






import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM






def 						a    (		lowerCamelCase_	,     lowerCamelCase_	,     lowerCamelCase_       ):






				'''simple docstring'''
				lowercase__     				=    AutoConfig.from_pretrained(lowerCamelCase_       )
				lowercase__     				=    FlaxAutoModelForSeqaSeqLM.from_config(config=lowerCamelCase_       )
				lowercase__     				=    checkpoints.load_tax_checkpoint(lowerCamelCase_       )

				lowercase__     				=    '''wi_0''' in tax_model['''target''']['''encoder''']['''layers_0''']['''mlp''']

				if config.model_type == "t5":
								lowercase__     				=    '''SelfAttention'''
				if config.model_type == "longt5" and config.encoder_attention_type == "local":
								lowercase__     				=    '''LocalSelfAttention'''
				elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
								lowercase__     				=    '''TransientGlobalSelfAttention'''
				else:
								raise ValueError(
								    '''Given config is expected to have `model_type=\'t5\'`, or `model_type=\'longt5` with `encoder_attention_type`'''
								    ''' attribute with a value from [\'local\', \'transient-global].'''       )

				# Encoder
				for layer_index in range(config.num_layers       ):
								lowercase__     				=    F"""layers_{str(lowerCamelCase_       )}"""

								# Self-Attention
								lowercase__     				=    tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''key''']['''kernel''']
								lowercase__     				=    tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''out''']['''kernel''']
								lowercase__     				=    tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''query''']['''kernel''']
								lowercase__     				=    tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''value''']['''kernel''']

								# Global input layer norm
								if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
												lowercase__     				=    tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''T5LayerNorm_0''']['''scale''']

								# Layer Normalization
								lowercase__     				=    tax_model['''target''']['''encoder'''][layer_name]['''pre_attention_layer_norm''']['''scale''']

								if split_mlp_wi:
												lowercase__     				=    tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
												lowercase__     				=    tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
								else:
												lowercase__     				=    tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

								lowercase__     				=    tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

								# Layer Normalization
								lowercase__     				=    tax_model['''target''']['''encoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

								# Assigning
								lowercase__     				=    flax_model.params['''encoder''']['''block'''][str(lowerCamelCase_       )]['''layer''']
								lowercase__     				=    tax_attention_key
								lowercase__     				=    tax_attention_out
								lowercase__     				=    tax_attention_query
								lowercase__     				=    tax_attention_value

								lowercase__     				=    tax_attention_layer_norm

								# Global input layer norm
								if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
												lowercase__     				=    tax_global_layer_norm

								if split_mlp_wi:
												lowercase__     				=    tax_mlp_wi_a
												lowercase__     				=    tax_mlp_wi_a
								else:
												lowercase__     				=    tax_mlp_wi

								lowercase__     				=    tax_mlp_wo
								lowercase__     				=    tax_mlp_layer_norm

								lowercase__     				=    flax_model_encoder_layer_block

				# Only for layer 0:
				lowercase__     				=    tax_model['''target''']['''encoder''']['''relpos_bias''']['''rel_embedding'''].T
				lowercase__     				=    tax_encoder_rel_embedding

				# Side/global relative position_bias + layer norm
				if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
								lowercase__     				=    tax_model['''target''']['''encoder''']['''side_relpos_bias''']['''rel_embedding'''].T
								lowercase__     				=    tax_encoder_global_rel_embedding

				# Assigning
				lowercase__     				=    tax_model['''target''']['''encoder''']['''encoder_norm''']['''scale''']
				lowercase__     				=    tax_encoder_norm

				# Decoder
				for layer_index in range(config.num_layers       ):
								lowercase__     				=    F"""layers_{str(lowerCamelCase_       )}"""

								# Self-Attention
								lowercase__     				=    tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''key''']['''kernel''']
								lowercase__     				=    tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''out''']['''kernel''']
								lowercase__     				=    tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''query''']['''kernel''']
								lowercase__     				=    tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''value''']['''kernel''']

								# Layer Normalization
								lowercase__     				=    tax_model['''target''']['''decoder'''][layer_name]['''pre_self_attention_layer_norm'''][
								    '''scale'''
								]

								# Encoder-Decoder-Attention
								lowercase__     				=    tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']
								lowercase__     				=    tax_enc_dec_attention_module['''key''']['''kernel''']
								lowercase__     				=    tax_enc_dec_attention_module['''out''']['''kernel''']
								lowercase__     				=    tax_enc_dec_attention_module['''query''']['''kernel''']
								lowercase__     				=    tax_enc_dec_attention_module['''value''']['''kernel''']

								# Layer Normalization
								lowercase__     				=    tax_model['''target''']['''decoder'''][layer_name]['''pre_cross_attention_layer_norm''']['''scale''']

								# MLP
								if split_mlp_wi:
												lowercase__     				=    tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
												lowercase__     				=    tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
								else:
												lowercase__     				=    tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

								lowercase__     				=    tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

								# Layer Normalization
								lowercase__     				=    tax_model['''target''']['''decoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

								# Assigning
								lowercase__     				=    flax_model.params['''decoder''']['''block'''][str(lowerCamelCase_       )]['''layer''']
								lowercase__     				=    tax_attention_key
								lowercase__     				=    tax_attention_out
								lowercase__     				=    tax_attention_query
								lowercase__     				=    tax_attention_value

								lowercase__     				=    tax_pre_attention_layer_norm

								lowercase__     				=    tax_enc_dec_attention_key
								lowercase__     				=    tax_enc_dec_attention_out
								lowercase__     				=    tax_enc_dec_attention_query
								lowercase__     				=    tax_enc_dec_attention_value

								lowercase__     				=    tax_cross_layer_norm

								if split_mlp_wi:
												lowercase__     				=    tax_mlp_wi_a
												lowercase__     				=    tax_mlp_wi_a
								else:
												lowercase__     				=    tax_mlp_wi

								lowercase__     				=    tax_mlp_wo

								lowercase__     				=    txa_mlp_layer_norm

								lowercase__     				=    flax_model_decoder_layer_block

				# Decoder Normalization
				lowercase__     				=    tax_model['''target''']['''decoder''']['''decoder_norm''']['''scale''']
				lowercase__     				=    txa_decoder_norm

				# Only for layer 0:
				lowercase__     				=    tax_model['''target''']['''decoder''']['''relpos_bias''']['''rel_embedding'''].T
				lowercase__     				=    tax_decoder_rel_embedding

				# Token Embeddings
				lowercase__     				=    tax_model['''target''']['''token_embedder''']['''embedding''']
				lowercase__     				=    txa_token_embeddings

				# LM Head (only in v1.1 and LongT5 checkpoints)
				if "logits_dense" in tax_model["target"]["decoder"]:
								lowercase__     				=    tax_model['''target''']['''decoder''']['''logits_dense''']['''kernel''']

				flax_model.save_pretrained(lowerCamelCase_       )
				print('''T5X Model was sucessfully converted!'''       )


if __name__ == "__main__":
							A__			:    List[str]         =				argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '--t5x_checkpoint_path', default=None, type=str, required=True, help='Path the T5X checkpoint.'
							)
							parser.add_argument('--config_name', default=None, type=str, required=True, help='Config name of LongT5/T5 model.')
							parser.add_argument(
							    '--flax_dump_folder_path', default=None, type=str, required=True, help='Path to the output FLAX model.'
							)
							A__			:    Dict         =				parser.parse_args()
							convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)












# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin, SchedulerOutput
@dataclass
class    _UpperCAmelCase      ( A__			):

				"""simple docstring"""




				lowercase__       							= 42
				lowercase__       							= 42



class    _UpperCAmelCase      ( A__	,A__			):

				"""simple docstring"""




				lowercase__       							= 1




				@register_to_config
				def __init__(  self		:					Union[str, Any],						lowerCamelCase		:					int = 2_000,						lowerCamelCase		:					float = 0.15,						lowerCamelCase		:					float = 0.01,						lowerCamelCase		:					float = 1348.0,						lowerCamelCase		:					float = 1E-5,						lowerCamelCase		:					int = 1,						):

								'''simple docstring'''
								# standard deviation of the initial noise distribution
								lowercase__     				=    sigma_max

								# setable values
								lowercase__     				=    None

								self.set_sigmas(lowerCamelCase,						lowerCamelCase,						lowerCamelCase,						lowerCamelCase						)




				def        lowercase__     (  self		:					List[str],						lowerCamelCase		:					torch.FloatTensor,						lowerCamelCase		:					Optional[int] = None						):

								'''simple docstring'''
								return sample




				def        lowercase__     (  self		:					Dict,						lowerCamelCase		:					int,						lowerCamelCase		:					float = None,						lowerCamelCase		:					Union[str, torch.device] = None						):

								'''simple docstring'''
								lowercase__     				=    sampling_eps if sampling_eps is not None else self.config.sampling_eps

								lowercase__     				=    torch.linspace(1,						lowerCamelCase,						lowerCamelCase,						device=lowerCamelCase						)




				def        lowercase__     (  self		:					str,						lowerCamelCase		:					int,						lowerCamelCase		:					float = None,						lowerCamelCase		:					float = None,						lowerCamelCase		:					float = None						):

								'''simple docstring'''
								lowercase__     				=    sigma_min if sigma_min is not None else self.config.sigma_min
								lowercase__     				=    sigma_max if sigma_max is not None else self.config.sigma_max
								lowercase__     				=    sampling_eps if sampling_eps is not None else self.config.sampling_eps
								if self.timesteps is None:
												self.set_timesteps(lowerCamelCase,						lowerCamelCase						)

								lowercase__     				=    sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
								lowercase__     				=    torch.exp(torch.linspace(math.log(lowerCamelCase						),						math.log(lowerCamelCase						),						lowerCamelCase						)						)
								lowercase__     				=    torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]						)




				def        lowercase__     (  self		:					Optional[int],						lowerCamelCase		:					str,						lowerCamelCase		:					str						):

								'''simple docstring'''
								return torch.where(
								    timesteps == 0,						torch.zeros_like(t.to(timesteps.device						)						),						self.discrete_sigmas[timesteps - 1].to(timesteps.device						),						)




				def        lowercase__     (  self		:					Tuple,						lowerCamelCase		:					torch.FloatTensor,						lowerCamelCase		:					int,						lowerCamelCase		:					torch.FloatTensor,						lowerCamelCase		:					Optional[torch.Generator] = None,						lowerCamelCase		:					bool = True,						):

								'''simple docstring'''
								if self.timesteps is None:
												raise ValueError(
												    '''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'''						)

								lowercase__     				=    timestep * torch.ones(
								    sample.shape[0],						device=sample.device						)  # torch.repeat_interleave(timestep, sample.shape[0])
								lowercase__     				=    (timestep * (len(self.timesteps						) - 1)).long()

								# mps requires indices to be in the same device, so we use cpu as is the default with cuda
								lowercase__     				=    timesteps.to(self.discrete_sigmas.device						)

								lowercase__     				=    self.discrete_sigmas[timesteps].to(sample.device						)
								lowercase__     				=    self.get_adjacent_sigma(lowerCamelCase,						lowerCamelCase						).to(sample.device						)
								lowercase__     				=    torch.zeros_like(lowerCamelCase						)
								lowercase__     				=    (sigma**2 - adjacent_sigma**2) ** 0.5

								# equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
								# also equation 47 shows the analog from SDE models to ancestral sampling methods
								lowercase__     				=    diffusion.flatten()
								while len(diffusion.shape						) < len(sample.shape						):
												lowercase__     				=    diffusion.unsqueeze(-1						)
								lowercase__     				=    drift - diffusion**2 * model_output

								#  equation 6: sample noise for the diffusion term of
								lowercase__     				=    randn_tensor(
								    sample.shape,						layout=sample.layout,						generator=lowerCamelCase,						device=sample.device,						dtype=sample.dtype						)
								lowercase__     				=    sample - drift  # subtract because `dt` is a small negative timestep
								# TODO is the variable diffusion the correct scaling term for the noise?
								lowercase__     				=    prev_sample_mean + diffusion * noise  # add impact of diffusion field g

								if not return_dict:
												return (prev_sample, prev_sample_mean)

								return SdeVeOutput(prev_sample=lowerCamelCase,						prev_sample_mean=lowerCamelCase						)




				def        lowercase__     (  self		:					int,						lowerCamelCase		:					torch.FloatTensor,						lowerCamelCase		:					torch.FloatTensor,						lowerCamelCase		:					Optional[torch.Generator] = None,						lowerCamelCase		:					bool = True,						):

								'''simple docstring'''
								if self.timesteps is None:
												raise ValueError(
												    '''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'''						)

								# For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
								# sample noise for correction
								lowercase__     				=    randn_tensor(sample.shape,						layout=sample.layout,						generator=lowerCamelCase						).to(sample.device						)

								# compute step size from the model_output, the noise, and the snr
								lowercase__     				=    torch.norm(model_output.reshape(model_output.shape[0],						-1						),						dim=-1						).mean()
								lowercase__     				=    torch.norm(noise.reshape(noise.shape[0],						-1						),						dim=-1						).mean()
								lowercase__     				=    (self.config.snr * noise_norm / grad_norm) ** 2 * 2
								lowercase__     				=    step_size * torch.ones(sample.shape[0]						).to(sample.device						)
								# self.repeat_scalar(step_size, sample.shape[0])

								# compute corrected sample: model_output term and noise term
								lowercase__     				=    step_size.flatten()
								while len(step_size.shape						) < len(sample.shape						):
												lowercase__     				=    step_size.unsqueeze(-1						)
								lowercase__     				=    sample + step_size * model_output
								lowercase__     				=    prev_sample_mean + ((step_size * 2) ** 0.5) * noise

								if not return_dict:
												return (prev_sample,)

								return SchedulerOutput(prev_sample=lowerCamelCase						)




				def        lowercase__     (  self		:					List[str],						lowerCamelCase		:					torch.FloatTensor,						lowerCamelCase		:					torch.FloatTensor,						lowerCamelCase		:					torch.FloatTensor,						):

								'''simple docstring'''
								# Make sure sigmas and timesteps have the same device and dtype as original_samples
								lowercase__     				=    timesteps.to(original_samples.device						)
								lowercase__     				=    self.discrete_sigmas.to(original_samples.device						)[timesteps]
								lowercase__     				=    (
								    noise * sigmas[:, None, None, None]
								    if noise is not None
								    else torch.randn_like(lowerCamelCase						) * sigmas[:, None, None, None]
								)
								lowercase__     				=    noise + original_samples
								return noisy_samples




				def __len__(  self		:					Union[str, Any]						):

								'''simple docstring'''
								return self.config.num_train_timesteps




