import json
import os
import tempfile

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features import ArrayaD
from utils import generate_examples, get_duration


lowercase__      :    Optional[Any]          =     (3_0, 4_8_7)
lowercase__      :    Optional[Any]          =     (3_6, 1_0_2_4)
lowercase__      :    Tuple          =     (1_0_0, 1_0_0)
lowercase__      :    Optional[Any]          =     1_0_0

lowercase__      :    Optional[int]          =     datasets.Features(
    {'''text''': ArrayaD(SHAPE_TEST_1, dtype='''float32'''), '''image''': ArrayaD(SHAPE_TEST_2, dtype='''float32''')}
)

lowercase__     ,   lowercase__      :    List[Any]          =     os.path.split(__file__)
lowercase__      :    Union[str, Any]          =     os.path.join(RESULTS_BASEPATH, '''results''', RESULTS_FILENAME.replace('''.py''', '''.json'''))

@get_duration
def        SCREAMING_SNAKE_CASE_					(						snake_case__       ,  snake_case__       ,  snake_case__							)					->     str:
							with ArrowWriter(features=snake_case__       ,  path=os.path.join(snake_case__       ,  '''beta.arrow'''							)							) as writer:
														for key, record in dummy_data:
																					lowerCAmelCase     		=		my_features.encode_example(snake_case__							)
																					writer.write(snake_case__							)
														lowerCAmelCase		,				lowerCAmelCase     		=		writer.finalize()

@get_duration
def        SCREAMING_SNAKE_CASE_					(						snake_case__       ,  snake_case__							)					->     List[str]:
							lowerCAmelCase     		=		datasets.Dataset.from_file(
							    filename=os.path.join(snake_case__       ,  '''beta.arrow'''							)       ,  info=datasets.DatasetInfo(features=snake_case__							)							)
							for _ in dataset:
														pass

@get_duration
def        SCREAMING_SNAKE_CASE_					(						snake_case__       ,  snake_case__							)					->     Tuple:
							lowerCAmelCase     		=		datasets.Dataset.from_file(
							    filename=os.path.join(snake_case__       ,  '''beta.arrow'''							)       ,  info=datasets.DatasetInfo(features=snake_case__							)							)
							dataset.set_format('''numpy'''							)
							for _ in dataset:
														pass

@get_duration
def        SCREAMING_SNAKE_CASE_					(						snake_case__       ,  snake_case__							)					->     Union[str, Any]:
							lowerCAmelCase     		=		1_0
							lowerCAmelCase     		=		datasets.Dataset.from_file(
							    filename=os.path.join(snake_case__       ,  '''beta.arrow'''							)       ,  info=datasets.DatasetInfo(features=snake_case__							)							)
							for i in range(0       ,  len(snake_case__							)       ,  snake_case__							):
														lowerCAmelCase     		=		dataset[i : i + batch_size]

@get_duration
def        SCREAMING_SNAKE_CASE_					(						snake_case__       ,  snake_case__							)					->     Union[str, Any]:
							lowerCAmelCase     		=		1_0
							lowerCAmelCase     		=		datasets.Dataset.from_file(
							    filename=os.path.join(snake_case__       ,  '''beta.arrow'''							)       ,  info=datasets.DatasetInfo(features=snake_case__							)							)
							dataset.set_format('''numpy'''							)
							for i in range(0       ,  len(snake_case__							)       ,  snake_case__							):
														lowerCAmelCase     		=		dataset[i : i + batch_size]

@get_duration
def        SCREAMING_SNAKE_CASE_					(						snake_case__       ,  snake_case__							)					->     Tuple:
							lowerCAmelCase     		=		datasets.Dataset.from_file(
							    filename=os.path.join(snake_case__       ,  '''beta.arrow'''							)       ,  info=datasets.DatasetInfo(features=snake_case__							)							)
							for col in feats:
														lowerCAmelCase     		=		dataset[col]

@get_duration
def        SCREAMING_SNAKE_CASE_					(						snake_case__       ,  snake_case__							)					->     Optional[int]:
							lowerCAmelCase     		=		datasets.Dataset.from_file(
							    filename=os.path.join(snake_case__       ,  '''beta.arrow'''							)       ,  info=datasets.DatasetInfo(features=snake_case__							)							)
							dataset.set_format('''numpy'''							)
							for col in feats:
														lowerCAmelCase     		=		dataset[col]

def        SCREAMING_SNAKE_CASE_					(						)					->     Optional[Any]:
							lowerCAmelCase     		=		{}
							lowerCAmelCase     		=		(
							    read_unformated,
							    read_formatted_as_numpy,
							    read_batch_unformated,
							    read_batch_formatted_as_numpy,
							    read_col_unformated,
							    read_col_formatted_as_numpy,
							)
							with tempfile.TemporaryDirectory() as tmp_dir:
														lowerCAmelCase     		=		datasets.Features({'''image''': ArrayaD(snake_case__       ,  dtype='''float32'''							)}							)
														lowerCAmelCase     		=		generate_examples(features=snake_case__       ,  num_examples=snake_case__							)
														lowerCAmelCase     		=		write(snake_case__       ,  snake_case__       ,  snake_case__							)
														for read_func in read_functions:
																					lowerCAmelCase     		=		read_func(snake_case__       ,  snake_case__							)

							with tempfile.TemporaryDirectory() as tmp_dir:
														# don't use fixed length for fair comparison
														# feats = datasets.Features(
														#     {"image": datasets.Sequence(datasets.Sequence(datasets.Value("float32"), SPEED_TEST_SHAPE[1]), SPEED_TEST_SHAPE[0])}
														# )
														lowerCAmelCase     		=		datasets.Features({'''image''': datasets.Sequence(datasets.Sequence(datasets.Value('''float32'''							)							)							)}							)
														lowerCAmelCase     		=		generate_examples(
														    features=snake_case__       ,  num_examples=snake_case__       ,  seq_shapes={'''image''': SPEED_TEST_SHAPE}							)
														lowerCAmelCase     		=		write(snake_case__       ,  snake_case__       ,  snake_case__							)
														for read_func in read_functions:
																					lowerCAmelCase     		=		read_func(snake_case__       ,  snake_case__							)

							with tempfile.TemporaryDirectory() as tmp_dir:
														# don't use fixed length for fair comparison
														# feats = datasets.Features(
														#     {"image": datasets.Sequence(datasets.Value("float32"), SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1])}
														# )
														lowerCAmelCase     		=		datasets.Features({'''image''': datasets.Sequence(datasets.Value('''float32'''							)							)}							)
														lowerCAmelCase     		=		generate_examples(
														    features=snake_case__       ,  num_examples=snake_case__       ,  seq_shapes={'''image''': [SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1]]}       ,  )
														lowerCAmelCase     		=		write(snake_case__       ,  snake_case__       ,  snake_case__							)
														for read_func in read_functions:
																					lowerCAmelCase     		=		read_func(snake_case__       ,  snake_case__							)

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


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




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


lowercase__      :    str          =     [nn.LayerNorm]

lowercase__      :    str          =     logging.get_logger(__name__)

lowercase__      :    List[Any]          =     version.parse(version.parse(torch.__version__).base_version)

lowercase__      :    Dict          =     parsed_torch_version_base >= version.parse('''2.0''')
lowercase__      :    int          =     parsed_torch_version_base >= version.parse('''1.12''')
lowercase__      :    Optional[int]          =     parsed_torch_version_base >= version.parse('''1.11''')
lowercase__      :    int          =     parsed_torch_version_base < version.parse('''1.11''')

def        SCREAMING_SNAKE_CASE_					(						snake_case__       ,  snake_case__       ,  snake_case__       ,  snake_case__       ,  self							)					->     Union[str, Any]:
							from torch import _softmax_backward_data

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

def        SCREAMING_SNAKE_CASE_					(						snake_case__       ,  snake_case__       ,  snake_case__ = 0							)					->     nn.Linear:
							lowerCAmelCase     		=		index.to(layer.weight.device							)
							lowerCAmelCase     		=		layer.weight.index_select(snake_case__       ,  snake_case__							).clone().detach()
							if layer.bias is not None:
														if dim == 1:
																					lowerCAmelCase     		=		layer.bias.clone().detach()
														else:
																					lowerCAmelCase     		=		layer.bias[index].clone().detach()
							lowerCAmelCase     		=		list(layer.weight.size()							)
							lowerCAmelCase     		=		len(snake_case__							)
							lowerCAmelCase     		=		nn.Linear(new_size[1]       ,  new_size[0]       ,  bias=layer.bias is not None							).to(layer.weight.device							)
							lowerCAmelCase     		=		False
							new_layer.weight.copy_(W.contiguous()							)
							lowerCAmelCase     		=		True
							if layer.bias is not None:
														lowerCAmelCase     		=		False
														new_layer.bias.copy_(b.contiguous()							)
														lowerCAmelCase     		=		True
							return new_layer




class        lowercase_  (  nn.Module						):



	"""simple docstring"""





	def __init__(							self		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE    )						->Optional[Any]:
								super().__init__()
								lowerCAmelCase     		=		nf
								lowerCAmelCase     		=		nn.Parameter(torch.empty(__SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE    )    )
								lowerCAmelCase     		=		nn.Parameter(torch.zeros(__SCREAMING_SNAKE_CASE    )    )
								nn.init.normal_(self.weight		,      std=0.0_2    )




	def       SCREAMING_SNAKE_CASE_    (							self		,      __SCREAMING_SNAKE_CASE    )						->List[str]:
								lowerCAmelCase     		=		x.size()[:-1] + (self.nf,)
								lowerCAmelCase     		=		torch.addmm(self.bias		,      x.view(-1		,      x.size(-1    )    )		,      self.weight    )
								lowerCAmelCase     		=		x.view(__SCREAMING_SNAKE_CASE    )
								return x

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

def        SCREAMING_SNAKE_CASE_					(						snake_case__       ,  snake_case__       ,  snake_case__ = None							)					->     Union[nn.Linear, ConvaD]:
							if isinstance(snake_case__       ,  nn.Linear							):
														return prune_linear_layer(snake_case__       ,  snake_case__       ,  dim=0 if dim is None else dim							)
							elif isinstance(snake_case__       ,  snake_case__							):
														return prune_convad_layer(snake_case__       ,  snake_case__       ,  dim=1 if dim is None else dim							)
							else:
														raise ValueError(f"Can't prune layer of class {layer.__class__}"							)

def        SCREAMING_SNAKE_CASE_					(						snake_case__       ,  snake_case__       ,  snake_case__       ,  *snake_case__							)					->     torch.Tensor:
							assert len(snake_case__							) > 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
							lowerCAmelCase     		=		len(inspect.signature(snake_case__							).parameters							)
							if num_args_in_forward_chunk_fn != len(snake_case__							):
														raise ValueError(
														    f"forward_chunk_fn expects {num_args_in_forward_chunk_fn} arguments, but only {len(snake_case__							)} input "
														    '''tensors are given'''							)

							if chunk_size > 0:
														lowerCAmelCase     		=		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}"							)

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

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

							return forward_fn(*snake_case__							)

def        SCREAMING_SNAKE_CASE_					(						snake_case__       ,  snake_case__       ,  snake_case__       ,  snake_case__							)					->     Tuple[Set[int], torch.LongTensor]:
							lowerCAmelCase     		=		torch.ones(snake_case__       ,  snake_case__							)
							lowerCAmelCase     		=		set(snake_case__							) - 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
														lowerCAmelCase     		=		head - sum(1 if h < head else 0 for h in already_pruned_heads							)
														lowerCAmelCase     		=		0
							lowerCAmelCase     		=		mask.view(-1							).contiguous().eq(1							)
							lowerCAmelCase     		=		torch.arange(len(snake_case__							)							)[mask].long()
							return heads, index

def        SCREAMING_SNAKE_CASE_					(						*snake_case__       ,  snake_case__ = None							)					->     Tuple[torch.Tensor, ...]:
							return torch.meshgrid(*snake_case__       ,  indexing=snake_case__							)






def        SCREAMING_SNAKE_CASE_					(						snake_case__							)					->     Tuple[torch.device, int, int]:
							return tensor.device, storage_ptr(snake_case__							), storage_size(snake_case__							)



