



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()
A__	      =       logging.get_logger(__name__)


def 		_UpperCAmelCase				(      snake_case	,						snake_case=False			):





					"""simple docstring"""

					_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 		_UpperCAmelCase				(      snake_case	,						snake_case	,						snake_case=False			):





					"""simple docstring"""

					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 		_UpperCAmelCase				(      snake_case			):





					"""simple docstring"""

					_lowerCAmelCase						=    ["""head.weight""", """head.bias"""]
					for k in ignore_keys:
										state_dict.pop(snake_case	,						snake_case			)


def 		_UpperCAmelCase				(      snake_case	,						snake_case	,						snake_case			):





					"""simple docstring"""

					_lowerCAmelCase						=    dct.pop(snake_case			)
					_lowerCAmelCase						=    val


def 		_UpperCAmelCase				(      ):





					"""simple docstring"""

					_lowerCAmelCase						=    """http://images.cocodataset.org/val2017/000000039769.jpg"""
					_lowerCAmelCase						=    Image.open(requests.get(snake_case	,						stream=snake_case			).raw			)
					return im


@torch.no_grad()
def 		_UpperCAmelCase				(      snake_case	,						snake_case	,						snake_case=True			):





					"""simple docstring"""

					_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(snake_case	,						snake_case	,						repo_type="""dataset"""			)	,						"""r"""			)			)
										_lowerCAmelCase						=    {int(snake_case			): 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"""	,						snake_case			)
					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_(snake_case			)
					_lowerCAmelCase						=    create_rename_keys(snake_case	,						base_model=snake_case			)
					for src, dest in rename_keys:
										rename_key(snake_case	,						snake_case	,						snake_case			)
					read_in_q_k_v(snake_case	,						snake_case	,						snake_case			)

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

					# 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(snake_case			)

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

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


if __name__ == "__main__":
	A__	      =       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)
	A__	      =       parser.parse_args()
	convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase							:  Optional[int]   	=						{"""configuration_wavlm""": ["""WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """WavLMConfig"""]}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase							:  Any   	=						[
						    """WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """WavLMForAudioFrameClassification""",
						    """WavLMForCTC""",
						    """WavLMForSequenceClassification""",
						    """WavLMForXVector""",
						    """WavLMModel""",
						    """WavLMPreTrainedModel""",
						]

if TYPE_CHECKING:
						from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_wavlm import (
												    WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
												    WavLMForAudioFrameClassification,
												    WavLMForCTC,
												    WavLMForSequenceClassification,
												    WavLMForXVector,
												    WavLMModel,
												    WavLMPreTrainedModel,
												)

else:
						import sys

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






