





'''simple docstring'''






import flax.linen as nn
import jax.numpy as jnp

from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD




class 			UpperCAmelCase					(      nn.Module       ):
						_lowercase:      List[Any]											=       42
						_lowercase:      int											=       42
						_lowercase:      Optional[int]											=       0.0
						_lowercase:      int											=       1
						_lowercase:      int											=       1
						_lowercase:      List[Any]											=       True
						_lowercase:      Tuple											=       False
						_lowercase:      Tuple											=       False
						_lowercase:      Tuple											=       False
						_lowercase:      str											=       jnp.floataa




						def     lowercase__   (      self      :			Optional[int]  )    ->				Optional[Any]:
												_lowerCAmelCase												=							[]
												_lowerCAmelCase												=							[]

												for i in range(self.num_layers  ):
																		_lowerCAmelCase												=							self.in_channels if i == 0 else self.out_channels

																		_lowerCAmelCase												=							FlaxResnetBlockaD(
																		    in_channels=lowercase_      ,      out_channels=self.out_channels      ,      dropout_prob=self.dropout      ,      dtype=self.dtype      ,      )
																		resnets.append(lowercase_  )

																		_lowerCAmelCase												=							FlaxTransformeraDModel(
																		    in_channels=self.out_channels      ,      n_heads=self.num_attention_heads      ,      d_head=self.out_channels // self.num_attention_heads      ,      depth=1      ,      use_linear_projection=self.use_linear_projection      ,      only_cross_attention=self.only_cross_attention      ,      use_memory_efficient_attention=self.use_memory_efficient_attention      ,      dtype=self.dtype      ,      )
																		attentions.append(lowercase_  )

												_lowerCAmelCase												=							resnets
												_lowerCAmelCase												=							attentions

												if self.add_downsample:
																		_lowerCAmelCase												=							FlaxDownsampleaD(self.out_channels      ,      dtype=self.dtype  )




						def __call__(      self      :			int      ,      __snake_case      :			str      ,      __snake_case      :			List[str]      ,      __snake_case      :			Union[str, Any]      ,      __snake_case      :			Union[str, Any]=True  )    ->				List[Any]:
												_lowerCAmelCase												=							()

												for resnet, attn in zip(self.resnets      ,      self.attentions  ):
																		_lowerCAmelCase												=							resnet(lowercase_      ,      lowercase_      ,      deterministic=lowercase_  )
																		_lowerCAmelCase												=							attn(lowercase_      ,      lowercase_      ,      deterministic=lowercase_  )
																		output_states += (hidden_states,)

												if self.add_downsample:
																		_lowerCAmelCase												=							self.downsamplers_a(lowercase_  )
																		output_states += (hidden_states,)

												return hidden_states, output_states




class 			UpperCAmelCase					(      nn.Module       ):
						_lowercase:      int											=       42
						_lowercase:      int											=       42
						_lowercase:      Any											=       0.0
						_lowercase:      List[str]											=       1
						_lowercase:      Optional[int]											=       True
						_lowercase:      Optional[Any]											=       jnp.floataa




						def     lowercase__   (      self      :			str  )    ->				int:
												_lowerCAmelCase												=							[]

												for i in range(self.num_layers  ):
																		_lowerCAmelCase												=							self.in_channels if i == 0 else self.out_channels

																		_lowerCAmelCase												=							FlaxResnetBlockaD(
																		    in_channels=lowercase_      ,      out_channels=self.out_channels      ,      dropout_prob=self.dropout      ,      dtype=self.dtype      ,      )
																		resnets.append(lowercase_  )
												_lowerCAmelCase												=							resnets

												if self.add_downsample:
																		_lowerCAmelCase												=							FlaxDownsampleaD(self.out_channels      ,      dtype=self.dtype  )




						def __call__(      self      :			Dict      ,      __snake_case      :			int      ,      __snake_case      :			Dict      ,      __snake_case      :			Tuple=True  )    ->				Any:
												_lowerCAmelCase												=							()

												for resnet in self.resnets:
																		_lowerCAmelCase												=							resnet(lowercase_      ,      lowercase_      ,      deterministic=lowercase_  )
																		output_states += (hidden_states,)

												if self.add_downsample:
																		_lowerCAmelCase												=							self.downsamplers_a(lowercase_  )
																		output_states += (hidden_states,)

												return hidden_states, output_states




class 			UpperCAmelCase					(      nn.Module       ):
						_lowercase:      Any											=       42
						_lowercase:      Union[str, Any]											=       42
						_lowercase:      Optional[int]											=       42
						_lowercase:      Optional[int]											=       0.0
						_lowercase:      Tuple											=       1
						_lowercase:      int											=       1
						_lowercase:      int											=       True
						_lowercase:      List[Any]											=       False
						_lowercase:      List[Any]											=       False
						_lowercase:      str											=       False
						_lowercase:      str											=       jnp.floataa




						def     lowercase__   (      self      :			int  )    ->				Tuple:
												_lowerCAmelCase												=							[]
												_lowerCAmelCase												=							[]

												for i in range(self.num_layers  ):
																		_lowerCAmelCase												=							self.in_channels if (i == self.num_layers - 1) else self.out_channels
																		_lowerCAmelCase												=							self.prev_output_channel if i == 0 else self.out_channels

																		_lowerCAmelCase												=							FlaxResnetBlockaD(
																		    in_channels=resnet_in_channels + res_skip_channels      ,      out_channels=self.out_channels      ,      dropout_prob=self.dropout      ,      dtype=self.dtype      ,      )
																		resnets.append(lowercase_  )

																		_lowerCAmelCase												=							FlaxTransformeraDModel(
																		    in_channels=self.out_channels      ,      n_heads=self.num_attention_heads      ,      d_head=self.out_channels // self.num_attention_heads      ,      depth=1      ,      use_linear_projection=self.use_linear_projection      ,      only_cross_attention=self.only_cross_attention      ,      use_memory_efficient_attention=self.use_memory_efficient_attention      ,      dtype=self.dtype      ,      )
																		attentions.append(lowercase_  )

												_lowerCAmelCase												=							resnets
												_lowerCAmelCase												=							attentions

												if self.add_upsample:
																		_lowerCAmelCase												=							FlaxUpsampleaD(self.out_channels      ,      dtype=self.dtype  )




						def __call__(      self      :			Dict      ,      __snake_case      :			str      ,      __snake_case      :			int      ,      __snake_case      :			int      ,      __snake_case      :			Tuple      ,      __snake_case      :			int=True  )    ->				int:
												for resnet, attn in zip(self.resnets      ,      self.attentions  ):
																		# pop res hidden states
																		_lowerCAmelCase												=							res_hidden_states_tuple[-1]
																		_lowerCAmelCase												=							res_hidden_states_tuple[:-1]
																		_lowerCAmelCase												=							jnp.concatenate((hidden_states, res_hidden_states)      ,      axis=-1  )

																		_lowerCAmelCase												=							resnet(lowercase_      ,      lowercase_      ,      deterministic=lowercase_  )
																		_lowerCAmelCase												=							attn(lowercase_      ,      lowercase_      ,      deterministic=lowercase_  )

												if self.add_upsample:
																		_lowerCAmelCase												=							self.upsamplers_a(lowercase_  )

												return hidden_states




class 			UpperCAmelCase					(      nn.Module       ):
						_lowercase:      List[Any]											=       42
						_lowercase:      List[Any]											=       42
						_lowercase:      List[Any]											=       42
						_lowercase:      List[str]											=       0.0
						_lowercase:      Union[str, Any]											=       1
						_lowercase:      int											=       True
						_lowercase:      List[str]											=       jnp.floataa




						def     lowercase__   (      self      :			Optional[int]  )    ->				Tuple:
												_lowerCAmelCase												=							[]

												for i in range(self.num_layers  ):
																		_lowerCAmelCase												=							self.in_channels if (i == self.num_layers - 1) else self.out_channels
																		_lowerCAmelCase												=							self.prev_output_channel if i == 0 else self.out_channels

																		_lowerCAmelCase												=							FlaxResnetBlockaD(
																		    in_channels=resnet_in_channels + res_skip_channels      ,      out_channels=self.out_channels      ,      dropout_prob=self.dropout      ,      dtype=self.dtype      ,      )
																		resnets.append(lowercase_  )

												_lowerCAmelCase												=							resnets

												if self.add_upsample:
																		_lowerCAmelCase												=							FlaxUpsampleaD(self.out_channels      ,      dtype=self.dtype  )




						def __call__(      self      :			Optional[int]      ,      __snake_case      :			Optional[Any]      ,      __snake_case      :			Optional[Any]      ,      __snake_case      :			Union[str, Any]      ,      __snake_case      :			List[Any]=True  )    ->				Any:
												for resnet in self.resnets:
																		# pop res hidden states
																		_lowerCAmelCase												=							res_hidden_states_tuple[-1]
																		_lowerCAmelCase												=							res_hidden_states_tuple[:-1]
																		_lowerCAmelCase												=							jnp.concatenate((hidden_states, res_hidden_states)      ,      axis=-1  )

																		_lowerCAmelCase												=							resnet(lowercase_      ,      lowercase_      ,      deterministic=lowercase_  )

												if self.add_upsample:
																		_lowerCAmelCase												=							self.upsamplers_a(lowercase_  )

												return hidden_states




class 			UpperCAmelCase					(      nn.Module       ):
						_lowercase:      Union[str, Any]											=       42
						_lowercase:      List[Any]											=       0.0
						_lowercase:      Tuple											=       1
						_lowercase:      Any											=       1
						_lowercase:      List[str]											=       False
						_lowercase:      int											=       False
						_lowercase:      Tuple											=       jnp.floataa




						def     lowercase__   (      self      :			Optional[int]  )    ->				Optional[Any]:
												# there is always at least one resnet
												_lowerCAmelCase												=							[
												    FlaxResnetBlockaD(
												        in_channels=self.in_channels      ,      out_channels=self.in_channels      ,      dropout_prob=self.dropout      ,      dtype=self.dtype      ,      )
												]

												_lowerCAmelCase												=							[]

												for _ in range(self.num_layers  ):
																		_lowerCAmelCase												=							FlaxTransformeraDModel(
																		    in_channels=self.in_channels      ,      n_heads=self.num_attention_heads      ,      d_head=self.in_channels // self.num_attention_heads      ,      depth=1      ,      use_linear_projection=self.use_linear_projection      ,      use_memory_efficient_attention=self.use_memory_efficient_attention      ,      dtype=self.dtype      ,      )
																		attentions.append(lowercase_  )

																		_lowerCAmelCase												=							FlaxResnetBlockaD(
																		    in_channels=self.in_channels      ,      out_channels=self.in_channels      ,      dropout_prob=self.dropout      ,      dtype=self.dtype      ,      )
																		resnets.append(lowercase_  )

												_lowerCAmelCase												=							resnets
												_lowerCAmelCase												=							attentions




						def __call__(      self      :			Optional[Any]      ,      __snake_case      :			List[str]      ,      __snake_case      :			List[str]      ,      __snake_case      :			Dict      ,      __snake_case      :			str=True  )    ->				Tuple:
												_lowerCAmelCase												=							self.resnets[0](lowercase_      ,      lowercase_  )
												for attn, resnet in zip(self.attentions      ,      self.resnets[1:]  ):
																		_lowerCAmelCase												=							attn(lowercase_      ,      lowercase_      ,      deterministic=lowercase_  )
																		_lowerCAmelCase												=							resnet(lowercase_      ,      lowercase_      ,      deterministic=lowercase_  )

												return hidden_states













'''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        UpperCamelCase__				(		lowerCAmelCase=None							,     lowerCAmelCase=None  ):





      """simple docstring"""


      return field(default_factory=lambda: default							,     metadata=lowerCAmelCase  )




@dataclass
class 			UpperCAmelCase					:
      _lowercase:      str											=       field(
          metadata={'''help''': '''The csv file to plot.'''}							,		)
      _lowercase:      bool											=       field(
          default=snake_case_							,		metadata={'''help''': '''Whether to plot along batch size or sequence length. Defaults to sequence length.'''}							,		)
      _lowercase:      bool											=       field(
          default=snake_case_							,		metadata={'''help''': '''Whether the csv file has time results or memory results. Defaults to memory results.'''}							,		)
      _lowercase:      bool											=       field(
          default=snake_case_							,		metadata={'''help''': '''Disable logarithmic scale when plotting'''}							,		)
      _lowercase:      bool											=       field(
          default=snake_case_							,		metadata={
              '''help''': '''Whether the csv file has training results or inference results. Defaults to inference results.'''
          }							,		)
      _lowercase:      Optional[str]											=       field(
          default=snake_case_							,		metadata={'''help''': '''Filename under which the plot will be saved. If unused no plot is saved.'''}							,		)
      _lowercase:      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        UpperCamelCase__				(		lowerCAmelCase  ):





      """simple docstring"""


      try:
            int(lowerCAmelCase  )
            return True
      except ValueError:
            return False
def        UpperCamelCase__				(		lowerCAmelCase  ):





      """simple docstring"""


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




class 			UpperCAmelCase					:




      def __init__(      self      :			List[str]      ,      __snake_case      :			Union[str, Any]  )    ->				int:
            _lowerCAmelCase												=							args
            _lowerCAmelCase												=							defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}  )

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




      def     lowercase__   (      self      :			Dict  )    ->				str:
            _lowerCAmelCase					,							_lowerCAmelCase												=							plt.subplots()
            _lowerCAmelCase												=							"""Time usage""" if self.args.is_time else """Memory usage"""
            _lowerCAmelCase												=							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												=							sorted(set(self.result_dict[model_name]["""bsz"""]  )  )
                  _lowerCAmelCase												=							sorted(set(self.result_dict[model_name]["""seq_len"""]  )  )
                  _lowerCAmelCase												=							self.result_dict[model_name]["""result"""]

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

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

                        _lowerCAmelCase												=							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												=							title_str[:-4]
            _lowerCAmelCase												=							"""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        UpperCamelCase__				(		):





      """simple docstring"""


      _lowerCAmelCase												=							HfArgumentParser(lowerCAmelCase  )
      _lowerCAmelCase												=							parser.parse_args_into_dataclasses()[0]
      _lowerCAmelCase												=							Plot(args=lowerCAmelCase  )
      plot.plot()


if __name__ == "__main__":
      main()






