





import copy
import os
from typing import TYPE_CHECKING, List, Union


if TYPE_CHECKING:
					pass

from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase__           =			logging.get_logger(__name__)

UpperCamelCase__           =			{
    "kakaobrain/align-base": "https://huggingface.co/kakaobrain/align-base/resolve/main/config.json",
}

class 							__SCREAMING_SNAKE_CASE       (  __a							):
							snake_case  :   str      =							"""align_text_model"""






							def __init__(    self	,			__lowerCAmelCase=30522	,			__lowerCAmelCase=768	,			__lowerCAmelCase=12	,			__lowerCAmelCase=12	,			__lowerCAmelCase=3072	,			__lowerCAmelCase="gelu"	,			__lowerCAmelCase=0.1	,			__lowerCAmelCase=0.1	,			__lowerCAmelCase=512	,			__lowerCAmelCase=2	,			__lowerCAmelCase=0.02	,			__lowerCAmelCase=1E-12	,			__lowerCAmelCase=0	,			__lowerCAmelCase="absolute"	,			__lowerCAmelCase=True	,			**__lowerCAmelCase	,			):
														super().__init__(**lowercase_	)

														UpperCamelCase__					  =      vocab_size
														UpperCamelCase__					  =      hidden_size
														UpperCamelCase__					  =      num_hidden_layers
														UpperCamelCase__					  =      num_attention_heads
														UpperCamelCase__					  =      hidden_act
														UpperCamelCase__					  =      intermediate_size
														UpperCamelCase__					  =      hidden_dropout_prob
														UpperCamelCase__					  =      attention_probs_dropout_prob
														UpperCamelCase__					  =      max_position_embeddings
														UpperCamelCase__					  =      type_vocab_size
														UpperCamelCase__					  =      initializer_range
														UpperCamelCase__					  =      layer_norm_eps
														UpperCamelCase__					  =      position_embedding_type
														UpperCamelCase__					  =      use_cache
														UpperCamelCase__					  =      pad_token_id







							@classmethod
							def 					_lowerCamelCase		(    cls	,			__lowerCAmelCase	,			**__lowerCAmelCase	):
														cls._set_token_in_kwargs(lowercase_	)

														UpperCamelCase__  ,  UpperCamelCase__					  =      cls.get_config_dict(lowercase_	,			**lowercase_	)

														# get the text config dict if we are loading from AlignConfig
														if config_dict.get("""model_type"""	) == "align":
																					UpperCamelCase__					  =      config_dict["""text_config"""]

														if "model_type" in config_dict and hasattr(cls	,			"""model_type"""	) and config_dict["model_type"] != cls.model_type:
																					logger.warning(
																					    f"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
																					    f"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""	)

														return cls.from_dict(lowercase_	,			**lowercase_	)






class 							__SCREAMING_SNAKE_CASE       (  __a							):
							snake_case  :   List[Any]      =							"""align_vision_model"""






							def __init__(    self	,			__lowerCAmelCase = 3	,			__lowerCAmelCase = 600	,			__lowerCAmelCase = 2.0	,			__lowerCAmelCase = 3.1	,			__lowerCAmelCase = 8	,			__lowerCAmelCase = [3, 3, 5, 3, 5, 5, 3]	,			__lowerCAmelCase = [32, 16, 24, 40, 80, 112, 192]	,			__lowerCAmelCase = [16, 24, 40, 80, 112, 192, 320]	,			__lowerCAmelCase = []	,			__lowerCAmelCase = [1, 2, 2, 2, 1, 2, 1]	,			__lowerCAmelCase = [1, 2, 2, 3, 3, 4, 1]	,			__lowerCAmelCase = [1, 6, 6, 6, 6, 6, 6]	,			__lowerCAmelCase = 0.25	,			__lowerCAmelCase = "swish"	,			__lowerCAmelCase = 2560	,			__lowerCAmelCase = "mean"	,			__lowerCAmelCase = 0.02	,			__lowerCAmelCase = 0.001	,			__lowerCAmelCase = 0.99	,			__lowerCAmelCase = 0.2	,			**__lowerCAmelCase	,			):
														super().__init__(**lowercase_	)

														UpperCamelCase__					  =      num_channels
														UpperCamelCase__					  =      image_size
														UpperCamelCase__					  =      width_coefficient
														UpperCamelCase__					  =      depth_coefficient
														UpperCamelCase__					  =      depth_divisor
														UpperCamelCase__					  =      kernel_sizes
														UpperCamelCase__					  =      in_channels
														UpperCamelCase__					  =      out_channels
														UpperCamelCase__					  =      depthwise_padding
														UpperCamelCase__					  =      strides
														UpperCamelCase__					  =      num_block_repeats
														UpperCamelCase__					  =      expand_ratios
														UpperCamelCase__					  =      squeeze_expansion_ratio
														UpperCamelCase__					  =      hidden_act
														UpperCamelCase__					  =      hidden_dim
														UpperCamelCase__					  =      pooling_type
														UpperCamelCase__					  =      initializer_range
														UpperCamelCase__					  =      batch_norm_eps
														UpperCamelCase__					  =      batch_norm_momentum
														UpperCamelCase__					  =      drop_connect_rate
														UpperCamelCase__					  =      sum(lowercase_	) * 4







							@classmethod
							def 					_lowerCamelCase		(    cls	,			__lowerCAmelCase	,			**__lowerCAmelCase	):
														cls._set_token_in_kwargs(lowercase_	)

														UpperCamelCase__  ,  UpperCamelCase__					  =      cls.get_config_dict(lowercase_	,			**lowercase_	)

														# get the vision config dict if we are loading from AlignConfig
														if config_dict.get("""model_type"""	) == "align":
																					UpperCamelCase__					  =      config_dict["""vision_config"""]

														if "model_type" in config_dict and hasattr(cls	,			"""model_type"""	) and config_dict["model_type"] != cls.model_type:
																					logger.warning(
																					    f"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
																					    f"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""	)

														return cls.from_dict(lowercase_	,			**lowercase_	)






class 							__SCREAMING_SNAKE_CASE       (  __a							):
							snake_case  :   Any      =							"""align"""
							snake_case  :   int      =							True






							def __init__(    self	,			__lowerCAmelCase=None	,			__lowerCAmelCase=None	,			__lowerCAmelCase=640	,			__lowerCAmelCase=1.0	,			__lowerCAmelCase=0.02	,			**__lowerCAmelCase	,			):
														super().__init__(**lowercase_	)

														if text_config is None:
																					UpperCamelCase__					  =      {}
																					logger.info("""text_config is None. Initializing the AlignTextConfig with default values."""	)

														if vision_config is None:
																					UpperCamelCase__					  =      {}
																					logger.info("""vision_config is None. Initializing the AlignVisionConfig with default values."""	)

														UpperCamelCase__					  =      AlignTextConfig(**lowercase_	)
														UpperCamelCase__					  =      AlignVisionConfig(**lowercase_	)

														UpperCamelCase__					  =      projection_dim
														UpperCamelCase__					  =      temperature_init_value
														UpperCamelCase__					  =      initializer_range






							@classmethod
							def 					_lowerCamelCase		(    cls	,			__lowerCAmelCase	,			__lowerCAmelCase	,			**__lowerCAmelCase	):
														return cls(text_config=text_config.to_dict()	,			vision_config=vision_config.to_dict()	,			**lowercase_	)







							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      copy.deepcopy(self.__dict__	)
														UpperCamelCase__					  =      self.text_config.to_dict()
														UpperCamelCase__					  =      self.vision_config.to_dict()
														UpperCamelCase__					  =      self.__class__.model_type
														return output





'''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   A_      (       SCREAMING_SNAKE_CASE_=None ,	SCREAMING_SNAKE_CASE_=None						)			->str:
  return field(default_factory=lambda: default ,	metadata=SCREAMING_SNAKE_CASE_						)





@dataclass
class       _a    :


  """simple docstring"""

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






def   A_      (       SCREAMING_SNAKE_CASE_						)			->Dict:
  try:
    int(SCREAMING_SNAKE_CASE_						)
    return True
  except ValueError:
    return False

def   A_      (       SCREAMING_SNAKE_CASE_						)			->int:
  try:
    float(SCREAMING_SNAKE_CASE_						)
    return True
  except ValueError:
    return False





class       _a    :


  """simple docstring"""

  def __init__(		self      :       str   ,				lowercase_      :       List[str]      ):







    '''simple docstring'''


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

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





  def 		lowerCamelCase__  (		self      :       str      ):







    '''simple docstring'''


    lowercase_     ,	lowercase_     =       plt.subplots()
    lowercase_     =       """Time usage""" if self.args.is_time else """Memory usage"""
    lowercase_     =       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()      ):
      lowercase_     =       sorted(set(self.result_dict[model_name]["""bsz"""]      )      )
      lowercase_     =       sorted(set(self.result_dict[model_name]["""seq_len"""]      )      )
      lowercase_     =       self.result_dict[model_name]["""result"""]

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

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

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

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

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

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

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

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






def   A_      (       )			->Tuple:
  lowercase_     =       HfArgumentParser(SCREAMING_SNAKE_CASE_						)
  lowercase_     =       parser.parse_args_into_dataclasses()[0]
  lowercase_     =       Plot(args=SCREAMING_SNAKE_CASE_						)
  plot.plot()


if __name__ == "__main__":
       main()

