





'''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 timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import (
    BitConfig,
    ViTHybridConfig,
    ViTHybridForImageClassification,
    ViTHybridImageProcessor,
    ViTHybridModel,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
__UpperCamelCase					    =	logging.get_logger(__name__)







def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase=False ) ->				str:



			"""simple docstring"""


			__snake_case					:      Any							      =     []

			# fmt: off
			# stem:
			rename_keys.append(("""cls_token""", """vit.embeddings.cls_token""") )
			rename_keys.append(("""pos_embed""", """vit.embeddings.position_embeddings""") )

			rename_keys.append(("""patch_embed.proj.weight""", """vit.embeddings.patch_embeddings.projection.weight""") )
			rename_keys.append(("""patch_embed.proj.bias""", """vit.embeddings.patch_embeddings.projection.bias""") )

			# backbone
			rename_keys.append(("""patch_embed.backbone.stem.conv.weight""", """vit.embeddings.patch_embeddings.backbone.bit.embedder.convolution.weight""") )
			rename_keys.append(("""patch_embed.backbone.stem.norm.weight""", """vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.weight""") )
			rename_keys.append(("""patch_embed.backbone.stem.norm.bias""", """vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.bias""") )

			for stage_idx in range(len(config.backbone_config.depths ) ):
						for layer_idx in range(config.backbone_config.depths[stage_idx] ):
									rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv1.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv1.weight''') )
									rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.weight''') )
									rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.bias''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.bias''') )
									rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv2.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv2.weight''') )
									rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.weight''') )
									rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.bias''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.bias''') )
									rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv3.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv3.weight''') )
									rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.weight''') )
									rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.bias''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.bias''') )

						rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.conv.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.conv.weight''') )
						rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.weight''') )
						rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.bias''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.bias''') )

			# transformer encoder
			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''') )

			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"
						__snake_case					:      int							      =     [(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"""),
						    ] )
			# fmt: on

			return rename_keys







def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase=False ) ->				Optional[int]:



			"""simple docstring"""


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







def 					_a						(       _lowerCamelCase ) ->				int:



			"""simple docstring"""


			__snake_case					:      List[Any]							      =     ["""head.weight""", """head.bias"""]
			for k in ignore_keys:
						state_dict.pop(UpperCAmelCase__    ,	UpperCAmelCase__ )







def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase ) ->				List[str]:



			"""simple docstring"""


			__snake_case					:      Tuple							      =     dct.pop(UpperCAmelCase__ )
			__snake_case					:      Any							      =     val







def 					_a						(       ) ->				Optional[Any]:



			"""simple docstring"""


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







@torch.no_grad()
def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase=False ) ->				Tuple:



			"""simple docstring"""


			__snake_case					:      Dict							      =     BitConfig(
			    global_padding="""same"""    ,	layer_type="""bottleneck"""    ,	depths=(3, 4, 9)    ,	out_features=["""stage3"""]    ,	embedding_dynamic_padding=UpperCAmelCase__    ,	)
			__snake_case					:      Optional[Any]							      =     ViTHybridConfig(backbone_config=UpperCAmelCase__    ,	image_size=384    ,	num_labels=1000 )
			__snake_case					:      Optional[int]							      =     False

			# load original model from timm
			__snake_case					:      Tuple							      =     timm.create_model(UpperCAmelCase__    ,	pretrained=UpperCAmelCase__ )
			timm_model.eval()

			# load state_dict of original model, remove and rename some keys
			__snake_case					:      str							      =     timm_model.state_dict()
			if base_model:
						remove_classification_head_(UpperCAmelCase__ )
			__snake_case					:      str							      =     create_rename_keys(UpperCAmelCase__    ,	UpperCAmelCase__ )
			for src, dest in rename_keys:
						rename_key(UpperCAmelCase__    ,	UpperCAmelCase__    ,	UpperCAmelCase__ )
			read_in_q_k_v(UpperCAmelCase__    ,	UpperCAmelCase__    ,	UpperCAmelCase__ )

			__snake_case					:      List[str]							      =     """huggingface/label-files"""
			__snake_case					:      Dict							      =     """imagenet-1k-id2label.json"""
			__snake_case					:      Optional[int]							      =     json.load(open(hf_hub_download(UpperCAmelCase__    ,	UpperCAmelCase__    ,	repo_type="""dataset""" )    ,	"""r""" ) )
			__snake_case					:      Optional[int]							      =     {int(UpperCAmelCase__ ): v for k, v in idalabel.items()}
			__snake_case					:      Any							      =     idalabel
			__snake_case					:      List[str]							      =     {v: k for k, v in idalabel.items()}

			# load HuggingFace model
			if vit_name[-5:] == "in21k":
						__snake_case					:      Tuple							      =     ViTHybridModel(UpperCAmelCase__ ).eval()
			else:
						__snake_case					:      Tuple							      =     ViTHybridForImageClassification(UpperCAmelCase__ ).eval()
			model.load_state_dict(UpperCAmelCase__ )

			# create image processor
			__snake_case					:      Any							      =     create_transform(**resolve_data_config({}    ,	model=UpperCAmelCase__ ) )
			__snake_case					:      List[Any]							      =     transform.transforms

			__snake_case					:      Union[str, Any]							      =     {
			    """bilinear""": PILImageResampling.BILINEAR,
			    """bicubic""": PILImageResampling.BICUBIC,
			    """nearest""": PILImageResampling.NEAREST,
			}

			__snake_case					:      Tuple							      =     ViTHybridImageProcessor(
			    do_resize=UpperCAmelCase__    ,	size={"""shortest_edge""": timm_transforms[0].size}    ,	resample=pillow_resamplings[timm_transforms[0].interpolation.value]    ,	do_center_crop=UpperCAmelCase__    ,	crop_size={"""height""": timm_transforms[1].size[0], """width""": timm_transforms[1].size[1]}    ,	do_normalize=UpperCAmelCase__    ,	image_mean=timm_transforms[-1].mean.tolist()    ,	image_std=timm_transforms[-1].std.tolist()    ,	)

			__snake_case					:      Tuple							      =     prepare_img()
			__snake_case					:      Any							      =     transform(UpperCAmelCase__ ).unsqueeze(0 )
			__snake_case					:      Union[str, Any]							      =     processor(UpperCAmelCase__    ,	return_tensors="""pt""" ).pixel_values

			# verify pixel values
			assert torch.allclose(UpperCAmelCase__    ,	UpperCAmelCase__ )

			# verify logits
			with torch.no_grad():
						__snake_case					:      Optional[int]							      =     model(UpperCAmelCase__ )
						__snake_case					:      Optional[Any]							      =     outputs.logits

			print("""Predicted class:"""    ,	logits.argmax(-1 ).item() )
			if base_model:
						__snake_case					:      List[Any]							      =     timm_model.forward_features(UpperCAmelCase__ )
						assert timm_pooled_output.shape == outputs.pooler_output.shape
						assert torch.allclose(UpperCAmelCase__    ,	outputs.pooler_output    ,	atol=1E-3 )
			else:
						__snake_case					:      int							      =     timm_model(UpperCAmelCase__ )
						assert timm_logits.shape == outputs.logits.shape
						assert torch.allclose(UpperCAmelCase__    ,	outputs.logits    ,	atol=1E-3 )
			print("""Looks ok!""" )

			if pytorch_dump_folder_path is not None:
						Path(UpperCAmelCase__ ).mkdir(exist_ok=UpperCAmelCase__ )
						print(F'''Saving model {vit_name} to {pytorch_dump_folder_path}''' )
						model.save_pretrained(UpperCAmelCase__ )
						print(F'''Saving processor to {pytorch_dump_folder_path}''' )
						processor.save_pretrained(UpperCAmelCase__ )

			if push_to_hub:
						print(F'''Pushing model and processor to the hub {vit_name}''' )
						model.push_to_hub(F'''ybelkada/{vit_name}''' )
						processor.push_to_hub(F'''ybelkada/{vit_name}''' )


if __name__ == "__main__":
	__UpperCamelCase					    =	argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    "--vit_name",
	    default="vit_base_r50_s16_384",
	    type=str,
	    help="Name of the hybrid 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."
	)
	parser.add_argument(
	    "--push_to_hub", action="store_true", help="Whether to upload the model to the HuggingFace hub."
	)

	__UpperCamelCase					    =	parser.parse_args()
	convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


lowercase   =	{
    '''configuration_layoutlmv2''': ['''LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LayoutLMv2Config'''],
    '''processing_layoutlmv2''': ['''LayoutLMv2Processor'''],
    '''tokenization_layoutlmv2''': ['''LayoutLMv2Tokenizer'''],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase   =	['''LayoutLMv2TokenizerFast''']

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase   =	['''LayoutLMv2FeatureExtractor''']
		lowercase   =	['''LayoutLMv2ImageProcessor''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase   =	[
		    '''LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''LayoutLMv2ForQuestionAnswering''',
		    '''LayoutLMv2ForSequenceClassification''',
		    '''LayoutLMv2ForTokenClassification''',
		    '''LayoutLMv2Layer''',
		    '''LayoutLMv2Model''',
		    '''LayoutLMv2PreTrainedModel''',
		]

if TYPE_CHECKING:
		from .configuration_layoutlmva import LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMvaConfig
		from .processing_layoutlmva import LayoutLMvaProcessor
		from .tokenization_layoutlmva import LayoutLMvaTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor, LayoutLMvaImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_layoutlmva import (
				    LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST,
				    LayoutLMvaForQuestionAnswering,
				    LayoutLMvaForSequenceClassification,
				    LayoutLMvaForTokenClassification,
				    LayoutLMvaLayer,
				    LayoutLMvaModel,
				    LayoutLMvaPreTrainedModel,
				)
else:
		import sys

		lowercase   =	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





