
import importlib
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Union

import torch

from ..utils import BaseOutput


_UpperCAmelCase      :				Any          = "scheduler_config.json"


class        lowercase      (	_SCREAMING_SNAKE_CASE     ):
		__lowercase : str			  =       1
		__lowercase : Dict			  =       2
		__lowercase : Dict			  =       3
		__lowercase : Union[str, Any]			  =       4
		__lowercase : str			  =       5
		__lowercase : Optional[int]			  =       6
		__lowercase : Dict			  =       7
		__lowercase : Dict			  =       8
		__lowercase : Optional[Any]			  =       9
		__lowercase : List[Any]			  =       10
		__lowercase : str			  =       11
		__lowercase : Tuple			  =       12
		__lowercase : List[Any]			  =       13
		__lowercase : List[Any]			  =       14






@dataclass
class        lowercase      (	_SCREAMING_SNAKE_CASE     ):
		__lowercase : torch.FloatTensor






class        lowercase      :
		__lowercase : Optional[int]			  =       SCHEDULER_CONFIG_NAME
		__lowercase : Optional[int]			  =       []
		__lowercase : str			  =       True



		@classmethod
		def __UpperCamelCase				(				cls       ,   A_ = None       ,   A_ = None       ,   A_=False       ,   **A_       ,   )						->      Dict:
				"""simple docstring"""
				UpperCamelCase			,						UpperCamelCase			,						UpperCamelCase						=  cls.load_config(
				    pretrained_model_name_or_path=A_       ,   subfolder=A_       ,   return_unused_kwargs=A_       ,   return_commit_hash=A_       ,   **A_       ,   )
				return cls.from_config(A_       ,   return_unused_kwargs=A_       ,   **A_ )



		def __UpperCamelCase				(				self       ,   A_       ,   A_ = False       ,   **A_ )						->      Tuple:
				"""simple docstring"""
				self.save_config(save_directory=A_       ,   push_to_hub=A_       ,   **A_ )



		@property
		def __UpperCamelCase				(				self )						->      List[Any]:
				"""simple docstring"""
				return self._get_compatibles()



		@classmethod
		def __UpperCamelCase				(				cls )						->      int:
				"""simple docstring"""
				UpperCamelCase						=  list(set([cls.__name__] + cls._compatibles ) )
				UpperCamelCase						=  importlib.import_module(__name__.split('.' )[0] )
				UpperCamelCase						=  [
				    getattr(A_       ,   A_ ) for c in compatible_classes_str if hasattr(A_       ,   A_ )
				]
				return compatible_classes


import pytest

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


@pytest.mark.parametrize('dataset_size'					, [None, 400 * 2**20, 600 * 2**20]				)
@pytest.mark.parametrize('input_in_memory_max_size'					, ['default', 0, 100 * 2**20, 900 * 2**20]				)
def    A (      lowercase					, lowercase					, lowercase				)     ->						Union[str, Any]:
		'''simple docstring'''

		if input_in_memory_max_size != "default":
				monkeypatch.setattr(datasets.config					, 'IN_MEMORY_MAX_SIZE'					, lowercase				)
		UpperCamelCase						=  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:
				UpperCamelCase						=  dataset_size < in_memory_max_size
		else:
				UpperCamelCase						=  False
		UpperCamelCase						=  is_small_dataset(lowercase				)
		assert result == expected
