
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(SCREAMING_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 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    int:
       lowercase							:     Tuple       	=      _distribute_shards(**SCREAMING_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 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    List[str]:
       lowercase							:     Optional[int]       	=      _split_gen_kwargs(SCREAMING_SNAKE_CASE__					,	SCREAMING_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 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    List[Any]:
       if expected is RuntimeError:
              with pytest.raises(SCREAMING_SNAKE_CASE__				):
                     _number_of_shards_in_gen_kwargs(SCREAMING_SNAKE_CASE__				)
       else:
              lowercase							:     str       	=      _number_of_shards_in_gen_kwargs(SCREAMING_SNAKE_CASE__				)
              assert out == expected



"""simple docstring"""





from typing import List, Optional, Union

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


_a       =							logging.get_logger(__name__)

_a       =							{
    'huggingface/time-series-transformer-tourism-monthly': (
        'https://huggingface.co/huggingface/time-series-transformer-tourism-monthly/resolve/main/config.json'
    ),
    # See all TimeSeriesTransformer models at https://huggingface.co/models?filter=time_series_transformer
}




class 					_lowerCAmelCase  (	lowercase   ):







 """simple docstring"""







 __UpperCAmelCase	:					int   		=	"time_series_transformer"
 __UpperCAmelCase	:					Any   		=	{
     "hidden_size": "d_model",
     "num_attention_heads": "encoder_attention_heads",
     "num_hidden_layers": "encoder_layers",
 }


 def __init__(			self							:			int,					UpperCAmelCase__							:			Optional[int] = None,					UpperCAmelCase__							:			Optional[int] = None,					UpperCAmelCase__							:			str = "student_t",					UpperCAmelCase__							:			str = "nll",					UpperCAmelCase__							:			int = 1,					UpperCAmelCase__							:			List[int] = [1, 2, 3, 4, 5, 6, 7],					UpperCAmelCase__							:			Optional[Union[str, bool]] = "mean",					UpperCAmelCase__							:			int = 0,					UpperCAmelCase__							:			int = 0,					UpperCAmelCase__							:			int = 0,					UpperCAmelCase__							:			int = 0,					UpperCAmelCase__							:			Optional[List[int]] = None,					UpperCAmelCase__							:			Optional[List[int]] = None,					UpperCAmelCase__							:			int = 3_2,					UpperCAmelCase__							:			int = 3_2,					UpperCAmelCase__							:			int = 2,					UpperCAmelCase__							:			int = 2,					UpperCAmelCase__							:			int = 2,					UpperCAmelCase__							:			int = 2,					UpperCAmelCase__							:			bool = True,					UpperCAmelCase__							:			str = "gelu",					UpperCAmelCase__							:			int = 6_4,					UpperCAmelCase__							:			float = 0.1,					UpperCAmelCase__							:			float = 0.1,					UpperCAmelCase__							:			float = 0.1,					UpperCAmelCase__							:			float = 0.1,					UpperCAmelCase__							:			float = 0.1,					UpperCAmelCase__							:			int = 1_0_0,					UpperCAmelCase__							:			float = 0.02,					UpperCAmelCase__							:			Any=True,					**UpperCAmelCase__							:			List[str],					):
    # time series specific configuration
    __lowercase								=						prediction_length
    __lowercase								=						context_length or prediction_length
    __lowercase								=						distribution_output
    __lowercase								=						loss
    __lowercase								=						input_size
    __lowercase								=						num_time_features
    __lowercase								=						lags_sequence
    __lowercase								=						scaling
    __lowercase								=						num_dynamic_real_features
    __lowercase								=						num_static_real_features
    __lowercase								=						num_static_categorical_features
    if cardinality and num_static_categorical_features > 0:
       if len(UpperCAmelCase__	) != num_static_categorical_features:
          raise ValueError(
              "The cardinality should be a list of the same length as `num_static_categorical_features`"	)
       __lowercase								=						cardinality
    else:
       __lowercase								=						[0]
    if embedding_dimension and num_static_categorical_features > 0:
       if len(UpperCAmelCase__	) != num_static_categorical_features:
          raise ValueError(
              "The embedding dimension should be a list of the same length as `num_static_categorical_features`"	)
       __lowercase								=						embedding_dimension
    else:
       __lowercase								=						[min(5_0,					(cat + 1) // 2	) for cat in self.cardinality]
    __lowercase								=						num_parallel_samples

    # Transformer architecture configuration
    __lowercase								=						input_size * len(UpperCAmelCase__	) + self._number_of_features
    __lowercase								=						d_model
    __lowercase								=						encoder_attention_heads
    __lowercase								=						decoder_attention_heads
    __lowercase								=						encoder_ffn_dim
    __lowercase								=						decoder_ffn_dim
    __lowercase								=						encoder_layers
    __lowercase								=						decoder_layers

    __lowercase								=						dropout
    __lowercase								=						attention_dropout
    __lowercase								=						activation_dropout
    __lowercase								=						encoder_layerdrop
    __lowercase								=						decoder_layerdrop

    __lowercase								=						activation_function
    __lowercase								=						init_std

    __lowercase								=						use_cache

    super().__init__(is_encoder_decoder=UpperCAmelCase__,					**UpperCAmelCase__	)



 @property
 def 					_lowercase (			self							:			Optional[Any]	):
    return (
        sum(self.embedding_dimension	)
        + self.num_dynamic_real_features
        + self.num_time_features
        + self.num_static_real_features
        + self.input_size * 2  # the log1p(abs(loc)) and log(scale) features
    )




