



'''simple docstring'''



import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path

import timm
import torch
from huggingface_hub import hf_hub_download

from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase_     :       Optional[Any]								=						logging.get_logger()
def 					UpperCAmelCase   (						A  :				int					,		A  :				str					,		A  :				LevitConfig					,		A  :				Path					,		A  :				bool = True				):
							print(F"""Converting {name}..."""				)

							with torch.no_grad():
														if hidden_sizes == 128:
																					if name[-1] == "S":
																												SCREAMING_SNAKE_CASE					:      int     					=			timm.create_model('''levit_128s'''					,		pretrained=A				)
																					else:
																												SCREAMING_SNAKE_CASE					:      List[Any]     					=			timm.create_model('''levit_128'''					,		pretrained=A				)
														if hidden_sizes == 192:
																					SCREAMING_SNAKE_CASE					:      str     					=			timm.create_model('''levit_192'''					,		pretrained=A				)
														if hidden_sizes == 256:
																					SCREAMING_SNAKE_CASE					:      Optional[int]     					=			timm.create_model('''levit_256'''					,		pretrained=A				)
														if hidden_sizes == 384:
																					SCREAMING_SNAKE_CASE					:      Tuple     					=			timm.create_model('''levit_384'''					,		pretrained=A				)

														from_model.eval()
														SCREAMING_SNAKE_CASE					:      Optional[int]     					=			LevitForImageClassificationWithTeacher(A				).eval()
														SCREAMING_SNAKE_CASE					:      int     					=			OrderedDict()

														SCREAMING_SNAKE_CASE					:      Optional[int]     					=			from_model.state_dict()
														SCREAMING_SNAKE_CASE					:      int     					=			list(from_model.state_dict().keys()				)
														SCREAMING_SNAKE_CASE					:      Optional[int]     					=			list(our_model.state_dict().keys()				)
														print(len(A				)					,		len(A				)				)
														for i in range(len(A				)				):
																					SCREAMING_SNAKE_CASE					:      Dict     					=			weights[og_keys[i]]
														our_model.load_state_dict(A				)

														SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			torch.randn((2, 3, 224, 224)				)
														SCREAMING_SNAKE_CASE					:      Optional[int]     					=			from_model(A				)
														SCREAMING_SNAKE_CASE					:      int     					=			our_model(A				).logits

							assert torch.allclose(A					,		A				), "The model logits don't match the original one."

							SCREAMING_SNAKE_CASE					:      Any     					=			name
							print(A				)

							if push_to_hub:
														our_model.save_pretrained(save_directory / checkpoint_name				)
														SCREAMING_SNAKE_CASE					:      Any     					=			LevitImageProcessor()
														image_processor.save_pretrained(save_directory / checkpoint_name				)

														print(F"""Pushed {checkpoint_name}"""				)
def 					UpperCAmelCase   (						A  :				Path					,		A  :				str = None					,		A  :				bool = True				):
							SCREAMING_SNAKE_CASE					:      List[str]     					=			'''imagenet-1k-id2label.json'''
							SCREAMING_SNAKE_CASE					:      List[Any]     					=			1000
							SCREAMING_SNAKE_CASE					:      Dict     					=			(1, num_labels)

							SCREAMING_SNAKE_CASE					:      Tuple     					=			'''huggingface/label-files'''
							SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			num_labels
							SCREAMING_SNAKE_CASE					:      List[str]     					=			json.load(open(hf_hub_download(A					,		A					,		repo_type='''dataset'''				)					,		'''r'''				)				)
							SCREAMING_SNAKE_CASE					:      int     					=			{int(A				): v for k, v in idalabel.items()}

							SCREAMING_SNAKE_CASE					:      Any     					=			idalabel
							SCREAMING_SNAKE_CASE					:      List[str]     					=			{v: k for k, v in idalabel.items()}

							SCREAMING_SNAKE_CASE					:      Any     					=			partial(A					,		num_labels=A					,		idalabel=A					,		labelaid=A				)

							SCREAMING_SNAKE_CASE					:      Optional[int]     					=			{
							    '''levit-128S''': 128,
							    '''levit-128''': 128,
							    '''levit-192''': 192,
							    '''levit-256''': 256,
							    '''levit-384''': 384,
							}

							SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			{
							    '''levit-128S''': ImageNetPreTrainedConfig(
							        hidden_sizes=[128, 256, 384]					,		num_attention_heads=[4, 6, 8]					,		depths=[2, 3, 4]					,		key_dim=[16, 16, 16]					,		drop_path_rate=0					,		),
							    '''levit-128''': ImageNetPreTrainedConfig(
							        hidden_sizes=[128, 256, 384]					,		num_attention_heads=[4, 8, 12]					,		depths=[4, 4, 4]					,		key_dim=[16, 16, 16]					,		drop_path_rate=0					,		),
							    '''levit-192''': ImageNetPreTrainedConfig(
							        hidden_sizes=[192, 288, 384]					,		num_attention_heads=[3, 5, 6]					,		depths=[4, 4, 4]					,		key_dim=[32, 32, 32]					,		drop_path_rate=0					,		),
							    '''levit-256''': ImageNetPreTrainedConfig(
							        hidden_sizes=[256, 384, 512]					,		num_attention_heads=[4, 6, 8]					,		depths=[4, 4, 4]					,		key_dim=[32, 32, 32]					,		drop_path_rate=0					,		),
							    '''levit-384''': ImageNetPreTrainedConfig(
							        hidden_sizes=[384, 512, 768]					,		num_attention_heads=[6, 9, 12]					,		depths=[4, 4, 4]					,		key_dim=[32, 32, 32]					,		drop_path_rate=0.1					,		),
							}

							if model_name:
														convert_weight_and_push(
														    names_to_hidden_sizes[model_name]					,		A					,		names_to_config[model_name]					,		A					,		A				)
							else:
														for model_name, config in names_to_config.items():
																					convert_weight_and_push(names_to_hidden_sizes[model_name]					,		A					,		A					,		A					,		A				)
							return config, expected_shape


if __name__ == "__main__":
							lowerCAmelCase_     :       Optional[int]								=						argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '--model_name',
							    default=None,
							    type=str,
							    help='The name of the model you wish to convert, it must be one of the supported Levit* architecture,',
							)
							parser.add_argument(
							    '--pytorch_dump_folder_path',
							    default='levit-dump-folder/',
							    type=Path,
							    required=False,
							    help='Path to the output PyTorch model directory.',
							)
							parser.add_argument('--push_to_hub', action='store_true', help='Push model and image processor to the hub')
							parser.add_argument(
							    '--no-push_to_hub',
							    dest='push_to_hub',
							    action='store_false',
							    help='Do not push model and image processor to the hub',
							)

							lowerCAmelCase_     :       str								=						parser.parse_args()
							lowerCAmelCase_     :       Path								=						args.pytorch_dump_folder_path
							pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
							convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)









'''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'):
							lowerCAmelCase_     :       Union[str, Any]								=						True
							from torch.cuda.amp import autocast


lowerCAmelCase_     :       Union[str, Any]								=						logging.getLogger(__name__)
@dataclass
class 	lowerCamelCase_							:
						_lowerCAmelCase     :   str	     =				field(
						    metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}			)
						_lowerCAmelCase     :   Optional[str]	     =				field(
						    default=snake_case_    ,				metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}    ,				)
						_lowerCAmelCase     :   Optional[bool]	     =				field(
						    default=snake_case_    ,				metadata={'help': 'Whether to freeze the feature extractor layers of the model.'}			)
						_lowerCAmelCase     :   Optional[bool]	     =				field(
						    default=snake_case_    ,				metadata={'help': 'Whether to log verbose messages or not.'}    ,				)
						_lowerCAmelCase     :   Optional[float]	     =				field(
						    default=2.0    ,				metadata={'help': 'Maximum temperature for gumbel softmax.'}			)
						_lowerCAmelCase     :   Optional[float]	     =				field(
						    default=0.5    ,				metadata={'help': 'Minimum temperature for gumbel softmax.'}			)
						_lowerCAmelCase     :   Optional[float]	     =				field(
						    default=0.99_9995    ,				metadata={'help': 'Decay of gumbel temperature during training.'}			)


def 					UpperCAmelCase   (						A  :				ModelArguments					,		A  :				TrainingArguments				):
							logging.basicConfig(
							    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''					,		datefmt='''%m/%d/%Y %H:%M:%S'''					,		handlers=[logging.StreamHandler(sys.stdout				)]					,		)
							SCREAMING_SNAKE_CASE					:      List[Any]     					=			logging.WARNING
							if model_args.verbose_logging:
														SCREAMING_SNAKE_CASE					:      Dict     					=			logging.DEBUG
							elif trainer_utils.is_main_process(training_args.local_rank				):
														SCREAMING_SNAKE_CASE					:      Any     					=			logging.INFO
							logger.setLevel(A				)
@dataclass
class 	lowerCamelCase_							:
						_lowerCAmelCase     :   str	     =				field(
						    default=snake_case_    ,				metadata={'help': 'The name of the dataset to use (via the datasets library).'}			)
						_lowerCAmelCase     :   Optional[str]	     =				field(
						    default=snake_case_    ,				metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'}			)
						_lowerCAmelCase     :   Optional[str]	     =				field(
						    default='train'    ,				metadata={
						        'help': 'The name of the training data set split to use (via the datasets library). Defaults to \'train\''
						    }    ,				)
						_lowerCAmelCase     :   Optional[str]	     =				field(
						    default='validation'    ,				metadata={
						        'help': (
						            'The name of the validation data set split to use (via the datasets library). Defaults to \'validation\''
						        )
						    }    ,				)
						_lowerCAmelCase     :   Optional[str]	     =				field(
						    default='file'    ,				metadata={'help': 'Column in the dataset that contains speech file path. Defaults to \'file\''}    ,				)
						_lowerCAmelCase     :   bool	     =				field(
						    default=snake_case_    ,				metadata={'help': 'Overwrite the cached preprocessed datasets or not.'}			)
						_lowerCAmelCase     :   Optional[int]	     =				field(
						    default=1    ,				metadata={
						        'help': 'The percentage of the train set used as validation set in case there\'s no validation split'
						    }    ,				)
						_lowerCAmelCase     :   Optional[int]	     =				field(
						    default=snake_case_    ,				metadata={'help': 'The number of processes to use for the preprocessing.'}    ,				)
						_lowerCAmelCase     :   Optional[float]	     =				field(
						    default=20.0    ,				metadata={'help': 'Filter audio files that are longer than `max_duration_in_seconds` seconds'}			)


@dataclass
class 	lowerCamelCase_							:
						_lowerCAmelCase     :   WavaVecaForPreTraining
						_lowerCAmelCase     :   WavaVecaFeatureExtractor
						_lowerCAmelCase     :   Union[bool, str]	     =				"longest"
						_lowerCAmelCase     :   Optional[int]	     =				None
						_lowerCAmelCase     :   Optional[int]	     =				None







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

													"""simple docstring"""
													# reformat list to dict and set to pytorch format
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			self.feature_extractor.pad(
													    lowerCAmelCase__				,    max_length=self.max_length				,    padding=self.padding				,    pad_to_multiple_of=self.pad_to_multiple_of				,    return_tensors='''pt'''				,    )
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			self.model._get_feat_extract_output_lengths(batch['''input_values'''].shape[-1]				)

													SCREAMING_SNAKE_CASE					:      Tuple     					=			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
																				SCREAMING_SNAKE_CASE					:      List[Any]     					=			self.model._get_feat_extract_output_lengths(batch['''attention_mask'''].sum(-1				)				).to(
																				    torch.long				)

																				SCREAMING_SNAKE_CASE					:      List[Any]     					=			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
																				SCREAMING_SNAKE_CASE					:      Optional[int]     					=			1
																				SCREAMING_SNAKE_CASE					:      Dict     					=			attention_mask.flip([-1]				).cumsum(-1				).flip([-1]				).bool()

													# sample randomly masked indices
													SCREAMING_SNAKE_CASE					:      List[str]     					=			_compute_mask_indices(
													    (batch_size, mask_indices_seq_length)				,    self.model.config.mask_time_prob				,    self.model.config.mask_time_length				,    attention_mask=lowerCAmelCase__				,    min_masks=2				,    )

													return batch


class 	lowerCamelCase_							(					snake_case_			):







						def __init__(  self   :      str				,    *lowerCAmelCase__   :      int				,    lowerCAmelCase__   :      Any=1				,    lowerCAmelCase__   :      Dict=0				,    lowerCAmelCase__   :      Tuple=1.0				,    **lowerCAmelCase__   :      Optional[Any]				):

													"""simple docstring"""
													super().__init__(*lowerCAmelCase__				,    **lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			0
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			max_gumbel_temp
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			min_gumbel_temp
													SCREAMING_SNAKE_CASE					:      Tuple     					=			gumbel_temp_decay







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

													"""simple docstring"""
													model.train()
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			self._prepare_inputs(lowerCAmelCase__				)

													if self.use_amp:
																				with autocast():
																											SCREAMING_SNAKE_CASE					:      Any     					=			self.compute_loss(lowerCAmelCase__				,    lowerCAmelCase__				)
													else:
																				SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			self.compute_loss(lowerCAmelCase__				,    lowerCAmelCase__				)

													if self.args.n_gpu > 1 or self.deepspeed:
																				if model.module.config.ctc_loss_reduction == "mean":
																											SCREAMING_SNAKE_CASE					:      Any     					=			loss.mean()
																				elif model.module.config.ctc_loss_reduction == "sum":
																											SCREAMING_SNAKE_CASE					:      Tuple     					=			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:
																				SCREAMING_SNAKE_CASE					:      List[Any]     					=			loss / self.args.gradient_accumulation_steps

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

							SCREAMING_SNAKE_CASE					:      Optional[int]     					=			HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)				)

							SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      List[Any]     					=			parser.parse_args_into_dataclasses()
							configure_logger(A					,		A				)

							# Downloading and loading a dataset from the hub.
							SCREAMING_SNAKE_CASE					:      str     					=			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"
														SCREAMING_SNAKE_CASE					:      Any     					=			DatasetDict()
														SCREAMING_SNAKE_CASE					:      Any     					=			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					,		)
														SCREAMING_SNAKE_CASE					:      List[str]     					=			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"
														SCREAMING_SNAKE_CASE					:      str     					=			DatasetDict()
														SCREAMING_SNAKE_CASE					:      Any     					=			load_dataset(
														    data_args.dataset_name					,		data_args.dataset_config_name					,		split='''validation'''					,		cache_dir=model_args.cache_dir					,		)
														SCREAMING_SNAKE_CASE					:      Tuple     					=			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
							SCREAMING_SNAKE_CASE					:      int     					=			WavaVecaFeatureExtractor.from_pretrained(
							    model_args.model_name_or_path					,		cache_dir=model_args.cache_dir					,		do_normalize=A				)

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

							# load audio files into numpy arrays
							SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			datasets.map(
							    A					,		num_proc=data_args.preprocessing_num_workers					,		remove_columns=datasets['''train'''].column_names				)

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

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

							# normalize and transform to `BatchFeatures`
							SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			vectorized_datasets.map(
							    A					,		batched=A					,		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
							SCREAMING_SNAKE_CASE					:      Tuple     					=			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\''''				)

							SCREAMING_SNAKE_CASE					:      str     					=			WavaVecaForPreTraining(A				)

							SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			DataCollatorForWavaVecaPretraining(model=A					,		feature_extractor=A				)

							SCREAMING_SNAKE_CASE					:      Dict     					=			WavaVecaPreTrainer(
							    model=A					,		data_collator=A					,		args=A					,		train_dataset=vectorized_datasets['''train''']					,		eval_dataset=vectorized_datasets['''validation''']					,		tokenizer=A					,		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()




