import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging


logging.set_verbosity_info()
snake_case__				= logging.get_logger(__name__)
def       __magic_name__(  __UpperCAmelCase		)						->    Union[str, Any]:



      '''simple docstring'''

      if "resnet-50" in model_name:
            _lowerCamelCase			    =				ResNetConfig.from_pretrained('''microsoft/resnet-50'''		)
      elif "resnet-101" in model_name:
            _lowerCamelCase			    =				ResNetConfig.from_pretrained('''microsoft/resnet-101'''		)
      else:
            raise ValueError('''Model name should include either resnet50 or resnet101'''		)

      _lowerCamelCase			    =				DetrConfig(use_timm_backbone=__UpperCAmelCase ,						backbone_config=__UpperCAmelCase		)

      # set label attributes
      _lowerCamelCase			    =				'''panoptic''' in model_name
      if is_panoptic:
            _lowerCamelCase			    =				250
      else:
            _lowerCamelCase			    =				91
            _lowerCamelCase			    =				'''huggingface/label-files'''
            _lowerCamelCase			    =				'''coco-detection-id2label.json'''
            _lowerCamelCase			    =				json.load(open(hf_hub_download(__UpperCAmelCase ,						__UpperCAmelCase ,						repo_type='''dataset'''		) ,						'''r'''		)		)
            _lowerCamelCase			    =				{int(__UpperCAmelCase		): v for k, v in idalabel.items()}
            _lowerCamelCase			    =				idalabel
            _lowerCamelCase			    =				{v: k for k, v in idalabel.items()}

      return config, is_panoptic
def       __magic_name__(  __UpperCAmelCase		)						->    int:



      '''simple docstring'''

      _lowerCamelCase			    =				[]

      # stem
      # fmt: off
      rename_keys.append(('''backbone.0.body.conv1.weight''', '''backbone.conv_encoder.model.embedder.embedder.convolution.weight''')		)
      rename_keys.append(('''backbone.0.body.bn1.weight''', '''backbone.conv_encoder.model.embedder.embedder.normalization.weight''')		)
      rename_keys.append(('''backbone.0.body.bn1.bias''', '''backbone.conv_encoder.model.embedder.embedder.normalization.bias''')		)
      rename_keys.append(('''backbone.0.body.bn1.running_mean''', '''backbone.conv_encoder.model.embedder.embedder.normalization.running_mean''')		)
      rename_keys.append(('''backbone.0.body.bn1.running_var''', '''backbone.conv_encoder.model.embedder.embedder.normalization.running_var''')		)
      # stages
      for stage_idx in range(len(config.backbone_config.depths		)		):
            for layer_idx in range(config.backbone_config.depths[stage_idx]		):
                  # shortcut
                  if layer_idx == 0:
                        rename_keys.append(
                            (
                                F'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight',
                                F'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight',
                            )		)
                        rename_keys.append(
                            (
                                F'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight',
                                F'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight',
                            )		)
                        rename_keys.append(
                            (
                                F'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias',
                                F'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias',
                            )		)
                        rename_keys.append(
                            (
                                F'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean',
                                F'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean',
                            )		)
                        rename_keys.append(
                            (
                                F'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var',
                                F'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var',
                            )		)
                  # 3 convs
                  for i in range(3		):
                        rename_keys.append(
                            (
                                F'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight',
                                F'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight',
                            )		)
                        rename_keys.append(
                            (
                                F'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight',
                                F'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight',
                            )		)
                        rename_keys.append(
                            (
                                F'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias',
                                F'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias',
                            )		)
                        rename_keys.append(
                            (
                                F'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean',
                                F'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean',
                            )		)
                        rename_keys.append(
                            (
                                F'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var',
                                F'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var',
                            )		)
    # fmt: on

      for i in range(config.encoder_layers		):
            # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
            rename_keys.append(
                (
                    F'transformer.encoder.layers.{i}.self_attn.out_proj.weight',
                    F'encoder.layers.{i}.self_attn.out_proj.weight',
                )		)
            rename_keys.append(
                (F'transformer.encoder.layers.{i}.self_attn.out_proj.bias', F'encoder.layers.{i}.self_attn.out_proj.bias')		)
            rename_keys.append((F'transformer.encoder.layers.{i}.linear1.weight', F'encoder.layers.{i}.fc1.weight')		)
            rename_keys.append((F'transformer.encoder.layers.{i}.linear1.bias', F'encoder.layers.{i}.fc1.bias')		)
            rename_keys.append((F'transformer.encoder.layers.{i}.linear2.weight', F'encoder.layers.{i}.fc2.weight')		)
            rename_keys.append((F'transformer.encoder.layers.{i}.linear2.bias', F'encoder.layers.{i}.fc2.bias')		)
            rename_keys.append(
                (F'transformer.encoder.layers.{i}.norm1.weight', F'encoder.layers.{i}.self_attn_layer_norm.weight')		)
            rename_keys.append(
                (F'transformer.encoder.layers.{i}.norm1.bias', F'encoder.layers.{i}.self_attn_layer_norm.bias')		)
            rename_keys.append(
                (F'transformer.encoder.layers.{i}.norm2.weight', F'encoder.layers.{i}.final_layer_norm.weight')		)
            rename_keys.append((F'transformer.encoder.layers.{i}.norm2.bias', F'encoder.layers.{i}.final_layer_norm.bias')		)
            # decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
            rename_keys.append(
                (
                    F'transformer.decoder.layers.{i}.self_attn.out_proj.weight',
                    F'decoder.layers.{i}.self_attn.out_proj.weight',
                )		)
            rename_keys.append(
                (F'transformer.decoder.layers.{i}.self_attn.out_proj.bias', F'decoder.layers.{i}.self_attn.out_proj.bias')		)
            rename_keys.append(
                (
                    F'transformer.decoder.layers.{i}.multihead_attn.out_proj.weight',
                    F'decoder.layers.{i}.encoder_attn.out_proj.weight',
                )		)
            rename_keys.append(
                (
                    F'transformer.decoder.layers.{i}.multihead_attn.out_proj.bias',
                    F'decoder.layers.{i}.encoder_attn.out_proj.bias',
                )		)
            rename_keys.append((F'transformer.decoder.layers.{i}.linear1.weight', F'decoder.layers.{i}.fc1.weight')		)
            rename_keys.append((F'transformer.decoder.layers.{i}.linear1.bias', F'decoder.layers.{i}.fc1.bias')		)
            rename_keys.append((F'transformer.decoder.layers.{i}.linear2.weight', F'decoder.layers.{i}.fc2.weight')		)
            rename_keys.append((F'transformer.decoder.layers.{i}.linear2.bias', F'decoder.layers.{i}.fc2.bias')		)
            rename_keys.append(
                (F'transformer.decoder.layers.{i}.norm1.weight', F'decoder.layers.{i}.self_attn_layer_norm.weight')		)
            rename_keys.append(
                (F'transformer.decoder.layers.{i}.norm1.bias', F'decoder.layers.{i}.self_attn_layer_norm.bias')		)
            rename_keys.append(
                (F'transformer.decoder.layers.{i}.norm2.weight', F'decoder.layers.{i}.encoder_attn_layer_norm.weight')		)
            rename_keys.append(
                (F'transformer.decoder.layers.{i}.norm2.bias', F'decoder.layers.{i}.encoder_attn_layer_norm.bias')		)
            rename_keys.append(
                (F'transformer.decoder.layers.{i}.norm3.weight', F'decoder.layers.{i}.final_layer_norm.weight')		)
            rename_keys.append((F'transformer.decoder.layers.{i}.norm3.bias', F'decoder.layers.{i}.final_layer_norm.bias')		)

      # convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
      rename_keys.extend(
          [
              ('''input_proj.weight''', '''input_projection.weight'''),
              ('''input_proj.bias''', '''input_projection.bias'''),
              ('''query_embed.weight''', '''query_position_embeddings.weight'''),
              ('''transformer.decoder.norm.weight''', '''decoder.layernorm.weight'''),
              ('''transformer.decoder.norm.bias''', '''decoder.layernorm.bias'''),
              ('''class_embed.weight''', '''class_labels_classifier.weight'''),
              ('''class_embed.bias''', '''class_labels_classifier.bias'''),
              ('''bbox_embed.layers.0.weight''', '''bbox_predictor.layers.0.weight'''),
              ('''bbox_embed.layers.0.bias''', '''bbox_predictor.layers.0.bias'''),
              ('''bbox_embed.layers.1.weight''', '''bbox_predictor.layers.1.weight'''),
              ('''bbox_embed.layers.1.bias''', '''bbox_predictor.layers.1.bias'''),
              ('''bbox_embed.layers.2.weight''', '''bbox_predictor.layers.2.weight'''),
              ('''bbox_embed.layers.2.bias''', '''bbox_predictor.layers.2.bias'''),
          ]		)

      return rename_keys
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase		)						->    Union[str, Any]:



      '''simple docstring'''

      _lowerCamelCase			    =				state_dict.pop(__UpperCAmelCase		)
      _lowerCamelCase			    =				val
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase=False		)						->    Optional[int]:



      '''simple docstring'''

      _lowerCamelCase			    =				''''''
      if is_panoptic:
            _lowerCamelCase			    =				'''detr.'''

      # first: transformer encoder
      for i in range(6		):
            # read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
            _lowerCamelCase			    =				state_dict.pop(F'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'		)
            _lowerCamelCase			    =				state_dict.pop(F'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias'		)
            # next, add query, keys and values (in that order) to the state dict
            _lowerCamelCase			    =				in_proj_weight[:256, :]
            _lowerCamelCase			    =				in_proj_bias[:256]
            _lowerCamelCase			    =				in_proj_weight[256:512, :]
            _lowerCamelCase			    =				in_proj_bias[256:512]
            _lowerCamelCase			    =				in_proj_weight[-256:, :]
            _lowerCamelCase			    =				in_proj_bias[-256:]
      # next: transformer decoder (which is a bit more complex because it also includes cross-attention)
      for i in range(6		):
            # read in weights + bias of input projection layer of self-attention
            _lowerCamelCase			    =				state_dict.pop(F'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'		)
            _lowerCamelCase			    =				state_dict.pop(F'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias'		)
            # next, add query, keys and values (in that order) to the state dict
            _lowerCamelCase			    =				in_proj_weight[:256, :]
            _lowerCamelCase			    =				in_proj_bias[:256]
            _lowerCamelCase			    =				in_proj_weight[256:512, :]
            _lowerCamelCase			    =				in_proj_bias[256:512]
            _lowerCamelCase			    =				in_proj_weight[-256:, :]
            _lowerCamelCase			    =				in_proj_bias[-256:]
            # read in weights + bias of input projection layer of cross-attention
            _lowerCamelCase			    =				state_dict.pop(
                F'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'		)
            _lowerCamelCase			    =				state_dict.pop(F'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias'		)
            # next, add query, keys and values (in that order) of cross-attention to the state dict
            _lowerCamelCase			    =				in_proj_weight_cross_attn[:256, :]
            _lowerCamelCase			    =				in_proj_bias_cross_attn[:256]
            _lowerCamelCase			    =				in_proj_weight_cross_attn[256:512, :]
            _lowerCamelCase			    =				in_proj_bias_cross_attn[256:512]
            _lowerCamelCase			    =				in_proj_weight_cross_attn[-256:, :]
            _lowerCamelCase			    =				in_proj_bias_cross_attn[-256:]
def       __magic_name__(  )						->    int:



      '''simple docstring'''

      _lowerCamelCase			    =				'''http://images.cocodataset.org/val2017/000000039769.jpg'''
      _lowerCamelCase			    =				Image.open(requests.get(__UpperCAmelCase ,						stream=__UpperCAmelCase		).raw		)

      return im
@torch.no_grad()
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase=None ,						__UpperCAmelCase=False		)						->    Tuple:



      '''simple docstring'''

      _lowerCamelCase    ,					_lowerCamelCase			    =				get_detr_config(__UpperCAmelCase		)

      # load original model from torch hub
      _lowerCamelCase			    =				{
          '''detr-resnet-50''': '''detr_resnet50''',
          '''detr-resnet-101''': '''detr_resnet101''',
      }
      logger.info(F'Converting model {model_name}...'		)
      _lowerCamelCase			    =				torch.hub.load('''facebookresearch/detr''' ,						model_name_to_original_name[model_name] ,						pretrained=__UpperCAmelCase		).eval()
      _lowerCamelCase			    =				detr.state_dict()
      # rename keys
      for src, dest in create_rename_keys(__UpperCAmelCase		):
            if is_panoptic:
                  _lowerCamelCase			    =				'''detr.''' + src
            rename_key(__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase		)
      # query, key and value matrices need special treatment
      read_in_q_k_v(__UpperCAmelCase ,						is_panoptic=__UpperCAmelCase		)
      # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
      _lowerCamelCase			    =				'''detr.model.''' if is_panoptic else '''model.'''
      for key in state_dict.copy().keys():
            if is_panoptic:
                  if (
                      key.startswith('''detr'''		)
                      and not key.startswith('''class_labels_classifier'''		)
                      and not key.startswith('''bbox_predictor'''		)
                  ):
                        _lowerCamelCase			    =				state_dict.pop(__UpperCAmelCase		)
                        _lowerCamelCase			    =				val
                  elif "class_labels_classifier" in key or "bbox_predictor" in key:
                        _lowerCamelCase			    =				state_dict.pop(__UpperCAmelCase		)
                        _lowerCamelCase			    =				val
                  elif key.startswith('''bbox_attention'''		) or key.startswith('''mask_head'''		):
                        continue
                  else:
                        _lowerCamelCase			    =				state_dict.pop(__UpperCAmelCase		)
                        _lowerCamelCase			    =				val
            else:
                  if not key.startswith('''class_labels_classifier'''		) and not key.startswith('''bbox_predictor'''		):
                        _lowerCamelCase			    =				state_dict.pop(__UpperCAmelCase		)
                        _lowerCamelCase			    =				val

    # finally, create HuggingFace model and load state dict
      _lowerCamelCase			    =				DetrForSegmentation(__UpperCAmelCase		) if is_panoptic else DetrForObjectDetection(__UpperCAmelCase		)
      model.load_state_dict(__UpperCAmelCase		)
      model.eval()

      # verify our conversion on an image
      _lowerCamelCase			    =				'''coco_panoptic''' if is_panoptic else '''coco_detection'''
      _lowerCamelCase			    =				DetrImageProcessor(format=__UpperCAmelCase		)

      _lowerCamelCase			    =				processor(images=prepare_img() ,						return_tensors='''pt'''		)
      _lowerCamelCase			    =				encoding['''pixel_values''']

      _lowerCamelCase			    =				detr(__UpperCAmelCase		)
      _lowerCamelCase			    =				model(__UpperCAmelCase		)

      assert torch.allclose(outputs.logits ,						original_outputs['''pred_logits'''] ,						atol=1E-3		)
      assert torch.allclose(outputs.pred_boxes ,						original_outputs['''pred_boxes'''] ,						atol=1E-3		)
      if is_panoptic:
            assert torch.allclose(outputs.pred_masks ,						original_outputs['''pred_masks'''] ,						atol=1E-4		)
      print('''Looks ok!'''		)

      if pytorch_dump_folder_path is not None:
            # Save model and image processor
            logger.info(F'Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'		)
            Path(__UpperCAmelCase		).mkdir(exist_ok=__UpperCAmelCase		)
            model.save_pretrained(__UpperCAmelCase		)
            processor.save_pretrained(__UpperCAmelCase		)

      if push_to_hub:
            # Upload model and image processor to the hub
            logger.info('''Uploading PyTorch model and image processor to the hub...'''		)
            model.push_to_hub(F'nielsr/{model_name}'		)
            processor.push_to_hub(F'nielsr/{model_name}'		)


if __name__ == "__main__":
     snake_case__				= argparse.ArgumentParser()

     parser.add_argument(
         '--model_name',
         default='detr-resnet-50',
         type=str,
         choices=['detr-resnet-50', 'detr-resnet-101'],
         help='Name of the DETR model you\'d like to convert.',
     )
     parser.add_argument(
         '--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
     )
     parser.add_argument('--push_to_hub', action='store_true', help='Whether to push the model to the hub or not.')
     snake_case__				= parser.parse_args()
     convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
import logging

import numpy as np
import pytest
from scipy.linalg import eigh

logging.basicConfig(level=logging.INFO, format='%(message)s')
def       __magic_name__(  __UpperCAmelCase		)						->    np.ndarray:



      '''simple docstring'''

      return input_array.reshape((input_array.size, 1)		)
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase		)						->    np.ndarray:



      '''simple docstring'''

      _lowerCamelCase			    =				np.nan
      for i in range(__UpperCAmelCase		):
            _lowerCamelCase			    =				features[:, labels == i]
            _lowerCamelCase			    =				data.mean(1		)
            # Centralize the data of class i
            _lowerCamelCase			    =				data - column_reshape(__UpperCAmelCase		)
            if i > 0:
                  # If covariance_sum is not None
                  covariance_sum += np.dot(__UpperCAmelCase ,						centered_data.T		)
            else:
                  # If covariance_sum is np.nan (i.e. first loop)
                  _lowerCamelCase			    =				np.dot(__UpperCAmelCase ,						centered_data.T		)

      return covariance_sum / features.shape[1]
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase		)						->    np.ndarray:



      '''simple docstring'''

      _lowerCamelCase			    =				features.mean(1		)
      _lowerCamelCase			    =				np.nan
      for i in range(__UpperCAmelCase		):
            _lowerCamelCase			    =				features[:, labels == i]
            _lowerCamelCase			    =				data.shape[1]
            _lowerCamelCase			    =				data.mean(1		)
            if i > 0:
                  # If covariance_sum is not None
                  covariance_sum += device_data * np.dot(
                      column_reshape(__UpperCAmelCase		) - column_reshape(__UpperCAmelCase		) ,						(column_reshape(__UpperCAmelCase		) - column_reshape(__UpperCAmelCase		)).T ,						)
            else:
                  # If covariance_sum is np.nan (i.e. first loop)
                  _lowerCamelCase			    =				device_data * np.dot(
                      column_reshape(__UpperCAmelCase		) - column_reshape(__UpperCAmelCase		) ,						(column_reshape(__UpperCAmelCase		) - column_reshape(__UpperCAmelCase		)).T ,						)

      return covariance_sum / features.shape[1]
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase		)						->    np.ndarray:



      '''simple docstring'''

      if features.any():
            _lowerCamelCase			    =				features.mean(1		)
            # Center the dataset
            _lowerCamelCase			    =				features - np.reshape(__UpperCAmelCase ,						(data_mean.size, 1)		)
            _lowerCamelCase			    =				np.dot(__UpperCAmelCase ,						centered_data.T		) / features.shape[1]
            _lowerCamelCase    ,					_lowerCamelCase			    =				np.linalg.eigh(__UpperCAmelCase		)
            # Take all the columns in the reverse order (-1), and then takes only the first
            _lowerCamelCase			    =				eigenvectors[:, ::-1][:, 0:dimensions]
            # Project the database on the new space
            _lowerCamelCase			    =				np.dot(filtered_eigenvectors.T ,						__UpperCAmelCase		)
            logging.info('''Principal Component Analysis computed'''		)

            return projected_data
      else:
            logging.basicConfig(level=logging.ERROR ,						format='''%(message)s''' ,						force=__UpperCAmelCase		)
            logging.error('''Dataset empty'''		)
            raise AssertionError
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase		)						->    np.ndarray:



      '''simple docstring'''

      assert classes > dimensions

      # Check if features have been already loaded
      if features.any:
            _lowerCamelCase    ,					_lowerCamelCase			    =				eigh(
                covariance_between_classes(__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase		) ,						covariance_within_classes(__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase		) ,						)
            _lowerCamelCase			    =				eigenvectors[:, ::-1][:, :dimensions]
            _lowerCamelCase    ,					_lowerCamelCase    ,					_lowerCamelCase			    =				np.linalg.svd(__UpperCAmelCase		)
            _lowerCamelCase			    =				svd_matrix[:, 0:dimensions]
            _lowerCamelCase			    =				np.dot(filtered_svd_matrix.T ,						__UpperCAmelCase		)
            logging.info('''Linear Discriminant Analysis computed'''		)

            return projected_data
      else:
            logging.basicConfig(level=logging.ERROR ,						format='''%(message)s''' ,						force=__UpperCAmelCase		)
            logging.error('''Dataset empty'''		)
            raise AssertionError
def       __magic_name__(  )						->    None:



      '''simple docstring'''

      _lowerCamelCase			    =				np.array([[1, 2, 3, 4, 5], [2, 3, 4, 5, 6], [3, 4, 5, 6, 7]]		)
      _lowerCamelCase			    =				np.array([0, 0, 0, 1, 1]		)
      _lowerCamelCase			    =				2
      _lowerCamelCase			    =				2

      # Assert that the function raises an AssertionError if dimensions > classes
      with pytest.raises(__UpperCAmelCase		) as error_info:
            _lowerCamelCase			    =				linear_discriminant_analysis(
                __UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase		)
            if isinstance(__UpperCAmelCase ,						np.ndarray		):
                  raise AssertionError(
                      '''Did not raise AssertionError for dimensions > classes'''		)
            assert error_info.type is AssertionError
def       __magic_name__(  )						->    None:



      '''simple docstring'''

      _lowerCamelCase			    =				np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]		)
      _lowerCamelCase			    =				2
      _lowerCamelCase			    =				np.array([[6.9_2_8_2_0_3_2_3, 8.6_6_0_2_5_4_0_4, 1_0.3_9_2_3_0_4_8_5], [3.0, 3.0, 3.0]]		)

      with pytest.raises(__UpperCAmelCase		) as error_info:
            _lowerCamelCase			    =				principal_component_analysis(__UpperCAmelCase ,						__UpperCAmelCase		)
            if not np.allclose(__UpperCAmelCase ,						__UpperCAmelCase		):
                  raise AssertionError
            assert error_info.type is AssertionError


if __name__ == "__main__":
     import doctest

     doctest.testmod()