



"""simple docstring"""






import argparse
from pathlib import Path

import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version

from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse('0.12.2'):
	raise Exception('requires fairseq >= 0.12.2')
if version.parse(fairseq.__version__) > version.parse('2'):
	raise Exception('requires fairseq < v2')

logging.set_verbosity_info()
__snake_case				:  List[Any] =       logging.get_logger(__name__)

__snake_case				:  Optional[int] =       'Hello, World!'
__snake_case				:  Tuple =       'en_XX'


def      _lowercase							(    __snake_case   ,__snake_case   ,__snake_case      )		->  int:
			__lowerCAmelCase :    Dict				=			Path("data_bin"      )
			__lowerCAmelCase :    Dict				=			FairseqXmodModel.from_pretrained(
			    model_name_or_path=str(Path(__snake_case      ).parent      )   ,checkpoint_file=Path(__snake_case      ).name   ,_name="xmod_base"   ,arch="xmod_base"   ,task="multilingual_masked_lm"   ,data_name_or_path=str(__snake_case      )   ,bpe="sentencepiece"   ,sentencepiece_model=str(Path(__snake_case      ).parent / "sentencepiece.bpe.model"      )   ,src_dict=str(data_dir / "dict.txt"      )   ,)
			xmod.eval()  # disable dropout
			print(__snake_case      )

			__lowerCAmelCase :    int				=			xmod.model.encoder.sentence_encoder
			__lowerCAmelCase :    List[Any]				=			XmodConfig(
			    vocab_size=xmod_sent_encoder.embed_tokens.num_embeddings   ,hidden_size=xmod.cfg.model.encoder_embed_dim   ,num_hidden_layers=xmod.cfg.model.encoder_layers   ,num_attention_heads=xmod.cfg.model.encoder_attention_heads   ,intermediate_size=xmod.cfg.model.encoder_ffn_embed_dim   ,max_position_embeddings=514   ,type_vocab_size=1   ,layer_norm_eps=1e-5   ,pre_norm=xmod.cfg.model.encoder_normalize_before   ,adapter_reduction_factor=getattr(xmod.cfg.model   ,"bottleneck"   ,2      )   ,adapter_layer_norm=xmod.cfg.model.adapter_layer_norm   ,adapter_reuse_layer_norm=xmod.cfg.model.adapter_reuse_layer_norm   ,ln_before_adapter=xmod.cfg.model.ln_before_adapter   ,languages=xmod.cfg.model.languages   ,)
			if classification_head:
						__lowerCAmelCase :    List[Any]				=			xmod.model.classification_heads["mnli"].out_proj.weight.shape[0]

			print("Our X-MOD config:"   ,__snake_case      )

			__lowerCAmelCase :    Any				=			XmodForSequenceClassification(__snake_case      ) if classification_head else XmodForMaskedLM(__snake_case      )
			model.eval()

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

			__lowerCAmelCase :    List[str]				=			xmod_sent_encoder.layernorm_embedding.weight
			__lowerCAmelCase :    Tuple				=			xmod_sent_encoder.layernorm_embedding.bias

			for i in range(config.num_hidden_layers      ):
						# Encoder: start of layer
						__lowerCAmelCase :    Optional[int]				=			model.roberta.encoder.layer[i]
						__lowerCAmelCase :    int				=			xmod_sent_encoder.layers[i]

						# self attention
						__lowerCAmelCase :    Optional[Any]				=			layer.attention.self
						if not (
						    xmod_layer.self_attn.k_proj.weight.data.shape
						    == xmod_layer.self_attn.q_proj.weight.data.shape
						    == xmod_layer.self_attn.v_proj.weight.data.shape
						    == torch.Size((config.hidden_size, config.hidden_size)      )
						):
									raise AssertionError("Dimensions of self-attention weights do not match."      )

						__lowerCAmelCase :    Optional[int]				=			xmod_layer.self_attn.q_proj.weight
						__lowerCAmelCase :    int				=			xmod_layer.self_attn.q_proj.bias
						__lowerCAmelCase :    Union[str, Any]				=			xmod_layer.self_attn.k_proj.weight
						__lowerCAmelCase :    int				=			xmod_layer.self_attn.k_proj.bias
						__lowerCAmelCase :    Union[str, Any]				=			xmod_layer.self_attn.v_proj.weight
						__lowerCAmelCase :    str				=			xmod_layer.self_attn.v_proj.bias

						# self-attention output
						__lowerCAmelCase :    str				=			layer.attention.output
						if self_output.dense.weight.shape != xmod_layer.self_attn.out_proj.weight.shape:
									raise AssertionError("Dimensions of self-attention output weights do not match."      )
						__lowerCAmelCase :    List[Any]				=			xmod_layer.self_attn.out_proj.weight
						__lowerCAmelCase :    Any				=			xmod_layer.self_attn.out_proj.bias
						__lowerCAmelCase :    Any				=			xmod_layer.self_attn_layer_norm.weight
						__lowerCAmelCase :    int				=			xmod_layer.self_attn_layer_norm.bias

						# intermediate
						__lowerCAmelCase :    List[str]				=			layer.intermediate
						if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
									raise AssertionError("Dimensions of intermediate weights do not match."      )
						__lowerCAmelCase :    Dict				=			xmod_layer.fca.weight
						__lowerCAmelCase :    Tuple				=			xmod_layer.fca.bias

						# output
						__lowerCAmelCase :    str				=			layer.output
						if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
									raise AssertionError("Dimensions of feed-forward weights do not match."      )
						__lowerCAmelCase :    List[str]				=			xmod_layer.fca.weight
						__lowerCAmelCase :    int				=			xmod_layer.fca.bias
						__lowerCAmelCase :    Any				=			xmod_layer.final_layer_norm.weight
						__lowerCAmelCase :    List[str]				=			xmod_layer.final_layer_norm.bias
						if bert_output.adapter_layer_norm is not None:
									__lowerCAmelCase :    Optional[int]				=			xmod_layer.adapter_layer_norm.weight
									__lowerCAmelCase :    Tuple				=			xmod_layer.adapter_layer_norm.bias

						if sorted(bert_output.adapter_modules.keys()      ) != sorted(xmod_layer.adapter_modules.keys()      ):
									raise AssertionError("Lists of language adapters do not match."      )
						for lang_code, adapter in xmod_layer.adapter_modules.items():
									__lowerCAmelCase :    Union[str, Any]				=			bert_output.adapter_modules[lang_code]
									__lowerCAmelCase :    List[str]				=			xmod_layer.adapter_modules[lang_code]
									__lowerCAmelCase :    Union[str, Any]				=			from_adapter.fca.weight
									__lowerCAmelCase :    Tuple				=			from_adapter.fca.bias
									__lowerCAmelCase :    Dict				=			from_adapter.fca.weight
									__lowerCAmelCase :    List[str]				=			from_adapter.fca.bias

						# end of layer

			if xmod_sent_encoder.layer_norm is not None:
						__lowerCAmelCase :    Tuple				=			xmod_sent_encoder.layer_norm.weight
						__lowerCAmelCase :    Optional[int]				=			xmod_sent_encoder.layer_norm.bias

			if classification_head:
						__lowerCAmelCase :    str				=			xmod.model.classification_heads["mnli"].dense.weight
						__lowerCAmelCase :    List[str]				=			xmod.model.classification_heads["mnli"].dense.bias
						__lowerCAmelCase :    Tuple				=			xmod.model.classification_heads["mnli"].out_proj.weight
						__lowerCAmelCase :    Dict				=			xmod.model.classification_heads["mnli"].out_proj.bias
			else:
						# LM Head
						__lowerCAmelCase :    int				=			xmod.model.encoder.lm_head.dense.weight
						__lowerCAmelCase :    Optional[Any]				=			xmod.model.encoder.lm_head.dense.bias
						__lowerCAmelCase :    int				=			xmod.model.encoder.lm_head.layer_norm.weight
						__lowerCAmelCase :    Tuple				=			xmod.model.encoder.lm_head.layer_norm.bias
						__lowerCAmelCase :    List[Any]				=			xmod.model.encoder.lm_head.weight
						__lowerCAmelCase :    Dict				=			xmod.model.encoder.lm_head.bias

			# Let's check that we get the same results.
			__lowerCAmelCase :    Optional[int]				=			xmod.encode(__snake_case      ).unsqueeze(0      )  # batch of size 1
			model.roberta.set_default_language(__snake_case      )

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

			Path(__snake_case      ).mkdir(parents=__snake_case   ,exist_ok=__snake_case      )
			print(F"""Saving model to {pytorch_dump_folder_path}"""      )
			model.save_pretrained(__snake_case      )


if __name__ == "__main__":
	__snake_case				:  Optional[int] =       argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '--xmod_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.'
	)
	__snake_case				:  Union[str, Any] =       parser.parse_args()
	convert_xmod_checkpoint_to_pytorch(
	    args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
	)




"""simple docstring"""






import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression

# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split

# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import PolynomialFeatures

# Importing the dataset
__snake_case				:  Optional[Any] =       pd.read_csv(
    'https://s3.us-west-2.amazonaws.com/public.gamelab.fun/dataset/'
    'position_salaries.csv'
)
__snake_case				:  int =       dataset.iloc[:, 1:2].values
__snake_case				:  Tuple =       dataset.iloc[:, 2].values


__snake_case ,						__snake_case ,						__snake_case ,						__snake_case				:  int =       train_test_split(X, y, test_size=0.2, random_state=0)


__snake_case				:  Tuple =       PolynomialFeatures(degree=4)
__snake_case				:  Any =       poly_reg.fit_transform(X)
__snake_case				:  List[str] =       LinearRegression()
pol_reg.fit(X_poly, y)


def      _lowercase							(    )		->  List[Any]:
			plt.scatter(__snake_case   ,__snake_case   ,color="red"      )
			plt.plot(__snake_case   ,pol_reg.predict(poly_reg.fit_transform(__snake_case      )      )   ,color="blue"      )
			plt.title("Truth or Bluff (Linear Regression)"      )
			plt.xlabel("Position level"      )
			plt.ylabel("Salary"      )
			plt.show()


if __name__ == "__main__":
	viz_polymonial()

	# Predicting a new result with Polymonial Regression
	pol_reg.predict(poly_reg.fit_transform([[5.5]]))
	# output should be 132148.43750003