





import os
import textwrap

import pyarrow as pa
import pytest

from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv

from ..utils import require_pil




@pytest.fixture
def        __UpperCamelCase					(    lowerCAmelCase__							:    str  ):
 __a			:  Any		    =  tmp_path / """file.csv"""
 __a			:  Any		    =  textwrap.dedent(
     '''\
        header1,header2
        1,2
        10,20
        '''  )
 with open(lowerCamelCase_  ,       '''w'''  ) as f:
  f.write(lowerCamelCase_  )
 return str(lowerCamelCase_  )




@pytest.fixture
def        __UpperCamelCase					(    lowerCAmelCase__							:    str  ):
 __a			:  Optional[int]		    =  tmp_path / """malformed_file.csv"""
 __a			:  int		    =  textwrap.dedent(
     '''\
        header1,header2
        1,2
        10,20,
        '''  )
 with open(lowerCamelCase_  ,       '''w'''  ) as f:
  f.write(lowerCamelCase_  )
 return str(lowerCamelCase_  )




@pytest.fixture
def        __UpperCamelCase					(    lowerCAmelCase__							:    Optional[Any]  ,       lowerCAmelCase__							:    int  ):
 __a			:  str		    =  tmp_path / """csv_with_image.csv"""
 __a			:  int		    =  textwrap.dedent(
     f"\\n        image\n        {image_file}\n        "  )
 with open(lowerCamelCase_  ,       '''w'''  ) as f:
  f.write(lowerCamelCase_  )
 return str(lowerCamelCase_  )




@pytest.fixture
def        __UpperCamelCase					(    lowerCAmelCase__							:    Any  ):
 __a			:  int		    =  tmp_path / """csv_with_label.csv"""
 __a			:  Tuple		    =  textwrap.dedent(
     '''\
        label
        good
        bad
        good
        '''  )
 with open(lowerCamelCase_  ,       '''w'''  ) as f:
  f.write(lowerCamelCase_  )
 return str(lowerCamelCase_  )




@pytest.fixture
def        __UpperCamelCase					(    lowerCAmelCase__							:    Union[str, Any]  ):
 __a			:  List[str]		    =  tmp_path / """csv_with_int_list.csv"""
 __a			:  str		    =  textwrap.dedent(
     '''\
        int_list
        1 2 3
        4 5 6
        7 8 9
        '''  )
 with open(lowerCamelCase_  ,       '''w'''  ) as f:
  f.write(lowerCamelCase_  )
 return str(lowerCamelCase_  )




def        __UpperCamelCase					(    lowerCAmelCase__							:    Union[str, Any]  ,       lowerCAmelCase__							:    int  ,       lowerCAmelCase__							:    Tuple  ):
 __a			:  int		    =  Csv()
 __a			:  Optional[Any]		    =  csv._generate_tables([[csv_file, malformed_csv_file]]  )
 with pytest.raises(lowerCamelCase_  ,       match='''Error tokenizing data'''  ):
  for _ in generator:
   pass
 assert any(
     record.levelname == '''ERROR'''
     and '''Failed to read file''' in record.message
     and os.path.basename(lowerCamelCase_  ) in record.message
     for record in caplog.records  )




@require_pil
def        __UpperCamelCase					(    lowerCAmelCase__							:    Tuple  ):
 with open(lowerCamelCase_  ,       encoding='''utf-8'''  ) as f:
  __a			:  Tuple		    =  f.read().splitlines()[1]
 __a			:  str		    =  Csv(encoding='''utf-8'''  ,       features=Features({'''image''': Image()}  )  )
 __a			:  Tuple		    =  csv._generate_tables([[csv_file_with_image]]  )
 __a			:  Optional[Any]		    =  pa.concat_tables([table for _, table in generator]  )
 assert pa_table.schema.field('''image'''  ).type == Image()()
 __a			:  List[str]		    =  pa_table.to_pydict()["""image"""]
 assert generated_content == [{"path": image_file, "bytes": None}]




def        __UpperCamelCase					(    lowerCAmelCase__							:    int  ):
 with open(lowerCamelCase_  ,       encoding='''utf-8'''  ) as f:
  __a			:  List[Any]		    =  f.read().splitlines()[1:]
 __a			:  Union[str, Any]		    =  Csv(encoding='''utf-8'''  ,       features=Features({'''label''': ClassLabel(names=['''good''', '''bad''']  )}  )  )
 __a			:  Optional[Any]		    =  csv._generate_tables([[csv_file_with_label]]  )
 __a			:  Optional[int]		    =  pa.concat_tables([table for _, table in generator]  )
 assert pa_table.schema.field('''label'''  ).type == ClassLabel(names=['''good''', '''bad''']  )()
 __a			:  Union[str, Any]		    =  pa_table.to_pydict()["""label"""]
 assert generated_content == [ClassLabel(names=['''good''', '''bad''']  ).straint(lowerCamelCase_  ) for label in labels]




def        __UpperCamelCase					(    lowerCAmelCase__							:    Union[str, Any]  ):
 __a			:  str		    =  Csv(encoding='''utf-8'''  ,       sep=''','''  ,       converters={'''int_list''': lambda lowerCAmelCase__  : [int(lowerCamelCase_  ) for i in x.split()]}  )
 __a			:  Optional[Any]		    =  csv._generate_tables([[csv_file_with_int_list]]  )
 __a			:  Tuple		    =  pa.concat_tables([table for _, table in generator]  )
 assert pa.types.is_list(pa_table.schema.field('''int_list'''  ).type  )
 __a			:  Dict		    =  pa_table.to_pydict()["""int_list"""]
 assert generated_content == [[1, 2, 3], [4, 5, 6], [7, 8, 9]]


import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


lowercase__          =50000
lowercase__          =5000

lowercase__       ,      lowercase__          =os.path.split(__file__)
lowercase__          =os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))




@get_duration
def        __UpperCamelCase					(    lowerCAmelCase__							:    datasets.Dataset  ,       lowerCAmelCase__							:    List[str]  ):
 for i in range(lowerCAmelCase__  ):
  __a			:  str		    =  dataset[i]




@get_duration
def        __UpperCamelCase					(    lowerCAmelCase__							:    datasets.Dataset  ,       lowerCAmelCase__							:    Optional[Any]  ,       lowerCAmelCase__							:    Tuple  ):
 for i in range(0  ,       len(lowerCAmelCase__  )  ,       lowerCAmelCase__  ):
  __a			:  Optional[int]		    =  dataset[i : i + batch_size]




@get_duration
def        __UpperCamelCase					(    lowerCAmelCase__							:    datasets.Dataset  ,       lowerCAmelCase__							:    Union[str, Any]  ,       lowerCAmelCase__							:    str  ):
 with dataset.formatted_as(type=lowerCAmelCase__  ):
  for i in range(lowerCAmelCase__  ):
   __a			:  Dict		    =  dataset[i]




@get_duration
def        __UpperCamelCase					(    lowerCAmelCase__							:    datasets.Dataset  ,       lowerCAmelCase__							:    List[str]  ,       lowerCAmelCase__							:    Optional[Any]  ,       lowerCAmelCase__							:    int  ):
 with dataset.formatted_as(type=lowerCAmelCase__  ):
  for i in range(0  ,       lowerCAmelCase__  ,       lowerCAmelCase__  ):
   __a			:  int		    =  dataset[i : i + batch_size]




def        __UpperCamelCase					(    ):
 __a			:  Any		    =  {'''num examples''': SPEED_TEST_N_EXAMPLES}
 __a			:  List[Any]		    =  [
     (read, {'''length''': SMALL_TEST}),
     (read, {'''length''': SPEED_TEST_N_EXAMPLES}),
     (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 1_0}),
     (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 1_0_0}),
     (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 1_0_0_0}),
     (read_formatted, {'''type''': '''numpy''', '''length''': SMALL_TEST}),
     (read_formatted, {'''type''': '''pandas''', '''length''': SMALL_TEST}),
     (read_formatted, {'''type''': '''torch''', '''length''': SMALL_TEST}),
     (read_formatted, {'''type''': '''tensorflow''', '''length''': SMALL_TEST}),
     (read_formatted_batch, {'''type''': '''numpy''', '''length''': SMALL_TEST, '''batch_size''': 1_0}),
     (read_formatted_batch, {'''type''': '''numpy''', '''length''': SMALL_TEST, '''batch_size''': 1_0_0_0}),
 ]

 __a			:  Union[str, Any]		    =  [
     (read, {'''length''': SMALL_TEST}),
     (read, {'''length''': SPEED_TEST_N_EXAMPLES}),
     (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 1_0}),
     (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 1_0_0}),
     (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 1_0_0_0}),
     (read_formatted, {'''type''': '''numpy''', '''length''': SMALL_TEST}),
     (read_formatted_batch, {'''type''': '''numpy''', '''length''': SMALL_TEST, '''batch_size''': 1_0}),
     (read_formatted_batch, {'''type''': '''numpy''', '''length''': SMALL_TEST, '''batch_size''': 1_0_0_0}),
 ]
 with tempfile.TemporaryDirectory() as tmp_dir:
  print('''generating dataset'''  )
  __a			:  Optional[Any]		    =  datasets.Features(
      {'''list''': datasets.Sequence(datasets.Value('''float32'''  )  ), '''numbers''': datasets.Value('''float32'''  )}  )
  __a			:  Optional[int]		    =  generate_example_dataset(
      os.path.join(lowerCAmelCase__  ,       '''dataset.arrow'''  )  ,       lowerCAmelCase__  ,       num_examples=lowerCAmelCase__  ,       seq_shapes={'''list''': (1_0_0,)}  ,       )
  print('''first set of iterations'''  )
  for func, kwargs in functions:
   print(func.__name__  ,       str(lowerCAmelCase__  )  )
   __a			:  str		    =  func(lowerCAmelCase__  ,       **lowerCAmelCase__  )

  print('''shuffling dataset'''  )
  __a			:  int		    =  dataset.shuffle()
  print('''Second set of iterations (after shuffling'''  )
  for func, kwargs in functions_shuffled:
   print('''shuffled '''  ,       func.__name__  ,       str(lowerCAmelCase__  )  )
   __a			:  List[Any]		    =  func(
       lowerCAmelCase__  ,       **lowerCAmelCase__  )

 with open(lowerCAmelCase__  ,       '''wb'''  ) as f:
  f.write(json.dumps(lowerCAmelCase__  ).encode('''utf-8'''  )  )


if __name__ == "__main__":  # useful to run the profiler
      benchmark_iterating()
