





import os
import textwrap

import pyarrow as pa
import pytest

from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv

from ..utils import require_pil







@pytest.fixture
def        __lowerCamelCase (							__a       :Optional[int]   )		->    Union[str, Any]:






		"""simple docstring"""

		A__  				=       tmp_path / """file.csv"""
		A__  				=       textwrap.dedent(
		    """\
        header1,header2
        1,2
        10,20
        """   )
		with open(__a   ,					"""w"""   ) as f:
				f.write(__a   )
		return str(__a   )







@pytest.fixture
def        __lowerCamelCase (							__a       :Optional[Any]   )		->    Dict:






		"""simple docstring"""

		A__  				=       tmp_path / """malformed_file.csv"""
		A__  				=       textwrap.dedent(
		    """\
        header1,header2
        1,2
        10,20,
        """   )
		with open(__a   ,					"""w"""   ) as f:
				f.write(__a   )
		return str(__a   )







@pytest.fixture
def        __lowerCamelCase (							__a       :Optional[Any]   ,					__a       :Tuple   )		->    Optional[Any]:






		"""simple docstring"""

		A__  				=       tmp_path / """csv_with_image.csv"""
		A__  				=       textwrap.dedent(
		    F'\\n        image\n        {image_file}\n        '   )
		with open(__a   ,					"""w"""   ) as f:
				f.write(__a   )
		return str(__a   )







@pytest.fixture
def        __lowerCamelCase (							__a       :Tuple   )		->    int:






		"""simple docstring"""

		A__  				=       tmp_path / """csv_with_label.csv"""
		A__  				=       textwrap.dedent(
		    """\
        label
        good
        bad
        good
        """   )
		with open(__a   ,					"""w"""   ) as f:
				f.write(__a   )
		return str(__a   )







@pytest.fixture
def        __lowerCamelCase (							__a       :Dict   )		->    Dict:






		"""simple docstring"""

		A__  				=       tmp_path / """csv_with_int_list.csv"""
		A__  				=       textwrap.dedent(
		    """\
        int_list
        1 2 3
        4 5 6
        7 8 9
        """   )
		with open(__a   ,					"""w"""   ) as f:
				f.write(__a   )
		return str(__a   )







def        __lowerCamelCase (							__a       :Optional[Any]   ,					__a       :Dict   ,					__a       :Union[str, Any]   )		->    Union[str, Any]:






		"""simple docstring"""

		A__  				=       Csv()
		A__  				=       csv._generate_tables([[csv_file, malformed_csv_file]]   )
		with pytest.raises(__a   ,					match="""Error tokenizing data"""   ):
				for _ in generator:
						pass
		assert any(
		    record.levelname == """ERROR"""
		    and """Failed to read file""" in record.message
		    and os.path.basename(__a   ) in record.message
		    for record in caplog.records   )







@require_pil
def        __lowerCamelCase (							__a       :Tuple   )		->    Optional[int]:






		"""simple docstring"""

		with open(__a   ,					encoding="""utf-8"""   ) as f:
				A__  				=       f.read().splitlines()[1]
		A__  				=       Csv(encoding="""utf-8"""   ,					features=Features({"""image""": Image()}   )   )
		A__  				=       csv._generate_tables([[csv_file_with_image]]   )
		A__  				=       pa.concat_tables([table for _, table in generator]   )
		assert pa_table.schema.field("""image"""   ).type == Image()()
		A__  				=       pa_table.to_pydict()["""image"""]
		assert generated_content == [{"path": image_file, "bytes": None}]







def        __lowerCamelCase (							__a       :Tuple   )		->    Optional[int]:






		"""simple docstring"""

		with open(__a   ,					encoding="""utf-8"""   ) as f:
				A__  				=       f.read().splitlines()[1:]
		A__  				=       Csv(encoding="""utf-8"""   ,					features=Features({"""label""": ClassLabel(names=["""good""", """bad"""]   )}   )   )
		A__  				=       csv._generate_tables([[csv_file_with_label]]   )
		A__  				=       pa.concat_tables([table for _, table in generator]   )
		assert pa_table.schema.field("""label"""   ).type == ClassLabel(names=["""good""", """bad"""]   )()
		A__  				=       pa_table.to_pydict()["""label"""]
		assert generated_content == [ClassLabel(names=["""good""", """bad"""]   ).straint(__a   ) for label in labels]







def        __lowerCamelCase (							__a       :Dict   )		->    Dict:






		"""simple docstring"""

		A__  				=       Csv(encoding="""utf-8"""   ,					sep=""","""   ,					converters={"""int_list""": lambda __a   : [int(__a   ) for i in x.split()]}   )
		A__  				=       csv._generate_tables([[csv_file_with_int_list]]   )
		A__  				=       pa.concat_tables([table for _, table in generator]   )
		assert pa.types.is_list(pa_table.schema.field("""int_list"""   ).type   )
		A__  				=       pa_table.to_pydict()["""int_list"""]
		assert generated_content == [[1, 2, 3], [4, 5, 6], [7, 8, 9]]










from typing import List, Optional, Union

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


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

A   : List[str]			=				{
    '''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   A      (SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''




			__lowerCamelCase     :	Any          =   '''time_series_transformer'''
			__lowerCamelCase     :	Dict          =   {
			    '''hidden_size''': '''d_model''',
			    '''num_attention_heads''': '''encoder_attention_heads''',
			    '''num_hidden_layers''': '''encoder_layers''',
			}






			def __init__(      self       :   Union[str, Any]							,     __lowerCAmelCase       :   Optional[int] = None							,     __lowerCAmelCase       :   Optional[int] = None							,     __lowerCAmelCase       :   str = "student_t"							,     __lowerCAmelCase       :   str = "nll"							,     __lowerCAmelCase       :   int = 1							,     __lowerCAmelCase       :   List[int] = [1, 2, 3, 4, 5, 6, 7]							,     __lowerCAmelCase       :   Optional[Union[str, bool]] = "mean"							,     __lowerCAmelCase       :   int = 0							,     __lowerCAmelCase       :   int = 0							,     __lowerCAmelCase       :   int = 0							,     __lowerCAmelCase       :   int = 0							,     __lowerCAmelCase       :   Optional[List[int]] = None							,     __lowerCAmelCase       :   Optional[List[int]] = None							,     __lowerCAmelCase       :   int = 32							,     __lowerCAmelCase       :   int = 32							,     __lowerCAmelCase       :   int = 2							,     __lowerCAmelCase       :   int = 2							,     __lowerCAmelCase       :   int = 2							,     __lowerCAmelCase       :   int = 2							,     __lowerCAmelCase       :   bool = True							,     __lowerCAmelCase       :   str = "gelu"							,     __lowerCAmelCase       :   int = 64							,     __lowerCAmelCase       :   float = 0.1							,     __lowerCAmelCase       :   float = 0.1							,     __lowerCAmelCase       :   float = 0.1							,     __lowerCAmelCase       :   float = 0.1							,     __lowerCAmelCase       :   float = 0.1							,     __lowerCAmelCase       :   int = 1_00							,     __lowerCAmelCase       :   float = 0.0_2							,     __lowerCAmelCase       :   Optional[Any]=True							,     **__lowerCAmelCase       :   Union[str, Any]							,     )     ->     Optional[int]:







					"""simple docstring"""






					A__  				=       prediction_length
					A__  				=       context_length or prediction_length
					A__  				=       distribution_output
					A__  				=       loss
					A__  				=       input_size
					A__  				=       num_time_features
					A__  				=       lags_sequence
					A__  				=       scaling
					A__  				=       num_dynamic_real_features
					A__  				=       num_static_real_features
					A__  				=       num_static_categorical_features
					if cardinality and num_static_categorical_features > 0:
							if len(__lowerCAmelCase       ) != num_static_categorical_features:
									raise ValueError(
									    """The cardinality should be a list of the same length as `num_static_categorical_features`"""       )
							A__  				=       cardinality
					else:
							A__  				=       [0]
					if embedding_dimension and num_static_categorical_features > 0:
							if len(__lowerCAmelCase       ) != num_static_categorical_features:
									raise ValueError(
									    """The embedding dimension should be a list of the same length as `num_static_categorical_features`"""       )
							A__  				=       embedding_dimension
					else:
							A__  				=       [min(50							,     (cat + 1) // 2       ) for cat in self.cardinality]
					A__  				=       num_parallel_samples

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

					A__  				=       dropout
					A__  				=       attention_dropout
					A__  				=       activation_dropout
					A__  				=       encoder_layerdrop
					A__  				=       decoder_layerdrop

					A__  				=       activation_function
					A__  				=       init_std

					A__  				=       use_cache

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







			@property
			def        a_	(      self       :   List[Any]       )     ->     int:







					"""simple docstring"""






					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
					)



