

import pytest

import datasets.config
from datasets.utils.info_utils import is_small_dataset





@pytest.mark.parametrize('''dataset_size'''     ,				[None, 4_00 * 2**20, 6_00 * 2**20]			)
@pytest.mark.parametrize('''input_in_memory_max_size'''     ,				['''default''', 0, 1_00 * 2**20, 9_00 * 2**20]			)
def      A__					(			__A  :						Optional[Any]     ,				__A  :						Union[str, Any]     ,				__A  :						Dict			)					->Optional[Any]:
      if input_in_memory_max_size != "default":
            monkeypatch.setattr(datasets.config     ,				'''IN_MEMORY_MAX_SIZE'''     ,				snake_case__			)
      __A    =datasets.config.IN_MEMORY_MAX_SIZE
      if input_in_memory_max_size == "default":
            assert in_memory_max_size == 0
      else:
            assert in_memory_max_size == input_in_memory_max_size
      if dataset_size and in_memory_max_size:
            __A    =dataset_size < in_memory_max_size
      else:
            __A    =False
      __A    =is_small_dataset(snake_case__			)
      assert result == expected










"""simple docstring"""
from typing import Dict

from .base import GenericTensor, Pipeline



class   SCREAMING_SNAKE_CASE	(					_SCREAMING_SNAKE_CASE    ):





	"""simple docstring"""




	def  lowerCamelCase(self  ,  lowerCAmelCase_=None  ,  lowerCAmelCase_=None  ,  lowerCAmelCase_=None  ,  **lowerCAmelCase_				):
		if tokenize_kwargs is None:
			A_		:    Dict						     = {}

		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)"""				)
			A_		:    Optional[Any]						     = truncation

		A_		:    Optional[int]						     = tokenize_kwargs

		A_		:    Optional[Any]						     = {}
		if return_tensors is not None:
			A_		:    Optional[Any]						     = return_tensors

		return preprocess_params, {}, postprocess_params





	def  lowerCamelCase(self  ,  lowerCAmelCase_  ,  **lowerCAmelCase_				):
		A_		:    List[Any]						     = self.framework
		A_		:    Optional[int]						     = self.tokenizer(lowerCAmelCase_  ,  return_tensors=lowerCAmelCase_  ,  **lowerCAmelCase_				)
		return model_inputs





	def  lowerCamelCase(self  ,  lowerCAmelCase_				):
		A_		:    List[Any]						     = self.model(**lowerCAmelCase_				)
		return model_outputs





	def  lowerCamelCase(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_=False				):
		# [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  ,  *lowerCAmelCase_  ,  **lowerCAmelCase_				):
		return super().__call__(*lowerCAmelCase_  ,  **lowerCAmelCase_				)

