



"""simple docstring"""




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()
__A             =logging.get_logger(__name__)

__A             ="Hello world! cécé herlolip"

def 	a						(				_UpperCAmelCase							: str			,       _UpperCAmelCase							: str			,       _UpperCAmelCase							: bool						):







		'''simple docstring'''





		__UpperCAmelCase   :					int													=     FairseqRobertaModel.from_pretrained(_UpperCAmelCase						)
		roberta.eval()  # disable dropout
		__UpperCAmelCase   :					List[str]													=     roberta.model.encoder.sentence_encoder
		__UpperCAmelCase   :					Any													=     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=5_14			,       type_vocab_size=1			,       layer_norm_eps=1e-5			,       )
		if classification_head:
				__UpperCAmelCase   :					Union[str, Any]													=     roberta.model.classification_heads['''mnli'''].out_proj.weight.shape[0]
		print('''Our BERT config:'''			,       _UpperCAmelCase						)

		__UpperCAmelCase   :					Optional[Any]													=     RobertaForSequenceClassification(_UpperCAmelCase						) if classification_head else RobertaForMaskedLM(_UpperCAmelCase						)
		model.eval()

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

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

				# self attention
				__UpperCAmelCase   :					BertSelfAttention													=     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)						)
				)

				__UpperCAmelCase   :					Any													=     roberta_layer.self_attn.q_proj.weight
				__UpperCAmelCase   :					str													=     roberta_layer.self_attn.q_proj.bias
				__UpperCAmelCase   :					Any													=     roberta_layer.self_attn.k_proj.weight
				__UpperCAmelCase   :					Tuple													=     roberta_layer.self_attn.k_proj.bias
				__UpperCAmelCase   :					List[str]													=     roberta_layer.self_attn.v_proj.weight
				__UpperCAmelCase   :					Tuple													=     roberta_layer.self_attn.v_proj.bias

				# self-attention output
				__UpperCAmelCase   :					BertSelfOutput													=     layer.attention.output
				assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
				__UpperCAmelCase   :					Union[str, Any]													=     roberta_layer.self_attn.out_proj.weight
				__UpperCAmelCase   :					Any													=     roberta_layer.self_attn.out_proj.bias
				__UpperCAmelCase   :					Optional[int]													=     roberta_layer.self_attn_layer_norm.weight
				__UpperCAmelCase   :					int													=     roberta_layer.self_attn_layer_norm.bias

				# intermediate
				__UpperCAmelCase   :					BertIntermediate													=     layer.intermediate
				assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
				__UpperCAmelCase   :					List[str]													=     roberta_layer.fca.weight
				__UpperCAmelCase   :					List[Any]													=     roberta_layer.fca.bias

				# output
				__UpperCAmelCase   :					BertOutput													=     layer.output
				assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
				__UpperCAmelCase   :					Any													=     roberta_layer.fca.weight
				__UpperCAmelCase   :					Tuple													=     roberta_layer.fca.bias
				__UpperCAmelCase   :					int													=     roberta_layer.final_layer_norm.weight
				__UpperCAmelCase   :					int													=     roberta_layer.final_layer_norm.bias
				# end of layer

		if classification_head:
				__UpperCAmelCase   :					Optional[int]													=     roberta.model.classification_heads['''mnli'''].dense.weight
				__UpperCAmelCase   :					List[str]													=     roberta.model.classification_heads['''mnli'''].dense.bias
				__UpperCAmelCase   :					List[Any]													=     roberta.model.classification_heads['''mnli'''].out_proj.weight
				__UpperCAmelCase   :					Any													=     roberta.model.classification_heads['''mnli'''].out_proj.bias
		else:
				# LM Head
				__UpperCAmelCase   :					List[str]													=     roberta.model.encoder.lm_head.dense.weight
				__UpperCAmelCase   :					int													=     roberta.model.encoder.lm_head.dense.bias
				__UpperCAmelCase   :					Union[str, Any]													=     roberta.model.encoder.lm_head.layer_norm.weight
				__UpperCAmelCase   :					List[Any]													=     roberta.model.encoder.lm_head.layer_norm.bias
				__UpperCAmelCase   :					Union[str, Any]													=     roberta.model.encoder.lm_head.weight
				__UpperCAmelCase   :					Optional[Any]													=     roberta.model.encoder.lm_head.bias

		# Let's check that we get the same results.
		__UpperCAmelCase   :					torch.Tensor													=     roberta.encode(_UpperCAmelCase						).unsqueeze(0						)  # batch of size 1

		__UpperCAmelCase   :					Tuple													=     model(_UpperCAmelCase						)[0]
		if classification_head:
				__UpperCAmelCase   :					Any													=     roberta.model.classification_heads['''mnli'''](roberta.extract_features(_UpperCAmelCase						)						)
		else:
				__UpperCAmelCase   :					List[Any]													=     roberta.model(_UpperCAmelCase						)[0]
		print(our_output.shape			,       their_output.shape						)
		__UpperCAmelCase   :					str													=     torch.max(torch.abs(our_output - their_output						)						).item()
		print(f'max_absolute_diff = {max_absolute_diff}'						)  # ~ 1e-7
		__UpperCAmelCase   :					Dict													=     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'''						)

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


if __name__ == "__main__":
				__A             =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."
				)
				__A             =parser.parse_args()
				convert_roberta_checkpoint_to_pytorch(
				    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
				)










from ...configuration_utils import PretrainedConfig
from ...utils import logging


__A             =logging.get_logger(__name__)

__A             ={
    "tau/splinter-base": "https://huggingface.co/tau/splinter-base/resolve/main/config.json",
    "tau/splinter-base-qass": "https://huggingface.co/tau/splinter-base-qass/resolve/main/config.json",
    "tau/splinter-large": "https://huggingface.co/tau/splinter-large/resolve/main/config.json",
    "tau/splinter-large-qass": "https://huggingface.co/tau/splinter-large-qass/resolve/main/config.json",
    # See all Splinter models at https://huggingface.co/models?filter=splinter
}

class 		UpperCAmelCase__      (      __UpperCamelCase       ):




			'''simple docstring'''






			UpperCamelCase      						=	"""splinter"""




			def __init__(       self							:			Any	,	a_							:			Optional[int]=3_05_22	,	a_							:			Union[str, Any]=7_68	,	a_							:			int=12	,	a_							:			int=12	,	a_							:			List[Any]=30_72	,	a_							:			Union[str, Any]="gelu"	,	a_							:			Any=0.1	,	a_							:			Union[str, Any]=0.1	,	a_							:			Any=5_12	,	a_							:			str=2	,	a_							:			Dict=0.0_2	,	a_							:			int=1e-12	,	a_							:			str=True	,	a_							:			Tuple=0	,	a_							:			List[Any]=1_04	,	**a_							:			str	,	):
					'''simple docstring'''





					super().__init__(pad_token_id=a_	,	**a_			)

					__UpperCAmelCase   :					Union[str, Any]													=     vocab_size
					__UpperCAmelCase   :					List[Any]													=     max_position_embeddings
					__UpperCAmelCase   :					str													=     hidden_size
					__UpperCAmelCase   :					Union[str, Any]													=     num_hidden_layers
					__UpperCAmelCase   :					Any													=     num_attention_heads
					__UpperCAmelCase   :					Any													=     intermediate_size
					__UpperCAmelCase   :					Tuple													=     hidden_act
					__UpperCAmelCase   :					str													=     hidden_dropout_prob
					__UpperCAmelCase   :					Any													=     attention_probs_dropout_prob
					__UpperCAmelCase   :					Optional[int]													=     initializer_range
					__UpperCAmelCase   :					Any													=     type_vocab_size
					__UpperCAmelCase   :					int													=     layer_norm_eps
					__UpperCAmelCase   :					List[Any]													=     use_cache
					__UpperCAmelCase   :					Optional[int]													=     question_token_id





