
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()
__lowerCAmelCase					   =       logging.get_logger(__name__)


def    a      ( a		,						a=False	)     ->List[str]:




			'''simple docstring'''


			SCREAMING_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'''),
						        ('''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"
						SCREAMING_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    a      ( a		,						a		,						a=False	)     ->Optional[Any]:




			'''simple docstring'''


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


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




			'''simple docstring'''


			SCREAMING_SNAKE_CASE     						= ['''head.weight''', '''head.bias''']
			for k in ignore_keys:
						state_dict.pop(a		,						a	)


def    a      ( a		,						a		,						a	)     ->int:




			'''simple docstring'''


			SCREAMING_SNAKE_CASE     						= dct.pop(a	)
			SCREAMING_SNAKE_CASE     						= val


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




			'''simple docstring'''


			SCREAMING_SNAKE_CASE     						= '''http://images.cocodataset.org/val2017/000000039769.jpg'''
			SCREAMING_SNAKE_CASE     						= Image.open(requests.get(a		,						stream=a	).raw	)
			return im


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




			'''simple docstring'''


			SCREAMING_SNAKE_CASE     						= ViTConfig()
			SCREAMING_SNAKE_CASE     						= False
			# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
			if vit_name[-5:] == "in21k":
						SCREAMING_SNAKE_CASE     						= True
						SCREAMING_SNAKE_CASE     						= int(vit_name[-12:-10]	)
						SCREAMING_SNAKE_CASE     						= int(vit_name[-9:-6]	)
			else:
						SCREAMING_SNAKE_CASE     						= 1000
						SCREAMING_SNAKE_CASE     						= '''huggingface/label-files'''
						SCREAMING_SNAKE_CASE     						= '''imagenet-1k-id2label.json'''
						SCREAMING_SNAKE_CASE     						= json.load(open(hf_hub_download(a		,						a		,						repo_type='''dataset'''	)		,						'''r'''	)	)
						SCREAMING_SNAKE_CASE     						= {int(a	): v for k, v in idalabel.items()}
						SCREAMING_SNAKE_CASE     						= idalabel
						SCREAMING_SNAKE_CASE     						= {v: k for k, v in idalabel.items()}
						SCREAMING_SNAKE_CASE     						= int(vit_name[-6:-4]	)
						SCREAMING_SNAKE_CASE     						= int(vit_name[-3:]	)
			# size of the architecture
			if "deit" in vit_name:
						if vit_name[9:].startswith('''tiny'''	):
									SCREAMING_SNAKE_CASE     						= 192
									SCREAMING_SNAKE_CASE     						= 768
									SCREAMING_SNAKE_CASE     						= 12
									SCREAMING_SNAKE_CASE     						= 3
						elif vit_name[9:].startswith('''small'''	):
									SCREAMING_SNAKE_CASE     						= 384
									SCREAMING_SNAKE_CASE     						= 1536
									SCREAMING_SNAKE_CASE     						= 12
									SCREAMING_SNAKE_CASE     						= 6
						else:
									pass
			else:
						if vit_name[4:].startswith('''small'''	):
									SCREAMING_SNAKE_CASE     						= 768
									SCREAMING_SNAKE_CASE     						= 2304
									SCREAMING_SNAKE_CASE     						= 8
									SCREAMING_SNAKE_CASE     						= 8
						elif vit_name[4:].startswith('''base'''	):
									pass
						elif vit_name[4:].startswith('''large'''	):
									SCREAMING_SNAKE_CASE     						= 1024
									SCREAMING_SNAKE_CASE     						= 4096
									SCREAMING_SNAKE_CASE     						= 24
									SCREAMING_SNAKE_CASE     						= 16
						elif vit_name[4:].startswith('''huge'''	):
									SCREAMING_SNAKE_CASE     						= 1280
									SCREAMING_SNAKE_CASE     						= 5120
									SCREAMING_SNAKE_CASE     						= 32
									SCREAMING_SNAKE_CASE     						= 16

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

			# load state_dict of original model, remove and rename some keys
			SCREAMING_SNAKE_CASE     						= timm_model.state_dict()
			if base_model:
						remove_classification_head_(a	)
			SCREAMING_SNAKE_CASE     						= 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":
						SCREAMING_SNAKE_CASE     						= ViTModel(a	).eval()
			else:
						SCREAMING_SNAKE_CASE     						= ViTForImageClassification(a	).eval()
			model.load_state_dict(a	)

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

			if base_model:
						SCREAMING_SNAKE_CASE     						= 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:
						SCREAMING_SNAKE_CASE     						= 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__":
							__lowerCAmelCase					   =       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.'
							)

							__lowerCAmelCase					   =       parser.parse_args()
							convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__lowerCAmelCase					   =       {
    'configuration_bridgetower': [
        'BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'BridgeTowerConfig',
        'BridgeTowerTextConfig',
        'BridgeTowerVisionConfig',
    ],
    'processing_bridgetower': ['BridgeTowerProcessor'],
}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase					   =       ['BridgeTowerImageProcessor']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase					   =       [
							    'BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'BridgeTowerForContrastiveLearning',
							    'BridgeTowerForImageAndTextRetrieval',
							    'BridgeTowerForMaskedLM',
							    'BridgeTowerModel',
							    'BridgeTowerPreTrainedModel',
							]


if TYPE_CHECKING:
							from .configuration_bridgetower import (
							    BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    BridgeTowerConfig,
							    BridgeTowerTextConfig,
							    BridgeTowerVisionConfig,
							)
							from .processing_bridgetower import BridgeTowerProcessor

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .image_processing_bridgetower import BridgeTowerImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_bridgetower import (
														    BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    BridgeTowerForContrastiveLearning,
														    BridgeTowerForImageAndTextRetrieval,
														    BridgeTowerForMaskedLM,
														    BridgeTowerModel,
														    BridgeTowerPreTrainedModel,
														)


else:
							import sys

							__lowerCAmelCase					   =       _LazyModule(__name__, globals()['__file__'], _import_structure)