import argparse
import logging
import os
import re

import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    DataCollatorForLanguageModeling,
    PushToHubCallback,
    TFAutoModelForMaskedLM,
    create_optimizer,
)


snake_case__				= logging.getLogger(__name__)

snake_case__				= tf.data.AUTOTUNE
def       __magic_name__(  )						->    Optional[Any]:



      '''simple docstring'''

      _lowerCamelCase			    =				argparse.ArgumentParser(description='''Train a masked language model on TPU.'''		)
      parser.add_argument(
          '''--pretrained_model_config''' ,						type=__UpperCAmelCase ,						default='''roberta-base''' ,						help='''The model config to use. Note that we don\'t copy the model\'s weights, only the config!''' ,						)
      parser.add_argument(
          '''--tokenizer''' ,						type=__UpperCAmelCase ,						default='''unigram-tokenizer-wikitext''' ,						help='''The name of the tokenizer to load. We use the pretrained tokenizer to initialize the model\'s vocab size.''' ,						)

      parser.add_argument(
          '''--per_replica_batch_size''' ,						type=__UpperCAmelCase ,						default=8 ,						help='''Batch size per TPU core.''' ,						)

      parser.add_argument(
          '''--no_tpu''' ,						action='''store_true''' ,						help='''If set, run on CPU and don\'t try to initialize a TPU. Useful for debugging on non-TPU instances.''' ,						)

      parser.add_argument(
          '''--tpu_name''' ,						type=__UpperCAmelCase ,						help='''Name of TPU resource to initialize. Should be blank on Colab, and \'local\' on TPU VMs.''' ,						default='''local''' ,						)

      parser.add_argument(
          '''--tpu_zone''' ,						type=__UpperCAmelCase ,						help='''Google cloud zone that TPU resource is located in. Only used for non-Colab TPU nodes.''' ,						)

      parser.add_argument(
          '''--gcp_project''' ,						type=__UpperCAmelCase ,						help='''Google cloud project name. Only used for non-Colab TPU nodes.'''		)

      parser.add_argument(
          '''--bfloat16''' ,						action='''store_true''' ,						help='''Use mixed-precision bfloat16 for training. This is the recommended lower-precision format for TPU.''' ,						)

      parser.add_argument(
          '''--train_dataset''' ,						type=__UpperCAmelCase ,						help='''Path to training dataset to load. If the path begins with `gs://`'''
          ''' then the dataset will be loaded from a Google Cloud Storage bucket.''' ,						)

      parser.add_argument(
          '''--shuffle_buffer_size''' ,						type=__UpperCAmelCase ,						default=2**18 ,						help='''Size of the shuffle buffer (in samples)''' ,						)

      parser.add_argument(
          '''--eval_dataset''' ,						type=__UpperCAmelCase ,						help='''Path to evaluation dataset to load. If the path begins with `gs://`'''
          ''' then the dataset will be loaded from a Google Cloud Storage bucket.''' ,						)

      parser.add_argument(
          '''--num_epochs''' ,						type=__UpperCAmelCase ,						default=1 ,						help='''Number of epochs to train for.''' ,						)

      parser.add_argument(
          '''--learning_rate''' ,						type=__UpperCAmelCase ,						default=1E-4 ,						help='''Learning rate to use for training.''' ,						)

      parser.add_argument(
          '''--weight_decay_rate''' ,						type=__UpperCAmelCase ,						default=1E-3 ,						help='''Weight decay rate to use for training.''' ,						)

      parser.add_argument(
          '''--max_length''' ,						type=__UpperCAmelCase ,						default=512 ,						help='''Maximum length of tokenized sequences. Should match the setting used in prepare_tfrecord_shards.py''' ,						)

      parser.add_argument(
          '''--mlm_probability''' ,						type=__UpperCAmelCase ,						default=0.1_5 ,						help='''Fraction of tokens to mask during training.''' ,						)

      parser.add_argument('''--output_dir''' ,						type=__UpperCAmelCase ,						required=__UpperCAmelCase ,						help='''Path to save model checkpoints to.'''		)
      parser.add_argument('''--hub_model_id''' ,						type=__UpperCAmelCase ,						help='''Model ID to upload to on the Hugging Face Hub.'''		)

      _lowerCamelCase			    =				parser.parse_args()
      return args
def       __magic_name__(  __UpperCAmelCase		)						->    Optional[int]:



      '''simple docstring'''

      try:
            if args.tpu_name:
                  _lowerCamelCase			    =				tf.distribute.cluster_resolver.TPUClusterResolver(
                      args.tpu_name ,						zone=args.tpu_zone ,						project=args.gcp_project		)
            else:
                  _lowerCamelCase			    =				tf.distribute.cluster_resolver.TPUClusterResolver()
      except ValueError:
            raise RuntimeError(
                '''Couldn\'t connect to TPU! Most likely you need to specify --tpu_name, --tpu_zone, or '''
                '''--gcp_project. When running on a TPU VM, use --tpu_name local.'''		)

      tf.config.experimental_connect_to_cluster(__UpperCAmelCase		)
      tf.tpu.experimental.initialize_tpu_system(__UpperCAmelCase		)

      return tpu
def       __magic_name__(  __UpperCAmelCase		)						->    Union[str, Any]:



      '''simple docstring'''

      _lowerCamelCase			    =				0
      for file in file_list:
            _lowerCamelCase			    =				file.split('''/'''		)[-1]
            _lowerCamelCase			    =				re.search(r'''-\d+-(\d+)\.tfrecord''' ,						__UpperCAmelCase		).group(1		)
            _lowerCamelCase			    =				int(__UpperCAmelCase		)
            num_samples += sample_count

      return num_samples
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase=None		)						->    str:



      '''simple docstring'''

      _lowerCamelCase			    =				count_samples(__UpperCAmelCase		)
      _lowerCamelCase			    =				tf.data.Dataset.from_tensor_slices(__UpperCAmelCase		)
      if shuffle:
            _lowerCamelCase			    =				dataset.shuffle(len(__UpperCAmelCase		)		)
      _lowerCamelCase			    =				tf.data.TFRecordDataset(__UpperCAmelCase ,						num_parallel_reads=__UpperCAmelCase		)
      # TF can't infer the total sample count because it doesn't read all the records yet, so we assert it here
      _lowerCamelCase			    =				dataset.apply(tf.data.experimental.assert_cardinality(__UpperCAmelCase		)		)
      _lowerCamelCase			    =				dataset.map(__UpperCAmelCase ,						num_parallel_calls=__UpperCAmelCase		)
      if shuffle:
            assert shuffle_buffer_size is not None
            _lowerCamelCase			    =				dataset.shuffle(args.shuffle_buffer_size		)
      _lowerCamelCase			    =				dataset.batch(__UpperCAmelCase ,						drop_remainder=__UpperCAmelCase		)
      _lowerCamelCase			    =				dataset.map(__UpperCAmelCase ,						num_parallel_calls=__UpperCAmelCase		)
      _lowerCamelCase			    =				dataset.prefetch(__UpperCAmelCase		)
      return dataset
def       __magic_name__(  __UpperCAmelCase		)						->    Optional[int]:



      '''simple docstring'''

      if not args.no_tpu:
            _lowerCamelCase			    =				initialize_tpu(__UpperCAmelCase		)
            _lowerCamelCase			    =				tf.distribute.TPUStrategy(__UpperCAmelCase		)
      else:
            _lowerCamelCase			    =				tf.distribute.OneDeviceStrategy(device='''/gpu:0'''		)

      if args.bfloataa:
            tf.keras.mixed_precision.set_global_policy('''mixed_bfloat16'''		)

      _lowerCamelCase			    =				AutoTokenizer.from_pretrained(args.tokenizer		)
      _lowerCamelCase			    =				AutoConfig.from_pretrained(args.pretrained_model_config		)
      _lowerCamelCase			    =				tokenizer.vocab_size

      _lowerCamelCase			    =				tf.io.gfile.glob(os.path.join(args.train_dataset ,						'''*.tfrecord'''		)		)
      if not training_records:
            raise ValueError(F'No .tfrecord files found in {args.train_dataset}.'		)
      _lowerCamelCase			    =				tf.io.gfile.glob(os.path.join(args.eval_dataset ,						'''*.tfrecord'''		)		)
      if not eval_records:
            raise ValueError(F'No .tfrecord files found in {args.eval_dataset}.'		)

      _lowerCamelCase			    =				count_samples(__UpperCAmelCase		)

      _lowerCamelCase			    =				num_train_samples // (args.per_replica_batch_size * strategy.num_replicas_in_sync)
      _lowerCamelCase			    =				steps_per_epoch * args.num_epochs

      with strategy.scope():
            _lowerCamelCase			    =				TFAutoModelForMaskedLM.from_config(__UpperCAmelCase		)
            model(model.dummy_inputs		)  # Pass some dummy inputs through the model to ensure all the weights are built
            _lowerCamelCase    ,					_lowerCamelCase			    =				create_optimizer(
                num_train_steps=__UpperCAmelCase ,						num_warmup_steps=total_train_steps // 20 ,						init_lr=args.learning_rate ,						weight_decay_rate=args.weight_decay_rate ,						)

            # Transformers models compute the right loss for their task by default when labels are passed, and will
            # use this for training unless you specify your own loss function in compile().
            model.compile(optimizer=__UpperCAmelCase ,						metrics=['''accuracy''']		)

      def decode_fn(__UpperCAmelCase		):
            _lowerCamelCase			    =				{
                '''input_ids''': tf.io.FixedLenFeature(dtype=tf.intaa ,						shape=(args.max_length,)		),
                '''attention_mask''': tf.io.FixedLenFeature(dtype=tf.intaa ,						shape=(args.max_length,)		),
            }
            return tf.io.parse_single_example(__UpperCAmelCase ,						__UpperCAmelCase		)

      # Many of the data collators in Transformers are TF-compilable when return_tensors == "tf", so we can
      # use their methods in our data pipeline.
      _lowerCamelCase			    =				DataCollatorForLanguageModeling(
          tokenizer=__UpperCAmelCase ,						mlm_probability=args.mlm_probability ,						mlm=__UpperCAmelCase ,						return_tensors='''tf'''		)

      def mask_with_collator(__UpperCAmelCase		):
            # TF really needs an isin() function
            _lowerCamelCase			    =				(
                ~tf.cast(batch['''attention_mask'''] ,						tf.bool		)
                | (batch['''input_ids'''] == tokenizer.cls_token_id)
                | (batch['''input_ids'''] == tokenizer.sep_token_id)
            )
            _lowerCamelCase    ,					_lowerCamelCase			    =				data_collator.tf_mask_tokens(
                batch['''input_ids'''] ,						vocab_size=len(__UpperCAmelCase		) ,						mask_token_id=tokenizer.mask_token_id ,						special_tokens_mask=__UpperCAmelCase ,						)
            return batch

      _lowerCamelCase			    =				args.per_replica_batch_size * strategy.num_replicas_in_sync

      _lowerCamelCase			    =				prepare_dataset(
          __UpperCAmelCase ,						decode_fn=__UpperCAmelCase ,						mask_fn=__UpperCAmelCase ,						batch_size=__UpperCAmelCase ,						shuffle=__UpperCAmelCase ,						shuffle_buffer_size=args.shuffle_buffer_size ,						)

      _lowerCamelCase			    =				prepare_dataset(
          __UpperCAmelCase ,						decode_fn=__UpperCAmelCase ,						mask_fn=__UpperCAmelCase ,						batch_size=__UpperCAmelCase ,						shuffle=__UpperCAmelCase ,						)

      _lowerCamelCase			    =				[]
      if args.hub_model_id:
            callbacks.append(
                PushToHubCallback(output_dir=args.output_dir ,						hub_model_id=args.hub_model_id ,						tokenizer=__UpperCAmelCase		)		)

      model.fit(
          __UpperCAmelCase ,						validation_data=__UpperCAmelCase ,						epochs=args.num_epochs ,						callbacks=__UpperCAmelCase ,						)

      model.save_pretrained(args.output_dir		)


if __name__ == "__main__":
     snake_case__				= parse_args()
     main(args)
from math import acos, sin
from typing import List, Tuple, Union

import numpy as np
import torch
from PIL import Image

from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
from .mel import Mel



class  UpperCamelCase				( __lowercase ):

     '''simple docstring'''

     A_										=  ['vqvae']



     def __init__(					self				,				A_				,				A_				,				A_				,				A_				,				) ->				Tuple:






           """simple docstring"""
           super().__init__()
           self.register_modules(unet=A_				,				scheduler=A_				,				mel=A_				,				vqvae=A_						)



     def 		UpperCamelCase_						(					self						) ->				int:






           """simple docstring"""
           return 50 if isinstance(self.scheduler				,				A_						) else 10_00



     @torch.no_grad()
     def __call__(					self				,				A_ = 1				,				A_ = None				,				A_ = None				,				A_ = 0				,				A_ = 0				,				A_ = None				,				A_ = None				,				A_ = 0				,				A_ = 0				,				A_ = None				,				A_ = 0				,				A_ = None				,				A_ = None				,				A_=True				,				) ->				Union[
         Union[AudioPipelineOutput, ImagePipelineOutput],
         Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]],
     ]:






           """simple docstring"""
           _lowerCamelCase			    =				steps or self.get_default_steps()
           self.scheduler.set_timesteps(A_						)
           _lowerCamelCase			    =				step_generator or generator
           # For backwards compatibility
           if type(self.unet.config.sample_size						) == int:
                 _lowerCamelCase			    =				(self.unet.config.sample_size, self.unet.config.sample_size)
           if noise is None:
                 _lowerCamelCase			    =				randn_tensor(
                     (
                         batch_size,
                         self.unet.config.in_channels,
                         self.unet.config.sample_size[0],
                         self.unet.config.sample_size[1],
                     )				,				generator=A_				,				device=self.device				,				)
           _lowerCamelCase			    =				noise
           _lowerCamelCase			    =				None

           if audio_file is not None or raw_audio is not None:
                 self.mel.load_audio(A_				,				A_						)
                 _lowerCamelCase			    =				self.mel.audio_slice_to_image(A_						)
                 _lowerCamelCase			    =				np.frombuffer(input_image.tobytes()				,				dtype='''uint8'''						).reshape(
                     (input_image.height, input_image.width)						)
                 _lowerCamelCase			    =				(input_image / 2_55) * 2 - 1
                 _lowerCamelCase			    =				torch.tensor(input_image[np.newaxis, :, :]				,				dtype=torch.float						).to(self.device						)

                 if self.vqvae is not None:
                       _lowerCamelCase			    =				self.vqvae.encode(torch.unsqueeze(A_				,				0						)						).latent_dist.sample(
                           generator=A_						)[0]
                       _lowerCamelCase			    =				self.vqvae.config.scaling_factor * input_images

                 if start_step > 0:
                       _lowerCamelCase			    =				self.scheduler.add_noise(A_				,				A_				,				self.scheduler.timesteps[start_step - 1]						)

                 _lowerCamelCase			    =				(
                     self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
                 )
                 _lowerCamelCase			    =				int(mask_start_secs * pixels_per_second						)
                 _lowerCamelCase			    =				int(mask_end_secs * pixels_per_second						)
                 _lowerCamelCase			    =				self.scheduler.add_noise(A_				,				A_				,				torch.tensor(self.scheduler.timesteps[start_step:]						)						)

           for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:]						)						):
                 if isinstance(self.unet				,				A_						):
                       _lowerCamelCase			    =				self.unet(A_				,				A_				,				A_						)['''sample''']
                 else:
                       _lowerCamelCase			    =				self.unet(A_				,				A_						)['''sample''']

                 if isinstance(self.scheduler				,				A_						):
                       _lowerCamelCase			    =				self.scheduler.step(
                           model_output=A_				,				timestep=A_				,				sample=A_				,				eta=A_				,				generator=A_				,				)['''prev_sample''']
                 else:
                       _lowerCamelCase			    =				self.scheduler.step(
                           model_output=A_				,				timestep=A_				,				sample=A_				,				generator=A_				,				)['''prev_sample''']

                 if mask is not None:
                       if mask_start > 0:
                             _lowerCamelCase			    =				mask[:, step, :, :mask_start]
                       if mask_end > 0:
                             _lowerCamelCase			    =				mask[:, step, :, -mask_end:]

           if self.vqvae is not None:
                 # 0.18215 was scaling factor used in training to ensure unit variance
                 _lowerCamelCase			    =				1 / self.vqvae.config.scaling_factor * images
                 _lowerCamelCase			    =				self.vqvae.decode(A_						)['''sample''']

           _lowerCamelCase			    =				(images / 2 + 0.5).clamp(0				,				1						)
           _lowerCamelCase			    =				images.cpu().permute(0				,				2				,				3				,				1						).numpy()
           _lowerCamelCase			    =				(images * 2_55).round().astype('''uint8'''						)
           _lowerCamelCase			    =				list(
               (Image.fromarray(_[:, :, 0]						) for _ in images)
               if images.shape[3] == 1
               else (Image.fromarray(A_				,				mode='''RGB'''						).convert('''L'''						) for _ in images)						)

           _lowerCamelCase			    =				[self.mel.image_to_audio(A_						) for _ in images]
           if not return_dict:
                 return images, (self.mel.get_sample_rate(), audios)

           return BaseOutput(**AudioPipelineOutput(np.array(A_						)[:, np.newaxis, :]						)				,				**ImagePipelineOutput(A_						)						)



     @torch.no_grad()
     def 		UpperCamelCase_						(					self				,				A_				,				A_ = 50						) ->				np.ndarray:






           """simple docstring"""
           assert isinstance(self.scheduler				,				A_						)
           self.scheduler.set_timesteps(A_						)
           _lowerCamelCase			    =				np.array(
               [np.frombuffer(image.tobytes()				,				dtype='''uint8'''						).reshape((1, image.height, image.width)						) for image in images]						)
           _lowerCamelCase			    =				(sample / 2_55) * 2 - 1
           _lowerCamelCase			    =				torch.Tensor(A_						).to(self.device						)

           for t in self.progress_bar(torch.flip(self.scheduler.timesteps				,				(0,)						)						):
                 _lowerCamelCase			    =				t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
                 _lowerCamelCase			    =				self.scheduler.alphas_cumprod[t]
                 _lowerCamelCase			    =				(
                     self.scheduler.alphas_cumprod[prev_timestep]
                     if prev_timestep >= 0
                     else self.scheduler.final_alpha_cumprod
                 )
                 _lowerCamelCase			    =				1 - alpha_prod_t
                 _lowerCamelCase			    =				self.unet(A_				,				A_						)['''sample''']
                 _lowerCamelCase			    =				(1 - alpha_prod_t_prev) ** 0.5 * model_output
                 _lowerCamelCase			    =				(sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
                 _lowerCamelCase			    =				sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

           return sample





     @staticmethod
     def 		UpperCamelCase_						(					A_				,				A_				,				A_						) ->				torch.Tensor:






           """simple docstring"""
           _lowerCamelCase			    =				acos(torch.dot(torch.flatten(A_						)				,				torch.flatten(A_						)						) / torch.norm(A_						) / torch.norm(A_						)						)
           return sin((1 - alpha) * theta						) * xa / sin(A_						) + sin(alpha * theta						) * xa / sin(A_						)