





'''simple docstring'''




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 			_lowerCAmelCase     (    __snake_case    :					List[Any]     )	->	int:
     __A		:					str						=			[]
     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 			_lowerCAmelCase     (    __snake_case    :					int					,				__snake_case    :					Any     )	->	Optional[int]:
     __A		:					int						=			[]
     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 			_lowerCAmelCase     (    __snake_case    :					Union[str, Any]     )	->	Optional[Any]:
     __A		:					Dict						=			[]
     token.append((f'cvt.encoder.stages.{idx}.cls_token', 'stage2.cls_token')     )
     return token






def 			_lowerCAmelCase     (    )	->	List[Any]:
     __A		:					Union[str, Any]						=			[]
     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 			_lowerCAmelCase     (    __snake_case    :					Union[str, Any]					,				__snake_case    :					Tuple					,				__snake_case    :					Optional[Any]					,				__snake_case    :					List[Any]     )	->	List[str]:
     __A		:					Any						=			'''imagenet-1k-id2label.json'''
     __A		:					List[str]						=			10_00

     __A		:					Optional[int]						=			'''huggingface/label-files'''
     __A		:					List[str]						=			num_labels
     __A		:					List[Any]						=			json.load(open(cached_download(hf_hub_url(UpperCAmelCase_					,				UpperCAmelCase_					,				repo_type='dataset'     )     )					,				'r'     )     )
     __A		:					Union[str, Any]						=			{int(UpperCAmelCase_     ): v for k, v in idalabel.items()}

     __A		:					Optional[Any]						=			idalabel
     __A		:					Union[str, Any]						=			{v: k for k, v in idalabel.items()}

     __A		:					int						=			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":
          __A		:					Tuple						=			[1, 2, 10]

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

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

     __A		:					List[str]						=			CvtForImageClassification(UpperCAmelCase_     )
     __A		:					str						=			AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'     )
     __A		:					str						=			image_size
     __A		:					List[str]						=			torch.load(UpperCAmelCase_					,				map_location=torch.device('cpu'     )     )

     __A		:					Tuple						=			OrderedDict()
     __A		:					List[Any]						=			[]

     for idx in range(len(config.depth     )     ):
          if config.cls_token[idx]:
               __A		:					Optional[Any]						=			list_of_state_dict + cls_token(UpperCAmelCase_     )
          __A		:					Union[str, Any]						=			list_of_state_dict + embeddings(UpperCAmelCase_     )
          for cnt in range(config.depth[idx]     ):
               __A		:					Optional[int]						=			list_of_state_dict + attention(UpperCAmelCase_					,				UpperCAmelCase_     )

     __A		:					int						=			list_of_state_dict + final()
     for gg in list_of_state_dict:
          print(UpperCAmelCase_     )
     for i in range(len(UpperCAmelCase_     )     ):
          __A		:					List[str]						=			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__":
  lowercase__       : Optional[Any]         =       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.'''
  )

  lowercase__       : Any         =       parser.parse_args()
  convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)



import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()







class 						_snake_case    ( unittest.TestCase				):




 @property
 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       torch.manual_seed(0       )
       a		:List[Any]									=			UNetaDModel(
           block_out_channels=(32, 64)   ,    layers_per_block=2   ,    sample_size=32   ,    in_channels=3   ,    out_channels=3   ,    down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')   ,    up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')   ,    )
       return model




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:Union[str, Any]									=			self.dummy_uncond_unet
       a		:Any									=			PNDMScheduler()

       a		:Any									=			PNDMPipeline(unet=_lowerCamelCase   ,    scheduler=_lowerCamelCase       )
       pndm.to(_lowerCamelCase       )
       pndm.set_progress_bar_config(disable=_lowerCamelCase       )

       a		:Any									=			torch.manual_seed(0       )
       a		:List[str]									=			pndm(generator=_lowerCamelCase   ,    num_inference_steps=20   ,    output_type='''numpy'''       ).images

       a		:int									=			torch.manual_seed(0       )
       a		:str									=			pndm(generator=_lowerCamelCase   ,    num_inference_steps=20   ,    output_type='''numpy'''   ,    return_dict=_lowerCamelCase       )[0]

       a		:int									=			image[0, -3:, -3:, -1]
       a		:List[str]									=			image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       a		:str									=			np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0]       )

       assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-2
       assert np.abs(image_from_tuple_slice.flatten() - expected_slice       ).max() < 1e-2







@slow
@require_torch
class 						_snake_case    ( unittest.TestCase				):




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:int									=			'''google/ddpm-cifar10-32'''

       a		:str									=			UNetaDModel.from_pretrained(_lowerCamelCase       )
       a		:Optional[Any]									=			PNDMScheduler()

       a		:Union[str, Any]									=			PNDMPipeline(unet=_lowerCamelCase   ,    scheduler=_lowerCamelCase       )
       pndm.to(_lowerCamelCase       )
       pndm.set_progress_bar_config(disable=_lowerCamelCase       )
       a		:Dict									=			torch.manual_seed(0       )
       a		:List[str]									=			pndm(generator=_lowerCamelCase   ,    output_type='''numpy'''       ).images

       a		:List[Any]									=			image[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       a		:int									=			np.array([0.1564, 0.1_4645, 0.1406, 0.1_4715, 0.1_2425, 0.1_4045, 0.1_3115, 0.1_2175, 0.125]       )

       assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-2





