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__							)




import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter

from transformers import HfArgumentParser

def        SCREAMING_SNAKE_CASE_					(						snake_case__=None       ,  snake_case__=None							)					->     Optional[Any]:
							return field(default_factory=lambda: default       ,  metadata=snake_case__							)




@dataclass
class        lowercase_  :



	"""simple docstring"""





	UpperCAmelCase_  : str       =    field(
	    metadata={"""help""": """The csv file to plot."""}				,					)
	UpperCAmelCase_  : bool       =    field(
	    default=UpperCamelCase_				,					metadata={"""help""": """Whether to plot along batch size or sequence length. Defaults to sequence length."""}				,					)
	UpperCAmelCase_  : bool       =    field(
	    default=UpperCamelCase_				,					metadata={"""help""": """Whether the csv file has time results or memory results. Defaults to memory results."""}				,					)
	UpperCAmelCase_  : bool       =    field(
	    default=UpperCamelCase_				,					metadata={"""help""": """Disable logarithmic scale when plotting"""}				,					)
	UpperCAmelCase_  : bool       =    field(
	    default=UpperCamelCase_				,					metadata={
	        """help""": """Whether the csv file has training results or inference results. Defaults to inference results."""
	    }				,					)
	UpperCAmelCase_  : Optional[str]       =    field(
	    default=UpperCamelCase_				,					metadata={"""help""": """Filename under which the plot will be saved. If unused no plot is saved."""}				,					)
	UpperCAmelCase_  : Optional[List[str]]       =    list_field(
	    default=UpperCamelCase_				,					metadata={"""help""": """List of model names that are used instead of the ones in the csv file."""}						)

def        SCREAMING_SNAKE_CASE_					(						snake_case__							)					->     Dict:
							try:
														int(snake_case__							)
														return True
							except ValueError:
														return False

def        SCREAMING_SNAKE_CASE_					(						snake_case__							)					->     Union[str, Any]:
							try:
														float(snake_case__							)
														return True
							except ValueError:
														return False




class        lowercase_  :



	"""simple docstring"""





	def __init__(							self		,      __SCREAMING_SNAKE_CASE    )						->Optional[int]:
								lowerCAmelCase     		=		args
								lowerCAmelCase     		=		defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}    )

								with open(self.args.csv_file		,      newline=''''''    ) as csv_file:
															lowerCAmelCase     		=		csv.DictReader(__SCREAMING_SNAKE_CASE    )
															for row in reader:
																						lowerCAmelCase     		=		row['''model''']
																						self.result_dict[model_name]["bsz"].append(int(row['''batch_size''']    )    )
																						self.result_dict[model_name]["seq_len"].append(int(row['''sequence_length''']    )    )
																						if can_convert_to_int(row['''result''']    ):
																													# value is not None
																													lowerCAmelCase     		=		int(row['''result''']    )
																						elif can_convert_to_float(row['''result''']    ):
																													# value is not None
																													lowerCAmelCase     		=		float(row['''result''']    )




	def       SCREAMING_SNAKE_CASE_    (							self    )						->Tuple:
								lowerCAmelCase		,				lowerCAmelCase     		=		plt.subplots()
								lowerCAmelCase     		=		'''Time usage''' if self.args.is_time else '''Memory usage'''
								lowerCAmelCase     		=		title_str + ''' for training''' if self.args.is_train else title_str + ''' for inference'''

								if not self.args.no_log_scale:
															# set logarithm scales
															ax.set_xscale('''log'''    )
															ax.set_yscale('''log'''    )

								for axis in [ax.xaxis, ax.yaxis]:
															axis.set_major_formatter(ScalarFormatter()    )

								for model_name_idx, model_name in enumerate(self.result_dict.keys()    ):
															lowerCAmelCase     		=		sorted(set(self.result_dict[model_name]['''bsz''']    )    )
															lowerCAmelCase     		=		sorted(set(self.result_dict[model_name]['''seq_len''']    )    )
															lowerCAmelCase     		=		self.result_dict[model_name]['''result''']

															((lowerCAmelCase)		,				(lowerCAmelCase))     		=		(
															    (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
															)

															lowerCAmelCase     		=		(
															    model_name if self.args.short_model_names is None else self.args.short_model_names[model_name_idx]
															)

															for inner_loop_value in inner_loop_array:
																						if self.args.plot_along_batch:
																													lowerCAmelCase     		=		np.asarray(
																													    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]		,      dtype=__SCREAMING_SNAKE_CASE		,      )
																						else:
																													lowerCAmelCase     		=		np.asarray(
																													    [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]		,      dtype=np.floataa		,      )

																						((lowerCAmelCase)		,				(lowerCAmelCase))     		=		(
																						    ('''batch_size''', '''len''') if self.args.plot_along_batch else ('''in #tokens''', '''bsz''')
																						)

																						lowerCAmelCase     		=		np.asarray(__SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE    )[: len(__SCREAMING_SNAKE_CASE    )]
																						plt.scatter(
																						    __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      label=F"{label_model_name} - {inner_loop_label}: {inner_loop_value}"    )
																						plt.plot(__SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      '''--'''    )

															title_str += F" {label_model_name} vs."

								lowerCAmelCase     		=		title_str[:-4]
								lowerCAmelCase     		=		'''Time in s''' if self.args.is_time else '''Memory in MB'''

								# plot
								plt.title(__SCREAMING_SNAKE_CASE    )
								plt.xlabel(__SCREAMING_SNAKE_CASE    )
								plt.ylabel(__SCREAMING_SNAKE_CASE    )
								plt.legend()

								if self.args.figure_png_file is not None:
															plt.savefig(self.args.figure_png_file    )
								else:
															plt.show()

def        SCREAMING_SNAKE_CASE_					(						)					->     List[str]:
							lowerCAmelCase     		=		HfArgumentParser(snake_case__							)
							lowerCAmelCase     		=		parser.parse_args_into_dataclasses()[0]
							lowerCAmelCase     		=		Plot(args=snake_case__							)
							plot.plot()


if __name__ == "__main__":
	main()



