

'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase :      Optional[int]					 =     {
    "configuration_roformer": ["ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoFormerConfig", "RoFormerOnnxConfig"],
    "tokenization_roformer": ["RoFormerTokenizer"],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase :      List[str]					 =     ["RoFormerTokenizerFast"]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase :      Optional[Any]					 =     [
         "ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
         "RoFormerForCausalLM",
         "RoFormerForMaskedLM",
         "RoFormerForMultipleChoice",
         "RoFormerForQuestionAnswering",
         "RoFormerForSequenceClassification",
         "RoFormerForTokenClassification",
         "RoFormerLayer",
         "RoFormerModel",
         "RoFormerPreTrainedModel",
         "load_tf_weights_in_roformer",
     ]


try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase :      str					 =     [
         "TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
         "TFRoFormerForCausalLM",
         "TFRoFormerForMaskedLM",
         "TFRoFormerForMultipleChoice",
         "TFRoFormerForQuestionAnswering",
         "TFRoFormerForSequenceClassification",
         "TFRoFormerForTokenClassification",
         "TFRoFormerLayer",
         "TFRoFormerModel",
         "TFRoFormerPreTrainedModel",
     ]


try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase :      Optional[Any]					 =     [
         "FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
         "FlaxRoFormerForMaskedLM",
         "FlaxRoFormerForMultipleChoice",
         "FlaxRoFormerForQuestionAnswering",
         "FlaxRoFormerForSequenceClassification",
         "FlaxRoFormerForTokenClassification",
         "FlaxRoFormerModel",
         "FlaxRoFormerPreTrainedModel",
     ]


if TYPE_CHECKING:
     from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
     from .tokenization_roformer import RoFormerTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_roformer_fast import RoFormerTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_roformer import (
              ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
              RoFormerForCausalLM,
              RoFormerForMaskedLM,
              RoFormerForMultipleChoice,
              RoFormerForQuestionAnswering,
              RoFormerForSequenceClassification,
              RoFormerForTokenClassification,
              RoFormerLayer,
              RoFormerModel,
              RoFormerPreTrainedModel,
              load_tf_weights_in_roformer,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_roformer import (
              TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFRoFormerForCausalLM,
              TFRoFormerForMaskedLM,
              TFRoFormerForMultipleChoice,
              TFRoFormerForQuestionAnswering,
              TFRoFormerForSequenceClassification,
              TFRoFormerForTokenClassification,
              TFRoFormerLayer,
              TFRoFormerModel,
              TFRoFormerPreTrainedModel,
          )

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_roformer import (
              FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
              FlaxRoFormerForMaskedLM,
              FlaxRoFormerForMultipleChoice,
              FlaxRoFormerForQuestionAnswering,
              FlaxRoFormerForSequenceClassification,
              FlaxRoFormerForTokenClassification,
              FlaxRoFormerModel,
              FlaxRoFormerPreTrainedModel,
          )


else:
     import sys

     lowerCamelCase :      List[str]					 =     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




'''simple docstring'''







import pytest

from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs


@pytest.mark.parametrize(
    """kwargs, expected"""    ,[
        ({"""num_shards""": 0, """max_num_jobs""": 1}, []),
        ({"""num_shards""": 10, """max_num_jobs""": 1}, [range(10  )]),
        ({"""num_shards""": 10, """max_num_jobs""": 10}, [range(_UpperCAmelCase    ,i + 1  ) for i in range(10  )]),
        ({"""num_shards""": 1, """max_num_jobs""": 10}, [range(1  )]),
        ({"""num_shards""": 10, """max_num_jobs""": 3}, [range(0    ,4  ), range(4    ,7  ), range(7    ,10  )]),
        ({"""num_shards""": 3, """max_num_jobs""": 10}, [range(0    ,1  ), range(1    ,2  ), range(2    ,3  )]),
    ]    ,)
def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	Union[str, Any]    ,_UpperCAmelCase	:	Dict  )  ->			Optional[Any]:
       _a    : Tuple     					=_distribute_shards(**_UpperCAmelCase  )
       assert out == expected


@pytest.mark.parametrize(
    """gen_kwargs, max_num_jobs, expected"""    ,[
        ({"""foo""": 0}, 10, [{"""foo""": 0}]),
        ({"""shards""": [0, 1, 2, 3]}, 1, [{"""shards""": [0, 1, 2, 3]}]),
        ({"""shards""": [0, 1, 2, 3]}, 4, [{"""shards""": [0]}, {"""shards""": [1]}, {"""shards""": [2]}, {"""shards""": [3]}]),
        ({"""shards""": [0, 1]}, 4, [{"""shards""": [0]}, {"""shards""": [1]}]),
        ({"""shards""": [0, 1, 2, 3]}, 2, [{"""shards""": [0, 1]}, {"""shards""": [2, 3]}]),
    ]    ,)
def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	Tuple    ,_UpperCAmelCase	:	List[Any]    ,_UpperCAmelCase	:	Union[str, Any]  )  ->			List[str]:
       _a    : List[str]     					=_split_gen_kwargs(_UpperCAmelCase    ,_UpperCAmelCase  )
       assert out == expected


@pytest.mark.parametrize(
    """gen_kwargs, expected"""    ,[
        ({"""foo""": 0}, 1),
        ({"""shards""": [0]}, 1),
        ({"""shards""": [0, 1, 2, 3]}, 4),
        ({"""shards""": [0, 1, 2, 3], """foo""": 0}, 4),
        ({"""shards""": [0, 1, 2, 3], """other""": (0, 1)}, 4),
        ({"""shards""": [0, 1, 2, 3], """shards2""": [0, 1]}, RuntimeError),
    ]    ,)
def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	Optional[Any]    ,_UpperCAmelCase	:	List[Any]  )  ->			Union[str, Any]:
       if expected is RuntimeError:
              with pytest.raises(_UpperCAmelCase  ):
                     _number_of_shards_in_gen_kwargs(_UpperCAmelCase  )
       else:
              _a    : Dict     					=_number_of_shards_in_gen_kwargs(_UpperCAmelCase  )
              assert out == expected


