
'''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()
lowercase    :							Optional[Any]      =					logging.get_logger(__name__)







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A=False	)       ->	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'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"
		_snake_case   						=		[(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 				SCREAMING_SNAKE_CASE__	(   __A			,				__A			,				__A=False	)       ->	Union[str, Any]:
	for i in range(config.num_hidden_layers	):
		if base_model:
			_snake_case   						=		''
		else:
			_snake_case   						=		'vit.'
		# 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 				SCREAMING_SNAKE_CASE__	(   __A	)       ->	List[str]:
	_snake_case   						=		['head.weight', 'head.bias']
	for k in ignore_keys:
		state_dict.pop(__A			,				__A	)







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A			,				__A	)       ->	Optional[int]:
	_snake_case   						=		dct.pop(__A	)
	_snake_case   						=		val







def 				SCREAMING_SNAKE_CASE__	(   )       ->	Any:
	_snake_case   						=		'http://images.cocodataset.org/val2017/000000039769.jpg'
	_snake_case   						=		Image.open(requests.get(__A			,				stream=__A	).raw	)
	return im







@torch.no_grad()
def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A			,				__A=True	)       ->	Optional[int]:
	_snake_case   						=		ViTConfig()
	# patch_size
	if model_name[-1] == "8":
		_snake_case   						=		8
	# set labels if required
	if not base_model:
		_snake_case   						=		1_000
		_snake_case   						=		'huggingface/label-files'
		_snake_case   						=		'imagenet-1k-id2label.json'
		_snake_case   						=		json.load(open(hf_hub_download(__A			,				__A			,				repo_type='dataset'	)			,				'r'	)	)
		_snake_case   						=		{int(__A	): v for k, v in idalabel.items()}
		_snake_case   						=		idalabel
		_snake_case   						=		{v: k for k, v in idalabel.items()}
	# size of the architecture
	if model_name in ["dino_vits8", "dino_vits16"]:
		_snake_case   						=		384
		_snake_case   						=		1_536
		_snake_case   						=		12
		_snake_case   						=		6

	# load original model from torch hub
	_snake_case   						=		torch.hub.load('facebookresearch/dino:main'			,				__A	)
	original_model.eval()

	# load state_dict of original model, remove and rename some keys
	_snake_case   						=		original_model.state_dict()
	if base_model:
		remove_classification_head_(__A	)
	_snake_case   						=		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:
		_snake_case   						=		ViTModel(__A			,				add_pooling_layer=__A	).eval()
	else:
		_snake_case   						=		ViTForImageClassification(__A	).eval()
	model.load_state_dict(__A	)

	# Check outputs on an image, prepared by ViTImageProcessor
	_snake_case   						=		ViTImageProcessor()
	_snake_case   						=		image_processor(images=prepare_img()			,				return_tensors='pt'	)
	_snake_case   						=		encoding['pixel_values']
	_snake_case   						=		model(__A	)

	if base_model:
		_snake_case   						=		original_model(__A	)
		assert torch.allclose(__A			,				outputs.last_hidden_state[:, 0, :]			,				atol=1e-1	)
	else:
		_snake_case   						=		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__":
					lowercase    :							List[str]      =					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)
					lowercase    :							Union[str, Any]      =					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


lowercase    :							Tuple      =					{
    "configuration_xlm": ["XLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMConfig", "XLMOnnxConfig"],
    "tokenization_xlm": ["XLMTokenizer"],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase    :							int      =					[
					    "XLM_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "XLMForMultipleChoice",
					    "XLMForQuestionAnswering",
					    "XLMForQuestionAnsweringSimple",
					    "XLMForSequenceClassification",
					    "XLMForTokenClassification",
					    "XLMModel",
					    "XLMPreTrainedModel",
					    "XLMWithLMHeadModel",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase    :							Tuple      =					[
					    "TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "TFXLMForMultipleChoice",
					    "TFXLMForQuestionAnsweringSimple",
					    "TFXLMForSequenceClassification",
					    "TFXLMForTokenClassification",
					    "TFXLMMainLayer",
					    "TFXLMModel",
					    "TFXLMPreTrainedModel",
					    "TFXLMWithLMHeadModel",
					]


if TYPE_CHECKING:
					from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
					from .tokenization_xlm import XLMTokenizer

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_xlm import (
										    XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
										    XLMForMultipleChoice,
										    XLMForQuestionAnswering,
										    XLMForQuestionAnsweringSimple,
										    XLMForSequenceClassification,
										    XLMForTokenClassification,
										    XLMModel,
										    XLMPreTrainedModel,
										    XLMWithLMHeadModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_xlm import (
										    TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFXLMForMultipleChoice,
										    TFXLMForQuestionAnsweringSimple,
										    TFXLMForSequenceClassification,
										    TFXLMForTokenClassification,
										    TFXLMMainLayer,
										    TFXLMModel,
										    TFXLMPreTrainedModel,
										    TFXLMWithLMHeadModel,
										)

else:
					import sys

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

