




import inspect
from typing import Callable, List, Optional, Set, Tuple, Union

import torch
from packaging import version
from safetensors.torch import storage_ptr, storage_size
from torch import nn

from .utils import logging


lowerCamelCase_  						=       [nn.LayerNorm]

lowerCamelCase_  						=       logging.get_logger(__name__)

lowerCamelCase_  						=       version.parse(version.parse(torch.__version__).base_version)

lowerCamelCase_  						=       parsed_torch_version_base >= version.parse("2.0")
lowerCamelCase_  						=       parsed_torch_version_base >= version.parse("1.12")
lowerCamelCase_  						=       parsed_torch_version_base >= version.parse("1.11")
lowerCamelCase_  						=       parsed_torch_version_base < version.parse("1.11")



def      UpperCAmelCase_   (     __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    self     ):
    from torch import _softmax_backward_data

    if is_torch_less_than_1_11:
        return _softmax_backward_data(__UpperCamelCase,    __UpperCamelCase,    parent.dim,    self     )
    else:
        return _softmax_backward_data(__UpperCamelCase,    __UpperCamelCase,    parent.dim,    self.dtype     )



def      UpperCAmelCase_   (     __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase = 0     ):
    SCREAMING_SNAKE_CASE__       =index.to(layer.weight.device     )
    SCREAMING_SNAKE_CASE__       =layer.weight.index_select(__UpperCamelCase,    __UpperCamelCase     ).clone().detach()
    if layer.bias is not None:
        if dim == 1:
            SCREAMING_SNAKE_CASE__       =layer.bias.clone().detach()
        else:
            SCREAMING_SNAKE_CASE__       =layer.bias[index].clone().detach()
    SCREAMING_SNAKE_CASE__       =list(layer.weight.size()     )
    SCREAMING_SNAKE_CASE__       =len(__UpperCamelCase     )
    SCREAMING_SNAKE_CASE__       =nn.Linear(new_size[1],    new_size[0],    bias=layer.bias is not None     ).to(layer.weight.device     )
    SCREAMING_SNAKE_CASE__       =False
    new_layer.weight.copy_(W.contiguous()     )
    SCREAMING_SNAKE_CASE__       =True
    if layer.bias is not None:
        SCREAMING_SNAKE_CASE__       =False
        new_layer.bias.copy_(b.contiguous()     )
        SCREAMING_SNAKE_CASE__       =True
    return new_layer
class 				__a (			nn.Module					):


     """simple docstring"""


     def __init__(     self      :			Optional[Any]    ,_UpperCamelCase      :			Optional[int]    ,_UpperCamelCase      :			Any						)		->       List[Any]:


         '''simple docstring'''





         super().__init__()
         SCREAMING_SNAKE_CASE__       =nf
         SCREAMING_SNAKE_CASE__       =nn.Parameter(torch.empty(_UpperCamelCase    ,_UpperCamelCase						)						)
         SCREAMING_SNAKE_CASE__       =nn.Parameter(torch.zeros(_UpperCamelCase						)						)
         nn.init.normal_(self.weight    ,std=0.02						)




     def   __A    (     self      :			List[str]    ,_UpperCamelCase      :			str						)		->       Tuple:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       =x.size()[:-1] + (self.nf,)
         SCREAMING_SNAKE_CASE__       =torch.addmm(self.bias    ,x.view(-1    ,x.size(-1						)						)    ,self.weight						)
         SCREAMING_SNAKE_CASE__       =x.view(_UpperCamelCase						)
         return x






def      UpperCAmelCase_   (     __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase = 1     ):
    SCREAMING_SNAKE_CASE__       =index.to(layer.weight.device     )
    SCREAMING_SNAKE_CASE__       =layer.weight.index_select(__UpperCamelCase,    __UpperCamelCase     ).clone().detach()
    if dim == 0:
        SCREAMING_SNAKE_CASE__       =layer.bias.clone().detach()
    else:
        SCREAMING_SNAKE_CASE__       =layer.bias[index].clone().detach()
    SCREAMING_SNAKE_CASE__       =list(layer.weight.size()     )
    SCREAMING_SNAKE_CASE__       =len(__UpperCamelCase     )
    SCREAMING_SNAKE_CASE__       =ConvaD(new_size[1],    new_size[0]     ).to(layer.weight.device     )
    SCREAMING_SNAKE_CASE__       =False
    new_layer.weight.copy_(W.contiguous()     )
    SCREAMING_SNAKE_CASE__       =True
    SCREAMING_SNAKE_CASE__       =False
    new_layer.bias.copy_(b.contiguous()     )
    SCREAMING_SNAKE_CASE__       =True
    return new_layer



def      UpperCAmelCase_   (     __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase = None     ):
    if isinstance(__UpperCamelCase,    nn.Linear     ):
        return prune_linear_layer(__UpperCamelCase,    __UpperCamelCase,    dim=0 if dim is None else dim     )
    elif isinstance(__UpperCamelCase,    __UpperCamelCase     ):
        return prune_convad_layer(__UpperCamelCase,    __UpperCamelCase,    dim=1 if dim is None else dim     )
    else:
        raise ValueError(f"""Can't prune layer of class {layer.__class__}"""     )



def      UpperCAmelCase_   (     __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    *__UpperCamelCase     ):
    assert len(__UpperCamelCase     ) > 0, f"""{input_tensors} has to be a tuple/list of tensors"""

    # inspect.signature exist since python 3.5 and is a python method -> no problem with backward compatibility
    SCREAMING_SNAKE_CASE__       =len(inspect.signature(__UpperCamelCase     ).parameters     )
    if num_args_in_forward_chunk_fn != len(__UpperCamelCase     ):
        raise ValueError(
            f"""forward_chunk_fn expects {num_args_in_forward_chunk_fn} arguments, but only {len(__UpperCamelCase     )} input """
            """tensors are given"""     )

    if chunk_size > 0:
        SCREAMING_SNAKE_CASE__       =input_tensors[0].shape[chunk_dim]
        for input_tensor in input_tensors:
            if input_tensor.shape[chunk_dim] != tensor_shape:
                raise ValueError(
                    f"""All input tenors have to be of the same shape: {tensor_shape}, """
                    f"""found shape {input_tensor.shape[chunk_dim]}"""     )

        if input_tensors[0].shape[chunk_dim] % chunk_size != 0:
            raise ValueError(
                f"""The dimension to be chunked {input_tensors[0].shape[chunk_dim]} has to be a multiple of the chunk """
                f"""size {chunk_size}"""     )

        SCREAMING_SNAKE_CASE__       =input_tensors[0].shape[chunk_dim] // chunk_size

        # chunk input tensor into tuples
        SCREAMING_SNAKE_CASE__       =tuple(input_tensor.chunk(__UpperCamelCase,    dim=__UpperCamelCase     ) for input_tensor in input_tensors     )
        # apply forward fn to every tuple
        SCREAMING_SNAKE_CASE__       =tuple(forward_fn(*__UpperCamelCase     ) for input_tensors_chunk in zip(*__UpperCamelCase     )     )
        # concatenate output at same dimension
        return torch.cat(__UpperCamelCase,    dim=__UpperCamelCase     )

    return forward_fn(*__UpperCamelCase     )



def      UpperCAmelCase_   (     __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase     ):
    SCREAMING_SNAKE_CASE__       =torch.ones(__UpperCamelCase,    __UpperCamelCase     )
    SCREAMING_SNAKE_CASE__       =set(__UpperCamelCase     ) - already_pruned_heads  # Convert to set and remove already pruned heads
    for head in heads:
        # Compute how many pruned heads are before the head and move the index accordingly
        SCREAMING_SNAKE_CASE__       =head - sum(1 if h < head else 0 for h in already_pruned_heads     )
        SCREAMING_SNAKE_CASE__       =0
    SCREAMING_SNAKE_CASE__       =mask.view(-1     ).contiguous().eq(1     )
    SCREAMING_SNAKE_CASE__       =torch.arange(len(__UpperCamelCase     )     )[mask].long()
    return heads, index



def      UpperCAmelCase_   (     *__UpperCamelCase,    __UpperCamelCase = None     ):
    return torch.meshgrid(*__UpperCamelCase,    indexing=__UpperCamelCase     )




def      UpperCAmelCase_   (     __UpperCamelCase     ):
    return tensor.device, storage_ptr(__UpperCamelCase     ), storage_size(__UpperCamelCase     )












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 				__a (			__lowerCamelCase					):


     """simple docstring"""


     def   __A    (     self      :			Optional[Any]    ,_UpperCamelCase      :			Optional[int]    ,_UpperCamelCase      :			Optional[Any]=False						)		->       Tuple:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__      ,		SCREAMING_SNAKE_CASE__      ,		SCREAMING_SNAKE_CASE__       =(
             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(_UpperCamelCase						)    ,"""wb"""						).close()
         SCREAMING_SNAKE_CASE__       =pa.Table.from_pydict({"""filename""": [Path(_UpperCamelCase						).name] * 1_0_0}						)
         if take == -1:
             SCREAMING_SNAKE_CASE__       =len(_UpperCamelCase						) - skip
         if skip is not None and take is not None:
             SCREAMING_SNAKE_CASE__       =pa_table.slice(_UpperCamelCase    ,_UpperCamelCase						)
         return pa_table






class 				__a (			__lowerCamelCase					):


     """simple docstring"""


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


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       ="""my_name"""
         SCREAMING_SNAKE_CASE__       =SplitInfo(name="""train"""    ,num_examples=1_0_0						)
         SCREAMING_SNAKE_CASE__       =SplitInfo(name="""test"""    ,num_examples=1_0_0						)
         SCREAMING_SNAKE_CASE__       =[train_info, test_info]
         SCREAMING_SNAKE_CASE__       =SplitDict()
         split_dict.add(_UpperCamelCase						)
         split_dict.add(_UpperCamelCase						)
         SCREAMING_SNAKE_CASE__       =DatasetInfo(splits=_UpperCamelCase						)

         with tempfile.TemporaryDirectory() as tmp_dir:
             SCREAMING_SNAKE_CASE__       =ReaderTest(_UpperCamelCase    ,_UpperCamelCase						)

             SCREAMING_SNAKE_CASE__       ="""test[:33%]"""
             SCREAMING_SNAKE_CASE__       =Dataset(**reader.read(_UpperCamelCase    ,_UpperCamelCase    ,_UpperCamelCase						)						)
             self.assertEqual(dset["""filename"""][0]    ,f"""{name}-test"""						)
             self.assertEqual(dset.num_rows    ,3_3						)
             self.assertEqual(dset.num_columns    ,1						)

             SCREAMING_SNAKE_CASE__       =["""train""", """test[:33%]"""]
             SCREAMING_SNAKE_CASE__       =[Split.TRAIN, ReadInstruction.from_spec("""test[:33%]"""						)]
             for instructions in [instructionsa, instructionsa]:
                 SCREAMING_SNAKE_CASE__       =[reader.read(_UpperCamelCase    ,_UpperCamelCase    ,_UpperCamelCase						) for instr in instructions]
                 SCREAMING_SNAKE_CASE__      ,		SCREAMING_SNAKE_CASE__       =(Dataset(**_UpperCamelCase						) for dataset_kwargs in datasets_kwargs)
                 self.assertEqual(train_dset["""filename"""][0]    ,f"""{name}-train"""						)
                 self.assertEqual(train_dset.num_rows    ,1_0_0						)
                 self.assertEqual(train_dset.num_columns    ,1						)
                 self.assertIsInstance(train_dset.split    ,_UpperCamelCase						)
                 self.assertEqual(str(train_dset.split						)    ,"""train"""						)
                 self.assertEqual(test_dset["""filename"""][0]    ,f"""{name}-test"""						)
                 self.assertEqual(test_dset.num_rows    ,3_3						)
                 self.assertEqual(test_dset.num_columns    ,1						)
                 self.assertIsInstance(test_dset.split    ,_UpperCamelCase						)
                 self.assertEqual(str(test_dset.split						)    ,"""test[:33%]"""						)
                 del train_dset, test_dset




     def   __A    (     self      :			Tuple						)		->       str:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       ="""my_name"""
         SCREAMING_SNAKE_CASE__       =SplitInfo(name="""train"""    ,num_examples=1_0_0_0    ,shard_lengths=[1_0_0] * 1_0						)
         SCREAMING_SNAKE_CASE__       =[train_info]
         SCREAMING_SNAKE_CASE__       =SplitDict()
         split_dict.add(_UpperCamelCase						)
         SCREAMING_SNAKE_CASE__       =DatasetInfo(splits=_UpperCamelCase						)

         with tempfile.TemporaryDirectory() as tmp_dir:
             SCREAMING_SNAKE_CASE__       =ReaderTest(_UpperCamelCase    ,_UpperCamelCase						)

             SCREAMING_SNAKE_CASE__       ="""train[:33%]"""
             SCREAMING_SNAKE_CASE__       =Dataset(**reader.read(_UpperCamelCase    ,_UpperCamelCase    ,_UpperCamelCase						)						)
             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_3_0						)
             self.assertEqual(dset.num_columns    ,1						)




     def   __A    (     self      :			Dict						)		->       Union[str, Any]:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       =SplitInfo(name="""train"""    ,num_examples=1_0_0						)
         SCREAMING_SNAKE_CASE__       =SplitInfo(name="""test"""    ,num_examples=1_0_0						)
         SCREAMING_SNAKE_CASE__       =SplitDict()
         split_dict.add(_UpperCamelCase						)
         split_dict.add(_UpperCamelCase						)
         SCREAMING_SNAKE_CASE__       =DatasetInfo(splits=_UpperCamelCase						)

         with tempfile.TemporaryDirectory() as tmp_dir:
             SCREAMING_SNAKE_CASE__       =ReaderTest(_UpperCamelCase    ,_UpperCamelCase						)

             SCREAMING_SNAKE_CASE__       =[
                 {"""filename""": os.path.join(_UpperCamelCase    ,"""train"""						)},
                 {"""filename""": os.path.join(_UpperCamelCase    ,"""test"""						), """skip""": 1_0, """take""": 1_0},
             ]
             SCREAMING_SNAKE_CASE__       =Dataset(**reader.read_files(_UpperCamelCase    ,original_instructions="""train+test[10:20]"""						)						)
             self.assertEqual(dset.num_rows    ,1_1_0						)
             self.assertEqual(dset.num_columns    ,1						)
             del dset






@pytest.mark.parametrize("""in_memory""",    [False, True]     )
def      UpperCAmelCase_   (     __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase     ):
    SCREAMING_SNAKE_CASE__       =arrow_file
    with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
        SCREAMING_SNAKE_CASE__       =ArrowReader.read_table(__UpperCamelCase,    in_memory=__UpperCamelCase     )
    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_   (     __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase     ):
    SCREAMING_SNAKE_CASE__       =arrow_file
    SCREAMING_SNAKE_CASE__       =ArrowReader("""""",    __UpperCamelCase     )
    with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
        SCREAMING_SNAKE_CASE__       =reader.read_files([{"""filename""": filename}],    in_memory=__UpperCamelCase     )
    assert dataset_kwargs.keys() == {"arrow_table", "info", "split"}
    SCREAMING_SNAKE_CASE__       =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_   (     ):
    assert ReadInstruction("""train""",    to=10,    unit="""abs"""     ).to_spec() == "train[:10]"
    assert ReadInstruction("""train""",    from_=-80,    to=10,    unit="""%"""     ).to_spec() == "train[-80%:10%]"

    SCREAMING_SNAKE_CASE__       ="""train+test"""
    assert ReadInstruction.from_spec(__UpperCamelCase     ).to_spec() == spec_train_test

    SCREAMING_SNAKE_CASE__       ="""train[2:10]"""
    assert ReadInstruction.from_spec(__UpperCamelCase     ).to_spec() == spec_train_abs

    SCREAMING_SNAKE_CASE__       ="""train[15%:-20%]"""
    assert ReadInstruction.from_spec(__UpperCamelCase     ).to_spec() == spec_train_pct

    SCREAMING_SNAKE_CASE__       ="""train[:10%](closest)"""
    assert ReadInstruction.from_spec(__UpperCamelCase     ).to_spec() == "train[:10%]"

    SCREAMING_SNAKE_CASE__       ="""train[:10%](pct1_dropremainder)"""
    assert ReadInstruction.from_spec(__UpperCamelCase     ).to_spec() == spec_train_pct_rounding

    SCREAMING_SNAKE_CASE__       ="""train[:10%](pct1_dropremainder)+test[-10%:](pct1_dropremainder)"""
    assert ReadInstruction.from_spec(__UpperCamelCase     ).to_spec() == spec_train_test_pct_rounding



def      UpperCAmelCase_   (     ):
    SCREAMING_SNAKE_CASE__       ="""dummy"""
    SCREAMING_SNAKE_CASE__       =[SplitInfo(name="""train""",    num_examples=100     )]
    SCREAMING_SNAKE_CASE__       ="""train[:33%]"""
    SCREAMING_SNAKE_CASE__       ="""arrow"""
    SCREAMING_SNAKE_CASE__       ="""prefix"""

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

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




@pytest.mark.parametrize("""name, expected_exception""",    [(None, TypeError), ("""""", ValueError)]     )
def      UpperCAmelCase_   (     __UpperCamelCase,    __UpperCamelCase     ):
    SCREAMING_SNAKE_CASE__       =[SplitInfo(name="""train""",    num_examples=100     )]
    SCREAMING_SNAKE_CASE__       ="""train"""
    SCREAMING_SNAKE_CASE__       ="""arrow"""
    SCREAMING_SNAKE_CASE__       ="""prefix_path"""
    with pytest.raises(__UpperCamelCase     ):
        SCREAMING_SNAKE_CASE__       =make_file_instructions(__UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase     )






