
'''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 		A_(     A						:		str		,     A						:		Optional[int]		,     A						:		List[str]):
							return params[f'''{prefix}/{prefix}/relpos_bias/rel_embedding'''][:, i, :]







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







def 		A_(     A						:		List[Any]		,     A						:		Dict		,     A						:		str		,     A						:		Optional[Any]=False):
							if split_mlp_wi:
														UpperCamelCase   			=     params[f'''{prefix}/{prefix}/mlp/wi_0/kernel'''][:, i, :]
														UpperCamelCase   			=     params[f'''{prefix}/{prefix}/mlp/wi_1/kernel'''][:, i, :]
														UpperCamelCase   			=     (wi_a, wi_a)
							else:
														UpperCamelCase   			=     params[f'''{prefix}/{prefix}/mlp/wi/kernel'''][:, i, :]

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







def 		A_(     A						:		List[Any]		,     A						:		Union[str, Any]		,     A						:		Optional[Any]		,     A						:		Optional[int]):
							return params[f'''{prefix}/{prefix}/{layer_name}/scale'''][:, i]







def 		A_(     A						:		dict		,     *, A						:		int		,     A						:		bool		,     A						:		bool = False):
							UpperCamelCase   			=     traverse_util.flatten_dict(variables['target'])
							UpperCamelCase   			=     {'/'.join(A): v for k, v in old.items()}

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

							UpperCamelCase   			=     collections.OrderedDict()

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

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

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

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

							if not scalable_attention:
														UpperCamelCase   			=     tax_relpos_bias_lookup(
														    A		,     0		,     'encoder').T
														UpperCamelCase   			=     tax_relpos_bias_lookup(
														    A		,     0		,     'decoder').T

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

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

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

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

														UpperCamelCase   			=     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:
																					UpperCamelCase   			=     old['decoder/logits_dense/kernel'].T

							return new







def 		A_(     A						:		Any		,     A						:		bool):
							UpperCamelCase   			=     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:
														UpperCamelCase   			=     state_dict['shared.weight']

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

							return state_dict







def 		A_(     A						:		List[str]		,     A						:		Optional[Any]		,     A						:		str		,     A						:		int		,     A						:		List[Any]):
							UpperCamelCase   			=     checkpoints.load_tax_checkpoint(A)
							UpperCamelCase   			=     convert_tax_to_pytorch(
							    A		,     num_layers=config.num_layers		,     is_encoder_only=A		,     scalable_attention=A)
							UpperCamelCase   			=     make_state_dict(A		,     A)
							model.load_state_dict(A		,     strict=A)







def 		A_(     A						:		Optional[Any]		,     A						:		List[str]		,     A						:		List[Any]		,     A						:		bool = False		,     A						:		bool = False		,     ):
							UpperCamelCase   			=     MTaConfig.from_json_file(A)
							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:
														UpperCamelCase   			=     UMTaEncoderModel(A)
							else:
														UpperCamelCase   			=     UMTaForConditionalGeneration(A)

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

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

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


if __name__ == "__main__":
						lowerCAmelCase					:    Optional[Any]           = 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,
						)
						lowerCAmelCase					:    Dict           = 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,
						)





'''simple docstring'''


import math
import sys

def   A_      (       SCREAMING_SNAKE_CASE_						)			->int:
  if number != int(SCREAMING_SNAKE_CASE_						):
    raise ValueError("""the value of input must be a natural number"""						)
  if number < 0:
    raise ValueError("""the value of input must not be a negative number"""						)
  if number == 0:
    return 1
  lowercase_     =       [-1] * (number + 1)
  lowercase_     =       0
  for i in range(1 ,	number + 1						):
    lowercase_     =       sys.maxsize
    lowercase_     =       int(math.sqrt(SCREAMING_SNAKE_CASE_						)						)
    for j in range(1 ,	root + 1						):
      lowercase_     =       1 + answers[i - (j**2)]
      lowercase_     =       min(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)
    lowercase_     =       answer
  return answers[number]


if __name__ == "__main__":
       import doctest

       doctest.testmod()

