





"""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 DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging


logging.set_verbosity_info()
__A											=						logging.get_logger(__name__)
def 					__A  (_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE=False     )   ->Optional[int]:





  """simple docstring"""


  lowerCAmelCase__     :Optional[int]											=				[]
  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"vit.encoder.layer.{i}.layernorm_before.weight")     )
    rename_keys.append((F"blocks.{i}.norm1.bias", F"vit.encoder.layer.{i}.layernorm_before.bias")     )
    rename_keys.append((F"blocks.{i}.attn.proj.weight", F"vit.encoder.layer.{i}.attention.output.dense.weight")     )
    rename_keys.append((F"blocks.{i}.attn.proj.bias", F"vit.encoder.layer.{i}.attention.output.dense.bias")     )
    rename_keys.append((F"blocks.{i}.norm2.weight", F"vit.encoder.layer.{i}.layernorm_after.weight")     )
    rename_keys.append((F"blocks.{i}.norm2.bias", F"vit.encoder.layer.{i}.layernorm_after.bias")     )
    rename_keys.append((F"blocks.{i}.mlp.fc1.weight", F"vit.encoder.layer.{i}.intermediate.dense.weight")     )
    rename_keys.append((F"blocks.{i}.mlp.fc1.bias", F"vit.encoder.layer.{i}.intermediate.dense.bias")     )
    rename_keys.append((F"blocks.{i}.mlp.fc2.weight", F"vit.encoder.layer.{i}.output.dense.weight")     )
    rename_keys.append((F"blocks.{i}.mlp.fc2.bias", F"vit.encoder.layer.{i}.output.dense.bias")     )

  # projection layer + position embeddings
  rename_keys.extend(
      [
          ('cls_token', 'vit.embeddings.cls_token'),
          ('patch_embed.proj.weight', 'vit.embeddings.patch_embeddings.projection.weight'),
          ('patch_embed.proj.bias', 'vit.embeddings.patch_embeddings.projection.bias'),
          ('pos_embed', 'vit.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 "vit" from all keys that start with "vit"
    lowerCAmelCase__     :Union[str, Any]											=				[(pair[0], pair[1][4:]) if pair[1].startswith('vit'     ) else pair for pair in rename_keys]
  else:
    # layernorm + classification head
    rename_keys.extend(
        [
            ('norm.weight', 'vit.layernorm.weight'),
            ('norm.bias', 'vit.layernorm.bias'),
            ('head.weight', 'classifier.weight'),
            ('head.bias', 'classifier.bias'),
        ]     )

  return rename_keys
def 					__A  (_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE=False     )   ->int:





  """simple docstring"""


  for i in range(config.num_hidden_layers     ):
    if base_model:
      lowerCAmelCase__     :List[str]											=				''
    else:
      lowerCAmelCase__     :List[str]											=				'vit.'
    # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
    lowerCAmelCase__     :List[Any]											=				state_dict.pop(F"blocks.{i}.attn.qkv.weight"     )
    lowerCAmelCase__     :int											=				state_dict.pop(F"blocks.{i}.attn.qkv.bias"     )
    # next, add query, keys and values (in that order) to the state dict
    lowerCAmelCase__     :Optional[int]											=				in_proj_weight[
        : config.hidden_size, :
    ]
    lowerCAmelCase__     :Optional[Any]											=				in_proj_bias[: config.hidden_size]
    lowerCAmelCase__     :Union[str, Any]											=				in_proj_weight[
        config.hidden_size : config.hidden_size * 2, :
    ]
    lowerCAmelCase__     :Optional[int]											=				in_proj_bias[
        config.hidden_size : config.hidden_size * 2
    ]
    lowerCAmelCase__     :Dict											=				in_proj_weight[
        -config.hidden_size :, :
    ]
    lowerCAmelCase__     :str											=				in_proj_bias[-config.hidden_size :]
def 					__A  (_SCREAMING_SNAKE_CASE     )   ->Any:





  """simple docstring"""


  lowerCAmelCase__     :Union[str, Any]											=				['head.weight', 'head.bias']
  for k in ignore_keys:
    state_dict.pop(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )
def 					__A  (_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )   ->int:





  """simple docstring"""


  lowerCAmelCase__     :Optional[int]											=				dct.pop(_SCREAMING_SNAKE_CASE     )
  lowerCAmelCase__     :Tuple											=				val
def 					__A  ()   ->Union[str, Any]:





  """simple docstring"""


  lowerCAmelCase__     :Tuple											=				'http://images.cocodataset.org/val2017/000000039769.jpg'
  lowerCAmelCase__     :List[str]											=				Image.open(requests.get(_SCREAMING_SNAKE_CASE	,  stream=_SCREAMING_SNAKE_CASE     ).raw     )
  return im
@torch.no_grad()
def 					__A  (_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )   ->Dict:





  """simple docstring"""


  lowerCAmelCase__     :int											=				ViTConfig()
  lowerCAmelCase__     :Dict											=				False
  # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
  if vit_name[-5:] == "in21k":
    lowerCAmelCase__     :Union[str, Any]											=				True
    lowerCAmelCase__     :Any											=				int(vit_name[-12:-10]     )
    lowerCAmelCase__     :Any											=				int(vit_name[-9:-6]     )
  else:
    lowerCAmelCase__     :int											=				1000
    lowerCAmelCase__     :Optional[Any]											=				'huggingface/label-files'
    lowerCAmelCase__     :Optional[Any]											=				'imagenet-1k-id2label.json'
    lowerCAmelCase__     :Tuple											=				json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  repo_type='dataset'     )	,  'r'     )     )
    lowerCAmelCase__     :Dict											=				{int(_SCREAMING_SNAKE_CASE     ): v for k, v in idalabel.items()}
    lowerCAmelCase__     :Any											=				idalabel
    lowerCAmelCase__     :Optional[Any]											=				{v: k for k, v in idalabel.items()}
    lowerCAmelCase__     :List[Any]											=				int(vit_name[-6:-4]     )
    lowerCAmelCase__     :Union[str, Any]											=				int(vit_name[-3:]     )
  # size of the architecture
  if "deit" in vit_name:
    if vit_name[9:].startswith('tiny'     ):
      lowerCAmelCase__     :List[Any]											=				192
      lowerCAmelCase__     :Dict											=				768
      lowerCAmelCase__     :Union[str, Any]											=				12
      lowerCAmelCase__     :List[Any]											=				3
    elif vit_name[9:].startswith('small'     ):
      lowerCAmelCase__     :int											=				384
      lowerCAmelCase__     :Tuple											=				1536
      lowerCAmelCase__     :List[Any]											=				12
      lowerCAmelCase__     :List[Any]											=				6
    else:
      pass
  else:
    if vit_name[4:].startswith('small'     ):
      lowerCAmelCase__     :Tuple											=				768
      lowerCAmelCase__     :Dict											=				2304
      lowerCAmelCase__     :Tuple											=				8
      lowerCAmelCase__     :List[Any]											=				8
    elif vit_name[4:].startswith('base'     ):
      pass
    elif vit_name[4:].startswith('large'     ):
      lowerCAmelCase__     :Optional[Any]											=				1024
      lowerCAmelCase__     :Union[str, Any]											=				4096
      lowerCAmelCase__     :List[Any]											=				24
      lowerCAmelCase__     :Tuple											=				16
    elif vit_name[4:].startswith('huge'     ):
      lowerCAmelCase__     :Dict											=				1280
      lowerCAmelCase__     :Union[str, Any]											=				5120
      lowerCAmelCase__     :Union[str, Any]											=				32
      lowerCAmelCase__     :Tuple											=				16

    # load original model from timm
  lowerCAmelCase__     :List[str]											=				timm.create_model(_SCREAMING_SNAKE_CASE	,  pretrained=_SCREAMING_SNAKE_CASE     )
  timm_model.eval()

  # load state_dict of original model, remove and rename some keys
  lowerCAmelCase__     :Any											=				timm_model.state_dict()
  if base_model:
    remove_classification_head_(_SCREAMING_SNAKE_CASE     )
  lowerCAmelCase__     :Dict											=				create_rename_keys(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )
  for src, dest in rename_keys:
    rename_key(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )
  read_in_q_k_v(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )

  # load HuggingFace model
  if vit_name[-5:] == "in21k":
    lowerCAmelCase__     :str											=				ViTModel(_SCREAMING_SNAKE_CASE     ).eval()
  else:
    lowerCAmelCase__     :List[Any]											=				ViTForImageClassification(_SCREAMING_SNAKE_CASE     ).eval()
  model.load_state_dict(_SCREAMING_SNAKE_CASE     )

  # Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
  if "deit" in vit_name:
    lowerCAmelCase__     :Optional[Any]											=				DeiTImageProcessor(size=config.image_size     )
  else:
    lowerCAmelCase__     :str											=				ViTImageProcessor(size=config.image_size     )
  lowerCAmelCase__     :List[str]											=				image_processor(images=prepare_img()	,  return_tensors='pt'     )
  lowerCAmelCase__     :List[Any]											=				encoding['pixel_values']
  lowerCAmelCase__     :Any											=				model(_SCREAMING_SNAKE_CASE     )

  if base_model:
    lowerCAmelCase__     :Dict											=				timm_model.forward_features(_SCREAMING_SNAKE_CASE     )
    assert timm_pooled_output.shape == outputs.pooler_output.shape
    assert torch.allclose(_SCREAMING_SNAKE_CASE	,  outputs.pooler_output	,  atol=1e-3     )
  else:
    lowerCAmelCase__     :Tuple											=				timm_model(_SCREAMING_SNAKE_CASE     )
    assert timm_logits.shape == outputs.logits.shape
    assert torch.allclose(_SCREAMING_SNAKE_CASE	,  outputs.logits	,  atol=1e-3     )

  Path(_SCREAMING_SNAKE_CASE     ).mkdir(exist_ok=_SCREAMING_SNAKE_CASE     )
  print(F"Saving model {vit_name} to {pytorch_dump_folder_path}"     )
  model.save_pretrained(_SCREAMING_SNAKE_CASE     )
  print(F"Saving image processor to {pytorch_dump_folder_path}"     )
  image_processor.save_pretrained(_SCREAMING_SNAKE_CASE     )


if __name__ == "__main__":
  __A											=						argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--vit_name""",
      default="""vit_base_patch16_224""",
      type=str,
      help="""Name of the ViT 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."""
  )

  __A											=						parser.parse_args()
  convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)







"""simple docstring"""





# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__A											=						{
    """configuration_mgp_str""": ["""MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MgpstrConfig"""],
    """processing_mgp_str""": ["""MgpstrProcessor"""],
    """tokenization_mgp_str""": ["""MgpstrTokenizer"""],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __A											=						[
      """MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """MgpstrModel""",
      """MgpstrPreTrainedModel""",
      """MgpstrForSceneTextRecognition""",
  ]

if TYPE_CHECKING:
  from .configuration_mgp_str import MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP, MgpstrConfig
  from .processing_mgp_str import MgpstrProcessor
  from .tokenization_mgp_str import MgpstrTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_mgp_str import (
        MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST,
        MgpstrForSceneTextRecognition,
        MgpstrModel,
        MgpstrPreTrainedModel,
    )
else:
  import sys

  __A											=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
