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 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Optional[int]	)							->					int:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      tmp_path / """file.csv"""
  SCREAMING_SNAKE_CASE__										=      textwrap.dedent(
      """\
        header1,header2
        1,2
        10,20
        """	)
  with open(__UpperCamelCase ,     """w"""	) as f:
    f.write(__UpperCamelCase	)
  return str(__UpperCamelCase	)



@pytest.fixture
def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       List[str]	)							->					Dict:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      tmp_path / """malformed_file.csv"""
  SCREAMING_SNAKE_CASE__										=      textwrap.dedent(
      """\
        header1,header2
        1,2
        10,20,
        """	)
  with open(__UpperCamelCase ,     """w"""	) as f:
    f.write(__UpperCamelCase	)
  return str(__UpperCamelCase	)



@pytest.fixture
def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       List[Any] ,     __UpperCamelCase    :       Optional[int]	)							->					Tuple:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      tmp_path / """csv_with_image.csv"""
  SCREAMING_SNAKE_CASE__										=      textwrap.dedent(
      f"""\
        image
        {image_file}
        """	)
  with open(__UpperCamelCase ,     """w"""	) as f:
    f.write(__UpperCamelCase	)
  return str(__UpperCamelCase	)



@pytest.fixture
def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Dict	)							->					Any:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      tmp_path / """csv_with_label.csv"""
  SCREAMING_SNAKE_CASE__										=      textwrap.dedent(
      """\
        label
        good
        bad
        good
        """	)
  with open(__UpperCamelCase ,     """w"""	) as f:
    f.write(__UpperCamelCase	)
  return str(__UpperCamelCase	)



@pytest.fixture
def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       str	)							->					int:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      tmp_path / """csv_with_int_list.csv"""
  SCREAMING_SNAKE_CASE__										=      textwrap.dedent(
      """\
        int_list
        1 2 3
        4 5 6
        7 8 9
        """	)
  with open(__UpperCamelCase ,     """w"""	) as f:
    f.write(__UpperCamelCase	)
  return str(__UpperCamelCase	)



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Tuple ,     __UpperCamelCase    :       str ,     __UpperCamelCase    :       List[Any]	)							->					Any:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      Csv()
  SCREAMING_SNAKE_CASE__										=      csv._generate_tables([[csv_file, malformed_csv_file]]	)
  with pytest.raises(__UpperCamelCase ,     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(__UpperCamelCase	) in record.message
      for record in caplog.records	)



@require_pil
def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Any	)							->					Optional[Any]:







  """simple docstring"""





  with open(__UpperCamelCase ,     encoding="""utf-8"""	) as f:
    SCREAMING_SNAKE_CASE__										=      f.read().splitlines()[1]
  SCREAMING_SNAKE_CASE__										=      Csv(encoding="""utf-8""" ,     features=Features({"""image""": Image()}	)	)
  SCREAMING_SNAKE_CASE__										=      csv._generate_tables([[csv_file_with_image]]	)
  SCREAMING_SNAKE_CASE__										=      pa.concat_tables([table for _, table in generator]	)
  assert pa_table.schema.field("""image"""	).type == Image()()
  SCREAMING_SNAKE_CASE__										=      pa_table.to_pydict()["""image"""]
  assert generated_content == [{"path": image_file, "bytes": None}]



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       int	)							->					str:







  """simple docstring"""





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






def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Dict	)							->					Optional[int]:







  """simple docstring"""





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

import re

from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P


# Sentinels
__lowerCamelCase			: List[str]							   =		object()

# For specifying empty leaf dict `{}`
__lowerCamelCase			: Optional[int]							   =		object()



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       List[Any] ,     __UpperCamelCase    :       List[str]	)							->					Optional[Any]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      tuple((re.compile(x + """$"""	) for x in qs)	)
  for i in range(len(__UpperCamelCase	) - len(__UpperCamelCase	) + 1	):
    SCREAMING_SNAKE_CASE__										=      [x.match(__UpperCamelCase	) for x, y in zip(__UpperCamelCase ,     ks[i:]	)]
    if matches and all(__UpperCamelCase	):
      return True
  return False



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Optional[int]	)							->					List[str]:







  """simple docstring"""





  def replace(__UpperCamelCase    :       Tuple ,     __UpperCamelCase    :       Any	):
    for rule, replacement in rules:
      if _match(__UpperCamelCase ,     __UpperCamelCase	):
        return replacement
    return val

  return replace



def 	__SCREAMING_SNAKE_CASE  (					)							->					List[Any]:







  """simple docstring"""





  return [
      # embeddings
      (("transformer", "wpe", "embedding"), P("""mp""" ,     __UpperCamelCase	)),
      (("transformer", "wte", "embedding"), P("""mp""" ,     __UpperCamelCase	)),
      # atention
      (("attention", "(q_proj|k_proj|v_proj)", "kernel"), P(__UpperCamelCase ,     """mp"""	)),
      (("attention", "out_proj", "kernel"), P("""mp""" ,     __UpperCamelCase	)),
      (("attention", "out_proj", "bias"), None),
      # mlp
      (("mlp", "c_fc", "kernel"), P(__UpperCamelCase ,     """mp"""	)),
      (("mlp", "c_fc", "bias"), P("""mp"""	)),
      (("mlp", "c_proj", "kernel"), P("""mp""" ,     __UpperCamelCase	)),
      (("mlp", "c_proj", "bias"), None),
      # layer norms
      ((r"ln_\d+", "bias"), None),
      ((r"\d+", r"ln_\d+", "scale"), None),
      (("ln_f", "bias"), None),
      (("ln_f", "scale"), None),
  ]






def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Union[str, Any]	)							->					Dict:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      _get_partition_rules()
  SCREAMING_SNAKE_CASE__										=      _replacement_rules(__UpperCamelCase	)
  SCREAMING_SNAKE_CASE__										=      {k: _unmatched for k in flatten_dict(__UpperCamelCase	)}
  SCREAMING_SNAKE_CASE__										=      {k: replace(__UpperCamelCase ,     __UpperCamelCase	) for k, v in initd.items()}
  assert _unmatched not in result.values(), "Incomplete partition spec."
  return freeze(unflatten_dict(__UpperCamelCase	)	)
