


"""simple docstring"""







import logging
import os
from typing import List, Tuple

import numpy as np
import psutil
import torch
import torch.distributed as dist

from transformers import RagRetriever


a__       :							str        =  logging.getLogger(__name__)




class 				UpperCamelCase_			(				__lowerCAmelCase):

 """simple docstring"""





 def __init__(				self    :       Tuple    ,    UpperCAmelCase__    :       List[Any]    ,    UpperCAmelCase__    :       Any    ,    UpperCAmelCase__    :       Dict    ,    UpperCAmelCase__    :       List[str]=None     ) ->			Tuple:
      super().__init__(
          lowerCAmelCase_    ,    question_encoder_tokenizer=lowerCAmelCase_    ,    generator_tokenizer=lowerCAmelCase_    ,    index=lowerCAmelCase_    ,    init_retrieval=lowerCAmelCase_    ,    )
      __SCREAMING_SNAKE_CASE		     =       None




 def 						UpperCAmelCase_   (				self    :       Any    ,    UpperCAmelCase__    :       List[Any]     ) ->			int:
      logger.info("initializing retrieval"     )

      # initializing a separate process group for retrieval as the default
      # nccl backend doesn't support gather/scatter operations while gloo
      # is too slow to replace nccl for the core gpu communication
      if dist.is_initialized():
           logger.info("dist initialized"     )
           # needs to be set manually
           __SCREAMING_SNAKE_CASE		     =       self._infer_socket_ifname()
           # avoid clash with the NCCL port
           __SCREAMING_SNAKE_CASE		     =       str(distributed_port + 1     )
           __SCREAMING_SNAKE_CASE		     =       dist.new_group(ranks=lowerCAmelCase_    ,    backend="gloo"     )

      # initialize retriever only on the main worker
      if not dist.is_initialized() or self._is_main():
           logger.info("dist not initialized / main"     )
           self.index.init_index()

      # all processes wait untill the retriever is initialized by the main process
      if dist.is_initialized():
           torch.distributed.barrier(group=self.process_group     )




 def 						UpperCAmelCase_   (				self    :       int     ) ->			int:
      return dist.get_rank(group=self.process_group     ) == 0




 def 						UpperCAmelCase_   (				self    :       str    ,    UpperCAmelCase__    :       Any    ,    UpperCAmelCase__    :       Any    ,    UpperCAmelCase__    :       Optional[int]=torch.floataa     ) ->			List[str]:
      __SCREAMING_SNAKE_CASE		     =       torch.empty(lowerCAmelCase_    ,    dtype=lowerCAmelCase_     )
      dist.scatter(lowerCAmelCase_    ,    src=0    ,    scatter_list=lowerCAmelCase_    ,    group=self.process_group     )
      return target_tensor




 def 						UpperCAmelCase_   (				self    :       int     ) ->			Any:
      __SCREAMING_SNAKE_CASE		     =       psutil.net_if_addrs()
      # a hacky way to deal with varying network interface names
      __SCREAMING_SNAKE_CASE		     =       next((addr for addr in addrs if addr.startswith("e"     ))    ,    lowerCAmelCase_     )
      return ifname




 def 						UpperCAmelCase_   (				self    :       str    ,    UpperCAmelCase__    :       Optional[int]    ,    UpperCAmelCase__    :       Dict     ) ->			Tuple[np.ndarray, List[dict]]:

      # single GPU training
      if not dist.is_initialized():
           __SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE		     =       self._main_retrieve(lowerCAmelCase_    ,    lowerCAmelCase_     )
           return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(lowerCAmelCase_     )

      # distributed training
      __SCREAMING_SNAKE_CASE		     =       dist.get_world_size(group=self.process_group     )

      # gather logic
      __SCREAMING_SNAKE_CASE		     =       None
      if self._is_main():
           __SCREAMING_SNAKE_CASE		     =       [torch.empty(question_hidden_states.shape    ,    dtype=torch.floataa     ) for _ in range(lowerCAmelCase_     )]
      dist.gather(torch.tensor(lowerCAmelCase_     )    ,    dst=0    ,    gather_list=lowerCAmelCase_    ,    group=self.process_group     )

      # scatter logic
      __SCREAMING_SNAKE_CASE		     =       question_hidden_states.shape[0]
      __SCREAMING_SNAKE_CASE		     =       []
      __SCREAMING_SNAKE_CASE		     =       []
      if self._is_main():
           assert len(lowerCAmelCase_     ) == world_size
           __SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE		     =       self._main_retrieve(torch.cat(lowerCAmelCase_     ).numpy()    ,    lowerCAmelCase_     )
           __SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE		     =       torch.tensor(lowerCAmelCase_     ), torch.tensor(lowerCAmelCase_     )
           __SCREAMING_SNAKE_CASE		     =       self._chunk_tensor(lowerCAmelCase_    ,    lowerCAmelCase_     )
           __SCREAMING_SNAKE_CASE		     =       self._chunk_tensor(lowerCAmelCase_    ,    lowerCAmelCase_     )
      __SCREAMING_SNAKE_CASE		     =       self._scattered(lowerCAmelCase_    ,    [n_queries, n_docs]    ,    target_type=torch.intaa     )
      __SCREAMING_SNAKE_CASE		     =       self._scattered(lowerCAmelCase_    ,    [n_queries, n_docs, question_hidden_states.shape[1]]     )

      return retrieved_doc_embeds.numpy(), doc_ids.numpy(), self.index.get_doc_dicts(lowerCAmelCase_     )




"""simple docstring"""







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      UpperCAmelCase__ (lowerCAmelCase_=None						,		lowerCAmelCase_=None     ):




					'''simple docstring'''


					return field(default_factory=lambda: default						,		metadata=lowerCAmelCase_     )




@dataclass
class 				UpperCamelCase_			:

	"""simple docstring"""





	snake_case__       :						str					      =    field(
	    metadata={"help": "The csv file to plot."}    ,						)
	snake_case__       :						bool					      =    field(
	    default=UpperCamelCase    ,						metadata={"help": "Whether to plot along batch size or sequence length. Defaults to sequence length."}    ,						)
	snake_case__       :						bool					      =    field(
	    default=UpperCamelCase    ,						metadata={"help": "Whether the csv file has time results or memory results. Defaults to memory results."}    ,						)
	snake_case__       :						bool					      =    field(
	    default=UpperCamelCase    ,						metadata={"help": "Disable logarithmic scale when plotting"}    ,						)
	snake_case__       :						bool					      =    field(
	    default=UpperCamelCase    ,						metadata={
	        "help": "Whether the csv file has training results or inference results. Defaults to inference results."
	    }    ,						)
	snake_case__       :						Optional[str]					      =    field(
	    default=UpperCamelCase    ,						metadata={"help": "Filename under which the plot will be saved. If unused no plot is saved."}    ,						)
	snake_case__       :						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      UpperCAmelCase__ (lowerCAmelCase_     ):




					'''simple docstring'''


					try:
										int(lowerCAmelCase_     )
										return True
					except ValueError:
										return False




def      UpperCAmelCase__ (lowerCAmelCase_     ):




					'''simple docstring'''


					try:
										float(lowerCAmelCase_     )
										return True
					except ValueError:
										return False




class 				UpperCamelCase_			:

	"""simple docstring"""





	def __init__(				self    :       str    ,    UpperCAmelCase__    :       Dict     ) ->			Optional[Any]:
						__SCREAMING_SNAKE_CASE		     =       args
						__SCREAMING_SNAKE_CASE		     =       defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}     )

						with open(self.args.csv_file    ,    newline=""     ) as csv_file:
											__SCREAMING_SNAKE_CASE		     =       csv.DictReader(UpperCAmelCase__     )
											for row in reader:
																__SCREAMING_SNAKE_CASE		     =       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
																					__SCREAMING_SNAKE_CASE		     =       int(row["result"]     )
																elif can_convert_to_float(row["result"]     ):
																					# value is not None
																					__SCREAMING_SNAKE_CASE		     =       float(row["result"]     )




	def 						UpperCAmelCase_   (				self    :       List[str]     ) ->			Tuple:
						__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE		     =       plt.subplots()
						__SCREAMING_SNAKE_CASE		     =       "Time usage" if self.args.is_time else "Memory usage"
						__SCREAMING_SNAKE_CASE		     =       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()     ):
											__SCREAMING_SNAKE_CASE		     =       sorted(set(self.result_dict[model_name]["bsz"]     )     )
											__SCREAMING_SNAKE_CASE		     =       sorted(set(self.result_dict[model_name]["seq_len"]     )     )
											__SCREAMING_SNAKE_CASE		     =       self.result_dict[model_name]["result"]

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

											__SCREAMING_SNAKE_CASE		     =       (
											    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:
																					__SCREAMING_SNAKE_CASE		     =       np.asarray(
																					    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]    ,    dtype=UpperCAmelCase__    ,    )
																else:
																					__SCREAMING_SNAKE_CASE		     =       np.asarray(
																					    [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]    ,    dtype=np.floataa    ,    )

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

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

											title_str += F""" {label_model_name} vs."""

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

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

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




def      UpperCAmelCase__ ():




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE		     =       HfArgumentParser(lowerCAmelCase_     )
					__SCREAMING_SNAKE_CASE		     =       parser.parse_args_into_dataclasses()[0]
					__SCREAMING_SNAKE_CASE		     =       Plot(args=lowerCAmelCase_     )
					plot.plot()


if __name__ == "__main__":
		main()
