




'''simple docstring'''


import argparse
import collections

import torch
from flax import traverse_util
from tax import checkpoints

from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()
def 						lowerCamelCase__   (				a    ,	a    ,	a    ,	a="attention"							):
						__snake_case						 =   params[f'{prefix}/layers_{i}/{layer_name}/key/kernel']
						__snake_case						 =   params[f'{prefix}/layers_{i}/{layer_name}/out/kernel']
						__snake_case						 =   params[f'{prefix}/layers_{i}/{layer_name}/query/kernel']
						__snake_case						 =   params[f'{prefix}/layers_{i}/{layer_name}/value/kernel']
						return k, o, q, v
def 						lowerCamelCase__   (				a    ,	a    ,	a    ,	a=False							):
						if split_mlp_wi:
												__snake_case						 =   params[f'{prefix}/layers_{i}/mlp/wi_0/kernel']
												__snake_case						 =   params[f'{prefix}/layers_{i}/mlp/wi_1/kernel']
												__snake_case						 =   (wi_a, wi_a)
						else:
												__snake_case						 =   params[f'{prefix}/layers_{i}/mlp/wi/kernel']

						__snake_case						 =   params[f'{prefix}/layers_{i}/mlp/wo/kernel']
						return wi, wo
def 						lowerCamelCase__   (				a    ,	a    ,	a    ,	a							):
						return params[f'{prefix}/layers_{i}/{layer_name}/scale']
def 						lowerCamelCase__   (				a    ,	*, a    ,	a							):
						__snake_case						 =   traverse_util.flatten_dict(variables['target']							)
						__snake_case						 =   {'/'.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
						__snake_case						 =   'encoder/layers_0/mlp/wi_0/kernel' in old
						print('Split MLP:'    ,	a							)

						__snake_case						 =   collections.OrderedDict()

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

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

												# Block i, layer 1 (MLP).
												__snake_case						 =   tax_layer_norm_lookup(a    ,	a    ,	'encoder'    ,	'pre_mlp_layer_norm'							)
												__snake_case ,       __snake_case						 =   tax_mlp_lookup(a    ,	a    ,	'encoder'    ,	a							)
												__snake_case						 =   layer_norm
												if split_mlp_wi:
																		__snake_case						 =   wi[0].T
																		__snake_case						 =   wi[1].T
												else:
																		__snake_case						 =   wi.T
												__snake_case						 =   wo.T

						__snake_case						 =   old[
						    'encoder/relpos_bias/rel_embedding'
						].T
						__snake_case						 =   old['encoder/encoder_norm/scale']

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

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

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

												__snake_case						 =   old['decoder/decoder_norm/scale']
												__snake_case						 =   old[
												    'decoder/relpos_bias/rel_embedding'
												].T

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

						return new
def 						lowerCamelCase__   (				a    ,	a							):
						__snake_case						 =   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:
												__snake_case						 =   state_dict['shared.weight']

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

						return state_dict
def 						lowerCamelCase__   (				a    ,	a    ,	a    ,	a							):
						__snake_case						 =   checkpoints.load_tax_checkpoint(a							)
						__snake_case						 =   convert_tax_to_pytorch(a    ,	num_layers=config.num_layers    ,	is_encoder_only=a							)
						__snake_case						 =   make_state_dict(a    ,	a							)
						model.load_state_dict(a    ,	strict=a							)
def 						lowerCamelCase__   (				a    ,	a    ,	a    ,	a = False							):
						__snake_case						 =   TaConfig.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:
												__snake_case						 =   TaEncoderModel(a							)
						else:
												__snake_case						 =   TaForConditionalGeneration(a							)

						# Load weights from tf checkpoint
						load_tax_weights_in_ta(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__":
	_lowercase  =			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
	)
	_lowercase  =			parser.parse_args()
	convert_tax_checkpoint_to_pytorch(
	    args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
	)







'''simple docstring'''


def 						lowerCamelCase__   (				a							):
						__snake_case						 =   int(a							)
						if n_element < 1:
												__snake_case						 =   ValueError('a should be a positive number'							)
												raise my_error

						__snake_case						 =   [1]
						__snake_case ,       __snake_case ,       __snake_case						 =   (0, 0, 0)
						__snake_case						 =   1
						while index < n_element:
												while hamming_list[i] * 2 <= hamming_list[-1]:
																		i += 1
												while hamming_list[j] * 3 <= hamming_list[-1]:
																		j += 1
												while hamming_list[k] * 5 <= hamming_list[-1]:
																		k += 1
												hamming_list.append(
												    min(hamming_list[i] * 2    ,	hamming_list[j] * 3    ,	hamming_list[k] * 5							)							)
												index += 1
						return hamming_list


if __name__ == "__main__":
	_lowercase  =			input("""Enter the last number (nth term) of the Hamming Number Series: """)
	print("""Formula of Hamming Number Series => 2^i * 3^j * 5^k""")
	_lowercase  =			hamming(int(n))
	print("""-----------------------------------------------------""")
	print(f'''The list with nth numbers is: {hamming_numbers}''')
	print("""-----------------------------------------------------""")

