'''simple docstring'''

import inspect
import unittest

import numpy as np

from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import MaskFormerForInstanceSegmentation, MaskFormerModel

 if is_vision_available():
  from transformers import MaskFormerImageProcessor

if is_vision_available():
 from PIL import Image


class     __UpperCAmelCase       :





  def __init__(	self    ,     _lowerCamelCase    ,     _lowerCamelCase=2    ,     _lowerCamelCase=True    ,     _lowerCamelCase=False    ,     _lowerCamelCase=10    ,     _lowerCamelCase=3    ,     _lowerCamelCase=32 * 4    ,     _lowerCamelCase=32 * 6    ,     _lowerCamelCase=4    ,     _lowerCamelCase=32    ,     ):
     lowerCAmelCase_			=      parent
     lowerCAmelCase_			=      batch_size
     lowerCAmelCase_			=      is_training
     lowerCAmelCase_			=      use_auxiliary_loss
     lowerCAmelCase_			=      num_queries
     lowerCAmelCase_			=      num_channels
     lowerCAmelCase_			=      min_size
     lowerCAmelCase_			=      max_size
     lowerCAmelCase_			=      num_labels
     lowerCAmelCase_			=      mask_feature_size





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size] ).to(
         _lowerCamelCase )

     lowerCAmelCase_			=      torch.ones([self.batch_size, self.min_size, self.max_size]    ,     device=_lowerCamelCase )

     lowerCAmelCase_			=      (
         torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]    ,     device=_lowerCamelCase ) > 0.5
     ).float()
     lowerCAmelCase_			=      (torch.rand((self.batch_size, self.num_labels)    ,     device=_lowerCamelCase ) > 0.5).long()

     lowerCAmelCase_			=      self.get_config()
     return config, pixel_values, pixel_mask, mask_labels, class_labels





  def 						UpperCAmelCase_	(	self ):
     return MaskFormerConfig.from_backbone_and_decoder_configs(
         backbone_config=SwinConfig(
             depths=[1, 1, 1, 1]    ,     )    ,     decoder_config=DetrConfig(
             decoder_ffn_dim=128    ,     num_queries=self.num_queries    ,     decoder_attention_heads=2    ,     d_model=self.mask_feature_size    ,     )    ,     mask_feature_size=self.mask_feature_size    ,     fpn_feature_size=self.mask_feature_size    ,     num_channels=self.num_channels    ,     num_labels=self.num_labels    ,     )





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_	,lowerCAmelCase_	,lowerCAmelCase_	,lowerCAmelCase_	,lowerCAmelCase_			=      self.prepare_config_and_inputs()
     lowerCAmelCase_			=      {'''pixel_values''': pixel_values, '''pixel_mask''': pixel_mask}
     return config, inputs_dict





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     _lowerCamelCase ):
     lowerCAmelCase_			=      output.encoder_hidden_states
     lowerCAmelCase_			=      output.pixel_decoder_hidden_states
     lowerCAmelCase_			=      output.transformer_decoder_hidden_states

     self.parent.assertTrue(len(_lowerCamelCase )    ,     len(config.backbone_config.depths ) )
     self.parent.assertTrue(len(_lowerCamelCase )    ,     len(config.backbone_config.depths ) )
     self.parent.assertTrue(len(_lowerCamelCase )    ,     config.decoder_config.decoder_layers )





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase=False ):
     with torch.no_grad():
        lowerCAmelCase_			=      MaskFormerModel(config=_lowerCamelCase )
        model.to(_lowerCamelCase )
        model.eval()

        lowerCAmelCase_			=      model(pixel_values=_lowerCamelCase    ,     pixel_mask=_lowerCamelCase )
        lowerCAmelCase_			=      model(_lowerCamelCase    ,     output_hidden_states=_lowerCamelCase )
     # the correct shape of output.transformer_decoder_hidden_states ensure the correcteness of the
     # encoder and pixel decoder
     self.parent.assertEqual(
         output.transformer_decoder_last_hidden_state.shape    ,     (self.batch_size, self.num_queries, self.mask_feature_size)    ,     )
     # let's ensure the other two hidden state exists
     self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None )
     self.parent.assertTrue(output.encoder_last_hidden_state is not None )

     if output_hidden_states:
        self.check_output_hidden_state(_lowerCamelCase    ,     _lowerCamelCase )







  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase ):
     lowerCAmelCase_			=      MaskFormerForInstanceSegmentation(config=_lowerCamelCase )
     model.to(_lowerCamelCase )
     model.eval()

     def comm_check_on_output(_lowerCamelCase ):
        # let's still check that all the required stuff is there
        self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None )
        self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None )
        self.parent.assertTrue(result.encoder_last_hidden_state is not None )
        # okay, now we need to check the logits shape
        # due to the encoder compression, masks have a //4 spatial size
        self.parent.assertEqual(
            result.masks_queries_logits.shape    ,     (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4)    ,     )
        # + 1 for null class
        self.parent.assertEqual(
            result.class_queries_logits.shape    ,     (self.batch_size, self.num_queries, self.num_labels + 1) )

     with torch.no_grad():
        lowerCAmelCase_			=      model(pixel_values=_lowerCamelCase    ,     pixel_mask=_lowerCamelCase )
        lowerCAmelCase_			=      model(_lowerCamelCase )

        comm_check_on_output(_lowerCamelCase )

        lowerCAmelCase_			=      model(
            pixel_values=_lowerCamelCase    ,     pixel_mask=_lowerCamelCase    ,     mask_labels=_lowerCamelCase    ,     class_labels=_lowerCamelCase )

     comm_check_on_output(_lowerCamelCase )

     self.parent.assertTrue(result.loss is not None )
     self.parent.assertEqual(result.loss.shape    ,     torch.Size([1] ) )






@require_torch
class     __UpperCAmelCase       ( __a						,       __a						,       unittest.TestCase		):
  __A :				List[str]											= (MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
  __A :				Optional[int]											= (
      {'feature-extraction': MaskFormerModel, 'image-segmentation': MaskFormerForInstanceSegmentation}
      if is_torch_available()
      else {}
  )

  __A :				List[Any]											= False
  __A :				Optional[int]											= False
  __A :				str											= False
  __A :				Optional[int]											= False





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      MaskFormerModelTester(self )
     lowerCAmelCase_			=      ConfigTester(self    ,     config_class=_lowerCamelCase    ,     has_text_modality=_lowerCamelCase )





  def 						UpperCAmelCase_	(	self ):
     self.config_tester.run_common_tests()





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_	,lowerCAmelCase_			=      self.model_tester.prepare_config_and_inputs_for_common()
     self.model_tester.create_and_check_maskformer_model(_lowerCamelCase    ,     **_lowerCamelCase    ,     output_hidden_states=_lowerCamelCase )





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*_lowerCamelCase )





  @unittest.skip(reason='''MaskFormer does not use inputs_embeds''' )
  def 						UpperCAmelCase_	(	self ):
     pass





  @unittest.skip(reason='''MaskFormer does not have a get_input_embeddings method''' )
  def 						UpperCAmelCase_	(	self ):
     pass





  @unittest.skip(reason='''MaskFormer is not a generative model''' )
  def 						UpperCAmelCase_	(	self ):
     pass





  @unittest.skip(reason='''MaskFormer does not use token embeddings''' )
  def 						UpperCAmelCase_	(	self ):
     pass





  @require_torch_multi_gpu
  @unittest.skip(
      reason='''MaskFormer has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`''' )
  def 						UpperCAmelCase_	(	self ):
     pass





  @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
  def 						UpperCAmelCase_	(	self ):
     pass





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_	,lowerCAmelCase_			=      self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
        lowerCAmelCase_			=      model_class(_lowerCamelCase )
        lowerCAmelCase_			=      inspect.signature(model.forward )
        # signature.parameters is an OrderedDict => so arg_names order is deterministic
        lowerCAmelCase_			=      [*signature.parameters.keys()]

        lowerCAmelCase_			=      ['''pixel_values''']
        self.assertListEqual(arg_names[:1]    ,     _lowerCamelCase )





  @slow
  def 						UpperCAmelCase_	(	self ):
     for model_name in ["facebook/maskformer-swin-small-coco"]:
        lowerCAmelCase_			=      MaskFormerModel.from_pretrained(_lowerCamelCase )
        self.assertIsNotNone(_lowerCamelCase )





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      (self.model_tester.min_size,) * 2
     lowerCAmelCase_			=      {
         '''pixel_values''': torch.randn((2, 3, *size)    ,     device=_lowerCamelCase ),
         '''mask_labels''': torch.randn((2, 10, *size)    ,     device=_lowerCamelCase ),
         '''class_labels''': torch.zeros(2    ,     10    ,     device=_lowerCamelCase ).long(),
     }

     lowerCAmelCase_			=      MaskFormerForInstanceSegmentation(MaskFormerConfig() ).to(_lowerCamelCase )
     lowerCAmelCase_			=      model(**_lowerCamelCase )
     self.assertTrue(outputs.loss is not None )





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_	,lowerCAmelCase_			=      self.model_tester.prepare_config_and_inputs_for_common()
     self.model_tester.create_and_check_maskformer_model(_lowerCamelCase    ,     **_lowerCamelCase    ,     output_hidden_states=_lowerCamelCase )





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_	,lowerCAmelCase_			=      self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
        lowerCAmelCase_			=      model_class(_lowerCamelCase ).to(_lowerCamelCase )
        lowerCAmelCase_			=      model(**_lowerCamelCase    ,     output_attentions=_lowerCamelCase )
        self.assertTrue(outputs.attentions is not None )





  def 						UpperCAmelCase_	(	self ):
     if not self.model_tester.is_training:
        return
     # only MaskFormerForInstanceSegmentation has the loss
     lowerCAmelCase_			=      self.all_model_classes[1]
     lowerCAmelCase_	,lowerCAmelCase_	,lowerCAmelCase_	,lowerCAmelCase_	,lowerCAmelCase_			=      self.model_tester.prepare_config_and_inputs()

     lowerCAmelCase_			=      model_class(_lowerCamelCase )
     model.to(_lowerCamelCase )
     model.train()

     lowerCAmelCase_			=      model(_lowerCamelCase    ,     mask_labels=_lowerCamelCase    ,     class_labels=_lowerCamelCase ).loss
     loss.backward()







  def 						UpperCAmelCase_	(	self ):
     # only MaskFormerForInstanceSegmentation has the loss
     lowerCAmelCase_			=      self.all_model_classes[1]
     lowerCAmelCase_	,lowerCAmelCase_	,lowerCAmelCase_	,lowerCAmelCase_	,lowerCAmelCase_			=      self.model_tester.prepare_config_and_inputs()
     lowerCAmelCase_			=      True
     lowerCAmelCase_			=      True

     lowerCAmelCase_			=      model_class(_lowerCamelCase )
     model.to(_lowerCamelCase )
     model.train()

     lowerCAmelCase_			=      model(_lowerCamelCase    ,     mask_labels=_lowerCamelCase    ,     class_labels=_lowerCamelCase )

     lowerCAmelCase_			=      outputs.encoder_hidden_states[0]
     encoder_hidden_states.retain_grad()

     lowerCAmelCase_			=      outputs.pixel_decoder_hidden_states[0]
     pixel_decoder_hidden_states.retain_grad()
     # we requires_grad=True in inputs_embeds (line 2152), the original implementation don't
     lowerCAmelCase_			=      outputs.transformer_decoder_hidden_states[0]
     transformer_decoder_hidden_states.retain_grad()

     lowerCAmelCase_			=      outputs.attentions[0]
     attentions.retain_grad()

     outputs.loss.backward(retain_graph=_lowerCamelCase )

     self.assertIsNotNone(encoder_hidden_states.grad )
     self.assertIsNotNone(pixel_decoder_hidden_states.grad )
     self.assertIsNotNone(transformer_decoder_hidden_states.grad )
     self.assertIsNotNone(attentions.grad )






A_     :						str    							=1e-4



def snake_case_      (					)			->					List[str]:
   lowerCAmelCase_			=      Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''')
   return image






@require_vision
@slow
class     __UpperCAmelCase       ( unittest.TestCase		):





  @cached_property
  def 						UpperCAmelCase_	(	self ):
     return (
         MaskFormerImageProcessor.from_pretrained('''facebook/maskformer-swin-small-coco''' )
         if is_vision_available()
         else None
     )





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      MaskFormerModel.from_pretrained('''facebook/maskformer-swin-small-coco''' ).to(_lowerCamelCase )
     lowerCAmelCase_			=      self.default_image_processor
     lowerCAmelCase_			=      prepare_img()
     lowerCAmelCase_			=      image_processor(_lowerCamelCase    ,     return_tensors='''pt''' ).to(_lowerCamelCase )
     lowerCAmelCase_			=      inputs['''pixel_values'''].shape
     # check size is divisible by 32
     self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
     # check size
     self.assertEqual(_lowerCamelCase    ,     (1, 3, 800, 1088) )

     with torch.no_grad():
        lowerCAmelCase_			=      model(**_lowerCamelCase )

     lowerCAmelCase_			=      torch.tensor(
         [[-0.04_82, 0.92_28, 0.49_51], [-0.25_47, 0.80_17, 0.85_27], [-0.00_69, 0.33_85, -0.00_89]] ).to(_lowerCamelCase )
     self.assertTrue(
         torch.allclose(
             outputs.encoder_last_hidden_state[0, 0, :3, :3]    ,     _lowerCamelCase    ,     atol=_lowerCamelCase ) )

     lowerCAmelCase_			=      torch.tensor(
         [[-0.84_22, -0.84_34, -0.97_18], [-1.01_44, -0.55_65, -0.41_95], [-1.00_38, -0.44_84, -0.19_61]] ).to(_lowerCamelCase )
     self.assertTrue(
         torch.allclose(
             outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]    ,     _lowerCamelCase    ,     atol=_lowerCamelCase ) )

     lowerCAmelCase_			=      torch.tensor(
         [[0.28_52, -0.01_59, 0.97_35], [0.62_54, 0.18_58, 0.85_29], [-0.06_80, -0.41_16, 1.84_13]] ).to(_lowerCamelCase )
     self.assertTrue(
         torch.allclose(
             outputs.transformer_decoder_last_hidden_state[0, :3, :3]    ,     _lowerCamelCase    ,     atol=_lowerCamelCase ) )





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      (
         MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-swin-small-coco''' )
         .to(_lowerCamelCase )
         .eval()
     )
     lowerCAmelCase_			=      self.default_image_processor
     lowerCAmelCase_			=      prepare_img()
     lowerCAmelCase_			=      image_processor(_lowerCamelCase    ,     return_tensors='''pt''' ).to(_lowerCamelCase )
     lowerCAmelCase_			=      inputs['''pixel_values'''].shape
     # check size is divisible by 32
     self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
     # check size
     self.assertEqual(_lowerCamelCase    ,     (1, 3, 800, 1088) )

     with torch.no_grad():
        lowerCAmelCase_			=      model(**_lowerCamelCase )
     # masks_queries_logits
     lowerCAmelCase_			=      outputs.masks_queries_logits
     self.assertEqual(
         masks_queries_logits.shape    ,     (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)    ,     )
     lowerCAmelCase_			=      [
         [-1.3_73_71_24, -1.7_72_49_37, -1.9_36_42_33],
         [-1.5_97_72_81, -1.9_86_79_39, -2.1_52_36_95],
         [-1.5_79_53_98, -1.9_26_98_32, -2.09_39_42],
     ]
     lowerCAmelCase_			=      torch.tensor(_lowerCamelCase ).to(_lowerCamelCase )
     self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]    ,     _lowerCamelCase    ,     atol=_lowerCamelCase ) )
     # class_queries_logits
     lowerCAmelCase_			=      outputs.class_queries_logits
     self.assertEqual(
         class_queries_logits.shape    ,     (1, model.config.decoder_config.num_queries, model.config.num_labels + 1) )
     lowerCAmelCase_			=      torch.tensor(
         [
             [1.6512E00, -5.2572E00, -3.3519E00],
             [3.6169E-02, -5.9025E00, -2.9313E00],
             [1.0766E-04, -7.7630E00, -5.1263E00],
         ] ).to(_lowerCamelCase )
     self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]    ,     _lowerCamelCase    ,     atol=_lowerCamelCase ) )





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      (
         MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-resnet101-coco-stuff''' )
         .to(_lowerCamelCase )
         .eval()
     )
     lowerCAmelCase_			=      self.default_image_processor
     lowerCAmelCase_			=      prepare_img()
     lowerCAmelCase_			=      image_processor(_lowerCamelCase    ,     return_tensors='''pt''' ).to(_lowerCamelCase )
     lowerCAmelCase_			=      inputs['''pixel_values'''].shape
     # check size is divisible by 32
     self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
     # check size
     self.assertEqual(_lowerCamelCase    ,     (1, 3, 800, 1088) )

     with torch.no_grad():
        lowerCAmelCase_			=      model(**_lowerCamelCase )
     # masks_queries_logits
     lowerCAmelCase_			=      outputs.masks_queries_logits
     self.assertEqual(
         masks_queries_logits.shape    ,     (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)    ,     )
     lowerCAmelCase_			=      [[-0.90_46, -2.63_66, -4.60_62], [-3.41_79, -5.78_90, -8.80_57], [-4.91_79, -7.65_60, -10.77_11]]
     lowerCAmelCase_			=      torch.tensor(_lowerCamelCase ).to(_lowerCamelCase )
     self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]    ,     _lowerCamelCase    ,     atol=_lowerCamelCase ) )
     # class_queries_logits
     lowerCAmelCase_			=      outputs.class_queries_logits
     self.assertEqual(
         class_queries_logits.shape    ,     (1, model.config.decoder_config.num_queries, model.config.num_labels + 1) )
     lowerCAmelCase_			=      torch.tensor(
         [[4.71_88, -3.25_85, -2.88_57], [6.68_71, -2.91_81, -1.24_87], [7.24_49, -2.27_64, -2.18_74]] ).to(_lowerCamelCase )
     self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]    ,     _lowerCamelCase    ,     atol=_lowerCamelCase ) )







  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      (
         MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-swin-small-coco''' )
         .to(_lowerCamelCase )
         .eval()
     )
     lowerCAmelCase_			=      self.default_image_processor

     lowerCAmelCase_			=      image_processor(
         [np.zeros((3, 800, 1333) ), np.zeros((3, 800, 1333) )]    ,     segmentation_maps=[np.zeros((384, 384) ).astype(np.floataa ), np.zeros((384, 384) ).astype(np.floataa )]    ,     return_tensors='''pt'''    ,     )

     lowerCAmelCase_			=      inputs['''pixel_values'''].to(_lowerCamelCase )
     lowerCAmelCase_			=      [el.to(_lowerCamelCase ) for el in inputs['''mask_labels''']]
     lowerCAmelCase_			=      [el.to(_lowerCamelCase ) for el in inputs['''class_labels''']]

     with torch.no_grad():
        lowerCAmelCase_			=      model(**_lowerCamelCase )

     self.assertTrue(outputs.loss is not None )






'''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 snake_case_      (					__snake_case  :							Tuple=None	,   __snake_case  :							int=None)			->					int:
   return field(default_factory=lambda: default	,   metadata=__snake_case)


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






def snake_case_      (					__snake_case  :							Optional[Any])			->					Dict:
   try:
      int(__snake_case)
      return True
   except ValueError:
      return False



def snake_case_      (					__snake_case  :							Dict)			->					int:
   try:
      float(__snake_case)
      return True
   except ValueError:
      return False


class     __UpperCAmelCase       :





  def __init__(	self    ,     _lowerCamelCase ):
     lowerCAmelCase_			=      args
     lowerCAmelCase_			=      defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}} )

     with open(self.args.csv_file    ,     newline='''''' ) as csv_file:
        lowerCAmelCase_			=      csv.DictReader(_lowerCamelCase )
        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 						UpperCAmelCase_	(	self ):
     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=_lowerCamelCase    ,     )
           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(_lowerCamelCase    ,     _lowerCamelCase )[: len(_lowerCamelCase )]
           plt.scatter(
               _lowerCamelCase    ,     _lowerCamelCase    ,     label=F'''{label_model_name} - {inner_loop_label}: {inner_loop_value}''' )
           plt.plot(_lowerCamelCase    ,     _lowerCamelCase    ,     '''--''' )

        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(_lowerCamelCase )
     plt.xlabel(_lowerCamelCase )
     plt.ylabel(_lowerCamelCase )
     plt.legend()

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






def snake_case_      (					)			->					Tuple:
   lowerCAmelCase_			=      HfArgumentParser(__snake_case)
   lowerCAmelCase_			=      parser.parse_args_into_dataclasses()[0]
   lowerCAmelCase_			=      Plot(args=__snake_case)
   plot.plot()


if __name__ == "__main__":
 main()





