


import unittest

from .lib import (
    Matrix,
    Vector,
    axpy,
    square_zero_matrix,
    unit_basis_vector,
    zero_vector,
)

class   A__ (   unittest.TestCase     ):







    """simple docstring"""

    def 		a_      (	self   ):
         snake_case					=    Vector([1, 2, 3]   )
         self.assertEqual(x.component(0   )      , 1   )
         self.assertEqual(x.component(2   )      , 3   )
         snake_case					=    Vector()
    def 		a_      (	self   ):
         snake_case					=    Vector([0, 0, 0, 0, 0, 1]   )
         self.assertEqual(str(__snake_case   )      , '''(0,0,0,0,0,1)'''   )
    def 		a_      (	self   ):
         snake_case					=    Vector([1, 2, 3, 4]   )
         self.assertEqual(len(__snake_case   )      , 4   )
    def 		a_      (	self   ):
         snake_case					=    Vector([1, 2]   )
         snake_case					=    Vector([1, 2, 3, 4, 5]   )
         snake_case					=    Vector([0, 0, 0, 0, 0, 0, 0, 0, 0, 0]   )
         snake_case					=    Vector([1, -1, 1, -1, 2, -3, 4, -5]   )
         self.assertAlmostEqual(x.euclidean_length()      , 2.236      , 3   )
         self.assertAlmostEqual(y.euclidean_length()      , 7.416      , 3   )
         self.assertEqual(z.euclidean_length()      , 0   )
         self.assertAlmostEqual(w.euclidean_length()      , 7.616      , 3   )
    def 		a_      (	self   ):
         snake_case					=    Vector([1, 2, 3]   )
         snake_case					=    Vector([1, 1, 1]   )
         self.assertEqual((x + y).component(0   )      , 2   )
         self.assertEqual((x + y).component(1   )      , 3   )
         self.assertEqual((x + y).component(2   )      , 4   )
    def 		a_      (	self   ):
         snake_case					=    Vector([1, 2, 3]   )
         snake_case					=    Vector([1, 1, 1]   )
         self.assertEqual((x - y).component(0   )      , 0   )
         self.assertEqual((x - y).component(1   )      , 1   )
         self.assertEqual((x - y).component(2   )      , 2   )
    def 		a_      (	self   ):
         snake_case					=    Vector([1, 2, 3]   )
         snake_case					=    Vector([2, -1, 4]   )  # for test of dot product
         snake_case					=    Vector([1, -2, -1]   )
         self.assertEqual(str(x * 3.0   )      , '''(3.0,6.0,9.0)'''   )
         self.assertEqual((a * b)      , 0   )
    def 		a_      (	self   ):
         self.assertEqual(str(zero_vector(1_0   )   ).count('''0'''   )      , 1_0   )
    def 		a_      (	self   ):
         self.assertEqual(str(unit_basis_vector(3      , 1   )   )      , '''(0,1,0)'''   )
    def 		a_      (	self   ):
         snake_case					=    Vector([1, 2, 3]   )
         snake_case					=    Vector([1, 0, 1]   )
         self.assertEqual(str(axpy(2      , __snake_case      , __snake_case   )   )      , '''(3,4,7)'''   )
    def 		a_      (	self   ):
         snake_case					=    Vector([1, 0, 0, 0, 0, 0]   )
         snake_case					=    x.copy()
         self.assertEqual(str(__snake_case   )      , str(__snake_case   )   )
    def 		a_      (	self   ):
         snake_case					=    Vector([1, 0, 0]   )
         x.change_component(0      , 0   )
         x.change_component(1      , 1   )
         self.assertEqual(str(__snake_case   )      , '''(0,1,0)'''   )
    def 		a_      (	self   ):
         snake_case					=    Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]      , 3      , 3   )
         self.assertEqual('''|1,2,3|\n|2,4,5|\n|6,7,8|\n'''      , str(__snake_case   )   )
    def 		a_      (	self   ):
         snake_case					=    Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]      , 3      , 3   )
         snake_case					=    [[-3, -1_4, -1_0], [-5, -1_0, -5], [-2, -1, 0]]
         for x in range(a.height()   ):
              for y in range(a.width()   ):
                   self.assertEqual(minors[x][y]      , a.minor(__snake_case      , __snake_case   )   )
    def 		a_      (	self   ):
         snake_case					=    Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]      , 3      , 3   )
         snake_case					=    [[-3, 1_4, -1_0], [5, -1_0, 5], [-2, 1, 0]]
         for x in range(a.height()   ):
              for y in range(a.width()   ):
                   self.assertEqual(cofactors[x][y]      , a.cofactor(__snake_case      , __snake_case   )   )
    def 		a_      (	self   ):
         snake_case					=    Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]      , 3      , 3   )
         self.assertEqual(-5      , a.determinant()   )
    def 		a_      (	self   ):
         snake_case					=    Matrix([[1, 2, 3], [4, 5, 6], [7, 8, 9]]      , 3      , 3   )
         snake_case					=    Vector([1, 2, 3]   )
         self.assertEqual('''(14,32,50)'''      , str(a * x   )   )
         self.assertEqual('''|2,4,6|\n|8,10,12|\n|14,16,18|\n'''      , str(a * 2   )   )
    def 		a_      (	self   ):
         snake_case					=    Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]      , 3      , 3   )
         a.change_component(0      , 2      , 5   )
         self.assertEqual('''|1,2,5|\n|2,4,5|\n|6,7,8|\n'''      , str(__snake_case   )   )
    def 		a_      (	self   ):
         snake_case					=    Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]      , 3      , 3   )
         self.assertEqual(7      , a.component(2      , 1   )      , 0.01   )
    def 		a_      (	self   ):
         snake_case					=    Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]      , 3      , 3   )
         snake_case					=    Matrix([[1, 2, 7], [2, 4, 5], [6, 7, 1_0]]      , 3      , 3   )
         self.assertEqual('''|2,4,10|\n|4,8,10|\n|12,14,18|\n'''      , str(a + b   )   )
    def 		a_      (	self   ):
         snake_case					=    Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]      , 3      , 3   )
         snake_case					=    Matrix([[1, 2, 7], [2, 4, 5], [6, 7, 1_0]]      , 3      , 3   )
         self.assertEqual('''|0,0,-4|\n|0,0,0|\n|0,0,-2|\n'''      , str(a - b   )   )




    def 		a_      (	self   ):
         self.assertEqual(
             '''|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n'''      , str(square_zero_matrix(5   )   )      , )


if __name__ == "__main__":
 unittest.main()








import argparse
import json
from collections import OrderedDict

import torch
from huggingface_hub import cached_download, hf_hub_url

from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification
def       UpperCAmelCase__						(UpperCamelCase_    ):



     """simple docstring"""






     snake_case					=    []
     embed.append(
         (
             F'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.weight''',
             F'''stage{idx}.patch_embed.proj.weight''',
         )    )
     embed.append(
         (
             F'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.bias''',
             F'''stage{idx}.patch_embed.proj.bias''',
         )    )
     embed.append(
         (
             F'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.weight''',
             F'''stage{idx}.patch_embed.norm.weight''',
         )    )
     embed.append(
         (
             F'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.bias''',
             F'''stage{idx}.patch_embed.norm.bias''',
         )    )
     return embed
def       UpperCAmelCase__						(UpperCamelCase_							,UpperCamelCase_    ):



     """simple docstring"""






     snake_case					=    []
     attention_weights.append(
         (
             F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.convolution.weight''',
             F'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.conv.weight''',
         )    )
     attention_weights.append(
         (
             F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.weight''',
             F'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.weight''',
         )    )
     attention_weights.append(
         (
             F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.bias''',
             F'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.bias''',
         )    )
     attention_weights.append(
         (
             F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_mean''',
             F'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_mean''',
         )    )
     attention_weights.append(
         (
             F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_var''',
             F'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_var''',
         )    )
     attention_weights.append(
         (
             F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.num_batches_tracked''',
             F'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.num_batches_tracked''',
         )    )
     attention_weights.append(
         (
             F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.convolution.weight''',
             F'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.conv.weight''',
         )    )
     attention_weights.append(
         (
             F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.weight''',
             F'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.weight''',
         )    )
     attention_weights.append(
         (
             F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.bias''',
             F'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.bias''',
         )    )
     attention_weights.append(
         (
             F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_mean''',
             F'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_mean''',
         )    )
     attention_weights.append(
         (
             F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_var''',
             F'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_var''',
         )    )
     attention_weights.append(
         (
             F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.num_batches_tracked''',
             F'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.num_batches_tracked''',
         )    )
     attention_weights.append(
         (
             F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.convolution.weight''',
             F'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.conv.weight''',
         )    )
     attention_weights.append(
         (
             F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.weight''',
             F'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.weight''',
         )    )
     attention_weights.append(
         (
             F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.bias''',
             F'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.bias''',
         )    )
     attention_weights.append(
         (
             F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_mean''',
             F'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_mean''',
         )    )
     attention_weights.append(
         (
             F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_var''',
             F'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_var''',
         )    )
     attention_weights.append(
         (
             F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.num_batches_tracked''',
             F'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.num_batches_tracked''',
         )    )
     attention_weights.append(
         (
             F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.weight''',
             F'''stage{idx}.blocks.{cnt}.attn.proj_q.weight''',
         )    )
     attention_weights.append(
         (
             F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.bias''',
             F'''stage{idx}.blocks.{cnt}.attn.proj_q.bias''',
         )    )
     attention_weights.append(
         (
             F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.weight''',
             F'''stage{idx}.blocks.{cnt}.attn.proj_k.weight''',
         )    )
     attention_weights.append(
         (
             F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.bias''',
             F'''stage{idx}.blocks.{cnt}.attn.proj_k.bias''',
         )    )
     attention_weights.append(
         (
             F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.weight''',
             F'''stage{idx}.blocks.{cnt}.attn.proj_v.weight''',
         )    )
     attention_weights.append(
         (
             F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.bias''',
             F'''stage{idx}.blocks.{cnt}.attn.proj_v.bias''',
         )    )
     attention_weights.append(
         (
             F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.weight''',
             F'''stage{idx}.blocks.{cnt}.attn.proj.weight''',
         )    )
     attention_weights.append(
         (
             F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.bias''',
             F'''stage{idx}.blocks.{cnt}.attn.proj.bias''',
         )    )
     attention_weights.append(
         (F'''cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.weight''', F'''stage{idx}.blocks.{cnt}.mlp.fc1.weight''')    )
     attention_weights.append(
         (F'''cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.bias''', F'''stage{idx}.blocks.{cnt}.mlp.fc1.bias''')    )
     attention_weights.append(
         (F'''cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.weight''', F'''stage{idx}.blocks.{cnt}.mlp.fc2.weight''')    )
     attention_weights.append(
         (F'''cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.bias''', F'''stage{idx}.blocks.{cnt}.mlp.fc2.bias''')    )
     attention_weights.append(
         (F'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.weight''', F'''stage{idx}.blocks.{cnt}.norm1.weight''')    )
     attention_weights.append(
         (F'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.bias''', F'''stage{idx}.blocks.{cnt}.norm1.bias''')    )
     attention_weights.append(
         (F'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.weight''', F'''stage{idx}.blocks.{cnt}.norm2.weight''')    )
     attention_weights.append(
         (F'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.bias''', F'''stage{idx}.blocks.{cnt}.norm2.bias''')    )
     return attention_weights
def       UpperCAmelCase__						(UpperCamelCase_    ):



     """simple docstring"""






     snake_case					=    []
     token.append((F'''cvt.encoder.stages.{idx}.cls_token''', '''stage2.cls_token''')    )
     return token
def       UpperCAmelCase__						():



     """simple docstring"""






     snake_case					=    []
     head.append(('''layernorm.weight''', '''norm.weight''')    )
     head.append(('''layernorm.bias''', '''norm.bias''')    )
     head.append(('''classifier.weight''', '''head.weight''')    )
     head.append(('''classifier.bias''', '''head.bias''')    )
     return head
def       UpperCAmelCase__						(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_    ):



     """simple docstring"""






     snake_case					=    '''imagenet-1k-id2label.json'''
     snake_case					=    10_00

     snake_case					=    '''huggingface/label-files'''
     snake_case					=    num_labels
     snake_case					=    json.load(open(cached_download(hf_hub_url(UpperCamelCase_							,UpperCamelCase_							,repo_type='''dataset'''    )    )							,'''r'''    )    )
     snake_case					=    {int(UpperCamelCase_    ): v for k, v in idalabel.items()}

     snake_case					=    idalabel
     snake_case					=    {v: k for k, v in idalabel.items()}

     snake_case					=    snake_case					=    CvtConfig(num_labels=UpperCamelCase_							,idalabel=UpperCamelCase_							,labelaid=UpperCamelCase_    )

     # For depth size 13 (13 = 1+2+10)
     if cvt_model.rsplit('''/'''							,1    )[-1][4:6] == "13":
          snake_case					=    [1, 2, 10]

     # For depth size 21 (21 = 1+4+16)
     elif cvt_model.rsplit('''/'''							,1    )[-1][4:6] == "21":
          snake_case					=    [1, 4, 16]

     # For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
     else:
          snake_case					=    [2, 2, 20]
          snake_case					=    [3, 12, 16]
          snake_case					=    [1_92, 7_68, 10_24]

     snake_case					=    CvtForImageClassification(UpperCamelCase_    )
     snake_case					=    AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k'''    )
     snake_case					=    image_size
     snake_case					=    torch.load(UpperCamelCase_							,map_location=torch.device('''cpu'''    )    )

     snake_case					=    OrderedDict()
     snake_case					=    []

     for idx in range(len(config.depth    )    ):
          if config.cls_token[idx]:
               snake_case					=    list_of_state_dict + cls_token(UpperCamelCase_    )
          snake_case					=    list_of_state_dict + embeddings(UpperCamelCase_    )
          for cnt in range(config.depth[idx]    ):
               snake_case					=    list_of_state_dict + attention(UpperCamelCase_							,UpperCamelCase_    )

     snake_case					=    list_of_state_dict + final()
     for gg in list_of_state_dict:
          print(UpperCamelCase_    )
     for i in range(len(UpperCamelCase_    )    ):
          snake_case					=    original_weights[list_of_state_dict[i][1]]

     model.load_state_dict(UpperCamelCase_    )
     model.save_pretrained(UpperCamelCase_    )
     image_processor.save_pretrained(UpperCamelCase_    )


# Download the weights from zoo: https://1drv.ms/u/s!AhIXJn_J-blW9RzF3rMW7SsLHa8h?e=blQ0Al

if __name__ == "__main__":
 _SCREAMING_SNAKE_CASE      :			Optional[int]      				=   argparse.ArgumentParser()
 parser.add_argument(
     "--cvt_model",
     default="cvt-w24",
     type=str,
     help="Name of the cvt model you'd like to convert.",
 )
 parser.add_argument(
     "--image_size",
     default=3_84,
     type=int,
     help="Input Image Size",
 )
 parser.add_argument(
     "--cvt_file_name",
     default=R"cvtmodels\CvT-w24-384x384-IN-22k.pth",
     type=str,
     help="Input Image Size",
 )
 parser.add_argument(
     "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
 )

 _SCREAMING_SNAKE_CASE      :			Optional[int]      				=   parser.parse_args()
 convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)




