



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 					a	(  __lowerCamelCase       ):



  def 	lowerCamelCase__       (						self :					Optional[Any]		)     ->					Union[str, Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Optional[Any]     			=pa.array(TypedSequence([1, 2, 3]		)		)
     self.assertEqual(arr.type       ,		pa.intaa()		)



  def 	lowerCamelCase__       (						self :					Optional[int]		)     ->					str:


     '''simple docstring'''




     with self.assertRaises(SCREAMING_SNAKE_CASE_		):
        SCREAMING_SNAKE_CASE_: Optional[int]     			=pa.array(TypedSequence([1, 2, 3]		)       ,		type=pa.intaa()		)



  def 	lowerCamelCase__       (						self :					Optional[int]		)     ->					List[str]:


     '''simple docstring'''




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



  def 	lowerCamelCase__       (						self :					Tuple		)     ->					Dict:


     '''simple docstring'''




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



  def 	lowerCamelCase__       (						self :					List[Any]		)     ->					List[str]:


     '''simple docstring'''




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



  def 	lowerCamelCase__       (						self :					int		)     ->					List[Any]:


     '''simple docstring'''




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



  def 	lowerCamelCase__       (						self :					List[Any]		)     ->					Any:


     '''simple docstring'''




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



  def 	lowerCamelCase__       (						self :					Any		)     ->					Tuple:


     '''simple docstring'''




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



  def 	lowerCamelCase__       (						self :					Any		)     ->					List[Any]:


     '''simple docstring'''




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



  def 	lowerCamelCase__       (						self :					str		)     ->					int:


     '''simple docstring'''




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



  def 	lowerCamelCase__       (						self :					List[str]		)     ->					Tuple:


     '''simple docstring'''




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





  @require_pil
  def 	lowerCamelCase__       (						self :					str		)     ->					str:


     '''simple docstring'''




     import PIL.Image

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







def      __magic_name__				(							lowercase							,			lowercase ):
   SCREAMING_SNAKE_CASE_: str     			=pa.BufferReader(__a ) if isinstance(__a							,			pa.Buffer ) else pa.memory_map(__a )
   SCREAMING_SNAKE_CASE_: Optional[Any]     			=pa.ipc.open_stream(__a )
   SCREAMING_SNAKE_CASE_: List[str]     			=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      __magic_name__				(							lowercase							,			lowercase ):
   SCREAMING_SNAKE_CASE_: Dict     			=pa.BufferOutputStream()
   SCREAMING_SNAKE_CASE_: int     			=pa.schema(__a ) if fields else None
   with ArrowWriter(stream=__a							,			schema=__a							,			writer_batch_size=__a ) as writer:
      writer.write({"""col_1""": """foo""", """col_2""": 1} )
      writer.write({"""col_1""": """bar""", """col_2""": 2} )
      SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: List[Any]     			=writer.finalize()
   assert num_examples == 2
   assert num_bytes > 0
   if not fields:
      SCREAMING_SNAKE_CASE_: Any     			={"""col_1""": pa.string(), """col_2""": pa.intaa()}
   assert writer._schema == pa.schema(__a							,			metadata=writer._schema.metadata )
   _check_output(output.getvalue()							,			expected_num_chunks=num_examples if writer_batch_size == 1 else 1 )





def      __magic_name__				(							):
   SCREAMING_SNAKE_CASE_: List[Any]     			=pa.BufferOutputStream()
   SCREAMING_SNAKE_CASE_: Union[str, Any]     			=Features({"""labels""": ClassLabel(names=["""neg""", """pos"""] )} )
   with ArrowWriter(stream=__a							,			features=__a ) as writer:
      writer.write({"""labels""": 0} )
      writer.write({"""labels""": 1} )
      SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: Any     			=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_: Tuple     			=pa.BufferReader(output.getvalue() )
   SCREAMING_SNAKE_CASE_: int     			=pa.ipc.open_stream(__a )
   SCREAMING_SNAKE_CASE_: List[Any]     			=f.read_all()
   SCREAMING_SNAKE_CASE_: Dict     			=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(__a )





@pytest.mark.parametrize("""writer_batch_size"""							,			[None, 1, 10] )
def      __magic_name__				(							lowercase ):
   SCREAMING_SNAKE_CASE_: List[Any]     			=pa.BufferOutputStream()
   with ArrowWriter(
       stream=__a							,			writer_batch_size=__a							,			hash_salt="""split_name"""							,			check_duplicates=__a							,			) as writer:
      with pytest.raises(__a ):
         writer.write({"""col_1""": """foo""", """col_2""": 1}							,			key=[1, 2] )
         SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: Optional[int]     			=writer.finalize()





@pytest.mark.parametrize("""writer_batch_size"""							,			[None, 2, 10] )
def      __magic_name__				(							lowercase ):
   SCREAMING_SNAKE_CASE_: Optional[Any]     			=pa.BufferOutputStream()
   with ArrowWriter(
       stream=__a							,			writer_batch_size=__a							,			hash_salt="""split_name"""							,			check_duplicates=__a							,			) as writer:
      with pytest.raises(__a ):
         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_: str     			=writer.finalize()





@pytest.mark.parametrize("""writer_batch_size"""							,			[None, 2, 10] )
def      __magic_name__				(							lowercase ):
   SCREAMING_SNAKE_CASE_: List[str]     			=pa.BufferOutputStream()
   with ArrowWriter(
       stream=__a							,			writer_batch_size=__a							,			hash_salt="""split_name"""							,			check_duplicates=__a							,			) 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_: Dict     			=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      __magic_name__				(							lowercase							,			lowercase ):
   SCREAMING_SNAKE_CASE_: Optional[Any]     			=pa.BufferOutputStream()
   SCREAMING_SNAKE_CASE_: str     			=pa.schema(__a ) if fields else None
   with ArrowWriter(stream=__a							,			schema=__a							,			writer_batch_size=__a ) 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_: Optional[int]     			=writer.finalize()
   assert num_examples == 2
   assert num_bytes > 0
   if not fields:
      SCREAMING_SNAKE_CASE_: str     			={"""col_1""": pa.string(), """col_2""": pa.intaa()}
   assert writer._schema == pa.schema(__a							,			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      __magic_name__				(							lowercase							,			lowercase ):
   SCREAMING_SNAKE_CASE_: int     			=pa.BufferOutputStream()
   SCREAMING_SNAKE_CASE_: Union[str, Any]     			=pa.schema(__a ) if fields else None
   with ArrowWriter(stream=__a							,			schema=__a							,			writer_batch_size=__a ) as writer:
      writer.write_table(pa.Table.from_pydict({"""col_1""": ["""foo""", """bar"""], """col_2""": [1, 2]} ) )
      SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: str     			=writer.finalize()
   assert num_examples == 2
   assert num_bytes > 0
   if not fields:
      SCREAMING_SNAKE_CASE_: Optional[int]     			={"""col_1""": pa.string(), """col_2""": pa.intaa()}
   assert writer._schema == pa.schema(__a							,			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      __magic_name__				(							lowercase							,			lowercase ):
   SCREAMING_SNAKE_CASE_: int     			=pa.BufferOutputStream()
   SCREAMING_SNAKE_CASE_: Optional[int]     			=pa.schema(__a ) if fields else None
   with ArrowWriter(stream=__a							,			schema=__a							,			writer_batch_size=__a ) 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_: int     			=writer.finalize()
   assert num_examples == 2
   assert num_bytes > 0
   if not fields:
      SCREAMING_SNAKE_CASE_: List[str]     			={"""col_1""": pa.string(), """col_2""": pa.intaa()}
   assert writer._schema == pa.schema(__a							,			metadata=writer._schema.metadata )
   _check_output(output.getvalue()							,			expected_num_chunks=num_examples if writer_batch_size == 1 else 1 )





def      __magic_name__				(							):




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





def      __magic_name__				(							lowercase ):




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





def      __magic_name__				(							lowercase							,			lowercase ):




   if isinstance(lst[0]							,			__a ):
      change_first_primitive_element_in_list(lst[0]							,			__a )
   else:
      SCREAMING_SNAKE_CASE_: List[str]     			=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      __magic_name__				(							lowercase							,			lowercase							,			lowercase ):
   SCREAMING_SNAKE_CASE_: Dict     			=pa.array(TypedSequence(__a							,			optimized_int_type=__a ) )
   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      __magic_name__				(							lowercase							,			lowercase							,			lowercase ):
   SCREAMING_SNAKE_CASE_: str     			=pa.array(OptimizedTypedSequence(__a							,			col=__a ) )
   assert get_base_dtype(arr.type ) == expected_dtype

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





@pytest.mark.parametrize("""raise_exception"""							,			[False, True] )
def      __magic_name__				(							lowercase							,			lowercase ):
   SCREAMING_SNAKE_CASE_: Dict     			=str(tmp_path / """dataset-train.arrow""" )
   try:
      with ArrowWriter(path=__a ) as writer:
         if raise_exception:
            raise pa.lib.ArrowInvalid()
         else:
            writer.stream.close()
   except pa.lib.ArrowInvalid:
      pass
   finally:
      assert writer.stream.closed





def      __magic_name__				(							lowercase ):
   SCREAMING_SNAKE_CASE_: List[str]     			="""mock://dataset-train.arrow"""
   with ArrowWriter(path=__a							,			storage_options=mockfs.storage_options ) as writer:
      assert isinstance(writer._fs							,			type(__a ) )
      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_: Optional[int]     			=writer.finalize()
   assert num_examples == 2
   assert num_bytes > 0
   assert mockfs.exists(__a )





def      __magic_name__				(							):
   SCREAMING_SNAKE_CASE_: List[Any]     			=pa.BufferOutputStream()
   with ParquetWriter(stream=__a ) as writer:
      writer.write({"""col_1""": """foo""", """col_2""": 1} )
      writer.write({"""col_1""": """bar""", """col_2""": 2} )
      SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: int     			=writer.finalize()
   assert num_examples == 2
   assert num_bytes > 0
   SCREAMING_SNAKE_CASE_: Optional[Any]     			=pa.BufferReader(output.getvalue() )
   SCREAMING_SNAKE_CASE_: Dict     			=pq.read_table(__a )
   assert pa_table.to_pydict() == {"col_1": ["foo", "bar"], "col_2": [1, 2]}





@require_pil
@pytest.mark.parametrize("""embed_local_files"""							,			[False, True] )
def      __magic_name__				(							lowercase							,			lowercase ):
   import PIL.Image

   SCREAMING_SNAKE_CASE_: Optional[Any]     			=str(tmp_path / """test_image_rgb.jpg""" )
   PIL.Image.fromarray(np.zeros((5, 5)							,			dtype=np.uinta ) ).save(__a							,			format="""png""" )
   SCREAMING_SNAKE_CASE_: Any     			=pa.BufferOutputStream()
   with ParquetWriter(
       stream=__a							,			features=Features({"""image""": Image()} )							,			embed_local_files=__a ) as writer:
      writer.write({"""image""": image_path} )
      writer.finalize()
   SCREAMING_SNAKE_CASE_: Optional[int]     			=pa.BufferReader(output.getvalue() )
   SCREAMING_SNAKE_CASE_: int     			=pq.read_table(__a )
   SCREAMING_SNAKE_CASE_: List[str]     			=pa_table.to_pydict()
   if embed_local_files:
      assert isinstance(out["""image"""][0]["""path"""]							,			__a )
      with open(__a							,			"""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      __magic_name__				(							):
   SCREAMING_SNAKE_CASE_: Tuple     			=pa.schema([pa.field("""col_1"""							,			pa.string()							,			nullable=__a )] )
   SCREAMING_SNAKE_CASE_: Union[str, Any]     			=pa.BufferOutputStream()
   with ArrowWriter(stream=__a ) as writer:
      writer._build_writer(inferred_schema=__a )
      assert writer._schema == pa.schema([pa.field("""col_1"""							,			pa.string() )] )








"""simple docstring"""
import numpy as np
import torch
from torch.utils.data import DataLoader

from accelerate.utils.dataclasses import DistributedType
class 					a	:



  def __init__(						self :					Union[str, Any]       ,		lowerCAmelCase :					List[str]=2       ,		lowerCAmelCase :					int=3       ,		lowerCAmelCase :					Optional[Any]=64       ,		lowerCAmelCase :					Union[str, Any]=None		)     ->					Optional[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=np.random.default_rng(lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: Optional[int]     			=length
     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=rng.normal(size=(length,)		).astype(np.floataa		)
     SCREAMING_SNAKE_CASE_: Tuple     			=a * self.x + b + rng.normal(scale=0.1       ,		size=(length,)		).astype(np.floataa		)



  def __len__(						self :					List[Any]		)     ->					str:


     '''simple docstring'''




     return self.length





  def __getitem__(						self :					Union[str, Any]       ,		lowerCAmelCase :					Any		)     ->					List[str]:


     '''simple docstring'''




     return {"x": self.x[i], "y": self.y[i]}







class 					a	(  torch.nn.Module       ):



  def __init__(						self :					Optional[int]       ,		lowerCAmelCase :					str=0       ,		lowerCAmelCase :					List[Any]=0       ,		lowerCAmelCase :					Optional[int]=False		)     ->					Tuple:


     '''simple docstring'''




     super().__init__()
     SCREAMING_SNAKE_CASE_: int     			=torch.nn.Parameter(torch.tensor([2, 3]		).float()		)
     SCREAMING_SNAKE_CASE_: int     			=torch.nn.Parameter(torch.tensor([2, 3]		).float()		)
     SCREAMING_SNAKE_CASE_: Dict     			=True





  def 	lowerCamelCase__       (						self :					str       ,		lowerCAmelCase :					Tuple=None		)     ->					int:


     '''simple docstring'''




     if self.first_batch:
        print(f'''Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'''		)
        SCREAMING_SNAKE_CASE_: Union[str, Any]     			=False
     return x * self.a[0] + self.b[0]







class 					a	(  torch.nn.Module       ):



  def __init__(						self :					Union[str, Any]       ,		lowerCAmelCase :					Any=0       ,		lowerCAmelCase :					List[Any]=0       ,		lowerCAmelCase :					List[Any]=False		)     ->					str:


     '''simple docstring'''




     super().__init__()
     SCREAMING_SNAKE_CASE_: List[str]     			=torch.nn.Parameter(torch.tensor(lowerCAmelCase		).float()		)
     SCREAMING_SNAKE_CASE_: Optional[int]     			=torch.nn.Parameter(torch.tensor(lowerCAmelCase		).float()		)
     SCREAMING_SNAKE_CASE_: List[Any]     			=True





  def 	lowerCamelCase__       (						self :					List[str]       ,		lowerCAmelCase :					int=None		)     ->					Any:


     '''simple docstring'''




     if self.first_batch:
        print(f'''Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'''		)
        SCREAMING_SNAKE_CASE_: Optional[int]     			=False
     return x * self.a + self.b







def      __magic_name__				(							lowercase							,			lowercase = 16 ):
   from datasets import load_dataset
   from transformers import AutoTokenizer

   SCREAMING_SNAKE_CASE_: Optional[Any]     			=AutoTokenizer.from_pretrained("""bert-base-cased""" )
   SCREAMING_SNAKE_CASE_: Optional[int]     			={"""train""": """tests/test_samples/MRPC/train.csv""", """validation""": """tests/test_samples/MRPC/dev.csv"""}
   SCREAMING_SNAKE_CASE_: Any     			=load_dataset("""csv"""							,			data_files=lowercase )
   SCREAMING_SNAKE_CASE_: Any     			=datasets["""train"""].unique("""label""" )

   SCREAMING_SNAKE_CASE_: List[Any]     			={v: i for i, v in enumerate(lowercase )}

   def tokenize_function(lowercase ):
      # max_length=None => use the model max length (it's actually the default)
      SCREAMING_SNAKE_CASE_: Dict     			=tokenizer(
          examples["""sentence1"""]							,			examples["""sentence2"""]							,			truncation=lowercase							,			max_length=lowercase							,			padding="""max_length""" )
      if "label" in examples:
         SCREAMING_SNAKE_CASE_: Optional[int]     			=[label_to_id[l] for l in examples["""label"""]]
      return outputs

   # Apply the method we just defined to all the examples in all the splits of the dataset
   SCREAMING_SNAKE_CASE_: List[Any]     			=datasets.map(
       lowercase							,			batched=lowercase							,			remove_columns=["""sentence1""", """sentence2""", """label"""]							,			)

   def collate_fn(lowercase ):
      # On TPU it's best to pad everything to the same length or training will be very slow.
      if accelerator.distributed_type == DistributedType.TPU:
         return tokenizer.pad(lowercase							,			padding="""max_length"""							,			max_length=128							,			return_tensors="""pt""" )
      return tokenizer.pad(lowercase							,			padding="""longest"""							,			return_tensors="""pt""" )

   # Instantiate dataloaders.
   SCREAMING_SNAKE_CASE_: Optional[int]     			=DataLoader(tokenized_datasets["""train"""]							,			shuffle=lowercase							,			collate_fn=lowercase							,			batch_size=2 )
   SCREAMING_SNAKE_CASE_: Dict     			=DataLoader(tokenized_datasets["""validation"""]							,			shuffle=lowercase							,			collate_fn=lowercase							,			batch_size=1 )

   return train_dataloader, eval_dataloader



