


import argparse
import json
from pathlib import Path

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

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


logging.set_verbosity_info()
__UpperCamelCase     		=							logging.get_logger(__name__)
def 							UpperCamelCase_(					_A :str					,  _A :List[Any]=False	)->		Optional[Any]:
			UpperCamelCase__       =							[]
			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"),
						    ]	)

						# if just the base model, we should remove "vit" from all keys that start with "vit"
						UpperCamelCase__       =							[(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 							UpperCamelCase_(					_A :Tuple					,  _A :List[str]					,  _A :Optional[int]=False	)->		Union[str, Any]:
			for i in range(config.num_hidden_layers	):
						if base_model:
									UpperCamelCase__       =							""
						else:
									UpperCamelCase__       =							"vit."
						# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
						UpperCamelCase__       =							state_dict.pop(F'''blocks.{i}.attn.qkv.weight'''	)
						UpperCamelCase__       =							state_dict.pop(F'''blocks.{i}.attn.qkv.bias'''	)
						# next, add query, keys and values (in that order) to the state dict
						UpperCamelCase__       =							in_proj_weight[
						    : config.hidden_size, :
						]
						UpperCamelCase__       =							in_proj_bias[: config.hidden_size]
						UpperCamelCase__       =							in_proj_weight[
						    config.hidden_size : config.hidden_size * 2, :
						]
						UpperCamelCase__       =							in_proj_bias[
						    config.hidden_size : config.hidden_size * 2
						]
						UpperCamelCase__       =							in_proj_weight[
						    -config.hidden_size :, :
						]
						UpperCamelCase__       =							in_proj_bias[-config.hidden_size :]
def 							UpperCamelCase_(					_A :Any	)->		int:
			UpperCamelCase__       =							["head.weight", "head.bias"]
			for k in ignore_keys:
						state_dict.pop(_A					,  _A	)
def 							UpperCamelCase_(					_A :str					,  _A :Optional[Any]					,  _A :str	)->		Dict:
			UpperCamelCase__       =							dct.pop(_A	)
			UpperCamelCase__       =							val
def 							UpperCamelCase_(					)->		Union[str, Any]:
			UpperCamelCase__       =							"http://images.cocodataset.org/val2017/000000039769.jpg"
			UpperCamelCase__       =							Image.open(requests.get(_A					,  stream=_A	).raw	)
			return im
@torch.no_grad()
def 							UpperCamelCase_(					_A :str					,  _A :Any					,  _A :Union[str, Any]=True	)->		Tuple:
			UpperCamelCase__       =							ViTConfig()
			# patch_size
			if model_name[-1] == "8":
						UpperCamelCase__       =							8
			# set labels if required
			if not base_model:
						UpperCamelCase__       =							10_00
						UpperCamelCase__       =							"huggingface/label-files"
						UpperCamelCase__       =							"imagenet-1k-id2label.json"
						UpperCamelCase__       =							json.load(open(hf_hub_download(_A					,  _A					,  repo_type="dataset"	)					,  "r"	)	)
						UpperCamelCase__       =							{int(_A	): v for k, v in idalabel.items()}
						UpperCamelCase__       =							idalabel
						UpperCamelCase__       =							{v: k for k, v in idalabel.items()}
			# size of the architecture
			if model_name in ["dino_vits8", "dino_vits16"]:
						UpperCamelCase__       =							3_84
						UpperCamelCase__       =							15_36
						UpperCamelCase__       =							12
						UpperCamelCase__       =							6

			# load original model from torch hub
			UpperCamelCase__       =							torch.hub.load("facebookresearch/dino:main"					,  _A	)
			original_model.eval()

			# load state_dict of original model, remove and rename some keys
			UpperCamelCase__       =							original_model.state_dict()
			if base_model:
						remove_classification_head_(_A	)
			UpperCamelCase__       =							create_rename_keys(_A					,  base_model=_A	)
			for src, dest in rename_keys:
						rename_key(_A					,  _A					,  _A	)
			read_in_q_k_v(_A					,  _A					,  _A	)

			# load HuggingFace model
			if base_model:
						UpperCamelCase__       =							ViTModel(_A					,  add_pooling_layer=_A	).eval()
			else:
						UpperCamelCase__       =							ViTForImageClassification(_A	).eval()
			model.load_state_dict(_A	)

			# Check outputs on an image, prepared by ViTImageProcessor
			UpperCamelCase__       =							ViTImageProcessor()
			UpperCamelCase__       =							image_processor(images=prepare_img()					,  return_tensors="pt"	)
			UpperCamelCase__       =							encoding["pixel_values"]
			UpperCamelCase__       =							model(_A	)

			if base_model:
						UpperCamelCase__       =							original_model(_A	)
						assert torch.allclose(_A					,  outputs.last_hidden_state[:, 0, :]					,  atol=1e-1	)
			else:
						UpperCamelCase__       =							original_model(_A	)
						assert logits.shape == outputs.logits.shape
						assert torch.allclose(_A					,  outputs.logits					,  atol=1e-3	)

			Path(_A	).mkdir(exist_ok=_A	)
			print(F'''Saving model {model_name} to {pytorch_dump_folder_path}'''	)
			model.save_pretrained(_A	)
			print(F'''Saving image processor to {pytorch_dump_folder_path}'''	)
			image_processor.save_pretrained(_A	)


if __name__ == "__main__":
						__UpperCamelCase     		=							argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '--model_name',
						    default='dino_vitb16',
						    type=str,
						    help='Name of the model trained with DINO you\'d like to convert.',
						)
						parser.add_argument(
						    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
						)
						parser.add_argument(
						    '--base_model',
						    action='store_true',
						    help='Whether to only convert the base model (no projection head weights).',
						)

						parser.set_defaults(base_model=True)
						__UpperCamelCase     		=							parser.parse_args()
						convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)





'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


_a					:			Any							=    {
    'configuration_groupvit': [
        'GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'GroupViTConfig',
        'GroupViTOnnxConfig',
        'GroupViTTextConfig',
        'GroupViTVisionConfig',
    ],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _a					:			Union[str, Any]							=    [
      'GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
      'GroupViTModel',
      'GroupViTPreTrainedModel',
      'GroupViTTextModel',
      'GroupViTVisionModel',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _a					:			Tuple							=    [
      'TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
      'TFGroupViTModel',
      'TFGroupViTPreTrainedModel',
      'TFGroupViTTextModel',
      'TFGroupViTVisionModel',
  ]

if TYPE_CHECKING:
  from .configuration_groupvit import (
      GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
      GroupViTConfig,
      GroupViTOnnxConfig,
      GroupViTTextConfig,
      GroupViTVisionConfig,
  )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_groupvit import (
        GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
        GroupViTModel,
        GroupViTPreTrainedModel,
        GroupViTTextModel,
        GroupViTVisionModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_groupvit import (
        TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFGroupViTModel,
        TFGroupViTPreTrainedModel,
        TFGroupViTTextModel,
        TFGroupViTVisionModel,
    )

else:
  import sys

  _a					:			Union[str, Any]							=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


