




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


lowercase__    :					List[str]					=						logging.get_logger(__name__)

lowercase__    :					str					=						"MMBTConfig"
class 		UpperCAmelCase       (    nn.Module      ):







						'''simple docstring'''





						def __init__(						self							:					int	,     __lowercase							:					Any	,     __lowercase							:					List[str]	,     __lowercase							:					str						):






							"""simple docstring"""






							super().__init__()
							snake_case_		   =					config
							snake_case_		   =					encoder
							snake_case_		   =					nn.Linear(config.modal_hidden_size	,     config.hidden_size						)
							snake_case_		   =					embeddings.position_embeddings
							snake_case_		   =					embeddings.token_type_embeddings
							snake_case_		   =					embeddings.word_embeddings
							snake_case_		   =					embeddings.LayerNorm
							snake_case_		   =					nn.Dropout(p=config.hidden_dropout_prob						)






						def 	snake_case__						(						self							:					Optional[int]	,     __lowercase							:					Union[str, Any]	,     __lowercase							:					int=None	,     __lowercase							:					List[Any]=None	,     __lowercase							:					str=None	,     __lowercase							:					List[Any]=None						):






							"""simple docstring"""






							snake_case_		   =					self.proj_embeddings(self.encoder(__lowercase						)						)
							snake_case_		   =					token_embeddings.size(1						)

							if start_token is not None:
								snake_case_		   =					self.word_embeddings(__lowercase						)
								seq_length += 1
								snake_case_		   =					torch.cat([start_token_embeds.unsqueeze(1						), token_embeddings]	,     dim=1						)

							if end_token is not None:
								snake_case_		   =					self.word_embeddings(__lowercase						)
								seq_length += 1
								snake_case_		   =					torch.cat([token_embeddings, end_token_embeds.unsqueeze(1						)]	,     dim=1						)

							if position_ids is None:
								snake_case_		   =					torch.arange(__lowercase	,     dtype=torch.long	,     device=input_modal.device						)
								snake_case_		   =					position_ids.unsqueeze(0						).expand(input_modal.size(0						)	,     __lowercase						)

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

							snake_case_		   =					self.position_embeddings(__lowercase						)
							snake_case_		   =					self.token_type_embeddings(__lowercase						)
							snake_case_		   =					token_embeddings + position_embeddings + token_type_embeddings
							snake_case_		   =					self.LayerNorm(__lowercase						)
							snake_case_		   =					self.dropout(__lowercase						)
							return embeddings







lowercase__    :					Optional[Any]					=						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"

lowercase__    :					Union[str, Any]					=						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.'''	,       UpperCAmelCase__	,       )
class 		UpperCAmelCase       (    nn.Module	,       UpperCAmelCase__      ):







						'''simple docstring'''





						def __init__(						self							:					Optional[int]	,     __lowercase							:					Optional[int]	,     __lowercase							:					str	,     __lowercase							:					Union[str, Any]						):






							"""simple docstring"""






							super().__init__()
							snake_case_		   =					config
							snake_case_		   =					transformer
							snake_case_		   =					ModalEmbeddings(__lowercase	,     __lowercase	,     transformer.embeddings						)






						@add_start_docstrings_to_model_forward(__lowercase						)
						@replace_return_docstrings(output_type=__lowercase	,     config_class=_CONFIG_FOR_DOC						)
						def 	snake_case__						(						self							:					Union[str, Any]	,     __lowercase							:					Union[str, Any]	,     __lowercase							:					str=None	,     __lowercase							:					int=None	,     __lowercase							:					Union[str, Any]=None	,     __lowercase							:					Union[str, Any]=None	,     __lowercase							:					int=None	,     __lowercase							:					Optional[int]=None	,     __lowercase							:					int=None	,     __lowercase							:					Optional[Any]=None	,     __lowercase							:					str=None	,     __lowercase							:					List[str]=None	,     __lowercase							:					List[Any]=None	,     __lowercase							:					List[str]=None	,     __lowercase							:					Any=None	,     __lowercase							:					Tuple=None	,     __lowercase							:					int=None	,     ):






							"""simple docstring"""






							snake_case_		   =					output_attentions if output_attentions is not None else self.config.output_attentions
							snake_case_		   =					(
							    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
							)
							snake_case_		   =					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:
								snake_case_		   =					input_ids.size()
							elif inputs_embeds is not None:
								snake_case_		   =					inputs_embeds.size()[:-1]
							else:
								raise ValueError("You have to specify either input_ids or inputs_embeds"						)

							snake_case_		   =					input_ids.device if input_ids is not None else inputs_embeds.device

							snake_case_		   =					self.modal_encoder(
							    __lowercase	,     start_token=__lowercase	,     end_token=__lowercase	,     position_ids=__lowercase	,     token_type_ids=__lowercase	,     )

							snake_case_		   =					modal_embeddings.size()[:-1]

							if token_type_ids is None:
								snake_case_		   =					torch.ones(__lowercase	,     dtype=torch.long	,     device=__lowercase						)

							snake_case_		   =					self.transformer.embeddings(
							    input_ids=__lowercase	,     position_ids=__lowercase	,     token_type_ids=__lowercase	,     inputs_embeds=__lowercase						)

							snake_case_		   =					torch.cat([modal_embeddings, txt_embeddings]	,     1						)

							snake_case_		   =					embedding_output.size()[:-1]

							if attention_mask is None:
								snake_case_		   =					torch.ones(__lowercase	,     device=__lowercase						)
							else:
								snake_case_		   =					torch.cat(
								    [torch.ones(__lowercase	,     device=__lowercase	,     dtype=torch.long						), attention_mask]	,     dim=1						)
							if encoder_attention_mask is None:
								snake_case_		   =					torch.ones(__lowercase	,     device=__lowercase						)
							else:
								snake_case_		   =					torch.cat(
								    [torch.ones(__lowercase	,     device=__lowercase						), encoder_attention_mask]	,     dim=1						)

							snake_case_		   =					self.get_extended_attention_mask(__lowercase	,     __lowercase						)
							snake_case_		   =					self.invert_attention_mask(__lowercase						)
							snake_case_		   =					self.get_head_mask(__lowercase	,     self.config.num_hidden_layers						)

							snake_case_		   =					self.transformer.encoder(
							    __lowercase	,     attention_mask=__lowercase	,     head_mask=__lowercase	,     encoder_hidden_states=__lowercase	,     encoder_attention_mask=__lowercase	,     output_attentions=__lowercase	,     output_hidden_states=__lowercase	,     return_dict=__lowercase	,     )

							snake_case_		   =					encoder_outputs[0]
							snake_case_		   =					self.transformer.pooler(__lowercase						)

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

							return BaseModelOutputWithPooling(
							    last_hidden_state=__lowercase	,     pooler_output=__lowercase	,     hidden_states=encoder_outputs.hidden_states	,     attentions=encoder_outputs.attentions	,     )






						def 	snake_case__						(						self							:					Optional[Any]						):






							"""simple docstring"""






							return self.embeddings.word_embeddings






						def 	snake_case__						(						self							:					Tuple	,     __lowercase							:					Dict						):






							"""simple docstring"""






							snake_case_		   =					value







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







						'''simple docstring'''





						def __init__(						self							:					List[str]	,     __lowercase							:					Dict	,     __lowercase							:					Optional[Any]	,     __lowercase							:					Optional[Any]						):






							"""simple docstring"""






							super().__init__()
							snake_case_		   =					config.num_labels

							snake_case_		   =					MMBTModel(__lowercase	,     __lowercase	,     __lowercase						)
							snake_case_		   =					nn.Dropout(config.hidden_dropout_prob						)
							snake_case_		   =					nn.Linear(config.hidden_size	,     config.num_labels						)






						def 	snake_case__						(						self							:					Optional[int]	,     __lowercase							:					Optional[Any]	,     __lowercase							:					List[Any]=None	,     __lowercase							:					str=None	,     __lowercase							:					Optional[int]=None	,     __lowercase							:					List[str]=None	,     __lowercase							:					List[Any]=None	,     __lowercase							:					Union[str, Any]=None	,     __lowercase							:					List[Any]=None	,     __lowercase							:					str=None	,     __lowercase							:					str=None	,     __lowercase							:					Union[str, Any]=None	,     __lowercase							:					Any=None	,     __lowercase							:					int=None	,     ):






							"""simple docstring"""






							snake_case_		   =					return_dict if return_dict is not None else self.config.use_return_dict

							snake_case_		   =					self.mmbt(
							    input_modal=__lowercase	,     input_ids=__lowercase	,     modal_start_tokens=__lowercase	,     modal_end_tokens=__lowercase	,     attention_mask=__lowercase	,     token_type_ids=__lowercase	,     modal_token_type_ids=__lowercase	,     position_ids=__lowercase	,     modal_position_ids=__lowercase	,     head_mask=__lowercase	,     inputs_embeds=__lowercase	,     return_dict=__lowercase	,     )

							snake_case_		   =					outputs[1]

							snake_case_		   =					self.dropout(__lowercase						)
							snake_case_		   =					self.classifier(__lowercase						)

							snake_case_		   =					None
							if labels is not None:
								if self.num_labels == 1:
									#  We are doing regression
									snake_case_		   =					MSELoss()
									snake_case_		   =					loss_fct(logits.view(-1						)	,     labels.view(-1						)						)
								else:
									snake_case_		   =					CrossEntropyLoss()
									snake_case_		   =					loss_fct(logits.view(-1	,     self.num_labels						)	,     labels.view(-1						)						)

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

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






import argparse
import pathlib

import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import RobertaConfig, RobertaForMaskedLM, RobertaForSequenceClassification
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse("0.9.0"):
		raise Exception("requires fairseq >= 0.9.0")


logging.set_verbosity_info()
lowercase__    :					Dict					=						logging.get_logger(__name__)

lowercase__    :					Any					=						"Hello world! cécé herlolip"


def 						lowerCamelCase__   (		_A					,     _A					,     _A							):







	'''simple docstring'''





	snake_case_		   =					FairseqRobertaModel.from_pretrained(_A							)
	roberta.eval()  # disable dropout
	snake_case_		   =					roberta.model.encoder.sentence_encoder
	snake_case_		   =					RobertaConfig(
	    vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings					,     hidden_size=roberta.args.encoder_embed_dim					,     num_hidden_layers=roberta.args.encoder_layers					,     num_attention_heads=roberta.args.encoder_attention_heads					,     intermediate_size=roberta.args.encoder_ffn_embed_dim					,     max_position_embeddings=514					,     type_vocab_size=1					,     layer_norm_eps=1E-5					,     )
	if classification_head:
		snake_case_		   =					roberta.model.classification_heads["mnli"].out_proj.weight.shape[0]
	print("Our BERT config:"					,     _A							)

	snake_case_		   =					RobertaForSequenceClassification(_A							) if classification_head else RobertaForMaskedLM(_A							)
	model.eval()

	# Now let's copy all the weights.
	# Embeddings
	snake_case_		   =					roberta_sent_encoder.embed_tokens.weight
	snake_case_		   =					roberta_sent_encoder.embed_positions.weight
	snake_case_		   =					torch.zeros_like(
	    model.roberta.embeddings.token_type_embeddings.weight							)  # just zero them out b/c RoBERTa doesn't use them.
	snake_case_		   =					roberta_sent_encoder.emb_layer_norm.weight
	snake_case_		   =					roberta_sent_encoder.emb_layer_norm.bias

	for i in range(config.num_hidden_layers							):
		# Encoder: start of layer
		snake_case_		   =					model.roberta.encoder.layer[i]
		snake_case_		   =					roberta_sent_encoder.layers[i]

		# self attention
		snake_case_		   =					layer.attention.self
		assert (
		    roberta_layer.self_attn.k_proj.weight.data.shape
		    == roberta_layer.self_attn.q_proj.weight.data.shape
		    == roberta_layer.self_attn.v_proj.weight.data.shape
		    == torch.Size((config.hidden_size, config.hidden_size)							)
		)

		snake_case_		   =					roberta_layer.self_attn.q_proj.weight
		snake_case_		   =					roberta_layer.self_attn.q_proj.bias
		snake_case_		   =					roberta_layer.self_attn.k_proj.weight
		snake_case_		   =					roberta_layer.self_attn.k_proj.bias
		snake_case_		   =					roberta_layer.self_attn.v_proj.weight
		snake_case_		   =					roberta_layer.self_attn.v_proj.bias

		# self-attention output
		snake_case_		   =					layer.attention.output
		assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
		snake_case_		   =					roberta_layer.self_attn.out_proj.weight
		snake_case_		   =					roberta_layer.self_attn.out_proj.bias
		snake_case_		   =					roberta_layer.self_attn_layer_norm.weight
		snake_case_		   =					roberta_layer.self_attn_layer_norm.bias

		# intermediate
		snake_case_		   =					layer.intermediate
		assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
		snake_case_		   =					roberta_layer.fca.weight
		snake_case_		   =					roberta_layer.fca.bias

		# output
		snake_case_		   =					layer.output
		assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
		snake_case_		   =					roberta_layer.fca.weight
		snake_case_		   =					roberta_layer.fca.bias
		snake_case_		   =					roberta_layer.final_layer_norm.weight
		snake_case_		   =					roberta_layer.final_layer_norm.bias
		# end of layer

	if classification_head:
		snake_case_		   =					roberta.model.classification_heads["mnli"].dense.weight
		snake_case_		   =					roberta.model.classification_heads["mnli"].dense.bias
		snake_case_		   =					roberta.model.classification_heads["mnli"].out_proj.weight
		snake_case_		   =					roberta.model.classification_heads["mnli"].out_proj.bias
	else:
		# LM Head
		snake_case_		   =					roberta.model.encoder.lm_head.dense.weight
		snake_case_		   =					roberta.model.encoder.lm_head.dense.bias
		snake_case_		   =					roberta.model.encoder.lm_head.layer_norm.weight
		snake_case_		   =					roberta.model.encoder.lm_head.layer_norm.bias
		snake_case_		   =					roberta.model.encoder.lm_head.weight
		snake_case_		   =					roberta.model.encoder.lm_head.bias

	# Let's check that we get the same results.
	snake_case_		   =					roberta.encode(_A							).unsqueeze(0							)  # batch of size 1

	snake_case_		   =					model(_A							)[0]
	if classification_head:
		snake_case_		   =					roberta.model.classification_heads["mnli"](roberta.extract_features(_A							)							)
	else:
		snake_case_		   =					roberta.model(_A							)[0]
	print(our_output.shape					,     their_output.shape							)
	snake_case_		   =					torch.max(torch.abs(our_output - their_output							)							).item()
	print(f"max_absolute_diff = {max_absolute_diff}"							)  # ~ 1e-7
	snake_case_		   =					torch.allclose(_A					,     _A					,     atol=1E-3							)
	print("Do both models output the same tensors?"					,     "🔥" if success else "💩"							)
	if not success:
		raise Exception("Something went wRoNg"							)

	pathlib.Path(_A							).mkdir(parents=_A					,     exist_ok=_A							)
	print(f"Saving model to {pytorch_dump_folder_path}"							)
	model.save_pretrained(_A							)


if __name__ == "__main__":
		lowercase__    :					int					=						argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    "--roberta_checkpoint_path", default=None, type=str, required=True, help="Path the official PyTorch dump."
		)
		parser.add_argument(
		    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
		)
		parser.add_argument(
		    "--classification_head", action="store_true", help="Whether to convert a final classification head."
		)
		lowercase__    :					Optional[Any]					=						parser.parse_args()
		convert_roberta_checkpoint_to_pytorch(
		    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
		)
