"""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 						_lowercase				(  _SCREAMING_SNAKE_CASE				:				int=None  ,       _SCREAMING_SNAKE_CASE				:				Optional[Any]=None						)    ->     str:







     '''simple docstring'''


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






@dataclass
class 							__snake_case:


    '''simple docstring'''





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







def 						_lowercase				(  _SCREAMING_SNAKE_CASE				:				Union[str, Any]						)    ->     List[str]:







     '''simple docstring'''


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




def 						_lowercase				(  _SCREAMING_SNAKE_CASE				:				Optional[int]						)    ->     int:







     '''simple docstring'''


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






class 							__snake_case:


    '''simple docstring'''





    def __init__(  self					,       __lowerCamelCase	):




         '''simple docstring'''



         __A					:					Optional[int]								=		args
         __A					:					int								=		defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}	)

         with open(self.args.csv_file					,       newline=''	) as csv_file:
              __A					:					int								=		csv.DictReader(SCREAMING_SNAKE_CASE_	)
              for row in reader:
                   __A					:					Any								=		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
                        __A					:					Union[str, Any]								=		int(row['result']	)
                   elif can_convert_to_float(row['result']	):
                        # value is not None
                        __A					:					Union[str, Any]								=		float(row['result']	)

    def 						_a							(  self	):




         '''simple docstring'''



         __A					:					int								=		plt.subplots()
         __A					:					Dict								=		'''Time usage''' if self.args.is_time else '''Memory usage'''
         __A					:					Optional[Any]								=		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()	):
              __A					:					Optional[int]								=		sorted(set(self.result_dict[model_name]['bsz']	)	)
              __A					:					Tuple								=		sorted(set(self.result_dict[model_name]['seq_len']	)	)
              __A					:					Union[str, Any]								=		self.result_dict[model_name]['''result''']

              (__A)					:					List[str]								=		(
                  (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
              )

              __A					:					Tuple								=		(
                  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:
                        __A					:					List[Any]								=		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:
                        __A					:					List[Any]								=		np.asarray(
                            [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]					,       dtype=np.floataa					,       )

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

                   __A					:					Optional[int]								=		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.'

         __A					:					Dict								=		title_str[:-4]
         __A					:					Dict								=		'''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 						_lowercase				(  )    ->     Tuple:







     '''simple docstring'''


     __A					:					List[Any]								=		HfArgumentParser(_SCREAMING_SNAKE_CASE						)
     __A					:					int								=		parser.parse_args_into_dataclasses()[0]
     __A					:					Tuple								=		Plot(args=_SCREAMING_SNAKE_CASE						)
     plot.plot()


if __name__ == "__main__":
       main()









"""simple docstring"""





from typing import TYPE_CHECKING

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


_lowercase						   =     {
    """configuration_utils""": ["""GenerationConfig"""],
    """streamers""": ["""TextIteratorStreamer""", """TextStreamer"""],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_lowercase						   =     [
							    """Constraint""",
							    """ConstraintListState""",
							    """DisjunctiveConstraint""",
							    """PhrasalConstraint""",
							]
							_lowercase						   =     [
							    """BeamHypotheses""",
							    """BeamScorer""",
							    """BeamSearchScorer""",
							    """ConstrainedBeamSearchScorer""",
							]
							_lowercase						   =     [
							    """EpsilonLogitsWarper""",
							    """EtaLogitsWarper""",
							    """ForcedBOSTokenLogitsProcessor""",
							    """ForcedEOSTokenLogitsProcessor""",
							    """HammingDiversityLogitsProcessor""",
							    """InfNanRemoveLogitsProcessor""",
							    """LogitsProcessor""",
							    """LogitsProcessorList""",
							    """LogitsWarper""",
							    """MinLengthLogitsProcessor""",
							    """MinNewTokensLengthLogitsProcessor""",
							    """NoBadWordsLogitsProcessor""",
							    """NoRepeatNGramLogitsProcessor""",
							    """PrefixConstrainedLogitsProcessor""",
							    """RepetitionPenaltyLogitsProcessor""",
							    """SequenceBiasLogitsProcessor""",
							    """EncoderRepetitionPenaltyLogitsProcessor""",
							    """TemperatureLogitsWarper""",
							    """TopKLogitsWarper""",
							    """TopPLogitsWarper""",
							    """TypicalLogitsWarper""",
							    """EncoderNoRepeatNGramLogitsProcessor""",
							    """ExponentialDecayLengthPenalty""",
							    """LogitNormalization""",
							]
							_lowercase						   =     [
							    """MaxNewTokensCriteria""",
							    """MaxLengthCriteria""",
							    """MaxTimeCriteria""",
							    """StoppingCriteria""",
							    """StoppingCriteriaList""",
							    """validate_stopping_criteria""",
							]
							_lowercase						   =     [
							    """GenerationMixin""",
							    """top_k_top_p_filtering""",
							    """GreedySearchEncoderDecoderOutput""",
							    """GreedySearchDecoderOnlyOutput""",
							    """SampleEncoderDecoderOutput""",
							    """SampleDecoderOnlyOutput""",
							    """BeamSearchEncoderDecoderOutput""",
							    """BeamSearchDecoderOnlyOutput""",
							    """BeamSampleEncoderDecoderOutput""",
							    """BeamSampleDecoderOnlyOutput""",
							    """ContrastiveSearchEncoderDecoderOutput""",
							    """ContrastiveSearchDecoderOnlyOutput""",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_lowercase						   =     [
							    """TFForcedBOSTokenLogitsProcessor""",
							    """TFForcedEOSTokenLogitsProcessor""",
							    """TFLogitsProcessor""",
							    """TFLogitsProcessorList""",
							    """TFLogitsWarper""",
							    """TFMinLengthLogitsProcessor""",
							    """TFNoBadWordsLogitsProcessor""",
							    """TFNoRepeatNGramLogitsProcessor""",
							    """TFRepetitionPenaltyLogitsProcessor""",
							    """TFTemperatureLogitsWarper""",
							    """TFTopKLogitsWarper""",
							    """TFTopPLogitsWarper""",
							    """TFForceTokensLogitsProcessor""",
							    """TFSuppressTokensAtBeginLogitsProcessor""",
							    """TFSuppressTokensLogitsProcessor""",
							]
							_lowercase						   =     [
							    """TFGenerationMixin""",
							    """tf_top_k_top_p_filtering""",
							    """TFGreedySearchDecoderOnlyOutput""",
							    """TFGreedySearchEncoderDecoderOutput""",
							    """TFSampleEncoderDecoderOutput""",
							    """TFSampleDecoderOnlyOutput""",
							    """TFBeamSearchEncoderDecoderOutput""",
							    """TFBeamSearchDecoderOnlyOutput""",
							    """TFBeamSampleEncoderDecoderOutput""",
							    """TFBeamSampleDecoderOnlyOutput""",
							    """TFContrastiveSearchEncoderDecoderOutput""",
							    """TFContrastiveSearchDecoderOnlyOutput""",
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_lowercase						   =     [
							    """FlaxForcedBOSTokenLogitsProcessor""",
							    """FlaxForcedEOSTokenLogitsProcessor""",
							    """FlaxLogitsProcessor""",
							    """FlaxLogitsProcessorList""",
							    """FlaxLogitsWarper""",
							    """FlaxMinLengthLogitsProcessor""",
							    """FlaxTemperatureLogitsWarper""",
							    """FlaxTopKLogitsWarper""",
							    """FlaxTopPLogitsWarper""",
							]
							_lowercase						   =     [
							    """FlaxGenerationMixin""",
							    """FlaxGreedySearchOutput""",
							    """FlaxSampleOutput""",
							    """FlaxBeamSearchOutput""",
							]

if TYPE_CHECKING:
							from .configuration_utils import GenerationConfig
							from .streamers import TextIteratorStreamer, TextStreamer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .beam_constraints import Constraint, ConstraintListState, DisjunctiveConstraint, PhrasalConstraint
														from .beam_search import BeamHypotheses, BeamScorer, BeamSearchScorer, ConstrainedBeamSearchScorer
														from .logits_process import (
														    EncoderNoRepeatNGramLogitsProcessor,
														    EncoderRepetitionPenaltyLogitsProcessor,
														    EpsilonLogitsWarper,
														    EtaLogitsWarper,
														    ExponentialDecayLengthPenalty,
														    ForcedBOSTokenLogitsProcessor,
														    ForcedEOSTokenLogitsProcessor,
														    HammingDiversityLogitsProcessor,
														    InfNanRemoveLogitsProcessor,
														    LogitNormalization,
														    LogitsProcessor,
														    LogitsProcessorList,
														    LogitsWarper,
														    MinLengthLogitsProcessor,
														    MinNewTokensLengthLogitsProcessor,
														    NoBadWordsLogitsProcessor,
														    NoRepeatNGramLogitsProcessor,
														    PrefixConstrainedLogitsProcessor,
														    RepetitionPenaltyLogitsProcessor,
														    SequenceBiasLogitsProcessor,
														    TemperatureLogitsWarper,
														    TopKLogitsWarper,
														    TopPLogitsWarper,
														    TypicalLogitsWarper,
														)
														from .stopping_criteria import (
														    MaxLengthCriteria,
														    MaxNewTokensCriteria,
														    MaxTimeCriteria,
														    StoppingCriteria,
														    StoppingCriteriaList,
														    validate_stopping_criteria,
														)
														from .utils import (
														    BeamSampleDecoderOnlyOutput,
														    BeamSampleEncoderDecoderOutput,
														    BeamSearchDecoderOnlyOutput,
														    BeamSearchEncoderDecoderOutput,
														    ContrastiveSearchDecoderOnlyOutput,
														    ContrastiveSearchEncoderDecoderOutput,
														    GenerationMixin,
														    GreedySearchDecoderOnlyOutput,
														    GreedySearchEncoderDecoderOutput,
														    SampleDecoderOnlyOutput,
														    SampleEncoderDecoderOutput,
														    top_k_top_p_filtering,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tf_logits_process import (
														    TFForcedBOSTokenLogitsProcessor,
														    TFForcedEOSTokenLogitsProcessor,
														    TFForceTokensLogitsProcessor,
														    TFLogitsProcessor,
														    TFLogitsProcessorList,
														    TFLogitsWarper,
														    TFMinLengthLogitsProcessor,
														    TFNoBadWordsLogitsProcessor,
														    TFNoRepeatNGramLogitsProcessor,
														    TFRepetitionPenaltyLogitsProcessor,
														    TFSuppressTokensAtBeginLogitsProcessor,
														    TFSuppressTokensLogitsProcessor,
														    TFTemperatureLogitsWarper,
														    TFTopKLogitsWarper,
														    TFTopPLogitsWarper,
														)
														from .tf_utils import (
														    TFBeamSampleDecoderOnlyOutput,
														    TFBeamSampleEncoderDecoderOutput,
														    TFBeamSearchDecoderOnlyOutput,
														    TFBeamSearchEncoderDecoderOutput,
														    TFContrastiveSearchDecoderOnlyOutput,
														    TFContrastiveSearchEncoderDecoderOutput,
														    TFGenerationMixin,
														    TFGreedySearchDecoderOnlyOutput,
														    TFGreedySearchEncoderDecoderOutput,
														    TFSampleDecoderOnlyOutput,
														    TFSampleEncoderDecoderOutput,
														    tf_top_k_top_p_filtering,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .flax_logits_process import (
														    FlaxForcedBOSTokenLogitsProcessor,
														    FlaxForcedEOSTokenLogitsProcessor,
														    FlaxLogitsProcessor,
														    FlaxLogitsProcessorList,
														    FlaxLogitsWarper,
														    FlaxMinLengthLogitsProcessor,
														    FlaxTemperatureLogitsWarper,
														    FlaxTopKLogitsWarper,
														    FlaxTopPLogitsWarper,
														)
														from .flax_utils import FlaxBeamSearchOutput, FlaxGenerationMixin, FlaxGreedySearchOutput, FlaxSampleOutput
else:
							import sys

							_lowercase						   =     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


