


"""simple docstring"""







import argparse
import logging
import os

import datasets
import tensorflow as tf

from transformers import AutoTokenizer


lowerCamelCase_   :   List[Any]									=  logging.getLogger(__name__)
def _A						(						):







    """simple docstring"""
    a 		=argparse.ArgumentParser(
        description='''Prepare TFRecord shards from pre-tokenized samples of the wikitext dataset.'''		)
    parser.add_argument(
        '''--dataset_name'''       ,    type=lowercase       ,    default='''wikitext'''       ,    help='''Name of the training. Explore datasets at: hf.co/datasets.'''       ,    )
    parser.add_argument(
        '''--dataset_config'''       ,    type=lowercase       ,    default='''wikitext-103-raw-v1'''       ,    help='''Configuration name of the dataset.'''		)
    parser.add_argument(
        '''--tokenizer_name_or_path'''       ,    type=lowercase       ,    default='''sayakpaul/unigram-tokenizer-wikitext'''       ,    help='''Tokenizer identifier. Can be a local filepath or a Hub identifier.'''       ,    )
    parser.add_argument(
        '''--shard_size'''       ,    type=lowercase       ,    default=10_00       ,    help='''Number of entries to go in a single shard.'''       ,    )
    parser.add_argument('''--split'''       ,    type=lowercase       ,    default='''train'''       ,    choices=['''train''', '''test''', '''validation''']		)
    parser.add_argument(
        '''--limit'''       ,    default=lowercase       ,    type=lowercase       ,    help='''Limit the number of shards (used for debugging).'''       ,    )
    parser.add_argument(
        '''--max_length'''       ,    type=lowercase       ,    default=5_12       ,    help='''Maximum sequence length. For training on TPUs, it helps to have a maximum'''
        ''' sequence length that is a multiple of 8.'''       ,    )
    parser.add_argument(
        '''--output_dir'''       ,    default='''tf-tpu'''       ,    type=lowercase       ,    help='''Output directory where the TFRecord shards will be saved. If the'''
        ''' path is appended with `gs://` (\'gs://tf-tpu\', for example) then the TFRecord'''
        ''' shards will be directly saved to a Google Cloud Storage bucket.'''       ,    )

    a 		=parser.parse_args()
    return args
def _A						(						lowercase		):







    """simple docstring"""
    def fn(lowercase		):
        return tokenizer(examples['''text''']		)

    return fn
def _A						(						lowercase		):







    """simple docstring"""
    a 		=[]
    for i in range(len(tokenized_data['''input_ids''']		)		):
        a 		={
            '''input_ids''': tf.train.Feature(intaa_list=tf.train.IntaaList(value=tokenized_data['''input_ids'''][i]		)		),
            '''attention_mask''': tf.train.Feature(
                intaa_list=tf.train.IntaaList(value=tokenized_data['''attention_mask'''][i]		)		),
        }
        a 		=tf.train.Features(feature=lowercase		)
        a 		=tf.train.Example(features=lowercase		)
        a 		=example.SerializeToString()
        records.append(lowercase		)
    return records
def _A						(						lowercase		):







    """simple docstring"""
    a 		=datasets.load_dataset(args.dataset_name       ,    args.dataset_config       ,    split=args.split		)

    if args.limit is not None:
        a 		=min(len(lowercase		)       ,    args.limit		)
        a 		=dataset.select(range(lowercase		)		)
        print(f'''Limiting the dataset to {args.limit} entries.'''		)

    a 		=AutoTokenizer.from_pretrained(args.tokenizer_name_or_path		)

    # Handle output directory creation.
    # For serializing into a Google Cloud Storage Bucket, one needs to first
    # create a bucket.
    if "gs" not in args.output_dir:
        if not os.path.exists(args.output_dir		):
            os.makedirs(args.output_dir		)
        a 		=os.path.join(args.output_dir       ,    args.split		)
        if not os.path.exists(lowercase		):
            os.makedirs(lowercase		)
    else:
        a 		=os.path.join(args.output_dir       ,    args.split		)

    # Tokenize the whole dataset at once.
    a 		=tokenize_function(lowercase		)
    a 		=dataset.map(lowercase       ,    batched=lowercase       ,    num_proc=4       ,    remove_columns=['''text''']		)

    # We need to concatenate all our texts together, and then split the result
    # into chunks of a fixed size, which we will call block_size. To do this, we
    # will use the map method again, with the option batched=True. When we use batched=True,
    # the function we pass to map() will be passed multiple inputs at once, allowing us
    # to group them into more or fewer examples than we had in the input.
    # This allows us to create our new fixed-length samples. The advantage of this
    # method is that we don't lose a whole lot of content from the dataset compared to the
    # case where we simply tokenize with a pre-defined max_length.

    def group_texts(lowercase		):
        # Concatenate all texts.
        a 		={k: sum(examples[k]       ,    []		) for k in examples.keys()}
        a 		=len(concatenated_examples[list(examples.keys()		)[0]]		)
        # We drop the small remainder, though you could add padding instead if the model supports it
        # In this, as in all things, we advise you to follow your heart 🫀
        a 		=(total_length // args.max_length) * args.max_length
        # Split by chunks of max_len.
        a 		={
            k: [t[i : i + args.max_length] for i in range(0       ,    lowercase       ,    args.max_length		)]
            for k, t in concatenated_examples.items()
        }
        return result

    a 		=dataset_tokenized.map(lowercase       ,    batched=lowercase       ,    batch_size=10_00       ,    num_proc=4		)

    a 		=0
    a 		=0
    for shard in range(0       ,    len(lowercase		)       ,    args.shard_size		):
        a 		=grouped_dataset[shard : shard + args.shard_size]
        a 		=len(dataset_snapshot['''input_ids''']		)
        a 		=os.path.join(lowercase       ,    f'''dataset-{shard_count}-{records_containing}.tfrecord'''		)
        a 		=get_serialized_examples(lowercase		)

        with tf.io.TFRecordWriter(lowercase		) as out_file:
            for i in range(len(lowercase		)		):
                a 		=serialized_examples[i]
                out_file.write(lowercase		)
            print('''Wrote file {} containing {} records'''.format(lowercase       ,    lowercase		)		)

        shard_count += 1
        total_records += records_containing

    with open(f'''split-{args.split}-records-count.txt'''       ,    '''w'''		) as f:
        print(f'''Total {args.split} records: {total_records}'''       ,    file=lowercase		)


if __name__ == "__main__":
       lowerCamelCase_   :   Dict									=  parse_args()
       main(args)

import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest






class   UpperCAmelCase		(						A_ ):
   A__					:		Dict						     =     (DDIMParallelScheduler,)
   A__					:		Tuple						     =     (("eta", 0.0), ("num_inference_steps", 50))



   def 				_SCREAMING_SNAKE_CASE   (self		:					Tuple    ,						**snake_case__		:					Optional[int] )		->	Optional[Any]:


          '''simple docstring'''


          snake_case       :   Any							=      {
              "num_train_timesteps": 10_00,
              "beta_start": 0.0001,
              "beta_end": 0.02,
              "beta_schedule": "linear",
              "clip_sample": True,
          }

          config.update(**snake_case__ )
          return config



   def 				_SCREAMING_SNAKE_CASE   (self		:					Dict    ,						**snake_case__		:					Optional[int] )		->	Any:


          '''simple docstring'''


          snake_case       :   List[Any]							=      self.scheduler_classes[0]
          snake_case       :   Any							=      self.get_scheduler_config(**snake_case__ )
          snake_case       :   Any							=      scheduler_class(**snake_case__ )

          snake_case				,					snake_case       :   Union[str, Any]							=      10, 0.0

          snake_case       :   List[Any]							=      self.dummy_model()
          snake_case       :   Any							=      self.dummy_sample_deter

          scheduler.set_timesteps(snake_case__ )

          for t in scheduler.timesteps:
                 snake_case       :   Optional[int]							=      model(snake_case__    ,						snake_case__ )
                 snake_case       :   List[str]							=      scheduler.step(snake_case__    ,						snake_case__    ,						snake_case__    ,						snake_case__ ).prev_sample

          return sample



   def 				_SCREAMING_SNAKE_CASE   (self		:					Union[str, Any] )		->	str:


          '''simple docstring'''


          for timesteps in [1_00, 5_00, 10_00]:
                 self.check_over_configs(num_train_timesteps=snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					str )		->	int:


          '''simple docstring'''


          for steps_offset in [0, 1]:
                 self.check_over_configs(steps_offset=snake_case__ )

          snake_case       :   Optional[int]							=      self.scheduler_classes[0]
          snake_case       :   Optional[int]							=      self.get_scheduler_config(steps_offset=1 )
          snake_case       :   Union[str, Any]							=      scheduler_class(**snake_case__ )
          scheduler.set_timesteps(5 )
          assert torch.equal(scheduler.timesteps    ,						torch.LongTensor([8_01, 6_01, 4_01, 2_01, 1] ) )



   def 				_SCREAMING_SNAKE_CASE   (self		:					int )		->	Tuple:


          '''simple docstring'''


          for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]    ,						[0.002, 0.02, 0.2, 2] ):
                 self.check_over_configs(beta_start=snake_case__    ,						beta_end=snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Union[str, Any] )		->	Union[str, Any]:


          '''simple docstring'''


          for schedule in ["linear", "squaredcos_cap_v2"]:
                 self.check_over_configs(beta_schedule=snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					str )		->	Dict:


          '''simple docstring'''


          for prediction_type in ["epsilon", "v_prediction"]:
                 self.check_over_configs(prediction_type=snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[Any] )		->	List[str]:


          '''simple docstring'''


          for clip_sample in [True, False]:
                 self.check_over_configs(clip_sample=snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					List[Any] )		->	List[Any]:


          '''simple docstring'''


          for timestep_spacing in ["trailing", "leading"]:
                 self.check_over_configs(timestep_spacing=snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[int] )		->	List[Any]:


          '''simple docstring'''


          for rescale_betas_zero_snr in [True, False]:
                 self.check_over_configs(rescale_betas_zero_snr=snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[Any] )		->	Optional[Any]:


          '''simple docstring'''


          self.check_over_configs(thresholding=snake_case__ )
          for threshold in [0.5, 1.0, 2.0]:
                 for prediction_type in ["epsilon", "v_prediction"]:
                        self.check_over_configs(
                            thresholding=snake_case__    ,						prediction_type=snake_case__    ,						sample_max_value=snake_case__    ,						)



   def 				_SCREAMING_SNAKE_CASE   (self		:					Any )		->	Any:


          '''simple docstring'''


          for t in [1, 10, 49]:
                 self.check_over_forward(time_step=snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[int] )		->	Any:


          '''simple docstring'''


          for t, num_inference_steps in zip([1, 10, 50]    ,						[10, 50, 5_00] ):
                 self.check_over_forward(time_step=snake_case__    ,						num_inference_steps=snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					List[str] )		->	Optional[Any]:


          '''simple docstring'''


          for t, eta in zip([1, 10, 49]    ,						[0.0, 0.5, 1.0] ):
                 self.check_over_forward(time_step=snake_case__    ,						eta=snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[Any] )		->	Optional[int]:


          '''simple docstring'''


          snake_case       :   Dict							=      self.scheduler_classes[0]
          snake_case       :   Tuple							=      self.get_scheduler_config()
          snake_case       :   Dict							=      scheduler_class(**snake_case__ )

          assert torch.sum(torch.abs(scheduler._get_variance(0    ,						0 ) - 0.0 ) ) < 1e-5
          assert torch.sum(torch.abs(scheduler._get_variance(4_20    ,						4_00 ) - 0.14771 ) ) < 1e-5
          assert torch.sum(torch.abs(scheduler._get_variance(9_80    ,						9_60 ) - 0.32460 ) ) < 1e-5
          assert torch.sum(torch.abs(scheduler._get_variance(0    ,						0 ) - 0.0 ) ) < 1e-5
          assert torch.sum(torch.abs(scheduler._get_variance(4_87    ,						4_86 ) - 0.00979 ) ) < 1e-5
          assert torch.sum(torch.abs(scheduler._get_variance(9_99    ,						9_98 ) - 0.02 ) ) < 1e-5



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[int] )		->	Dict:


          '''simple docstring'''


          snake_case       :   Union[str, Any]							=      self.scheduler_classes[0]
          snake_case       :   List[Any]							=      self.get_scheduler_config()
          snake_case       :   int							=      scheduler_class(**snake_case__ )

          snake_case				,					snake_case       :   Any							=      10, 0.0
          scheduler.set_timesteps(snake_case__ )

          snake_case       :   Optional[Any]							=      self.dummy_model()
          snake_case       :   str							=      self.dummy_sample_deter
          snake_case       :   Dict							=      self.dummy_sample_deter + 0.1
          snake_case       :   Dict							=      self.dummy_sample_deter - 0.1

          snake_case       :   Optional[Any]							=      samplea.shape[0]
          snake_case       :   str							=      torch.stack([samplea, samplea, samplea]    ,						dim=0 )
          snake_case       :   Tuple							=      torch.arange(snake_case__ )[0:3, None].repeat(1    ,						snake_case__ )

          snake_case       :   Tuple							=      model(samples.flatten(0    ,						1 )    ,						timesteps.flatten(0    ,						1 ) )
          snake_case       :   List[str]							=      scheduler.batch_step_no_noise(snake_case__    ,						timesteps.flatten(0    ,						1 )    ,						samples.flatten(0    ,						1 )    ,						snake_case__ )

          snake_case       :   Dict							=      torch.sum(torch.abs(snake_case__ ) )
          snake_case       :   List[Any]							=      torch.mean(torch.abs(snake_case__ ) )

          assert abs(result_sum.item() - 1147.7904 ) < 1e-2
          assert abs(result_mean.item() - 0.4982 ) < 1e-3



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[Any] )		->	Optional[Any]:


          '''simple docstring'''


          snake_case       :   List[Any]							=      self.full_loop()

          snake_case       :   Optional[Any]							=      torch.sum(torch.abs(snake_case__ ) )
          snake_case       :   List[Any]							=      torch.mean(torch.abs(snake_case__ ) )

          assert abs(result_sum.item() - 172.0067 ) < 1e-2
          assert abs(result_mean.item() - 0.223967 ) < 1e-3



   def 				_SCREAMING_SNAKE_CASE   (self		:					str )		->	Union[str, Any]:


          '''simple docstring'''


          snake_case       :   Dict							=      self.full_loop(prediction_type="v_prediction" )

          snake_case       :   int							=      torch.sum(torch.abs(snake_case__ ) )
          snake_case       :   Optional[int]							=      torch.mean(torch.abs(snake_case__ ) )

          assert abs(result_sum.item() - 52.5302 ) < 1e-2
          assert abs(result_mean.item() - 0.0684 ) < 1e-3



   def 				_SCREAMING_SNAKE_CASE   (self		:					Any )		->	Optional[Any]:


          '''simple docstring'''


          snake_case       :   Dict							=      self.full_loop(set_alpha_to_one=snake_case__    ,						beta_start=0.01 )
          snake_case       :   str							=      torch.sum(torch.abs(snake_case__ ) )
          snake_case       :   Optional[Any]							=      torch.mean(torch.abs(snake_case__ ) )

          assert abs(result_sum.item() - 149.8295 ) < 1e-2
          assert abs(result_mean.item() - 0.1951 ) < 1e-3



   def 				_SCREAMING_SNAKE_CASE   (self		:					int )		->	Optional[Any]:


          '''simple docstring'''


          snake_case       :   int							=      self.full_loop(set_alpha_to_one=snake_case__    ,						beta_start=0.01 )
          snake_case       :   Tuple							=      torch.sum(torch.abs(snake_case__ ) )
          snake_case       :   List[Any]							=      torch.mean(torch.abs(snake_case__ ) )

          assert abs(result_sum.item() - 149.0784 ) < 1e-2
          assert abs(result_mean.item() - 0.1941 ) < 1e-3





