





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      (       _UpperCAmelCase=None						,	_UpperCAmelCase=None )			->     Dict:
    return field(default_factory=lambda: default						,	metadata=__A )

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


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     Dict:


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


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     int:


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

class    lowerCAmelCase							:
 def __init__(      self  :      Optional[int]			,     UpperCAmelCase  :      Union[str, Any]	)			->	Dict:
     lowerCamelCase__    :      List[Any]					 =		args
     lowerCamelCase__    :      Optional[Any]					 =		defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}	)

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







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

         ((lowerCamelCase__) ,							(lowerCamelCase__))    :      int					 =		(
             (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
         )

         lowerCamelCase__    :      int					 =		(
             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:
                 lowerCamelCase__    :      Any					 =		np.asarray(
                     [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]			,     dtype=_snake_case			,     )
             else:
                 lowerCamelCase__    :      Tuple					 =		np.asarray(
                     [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]			,     dtype=np.floataa			,     )

             ((lowerCamelCase__) ,							(lowerCamelCase__))    :      Optional[int]					 =		(
                 ('batch_size', 'len') if self.args.plot_along_batch else ('in #tokens', 'bsz')
             )

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

         title_str += F""" {label_model_name} vs."""

     lowerCamelCase__    :      Any					 =		title_str[:-4]
     lowerCamelCase__    :      Union[str, Any]					 =		'Time in s' if self.args.is_time else 'Memory in MB'

     # plot
     plt.title(_snake_case	)
     plt.xlabel(_snake_case	)
     plt.ylabel(_snake_case	)
     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      (       )			->     Union[str, Any]:
    lowerCamelCase__    :      int					 =		HfArgumentParser(__A )
    lowerCamelCase__    :      Any					 =		parser.parse_args_into_dataclasses()[0]
    lowerCamelCase__    :      Optional[Any]					 =		Plot(args=__A )
    plot.plot()


if __name__ == "__main__":
    main()











import warnings
from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType







class __snake_case			(			a		):
   UpperCAmelCase__  :    Dict          =  ['''image_processor''', '''tokenizer''']
   UpperCAmelCase__  :    Dict          =  '''FlavaImageProcessor'''
   UpperCAmelCase__  :    Dict          =  ('''BertTokenizer''', '''BertTokenizerFast''')







   def __init__(      self		:						Union[str, Any]					,						_snake_case		:						List[str]=None					,						_snake_case		:						str=None					,						**_snake_case		:						int):

        """simple docstring"""


        UpperCAmelCase_          =							None
        if "feature_extractor" in kwargs:
             warnings.warn(
                 '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
                 ''' instead.'''					,						_snake_case					,						)
             UpperCAmelCase_          =							kwargs.pop('''feature_extractor''')

        UpperCAmelCase_          =							image_processor if image_processor is not None else feature_extractor
        if image_processor is None:
             raise ValueError('''You need to specify an `image_processor`.''')
        if tokenizer is None:
             raise ValueError('''You need to specify a `tokenizer`.''')

        super().__init__(_snake_case					,						_snake_case)
        UpperCAmelCase_          =							self.image_processor







   def __call__(      self		:						List[Any]					,						_snake_case		:						Optional[ImageInput] = None					,						_snake_case		:						Optional[Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]]] = None					,						_snake_case		:						bool = True					,						_snake_case		:						Union[bool, str, PaddingStrategy] = False					,						_snake_case		:						Union[bool, str, TruncationStrategy] = False					,						_snake_case		:						Optional[int] = None					,						_snake_case		:						int = 0					,						_snake_case		:						Optional[int] = None					,						_snake_case		:						Optional[bool] = None					,						_snake_case		:						Optional[bool] = None					,						_snake_case		:						Optional[bool] = None					,						_snake_case		:						Optional[bool] = None					,						_snake_case		:						bool = False					,						_snake_case		:						bool = False					,						_snake_case		:						bool = False					,						_snake_case		:						bool = False					,						_snake_case		:						bool = True					,						_snake_case		:						Optional[Union[str, TensorType]] = None					,						**_snake_case		:						Any					,						):

        """simple docstring"""


        if text is None and images is None:
             raise ValueError('''You have to specify either text or images. Both cannot be none.''')

        if text is not None:
             UpperCAmelCase_          =							self.tokenizer(
                 text=_snake_case					,						add_special_tokens=_snake_case					,						padding=_snake_case					,						truncation=_snake_case					,						max_length=_snake_case					,						stride=_snake_case					,						pad_to_multiple_of=_snake_case					,						return_token_type_ids=_snake_case					,						return_attention_mask=_snake_case					,						return_overflowing_tokens=_snake_case					,						return_special_tokens_mask=_snake_case					,						return_offsets_mapping=_snake_case					,						return_length=_snake_case					,						verbose=_snake_case					,						return_tensors=_snake_case					,						**_snake_case					,						)
        if images is not None:
             UpperCAmelCase_          =							self.image_processor(
                 _snake_case					,						return_image_mask=_snake_case					,						return_codebook_pixels=_snake_case					,						return_tensors=_snake_case					,						**_snake_case					,						)

        if text is not None and images is not None:
             encoding.update(_snake_case)
             return encoding
        elif text is not None:
             return encoding
        else:
             return BatchEncoding(data=dict(**_snake_case)					,						tensor_type=_snake_case)







   def lowerCamelCase						(      self		:						Any					,						*_snake_case		:						Optional[Any]					,						**_snake_case		:						int):

        """simple docstring"""


        return self.tokenizer.batch_decode(*_snake_case					,						**_snake_case)







   def lowerCamelCase						(      self		:						Optional[int]					,						*_snake_case		:						int					,						**_snake_case		:						Dict):

        """simple docstring"""


        return self.tokenizer.decode(*_snake_case					,						**_snake_case)







   @property
   def lowerCamelCase						(      self		:						str):

        """simple docstring"""


        UpperCAmelCase_          =							self.tokenizer.model_input_names
        UpperCAmelCase_          =							self.image_processor.model_input_names
        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))







   @property
   def lowerCamelCase						(      self		:						str):

        """simple docstring"""


        warnings.warn(
            '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''					,						_snake_case					,						)
        return self.image_processor_class







   @property
   def lowerCamelCase						(      self		:						Any):

        """simple docstring"""


        warnings.warn(
            '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''					,						_snake_case					,						)
        return self.image_processor






