



'''simple docstring'''







import math
from typing import Optional

import torch
import torch.utils.checkpoint as checkpoint
from torch import nn

from ....modeling_utils import PreTrainedModel
from ....utils import add_start_docstrings, logging
from ...bert.modeling_bert import BertModel
from .configuration_retribert import RetriBertConfig


SCREAMING_SNAKE_CASE__     : Optional[int]				=   logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__     : int				=   [
    """yjernite/retribert-base-uncased""",
    # See all RetriBert models at https://huggingface.co/models?filter=retribert
]




class 				__lowerCAmelCase(							lowerCAmelCase__      ):
			__snake_case  :		Dict		=      RetriBertConfig
			__snake_case  :		List[Any]		=      None
			__snake_case  :		Union[str, Any]		=      'retribert'






			def 				_lowercase	(   self  :							List[Any]      ,       SCREAMING_SNAKE_CASE  :							List[Any]	):




					"""simple docstring"""







					if isinstance(SCREAMING_SNAKE_CASE      ,       nn.Linear	):
							module.weight.data.normal_(mean=0.0      ,       std=self.config.initializer_range	)
							if module.bias is not None:
									module.bias.data.zero_()
					elif isinstance(SCREAMING_SNAKE_CASE      ,       nn.Embedding	):
							module.weight.data.normal_(mean=0.0      ,       std=self.config.initializer_range	)
							if module.padding_idx is not None:
									module.weight.data[module.padding_idx].zero_()
					elif isinstance(SCREAMING_SNAKE_CASE      ,       nn.LayerNorm	):
							module.bias.data.zero_()
							module.weight.data.fill_(1.0	)


SCREAMING_SNAKE_CASE__     : str				=   r"""

    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
    etc.)

    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
    and behavior.

    Parameters:
        config ([`RetriBertConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""




@add_start_docstrings(
    'Bert Based model to embed queries or document for document retrieval.'		,			lowerCAmelCase__		,			)
class 				__lowerCAmelCase(							lowerCAmelCase__      ):






			def __init__(   self  :							Any      ,       SCREAMING_SNAKE_CASE  :							RetriBertConfig	):




					"""simple docstring"""







					super().__init__(SCREAMING_SNAKE_CASE	)
					SCREAMING_SNAKE_CASE_						:Any										=      config.projection_dim

					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      BertModel(SCREAMING_SNAKE_CASE	)
					SCREAMING_SNAKE_CASE_						:Any										=      None if config.share_encoders else BertModel(SCREAMING_SNAKE_CASE	)
					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      nn.Dropout(config.hidden_dropout_prob	)
					SCREAMING_SNAKE_CASE_						:List[str]										=      nn.Linear(config.hidden_size      ,       config.projection_dim      ,       bias=SCREAMING_SNAKE_CASE	)
					SCREAMING_SNAKE_CASE_						:Optional[int]										=      nn.Linear(config.hidden_size      ,       config.projection_dim      ,       bias=SCREAMING_SNAKE_CASE	)

					SCREAMING_SNAKE_CASE_						:int										=      nn.CrossEntropyLoss(reduction='mean'	)

					# Initialize weights and apply final processing
					self.post_init()






			def 				_lowercase	(   self  :							Dict      ,       SCREAMING_SNAKE_CASE  :							Tuple      ,       SCREAMING_SNAKE_CASE  :							str      ,       SCREAMING_SNAKE_CASE  :							List[Any]      ,       SCREAMING_SNAKE_CASE  :							Tuple=-1      ,       ):




					"""simple docstring"""







					if checkpoint_batch_size < 0 or input_ids.shape[0] < checkpoint_batch_size:
							return sent_encoder(SCREAMING_SNAKE_CASE      ,       attention_mask=SCREAMING_SNAKE_CASE	)[1]
					else:
							# prepare implicit variables
							SCREAMING_SNAKE_CASE_						:Tuple										=      input_ids.device
							SCREAMING_SNAKE_CASE_						:Any										=      input_ids.size()
							SCREAMING_SNAKE_CASE_						:Any										=      torch.zeros(SCREAMING_SNAKE_CASE      ,       dtype=torch.long      ,       device=SCREAMING_SNAKE_CASE	)
							SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      [None] * sent_encoder.config.num_hidden_layers
							SCREAMING_SNAKE_CASE_						:torch.Tensor										=      sent_encoder.get_extended_attention_mask(
							    SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE	)

							# define function for checkpointing
							def partial_encode(*SCREAMING_SNAKE_CASE  :							Union[str, Any]	):
									SCREAMING_SNAKE_CASE_						:List[Any]										=      sent_encoder.encoder(
									    inputs[0]      ,       attention_mask=inputs[1]      ,       head_mask=SCREAMING_SNAKE_CASE      ,       )
									SCREAMING_SNAKE_CASE_						:int										=      encoder_outputs[0]
									SCREAMING_SNAKE_CASE_						:Optional[Any]										=      sent_encoder.pooler(SCREAMING_SNAKE_CASE	)
									return pooled_output

							# run embedding layer on everything at once
							SCREAMING_SNAKE_CASE_						:Dict										=      sent_encoder.embeddings(
							    input_ids=SCREAMING_SNAKE_CASE      ,       position_ids=SCREAMING_SNAKE_CASE      ,       token_type_ids=SCREAMING_SNAKE_CASE      ,       inputs_embeds=SCREAMING_SNAKE_CASE	)
							# run encoding and pooling on one mini-batch at a time
							SCREAMING_SNAKE_CASE_						:str										=      []
							for b in range(math.ceil(input_ids.shape[0] / checkpoint_batch_size	)	):
									SCREAMING_SNAKE_CASE_						:Optional[int]										=      embedding_output[b * checkpoint_batch_size : (b + 1) * checkpoint_batch_size]
									SCREAMING_SNAKE_CASE_						:Optional[Any]										=      extended_attention_mask[b * checkpoint_batch_size : (b + 1) * checkpoint_batch_size]
									SCREAMING_SNAKE_CASE_						:int										=      checkpoint.checkpoint(SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE	)
									pooled_output_list.append(SCREAMING_SNAKE_CASE	)
							return torch.cat(SCREAMING_SNAKE_CASE      ,       dim=0	)






			def 				_lowercase	(   self  :							Optional[Any]      ,       SCREAMING_SNAKE_CASE  :							List[str]      ,       SCREAMING_SNAKE_CASE  :							Any=None      ,       SCREAMING_SNAKE_CASE  :							str=-1      ,       ):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Dict										=      self.embed_sentences_checkpointed(
					    SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE      ,       self.bert_query      ,       SCREAMING_SNAKE_CASE      ,       )
					return self.project_query(SCREAMING_SNAKE_CASE	)






			def 				_lowercase	(   self  :							str      ,       SCREAMING_SNAKE_CASE  :							Tuple      ,       SCREAMING_SNAKE_CASE  :							List[str]=None      ,       SCREAMING_SNAKE_CASE  :							int=-1      ,       ):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:int										=      self.embed_sentences_checkpointed(
					    SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE      ,       self.bert_query if self.bert_doc is None else self.bert_doc      ,       SCREAMING_SNAKE_CASE      ,       )
					return self.project_doc(SCREAMING_SNAKE_CASE	)






			def 				_lowercase	(   self  :							Any      ,       SCREAMING_SNAKE_CASE  :							torch.LongTensor      ,       SCREAMING_SNAKE_CASE  :							Optional[torch.FloatTensor]      ,       SCREAMING_SNAKE_CASE  :							torch.LongTensor      ,       SCREAMING_SNAKE_CASE  :							Optional[torch.FloatTensor]      ,       SCREAMING_SNAKE_CASE  :							int = -1      ,       ):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      input_ids_query.device
					SCREAMING_SNAKE_CASE_						:Any										=      self.embed_questions(SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE	)
					SCREAMING_SNAKE_CASE_						:List[str]										=      self.embed_answers(SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE	)
					SCREAMING_SNAKE_CASE_						:Dict										=      torch.mm(SCREAMING_SNAKE_CASE      ,       a_reps.t()	)
					SCREAMING_SNAKE_CASE_						:str										=      self.ce_loss(SCREAMING_SNAKE_CASE      ,       torch.arange(compare_scores.shape[1]	).to(SCREAMING_SNAKE_CASE	)	)
					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      self.ce_loss(compare_scores.t()      ,       torch.arange(compare_scores.shape[0]	).to(SCREAMING_SNAKE_CASE	)	)
					SCREAMING_SNAKE_CASE_						:Optional[int]										=      (loss_qa + loss_aq) / 2
					return loss


import argparse

# utilities to import the model weights and config file
import os
import pickle as pkl

# PyTorch + new model classes
import torch
from torch import nn

from transformers import AutoTokenizer, MegaConfig, MegaForMaskedLM


# import the EncoderLayer class used to pretrain
# !! NOTE !! this requires the version of fairseq that is built when you install the Mega source
try:
	from fairseq.modules.mega_layer import MegaEncoderLayer
except ImportError:
	raise ImportError('You need to install the version of fairseq from the Mega repo!')





class 		__lowerCamelCase     (   nn.Module		):



							def __init__(     self	,				__snake_case	,				__snake_case	,				__snake_case							)						->     Dict:






								"""simple docstring"""



								super().__init__()
								UpperCAmelCase:					int 						=     mega_args
								UpperCAmelCase:					Union[str, Any] 						=     nn.Embedding(__snake_case	,				self.mega_args.encoder_embed_dim							)
								UpperCAmelCase:					List[str] 						=     nn.ModuleList([MegaEncoderLayer(self.mega_args							) for _ in range(__snake_case							)]							)
								UpperCAmelCase:					List[str] 						=     depth




							def     A__ (     self	,				__snake_case	,				__snake_case	,				__snake_case=True	,				__snake_case=0							)						->     List[str]:






								"""simple docstring"""



								if batch_first:
									UpperCAmelCase:					int 						=     input_ids.T

								# to make things more confusing, Mega expects the attention mask to
								# be (batch, time), but with values of 0 (normal token) and 1 (ignore token)
								# which is the opposite of what HF returns
								if ignore_mask_value == 0:
									UpperCAmelCase:					Union[str, Any] 						=     1 - attention_mask

								# get token embeddings from IDs
								UpperCAmelCase:					Dict 						=     self.embedding_layer(__snake_case							)

								# pass through the Mega layers
								# input is (time, batch, encoder dim) and output is the same
								for encoder in self.encoders:
									UpperCAmelCase:					Any 						=     encoder(__snake_case	,				__snake_case							)

								# return according to the shape specified
								if batch_first:
									# (T, B, H) --> (B, T, H)
									return torch.transpose(__snake_case	,				0	,				1							)
								else:
									return embeds





class 		__lowerCamelCase     (   nn.Module		):



							def __init__(     self	,				__snake_case	,				__snake_case	,				__snake_case							)						->     Any:






								"""simple docstring"""



								super().__init__()
								UpperCAmelCase:					int 						=     MegaLM(__snake_case	,				__snake_case	,				__snake_case							)
								UpperCAmelCase:					Tuple 						=     nn.Linear(mega_args.encoder_embed_dim	,				__snake_case							)
								UpperCAmelCase:					List[str] 						=     nn.Dropout(p=0.1							)




							def     A__ (     self	,				__snake_case	,				__snake_case	,				__snake_case=True	,				__snake_case=0							)						->     Optional[Any]:






								"""simple docstring"""



								UpperCAmelCase:					str 						=     self.mega(__snake_case	,				__snake_case	,				__snake_case	,				__snake_case							)
								return self.mlm_head(self.dropout(__snake_case							)							)



def      __UpperCAmelCase    (  snake_case_							:		Optional[Any]				,					snake_case_							:		Dict				,					snake_case_							:		Tuple				):



	'''simple docstring'''



	with open(os.path.join(snake_case_				,					"model_args.pkl"				)				,					"rb"				) as f:
		UpperCAmelCase:					Union[str, Any] 						=     pkl.load(snake_case_				)

	# load the original encoder
	UpperCAmelCase:					Tuple 						=     OriginalMegaForMaskedLM(**snake_case_				).eval()

	# load its weights
	print(
	    "Original Mega encoder:"				,					original_mlm.mega.load_state_dict(
	        torch.load(os.path.join(snake_case_				,					"encoder_weights.pt"				)				,					map_location="cpu"				)				)				,					)
	print(
	    "Original Mega MLM layer:"				,					original_mlm.mlm_head.load_state_dict(
	        torch.load(os.path.join(snake_case_				,					"mlm_head_weights.pt"				)				,					map_location="cpu"				)				)				,					)

	# create a new config from the old one
	UpperCAmelCase:					int 						=     MegaConfig(
	    num_hidden_layers=mega_original_args["depth"]				,					vocab_size=mega_original_args["vocab_size"]				,					hidden_size=mega_original_args["mega_args"].encoder_embed_dim				,					shared_representation_size=mega_original_args["mega_args"].encoder_z_dim				,					intermediate_size=mega_original_args["mega_args"].encoder_hidden_dim				,					ema_projection_size=mega_original_args["mega_args"].encoder_n_dim				,					dropout_prob=mega_original_args["mega_args"].dropout				,					attention_probs_dropout_prob=mega_original_args["mega_args"].attention_dropout				,					hidden_dropout_prob=mega_original_args["mega_args"].hidden_dropout				,					activation=mega_original_args["mega_args"].activation_fn				,					attention_activation=mega_original_args["mega_args"].attention_activation_fn				,					bidirectional=mega_original_args["mega_args"].bidirectional				,					use_chunking=mega_original_args["mega_args"].encoder_chunk_size > 0				,					chunk_size=mega_original_args["mega_args"].encoder_chunk_size				,					truncation=mega_original_args["mega_args"].truncation_length				,					normalization_type=mega_original_args["mega_args"].normalization_type				,					normalize_before_mega=snake_case_				,					norm_affine=snake_case_				,					use_feature_dropout=mega_original_args["mega_args"].feature_dropout				,					relative_positional_bias=mega_original_args["mega_args"].rel_pos_bias				,					max_positions=mega_original_args["mega_args"].max_source_positions				,					nffn_hidden_size=mega_original_args["mega_args"].encoder_ffn_embed_dim				,					normalize_before_ffn=mega_original_args["mega_args"].normalize_before				,					nffn_activation_dropout_prob=0.0				,					add_token_type_embeddings=snake_case_				,					add_lm_hidden_dense_layer=snake_case_				,					)

	UpperCAmelCase:					Union[str, Any] 						=     MegaForMaskedLM(snake_case_				).eval()

	# the originl checkpoint just uses nn.Embedding for the word embeddings
	# we use a wrapper module for embeddings to add support for positional embeddings
	UpperCAmelCase:					str 						=     original_mlm.mega.embedding_layer.weight

	# modify the state dictionary of the original checkpoint to account for naming issues in the Hugging Face
	# ecosystem -- any names containing "beta" or "gamma" aren't safe to use and are renamed upon _load_pretrained,
	# also renaming previously confusing parameter names
	UpperCAmelCase:					List[str] 						=     original_mlm.mega.encoders.state_dict()
	UpperCAmelCase:					Optional[Any] 						=     {}
	for module_name in original_state_dict.keys():
		UpperCAmelCase:					Optional[Any] 						=     None
		# have to handle gamma, beta, and alpha differently due to their use
		# in multiple modules within the original repository;
		# beta is used in EMA, MovingAverageGatedAttention, and RotaryRelativePositionalBias, and must be renamed due to flax/tf weights
		# the EMA sublayer was renamed from "move" to "ema_gate" for readability, so that is also done here
		if "beta" in module_name:
			# EMA sub-layers were always called "move" in the original repo
			if "move.beta" in module_name:
				UpperCAmelCase:					Any 						=     module_name.replace("move.beta"				,					"ema_gate.ema_expansion_matrix"				)
			elif "mega_layer.beta" in module_name:
				UpperCAmelCase:					Optional[int] 						=     module_name.replace("beta"				,					"qk_bias"				)
			else:
				UpperCAmelCase:					List[str] 						=     module_name.replace("beta"				,					"b_param"				)
        # beta is used in EMA and MovingAverageGatedAttention, and must be renamed due to flax/tf weights
		elif "gamma" in module_name:
			if "move.gamma" in module_name:
				UpperCAmelCase:					Union[str, Any] 						=     module_name.replace("move.gamma"				,					"ema_gate.kernel_projection_matrix"				)
			elif "mega_layer.gamma" in module_name:
				UpperCAmelCase:					Any 						=     module_name.replace("gamma"				,					"qk_weight"				)
			else:
				UpperCAmelCase:					List[Any] 						=     module_name.replace("gamma"				,					"g_param"				)
        # alpha is used in EMA and positional bias; renaming to improve readability
		elif "move.alpha" in module_name:
			UpperCAmelCase:					Tuple 						=     module_name.replace("move.alpha"				,					"ema_gate.decay_factor"				)
		# delta is only used in EMA; renaming to improve readability
		elif "move.delta" in module_name:
			UpperCAmelCase:					Any 						=     module_name.replace("move.delta"				,					"ema_gate.damping_factor"				)
		# omega is only used in EMA; renaming to improve readability
		elif "omega" in module_name:
			UpperCAmelCase:					Optional[int] 						=     module_name.replace("move.omega"				,					"ema_gate.residual_weight"				)

		if new_module_name:
			UpperCAmelCase:					List[Any] 						=     new_module_name

	if len(snake_case_				) != 0:
		print(F'Renaming these keys: {updated_keys.keys()}'				)
	else:
		print("No need to rename state dict entries"				)
	for old, new in updated_keys.items():
		UpperCAmelCase:					str 						=     original_state_dict.pop(snake_case_				)

	# now attempt to load the state dictionary with updated names
	# note that we now call it `mega.layers` instead of `mega.encoders` due to hugging face style
	print("HF Mega encoder:"				,					hf_mlm.mega.layers.load_state_dict(snake_case_				)				)

	# load the MLM head weights directly
	print(
	    "HF Mega MLM layer:"				,					hf_mlm.mlm_head.load_state_dict(
	        torch.load(os.path.join(snake_case_				,					"mlm_head_weights.pt"				)				,					map_location="cpu"				)				)				,					)

	# test on a randomly generated input sequence
	UpperCAmelCase:					Optional[int] 						=     torch.randint(0				,					hf_config.vocab_size				,					size=(4, 2_5_6)				)
	UpperCAmelCase:					int 						=     torch.ones_like(snake_case_				)
	# mask a few tokens to make sure masking is applied appropriately :)
	UpperCAmelCase:					int 						=     0

	# run forward passes
	UpperCAmelCase:					Optional[int] 						=     original_mlm(snake_case_				,					snake_case_				,					batch_first=snake_case_				,					ignore_mask_value=0				)
	UpperCAmelCase:					Optional[int] 						=     hf_mlm(snake_case_				,					snake_case_				)[0]

	# print shapes and diff
	print(F'original output {original_output.shape}'				)
	print(F'hf output {hf_output.shape}'				)
	print(F'max diff: {(original_output - hf_output).max()}'				)  # 0.0
	UpperCAmelCase:					Tuple 						=     torch.allclose(snake_case_				,					snake_case_				,					atol=1e-3				)

	if success:
		print("Yay!"				)
		hf_mlm.save_pretrained(snake_case_				)
	else:
		raise RuntimeError(F'Something\'s broken :(\nOriginal:\n{original_output}\n\nHF\n{hf_output}\n{hf_mlm}'				)

	if includes_tokenizer:
		print("Transferring tokenizer"				)
		UpperCAmelCase:					List[str] 						=     AutoTokenizer.from_pretrained(snake_case_				)
		tokenizer.save_pretrained(snake_case_				)


if __name__ == "__main__":
	snake_case_      :					str				      =     argparse.ArgumentParser()

	parser.add_argument(
	    '--pretrained_checkpoint_path',
	    default=None,
	    type=str,
	    required=True,
	    help='Point to the directory containing your model weights using the official Mega repo',
	)

	parser.add_argument(
	    '--output_path', default=None, type=str, required=True, help='Location to save the Hugging Face version'
	)

	parser.add_argument(
	    '--includes_tokenizer',
	    action='store_true',
	    help='Use this flag if there is a Hugging Face tokenizer in the original checkpoint repo',
	)

	snake_case_      :					str				      =     parser.parse_args()

	convert_checkpoint_to_huggingface(args.pretrained_checkpoint_path, args.output_path, args.includes_tokenizer)
