






'''simple docstring'''



import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						List[Any]     )	->      List[Any]:

   """simple docstring"""
   __a					  =     os.path.join(args.tf_model_dir,       'parameters.json'     )
   __a					  =     json.loads(open(__a     ).read()     )
   if not params:
      raise ValueError(
          f"""It seems that the json file at {parameter_file} is empty. Make sure you have a correct json file."""     )
   if not args.output.endswith('.pt'     ):
      __a					  =     args.output + '.pt'
   __a					  =     OrderedDict()
   with tf.device('/CPU:0'     ):
      __a					  =     tf.train.load_checkpoint(args.tf_model_dir     )
      __a					  =     reader.get_variable_to_shape_map()
      for key_name in shapes.keys():
         __a					  =     reader.get_tensor(__a     ).astype(np.floataa     )
         if key_name.endswith('/adam_m'     ) or key_name.endswith('/adam_v'     ):
            continue
         if key_name.startswith('pasts/'     ):
            if key_name.startswith('pasts/mlp'     ):
               __a					  =     int(key_name[9]     )
            elif key_name.startswith('pasts/out'     ):
               __a					  =     8
            __a					  =     'model.sqout.%d.weight' % (player * 2)  # enter to nn.Sequencial with Tanh, so 2 at a time
            __a					  =     vnp.transpose([1, 0]     ).copy()  # Mesh-Tensorflow is a diagonal matrix
            __a					  =     torch.tensor(__a     )
         elif key_name.startswith('model/moe'     ):
            __a					  =     int(key_name[9:].split('/'     )[0]     )
            if key_name.endswith('/switch_gating/kernel'     ):
               __a					  =     'model.blocks.%d.feed_forward.mlp.router.classifier.weight' % player
               __a					  =     vnp.transpose([1, 0]     ).copy()  # Mesh-Tensorflow is a diagonal matrix
               __a					  =     torch.tensor(__a     )
            elif key_name.endswith('/softmlp/kernel'     ):
               __a					  =     'model.blocks.%d.feed_forward.soft_bypass_mlp.weight' % player
               __a					  =     vnp.transpose([1, 0]     ).copy()  # Mesh-Tensorflow is a diagonal matrix
               __a					  =     torch.tensor(__a     )
            elif key_name.endswith('/wo/kernel'     ) or key_name.endswith('/wi/kernel'     ):
               __a					  =     key_name[-9:-7]
               for i in range(16     ):
                  __a					  =     'model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight' % (player, i, nlayer)
                  __a					  =     (
                      vnp[i].transpose([1, 0]     ).copy()
                  )  # In Mesh-Tensorflow, it is one array, so it is divided
                  __a					  =     torch.tensor(__a     )
         elif key_name.startswith('model/mlp'     ):
            __a					  =     int(key_name[9:].split('/'     )[0]     )
            if key_name.endswith('/p1/kernel'     ):
               __a					  =     'model.blocks.%d.feed_forward.mlp.wi.weight' % player
               __a					  =     vnp.transpose([1, 0]     ).copy()  # Mesh-Tensorflow is a diagonal matrix
               __a					  =     torch.tensor(__a     )
            elif key_name.endswith('/p1/bias'     ):
               __a					  =     'model.blocks.%d.feed_forward.mlp.wi.bias' % player
               __a					  =     vnp.copy()  # same because it is one dimensional
               __a					  =     torch.tensor(__a     )
            elif key_name.endswith('/p2/kernel'     ):
               __a					  =     'model.blocks.%d.feed_forward.mlp.wo.weight' % player
               __a					  =     vnp.transpose([1, 0]     ).copy()  # Mesh-Tensorflow is a diagonal matrix
               __a					  =     torch.tensor(__a     )
            elif key_name.endswith('/p2/bias'     ):
               __a					  =     'model.blocks.%d.feed_forward.mlp.wo.bias' % player
               __a					  =     vnp.copy()  # same because it is one dimensional
               __a					  =     torch.tensor(__a     )
         elif key_name.startswith('model/ln'     ):
            __a					  =     int(key_name[8:].split('/'     )[0]     )
            if key_name.endswith('/b'     ):
               __a					  =     'model.blocks.%d.feed_forward.norm.bias' % player
               __a					  =     vnp.copy()  # same because it is one dimensional
               __a					  =     torch.tensor(__a     )
            elif key_name.endswith('/g'     ):
               __a					  =     'model.blocks.%d.feed_forward.norm.weight' % player
               __a					  =     vnp.copy()  # same because it is one dimensional
               __a					  =     torch.tensor(__a     )
         elif key_name.startswith('model/att'     ):
            __a					  =     int(key_name[9:].split('/'     )[0]     )
            if key_name.endswith('/qkv/kernel'     ):
               __a					  =     vnp.copy()  # Compute same dimension as Mesh-tensorflow using einsum
               __a					  =     state[:, 0, :, :]
               __a					  =     state[:, 1, :, :]
               __a					  =     state[:, 2, :, :]
               __a					  =     (
                   state_q.reshape([state_q.shape[0], state_q.shape[1] * state_q.shape[2]]     )
                   .transpose([1, 0]     )
                   .copy()
               )  # Mesh-Tensorflow is a diagonal matrix
               __a					  =     (
                   state_k.reshape([state_k.shape[0], state_k.shape[1] * state_k.shape[2]]     )
                   .transpose([1, 0]     )
                   .copy()
               )  # Mesh-Tensorflow is a diagonal matrix
               __a					  =     (
                   state_v.reshape([state_v.shape[0], state_v.shape[1] * state_v.shape[2]]     )
                   .transpose([1, 0]     )
                   .copy()
               )  # Mesh-Tensorflow is a diagonal matrix
               __a					  =     'model.blocks.%d.self_attn.self_attn.q_proj.weight' % player
               __a					  =     torch.tensor(__a     )
               __a					  =     'model.blocks.%d.self_attn.self_attn.k_proj.weight' % player
               __a					  =     torch.tensor(__a     )
               __a					  =     'model.blocks.%d.self_attn.self_attn.v_proj.weight' % player
               __a					  =     torch.tensor(__a     )
            elif key_name.endswith('/o/kernel'     ):
               __a					  =     'model.blocks.%d.self_attn.self_attn.out_proj.weight' % player
               __a					  =     (
                   vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]]     ).transpose([1, 0]     ).copy()
               )  # Mesh-Tensorflow is a diagonal matrix
               __a					  =     torch.tensor(__a     )
         elif key_name.startswith('model/an'     ):
            __a					  =     int(key_name[8:].split('/'     )[0]     )
            if key_name.endswith('/b'     ):
               __a					  =     'model.blocks.%d.self_attn.norm.bias' % player
               __a					  =     vnp.copy()  # same because it is one dimensional
               __a					  =     torch.tensor(__a     )
            elif key_name.endswith('/g'     ):
               __a					  =     'model.blocks.%d.self_attn.norm.weight' % player
               __a					  =     vnp.copy()  # same because it is one dimensional
               __a					  =     torch.tensor(__a     )
         elif (
             key_name.startswith('model/wte'     )
             or key_name.startswith('model/wpe'     )
             or key_name.startswith('model/ete'     )
         ):
            __a					  =     {'wte': 'embed_tokens', 'wpe': 'position_embeddings', 'ete': 'extra_position_embeddings'}[
                key_name[-3:]
            ]
            __a					  =     'model.%s.weight' % nlayer
            __a					  =     vnp.copy()  # same in embedded
            __a					  =     torch.tensor(__a     )
            if key_name.startswith('model/wte'     ):
               __a					  =     'lm_head.weight'
               __a					  =     vnp.copy()  # same in embedded
               __a					  =     torch.tensor(__a     )
         elif key_name.startswith('model/wob'     ):
            __a					  =     'final_logits_bias'
            __a					  =     vnp.copy()  # same in embedded
            __a					  =     state.reshape((1, -1)     )
            __a					  =     torch.tensor(__a     )
         elif key_name == "model/dense/kernel":
            __a					  =     'model.last_project.weight'
            __a					  =     vnp.transpose([1, 0]     ).copy()  # Mesh-Tensorflow is a diagonal matrix
            __a					  =     torch.tensor(__a     )
         elif key_name == "model/dense_1/bias":
            __a					  =     'model.last_project.bias'
            __a					  =     vnp.copy()  # same because it is one dimensional
            __a					  =     torch.tensor(__a     )
   torch.save(__a,       args.output     )


if __name__ == "__main__":
 __UpperCamelCase   :							Dict         =   argparse.ArgumentParser(
     description="""model converter.""", formatter_class=argparse.ArgumentDefaultsHelpFormatter
 )
 parser.add_argument("""--tf_model_dir""", metavar="""PATH""", type=str, required=True, help="""import model""")
 parser.add_argument("""--output""", metavar="""PATH""", type=str, required=True, help="""output model""")
 __UpperCamelCase   :							Union[str, Any]         =   parser.parse_args()
 convert_tf_gptsan_to_pt(args)




"""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_					(				__a   :			dict						):




			'''simple docstring'''



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

def     UpperCAmelCase_					(				__a   :			np.ndarray      ,							__a   :			np.ndarray						):




			'''simple docstring'''



			_lowerCamelCase					:					Dict	       =    XGBClassifier()
			classifier.fit(__a      ,							__a						)
			return classifier

def     UpperCAmelCase_					(				):




			'''simple docstring'''



			_lowerCamelCase					:					List[str]	       =    load_iris()
			_lowerCamelCase       ,							_lowerCamelCase					:					Optional[Any]	       =    data_handling(__a						)
			_lowerCamelCase       ,							_lowerCamelCase       ,							_lowerCamelCase       ,							_lowerCamelCase					:					int	       =    train_test_split(
			    __a      ,							__a      ,							test_size=0.2_5						)

			_lowerCamelCase					:					Optional[Any]	       =    iris['target_names']

			# Create an XGBoost Classifier from the training data
			_lowerCamelCase					:					Tuple	       =    xgboost(__a      ,							__a						)

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


if __name__ == "__main__":
	import doctest

	doctest.testmod(verbose=True)
	main()





