



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 A						(       __UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase="attention"					)						->      Tuple:
			A__       =    params[f'''{prefix}/layers_{i}/{layer_name}/key/kernel''']
			A__       =    params[f'''{prefix}/layers_{i}/{layer_name}/out/kernel''']
			A__       =    params[f'''{prefix}/layers_{i}/{layer_name}/query/kernel''']
			A__       =    params[f'''{prefix}/layers_{i}/{layer_name}/value/kernel''']
			return k, o, q, v


def A						(       __UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase=False					)						->      Optional[int]:
			if split_mlp_wi:
						A__       =    params[f'''{prefix}/layers_{i}/mlp/wi_0/kernel''']
						A__       =    params[f'''{prefix}/layers_{i}/mlp/wi_1/kernel''']
						A__       =    (wi_a, wi_a)
			else:
						A__       =    params[f'''{prefix}/layers_{i}/mlp/wi/kernel''']

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


def A						(       __UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase					)						->      Union[str, Any]:
			return params[f'''{prefix}/layers_{i}/{layer_name}/scale''']


def A						(       __UpperCamelCase ,	*, __UpperCamelCase ,	__UpperCamelCase					)						->      int:
			A__       =    traverse_util.flatten_dict(variables['target']					)
			A__       =    {"""/""".join(UpperCAmelCase__					): v for k, v in old.items()}

			# v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
			A__       =    """encoder/layers_0/mlp/wi_0/kernel""" in old
			print('Split MLP:' ,	UpperCAmelCase__					)

			A__       =    collections.OrderedDict()

			# Shared embeddings.
			A__       =    old["""token_embedder/embedding"""]

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

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

			A__       =    old[
			    """encoder/relpos_bias/rel_embedding"""
			].T
			A__       =    old["""encoder/encoder_norm/scale"""]

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

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

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

						A__       =    old["""decoder/decoder_norm/scale"""]
						A__       =    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:
									A__       =    old["""decoder/logits_dense/kernel"""].T

			return new


def A						(       __UpperCamelCase ,	__UpperCamelCase					)						->      int:
			A__       =    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:
						A__       =    state_dict["""shared.weight"""]

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

			return state_dict


def A						(       __UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase					)						->      Any:
			A__       =    checkpoints.load_tax_checkpoint(UpperCAmelCase__					)
			A__       =    convert_tax_to_pytorch(UpperCAmelCase__ ,	num_layers=config.num_layers ,	is_encoder_only=UpperCAmelCase__					)
			A__       =    make_state_dict(UpperCAmelCase__ ,	UpperCAmelCase__					)
			model.load_state_dict(UpperCAmelCase__ ,	strict=UpperCAmelCase__					)


def A						(       __UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase = False					)						->      List[str]:
			A__       =    TaConfig.from_json_file(UpperCAmelCase__					)
			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:
						A__       =    TaEncoderModel(UpperCAmelCase__					)
			else:
						A__       =    TaForConditionalGeneration(UpperCAmelCase__					)

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

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

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


if __name__ == "__main__":
							SCREAMING_SNAKE_CASE__       =      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
							)
							SCREAMING_SNAKE_CASE__       =      parser.parse_args()
							convert_tax_checkpoint_to_pytorch(
							    args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
							)









import math
import random


def A						(       __UpperCamelCase ,	__UpperCamelCase = False					)						->      float:
			if deriv:
						return value * (1 - value)
			return 1 / (1 + math.exp(-value					))


# Initial Value
SCREAMING_SNAKE_CASE__       =      0.02


def A						(       __UpperCamelCase ,	__UpperCamelCase					)						->      float:
			A__       =    float(2 * (random.randint(1 ,	100					)) - 1					)

			for _ in range(__UpperCamelCase					):
						# Forward propagation
						A__       =    sigmoid_function(INITIAL_VALUE * weight					)
						# How much did we miss?
						A__       =    (expected / 100) - layer_a
						# Error delta
						A__       =    layer_1_error * sigmoid_function(__UpperCamelCase ,	__UpperCamelCase					)
						# Update weight
						weight += INITIAL_VALUE * layer_1_delta

			return layer_a * 100


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							SCREAMING_SNAKE_CASE__       =      int(input('''Expected value: '''))
							SCREAMING_SNAKE_CASE__       =      int(input('''Number of propagations: '''))
							print(forward_propagation(expected, number_propagations))




