import importlib.metadata
import warnings
from copy import deepcopy

from packaging import version

from ..utils import logging
from .import_utils import is_accelerate_available, is_bitsandbytes_available


if is_bitsandbytes_available():
   import bitsandbytes as bnb
   import torch
   import torch.nn as nn

   from ..pytorch_utils import ConvaD

if is_accelerate_available():
   from accelerate import init_empty_weights
   from accelerate.utils import find_tied_parameters

__lowerCamelCase    :				Tuple							     =	logging.get_logger(__name__)



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Dict				,  __UpperCamelCase					: Optional[int]				,  __UpperCamelCase					: Optional[int]				,  __UpperCamelCase					: Any=None				,  __UpperCamelCase					: Optional[Any]=None			)							->      Dict:







  """simple docstring"""





  if "." in tensor_name:
    SCREAMING_SNAKE_CASE__    							=   tensor_name.split("""."""			)
    for split in splits[:-1]:
      SCREAMING_SNAKE_CASE__    							=   getattr(__UpperCamelCase				,  __UpperCamelCase			)
      if new_module is None:
        raise ValueError(f"""{module} has no attribute {split}."""			)
      SCREAMING_SNAKE_CASE__    							=   new_module
    SCREAMING_SNAKE_CASE__    							=   splits[-1]

  if tensor_name not in module._parameters and tensor_name not in module._buffers:
    raise ValueError(f"""{module} does not have a parameter or a buffer named {tensor_name}."""			)
  SCREAMING_SNAKE_CASE__    							=   tensor_name in module._buffers
  SCREAMING_SNAKE_CASE__    							=   getattr(__UpperCamelCase				,  __UpperCamelCase			)

  if old_value.device == torch.device("""meta"""			) and device not in ["meta", torch.device("""meta"""			)] and value is None:
    raise ValueError(f"""{tensor_name} is on the meta device, we need a `value` to put in on {device}."""			)

  SCREAMING_SNAKE_CASE__    							=   False
  SCREAMING_SNAKE_CASE__    							=   False
  if is_buffer or not is_bitsandbytes_available():
    SCREAMING_SNAKE_CASE__    							=   False
    SCREAMING_SNAKE_CASE__    							=   False
  else:
    SCREAMING_SNAKE_CASE__    							=   hasattr(bnb.nn				,  """Params4bit"""			) and isinstance(module._parameters[tensor_name]				,  bnb.nn.Paramsabit			)
    SCREAMING_SNAKE_CASE__    							=   isinstance(module._parameters[tensor_name]				,  bnb.nn.IntaParams			)

  if is_abit or is_abit:
    SCREAMING_SNAKE_CASE__    							=   module._parameters[tensor_name]
    if param.device.type != "cuda":
      if value is None:
        SCREAMING_SNAKE_CASE__    							=   old_value.to(__UpperCamelCase			)
      elif isinstance(__UpperCamelCase				,  torch.Tensor			):
        SCREAMING_SNAKE_CASE__    							=   value.to("""cpu"""			)
        if value.dtype == torch.inta:
          SCREAMING_SNAKE_CASE__    							=   version.parse(importlib.metadata.version("""bitsandbytes"""			)			) > version.parse(
              """0.37.2"""			)
          if not is_abit_serializable:
            raise ValueError(
                """Detected int8 weights but the version of bitsandbytes is not compatible with int8 serialization. """
                """Make sure to download the latest `bitsandbytes` version. `pip install --upgrade bitsandbytes`."""			)
      else:
        SCREAMING_SNAKE_CASE__    							=   torch.tensor(__UpperCamelCase				,  device="""cpu"""			)

      # Support models using `Conv1D` in place of `nn.Linear` (e.g. gpt2) by transposing the weight matrix prior to quantization.
      # Since weights are saved in the correct "orientation", we skip transposing when loading.
      if issubclass(module.source_cls				,  __UpperCamelCase			) and fpaa_statistics is None:
        SCREAMING_SNAKE_CASE__    							=   new_value.T

      SCREAMING_SNAKE_CASE__    							=   old_value.__dict__
      if is_abit:
        SCREAMING_SNAKE_CASE__    							=   bnb.nn.IntaParams(__UpperCamelCase				,  requires_grad=__UpperCamelCase				,  **__UpperCamelCase			).to(__UpperCamelCase			)
      elif is_abit:
        SCREAMING_SNAKE_CASE__    							=   bnb.nn.Paramsabit(__UpperCamelCase				,  requires_grad=__UpperCamelCase				,  **__UpperCamelCase			).to(__UpperCamelCase			)

      SCREAMING_SNAKE_CASE__    							=   new_value
      if fpaa_statistics is not None:
        setattr(module.weight				,  """SCB"""				,  fpaa_statistics.to(__UpperCamelCase			)			)

  else:
    if value is None:
      SCREAMING_SNAKE_CASE__    							=   old_value.to(__UpperCamelCase			)
    elif isinstance(__UpperCamelCase				,  torch.Tensor			):
      SCREAMING_SNAKE_CASE__    							=   value.to(__UpperCamelCase			)
    else:
      SCREAMING_SNAKE_CASE__    							=   torch.tensor(__UpperCamelCase				,  device=__UpperCamelCase			)

    if is_buffer:
      SCREAMING_SNAKE_CASE__    							=   new_value
    else:
      SCREAMING_SNAKE_CASE__    							=   nn.Parameter(__UpperCamelCase				,  requires_grad=old_value.requires_grad			)
      SCREAMING_SNAKE_CASE__    							=   new_value



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Any				,  __UpperCamelCase					: int=None				,  __UpperCamelCase					: Dict=None				,  __UpperCamelCase					: Tuple=None				,  __UpperCamelCase					: List[Any]=False			)							->      int:







  """simple docstring"""





  for name, module in model.named_children():
    if current_key_name is None:
      SCREAMING_SNAKE_CASE__    							=   []
    current_key_name.append(__UpperCamelCase			)

    if (isinstance(__UpperCamelCase				,  nn.Linear			) or isinstance(__UpperCamelCase				,  __UpperCamelCase			)) and name not in modules_to_not_convert:
      # Check if the current key is not in the `modules_to_not_convert`
      if not any(key in """.""".join(__UpperCamelCase			) for key in modules_to_not_convert			):
        with init_empty_weights():
          if isinstance(__UpperCamelCase				,  __UpperCamelCase			):
            SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   module.weight.shape
          else:
            SCREAMING_SNAKE_CASE__    							=   module.in_features
            SCREAMING_SNAKE_CASE__    							=   module.out_features

          if quantization_config.quantization_method() == "llm_int8":
            SCREAMING_SNAKE_CASE__    							=   bnb.nn.LinearabitLt(
                __UpperCamelCase				,  __UpperCamelCase				,  module.bias is not None				,  has_fpaa_weights=quantization_config.llm_inta_has_fpaa_weight				,  threshold=quantization_config.llm_inta_threshold				,  )
            SCREAMING_SNAKE_CASE__    							=   True
          else:
            if (
                quantization_config.llm_inta_skip_modules is not None
                and name in quantization_config.llm_inta_skip_modules
            ):
              pass
            else:
              SCREAMING_SNAKE_CASE__    							=   bnb.nn.Linearabit(
                  __UpperCamelCase				,  __UpperCamelCase				,  module.bias is not None				,  quantization_config.bnb_abit_compute_dtype				,  compress_statistics=quantization_config.bnb_abit_use_double_quant				,  quant_type=quantization_config.bnb_abit_quant_type				,  )
              SCREAMING_SNAKE_CASE__    							=   True
                    # Store the module class in case we need to transpose the weight later
          SCREAMING_SNAKE_CASE__    							=   type(__UpperCamelCase			)
          # Force requires grad to False to avoid unexpected errors
          model._modules[name].requires_grad_(__UpperCamelCase			)
    if len(list(module.children()			)			) > 0:
      SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   _replace_with_bnb_linear(
          __UpperCamelCase				,  __UpperCamelCase				,  __UpperCamelCase				,  __UpperCamelCase				,  has_been_replaced=__UpperCamelCase				,  )
    # Remove the last key for recursion
    current_key_name.pop(-1			)
  return model, has_been_replaced



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Tuple				,  __UpperCamelCase					: Union[str, Any]=None				,  __UpperCamelCase					: Dict=None				,  __UpperCamelCase					: str=None			)							->      Optional[Any]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   ["""lm_head"""] if modules_to_not_convert is None else modules_to_not_convert
  SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   _replace_with_bnb_linear(
      __UpperCamelCase				,  __UpperCamelCase				,  __UpperCamelCase				,  __UpperCamelCase			)

  if not has_been_replaced:
    logger.warning(
        """You are loading your model in 8bit or 4bit but no linear modules were found in your model."""
        """ Please double check your model architecture, or submit an issue on github if you think this is"""
        """ a bug."""			)

  return model



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







  """simple docstring"""





  warnings.warn(
      """`replace_8bit_linear` will be deprecated in a future version, please use `replace_with_bnb_linear` instead"""				,  __UpperCamelCase				,  )
  return replace_with_bnb_linear(*__UpperCamelCase				,  **__UpperCamelCase			)



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







  """simple docstring"""





  warnings.warn(
      """`set_module_8bit_tensor_to_device` will be deprecated in a future version, please use `set_module_quantized_tensor_to_device` instead"""				,  __UpperCamelCase				,  )
  return set_module_quantized_tensor_to_device(*__UpperCamelCase				,  **__UpperCamelCase			)






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







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   deepcopy(__UpperCamelCase			)  # this has 0 cost since it is done inside `init_empty_weights` context manager`
  tied_model.tie_weights()

  SCREAMING_SNAKE_CASE__    							=   find_tied_parameters(__UpperCamelCase			)
  # For compatibility with Accelerate < 0.18
  if isinstance(__UpperCamelCase				,  __UpperCamelCase			):
    SCREAMING_SNAKE_CASE__    							=   sum(list(tied_params.values()			)				,  []			) + list(tied_params.keys()			)
  else:
    SCREAMING_SNAKE_CASE__    							=   sum(__UpperCamelCase				,  []			)
  SCREAMING_SNAKE_CASE__    							=   len(__UpperCamelCase			) > 0

  # Check if it is a base model
  SCREAMING_SNAKE_CASE__    							=   not hasattr(__UpperCamelCase				,  model.base_model_prefix			)

  # Ignore this for base models (BertModel, GPT2Model, etc.)
  if (not has_tied_params) and is_base_model:
    return []

  # otherwise they have an attached head
  SCREAMING_SNAKE_CASE__    							=   list(model.named_children()			)
  SCREAMING_SNAKE_CASE__    							=   [list_modules[-1][0]]

  # add last module together with tied weights
  SCREAMING_SNAKE_CASE__    							=   set(__UpperCamelCase			) - set(__UpperCamelCase			)
  SCREAMING_SNAKE_CASE__    							=   list(set(__UpperCamelCase			)			) + list(__UpperCamelCase			)

  # remove ".weight" from the keys
  SCREAMING_SNAKE_CASE__    							=   [""".weight""", """.bias"""]
  SCREAMING_SNAKE_CASE__    							=   []
  for name in list_untouched:
    for name_to_remove in names_to_remove:
      if name_to_remove in name:
        SCREAMING_SNAKE_CASE__    							=   name.replace(__UpperCamelCase				,  """"""			)
    filtered_module_names.append(__UpperCamelCase			)

  return filtered_module_names

import copy
import os
import tempfile
from unittest import TestCase
from unittest.mock import patch

import numpy as np
import pyarrow as pa
import pyarrow.parquet as pq
import pytest

from datasets.arrow_writer import ArrowWriter, OptimizedTypedSequence, ParquetWriter, TypedSequence
from datasets.features import ArrayaD, ClassLabel, Features, Image, Value
from datasets.features.features import ArrayaDExtensionType, cast_to_python_objects
from datasets.keyhash import DuplicatedKeysError, InvalidKeyError

from .utils import require_pil







class __snake_case       (		lowerCamelCase_	):




   def 			__a		(					self    :       Optional[Any]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   pa.array(TypedSequence([1, 2, 3]   )   )
     self.assertEqual(arr.type ,			pa.intaa()   )




   def 			__a		(					self    :       Tuple   ):



     """simple docstring"""






     with self.assertRaises(_lowercase   ):
       SCREAMING_SNAKE_CASE__    							=   pa.array(TypedSequence([1, 2, 3]   ) ,			type=pa.intaa()   )




   def 			__a		(					self    :       int   ):



     """simple docstring"""






     with self.assertRaises(_lowercase   ):
       SCREAMING_SNAKE_CASE__    							=   pa.array(TypedSequence([1, 2, 3] ,			try_type=Value("""bool"""   ) ,			type=Value("""int64"""   )   )   )




   def 			__a		(					self    :       List[str]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   pa.array(TypedSequence([1, 2, 3] ,			type=Value("""int32"""   )   )   )
     self.assertEqual(arr.type ,			pa.intaa()   )




   def 			__a		(					self    :       Optional[Any]   ):



     """simple docstring"""






     with self.assertRaises((TypeError, pa.lib.ArrowInvalid)   ):
       SCREAMING_SNAKE_CASE__    							=   pa.array(TypedSequence(["""foo""", """bar"""] ,			type=Value("""int64"""   )   )   )




   def 			__a		(					self    :       str   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   pa.array(TypedSequence([1, 2, 3] ,			try_type=Value("""int32"""   )   )   )
     self.assertEqual(arr.type ,			pa.intaa()   )




   def 			__a		(					self    :       Any   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   pa.array(TypedSequence(["""foo""", """bar"""] ,			try_type=Value("""int64"""   )   )   )
     self.assertEqual(arr.type ,			pa.string()   )




   def 			__a		(					self    :       Tuple   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   pa.array(TypedSequence([[[1, 2, 3]]] ,			type=ArrayaD((1, 3) ,			"""int64"""   )   )   )
     self.assertEqual(arr.type ,			ArrayaDExtensionType((1, 3) ,			"""int64"""   )   )




   def 			__a		(					self    :       Dict   ):



     """simple docstring"""






     with self.assertRaises((TypeError, pa.lib.ArrowInvalid)   ):
       SCREAMING_SNAKE_CASE__    							=   pa.array(TypedSequence(["""foo""", """bar"""] ,			type=ArrayaD((1, 3) ,			"""int64"""   )   )   )




   def 			__a		(					self    :       Optional[int]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   pa.array(TypedSequence([[[1, 2, 3]]] ,			try_type=ArrayaD((1, 3) ,			"""int64"""   )   )   )
     self.assertEqual(arr.type ,			ArrayaDExtensionType((1, 3) ,			"""int64"""   )   )




   def 			__a		(					self    :       int   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   pa.array(TypedSequence(["""foo""", """bar"""] ,			try_type=ArrayaD((1, 3) ,			"""int64"""   )   )   )
     self.assertEqual(arr.type ,			pa.string()   )




   @require_pil
   def 			__a		(					self    :       List[str]   ):



     """simple docstring"""






     import PIL.Image

     SCREAMING_SNAKE_CASE__    							=   PIL.Image.fromarray(np.arange(10 ,			dtype=np.uinta   ).reshape(2 ,			5   )   )
     with patch(
         """datasets.arrow_writer.cast_to_python_objects""" ,			side_effect=_lowercase   ) as mock_cast_to_python_objects:
       SCREAMING_SNAKE_CASE__    							=   pa.array(TypedSequence([{"""path""": None, """bytes""": b"""image_bytes"""}, pil_image] ,			type=Image()   )   )
       SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   mock_cast_to_python_objects.call_args_list[-1]
       self.assertIn("""optimize_list_casting""" ,			_lowercase   )
       self.assertFalse(kwargs["""optimize_list_casting"""]   )





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







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   pa.BufferReader(__UpperCamelCase			) if isinstance(__UpperCamelCase				,  pa.Buffer			) else pa.memory_map(__UpperCamelCase			)
  SCREAMING_SNAKE_CASE__    							=   pa.ipc.open_stream(__UpperCamelCase			)
  SCREAMING_SNAKE_CASE__    							=   f.read_all()
  assert len(pa_table.to_batches()			) == expected_num_chunks
  assert pa_table.to_pydict() == {"col_1": ["foo", "bar"], "col_2": [1, 2]}
  del pa_table



@pytest.mark.parametrize("""writer_batch_size"""				,  [None, 1, 10]			)
@pytest.mark.parametrize(
    """fields"""				,  [None, {"""col_1""": pa.string(), """col_2""": pa.intaa()}, {"""col_1""": pa.string(), """col_2""": pa.intaa()}]			)
def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Union[str, Any]				,  __UpperCamelCase					: int			)							->      List[str]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   pa.BufferOutputStream()
  SCREAMING_SNAKE_CASE__    							=   pa.schema(__UpperCamelCase			) if fields else None
  with ArrowWriter(stream=__UpperCamelCase				,  schema=__UpperCamelCase				,  writer_batch_size=__UpperCamelCase			) as writer:
    writer.write({"""col_1""": """foo""", """col_2""": 1}			)
    writer.write({"""col_1""": """bar""", """col_2""": 2}			)
    SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   writer.finalize()
  assert num_examples == 2
  assert num_bytes > 0
  if not fields:
    SCREAMING_SNAKE_CASE__    							=   {"""col_1""": pa.string(), """col_2""": pa.intaa()}
  assert writer._schema == pa.schema(__UpperCamelCase				,  metadata=writer._schema.metadata			)
  _check_output(output.getvalue()				,  expected_num_chunks=num_examples if writer_batch_size == 1 else 1			)



def 			__SCREAMING_SNAKE_CASE						(      )							->      int:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   pa.BufferOutputStream()
  SCREAMING_SNAKE_CASE__    							=   Features({"""labels""": ClassLabel(names=["""neg""", """pos"""]			)}			)
  with ArrowWriter(stream=__UpperCamelCase				,  features=__UpperCamelCase			) as writer:
    writer.write({"""labels""": 0}			)
    writer.write({"""labels""": 1}			)
    SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   writer.finalize()
  assert num_examples == 2
  assert num_bytes > 0
  assert writer._schema == features.arrow_schema
  assert writer._schema.metadata == features.arrow_schema.metadata
  SCREAMING_SNAKE_CASE__    							=   pa.BufferReader(output.getvalue()			)
  SCREAMING_SNAKE_CASE__    							=   pa.ipc.open_stream(__UpperCamelCase			)
  SCREAMING_SNAKE_CASE__    							=   f.read_all()
  SCREAMING_SNAKE_CASE__    							=   pa_table.schema
  assert pa_table.num_rows == 2
  assert schema == features.arrow_schema
  assert schema.metadata == features.arrow_schema.metadata
  assert features == Features.from_arrow_schema(__UpperCamelCase			)



@pytest.mark.parametrize("""writer_batch_size"""				,  [None, 1, 10]			)
def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: List[str]			)							->      List[str]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   pa.BufferOutputStream()
  with ArrowWriter(
      stream=__UpperCamelCase				,  writer_batch_size=__UpperCamelCase				,  hash_salt="""split_name"""				,  check_duplicates=__UpperCamelCase				,  ) as writer:
    with pytest.raises(__UpperCamelCase			):
      writer.write({"""col_1""": """foo""", """col_2""": 1}				,  key=[1, 2]			)
      SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   writer.finalize()



@pytest.mark.parametrize("""writer_batch_size"""				,  [None, 2, 10]			)
def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Dict			)							->      Dict:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   pa.BufferOutputStream()
  with ArrowWriter(
      stream=__UpperCamelCase				,  writer_batch_size=__UpperCamelCase				,  hash_salt="""split_name"""				,  check_duplicates=__UpperCamelCase				,  ) as writer:
    with pytest.raises(__UpperCamelCase			):
      writer.write({"""col_1""": """foo""", """col_2""": 1}				,  key=10			)
      writer.write({"""col_1""": """bar""", """col_2""": 2}				,  key=10			)
      SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   writer.finalize()



@pytest.mark.parametrize("""writer_batch_size"""				,  [None, 2, 10]			)
def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: List[str]			)							->      int:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   pa.BufferOutputStream()
  with ArrowWriter(
      stream=__UpperCamelCase				,  writer_batch_size=__UpperCamelCase				,  hash_salt="""split_name"""				,  check_duplicates=__UpperCamelCase				,  ) as writer:
    writer.write({"""col_1""": """foo""", """col_2""": 1}				,  key=1			)
    writer.write({"""col_1""": """bar""", """col_2""": 2}				,  key=2			)
    SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   writer.finalize()
  assert num_examples == 2
  assert num_bytes > 0
  _check_output(output.getvalue()				,  expected_num_chunks=num_examples if writer_batch_size == 1 else 1			)



@pytest.mark.parametrize("""writer_batch_size"""				,  [None, 1, 10]			)
@pytest.mark.parametrize(
    """fields"""				,  [None, {"""col_1""": pa.string(), """col_2""": pa.intaa()}, {"""col_1""": pa.string(), """col_2""": pa.intaa()}]			)
def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Optional[int]				,  __UpperCamelCase					: Union[str, Any]			)							->      Optional[int]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   pa.BufferOutputStream()
  SCREAMING_SNAKE_CASE__    							=   pa.schema(__UpperCamelCase			) if fields else None
  with ArrowWriter(stream=__UpperCamelCase				,  schema=__UpperCamelCase				,  writer_batch_size=__UpperCamelCase			) as writer:
    writer.write_batch({"""col_1""": ["""foo""", """bar"""], """col_2""": [1, 2]}			)
    writer.write_batch({"""col_1""": [], """col_2""": []}			)
    SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   writer.finalize()
  assert num_examples == 2
  assert num_bytes > 0
  if not fields:
    SCREAMING_SNAKE_CASE__    							=   {"""col_1""": pa.string(), """col_2""": pa.intaa()}
  assert writer._schema == pa.schema(__UpperCamelCase				,  metadata=writer._schema.metadata			)
  _check_output(output.getvalue()				,  expected_num_chunks=num_examples if writer_batch_size == 1 else 1			)



@pytest.mark.parametrize("""writer_batch_size"""				,  [None, 1, 10]			)
@pytest.mark.parametrize(
    """fields"""				,  [None, {"""col_1""": pa.string(), """col_2""": pa.intaa()}, {"""col_1""": pa.string(), """col_2""": pa.intaa()}]			)
def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: int				,  __UpperCamelCase					: Union[str, Any]			)							->      Any:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   pa.BufferOutputStream()
  SCREAMING_SNAKE_CASE__    							=   pa.schema(__UpperCamelCase			) if fields else None
  with ArrowWriter(stream=__UpperCamelCase				,  schema=__UpperCamelCase				,  writer_batch_size=__UpperCamelCase			) as writer:
    writer.write_table(pa.Table.from_pydict({"""col_1""": ["""foo""", """bar"""], """col_2""": [1, 2]}			)			)
    SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   writer.finalize()
  assert num_examples == 2
  assert num_bytes > 0
  if not fields:
    SCREAMING_SNAKE_CASE__    							=   {"""col_1""": pa.string(), """col_2""": pa.intaa()}
  assert writer._schema == pa.schema(__UpperCamelCase				,  metadata=writer._schema.metadata			)
  _check_output(output.getvalue()				,  expected_num_chunks=num_examples if writer_batch_size == 1 else 1			)



@pytest.mark.parametrize("""writer_batch_size"""				,  [None, 1, 10]			)
@pytest.mark.parametrize(
    """fields"""				,  [None, {"""col_1""": pa.string(), """col_2""": pa.intaa()}, {"""col_1""": pa.string(), """col_2""": pa.intaa()}]			)
def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Union[str, Any]				,  __UpperCamelCase					: List[str]			)							->      str:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   pa.BufferOutputStream()
  SCREAMING_SNAKE_CASE__    							=   pa.schema(__UpperCamelCase			) if fields else None
  with ArrowWriter(stream=__UpperCamelCase				,  schema=__UpperCamelCase				,  writer_batch_size=__UpperCamelCase			) as writer:
    writer.write_row(pa.Table.from_pydict({"""col_1""": ["""foo"""], """col_2""": [1]}			)			)
    writer.write_row(pa.Table.from_pydict({"""col_1""": ["""bar"""], """col_2""": [2]}			)			)
    SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   writer.finalize()
  assert num_examples == 2
  assert num_bytes > 0
  if not fields:
    SCREAMING_SNAKE_CASE__    							=   {"""col_1""": pa.string(), """col_2""": pa.intaa()}
  assert writer._schema == pa.schema(__UpperCamelCase				,  metadata=writer._schema.metadata			)
  _check_output(output.getvalue()				,  expected_num_chunks=num_examples if writer_batch_size == 1 else 1			)



def 			__SCREAMING_SNAKE_CASE						(      )							->      List[Any]:







  """simple docstring"""





  with tempfile.TemporaryDirectory() as tmp_dir:
    SCREAMING_SNAKE_CASE__    							=   {"""col_1""": pa.string(), """col_2""": pa.intaa()}
    SCREAMING_SNAKE_CASE__    							=   os.path.join(__UpperCamelCase				,  """test.arrow"""			)
    with ArrowWriter(path=__UpperCamelCase				,  schema=pa.schema(__UpperCamelCase			)			) as writer:
      writer.write_batch({"""col_1""": ["""foo""", """bar"""], """col_2""": [1, 2]}			)
      SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   writer.finalize()
    assert num_examples == 2
    assert num_bytes > 0
    assert writer._schema == pa.schema(__UpperCamelCase				,  metadata=writer._schema.metadata			)
    _check_output(__UpperCamelCase				,  1			)



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: int			)							->      Tuple:







  """simple docstring"""





  if pa.types.is_list(__UpperCamelCase			):
    return get_base_dtype(arr_type.value_type			)
  else:
    return arr_type



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







  """simple docstring"""





  if isinstance(lst[0]				,  __UpperCamelCase			):
    change_first_primitive_element_in_list(lst[0]				,  __UpperCamelCase			)
  else:
    SCREAMING_SNAKE_CASE__    							=   value



@pytest.mark.parametrize("""optimized_int_type, expected_dtype"""				,  [(None, pa.intaa()), (Value("""int32"""			), pa.intaa())]			)
@pytest.mark.parametrize("""sequence"""				,  [[1, 2, 3], [[1, 2, 3]], [[[1, 2, 3]]]]			)
def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Union[str, Any]				,  __UpperCamelCase					: Optional[int]				,  __UpperCamelCase					: Dict			)							->      Any:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   pa.array(TypedSequence(__UpperCamelCase				,  optimized_int_type=__UpperCamelCase			)			)
  assert get_base_dtype(arr.type			) == expected_dtype



@pytest.mark.parametrize(
    """col, expected_dtype"""				,  [
        ("""attention_mask""", pa.inta()),
        ("""special_tokens_mask""", pa.inta()),
        ("""token_type_ids""", pa.inta()),
        ("""input_ids""", pa.intaa()),
        ("""other""", pa.intaa()),
    ]				,  )
@pytest.mark.parametrize("""sequence"""				,  [[1, 2, 3], [[1, 2, 3]], [[[1, 2, 3]]]]			)
def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: List[Any]				,  __UpperCamelCase					: Dict				,  __UpperCamelCase					: List[str]			)							->      Optional[int]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   pa.array(OptimizedTypedSequence(__UpperCamelCase				,  col=__UpperCamelCase			)			)
  assert get_base_dtype(arr.type			) == expected_dtype

  # not in range
  if col != "other":
    # avoids errors due to in-place modifications
    SCREAMING_SNAKE_CASE__    							=   copy.deepcopy(__UpperCamelCase			)
    SCREAMING_SNAKE_CASE__    							=   np.iinfo(expected_dtype.to_pandas_dtype()			).max + 1
    change_first_primitive_element_in_list(__UpperCamelCase				,  __UpperCamelCase			)
    SCREAMING_SNAKE_CASE__    							=   pa.array(OptimizedTypedSequence(__UpperCamelCase				,  col=__UpperCamelCase			)			)
    assert get_base_dtype(arr.type			) == pa.intaa()



@pytest.mark.parametrize("""raise_exception"""				,  [False, True]			)
def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Optional[int]				,  __UpperCamelCase					: Union[str, Any]			)							->      str:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   str(tmp_path / """dataset-train.arrow"""			)
  try:
    with ArrowWriter(path=__UpperCamelCase			) as writer:
      if raise_exception:
        raise pa.lib.ArrowInvalid()
      else:
        writer.stream.close()
  except pa.lib.ArrowInvalid:
    pass
  finally:
    assert writer.stream.closed



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Tuple			)							->      str:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   """mock://dataset-train.arrow"""
  with ArrowWriter(path=__UpperCamelCase				,  storage_options=mockfs.storage_options			) as writer:
    assert isinstance(writer._fs				,  type(__UpperCamelCase			)			)
    assert writer._fs.storage_options == mockfs.storage_options
    writer.write({"""col_1""": """foo""", """col_2""": 1}			)
    writer.write({"""col_1""": """bar""", """col_2""": 2}			)
    SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   writer.finalize()
  assert num_examples == 2
  assert num_bytes > 0
  assert mockfs.exists(__UpperCamelCase			)



def 			__SCREAMING_SNAKE_CASE						(      )							->      str:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   pa.BufferOutputStream()
  with ParquetWriter(stream=__UpperCamelCase			) as writer:
    writer.write({"""col_1""": """foo""", """col_2""": 1}			)
    writer.write({"""col_1""": """bar""", """col_2""": 2}			)
    SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   writer.finalize()
  assert num_examples == 2
  assert num_bytes > 0
  SCREAMING_SNAKE_CASE__    							=   pa.BufferReader(output.getvalue()			)
  SCREAMING_SNAKE_CASE__    							=   pq.read_table(__UpperCamelCase			)
  assert pa_table.to_pydict() == {"col_1": ["foo", "bar"], "col_2": [1, 2]}



@require_pil
@pytest.mark.parametrize("""embed_local_files"""				,  [False, True]			)
def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Tuple				,  __UpperCamelCase					: Tuple			)							->      Optional[Any]:







  """simple docstring"""





  import PIL.Image

  SCREAMING_SNAKE_CASE__    							=   str(tmp_path / """test_image_rgb.jpg"""			)
  PIL.Image.fromarray(np.zeros((5, 5)				,  dtype=np.uinta			)			).save(__UpperCamelCase				,  format="""png"""			)
  SCREAMING_SNAKE_CASE__    							=   pa.BufferOutputStream()
  with ParquetWriter(
      stream=__UpperCamelCase				,  features=Features({"""image""": Image()}			)				,  embed_local_files=__UpperCamelCase			) as writer:
    writer.write({"""image""": image_path}			)
    writer.finalize()
  SCREAMING_SNAKE_CASE__    							=   pa.BufferReader(output.getvalue()			)
  SCREAMING_SNAKE_CASE__    							=   pq.read_table(__UpperCamelCase			)
  SCREAMING_SNAKE_CASE__    							=   pa_table.to_pydict()
  if embed_local_files:
    assert isinstance(out["""image"""][0]["""path"""]				,  __UpperCamelCase			)
    with open(__UpperCamelCase				,  """rb"""			) as f:
      assert out["image"][0]["bytes"] == f.read()
  else:
    assert out["image"][0]["path"] == image_path
    assert out["image"][0]["bytes"] is None






def 			__SCREAMING_SNAKE_CASE						(      )							->      List[Any]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   pa.schema([pa.field("""col_1"""				,  pa.string()				,  nullable=__UpperCamelCase			)]			)
  SCREAMING_SNAKE_CASE__    							=   pa.BufferOutputStream()
  with ArrowWriter(stream=__UpperCamelCase			) as writer:
    writer._build_writer(inferred_schema=__UpperCamelCase			)
    assert writer._schema == pa.schema([pa.field("""col_1"""				,  pa.string()			)]			)
