





"""simple docstring"""




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__  :     str   			=logging.get_logger(__name__)





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





def        _lowercase   (  _UpperCAmelCase			)  ->					Optional[Any]:
				lowerCamelCase			=["""head.weight""", """head.bias"""]
				for k in ignore_keys:
								state_dict.pop(_UpperCAmelCase     , _UpperCAmelCase			)





def        _lowercase   (  _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase			)  ->					Optional[Any]:
				lowerCamelCase			=dct.pop(_UpperCAmelCase			)
				lowerCamelCase			=val





def        _lowercase   (  )  ->					Optional[Any]:
				lowerCamelCase			="""http://images.cocodataset.org/val2017/000000039769.jpg"""
				lowerCamelCase			=Image.open(requests.get(_UpperCAmelCase     , stream=_UpperCAmelCase			).raw			)
				return im





@torch.no_grad()
def        _lowercase   (  _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase=True			)  ->					List[str]:
				lowerCamelCase			=ViTConfig()
				# patch_size
				if model_name[-1] == "8":
								lowerCamelCase			=8
				# set labels if required
				if not base_model:
								lowerCamelCase			=10_00
								lowerCamelCase			="""huggingface/label-files"""
								lowerCamelCase			="""imagenet-1k-id2label.json"""
								lowerCamelCase			=json.load(open(hf_hub_download(_UpperCAmelCase     , _UpperCAmelCase     , repo_type="""dataset"""			)     , """r"""			)			)
								lowerCamelCase			={int(_UpperCAmelCase			): v for k, v in idalabel.items()}
								lowerCamelCase			=idalabel
								lowerCamelCase			={v: k for k, v in idalabel.items()}
				# size of the architecture
				if model_name in ["dino_vits8", "dino_vits16"]:
								lowerCamelCase			=3_84
								lowerCamelCase			=15_36
								lowerCamelCase			=12
								lowerCamelCase			=6

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

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

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

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

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

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


if __name__ == "__main__":
							UpperCAmelCase__  :     Dict   			=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__  :     Tuple   			=parser.parse_args()
							convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)












from .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
from .squad import SquadExample, SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
from .utils import DataProcessor, InputExample, InputFeatures, SingleSentenceClassificationProcessor
from .xnli import xnli_output_modes, xnli_processors, xnli_tasks_num_labels





