



import logging
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union

import librosa
import torch
from datasets import DatasetDict, load_dataset
from packaging import version
from torch import nn

from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaForPreTraining,
    is_apex_available,
    trainer_utils,
)
from transformers.models.wavaveca.modeling_wavaveca import _compute_mask_indices


if is_apex_available():
   from apex import amp

if version.parse(version.parse(torch.__version__).base_version) >= version.parse("1.6"):
   _lowercase						  =		True
   from torch.cuda.amp import autocast


_lowercase						  =		logging.getLogger(__name__)



@dataclass
class     _UpperCAmelCase			:
     UpperCamelCase__		    =				field(
         metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}     )
     UpperCamelCase__		    =				field(
         default=A__			,      metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}			,      )
     UpperCamelCase__		    =				field(
         default=A__			,      metadata={'''help''': '''Whether to freeze the feature extractor layers of the model.'''}     )
     UpperCamelCase__		    =				field(
         default=A__			,      metadata={'''help''': '''Whether to log verbose messages or not.'''}			,      )
     UpperCamelCase__		    =				field(
         default=2.0			,      metadata={'''help''': '''Maximum temperature for gumbel softmax.'''}     )
     UpperCamelCase__		    =				field(
         default=0.5			,      metadata={'''help''': '''Minimum temperature for gumbel softmax.'''}     )
     UpperCamelCase__		    =				field(
         default=0.99_9995			,      metadata={'''help''': '''Decay of gumbel temperature during training.'''}     )







def 		lowerCAmelCase__    (					UpperCamelCase_				:       ModelArguments ,       UpperCamelCase_				:       TrainingArguments	)->  int:
    logging.basicConfig(
        format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''' ,       datefmt='''%m/%d/%Y %H:%M:%S''' ,       handlers=[logging.StreamHandler(sys.stdout	)] ,       )
    A__		 =					logging.WARNING
    if model_args.verbose_logging:
        A__		 =					logging.DEBUG
    elif trainer_utils.is_main_process(training_args.local_rank	):
        A__		 =					logging.INFO
    logger.setLevel(UpperCamelCase_	)



@dataclass
class     _UpperCAmelCase			:
     UpperCamelCase__		    =				field(
         default=A__			,      metadata={'''help''': '''The name of the dataset to use (via the datasets library).'''}     )
     UpperCamelCase__		    =				field(
         default=A__			,      metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}     )
     UpperCamelCase__		    =				field(
         default='''train'''			,      metadata={
             '''help''': '''The name of the training data set split to use (via the datasets library). Defaults to \'train\''''
         }			,      )
     UpperCamelCase__		    =				field(
         default='''validation'''			,      metadata={
             '''help''': (
                 '''The name of the validation data set split to use (via the datasets library). Defaults to \'validation\''''
             )
         }			,      )
     UpperCamelCase__		    =				field(
         default='''file'''			,      metadata={'''help''': '''Column in the dataset that contains speech file path. Defaults to \'file\''''}			,      )
     UpperCamelCase__		    =				field(
         default=A__			,      metadata={'''help''': '''Overwrite the cached preprocessed datasets or not.'''}     )
     UpperCamelCase__		    =				field(
         default=1			,      metadata={
             '''help''': '''The percentage of the train set used as validation set in case there\'s no validation split'''
         }			,      )
     UpperCamelCase__		    =				field(
         default=A__			,      metadata={'''help''': '''The number of processes to use for the preprocessing.'''}			,      )
     UpperCamelCase__		    =				field(
         default=20.0			,      metadata={'''help''': '''Filter audio files that are longer than `max_duration_in_seconds` seconds'''}     )







@dataclass
class     _UpperCAmelCase			:
     UpperCamelCase__		    =				42
     UpperCamelCase__		    =				42
     UpperCamelCase__		    =				"longest"
     UpperCamelCase__		    =				None
     UpperCamelCase__		    =				None




     def __call__(				self ,						a__):
         # reformat list to dict and set to pytorch format
         A__		 =					self.feature_extractor.pad(
             a__ ,						max_length=self.max_length ,						padding=self.padding ,						pad_to_multiple_of=self.pad_to_multiple_of ,						return_tensors='''pt''' ,						)
         A__		 =					self.model._get_feat_extract_output_lengths(batch['''input_values'''].shape[-1])

         A__		 =					batch['''input_values'''].shape[0]

         # make sure that no loss is computed on padded inputs
         if batch["attention_mask"] is not None:
             # compute real output lengths according to convolution formula
             A__		 =					self.model._get_feat_extract_output_lengths(batch['''attention_mask'''].sum(-1)).to(
                 torch.long)

             A__		 =					torch.zeros(
                 (batch_size, mask_indices_seq_length) ,						dtype=torch.long ,						device=batch['''input_values'''].device)

             # these two operations makes sure that all values
             # before the output lengths indices are attended to
             A__		 =					1
             A__		 =					attention_mask.flip([-1]).cumsum(-1).flip([-1]).bool()

         # sample randomly masked indices
         A__		 =					_compute_mask_indices(
             (batch_size, mask_indices_seq_length) ,						self.model.config.mask_time_prob ,						self.model.config.mask_time_length ,						attention_mask=a__ ,						min_masks=2 ,						)

         return batch







class     _UpperCAmelCase			(       A__     ):




     def __init__(				self ,						*a__ ,						a__=1 ,						a__=0 ,						a__=1.0 ,						**a__):
         super().__init__(*a__ ,						**a__)
         A__		 =					0
         A__		 =					max_gumbel_temp
         A__		 =					min_gumbel_temp
         A__		 =					gumbel_temp_decay




     def 		snake_case_		(				self ,						a__ ,						a__):
         model.train()
         A__		 =					self._prepare_inputs(a__)

         if self.use_amp:
             with autocast():
                 A__		 =					self.compute_loss(a__ ,						a__)
         else:
             A__		 =					self.compute_loss(a__ ,						a__)

         if self.args.n_gpu > 1 or self.deepspeed:
             if model.module.config.ctc_loss_reduction == "mean":
                 A__		 =					loss.mean()
             elif model.module.config.ctc_loss_reduction == "sum":
                 A__		 =					loss.sum() / (inputs['''mask_time_indices''']).sum()
             else:
                 raise ValueError(F"{model.config.ctc_loss_reduction} is not valid. Choose one of ['mean', 'sum']")

         if self.args.gradient_accumulation_steps > 1:
             A__		 =					loss / self.args.gradient_accumulation_steps

         if self.use_amp:
             self.scaler.scale(a__).backward()
         elif self.use_apex:
             with amp.scale_loss(a__ ,						self.optimizer) as scaled_loss:
                 scaled_loss.backward()
         elif self.deepspeed:
             self.deepspeed.backward(a__)
         else:
             loss.backward()

         self.num_update_step += 1
         # make sure gumbel softmax temperature is decayed
         if self.args.n_gpu > 1 or self.deepspeed:
             model.module.set_gumbel_temperature(
                 max(self.max_gumbel_temp * self.gumbel_temp_decay**self.num_update_step ,						self.min_gumbel_temp))
         else:
             model.set_gumbel_temperature(
                 max(self.max_gumbel_temp * self.gumbel_temp_decay**self.num_update_step ,						self.min_gumbel_temp))

         return loss.detach()







def 		lowerCAmelCase__    (					)->  Optional[Any]:
    # See all possible arguments in src/transformers/training_args.py
    # or by passing the --help flag to this script.
    # We now keep distinct sets of args, for a cleaner separation of concerns.

    A__		 =					HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)	)

    A__ ,	A__ ,	A__		 =					parser.parse_args_into_dataclasses()
    configure_logger(UpperCamelCase_ ,       UpperCamelCase_	)

    # Downloading and loading a dataset from the hub.
    A__		 =					load_dataset(data_args.dataset_name ,       data_args.dataset_config_name ,       cache_dir=model_args.cache_dir	)

    if "validation" not in datasets.keys():
        # make sure only "validation" and "train" keys remain"
        A__		 =					DatasetDict()
        A__		 =					load_dataset(
            data_args.dataset_name ,       data_args.dataset_config_name ,       split=f"{data_args.train_split_name}[:{data_args.validation_split_percentage}%]" ,       cache_dir=model_args.cache_dir ,       )
        A__		 =					load_dataset(
            data_args.dataset_name ,       data_args.dataset_config_name ,       split=f"{data_args.train_split_name}[{data_args.validation_split_percentage}%:]" ,       cache_dir=model_args.cache_dir ,       )
    else:
        # make sure only "validation" and "train" keys remain"
        A__		 =					DatasetDict()
        A__		 =					load_dataset(
            data_args.dataset_name ,       data_args.dataset_config_name ,       split='''validation''' ,       cache_dir=model_args.cache_dir ,       )
        A__		 =					load_dataset(
            data_args.dataset_name ,       data_args.dataset_config_name ,       split=f"{data_args.train_split_name}" ,       cache_dir=model_args.cache_dir ,       )

    # only normalized-inputs-training is supported
    A__		 =					WavaVecaFeatureExtractor.from_pretrained(
        model_args.model_name_or_path ,       cache_dir=model_args.cache_dir ,       do_normalize=UpperCamelCase_	)

    def prepare_dataset(UpperCamelCase_				:       str	):
        # check that all files have the correct sampling rate
        A__ ,	A__		 =					librosa.load(batch[data_args.speech_file_column] ,       sr=feature_extractor.sampling_rate	)
        return batch

    # load audio files into numpy arrays
    A__		 =					datasets.map(
        UpperCamelCase_ ,       num_proc=data_args.preprocessing_num_workers ,       remove_columns=datasets['''train'''].column_names	)

    # filter audio files that are too long
    A__		 =					vectorized_datasets.filter(
        lambda UpperCamelCase_	: len(data['''speech''']	) < int(data_args.max_duration_in_seconds * feature_extractor.sampling_rate	)	)

    def normalize(UpperCamelCase_				:       Optional[int]	):
        return feature_extractor(batch['''speech'''] ,       sampling_rate=feature_extractor.sampling_rate	)

    # normalize and transform to `BatchFeatures`
    A__		 =					vectorized_datasets.map(
        UpperCamelCase_ ,       batched=UpperCamelCase_ ,       num_proc=data_args.preprocessing_num_workers ,       load_from_cache_file=not data_args.overwrite_cache ,       remove_columns=vectorized_datasets['''train'''].column_names ,       )

    # pretraining is only supported for "newer" stable layer norm architecture
    # apply_spec_augment has to be True, mask_feature_prob has to be 0.0
    A__		 =					WavaVecaConfig.from_pretrained(
        model_args.model_name_or_path ,       cache_dir=model_args.cache_dir ,       gradient_checkpointing=training_args.gradient_checkpointing ,       )

    if not config.do_stable_layer_norm or config.feat_extract_norm != "layer":
        raise ValueError(
            '''PreTraining is only supported for ``config.do_stable_layer_norm=True`` and'''
            ''' ``config.feat_extract_norm=\'layer\''''	)

    A__		 =					WavaVecaForPreTraining(UpperCamelCase_	)

    A__		 =					DataCollatorForWavaVecaPretraining(model=UpperCamelCase_ ,       feature_extractor=UpperCamelCase_	)

    A__		 =					WavaVecaPreTrainer(
        model=UpperCamelCase_ ,       data_collator=UpperCamelCase_ ,       args=UpperCamelCase_ ,       train_dataset=vectorized_datasets['''train'''] ,       eval_dataset=vectorized_datasets['''validation'''] ,       tokenizer=UpperCamelCase_ ,       max_gumbel_temp=model_args.max_gumbel_temperature ,       min_gumbel_temp=model_args.min_gumbel_temperature ,       gumbel_temp_decay=model_args.gumbel_temperature_decay ,       )
    trainer.train()


if __name__ == "__main__":
   main()





import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


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

   parser.add_argument(
       "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
   )
   parser.add_argument(
       "--original_config_file",
       type=str,
       required=True,
       help="The YAML config file corresponding to the original architecture.",
   )
   parser.add_argument(
       "--num_in_channels",
       default=None,
       type=int,
       help="The number of input channels. If `None` number of input channels will be automatically inferred.",
   )
   parser.add_argument(
       "--image_size",
       default=512,
       type=int,
       help=(
           "The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2"
           " Base. Use 768 for Stable Diffusion v2."
       ),
   )
   parser.add_argument(
       "--extract_ema",
       action="store_true",
       help=(
           "Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights"
           " or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield"
           " higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning."
       ),
   )
   parser.add_argument(
       "--upcast_attention",
       action="store_true",
       help=(
           "Whether the attention computation should always be upcasted. This is necessary when running stable"
           " diffusion 2.1."
       ),
   )
   parser.add_argument(
       "--from_safetensors",
       action="store_true",
       help="If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.",
   )
   parser.add_argument(
       "--to_safetensors",
       action="store_true",
       help="Whether to store pipeline in safetensors format or not.",
   )
   parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
   parser.add_argument("--device", type=str, help="Device to use (e.g. cpu, cuda:0, cuda:1, etc.)")



   def 		lowerCAmelCase__    (					UpperCamelCase_				:       Any	)->  Union[str, Any]:
       if string == "True":
           return True
       elif string == "False":
           return False
       else:
           raise ValueError(f"could not parse string as bool {string}"	)

   parser.add_argument(
       "--use_linear_projection", help="Override for use linear projection", required=False, type=parse_bool
   )

   parser.add_argument("--cross_attention_dim", help="Override for cross attention_dim", required=False, type=int)

   _lowercase						  =		parser.parse_args()

   _lowercase						  =		download_controlnet_from_original_ckpt(
       checkpoint_path=args.checkpoint_path,
       original_config_file=args.original_config_file,
       image_size=args.image_size,
       extract_ema=args.extract_ema,
       num_in_channels=args.num_in_channels,
       upcast_attention=args.upcast_attention,
       from_safetensors=args.from_safetensors,
       device=args.device,
       use_linear_projection=args.use_linear_projection,
       cross_attention_dim=args.cross_attention_dim,
   )



   controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
