


"""simple docstring"""







import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration




def      UpperCAmelCase__ (lowerCAmelCase_     ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE		     =       [
					    "encoder.version",
					    "decoder.version",
					    "model.encoder.version",
					    "model.decoder.version",
					    "decoder.output_projection.weight",
					    "_float_tensor",
					    "encoder.embed_positions._float_tensor",
					    "decoder.embed_positions._float_tensor",
					]
					for k in ignore_keys:
										state_dict.pop(lowerCAmelCase_						,		lowerCAmelCase_     )




def      UpperCAmelCase__ (lowerCAmelCase_     ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE		     =       list(s_dict.keys()     )
					for key in keys:
										if "transformer_layers" in key:
															__SCREAMING_SNAKE_CASE		     =       s_dict.pop(lowerCAmelCase_     )
										elif "subsample" in key:
															__SCREAMING_SNAKE_CASE		     =       s_dict.pop(lowerCAmelCase_     )




def      UpperCAmelCase__ (lowerCAmelCase_     ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE		     =       emb.weight.shape
					__SCREAMING_SNAKE_CASE		     =       nn.Linear(lowerCAmelCase_						,		lowerCAmelCase_						,		bias=lowerCAmelCase_     )
					__SCREAMING_SNAKE_CASE		     =       emb.weight.data
					return lin_layer




def      UpperCAmelCase__ (lowerCAmelCase_						,		lowerCAmelCase_     ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE		     =       torch.load(lowerCAmelCase_						,		map_location="cpu"     )
					__SCREAMING_SNAKE_CASE		     =       mam_aaa["args"]
					__SCREAMING_SNAKE_CASE		     =       mam_aaa["model"]
					__SCREAMING_SNAKE_CASE		     =       state_dict["decoder.output_projection.weight"]

					remove_ignore_keys_(lowerCAmelCase_     )
					rename_keys(lowerCAmelCase_     )

					__SCREAMING_SNAKE_CASE		     =       state_dict["decoder.embed_tokens.weight"].shape[0]

					__SCREAMING_SNAKE_CASE		     =       args.share_decoder_input_output_embed

					__SCREAMING_SNAKE_CASE		     =       [int(lowerCAmelCase_     ) for i in args.conv_kernel_sizes.split(","     )]
					__SCREAMING_SNAKE_CASE		     =       SpeechaTextConfig(
					    vocab_size=lowerCAmelCase_						,		max_source_positions=args.max_source_positions						,		max_target_positions=args.max_target_positions						,		encoder_layers=args.encoder_layers						,		decoder_layers=args.decoder_layers						,		encoder_attention_heads=args.encoder_attention_heads						,		decoder_attention_heads=args.decoder_attention_heads						,		encoder_ffn_dim=args.encoder_ffn_embed_dim						,		decoder_ffn_dim=args.decoder_ffn_embed_dim						,		d_model=args.encoder_embed_dim						,		dropout=args.dropout						,		attention_dropout=args.attention_dropout						,		activation_dropout=args.activation_dropout						,		activation_function="relu"						,		num_conv_layers=len(lowerCAmelCase_     )						,		conv_channels=args.conv_channels						,		conv_kernel_sizes=lowerCAmelCase_						,		input_feat_per_channel=args.input_feat_per_channel						,		input_channels=args.input_channels						,		tie_word_embeddings=lowerCAmelCase_						,		num_beams=5						,		max_length=200						,		use_cache=lowerCAmelCase_						,		decoder_start_token_id=2						,		early_stopping=lowerCAmelCase_						,		)

					__SCREAMING_SNAKE_CASE		     =       SpeechaTextForConditionalGeneration(lowerCAmelCase_     )
					__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE		     =       model.model.load_state_dict(lowerCAmelCase_						,		strict=lowerCAmelCase_     )
					if len(lowerCAmelCase_     ) > 0 and not set(lowerCAmelCase_     ) <= {
					    "encoder.embed_positions.weights",
					    "decoder.embed_positions.weights",
					}:
										raise ValueError(
										    "Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights`  are allowed to be missing,"
										    f""" but all the following weights are missing {missing}"""     )

					if tie_embeds:
										__SCREAMING_SNAKE_CASE		     =       make_linear_from_emb(model.model.decoder.embed_tokens     )
					else:
										__SCREAMING_SNAKE_CASE		     =       lm_head_weights

					model.save_pretrained(lowerCAmelCase_     )


if __name__ == "__main__":
		a__       :							Any        =  argparse.ArgumentParser()
		# Required parameters
		parser.add_argument('''--fairseq_path''', type=str, help='''Path to the fairseq model (.pt) file.''')
		parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
		a__       :							Tuple        =  parser.parse_args()
		convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)




"""simple docstring"""







import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier




def      UpperCAmelCase__ (lowerCAmelCase_     ):




					'''simple docstring'''


					return (data["data"], data["target"])




def      UpperCAmelCase__ (lowerCAmelCase_						,		lowerCAmelCase_     ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE		     =       XGBClassifier()
					classifier.fit(lowerCAmelCase_						,		lowerCAmelCase_     )
					return classifier




def      UpperCAmelCase__ ():




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE		     =       load_iris()
					__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE		     =       data_handling(lowerCAmelCase_     )
					__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE		     =       train_test_split(
					    lowerCAmelCase_						,		lowerCAmelCase_						,		test_size=0.25     )

					__SCREAMING_SNAKE_CASE		     =       iris["target_names"]

					# Create an XGBoost Classifier from the training data
					__SCREAMING_SNAKE_CASE		     =       xgboost(lowerCAmelCase_						,		lowerCAmelCase_     )

					# Display the confusion matrix of the classifier with both training and test sets
					ConfusionMatrixDisplay.from_estimator(
					    lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_						,		display_labels=lowerCAmelCase_						,		cmap="Blues"						,		normalize="true"						,		)
					plt.title("Normalized Confusion Matrix - IRIS Dataset"     )
					plt.show()


if __name__ == "__main__":
		import doctest

		doctest.testmod(verbose=True)
		main()
