

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'):
	A_       : List[Any]         =			True
	from torch.cuda.amp import autocast


A_       : Union[str, Any]         =			logging.getLogger(__name__)



@dataclass
class     _a						:


					'''simple docstring'''






					UpperCAmelCase__:   str  							=       field(
					    metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}				)
					UpperCAmelCase__:   Optional[str]  							=       field(
					    default=__magic_name__		,   metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}		,   )
					UpperCAmelCase__:   Optional[bool]  							=       field(
					    default=__magic_name__		,   metadata={'''help''': '''Whether to freeze the feature extractor layers of the model.'''}				)
					UpperCAmelCase__:   Optional[bool]  							=       field(
					    default=__magic_name__		,   metadata={'''help''': '''Whether to log verbose messages or not.'''}		,   )
					UpperCAmelCase__:   Optional[float]  							=       field(
					    default=2.0		,   metadata={'''help''': '''Maximum temperature for gumbel softmax.'''}				)
					UpperCAmelCase__:   Optional[float]  							=       field(
					    default=0.5		,   metadata={'''help''': '''Minimum temperature for gumbel softmax.'''}				)
					UpperCAmelCase__:   Optional[float]  							=       field(
					    default=0.99_99_95		,   metadata={'''help''': '''Decay of gumbel temperature during training.'''}				)



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



@dataclass
class     _a						:


					'''simple docstring'''






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



@dataclass
class     _a						:


					'''simple docstring'''






					UpperCAmelCase__:   WavaVecaForPreTraining
					UpperCAmelCase__:   WavaVecaFeatureExtractor
					UpperCAmelCase__:   Union[bool, str]  							=       "longest"
					UpperCAmelCase__:   Optional[int]  							=       None
					UpperCAmelCase__:   Optional[int]  							=       None






					def __call__( self    ,       A__			):
						# reformat list to dict and set to pytorch format
						A__							:    Optional[int]        =		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__							:    Dict        =		self.model._get_feat_extract_output_lengths(batch["""input_values"""].shape[-1]			)

						A__							:    List[Any]        =		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__							:    Any        =		self.model._get_feat_extract_output_lengths(batch["""attention_mask"""].sum(-1			)			).to(
							    torch.long			)

							A__							:    Optional[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
							A__							:    int        =		1
							A__							:    int        =		attention_mask.flip([-1]			).cumsum(-1			).flip([-1]			).bool()

						# sample randomly masked indices
						A__							:    Optional[Any]        =		_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     _a						(__magic_name__				):


					'''simple docstring'''






					def __init__( self    ,       *A__    ,       A__=1    ,       A__=0    ,       A__=1.0    ,       **A__			):
						super().__init__(*A__    ,       **A__			)
						A__							:    Union[str, Any]        =		0
						A__							:    Optional[Any]        =		max_gumbel_temp
						A__							:    List[Any]        =		min_gumbel_temp
						A__							:    List[Any]        =		gumbel_temp_decay






					def   __A			( self    ,       A__    ,       A__			):
						model.train()
						A__							:    Optional[Any]        =		self._prepare_inputs(A__			)

						if self.use_amp:
							with autocast():
								A__							:    Optional[int]        =		self.compute_loss(A__    ,       A__			)
						else:
							A__							:    int        =		self.compute_loss(A__    ,       A__			)

						if self.args.n_gpu > 1 or self.deepspeed:
							if model.module.config.ctc_loss_reduction == "mean":
								A__							:    List[Any]        =		loss.mean()
							elif model.module.config.ctc_loss_reduction == "sum":
								A__							:    List[str]        =		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__							:    Dict        =		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 						UpperCamelCase					()     ->      Union[str, 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__							:    Tuple        =		HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)					)

	A__   ,   A__   ,   A__							:    List[Any]        =		parser.parse_args_into_dataclasses()
	configure_logger(lowercase_    ,      lowercase_					)

	# Downloading and loading a dataset from the hub.
	A__							:    List[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"
		A__							:    Dict        =		DatasetDict()
		A__							:    Optional[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    ,      )
		A__							:    List[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    ,      )
	else:
		# make sure only "validation" and "train" keys remain"
		A__							:    Optional[Any]        =		DatasetDict()
		A__							:    Optional[int]        =		load_dataset(
		    data_args.dataset_name    ,      data_args.dataset_config_name    ,      split="""validation"""    ,      cache_dir=model_args.cache_dir    ,      )
		A__							:    List[str]        =		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__							:    Optional[Any]        =		WavaVecaFeatureExtractor.from_pretrained(
	    model_args.model_name_or_path    ,      cache_dir=model_args.cache_dir    ,      do_normalize=lowercase_					)

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

	# load audio files into numpy arrays
	A__							:    List[Any]        =		datasets.map(
	    lowercase_    ,      num_proc=data_args.preprocessing_num_workers    ,      remove_columns=datasets["""train"""].column_names					)

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

	def normalize(lowercase_: Union[str, Any]					):
		return feature_extractor(batch["""speech"""]    ,      sampling_rate=feature_extractor.sampling_rate					)

	# normalize and transform to `BatchFeatures`
	A__							:    List[str]        =		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
	A__							:    int        =		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__							:    int        =		WavaVecaForPreTraining(lowercase_					)

	A__							:    Tuple        =		DataCollatorForWavaVecaPretraining(model=lowercase_    ,      feature_extractor=lowercase_					)

	A__							:    Optional[int]        =		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()






import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel

from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()



class     _a						(unittest.TestCase				):


					'''simple docstring'''






					@property
					def   __A			( self			):
						torch.manual_seed(0			)
						A__							:    int        =		UNetaDModel(
						    block_out_channels=(32, 64)    ,       layers_per_block=2    ,       sample_size=32    ,       in_channels=3    ,       out_channels=3    ,       down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")    ,       up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")    ,       )
						return model






					@property
					def   __A			( self			):
						torch.manual_seed(0			)
						A__							:    Dict        =		VQModel(
						    block_out_channels=[32, 64]    ,       in_channels=3    ,       out_channels=3    ,       down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]    ,       up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]    ,       latent_channels=3    ,       )
						return model






					@property
					def   __A			( self			):
						torch.manual_seed(0			)
						A__							:    int        =		CLIPTextConfig(
						    bos_token_id=0    ,       eos_token_id=2    ,       hidden_size=32    ,       intermediate_size=37    ,       layer_norm_eps=1e-05    ,       num_attention_heads=4    ,       num_hidden_layers=5    ,       pad_token_id=1    ,       vocab_size=1000    ,       )
						return CLIPTextModel(A__			)






					def   __A			( self			):
						A__							:    int        =		self.dummy_uncond_unet
						A__							:    Optional[int]        =		DDIMScheduler()
						A__							:    List[Any]        =		self.dummy_vq_model

						A__							:    Optional[int]        =		LDMPipeline(unet=A__    ,       vqvae=A__    ,       scheduler=A__			)
						ldm.to(A__			)
						ldm.set_progress_bar_config(disable=A__			)

						A__							:    Tuple        =		torch.manual_seed(0			)
						A__							:    List[Any]        =		ldm(generator=A__    ,       num_inference_steps=2    ,       output_type="""numpy"""			).images

						A__							:    Optional[int]        =		torch.manual_seed(0			)
						A__							:    List[Any]        =		ldm(generator=A__    ,       num_inference_steps=2    ,       output_type="""numpy"""    ,       return_dict=A__			)[0]

						A__							:    str        =		image[0, -3:, -3:, -1]
						A__							:    Tuple        =		image_from_tuple[0, -3:, -3:, -1]

						assert image.shape == (1, 64, 64, 3)
						A__							:    Dict        =		np.array([0.8_5_1_2, 0.8_1_8, 0.6_4_1_1, 0.6_8_0_8, 0.4_4_6_5, 0.5_6_1_8, 0.4_6, 0.6_2_3_1, 0.5_1_7_2]			)
						A__							:    List[Any]        =		1e-2 if torch_device != """mps""" else 3e-2

						assert np.abs(image_slice.flatten() - expected_slice			).max() < tolerance
						assert np.abs(image_from_tuple_slice.flatten() - expected_slice			).max() < tolerance



@slow
@require_torch
class     _a						(unittest.TestCase				):


					'''simple docstring'''






					def   __A			( self			):
						A__							:    Optional[Any]        =		LDMPipeline.from_pretrained("""CompVis/ldm-celebahq-256"""			)
						ldm.to(A__			)
						ldm.set_progress_bar_config(disable=A__			)

						A__							:    Tuple        =		torch.manual_seed(0			)
						A__							:    List[Any]        =		ldm(generator=A__    ,       num_inference_steps=5    ,       output_type="""numpy"""			).images

						A__							:    int        =		image[0, -3:, -3:, -1]

						assert image.shape == (1, 256, 256, 3)
						A__							:    Any        =		np.array([0.4_3_9_9, 0.4_4_9_7_5, 0.4_6_8_2_5, 0.4_7_4, 0.4_3_5_9, 0.4_5_8_1, 0.4_5_0_9_5, 0.4_3_4_1, 0.4_4_4_7]			)
						A__							:    int        =		1e-2 if torch_device != """mps""" else 3e-2

						assert np.abs(image_slice.flatten() - expected_slice			).max() < tolerance



