


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   lowerCamelCase(      a__=None		,a__=None):
				return field(default_factory=lambda: default		,metadata=a__)

@dataclass
class   A__							:
						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   lowerCamelCase(      a__):
				try:
								int(a__)
								return True
				except ValueError:
								return False







def   lowerCamelCase(      a__):
				try:
								float(a__)
								return True
				except ValueError:
								return False

class   A__							:




						def __init__(				self    :					Optional[int] ,			_a    :					List[str]     )							->   Dict:


										"""simple docstring"""





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

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




						def 							__UpperCamelCase (				self    :					Optional[int]     )							->   Dict:


										"""simple docstring"""





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

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

														_SCREAMING_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:
																						_SCREAMING_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:
																						_SCREAMING_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 ,			)

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

																		_SCREAMING_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."

										_SCREAMING_SNAKE_CASE	     =title_str[:-4]
										_SCREAMING_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   lowerCamelCase(      ):
				_SCREAMING_SNAKE_CASE	     =HfArgumentParser(a__)
				_SCREAMING_SNAKE_CASE	     =parser.parse_args_into_dataclasses()[0]
				_SCREAMING_SNAKE_CASE	     =Plot(args=a__)
				plot.plot()


if __name__ == "__main__":
							main()






"""simple docstring"""





from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


_lowerCamelCase       =	logging.get_logger(__name__)

_lowerCamelCase       =	{
    '''facebook/m2m100_418M''': '''https://huggingface.co/facebook/m2m100_418M/resolve/main/config.json''',
    # See all M2M100 models at https://huggingface.co/models?filter=m2m_100
}





class 			snake_case		(   __UpperCAmelCase   ):
      lowerCamelCase__						= '''m2m_100'''
      lowerCamelCase__						= ['''past_key_values''']
      lowerCamelCase__						= {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}

      def __init__(			self   :int						,							_lowerCamelCase   :Union[str, Any]=1_2_8_1_1_2						,							_lowerCamelCase   :str=1_0_2_4						,							_lowerCamelCase   :Any=1_2						,							_lowerCamelCase   :Dict=4_0_9_6						,							_lowerCamelCase   :List[str]=1_6						,							_lowerCamelCase   :Union[str, Any]=1_2						,							_lowerCamelCase   :Any=4_0_9_6						,							_lowerCamelCase   :str=1_6						,							_lowerCamelCase   :Union[str, Any]=0.0_5						,							_lowerCamelCase   :Any=0.0_5						,							_lowerCamelCase   :int=True						,							_lowerCamelCase   :Dict=True						,							_lowerCamelCase   :List[str]="relu"						,							_lowerCamelCase   :List[Any]=1_0_2_4						,							_lowerCamelCase   :Dict=0.1						,							_lowerCamelCase   :List[Any]=0.1						,							_lowerCamelCase   :Any=0.0						,							_lowerCamelCase   :Tuple=0.0_2						,							_lowerCamelCase   :Dict=2						,							_lowerCamelCase   :List[str]=True						,							_lowerCamelCase   :Any=1						,							_lowerCamelCase   :Dict=0						,							_lowerCamelCase   :List[Any]=2						,							**_lowerCamelCase   :Optional[Any]						,							):
       __SCREAMING_SNAKE_CASE :			Any												=       vocab_size
       __SCREAMING_SNAKE_CASE :			Any												=       max_position_embeddings
       __SCREAMING_SNAKE_CASE :			int												=       d_model
       __SCREAMING_SNAKE_CASE :			List[Any]												=       encoder_ffn_dim
       __SCREAMING_SNAKE_CASE :			Dict												=       encoder_layers
       __SCREAMING_SNAKE_CASE :			int												=       encoder_attention_heads
       __SCREAMING_SNAKE_CASE :			Dict												=       decoder_ffn_dim
       __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       decoder_layers
       __SCREAMING_SNAKE_CASE :			Optional[Any]												=       decoder_attention_heads
       __SCREAMING_SNAKE_CASE :			Optional[Any]												=       dropout
       __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       attention_dropout
       __SCREAMING_SNAKE_CASE :			List[str]												=       activation_dropout
       __SCREAMING_SNAKE_CASE :			List[Any]												=       activation_function
       __SCREAMING_SNAKE_CASE :			Any												=       init_std
       __SCREAMING_SNAKE_CASE :			List[Any]												=       encoder_layerdrop
       __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       decoder_layerdrop
       __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       use_cache
       __SCREAMING_SNAKE_CASE :			List[str]												=       encoder_layers
       __SCREAMING_SNAKE_CASE :			str												=       scale_embedding  # scale factor will be sqrt(d_model) if True

       super().__init__(
           pad_token_id=_lowerCamelCase						,							bos_token_id=_lowerCamelCase						,							eos_token_id=_lowerCamelCase						,							is_encoder_decoder=_lowerCamelCase						,							decoder_start_token_id=_lowerCamelCase						,							**_lowerCamelCase						,							)







class 			snake_case		(   __UpperCAmelCase   ):

      @property
      def 						SCREAMING_SNAKE_CASE_			(			self   :int						):
       __SCREAMING_SNAKE_CASE :			str												=       OrderedDict(
           [
               ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
               ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
           ]						)

       if self.use_past:
        __SCREAMING_SNAKE_CASE :			Dict												=       {0: '''batch'''}
        __SCREAMING_SNAKE_CASE :			Any												=       {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
       else:
        __SCREAMING_SNAKE_CASE :			int												=       {0: '''batch''', 1: '''decoder_sequence'''}
        __SCREAMING_SNAKE_CASE :			List[Any]												=       {0: '''batch''', 1: '''decoder_sequence'''}

       if self.use_past:
        self.fill_with_past_key_values_(_lowerCamelCase						,							direction='''inputs'''						)
       return common_inputs

      def 						SCREAMING_SNAKE_CASE_			(			self   :Tuple						,							_lowerCamelCase   :PreTrainedTokenizer						,							_lowerCamelCase   :int = -1						,							_lowerCamelCase   :int = -1						,							_lowerCamelCase   :bool = False						,							_lowerCamelCase   :Optional[TensorType] = None						,							):
       # Copied from OnnxConfig.generate_dummy_inputs
       # Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
       # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
       __SCREAMING_SNAKE_CASE :			List[Any]												=       compute_effective_axis_dimension(
           _lowerCamelCase						,							fixed_dimension=OnnxConfig.default_fixed_batch						,							num_token_to_add=0						)

       # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
       __SCREAMING_SNAKE_CASE :			List[str]												=       tokenizer.num_special_tokens_to_add(_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE :			int												=       compute_effective_axis_dimension(
           _lowerCamelCase						,							fixed_dimension=OnnxConfig.default_fixed_sequence						,							num_token_to_add=_lowerCamelCase						)

       # Generate dummy inputs according to compute batch and sequence
       __SCREAMING_SNAKE_CASE :			int												=       [''' '''.join([tokenizer.unk_token]						) * seq_length] * batch_size
       __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       dict(tokenizer(_lowerCamelCase						,							return_tensors=_lowerCamelCase						)						)
       return common_inputs

      def 						SCREAMING_SNAKE_CASE_			(			self   :str						,							_lowerCamelCase   :PreTrainedTokenizer						,							_lowerCamelCase   :int = -1						,							_lowerCamelCase   :int = -1						,							_lowerCamelCase   :bool = False						,							_lowerCamelCase   :Optional[TensorType] = None						,							):
       __SCREAMING_SNAKE_CASE :			Dict												=       self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           _lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						)

       # Generate decoder inputs
       __SCREAMING_SNAKE_CASE :			Dict												=       seq_length if not self.use_past else 1
       __SCREAMING_SNAKE_CASE :			Any												=       self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           _lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE :			Optional[int]												=       {f'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
       __SCREAMING_SNAKE_CASE :			Optional[Any]												=       dict(**_lowerCamelCase						,							**_lowerCamelCase						)

       if self.use_past:
        if not is_torch_available():
         raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''						)
        else:
         import torch
        __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE :			int												=       common_inputs['''input_ids'''].shape
        __SCREAMING_SNAKE_CASE :			Tuple												=       common_inputs['''decoder_input_ids'''].shape[1]
        __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE :			Optional[Any]												=       self.num_attention_heads
        __SCREAMING_SNAKE_CASE :			List[str]												=       (
            batch,
            num_encoder_attention_heads,
            encoder_seq_length,
            self._config.hidden_size // num_encoder_attention_heads,
        )
        __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       decoder_seq_length + 3
        __SCREAMING_SNAKE_CASE :			List[str]												=       (
            batch,
            num_decoder_attention_heads,
            decoder_past_length,
            self._config.hidden_size // num_decoder_attention_heads,
        )

        __SCREAMING_SNAKE_CASE :			Any												=       torch.cat(
            [common_inputs['''decoder_attention_mask'''], torch.ones(_lowerCamelCase						,							_lowerCamelCase						)]						,							dim=1						)

        __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       []
        # If the number of encoder and decoder layers are present in the model configuration, both are considered
        __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       self.num_layers
        __SCREAMING_SNAKE_CASE :			Any												=       min(_lowerCamelCase						,							_lowerCamelCase						)
        __SCREAMING_SNAKE_CASE :			Tuple												=       max(_lowerCamelCase						,							_lowerCamelCase						) - min_num_layers
        __SCREAMING_SNAKE_CASE :			Optional[int]												=       '''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''

        for _ in range(_lowerCamelCase						):
         common_inputs["past_key_values"].append(
             (
                 torch.zeros(_lowerCamelCase						),
                 torch.zeros(_lowerCamelCase						),
                 torch.zeros(_lowerCamelCase						),
                 torch.zeros(_lowerCamelCase						),
             )						)
        # TODO: test this.
        __SCREAMING_SNAKE_CASE :			Any												=       encoder_shape if remaining_side_name == '''encoder''' else decoder_shape
        for _ in range(_lowerCamelCase						,							_lowerCamelCase						):
         common_inputs["past_key_values"].append((torch.zeros(_lowerCamelCase						), torch.zeros(_lowerCamelCase						))						)
       return common_inputs



      lowerCamelCase__						= _generate_dummy_inputs_for_default_and_seqaseq_lm

