
"""simple docstring"""



import argparse
import os
from pathlib import Path

import fairseq
import torch
from packaging import version
from torch import nn

from transformers import (
    BartConfig,
    BartForConditionalGeneration,
    BartForSequenceClassification,
    BartModel,
    BartTokenizer,
)
from transformers.utils import logging


UpperCAmelCase__          = ['bart.large', 'bart.large.mnli', 'bart.large.cnn', 'bart_xsum/model.pt']
UpperCAmelCase__          = {'bart.large': BartModel, 'bart.large.mnli': BartForSequenceClassification}
if version.parse(fairseq.__version__) < version.parse('0.9.0'):
 raise Exception('requires fairseq >= 0.9.0')


logging.set_verbosity_info()
UpperCAmelCase__          = logging.get_logger(__name__)

UpperCAmelCase__          = ' Hello world! cécé herlolip'

UpperCAmelCase__          = [
    ('model.classification_heads.mnli.dense.weight', 'classification_head.dense.weight'),
    ('model.classification_heads.mnli.dense.bias', 'classification_head.dense.bias'),
    ('model.classification_heads.mnli.out_proj.weight', 'classification_head.out_proj.weight'),
    ('model.classification_heads.mnli.out_proj.bias', 'classification_head.out_proj.bias'),
]
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Optional[Any]		)     ->					Optional[int]:
  _snake_case					     =       [
      '''encoder.version''',
      '''decoder.version''',
      '''model.encoder.version''',
      '''model.decoder.version''',
      '''_float_tensor''',
  ]
  for k in ignore_keys:
    state_dict.pop(__lowerCamelCase ,					__lowerCamelCase		)
def 					_UpperCAmelCase      ( __lowerCamelCase   :       List[Any] ,					__lowerCamelCase   :       int ,					__lowerCamelCase   :       int		)     ->					int:
  _snake_case					     =       dct.pop(__lowerCamelCase		)
  _snake_case					     =       val
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Dict		)     ->					str:
  _snake_case					     =       torch.load(__lowerCamelCase ,					map_location='''cpu'''		)
  _snake_case					     =       torch.hub.load('''pytorch/fairseq''' ,					'''bart.large.cnn'''		).eval()
  hub_interface.model.load_state_dict(sd['''model''']		)
  return hub_interface
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Optional[int]		)     ->					Union[str, Any]:
  _snake_case     ,					_snake_case					     =       emb.weight.shape
  _snake_case					     =       nn.Linear(__lowerCamelCase ,					__lowerCamelCase ,					bias=__lowerCamelCase		)
  _snake_case					     =       emb.weight.data
  return lin_layer
@torch.no_grad()
def 					_UpperCAmelCase      ( __lowerCamelCase   :       List[Any] ,					__lowerCamelCase   :       Tuple ,					__lowerCamelCase   :       Union[str, Any]=None		)     ->					List[Any]:
  if not os.path.exists(__lowerCamelCase		):
    _snake_case					     =       torch.hub.load('''pytorch/fairseq''' ,					__lowerCamelCase		).eval()
  else:
    _snake_case					     =       load_xsum_checkpoint(__lowerCamelCase		)

  bart.model.upgrade_state_dict(bart.model.state_dict()		)
  if hf_checkpoint_name is None:
    _snake_case					     =       checkpoint_path.replace('''.''' ,					'''-'''		)
  _snake_case					     =       BartConfig.from_pretrained(__lowerCamelCase		)
  _snake_case					     =       bart.encode(__lowerCamelCase		).unsqueeze(0		)
  _snake_case					     =       BartTokenizer.from_pretrained(__lowerCamelCase		).encode(__lowerCamelCase ,					return_tensors='''pt'''		).unsqueeze(0		)
  if not torch.eq(__lowerCamelCase ,					__lowerCamelCase		).all():
    raise ValueError(
        f'''converted tokenizer and pretrained tokenizer returned different output: {tokens} != {tokensa}'''		)

  if checkpoint_path == "bart.large.mnli":
    _snake_case					     =       bart.state_dict()
    remove_ignore_keys_(__lowerCamelCase		)
    _snake_case					     =       state_dict['''model.decoder.embed_tokens.weight''']
    for src, dest in mnli_rename_keys:
      rename_key(__lowerCamelCase ,					__lowerCamelCase ,					__lowerCamelCase		)
    _snake_case					     =       BartForSequenceClassification(__lowerCamelCase		).eval()
    model.load_state_dict(__lowerCamelCase		)
    _snake_case					     =       bart.predict('''mnli''' ,					__lowerCamelCase ,					return_logits=__lowerCamelCase		)
    _snake_case					     =       model(__lowerCamelCase		)[0]  # logits
  else:  # no classification heads to worry about
    _snake_case					     =       bart.model.state_dict()
    remove_ignore_keys_(__lowerCamelCase		)
    _snake_case					     =       state_dict['''decoder.embed_tokens.weight''']
    _snake_case					     =       bart.extract_features(__lowerCamelCase		)
    if hf_checkpoint_name == "facebook/bart-large":
      _snake_case					     =       BartModel(__lowerCamelCase		).eval()
      model.load_state_dict(__lowerCamelCase		)
      _snake_case					     =       model(__lowerCamelCase		).model[0]
    else:
      _snake_case					     =       BartForConditionalGeneration(__lowerCamelCase		).eval()  # an existing summarization ckpt
      model.model.load_state_dict(__lowerCamelCase		)
      if hasattr(__lowerCamelCase ,					'''lm_head'''		):
        _snake_case					     =       make_linear_from_emb(model.model.shared		)
      _snake_case					     =       model.model(__lowerCamelCase		)[0]

    # Check results
  if fairseq_output.shape != new_model_outputs.shape:
    raise ValueError(
        f'''`fairseq_output` shape and `new_model_output` shape are different: {fairseq_output.shape=}, {new_model_outputs.shape}'''		)
  if (fairseq_output != new_model_outputs).any().item():
    raise ValueError('''Some values in `fairseq_output` are different from `new_model_outputs`'''		)
  Path(__lowerCamelCase		).mkdir(exist_ok=__lowerCamelCase		)
  model.save_pretrained(__lowerCamelCase		)


if __name__ == "__main__":
 UpperCAmelCase__          = argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     'fairseq_path', type=str, help='bart.large, bart.large.cnn or a path to a model.pt on local filesystem.'
 )
 parser.add_argument('pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
 parser.add_argument(
     '--hf_config', default=None, type=str, help='Which huggingface architecture to use: bart-large-xsum'
 )
 UpperCAmelCase__          = parser.parse_args()
 convert_bart_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, hf_checkpoint_name=args.hf_config)





"""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)



