





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 				SCREAMING_SNAKE_CASE__    (     __a=None ,			__a=None ):
	return field(default_factory=lambda: default ,			metadata=__a )
@dataclass
class SCREAMING_SNAKE_CASE_    :
						__magic_name__:      str			   =							field(
						    metadata={"help": "The csv file to plot."}							,					)
						__magic_name__:      bool			   =							field(
						    default=snake_case_							,					metadata={"help": "Whether to plot along batch size or sequence length. Defaults to sequence length."}							,					)
						__magic_name__:      bool			   =							field(
						    default=snake_case_							,					metadata={"help": "Whether the csv file has time results or memory results. Defaults to memory results."}							,					)
						__magic_name__:      bool			   =							field(
						    default=snake_case_							,					metadata={"help": "Disable logarithmic scale when plotting"}							,					)
						__magic_name__:      bool			   =							field(
						    default=snake_case_							,					metadata={
						        "help": "Whether the csv file has training results or inference results. Defaults to inference results."
						    }							,					)
						__magic_name__:      Optional[str]			   =							field(
						    default=snake_case_							,					metadata={"help": "Filename under which the plot will be saved. If unused no plot is saved."}							,					)
						__magic_name__:      Optional[List[str]]			   =							list_field(
						    default=snake_case_							,					metadata={"help": "List of model names that are used instead of the ones in the csv file."}       )


def 				SCREAMING_SNAKE_CASE__    (     __a ):
	try:
		int(__a )
		return True
	except ValueError:
		return False


def 				SCREAMING_SNAKE_CASE__    (     __a ):
	try:
		float(__a )
		return True
	except ValueError:
		return False
class SCREAMING_SNAKE_CASE_    :






						def __init__(   self   :   Dict		, _A   :   List[Any]						)				->     str:







							"""simple docstring"""


							snake_case_		:      int           =						args
							snake_case_		:      List[Any]           =						defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}						)

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






						def UpperCAmelCase_					(   self   :   Any						)				->     Optional[Any]:







							"""simple docstring"""


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

								((snake_case_)    ,(snake_case_))		:      Optional[int]           =						(
								    (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
								)

								snake_case_		:      List[str]           =						(
								    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_		:      int           =						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_		:      str           =						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_))		:      Union[str, Any]           =						(
									    ('batch_size', 'len') if self.args.plot_along_batch else ('in #tokens', 'bsz')
									)

									snake_case_		:      Tuple           =						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_		:      Optional[int]           =						title_str[:-4]
							snake_case_		:      Tuple           =						'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 				SCREAMING_SNAKE_CASE__    (     ):
	snake_case_		:      Dict           =						HfArgumentParser(__a )
	snake_case_		:      List[str]           =						parser.parse_args_into_dataclasses()[0]
	snake_case_		:      int           =						Plot(args=__a )
	plot.plot()


if __name__ == "__main__":
						main()



import os
import pathlib
import tempfile
import uuid

import numpy as np

from ..utils import is_soundfile_availble, is_torch_available, is_vision_available, logging


lowerCamelCase_				:							Any       =    logging.get_logger(__name__)

if is_vision_available():
 import PIL.Image
 from PIL import Image
 from PIL.Image import Image as ImageType
else:
 lowerCamelCase_				:							Union[str, Any]       =    object

if is_torch_available():
 import torch

if is_soundfile_availble():
 import soundfile as sf







class  _lowerCamelCase			:

     def __init__(  self							,       SCREAMING_SNAKE_CASE_    ):
            __snake_case						       =   value

     def __str__(  self    ):
            return self.to_string()

     def 						__lowerCamelCase				(  self    ):
            logger.error(
                'This is a raw AgentType of unknown type. Display in notebooks and string conversion will be unreliable'    )
            return self._value

     def 						__lowerCamelCase				(  self    ):
            logger.error(
                'This is a raw AgentType of unknown type. Display in notebooks and string conversion will be unreliable'    )
            return str(self._value    )







class  _lowerCamelCase			(lowerCamelCase							,					lowerCamelCase					):

     def 						__lowerCamelCase				(  self    ):
            return self._value

     def 						__lowerCamelCase				(  self    ):
            return self._value







class  _lowerCamelCase			(lowerCamelCase							,					lowerCamelCase					):

     def __init__(  self							,       SCREAMING_SNAKE_CASE_    ):
            super().__init__(SCREAMING_SNAKE_CASE_    )

            if not is_vision_available():
                   raise ImportError('PIL must be installed in order to handle images.'    )

            __snake_case						       =   None
            __snake_case						       =   None
            __snake_case						       =   None

            if isinstance(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    ):
                   __snake_case						       =   value
            elif isinstance(SCREAMING_SNAKE_CASE_							,       (str, pathlib.Path)    ):
                   __snake_case						       =   value
            elif isinstance(SCREAMING_SNAKE_CASE_							,       torch.Tensor    ):
                   __snake_case						       =   value
            else:
                   raise ValueError(f'''Unsupported type for {self.__class__.__name__}: {type(SCREAMING_SNAKE_CASE_    )}'''    )

     def 						__lowerCamelCase				(  self							,       SCREAMING_SNAKE_CASE_=None							,       SCREAMING_SNAKE_CASE_=None    ):
            from IPython.display import Image, display

            display(Image(self.to_string()    )    )

     def 						__lowerCamelCase				(  self    ):
            if self._raw is not None:
                   return self._raw

            if self._path is not None:
                   __snake_case						       =   Image.open(self._path    )
                   return self._raw

     def 						__lowerCamelCase				(  self    ):
            if self._path is not None:
                   return self._path

            if self._raw is not None:
                   __snake_case						       =   tempfile.mkdtemp()
                   __snake_case						       =   os.path.join(SCREAMING_SNAKE_CASE_							,       str(uuid.uuida()    ) + '.png'    )
                   self._raw.save(self._path    )

                   return self._path

            if self._tensor is not None:
                   __snake_case						       =   self._tensor.cpu().detach().numpy()

                   # There is likely simpler than load into image into save
                   __snake_case						       =   Image.fromarray((array * 255).astype(np.uinta    )    )

                   __snake_case						       =   tempfile.mkdtemp()
                   __snake_case						       =   os.path.join(SCREAMING_SNAKE_CASE_							,       str(uuid.uuida()    ) + '.png'    )

                   img.save(self._path    )

                   return self._path







class  _lowerCamelCase			(lowerCamelCase					):

     def __init__(  self							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_=16_000    ):
            super().__init__(SCREAMING_SNAKE_CASE_    )

            if not is_soundfile_availble():
                   raise ImportError('soundfile must be installed in order to handle audio.'    )

            __snake_case						       =   None
            __snake_case						       =   None

            __snake_case						       =   samplerate

            if isinstance(SCREAMING_SNAKE_CASE_							,       (str, pathlib.Path)    ):
                   __snake_case						       =   value
            elif isinstance(SCREAMING_SNAKE_CASE_							,       torch.Tensor    ):
                   __snake_case						       =   value
            else:
                   raise ValueError(f'''Unsupported audio type: {type(SCREAMING_SNAKE_CASE_    )}'''    )

     def 						__lowerCamelCase				(  self							,       SCREAMING_SNAKE_CASE_=None							,       SCREAMING_SNAKE_CASE_=None    ):
            from IPython.display import Audio, display

            display(Audio(self.to_string()							,       rate=self.samplerate    )    )

     def 						__lowerCamelCase				(  self    ):
            if self._tensor is not None:
                   return self._tensor

            if self._path is not None:
                   __snake_case       ,       __snake_case						       =   sf.read(self._path    )
                   __snake_case						       =   torch.tensor(SCREAMING_SNAKE_CASE_    )
                   return self._tensor

     def 						__lowerCamelCase				(  self    ):
            if self._path is not None:
                   return self._path

            if self._tensor is not None:
                   __snake_case						       =   tempfile.mkdtemp()
                   __snake_case						       =   os.path.join(SCREAMING_SNAKE_CASE_							,       str(uuid.uuida()    ) + '.wav'    )
                   sf.write(self._path							,       self._tensor							,       samplerate=self.samplerate    )
                   return self._path





lowerCamelCase_				:							Optional[Any]       =    {"text": AgentText, "image": AgentImage, "audio": AgentAudio}
lowerCamelCase_				:							Any       =    {str: AgentText}

if is_vision_available():
 lowerCamelCase_				:							Optional[int]       =    AgentImage
def        __lowercase(							*__snake_case				:		Optional[Any]    ,**__snake_case				:		str					)       ->   Tuple:
       __snake_case						       =   [(arg.to_raw() if isinstance(__snake_case    ,__snake_case					) else arg) for arg in args]
       __snake_case						       =   {k: (v.to_raw() if isinstance(__snake_case    ,__snake_case					) else v) for k, v in kwargs.items()}
       return args, kwargs
def        __lowercase(							__snake_case				:		Optional[int]    ,__snake_case				:		List[str]=None					)       ->   Tuple:
       if isinstance(__snake_case    ,__snake_case					):
              __snake_case						       =   {}
              for i, (k, v) in enumerate(outputs.items()					):
                     if output_types is not None:
                            # If the class has defined outputs, we can map directly according to the class definition
                            if output_types[i] in AGENT_TYPE_MAPPING:
                                   __snake_case						       =   AGENT_TYPE_MAPPING[output_types[i]](__snake_case					)
                            else:
                                   __snake_case						       =   AgentType(__snake_case					)

                     else:
                            # If the class does not have defined output, then we map according to the type
                            for _k, _v in INSTANCE_TYPE_MAPPING.items():
                                   if isinstance(__snake_case    ,_k					):
                                          __snake_case						       =   _v(__snake_case					)
                            if k not in decoded_outputs:
                                   __snake_case						       =   AgentType[v]

       elif isinstance(__snake_case    ,(list, tuple)					):
              __snake_case						       =   type(__snake_case					)()
              for i, v in enumerate(__snake_case					):
                     if output_types is not None:
                            # If the class has defined outputs, we can map directly according to the class definition
                            if output_types[i] in AGENT_TYPE_MAPPING:
                                   decoded_outputs.append(AGENT_TYPE_MAPPING[output_types[i]](__snake_case					)					)
                            else:
                                   decoded_outputs.append(AgentType(__snake_case					)					)
                     else:
                            # If the class does not have defined output, then we map according to the type
                            __snake_case						       =   False
                            for _k, _v in INSTANCE_TYPE_MAPPING.items():
                                   if isinstance(__snake_case    ,_k					):
                                          decoded_outputs.append(_v(__snake_case					)					)
                                          __snake_case						       =   True

                            if not found:
                                   decoded_outputs.append(AgentType(__snake_case					)					)

       else:
              if output_types[0] in AGENT_TYPE_MAPPING:
                     # If the class has defined outputs, we can map directly according to the class definition
                     __snake_case						       =   AGENT_TYPE_MAPPING[output_types[0]](__snake_case					)

              else:
                     # If the class does not have defined output, then we map according to the type
                     for _k, _v in INSTANCE_TYPE_MAPPING.items():
                            if isinstance(__snake_case    ,_k					):
                                   return _v(__snake_case					)
                     return AgentType(__snake_case					)

       return decoded_outputs
