
'''simple docstring'''





import argparse
import collections

import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints

from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()



def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__	):
					'''simple docstring'''
					return params[f'{prefix}/{prefix}/relpos_bias/rel_embedding'][:, i, :]



def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__="attention"	):
					'''simple docstring'''
					lowercase					=		lowercase					=		np.ascontiguousarray(params[f'{prefix}/{prefix}/{layer_name}/key/kernel'][:, i, :, :]	)
					lowercase					=		k_tmp.reshape(k_tmp.shape[0]				,    k_tmp.shape[1] * k_tmp.shape[2]	)
					lowercase					=		np.ascontiguousarray(params[f'{prefix}/{prefix}/{layer_name}/out/kernel'][:, i, :, :]	)
					lowercase					=		o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1]				,    o_tmp.shape[2]	)
					lowercase					=		np.ascontiguousarray(params[f'{prefix}/{prefix}/{layer_name}/query/kernel'][:, i, :, :]	)
					lowercase					=		q_tmp.reshape(q_tmp.shape[0]				,    q_tmp.shape[1] * q_tmp.shape[2]	)
					lowercase					=		np.ascontiguousarray(params[f'{prefix}/{prefix}/{layer_name}/value/kernel'][:, i, :, :]	)
					lowercase					=		v_tmp.reshape(v_tmp.shape[0]				,    v_tmp.shape[1] * v_tmp.shape[2]	)
					return k, o, q, v



def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__=False	):
					'''simple docstring'''
					if split_mlp_wi:
										lowercase					=		params[f'{prefix}/{prefix}/mlp/wi_0/kernel'][:, i, :]
										lowercase					=		params[f'{prefix}/{prefix}/mlp/wi_1/kernel'][:, i, :]
										lowercase					=		(wi_a, wi_a)
					else:
										lowercase					=		params[f'{prefix}/{prefix}/mlp/wi/kernel'][:, i, :]

					lowercase					=		params[f'{prefix}/{prefix}/mlp/wo/kernel'][:, i, :]
					return wi, wo



def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__	):
					'''simple docstring'''
					return params[f'{prefix}/{prefix}/{layer_name}/scale'][:, i]



def  UpperCamelCase					(							lowerCAmelCase__				,    *, lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__ = False	):
					'''simple docstring'''
					lowercase					=		traverse_util.flatten_dict(variables['''target''']	)
					lowercase					=		{'''/'''.join(lowerCAmelCase__	): v for k, v in old.items()}

					# v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
					lowercase					=		'''encoder/encoder/mlp/wi_0/kernel''' in old
					print('''Split MLP:'''				,    lowerCAmelCase__	)

					lowercase					=		collections.OrderedDict()

					# Shared embeddings.
					lowercase					=		old['''token_embedder/embedding''']

					# Encoder.
					for i in range(lowerCAmelCase__	):
										# Block i, layer 0 (Self Attention).
										lowercase					=		tax_layer_norm_lookup(lowerCAmelCase__				,    lowerCAmelCase__				,    '''encoder'''				,    '''pre_attention_layer_norm'''	)
										lowercase			,		lowercase			,		lowercase			,		lowercase					=		tax_attention_lookup(lowerCAmelCase__				,    lowerCAmelCase__				,    '''encoder'''				,    '''attention'''	)
										lowercase					=		layer_norm
										lowercase					=		k.T
										lowercase					=		o.T
										lowercase					=		q.T
										lowercase					=		v.T

										# Block i, layer 1 (MLP).
										lowercase					=		tax_layer_norm_lookup(lowerCAmelCase__				,    lowerCAmelCase__				,    '''encoder'''				,    '''pre_mlp_layer_norm'''	)
										lowercase			,		lowercase					=		tax_mlp_lookup(lowerCAmelCase__				,    lowerCAmelCase__				,    '''encoder'''				,    lowerCAmelCase__	)
										lowercase					=		layer_norm
										if split_mlp_wi:
															lowercase					=		wi[0].T
															lowercase					=		wi[1].T
										else:
															lowercase					=		wi.T
										lowercase					=		wo.T
										if scalable_attention:
															# convert the rel_embedding of each layer
															lowercase					=		tax_relpos_bias_lookup(
															    lowerCAmelCase__				,    lowerCAmelCase__				,    '''encoder'''	).T

					lowercase					=		old['''encoder/encoder_norm/scale''']

					if not scalable_attention:
										lowercase					=		tax_relpos_bias_lookup(
										    lowerCAmelCase__				,    0				,    '''encoder'''	).T
										lowercase					=		tax_relpos_bias_lookup(
										    lowerCAmelCase__				,    0				,    '''decoder'''	).T

					if not is_encoder_only:
										# Decoder.
										for i in range(lowerCAmelCase__	):
															# Block i, layer 0 (Self Attention).
															lowercase					=		tax_layer_norm_lookup(lowerCAmelCase__				,    lowerCAmelCase__				,    '''decoder'''				,    '''pre_self_attention_layer_norm'''	)
															lowercase			,		lowercase			,		lowercase			,		lowercase					=		tax_attention_lookup(lowerCAmelCase__				,    lowerCAmelCase__				,    '''decoder'''				,    '''self_attention'''	)
															lowercase					=		layer_norm
															lowercase					=		k.T
															lowercase					=		o.T
															lowercase					=		q.T
															lowercase					=		v.T

															# Block i, layer 1 (Cross Attention).
															lowercase					=		tax_layer_norm_lookup(lowerCAmelCase__				,    lowerCAmelCase__				,    '''decoder'''				,    '''pre_cross_attention_layer_norm'''	)
															lowercase			,		lowercase			,		lowercase			,		lowercase					=		tax_attention_lookup(lowerCAmelCase__				,    lowerCAmelCase__				,    '''decoder'''				,    '''encoder_decoder_attention'''	)
															lowercase					=		layer_norm
															lowercase					=		k.T
															lowercase					=		o.T
															lowercase					=		q.T
															lowercase					=		v.T

															# Block i, layer 2 (MLP).
															lowercase					=		tax_layer_norm_lookup(lowerCAmelCase__				,    lowerCAmelCase__				,    '''decoder'''				,    '''pre_mlp_layer_norm'''	)
															lowercase			,		lowercase					=		tax_mlp_lookup(lowerCAmelCase__				,    lowerCAmelCase__				,    '''decoder'''				,    lowerCAmelCase__	)
															lowercase					=		layer_norm
															if split_mlp_wi:
																				lowercase					=		wi[0].T
																				lowercase					=		wi[1].T
															else:
																				lowercase					=		wi.T
															lowercase					=		wo.T

															if scalable_attention:
																				# convert the rel_embedding of each layer
																				lowercase					=		tax_relpos_bias_lookup(lowerCAmelCase__				,    lowerCAmelCase__				,    '''decoder'''	).T

										lowercase					=		old['''decoder/decoder_norm/scale''']

										# LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
										if "decoder/logits_dense/kernel" in old:
															lowercase					=		old['''decoder/logits_dense/kernel'''].T

					return new



def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__	):
					'''simple docstring'''
					lowercase					=		collections.OrderedDict([(k, torch.from_numpy(v.copy()	)) for (k, v) in converted_params.items()]	)

					# Add what is missing.
					if "encoder.embed_tokens.weight" not in state_dict:
										lowercase					=		state_dict['''shared.weight''']

					if not is_encoder_only:
										if "decoder.embed_tokens.weight" not in state_dict:
															lowercase					=		state_dict['''shared.weight''']

										if "lm_head.weight" not in state_dict:  # For old 1.0 models.
															print('''Using shared word embeddings as lm_head.'''	)
															lowercase					=		state_dict['''shared.weight''']

					return state_dict



def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__	):
					'''simple docstring'''
					lowercase					=		checkpoints.load_tax_checkpoint(lowerCAmelCase__	)
					lowercase					=		convert_tax_to_pytorch(
					    lowerCAmelCase__				,    num_layers=config.num_layers				,    is_encoder_only=lowerCAmelCase__				,    scalable_attention=lowerCAmelCase__	)
					lowercase					=		make_state_dict(lowerCAmelCase__				,    lowerCAmelCase__	)
					model.load_state_dict(lowerCAmelCase__				,    strict=lowerCAmelCase__	)



def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__ = False				,    lowerCAmelCase__ = False				,    ):
					'''simple docstring'''
					lowercase					=		MTaConfig.from_json_file(lowerCAmelCase__	)
					print(f'Building PyTorch model from configuration: {config}'	)
					# Non-v1.1 checkpoints could also use T5Model, but this works for all.
					# The v1.0 checkpoints will simply have an LM head that is the word embeddings.
					if is_encoder_only:
										lowercase					=		UMTaEncoderModel(lowerCAmelCase__	)
					else:
										lowercase					=		UMTaForConditionalGeneration(lowerCAmelCase__	)

					# Load weights from tf checkpoint
					load_tax_weights_in_ta(lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__	)

					# Save pytorch-model
					print(f'Save PyTorch model to {pytorch_dump_path}'	)
					model.save_pretrained(lowerCAmelCase__	)

					# Verify that we can load the checkpoint.
					model.from_pretrained(lowerCAmelCase__	)
					print('''Done'''	)


if __name__ == "__main__":
							lowercase__	:Tuple										=					argparse.ArgumentParser(description="Converts a native T5X checkpoint into a PyTorch checkpoint.")
							# Required parameters
							parser.add_argument(
							    "--t5x_checkpoint_path", default=None, type=str, required=True, help="Path to the T5X checkpoint."
							)
							parser.add_argument(
							    "--config_file",
							    default=None,
							    type=str,
							    required=True,
							    help="The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.",
							)
							parser.add_argument(
							    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
							)
							parser.add_argument(
							    "--is_encoder_only", action="store_true", help="Check if the model is encoder-decoder model", default=False
							)
							parser.add_argument(
							    "--scalable_attention",
							    action="store_true",
							    help="Whether the model uses scaled attention (umt5 model)",
							    default=False,
							)
							lowercase__	:List[Any]										=					parser.parse_args()
							convert_tax_checkpoint_to_pytorch(
							    args.tax_checkpoint_path,
							    args.config_file,
							    args.pytorch_dump_path,
							    args.is_encoder_only,
							    args.scalable_attention,
							)






class        lowercase	:




							def __init__(       self     ,A__):
												lowercase					=		val
												lowercase					=		None
												lowercase					=		None





							def 				A__   (       self     ,A__):
												if self.val:
																	if val < self.val:
																						if self.left is None:
																											lowercase					=		Node(A__)
																						else:
																											self.left.insert(A__)
																	elif val > self.val:
																						if self.right is None:
																											lowercase					=		Node(A__)
																						else:
																											self.right.insert(A__)
												else:
																	lowercase					=		val



def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__	):
					'''simple docstring'''
					# Recursive traversal
					if root:
										inorder(root.left				,    lowerCAmelCase__	)
										res.append(root.val	)
										inorder(root.right				,    lowerCAmelCase__	)



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					# Build BST
					if len(lowerCAmelCase__	) == 0:
										return arr
					lowercase					=		Node(arr[0]	)
					for i in range(1				,    len(lowerCAmelCase__	)	):
										root.insert(arr[i]	)
					# Traverse BST in order.
					lowercase					=		[]
					inorder(lowerCAmelCase__				,    lowerCAmelCase__	)
					return res


if __name__ == "__main__":
							print(tree_sort([10, 1, 3, 2, 9, 14, 13]))




