


'''simple docstring'''







import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch







def _UpperCamelCase       (     __A    )							->		List[str]:


      '''simple docstring'''



      UpperCamelCase__										=				os.path.join(args.tf_model_dir      ,			"parameters.json"    )
      UpperCamelCase__										=				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"    ):
            UpperCamelCase__										=				args.output + ".pt"
      UpperCamelCase__										=				OrderedDict()
      with tf.device("/CPU:0"    ):
            UpperCamelCase__										=				tf.train.load_checkpoint(args.tf_model_dir    )
            UpperCamelCase__										=				reader.get_variable_to_shape_map()
            for key_name in shapes.keys():
                  UpperCamelCase__										=				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"    ):
                              UpperCamelCase__										=				int(key_name[9]    )
                        elif key_name.startswith("pasts/out"    ):
                              UpperCamelCase__										=				8
                        UpperCamelCase__										=				"model.sqout.%d.weight" % (player * 2)  # enter to nn.Sequencial with Tanh, so 2 at a time
                        UpperCamelCase__										=				vnp.transpose([1, 0]    ).copy()  # Mesh-Tensorflow is a diagonal matrix
                        UpperCamelCase__										=				torch.tensor(__A    )
                  elif key_name.startswith("model/moe"    ):
                        UpperCamelCase__										=				int(key_name[9:].split("/"    )[0]    )
                        if key_name.endswith("/switch_gating/kernel"    ):
                              UpperCamelCase__										=				"model.blocks.%d.feed_forward.mlp.router.classifier.weight" % player
                              UpperCamelCase__										=				vnp.transpose([1, 0]    ).copy()  # Mesh-Tensorflow is a diagonal matrix
                              UpperCamelCase__										=				torch.tensor(__A    )
                        elif key_name.endswith("/softmlp/kernel"    ):
                              UpperCamelCase__										=				"model.blocks.%d.feed_forward.soft_bypass_mlp.weight" % player
                              UpperCamelCase__										=				vnp.transpose([1, 0]    ).copy()  # Mesh-Tensorflow is a diagonal matrix
                              UpperCamelCase__										=				torch.tensor(__A    )
                        elif key_name.endswith("/wo/kernel"    ) or key_name.endswith("/wi/kernel"    ):
                              UpperCamelCase__										=				key_name[-9:-7]
                              for i in range(16    ):
                                    UpperCamelCase__										=				"model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight" % (player, i, nlayer)
                                    UpperCamelCase__										=				(
                                        vnp[i].transpose([1, 0]    ).copy()
                                    )  # In Mesh-Tensorflow, it is one array, so it is divided
                                    UpperCamelCase__										=				torch.tensor(__A    )
                  elif key_name.startswith("model/mlp"    ):
                        UpperCamelCase__										=				int(key_name[9:].split("/"    )[0]    )
                        if key_name.endswith("/p1/kernel"    ):
                              UpperCamelCase__										=				"model.blocks.%d.feed_forward.mlp.wi.weight" % player
                              UpperCamelCase__										=				vnp.transpose([1, 0]    ).copy()  # Mesh-Tensorflow is a diagonal matrix
                              UpperCamelCase__										=				torch.tensor(__A    )
                        elif key_name.endswith("/p1/bias"    ):
                              UpperCamelCase__										=				"model.blocks.%d.feed_forward.mlp.wi.bias" % player
                              UpperCamelCase__										=				vnp.copy()  # same because it is one dimensional
                              UpperCamelCase__										=				torch.tensor(__A    )
                        elif key_name.endswith("/p2/kernel"    ):
                              UpperCamelCase__										=				"model.blocks.%d.feed_forward.mlp.wo.weight" % player
                              UpperCamelCase__										=				vnp.transpose([1, 0]    ).copy()  # Mesh-Tensorflow is a diagonal matrix
                              UpperCamelCase__										=				torch.tensor(__A    )
                        elif key_name.endswith("/p2/bias"    ):
                              UpperCamelCase__										=				"model.blocks.%d.feed_forward.mlp.wo.bias" % player
                              UpperCamelCase__										=				vnp.copy()  # same because it is one dimensional
                              UpperCamelCase__										=				torch.tensor(__A    )
                  elif key_name.startswith("model/ln"    ):
                        UpperCamelCase__										=				int(key_name[8:].split("/"    )[0]    )
                        if key_name.endswith("/b"    ):
                              UpperCamelCase__										=				"model.blocks.%d.feed_forward.norm.bias" % player
                              UpperCamelCase__										=				vnp.copy()  # same because it is one dimensional
                              UpperCamelCase__										=				torch.tensor(__A    )
                        elif key_name.endswith("/g"    ):
                              UpperCamelCase__										=				"model.blocks.%d.feed_forward.norm.weight" % player
                              UpperCamelCase__										=				vnp.copy()  # same because it is one dimensional
                              UpperCamelCase__										=				torch.tensor(__A    )
                  elif key_name.startswith("model/att"    ):
                        UpperCamelCase__										=				int(key_name[9:].split("/"    )[0]    )
                        if key_name.endswith("/qkv/kernel"    ):
                              UpperCamelCase__										=				vnp.copy()  # Compute same dimension as Mesh-tensorflow using einsum
                              UpperCamelCase__										=				state[:, 0, :, :]
                              UpperCamelCase__										=				state[:, 1, :, :]
                              UpperCamelCase__										=				state[:, 2, :, :]
                              UpperCamelCase__										=				(
                                  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
                              UpperCamelCase__										=				(
                                  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
                              UpperCamelCase__										=				(
                                  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
                              UpperCamelCase__										=				"model.blocks.%d.self_attn.self_attn.q_proj.weight" % player
                              UpperCamelCase__										=				torch.tensor(__A    )
                              UpperCamelCase__										=				"model.blocks.%d.self_attn.self_attn.k_proj.weight" % player
                              UpperCamelCase__										=				torch.tensor(__A    )
                              UpperCamelCase__										=				"model.blocks.%d.self_attn.self_attn.v_proj.weight" % player
                              UpperCamelCase__										=				torch.tensor(__A    )
                        elif key_name.endswith("/o/kernel"    ):
                              UpperCamelCase__										=				"model.blocks.%d.self_attn.self_attn.out_proj.weight" % player
                              UpperCamelCase__										=				(
                                  vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]]    ).transpose([1, 0]    ).copy()
                              )  # Mesh-Tensorflow is a diagonal matrix
                              UpperCamelCase__										=				torch.tensor(__A    )
                  elif key_name.startswith("model/an"    ):
                        UpperCamelCase__										=				int(key_name[8:].split("/"    )[0]    )
                        if key_name.endswith("/b"    ):
                              UpperCamelCase__										=				"model.blocks.%d.self_attn.norm.bias" % player
                              UpperCamelCase__										=				vnp.copy()  # same because it is one dimensional
                              UpperCamelCase__										=				torch.tensor(__A    )
                        elif key_name.endswith("/g"    ):
                              UpperCamelCase__										=				"model.blocks.%d.self_attn.norm.weight" % player
                              UpperCamelCase__										=				vnp.copy()  # same because it is one dimensional
                              UpperCamelCase__										=				torch.tensor(__A    )
                  elif (
                      key_name.startswith("model/wte"    )
                      or key_name.startswith("model/wpe"    )
                      or key_name.startswith("model/ete"    )
                  ):
                        UpperCamelCase__										=				{"wte": "embed_tokens", "wpe": "position_embeddings", "ete": "extra_position_embeddings"}[
                            key_name[-3:]
                        ]
                        UpperCamelCase__										=				"model.%s.weight" % nlayer
                        UpperCamelCase__										=				vnp.copy()  # same in embedded
                        UpperCamelCase__										=				torch.tensor(__A    )
                        if key_name.startswith("model/wte"    ):
                              UpperCamelCase__										=				"lm_head.weight"
                              UpperCamelCase__										=				vnp.copy()  # same in embedded
                              UpperCamelCase__										=				torch.tensor(__A    )
                  elif key_name.startswith("model/wob"    ):
                        UpperCamelCase__										=				"final_logits_bias"
                        UpperCamelCase__										=				vnp.copy()  # same in embedded
                        UpperCamelCase__										=				state.reshape((1, -1)    )
                        UpperCamelCase__										=				torch.tensor(__A    )
                  elif key_name == "model/dense/kernel":
                        UpperCamelCase__										=				"model.last_project.weight"
                        UpperCamelCase__										=				vnp.transpose([1, 0]    ).copy()  # Mesh-Tensorflow is a diagonal matrix
                        UpperCamelCase__										=				torch.tensor(__A    )
                  elif key_name == "model/dense_1/bias":
                        UpperCamelCase__										=				"model.last_project.bias"
                        UpperCamelCase__										=				vnp.copy()  # same because it is one dimensional
                        UpperCamelCase__										=				torch.tensor(__A    )
      torch.save(__A      ,			args.output    )


if __name__ == "__main__":
  a__  :					Dict        =       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')
  a__  :					int        =       parser.parse_args()
  convert_tf_gptsan_to_pt(args)







"""simple docstring"""



import argparse
import json
from pathlib import Path

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

from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase__          = logging.get_logger(__name__)
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Any ,					__lowerCamelCase   :       Union[str, Any]=False		)     ->					Optional[int]:
  _snake_case					     =       []
  for i in range(config.num_hidden_layers		):
    # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
    rename_keys.append((f'''blocks.{i}.norm1.weight''', f'''deit.encoder.layer.{i}.layernorm_before.weight''')		)
    rename_keys.append((f'''blocks.{i}.norm1.bias''', f'''deit.encoder.layer.{i}.layernorm_before.bias''')		)
    rename_keys.append((f'''blocks.{i}.attn.proj.weight''', f'''deit.encoder.layer.{i}.attention.output.dense.weight''')		)
    rename_keys.append((f'''blocks.{i}.attn.proj.bias''', f'''deit.encoder.layer.{i}.attention.output.dense.bias''')		)
    rename_keys.append((f'''blocks.{i}.norm2.weight''', f'''deit.encoder.layer.{i}.layernorm_after.weight''')		)
    rename_keys.append((f'''blocks.{i}.norm2.bias''', f'''deit.encoder.layer.{i}.layernorm_after.bias''')		)
    rename_keys.append((f'''blocks.{i}.mlp.fc1.weight''', f'''deit.encoder.layer.{i}.intermediate.dense.weight''')		)
    rename_keys.append((f'''blocks.{i}.mlp.fc1.bias''', f'''deit.encoder.layer.{i}.intermediate.dense.bias''')		)
    rename_keys.append((f'''blocks.{i}.mlp.fc2.weight''', f'''deit.encoder.layer.{i}.output.dense.weight''')		)
    rename_keys.append((f'''blocks.{i}.mlp.fc2.bias''', f'''deit.encoder.layer.{i}.output.dense.bias''')		)

  # projection layer + position embeddings
  rename_keys.extend(
      [
          ('''cls_token''', '''deit.embeddings.cls_token'''),
          ('''dist_token''', '''deit.embeddings.distillation_token'''),
          ('''patch_embed.proj.weight''', '''deit.embeddings.patch_embeddings.projection.weight'''),
          ('''patch_embed.proj.bias''', '''deit.embeddings.patch_embeddings.projection.bias'''),
          ('''pos_embed''', '''deit.embeddings.position_embeddings'''),
      ]		)

  if base_model:
    # layernorm + pooler
    rename_keys.extend(
        [
            ('''norm.weight''', '''layernorm.weight'''),
            ('''norm.bias''', '''layernorm.bias'''),
            ('''pre_logits.fc.weight''', '''pooler.dense.weight'''),
            ('''pre_logits.fc.bias''', '''pooler.dense.bias'''),
        ]		)

    # if just the base model, we should remove "deit" from all keys that start with "deit"
    _snake_case					     =       [(pair[0], pair[1][4:]) if pair[1].startswith('''deit'''		) else pair for pair in rename_keys]
  else:
    # layernorm + classification heads
    rename_keys.extend(
        [
            ('''norm.weight''', '''deit.layernorm.weight'''),
            ('''norm.bias''', '''deit.layernorm.bias'''),
            ('''head.weight''', '''cls_classifier.weight'''),
            ('''head.bias''', '''cls_classifier.bias'''),
            ('''head_dist.weight''', '''distillation_classifier.weight'''),
            ('''head_dist.bias''', '''distillation_classifier.bias'''),
        ]		)

  return rename_keys
def 					_UpperCAmelCase      ( __lowerCamelCase   :       int ,					__lowerCamelCase   :       List[str] ,					__lowerCamelCase   :       Tuple=False		)     ->					Tuple:
  for i in range(config.num_hidden_layers		):
    if base_model:
      _snake_case					     =       ''''''
    else:
      _snake_case					     =       '''deit.'''
    # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
    _snake_case					     =       state_dict.pop(f'''blocks.{i}.attn.qkv.weight'''		)
    _snake_case					     =       state_dict.pop(f'''blocks.{i}.attn.qkv.bias'''		)
    # next, add query, keys and values (in that order) to the state dict
    _snake_case					     =       in_proj_weight[
        : config.hidden_size, :
    ]
    _snake_case					     =       in_proj_bias[: config.hidden_size]
    _snake_case					     =       in_proj_weight[
        config.hidden_size : config.hidden_size * 2, :
    ]
    _snake_case					     =       in_proj_bias[
        config.hidden_size : config.hidden_size * 2
    ]
    _snake_case					     =       in_proj_weight[
        -config.hidden_size :, :
    ]
    _snake_case					     =       in_proj_bias[-config.hidden_size :]
def 					_UpperCAmelCase      ( __lowerCamelCase   :       str ,					__lowerCamelCase   :       Tuple ,					__lowerCamelCase   :       Tuple		)     ->					Tuple:
  _snake_case					     =       dct.pop(__lowerCamelCase		)
  _snake_case					     =       val
def 					_UpperCAmelCase      ( )     ->					Dict:
  _snake_case					     =       '''http://images.cocodataset.org/val2017/000000039769.jpg'''
  _snake_case					     =       Image.open(requests.get(__lowerCamelCase ,					stream=__lowerCamelCase		).raw		)
  return im
@torch.no_grad()
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Union[str, Any] ,					__lowerCamelCase   :       str		)     ->					str:
  _snake_case					     =       DeiTConfig()
  # all deit models have fine-tuned heads
  _snake_case					     =       False
  # dataset (fine-tuned on ImageNet 2012), patch_size and image_size
  _snake_case					     =       10_00
  _snake_case					     =       '''huggingface/label-files'''
  _snake_case					     =       '''imagenet-1k-id2label.json'''
  _snake_case					     =       json.load(open(hf_hub_download(__lowerCamelCase ,					__lowerCamelCase ,					repo_type='''dataset'''		) ,					'''r'''		)		)
  _snake_case					     =       {int(__lowerCamelCase		): v for k, v in idalabel.items()}
  _snake_case					     =       idalabel
  _snake_case					     =       {v: k for k, v in idalabel.items()}
  _snake_case					     =       int(deit_name[-6:-4]		)
  _snake_case					     =       int(deit_name[-3:]		)
  # size of the architecture
  if deit_name[9:].startswith('''tiny'''		):
    _snake_case					     =       1_92
    _snake_case					     =       7_68
    _snake_case					     =       12
    _snake_case					     =       3
  elif deit_name[9:].startswith('''small'''		):
    _snake_case					     =       3_84
    _snake_case					     =       15_36
    _snake_case					     =       12
    _snake_case					     =       6
  if deit_name[9:].startswith('''base'''		):
    pass
  elif deit_name[4:].startswith('''large'''		):
    _snake_case					     =       10_24
    _snake_case					     =       40_96
    _snake_case					     =       24
    _snake_case					     =       16

  # load original model from timm
  _snake_case					     =       timm.create_model(__lowerCamelCase ,					pretrained=__lowerCamelCase		)
  timm_model.eval()

  # load state_dict of original model, remove and rename some keys
  _snake_case					     =       timm_model.state_dict()
  _snake_case					     =       create_rename_keys(__lowerCamelCase ,					__lowerCamelCase		)
  for src, dest in rename_keys:
    rename_key(__lowerCamelCase ,					__lowerCamelCase ,					__lowerCamelCase		)
  read_in_q_k_v(__lowerCamelCase ,					__lowerCamelCase ,					__lowerCamelCase		)

  # load HuggingFace model
  _snake_case					     =       DeiTForImageClassificationWithTeacher(__lowerCamelCase		).eval()
  model.load_state_dict(__lowerCamelCase		)

  # Check outputs on an image, prepared by DeiTImageProcessor
  _snake_case					     =       int(
      (2_56 / 2_24) * config.image_size		)  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
  _snake_case					     =       DeiTImageProcessor(size=__lowerCamelCase ,					crop_size=config.image_size		)
  _snake_case					     =       image_processor(images=prepare_img() ,					return_tensors='''pt'''		)
  _snake_case					     =       encoding['''pixel_values''']
  _snake_case					     =       model(__lowerCamelCase		)

  _snake_case					     =       timm_model(__lowerCamelCase		)
  assert timm_logits.shape == outputs.logits.shape
  assert torch.allclose(__lowerCamelCase ,					outputs.logits ,					atol=1E-3		)

  Path(__lowerCamelCase		).mkdir(exist_ok=__lowerCamelCase		)
  print(f'''Saving model {deit_name} to {pytorch_dump_folder_path}'''		)
  model.save_pretrained(__lowerCamelCase		)
  print(f'''Saving image processor to {pytorch_dump_folder_path}'''		)
  image_processor.save_pretrained(__lowerCamelCase		)


if __name__ == "__main__":
 UpperCAmelCase__          = argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '--deit_name',
     default='vit_deit_base_distilled_patch16_224',
     type=str,
     help='Name of the DeiT timm model you\'d like to convert.',
 )
 parser.add_argument(
     '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
 )

 UpperCAmelCase__          = parser.parse_args()
 convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)



