





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  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						dict				)  ->							tuple:
    return (data["data"], data["target"])




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						np.ndarray	,					__UpperCamelCase      :						np.ndarray				)  ->							XGBClassifier:
    UpperCAmelCase_										=					XGBClassifier()
    classifier.fit(__UpperCamelCase	,					__UpperCamelCase				)
    return classifier




def  SCREAMING_SNAKE_CASE     (	)  ->							None:
    UpperCAmelCase_										=					load_iris()
    UpperCAmelCase_			,							UpperCAmelCase_										=					data_handling(__UpperCamelCase				)
    UpperCAmelCase_			,							UpperCAmelCase_			,							UpperCAmelCase_			,							UpperCAmelCase_										=					train_test_split(
        __UpperCamelCase	,					__UpperCamelCase	,					test_size=0.25				)

    UpperCAmelCase_										=					iris['''target_names''']

    # Create an XGBoost Classifier from the training data
    UpperCAmelCase_										=					xgboost(__UpperCamelCase	,					__UpperCamelCase				)

    # Display the confusion matrix of the classifier with both training and test sets
    ConfusionMatrixDisplay.from_estimator(
        __UpperCamelCase	,					__UpperCamelCase	,					__UpperCamelCase	,					display_labels=__UpperCamelCase	,					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

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Tuple	,					__UpperCamelCase      :						List[str]	,					__UpperCamelCase      :						str				)  ->							str:
    UpperCAmelCase_										=					AutoConfig.from_pretrained(__UpperCamelCase				)
    UpperCAmelCase_										=					FlaxAutoModelForSeqaSeqLM.from_config(config=__UpperCamelCase				)
    UpperCAmelCase_										=					checkpoints.load_tax_checkpoint(__UpperCamelCase				)

    UpperCAmelCase_										=					'''wi_0''' in tax_model['''target''']['''encoder''']['''layers_0''']['''mlp''']

    if config.model_type == "t5":
        UpperCAmelCase_										=					'''SelfAttention'''
    if config.model_type == "longt5" and config.encoder_attention_type == "local":
        UpperCAmelCase_										=					'''LocalSelfAttention'''
    elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
        UpperCAmelCase_										=					'''TransientGlobalSelfAttention'''
    else:
        raise ValueError(
            '''Given config is expected to have `model_type=\'t5\'`, or `model_type=\'longt5` with `encoder_attention_type`'''
            ''' attribute with a value from [\'local\', \'transient-global].'''				)

    # Encoder
    for layer_index in range(config.num_layers				):
        UpperCAmelCase_										=					f'layers_{str(__UpperCamelCase				)}'

        # Self-Attention
        UpperCAmelCase_										=					tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''key''']['''kernel''']
        UpperCAmelCase_										=					tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''out''']['''kernel''']
        UpperCAmelCase_										=					tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''query''']['''kernel''']
        UpperCAmelCase_										=					tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''value''']['''kernel''']

        # Global input layer norm
        if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
            UpperCAmelCase_										=					tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''T5LayerNorm_0''']['''scale''']

        # Layer Normalization
        UpperCAmelCase_										=					tax_model['''target''']['''encoder'''][layer_name]['''pre_attention_layer_norm''']['''scale''']

        if split_mlp_wi:
            UpperCAmelCase_										=					tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
            UpperCAmelCase_										=					tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
        else:
            UpperCAmelCase_										=					tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

        UpperCAmelCase_										=					tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

        # Layer Normalization
        UpperCAmelCase_										=					tax_model['''target''']['''encoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

        # Assigning
        UpperCAmelCase_										=					flax_model.params['''encoder''']['''block'''][str(__UpperCamelCase				)]['''layer''']
        UpperCAmelCase_										=					tax_attention_key
        UpperCAmelCase_										=					tax_attention_out
        UpperCAmelCase_										=					tax_attention_query
        UpperCAmelCase_										=					tax_attention_value

        UpperCAmelCase_										=					tax_attention_layer_norm

        # Global input layer norm
        if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
            UpperCAmelCase_										=					tax_global_layer_norm

        if split_mlp_wi:
            UpperCAmelCase_										=					tax_mlp_wi_a
            UpperCAmelCase_										=					tax_mlp_wi_a
        else:
            UpperCAmelCase_										=					tax_mlp_wi

        UpperCAmelCase_										=					tax_mlp_wo
        UpperCAmelCase_										=					tax_mlp_layer_norm

        UpperCAmelCase_										=					flax_model_encoder_layer_block

    # Only for layer 0:
    UpperCAmelCase_										=					tax_model['''target''']['''encoder''']['''relpos_bias''']['''rel_embedding'''].T
    UpperCAmelCase_										=					tax_encoder_rel_embedding

    # Side/global relative position_bias + layer norm
    if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
        UpperCAmelCase_										=					tax_model['''target''']['''encoder''']['''side_relpos_bias''']['''rel_embedding'''].T
        UpperCAmelCase_										=					tax_encoder_global_rel_embedding

    # Assigning
    UpperCAmelCase_										=					tax_model['''target''']['''encoder''']['''encoder_norm''']['''scale''']
    UpperCAmelCase_										=					tax_encoder_norm

    # Decoder
    for layer_index in range(config.num_layers				):
        UpperCAmelCase_										=					f'layers_{str(__UpperCamelCase				)}'

        # Self-Attention
        UpperCAmelCase_										=					tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''key''']['''kernel''']
        UpperCAmelCase_										=					tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''out''']['''kernel''']
        UpperCAmelCase_										=					tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''query''']['''kernel''']
        UpperCAmelCase_										=					tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''value''']['''kernel''']

        # Layer Normalization
        UpperCAmelCase_										=					tax_model['''target''']['''decoder'''][layer_name]['''pre_self_attention_layer_norm'''][
            '''scale'''
        ]

        # Encoder-Decoder-Attention
        UpperCAmelCase_										=					tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']
        UpperCAmelCase_										=					tax_enc_dec_attention_module['''key''']['''kernel''']
        UpperCAmelCase_										=					tax_enc_dec_attention_module['''out''']['''kernel''']
        UpperCAmelCase_										=					tax_enc_dec_attention_module['''query''']['''kernel''']
        UpperCAmelCase_										=					tax_enc_dec_attention_module['''value''']['''kernel''']

        # Layer Normalization
        UpperCAmelCase_										=					tax_model['''target''']['''decoder'''][layer_name]['''pre_cross_attention_layer_norm''']['''scale''']

        # MLP
        if split_mlp_wi:
            UpperCAmelCase_										=					tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
            UpperCAmelCase_										=					tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
        else:
            UpperCAmelCase_										=					tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

        UpperCAmelCase_										=					tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

        # Layer Normalization
        UpperCAmelCase_										=					tax_model['''target''']['''decoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

        # Assigning
        UpperCAmelCase_										=					flax_model.params['''decoder''']['''block'''][str(__UpperCamelCase				)]['''layer''']
        UpperCAmelCase_										=					tax_attention_key
        UpperCAmelCase_										=					tax_attention_out
        UpperCAmelCase_										=					tax_attention_query
        UpperCAmelCase_										=					tax_attention_value

        UpperCAmelCase_										=					tax_pre_attention_layer_norm

        UpperCAmelCase_										=					tax_enc_dec_attention_key
        UpperCAmelCase_										=					tax_enc_dec_attention_out
        UpperCAmelCase_										=					tax_enc_dec_attention_query
        UpperCAmelCase_										=					tax_enc_dec_attention_value

        UpperCAmelCase_										=					tax_cross_layer_norm

        if split_mlp_wi:
            UpperCAmelCase_										=					tax_mlp_wi_a
            UpperCAmelCase_										=					tax_mlp_wi_a
        else:
            UpperCAmelCase_										=					tax_mlp_wi

        UpperCAmelCase_										=					tax_mlp_wo

        UpperCAmelCase_										=					txa_mlp_layer_norm

        UpperCAmelCase_										=					flax_model_decoder_layer_block

    # Decoder Normalization
    UpperCAmelCase_										=					tax_model['''target''']['''decoder''']['''decoder_norm''']['''scale''']
    UpperCAmelCase_										=					txa_decoder_norm

    # Only for layer 0:
    UpperCAmelCase_										=					tax_model['''target''']['''decoder''']['''relpos_bias''']['''rel_embedding'''].T
    UpperCAmelCase_										=					tax_decoder_rel_embedding

    # Token Embeddings
    UpperCAmelCase_										=					tax_model['''target''']['''token_embedder''']['''embedding''']
    UpperCAmelCase_										=					txa_token_embeddings

    # LM Head (only in v1.1 and LongT5 checkpoints)
    if "logits_dense" in tax_model["target"]["decoder"]:
        UpperCAmelCase_										=					tax_model['''target''']['''decoder''']['''logits_dense''']['''kernel''']

    flax_model.save_pretrained(__UpperCamelCase				)
    print('''T5X Model was sucessfully converted!'''				)


if __name__ == "__main__":
 _lowerCamelCase      			=						argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '--t5x_checkpoint_path', default=None, type=str, required=True, help='Path the T5X checkpoint.'
 )
 parser.add_argument('--config_name', default=None, type=str, required=True, help='Config name of LongT5/T5 model.')
 parser.add_argument(
     '--flax_dump_folder_path', default=None, type=str, required=True, help='Path to the output FLAX model.'
 )
 _lowerCamelCase      			=						parser.parse_args()
 convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)





