






'''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,
)


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


UpperCamelCase__       =	namedtuple('Batch', ['document_names', 'batch_size', 'src', 'segs', 'mask_src', 'tgt_str'])




def 						__SCREAMING_SNAKE_CASE    (     _UpperCamelCase							):


   """simple docstring"""




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

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

   if args.compute_rouge:
      lowercase_		:			Optional[int]							=   []
      lowercase_		:			List[Any]							=   []

      import nltk
      import rouge

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

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

   lowercase_		:			List[str]							=   build_data_iterator(__lowerCAmelCase  ,						__lowerCAmelCase							)
   lowercase_		:			Dict							=   build_predictor(__lowerCAmelCase  ,						__lowerCAmelCase  ,						__lowerCAmelCase  ,						__lowerCAmelCase							)

   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(__lowerCAmelCase							):
      lowercase_		:			str							=   predictor.translate_batch(__lowerCAmelCase							)
      lowercase_		:			Optional[Any]							=   predictor.from_batch(__lowerCAmelCase							)
      lowercase_		:			Dict							=   [format_summary(__lowerCAmelCase							) for t in translations]
      save_summaries(__lowerCAmelCase  ,						args.summaries_output_dir  ,						batch.document_names							)

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

   if args.compute_rouge:
      lowercase_		:			Union[str, Any]							=   rouge_evaluator.get_scores(__lowerCAmelCase  ,						__lowerCAmelCase							)
      lowercase_		:			Optional[int]							=   format_rouge_scores(__lowerCAmelCase							)
      save_rouge_scores(__lowerCAmelCase							)
      print(__lowerCAmelCase							)




def 						__SCREAMING_SNAKE_CASE    (     _UpperCamelCase  ,						_UpperCamelCase  ,						_UpperCamelCase							):


   """simple docstring"""




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

      lowercase_		:			Dict							=   os.path.join(__lowerCAmelCase  ,						__lowerCAmelCase							)
      with open(__lowerCAmelCase  ,						"w"							) as output:
         output.write(__lowerCAmelCase							)




def 						__SCREAMING_SNAKE_CASE    (     _UpperCamelCase							):


   """simple docstring"""




   lowercase_			,						lowercase_			,						lowercase_		:			List[str]							=   translation
   lowercase_		:			Optional[int]							=   (
       raw_summary.replace("[unused0]"  ,						""							)
       .replace("[unused3]"  ,						""							)
       .replace("[PAD]"  ,						""							)
       .replace("[unused1]"  ,						""							)
       .replace(R" +"  ,						" "							)
       .replace(" [unused2] "  ,						". "							)
       .replace("[unused2]"  ,						""							)
       .strip()
   )

   return summary




def 						__SCREAMING_SNAKE_CASE    (     _UpperCamelCase							):


   """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 						__SCREAMING_SNAKE_CASE    (     _UpperCamelCase							):


   """simple docstring"""




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




def 						__SCREAMING_SNAKE_CASE    (     _UpperCamelCase  ,						_UpperCamelCase							):


   """simple docstring"""




   lowercase_		:			Tuple							=   load_and_cache_examples(__lowerCAmelCase  ,						__lowerCAmelCase							)
   lowercase_		:			Union[str, Any]							=   SequentialSampler(__lowerCAmelCase							)

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

   lowercase_		:			Tuple							=   DataLoader(
       __lowerCAmelCase  ,						sampler=__lowerCAmelCase  ,						batch_size=args.batch_size  ,						collate_fn=__lowerCAmelCase  ,						)

   return iterator




def 						__SCREAMING_SNAKE_CASE    (     _UpperCamelCase  ,						_UpperCamelCase							):


   """simple docstring"""




   lowercase_		:			int							=   CNNDMDataset(args.documents_dir							)
   return dataset




def 						__SCREAMING_SNAKE_CASE    (     _UpperCamelCase  ,						_UpperCamelCase  ,						_UpperCamelCase  ,						_UpperCamelCase							):


   """simple docstring"""




   lowercase_		:			str							=   [x for x in data if not len(x[1]							) == 0]  # remove empty_files
   lowercase_		:			Dict							=   [name for name, _, _ in data]
   lowercase_		:			List[Any]							=   [" ".join(__lowerCAmelCase							) for _, _, summary_list in data]

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

   lowercase_		:			Optional[Any]							=   Batch(
       document_names=__lowerCAmelCase  ,						batch_size=len(__lowerCAmelCase							)  ,						src=encoded_stories.to(__lowerCAmelCase							)  ,						segs=encoder_token_type_ids.to(__lowerCAmelCase							)  ,						mask_src=encoder_mask.to(__lowerCAmelCase							)  ,						tgt_str=__lowerCAmelCase  ,						)

   return batch




def 						__SCREAMING_SNAKE_CASE    (     _UpperCamelCase  ,						_UpperCamelCase							):


   """simple docstring"""




   lowercase_		:			Union[str, Any]							=   summary_tokens.to("cpu"							).numpy()
   lowercase_		:			Tuple							=   tokenizer.decode(__lowerCAmelCase							)
   lowercase_		:			Optional[Any]							=   summary.split("."							)
   lowercase_		:			str							=   [s + "." for s in sentences]
   return sentences




def 						__SCREAMING_SNAKE_CASE    (     ):


   """simple docstring"""




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

   # Select device (distibuted not available)
   lowercase_		:			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:
      lowercase_		:			List[Any]							=   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=__lowerCAmelCase							)

   evaluate(__lowerCAmelCase							)




def 						__SCREAMING_SNAKE_CASE    (     _UpperCamelCase							):


   """simple docstring"""




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

   lowercase_		:			List[Any]							=   os.listdir(__lowerCAmelCase							)
   if len(__lowerCAmelCase							) == 0:
      return False

   return True


if __name__ == "__main__":
 main()





"""simple docstring"""


import argparse

import huggingface_hub
import k_diffusion as K
import torch

from diffusers import UNetaDConditionModel


a							='pcuenq/k-upscaler'





def 			lowerCamelCase_ (					__lowerCAmelCase   ,    __lowerCAmelCase   ,    *, __lowerCAmelCase   ,    __lowerCAmelCase				)							->					Dict:




						'''simple docstring'''



						lowerCamelCase__					={
						    # norm1
						    F'''{diffusers_resnet_prefix}.norm1.linear.weight''': checkpoint[F'''{resnet_prefix}.main.0.mapper.weight'''],
						    F'''{diffusers_resnet_prefix}.norm1.linear.bias''': checkpoint[F'''{resnet_prefix}.main.0.mapper.bias'''],
						    # conv1
						    F'''{diffusers_resnet_prefix}.conv1.weight''': checkpoint[F'''{resnet_prefix}.main.2.weight'''],
						    F'''{diffusers_resnet_prefix}.conv1.bias''': checkpoint[F'''{resnet_prefix}.main.2.bias'''],
						    # norm2
						    F'''{diffusers_resnet_prefix}.norm2.linear.weight''': checkpoint[F'''{resnet_prefix}.main.4.mapper.weight'''],
						    F'''{diffusers_resnet_prefix}.norm2.linear.bias''': checkpoint[F'''{resnet_prefix}.main.4.mapper.bias'''],
						    # conv2
						    F'''{diffusers_resnet_prefix}.conv2.weight''': checkpoint[F'''{resnet_prefix}.main.6.weight'''],
						    F'''{diffusers_resnet_prefix}.conv2.bias''': checkpoint[F'''{resnet_prefix}.main.6.bias'''],
						}

						if resnet.conv_shortcut is not None:
												rv.update(
												    {
												        F'''{diffusers_resnet_prefix}.conv_shortcut.weight''': checkpoint[F'''{resnet_prefix}.skip.weight'''],
												    }				)

						return rv





def 			lowerCamelCase_ (					__lowerCAmelCase   ,    *, __lowerCAmelCase   ,    __lowerCAmelCase				)							->					int:




						'''simple docstring'''



						lowerCamelCase__					,      lowerCamelCase__					,      lowerCamelCase__					=checkpoint[F'''{attention_prefix}.qkv_proj.weight'''].chunk(3   ,    dim=0				)
						lowerCamelCase__					,      lowerCamelCase__					,      lowerCamelCase__					=checkpoint[F'''{attention_prefix}.qkv_proj.bias'''].chunk(3   ,    dim=0				)
						lowerCamelCase__					={
						    # norm
						    F'''{diffusers_attention_prefix}.norm1.linear.weight''': checkpoint[F'''{attention_prefix}.norm_in.mapper.weight'''],
						    F'''{diffusers_attention_prefix}.norm1.linear.bias''': checkpoint[F'''{attention_prefix}.norm_in.mapper.bias'''],
						    # to_q
						    F'''{diffusers_attention_prefix}.attn1.to_q.weight''': weight_q.squeeze(-1				).squeeze(-1				),
						    F'''{diffusers_attention_prefix}.attn1.to_q.bias''': bias_q,
						    # to_k
						    F'''{diffusers_attention_prefix}.attn1.to_k.weight''': weight_k.squeeze(-1				).squeeze(-1				),
						    F'''{diffusers_attention_prefix}.attn1.to_k.bias''': bias_k,
						    # to_v
						    F'''{diffusers_attention_prefix}.attn1.to_v.weight''': weight_v.squeeze(-1				).squeeze(-1				),
						    F'''{diffusers_attention_prefix}.attn1.to_v.bias''': bias_v,
						    # to_out
						    F'''{diffusers_attention_prefix}.attn1.to_out.0.weight''': checkpoint[F'''{attention_prefix}.out_proj.weight''']
						    .squeeze(-1				)
						    .squeeze(-1				),
						    F'''{diffusers_attention_prefix}.attn1.to_out.0.bias''': checkpoint[F'''{attention_prefix}.out_proj.bias'''],
						}

						return rv





def 			lowerCamelCase_ (					__lowerCAmelCase   ,    *, __lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase				)							->					str:




						'''simple docstring'''



						lowerCamelCase__					,      lowerCamelCase__					=checkpoint[F'''{attention_prefix}.kv_proj.weight'''].chunk(2   ,    dim=0				)
						lowerCamelCase__					,      lowerCamelCase__					=checkpoint[F'''{attention_prefix}.kv_proj.bias'''].chunk(2   ,    dim=0				)

						lowerCamelCase__					={
						    # norm2 (ada groupnorm)
						    F'''{diffusers_attention_prefix}.norm{diffusers_attention_index}.linear.weight''': checkpoint[
						        F'''{attention_prefix}.norm_dec.mapper.weight'''
						    ],
						    F'''{diffusers_attention_prefix}.norm{diffusers_attention_index}.linear.bias''': checkpoint[
						        F'''{attention_prefix}.norm_dec.mapper.bias'''
						    ],
						    # layernorm on encoder_hidden_state
						    F'''{diffusers_attention_prefix}.attn{diffusers_attention_index}.norm_cross.weight''': checkpoint[
						        F'''{attention_prefix}.norm_enc.weight'''
						    ],
						    F'''{diffusers_attention_prefix}.attn{diffusers_attention_index}.norm_cross.bias''': checkpoint[
						        F'''{attention_prefix}.norm_enc.bias'''
						    ],
						    # to_q
						    F'''{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_q.weight''': checkpoint[
						        F'''{attention_prefix}.q_proj.weight'''
						    ]
						    .squeeze(-1				)
						    .squeeze(-1				),
						    F'''{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_q.bias''': checkpoint[
						        F'''{attention_prefix}.q_proj.bias'''
						    ],
						    # to_k
						    F'''{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_k.weight''': weight_k.squeeze(-1				).squeeze(-1				),
						    F'''{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_k.bias''': bias_k,
						    # to_v
						    F'''{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_v.weight''': weight_v.squeeze(-1				).squeeze(-1				),
						    F'''{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_v.bias''': bias_v,
						    # to_out
						    F'''{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_out.0.weight''': checkpoint[
						        F'''{attention_prefix}.out_proj.weight'''
						    ]
						    .squeeze(-1				)
						    .squeeze(-1				),
						    F'''{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_out.0.bias''': checkpoint[
						        F'''{attention_prefix}.out_proj.bias'''
						    ],
						}

						return rv





def 			lowerCamelCase_ (					__lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase				)							->					Optional[int]:




						'''simple docstring'''



						lowerCamelCase__					="inner_model.u_net.u_blocks" if block_type == "up" else "inner_model.u_net.d_blocks"
						lowerCamelCase__					=F'''{block_prefix}.{block_idx}'''

						lowerCamelCase__					={}

						if not hasattr(__lowerCAmelCase   ,    "attentions"				):
												lowerCamelCase__					=1  # resnet only
						elif not block.attentions[0].add_self_attention:
												lowerCamelCase__					=2  # resnet -> cross-attention
						else:
												lowerCamelCase__					=3  # resnet -> self-attention -> cross-attention)

						for resnet_idx, resnet in enumerate(block.resnets				):
												# diffusers_resnet_prefix = f"{diffusers_up_block_prefix}.resnets.{resnet_idx}"
												lowerCamelCase__					=F'''{block_type}_blocks.{block_idx}.resnets.{resnet_idx}'''
												lowerCamelCase__					=n * resnet_idx if block_type == "up" else n * resnet_idx + 1
												lowerCamelCase__					=F'''{block_prefix}.{idx}''' if block_type == "up" else F'''{block_prefix}.{idx}'''

												diffusers_checkpoint.update(
												    resnet_to_diffusers_checkpoint(
												        __lowerCAmelCase   ,    __lowerCAmelCase   ,    diffusers_resnet_prefix=__lowerCAmelCase   ,    resnet_prefix=__lowerCAmelCase				)				)

						if hasattr(__lowerCAmelCase   ,    "attentions"				):
												for attention_idx, attention in enumerate(block.attentions				):
																		lowerCamelCase__					=F'''{block_type}_blocks.{block_idx}.attentions.{attention_idx}'''
																		lowerCamelCase__					=n * attention_idx + 1 if block_type == "up" else n * attention_idx + 2
																		lowerCamelCase__					=F'''{block_prefix}.{idx}'''
																		lowerCamelCase__					=F'''{block_prefix}.{idx }'''
																		lowerCamelCase__					=1 if not attention.add_self_attention else 2
																		lowerCamelCase__					=(
																		    n * attention_idx + cross_attention_index
																		    if block_type == "up"
																		    else n * attention_idx + cross_attention_index + 1
																		)
																		lowerCamelCase__					=F'''{block_prefix}.{idx }'''

																		diffusers_checkpoint.update(
																		    cross_attn_to_diffusers_checkpoint(
																		        __lowerCAmelCase   ,    diffusers_attention_prefix=__lowerCAmelCase   ,    diffusers_attention_index=2   ,    attention_prefix=__lowerCAmelCase   ,    )				)

																		if attention.add_self_attention is True:
																								diffusers_checkpoint.update(
																								    self_attn_to_diffusers_checkpoint(
																								        __lowerCAmelCase   ,    diffusers_attention_prefix=__lowerCAmelCase   ,    attention_prefix=__lowerCAmelCase   ,    )				)

						return diffusers_checkpoint





def 			lowerCamelCase_ (					__lowerCAmelCase   ,    __lowerCAmelCase				)							->					Tuple:




						'''simple docstring'''



						lowerCamelCase__					={}

						# pre-processing
						diffusers_checkpoint.update(
						    {
						        "conv_in.weight": checkpoint["inner_model.proj_in.weight"],
						        "conv_in.bias": checkpoint["inner_model.proj_in.bias"],
						    }				)

						# timestep and class embedding
						diffusers_checkpoint.update(
						    {
						        "time_proj.weight": checkpoint["inner_model.timestep_embed.weight"].squeeze(-1				),
						        "time_embedding.linear_1.weight": checkpoint["inner_model.mapping.0.weight"],
						        "time_embedding.linear_1.bias": checkpoint["inner_model.mapping.0.bias"],
						        "time_embedding.linear_2.weight": checkpoint["inner_model.mapping.2.weight"],
						        "time_embedding.linear_2.bias": checkpoint["inner_model.mapping.2.bias"],
						        "time_embedding.cond_proj.weight": checkpoint["inner_model.mapping_cond.weight"],
						    }				)

						# down_blocks
						for down_block_idx, down_block in enumerate(model.down_blocks				):
												diffusers_checkpoint.update(block_to_diffusers_checkpoint(__lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase   ,    "down"				)				)

						# up_blocks
						for up_block_idx, up_block in enumerate(model.up_blocks				):
												diffusers_checkpoint.update(block_to_diffusers_checkpoint(__lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase   ,    "up"				)				)

						# post-processing
						diffusers_checkpoint.update(
						    {
						        "conv_out.weight": checkpoint["inner_model.proj_out.weight"],
						        "conv_out.bias": checkpoint["inner_model.proj_out.bias"],
						    }				)

						return diffusers_checkpoint





def 			lowerCamelCase_ (					__lowerCAmelCase				)							->					Optional[int]:




						'''simple docstring'''



						lowerCamelCase__					=original_config["input_channels"] + original_config["unet_cond_dim"]
						lowerCamelCase__					=original_config["input_channels"] + (1 if original_config["has_variance"] else 0)

						lowerCamelCase__					=original_config["channels"]

						assert (
						    len(set(original_config["depths"]				)				) == 1
						), "UNet2DConditionModel currently do not support blocks with different number of layers"
						lowerCamelCase__					=original_config["depths"][0]

						lowerCamelCase__					=original_config["mapping_cond_dim"]
						lowerCamelCase__					=original_config["cross_cond_dim"]

						lowerCamelCase__					=[]
						lowerCamelCase__					=[]
						for s, c in zip(original_config["self_attn_depths"]   ,    original_config["cross_attn_depths"]				):
												if s:
																		lowerCamelCase__					="self"
																		lowerCamelCase__					="cross" if c else None
												elif c:
																		lowerCamelCase__					="cross"
																		lowerCamelCase__					=None
												else:
																		lowerCamelCase__					=None
																		lowerCamelCase__					=None
												attna_types.append(__lowerCAmelCase				)
												attna_types.append(__lowerCAmelCase				)

						lowerCamelCase__					=UNetaDConditionModel(
						    in_channels=__lowerCAmelCase   ,    out_channels=__lowerCAmelCase   ,    down_block_types=("KDownBlock2D", "KCrossAttnDownBlock2D", "KCrossAttnDownBlock2D", "KCrossAttnDownBlock2D")   ,    mid_block_type=__lowerCAmelCase   ,    up_block_types=("KCrossAttnUpBlock2D", "KCrossAttnUpBlock2D", "KCrossAttnUpBlock2D", "KUpBlock2D")   ,    block_out_channels=__lowerCAmelCase   ,    layers_per_block=__lowerCAmelCase   ,    act_fn="gelu"   ,    norm_num_groups=__lowerCAmelCase   ,    cross_attention_dim=__lowerCAmelCase   ,    attention_head_dim=64   ,    time_cond_proj_dim=__lowerCAmelCase   ,    resnet_time_scale_shift="scale_shift"   ,    time_embedding_type="fourier"   ,    timestep_post_act="gelu"   ,    conv_in_kernel=1   ,    conv_out_kernel=1   ,    )

						return unet





def 			lowerCamelCase_ (					__lowerCAmelCase				)							->					int:




						'''simple docstring'''



						lowerCamelCase__					=torch.device("cuda" if torch.cuda.is_available() else "cpu"				)

						lowerCamelCase__					=huggingface_hub.hf_hub_download(__lowerCAmelCase   ,    "config_laion_text_cond_latent_upscaler_2.json"				)
						lowerCamelCase__					=huggingface_hub.hf_hub_download(
						    __lowerCAmelCase   ,    "laion_text_cond_latent_upscaler_2_1_00470000_slim.pth"				)
						print(F'''loading original model configuration from {orig_config_path}'''				)
						print(F'''loading original model checkpoint from {orig_weights_path}'''				)

						print("converting to diffusers unet"				)
						lowerCamelCase__					=K.config.load_config(open(__lowerCAmelCase				)				)["model"]
						lowerCamelCase__					=unet_model_from_original_config(__lowerCAmelCase				)

						lowerCamelCase__					=torch.load(__lowerCAmelCase   ,    map_location=__lowerCAmelCase				)["model_ema"]
						lowerCamelCase__					=unet_to_diffusers_checkpoint(__lowerCAmelCase   ,    __lowerCAmelCase				)

						model.load_state_dict(__lowerCAmelCase   ,    strict=__lowerCAmelCase				)
						model.save_pretrained(args.dump_path				)
						print(F'''saving converted unet model in {args.dump_path}'''				)


if __name__ == "__main__":
	a							=argparse.ArgumentParser()

	parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')
	a							=parser.parse_args()

	main(args)


