


"""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()
__SCREAMING_SNAKE_CASE  :						Any    							=      logging.get_logger(__name__)



def        lowerCAmelCase_(   lowercase_     :							Tuple						,   lowercase_     :							str=False							)	->       Union[str, Any]:
				_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        lowerCAmelCase_(   lowercase_     :							str						,   lowercase_     :							Tuple						,   lowercase_     :							Tuple=False							)	->       Dict:
				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        lowerCAmelCase_(   lowercase_     :							int							)	->       Tuple:
				_lowerCamelCase        =				["head.weight", "head.bias"]
				for k in ignore_keys:
								state_dict.pop(_lowerCAmelCase						,   _lowerCAmelCase							)



def        lowerCAmelCase_(   lowercase_     :							Tuple						,   lowercase_     :							Tuple						,   lowercase_     :							Tuple							)	->       Any:
				_lowerCamelCase        =				dct.pop(_lowerCAmelCase							)
				_lowerCamelCase        =				val



def        lowerCAmelCase_(   )	->       Any:
				_lowerCamelCase        =				"http://images.cocodataset.org/val2017/000000039769.jpg"
				_lowerCamelCase        =				Image.open(requests.get(_lowerCAmelCase						,   stream=_lowerCAmelCase							).raw							)
				return im



@torch.no_grad()
def        lowerCAmelCase_(   lowercase_     :							List[str]						,   lowercase_     :							List[str]						,   lowercase_     :							Dict=False							)	->       List[str]:
				_lowerCamelCase        =				BitConfig(
				    global_padding='''same'''						,   layer_type='''bottleneck'''						,   depths=(3, 4, 9)						,   out_features=['''stage3''']						,   embedding_dynamic_padding=_lowerCAmelCase						,   )
				_lowerCamelCase        =				ViTHybridConfig(backbone_config=_lowerCAmelCase						,   image_size=3_84						,   num_labels=10_00							)
				_lowerCamelCase        =				False

				# load original model from timm
				_lowerCamelCase        =				timm.create_model(_lowerCAmelCase						,   pretrained=_lowerCAmelCase							)
				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_(_lowerCAmelCase							)
				_lowerCamelCase        =				create_rename_keys(_lowerCAmelCase						,   _lowerCAmelCase							)
				for src, dest in rename_keys:
								rename_key(_lowerCAmelCase						,   _lowerCAmelCase						,   _lowerCAmelCase							)
				read_in_q_k_v(_lowerCAmelCase						,   _lowerCAmelCase						,   _lowerCAmelCase							)

				_lowerCamelCase        =				"huggingface/label-files"
				_lowerCamelCase        =				"imagenet-1k-id2label.json"
				_lowerCamelCase        =				json.load(open(hf_hub_download(_lowerCAmelCase						,   _lowerCAmelCase						,   repo_type='''dataset'''							)						,   '''r'''							)							)
				_lowerCamelCase        =				{int(_lowerCAmelCase							): 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(_lowerCAmelCase							).eval()
				else:
								_lowerCamelCase        =				ViTHybridForImageClassification(_lowerCAmelCase							).eval()
				model.load_state_dict(_lowerCAmelCase							)

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

				_lowerCamelCase        =				{
				    "bilinear": PILImageResampling.BILINEAR,
				    "bicubic": PILImageResampling.BICUBIC,
				    "nearest": PILImageResampling.NEAREST,
				}

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

				_lowerCamelCase        =				prepare_img()
				_lowerCamelCase        =				transform(_lowerCAmelCase							).unsqueeze(0							)
				_lowerCamelCase        =				processor(_lowerCAmelCase						,   return_tensors='''pt'''							).pixel_values

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

				# verify logits
				with torch.no_grad():
								_lowerCamelCase        =				model(_lowerCAmelCase							)
								_lowerCamelCase        =				outputs.logits

				print('''Predicted class:'''						,   logits.argmax(-1							).item()							)
				if base_model:
								_lowerCamelCase        =				timm_model.forward_features(_lowerCAmelCase							)
								assert timm_pooled_output.shape == outputs.pooler_output.shape
								assert torch.allclose(_lowerCAmelCase						,   outputs.pooler_output						,   atol=1e-3							)
				else:
								_lowerCamelCase        =				timm_model(_lowerCAmelCase							)
								assert timm_logits.shape == outputs.logits.shape
								assert torch.allclose(_lowerCAmelCase						,   outputs.logits						,   atol=1e-3							)
				print('''Looks ok!'''							)

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

				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__":
			__SCREAMING_SNAKE_CASE  :						int    							=      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.'''
			)

			__SCREAMING_SNAKE_CASE  :						Union[str, Any]    							=      parser.parse_args()
			convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)





"""simple docstring"""
import argparse
import json
import os

import torch

from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken



@torch.no_grad()
def        lowerCAmelCase_(   lowercase_     :							int						,   lowercase_     :							List[Any]						,   lowercase_     :							List[Any]						,   lowercase_     :							Tuple						,   lowercase_     :							List[str]							)	->       Dict:
				# Load configuration defined in the metadata file
				with open(lowercase_							) as metadata_file:
								_lowerCamelCase        =				json.load(lowercase_							)
				_lowerCamelCase        =				LukeConfig(use_entity_aware_attention=lowercase_						,   **metadata['''model_config''']							)

				# Load in the weights from the checkpoint_path
				_lowerCamelCase        =				torch.load(lowercase_						,   map_location='''cpu'''							)

				# Load the entity vocab file
				_lowerCamelCase        =				load_entity_vocab(lowercase_							)

				_lowerCamelCase        =				RobertaTokenizer.from_pretrained(metadata['''model_config''']['''bert_model_name''']							)

				# Add special tokens to the token vocabulary for downstream tasks
				_lowerCamelCase        =				AddedToken('''<ent>'''						,   lstrip=lowercase_						,   rstrip=lowercase_							)
				_lowerCamelCase        =				AddedToken('''<ent2>'''						,   lstrip=lowercase_						,   rstrip=lowercase_							)
				tokenizer.add_special_tokens({'''additional_special_tokens''': [entity_token_a, entity_token_a]}							)
				config.vocab_size += 2

				print(F"""Saving tokenizer to {pytorch_dump_folder_path}"""							)
				tokenizer.save_pretrained(lowercase_							)
				with open(os.path.join(lowercase_						,   LukeTokenizer.vocab_files_names['''entity_vocab_file''']							)						,   '''w'''							) as f:
								json.dump(lowercase_						,   lowercase_							)

				_lowerCamelCase        =				LukeTokenizer.from_pretrained(lowercase_							)

				# Initialize the embeddings of the special tokens
				_lowerCamelCase        =				state_dict['''embeddings.word_embeddings.weight''']
				_lowerCamelCase        =				word_emb[tokenizer.convert_tokens_to_ids(['''@''']							)[0]].unsqueeze(0							)
				_lowerCamelCase        =				word_emb[tokenizer.convert_tokens_to_ids(['''#''']							)[0]].unsqueeze(0							)
				_lowerCamelCase        =				torch.cat([word_emb, ent_emb, enta_emb]							)

				# Initialize the query layers of the entity-aware self-attention mechanism
				for layer_index in range(config.num_hidden_layers							):
								for matrix_name in ["query.weight", "query.bias"]:
												_lowerCamelCase        =				F"""encoder.layer.{layer_index}.attention.self."""
												_lowerCamelCase        =				state_dict[prefix + matrix_name]
												_lowerCamelCase        =				state_dict[prefix + matrix_name]
												_lowerCamelCase        =				state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
				_lowerCamelCase        =				state_dict['''entity_embeddings.entity_embeddings.weight''']
				_lowerCamelCase        =				entity_emb[entity_vocab['''[MASK]''']]

				_lowerCamelCase        =				LukeModel(config=lowercase_							).eval()

				_lowerCamelCase  ,     _lowerCamelCase        =				model.load_state_dict(lowercase_						,   strict=lowercase_							)
				if not (len(lowercase_							) == 1 and missing_keys[0] == "embeddings.position_ids"):
								raise ValueError(F"""Missing keys {", ".join(lowercase_							)}. Expected only missing embeddings.position_ids"""							)
				if not (all(key.startswith('''entity_predictions'''							) or key.startswith('''lm_head'''							) for key in unexpected_keys							)):
								raise ValueError(
								    '''Unexpected keys'''
								    F""" {", ".join([key for key in unexpected_keys if not (key.startswith("entity_predictions"							) or key.startswith("lm_head"							))]							)}"""							)

				# Check outputs
				_lowerCamelCase        =				LukeTokenizer.from_pretrained(lowercase_						,   task='''entity_classification'''							)

				_lowerCamelCase        =				(
				    '''Top seed Ana Ivanovic said on Thursday she could hardly believe her luck as a fortuitous netcord helped the'''
				    ''' new world number one avoid a humiliating second- round exit at Wimbledon .'''
				)
				_lowerCamelCase        =				(39, 42)
				_lowerCamelCase        =				tokenizer(lowercase_						,   entity_spans=[span]						,   add_prefix_space=lowercase_						,   return_tensors='''pt'''							)

				_lowerCamelCase        =				model(**lowercase_							)

				# Verify word hidden states
				if model_size == "large":
								_lowerCamelCase        =				torch.Size((1, 42, 10_24)							)
								_lowerCamelCase        =				torch.tensor(
								    [[0.0_1_3_3, 0.0_8_6_5, 0.0_0_9_5], [0.3_0_9_3, -0.2_5_7_6, -0.7_4_1_8], [-0.1_7_2_0, -0.2_1_1_7, -0.2_8_6_9]]							)
				else:  # base
								_lowerCamelCase        =				torch.Size((1, 42, 7_68)							)
								_lowerCamelCase        =				torch.tensor([[0.0_0_3_7, 0.1_3_6_8, -0.0_0_9_1], [0.1_0_9_9, 0.3_3_2_9, -0.1_0_9_5], [0.0_7_6_5, 0.5_3_3_5, 0.1_1_7_9]]							)

				if not (outputs.last_hidden_state.shape == expected_shape):
								raise ValueError(
								    F"""Outputs.last_hidden_state.shape is {outputs.last_hidden_state.shape}, Expected shape is {expected_shape}"""							)
				if not torch.allclose(outputs.last_hidden_state[0, :3, :3]						,   lowercase_						,   atol=1e-4							):
								raise ValueError

				# Verify entity hidden states
				if model_size == "large":
								_lowerCamelCase        =				torch.Size((1, 1, 10_24)							)
								_lowerCamelCase        =				torch.tensor([[0.0_4_6_6, -0.0_1_0_6, -0.0_1_7_9]]							)
				else:  # base
								_lowerCamelCase        =				torch.Size((1, 1, 7_68)							)
								_lowerCamelCase        =				torch.tensor([[0.1_4_5_7, 0.1_0_4_4, 0.0_1_7_4]]							)

				if not (outputs.entity_last_hidden_state.shape != expected_shape):
								raise ValueError(
								    F"""Outputs.entity_last_hidden_state.shape is {outputs.entity_last_hidden_state.shape}, Expected shape is"""
								    F""" {expected_shape}"""							)
				if not torch.allclose(outputs.entity_last_hidden_state[0, :3, :3]						,   lowercase_						,   atol=1e-4							):
								raise ValueError

				# Finally, save our PyTorch model and tokenizer
				print('''Saving PyTorch model to {}'''.format(lowercase_							)							)
				model.save_pretrained(lowercase_							)



def        lowerCAmelCase_(   lowercase_     :							Optional[Any]							)	->       Any:
				_lowerCamelCase        =				{}
				with open(lowercase_						,   '''r'''						,   encoding='''utf-8'''							) as f:
								for index, line in enumerate(lowercase_							):
												_lowerCamelCase  ,     _lowerCamelCase        =				line.rstrip().split('''\t'''							)
												_lowerCamelCase        =				index

				return entity_vocab


if __name__ == "__main__":
			__SCREAMING_SNAKE_CASE  :						List[str]    							=      argparse.ArgumentParser()
			# Required parameters
			parser.add_argument('''--checkpoint_path''', type=str, help='''Path to a pytorch_model.bin file.''')
			parser.add_argument(
			    '''--metadata_path''', default=None, type=str, help='''Path to a metadata.json file, defining the configuration.'''
			)
			parser.add_argument(
			    '''--entity_vocab_path''',
			    default=None,
			    type=str,
			    help='''Path to an entity_vocab.tsv file, containing the entity vocabulary.''',
			)
			parser.add_argument(
			    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to where to dump the output PyTorch model.'''
			)
			parser.add_argument(
			    '''--model_size''', default='''base''', type=str, choices=['''base''', '''large'''], help='''Size of the model to be converted.'''
			)
			__SCREAMING_SNAKE_CASE  :						int    							=      parser.parse_args()
			convert_luke_checkpoint(
			    args.checkpoint_path,
			    args.metadata_path,
			    args.entity_vocab_path,
			    args.pytorch_dump_folder_path,
			    args.model_size,
			)
