
import glob
import os
from typing import Dict, List, Union

import torch

from diffusers.utils import is_safetensors_available


if is_safetensors_available():
       import safetensors.torch

from huggingface_hub import snapshot_download

from diffusers import DiffusionPipeline, __version__
from diffusers.schedulers.scheduling_utils import SCHEDULER_CONFIG_NAME
from diffusers.utils import CONFIG_NAME, DIFFUSERS_CACHE, ONNX_WEIGHTS_NAME, WEIGHTS_NAME





class 						__lowercase(     SCREAMING_SNAKE_CASE   ):


       """simple docstring"""







       def __init__(     self					:			Optional[Any]       )					->   List[str]:
         self.register_to_config()
         super().__init__()



       def SCREAMING_SNAKE_CASE_				(     self					:			List[str]  ,      _lowerCAmelCase					:			Tuple  ,      _lowerCAmelCase					:			Union[str, Any]       )					->   List[Any]:
         if dicta == dicta:
           return True
         else:
           _lowerCAmelCase	,       _lowerCAmelCase						=		self._remove_meta_keys(lowerCAmelCase_       )
           _lowerCAmelCase	,       _lowerCAmelCase						=		self._remove_meta_keys(lowerCAmelCase_       )
           if configa == configa:
             print(F'''Warning !: Mismatch in keys {meta_keysa} and {meta_keysa}.'''       )
             return True
         return False



       def SCREAMING_SNAKE_CASE_				(     self					:			str  ,      _lowerCAmelCase					:			Dict       )					->   Union[str, Any]:
         _lowerCAmelCase						=		[]
         _lowerCAmelCase						=		config_dict.copy()
         for key in config_dict.keys():
           if key.startswith('_'       ):
             temp_dict.pop(lowerCAmelCase_       )
             meta_keys.append(lowerCAmelCase_       )
         return (temp_dict, meta_keys)



       @torch.no_grad()
       def SCREAMING_SNAKE_CASE_				(     self					:			Dict  ,      _lowerCAmelCase					:			List[Union[str, os.PathLike]]  ,      **_lowerCAmelCase					:			Union[str, Any]       )					->   Tuple:
         _lowerCAmelCase						=		kwargs.pop('cache_dir'  ,      lowerCAmelCase_       )
         _lowerCAmelCase						=		kwargs.pop('resume_download'  ,      lowerCAmelCase_       )
         _lowerCAmelCase						=		kwargs.pop('force_download'  ,      lowerCAmelCase_       )
         _lowerCAmelCase						=		kwargs.pop('proxies'  ,      lowerCAmelCase_       )
         _lowerCAmelCase						=		kwargs.pop('local_files_only'  ,      lowerCAmelCase_       )
         _lowerCAmelCase						=		kwargs.pop('use_auth_token'  ,      lowerCAmelCase_       )
         _lowerCAmelCase						=		kwargs.pop('revision'  ,      lowerCAmelCase_       )
         _lowerCAmelCase						=		kwargs.pop('torch_dtype'  ,      lowerCAmelCase_       )
         _lowerCAmelCase						=		kwargs.pop('device_map'  ,      lowerCAmelCase_       )

         _lowerCAmelCase						=		kwargs.pop('alpha'  ,      0.5       )
         _lowerCAmelCase						=		kwargs.pop('interp'  ,      lowerCAmelCase_       )

         print('Received list'  ,      lowerCAmelCase_       )
         print(F'''Combining with alpha={alpha}, interpolation mode={interp}'''       )

         _lowerCAmelCase						=		len(lowerCAmelCase_       )
         # Ignore result from model_index_json comparision of the two checkpoints
         _lowerCAmelCase						=		kwargs.pop('force'  ,      lowerCAmelCase_       )

         # If less than 2 checkpoints, nothing to merge. If more than 3, not supported for now.
         if checkpoint_count > 3 or checkpoint_count < 2:
           raise ValueError(
               'Received incorrect number of checkpoints to merge. Ensure that either 2 or 3 checkpoints are being'
               ' passed.'       )

         print('Received the right number of checkpoints'       )
         # chkpt0, chkpt1 = pretrained_model_name_or_path_list[0:2]
         # chkpt2 = pretrained_model_name_or_path_list[2] if checkpoint_count == 3 else None

         # Validate that the checkpoints can be merged
         # Step 1: Load the model config and compare the checkpoints. We'll compare the model_index.json first while ignoring the keys starting with '_'
         _lowerCAmelCase						=		[]
         for pretrained_model_name_or_path in pretrained_model_name_or_path_list:
           _lowerCAmelCase						=		DiffusionPipeline.load_config(
               lowerCAmelCase_  ,      cache_dir=lowerCAmelCase_  ,      resume_download=lowerCAmelCase_  ,      force_download=lowerCAmelCase_  ,      proxies=lowerCAmelCase_  ,      local_files_only=lowerCAmelCase_  ,      use_auth_token=lowerCAmelCase_  ,      revision=lowerCAmelCase_  ,      )
           config_dicts.append(lowerCAmelCase_       )

         _lowerCAmelCase						=		True
         for idx in range(1  ,      len(lowerCAmelCase_       )       ):
           comparison_result &= self._compare_model_configs(config_dicts[idx - 1]  ,      config_dicts[idx]       )
           if not force and comparison_result is False:
             raise ValueError('Incompatible checkpoints. Please check model_index.json for the models.'       )
             print(config_dicts[0]  ,      config_dicts[1]       )
         print('Compatible model_index.json files found'       )
         # Step 2: Basic Validation has succeeded. Let's download the models and save them into our local files.
         _lowerCAmelCase						=		[]
         for pretrained_model_name_or_path, config_dict in zip(lowerCAmelCase_  ,      lowerCAmelCase_       ):
           _lowerCAmelCase						=		[k for k in config_dict.keys() if not k.startswith('_'       )]
           _lowerCAmelCase						=		[os.path.join(lowerCAmelCase_  ,      '*'       ) for k in folder_names]
           allow_patterns += [
               WEIGHTS_NAME,
               SCHEDULER_CONFIG_NAME,
               CONFIG_NAME,
               ONNX_WEIGHTS_NAME,
               DiffusionPipeline.config_name,
           ]
           _lowerCAmelCase						=		config_dict.get('_class_name'       )
           _lowerCAmelCase						=		{'diffusers': __version__, 'pipeline_class': requested_pipeline_class}

           _lowerCAmelCase						=		(
               pretrained_model_name_or_path
               if os.path.isdir(lowerCAmelCase_       )
               else snapshot_download(
                   lowerCAmelCase_  ,      cache_dir=lowerCAmelCase_  ,      resume_download=lowerCAmelCase_  ,      proxies=lowerCAmelCase_  ,      local_files_only=lowerCAmelCase_  ,      use_auth_token=lowerCAmelCase_  ,      revision=lowerCAmelCase_  ,      allow_patterns=lowerCAmelCase_  ,      user_agent=lowerCAmelCase_  ,      )
           )
           print('Cached Folder'  ,      lowerCAmelCase_       )
           cached_folders.append(lowerCAmelCase_       )

         # Step 3:-
         # Load the first checkpoint as a diffusion pipeline and modify its module state_dict in place
         _lowerCAmelCase						=		DiffusionPipeline.from_pretrained(
             cached_folders[0]  ,      torch_dtype=lowerCAmelCase_  ,      device_map=lowerCAmelCase_       )
         final_pipe.to(self.device       )

         _lowerCAmelCase						=		None
         if len(lowerCAmelCase_       ) > 2:
           _lowerCAmelCase						=		os.path.join(cached_folders[2]       )

         if interp == "sigmoid":
           _lowerCAmelCase						=		CheckpointMergerPipeline.sigmoid
         elif interp == "inv_sigmoid":
           _lowerCAmelCase						=		CheckpointMergerPipeline.inv_sigmoid
         elif interp == "add_diff":
           _lowerCAmelCase						=		CheckpointMergerPipeline.add_difference
         else:
           _lowerCAmelCase						=		CheckpointMergerPipeline.weighted_sum

         # Find each module's state dict.
         for attr in final_pipe.config.keys():
           if not attr.startswith('_'       ):
             _lowerCAmelCase						=		os.path.join(cached_folders[1]  ,      lowerCAmelCase_       )
             if os.path.exists(lowerCAmelCase_       ):
               _lowerCAmelCase						=		[
                   *glob.glob(os.path.join(lowerCAmelCase_  ,      '*.safetensors'       )       ),
                   *glob.glob(os.path.join(lowerCAmelCase_  ,      '*.bin'       )       ),
               ]
               _lowerCAmelCase						=		files[0] if len(lowerCAmelCase_       ) > 0 else None
             if len(lowerCAmelCase_       ) < 3:
               _lowerCAmelCase						=		None
             else:
               _lowerCAmelCase						=		os.path.join(cached_folders[2]  ,      lowerCAmelCase_       )
               if os.path.exists(lowerCAmelCase_       ):
                 _lowerCAmelCase						=		[
                     *glob.glob(os.path.join(lowerCAmelCase_  ,      '*.safetensors'       )       ),
                     *glob.glob(os.path.join(lowerCAmelCase_  ,      '*.bin'       )       ),
                 ]
                 _lowerCAmelCase						=		files[0] if len(lowerCAmelCase_       ) > 0 else None
                # For an attr if both checkpoint_path_1 and 2 are None, ignore.
                # If atleast one is present, deal with it according to interp method, of course only if the state_dict keys match.
             if checkpoint_path_a is None and checkpoint_path_a is None:
               print(F'''Skipping {attr}: not present in 2nd or 3d model'''       )
               continue
             try:
               _lowerCAmelCase						=		getattr(lowerCAmelCase_  ,      lowerCAmelCase_       )
               if isinstance(lowerCAmelCase_  ,      lowerCAmelCase_       ):  # ignore requires_safety_checker boolean
                 continue
               _lowerCAmelCase						=		getattr(lowerCAmelCase_  ,      'state_dict'       )
               _lowerCAmelCase						=		theta_a()

               _lowerCAmelCase						=		getattr(lowerCAmelCase_  ,      'load_state_dict'       )
               _lowerCAmelCase						=		(
                   safetensors.torch.load_file(lowerCAmelCase_       )
                   if (is_safetensors_available() and checkpoint_path_a.endswith('.safetensors'       ))
                   else torch.load(lowerCAmelCase_  ,      map_location='cpu'       )
               )
               _lowerCAmelCase						=		None
               if checkpoint_path_a:
                 _lowerCAmelCase						=		(
                     safetensors.torch.load_file(lowerCAmelCase_       )
                     if (is_safetensors_available() and checkpoint_path_a.endswith('.safetensors'       ))
                     else torch.load(lowerCAmelCase_  ,      map_location='cpu'       )
                 )

               if not theta_a.keys() == theta_a.keys():
                 print(F'''Skipping {attr}: key mismatch'''       )
                 continue
               if theta_a and not theta_a.keys() == theta_a.keys():
                 print(F'''Skipping {attr}:y mismatch'''       )
             except Exception as e:
               print(F'''Skipping {attr} do to an unexpected error: {str(lowerCAmelCase_       )}'''       )
               continue
             print(F'''MERGING {attr}'''       )

             for key in theta_a.keys():
               if theta_a:
                 _lowerCAmelCase						=		theta_func(theta_a[key]  ,      theta_a[key]  ,      theta_a[key]  ,      lowerCAmelCase_       )
               else:
                 _lowerCAmelCase						=		theta_func(theta_a[key]  ,      theta_a[key]  ,      lowerCAmelCase_  ,      lowerCAmelCase_       )

             del theta_a
             del theta_a
             update_theta_a(lowerCAmelCase_       )

             del theta_a
         return final_pipe



       @staticmethod
       def SCREAMING_SNAKE_CASE_				(     _lowerCAmelCase					:			Any  ,      _lowerCAmelCase					:			str  ,      _lowerCAmelCase					:			Optional[Any]  ,      _lowerCAmelCase					:			List[Any]       )					->   Tuple:
         return ((1 - alpha) * thetaa) + (alpha * thetaa)



       @staticmethod
       def SCREAMING_SNAKE_CASE_				(     _lowerCAmelCase					:			Optional[int]  ,      _lowerCAmelCase					:			Optional[Any]  ,      _lowerCAmelCase					:			List[str]  ,      _lowerCAmelCase					:			Optional[int]       )					->   List[str]:
         _lowerCAmelCase						=		alpha * alpha * (3 - (2 * alpha))
         return thetaa + ((thetaa - thetaa) * alpha)



       @staticmethod
       def SCREAMING_SNAKE_CASE_				(     _lowerCAmelCase					:			Optional[int]  ,      _lowerCAmelCase					:			int  ,      _lowerCAmelCase					:			Dict  ,      _lowerCAmelCase					:			Union[str, Any]       )					->   Dict:
         import math

         _lowerCAmelCase						=		0.5 - math.sin(math.asin(1.0 - 2.0 * alpha       ) / 3.0       )
         return thetaa + ((thetaa - thetaa) * alpha)






       @staticmethod
       def SCREAMING_SNAKE_CASE_				(     _lowerCAmelCase					:			Dict  ,      _lowerCAmelCase					:			Tuple  ,      _lowerCAmelCase					:			Tuple  ,      _lowerCAmelCase					:			Optional[int]       )					->   List[str]:
         return thetaa + (thetaa - thetaa) * (1.0 - alpha)


'''simple docstring'''





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


A							     = namedtuple("Batch", ["document_names", "batch_size", "src", "segs", "mask_src", "tgt_str"])
def 	snake_case_							(				_a  :				str      ):
      snake_case_    							=				BertTokenizer.from_pretrained("bert-base-uncased"			,       do_lower_case=_a      )
      snake_case_    							=				BertAbs.from_pretrained("remi/bertabs-finetuned-extractive-abstractive-summarization"      )
      model.to(args.device      )
      model.eval()

      snake_case_    							=				{
          "BOS": tokenizer.vocab["[unused0]"],
          "EOS": tokenizer.vocab["[unused1]"],
          "PAD": tokenizer.vocab["[PAD]"],
      }

      if args.compute_rouge:
            snake_case_    							=				[]
            snake_case_    							=				[]

            import nltk
            import rouge

            nltk.download("punkt"      )
            snake_case_    							=				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.
      snake_case_    							=				""
      snake_case_    							=				""

      snake_case_    							=				build_data_iterator(_a			,       _a      )
      snake_case_    							=				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      ):
            snake_case_    							=				predictor.translate_batch(_a      )
            snake_case_    							=				predictor.from_batch(_a      )
            snake_case_    							=				[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:
            snake_case_    							=				rouge_evaluator.get_scores(_a			,       _a      )
            snake_case_    							=				format_rouge_scores(_a      )
            save_rouge_scores(_a      )
            print(_a      )
def 	snake_case_							(				_a  :				List[Any]			,       _a  :				List[str]			,       _a  :				Any      ):
      for summary, document_name in zip(_a			,       _a      ):
            # Prepare the summary file's name
            if "." in document_name:
                  snake_case_    							=				".".join(document_name.split("."      )[:-1]      )
                  snake_case_    							=				document_name.split("."      )[-1]
                  snake_case_    							=				bare_document_name + "_summary." + extension
            else:
                  snake_case_    							=				document_name + "_summary"

            snake_case_    							=				os.path.join(_a			,       _a      )
            with open(_a			,       "w"      ) as output:
                  output.write(_a      )
def 	snake_case_							(				_a  :				Optional[int]      ):
      snake_case_							,						snake_case_							,						snake_case_    							=				translation
      snake_case_    							=				(
          raw_summary.replace("[unused0]"			,       ""      )
          .replace("[unused3]"			,       ""      )
          .replace("[PAD]"			,       ""      )
          .replace("[unused1]"			,       ""      )
          .replace(R" +"			,       " "      )
          .replace(" [unused2] "			,       ". "      )
          .replace("[unused2]"			,       ""      )
          .strip()
      )

      return summary
def 	snake_case_							(				_a  :				str      ):
      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 	snake_case_							(				_a  :				str      ):
      with open("rouge_scores.txt"			,       "w"      ) as output:
            output.write(_a      )
def 	snake_case_							(				_a  :				Tuple			,       _a  :				Union[str, Any]      ):
      snake_case_    							=				load_and_cache_examples(_a			,       _a      )
      snake_case_    							=				SequentialSampler(_a      )

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

      snake_case_    							=				DataLoader(
          _a			,       sampler=_a			,       batch_size=args.batch_size			,       collate_fn=_a			,       )

      return iterator
def 	snake_case_							(				_a  :				Optional[Any]			,       _a  :				Tuple      ):
      snake_case_    							=				CNNDMDataset(args.documents_dir      )
      return dataset
def 	snake_case_							(				_a  :				int			,       _a  :				int			,       _a  :				str			,       _a  :				List[Any]      ):
      snake_case_    							=				[x for x in data if not len(x[1]      ) == 0]  # remove empty_files
      snake_case_    							=				[name for name, _, _ in data]
      snake_case_    							=				[" ".join(_a      ) for _, _, summary_list in data]

      snake_case_    							=				[encode_for_summarization(_a			,       _a			,       _a      ) for _, story, summary in data]
      snake_case_    							=				torch.tensor(
          [truncate_or_pad(_a			,       _a			,       tokenizer.pad_token_id      ) for story, _ in encoded_text]      )
      snake_case_    							=				compute_token_type_ids(_a			,       tokenizer.cls_token_id      )
      snake_case_    							=				build_mask(_a			,       tokenizer.pad_token_id      )

      snake_case_    							=				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 	snake_case_							(				_a  :				Tuple			,       _a  :				int      ):
      snake_case_    							=				summary_tokens.to("cpu"      ).numpy()
      snake_case_    							=				tokenizer.decode(_a      )
      snake_case_    							=				summary.split("."      )
      snake_case_    							=				[s + "." for s in sentences]
      return sentences
def 	snake_case_							(				):
      snake_case_    							=				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=0.9_5			,       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."			,       )
      snake_case_    							=				parser.parse_args()

      # Select device (distibuted not available)
      snake_case_    							=				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:
            snake_case_    							=				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 	snake_case_							(				_a  :				Optional[int]      ):
      if not os.path.exists(_a      ):
            return False

      snake_case_    							=				os.listdir(_a      )
      if len(_a      ) == 0:
            return False

      return True


if __name__ == "__main__":
     main()

