

'''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()
__snake_case					:      Dict 		=     logging.get_logger(__name__)
def 		__lowerCamelCase			(  __snake_case	:						Optional[Any],						__snake_case	:						Optional[Any]=False			)			->				Optional[int]:






						"""simple docstring"""





						A__	:  Tuple           =[]
						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"
												A__	:  Tuple           =[(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 		__lowerCamelCase			(  __snake_case	:						Any,						__snake_case	:						Union[str, Any],						__snake_case	:						Optional[Any]=False			)			->				List[str]:






						"""simple docstring"""





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






						"""simple docstring"""





						A__	:  List[Any]           =['head.weight', 'head.bias']
						for k in ignore_keys:
												state_dict.pop(__a,						__a			)
def 		__lowerCamelCase			(  __snake_case	:						List[str],						__snake_case	:						Any,						__snake_case	:						Union[str, Any]			)			->				Any:






						"""simple docstring"""





						A__	:  int           =dct.pop(__a			)
						A__	:  Tuple           =val
def 		__lowerCamelCase			(  )			->				Optional[Any]:






						"""simple docstring"""





						A__	:  str           ='http://images.cocodataset.org/val2017/000000039769.jpg'
						A__	:  Tuple           =Image.open(requests.get(__a,						stream=__a			).raw			)
						return im
@torch.no_grad()
def 		__lowerCamelCase			(  __snake_case	:						Optional[Any],						__snake_case	:						List[str]			)			->				Any:






						"""simple docstring"""





						A__	:  Tuple           =ViTConfig()
						A__	:  List[str]           =False
						# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
						if vit_name[-5:] == "in21k":
												A__	:  str           =True
												A__	:  Union[str, Any]           =int(vit_name[-12:-10]			)
												A__	:  Dict           =int(vit_name[-9:-6]			)
						else:
												A__	:  Dict           =1_000
												A__	:  Optional[Any]           ='huggingface/label-files'
												A__	:  List[Any]           ='imagenet-1k-id2label.json'
												A__	:  Union[str, Any]           =json.load(open(hf_hub_download(__a,						__a,						repo_type="""dataset"""			),						"""r"""			)			)
												A__	:  Optional[int]           ={int(__a			): v for k, v in idalabel.items()}
												A__	:  Optional[Any]           =idalabel
												A__	:  Union[str, Any]           ={v: k for k, v in idalabel.items()}
												A__	:  Tuple           =int(vit_name[-6:-4]			)
												A__	:  Union[str, Any]           =int(vit_name[-3:]			)
						# size of the architecture
						if "deit" in vit_name:
												if vit_name[9:].startswith("""tiny"""			):
																		A__	:  str           =192
																		A__	:  Optional[Any]           =768
																		A__	:  List[str]           =12
																		A__	:  int           =3
												elif vit_name[9:].startswith("""small"""			):
																		A__	:  List[Any]           =384
																		A__	:  List[Any]           =1_536
																		A__	:  Union[str, Any]           =12
																		A__	:  List[Any]           =6
												else:
																		pass
						else:
												if vit_name[4:].startswith("""small"""			):
																		A__	:  List[str]           =768
																		A__	:  Tuple           =2_304
																		A__	:  List[Any]           =8
																		A__	:  Optional[Any]           =8
												elif vit_name[4:].startswith("""base"""			):
																		pass
												elif vit_name[4:].startswith("""large"""			):
																		A__	:  int           =1_024
																		A__	:  str           =4_096
																		A__	:  Any           =24
																		A__	:  Optional[Any]           =16
												elif vit_name[4:].startswith("""huge"""			):
																		A__	:  Optional[int]           =1_280
																		A__	:  Any           =5_120
																		A__	:  Optional[Any]           =32
																		A__	:  Any           =16

    # load original model from timm
						A__	:  int           =timm.create_model(__a,						pretrained=__a			)
						timm_model.eval()

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

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

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

						if base_model:
												A__	:  str           =timm_model.forward_features(__a			)
												assert timm_pooled_output.shape == outputs.pooler_output.shape
												assert torch.allclose(__a,						outputs.pooler_output,						atol=1E-3			)
						else:
												A__	:  Optional[int]           =timm_model(__a			)
												assert timm_logits.shape == outputs.logits.shape
												assert torch.allclose(__a,						outputs.logits,						atol=1E-3			)

						Path(__a			).mkdir(exist_ok=__a			)
						print(f"Saving model {vit_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__":
		__snake_case					:      int 		=     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.'
		)

		__snake_case					:      List[str] 		=     parser.parse_args()
		convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)






"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


a_							=    {"""configuration_opt""": ["""OPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """OPTConfig"""]}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a_							=    [
	    """OPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """OPTForCausalLM""",
	    """OPTModel""",
	    """OPTPreTrainedModel""",
	    """OPTForSequenceClassification""",
	    """OPTForQuestionAnswering""",
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a_							=    ["""TFOPTForCausalLM""", """TFOPTModel""", """TFOPTPreTrainedModel"""]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a_							=    [
	    """FlaxOPTForCausalLM""",
	    """FlaxOPTModel""",
	    """FlaxOPTPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_opt import OPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OPTConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_opt import (
		    OPT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    OPTForCausalLM,
		    OPTForQuestionAnswering,
		    OPTForSequenceClassification,
		    OPTModel,
		    OPTPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_opt import TFOPTForCausalLM, TFOPTModel, TFOPTPreTrainedModel

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_opt import FlaxOPTForCausalLM, FlaxOPTModel, FlaxOPTPreTrainedModel

else:
	import sys

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





