


from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


SCREAMING_SNAKE_CASE	:Dict  		=     logging.get_logger(__name__)

SCREAMING_SNAKE_CASE	:Union[str, Any]  		=     {
    '''apple/mobilevitv2-1.0''': '''https://huggingface.co/apple/mobilevitv2-1.0/resolve/main/config.json''',
}


class     __lowerCAmelCase     ( a					):






  """simple docstring"""





  _SCREAMING_SNAKE_CASE		    =       "mobilevitv2"



  def __init__(     self       :       int    ,				_lowerCAmelCase       :       Tuple=3    ,				_lowerCAmelCase       :       str=2_5_6    ,				_lowerCAmelCase       :       List[Any]=2    ,				_lowerCAmelCase       :       Optional[Any]=2.0    ,				_lowerCAmelCase       :       Optional[int]="swish"    ,				_lowerCAmelCase       :       Optional[Any]=3    ,				_lowerCAmelCase       :       int=3_2    ,				_lowerCAmelCase       :       Optional[int]=0.1    ,				_lowerCAmelCase       :       str=0.02    ,				_lowerCAmelCase       :       Union[str, Any]=1e-5    ,				_lowerCAmelCase       :       Optional[Any]=5_1_2    ,				_lowerCAmelCase       :       Union[str, Any]=[6, 1_2, 1_8]    ,				_lowerCAmelCase       :       List[str]=0.1    ,				_lowerCAmelCase       :       Optional[int]=2_5_5    ,				_lowerCAmelCase       :       str=[2, 4, 3]    ,				_lowerCAmelCase       :       List[Any]=[1_2_8, 1_9_2, 2_5_6]    ,				_lowerCAmelCase       :       Optional[int]=1.0    ,				_lowerCAmelCase       :       Any=2    ,				_lowerCAmelCase       :       List[str]=0.0    ,				_lowerCAmelCase       :       Dict=0.0    ,				**_lowerCAmelCase       :       Union[str, Any]    ,				)   -> Optional[int]:




     """simple docstring"""
     super().__init__(**UpperCAmelCase_  )

     snake_case_					    =					num_channels
     snake_case_					    =					image_size
     snake_case_					    =					patch_size
     snake_case_					    =					expand_ratio
     snake_case_					    =					hidden_act
     snake_case_					    =					conv_kernel_size
     snake_case_					    =					output_stride
     snake_case_					    =					initializer_range
     snake_case_					    =					layer_norm_eps
     snake_case_					    =					n_attn_blocks
     snake_case_					    =					base_attn_unit_dims
     snake_case_					    =					width_multiplier
     snake_case_					    =					ffn_multiplier
     snake_case_					    =					ffn_dropout
     snake_case_					    =					attn_dropout
     snake_case_					    =					classifier_dropout_prob

     # decode head attributes for semantic segmentation
     snake_case_					    =					aspp_out_channels
     snake_case_					    =					atrous_rates
     snake_case_					    =					aspp_dropout_prob
     snake_case_					    =					semantic_loss_ignore_index






class     __lowerCAmelCase     ( a					):






  """simple docstring"""





  _SCREAMING_SNAKE_CASE		    =       version.parse('1.11'					)



  @property
  def 					lowerCAmelCase__  (     self       :       List[str]  )   -> List[Any]:




     """simple docstring"""
     return OrderedDict([("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"})]  )



  @property
  def 					lowerCAmelCase__  (     self       :       Optional[Any]  )   -> Tuple:




     """simple docstring"""
     if self.task == "image-classification":
        return OrderedDict([("logits", {0: "batch"})]  )
     else:
        return OrderedDict([("last_hidden_state", {0: "batch"}), ("pooler_output", {0: "batch"})]  )



  @property
  def 					lowerCAmelCase__  (     self       :       Tuple  )   -> Optional[Any]:




     """simple docstring"""
     return 1e-4








import csv
import os

import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.csv import CsvDatasetReader, CsvDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases



def 			_lowerCAmelCase						(							lowerCAmelCase_							:Optional[Any]		,						lowerCAmelCase_							:List[str]							)->Dict:
   '''simple docstring'''






   assert isinstance(lowerCAmelCase_		,						lowerCAmelCase_							)
   assert dataset.num_rows == 4
   assert dataset.num_columns == 3
   assert dataset.column_names == ["col_1", "col_2", "col_3"]
   for feature, expected_dtype in expected_features.items():
      assert dataset.features[feature].dtype == expected_dtype



@pytest.mark.parametrize("keep_in_memory"		,						[False, True]							)
def 			_lowerCAmelCase						(							lowerCAmelCase_							:Dict		,						lowerCAmelCase_							:List[Any]		,						lowerCAmelCase_							:List[str]							)->int:
   '''simple docstring'''






   snake_case_					    =					tmp_path / "cache"
   snake_case_					    =					{"col_1": "int64", "col_2": "int64", "col_3": "float64"}
   with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
      snake_case_					    =					CsvDatasetReader(lowerCAmelCase_		,						cache_dir=lowerCAmelCase_		,						keep_in_memory=lowerCAmelCase_							).read()
   _check_csv_dataset(lowerCAmelCase_		,						lowerCAmelCase_							)



@pytest.mark.parametrize(
    "features"		,						[
        None,
        {"col_1": "string", "col_2": "int64", "col_3": "float64"},
        {"col_1": "string", "col_2": "string", "col_3": "string"},
        {"col_1": "int32", "col_2": "int32", "col_3": "int32"},
        {"col_1": "float32", "col_2": "float32", "col_3": "float32"},
    ]		,						)
def 			_lowerCAmelCase						(							lowerCAmelCase_							:List[str]		,						lowerCAmelCase_							:Optional[Any]		,						lowerCAmelCase_							:Optional[Any]							)->Optional[int]:
   '''simple docstring'''






   snake_case_					    =					tmp_path / "cache"
   # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
   snake_case_					    =					{"col_1": "int64", "col_2": "int64", "col_3": "float64"}
   snake_case_					    =					features.copy() if features else default_expected_features
   snake_case_					    =					(
       Features({feature: Value(lowerCAmelCase_							) for feature, dtype in features.items()}							) if features is not None else None
   )
   snake_case_					    =					CsvDatasetReader(lowerCAmelCase_		,						features=lowerCAmelCase_		,						cache_dir=lowerCAmelCase_							).read()
   _check_csv_dataset(lowerCAmelCase_		,						lowerCAmelCase_							)



@pytest.mark.parametrize("split"		,						[None, NamedSplit("train"							), "train", "test"]							)
def 			_lowerCAmelCase						(							lowerCAmelCase_							:Optional[int]		,						lowerCAmelCase_							:Tuple		,						lowerCAmelCase_							:List[Any]							)->Optional[int]:
   '''simple docstring'''






   snake_case_					    =					tmp_path / "cache"
   snake_case_					    =					{"col_1": "int64", "col_2": "int64", "col_3": "float64"}
   snake_case_					    =					CsvDatasetReader(lowerCAmelCase_		,						cache_dir=lowerCAmelCase_		,						split=lowerCAmelCase_							).read()
   _check_csv_dataset(lowerCAmelCase_		,						lowerCAmelCase_							)
   assert dataset.split == split if split else "train"



@pytest.mark.parametrize("path_type"		,						[str, list]							)
def 			_lowerCAmelCase						(							lowerCAmelCase_							:Any		,						lowerCAmelCase_							:Dict		,						lowerCAmelCase_							:List[Any]							)->List[Any]:
   '''simple docstring'''






   if issubclass(lowerCAmelCase_		,						lowerCAmelCase_							):
      snake_case_					    =					csv_path
   elif issubclass(lowerCAmelCase_		,						lowerCAmelCase_							):
      snake_case_					    =					[csv_path]
   snake_case_					    =					tmp_path / "cache"
   snake_case_					    =					{"col_1": "int64", "col_2": "int64", "col_3": "float64"}
   snake_case_					    =					CsvDatasetReader(lowerCAmelCase_		,						cache_dir=lowerCAmelCase_							).read()
   _check_csv_dataset(lowerCAmelCase_		,						lowerCAmelCase_							)



def 			_lowerCAmelCase						(							lowerCAmelCase_							:List[str]		,						lowerCAmelCase_							:Dict		,						lowerCAmelCase_							:List[str]=("train",)							)->Optional[Any]:
   '''simple docstring'''






   assert isinstance(lowerCAmelCase_		,						lowerCAmelCase_							)
   for split in splits:
      snake_case_					    =					dataset_dict[split]
      assert dataset.num_rows == 4
      assert dataset.num_columns == 3
      assert dataset.column_names == ["col_1", "col_2", "col_3"]
      for feature, expected_dtype in expected_features.items():
         assert dataset.features[feature].dtype == expected_dtype



@pytest.mark.parametrize("keep_in_memory"		,						[False, True]							)
def 			_lowerCAmelCase						(							lowerCAmelCase_							:Union[str, Any]		,						lowerCAmelCase_							:Dict		,						lowerCAmelCase_							:List[Any]							)->Any:
   '''simple docstring'''






   snake_case_					    =					tmp_path / "cache"
   snake_case_					    =					{"col_1": "int64", "col_2": "int64", "col_3": "float64"}
   with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
      snake_case_					    =					CsvDatasetReader({"train": csv_path}		,						cache_dir=lowerCAmelCase_		,						keep_in_memory=lowerCAmelCase_							).read()
   _check_csv_datasetdict(lowerCAmelCase_		,						lowerCAmelCase_							)



@pytest.mark.parametrize(
    "features"		,						[
        None,
        {"col_1": "string", "col_2": "int64", "col_3": "float64"},
        {"col_1": "string", "col_2": "string", "col_3": "string"},
        {"col_1": "int32", "col_2": "int32", "col_3": "int32"},
        {"col_1": "float32", "col_2": "float32", "col_3": "float32"},
    ]		,						)
def 			_lowerCAmelCase						(							lowerCAmelCase_							:List[Any]		,						lowerCAmelCase_							:Optional[Any]		,						lowerCAmelCase_							:Any							)->str:
   '''simple docstring'''






   snake_case_					    =					tmp_path / "cache"
   # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
   snake_case_					    =					{"col_1": "int64", "col_2": "int64", "col_3": "float64"}
   snake_case_					    =					features.copy() if features else default_expected_features
   snake_case_					    =					(
       Features({feature: Value(lowerCAmelCase_							) for feature, dtype in features.items()}							) if features is not None else None
   )
   snake_case_					    =					CsvDatasetReader({"train": csv_path}		,						features=lowerCAmelCase_		,						cache_dir=lowerCAmelCase_							).read()
   _check_csv_datasetdict(lowerCAmelCase_		,						lowerCAmelCase_							)



@pytest.mark.parametrize("split"		,						[None, NamedSplit("train"							), "train", "test"]							)
def 			_lowerCAmelCase						(							lowerCAmelCase_							:str		,						lowerCAmelCase_							:Tuple		,						lowerCAmelCase_							:Any							)->str:
   '''simple docstring'''






   if split:
      snake_case_					    =					{split: csv_path}
   else:
      snake_case_					    =					{"train": csv_path, "test": csv_path}
   snake_case_					    =					tmp_path / "cache"
   snake_case_					    =					{"col_1": "int64", "col_2": "int64", "col_3": "float64"}
   snake_case_					    =					CsvDatasetReader(lowerCAmelCase_		,						cache_dir=lowerCAmelCase_							).read()
   _check_csv_datasetdict(lowerCAmelCase_		,						lowerCAmelCase_		,						splits=list(path.keys()							)							)
   assert all(dataset[split].split == split for split in path.keys()							)



def 			_lowerCAmelCase						(							lowerCAmelCase_							:Tuple							)->str:
   '''simple docstring'''






   with open(lowerCAmelCase_		,						encoding="utf-8"							) as csvfile:
      yield from csv.reader(lowerCAmelCase_							)



def 			_lowerCAmelCase						(							lowerCAmelCase_							:Tuple		,						lowerCAmelCase_							:Dict							)->Any:
   '''simple docstring'''






   snake_case_					    =					tmp_path / "cache"
   snake_case_					    =					os.path.join(lowerCAmelCase_		,						"tmp.csv"							)
   snake_case_					    =					CsvDatasetReader({"train": csv_path}		,						cache_dir=lowerCAmelCase_							).read()
   CsvDatasetWriter(dataset["train"]		,						lowerCAmelCase_		,						num_proc=1							).write()

   snake_case_					    =					iter_csv_file(lowerCAmelCase_							)
   snake_case_					    =					iter_csv_file(lowerCAmelCase_							)

   for rowa, rowa in zip(lowerCAmelCase_		,						lowerCAmelCase_							):
      assert rowa == rowa



def 			_lowerCAmelCase						(							lowerCAmelCase_							:str		,						lowerCAmelCase_							:Any							)->Union[str, Any]:
   '''simple docstring'''






   snake_case_					    =					tmp_path / "cache"
   snake_case_					    =					os.path.join(lowerCAmelCase_		,						"tmp.csv"							)
   snake_case_					    =					CsvDatasetReader({"train": csv_path}		,						cache_dir=lowerCAmelCase_							).read()
   CsvDatasetWriter(dataset["train"]		,						lowerCAmelCase_		,						num_proc=2							).write()

   snake_case_					    =					iter_csv_file(lowerCAmelCase_							)
   snake_case_					    =					iter_csv_file(lowerCAmelCase_							)

   for rowa, rowa in zip(lowerCAmelCase_		,						lowerCAmelCase_							):
      assert rowa == rowa



def 			_lowerCAmelCase						(							lowerCAmelCase_							:Any		,						lowerCAmelCase_							:Optional[int]							)->int:
   '''simple docstring'''






   snake_case_					    =					tmp_path / "cache"
   snake_case_					    =					os.path.join(lowerCAmelCase_		,						"tmp.csv"							)
   snake_case_					    =					CsvDatasetReader({"train": csv_path}		,						cache_dir=lowerCAmelCase_							).read()
   with pytest.raises(lowerCAmelCase_							):
      CsvDatasetWriter(dataset["train"]		,						lowerCAmelCase_		,						num_proc=0							)




