





'''simple docstring'''






from typing import List, Optional, TypeVar

from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets
from .splits import NamedSplit
from .utils import logging
from .utils.py_utils import Literal


__A	:	Optional[Any]     =     logging.get_logger(__name__)


__A	:	int     =     TypeVar('DatasetType', Dataset, IterableDataset)





def      UpperCAmelCase      (		lowerCamelCase_  :List[DatasetType]				,      lowerCamelCase_  :Optional[List[float]] = None				,      lowerCamelCase_  :Optional[int] = None				,      lowerCamelCase_  :Optional[DatasetInfo] = None				,      lowerCamelCase_  :Optional[NamedSplit] = None				,      lowerCamelCase_  :Literal["first_exhausted", "all_exhausted"] = "first_exhausted"				,      ):







		'''simple docstring'''
		from .arrow_dataset import Dataset
		from .iterable_dataset import IterableDataset

		if not datasets:
				raise ValueError("""Unable to interleave an empty list of datasets.""" )
		for i, dataset in enumerate(snake_case__ ):
				if not isinstance(snake_case__				,      (Dataset, IterableDataset) ):
						if isinstance(snake_case__				,      (DatasetDict, IterableDatasetDict) ):
								if not dataset:
										raise ValueError(
										    F'''Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} '''
										    """is an empty dataset dictionary.""" )
								raise ValueError(
								    F'''Dataset at position {i} has at least one split: {list(snake_case__ )}\n'''
								    F'''Please pick one to interleave with the other datasets, for example: dataset[\'{next(iter(snake_case__ ) )}\']''' )
						raise ValueError(
						    F'''Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(snake_case__ ).__name__}.''' )
				if i == 0:
						snake_case_       ,      snake_case_		:							Optional[int]			       =						(
						    (Dataset, IterableDataset) if isinstance(snake_case__				,      snake_case__ ) else (IterableDataset, Dataset)
						)
				elif not isinstance(snake_case__				,      snake_case__ ):
						raise ValueError(
						    F'''Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects.''' )
		if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
				raise ValueError(F'''{stopping_strategy} is not supported. Please enter a valid stopping_strategy.''' )
		if dataset_type is Dataset:
				return _interleave_map_style_datasets(
				    snake_case__				,      snake_case__				,      snake_case__				,      info=snake_case__				,      split=snake_case__				,      stopping_strategy=snake_case__ )
		else:
				return _interleave_iterable_datasets(
				    snake_case__				,      snake_case__				,      snake_case__				,      info=snake_case__				,      split=snake_case__				,      stopping_strategy=snake_case__ )






def      UpperCAmelCase      (		lowerCamelCase_  :List[DatasetType]				,      lowerCamelCase_  :Optional[DatasetInfo] = None				,      lowerCamelCase_  :Optional[NamedSplit] = None				,      lowerCamelCase_  :int = 0				,      ):







		'''simple docstring'''

		if not dsets:
				raise ValueError("""Unable to concatenate an empty list of datasets.""" )
		for i, dataset in enumerate(snake_case__ ):
				if not isinstance(snake_case__				,      (Dataset, IterableDataset) ):
						if isinstance(snake_case__				,      (DatasetDict, IterableDatasetDict) ):
								if not dataset:
										raise ValueError(
										    F'''Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} '''
										    """is an empty dataset dictionary.""" )
								raise ValueError(
								    F'''Dataset at position {i} has at least one split: {list(snake_case__ )}\n'''
								    F'''Please pick one to interleave with the other datasets, for example: dataset[\'{next(iter(snake_case__ ) )}\']''' )
						raise ValueError(
						    F'''Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(snake_case__ ).__name__}.''' )
				if i == 0:
						snake_case_       ,      snake_case_		:							Dict			       =						(
						    (Dataset, IterableDataset) if isinstance(snake_case__				,      snake_case__ ) else (IterableDataset, Dataset)
						)
				elif not isinstance(snake_case__				,      snake_case__ ):
						raise ValueError(
						    F'''Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects.''' )
		if dataset_type is Dataset:
				return _concatenate_map_style_datasets(snake_case__				,      info=snake_case__				,      split=snake_case__				,      axis=snake_case__ )
		else:
				return _concatenate_iterable_datasets(snake_case__				,      info=snake_case__				,      split=snake_case__				,      axis=snake_case__ )



'''simple docstring'''






import tempfile
import unittest

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
    is_torch_available,
    require_optimum,
    require_torch,
    slow,
)


if is_torch_available():
							import torch
@require_torch
@require_optimum
@slow
class 						__UpperCamelCase	(						unittest.TestCase							):





							def 		a__   (  self		:List[str]			):
									snake_case_		:							Union[str, Any]			       =						"""hf-internal-testing/tiny-random-t5"""
									snake_case_		:							str			       =						AutoTokenizer.from_pretrained(_UpperCamelCase			)
									snake_case_		:							str			       =						AutoModelForSeqaSeqLM.from_pretrained(_UpperCamelCase			)

									snake_case_		:							List[str]			       =						tokenizer("""This is me"""  ,return_tensors="""pt"""			)

									snake_case_		:							List[str]			       =						model.to_bettertransformer()

									self.assertTrue(any("""BetterTransformer""" in mod.__class__.__name__ for _, mod in model.named_modules()			)			)

									snake_case_		:							List[str]			       =						model.generate(**_UpperCamelCase			)

									snake_case_		:							Dict			       =						model.reverse_bettertransformer()

									self.assertFalse(any("""BetterTransformer""" in mod.__class__.__name__ for _, mod in model.named_modules()			)			)

									with tempfile.TemporaryDirectory() as tmpdirname:
											model.save_pretrained(_UpperCamelCase			)

											snake_case_		:							List[Any]			       =						AutoModelForSeqaSeqLM.from_pretrained(_UpperCamelCase			)

											self.assertFalse(
											    any("""BetterTransformer""" in mod.__class__.__name__ for _, mod in model_reloaded.named_modules()			)			)

											snake_case_		:							Optional[Any]			       =						model_reloaded.generate(**_UpperCamelCase			)
											self.assertTrue(torch.allclose(_UpperCamelCase  ,_UpperCamelCase			)			)





							def 		a__   (  self		:Union[str, Any]			):
									snake_case_		:							Optional[Any]			       =						"""hf-internal-testing/tiny-random-t5"""
									snake_case_		:							str			       =						AutoModelForSeqaSeqLM.from_pretrained(_UpperCamelCase			)

									snake_case_		:							List[Any]			       =						model.to_bettertransformer()

									with tempfile.TemporaryDirectory() as tmpdirname:
											with self.assertRaises(_UpperCamelCase			):
													model.save_pretrained(_UpperCamelCase			)

											snake_case_		:							Any			       =						model.reverse_bettertransformer()
											model.save_pretrained(_UpperCamelCase			)