


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__			 =						logging.get_logger(__name__)


def 				lowerCamelCase__	(      a	:   Union[str, Any]	,  a	:   Union[str, Any]=False				)    ->	Optional[int]:





			"""simple docstring"""
			a__ :Any						  =    []
			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__ :Union[str, Any]						  =    [(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__	(      a	:   Dict	,  a	:   Dict	,  a	:   Tuple=False				)    ->	Union[str, Any]:





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


def 				lowerCamelCase__	(      a	:   List[str]				)    ->	Any:





			"""simple docstring"""
			a__ :Dict						  =    ["head.weight", "head.bias"]
			for k in ignore_keys:
						state_dict.pop(a	,  a				)


def 				lowerCamelCase__	(      a	:   List[Any]	,  a	:   List[str]	,  a	:   Dict				)    ->	Optional[int]:





			"""simple docstring"""
			a__ :Tuple						  =    dct.pop(a				)
			a__ :Any						  =    val


def 				lowerCamelCase__	(      )    ->	Union[str, Any]:





			"""simple docstring"""
			a__ :Union[str, Any]						  =    "http://images.cocodataset.org/val2017/000000039769.jpg"
			a__ :Optional[int]						  =    Image.open(requests.get(a	,  stream=a				).raw				)
			return im


@torch.no_grad()
def 				lowerCamelCase__	(      a	:   List[Any]	,  a	:   List[Any]				)    ->	List[str]:





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

    # load original model from timm
			a__ :str						  =    timm.create_model(a	,  pretrained=a				)
			timm_model.eval()

			# load state_dict of original model, remove and rename some keys
			a__ :Tuple						  =    timm_model.state_dict()
			if base_model:
						remove_classification_head_(a				)
			a__ :List[str]						  =    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__ :Optional[int]						  =    ViTModel(a				).eval()
			else:
						a__ :Any						  =    ViTForImageClassification(a				).eval()
			model.load_state_dict(a				)

			# Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
			if "deit" in vit_name:
						a__ :List[str]						  =    DeiTImageProcessor(size=config.image_size				)
			else:
						a__ :Union[str, Any]						  =    ViTImageProcessor(size=config.image_size				)
			a__ :Optional[int]						  =    image_processor(images=prepare_img()	,  return_tensors="pt"				)
			a__ :Any						  =    encoding["pixel_values"]
			a__ :Union[str, Any]						  =    model(a				)

			if base_model:
						a__ :int						  =    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__ :str						  =    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__			 =						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__			 =						parser.parse_args()
		convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)






# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse

from ...utils.dataclasses import (
    ComputeEnvironment,
    DistributedType,
    DynamoBackend,
    PrecisionType,
    SageMakerDistributedType,
)
from ..menu import BulletMenu


snake_case__			 =						[
    '''EAGER''',
    '''AOT_EAGER''',
    '''INDUCTOR''',
    '''NVFUSER''',
    '''AOT_NVFUSER''',
    '''AOT_CUDAGRAPHS''',
    '''OFI''',
    '''FX2TRT''',
    '''ONNXRT''',
    '''IPEX''',
]


def 				lowerCamelCase__	(      a	:   Optional[Any]	,  a	:   Tuple=None	,  a	:   int=None	,  a	:   List[str]=None				)    ->	int:





			"""simple docstring"""
			a__ :Tuple						  =    True
			while ask_again:
						a__ :str						  =    input(a				)
						try:
									if default is not None and len(a				) == 0:
												return default
									return convert_value(a				) if convert_value is not None else result
						except Exception:
									if error_message is not None:
												print(a				)


def 				lowerCamelCase__	(      a	:   int	,  a	:   Union[str, Any]=[]	,  a	:   str=None	,  a	:   str=0				)    ->	List[str]:





			"""simple docstring"""
			a__ :str						  =    BulletMenu(a	,  a				)
			a__ :int						  =    menu.run(default_choice=a				)
			return convert_value(a				) if convert_value is not None else result


def 				lowerCamelCase__	(      a	:   Optional[Any]				)    ->	Dict:





			"""simple docstring"""
			a__ :str						  =    int(a				)
			return ComputeEnvironment(["LOCAL_MACHINE", "AMAZON_SAGEMAKER"][value]				)


def 				lowerCamelCase__	(      a	:   Tuple				)    ->	List[str]:





			"""simple docstring"""
			a__ :Union[str, Any]						  =    int(a				)
			return DistributedType(["NO", "MULTI_CPU", "MULTI_XPU", "MULTI_GPU", "MULTI_NPU", "TPU"][value]				)


def 				lowerCamelCase__	(      a	:   List[Any]				)    ->	str:





			"""simple docstring"""
			a__ :int						  =    int(a				)
			return DynamoBackend(DYNAMO_BACKENDS[value]				).value


def 				lowerCamelCase__	(      a	:   Any				)    ->	Union[str, Any]:





			"""simple docstring"""
			a__ :Optional[Any]						  =    int(a				)
			return PrecisionType(["no", "fp16", "bf16", "fp8"][value]				)


def 				lowerCamelCase__	(      a	:   List[Any]				)    ->	Union[str, Any]:





			"""simple docstring"""
			a__ :Optional[Any]						  =    int(a				)
			return SageMakerDistributedType(["NO", "DATA_PARALLEL", "MODEL_PARALLEL"][value]				)


def 				lowerCamelCase__	(      a	:   Union[str, Any]				)    ->	List[str]:





			"""simple docstring"""
			return {"yes": True, "no": False}[value.lower()]







class 					lowerCAmelCase_ (       argparse.RawDescriptionHelpFormatter):




						def  _snake_case					(	self						:   Tuple   ,     __A						:   Tuple   ,     __A						:   int   ,     __A						:   Tuple   ,     __A						:   Any						)      ->Union[str, Any]:






									"""simple docstring"""






									a__ :Optional[int]						  =    super()._format_usage(__A   ,     __A   ,     __A   ,     __A						)
									a__ :Any						  =    usage.replace("<command> [<args>] "   ,     ""						)
									return usage


