





from unittest import TestCase

from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset



class    __lowerCAmelCase			(     _a	):






			def      lowerCamelCase    (self       )	->  Dict:


							'''simple docstring'''



							return [
							    {"col_1": 3, "col_2": "a"},
							    {"col_1": 2, "col_2": "b"},
							    {"col_1": 1, "col_2": "c"},
							    {"col_1": 0, "col_2": "d"},
							]






			def      lowerCamelCase    (self       )	->  Tuple:


							'''simple docstring'''



							snake_case_		:       Union[str, Any]           =    {'''col_1''': [3, 2, 1, 0], '''col_2''': ['''a''', '''b''', '''c''', '''d''']}
							return Dataset.from_dict(__magic_name__       )






			def      lowerCamelCase    (self       )	->  str:


							'''simple docstring'''



							snake_case_		:       int           =    self._create_example_records()
							snake_case_		:       List[str]           =    Dataset.from_list(__magic_name__       )
							self.assertListEqual(dset.column_names   ,				['''col_1''', '''col_2''']       )
							for i, r in enumerate(__magic_name__       ):
											self.assertDictEqual(__magic_name__   ,				example_records[i]       )






			def      lowerCamelCase    (self       )	->  List[Any]:


							'''simple docstring'''



							snake_case_		:       Dict           =    self._create_example_records()
							snake_case_		:       Tuple           =    Dataset.from_list(__magic_name__       )
							snake_case_		:       Optional[int]           =    Dataset.from_dict({k: [r[k] for r in example_records] for k in example_records[0]}       )
							self.assertEqual(dset.info   ,				dset_from_dict.info       )






			def      lowerCamelCase    (self       )	->  Any:  # checks what happens with missing columns


							'''simple docstring'''



							snake_case_		:       Dict           =    [{'''col_1''': 1}, {'''col_2''': '''x'''}]
							snake_case_		:       Any           =    Dataset.from_list(__magic_name__       )
							self.assertDictEqual(dset[0]   ,				{'''col_1''': 1}       )
							self.assertDictEqual(dset[1]   ,				{'''col_1''': None}       )  # NB: first record is used for columns






			def      lowerCamelCase    (self       )	->  Optional[int]:  # checks if the type can be inferred from the second record


							'''simple docstring'''



							snake_case_		:       int           =    [{'''col_1''': []}, {'''col_1''': [1, 2]}]
							snake_case_		:       str           =    Dataset.from_list(__magic_name__       )
							self.assertEqual(dset.info.features['''col_1''']   ,				Sequence(Value('''int64'''       )       )       )






			def      lowerCamelCase    (self       )	->  List[str]:


							'''simple docstring'''



							snake_case_		:       Union[str, Any]           =    Dataset.from_list([]       )
							self.assertEqual(len(__magic_name__       )   ,				0       )
							self.assertListEqual(dset.column_names   ,				[]       )








from typing import Dict

from .base import GenericTensor, Pipeline
class  _UpperCAmelCase   (			A__      ):

				"""simple docstring"""




				def 							lowercase__  (	self		:       Any,    lowerCamelCase		:       Tuple=None,    lowerCamelCase		:       Union[str, Any]=None,    lowerCamelCase		:       Union[str, Any]=None,    **lowerCamelCase		:       List[Any]    ):

								'''simple docstring'''
								if tokenize_kwargs is None:
												lowercase__  					=     {}

								if truncation is not None:
												if "truncation" in tokenize_kwargs:
																raise ValueError(
																    '''truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)'''    )
												lowercase__  					=     truncation

								lowercase__  					=     tokenize_kwargs

								lowercase__  					=     {}
								if return_tensors is not None:
												lowercase__  					=     return_tensors

								return preprocess_params, {}, postprocess_params




				def 							lowercase__  (	self		:       Tuple,    lowerCamelCase		:       Optional[int],    **lowerCamelCase		:       List[Any]    ):

								'''simple docstring'''
								lowercase__  					=     self.framework
								lowercase__  					=     self.tokenizer(lowerCamelCase,    return_tensors=lowerCamelCase,    **lowerCamelCase    )
								return model_inputs




				def 							lowercase__  (	self		:       Union[str, Any],    lowerCamelCase		:       Dict    ):

								'''simple docstring'''
								lowercase__  					=     self.model(**lowerCamelCase    )
								return model_outputs




				def 							lowercase__  (	self		:       Union[str, Any],    lowerCamelCase		:       Dict,    lowerCamelCase		:       Tuple=False    ):

								'''simple docstring'''
								# [0] is the first available tensor, logits or last_hidden_state.
								if return_tensors:
												return model_outputs[0]
								if self.framework == "pt":
												return model_outputs[0].tolist()
								elif self.framework == "tf":
												return model_outputs[0].numpy().tolist()




				def __call__(	self		:       List[str],    *lowerCamelCase		:       List[Any],    **lowerCamelCase		:       str    ):

								'''simple docstring'''
								return super().__call__(*lowerCamelCase,    **lowerCamelCase    )




