


'''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_					(				__SCREAMING_SNAKE_CASE :					Dict	,   __SCREAMING_SNAKE_CASE :					Union[str, Any]=False	,   __SCREAMING_SNAKE_CASE :					Tuple=False	,   __SCREAMING_SNAKE_CASE :					Optional[Any]="data2vec."       )     ->  List[str]:
					__SCREAMING_SNAKE_CASE					:							Union[str, Any]			   = '''backbone.''' if is_semantic else ''''''

					__SCREAMING_SNAKE_CASE					:							Tuple			   = []
					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_					(				__SCREAMING_SNAKE_CASE :					int	,   __SCREAMING_SNAKE_CASE :					Optional[int]	,   __SCREAMING_SNAKE_CASE :					int=False	,   __SCREAMING_SNAKE_CASE :					List[Any]=False	,   __SCREAMING_SNAKE_CASE :					Optional[Any]="data2vec_vision."       )     ->  Union[str, Any]:
					for i in range(config.num_hidden_layers       ):
										__SCREAMING_SNAKE_CASE					:							Tuple			   = '''backbone.''' if is_semantic else ''''''
										# queries, keys and values
										__SCREAMING_SNAKE_CASE					:							Optional[int]			   = state_dict.pop(f"""{prefix}blocks.{i}.attn.qkv.weight"""       )
										__SCREAMING_SNAKE_CASE					:							Dict			   = state_dict.pop(f"""{prefix}blocks.{i}.attn.q_bias"""       )
										__SCREAMING_SNAKE_CASE					:							str			   = state_dict.pop(f"""{prefix}blocks.{i}.attn.v_bias"""       )

										__SCREAMING_SNAKE_CASE					:							List[Any]			   = in_proj_weight[
										    : config.hidden_size, :
										]
										__SCREAMING_SNAKE_CASE					:							List[str]			   = q_bias
										__SCREAMING_SNAKE_CASE					:							Tuple			   = in_proj_weight[
										    config.hidden_size : config.hidden_size * 2, :
										]
										__SCREAMING_SNAKE_CASE					:							Union[str, Any]			   = in_proj_weight[
										    -config.hidden_size :, :
										]
										__SCREAMING_SNAKE_CASE					:							List[str]			   = v_bias

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

										__SCREAMING_SNAKE_CASE					:							Dict			   = gamma_a
										__SCREAMING_SNAKE_CASE					:							Dict			   = gamma_a

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

															__SCREAMING_SNAKE_CASE					:							Dict			   = table
															__SCREAMING_SNAKE_CASE					:							Tuple			   = index


def A_					(				)     ->  Optional[Any]:
					__SCREAMING_SNAKE_CASE					:							Optional[int]			   = argparse.ArgumentParser(
					    '''Convert Data2VecVision to HF for image classification and pretraining'''	,   add_help=__SCREAMING_SNAKE_CASE       )
					parser.add_argument('''--hf_checkpoint_name'''	,   type=__SCREAMING_SNAKE_CASE       )
					parser.add_argument('''--input_size'''	,   default=2_24	,   type=__SCREAMING_SNAKE_CASE	,   help='''images input size'''       )
					parser.add_argument('''--beit_checkpoint'''	,   default=''''''	,   help='''beit checkpoint'''       )

					return parser.parse_args()


def A_					(				__SCREAMING_SNAKE_CASE :					str	,   __SCREAMING_SNAKE_CASE :					int	,   __SCREAMING_SNAKE_CASE :					Any       )     ->  int:
					def load_state_dict(__SCREAMING_SNAKE_CASE :					Dict	,   __SCREAMING_SNAKE_CASE :					List[Any]	,   __SCREAMING_SNAKE_CASE :					List[Any]=""	,   __SCREAMING_SNAKE_CASE :					Dict="relative_position_index"       ):
										__SCREAMING_SNAKE_CASE					:							Optional[Any]			   = []
										__SCREAMING_SNAKE_CASE					:							Dict			   = []
										__SCREAMING_SNAKE_CASE					:							Optional[int]			   = []
										# copy state_dict so _load_from_state_dict can modify it
										__SCREAMING_SNAKE_CASE					:							int			   = getattr(__SCREAMING_SNAKE_CASE	,   '''_metadata'''	,   __SCREAMING_SNAKE_CASE       )
										__SCREAMING_SNAKE_CASE					:							List[str]			   = state_dict.copy()
										if metadata is not None:
															__SCREAMING_SNAKE_CASE					:							Optional[int]			   = metadata

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

										load(__SCREAMING_SNAKE_CASE	,   prefix=__SCREAMING_SNAKE_CASE       )

										__SCREAMING_SNAKE_CASE					:							Any			   = []
										__SCREAMING_SNAKE_CASE					:							int			   = []
										for key in missing_keys:
															__SCREAMING_SNAKE_CASE					:							Union[str, Any]			   = True
															for ignore_key in ignore_missing.split('''|'''       ):
																				if ignore_key in key:
																									__SCREAMING_SNAKE_CASE					:							Optional[int]			   = False
																									break
															if keep_flag:
																				warn_missing_keys.append(__SCREAMING_SNAKE_CASE       )
															else:
																				ignore_missing_keys.append(__SCREAMING_SNAKE_CASE       )

										__SCREAMING_SNAKE_CASE					:							Tuple			   = warn_missing_keys

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

					__SCREAMING_SNAKE_CASE					:							List[str]			   = {
					    '''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''': 10_00,
										        '''use_mean_pooling''': True,
										        '''init_scale''': 0.001,
										        '''use_rel_pos_bias''': True,
										    }       )

					__SCREAMING_SNAKE_CASE					:							Tuple			   = create_model(
					    '''beit_large_patch16_224''' if is_large else '''beit_base_patch16_224'''	,   **__SCREAMING_SNAKE_CASE	,   )
					__SCREAMING_SNAKE_CASE					:							Dict			   = model.patch_embed.patch_size
					__SCREAMING_SNAKE_CASE					:							str			   = (args.input_size // patch_size[0], args.input_size // patch_size[1])
					__SCREAMING_SNAKE_CASE					:							Tuple			   = torch.load(args.beit_checkpoint	,   map_location='''cpu'''       )

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

					__SCREAMING_SNAKE_CASE					:							List[Any]			   = list(checkpoint_model.keys()       )
					for key in all_keys:
										if "relative_position_index" in key:
															checkpoint_model.pop(__SCREAMING_SNAKE_CASE       )

										if "relative_position_bias_table" in key:
															__SCREAMING_SNAKE_CASE					:							int			   = checkpoint_model[key]
															__SCREAMING_SNAKE_CASE   ,   __SCREAMING_SNAKE_CASE					:							Union[str, Any]			   = rel_pos_bias.size()
															__SCREAMING_SNAKE_CASE   ,   __SCREAMING_SNAKE_CASE					:							Tuple			   = model.state_dict()[key].size()
															__SCREAMING_SNAKE_CASE					:							Optional[int]			   = model.patch_embed.patch_shape
															if dst_patch_shape[0] != dst_patch_shape[1]:
																				raise NotImplementedError()

					load_state_dict(__SCREAMING_SNAKE_CASE	,   __SCREAMING_SNAKE_CASE	,   prefix=''''''       )

					return model


def A_					(				)     ->  Union[str, Any]:
					__SCREAMING_SNAKE_CASE					:							Optional[int]			   = get_args()

					__SCREAMING_SNAKE_CASE					:							int			   = '''ft1k''' in args.hf_checkpoint_name
					__SCREAMING_SNAKE_CASE					:							Any			   = '''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
					__SCREAMING_SNAKE_CASE					:							List[Any]			   = DataaVecVisionConfig()
					if is_finetuned:
										__SCREAMING_SNAKE_CASE					:							Dict			   = True
										__SCREAMING_SNAKE_CASE					:							str			   = False
										__SCREAMING_SNAKE_CASE					:							Any			   = True
										__SCREAMING_SNAKE_CASE					:							List[Any]			   = 10_00

										__SCREAMING_SNAKE_CASE					:							Optional[Any]			   = '''huggingface/label-files'''
										__SCREAMING_SNAKE_CASE					:							Union[str, Any]			   = '''imagenet-1k-id2label.json'''
										__SCREAMING_SNAKE_CASE					:							int			   = json.load(open(hf_hub_download(__SCREAMING_SNAKE_CASE	,   __SCREAMING_SNAKE_CASE	,   repo_type='''dataset'''       )	,   '''r'''       )       )
										__SCREAMING_SNAKE_CASE					:							Optional[Any]			   = {int(__SCREAMING_SNAKE_CASE       ): v for k, v in idalabel.items()}
										__SCREAMING_SNAKE_CASE					:							Optional[int]			   = idalabel
										__SCREAMING_SNAKE_CASE					:							Dict			   = {v: k for k, v in idalabel.items()}
					else:
										__SCREAMING_SNAKE_CASE					:							int			   = False
										__SCREAMING_SNAKE_CASE					:							Dict			   = True
										__SCREAMING_SNAKE_CASE					:							str			   = False

					if is_large:
										__SCREAMING_SNAKE_CASE					:							int			   = 10_24
										__SCREAMING_SNAKE_CASE					:							List[str]			   = 40_96
										__SCREAMING_SNAKE_CASE					:							Any			   = 24
										__SCREAMING_SNAKE_CASE					:							List[Any]			   = 16

					# 2. Load Beit model
					__SCREAMING_SNAKE_CASE					:							Tuple			   = load_beit_model(__SCREAMING_SNAKE_CASE	,   __SCREAMING_SNAKE_CASE	,   __SCREAMING_SNAKE_CASE       )
					orig_model.eval()

					# 3. Forward Beit model
					__SCREAMING_SNAKE_CASE					:							Optional[Any]			   = BeitImageProcessor(size=config.image_size	,   do_center_crop=__SCREAMING_SNAKE_CASE       )
					__SCREAMING_SNAKE_CASE					:							Dict			   = Image.open('''../../../../tests/fixtures/tests_samples/COCO/000000039769.png'''       )
					__SCREAMING_SNAKE_CASE					:							Union[str, Any]			   = image_processor(images=__SCREAMING_SNAKE_CASE	,   return_tensors='''pt'''       )
					__SCREAMING_SNAKE_CASE					:							Dict			   = encoding['''pixel_values''']

					__SCREAMING_SNAKE_CASE					:							Dict			   = (pixel_values,) if is_finetuned else (pixel_values, None)
					with torch.no_grad():
										__SCREAMING_SNAKE_CASE					:							List[str]			   = orig_model(*__SCREAMING_SNAKE_CASE       )

					# 4. Load HF Data2VecVision model
					if is_finetuned:
										__SCREAMING_SNAKE_CASE					:							Any			   = DataaVecVisionForImageClassification(__SCREAMING_SNAKE_CASE       )
										hf_model.eval()
										__SCREAMING_SNAKE_CASE					:							str			   = False
										__SCREAMING_SNAKE_CASE					:							str			   = '''data2vec_vision.'''
					else:
										__SCREAMING_SNAKE_CASE					:							List[str]			   = DataaVecVisionModel(__SCREAMING_SNAKE_CASE       )
										hf_model.eval()
										__SCREAMING_SNAKE_CASE					:							str			   = True
										__SCREAMING_SNAKE_CASE					:							Optional[int]			   = ''''''

					__SCREAMING_SNAKE_CASE					:							List[str]			   = create_rename_keys(__SCREAMING_SNAKE_CASE	,   hf_prefix=__SCREAMING_SNAKE_CASE	,   has_lm_head=__SCREAMING_SNAKE_CASE       )
					__SCREAMING_SNAKE_CASE					:							int			   = orig_model.state_dict()
					for src, dest in rename_keys:
										__SCREAMING_SNAKE_CASE					:							List[str]			   = state_dict.pop(__SCREAMING_SNAKE_CASE       )
										__SCREAMING_SNAKE_CASE					:							Dict			   = val

					read_in_q_k_v(__SCREAMING_SNAKE_CASE	,   __SCREAMING_SNAKE_CASE	,   hf_prefix=__SCREAMING_SNAKE_CASE	,   has_lm_head=__SCREAMING_SNAKE_CASE       )
					__SCREAMING_SNAKE_CASE   ,   __SCREAMING_SNAKE_CASE					:							Optional[int]			   = hf_model.load_state_dict(__SCREAMING_SNAKE_CASE	,   strict=__SCREAMING_SNAKE_CASE       )
					print('''HF missing'''	,   __SCREAMING_SNAKE_CASE       )
					print('''HF unexpected_keys'''	,   __SCREAMING_SNAKE_CASE       )

					# 5. Forward HF Data2VecVision model
					with torch.no_grad():
										__SCREAMING_SNAKE_CASE					:							Any			   = hf_model(__SCREAMING_SNAKE_CASE       )

					__SCREAMING_SNAKE_CASE					:							Dict			   = hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state

					# 6. Compare
					__SCREAMING_SNAKE_CASE					:							str			   = torch.max(torch.abs(hf_output - orig_model_output       )       ).item()

					print(f"""max_absolute_diff = {max_absolute_diff}"""       )
					__SCREAMING_SNAKE_CASE					:							List[Any]			   = torch.allclose(__SCREAMING_SNAKE_CASE	,   __SCREAMING_SNAKE_CASE	,   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'''






from .balanced_parentheses import balanced_parentheses
from .stack import Stack


def A_					(				__SCREAMING_SNAKE_CASE :					str       )     ->  int:
					return {"+": 1, "-": 1, "*": 2, "/": 2, "^": 3}.get(__SCREAMING_SNAKE_CASE	,   -1       )


def A_					(				__SCREAMING_SNAKE_CASE :					str       )     ->  str:
					if not balanced_parentheses(__SCREAMING_SNAKE_CASE       ):
										raise ValueError('''Mismatched parentheses'''       )
					__SCREAMING_SNAKE_CASE					:							Stack[str]			   = Stack()
					__SCREAMING_SNAKE_CASE					:							Optional[Any]			   = []
					for char in expression_str:
										if char.isalpha() or char.isdigit():
															postfix.append(__SCREAMING_SNAKE_CASE       )
										elif char == "(":
															stack.push(__SCREAMING_SNAKE_CASE       )
										elif char == ")":
															while not stack.is_empty() and stack.peek() != "(":
																				postfix.append(stack.pop()       )
															stack.pop()
										else:
															while not stack.is_empty() and precedence(__SCREAMING_SNAKE_CASE       ) <= precedence(stack.peek()       ):
																				postfix.append(stack.pop()       )
															stack.push(__SCREAMING_SNAKE_CASE       )
					while not stack.is_empty():
										postfix.append(stack.pop()       )
					return " ".join(__SCREAMING_SNAKE_CASE       )


if __name__ == "__main__":
						from doctest import testmod

						testmod()
						_A      =     """a+b*(c^d-e)^(f+g*h)-i"""

						print("""Infix to Postfix Notation demonstration:\n""")
						print("""Infix notation: """ + expression)
						print("""Postfix notation: """ + infix_to_postfix(expression))


