import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline



def   __A		(			_A							,				_A							,				_A							,				_A							,				_A					):



 """simple docstring"""


 __a       =  StableDiffusionPipeline.from_pretrained(_A							,				torch_dtype=torch.floataa					)

 # load LoRA weight from .safetensors
 __a       =  load_file(_A					)

 __a       =  []

 # directly update weight in diffusers model
 for key in state_dict:
  # it is suggested to print out the key, it usually will be something like below
  # "lora_te_text_model_encoder_layers_0_self_attn_k_proj.lora_down.weight"

  # as we have set the alpha beforehand, so just skip
  if ".alpha" in key or key in visited:
   continue

  if "text" in key:
   __a       =  key.split("."					)[0].split(LORA_PREFIX_TEXT_ENCODER + "_"					)[-1].split("_"					)
   __a       =  pipeline.text_encoder
  else:
   __a       =  key.split("."					)[0].split(LORA_PREFIX_UNET + "_"					)[-1].split("_"					)
   __a       =  pipeline.unet

  # find the target layer
  __a       =  layer_infos.pop(0					)
  while len(_A					) > -1:
   try:
    __a       =  curr_layer.__getattr__(_A					)
    if len(_A					) > 0:
     __a       =  layer_infos.pop(0					)
    elif len(_A					) == 0:
     break
   except Exception:
    if len(_A					) > 0:
     temp_name += "_" + layer_infos.pop(0					)
    else:
     __a       =  layer_infos.pop(0					)

  __a       =  []
  if "lora_down" in key:
   pair_keys.append(key.replace("lora_down"							,				"lora_up"					)					)
   pair_keys.append(_A					)
  else:
   pair_keys.append(_A					)
   pair_keys.append(key.replace("lora_up"							,				"lora_down"					)					)

  # update weight
  if len(state_dict[pair_keys[0]].shape					) == 4:
   __a       =  state_dict[pair_keys[0]].squeeze(3					).squeeze(2					).to(torch.floataa					)
   __a       =  state_dict[pair_keys[1]].squeeze(3					).squeeze(2					).to(torch.floataa					)
   curr_layer.weight.data += alpha * torch.mm(_A							,				_A					).unsqueeze(2					).unsqueeze(3					)
  else:
   __a       =  state_dict[pair_keys[0]].to(torch.floataa					)
   __a       =  state_dict[pair_keys[1]].to(torch.floataa					)
   curr_layer.weight.data += alpha * torch.mm(_A							,				_A					)

  # update visited list
  for item in pair_keys:
   visited.append(_A					)

 return pipeline


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE :	List[Any]							     =		argparse.ArgumentParser()

  parser.add_argument(
      """--base_model_path""", default=None, type=str, required=True, help="""Path to the base model in diffusers format."""
  )
  parser.add_argument(
      """--checkpoint_path""", default=None, type=str, required=True, help="""Path to the checkpoint to convert."""
  )
  parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the output model.""")
  parser.add_argument(
      """--lora_prefix_unet""", default="""lora_unet""", type=str, help="""The prefix of UNet weight in safetensors"""
  )
  parser.add_argument(
      """--lora_prefix_text_encoder""",
      default="""lora_te""",
      type=str,
      help="""The prefix of text encoder weight in safetensors""",
  )
  parser.add_argument("""--alpha""", default=0.7_5, type=float, help="""The merging ratio in W = W0 + alpha * deltaW""")
  parser.add_argument(
      """--to_safetensors""", action="""store_true""", help="""Whether to store pipeline in safetensors format or not."""
  )
  parser.add_argument("""--device""", type=str, help="""Device to use (e.g. cpu, cuda:0, cuda:1, etc.)""")

  SCREAMING_SNAKE_CASE :	Optional[Any]							     =		parser.parse_args()

  SCREAMING_SNAKE_CASE :	Dict							     =		args.base_model_path
  SCREAMING_SNAKE_CASE :	List[str]							     =		args.checkpoint_path
  SCREAMING_SNAKE_CASE :	Tuple							     =		args.dump_path
  SCREAMING_SNAKE_CASE :	Optional[Any]							     =		args.lora_prefix_unet
  SCREAMING_SNAKE_CASE :	Dict							     =		args.lora_prefix_text_encoder
  SCREAMING_SNAKE_CASE :	str							     =		args.alpha

  SCREAMING_SNAKE_CASE :	List[Any]							     =		convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)

  SCREAMING_SNAKE_CASE :	Dict							     =		pipe.to(args.device)
  pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)



import argparse
import re
from typing import Dict

import torch
from datasets import Audio, Dataset, load_dataset, load_metric

from transformers import AutoFeatureExtractor, pipeline



def   __A		(			_A							,				_A					):



 """simple docstring"""


 __a       =  args.log_outputs
 __a       =  "_".join(args.dataset.split("/"					) + [args.config, args.split]					)

 # load metric
 __a       =  load_metric("wer"					)
 __a       =  load_metric("cer"					)

 # compute metrics
 __a       =  wer.compute(references=result["target"]							,				predictions=result["prediction"]					)
 __a       =  cer.compute(references=result["target"]							,				predictions=result["prediction"]					)

 # print & log results
 __a       =  f"""WER: {wer_result}\nCER: {cer_result}"""
 print(_A					)

 with open(f"""{dataset_id}_eval_results.txt"""							,				"w"					) as f:
  f.write(_A					)

 # log all results in text file. Possibly interesting for analysis
 if log_outputs is not None:
  __a       =  f"""log_{dataset_id}_predictions.txt"""
  __a       =  f"""log_{dataset_id}_targets.txt"""

  with open(_A							,				"w"					) as p, open(_A							,				"w"					) as t:
   # mapping function to write output
   def write_to_file(_A							,				_A					):
    p.write(f"""{i}""" + "\n"					)
    p.write(batch["prediction"] + "\n"					)
    t.write(f"""{i}""" + "\n"					)
    t.write(batch["target"] + "\n"					)

   result.map(_A							,				with_indices=_A					)



def   __A		(			_A					):



 """simple docstring"""


 __a       =  "[,?.!\-\;\:\"“%‘”�—’…–]"  # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training

 __a       =  re.sub(_A							,				""							,				text.lower()					)

 # In addition, we can normalize the target text, e.g. removing new lines characters etc...
 # note that order is important here!
 __a       =  ["\n\n", "\n", "   ", "  "]

 for t in token_sequences_to_ignore:
  __a       =  " ".join(text.split(_A					)					)

 return text



def   __A		(			_A					):



 """simple docstring"""


 __a       =  load_dataset(args.dataset							,				args.config							,				split=args.split							,				use_auth_token=_A					)

 # for testing: only process the first two examples as a test
 # dataset = dataset.select(range(10))

 # load processor
 __a       =  AutoFeatureExtractor.from_pretrained(args.model_id					)
 __a       =  feature_extractor.sampling_rate

 # resample audio
 __a       =  dataset.cast_column("audio"							,				Audio(sampling_rate=_A					)					)

 # load eval pipeline
 if args.device is None:
  __a       =  0 if torch.cuda.is_available() else -1
 __a       =  pipeline("automatic-speech-recognition"							,				model=args.model_id							,				device=args.device					)

 # map function to decode audio
 def map_to_pred(_A					):
  __a       =  asr(
      batch["audio"]["array"]							,				chunk_length_s=args.chunk_length_s							,				stride_length_s=args.stride_length_s					)

  __a       =  prediction["text"]
  __a       =  normalize_text(batch["sentence"]					)
  return batch

 # run inference on all examples
 __a       =  dataset.map(_A							,				remove_columns=dataset.column_names					)

 # compute and log_results
 # do not change function below
 log_results(_A							,				_A					)


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE :	Any							     =		argparse.ArgumentParser()

  parser.add_argument(
      """--model_id""", type=str, required=True, help="""Model identifier. Should be loadable with 🤗 Transformers"""
  )
  parser.add_argument(
      """--dataset""",
      type=str,
      required=True,
      help="""Dataset name to evaluate the `model_id`. Should be loadable with 🤗 Datasets""",
  )
  parser.add_argument(
      """--config""", type=str, required=True, help="""Config of the dataset. *E.g.* `'en'`  for Common Voice"""
  )
  parser.add_argument("""--split""", type=str, required=True, help="""Split of the dataset. *E.g.* `'test'`""")
  parser.add_argument(
      """--chunk_length_s""", type=float, default=None, help="""Chunk length in seconds. Defaults to 5 seconds."""
  )
  parser.add_argument(
      """--stride_length_s""", type=float, default=None, help="""Stride of the audio chunks. Defaults to 1 second."""
  )
  parser.add_argument(
      """--log_outputs""", action="""store_true""", help="""If defined, write outputs to log file for analysis."""
  )
  parser.add_argument(
      """--device""",
      type=int,
      default=None,
      help="""The device to run the pipeline on. -1 for CPU (default), 0 for the first GPU and so on.""",
  )
  SCREAMING_SNAKE_CASE :	Optional[Any]							     =		parser.parse_args()

  main(args)


