


'''simple docstring'''
import argparse
import json

import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.models import create_model

from transformers import (
    BeitImageProcessor,
    DataaVecVisionConfig,
    DataaVecVisionForImageClassification,
    DataaVecVisionModel,
)







def 			a_							(  UpperCamelCase_	,     UpperCamelCase_=False	,     UpperCamelCase_=False	,     UpperCamelCase_="data2vec."   ):
							A_ 						=		"backbone." if is_semantic else ""

							A_ 						=		[]
							for i in range(config.num_hidden_layers   ):
														# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
														rename_keys.append(
														    (f"{prefix}blocks.{i}.norm1.weight", f"{hf_prefix}encoder.layer.{i}.layernorm_before.weight")   )
														rename_keys.append((f"{prefix}blocks.{i}.norm1.bias", f"{hf_prefix}encoder.layer.{i}.layernorm_before.bias")   )
														rename_keys.append(
														    (f"{prefix}blocks.{i}.attn.proj.weight", f"{hf_prefix}encoder.layer.{i}.attention.output.dense.weight")   )
														rename_keys.append(
														    (f"{prefix}blocks.{i}.attn.proj.bias", f"{hf_prefix}encoder.layer.{i}.attention.output.dense.bias")   )
														rename_keys.append(
														    (f"{prefix}blocks.{i}.norm2.weight", f"{hf_prefix}encoder.layer.{i}.layernorm_after.weight")   )
														rename_keys.append((f"{prefix}blocks.{i}.norm2.bias", f"{hf_prefix}encoder.layer.{i}.layernorm_after.bias")   )
														rename_keys.append(
														    (f"{prefix}blocks.{i}.mlp.fc1.weight", f"{hf_prefix}encoder.layer.{i}.intermediate.dense.weight")   )
														rename_keys.append(
														    (f"{prefix}blocks.{i}.mlp.fc1.bias", f"{hf_prefix}encoder.layer.{i}.intermediate.dense.bias")   )
														rename_keys.append((f"{prefix}blocks.{i}.mlp.fc2.weight", f"{hf_prefix}encoder.layer.{i}.output.dense.weight")   )
														rename_keys.append((f"{prefix}blocks.{i}.mlp.fc2.bias", f"{hf_prefix}encoder.layer.{i}.output.dense.bias")   )

							# projection layer + position embeddings
							rename_keys.extend(
							    [
							        (f"{prefix}cls_token", f"{hf_prefix}embeddings.cls_token"),
							        (f"{prefix}patch_embed.proj.weight", f"{hf_prefix}embeddings.patch_embeddings.projection.weight"),
							        (f"{prefix}patch_embed.proj.bias", f"{hf_prefix}embeddings.patch_embeddings.projection.bias"),
							    ]   )

							if has_lm_head:
														# mask token + shared relative position bias + layernorm
														rename_keys.extend(
														    [
														        ("mask_token", f"{hf_prefix}embeddings.mask_token"),
														        (
														            "rel_pos_bias.relative_position_bias_table",
														            f"{hf_prefix}encoder.relative_position_bias.relative_position_bias_table",
														        ),
														        (
														            "rel_pos_bias.relative_position_index",
														            f"{hf_prefix}encoder.relative_position_bias.relative_position_index",
														        ),
														        ("norm.weight", "layernorm.weight"),
														        ("norm.bias", "layernorm.bias"),
														    ]   )
							elif is_semantic:
														# semantic segmentation classification heads
														rename_keys.extend(
														    [
														        ("decode_head.conv_seg.weight", "decode_head.classifier.weight"),
														        ("decode_head.conv_seg.bias", "decode_head.classifier.bias"),
														        ("auxiliary_head.conv_seg.weight", "auxiliary_head.classifier.weight"),
														        ("auxiliary_head.conv_seg.bias", "auxiliary_head.classifier.bias"),
														    ]   )
							else:
														# layernorm + classification head
														rename_keys.extend(
														    [
														        ("fc_norm.weight", f"{hf_prefix}pooler.layernorm.weight"),
														        ("fc_norm.bias", f"{hf_prefix}pooler.layernorm.bias"),
														        ("head.weight", "classifier.weight"),
														        ("head.bias", "classifier.bias"),
														    ]   )

							return rename_keys







def 			a_							(  UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_=False	,     UpperCamelCase_=False	,     UpperCamelCase_="data2vec_vision."   ):
							for i in range(config.num_hidden_layers   ):
														A_ 						=		"backbone." if is_semantic else ""
														# queries, keys and values
														A_ 						=		state_dict.pop(f"{prefix}blocks.{i}.attn.qkv.weight"   )
														A_ 						=		state_dict.pop(f"{prefix}blocks.{i}.attn.q_bias"   )
														A_ 						=		state_dict.pop(f"{prefix}blocks.{i}.attn.v_bias"   )

														A_ 						=		in_proj_weight[
														    : config.hidden_size, :
														]
														A_ 						=		q_bias
														A_ 						=		in_proj_weight[
														    config.hidden_size : config.hidden_size * 2, :
														]
														A_ 						=		in_proj_weight[
														    -config.hidden_size :, :
														]
														A_ 						=		v_bias

														# gamma_1 and gamma_2
														# we call them lambda because otherwise they are renamed when using .from_pretrained
														A_ 						=		state_dict.pop(f"{prefix}blocks.{i}.gamma_1"   )
														A_ 						=		state_dict.pop(f"{prefix}blocks.{i}.gamma_2"   )

														A_ 						=		gamma_a
														A_ 						=		gamma_a

														# relative_position bias table + index
														if not has_lm_head:
																					# each layer has its own relative position bias
																					A_ 						=		state_dict.pop(f"{prefix}blocks.{i}.attn.relative_position_bias_table"   )
																					A_ 						=		state_dict.pop(f"{prefix}blocks.{i}.attn.relative_position_index"   )

																					A_ 						=		table
																					A_ 						=		index







def 			a_							(  ):
							A_ 						=		argparse.ArgumentParser(
							    "Convert Data2VecVision to HF for image classification and pretraining"	,     add_help=UpperCamelCase_   )
							parser.add_argument("--hf_checkpoint_name"	,     type=UpperCamelCase_   )
							parser.add_argument("--input_size"	,     default=2_2_4	,     type=UpperCamelCase_	,     help="images input size"   )
							parser.add_argument("--beit_checkpoint"	,     default=""	,     help="beit checkpoint"   )

							return parser.parse_args()







def 			a_							(  UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_   ):
							def load_state_dict(UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_=""	,     UpperCamelCase_="relative_position_index"   ):
														A_ 						=		[]
														A_ 						=		[]
														A_ 						=		[]
														# copy state_dict so _load_from_state_dict can modify it
														A_ 						=		getattr(UpperCamelCase_	,     "_metadata"	,     UpperCamelCase_   )
														A_ 						=		state_dict.copy()
														if metadata is not None:
																					A_ 						=		metadata

														def load(UpperCamelCase_	,     UpperCamelCase_=""   ):
																					A_ 						=		{} if metadata is None else metadata.get(prefix[:-1]	,     {}   )
																					module._load_from_state_dict(
																					    UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_   )
																					for name, child in module._modules.items():
																												if child is not None:
																																			load(UpperCamelCase_	,     prefix + name + "."   )

														load(UpperCamelCase_	,     prefix=UpperCamelCase_   )

														A_ 						=		[]
														A_ 						=		[]
														for key in missing_keys:
																					A_ 						=		True
																					for ignore_key in ignore_missing.split("|"   ):
																												if ignore_key in key:
																																			A_ 						=		False
																																			break
																					if keep_flag:
																												warn_missing_keys.append(UpperCamelCase_   )
																					else:
																												ignore_missing_keys.append(UpperCamelCase_   )

														A_ 						=		warn_missing_keys

														if len(UpperCamelCase_   ) > 0:
																					print(
																					    "Weights of {} not initialized from pretrained model: {}".format(
																					        model.__class__.__name__	,     UpperCamelCase_   )   )
														if len(UpperCamelCase_   ) > 0:
																					print("Weights from pretrained model not used in {}: {}".format(model.__class__.__name__	,     UpperCamelCase_   )   )
														if len(UpperCamelCase_   ) > 0:
																					print(
																					    "Ignored weights of {} not initialized from pretrained model: {}".format(
																					        model.__class__.__name__	,     UpperCamelCase_   )   )
														if len(UpperCamelCase_   ) > 0:
																					print("\n".join(UpperCamelCase_   )   )

							A_ 						=		{
							    "pretrained": False,
							    "use_shared_rel_pos_bias": True,
							    "use_abs_pos_emb": False,
							    "init_values": 0.1,
							}

							if is_finetuned:
														model_kwargs.update(
														    {
														        "num_classes": 1_0_0_0,
														        "use_mean_pooling": True,
														        "init_scale": 0.001,
														        "use_rel_pos_bias": True,
														    }   )

							A_ 						=		create_model(
							    "beit_large_patch16_224" if is_large else "beit_base_patch16_224"	,     **UpperCamelCase_	,     )
							A_ 						=		model.patch_embed.patch_size
							A_ 						=		(args.input_size // patch_size[0], args.input_size // patch_size[1])
							A_ 						=		torch.load(args.beit_checkpoint	,     map_location="cpu"   )

							print(f"Load ckpt from {args.beit_checkpoint}"   )
							A_ 						=		None
							for model_key in ("model", "module"):
														if model_key in checkpoint:
																					A_ 						=		checkpoint[model_key]
																					print(f"Load state_dict by model_key = {model_key}"   )
																					break

							A_ 						=		list(checkpoint_model.keys()   )
							for key in all_keys:
														if "relative_position_index" in key:
																					checkpoint_model.pop(UpperCamelCase_   )

														if "relative_position_bias_table" in key:
																					A_ 						=		checkpoint_model[key]
																					A_						,				A_ 						=		rel_pos_bias.size()
																					A_						,				A_ 						=		model.state_dict()[key].size()
																					A_ 						=		model.patch_embed.patch_shape
																					if dst_patch_shape[0] != dst_patch_shape[1]:
																												raise NotImplementedError()

							load_state_dict(UpperCamelCase_	,     UpperCamelCase_	,     prefix=""   )

							return model







def 			a_							(  ):
							A_ 						=		get_args()

							A_ 						=		"ft1k" in args.hf_checkpoint_name
							A_ 						=		"large" in args.hf_checkpoint_name

							if is_finetuned:
														# To convert Beit's data2vec_vision to HF you need to copy
														# https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_finetune.py
														# into this folder.
														import modeling_finetune  # noqa: F401
							else:
														# To convert Beit's data2vec_vision to HF you need to copy
														# https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_cyclical.py
														# into this folder
														# IMPORTANT: Note that for now we've only converted the down-stream
														# model and not the full pretrained model. This means for the integration
														# test you need to add a `return x` after the following line:
														# https://github.com/facebookresearch/data2vec_vision/blob/af9a36349aaed59ae66e69b5dabeef2d62fdc5da/beit/modeling_cyclical.py#L197
														# to make the integration test pass.
														import modeling_cyclical  # noqa: F401

							# 1. Create model config
							A_ 						=		DataaVecVisionConfig()
							if is_finetuned:
														A_ 						=		True
														A_ 						=		False
														A_ 						=		True
														A_ 						=		1_0_0_0

														A_ 						=		"huggingface/label-files"
														A_ 						=		"imagenet-1k-id2label.json"
														A_ 						=		json.load(open(hf_hub_download(UpperCamelCase_	,     UpperCamelCase_	,     repo_type="dataset"   )	,     "r"   )   )
														A_ 						=		{int(UpperCamelCase_   ): v for k, v in idalabel.items()}
														A_ 						=		idalabel
														A_ 						=		{v: k for k, v in idalabel.items()}
							else:
														A_ 						=		False
														A_ 						=		True
														A_ 						=		False

							if is_large:
														A_ 						=		1_0_2_4
														A_ 						=		4_0_9_6
														A_ 						=		2_4
														A_ 						=		1_6

							# 2. Load Beit model
							A_ 						=		load_beit_model(UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_   )
							orig_model.eval()

							# 3. Forward Beit model
							A_ 						=		BeitImageProcessor(size=config.image_size	,     do_center_crop=UpperCamelCase_   )
							A_ 						=		Image.open("../../../../tests/fixtures/tests_samples/COCO/000000039769.png"   )
							A_ 						=		image_processor(images=UpperCamelCase_	,     return_tensors="pt"   )
							A_ 						=		encoding["pixel_values"]

							A_ 						=		(pixel_values,) if is_finetuned else (pixel_values, None)
							with torch.no_grad():
														A_ 						=		orig_model(*UpperCamelCase_   )

							# 4. Load HF Data2VecVision model
							if is_finetuned:
														A_ 						=		DataaVecVisionForImageClassification(UpperCamelCase_   )
														hf_model.eval()
														A_ 						=		False
														A_ 						=		"data2vec_vision."
							else:
														A_ 						=		DataaVecVisionModel(UpperCamelCase_   )
														hf_model.eval()
														A_ 						=		True
														A_ 						=		""

							A_ 						=		create_rename_keys(UpperCamelCase_	,     hf_prefix=UpperCamelCase_	,     has_lm_head=UpperCamelCase_   )
							A_ 						=		orig_model.state_dict()
							for src, dest in rename_keys:
														A_ 						=		state_dict.pop(UpperCamelCase_   )
														A_ 						=		val

							read_in_q_k_v(UpperCamelCase_	,     UpperCamelCase_	,     hf_prefix=UpperCamelCase_	,     has_lm_head=UpperCamelCase_   )
							A_						,				A_ 						=		hf_model.load_state_dict(UpperCamelCase_	,     strict=UpperCamelCase_   )
							print("HF missing"	,     UpperCamelCase_   )
							print("HF unexpected_keys"	,     UpperCamelCase_   )

							# 5. Forward HF Data2VecVision model
							with torch.no_grad():
														A_ 						=		hf_model(UpperCamelCase_   )

							A_ 						=		hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state

							# 6. Compare
							A_ 						=		torch.max(torch.abs(hf_output - orig_model_output   )   ).item()

							print(f"max_absolute_diff = {max_absolute_diff}"   )
							A_ 						=		torch.allclose(UpperCamelCase_	,     UpperCamelCase_	,     atol=1e-3   )
							print("Do both models output the same tensors?"	,     "🔥" if success else "💩"   )
							if not success:
														raise Exception("Something went wRoNg"   )

							# 7. Save
							print(f"Saving to {args.hf_checkpoint_name}"   )
							hf_model.save_pretrained(args.hf_checkpoint_name   )
							image_processor.save_pretrained(args.hf_checkpoint_name   )


if __name__ == "__main__":
							main()
							# Run the following to convert checkpoints
							#  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
							#          --beit_checkpoint ./pretrained_base.pt \
							#          --hf_checkpoint_name "./data2vec-vision-base"
							#  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
							#          --beit_checkpoint ./finetuned_base.pt \
							#          --hf_checkpoint_name "./data2vec-vision-base-ft1k"
							#  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
							#          --beit_checkpoint ./pretrained_large.pt \
							#          --hf_checkpoint_name "./data2vec-vision-large"
							#  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
							#          --beit_checkpoint ./finetuned_large.pt \
							#          --hf_checkpoint_name "./data2vec-vision-large-ft1k"









'''simple docstring'''
import argparse
import logging
import sys
from unittest.mock import patch

import run_glue_with_pabee

from transformers.testing_utils import TestCasePlus


logging.basicConfig(level=logging.DEBUG)

__SCREAMING_SNAKE_CASE				:							str		       =		logging.getLogger()







def 			a_							(  ):
							A_ 						=		argparse.ArgumentParser()
							parser.add_argument("-f"   )
							A_ 						=		parser.parse_args()
							return args.f







class 				__lowerCAmelCase      (      lowercase	):

						"""simple docstring"""






						def 							_UpperCAmelCase					(     self       :			List[Any]  ):
													A_ 						=		logging.StreamHandler(sys.stdout  )
													logger.addHandler(lowerCAmelCase  )

													A_ 						=		self.get_auto_remove_tmp_dir()
													A_ 						=		F"\n            run_glue_with_pabee.py\n            --model_type albert\n            --model_name_or_path albert-base-v2\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --task_name mrpc\n            --do_train\n            --do_eval\n            --per_gpu_train_batch_size=2\n            --per_gpu_eval_batch_size=1\n            --learning_rate=2e-5\n            --max_steps=50\n            --warmup_steps=2\n            --seed=42\n            --max_seq_length=128\n            ".split()

													with patch.object(lowerCAmelCase   ,     "argv"   ,     lowerCAmelCase  ):
																				A_ 						=		run_glue_with_pabee.main()
																				for value in result.values():
																											self.assertGreaterEqual(lowerCAmelCase   ,     0.7_5  )





