



"""simple docstring"""





import unittest

import numpy as np

from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
					import jax.numpy as jnp

					from transformers.models.roformer.modeling_flax_roformer import (
					    FlaxRoFormerForMaskedLM,
					    FlaxRoFormerForMultipleChoice,
					    FlaxRoFormerForQuestionAnswering,
					    FlaxRoFormerForSequenceClassification,
					    FlaxRoFormerForTokenClassification,
					    FlaxRoFormerModel,
					)




class 			lowercase__			(      unittest.TestCase    ):
							'''simple docstring'''







							def __init__(  self	,       snake_case	,       snake_case=13	,       snake_case=7	,       snake_case=True	,       snake_case=True	,       snake_case=True	,       snake_case=True	,       snake_case=99	,       snake_case=32	,       snake_case=5	,       snake_case=4	,       snake_case=37	,       snake_case="gelu"	,       snake_case=0.1	,       snake_case=0.1	,       snake_case=512	,       snake_case=16	,       snake_case=2	,       snake_case=0.02	,       snake_case=4	,       )			->							Tuple:
									_UpperCAmelCase							 =		parent
									_UpperCAmelCase							 =		batch_size
									_UpperCAmelCase							 =		seq_length
									_UpperCAmelCase							 =		is_training
									_UpperCAmelCase							 =		use_attention_mask
									_UpperCAmelCase							 =		use_token_type_ids
									_UpperCAmelCase							 =		use_labels
									_UpperCAmelCase							 =		vocab_size
									_UpperCAmelCase							 =		hidden_size
									_UpperCAmelCase							 =		num_hidden_layers
									_UpperCAmelCase							 =		num_attention_heads
									_UpperCAmelCase							 =		intermediate_size
									_UpperCAmelCase							 =		hidden_act
									_UpperCAmelCase							 =		hidden_dropout_prob
									_UpperCAmelCase							 =		attention_probs_dropout_prob
									_UpperCAmelCase							 =		max_position_embeddings
									_UpperCAmelCase							 =		type_vocab_size
									_UpperCAmelCase							 =		type_sequence_label_size
									_UpperCAmelCase							 =		initializer_range
									_UpperCAmelCase							 =		num_choices






							def        lowerCamelCase_   (  self						)			->							List[Any]:
									_UpperCAmelCase							 =		ids_tensor([self.batch_size, self.seq_length]	,       self.vocab_size						)

									_UpperCAmelCase							 =		None
									if self.use_attention_mask:
											_UpperCAmelCase							 =		random_attention_mask([self.batch_size, self.seq_length]						)

									_UpperCAmelCase							 =		None
									if self.use_token_type_ids:
											_UpperCAmelCase							 =		ids_tensor([self.batch_size, self.seq_length]	,       self.type_vocab_size						)

									_UpperCAmelCase							 =		RoFormerConfig(
									    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	,       is_decoder=snake_case	,       initializer_range=self.initializer_range	,       )

									return config, input_ids, token_type_ids, attention_mask






							def        lowerCamelCase_   (  self						)			->							int:
									_UpperCAmelCase							 =		self.prepare_config_and_inputs()
									_UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase							 =		config_and_inputs
									_UpperCAmelCase							 =		{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
									return config, inputs_dict







@require_flax
class 			lowercase__			(      A,					unittest.TestCase    ):
							'''simple docstring'''







							_UpperCAmelCase      				=					True

							_UpperCAmelCase      				=					(
							    (
							        FlaxRoFormerModel,
							        FlaxRoFormerForMaskedLM,
							        FlaxRoFormerForSequenceClassification,
							        FlaxRoFormerForTokenClassification,
							        FlaxRoFormerForMultipleChoice,
							        FlaxRoFormerForQuestionAnswering,
							    )
							    if is_flax_available()
							    else ()
							)






							def        lowerCamelCase_   (  self						)			->							Optional[Any]:
									_UpperCAmelCase							 =		FlaxRoFormerModelTester(self						)






							@slow
							def        lowerCamelCase_   (  self						)			->							Tuple:
									for model_class_name in self.all_model_classes:
											_UpperCAmelCase							 =		model_class_name.from_pretrained('junnyu/roformer_chinese_small'	,       from_pt=snake_case						)
											_UpperCAmelCase							 =		model(np.ones((1, 1)						)						)
											self.assertIsNotNone(snake_case						)







@require_flax
class 			lowercase__			(      unittest.TestCase    ):
							'''simple docstring'''







							@slow
							def        lowerCamelCase_   (  self						)			->							List[Any]:
									_UpperCAmelCase							 =		FlaxRoFormerForMaskedLM.from_pretrained('junnyu/roformer_chinese_base'						)
									_UpperCAmelCase							 =		jnp.array([[0, 1, 2, 3, 4, 5]]						)
									_UpperCAmelCase							 =		model(snake_case						)[0]

									_UpperCAmelCase							 =		50000

									_UpperCAmelCase							 =		(1, 6, vocab_size)
									self.assertEqual(output.shape	,       snake_case						)

									_UpperCAmelCase							 =		jnp.array(
									    [[[-0.1205, -1.0265, 0.2922], [-1.5134, 0.1974, 0.1519], [-5.0135, -3.9003, -0.8404]]]						)

									self.assertTrue(jnp.allclose(output[:, :3, :3]	,       snake_case	,       atol=1E-4						)						)







"""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


lowercase  							=					logging.get_logger(__name__)


lowercase  							=					TypeVar('''DatasetType''', Dataset, IterableDataset)




def 	UpperCAmelCase     (		A	:						List[DatasetType]     ,				A	:						Optional[List[float]] = None     ,				A	:						Optional[int] = None     ,				A	:						Optional[DatasetInfo] = None     ,				A	:						Optional[NamedSplit] = None     ,				A	:						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(A       ):
				if not isinstance(A     ,				(Dataset, IterableDataset)       ):
						if isinstance(A     ,				(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(A       )}\n'
								    f'Please pick one to interleave with the other datasets, for example: dataset[\'{next(iter(A       )       )}\']'       )
						raise ValueError(
						    f'Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(A       ).__name__}.'       )
				if i == 0:
						_UpperCAmelCase ,					_UpperCAmelCase							 =		(
						    (Dataset, IterableDataset) if isinstance(A     ,				A       ) else (IterableDataset, Dataset)
						)
				elif not isinstance(A     ,				A       ):
						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(
				    A     ,				A     ,				A     ,				info=A     ,				split=A     ,				stopping_strategy=A       )
		else:
				return _interleave_iterable_datasets(
				    A     ,				A     ,				A     ,				info=A     ,				split=A     ,				stopping_strategy=A       )






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

		'''simple docstring'''


		if not dsets:
				raise ValueError('Unable to concatenate an empty list of datasets.'       )
		for i, dataset in enumerate(A       ):
				if not isinstance(A     ,				(Dataset, IterableDataset)       ):
						if isinstance(A     ,				(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(A       )}\n'
								    f'Please pick one to interleave with the other datasets, for example: dataset[\'{next(iter(A       )       )}\']'       )
						raise ValueError(
						    f'Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(A       ).__name__}.'       )
				if i == 0:
						_UpperCAmelCase ,					_UpperCAmelCase							 =		(
						    (Dataset, IterableDataset) if isinstance(A     ,				A       ) else (IterableDataset, Dataset)
						)
				elif not isinstance(A     ,				A       ):
						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(A     ,				info=A     ,				split=A     ,				axis=A       )
		else:
				return _concatenate_iterable_datasets(A     ,				info=A     ,				split=A     ,				axis=A       )


