

import mpmath  # for roots of unity
import numpy as np






class       __A :






    def __init__(		self	:			Any			, UpperCAmelCase_	:			int=None			, UpperCAmelCase_	:			Union[str, Any]=None			):
        lowerCAmelCase :	List[str]				=					list(poly_a or [0]			)[:]
        lowerCAmelCase :	List[str]				=					list(poly_b or [0]			)[:]

        # Remove leading zero coefficients
        while self.polyA[-1] == 0:
            self.polyA.pop()
        lowerCAmelCase :	Any				=					len(self.polyA			)

        while self.polyB[-1] == 0:
            self.polyB.pop()
        lowerCAmelCase :	List[str]				=					len(self.polyB			)

        # Add 0 to make lengths equal a power of 2
        lowerCAmelCase :	str				=					int(
            2 ** np.ceil(np.loga(len(self.polyA			) + len(self.polyB			) - 1			)			)			)

        while len(self.polyA			) < self.c_max_length:
            self.polyA.append(0			)
        while len(self.polyB			) < self.c_max_length:
            self.polyB.append(0			)
        # A complex root used for the fourier transform
        lowerCAmelCase :	Union[str, Any]				=					complex(mpmath.root(x=1			, n=self.c_max_length			, k=1			)			)

        # The product
        lowerCAmelCase :	Any				=					self.__multiply()






    def 		lowercase__       (		self	:			str			, UpperCAmelCase_	:			int			):
        lowerCAmelCase :	Union[str, Any]				=					[[x] for x in self.polyA] if which == """A""" else [[x] for x in self.polyB]
        # Corner case
        if len(UpperCAmelCase_			) <= 1:
            return dft[0]
        #
        lowerCAmelCase :	Optional[Any]				=					self.c_max_length // 2
        while next_ncol > 0:
            lowerCAmelCase :	Tuple				=					[[] for i in range(UpperCAmelCase_			)]
            lowerCAmelCase :	int				=					self.root**next_ncol

            # First half of next step
            lowerCAmelCase :	Tuple				=					1
            for j in range(self.c_max_length // (next_ncol * 2)			):
                for i in range(UpperCAmelCase_			):
                    new_dft[i].append(dft[i][j] + current_root * dft[i + next_ncol][j]			)
                current_root *= root
            # Second half of next step
            lowerCAmelCase :	Any				=					1
            for j in range(self.c_max_length // (next_ncol * 2)			):
                for i in range(UpperCAmelCase_			):
                    new_dft[i].append(dft[i][j] - current_root * dft[i + next_ncol][j]			)
                current_root *= root
            # Update
            lowerCAmelCase :	Dict				=					new_dft
            lowerCAmelCase :	Optional[int]				=					next_ncol // 2
        return dft[0]






    def 		lowercase__       (		self	:			str			):
        lowerCAmelCase :	Optional[Any]				=					self.__dft('A'			)
        lowerCAmelCase :	List[str]				=					self.__dft('B'			)
        lowerCAmelCase :	Optional[int]				=					[[dft_a[i] * dft_b[i] for i in range(self.c_max_length			)]]
        del dft_a
        del dft_b

        # Corner Case
        if len(inverce_c[0]			) <= 1:
            return inverce_c[0]
        # Inverse DFT
        lowerCAmelCase :	Dict				=					2
        while next_ncol <= self.c_max_length:
            lowerCAmelCase :	Any				=					[[] for i in range(UpperCAmelCase_			)]
            lowerCAmelCase :	Optional[Any]				=					self.root ** (next_ncol // 2)
            lowerCAmelCase :	Optional[int]				=					1
            # First half of next step
            for j in range(self.c_max_length // next_ncol			):
                for i in range(next_ncol // 2			):
                    # Even positions
                    new_inverse_c[i].append(
                        (
                            inverce_c[i][j]
                            + inverce_c[i][j + self.c_max_length // next_ncol]
                        )
                        / 2			)
                    # Odd positions
                    new_inverse_c[i + next_ncol // 2].append(
                        (
                            inverce_c[i][j]
                            - inverce_c[i][j + self.c_max_length // next_ncol]
                        )
                        / (2 * current_root)			)
                current_root *= root
            # Update
            lowerCAmelCase :	str				=					new_inverse_c
            next_ncol *= 2
        # Unpack
        lowerCAmelCase :	List[str]				=					[round(x[0].real			, 8			) + round(x[0].imag			, 8			) * 1j for x in inverce_c]

        # Remove leading 0's
        while inverce_c[-1] == 0:
            inverce_c.pop()
        return inverce_c






    def __str__(		self	:			str			):
        lowerCAmelCase :	Optional[int]				=					"""A = """ + """ + """.join(
            f"{coef}*x^{i}" for coef, i in enumerate(self.polyA[: self.len_A]			)			)
        lowerCAmelCase :	List[Any]				=					"""B = """ + """ + """.join(
            f"{coef}*x^{i}" for coef, i in enumerate(self.polyB[: self.len_B]			)			)
        lowerCAmelCase :	Optional[Any]				=					"""A*B = """ + """ + """.join(
            f"{coef}*x^{i}" for coef, i in enumerate(self.product			)			)

        return f"{a}\n{b}\n{c}"


# Unit tests
if __name__ == "__main__":
 import doctest

 doctest.testmod()







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 						_snake_case(  SCREAMING_SNAKE_CASE__				) ->    Tuple:
       lowercase							:     Union[str, Any]       	=      []
       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 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    str:
       lowercase							:     Optional[Any]       	=      []
       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 						_snake_case(  SCREAMING_SNAKE_CASE__				) ->    Optional[Any]:
       lowercase							:     Optional[Any]       	=      []
       token.append((f"cvt.encoder.stages.{idx}.cls_token", """stage2.cls_token""")				)
       return token







def 						_snake_case(  ) ->    Dict:
       lowercase							:     Optional[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 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    str:
       lowercase							:     Any       	=      """imagenet-1k-id2label.json"""
       lowercase							:     List[str]       	=      1_000

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

       lowercase							:     Dict       	=      idalabel
       lowercase							:     List[str]       	=      {v: k for k, v in idalabel.items()}

       lowercase							:     List[str]       	=      CvtConfig(num_labels=SCREAMING_SNAKE_CASE__					,	idalabel=SCREAMING_SNAKE_CASE__					,	labelaid=SCREAMING_SNAKE_CASE__				)

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

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

       # For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
       else:
              lowercase							:     int       	=      [2, 2, 20]
              lowercase							:     Optional[int]       	=      [3, 12, 16]
              lowercase							:     str       	=      [192, 768, 1_024]

       lowercase							:     Union[str, Any]       	=      CvtForImageClassification(SCREAMING_SNAKE_CASE__				)
       lowercase							:     Union[str, Any]       	=      AutoImageProcessor.from_pretrained("""facebook/convnext-base-224-22k-1k"""				)
       lowercase							:     Optional[Any]       	=      image_size
       lowercase							:     Union[str, Any]       	=      torch.load(SCREAMING_SNAKE_CASE__					,	map_location=torch.device("""cpu"""				)				)

       lowercase							:     Optional[Any]       	=      OrderedDict()
       lowercase							:     Tuple       	=      []

       for idx in range(len(config.depth				)				):
              if config.cls_token[idx]:
                     lowercase							:     Optional[Any]       	=      list_of_state_dict + cls_token(SCREAMING_SNAKE_CASE__				)
              lowercase							:     str       	=      list_of_state_dict + embeddings(SCREAMING_SNAKE_CASE__				)
              for cnt in range(config.depth[idx]				):
                     lowercase							:     List[str]       	=      list_of_state_dict + attention(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)

       lowercase							:     List[str]       	=      list_of_state_dict + final()
       for gg in list_of_state_dict:
              print(SCREAMING_SNAKE_CASE__				)
       for i in range(len(SCREAMING_SNAKE_CASE__				)				):
              lowercase							:     Optional[Any]       	=      original_weights[list_of_state_dict[i][1]]

       model.load_state_dict(SCREAMING_SNAKE_CASE__				)
       model.save_pretrained(SCREAMING_SNAKE_CASE__				)
       image_processor.save_pretrained(SCREAMING_SNAKE_CASE__				)


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

if __name__ == "__main__":
  lowercase  :					Tuple						    =							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=384,
      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  :					Optional[int]						    =							parser.parse_args()
  convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)
