
'''simple docstring'''
__lowerCAmelCase  							= {
    '+': lambda x, y: x + y,
    '-': lambda x, y: x - y,
    '*': lambda x, y: x * y,
    '/': lambda x, y: x / y,
}






def       __SCREAMING_SNAKE_CASE   (						_SCREAMING_SNAKE_CASE   ):
							return c.isdigit()






def       __SCREAMING_SNAKE_CASE   (						_SCREAMING_SNAKE_CASE   ):
							_snake_case     =						[]

							# iterate over the string in reverse order
							for c in expression.split()[::-1]:
														# push operand to stack
														if is_operand(_SCREAMING_SNAKE_CASE   ):
																					stack.append(int(_SCREAMING_SNAKE_CASE   )   )

														else:
																					# pop values from stack can calculate the result
																					# push the result onto the stack again
																					_snake_case     =						stack.pop()
																					_snake_case     =						stack.pop()
																					stack.append(calc[c](_SCREAMING_SNAKE_CASE							,      _SCREAMING_SNAKE_CASE   )   )

							return stack.pop()


# Driver code
if __name__ == "__main__":
				__lowerCAmelCase  							= '+ 9 * 2 6'
				print(evaluate(test_expression))

				__lowerCAmelCase  							= '/ * 10 2 + 4 1 '
				print(evaluate(test_expression))

'''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       __SCREAMING_SNAKE_CASE   (						_SCREAMING_SNAKE_CASE							,      _SCREAMING_SNAKE_CASE=False							,      _SCREAMING_SNAKE_CASE=False							,      _SCREAMING_SNAKE_CASE="data2vec."   ):
							_snake_case     =						"""backbone.""" if is_semantic else """"""

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

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

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

														_snake_case     =						gamma_a
														_snake_case     =						gamma_a

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

																					_snake_case     =						table
																					_snake_case     =						index






def       __SCREAMING_SNAKE_CASE   (						):
							_snake_case     =						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=224							,      type=_SCREAMING_SNAKE_CASE							,      help="""images input size"""   )
							parser.add_argument("""--beit_checkpoint"""							,      default=""""""							,      help="""beit checkpoint"""   )

							return parser.parse_args()






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

														def load(_SCREAMING_SNAKE_CASE							,      _SCREAMING_SNAKE_CASE=""   ):
																					_snake_case     =						{} 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   )

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

														_snake_case     =						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   )   )

							_snake_case     =						{
							    """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""": 1000,
														        """use_mean_pooling""": True,
														        """init_scale""": 0.001,
														        """use_rel_pos_bias""": True,
														    }   )

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

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

							_snake_case     =						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:
																					_snake_case     =						checkpoint_model[key]
																					_snake_case,							_snake_case     =						rel_pos_bias.size()
																					_snake_case,							_snake_case     =						model.state_dict()[key].size()
																					_snake_case     =						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       __SCREAMING_SNAKE_CASE   (						):
							_snake_case     =						get_args()

							_snake_case     =						"""ft1k""" in args.hf_checkpoint_name
							_snake_case     =						"""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
							_snake_case     =						DataaVecVisionConfig()
							if is_finetuned:
														_snake_case     =						True
														_snake_case     =						False
														_snake_case     =						True
														_snake_case     =						1000

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

							if is_large:
														_snake_case     =						1024
														_snake_case     =						4096
														_snake_case     =						24
														_snake_case     =						16

							# 2. Load Beit model
							_snake_case     =						load_beit_model(_SCREAMING_SNAKE_CASE							,      _SCREAMING_SNAKE_CASE							,      _SCREAMING_SNAKE_CASE   )
							orig_model.eval()

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

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

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

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

							read_in_q_k_v(_SCREAMING_SNAKE_CASE							,      _SCREAMING_SNAKE_CASE							,      hf_prefix=_SCREAMING_SNAKE_CASE							,      has_lm_head=_SCREAMING_SNAKE_CASE   )
							_snake_case,							_snake_case     =						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():
														_snake_case     =						hf_model(_SCREAMING_SNAKE_CASE   )

							_snake_case     =						hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state

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

							print(f"""max_absolute_diff = {max_absolute_diff}"""   )
							_snake_case     =						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"