





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


UpperCAmelCase			:			Union[str, Any]	  = logging.get_logger(__name__)

UpperCAmelCase			:			Optional[Any]	  = {
    """apple/mobilevit-small""": """https://huggingface.co/apple/mobilevit-small/resolve/main/config.json""",
    """apple/mobilevit-x-small""": """https://huggingface.co/apple/mobilevit-x-small/resolve/main/config.json""",
    """apple/mobilevit-xx-small""": """https://huggingface.co/apple/mobilevit-xx-small/resolve/main/config.json""",
    """apple/deeplabv3-mobilevit-small""": (
        """https://huggingface.co/apple/deeplabv3-mobilevit-small/resolve/main/config.json"""
    ),
    """apple/deeplabv3-mobilevit-x-small""": (
        """https://huggingface.co/apple/deeplabv3-mobilevit-x-small/resolve/main/config.json"""
    ),
    """apple/deeplabv3-mobilevit-xx-small""": (
        """https://huggingface.co/apple/deeplabv3-mobilevit-xx-small/resolve/main/config.json"""
    ),
    # See all MobileViT models at https://huggingface.co/models?filter=mobilevit
}







class        __lowerCAmelCase (					UpperCamelCase__):
      _lowercase       :      Tuple        =    """mobilevit"""







      def __init__(					self	,   lowerCAmelCase__=3	,   lowerCAmelCase__=2_5_6	,   lowerCAmelCase__=2	,   lowerCAmelCase__=[1_4_4, 1_9_2, 2_4_0]	,   lowerCAmelCase__=[1_6, 3_2, 6_4, 9_6, 1_2_8, 1_6_0, 6_4_0]	,   lowerCAmelCase__=4	,   lowerCAmelCase__=2.0	,   lowerCAmelCase__=4.0	,   lowerCAmelCase__="silu"	,   lowerCAmelCase__=3	,   lowerCAmelCase__=3_2	,   lowerCAmelCase__=0.1	,   lowerCAmelCase__=0.0	,   lowerCAmelCase__=0.1	,   lowerCAmelCase__=0.02	,   lowerCAmelCase__=1E-5	,   lowerCAmelCase__=True	,   lowerCAmelCase__=2_5_6	,   lowerCAmelCase__=[6, 1_2, 1_8]	,   lowerCAmelCase__=0.1	,   lowerCAmelCase__=2_5_5	,   **lowerCAmelCase__	,   )     ->		Dict:



        '''simple docstring'''




        super().__init__(**lowerCAmelCase__							)

        a__			:      Any            =num_channels
        a__			:      Any            =image_size
        a__			:      Dict            =patch_size
        a__			:      Tuple            =hidden_sizes
        a__			:      List[Any]            =neck_hidden_sizes
        a__			:      str            =num_attention_heads
        a__			:      Dict            =mlp_ratio
        a__			:      Tuple            =expand_ratio
        a__			:      List[Any]            =hidden_act
        a__			:      str            =conv_kernel_size
        a__			:      int            =output_stride
        a__			:      int            =hidden_dropout_prob
        a__			:      Tuple            =attention_probs_dropout_prob
        a__			:      Tuple            =classifier_dropout_prob
        a__			:      List[str]            =initializer_range
        a__			:      Optional[int]            =layer_norm_eps
        a__			:      Any            =qkv_bias

        # decode head attributes for semantic segmentation
        a__			:      Dict            =aspp_out_channels
        a__			:      Optional[int]            =atrous_rates
        a__			:      Dict            =aspp_dropout_prob
        a__			:      str            =semantic_loss_ignore_index







class        __lowerCAmelCase (					UpperCamelCase__):
      _lowercase       :      Optional[int]        =    version.parse("""1.11""")







      @property
      def      _lowercase						(					self							)     ->		Mapping[str, Mapping[int, str]]:



        '''simple docstring'''




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







      @property
      def      _lowercase						(					self							)     ->		Mapping[str, Mapping[int, str]]:



        '''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      _lowercase						(					self							)     ->		float:



        '''simple docstring'''




        return 1E-4













import os
import tempfile
from pathlib import Path
from unittest import TestCase

import pyarrow as pa
import pytest

from datasets.arrow_dataset import Dataset
from datasets.arrow_reader import ArrowReader, BaseReader, FileInstructions, ReadInstruction, make_file_instructions
from datasets.info import DatasetInfo
from datasets.splits import NamedSplit, Split, SplitDict, SplitInfo

from .utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases







class        __lowerCAmelCase (					UpperCamelCase__):







      def      _lowercase						(					self	,   lowerCAmelCase__	,   lowerCAmelCase__=False							)     ->		List[Any]:



        '''simple docstring'''




        a__      ,							a__      ,							a__			:      Optional[int]            =(
            filename_skip_take["filename"],
            filename_skip_take["skip"] if "skip" in filename_skip_take else None,
            filename_skip_take["take"] if "take" in filename_skip_take else None,
        )
        open(os.path.join(lowerCAmelCase__							)	,   "wb"							).close()
        a__			:      Optional[int]            =pa.Table.from_pydict({"filename": [Path(lowerCAmelCase__							).name] * 1_0_0}							)
        if take == -1:
          a__			:      int            =len(lowerCAmelCase__							) - skip
        if skip is not None and take is not None:
          a__			:      Optional[Any]            =pa_table.slice(lowerCAmelCase__	,   lowerCAmelCase__							)
        return pa_table







class        __lowerCAmelCase (					UpperCamelCase__):







      def      _lowercase						(					self							)     ->		str:



        '''simple docstring'''




        a__			:      List[str]            ="my_name"
        a__			:      List[Any]            =SplitInfo(name="train"	,   num_examples=1_0_0							)
        a__			:      List[str]            =SplitInfo(name="test"	,   num_examples=1_0_0							)
        a__			:      List[Any]            =[train_info, test_info]
        a__			:      Union[str, Any]            =SplitDict()
        split_dict.add(lowerCAmelCase__							)
        split_dict.add(lowerCAmelCase__							)
        a__			:      Dict            =DatasetInfo(splits=lowerCAmelCase__							)

        with tempfile.TemporaryDirectory() as tmp_dir:
          a__			:      Optional[Any]            =ReaderTest(lowerCAmelCase__	,   lowerCAmelCase__							)

          a__			:      Any            ="test[:33%]"
          a__			:      int            =Dataset(**reader.read(lowerCAmelCase__	,   lowerCAmelCase__	,   lowerCAmelCase__							)							)
          self.assertEqual(dset["filename"][0]	,   F'''{name}-test'''							)
          self.assertEqual(dset.num_rows	,   3_3							)
          self.assertEqual(dset.num_columns	,   1							)

          a__			:      Tuple            =["train", "test[:33%]"]
          a__			:      str            =[Split.TRAIN, ReadInstruction.from_spec("test[:33%]"							)]
          for instructions in [instructionsa, instructionsa]:
            a__			:      Optional[int]            =[reader.read(lowerCAmelCase__	,   lowerCAmelCase__	,   lowerCAmelCase__							) for instr in instructions]
            a__      ,							a__			:      Optional[int]            =(Dataset(**lowerCAmelCase__							) for dataset_kwargs in datasets_kwargs)
            self.assertEqual(train_dset["filename"][0]	,   F'''{name}-train'''							)
            self.assertEqual(train_dset.num_rows	,   1_0_0							)
            self.assertEqual(train_dset.num_columns	,   1							)
            self.assertIsInstance(train_dset.split	,   lowerCAmelCase__							)
            self.assertEqual(str(train_dset.split							)	,   "train"							)
            self.assertEqual(test_dset["filename"][0]	,   F'''{name}-test'''							)
            self.assertEqual(test_dset.num_rows	,   3_3							)
            self.assertEqual(test_dset.num_columns	,   1							)
            self.assertIsInstance(test_dset.split	,   lowerCAmelCase__							)
            self.assertEqual(str(test_dset.split							)	,   "test[:33%]"							)
            del train_dset, test_dset







      def      _lowercase						(					self							)     ->		Optional[Any]:



        '''simple docstring'''




        a__			:      Any            ="my_name"
        a__			:      List[Any]            =SplitInfo(name="train"	,   num_examples=1_0_0_0	,   shard_lengths=[1_0_0] * 1_0							)
        a__			:      Optional[Any]            =[train_info]
        a__			:      Any            =SplitDict()
        split_dict.add(lowerCAmelCase__							)
        a__			:      Any            =DatasetInfo(splits=lowerCAmelCase__							)

        with tempfile.TemporaryDirectory() as tmp_dir:
          a__			:      Optional[int]            =ReaderTest(lowerCAmelCase__	,   lowerCAmelCase__							)

          a__			:      Union[str, Any]            ="train[:33%]"
          a__			:      Union[str, Any]            =Dataset(**reader.read(lowerCAmelCase__	,   lowerCAmelCase__	,   lowerCAmelCase__							)							)
          self.assertEqual(dset["filename"][0]	,   F'''{name}-train-00000-of-00010'''							)
          self.assertEqual(dset["filename"][-1]	,   F'''{name}-train-00003-of-00010'''							)
          self.assertEqual(dset.num_rows	,   3_3_0							)
          self.assertEqual(dset.num_columns	,   1							)







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



        '''simple docstring'''




        a__			:      Dict            =SplitInfo(name="train"	,   num_examples=1_0_0							)
        a__			:      str            =SplitInfo(name="test"	,   num_examples=1_0_0							)
        a__			:      int            =SplitDict()
        split_dict.add(lowerCAmelCase__							)
        split_dict.add(lowerCAmelCase__							)
        a__			:      int            =DatasetInfo(splits=lowerCAmelCase__							)

        with tempfile.TemporaryDirectory() as tmp_dir:
          a__			:      Tuple            =ReaderTest(lowerCAmelCase__	,   lowerCAmelCase__							)

          a__			:      Tuple            =[
              {"filename": os.path.join(lowerCAmelCase__	,   "train"							)},
              {"filename": os.path.join(lowerCAmelCase__	,   "test"							), "skip": 1_0, "take": 1_0},
          ]
          a__			:      Optional[Any]            =Dataset(**reader.read_files(lowerCAmelCase__	,   original_instructions="train+test[10:20]"							)							)
          self.assertEqual(dset.num_rows	,   1_1_0							)
          self.assertEqual(dset.num_columns	,   1							)
          del dset
@pytest.mark.parametrize("in_memory" , [False, True]		)
def   _A (	SCREAMING_SNAKE_CASE  :     Optional[Any] , SCREAMING_SNAKE_CASE  :     Optional[Any] , SCREAMING_SNAKE_CASE  :     Optional[Any]		):





  """simple docstring"""



  a__			:      int            =arrow_file
  with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
    a__			:      str            =ArrowReader.read_table(SCREAMING_SNAKE_CASE , in_memory=SCREAMING_SNAKE_CASE		)
  assert table.shape == dataset.data.shape
  assert set(table.column_names		) == set(dataset.data.column_names		)
  assert dict(table.to_pydict()		) == dict(dataset.data.to_pydict()		)  # to_pydict returns OrderedDict
@pytest.mark.parametrize("in_memory" , [False, True]		)
def   _A (	SCREAMING_SNAKE_CASE  :     Tuple , SCREAMING_SNAKE_CASE  :     List[Any] , SCREAMING_SNAKE_CASE  :     str		):





  """simple docstring"""



  a__			:      Optional[int]            =arrow_file
  a__			:      Tuple            =ArrowReader("" , SCREAMING_SNAKE_CASE		)
  with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
    a__			:      Union[str, Any]            =reader.read_files([{"filename": filename}] , in_memory=SCREAMING_SNAKE_CASE		)
  assert dataset_kwargs.keys() == {"arrow_table", "info", "split"}
  a__			:      Dict            =dataset_kwargs["arrow_table"]
  assert table.shape == dataset.data.shape
  assert set(table.column_names		) == set(dataset.data.column_names		)
  assert dict(table.to_pydict()		) == dict(dataset.data.to_pydict()		)  # to_pydict returns OrderedDict
def   _A (	):





  """simple docstring"""



  assert ReadInstruction("train" , to=10 , unit="abs"		).to_spec() == "train[:10]"
  assert ReadInstruction("train" , from_=-80 , to=10 , unit="%"		).to_spec() == "train[-80%:10%]"

  a__			:      List[Any]            ="train+test"
  assert ReadInstruction.from_spec(SCREAMING_SNAKE_CASE		).to_spec() == spec_train_test

  a__			:      Union[str, Any]            ="train[2:10]"
  assert ReadInstruction.from_spec(SCREAMING_SNAKE_CASE		).to_spec() == spec_train_abs

  a__			:      List[Any]            ="train[15%:-20%]"
  assert ReadInstruction.from_spec(SCREAMING_SNAKE_CASE		).to_spec() == spec_train_pct

  a__			:      Dict            ="train[:10%](closest)"
  assert ReadInstruction.from_spec(SCREAMING_SNAKE_CASE		).to_spec() == "train[:10%]"

  a__			:      Tuple            ="train[:10%](pct1_dropremainder)"
  assert ReadInstruction.from_spec(SCREAMING_SNAKE_CASE		).to_spec() == spec_train_pct_rounding

  a__			:      List[Any]            ="train[:10%](pct1_dropremainder)+test[-10%:](pct1_dropremainder)"
  assert ReadInstruction.from_spec(SCREAMING_SNAKE_CASE		).to_spec() == spec_train_test_pct_rounding
def   _A (	):





  """simple docstring"""



  a__			:      Optional[Any]            ="dummy"
  a__			:      Dict            =[SplitInfo(name="train" , num_examples=100		)]
  a__			:      Optional[Any]            ="train[:33%]"
  a__			:      int            ="arrow"
  a__			:      Any            ="prefix"

  a__			:      Tuple            =make_file_instructions(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE		)
  assert isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE		)
  assert file_instructions.num_examples == 33
  assert file_instructions.file_instructions == [
      {"filename": os.path.join(SCREAMING_SNAKE_CASE , f'''{name}-train.arrow'''		), "skip": 0, "take": 33}
  ]

  a__			:      List[str]            =[SplitInfo(name="train" , num_examples=100 , shard_lengths=[10] * 10		)]
  a__			:      Union[str, Any]            =make_file_instructions(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE		)
  assert isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE		)
  assert file_instructions.num_examples == 33
  assert file_instructions.file_instructions == [
      {"filename": os.path.join(SCREAMING_SNAKE_CASE , f'''{name}-train-00000-of-00010.arrow'''		), "skip": 0, "take": -1},
      {"filename": os.path.join(SCREAMING_SNAKE_CASE , f'''{name}-train-00001-of-00010.arrow'''		), "skip": 0, "take": -1},
      {"filename": os.path.join(SCREAMING_SNAKE_CASE , f'''{name}-train-00002-of-00010.arrow'''		), "skip": 0, "take": -1},
      {"filename": os.path.join(SCREAMING_SNAKE_CASE , f'''{name}-train-00003-of-00010.arrow'''		), "skip": 0, "take": 3},
  ]





@pytest.mark.parametrize("name, expected_exception" , [(None, TypeError), ("", ValueError)]		)
def   _A (	SCREAMING_SNAKE_CASE  :     Tuple , SCREAMING_SNAKE_CASE  :     Tuple		):





  """simple docstring"""



  a__			:      Dict            =[SplitInfo(name="train" , num_examples=100		)]
  a__			:      Any            ="train"
  a__			:      Optional[Any]            ="arrow"
  a__			:      List[Any]            ="prefix_path"
  with pytest.raises(SCREAMING_SNAKE_CASE		):
    a__			:      Optional[Any]            =make_file_instructions(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE		)






