




'''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(__snake_case  ,			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 	a_				( __snake_case				:     List[Any]  ,			__snake_case				:     Any  )				->		Any:



       """simple docstring"""
       lowerCamelCase_				=_distribute_shards(**__snake_case  )
       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 	a_				( __snake_case				:     Tuple  ,			__snake_case				:     Union[str, Any]  ,			__snake_case				:     Optional[int]  )				->		List[str]:



       """simple docstring"""
       lowerCamelCase_				=_split_gen_kwargs(__snake_case  ,			__snake_case  )
       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 	a_				( __snake_case				:     Dict  ,			__snake_case				:     List[Any]  )				->		Optional[Any]:



       """simple docstring"""
       if expected is RuntimeError:
              with pytest.raises(__snake_case  ):
                     _number_of_shards_in_gen_kwargs(__snake_case  )
       else:
              lowerCamelCase_				=_number_of_shards_in_gen_kwargs(__snake_case  )
              assert out == expected











'''simple docstring'''




from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase__       : List[Any]         =       logging.get_logger(__name__)

lowercase__       : Optional[int]         =       {
    '''unc-nlp/lxmert-base-uncased''': '''https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/config.json''',
}


class 					SCREAMING_SNAKE_CASE       (a__				):
     lowerCAmelCase						     =			'''lxmert'''
     lowerCAmelCase						     =			{}


     def __init__(		self		,      _UpperCAmelCase=3_0522		,      _UpperCAmelCase=768		,      _UpperCAmelCase=12		,      _UpperCAmelCase=9500		,      _UpperCAmelCase=1600		,      _UpperCAmelCase=400		,      _UpperCAmelCase=3072		,      _UpperCAmelCase="gelu"		,      _UpperCAmelCase=0.1		,      _UpperCAmelCase=0.1		,      _UpperCAmelCase=512		,      _UpperCAmelCase=2		,      _UpperCAmelCase=0.02		,      _UpperCAmelCase=1e-1_2		,      _UpperCAmelCase=9		,      _UpperCAmelCase=5		,      _UpperCAmelCase=5		,      _UpperCAmelCase=2048		,      _UpperCAmelCase=4		,      _UpperCAmelCase=6.67		,      _UpperCAmelCase=True		,      _UpperCAmelCase=True		,      _UpperCAmelCase=True		,      _UpperCAmelCase=True		,      _UpperCAmelCase=True		,      _UpperCAmelCase=True		,      _UpperCAmelCase=True		,      **_UpperCAmelCase		,      ):

          '''simple docstring'''






          __A		:					Tuple						=			vocab_size
          __A		:					int						=			hidden_size
          __A		:					str						=			num_attention_heads
          __A		:					Tuple						=			hidden_act
          __A		:					int						=			intermediate_size
          __A		:					str						=			hidden_dropout_prob
          __A		:					Optional[int]						=			attention_probs_dropout_prob
          __A		:					Optional[Any]						=			max_position_embeddings
          __A		:					Tuple						=			type_vocab_size
          __A		:					Optional[int]						=			initializer_range
          __A		:					Any						=			layer_norm_eps
          __A		:					Optional[Any]						=			num_qa_labels
          __A		:					Optional[int]						=			num_object_labels
          __A		:					Any						=			num_attr_labels
          __A		:					Union[str, Any]						=			l_layers
          __A		:					Optional[int]						=			x_layers
          __A		:					List[Any]						=			r_layers
          __A		:					Tuple						=			visual_feat_dim
          __A		:					Tuple						=			visual_pos_dim
          __A		:					Optional[int]						=			visual_loss_normalizer
          __A		:					int						=			task_matched
          __A		:					List[Any]						=			task_mask_lm
          __A		:					Optional[Any]						=			task_obj_predict
          __A		:					str						=			task_qa
          __A		:					List[Any]						=			visual_obj_loss
          __A		:					Optional[Any]						=			visual_attr_loss
          __A		:					Union[str, Any]						=			visual_feat_loss
          __A		:					Union[str, Any]						=			{'vision': r_layers, 'cross_encoder': x_layers, 'language': l_layers}
          super().__init__(**_UpperCAmelCase)