


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	(  SCREAMING_SNAKE_CASE     ):

       def 				UpperCAmelCase				(			self	,						a__	,						a__=False					):






        '''simple docstring'''






        snake_case  ,					snake_case  ,					snake_case   : List[str]	  =      (
            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(a__					)	,						'''wb'''					).close()
        snake_case   : Dict	  =      pa.Table.from_pydict({'''filename''': [Path(a__					).name] * 1_00}					)
        if take == -1:
         snake_case   : Dict	  =      len(a__					) - skip
        if skip is not None and take is not None:
         snake_case   : Optional[Any]	  =      pa_table.slice(a__	,						a__					)
        return pa_table







class      lowerCamelCase	(  SCREAMING_SNAKE_CASE     ):

       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        snake_case   : Tuple	  =      '''my_name'''
        snake_case   : List[str]	  =      SplitInfo(name='''train'''	,						num_examples=1_00					)
        snake_case   : Dict	  =      SplitInfo(name='''test'''	,						num_examples=1_00					)
        snake_case   : str	  =      [train_info, test_info]
        snake_case   : Tuple	  =      SplitDict()
        split_dict.add(a__					)
        split_dict.add(a__					)
        snake_case   : List[Any]	  =      DatasetInfo(splits=a__					)

        with tempfile.TemporaryDirectory() as tmp_dir:
         snake_case   : List[str]	  =      ReaderTest(a__	,						a__					)

         snake_case   : Optional[int]	  =      '''test[:33%]'''
         snake_case   : List[Any]	  =      Dataset(**reader.read(a__	,						a__	,						a__					)					)
         self.assertEqual(dset['''filename'''][0]	,						f'''{name}-test'''					)
         self.assertEqual(dset.num_rows	,						33					)
         self.assertEqual(dset.num_columns	,						1					)

         snake_case   : List[str]	  =      ['''train''', '''test[:33%]''']
         snake_case   : str	  =      [Split.TRAIN, ReadInstruction.from_spec('''test[:33%]'''					)]
         for instructions in [instructionsa, instructionsa]:
          snake_case   : List[Any]	  =      [reader.read(a__	,						a__	,						a__					) for instr in instructions]
          snake_case  ,					snake_case   : Tuple	  =      (Dataset(**a__					) for dataset_kwargs in datasets_kwargs)
          self.assertEqual(train_dset['''filename'''][0]	,						f'''{name}-train'''					)
          self.assertEqual(train_dset.num_rows	,						1_00					)
          self.assertEqual(train_dset.num_columns	,						1					)
          self.assertIsInstance(train_dset.split	,						a__					)
          self.assertEqual(str(train_dset.split					)	,						'''train'''					)
          self.assertEqual(test_dset['''filename'''][0]	,						f'''{name}-test'''					)
          self.assertEqual(test_dset.num_rows	,						33					)
          self.assertEqual(test_dset.num_columns	,						1					)
          self.assertIsInstance(test_dset.split	,						a__					)
          self.assertEqual(str(test_dset.split					)	,						'''test[:33%]'''					)
          del train_dset, test_dset

       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        snake_case   : Union[str, Any]	  =      '''my_name'''
        snake_case   : Union[str, Any]	  =      SplitInfo(name='''train'''	,						num_examples=10_00	,						shard_lengths=[1_00] * 10					)
        snake_case   : Dict	  =      [train_info]
        snake_case   : List[str]	  =      SplitDict()
        split_dict.add(a__					)
        snake_case   : int	  =      DatasetInfo(splits=a__					)

        with tempfile.TemporaryDirectory() as tmp_dir:
         snake_case   : Any	  =      ReaderTest(a__	,						a__					)

         snake_case   : List[str]	  =      '''train[:33%]'''
         snake_case   : Dict	  =      Dataset(**reader.read(a__	,						a__	,						a__					)					)
         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_30					)
         self.assertEqual(dset.num_columns	,						1					)



       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        snake_case   : int	  =      SplitInfo(name='''train'''	,						num_examples=1_00					)
        snake_case   : List[Any]	  =      SplitInfo(name='''test'''	,						num_examples=1_00					)
        snake_case   : Optional[int]	  =      SplitDict()
        split_dict.add(a__					)
        split_dict.add(a__					)
        snake_case   : List[str]	  =      DatasetInfo(splits=a__					)

        with tempfile.TemporaryDirectory() as tmp_dir:
         snake_case   : Any	  =      ReaderTest(a__	,						a__					)

         snake_case   : Any	  =      [
             {'''filename''': os.path.join(a__	,						'''train'''					)},
             {'''filename''': os.path.join(a__	,						'''test'''					), '''skip''': 10, '''take''': 10},
         ]
         snake_case   : Dict	  =      Dataset(**reader.read_files(a__	,						original_instructions='''train+test[10:20]'''					)					)
         self.assertEqual(dset.num_rows	,						1_10					)
         self.assertEqual(dset.num_columns	,						1					)
         del dset







@pytest.mark.parametrize('''in_memory''' ,     [False, True]       )
def    _UpperCAmelCase	(   __lowerCamelCase ,     __lowerCamelCase ,     __lowerCamelCase       )		->Optional[Any]:


 '''simple docstring'''
 snake_case   : Union[str, Any]	  =      arrow_file
 with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
  snake_case   : List[Any]	  =      ArrowReader.read_table(__lowerCamelCase ,     in_memory=__lowerCamelCase       )
 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    _UpperCAmelCase	(   __lowerCamelCase ,     __lowerCamelCase ,     __lowerCamelCase       )		->Union[str, Any]:


 '''simple docstring'''
 snake_case   : str	  =      arrow_file
 snake_case   : Tuple	  =      ArrowReader('''''' ,     __lowerCamelCase       )
 with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
  snake_case   : List[str]	  =      reader.read_files([{'''filename''': filename}] ,     in_memory=__lowerCamelCase       )
 assert dataset_kwargs.keys() == {"arrow_table", "info", "split"}
 snake_case   : str	  =      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    _UpperCAmelCase	(   )		->Any:


 '''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%]"

 snake_case   : List[str]	  =      '''train+test'''
 assert ReadInstruction.from_spec(__lowerCamelCase       ).to_spec() == spec_train_test

 snake_case   : Union[str, Any]	  =      '''train[2:10]'''
 assert ReadInstruction.from_spec(__lowerCamelCase       ).to_spec() == spec_train_abs

 snake_case   : List[str]	  =      '''train[15%:-20%]'''
 assert ReadInstruction.from_spec(__lowerCamelCase       ).to_spec() == spec_train_pct

 snake_case   : List[str]	  =      '''train[:10%](closest)'''
 assert ReadInstruction.from_spec(__lowerCamelCase       ).to_spec() == "train[:10%]"

 snake_case   : Optional[int]	  =      '''train[:10%](pct1_dropremainder)'''
 assert ReadInstruction.from_spec(__lowerCamelCase       ).to_spec() == spec_train_pct_rounding

 snake_case   : List[Any]	  =      '''train[:10%](pct1_dropremainder)+test[-10%:](pct1_dropremainder)'''
 assert ReadInstruction.from_spec(__lowerCamelCase       ).to_spec() == spec_train_test_pct_rounding






def    _UpperCAmelCase	(   )		->Dict:


 '''simple docstring'''
 snake_case   : Optional[int]	  =      '''dummy'''
 snake_case   : Union[str, Any]	  =      [SplitInfo(name='''train''' ,     num_examples=100       )]
 snake_case   : Optional[int]	  =      '''train[:33%]'''
 snake_case   : Dict	  =      '''arrow'''
 snake_case   : Dict	  =      '''prefix'''

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

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






@pytest.mark.parametrize('''name, expected_exception''' ,     [(None, TypeError), ('''''', ValueError)]       )
def    _UpperCAmelCase	(   __lowerCamelCase ,     __lowerCamelCase       )		->Dict:


 '''simple docstring'''
 snake_case   : Dict	  =      [SplitInfo(name='''train''' ,     num_examples=100       )]
 snake_case   : Any	  =      '''train'''
 snake_case   : Tuple	  =      '''arrow'''
 snake_case   : str	  =      '''prefix_path'''
 with pytest.raises(__lowerCamelCase       ):
  snake_case   : Tuple	  =      make_file_instructions(__lowerCamelCase ,     __lowerCamelCase ,     __lowerCamelCase ,     __lowerCamelCase ,     __lowerCamelCase       )










import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import MobileNetVaImageProcessor

class      lowerCamelCase	(  unittest.TestCase     ):

       def __init__(			self	,						a__	,						a__=7	,						a__=3	,						a__=18	,						a__=30	,						a__=4_00	,						a__=True	,						a__=None	,						a__=True	,						a__=None	,						):






        '''simple docstring'''






        snake_case   : str	  =      size if size is not None else {'''shortest_edge''': 20}
        snake_case   : List[str]	  =      crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
        snake_case   : Optional[Any]	  =      parent
        snake_case   : Dict	  =      batch_size
        snake_case   : List[str]	  =      num_channels
        snake_case   : Union[str, Any]	  =      image_size
        snake_case   : Optional[Any]	  =      min_resolution
        snake_case   : Union[str, Any]	  =      max_resolution
        snake_case   : Optional[int]	  =      do_resize
        snake_case   : Optional[int]	  =      size
        snake_case   : Optional[Any]	  =      do_center_crop
        snake_case   : Optional[int]	  =      crop_size



       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        return {
            "do_resize": self.do_resize,
            "size": self.size,
            "do_center_crop": self.do_center_crop,
            "crop_size": self.crop_size,
        }







@require_torch
@require_vision
class      lowerCamelCase	(  SCREAMING_SNAKE_CASE,			unittest.TestCase     ):
       _UpperCamelCase    		=      MobileNetVaImageProcessor if is_vision_available() else None

       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        snake_case   : List[str]	  =      MobileNetVaImageProcessingTester(self					)

       @property
       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        return self.image_processor_tester.prepare_image_processor_dict()

       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        snake_case   : Union[str, Any]	  =      self.image_processing_class(**self.image_processor_dict					)
        self.assertTrue(hasattr(a__	,						'''do_resize'''					)					)
        self.assertTrue(hasattr(a__	,						'''size'''					)					)
        self.assertTrue(hasattr(a__	,						'''do_center_crop'''					)					)
        self.assertTrue(hasattr(a__	,						'''center_crop'''					)					)

       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        snake_case   : str	  =      self.image_processing_class.from_dict(self.image_processor_dict					)
        self.assertEqual(image_processor.size	,						{'''shortest_edge''': 20}					)
        self.assertEqual(image_processor.crop_size	,						{'''height''': 18, '''width''': 18}					)

        snake_case   : Dict	  =      self.image_processing_class.from_dict(self.image_processor_dict	,						size=42	,						crop_size=84					)
        self.assertEqual(image_processor.size	,						{'''shortest_edge''': 42}					)
        self.assertEqual(image_processor.crop_size	,						{'''height''': 84, '''width''': 84}					)

       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        pass

       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        snake_case   : Union[str, Any]	  =      self.image_processing_class(**self.image_processor_dict					)
        # create random PIL images
        snake_case   : Tuple	  =      prepare_image_inputs(self.image_processor_tester	,						equal_resolution=a__					)
        for image in image_inputs:
         self.assertIsInstance(a__	,						Image.Image					)

        # Test not batched input
        snake_case   : List[str]	  =      image_processing(image_inputs[0]	,						return_tensors='''pt'''					).pixel_values
        self.assertEqual(
            encoded_images.shape	,						(
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['''height'''],
                self.image_processor_tester.crop_size['''width'''],
            )	,						)

        # Test batched
        snake_case   : List[Any]	  =      image_processing(a__	,						return_tensors='''pt'''					).pixel_values
        self.assertEqual(
            encoded_images.shape	,						(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['''height'''],
                self.image_processor_tester.crop_size['''width'''],
            )	,						)

       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        snake_case   : Optional[int]	  =      self.image_processing_class(**self.image_processor_dict					)
        # create random numpy tensors
        snake_case   : Tuple	  =      prepare_image_inputs(self.image_processor_tester	,						equal_resolution=a__	,						numpify=a__					)
        for image in image_inputs:
         self.assertIsInstance(a__	,						np.ndarray					)

        # Test not batched input
        snake_case   : Dict	  =      image_processing(image_inputs[0]	,						return_tensors='''pt'''					).pixel_values
        self.assertEqual(
            encoded_images.shape	,						(
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['''height'''],
                self.image_processor_tester.crop_size['''width'''],
            )	,						)

        # Test batched
        snake_case   : List[Any]	  =      image_processing(a__	,						return_tensors='''pt'''					).pixel_values
        self.assertEqual(
            encoded_images.shape	,						(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['''height'''],
                self.image_processor_tester.crop_size['''width'''],
            )	,						)



       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        snake_case   : List[str]	  =      self.image_processing_class(**self.image_processor_dict					)
        # create random PyTorch tensors
        snake_case   : List[Any]	  =      prepare_image_inputs(self.image_processor_tester	,						equal_resolution=a__	,						torchify=a__					)
        for image in image_inputs:
         self.assertIsInstance(a__	,						torch.Tensor					)

        # Test not batched input
        snake_case   : List[str]	  =      image_processing(image_inputs[0]	,						return_tensors='''pt'''					).pixel_values
        self.assertEqual(
            encoded_images.shape	,						(
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['''height'''],
                self.image_processor_tester.crop_size['''width'''],
            )	,						)

        # Test batched
        snake_case   : Any	  =      image_processing(a__	,						return_tensors='''pt'''					).pixel_values
        self.assertEqual(
            encoded_images.shape	,						(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['''height'''],
                self.image_processor_tester.crop_size['''width'''],
            )	,						)






