

'''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 XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobertaXLForSequenceClassification
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.models.roberta.modeling_roberta import RobertaAttention
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse("""1.0.0a"""):
					raise Exception("""requires fairseq >= 1.0.0a""")

logging.set_verbosity_info()
lowerCAmelCase__						:       int										=						logging.get_logger(__name__)

lowerCAmelCase__						:       Tuple										=						"""Hello world! cécé herlolip"""




def 	_a       (				__lowerCAmelCase		:      str   ,							__lowerCAmelCase		:      str   ,							__lowerCAmelCase		:      bool     ):

		"""simple docstring"""

		snake_case__						:       Dict       						=  FairseqRobertaModel.from_pretrained(UpperCAmelCase__     )
		roberta.eval()  # disable dropout
		snake_case__						:       Optional[int]       						=  roberta.model.encoder.sentence_encoder
		snake_case__						:       str       						=  XLMRobertaConfig(
		    vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings   ,							hidden_size=roberta.cfg.model.encoder_embed_dim   ,							num_hidden_layers=roberta.cfg.model.encoder_layers   ,							num_attention_heads=roberta.cfg.model.encoder_attention_heads   ,							intermediate_size=roberta.cfg.model.encoder_ffn_embed_dim   ,							max_position_embeddings=5_14   ,							type_vocab_size=1   ,							layer_norm_eps=1e-5   ,							)
		if classification_head:
				snake_case__						:       Dict       						=  roberta.model.classification_heads['''mnli'''].out_proj.weight.shape[0]

		print('''Our RoBERTa config:'''   ,							UpperCAmelCase__     )

		snake_case__						:       List[str]       						=  XLMRobertaXLForSequenceClassification(UpperCAmelCase__     ) if classification_head else XLMRobertaXLForMaskedLM(UpperCAmelCase__     )
		model.eval()

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

		snake_case__						:       str       						=  roberta_sent_encoder.layer_norm.weight
		snake_case__						:       Tuple       						=  roberta_sent_encoder.layer_norm.bias

		for i in range(config.num_hidden_layers     ):
				# Encoder: start of layer
				snake_case__						:       Optional[Any]       						=  model.roberta.encoder.layer[i]
				snake_case__						:       Union[str, Any]       						=  roberta_sent_encoder.layers[i]

				snake_case__						:       Optional[Any]       						=  layer.attention
				snake_case__						:       Optional[Any]       						=  roberta_layer.self_attn_layer_norm.weight
				snake_case__						:       Union[str, Any]       						=  roberta_layer.self_attn_layer_norm.bias

				# self attention
				snake_case__						:       Any       						=  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__						:       Dict       						=  roberta_layer.self_attn.q_proj.weight
				snake_case__						:       Tuple       						=  roberta_layer.self_attn.q_proj.bias
				snake_case__						:       Optional[int]       						=  roberta_layer.self_attn.k_proj.weight
				snake_case__						:       List[Any]       						=  roberta_layer.self_attn.k_proj.bias
				snake_case__						:       Dict       						=  roberta_layer.self_attn.v_proj.weight
				snake_case__						:       int       						=  roberta_layer.self_attn.v_proj.bias

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

				# this one is final layer norm
				snake_case__						:       Optional[int]       						=  roberta_layer.final_layer_norm.weight
				snake_case__						:       List[str]       						=  roberta_layer.final_layer_norm.bias

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

				# output
				snake_case__						:       Tuple       						=  layer.output
				assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
				snake_case__						:       Tuple       						=  roberta_layer.fca.weight
				snake_case__						:       Optional[Any]       						=  roberta_layer.fca.bias
				# end of layer

		if classification_head:
				snake_case__						:       Any       						=  roberta.model.classification_heads['''mnli'''].dense.weight
				snake_case__						:       Tuple       						=  roberta.model.classification_heads['''mnli'''].dense.bias
				snake_case__						:       Any       						=  roberta.model.classification_heads['''mnli'''].out_proj.weight
				snake_case__						:       Dict       						=  roberta.model.classification_heads['''mnli'''].out_proj.bias
		else:
				# LM Head
				snake_case__						:       Optional[int]       						=  roberta.model.encoder.lm_head.dense.weight
				snake_case__						:       Tuple       						=  roberta.model.encoder.lm_head.dense.bias
				snake_case__						:       Tuple       						=  roberta.model.encoder.lm_head.layer_norm.weight
				snake_case__						:       Optional[int]       						=  roberta.model.encoder.lm_head.layer_norm.bias
				snake_case__						:       Any       						=  roberta.model.encoder.lm_head.weight
				snake_case__						:       List[Any]       						=  roberta.model.encoder.lm_head.bias

		# Let's check that we get the same results.
		snake_case__						:       List[str]       						=  roberta.encode(UpperCAmelCase__     ).unsqueeze(0     )  # batch of size 1

		snake_case__						:       Optional[Any]       						=  model(UpperCAmelCase__     )[0]
		if classification_head:
				snake_case__						:       Any       						=  roberta.model.classification_heads['''mnli'''](roberta.extract_features(UpperCAmelCase__     )     )
		else:
				snake_case__						:       str       						=  roberta.model(UpperCAmelCase__     )[0]
		print(our_output.shape   ,							their_output.shape     )
		snake_case__						:       Any       						=  torch.max(torch.abs(our_output - their_output     )     ).item()
		print(F"""max_absolute_diff = {max_absolute_diff}"""     )  # ~ 1e-7
		snake_case__						:       Optional[int]       						=  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__":
					lowerCAmelCase__						:       Optional[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."""
					)
					lowerCAmelCase__						:       Union[str, Any]										=						parser.parse_args()
					convert_xlm_roberta_xl_checkpoint_to_pytorch(
					    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
					)






'''simple docstring'''


def 	_a       (				__lowerCAmelCase		:      int     ):

		"""simple docstring"""

		if p < 2:
				raise ValueError('''p should not be less than 2!'''     )
		elif p == 2:
				return True

		snake_case__						:       Any       						=  4
		snake_case__						:       int       						=  (1 << p) - 1
		for _ in range(p - 2     ):
				snake_case__						:       Tuple       						=  ((s * s) - 2) % m
		return s == 0


if __name__ == "__main__":
					print(lucas_lehmer_test(7))
					print(lucas_lehmer_test(11))



