





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  __UpperCamelCase   (     a)			->List[str]:
					lowerCamelCase__      =							BertTokenizer.from_pretrained("bert-base-uncased",  do_lower_case=__SCREAMING_SNAKE_CASE)
					lowerCamelCase__      =							BertAbs.from_pretrained("remi/bertabs-finetuned-extractive-abstractive-summarization")
					model.to(args.device)
					model.eval()

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

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

										import nltk
										import rouge

										nltk.download("punkt")
										lowerCamelCase__      =							rouge.Rouge(
										    metrics=["rouge-n", "rouge-l"],  max_n=2,  limit_length=__SCREAMING_SNAKE_CASE,  length_limit=args.beam_size,  length_limit_type="words",  apply_avg=__SCREAMING_SNAKE_CASE,  apply_best=__SCREAMING_SNAKE_CASE,  alpha=0.5,  weight_factor=1.2,  stemming=__SCREAMING_SNAKE_CASE,  )

					# these (unused) arguments are defined to keep the compatibility
					# with the legacy code and will be deleted in a next iteration.
					lowerCamelCase__      =							""""""
					lowerCamelCase__      =							""""""

					lowerCamelCase__      =							build_data_iterator(__SCREAMING_SNAKE_CASE,  __SCREAMING_SNAKE_CASE)
					lowerCamelCase__      =							build_predictor(__SCREAMING_SNAKE_CASE,  __SCREAMING_SNAKE_CASE,  __SCREAMING_SNAKE_CASE,  __SCREAMING_SNAKE_CASE)

					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(__SCREAMING_SNAKE_CASE):
										lowerCamelCase__      =							predictor.translate_batch(__SCREAMING_SNAKE_CASE)
										lowerCamelCase__      =							predictor.from_batch(__SCREAMING_SNAKE_CASE)
										lowerCamelCase__      =							[format_summary(__SCREAMING_SNAKE_CASE) for t in translations]
										save_summaries(__SCREAMING_SNAKE_CASE,  args.summaries_output_dir,  batch.document_names)

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

					if args.compute_rouge:
										lowerCamelCase__      =							rouge_evaluator.get_scores(__SCREAMING_SNAKE_CASE,  __SCREAMING_SNAKE_CASE)
										lowerCamelCase__      =							format_rouge_scores(__SCREAMING_SNAKE_CASE)
										save_rouge_scores(__SCREAMING_SNAKE_CASE)
										print(__SCREAMING_SNAKE_CASE)





def  __UpperCamelCase   (     a,  a,  a)			->str:

					for summary, document_name in zip(__SCREAMING_SNAKE_CASE,  __SCREAMING_SNAKE_CASE):
										# Prepare the summary file's name
										if "." in document_name:
															lowerCamelCase__      =							""".""".join(document_name.split(".")[:-1])
															lowerCamelCase__      =							document_name.split(".")[-1]
															lowerCamelCase__      =							bare_document_name + """_summary.""" + extension
										else:
															lowerCamelCase__      =							document_name + """_summary"""

										lowerCamelCase__      =							os.path.join(__SCREAMING_SNAKE_CASE,  __SCREAMING_SNAKE_CASE)
										with open(__SCREAMING_SNAKE_CASE,  "w") as output:
															output.write(__SCREAMING_SNAKE_CASE)





def  __UpperCamelCase   (     a)			->Any:
					lowerCamelCase__      =							translation
					lowerCamelCase__      =							(
					    raw_summary.replace("[unused0]",  "")
					    .replace("[unused3]",  "")
					    .replace("[PAD]",  "")
					    .replace("[unused1]",  "")
					    .replace(R" +",  " ")
					    .replace(" [unused2] ",  ". ")
					    .replace("[unused2]",  "")
					    .strip()
					)

					return summary





def  __UpperCamelCase   (     a)			->int:
					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)			->Dict:

					with open("rouge_scores.txt",  "w") as output:
										output.write(__SCREAMING_SNAKE_CASE)





def  __UpperCamelCase   (     a,  a)			->int:
					lowerCamelCase__      =							load_and_cache_examples(__SCREAMING_SNAKE_CASE,  __SCREAMING_SNAKE_CASE)
					lowerCamelCase__      =							SequentialSampler(__SCREAMING_SNAKE_CASE)

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

					lowerCamelCase__      =							DataLoader(
					    __SCREAMING_SNAKE_CASE,  sampler=__SCREAMING_SNAKE_CASE,  batch_size=args.batch_size,  collate_fn=__SCREAMING_SNAKE_CASE,  )

					return iterator





def  __UpperCamelCase   (     a,  a)			->Tuple:
					lowerCamelCase__      =							CNNDMDataset(args.documents_dir)
					return dataset





def  __UpperCamelCase   (     a,  a,  a,  a)			->Optional[Any]:
					lowerCamelCase__      =							[x for x in data if not len(x[1]) == 0]  # remove empty_files
					lowerCamelCase__      =							[name for name, _, _ in data]
					lowerCamelCase__      =							[""" """.join(__SCREAMING_SNAKE_CASE) for _, _, summary_list in data]

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

					lowerCamelCase__      =							Batch(
					    document_names=__SCREAMING_SNAKE_CASE,  batch_size=len(__SCREAMING_SNAKE_CASE),  src=encoded_stories.to(__SCREAMING_SNAKE_CASE),  segs=encoder_token_type_ids.to(__SCREAMING_SNAKE_CASE),  mask_src=encoder_mask.to(__SCREAMING_SNAKE_CASE),  tgt_str=__SCREAMING_SNAKE_CASE,  )

					return batch





def  __UpperCamelCase   (     a,  a)			->List[str]:
					lowerCamelCase__      =							summary_tokens.to("cpu").numpy()
					lowerCamelCase__      =							tokenizer.decode(__SCREAMING_SNAKE_CASE)
					lowerCamelCase__      =							summary.split(".")
					lowerCamelCase__      =							[s + """.""" for s in sentences]
					return sentences





def  __UpperCamelCase   (     )			->Optional[Any]:
					lowerCamelCase__      =							argparse.ArgumentParser()
					parser.add_argument(
					    "--documents_dir",  default=__SCREAMING_SNAKE_CASE,  type=__SCREAMING_SNAKE_CASE,  required=__SCREAMING_SNAKE_CASE,  help="The folder where the documents to summarize are located.",  )
					parser.add_argument(
					    "--summaries_output_dir",  default=__SCREAMING_SNAKE_CASE,  type=__SCREAMING_SNAKE_CASE,  required=__SCREAMING_SNAKE_CASE,  help="The folder in wich the summaries should be written. Defaults to the folder where the documents are",  )
					parser.add_argument(
					    "--compute_rouge",  default=__SCREAMING_SNAKE_CASE,  type=__SCREAMING_SNAKE_CASE,  required=__SCREAMING_SNAKE_CASE,  help="Compute the ROUGE metrics during evaluation. Only available for the CNN/DailyMail dataset.",  )
					# EVALUATION options
					parser.add_argument(
					    "--no_cuda",  default=__SCREAMING_SNAKE_CASE,  type=__SCREAMING_SNAKE_CASE,  help="Whether to force the execution on CPU.",  )
					parser.add_argument(
					    "--batch_size",  default=4,  type=__SCREAMING_SNAKE_CASE,  help="Batch size per GPU/CPU for training.",  )
					# BEAM SEARCH arguments
					parser.add_argument(
					    "--min_length",  default=50,  type=__SCREAMING_SNAKE_CASE,  help="Minimum number of tokens for the summaries.",  )
					parser.add_argument(
					    "--max_length",  default=200,  type=__SCREAMING_SNAKE_CASE,  help="Maixmum number of tokens for the summaries.",  )
					parser.add_argument(
					    "--beam_size",  default=5,  type=__SCREAMING_SNAKE_CASE,  help="The number of beams to start with for each example.",  )
					parser.add_argument(
					    "--alpha",  default=0.9_5,  type=__SCREAMING_SNAKE_CASE,  help="The value of alpha for the length penalty in the beam search.",  )
					parser.add_argument(
					    "--block_trigram",  default=__SCREAMING_SNAKE_CASE,  type=__SCREAMING_SNAKE_CASE,  help="Whether to block the existence of repeating trigrams in the text generated by beam search.",  )
					lowerCamelCase__      =							parser.parse_args()

					# Select device (distibuted not available)
					lowerCamelCase__      =							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:
										lowerCamelCase__      =							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=__SCREAMING_SNAKE_CASE)

					evaluate(__SCREAMING_SNAKE_CASE)





def  __UpperCamelCase   (     a)			->List[Any]:

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

					lowerCamelCase__      =							os.listdir(__SCREAMING_SNAKE_CASE)
					if len(__SCREAMING_SNAKE_CASE) == 0:
										return False

					return True


if __name__ == "__main__":
						main()



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

import torch
from torchvision.datasets.utils import download_url

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel


A__ :		Tuple					={512: 'DiT-XL-2-512x512.pt', 256: 'DiT-XL-2-256x256.pt'}







def 							A_  (    __SCREAMING_SNAKE_CASE    :	int			)					->							Tuple:





  """simple docstring"""

  __A		:  List[Any]       				=		F"pretrained_models/{model_name}"
  if not os.path.isfile(__SCREAMING_SNAKE_CASE			):
    os.makedirs("""pretrained_models"""      ,  exist_ok=__SCREAMING_SNAKE_CASE			)
    __A		:  int       				=		F"https://dl.fbaipublicfiles.com/DiT/models/{model_name}"
    download_url(__SCREAMING_SNAKE_CASE      ,  """pretrained_models"""			)
  __A		:  Optional[Any]       				=		torch.load(__SCREAMING_SNAKE_CASE      ,  map_location=lambda __SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE			: storage			)
  return model







def 							A_  (    __SCREAMING_SNAKE_CASE    :	Optional[int]			)					->							Dict:





  """simple docstring"""

  __A		:  Optional[Any]       				=		download_model(pretrained_models[args.image_size]			)

  __A		:  Tuple       				=		state_dict["""x_embedder.proj.weight"""]
  __A		:  Dict       				=		state_dict["""x_embedder.proj.bias"""]
  state_dict.pop("""x_embedder.proj.weight"""			)
  state_dict.pop("""x_embedder.proj.bias"""			)

  for depth in range(28			):
    __A		:  Union[str, Any]       				=		state_dict[
        """t_embedder.mlp.0.weight"""
    ]
    __A		:  int       				=		state_dict[
        """t_embedder.mlp.0.bias"""
    ]
    __A		:  List[Any]       				=		state_dict[
        """t_embedder.mlp.2.weight"""
    ]
    __A		:  str       				=		state_dict[
        """t_embedder.mlp.2.bias"""
    ]
    __A		:  Optional[int]       				=		state_dict[
        """y_embedder.embedding_table.weight"""
    ]

    __A		:  Union[str, Any]       				=		state_dict[
        F"blocks.{depth}.adaLN_modulation.1.weight"
    ]
    __A		:  List[Any]       				=		state_dict[
        F"blocks.{depth}.adaLN_modulation.1.bias"
    ]

    __A				,       __A				,       __A		:  Tuple       				=		torch.chunk(state_dict[F"blocks.{depth}.attn.qkv.weight"]      ,  3      ,  dim=0			)
    __A				,       __A				,       __A		:  Union[str, Any]       				=		torch.chunk(state_dict[F"blocks.{depth}.attn.qkv.bias"]      ,  3      ,  dim=0			)

    __A		:  Tuple       				=		q
    __A		:  Tuple       				=		q_bias
    __A		:  Tuple       				=		k
    __A		:  int       				=		k_bias
    __A		:  Union[str, Any]       				=		v
    __A		:  Optional[Any]       				=		v_bias

    __A		:  List[Any]       				=		state_dict[
        F"blocks.{depth}.attn.proj.weight"
    ]
    __A		:  str       				=		state_dict[F"blocks.{depth}.attn.proj.bias"]

    __A		:  str       				=		state_dict[F"blocks.{depth}.mlp.fc1.weight"]
    __A		:  Union[str, Any]       				=		state_dict[F"blocks.{depth}.mlp.fc1.bias"]
    __A		:  int       				=		state_dict[F"blocks.{depth}.mlp.fc2.weight"]
    __A		:  str       				=		state_dict[F"blocks.{depth}.mlp.fc2.bias"]

    state_dict.pop(F"blocks.{depth}.attn.qkv.weight"			)
    state_dict.pop(F"blocks.{depth}.attn.qkv.bias"			)
    state_dict.pop(F"blocks.{depth}.attn.proj.weight"			)
    state_dict.pop(F"blocks.{depth}.attn.proj.bias"			)
    state_dict.pop(F"blocks.{depth}.mlp.fc1.weight"			)
    state_dict.pop(F"blocks.{depth}.mlp.fc1.bias"			)
    state_dict.pop(F"blocks.{depth}.mlp.fc2.weight"			)
    state_dict.pop(F"blocks.{depth}.mlp.fc2.bias"			)
    state_dict.pop(F"blocks.{depth}.adaLN_modulation.1.weight"			)
    state_dict.pop(F"blocks.{depth}.adaLN_modulation.1.bias"			)

  state_dict.pop("""t_embedder.mlp.0.weight"""			)
  state_dict.pop("""t_embedder.mlp.0.bias"""			)
  state_dict.pop("""t_embedder.mlp.2.weight"""			)
  state_dict.pop("""t_embedder.mlp.2.bias"""			)
  state_dict.pop("""y_embedder.embedding_table.weight"""			)

  __A		:  List[str]       				=		state_dict["""final_layer.adaLN_modulation.1.weight"""]
  __A		:  Optional[int]       				=		state_dict["""final_layer.adaLN_modulation.1.bias"""]
  __A		:  List[str]       				=		state_dict["""final_layer.linear.weight"""]
  __A		:  Optional[int]       				=		state_dict["""final_layer.linear.bias"""]

  state_dict.pop("""final_layer.linear.weight"""			)
  state_dict.pop("""final_layer.linear.bias"""			)
  state_dict.pop("""final_layer.adaLN_modulation.1.weight"""			)
  state_dict.pop("""final_layer.adaLN_modulation.1.bias"""			)

  # DiT XL/2
  __A		:  Union[str, Any]       				=		TransformeraDModel(
      sample_size=args.image_size // 8      ,  num_layers=28      ,  attention_head_dim=72      ,  in_channels=4      ,  out_channels=8      ,  patch_size=2      ,  attention_bias=__SCREAMING_SNAKE_CASE      ,  num_attention_heads=16      ,  activation_fn="""gelu-approximate"""      ,  num_embeds_ada_norm=1000      ,  norm_type="""ada_norm_zero"""      ,  norm_elementwise_affine=__SCREAMING_SNAKE_CASE      ,  )
  transformer.load_state_dict(__SCREAMING_SNAKE_CASE      ,  strict=__SCREAMING_SNAKE_CASE			)

  __A		:  str       				=		DDIMScheduler(
      num_train_timesteps=1000      ,  beta_schedule="""linear"""      ,  prediction_type="""epsilon"""      ,  clip_sample=__SCREAMING_SNAKE_CASE      ,  )

  __A		:  List[Any]       				=		AutoencoderKL.from_pretrained(args.vae_model			)

  __A		:  Optional[int]       				=		DiTPipeline(transformer=__SCREAMING_SNAKE_CASE      ,  vae=__SCREAMING_SNAKE_CASE      ,  scheduler=__SCREAMING_SNAKE_CASE			)

  if args.save:
    pipeline.save_pretrained(args.checkpoint_path			)


if __name__ == "__main__":
     A__ :		List[str]					=argparse.ArgumentParser()

     parser.add_argument(
         '--image_size',
         default=256,
         type=int,
         required=False,
         help='Image size of pretrained model, either 256 or 512.',
     )
     parser.add_argument(
         '--vae_model',
         default='stabilityai/sd-vae-ft-ema',
         type=str,
         required=False,
         help='Path to pretrained VAE model, either stabilityai/sd-vae-ft-mse or stabilityai/sd-vae-ft-ema.',
     )
     parser.add_argument(
         '--save', default=True, type=bool, required=False, help='Whether to save the converted pipeline or not.'
     )
     parser.add_argument(
         '--checkpoint_path', default=None, type=str, required=True, help='Path to the output pipeline.'
     )

     A__ :		Dict					=parser.parse_args()
     main(args)



