



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_		(					snake_case__					)	->     tuple:



				"""simple docstring"""





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







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__					)	->     XGBClassifier:



				"""simple docstring"""





				lowerCAmelCase__		  =  XGBClassifier()
				classifier.fit(snake_case__    ,			snake_case__					)
				return classifier







def     UpperCAmelCase_		(					)	->     None:



				"""simple docstring"""





				lowerCAmelCase__		  =  load_iris()
				lowerCAmelCase__  ,   lowerCAmelCase__		  =  data_handling(snake_case__					)
				lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__		  =  train_test_split(
				    snake_case__    ,			snake_case__    ,			test_size=0.25					)

				lowerCAmelCase__		  =  iris['target_names']

				# Create an XGBoost Classifier from the training data
				lowerCAmelCase__		  =  xgboost(snake_case__    ,			snake_case__					)

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


if __name__ == "__main__":
	import doctest

	doctest.testmod(verbose=True)
	main()





import argparse
import json
import os

import fairseq
import torch
from torch import nn

from transformers import (
    SpeechaTextaConfig,
    SpeechaTextaForCausalLM,
    SpeechaTextaTokenizer,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


logging.set_verbosity_info()
_lowerCAmelCase	:   str    =   logging.get_logger(__name__)

_lowerCAmelCase	:   str    =   {
    "post_extract_proj": "feature_projection.projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "self_attn.k_proj": "encoder.layers.*.attention.k_proj",
    "self_attn.v_proj": "encoder.layers.*.attention.v_proj",
    "self_attn.q_proj": "encoder.layers.*.attention.q_proj",
    "self_attn.out_proj": "encoder.layers.*.attention.out_proj",
    "self_attn_layer_norm": "encoder.layers.*.layer_norm",
    "fc1": "encoder.layers.*.feed_forward.intermediate_dense",
    "fc2": "encoder.layers.*.feed_forward.output_dense",
    "final_layer_norm": "encoder.layers.*.final_layer_norm",
    "encoder.layer_norm": "encoder.layer_norm",
    "w2v_model.layer_norm": "feature_projection.layer_norm",
    "quantizer.weight_proj": "quantizer.weight_proj",
    "quantizer.vars": "quantizer.codevectors",
    "project_q": "project_q",
    "final_proj": "project_hid",
    "w2v_encoder.proj": "lm_head",
    "mask_emb": "masked_spec_embed",
}
_lowerCAmelCase	:   List[str]    =   [
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__    ,			snake_case__    ,			snake_case__    ,			snake_case__					)	->     Any:



				"""simple docstring"""





				for attribute in key.split('.'					):
								lowerCAmelCase__		  =  getattr(snake_case__    ,			snake_case__					)

				if weight_type is not None:
								lowerCAmelCase__		  =  getattr(snake_case__    ,			snake_case__					).shape
				else:
								lowerCAmelCase__		  =  hf_pointer.shape

				assert hf_shape == value.shape, (
				    f'Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'
				    f' {value.shape} for {full_name}'
				)

				if weight_type == "weight":
								lowerCAmelCase__		  =  value
				elif weight_type == "weight_g":
								lowerCAmelCase__		  =  value
				elif weight_type == "weight_v":
								lowerCAmelCase__		  =  value
				elif weight_type == "bias":
								lowerCAmelCase__		  =  value
				else:
								lowerCAmelCase__		  =  value

				logger.info(f'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'					)







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__					)	->     List[Any]:



				"""simple docstring"""





				lowerCAmelCase__		  =  []
				lowerCAmelCase__		  =  fairseq_model.state_dict()

				lowerCAmelCase__		  =  hf_model.feature_extractor

				# if encoder has different dim to decoder -> use proj_weight
				lowerCAmelCase__		  =  None

				for name, value in fairseq_dict.items():
								lowerCAmelCase__		  =  False
								if "conv_layers" in name:
												load_conv_layer(
												    snake_case__    ,			snake_case__    ,			snake_case__    ,			snake_case__    ,			hf_model.config.feat_extract_norm == 'group'    ,			)
												lowerCAmelCase__		  =  True
								elif name.split('.'					)[0] == "proj":
												lowerCAmelCase__		  =  fairseq_model.proj
												lowerCAmelCase__		  =  True
								else:
												for key, mapped_key in MAPPING.items():
																if key in name or key.split('w2v_model.'					)[-1] == name.split('.'					)[0]:
																				lowerCAmelCase__		  =  True
																				if "*" in mapped_key:
																								lowerCAmelCase__		  =  name.split(snake_case__					)[0].split('.'					)[-2]
																								lowerCAmelCase__		  =  mapped_key.replace('*'    ,			snake_case__					)
																				if "weight_g" in name:
																								lowerCAmelCase__		  =  'weight_g'
																				elif "weight_v" in name:
																								lowerCAmelCase__		  =  'weight_v'
																				elif "bias" in name:
																								lowerCAmelCase__		  =  'bias'
																				elif "weight" in name:
																								lowerCAmelCase__		  =  'weight'
																				else:
																								lowerCAmelCase__		  =  None
																				set_recursively(snake_case__    ,			snake_case__    ,			snake_case__    ,			snake_case__    ,			snake_case__					)
																continue
								if not is_used:
												unused_weights.append(snake_case__					)

				logger.warning(f'Unused weights: {unused_weights}'					)

				return proj_weight







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__    ,			snake_case__    ,			snake_case__    ,			snake_case__					)	->     List[Any]:



				"""simple docstring"""





				lowerCAmelCase__		  =  full_name.split('conv_layers.'					)[-1]
				lowerCAmelCase__		  =  name.split('.'					)
				lowerCAmelCase__		  =  int(items[0]					)
				lowerCAmelCase__		  =  int(items[1]					)

				if type_id == 0:
								if "bias" in name:
												assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
												    f'{full_name} has size {value.shape}, but'
												    f' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'
												)
												lowerCAmelCase__		  =  value
												logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.'					)
								elif "weight" in name:
												assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
												    f'{full_name} has size {value.shape}, but'
												    f' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'
												)
												lowerCAmelCase__		  =  value
												logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.'					)
				elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
								if "bias" in name:
												assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
												    f'{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'
												    " found."
												)
												lowerCAmelCase__		  =  value
												logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'					)
								elif "weight" in name:
												assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
												    f'{full_name} has size {value.shape}, but'
												    f' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'
												)
												lowerCAmelCase__		  =  value
												logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'					)
				else:
								unused_weights.append(snake_case__					)







def     UpperCAmelCase_		(					snake_case__					)	->     str:



				"""simple docstring"""





				lowerCAmelCase__  ,   lowerCAmelCase__		  =  emb.weight.shape
				lowerCAmelCase__		  =  nn.Linear(snake_case__    ,			snake_case__    ,			bias=snake_case__					)
				lowerCAmelCase__		  =  emb.weight.data
				return lin_layer







def     UpperCAmelCase_		(					snake_case__					)	->     Any:



				"""simple docstring"""





				with open(snake_case__    ,			'r'    ,			encoding='utf-8'					) as f:
								lowerCAmelCase__		  =  f.readlines()
								lowerCAmelCase__		  =  [line.split(' '					)[0] for line in lines]

				lowerCAmelCase__		  =  len(snake_case__					)

				lowerCAmelCase__		  =  {
				    '<s>': 0,
				    '<pad>': 1,
				    '</s>': 2,
				    '<unk>': 3,
				}

				vocab_dict.update(dict(zip(snake_case__    ,			range(4    ,			num_words + 4					)					)					)					)
				return vocab_dict







@torch.no_grad()
def     UpperCAmelCase_		(					snake_case__    ,			snake_case__    ,			snake_case__    ,			snake_case__    ,			snake_case__    ,			snake_case__    ,			snake_case__    ,			)	->     Any:



				"""simple docstring"""





				lowerCAmelCase__		  =  WavaVecaConfig.from_pretrained(snake_case__					)
				lowerCAmelCase__		  =  SpeechaTextaConfig.from_pretrained(
				    snake_case__    ,			vocab_size=snake_case__    ,			decoder_layers=snake_case__    ,			do_stable_layer_norm=snake_case__					)

				lowerCAmelCase__		  =  WavaVecaFeatureExtractor(
				    feature_size=1    ,			sampling_rate=16000    ,			padding_value=0    ,			do_normalize=snake_case__    ,			return_attention_mask=snake_case__    ,			)

				lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__		  =  fairseq.checkpoint_utils.load_model_ensemble_and_task(
				    [checkpoint_path]    ,			arg_overrides={'data': '/'.join(dict_path.split('/'					)[:-1]					)}					)
				lowerCAmelCase__		  =  model[0].eval()

				# set weights for wav2vec2 encoder
				lowerCAmelCase__		  =  WavaVecaModel(snake_case__					)
				lowerCAmelCase__		  =  recursively_load_weights_wavaveca(model.encoder    ,			snake_case__					)

				lowerCAmelCase__		  =  SpeechaTextaForCausalLM(snake_case__					)
				lowerCAmelCase__  ,   lowerCAmelCase__		  =  hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()    ,			strict=snake_case__					)

				# set output linear layer
				unexpected_keys.remove('embed_out'					)
				lowerCAmelCase__		  =  nn.Parameter(model.decoder.embed_out.detach()					)

				# layer norm is init to identity matrix so leaving it is fine
				logger.warning(f'The following keys are missing when loading the decoder weights: {missing_keys}'					)
				logger.warning(f'The following keys are unexpected when loading the decoder weights: {unexpected_keys}'					)

				lowerCAmelCase__		  =  SpeechEncoderDecoderModel(encoder=snake_case__    ,			decoder=snake_case__					)
				lowerCAmelCase__		  =  False

				# add projection layer
				lowerCAmelCase__		  =  nn.Parameter(projection_layer.weight					)
				lowerCAmelCase__		  =  nn.Parameter(projection_layer.bias					)

				lowerCAmelCase__		  =  create_vocab_dict(snake_case__					)

				with open(os.path.join(snake_case__    ,			'vocab.json'					)    ,			'w'					) as fp:
								json.dump(snake_case__    ,			snake_case__					)

				lowerCAmelCase__		  =  SpeechaTextaTokenizer(os.path.join(snake_case__    ,			'vocab.json'					)					)
				tokenizer.save_pretrained(snake_case__					)

				lowerCAmelCase__		  =  hf_wavavec.config.to_dict()
				lowerCAmelCase__		  =  tokenizer.pad_token_id
				lowerCAmelCase__		  =  tokenizer.bos_token_id
				lowerCAmelCase__		  =  tokenizer.eos_token_id
				lowerCAmelCase__		  =  'speech_to_text_2'
				lowerCAmelCase__		  =  'wav2vec2'

				lowerCAmelCase__		  =  SpeechEncoderDecoderConfig.from_dict(snake_case__					)

				hf_wavavec.save_pretrained(snake_case__					)
				feature_extractor.save_pretrained(snake_case__					)


if __name__ == "__main__":
	_lowerCAmelCase	:   int    =   argparse.ArgumentParser()
	parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
	parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
	parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
	parser.add_argument(
	    "--encoder_config_path",
	    default="facebook/wav2vec2-large-lv60",
	    type=str,
	    help="Path to hf encoder wav2vec2 checkpoint config",
	)
	parser.add_argument(
	    "--decoder_config_path",
	    default="facebook/s2t-small-mustc-en-fr-st",
	    type=str,
	    help="Path to hf decoder s2t checkpoint config",
	)
	parser.add_argument("--vocab_size", default=1_0_2_2_4, type=int, help="Vocab size of decoder")
	parser.add_argument("--num_decoder_layers", default=7, type=int, help="Number of decoder layers")

	_lowerCAmelCase	:   Optional[int]    =   parser.parse_args()
	convert_wavaveca_checkpoint(
	    args.checkpoint_path,
	    args.pytorch_dump_folder_path,
	    args.dict_path,
	    encoder_config_path=args.encoder_config_path,
	    decoder_config_path=args.decoder_config_path,
	    vocab_size=args.vocab_size,
	    num_decoder_layers=args.num_decoder_layers,
	)
