





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)       ->   int:
  return field(default_factory=lambda: default , metadata=SCREAMING_SNAKE_CASE__)







@dataclass
class    __snake_case	:





       '''simple docstring'''







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







def       A__							(   SCREAMING_SNAKE_CASE__)       ->   str:
  try:
    int(SCREAMING_SNAKE_CASE__)
    return True
  except ValueError:
    return False







def       A__							(   SCREAMING_SNAKE_CASE__)       ->   Union[str, Any]:
  try:
    float(SCREAMING_SNAKE_CASE__)
    return True
  except ValueError:
    return False







class    __snake_case	:





       '''simple docstring'''







       def __init__(  self			:   Tuple							,    A			:   Union[str, Any]		):
         __snake_case:						Any			  =						args
         __snake_case:						Any			  =						defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}		)

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






       def 							UpperCAmelCase__			(  self			:   List[str]		):
         __snake_case:						List[str]			  =						plt.subplots()
         __snake_case:						str			  =						"""Time usage""" if self.args.is_time else """Memory usage"""
         __snake_case:						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()		):
           __snake_case:						List[Any]			  =						sorted(set(self.result_dict[model_name]["""bsz"""]		)		)
           __snake_case:						List[Any]			  =						sorted(set(self.result_dict[model_name]["""seq_len"""]		)		)
           __snake_case:						List[str]			  =						self.result_dict[model_name]["""result"""]

           (__snake_case):						str			  =						(
               (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
           )

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

             __snake_case:						Union[str, Any]			  =						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:						Any			  =						title_str[:-4]
         __snake_case:						List[Any]			  =						"""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       A__							(   )       ->   Dict:
  __snake_case:						Union[str, Any]			  =						HfArgumentParser(SCREAMING_SNAKE_CASE__)
  __snake_case:						Optional[Any]			  =						parser.parse_args_into_dataclasses()[0]
  __snake_case:						List[Any]			  =						Plot(args=SCREAMING_SNAKE_CASE__)
  plot.plot()


if __name__ == "__main__":
      main()













import functools
import operator

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


__UpperCAmelCase	:       Optional[Any]    	=							logging.get_logger(__name__)

__UpperCAmelCase	:       Union[str, Any]    	=							{
    "asapp/sew-d-tiny-100k": "https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json",
    # See all SEW-D models at https://huggingface.co/models?filter=sew-d
}







class    __snake_case	(							__lowerCamelCase      ):





       '''simple docstring'''







       lowerCAmelCase__						=					"""sew-d"""




       def __init__(  self			:   Dict							,    A			:   Any=32							,    A			:   Dict=768							,    A			:   Optional[Any]=12							,    A			:   Union[str, Any]=12							,    A			:   Union[str, Any]=3_072							,    A			:   Optional[Any]=2							,    A			:   Union[str, Any]=512							,    A			:   List[Any]=256							,    A			:   Dict=True							,    A			:   Union[str, Any]=True							,    A			:   Optional[int]=("p2c", "c2p")							,    A			:   str="layer_norm"							,    A			:   Dict="gelu_python"							,    A			:   Tuple=0.1							,    A			:   Any=0.1							,    A			:   Tuple=0.1							,    A			:   Optional[int]=0.0							,    A			:   Any=0.1							,    A			:   Any=0.02							,    A			:   Dict=1E-7							,    A			:   str=1E-5							,    A			:   int="group"							,    A			:   int="gelu"							,    A			:   str=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)							,    A			:   Union[str, Any]=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)							,    A			:   List[Any]=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)							,    A			:   Optional[int]=False							,    A			:   int=128							,    A			:   int=16							,    A			:   Optional[Any]=True							,    A			:   List[Any]=0.05							,    A			:   Any=10							,    A			:   Dict=2							,    A			:   List[Any]=0.0							,    A			:   Union[str, Any]=10							,    A			:   int=0							,    A			:   List[Any]="mean"							,    A			:   Union[str, Any]=False							,    A			:   Any=False							,    A			:   Optional[int]=256							,    A			:   List[Any]=0							,    A			:   Any=1							,    A			:   List[Any]=2							,    **A			:   List[Any]							,    ):
         super().__init__(**A							,    pad_token_id=A							,    bos_token_id=A							,    eos_token_id=A		)
         __snake_case:						Optional[int]			  =						hidden_size
         __snake_case:						str			  =						feat_extract_norm
         __snake_case:						int			  =						feat_extract_activation
         __snake_case:						str			  =						list(A		)
         __snake_case:						Any			  =						list(A		)
         __snake_case:						str			  =						list(A		)
         __snake_case:						Union[str, Any]			  =						conv_bias
         __snake_case:						int			  =						num_conv_pos_embeddings
         __snake_case:						str			  =						num_conv_pos_embedding_groups
         __snake_case:						List[Any]			  =						len(self.conv_dim		)
         __snake_case:						List[str]			  =						num_hidden_layers
         __snake_case:						Union[str, Any]			  =						intermediate_size
         __snake_case:						Dict			  =						squeeze_factor
         __snake_case:						List[Any]			  =						max_position_embeddings
         __snake_case:						List[Any]			  =						position_buckets
         __snake_case:						List[str]			  =						share_att_key
         __snake_case:						int			  =						relative_attention
         __snake_case:						Union[str, Any]			  =						norm_rel_ebd
         __snake_case:						List[str]			  =						list(A		)
         __snake_case:						Tuple			  =						hidden_act
         __snake_case:						List[Any]			  =						num_attention_heads
         __snake_case:						str			  =						hidden_dropout
         __snake_case:						int			  =						attention_dropout
         __snake_case:						Dict			  =						activation_dropout
         __snake_case:						Any			  =						feat_proj_dropout
         __snake_case:						int			  =						final_dropout
         __snake_case:						List[Any]			  =						layer_norm_eps
         __snake_case:						List[str]			  =						feature_layer_norm_eps
         __snake_case:						List[Any]			  =						initializer_range
         __snake_case:						List[Any]			  =						vocab_size

         if (
             (len(self.conv_stride		) != self.num_feat_extract_layers)
             or (len(self.conv_kernel		) != self.num_feat_extract_layers)
             or (len(self.conv_dim		) != self.num_feat_extract_layers)
         ):
           raise ValueError(
               """Configuration for convolutional layers is incorrect."""
               """It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,"""
               f'''but is `len(config.conv_dim) = {len(self.conv_dim		)}`, `len(config.conv_stride)'''
               f'''= {len(self.conv_stride		)}`, `len(config.conv_kernel) = {len(self.conv_kernel		)}`.'''		)

         # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
         __snake_case:						List[Any]			  =						apply_spec_augment
         __snake_case:						List[Any]			  =						mask_time_prob
         __snake_case:						str			  =						mask_time_length
         __snake_case:						List[str]			  =						mask_time_min_masks
         __snake_case:						str			  =						mask_feature_prob
         __snake_case:						Optional[int]			  =						mask_feature_length
         __snake_case:						Dict			  =						mask_feature_min_masks

         # ctc loss
         __snake_case:						Any			  =						ctc_loss_reduction
         __snake_case:						str			  =						ctc_zero_infinity

         # sequence classification
         __snake_case:						Optional[Any]			  =						use_weighted_layer_sum
         __snake_case:						List[Any]			  =						classifier_proj_size






       @property
       def 							UpperCAmelCase__			(  self			:   int		):
         return functools.reduce(operator.mul							,    self.conv_stride							,    1		)






