
import unittest

from transformers import ErnieMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers import (
							    ErnieMForInformationExtraction,
							    ErnieMForMultipleChoice,
							    ErnieMForQuestionAnswering,
							    ErnieMForSequenceClassification,
							    ErnieMForTokenClassification,
							    ErnieMModel,
							)
							from transformers.models.ernie_m.modeling_ernie_m import ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST
class 				_UpperCAmelCase :

				"""simple docstring"""




				def __init__(					self						:   List[str],  lowerCamelCase						:   Tuple,  lowerCamelCase						:   Union[str, Any]=13,  lowerCamelCase						:   List[str]=7,  lowerCamelCase						:   str=True,  lowerCamelCase						:   List[Any]=True,  lowerCamelCase						:   int=True,  lowerCamelCase						:   List[str]=99,  lowerCamelCase						:   Union[str, Any]=32,  lowerCamelCase						:   Tuple=5,  lowerCamelCase						:   Dict=4,  lowerCamelCase						:   Dict=37,  lowerCamelCase						:   Union[str, Any]="gelu",  lowerCamelCase						:   Dict=0.1,  lowerCamelCase						:   Optional[int]=0.1,  lowerCamelCase						:   int=512,  lowerCamelCase						:   Dict=16,  lowerCamelCase						:   Optional[Any]=2,  lowerCamelCase						:   str=0.02,  lowerCamelCase						:   Dict=3,  lowerCamelCase						:   Optional[Any]=4,  lowerCamelCase						:   Dict=None,  ):

								'''simple docstring'''
								lowercase__       			=      parent
								lowercase__       			=      batch_size
								lowercase__       			=      seq_length
								lowercase__       			=      is_training
								lowercase__       			=      use_input_mask
								lowercase__       			=      use_labels
								lowercase__       			=      vocab_size
								lowercase__       			=      hidden_size
								lowercase__       			=      num_hidden_layers
								lowercase__       			=      num_attention_heads
								lowercase__       			=      intermediate_size
								lowercase__       			=      hidden_act
								lowercase__       			=      hidden_dropout_prob
								lowercase__       			=      attention_probs_dropout_prob
								lowercase__       			=      max_position_embeddings
								lowercase__       			=      type_vocab_size
								lowercase__       			=      type_sequence_label_size
								lowercase__       			=      initializer_range
								lowercase__       			=      num_labels
								lowercase__       			=      num_choices
								lowercase__       			=      scope




				def     lowercase__       (					self						:   Any  ):

								'''simple docstring'''
								lowercase__       			=      ids_tensor([self.batch_size, self.seq_length],  self.vocab_size  )

								lowercase__       			=      None
								if self.use_input_mask:
												lowercase__       			=      random_attention_mask([self.batch_size, self.seq_length]  )

								lowercase__       			=      None
								lowercase__       			=      None
								lowercase__       			=      None
								if self.use_labels:
												lowercase__       			=      ids_tensor([self.batch_size],  self.type_sequence_label_size  )
												lowercase__       			=      ids_tensor([self.batch_size, self.seq_length],  self.num_labels  )
												lowercase__       			=      ids_tensor([self.batch_size],  self.num_choices  )

								lowercase__       			=      self.get_config()

								return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels




				def     lowercase__       (					self						:   Tuple  ):

								'''simple docstring'''
								lowercase__       			=      ids_tensor([self.batch_size, self.seq_length],  self.vocab_size  )

								lowercase__       			=      None
								if self.use_input_mask:
												lowercase__       			=      random_attention_mask([self.batch_size, self.seq_length]  )
								lowercase__       			=      self.get_config()

								return config, input_ids, input_mask




				def     lowercase__       (					self						:   Optional[Any]  ):

								'''simple docstring'''
								return ErnieMConfig(
								    vocab_size=self.vocab_size,  hidden_size=self.hidden_size,  num_hidden_layers=self.num_hidden_layers,  num_attention_heads=self.num_attention_heads,  intermediate_size=self.intermediate_size,  hidden_act=self.hidden_act,  hidden_dropout_prob=self.hidden_dropout_prob,  attention_probs_dropout_prob=self.attention_probs_dropout_prob,  max_position_embeddings=self.max_position_embeddings,  type_vocab_size=self.type_vocab_size,  initializer_range=self.initializer_range,  )




				def     lowercase__       (					self						:   Optional[Any],  lowerCamelCase						:   Optional[int],  lowerCamelCase						:   List[Any],  lowerCamelCase						:   Tuple,  lowerCamelCase						:   List[Any],  lowerCamelCase						:   List[str],  lowerCamelCase						:   Union[str, Any]  ):

								'''simple docstring'''
								lowercase__       			=      ErnieMModel(config=lowerCamelCase  )
								model.to(lowerCamelCase  )
								model.eval()
								lowercase__       			=      model(lowerCamelCase,  return_dict=lowerCamelCase  )
								self.parent.assertEqual(result.last_hidden_state.shape,  (self.batch_size, self.seq_length, self.hidden_size)  )




				def     lowercase__       (					self						:   str,  lowerCamelCase						:   List[Any],  lowerCamelCase						:   Union[str, Any],  lowerCamelCase						:   Optional[Any],  lowerCamelCase						:   Optional[int],  lowerCamelCase						:   str,  lowerCamelCase						:   Dict  ):

								'''simple docstring'''
								lowercase__       			=      ErnieMForQuestionAnswering(config=lowerCamelCase  )
								model.to(lowerCamelCase  )
								model.eval()
								lowercase__       			=      model(
								    lowerCamelCase,  attention_mask=lowerCamelCase,  start_positions=lowerCamelCase,  end_positions=lowerCamelCase,  )
								self.parent.assertEqual(result.start_logits.shape,  (self.batch_size, self.seq_length)  )
								self.parent.assertEqual(result.end_logits.shape,  (self.batch_size, self.seq_length)  )




				def     lowercase__       (					self						:   Tuple,  lowerCamelCase						:   Dict,  lowerCamelCase						:   str,  lowerCamelCase						:   Optional[Any],  lowerCamelCase						:   List[Any],  lowerCamelCase						:   List[Any],  lowerCamelCase						:   Optional[int]  ):

								'''simple docstring'''
								lowercase__       			=      ErnieMForInformationExtraction(config=lowerCamelCase  )
								model.to(lowerCamelCase  )
								model.eval()
								lowercase__       			=      torch.ones_like(lowerCamelCase,  dtype=torch.floataa  )
								lowercase__       			=      model(
								    lowerCamelCase,  attention_mask=lowerCamelCase,  start_positions=lowerCamelCase,  end_positions=lowerCamelCase,  )
								self.parent.assertEqual(result.start_logits.shape,  (self.batch_size, self.seq_length)  )
								self.parent.assertEqual(result.end_logits.shape,  (self.batch_size, self.seq_length)  )




				def     lowercase__       (					self						:   Optional[Any],  lowerCamelCase						:   str,  lowerCamelCase						:   Optional[int],  lowerCamelCase						:   str,  lowerCamelCase						:   Union[str, Any],  lowerCamelCase						:   Optional[Any],  lowerCamelCase						:   Union[str, Any]  ):

								'''simple docstring'''
								lowercase__       			=      self.num_labels
								lowercase__       			=      ErnieMForSequenceClassification(lowerCamelCase  )
								model.to(lowerCamelCase  )
								model.eval()
								lowercase__       			=      model(lowerCamelCase,  attention_mask=lowerCamelCase,  labels=lowerCamelCase  )
								self.parent.assertEqual(result.logits.shape,  (self.batch_size, self.num_labels)  )




				def     lowercase__       (					self						:   Tuple,  lowerCamelCase						:   List[Any],  lowerCamelCase						:   str,  lowerCamelCase						:   int,  lowerCamelCase						:   List[Any],  lowerCamelCase						:   Optional[Any],  lowerCamelCase						:   List[str]  ):

								'''simple docstring'''
								lowercase__       			=      self.num_labels
								lowercase__       			=      ErnieMForTokenClassification(config=lowerCamelCase  )
								model.to(lowerCamelCase  )
								model.eval()
								input_ids.to(lowerCamelCase  )
								input_mask.to(lowerCamelCase  )
								token_labels.to(lowerCamelCase  )

								lowercase__       			=      model(lowerCamelCase,  attention_mask=lowerCamelCase,  labels=lowerCamelCase  )

								self.parent.assertEqual(result.logits.shape,  (self.batch_size, self.seq_length, self.num_labels)  )




				def     lowercase__       (					self						:   Optional[Any],  lowerCamelCase						:   str,  lowerCamelCase						:   int,  lowerCamelCase						:   str,  lowerCamelCase						:   Optional[int],  lowerCamelCase						:   Tuple,  lowerCamelCase						:   Optional[int]  ):

								'''simple docstring'''
								lowercase__       			=      self.num_choices
								lowercase__       			=      ErnieMForMultipleChoice(config=lowerCamelCase  )
								model.to(lowerCamelCase  )
								model.eval()
								lowercase__       			=      input_ids.unsqueeze(1  ).expand(-1,  self.num_choices,  -1  ).contiguous()
								lowercase__       			=      input_mask.unsqueeze(1  ).expand(-1,  self.num_choices,  -1  ).contiguous()
								lowercase__       			=      model(
								    lowerCamelCase,  attention_mask=lowerCamelCase,  labels=lowerCamelCase,  )
								self.parent.assertEqual(result.logits.shape,  (self.batch_size, self.num_choices)  )




				def     lowercase__       (					self						:   Optional[int]  ):

								'''simple docstring'''
								lowercase__       			=      self.prepare_config_and_inputs()
								(
								    (
								    lowercase__						
								)			,						(
								    lowercase__						
								)			,						(
								    lowercase__						
								)			,						(
								    lowercase__						
								)			,						(
								    lowercase__						
								)			,						(
								    lowercase__						
								)			,												
								)       			=      config_and_inputs
								lowercase__       			=      {'''input_ids''': input_ids, '''attention_mask''': input_mask}
								return config, inputs_dict



@require_torch
class 				_UpperCAmelCase (	A__		,A__		,unittest.TestCase     ):

				"""simple docstring"""




				lowercase__    							=	(
				    (
				        ErnieMModel,
				        ErnieMForMultipleChoice,
				        ErnieMForQuestionAnswering,
				        ErnieMForSequenceClassification,
				        ErnieMForTokenClassification,
				    )
				    if is_torch_available()
				    else ()
				)
				lowercase__    							=	()
				lowercase__    							=	(
				    {
				        """feature-extraction""": ErnieMModel,
				        """question-answering""": ErnieMForQuestionAnswering,
				        """text-classification""": ErnieMForSequenceClassification,
				        """token-classification""": ErnieMForTokenClassification,
				        """zero-shot""": ErnieMForSequenceClassification,
				    }
				    if is_torch_available()
				    else {}
				)
				lowercase__    							=	False




				def     lowercase__       (					self						:   List[Any],  lowerCamelCase						:   List[Any],  lowerCamelCase						:   Optional[Any],  lowerCamelCase						:   Optional[int],  lowerCamelCase						:   List[Any],  lowerCamelCase						:   Dict  ):

								'''simple docstring'''
								if pipeline_test_casse_name == "QAPipelineTests":
												return True

								return False




				def     lowercase__       (					self						:   Union[str, Any]  ):

								'''simple docstring'''
								lowercase__       			=      ErnieMModelTester(self  )
								lowercase__       			=      ConfigTester(self,  config_class=lowerCamelCase,  hidden_size=37  )




				def     lowercase__       (					self						:   str  ):

								'''simple docstring'''
								self.config_tester.run_common_tests()




				def     lowercase__       (					self						:   List[str]  ):

								'''simple docstring'''
								lowercase__       			=      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*lowerCamelCase  )




				def     lowercase__       (					self						:   Dict  ):

								'''simple docstring'''
								lowercase__       			=      self.model_tester.prepare_config_and_inputs()
								for type in ["absolute", "relative_key", "relative_key_query"]:
												lowercase__       			=      type
												self.model_tester.create_and_check_model(*lowerCamelCase  )




				def     lowercase__       (					self						:   Any  ):

								'''simple docstring'''
								lowercase__       			=      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_multiple_choice(*lowerCamelCase  )




				def     lowercase__       (					self						:   Optional[int]  ):

								'''simple docstring'''
								lowercase__       			=      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_question_answering(*lowerCamelCase  )




				def     lowercase__       (					self						:   List[Any]  ):

								'''simple docstring'''
								lowercase__       			=      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_sequence_classification(*lowerCamelCase  )




				def     lowercase__       (					self						:   Any  ):

								'''simple docstring'''
								lowercase__       			=      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_information_extraction(*lowerCamelCase  )




				def     lowercase__       (					self						:   int  ):

								'''simple docstring'''
								lowercase__       			=      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_token_classification(*lowerCamelCase  )




				@slow
				def     lowercase__       (					self						:   Dict  ):

								'''simple docstring'''
								for model_name in ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												lowercase__       			=      ErnieMModel.from_pretrained(lowerCamelCase  )
												self.assertIsNotNone(lowerCamelCase  )



@require_torch
class 				_UpperCAmelCase (	unittest.TestCase     ):

				"""simple docstring"""




				@slow
				def     lowercase__       (					self						:   Dict  ):

								'''simple docstring'''
								lowercase__       			=      ErnieMModel.from_pretrained('''susnato/ernie-m-base_pytorch'''  )
								model.eval()
								lowercase__       			=      torch.tensor([[0, 1, 2, 3, 4, 5]]  )
								lowercase__       			=      model(lowerCamelCase  )[0]

								# TODO Replace vocab size
								lowercase__       			=      768

								lowercase__       			=      torch.Size((1, 6, hidden_size)  )
								self.assertEqual(output.shape,  lowerCamelCase  )

								lowercase__       			=      torch.tensor(
								    [[[-0.0012, 0.1245, -0.0214], [-0.0742, 0.0244, -0.0771], [-0.0333, 0.1164, -0.1554]]]  )

								self.assertTrue(torch.allclose(output[:, :3, :3],  lowerCamelCase,  atol=1E-3  )  )












import importlib
import os
import platform
from pathlib import Path

from packaging import version

from .utils.logging import get_logger


A__     :      List[Any]             =			get_logger(__name__)

# Datasets
A__     :      Union[str, Any]             =			'https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets'
A__     :      Dict             =			'https://cdn-datasets.huggingface.co/datasets/datasets'
A__     :      str             =			'https://raw.githubusercontent.com/huggingface/datasets/{revision}/datasets/{path}/{name}'

# Metrics
A__     :      Optional[int]             =			'https://s3.amazonaws.com/datasets.huggingface.co/datasets/metrics'
A__     :      Optional[Any]             =			'https://cdn-datasets.huggingface.co/datasets/metric'
A__     :      Tuple             =			'https://raw.githubusercontent.com/huggingface/datasets/{revision}/metrics/{path}/{name}'

# Hub
A__     :      int             =			os.environ.get('HF_ENDPOINT', 'https://huggingface.co')
A__     :      str             =			HF_ENDPOINT + '/datasets/{repo_id}/resolve/{revision}/{path}'
A__     :      Any             =			'main'

A__     :      Tuple             =			version.parse(platform.python_version())

if PY_VERSION < version.parse('3.8'):
							import importlib_metadata
else:
							import importlib.metadata as importlib_metadata

# General environment variables accepted values for booleans
A__     :      List[str]             =			{'1', 'ON', 'YES', 'TRUE'}
A__     :      List[Any]             =			ENV_VARS_TRUE_VALUES.union({'AUTO'})


# Imports
A__     :      List[str]             =			version.parse(importlib_metadata.version('dill'))
A__     :      int             =			version.parse(importlib_metadata.version('pandas'))
A__     :      Optional[Any]             =			version.parse(importlib_metadata.version('pyarrow'))

A__     :      Union[str, Any]             =			os.environ.get('USE_TF', 'AUTO').upper()
A__     :      List[Any]             =			os.environ.get('USE_TORCH', 'AUTO').upper()
A__     :      Optional[Any]             =			os.environ.get('USE_JAX', 'AUTO').upper()

A__     :      List[Any]             =			'N/A'
A__     :      int             =			False

if USE_TORCH in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TF not in ENV_VARS_TRUE_VALUES:
							A__     :      List[str]             =			importlib.util.find_spec('torch') is not None
							if TORCH_AVAILABLE:
														try:
																					A__     :      Union[str, Any]             =			version.parse(importlib_metadata.version('torch'))
																					logger.info(F"PyTorch version {TORCH_VERSION} available.")
														except importlib_metadata.PackageNotFoundError:
																					pass
else:
							logger.info('Disabling PyTorch because USE_TF is set')

A__     :      int             =			'N/A'
A__     :      Optional[int]             =			False

if USE_TF in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TORCH not in ENV_VARS_TRUE_VALUES:
							A__     :      Optional[int]             =			importlib.util.find_spec('tensorflow') is not None
							if TF_AVAILABLE:
														# For the metadata, we have to look for both tensorflow and tensorflow-cpu
														for package in [
														    "tensorflow",
														    "tensorflow-cpu",
														    "tensorflow-gpu",
														    "tf-nightly",
														    "tf-nightly-cpu",
														    "tf-nightly-gpu",
														    "intel-tensorflow",
														    "tensorflow-rocm",
														    "tensorflow-macos",
														]:
																					try:
																												A__     :      Any             =			version.parse(importlib_metadata.version(package))
																					except importlib_metadata.PackageNotFoundError:
																												continue
																					else:
																												break
														else:
																					A__     :      Tuple             =			False
							if TF_AVAILABLE:
														if TF_VERSION.major < 2:
																					logger.info(F"TensorFlow found but with version {TF_VERSION}. `datasets` requires version 2 minimum.")
																					A__     :      Any             =			False
														else:
																					logger.info(F"TensorFlow version {TF_VERSION} available.")
else:
							logger.info('Disabling Tensorflow because USE_TORCH is set')


A__     :      Union[str, Any]             =			'N/A'
A__     :      str             =			False

if USE_JAX in ENV_VARS_TRUE_AND_AUTO_VALUES:
							A__     :      Union[str, Any]             =			importlib.util.find_spec('jax') is not None and importlib.util.find_spec('jaxlib') is not None
							if JAX_AVAILABLE:
														try:
																					A__     :      str             =			version.parse(importlib_metadata.version('jax'))
																					logger.info(F"JAX version {JAX_VERSION} available.")
														except importlib_metadata.PackageNotFoundError:
																					pass
else:
							logger.info('Disabling JAX because USE_JAX is set to False')


A__     :      str             =			os.environ.get('USE_BEAM', 'AUTO').upper()
A__     :      List[Any]             =			'N/A'
A__     :      Tuple             =			False
if USE_BEAM in ENV_VARS_TRUE_AND_AUTO_VALUES:
							try:
														A__     :      str             =			version.parse(importlib_metadata.version('apache_beam'))
														A__     :      Any             =			True
														logger.info(F"Apache Beam version {BEAM_VERSION} available.")
							except importlib_metadata.PackageNotFoundError:
														pass
else:
							logger.info('Disabling Apache Beam because USE_BEAM is set to False')


# Optional tools for data loading
A__     :      Optional[int]             =			importlib.util.find_spec('sqlalchemy') is not None

# Optional tools for feature decoding
A__     :      List[str]             =			importlib.util.find_spec('PIL') is not None
A__     :      Optional[Any]             =			importlib.util.find_spec('soundfile') is not None and version.parse(
    importlib.import_module('soundfile').__libsndfile_version__
) >= version.parse('1.0.31')
A__     :      Tuple             =			importlib.util.find_spec('soundfile') is not None and version.parse(
    importlib.import_module('soundfile').__libsndfile_version__
) >= version.parse('1.1.0')

# Optional compression tools
A__     :      List[Any]             =			importlib.util.find_spec('rarfile') is not None
A__     :      Union[str, Any]             =			importlib.util.find_spec('zstandard') is not None
A__     :      Any             =			importlib.util.find_spec('lz4') is not None
A__     :      int             =			importlib.util.find_spec('py7zr') is not None


# Cache location
A__     :      Tuple             =			'~/.cache'
A__     :      Dict             =			os.getenv('XDG_CACHE_HOME', DEFAULT_XDG_CACHE_HOME)
A__     :      Dict             =			os.path.join(XDG_CACHE_HOME, 'huggingface')
A__     :      Tuple             =			os.path.expanduser(os.getenv('HF_HOME', DEFAULT_HF_CACHE_HOME))

A__     :      str             =			os.path.join(HF_CACHE_HOME, 'datasets')
A__     :      List[Any]             =			Path(os.getenv('HF_DATASETS_CACHE', DEFAULT_HF_DATASETS_CACHE))

A__     :      Optional[int]             =			os.path.join(HF_CACHE_HOME, 'metrics')
A__     :      Optional[int]             =			Path(os.getenv('HF_METRICS_CACHE', DEFAULT_HF_METRICS_CACHE))

A__     :      List[str]             =			os.path.join(HF_CACHE_HOME, 'modules')
A__     :      List[Any]             =			Path(os.getenv('HF_MODULES_CACHE', DEFAULT_HF_MODULES_CACHE))

A__     :      Optional[int]             =			'downloads'
A__     :      Optional[int]             =			os.path.join(HF_DATASETS_CACHE, DOWNLOADED_DATASETS_DIR)
A__     :      int             =			Path(os.getenv('HF_DATASETS_DOWNLOADED_DATASETS_PATH', DEFAULT_DOWNLOADED_DATASETS_PATH))

A__     :      Optional[int]             =			'extracted'
A__     :      Union[str, Any]             =			os.path.join(DEFAULT_DOWNLOADED_DATASETS_PATH, EXTRACTED_DATASETS_DIR)
A__     :      Tuple             =			Path(os.getenv('HF_DATASETS_EXTRACTED_DATASETS_PATH', DEFAULT_EXTRACTED_DATASETS_PATH))

# Download count for the website
A__     :      List[str]             =			(
    os.environ.get('HF_UPDATE_DOWNLOAD_COUNTS', 'AUTO').upper() in ENV_VARS_TRUE_AND_AUTO_VALUES
)

# Batch size constants. For more info, see:
# https://github.com/apache/arrow/blob/master/docs/source/cpp/arrays.rst#size-limitations-and-recommendations)
A__     :      List[Any]             =			10_00

# Size of the preloaded record batch in `Dataset.__iter__`
A__     :      int             =			10

# Pickling tables works only for small tables (<4GiB)
# For big tables, we write them on disk instead
A__     :      str             =			4 << 30

# Max shard size in bytes (e.g. to shard parquet datasets in push_to_hub or download_and_prepare)
A__     :      Dict             =			'500MB'

# Parquet configuration
A__     :      Optional[Any]             =			1_00
A__     :      List[Any]             =			1_00
A__     :      Tuple             =			1_00

# Offline mode
A__     :      Optional[Any]             =			os.environ.get('HF_DATASETS_OFFLINE', 'AUTO').upper() in ENV_VARS_TRUE_VALUES

# In-memory
A__     :      Union[str, Any]             =			0  # Disabled
A__     :      Tuple             =			float(os.environ.get('HF_DATASETS_IN_MEMORY_MAX_SIZE', DEFAULT_IN_MEMORY_MAX_SIZE))

# File names
A__     :      str             =			'dataset.arrow'
A__     :      List[str]             =			'indices.arrow'
A__     :      List[Any]             =			'state.json'
A__     :      int             =			'dataset_info.json'
A__     :      Tuple             =			'dataset_infos.json'
A__     :      int             =			'LICENSE'
A__     :      Union[str, Any]             =			'metric_info.json'
A__     :      List[Any]             =			'dataset_dict.json'

A__     :      Optional[Any]             =			'datasets_modules'

A__     :      Dict             =			2_55

# Streaming
A__     :      Optional[Any]             =			20
A__     :      str             =			5

# Datasets without script
A__     :      Optional[int]             =			2_00
A__     :      Optional[Any]             =			10
A__     :      Any             =			2_00

# Progress bars
A__     :      Any             =			0.05  # 20 progress updates per sec




