

'''simple docstring'''

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'''):
       UpperCAmelCase					 =	True
       from torch.cuda.amp import autocast


UpperCAmelCase					 =	logging.getLogger(__name__)


@dataclass
class 	lowerCAmelCase  :
       lowerCAmelCase_												=			field(
           metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} )
       lowerCAmelCase_												=			field(
           default=A ,      metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"} ,      )
       lowerCAmelCase_												=			field(
           default=A ,      metadata={"help": "Whether to freeze the feature extractor layers of the model."} )
       lowerCAmelCase_												=			field(
           default=A ,      metadata={"help": "Whether to log verbose messages or not."} ,      )
       lowerCAmelCase_												=			field(
           default=2.0 ,      metadata={"help": "Maximum temperature for gumbel softmax."} )
       lowerCAmelCase_												=			field(
           default=0.5 ,      metadata={"help": "Minimum temperature for gumbel softmax."} )
       lowerCAmelCase_												=			field(
           default=0.9_9_9_9_9_5 ,      metadata={"help": "Decay of gumbel temperature during training."} )






def 					__UpperCamelCase				(						lowercase__    :	ModelArguments,       lowercase__    :	TrainingArguments			):






     '''simple docstring'''





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


@dataclass
class 	lowerCAmelCase  :
       lowerCAmelCase_												=			field(
           default=A ,      metadata={"help": "The name of the dataset to use (via the datasets library)."} )
       lowerCAmelCase_												=			field(
           default=A ,      metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} )
       lowerCAmelCase_												=			field(
           default="train" ,      metadata={
               "help": "The name of the training data set split to use (via the datasets library). Defaults to 'train'"
           } ,      )
       lowerCAmelCase_												=			field(
           default="validation" ,      metadata={
               "help": (
                   "The name of the validation data set split to use (via the datasets library). Defaults to 'validation'"
               )
           } ,      )
       lowerCAmelCase_												=			field(
           default="file" ,      metadata={"help": "Column in the dataset that contains speech file path. Defaults to 'file'"} ,      )
       lowerCAmelCase_												=			field(
           default=A ,      metadata={"help": "Overwrite the cached preprocessed datasets or not."} )
       lowerCAmelCase_												=			field(
           default=1 ,      metadata={
               "help": "The percentage of the train set used as validation set in case there's no validation split"
           } ,      )
       lowerCAmelCase_												=			field(
           default=A ,      metadata={"help": "The number of processes to use for the preprocessing."} ,      )
       lowerCAmelCase_												=			field(
           default=2_0.0 ,      metadata={"help": "Filter audio files that are longer than `max_duration_in_seconds` seconds"} )






@dataclass
class 	lowerCAmelCase  :
       lowerCAmelCase_												=			42
       lowerCAmelCase_												=			42
       lowerCAmelCase_												=			"longest"
       lowerCAmelCase_												=			None
       lowerCAmelCase_												=			None







       def __call__(				self      :			Optional[int]    ,   __lowercase      :			List[Dict[str, Union[List[int], torch.Tensor]]]   ):







            """simple docstring"""







            __lowercase      		=self.feature_extractor.pad(
                __lowercase    ,   max_length=self.max_length    ,   padding=self.padding    ,   pad_to_multiple_of=self.pad_to_multiple_of    ,   return_tensors='pt'    ,   )
            __lowercase      		=self.model._get_feat_extract_output_lengths(batch['input_values'].shape[-1]   )

            __lowercase      		=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
                 __lowercase      		=self.model._get_feat_extract_output_lengths(batch['attention_mask'].sum(-1   )   ).to(
                     torch.long   )

                 __lowercase      		=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
                 __lowercase      		=1
                 __lowercase      		=attention_mask.flip([-1]   ).cumsum(-1   ).flip([-1]   ).bool()

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

            return batch






class 	lowerCAmelCase  (   A ):







       def __init__(				self      :			Optional[Any]    ,   *__lowercase      :			str    ,   __lowercase      :			Optional[int]=1    ,   __lowercase      :			str=0    ,   __lowercase      :			Optional[Any]=1.0    ,   **__lowercase      :			List[Any]   ):







            """simple docstring"""







            super().__init__(*__lowercase    ,   **__lowercase   )
            __lowercase      		=0
            __lowercase      		=max_gumbel_temp
            __lowercase      		=min_gumbel_temp
            __lowercase      		=gumbel_temp_decay







       def       snake_case (				self      :			List[Any]    ,   __lowercase      :			nn.Module    ,   __lowercase      :			Dict[str, Union[torch.Tensor, Any]]   ):







            """simple docstring"""







            model.train()
            __lowercase      		=self._prepare_inputs(__lowercase   )

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

            if self.args.n_gpu > 1 or self.deepspeed:
                 if model.module.config.ctc_loss_reduction == "mean":
                      __lowercase      		=loss.mean()
                 elif model.module.config.ctc_loss_reduction == "sum":
                      __lowercase      		=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:
                 __lowercase      		=loss / self.args.gradient_accumulation_steps

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






     '''simple docstring'''





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

     __lowercase		,  __lowercase		,  __lowercase      		=parser.parse_args_into_dataclasses()
     configure_logger(lowercase__,       lowercase__			)

     # Downloading and loading a dataset from the hub.
     __lowercase      		=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"
          __lowercase      		=DatasetDict()
          __lowercase      		=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,       )
          __lowercase      		=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"
          __lowercase      		=DatasetDict()
          __lowercase      		=load_dataset(
              data_args.dataset_name,       data_args.dataset_config_name,       split='validation',       cache_dir=model_args.cache_dir,       )
          __lowercase      		=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
     __lowercase      		=WavaVecaFeatureExtractor.from_pretrained(
         model_args.model_name_or_path,       cache_dir=model_args.cache_dir,       do_normalize=lowercase__			)

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

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

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

     def normalize(lowercase__    :	List[Any]			):
          return feature_extractor(batch['speech'],       sampling_rate=feature_extractor.sampling_rate			)

     # normalize and transform to `BatchFeatures`
     __lowercase      		=vectorized_datasets.map(
         lowercase__,       batched=lowercase__,       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
     __lowercase      		=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\''			)

     __lowercase      		=WavaVecaForPreTraining(lowercase__			)

     __lowercase      		=DataCollatorForWavaVecaPretraining(model=lowercase__,       feature_extractor=lowercase__			)

     __lowercase      		=WavaVecaPreTrainer(
         model=lowercase__,       data_collator=lowercase__,       args=lowercase__,       train_dataset=vectorized_datasets['train'],       eval_dataset=vectorized_datasets['validation'],       tokenizer=lowercase__,       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()





'''simple docstring'''

import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase					 =	logging.get_logger(__name__)

def 					__UpperCamelCase				(						lowercase__    :	str			):






     '''simple docstring'''





     __lowercase      		=YolosConfig()

     # size of the architecture
     if "yolos_ti" in yolos_name:
          __lowercase      		=1_92
          __lowercase      		=7_68
          __lowercase      		=12
          __lowercase      		=3
          __lowercase      		=[8_00, 13_33]
          __lowercase      		=False
     elif yolos_name == "yolos_s_dWr":
          __lowercase      		=3_30
          __lowercase      		=14
          __lowercase      		=6
          __lowercase      		=13_20
     elif "yolos_s" in yolos_name:
          __lowercase      		=3_84
          __lowercase      		=15_36
          __lowercase      		=12
          __lowercase      		=6
     elif "yolos_b" in yolos_name:
          __lowercase      		=[8_00, 13_44]

     __lowercase      		=91
     __lowercase      		='huggingface/label-files'
     __lowercase      		='coco-detection-id2label.json'
     __lowercase      		=json.load(open(hf_hub_download(lowercase__,       lowercase__,       repo_type='dataset'			),       'r'			)			)
     __lowercase      		={int(lowercase__			): v for k, v in idalabel.items()}
     __lowercase      		=idalabel
     __lowercase      		={v: k for k, v in idalabel.items()}

     return config

def 					__UpperCamelCase				(						lowercase__    :	dict,       lowercase__    :	YolosConfig,       lowercase__    :	bool = False			):






     '''simple docstring'''





     for i in range(config.num_hidden_layers			):
          # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
          __lowercase      		=state_dict.pop(F'''blocks.{i}.attn.qkv.weight'''			)
          __lowercase      		=state_dict.pop(F'''blocks.{i}.attn.qkv.bias'''			)
          # next, add query, keys and values (in that order) to the state dict
          __lowercase      		=in_proj_weight[: config.hidden_size, :]
          __lowercase      		=in_proj_bias[: config.hidden_size]
          __lowercase      		=in_proj_weight[
              config.hidden_size : config.hidden_size * 2, :
          ]
          __lowercase      		=in_proj_bias[
              config.hidden_size : config.hidden_size * 2
          ]
          __lowercase      		=in_proj_weight[-config.hidden_size :, :]
          __lowercase      		=in_proj_bias[-config.hidden_size :]

def 					__UpperCamelCase				(						lowercase__    :	str			):






     '''simple docstring'''





     if "backbone" in name:
          __lowercase      		=name.replace('backbone',       'vit'			)
     if "cls_token" in name:
          __lowercase      		=name.replace('cls_token',       'embeddings.cls_token'			)
     if "det_token" in name:
          __lowercase      		=name.replace('det_token',       'embeddings.detection_tokens'			)
     if "mid_pos_embed" in name:
          __lowercase      		=name.replace('mid_pos_embed',       'encoder.mid_position_embeddings'			)
     if "pos_embed" in name:
          __lowercase      		=name.replace('pos_embed',       'embeddings.position_embeddings'			)
     if "patch_embed.proj" in name:
          __lowercase      		=name.replace('patch_embed.proj',       'embeddings.patch_embeddings.projection'			)
     if "blocks" in name:
          __lowercase      		=name.replace('blocks',       'encoder.layer'			)
     if "attn.proj" in name:
          __lowercase      		=name.replace('attn.proj',       'attention.output.dense'			)
     if "attn" in name:
          __lowercase      		=name.replace('attn',       'attention.self'			)
     if "norm1" in name:
          __lowercase      		=name.replace('norm1',       'layernorm_before'			)
     if "norm2" in name:
          __lowercase      		=name.replace('norm2',       'layernorm_after'			)
     if "mlp.fc1" in name:
          __lowercase      		=name.replace('mlp.fc1',       'intermediate.dense'			)
     if "mlp.fc2" in name:
          __lowercase      		=name.replace('mlp.fc2',       'output.dense'			)
     if "class_embed" in name:
          __lowercase      		=name.replace('class_embed',       'class_labels_classifier'			)
     if "bbox_embed" in name:
          __lowercase      		=name.replace('bbox_embed',       'bbox_predictor'			)
     if "vit.norm" in name:
          __lowercase      		=name.replace('vit.norm',       'vit.layernorm'			)

     return name

def 					__UpperCamelCase				(						lowercase__    :	dict,       lowercase__    :	YolosForObjectDetection			):






     '''simple docstring'''





     for key in orig_state_dict.copy().keys():
          __lowercase      		=orig_state_dict.pop(lowercase__			)

          if "qkv" in key:
               __lowercase      		=key.split('.'			)
               __lowercase      		=int(key_split[2]			)
               __lowercase      		=model.vit.encoder.layer[layer_num].attention.attention.all_head_size
               if "weight" in key:
                    __lowercase      		=val[:dim, :]
                    __lowercase      		=val[
                        dim : dim * 2, :
                    ]
                    __lowercase      		=val[-dim:, :]
               else:
                    __lowercase      		=val[:dim]
                    __lowercase      		=val[dim : dim * 2]
                    __lowercase      		=val[-dim:]
          else:
               __lowercase      		=val

     return orig_state_dict

def 					__UpperCamelCase				(						):






     '''simple docstring'''





     __lowercase      		='http://images.cocodataset.org/val2017/000000039769.jpg'
     __lowercase      		=Image.open(requests.get(lowercase__,       stream=lowercase__			).raw			)
     return im

@torch.no_grad()
def 					__UpperCamelCase				(						lowercase__    :	str,       lowercase__    :	str,       lowercase__    :	str,       lowercase__    :	bool = False			):






     '''simple docstring'''





     __lowercase      		=get_yolos_config(lowercase__			)

     # load original state_dict
     __lowercase      		=torch.load(lowercase__,       map_location='cpu'			)['model']

     # load 🤗 model
     __lowercase      		=YolosForObjectDetection(lowercase__			)
     model.eval()
     __lowercase      		=convert_state_dict(lowercase__,       lowercase__			)
     model.load_state_dict(lowercase__			)

     # Check outputs on an image, prepared by YolosImageProcessor
     __lowercase      		=8_00 if yolos_name != 'yolos_ti' else 5_12
     __lowercase      		=YolosImageProcessor(format='coco_detection',       size=lowercase__			)
     __lowercase      		=image_processor(images=prepare_img(),       return_tensors='pt'			)
     __lowercase      		=model(**lowercase__			)
     __lowercase		,  __lowercase      		=outputs.logits, outputs.pred_boxes

     __lowercase		,  __lowercase      		=None, None
     if yolos_name == "yolos_ti":
          __lowercase      		=torch.tensor(
              [[-39.5022, -11.9820, -17.6888], [-29.9574, -9.9769, -17.7691], [-42.3281, -20.7200, -30.6294]]			)
          __lowercase      		=torch.tensor(
              [[0.4021, 0.0836, 0.7979], [0.0184, 0.2609, 0.0364], [0.1781, 0.2004, 0.2095]]			)
     elif yolos_name == "yolos_s_200_pre":
          __lowercase      		=torch.tensor(
              [[-24.0248, -10.3024, -14.8290], [-42.0392, -16.8200, -27.4334], [-27.2743, -11.8154, -18.7148]]			)
          __lowercase      		=torch.tensor(
              [[0.2559, 0.5455, 0.4706], [0.2989, 0.7279, 0.1875], [0.7732, 0.4017, 0.4462]]			)
     elif yolos_name == "yolos_s_300_pre":
          __lowercase      		=torch.tensor(
              [[-36.2220, -14.4385, -23.5457], [-35.6970, -14.7583, -21.3935], [-31.5939, -13.6042, -16.8049]]			)
          __lowercase      		=torch.tensor(
              [[0.7614, 0.2316, 0.4728], [0.7168, 0.4495, 0.3855], [0.4996, 0.1466, 0.9996]]			)
     elif yolos_name == "yolos_s_dWr":
          __lowercase      		=torch.tensor(
              [[-42.8668, -24.1049, -41.1690], [-34.7456, -14.1274, -24.9194], [-33.7898, -12.1946, -25.6495]]			)
          __lowercase      		=torch.tensor(
              [[0.5587, 0.2773, 0.0605], [0.5004, 0.3014, 0.9994], [0.4999, 0.1548, 0.9994]]			)
     elif yolos_name == "yolos_base":
          __lowercase      		=torch.tensor(
              [[-40.6064, -24.3084, -32.6447], [-55.1990, -30.7719, -35.5877], [-51.4311, -33.3507, -35.6462]]			)
          __lowercase      		=torch.tensor(
              [[0.5555, 0.2794, 0.0655], [0.9049, 0.2664, 0.1894], [0.9183, 0.1984, 0.1635]]			)
     else:
          raise ValueError(F'''Unknown yolos_name: {yolos_name}'''			)

     assert torch.allclose(logits[0, :3, :3],       lowercase__,       atol=1E-4			)
     assert torch.allclose(pred_boxes[0, :3, :3],       lowercase__,       atol=1E-4			)

     Path(lowercase__			).mkdir(exist_ok=lowercase__			)
     print(F'''Saving model {yolos_name} to {pytorch_dump_folder_path}'''			)
     model.save_pretrained(lowercase__			)
     print(F'''Saving image processor to {pytorch_dump_folder_path}'''			)
     image_processor.save_pretrained(lowercase__			)

     if push_to_hub:
          __lowercase      		={
              'yolos_ti': 'yolos-tiny',
              'yolos_s_200_pre': 'yolos-small',
              'yolos_s_300_pre': 'yolos-small-300',
              'yolos_s_dWr': 'yolos-small-dwr',
              'yolos_base': 'yolos-base',
          }

          print('Pushing to the hub...'			)
          __lowercase      		=model_mapping[yolos_name]
          image_processor.push_to_hub(lowercase__,       organization='hustvl'			)
          model.push_to_hub(lowercase__,       organization='hustvl'			)


if __name__ == "__main__":
       UpperCAmelCase					 =	argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '''--yolos_name''',
           default='''yolos_s_200_pre''',
           type=str,
           help=(
               '''Name of the YOLOS model you\'d like to convert. Should be one of \'yolos_ti\', \'yolos_s_200_pre\','''
               ''' \'yolos_s_300_pre\', \'yolos_s_dWr\', \'yolos_base\'.'''
           ),
       )
       parser.add_argument(
           '''--checkpoint_path''', default=None, type=str, help='''Path to the original state dict (.pth file).'''
       )
       parser.add_argument(
           '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
       )
       parser.add_argument(
           '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
       )

       UpperCAmelCase					 =	parser.parse_args()
       convert_yolos_checkpoint(args.yolos_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)


