



import argparse
import json
import os
import re
from collections import OrderedDict
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from pprint import pprint
from typing import Dict, List, Tuple

import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams
from huggingface_hub import cached_download, hf_hub_url
from torch import Tensor
from vissl.models.model_helpers import get_trunk_forward_outputs

from transformers import AutoImageProcessor, RegNetConfig, RegNetForImageClassification, RegNetModel
from transformers.modeling_utils import PreTrainedModel
from transformers.utils import logging


logging.set_verbosity_info()
A__       =			logging.get_logger()





@dataclass
class    __lowerCAmelCase					:
				__lowerCamelCase					=							42
				__lowerCamelCase					=							field(default_factory=lowerCamelCase__			)
				__lowerCamelCase					=							field(default_factory=lowerCamelCase__			)
				__lowerCamelCase					=							field(default_factory=lowerCamelCase__			)




				def 				snake_case  (				self		,							_snake_case		,							_snake_case		,							_snake_case		,							_snake_case						):



									"""simple docstring"""





									_lowerCAmelCase         =       len(list(m.modules()						)						) == 1 or isinstance(_snake_case		,							nn.Convad						) or isinstance(_snake_case		,							nn.BatchNormad						)
									if has_not_submodules:
														self.traced.append(_snake_case						)
														_lowerCAmelCase         =       m




				def __call__(				self		,							_snake_case						):



									"""simple docstring"""





									for name, m in self.module.named_modules():
														self.handles.append(m.register_forward_hook(partial(self._forward_hook		,							name=_snake_case						)						)						)
									self.module(_snake_case						)
									[x.remove() for x in self.handles]
									return self




				@property
				def 				snake_case  (				self						):



									"""simple docstring"""





									return {k: v for k, v in self.nameamodule.items() if len(list(v.state_dict().keys()						)						) > 0}





class    __lowerCAmelCase					(	nn.Module			):




				def __init__(				self		,							_snake_case						):



									"""simple docstring"""





									super().__init__()

									_lowerCAmelCase         =       []
									# - get the stem
									feature_blocks.append(("""conv1""", model.stem)						)
									# - get all the feature blocks
									for k, v in model.trunk_output.named_children():
														assert k.startswith("""block"""						), F'Unexpected layer name {k}'
														_lowerCAmelCase         =       len(_snake_case						) + 1
														feature_blocks.append((F'res{block_index}', v)						)

									_lowerCAmelCase         =       nn.ModuleDict(_snake_case						)




				def 				snake_case  (				self		,							_snake_case						):



									"""simple docstring"""





									return get_trunk_forward_outputs(
									    _snake_case		,							out_feat_keys=_snake_case		,							feature_blocks=self._feature_blocks		,							)





class    __lowerCAmelCase					(	lowerCamelCase__			):




				def 				snake_case  (				self						):



									"""simple docstring"""





									return [(8, 2, 2, 8, 1.0), (8, 2, 7, 8, 1.0), (8, 2, 17, 8, 1.0), (8, 2, 1, 8, 1.0)]


def 						_UpperCAmelCase							( snake_case     ):





					"""simple docstring"""

					_lowerCAmelCase         =       RegNetConfig(depths=[2, 7, 17, 1]      ,		hidden_sizes=[8, 8, 8, 8]      ,		groups_width=8     )
					if "in1k" in model_name:
										_lowerCAmelCase         =       RegNetForImageClassification(snake_case     )
					else:
										_lowerCAmelCase         =       RegNetModel(snake_case     )
					# create from model (with small weights)
					_lowerCAmelCase         =       FakeRegNetVisslWrapper(
					    RegNet(FakeRegNetParams(depth=27      ,		group_width=10_10      ,		w_a=17_44      ,		w_a=620.83      ,		w_m=2.52     )     )     )

					with torch.no_grad():
										_lowerCAmelCase         =       from_model.eval()
										_lowerCAmelCase         =       our_model.eval()

										_lowerCAmelCase         =       torch.randn((1, 3, 32, 32)     )
										# trace both
										_lowerCAmelCase         =       Tracker(snake_case     )
										_lowerCAmelCase         =       dest_tracker(snake_case     ).parametrized

										pprint(dest_tracker.nameamodule     )
										_lowerCAmelCase         =       Tracker(snake_case     )
										_lowerCAmelCase         =       src_tracker(snake_case     ).parametrized

					# convert the keys -> module dict to keys -> params
					def to_params_dict(snake_case     ):
										_lowerCAmelCase         =       OrderedDict()
										for name, module in dict_with_modules.items():
															for param_name, param in module.state_dict().items():
																				_lowerCAmelCase         =       param
										return params_dict

					_lowerCAmelCase         =       {}

					_lowerCAmelCase         =       to_params_dict(snake_case     )
					_lowerCAmelCase         =       to_params_dict(snake_case     )

					for (src_key, src_param), (dest_key, dest_param) in zip(src_state_dict.items()      ,		dst_state_dict.items()     ):
										_lowerCAmelCase         =       dest_key
										logger.info(F'{src_key} -> {dest_key}'     )
					# if "in1k" was in the model_name it means it must have a classification head (was finetuned)
					if "in1k" in model_name:
										_lowerCAmelCase         =       """classifier.1.weight"""
										_lowerCAmelCase         =       """classifier.1.bias"""

					return from_to_ours_keys


def 						_UpperCAmelCase							( snake_case      ,		snake_case = None      ,		snake_case = True     ):





					"""simple docstring"""

					_lowerCAmelCase         =       """imagenet-1k-id2label.json"""
					_lowerCAmelCase         =       10_00

					_lowerCAmelCase         =       """huggingface/label-files"""
					_lowerCAmelCase         =       num_labels
					_lowerCAmelCase         =       json.load(open(cached_download(hf_hub_url(snake_case      ,		snake_case      ,		repo_type="""dataset"""     )     )      ,		"""r"""     )     )
					_lowerCAmelCase         =       {int(snake_case     ): v for k, v in idalabel.items()}

					_lowerCAmelCase         =       idalabel
					_lowerCAmelCase         =       {v: k for k, v in idalabel.items()}

					_lowerCAmelCase         =       partial(snake_case      ,		num_labels=snake_case      ,		idalabel=snake_case      ,		labelaid=snake_case     )

					_lowerCAmelCase         =       {
					    """regnet-y-10b-seer""": ImageNetPreTrainedConfig(
					        depths=[2, 7, 17, 1]      ,		hidden_sizes=[20_20, 40_40, 1_11_10, 2_82_80]      ,		groups_width=10_10     ),
					    # finetuned on imagenet
					    """regnet-y-10b-seer-in1k""": ImageNetPreTrainedConfig(
					        depths=[2, 7, 17, 1]      ,		hidden_sizes=[20_20, 40_40, 1_11_10, 2_82_80]      ,		groups_width=10_10     ),
					}

					# add seer weights logic
					def load_using_classy_vision(snake_case     ) -> Tuple[Dict, Dict]:
										_lowerCAmelCase         =       torch.hub.load_state_dict_from_url(snake_case      ,		model_dir=str(snake_case     )      ,		map_location="""cpu"""     )
										# check if we have a head, if yes add it
										_lowerCAmelCase         =       files["""classy_state_dict"""]["""base_model"""]["""model"""]
										return model_state_dict["trunk"], model_state_dict["heads"]

					_lowerCAmelCase         =       {
					    """regnet-y-10b-seer""": partial(
					        snake_case      ,		"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch"""      ,		),
					    """regnet-y-10b-seer-in1k""": partial(
					        snake_case      ,		"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch"""      ,		),
					}

					_lowerCAmelCase         =       get_from_to_our_keys(snake_case     )

					if not (save_directory / F'{model_name}.pth').exists():
										logger.info("""Loading original state_dict."""     )
										_lowerCAmelCase      ,      _lowerCAmelCase         =       names_to_from_model[model_name]()
										_lowerCAmelCase         =       from_state_dict_trunk
										if "in1k" in model_name:
															# add the head
															_lowerCAmelCase         =       {**from_state_dict_trunk, **from_state_dict_head}
										logger.info("""Done!"""     )

										_lowerCAmelCase         =       {}

										_lowerCAmelCase         =       list(from_state_dict.keys()     )
										_lowerCAmelCase         =       R"""\.block.-part."""
										# this is "interesting", so the original checkpoints have `block[0,1]-part` in each key name, we remove it
										for key in from_state_dict.keys():
															# remove the weird "block[0,1]-part" from the key
															_lowerCAmelCase         =       re.sub(snake_case      ,		""""""      ,		snake_case     )
															# now src_key from the model checkpoints is the one we got from the original model after tracing, so use it to get the correct destination key
															_lowerCAmelCase         =       from_to_ours_keys[src_key]
															# store the parameter with our key
															_lowerCAmelCase         =       from_state_dict[key]
															not_used_keys.remove(snake_case     )
										# check that all keys have been updated
										assert len(snake_case     ) == 0, F'Some keys where not used {",".join(snake_case     )}'

										logger.info(F'The following keys were not used: {",".join(snake_case     )}'     )

										# save our state dict to disk
										torch.save(snake_case      ,		save_directory / F'{model_name}.pth'     )

										del converted_state_dict
					else:
										logger.info("""The state_dict was already stored on disk."""     )
					if push_to_hub:
										logger.info(F'Token is {os.environ["HF_TOKEN"]}'     )
										logger.info("""Loading our model."""     )
										# create our model
										_lowerCAmelCase         =       names_to_config[model_name]
										_lowerCAmelCase         =       RegNetModel
										if "in1k" in model_name:
															_lowerCAmelCase         =       RegNetForImageClassification
										_lowerCAmelCase         =       our_model_func(snake_case     )
										# place our model to the meta device (so remove all the weights)
										our_model.to(torch.device("""meta"""     )     )
										logger.info("""Loading state_dict in our model."""     )
										# load state dict
										_lowerCAmelCase         =       our_model.state_dict().keys()
										PreTrainedModel._load_pretrained_model_low_mem(
										    snake_case      ,		snake_case      ,		[save_directory / F'{model_name}.pth']     )
										logger.info("""Finally, pushing!"""     )
										# push it to hub
										our_model.push_to_hub(
										    repo_path_or_name=save_directory / model_name      ,		commit_message="""Add model"""      ,		output_dir=save_directory / model_name      ,		)
										_lowerCAmelCase         =       3_84
										# we can use the convnext one
										_lowerCAmelCase         =       AutoImageProcessor.from_pretrained("""facebook/convnext-base-224-22k-1k"""      ,		size=snake_case     )
										image_processor.push_to_hub(
										    repo_path_or_name=save_directory / model_name      ,		commit_message="""Add image processor"""      ,		output_dir=save_directory / model_name      ,		)


if __name__ == "__main__":
	A__       =			argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    """--model_name""",
	    default=None,
	    type=str,
	    help=(
	        """The name of the model you wish to convert, it must be one of the supported regnet* architecture,"""
	        """ currently: regnetx-*, regnety-*. If `None`, all of them will the converted."""
	    ),
	)
	parser.add_argument(
	    """--pytorch_dump_folder_path""",
	    default=None,
	    type=Path,
	    required=True,
	    help="""Path to the output PyTorch model directory.""",
	)
	parser.add_argument(
	    """--push_to_hub""",
	    default=True,
	    type=bool,
	    required=False,
	    help="""If True, push model and image processor to the hub.""",
	)

	A__       =			parser.parse_args()

	A__       =			args.pytorch_dump_folder_path
	pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
	convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)










'''simple docstring'''

import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from ....modeling_outputs import BaseModelOutputWithPooling, SequenceClassifierOutput
from ....modeling_utils import ModuleUtilsMixin
from ....utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings


lowerCAmelCase__      :   str      	=			logging.get_logger(__name__)

lowerCAmelCase__      :   Optional[int]      	=			"MMBTConfig"


class      SCREAMING_SNAKE_CASE__      (   nn.Module		):







					"""simple docstring"""






					def __init__(   self       :      List[str]		,	UpperCAmelCase_       :      Union[str, Any]		,	UpperCAmelCase_       :      Union[str, Any]		,	UpperCAmelCase_       :      Dict		):







							"""simple docstring"""
							super().__init__()
							__UpperCAmelCase	:							Optional[int]     =     config
							__UpperCAmelCase	:							Union[str, Any]     =     encoder
							__UpperCAmelCase	:							Optional[Any]     =     nn.Linear(config.modal_hidden_size		,	config.hidden_size		)
							__UpperCAmelCase	:							str     =     embeddings.position_embeddings
							__UpperCAmelCase	:							List[str]     =     embeddings.token_type_embeddings
							__UpperCAmelCase	:							str     =     embeddings.word_embeddings
							__UpperCAmelCase	:							Tuple     =     embeddings.LayerNorm
							__UpperCAmelCase	:							str     =     nn.Dropout(p=config.hidden_dropout_prob		)






					def 		lowerCamelCase_   (   self       :      List[Any]		,	UpperCAmelCase_       :      Optional[int]		,	UpperCAmelCase_       :      str=None		,	UpperCAmelCase_       :      Tuple=None		,	UpperCAmelCase_       :      Optional[int]=None		,	UpperCAmelCase_       :      Tuple=None		):







							"""simple docstring"""
							__UpperCAmelCase	:							List[str]     =     self.proj_embeddings(self.encoder(UpperCAmelCase_		)		)
							__UpperCAmelCase	:							List[str]     =     token_embeddings.size(1		)

							if start_token is not None:
									__UpperCAmelCase	:							Optional[Any]     =     self.word_embeddings(UpperCAmelCase_		)
									seq_length += 1
									__UpperCAmelCase	:							int     =     torch.cat([start_token_embeds.unsqueeze(1		), token_embeddings]		,	dim=1		)

							if end_token is not None:
									__UpperCAmelCase	:							List[str]     =     self.word_embeddings(UpperCAmelCase_		)
									seq_length += 1
									__UpperCAmelCase	:							List[Any]     =     torch.cat([token_embeddings, end_token_embeds.unsqueeze(1		)]		,	dim=1		)

							if position_ids is None:
									__UpperCAmelCase	:							Dict     =     torch.arange(UpperCAmelCase_		,	dtype=torch.long		,	device=input_modal.device		)
									__UpperCAmelCase	:							Optional[int]     =     position_ids.unsqueeze(0		).expand(input_modal.size(0		)		,	UpperCAmelCase_		)

							if token_type_ids is None:
									__UpperCAmelCase	:							int     =     torch.zeros(
									    (input_modal.size(0		), seq_length)		,	dtype=torch.long		,	device=input_modal.device		)

							__UpperCAmelCase	:							int     =     self.position_embeddings(UpperCAmelCase_		)
							__UpperCAmelCase	:							Union[str, Any]     =     self.token_type_embeddings(UpperCAmelCase_		)
							__UpperCAmelCase	:							Union[str, Any]     =     token_embeddings + position_embeddings + token_type_embeddings
							__UpperCAmelCase	:							Tuple     =     self.LayerNorm(UpperCAmelCase_		)
							__UpperCAmelCase	:							int     =     self.dropout(UpperCAmelCase_		)
							return embeddings






lowerCAmelCase__      :   List[str]      	=			R"\n    MMBT model was proposed in [Supervised Multimodal Bitransformers for Classifying Images and\n    Text](https://github.com/facebookresearch/mmbt) by Douwe Kiela, Suvrat Bhooshan, Hamed Firooz, Davide Testuggine.\n    It's a supervised multimodal bitransformer model that fuses information from text and other image encoders, and\n    obtain state-of-the-art performance on various multimodal classification benchmark tasks.\n\n    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the\n    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads\n    etc.)\n\n    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.\n    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage\n    and behavior.\n\n    Parameters:\n        config ([`MMBTConfig`]): Model configuration class with all the parameters of the model.\n            Initializing with a config file does not load the weights associated with the model, only the\n            configuration.\n        transformer (`nn.Module`): A text transformer that is used by MMBT.\n            It should have embeddings, encoder, and pooler attributes.\n        encoder (`nn.Module`): Encoder for the second modality.\n            It should take in a batch of modal inputs and return k, n dimension embeddings.\n"

lowerCAmelCase__      :   Tuple      	=			R"\n    Args:\n        input_modal (`torch.FloatTensor` of shape `(batch_size, ***)`):\n            The other modality data. It will be the shape that the encoder for that type expects. e.g. With an Image\n            Encoder, the shape would be (batch_size, channels, height, width)\n        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):\n            Indices of input sequence tokens in the vocabulary. It does not expect [CLS] token to be added as it's\n            appended to the end of other modality embeddings. Indices can be obtained using [`AutoTokenizer`]. See\n            [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details.\n\n            [What are input IDs?](../glossary#input-ids)\n        modal_start_tokens (`torch.LongTensor` of shape `(batch_size,)`, *optional*):\n            Optional start token to be added to Other Modality Embedding. [CLS] Most commonly used for classification\n            tasks.\n        modal_end_tokens (`torch.LongTensor` of shape `(batch_size,)`, *optional*):\n            Optional end token to be added to Other Modality Embedding. [SEP] Most commonly used.\n        attention_mask (*optional*) `torch.FloatTensor` of shape `(batch_size, sequence_length)`:\n            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:\n\n            - 1 for tokens that are **not masked**,\n            - 0 for tokens that are **masked**.\n\n            [What are attention masks?](../glossary#attention-mask)\n        token_type_ids (*optional*) `torch.LongTensor` of shape `(batch_size, sequence_length)`:\n            Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,\n            1]`:\n\n            - 0 corresponds to a *sentence A* token,\n            - 1 corresponds to a *sentence B* token.\n\n            [What are token type IDs?](../glossary#token-type-ids)\n        modal_token_type_ids (*optional*) `torch.LongTensor` of shape `(batch_size, modal_sequence_length)`:\n            Segment token indices to indicate different portions of the non-text modality. The embeddings from these\n            tokens will be summed with the respective token embeddings for the non-text modality.\n        position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):\n            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,\n            config.max_position_embeddings - 1]`.\n\n            [What are position IDs?](../glossary#position-ids)\n        modal_position_ids (`torch.LongTensor` of shape `(batch_size, modal_sequence_length)`, *optional*):\n            Indices of positions of each input sequence tokens in the position embeddings for the non-text modality.\n            Selected in the range `[0, config.max_position_embeddings - 1]`.\n\n            [What are position IDs?](../glossary#position-ids)\n        head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):\n            Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:\n\n            - 1 indicates the head is **not masked**,\n            - 0 indicates the head is **masked**.\n\n        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, embedding_dim)`, *optional*):\n            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This\n            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the\n            model's internal embedding lookup matrix.\n        encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):\n            Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if\n            the model is configured as a decoder.\n        encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):\n            Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in\n            the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`:\n\n            - 1 for tokens that are **not masked**,\n            - 0 for tokens that are **masked**.\n\n        output_attentions (`bool`, *optional*):\n            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned\n            tensors for more detail.\n        output_hidden_states (`bool`, *optional*):\n            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n            more detail.\n        return_dict (`bool`, *optional*):\n            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n"


@add_start_docstrings(
    '''The bare MMBT Model outputting raw hidden-states without any specific head on top.'''							,snake_case__							,)
class      SCREAMING_SNAKE_CASE__      (   nn.Module							,snake_case__		):







					"""simple docstring"""






					def __init__(   self       :      Dict		,	UpperCAmelCase_       :      str		,	UpperCAmelCase_       :      int		,	UpperCAmelCase_       :      Optional[int]		):







							"""simple docstring"""
							super().__init__()
							__UpperCAmelCase	:							Optional[int]     =     config
							__UpperCAmelCase	:							str     =     transformer
							__UpperCAmelCase	:							List[str]     =     ModalEmbeddings(UpperCAmelCase_		,	UpperCAmelCase_		,	transformer.embeddings		)
					@add_start_docstrings_to_model_forward(UpperCAmelCase_		)
					@replace_return_docstrings(output_type=UpperCAmelCase_		,	config_class=_CONFIG_FOR_DOC		)
					def 		lowerCamelCase_   (   self       :      Tuple		,	UpperCAmelCase_       :      List[str]		,	UpperCAmelCase_       :      str=None		,	UpperCAmelCase_       :      Optional[int]=None		,	UpperCAmelCase_       :      Any=None		,	UpperCAmelCase_       :      Dict=None		,	UpperCAmelCase_       :      int=None		,	UpperCAmelCase_       :      Union[str, Any]=None		,	UpperCAmelCase_       :      Tuple=None		,	UpperCAmelCase_       :      Optional[int]=None		,	UpperCAmelCase_       :      Tuple=None		,	UpperCAmelCase_       :      List[Any]=None		,	UpperCAmelCase_       :      Union[str, Any]=None		,	UpperCAmelCase_       :      Union[str, Any]=None		,	UpperCAmelCase_       :      Any=None		,	UpperCAmelCase_       :      Optional[int]=None		,	UpperCAmelCase_       :      str=None		,	):







							"""simple docstring"""
							__UpperCAmelCase	:							Any     =     output_attentions if output_attentions is not None else self.config.output_attentions
							__UpperCAmelCase	:							List[Any]     =     (
							    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
							)
							__UpperCAmelCase	:							Union[str, Any]     =     return_dict if return_dict is not None else self.config.use_return_dict

							if input_ids is not None and inputs_embeds is not None:
									raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time"		)
							elif input_ids is not None:
									__UpperCAmelCase	:							Optional[int]     =     input_ids.size()
							elif inputs_embeds is not None:
									__UpperCAmelCase	:							Optional[Any]     =     inputs_embeds.size()[:-1]
							else:
									raise ValueError("You have to specify either input_ids or inputs_embeds"		)

							__UpperCAmelCase	:							Tuple     =     input_ids.device if input_ids is not None else inputs_embeds.device

							__UpperCAmelCase	:							str     =     self.modal_encoder(
							    UpperCAmelCase_		,	start_token=UpperCAmelCase_		,	end_token=UpperCAmelCase_		,	position_ids=UpperCAmelCase_		,	token_type_ids=UpperCAmelCase_		,	)

							__UpperCAmelCase	:							str     =     modal_embeddings.size()[:-1]

							if token_type_ids is None:
									__UpperCAmelCase	:							Union[str, Any]     =     torch.ones(UpperCAmelCase_		,	dtype=torch.long		,	device=UpperCAmelCase_		)

							__UpperCAmelCase	:							int     =     self.transformer.embeddings(
							    input_ids=UpperCAmelCase_		,	position_ids=UpperCAmelCase_		,	token_type_ids=UpperCAmelCase_		,	inputs_embeds=UpperCAmelCase_		)

							__UpperCAmelCase	:							Union[str, Any]     =     torch.cat([modal_embeddings, txt_embeddings]		,	1		)

							__UpperCAmelCase	:							int     =     embedding_output.size()[:-1]

							if attention_mask is None:
									__UpperCAmelCase	:							str     =     torch.ones(UpperCAmelCase_		,	device=UpperCAmelCase_		)
							else:
									__UpperCAmelCase	:							List[Any]     =     torch.cat(
									    [torch.ones(UpperCAmelCase_		,	device=UpperCAmelCase_		,	dtype=torch.long		), attention_mask]		,	dim=1		)
							if encoder_attention_mask is None:
									__UpperCAmelCase	:							int     =     torch.ones(UpperCAmelCase_		,	device=UpperCAmelCase_		)
							else:
									__UpperCAmelCase	:							str     =     torch.cat(
									    [torch.ones(UpperCAmelCase_		,	device=UpperCAmelCase_		), encoder_attention_mask]		,	dim=1		)

							__UpperCAmelCase	:							str     =     self.get_extended_attention_mask(UpperCAmelCase_		,	UpperCAmelCase_		)
							__UpperCAmelCase	:							List[str]     =     self.invert_attention_mask(UpperCAmelCase_		)
							__UpperCAmelCase	:							List[str]     =     self.get_head_mask(UpperCAmelCase_		,	self.config.num_hidden_layers		)

							__UpperCAmelCase	:							int     =     self.transformer.encoder(
							    UpperCAmelCase_		,	attention_mask=UpperCAmelCase_		,	head_mask=UpperCAmelCase_		,	encoder_hidden_states=UpperCAmelCase_		,	encoder_attention_mask=UpperCAmelCase_		,	output_attentions=UpperCAmelCase_		,	output_hidden_states=UpperCAmelCase_		,	return_dict=UpperCAmelCase_		,	)

							__UpperCAmelCase	:							Any     =     encoder_outputs[0]
							__UpperCAmelCase	:							List[str]     =     self.transformer.pooler(UpperCAmelCase_		)

							if not return_dict:
									return (sequence_output, pooled_output) + encoder_outputs[1:]

							return BaseModelOutputWithPooling(
							    last_hidden_state=UpperCAmelCase_		,	pooler_output=UpperCAmelCase_		,	hidden_states=encoder_outputs.hidden_states		,	attentions=encoder_outputs.attentions		,	)
					def 		lowerCamelCase_   (   self       :      Optional[int]		):







							"""simple docstring"""
							return self.embeddings.word_embeddings






					def 		lowerCamelCase_   (   self       :      Optional[Any]		,	UpperCAmelCase_       :      Union[str, Any]		):







							"""simple docstring"""
							__UpperCAmelCase	:							Any     =     value






@add_start_docstrings(
    '''
    MMBT Model with a sequence classification/regression head on top (a linear layer on top of the pooled output)
    '''							,snake_case__							,snake_case__							,)
class      SCREAMING_SNAKE_CASE__      (   nn.Module		):







					"""simple docstring"""






					def __init__(   self       :      int		,	UpperCAmelCase_       :      List[Any]		,	UpperCAmelCase_       :      Tuple		,	UpperCAmelCase_       :      int		):







							"""simple docstring"""
							super().__init__()
							__UpperCAmelCase	:							Optional[Any]     =     config.num_labels

							__UpperCAmelCase	:							List[Any]     =     MMBTModel(UpperCAmelCase_		,	UpperCAmelCase_		,	UpperCAmelCase_		)
							__UpperCAmelCase	:							Optional[Any]     =     nn.Dropout(config.hidden_dropout_prob		)
							__UpperCAmelCase	:							List[Any]     =     nn.Linear(config.hidden_size		,	config.num_labels		)






					def 		lowerCamelCase_   (   self       :      Optional[Any]		,	UpperCAmelCase_       :      int		,	UpperCAmelCase_       :      Union[str, Any]=None		,	UpperCAmelCase_       :      List[Any]=None		,	UpperCAmelCase_       :      Tuple=None		,	UpperCAmelCase_       :      Tuple=None		,	UpperCAmelCase_       :      List[str]=None		,	UpperCAmelCase_       :      List[Any]=None		,	UpperCAmelCase_       :      str=None		,	UpperCAmelCase_       :      Union[str, Any]=None		,	UpperCAmelCase_       :      int=None		,	UpperCAmelCase_       :      Optional[Any]=None		,	UpperCAmelCase_       :      Optional[int]=None		,	UpperCAmelCase_       :      Union[str, Any]=None		,	):







							"""simple docstring"""
							__UpperCAmelCase	:							Union[str, Any]     =     return_dict if return_dict is not None else self.config.use_return_dict

							__UpperCAmelCase	:							int     =     self.mmbt(
							    input_modal=UpperCAmelCase_		,	input_ids=UpperCAmelCase_		,	modal_start_tokens=UpperCAmelCase_		,	modal_end_tokens=UpperCAmelCase_		,	attention_mask=UpperCAmelCase_		,	token_type_ids=UpperCAmelCase_		,	modal_token_type_ids=UpperCAmelCase_		,	position_ids=UpperCAmelCase_		,	modal_position_ids=UpperCAmelCase_		,	head_mask=UpperCAmelCase_		,	inputs_embeds=UpperCAmelCase_		,	return_dict=UpperCAmelCase_		,	)

							__UpperCAmelCase	:							Optional[int]     =     outputs[1]

							__UpperCAmelCase	:							Dict     =     self.dropout(UpperCAmelCase_		)
							__UpperCAmelCase	:							List[str]     =     self.classifier(UpperCAmelCase_		)

							__UpperCAmelCase	:							Tuple     =     None
							if labels is not None:
									if self.num_labels == 1:
											#  We are doing regression
											__UpperCAmelCase	:							Optional[Any]     =     MSELoss()
											__UpperCAmelCase	:							Tuple     =     loss_fct(logits.view(-1		)		,	labels.view(-1		)		)
									else:
											__UpperCAmelCase	:							int     =     CrossEntropyLoss()
											__UpperCAmelCase	:							List[str]     =     loss_fct(logits.view(-1		,	self.num_labels		)		,	labels.view(-1		)		)

							if not return_dict:
									__UpperCAmelCase	:							Union[str, Any]     =     (logits,) + outputs[2:]
									return ((loss,) + output) if loss is not None else output

							return SequenceClassifierOutput(
							    loss=UpperCAmelCase_		,	logits=UpperCAmelCase_		,	hidden_states=outputs.hidden_states		,	attentions=outputs.attentions		,	)




