
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__        =						logging.get_logger(__name__)

lowerCamelCase__        =						"""Hello world! cécé herlolip"""
def 					lowerCAmelCase__     (    SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_	)							->						int:
			lowerCAmelCase__ : List[Any]   =						FairseqRobertaModel.from_pretrained(SCREAMING_SNAKE_CASE_	)
			roberta.eval()  # disable dropout
			lowerCAmelCase__ : Union[str, Any]   =						roberta.model.encoder.sentence_encoder
			lowerCAmelCase__ : Union[str, 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=514			,				type_vocab_size=1			,				layer_norm_eps=1e-5			,				)
			if classification_head:
						lowerCAmelCase__ : Dict   =						roberta.model.classification_heads['mnli'].out_proj.weight.shape[0]
			print('Our BERT config:'			,				SCREAMING_SNAKE_CASE_	)

			lowerCAmelCase__ : Optional[int]   =						RobertaForSequenceClassification(SCREAMING_SNAKE_CASE_	) if classification_head else RobertaForMaskedLM(SCREAMING_SNAKE_CASE_	)
			model.eval()

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

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

						# self attention
						lowerCAmelCase__ : 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)	)
						)

						lowerCAmelCase__ : Optional[Any]   =						roberta_layer.self_attn.q_proj.weight
						lowerCAmelCase__ : Dict   =						roberta_layer.self_attn.q_proj.bias
						lowerCAmelCase__ : Optional[Any]   =						roberta_layer.self_attn.k_proj.weight
						lowerCAmelCase__ : int   =						roberta_layer.self_attn.k_proj.bias
						lowerCAmelCase__ : Optional[int]   =						roberta_layer.self_attn.v_proj.weight
						lowerCAmelCase__ : List[Any]   =						roberta_layer.self_attn.v_proj.bias

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

						# intermediate
						lowerCAmelCase__ : BertIntermediate   =						layer.intermediate
						assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
						lowerCAmelCase__ : List[str]   =						roberta_layer.fca.weight
						lowerCAmelCase__ : Optional[int]   =						roberta_layer.fca.bias

						# output
						lowerCAmelCase__ : BertOutput   =						layer.output
						assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
						lowerCAmelCase__ : int   =						roberta_layer.fca.weight
						lowerCAmelCase__ : int   =						roberta_layer.fca.bias
						lowerCAmelCase__ : Dict   =						roberta_layer.final_layer_norm.weight
						lowerCAmelCase__ : Optional[Any]   =						roberta_layer.final_layer_norm.bias
						# end of layer

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

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

			lowerCAmelCase__ : str   =						model(SCREAMING_SNAKE_CASE_	)[0]
			if classification_head:
						lowerCAmelCase__ : int   =						roberta.model.classification_heads['mnli'](roberta.extract_features(SCREAMING_SNAKE_CASE_	)	)
			else:
						lowerCAmelCase__ : Dict   =						roberta.model(SCREAMING_SNAKE_CASE_	)[0]
			print(our_output.shape			,				their_output.shape	)
			lowerCAmelCase__ : int   =						torch.max(torch.abs(our_output - their_output	)	).item()
			print(F'''max_absolute_diff = {max_absolute_diff}'''	)  # ~ 1e-7
			lowerCAmelCase__ : Optional[Any]   =						torch.allclose(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				atol=1e-3	)
			print('Do both models output the same tensors?'			,				'🔥' if success else '💩'	)
			if not success:
						raise Exception('Something went wRoNg'	)

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


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

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

# Load iris file
lowerCamelCase__        =						load_iris()
iris.keys()


print(F"""Target names: \n {iris.target_names} """)
print(F"""\n Features: \n {iris.feature_names}""")

# Train set e Test set
lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__        =						train_test_split(
    iris["""data"""], iris["""target"""], random_state=4
)

# KNN

lowerCamelCase__        =						KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train, y_train)

# new array to test
lowerCamelCase__        =						[[1, 2, 1, 4], [2, 3, 4, 5]]

lowerCamelCase__        =						knn.predict(X_new)

print(
    F"""\nNew array: \n {X_new}\n\nTarget Names Prediction: \n"""
    F""" {iris["target_names"][prediction]}"""
)