






import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch
def 						_a    (      a :Union[str, Any] )							->			List[str]:
 a		  =      os.path.join(args.tf_model_dir	,							'''parameters.json''' )
 a		  =      json.loads(open(a ).read() )
 if not params:
  raise ValueError(
      F"""It seems that the json file at {parameter_file} is empty. Make sure you have a correct json file.""" )
 if not args.output.endswith('''.pt''' ):
  a		  =      args.output + '''.pt'''
 a		  =      OrderedDict()
 with tf.device('''/CPU:0''' ):
  a		  =      tf.train.load_checkpoint(args.tf_model_dir )
  a		  =      reader.get_variable_to_shape_map()
  for key_name in shapes.keys():
   a		  =      reader.get_tensor(a ).astype(np.floataa )
   if key_name.endswith('''/adam_m''' ) or key_name.endswith('''/adam_v''' ):
    continue
   if key_name.startswith('''pasts/''' ):
    if key_name.startswith('''pasts/mlp''' ):
     a		  =      int(key_name[9] )
    elif key_name.startswith('''pasts/out''' ):
     a		  =      8
    a		  =      '''model.sqout.%d.weight''' % (player * 2)  # enter to nn.Sequencial with Tanh, so 2 at a time
    a		  =      vnp.transpose([1, 0] ).copy()  # Mesh-Tensorflow is a diagonal matrix
    a		  =      torch.tensor(a )
   elif key_name.startswith('''model/moe''' ):
    a		  =      int(key_name[9:].split('''/''' )[0] )
    if key_name.endswith('''/switch_gating/kernel''' ):
     a		  =      '''model.blocks.%d.feed_forward.mlp.router.classifier.weight''' % player
     a		  =      vnp.transpose([1, 0] ).copy()  # Mesh-Tensorflow is a diagonal matrix
     a		  =      torch.tensor(a )
    elif key_name.endswith('''/softmlp/kernel''' ):
     a		  =      '''model.blocks.%d.feed_forward.soft_bypass_mlp.weight''' % player
     a		  =      vnp.transpose([1, 0] ).copy()  # Mesh-Tensorflow is a diagonal matrix
     a		  =      torch.tensor(a )
    elif key_name.endswith('''/wo/kernel''' ) or key_name.endswith('''/wi/kernel''' ):
     a		  =      key_name[-9:-7]
     for i in range(16 ):
      a		  =      '''model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight''' % (player, i, nlayer)
      a		  =      (
          vnp[i].transpose([1, 0] ).copy()
      )  # In Mesh-Tensorflow, it is one array, so it is divided
      a		  =      torch.tensor(a )
   elif key_name.startswith('''model/mlp''' ):
    a		  =      int(key_name[9:].split('''/''' )[0] )
    if key_name.endswith('''/p1/kernel''' ):
     a		  =      '''model.blocks.%d.feed_forward.mlp.wi.weight''' % player
     a		  =      vnp.transpose([1, 0] ).copy()  # Mesh-Tensorflow is a diagonal matrix
     a		  =      torch.tensor(a )
    elif key_name.endswith('''/p1/bias''' ):
     a		  =      '''model.blocks.%d.feed_forward.mlp.wi.bias''' % player
     a		  =      vnp.copy()  # same because it is one dimensional
     a		  =      torch.tensor(a )
    elif key_name.endswith('''/p2/kernel''' ):
     a		  =      '''model.blocks.%d.feed_forward.mlp.wo.weight''' % player
     a		  =      vnp.transpose([1, 0] ).copy()  # Mesh-Tensorflow is a diagonal matrix
     a		  =      torch.tensor(a )
    elif key_name.endswith('''/p2/bias''' ):
     a		  =      '''model.blocks.%d.feed_forward.mlp.wo.bias''' % player
     a		  =      vnp.copy()  # same because it is one dimensional
     a		  =      torch.tensor(a )
   elif key_name.startswith('''model/ln''' ):
    a		  =      int(key_name[8:].split('''/''' )[0] )
    if key_name.endswith('''/b''' ):
     a		  =      '''model.blocks.%d.feed_forward.norm.bias''' % player
     a		  =      vnp.copy()  # same because it is one dimensional
     a		  =      torch.tensor(a )
    elif key_name.endswith('''/g''' ):
     a		  =      '''model.blocks.%d.feed_forward.norm.weight''' % player
     a		  =      vnp.copy()  # same because it is one dimensional
     a		  =      torch.tensor(a )
   elif key_name.startswith('''model/att''' ):
    a		  =      int(key_name[9:].split('''/''' )[0] )
    if key_name.endswith('''/qkv/kernel''' ):
     a		  =      vnp.copy()  # Compute same dimension as Mesh-tensorflow using einsum
     a		  =      state[:, 0, :, :]
     a		  =      state[:, 1, :, :]
     a		  =      state[:, 2, :, :]
     a		  =      (
         state_q.reshape([state_q.shape[0], state_q.shape[1] * state_q.shape[2]] )
         .transpose([1, 0] )
         .copy()
     )  # Mesh-Tensorflow is a diagonal matrix
     a		  =      (
         state_k.reshape([state_k.shape[0], state_k.shape[1] * state_k.shape[2]] )
         .transpose([1, 0] )
         .copy()
     )  # Mesh-Tensorflow is a diagonal matrix
     a		  =      (
         state_v.reshape([state_v.shape[0], state_v.shape[1] * state_v.shape[2]] )
         .transpose([1, 0] )
         .copy()
     )  # Mesh-Tensorflow is a diagonal matrix
     a		  =      '''model.blocks.%d.self_attn.self_attn.q_proj.weight''' % player
     a		  =      torch.tensor(a )
     a		  =      '''model.blocks.%d.self_attn.self_attn.k_proj.weight''' % player
     a		  =      torch.tensor(a )
     a		  =      '''model.blocks.%d.self_attn.self_attn.v_proj.weight''' % player
     a		  =      torch.tensor(a )
    elif key_name.endswith('''/o/kernel''' ):
     a		  =      '''model.blocks.%d.self_attn.self_attn.out_proj.weight''' % player
     a		  =      (
         vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]] ).transpose([1, 0] ).copy()
     )  # Mesh-Tensorflow is a diagonal matrix
     a		  =      torch.tensor(a )
   elif key_name.startswith('''model/an''' ):
    a		  =      int(key_name[8:].split('''/''' )[0] )
    if key_name.endswith('''/b''' ):
     a		  =      '''model.blocks.%d.self_attn.norm.bias''' % player
     a		  =      vnp.copy()  # same because it is one dimensional
     a		  =      torch.tensor(a )
    elif key_name.endswith('''/g''' ):
     a		  =      '''model.blocks.%d.self_attn.norm.weight''' % player
     a		  =      vnp.copy()  # same because it is one dimensional
     a		  =      torch.tensor(a )
   elif (
       key_name.startswith('''model/wte''' )
       or key_name.startswith('''model/wpe''' )
       or key_name.startswith('''model/ete''' )
   ):
    a		  =      {'''wte''': '''embed_tokens''', '''wpe''': '''position_embeddings''', '''ete''': '''extra_position_embeddings'''}[
        key_name[-3:]
    ]
    a		  =      '''model.%s.weight''' % nlayer
    a		  =      vnp.copy()  # same in embedded
    a		  =      torch.tensor(a )
    if key_name.startswith('''model/wte''' ):
     a		  =      '''lm_head.weight'''
     a		  =      vnp.copy()  # same in embedded
     a		  =      torch.tensor(a )
   elif key_name.startswith('''model/wob''' ):
    a		  =      '''final_logits_bias'''
    a		  =      vnp.copy()  # same in embedded
    a		  =      state.reshape((1, -1) )
    a		  =      torch.tensor(a )
   elif key_name == "model/dense/kernel":
    a		  =      '''model.last_project.weight'''
    a		  =      vnp.transpose([1, 0] ).copy()  # Mesh-Tensorflow is a diagonal matrix
    a		  =      torch.tensor(a )
   elif key_name == "model/dense_1/bias":
    a		  =      '''model.last_project.bias'''
    a		  =      vnp.copy()  # same because it is one dimensional
    a		  =      torch.tensor(a )
 torch.save(a	,							args.output )


if __name__ == "__main__":
     UpperCAmelCase__				   =		argparse.ArgumentParser(
         description="model converter.", formatter_class=argparse.ArgumentDefaultsHelpFormatter
     )
     parser.add_argument("--tf_model_dir", metavar="PATH", type=str, required=True, help="import model")
     parser.add_argument("--output", metavar="PATH", type=str, required=True, help="output model")
     UpperCAmelCase__				   =		parser.parse_args()
     convert_tf_gptsan_to_pt(args)










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 						_a    (      a :List[Any] )							->			Optional[int]:
 a		  =      []
 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 						_a    (      a :List[Any]	,							a :Optional[int] )							->			Dict:
 a		  =      []
 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 						_a    (      a :Any )							->			List[Any]:
 a		  =      []
 token.append((F"""cvt.encoder.stages.{idx}.cls_token""", '''stage2.cls_token''') )
 return token
def 						_a    (      )							->			Optional[int]:
 a		  =      []
 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 						_a    (      a :Tuple	,							a :Optional[int]	,							a :List[Any]	,							a :Union[str, Any] )							->			Optional[int]:
 a		  =      '''imagenet-1k-id2label.json'''
 a		  =      1_000

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

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

 a		  =      a		  =      CvtConfig(num_labels=a	,							idalabel=a	,							labelaid=a )

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

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

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

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

 a		  =      OrderedDict()
 a		  =      []

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

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

 model.load_state_dict(a )
 model.save_pretrained(a )
 image_processor.save_pretrained(a )


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

if __name__ == "__main__":
     UpperCAmelCase__				   =		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."
     )

     UpperCAmelCase__				   =		parser.parse_args()
     convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)


