
"""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()
__lowerCAmelCase							:  List[Any] 				=		logging.get_logger(__name__)

__lowerCAmelCase							:  Optional[Any] 				=		'''Hello world! cécé herlolip'''

def __snake_case (      UpperCamelCase						,					UpperCamelCase						,					UpperCamelCase    )       ->    List[Any]:


		"""simple docstring"""





		a__   		=     FairseqRobertaModel.from_pretrained(UpperCamelCase    )
		roberta.eval()  # disable dropout
		a__   		=     roberta.model.encoder.sentence_encoder
		a__   		=     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:
				a__   		=     roberta.model.classification_heads['''mnli'''].out_proj.weight.shape[0]
		print('''Our BERT config:'''						,					UpperCamelCase    )

		a__   		=     RobertaForSequenceClassification(UpperCamelCase    ) if classification_head else RobertaForMaskedLM(UpperCamelCase    )
		model.eval()

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

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

				# self attention
				a__   		=     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)    )
				)

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

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

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

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

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

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

		a__   		=     model(UpperCamelCase    )[0]
		if classification_head:
				a__   		=     roberta.model.classification_heads['''mnli'''](roberta.extract_features(UpperCamelCase    )    )
		else:
				a__   		=     roberta.model(UpperCamelCase    )[0]
		print(our_output.shape						,					their_output.shape    )
		a__   		=     torch.max(torch.abs(our_output - their_output    )    ).item()
		print(f"max_absolute_diff = {max_absolute_diff}"    )  # ~ 1e-7
		a__   		=     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							:  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							:  Dict 				=		parser.parse_args()
					convert_roberta_checkpoint_to_pytorch(
					    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
					)






"""simple docstring"""




def __snake_case (      UpperCamelCase    )       ->    float:


		"""simple docstring"""





		return -num if num < 0 else num

def __snake_case (      UpperCamelCase    )       ->    int:


		"""simple docstring"""





		if len(UpperCamelCase    ) == 0:
				raise ValueError('''abs_min() arg is an empty sequence'''    )
		a__   		=     x[0]
		for i in x:
				if abs_val(UpperCamelCase    ) < abs_val(UpperCamelCase    ):
						a__   		=     i
		return j

def __snake_case (      UpperCamelCase    )       ->    int:


		"""simple docstring"""





		if len(UpperCamelCase    ) == 0:
				raise ValueError('''abs_max() arg is an empty sequence'''    )
		a__   		=     x[0]
		for i in x:
				if abs(UpperCamelCase    ) > abs(UpperCamelCase    ):
						a__   		=     i
		return j

def __snake_case (      UpperCamelCase    )       ->    int:


		"""simple docstring"""





		if len(UpperCamelCase    ) == 0:
				raise ValueError('''abs_max_sort() arg is an empty sequence'''    )
		return sorted(UpperCamelCase						,					key=UpperCamelCase    )[-1]

def __snake_case (      )       ->    Optional[int]:


		"""simple docstring"""





		assert abs_val(0    ) == 0
		assert abs_val(34    ) == 34
		assert abs_val(-100_000_000_000    ) == 100_000_000_000

		a__   		=     [-3, -1, 2, -11]
		assert abs_max(UpperCamelCase    ) == -11
		assert abs_max_sort(UpperCamelCase    ) == -11
		assert abs_min(UpperCamelCase    ) == -1


if __name__ == "__main__":
					import doctest

					doctest.testmod()

					test_abs_val()
					print(abs_val(-34))  # --> 34




