



import gc
import unittest

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

from diffusers import AutoencoderKL, DDIMScheduler, LDMTextToImagePipeline, UNetaDConditionModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_numpy,
    nightly,
    require_torch_gpu,
    slow,
    torch_device,
)

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()

class        UpperCAmelCase_  (       UpperCamelCase_   ,   unittest.TestCase	):

						'''simple docstring'''




						UpperCamelCase__      :			Tuple      =  LDMTextToImagePipeline
						UpperCamelCase__      :			Optional[Any]      =  TEXT_TO_IMAGE_PARAMS - {
						    '''negative_prompt''',
						    '''negative_prompt_embeds''',
						    '''cross_attention_kwargs''',
						    '''prompt_embeds''',
						}
						UpperCamelCase__      :			Dict      =  PipelineTesterMixin.required_optional_params - {
						    '''num_images_per_prompt''',
						    '''callback''',
						    '''callback_steps''',
						}
						UpperCamelCase__      :			Union[str, Any]      =  TEXT_TO_IMAGE_BATCH_PARAMS
						UpperCamelCase__      :			Any      =  False






						def 	_A     (   self		):







													'''simple docstring'''

													torch.manual_seed(0		)
													__SCREAMING_SNAKE_CASE									= UNetaDConditionModel(
													    block_out_channels=(32, 64)				,							layers_per_block=2				,							sample_size=32				,							in_channels=4				,							out_channels=4				,							down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')				,							up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')				,							cross_attention_dim=32				,							)
													__SCREAMING_SNAKE_CASE									= DDIMScheduler(
													    beta_start=0.0_0_0_8_5				,							beta_end=0.0_1_2				,							beta_schedule='scaled_linear'				,							clip_sample=_A				,							set_alpha_to_one=_A				,							)
													torch.manual_seed(0		)
													__SCREAMING_SNAKE_CASE									= AutoencoderKL(
													    block_out_channels=(32, 64)				,							in_channels=3				,							out_channels=3				,							down_block_types=('DownEncoderBlock2D', 'DownEncoderBlock2D')				,							up_block_types=('UpDecoderBlock2D', 'UpDecoderBlock2D')				,							latent_channels=4				,							)
													torch.manual_seed(0		)
													__SCREAMING_SNAKE_CASE									= 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=1_000				,							)
													__SCREAMING_SNAKE_CASE									= CLIPTextModel(_A		)
													__SCREAMING_SNAKE_CASE									= CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'		)

													__SCREAMING_SNAKE_CASE									= {
													    'unet': unet,
													    'scheduler': scheduler,
													    'vqvae': vae,
													    'bert': text_encoder,
													    'tokenizer': tokenizer,
													}
													return components






						def 	_A     (   self				,							_A				,							_A=0		):







													'''simple docstring'''

													if str(_A		).startswith('mps'		):
																				__SCREAMING_SNAKE_CASE									= torch.manual_seed(_A		)
													else:
																				__SCREAMING_SNAKE_CASE									= torch.Generator(device=_A		).manual_seed(_A		)
													__SCREAMING_SNAKE_CASE									= {
													    'prompt': 'A painting of a squirrel eating a burger',
													    'generator': generator,
													    'num_inference_steps': 2,
													    'guidance_scale': 6.0,
													    'output_type': 'numpy',
													}
													return inputs






						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= 'cpu'  # ensure determinism for the device-dependent torch.Generator

													__SCREAMING_SNAKE_CASE									= self.get_dummy_components()
													__SCREAMING_SNAKE_CASE									= LDMTextToImagePipeline(**_A		)
													pipe.to(_A		)
													pipe.set_progress_bar_config(disable=_A		)

													__SCREAMING_SNAKE_CASE									= self.get_dummy_inputs(_A		)
													__SCREAMING_SNAKE_CASE									= pipe(**_A		).images
													__SCREAMING_SNAKE_CASE									= image[0, -3:, -3:, -1]

													assert image.shape == (1, 16, 16, 3)
													__SCREAMING_SNAKE_CASE									= np.array([0.6_1_0_1, 0.6_1_5_6, 0.5_6_2_2, 0.4_8_9_5, 0.6_6_6_1, 0.3_8_0_4, 0.5_7_4_8, 0.6_1_3_6, 0.5_0_1_4]		)

													assert np.abs(image_slice.flatten() - expected_slice		).max() < 1e-3







@slow
@require_torch_gpu
class        UpperCAmelCase_  (       unittest.TestCase	):

						'''simple docstring'''




						def 	_A     (   self		):







													'''simple docstring'''

													super().tearDown()
													gc.collect()
													torch.cuda.empty_cache()






						def 	_A     (   self				,							_A				,							_A=torch.floataa				,							_A=0		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= torch.manual_seed(_A		)
													__SCREAMING_SNAKE_CASE									= np.random.RandomState(_A		).standard_normal((1, 4, 32, 32)		)
													__SCREAMING_SNAKE_CASE									= torch.from_numpy(_A		).to(device=_A				,							dtype=_A		)
													__SCREAMING_SNAKE_CASE									= {
													    'prompt': 'A painting of a squirrel eating a burger',
													    'latents': latents,
													    'generator': generator,
													    'num_inference_steps': 3,
													    'guidance_scale': 6.0,
													    'output_type': 'numpy',
													}
													return inputs






						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= LDMTextToImagePipeline.from_pretrained('CompVis/ldm-text2im-large-256'		).to(_A		)
													pipe.set_progress_bar_config(disable=_A		)

													__SCREAMING_SNAKE_CASE									= self.get_inputs(_A		)
													__SCREAMING_SNAKE_CASE									= pipe(**_A		).images
													__SCREAMING_SNAKE_CASE									= image[0, -3:, -3:, -1].flatten()

													assert image.shape == (1, 256, 256, 3)
													__SCREAMING_SNAKE_CASE									= np.array([0.5_1_8_2_5, 0.5_2_8_5_0, 0.5_2_5_4_3, 0.5_4_2_5_8, 0.5_2_3_0_4, 0.5_2_5_6_9, 0.5_4_3_6_3, 0.5_5_2_7_6, 0.5_6_8_7_8]		)
													__SCREAMING_SNAKE_CASE									= np.abs(expected_slice - image_slice		).max()
													assert max_diff < 1e-3







@nightly
@require_torch_gpu
class        UpperCAmelCase_  (       unittest.TestCase	):

						'''simple docstring'''




						def 	_A     (   self		):







													'''simple docstring'''

													super().tearDown()
													gc.collect()
													torch.cuda.empty_cache()






						def 	_A     (   self				,							_A				,							_A=torch.floataa				,							_A=0		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= torch.manual_seed(_A		)
													__SCREAMING_SNAKE_CASE									= np.random.RandomState(_A		).standard_normal((1, 4, 32, 32)		)
													__SCREAMING_SNAKE_CASE									= torch.from_numpy(_A		).to(device=_A				,							dtype=_A		)
													__SCREAMING_SNAKE_CASE									= {
													    'prompt': 'A painting of a squirrel eating a burger',
													    'latents': latents,
													    'generator': generator,
													    'num_inference_steps': 50,
													    'guidance_scale': 6.0,
													    'output_type': 'numpy',
													}
													return inputs






						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= LDMTextToImagePipeline.from_pretrained('CompVis/ldm-text2im-large-256'		).to(_A		)
													pipe.set_progress_bar_config(disable=_A		)

													__SCREAMING_SNAKE_CASE									= self.get_inputs(_A		)
													__SCREAMING_SNAKE_CASE									= pipe(**_A		).images[0]

													__SCREAMING_SNAKE_CASE									= load_numpy(
													    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/ldm_text2img/ldm_large_256_ddim.npy'		)
													__SCREAMING_SNAKE_CASE									= np.abs(expected_image - image		).max()
													assert max_diff < 1e-3











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__		:					Optional[int]							=True
			from torch.cuda.amp import autocast


lowerCAmelCase__		:					List[Any]							=logging.getLogger(__name__)

@dataclass
class        UpperCAmelCase_  :

						'''simple docstring'''




						UpperCamelCase__      :			str      =  field(
						    metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}	)
						UpperCamelCase__      :			Optional[str]      =  field(
						    default=UpperCamelCase_   ,   metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}   ,   )
						UpperCamelCase__      :			Optional[bool]      =  field(
						    default=UpperCamelCase_   ,   metadata={'''help''': '''Whether to freeze the feature extractor layers of the model.'''}	)
						UpperCamelCase__      :			Optional[bool]      =  field(
						    default=UpperCamelCase_   ,   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=0.9_9_9_9_9_5   ,   metadata={'''help''': '''Decay of gumbel temperature during training.'''}	)







def      __lowercase       (			a__							,       a__       )    ->			Dict:
							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									= logging.WARNING
							if model_args.verbose_logging:
														__SCREAMING_SNAKE_CASE									= logging.DEBUG
							elif trainer_utils.is_main_process(training_args.local_rank       ):
														__SCREAMING_SNAKE_CASE									= logging.INFO
							logger.setLevel(a__       )

@dataclass
class        UpperCAmelCase_  :

						'''simple docstring'''




						UpperCamelCase__      :			str      =  field(
						    default=UpperCamelCase_   ,   metadata={'''help''': '''The name of the dataset to use (via the datasets library).'''}	)
						UpperCamelCase__      :			Optional[str]      =  field(
						    default=UpperCamelCase_   ,   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=UpperCamelCase_   ,   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=UpperCamelCase_   ,   metadata={'''help''': '''The number of processes to use for the preprocessing.'''}   ,   )
						UpperCamelCase__      :			Optional[float]      =  field(
						    default=2_0.0   ,   metadata={'''help''': '''Filter audio files that are longer than `max_duration_in_seconds` seconds'''}	)







@dataclass
class        UpperCAmelCase_  :

						'''simple docstring'''




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






						def __call__(   self				,							_A		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= 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'				,							)
													__SCREAMING_SNAKE_CASE									= self.model._get_feat_extract_output_lengths(batch['input_values'].shape[-1]		)

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

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

													# sample randomly masked indices
													__SCREAMING_SNAKE_CASE									= _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_  (       UpperCamelCase_	):

						'''simple docstring'''




						def __init__(   self				,							*_A				,							_A=1				,							_A=0				,							_A=1.0				,							**_A		):







													'''simple docstring'''

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






						def 	_A     (   self				,							_A				,							_A		):







													'''simple docstring'''

													model.train()
													__SCREAMING_SNAKE_CASE									= self._prepare_inputs(_A		)

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

													if self.args.n_gpu > 1 or self.deepspeed:
																				if model.module.config.ctc_loss_reduction == "mean":
																											__SCREAMING_SNAKE_CASE									= loss.mean()
																				elif model.module.config.ctc_loss_reduction == "sum":
																											__SCREAMING_SNAKE_CASE									= 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									= 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      __lowercase       (			)    ->			Tuple:
							# 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									= HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)       )

							__SCREAMING_SNAKE_CASE							,	__SCREAMING_SNAKE_CASE							,	__SCREAMING_SNAKE_CASE									= parser.parse_args_into_dataclasses()
							configure_logger(a__							,       a__       )

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

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

							# load audio files into numpy arrays
							__SCREAMING_SNAKE_CASE									= 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									= vectorized_datasets.filter(
							    lambda a__       : len(data['speech']       ) < int(data_args.max_duration_in_seconds * feature_extractor.sampling_rate       )       )

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

							# normalize and transform to `BatchFeatures`
							__SCREAMING_SNAKE_CASE									= 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									= 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									= WavaVecaForPreTraining(a__       )

							__SCREAMING_SNAKE_CASE									= DataCollatorForWavaVecaPretraining(model=a__							,       feature_extractor=a__       )

							__SCREAMING_SNAKE_CASE									= 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()






