




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      UpperCAmelCase  (						UpperCamelCase__						,			UpperCamelCase__=False						,			UpperCamelCase__=False						,			UpperCamelCase__="data2vec."   )     ->     List[str]:







				'''simple docstring'''


				__lowerCAmelCase					=  """backbone.""" if is_semantic else """"""

				__lowerCAmelCase					=  []
				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      UpperCAmelCase  (						UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__=False						,			UpperCamelCase__=False						,			UpperCamelCase__="data2vec_vision."   )     ->     Optional[Any]:







				'''simple docstring'''


				for i in range(config.num_hidden_layers   ):
								__lowerCAmelCase					=  """backbone.""" if is_semantic else """"""
								# queries, keys and values
								__lowerCAmelCase					=  state_dict.pop(F'''{prefix}blocks.{i}.attn.qkv.weight'''   )
								__lowerCAmelCase					=  state_dict.pop(F'''{prefix}blocks.{i}.attn.q_bias'''   )
								__lowerCAmelCase					=  state_dict.pop(F'''{prefix}blocks.{i}.attn.v_bias'''   )

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

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

								__lowerCAmelCase					=  gamma_a
								__lowerCAmelCase					=  gamma_a

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

												__lowerCAmelCase					=  table
												__lowerCAmelCase					=  index





def      UpperCAmelCase  (						)     ->     Optional[Any]:







				'''simple docstring'''


				__lowerCAmelCase					=  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_24						,			type=UpperCamelCase__						,			help="""images input size"""   )
				parser.add_argument("""--beit_checkpoint"""						,			default=""""""						,			help="""beit checkpoint"""   )

				return parser.parse_args()





def      UpperCAmelCase  (						UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__   )     ->     Dict:







				'''simple docstring'''


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

								def load(UpperCamelCase__						,			UpperCamelCase__=""   ):
												__lowerCAmelCase					=  {} 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__   )

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

								__lowerCAmelCase					=  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__   )   )

				__lowerCAmelCase					=  {
				    """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,
								    }   )

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

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

				__lowerCAmelCase					=  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:
												__lowerCAmelCase					=  checkpoint_model[key]
												__lowerCAmelCase	,				__lowerCAmelCase					=  rel_pos_bias.size()
												__lowerCAmelCase	,				__lowerCAmelCase					=  model.state_dict()[key].size()
												__lowerCAmelCase					=  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      UpperCAmelCase  (						)     ->     List[str]:







				'''simple docstring'''


				__lowerCAmelCase					=  get_args()

				__lowerCAmelCase					=  """ft1k""" in args.hf_checkpoint_name
				__lowerCAmelCase					=  """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
				__lowerCAmelCase					=  DataaVecVisionConfig()
				if is_finetuned:
								__lowerCAmelCase					=  True
								__lowerCAmelCase					=  False
								__lowerCAmelCase					=  True
								__lowerCAmelCase					=  10_00

								__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()}
				else:
								__lowerCAmelCase					=  False
								__lowerCAmelCase					=  True
								__lowerCAmelCase					=  False

				if is_large:
								__lowerCAmelCase					=  10_24
								__lowerCAmelCase					=  40_96
								__lowerCAmelCase					=  24
								__lowerCAmelCase					=  16

				# 2. Load Beit model
				__lowerCAmelCase					=  load_beit_model(UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__   )
				orig_model.eval()

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

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

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

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

				read_in_q_k_v(UpperCamelCase__						,			UpperCamelCase__						,			hf_prefix=UpperCamelCase__						,			has_lm_head=UpperCamelCase__   )
				__lowerCAmelCase	,				__lowerCAmelCase					=  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():
								__lowerCAmelCase					=  hf_model(UpperCamelCase__   )

				__lowerCAmelCase					=  hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state

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

				print(F'''max_absolute_diff = {max_absolute_diff}'''   )
				__lowerCAmelCase					=  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 inspect
from typing import Callable, List, Optional, Set, Tuple, Union

import torch
from packaging import version
from safetensors.torch import storage_ptr, storage_size
from torch import nn

from .utils import logging


__A       			=      [nn.LayerNorm]

__A       			=      logging.get_logger(__name__)

__A       			=      version.parse(version.parse(torch.__version__).base_version)

__A       			=      parsed_torch_version_base >= version.parse("""2.0""")
__A       			=      parsed_torch_version_base >= version.parse("""1.12""")
__A       			=      parsed_torch_version_base >= version.parse("""1.11""")
__A       			=      parsed_torch_version_base < version.parse("""1.11""")

def 						UpperCamelCase				(	_lowerCAmelCase   :							Tuple				, _lowerCAmelCase   :							List[str]				, _lowerCAmelCase   :							Union[str, Any]				, _lowerCAmelCase   :							int				, self   :							Optional[Any]						):
    from torch import _softmax_backward_data

    if is_torch_less_than_1_11:
        return _softmax_backward_data(_lowerCAmelCase				, _lowerCAmelCase				, parent.dim				, self						)
    else:
        return _softmax_backward_data(_lowerCAmelCase				, _lowerCAmelCase				, parent.dim				, self.dtype						)

def 						UpperCamelCase				(	_lowerCAmelCase   :							nn.Linear				, _lowerCAmelCase   :							torch.LongTensor				, _lowerCAmelCase   :							int = 0						):
    __a			      =						index.to(layer.weight.device						)
    __a			      =						layer.weight.index_select(_lowerCAmelCase				, _lowerCAmelCase						).clone().detach()
    if layer.bias is not None:
        if dim == 1:
            __a			      =						layer.bias.clone().detach()
        else:
            __a			      =						layer.bias[index].clone().detach()
    __a			      =						list(layer.weight.size()						)
    __a			      =						len(_lowerCAmelCase						)
    __a			      =						nn.Linear(new_size[1]				, new_size[0]				, bias=layer.bias is not None						).to(layer.weight.device						)
    __a			      =						False
    new_layer.weight.copy_(W.contiguous()						)
    __a			      =						True
    if layer.bias is not None:
        __a			      =						False
        new_layer.bias.copy_(b.contiguous()						)
        __a			      =						True
    return new_layer






class 					a					(				nn.Module							):



  def __init__(		self   :    Optional[int]						,  lowerCamelCase_   :    Optional[Any]						,  lowerCamelCase_   :    List[Any]     )      -> List[str]:
      super().__init__()
      __a			      =						nf
      __a			      =						nn.Parameter(torch.empty(lowerCamelCase_						,  lowerCamelCase_     )     )
      __a			      =						nn.Parameter(torch.zeros(lowerCamelCase_     )     )
      nn.init.normal_(self.weight						,  std=0.02     )



  def        lowerCAmelCase_				(		self   :    int						,  lowerCamelCase_   :    Optional[Any]     )      -> Union[str, Any]:
      __a			      =						x.size()[:-1] + (self.nf,)
      __a			      =						torch.addmm(self.bias						,  x.view(-1						,  x.size(-1     )     )						,  self.weight     )
      __a			      =						x.view(lowerCamelCase_     )
      return x

def 						UpperCamelCase				(	_lowerCAmelCase   :							ConvaD				, _lowerCAmelCase   :							torch.LongTensor				, _lowerCAmelCase   :							int = 1						):
    __a			      =						index.to(layer.weight.device						)
    __a			      =						layer.weight.index_select(_lowerCAmelCase				, _lowerCAmelCase						).clone().detach()
    if dim == 0:
        __a			      =						layer.bias.clone().detach()
    else:
        __a			      =						layer.bias[index].clone().detach()
    __a			      =						list(layer.weight.size()						)
    __a			      =						len(_lowerCAmelCase						)
    __a			      =						ConvaD(new_size[1]				, new_size[0]						).to(layer.weight.device						)
    __a			      =						False
    new_layer.weight.copy_(W.contiguous()						)
    __a			      =						True
    __a			      =						False
    new_layer.bias.copy_(b.contiguous()						)
    __a			      =						True
    return new_layer

def 						UpperCamelCase				(	_lowerCAmelCase   :							Union[nn.Linear, ConvaD]				, _lowerCAmelCase   :							torch.LongTensor				, _lowerCAmelCase   :							Optional[int] = None						):
    if isinstance(_lowerCAmelCase				, nn.Linear						):
        return prune_linear_layer(_lowerCAmelCase				, _lowerCAmelCase				, dim=0 if dim is None else dim						)
    elif isinstance(_lowerCAmelCase				, _lowerCAmelCase						):
        return prune_convad_layer(_lowerCAmelCase				, _lowerCAmelCase				, dim=1 if dim is None else dim						)
    else:
        raise ValueError(f"""Can't prune layer of class {layer.__class__}"""						)

def 						UpperCamelCase				(	_lowerCAmelCase   :							Callable[..., torch.Tensor]				, _lowerCAmelCase   :							int				, _lowerCAmelCase   :							int				, *_lowerCAmelCase   :							List[Any]						):
    assert len(_lowerCAmelCase						) > 0, f"""{input_tensors} has to be a tuple/list of tensors"""

    # inspect.signature exist since python 3.5 and is a python method -> no problem with backward compatibility
    __a			      =						len(inspect.signature(_lowerCAmelCase						).parameters						)
    if num_args_in_forward_chunk_fn != len(_lowerCAmelCase						):
        raise ValueError(
            f"""forward_chunk_fn expects {num_args_in_forward_chunk_fn} arguments, but only {len(_lowerCAmelCase						)} input """
            """tensors are given"""						)

    if chunk_size > 0:
        __a			      =						input_tensors[0].shape[chunk_dim]
        for input_tensor in input_tensors:
            if input_tensor.shape[chunk_dim] != tensor_shape:
                raise ValueError(
                    f"""All input tenors have to be of the same shape: {tensor_shape}, """
                    f"""found shape {input_tensor.shape[chunk_dim]}"""						)

        if input_tensors[0].shape[chunk_dim] % chunk_size != 0:
            raise ValueError(
                f"""The dimension to be chunked {input_tensors[0].shape[chunk_dim]} has to be a multiple of the chunk """
                f"""size {chunk_size}"""						)

        __a			      =						input_tensors[0].shape[chunk_dim] // chunk_size

        # chunk input tensor into tuples
        __a			      =						tuple(input_tensor.chunk(_lowerCAmelCase				, dim=_lowerCAmelCase						) for input_tensor in input_tensors						)
        # apply forward fn to every tuple
        __a			      =						tuple(forward_fn(*_lowerCAmelCase						) for input_tensors_chunk in zip(*_lowerCAmelCase						)						)
        # concatenate output at same dimension
        return torch.cat(_lowerCAmelCase				, dim=_lowerCAmelCase						)

    return forward_fn(*_lowerCAmelCase						)

def 						UpperCamelCase				(	_lowerCAmelCase   :							List[int]				, _lowerCAmelCase   :							int				, _lowerCAmelCase   :							int				, _lowerCAmelCase   :							Set[int]						):
    __a			      =						torch.ones(_lowerCAmelCase				, _lowerCAmelCase						)
    __a			      =						set(_lowerCAmelCase						) - already_pruned_heads  # Convert to set and remove already pruned heads
    for head in heads:
        # Compute how many pruned heads are before the head and move the index accordingly
        __a			      =						head - sum(1 if h < head else 0 for h in already_pruned_heads						)
        __a			      =						0
    __a			      =						mask.view(-1						).contiguous().eq(1						)
    __a			      =						torch.arange(len(_lowerCAmelCase						)						)[mask].long()
    return heads, index

def 						UpperCamelCase				(	*_lowerCAmelCase   :							Union[torch.Tensor, List[torch.Tensor]]				, _lowerCAmelCase   :							Optional[str] = None						):
    return torch.meshgrid(*_lowerCAmelCase				, indexing=_lowerCAmelCase						)






def 						UpperCamelCase				(	_lowerCAmelCase   :							torch.Tensor						):
    return tensor.device, storage_ptr(_lowerCAmelCase						), storage_size(_lowerCAmelCase						)
