





'''simple docstring'''







from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


snake_case_  :							Union[str, Any]            =	logging.get_logger(__name__)

snake_case_  :							int            =	{
    "google/long-t5-local-base": "https://huggingface.co/google/long-t5-local-base/blob/main/config.json",
    "google/long-t5-local-large": "https://huggingface.co/google/long-t5-local-large/blob/main/config.json",
    "google/long-t5-tglobal-base": "https://huggingface.co/google/long-t5-tglobal-base/blob/main/config.json",
    "google/long-t5-tglobal-large": "https://huggingface.co/google/long-t5-tglobal-large/blob/main/config.json",
}


class        __a   (lowerCamelCase  ):
							__a       : Any          =							"longt5"
							__a       : Dict          =							["past_key_values"]
							__a       : int          =							{"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}

							def __init__(				self				:  Optional[Any]							,  __magic_name__				:  Optional[int]=3_21_28							,  __magic_name__				:  str=5_12							,  __magic_name__				:  Optional[Any]=64							,  __magic_name__				:  List[str]=20_48							,  __magic_name__				:  List[str]=6							,  __magic_name__				:  Tuple=None							,  __magic_name__				:  List[str]=8							,  __magic_name__				:  Union[str, Any]=1_27							,  __magic_name__				:  Any=16							,  __magic_name__				:  Any=32							,  __magic_name__				:  str=1_28							,  __magic_name__				:  Tuple=0.1							,  __magic_name__				:  Union[str, Any]=1E-6							,  __magic_name__				:  str=1.0							,  __magic_name__				:  int="relu"							,  __magic_name__				:  Tuple=True							,  __magic_name__				:  Tuple="local"							,  __magic_name__				:  str=True							,  __magic_name__				:  int=0							,  __magic_name__				:  Any=1							,  **__magic_name__				:  Optional[Any]							,  )       ->							List[Any]:







													"""simple docstring"""
													UpperCAmelCase_      :   Optional[int]      =	vocab_size
													UpperCAmelCase_      :   Tuple      =	d_model
													UpperCAmelCase_      :   Any      =	d_kv
													UpperCAmelCase_      :   Dict      =	d_ff
													UpperCAmelCase_      :   int      =	num_layers
													# default = symmetry
													UpperCAmelCase_      :   Optional[int]      =	num_decoder_layers if num_decoder_layers is not None else self.num_layers
													UpperCAmelCase_      :   List[str]      =	num_heads
													UpperCAmelCase_      :   Dict      =	local_radius
													UpperCAmelCase_      :   Union[str, Any]      =	global_block_size
													UpperCAmelCase_      :   Tuple      =	relative_attention_num_buckets
													UpperCAmelCase_      :   Dict      =	relative_attention_max_distance
													UpperCAmelCase_      :   Tuple      =	dropout_rate
													UpperCAmelCase_      :   List[str]      =	layer_norm_epsilon
													UpperCAmelCase_      :   str      =	initializer_factor
													UpperCAmelCase_      :   Dict      =	feed_forward_proj
													UpperCAmelCase_      :   Dict      =	encoder_attention_type
													UpperCAmelCase_      :   List[str]      =	use_cache

													UpperCAmelCase_      :   List[str]      =	self.feed_forward_proj.split('''-''' )
													UpperCAmelCase_      :   Tuple      =	act_info[-1]
													UpperCAmelCase_      :   List[str]      =	act_info[0] == '''gated'''

													if len(__magic_name__ ) > 1 and act_info[0] != "gated" or len(__magic_name__ ) > 2:
																			raise ValueError(
																			    F"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
																			    '''Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '''
																			    '''\'gated-gelu\' or \'relu\'''' )

													# for backwards compatibility
													if feed_forward_proj == "gated-gelu":
																			UpperCAmelCase_      :   Optional[Any]      =	'''gelu_new'''

													super().__init__(
													    pad_token_id=__magic_name__							,  eos_token_id=__magic_name__							,  is_encoder_decoder=__magic_name__							,  **__magic_name__							,  )





class        __a   (lowerCamelCase  ):

							@property
							def      UpperCAmelCase__						(				self				:  Optional[int] )       ->							Mapping[str, Mapping[int, str]]:







													"""simple docstring"""
													UpperCAmelCase_      :   str      =	{
													    '''input_ids''': {0: '''batch''', 1: '''encoder_sequence'''},
													    '''attention_mask''': {0: '''batch''', 1: '''encoder_sequence'''},
													}
													if self.use_past:
																			UpperCAmelCase_      :   Union[str, Any]      =	'''past_encoder_sequence + sequence'''
																			UpperCAmelCase_      :   int      =	{0: '''batch'''}
																			UpperCAmelCase_      :   Optional[int]      =	{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
													else:
																			UpperCAmelCase_      :   List[str]      =	{0: '''batch''', 1: '''decoder_sequence'''}
																			UpperCAmelCase_      :   int      =	{0: '''batch''', 1: '''decoder_sequence'''}

													if self.use_past:
																			self.fill_with_past_key_values_(__magic_name__							,  direction='''inputs''' )

													return common_inputs



							@property
							def      UpperCAmelCase__						(				self				:  Dict )       ->							int:







													"""simple docstring"""
													return 13






'''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 __a			(						__lowerCamelCase      :    Optional[Any]=None			,		__lowerCamelCase      :    List[str]=None					)						->				Any:
      '''simple docstring'''


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





@dataclass
class 				lowercase    :
   lowerCamelCase_            =field(
       metadata={'help': 'The csv file to plot.'}			,					)
   lowerCamelCase_            =field(
       default=__lowerCamelCase			,					metadata={'help': 'Whether to plot along batch size or sequence length. Defaults to sequence length.'}			,					)
   lowerCamelCase_            =field(
       default=__lowerCamelCase			,					metadata={'help': 'Whether the csv file has time results or memory results. Defaults to memory results.'}			,					)
   lowerCamelCase_            =field(
       default=__lowerCamelCase			,					metadata={'help': 'Disable logarithmic scale when plotting'}			,					)
   lowerCamelCase_            =field(
       default=__lowerCamelCase			,					metadata={
           'help': 'Whether the csv file has training results or inference results. Defaults to inference results.'
       }			,					)
   lowerCamelCase_            =field(
       default=__lowerCamelCase			,					metadata={'help': 'Filename under which the plot will be saved. If unused no plot is saved.'}			,					)
   lowerCamelCase_            =list_field(
       default=__lowerCamelCase			,					metadata={'help': 'List of model names that are used instead of the ones in the csv file.'}			)




def __a			(						__lowerCamelCase      :    int					)						->				Tuple:
      '''simple docstring'''


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


def __a			(						__lowerCamelCase      :    Union[str, Any]					)						->				str:
      '''simple docstring'''


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





class 				lowercase    :


   def __init__( self					:						Dict ,		__lowerCAmelCase					:						Tuple)   ->       Dict:
         lowercase_     			=   args
         lowercase_     			=   defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}})

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


   def __UpperCAmelCase							( self					:						Optional[int])   ->       Tuple:
         lowercase_			,				lowercase_     			=   plt.subplots()
         lowercase_     			=   "Time usage" if self.args.is_time else "Memory usage"
         lowercase_     			=   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()):
               lowercase_     			=   sorted(set(self.result_dict[model_name]["bsz"]))
               lowercase_     			=   sorted(set(self.result_dict[model_name]["seq_len"]))
               lowercase_     			=   self.result_dict[model_name]["result"]

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

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

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

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

               title_str += F' {label_model_name} vs.'

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

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

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




def __a			(						)						->				Tuple:
      '''simple docstring'''


      lowercase_     			=   HfArgumentParser(__lowerCamelCase					)
      lowercase_     			=   parser.parse_args_into_dataclasses()[0]
      lowercase_     			=   Plot(args=__lowerCamelCase					)
      plot.plot()


if __name__ == "__main__":
       main()






