''The value of alpha for the length penalty in the beam search.''' ,						)
    parser.add_argument(
        '''--block_trigram''' ,						default=a__ ,						type=a__ ,						help='''Whether to block the existence of repeating trigrams in the text generated by beam search.''' ,						)
    SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = parser.parse_args()

    # Select device (distibuted not available)
    SCREAMING_SNAKE_CASE				:				List[Any]				      = torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu'''   )

    # Check the existence of directories
    if not args.summaries_output_dir:
        SCREAMING_SNAKE_CASE				:				Tuple				      = args.documents_dir

    if not documents_dir_is_valid(args.documents_dir   ):
        raise FileNotFoundError(
            '''We could not find the directory you specified for the documents to summarize, or it was empty. Please'''
            ''' specify a valid path.'''   )
    os.makedirs(args.summaries_output_dir ,						exist_ok=a__   )

    evaluate(a__   )







def 				UpperCAmelCase_(    a__   ):



    """simple docstring"""



    if not os.path.exists(a__   ):
        return False

    SCREAMING_SNAKE_CASE				:				List[str]				      = os.listdir(a__   )
    if len(a__   ) == 0:
        return False

    return True


if __name__ == "__main__":
      main()
