
"""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 						_lowerCAmelCase				(				UpperCamelCase_=None  ,				UpperCamelCase_=None				):
		return field(default_factory=lambda: default  ,				metadata=UpperCamelCase_				)
@dataclass
class 						SCREAMING_SNAKE_CASE_ :




		"""simple docstring"""





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



def 						_lowerCAmelCase				(				UpperCamelCase_				):
		try:
				int(UpperCamelCase_				)
				return True
		except ValueError:
				return False



def 						_lowerCAmelCase				(				UpperCamelCase_				):
		try:
				float(UpperCamelCase_				)
				return True
		except ValueError:
				return False
class 						SCREAMING_SNAKE_CASE_ :




		"""simple docstring"""





		def __init__(     self							,  lowerCAmelCase__):
				__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(lowerCAmelCase__)
						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 	snake_case_     (     self):
				__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=lowerCAmelCase__							,  )
								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(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."

				__SCREAMING_SNAKE_CASE            = title_str[:-4]
				__SCREAMING_SNAKE_CASE            = """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 						_lowerCAmelCase				(				):
		__SCREAMING_SNAKE_CASE            = HfArgumentParser(UpperCamelCase_				)
		__SCREAMING_SNAKE_CASE            = parser.parse_args_into_dataclasses()[0]
		__SCREAMING_SNAKE_CASE            = Plot(args=UpperCamelCase_				)
		plot.plot()


if __name__ == "__main__":
							main()









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available


a_   :int        =      {
    "configuration_longt5": ["LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP", "LongT5Config", "LongT5OnnxConfig"],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   :List[str]        =      [
     "LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST",
     "LongT5EncoderModel",
     "LongT5ForConditionalGeneration",
     "LongT5Model",
     "LongT5PreTrainedModel",
 ]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   :int        =      [
     "FlaxLongT5ForConditionalGeneration",
     "FlaxLongT5Model",
     "FlaxLongT5PreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_longta import LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP, LongTaConfig, LongTaOnnxConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_longta import (
      LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST,
      LongTaEncoderModel,
      LongTaForConditionalGeneration,
      LongTaModel,
      LongTaPreTrainedModel,
  )

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_longta import (
      FlaxLongTaForConditionalGeneration,
      FlaxLongTaModel,
      FlaxLongTaPreTrainedModel,
  )


else:
 import sys

 a_   :Optional[int]        =      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






