
"""simple docstring"""




import tempfile
import unittest

from diffusers import (
    DDIMScheduler,
    DDPMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    PNDMScheduler,
    logging,
)
from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.utils.testing_utils import CaptureLogger




class 	UpperCamelCase      (			_UpperCAmelCase ):

				"""simple docstring"""



				UpperCAmelCase_							: Tuple		    =							"config.json"


				@register_to_config
				def __init__(							self						,      lowercase__=2						,      lowercase__=5						,      lowercase__=(2, 5)						,      lowercase__="for diffusion"						,      lowercase__=[1, 3]						,      )       ->       Optional[int]:
									"""simple docstring"""







									pass






class 	UpperCamelCase      (			_UpperCAmelCase ):

				"""simple docstring"""



				UpperCAmelCase_							: Union[str, Any]		    =							"config.json"


				@register_to_config
				def __init__(							self						,      lowercase__=2						,      lowercase__=5						,      lowercase__=(2, 5)						,      lowercase__="for diffusion"						,      lowercase__=[1, 3]						,      )       ->       Any:
									"""simple docstring"""







									pass






class 	UpperCamelCase      (			_UpperCAmelCase ):

				"""simple docstring"""



				UpperCAmelCase_							: str		    =							"config.json"


				@register_to_config
				def __init__(							self						,      lowercase__=2						,      lowercase__=5						,      lowercase__=(2, 5)						,      lowercase__="for diffusion"						,      lowercase__=[1, 3]						,      lowercase__=[1, 3]						,      )       ->       List[Any]:
									"""simple docstring"""







									pass






class 	UpperCamelCase      (			_UpperCAmelCase ):

				"""simple docstring"""



				UpperCAmelCase_							: Tuple		    =							"config.json"


				@register_to_config
				def __init__(							self						,      lowercase__=2						,      lowercase__=5						,      lowercase__=(2, 5)						,      lowercase__="for diffusion"						,      lowercase__=[1, 5]						,      lowercase__=[5, 4]						,      )       ->       Optional[int]:
									"""simple docstring"""







									pass






class 	UpperCamelCase      (			unittest.TestCase ):

				"""simple docstring"""



				def 				A   (							self     )       ->       int:
									"""simple docstring"""







									with self.assertRaises(_lowerCAmelCase     ):
														ConfigMixin.load_config('dummy_path'     )


				def 				A   (							self     )       ->       List[str]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      SampleObject()
									SCREAMING_SNAKE_CASE           =      obj.config
									assert config["a"] == 2
									assert config["b"] == 5
									assert config["c"] == (2, 5)
									assert config["d"] == "for diffusion"
									assert config["e"] == [1, 3]

									# init ignore private arguments
									SCREAMING_SNAKE_CASE           =      SampleObject(_name_or_path='lalala'     )
									SCREAMING_SNAKE_CASE           =      obj.config
									assert config["a"] == 2
									assert config["b"] == 5
									assert config["c"] == (2, 5)
									assert config["d"] == "for diffusion"
									assert config["e"] == [1, 3]

									# can override default
									SCREAMING_SNAKE_CASE           =      SampleObject(c=6     )
									SCREAMING_SNAKE_CASE           =      obj.config
									assert config["a"] == 2
									assert config["b"] == 5
									assert config["c"] == 6
									assert config["d"] == "for diffusion"
									assert config["e"] == [1, 3]

									# can use positional arguments.
									SCREAMING_SNAKE_CASE           =      SampleObject(1						,      c=6     )
									SCREAMING_SNAKE_CASE           =      obj.config
									assert config["a"] == 1
									assert config["b"] == 5
									assert config["c"] == 6
									assert config["d"] == "for diffusion"
									assert config["e"] == [1, 3]


				def 				A   (							self     )       ->       Optional[Any]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      SampleObject()
									SCREAMING_SNAKE_CASE           =      obj.config

									assert config["a"] == 2
									assert config["b"] == 5
									assert config["c"] == (2, 5)
									assert config["d"] == "for diffusion"
									assert config["e"] == [1, 3]

									with tempfile.TemporaryDirectory() as tmpdirname:
														obj.save_config(_lowerCAmelCase     )
														SCREAMING_SNAKE_CASE           =      SampleObject.from_config(SampleObject.load_config(_lowerCAmelCase     )     )
														SCREAMING_SNAKE_CASE           =      new_obj.config

									# unfreeze configs
									SCREAMING_SNAKE_CASE           =      dict(_lowerCAmelCase     )
									SCREAMING_SNAKE_CASE           =      dict(_lowerCAmelCase     )

									assert config.pop('c'     ) == (2, 5)  # instantiated as tuple
									assert new_config.pop('c'     ) == [2, 5]  # saved & loaded as list because of json
									config.pop('_use_default_values'     )
									assert config == new_config


				def 				A   (							self     )       ->       Optional[Any]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      logging.get_logger('diffusers.configuration_utils'     )
									# 30 for warning
									logger.setLevel(30     )

									with CaptureLogger(_lowerCAmelCase     ) as cap_logger:
														SCREAMING_SNAKE_CASE           =      DDIMScheduler.from_pretrained(
														    'hf-internal-testing/tiny-stable-diffusion-torch'						,      subfolder='scheduler'     )

									assert ddim.__class__ == DDIMScheduler
									# no warning should be thrown
									assert cap_logger.out == ""


				def 				A   (							self     )       ->       Tuple:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      logging.get_logger('diffusers.configuration_utils'     )
									# 30 for warning
									logger.setLevel(30     )

									with CaptureLogger(_lowerCAmelCase     ) as cap_logger:
														SCREAMING_SNAKE_CASE           =      EulerDiscreteScheduler.from_pretrained(
														    'hf-internal-testing/tiny-stable-diffusion-torch'						,      subfolder='scheduler'     )

									assert euler.__class__ == EulerDiscreteScheduler
									# no warning should be thrown
									assert cap_logger.out == ""


				def 				A   (							self     )       ->       List[str]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      logging.get_logger('diffusers.configuration_utils'     )
									# 30 for warning
									logger.setLevel(30     )

									with CaptureLogger(_lowerCAmelCase     ) as cap_logger:
														SCREAMING_SNAKE_CASE           =      EulerAncestralDiscreteScheduler.from_pretrained(
														    'hf-internal-testing/tiny-stable-diffusion-torch'						,      subfolder='scheduler'     )

									assert euler.__class__ == EulerAncestralDiscreteScheduler
									# no warning should be thrown
									assert cap_logger.out == ""


				def 				A   (							self     )       ->       Any:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      logging.get_logger('diffusers.configuration_utils'     )
									# 30 for warning
									logger.setLevel(30     )

									with CaptureLogger(_lowerCAmelCase     ) as cap_logger:
														SCREAMING_SNAKE_CASE           =      PNDMScheduler.from_pretrained(
														    'hf-internal-testing/tiny-stable-diffusion-torch'						,      subfolder='scheduler'     )

									assert pndm.__class__ == PNDMScheduler
									# no warning should be thrown
									assert cap_logger.out == ""


				def 				A   (							self     )       ->       int:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      logging.get_logger('diffusers.configuration_utils'     )
									# 30 for warning
									logger.setLevel(30     )

									with CaptureLogger(_lowerCAmelCase     ) as cap_logger:
														SCREAMING_SNAKE_CASE           =      DDPMScheduler.from_pretrained(
														    'hf-internal-testing/tiny-stable-diffusion-torch'						,      subfolder='scheduler'						,      prediction_type='sample'						,      beta_end=8						,      )

									with CaptureLogger(_lowerCAmelCase     ) as cap_logger_a:
														SCREAMING_SNAKE_CASE           =      DDPMScheduler.from_pretrained('google/ddpm-celebahq-256'						,      beta_start=88     )

									assert ddpm.__class__ == DDPMScheduler
									assert ddpm.config.prediction_type == "sample"
									assert ddpm.config.beta_end == 8
									assert ddpm_a.config.beta_start == 88

									# no warning should be thrown
									assert cap_logger.out == ""
									assert cap_logger_a.out == ""


				def 				A   (							self     )       ->       Tuple:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      logging.get_logger('diffusers.configuration_utils'     )
									# 30 for warning
									logger.setLevel(30     )

									with CaptureLogger(_lowerCAmelCase     ) as cap_logger:
														SCREAMING_SNAKE_CASE           =      DPMSolverMultistepScheduler.from_pretrained(
														    'hf-internal-testing/tiny-stable-diffusion-torch'						,      subfolder='scheduler'     )

									assert dpm.__class__ == DPMSolverMultistepScheduler
									# no warning should be thrown
									assert cap_logger.out == ""




				def 				A   (							self     )       ->       int:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      SampleObject()

									SCREAMING_SNAKE_CASE           =      {k: v for k, v in config.config.items() if not k.startswith('_'     )}

									# make sure that default config has all keys in `_use_default_values`
									assert set(config_dict.keys()     ) == set(config.config._use_default_values     )

									with tempfile.TemporaryDirectory() as tmpdirname:
														config.save_config(_lowerCAmelCase     )

														# now loading it with SampleObject2 should put f into `_use_default_values`
														SCREAMING_SNAKE_CASE           =      SampleObjecta.from_config(_lowerCAmelCase     )

														assert "f" in config._use_default_values
														assert config.f == [1, 3]

									# now loading the config, should **NOT** use [1, 3] for `f`, but the default [1, 4] value
									# **BECAUSE** it is part of `config._use_default_values`
									SCREAMING_SNAKE_CASE           =      SampleObjecta.from_config(config.config     )
									assert new_config.f == [5, 4]

									config.config._use_default_values.pop()
									SCREAMING_SNAKE_CASE           =      SampleObjecta.from_config(config.config     )
									assert new_config_a.f == [1, 3]

									# Nevertheless "e" should still be correctly loaded to [1, 3] from SampleObject2 instead of defaulting to [1, 5]
									assert new_config_a.e == [1, 3]




import argparse
import logging

import datasets
from tokenizers import Tokenizer, decoders, normalizers, pre_tokenizers, processors
from tokenizers.models import Unigram
from tokenizers.trainers import UnigramTrainer

from transformers import AlbertTokenizerFast


UpperCamelCase			:					List[str]         =							logging.getLogger(__name__)
def 				UpperCamelCase_	(					)->   Dict:




						'''simple docstring'''
						__magic_name__					=      argparse.ArgumentParser(description='Train a unigram tokenizer on the wikitext dataset.'  )
						parser.add_argument(
						    '--dataset_name'						,      type=__snake_case						,      default='wikitext'						,      help='Name of the training. Explore datasets at: hf.co/datasets.'						,      )
						parser.add_argument(
						    '--dataset_config'						,      type=__snake_case						,      default='wikitext-103-raw-v1'						,      help='Configuration name of the dataset.'  )
						parser.add_argument(
						    '--batch_size'						,      type=__snake_case						,      default=10_00						,      help='Batch size during training.'						,      )
						parser.add_argument(
						    '--vocab_size'						,      type=__snake_case						,      default=1_00_48						,      help='Size of the desired vocabulary.'						,      )
						parser.add_argument(
						    '--limit'						,      default=__snake_case						,      type=__snake_case						,      help='Limit the number of shards (used for debugging).'						,      )
						parser.add_argument(
						    '--export_to_hub'						,      action='store_true'						,      )

						__magic_name__					=      parser.parse_args()
						return args
def 				UpperCamelCase_	(					__snake_case      :    Any  )->   Optional[int]:




						'''simple docstring'''
						__magic_name__					=      datasets.load_dataset(args.dataset_name						,      args.dataset_config						,      split='train'  )

						if args.limit is not None:
												__magic_name__					=      min(len(__snake_case  )						,      args.limit  )
												__magic_name__					=      dataset.select(range(__snake_case  )  )
												logger.info(F'''Limiting the dataset to {args.limit} entries.'''  )

						def batch_iterator():
												for i in range(0						,      len(__snake_case  )						,      args.batch_size  ):
																		yield dataset[i : i + args.batch_size]["text"]

    # Prepare the tokenizer.
						__magic_name__					=      Tokenizer(Unigram()  )
						__magic_name__					=      normalizers.Sequence([normalizers.Replace('``'						,      '"'  ), normalizers.Replace('\'\''						,      '"'  )]  )
						__magic_name__					=      pre_tokenizers.Metaspace()

						# Prepare the trainer.
						__magic_name__					=      UnigramTrainer(
						    unk_token='<unk>'						,      special_tokens=['[CLS]', '[SEP]', '<unk>', '<pad>', '[MASK]']						,      vocab_size=args.vocab_size						,      )

						logger.info('Training the tokenizer.'  )
						tokenizer.train_from_iterator(batch_iterator()						,      trainer=__snake_case  )
						logger.info('Tokenizer training complete!'  )

						__magic_name__					=      tokenizer.token_to_id('[CLS]'  )
						__magic_name__					=      tokenizer.token_to_id('[SEP]'  )
						__magic_name__					=      processors.TemplateProcessing(
						    single='[CLS]:0 $A:0 [SEP]:0'						,      pair='[CLS]:0 $A:0 [SEP]:0 $B:1 [SEP]:1'						,      special_tokens=[
						        ('[CLS]', cls_token_id),
						        ('[SEP]', sep_token_id),
						    ]						,      )
						__magic_name__					=      decoders.Metaspace()

						if args.export_to_hub:
												logger.info('Exporting the trained tokenzier to Hub.'  )
												__magic_name__					=      AlbertTokenizerFast(tokenizer_object=__snake_case  )
												new_tokenizer.push_to_hub('unigram-tokenizer-dataset'  )


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

