




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_      (       A__=None			,		A__=None		)							->							Optional[int]:
    """simple docstring"""

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







@dataclass
class 					lowerCamelCase		:
     UpperCAmelCase__    :							str									=		field(
         metadata={"help": "The csv file to plot."}							,			)
     UpperCAmelCase__    :							bool									=		field(
         default=A_							,			metadata={"help": "Whether to plot along batch size or sequence length. Defaults to sequence length."}							,			)
     UpperCAmelCase__    :							bool									=		field(
         default=A_							,			metadata={"help": "Whether the csv file has time results or memory results. Defaults to memory results."}							,			)
     UpperCAmelCase__    :							bool									=		field(
         default=A_							,			metadata={"help": "Disable logarithmic scale when plotting"}							,			)
     UpperCAmelCase__    :							bool									=		field(
         default=A_							,			metadata={
             "help": "Whether the csv file has training results or inference results. Defaults to inference results."
         }							,			)
     UpperCAmelCase__    :							Optional[str]									=		field(
         default=A_							,			metadata={"help": "Filename under which the plot will be saved. If unused no plot is saved."}							,			)
     UpperCAmelCase__    :							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 						lowercase_      (       A__		)							->							List[str]:
    """simple docstring"""

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



def 						lowercase_      (       A__		)							->							Dict:
    """simple docstring"""

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







class 					lowerCamelCase		:





     def __init__(self				:   List[Any]    ,   _A				:   int       )						->						List[Any]:
         snake_case								= args
         snake_case								= defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}       )

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





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

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

             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:
                     snake_case								= np.asarray(
                         [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]    ,   dtype=_A    ,   )
                 else:
                     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    ,   )

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

                 snake_case								= np.asarray(_A    ,   _A       )[: len(_A       )]
                 plt.scatter(
                     _A    ,   _A    ,   label=f'{label_model_name} - {inner_loop_label}: {inner_loop_value}'       )
                 plt.plot(_A    ,   _A    ,   "--"       )

             title_str += f' {label_model_name} vs.'

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

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

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



def 						lowercase_      (       )							->							str:
    """simple docstring"""

    snake_case								= HfArgumentParser(A__		)
    snake_case								= parser.parse_args_into_dataclasses()[0]
    snake_case								= Plot(args=A__		)
    plot.plot()


if __name__ == "__main__":
   main()



import warnings
from collections import OrderedDict
from typing import Mapping

from packaging import version

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


_A   =			logging.get_logger(__name__)

_A   =			{
    "nvidia/segformer-b0-finetuned-ade-512-512": (
        "https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512/resolve/main/config.json"
    ),
    # See all SegFormer models at https://huggingface.co/models?filter=segformer
}







class 					lowerCamelCase		(  A_  ):
     UpperCAmelCase__    :							List[Any]									=		"segformer"





     def __init__(self				:   List[Any]    ,   _A				:   int=3    ,   _A				:   List[Any]=4    ,   _A				:   Any=[2, 2, 2, 2]    ,   _A				:   Dict=[8, 4, 2, 1]    ,   _A				:   List[Any]=[3_2, 6_4, 1_6_0, 2_5_6]    ,   _A				:   Tuple=[7, 3, 3, 3]    ,   _A				:   Optional[int]=[4, 2, 2, 2]    ,   _A				:   Dict=[1, 2, 5, 8]    ,   _A				:   int=[4, 4, 4, 4]    ,   _A				:   Dict="gelu"    ,   _A				:   Tuple=0.0    ,   _A				:   Optional[Any]=0.0    ,   _A				:   List[Any]=0.1    ,   _A				:   Union[str, Any]=0.02    ,   _A				:   Dict=0.1    ,   _A				:   List[Any]=1E-6    ,   _A				:   List[str]=2_5_6    ,   _A				:   Optional[Any]=2_5_5    ,   **_A				:   str    ,   )						->						Tuple:
         super().__init__(**_A       )

         if "reshape_last_stage" in kwargs and kwargs["reshape_last_stage"] is False:
             warnings.warn(
                 "Reshape_last_stage is set to False in this config. This argument is deprecated and will soon be"
                 " removed, as the behaviour will default to that of reshape_last_stage = True."    ,   _A    ,   )

         snake_case								= num_channels
         snake_case								= num_encoder_blocks
         snake_case								= depths
         snake_case								= sr_ratios
         snake_case								= hidden_sizes
         snake_case								= patch_sizes
         snake_case								= strides
         snake_case								= mlp_ratios
         snake_case								= num_attention_heads
         snake_case								= hidden_act
         snake_case								= hidden_dropout_prob
         snake_case								= attention_probs_dropout_prob
         snake_case								= classifier_dropout_prob
         snake_case								= initializer_range
         snake_case								= drop_path_rate
         snake_case								= layer_norm_eps
         snake_case								= decoder_hidden_size
         snake_case								= kwargs.get("reshape_last_stage"    ,   _A       )
         snake_case								= semantic_loss_ignore_index







class 					lowerCamelCase		(  A_  ):
     UpperCAmelCase__    :							Optional[Any]									=		version.parse("1.11"  )





     @property
     def 		UpperCAmelCase(self				:   Tuple       )						->						Mapping[str, Mapping[int, str]]:
         return OrderedDict(
             [
                 ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
             ]       )





     @property
     def 		UpperCAmelCase(self				:   Tuple       )						->						float:
         return 1E-4





     @property
     def 		UpperCAmelCase(self				:   List[str]       )						->						int:
         return 1_2

