


"""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()
lowercase_							=							logging.get_logger(__name__)
def      UpperCAmelCase     (       _lowercase :   int			,   _lowercase :   List[str]=False	)						-> List[Any]:


	"""simple docstring"""




	lowerCAmelCase_      							=			[]

	# 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"
		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'''),
		    ]	)
	# fmt: on

	return rename_keys
def      UpperCAmelCase     (       _lowercase :   str			,   _lowercase :   str			,   _lowercase :   Union[str, Any]=False	)						-> Dict:


	"""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     (       _lowercase :   Optional[Any]	)						-> Any:


	"""simple docstring"""




	lowerCAmelCase_      							=			['head.weight', 'head.bias']
	for k in ignore_keys:
		state_dict.pop(__UpperCAmelCase			,   __UpperCAmelCase	)
def      UpperCAmelCase     (       _lowercase :   Any			,   _lowercase :   List[Any]			,   _lowercase :   List[Any]	)						-> Any:


	"""simple docstring"""




	lowerCAmelCase_      							=			dct.pop(__UpperCAmelCase	)
	lowerCAmelCase_      							=			val
def      UpperCAmelCase     (       )						-> Tuple:


	"""simple docstring"""




	lowerCAmelCase_      							=			'http://images.cocodataset.org/val2017/000000039769.jpg'
	lowerCAmelCase_      							=			Image.open(requests.get(__UpperCAmelCase			,   stream=__UpperCAmelCase	).raw	)
	return im
@torch.no_grad()
def      UpperCAmelCase     (       _lowercase :   Optional[Any]			,   _lowercase :   Optional[Any]			,   _lowercase :   Tuple=False	)						-> Optional[Any]:


	"""simple docstring"""




	lowerCAmelCase_      							=			BitConfig(
	    global_padding='''same'''			,   layer_type='''bottleneck'''			,   depths=(3, 4, 9)			,   out_features=['''stage3''']			,   embedding_dynamic_padding=__UpperCAmelCase			,   )
	lowerCAmelCase_      							=			ViTHybridConfig(backbone_config=__UpperCAmelCase			,   image_size=3_8_4			,   num_labels=1_0_0_0	)
	lowerCAmelCase_      							=			False

	# load original model from timm
	lowerCAmelCase_      							=			timm.create_model(__UpperCAmelCase			,   pretrained=__UpperCAmelCase	)
	timm_model.eval()

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

	lowerCAmelCase_      							=			'huggingface/label-files'
	lowerCAmelCase_      							=			'imagenet-1k-id2label.json'
	lowerCAmelCase_      							=			json.load(open(hf_hub_download(__UpperCAmelCase			,   __UpperCAmelCase			,   repo_type='''dataset'''	)			,   '''r'''	)	)
	lowerCAmelCase_      							=			{int(__UpperCAmelCase	): v for k, v in idalabel.items()}
	lowerCAmelCase_      							=			idalabel
	lowerCAmelCase_      							=			{v: k for k, v in idalabel.items()}

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

	# create image processor
	lowerCAmelCase_      							=			create_transform(**resolve_data_config({}			,   model=__UpperCAmelCase	)	)
	lowerCAmelCase_      							=			transform.transforms

	lowerCAmelCase_      							=			{
	    'bilinear': PILImageResampling.BILINEAR,
	    'bicubic': PILImageResampling.BICUBIC,
	    'nearest': PILImageResampling.NEAREST,
	}

	lowerCAmelCase_      							=			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()			,   )

	lowerCAmelCase_      							=			prepare_img()
	lowerCAmelCase_      							=			transform(__UpperCAmelCase	).unsqueeze(0	)
	lowerCAmelCase_      							=			processor(__UpperCAmelCase			,   return_tensors='''pt'''	).pixel_values

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

	# verify logits
	with torch.no_grad():
		lowerCAmelCase_      							=			model(__UpperCAmelCase	)
		lowerCAmelCase_      							=			outputs.logits

	print('''Predicted class:'''			,   logits.argmax(-1	).item()	)
	if base_model:
		lowerCAmelCase_      							=			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:
		lowerCAmelCase_      							=			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__":
		lowercase_							=							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.'
		)

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




'''simple docstring'''

import argparse

from torch import nn

# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
    ProphetNetForConditionalGeneration as ProphetNetForConditionalGenerationOld,
)
from transformers_old.modeling_xlm_prophetnet import (
    XLMProphetNetForConditionalGeneration as XLMProphetNetForConditionalGenerationOld,
)

from transformers import ProphetNetForConditionalGeneration, XLMProphetNetForConditionalGeneration, logging


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







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase					)				->					Dict:





			"""simple docstring"""







			if "xprophetnet" in prophetnet_checkpoint_path:
						snake_case:    Optional[Any]      							=XLMProphetNetForConditionalGenerationOld.from_pretrained(__UpperCAmelCase					)
						snake_case							,  snake_case:    str      							=XLMProphetNetForConditionalGeneration.from_pretrained(
						    __UpperCAmelCase				,							output_loading_info=__UpperCAmelCase					)
			else:
						snake_case:    Optional[int]      							=ProphetNetForConditionalGenerationOld.from_pretrained(__UpperCAmelCase					)
						snake_case							,  snake_case:    List[str]      							=ProphetNetForConditionalGeneration.from_pretrained(
						    __UpperCAmelCase				,							output_loading_info=__UpperCAmelCase					)

			snake_case:    str      							=['key_proj', 'value_proj', 'query_proj']

			snake_case:    str      							={
			    'self_attn': 'ngram_self_attn',
			    'cross_attn': 'encoder_attn',
			    'cross_attn_layer_norm': 'encoder_attn_layer_norm',
			    'feed_forward_layer_norm': 'final_layer_norm',
			    'feed_forward': '',
			    'intermediate': 'fc1',
			    'output': 'fc2',
			    'key_proj': 'k_proj',
			    'query_proj': 'q_proj',
			    'value_proj': 'v_proj',
			    'word_embeddings': 'embed_tokens',
			    'embeddings_layer_norm': 'emb_layer_norm',
			    'relative_pos_embeddings': 'relative_linear',
			    'ngram_embeddings': 'ngram_input_embed',
			    'position_embeddings': 'embed_positions',
			}

			for key in loading_info["missing_keys"]:
						snake_case:    List[str]      							=key.split('.'					)

						if attributes[0] == "lm_head":
									snake_case:    Dict      							=prophet
									snake_case:    List[str]      							=prophet_old
						else:
									snake_case:    Any      							=prophet.prophetnet
									snake_case:    int      							=prophet_old.model

						snake_case:    int      							=False
						for attribute in attributes:
									if attribute in mapping:
												snake_case:    Dict      							=mapping[attribute]
												if not hasattr(__UpperCAmelCase				,							__UpperCAmelCase					) and len(__UpperCAmelCase					) > 0:
															snake_case:    Optional[int]      							=attribute
									elif hasattr(__UpperCAmelCase				,							__UpperCAmelCase					):
												snake_case:    Optional[Any]      							=attribute

									if attribute == "weight":
												assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
												snake_case:    Optional[Any]      							=old_model.weight
												logger.info(f'''{attribute} is initialized.'''					)
												snake_case:    List[str]      							=True
												break
									elif attribute == "bias":
												assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
												snake_case:    List[Any]      							=old_model.bias
												logger.info(f'''{attribute} is initialized'''					)
												snake_case:    List[Any]      							=True
												break
									elif attribute in special_keys and hasattr(__UpperCAmelCase				,							'in_proj_weight'					):
												snake_case:    Optional[int]      							=old_model.in_proj_weight.shape[0] // 3
												snake_case:    List[str]      							=getattr(__UpperCAmelCase				,							__UpperCAmelCase					)
												param.weight.shape == old_model.in_proj_weight[:embed_dim, :].shape, "Shapes have to match"
												param.bias.shape == old_model.in_proj_bias[:embed_dim].shape, "Shapes have to match"
												if attribute == "query_proj":
															snake_case:    int      							=nn.Parameter(old_model.in_proj_weight[:embed_dim, :]					)
															snake_case:    Any      							=nn.Parameter(old_model.in_proj_bias[:embed_dim]					)

												elif attribute == "key_proj":
															snake_case:    Union[str, Any]      							=nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]					)
															snake_case:    List[str]      							=nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]					)
												elif attribute == "value_proj":
															snake_case:    List[str]      							=nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]					)
															snake_case:    Union[str, Any]      							=nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]					)
												snake_case:    Any      							=True
												break
									elif attribute == "position_embeddings":
												assert (
												    model.position_embeddings.weight.shape[-1] == old_model.embed_positions.weight.shape[-1]
												), "Hidden size has to match"
												assert model.position_embeddings.weight.shape[0] == 5_12, "We want 512 position_embeddings."
												snake_case:    Dict      							=nn.Parameter(old_model.embed_positions.weight[:5_12, :]					)
												snake_case:    Dict      							=True
												break

									if attribute.isdigit():
												snake_case:    int      							=model[int(__UpperCAmelCase					)]
												snake_case:    Optional[int]      							=old_model[int(__UpperCAmelCase					)]
									else:
												snake_case:    List[str]      							=getattr(__UpperCAmelCase				,							__UpperCAmelCase					)

												if old_attribute == "":
															snake_case:    Union[str, Any]      							=old_model
												else:
															if not hasattr(__UpperCAmelCase				,							__UpperCAmelCase					):
																		raise ValueError(f'''{old_model} does not have {old_attribute}'''					)
															snake_case:    List[Any]      							=getattr(__UpperCAmelCase				,							__UpperCAmelCase					)

						if not is_key_init:
									raise ValueError(f'''{key} was not correctly initialized!'''					)

			print(f'''Saving model to {pytorch_dump_folder_path}'''					)
			prophet.save_pretrained(__UpperCAmelCase					)


if __name__ == "__main__":
	a							   =				argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '--prophetnet_checkpoint_path', default=None, type=str, required=True, help='Path the official PyTorch dump.'
	)
	parser.add_argument(
	    '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
	)
	a							   =				parser.parse_args()
	convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)




