





# 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        _a(    __A    ):
		lowerCamelCase__     :torch.FloatTensor
		lowerCamelCase__     :torch.FloatTensor






class        _a(    __A	,   __A    ):
		lowerCamelCase__     :List[Any]   				=		1







		@register_to_config
		def __init__(    self ,						__snake_case = 2_0_0_0 ,						__snake_case = 0.15 ,						__snake_case = 0.01 ,						__snake_case = 1_3_4_8.0 ,						__snake_case = 1E-5 ,						__snake_case = 1 ,						)      ->      Tuple:


							'''simple docstring'''

							_snake_case       :					Tuple				       =   sigma_max

							# setable values
							_snake_case       :					Union[str, Any]				       =   None

							self.set_sigmas(__snake_case ,						__snake_case ,						__snake_case ,						__snake_case				)







		def        lowercase       (    self ,						__snake_case ,						__snake_case = None				)      ->      torch.FloatTensor:


							'''simple docstring'''

							return sample







		def        lowercase       (    self ,						__snake_case ,						__snake_case = None ,						__snake_case = None				)      ->      int:


							'''simple docstring'''

							_snake_case       :					Any				       =   sampling_eps if sampling_eps is not None else self.config.sampling_eps

							_snake_case       :					List[Any]				       =   torch.linspace(1 ,						__snake_case ,						__snake_case ,						device=__snake_case				)







		def        lowercase       (    self ,						__snake_case ,						__snake_case = None ,						__snake_case = None ,						__snake_case = None				)      ->      List[str]:


							'''simple docstring'''

							_snake_case       :					Dict				       =   sigma_min if sigma_min is not None else self.config.sigma_min
							_snake_case       :					Union[str, Any]				       =   sigma_max if sigma_max is not None else self.config.sigma_max
							_snake_case       :					Optional[Any]				       =   sampling_eps if sampling_eps is not None else self.config.sampling_eps
							if self.timesteps is None:
												self.set_timesteps(__snake_case ,						__snake_case				)

							_snake_case       :					Optional[int]				       =   sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
							_snake_case       :					int				       =   torch.exp(torch.linspace(math.log(__snake_case				) ,						math.log(__snake_case				) ,						__snake_case				)				)
							_snake_case       :					Optional[Any]				       =   torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]				)







		def        lowercase       (    self ,						__snake_case ,						__snake_case				)      ->      Union[str, Any]:


							'''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 ,						__snake_case ,						__snake_case ,						__snake_case ,						__snake_case = None ,						__snake_case = True ,						)      ->      Union[SdeVeOutput, Tuple]:


							'''simple docstring'''

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

							_snake_case       :					Union[str, Any]				       =   timestep * torch.ones(
							    sample.shape[0] ,						device=sample.device				)  # torch.repeat_interleave(timestep, sample.shape[0])
							_snake_case       :					Optional[int]				       =   (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
							_snake_case       :					Dict				       =   timesteps.to(self.discrete_sigmas.device				)

							_snake_case       :					Tuple				       =   self.discrete_sigmas[timesteps].to(sample.device				)
							_snake_case       :					Optional[int]				       =   self.get_adjacent_sigma(__snake_case ,						__snake_case				).to(sample.device				)
							_snake_case       :					Union[str, Any]				       =   torch.zeros_like(__snake_case				)
							_snake_case       :					Tuple				       =   (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
							_snake_case       :					Optional[int]				       =   diffusion.flatten()
							while len(diffusion.shape				) < len(sample.shape				):
												_snake_case       :					Union[str, Any]				       =   diffusion.unsqueeze(-1				)
							_snake_case       :					Union[str, Any]				       =   drift - diffusion**2 * model_output

							#  equation 6: sample noise for the diffusion term of
							_snake_case       :					Tuple				       =   randn_tensor(
							    sample.shape ,						layout=sample.layout ,						generator=__snake_case ,						device=sample.device ,						dtype=sample.dtype				)
							_snake_case       :					Optional[Any]				       =   sample - drift  # subtract because `dt` is a small negative timestep
							# TODO is the variable diffusion the correct scaling term for the noise?
							_snake_case       :					Optional[Any]				       =   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=__snake_case ,						prev_sample_mean=__snake_case				)







		def        lowercase       (    self ,						__snake_case ,						__snake_case ,						__snake_case = None ,						__snake_case = True ,						)      ->      Union[SchedulerOutput, Tuple]:


							'''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
							_snake_case       :					Tuple				       =   randn_tensor(sample.shape ,						layout=sample.layout ,						generator=__snake_case				).to(sample.device				)

							# compute step size from the model_output, the noise, and the snr
							_snake_case       :					List[str]				       =   torch.norm(model_output.reshape(model_output.shape[0] ,						-1				) ,						dim=-1				).mean()
							_snake_case       :					List[Any]				       =   torch.norm(noise.reshape(noise.shape[0] ,						-1				) ,						dim=-1				).mean()
							_snake_case       :					Tuple				       =   (self.config.snr * noise_norm / grad_norm) ** 2 * 2
							_snake_case       :					Optional[int]				       =   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
							_snake_case       :					Tuple				       =   step_size.flatten()
							while len(step_size.shape				) < len(sample.shape				):
												_snake_case       :					str				       =   step_size.unsqueeze(-1				)
							_snake_case       :					Tuple				       =   sample + step_size * model_output
							_snake_case       :					str				       =   prev_sample_mean + ((step_size * 2) ** 0.5) * noise

							if not return_dict:
												return (prev_sample,)

							return SchedulerOutput(prev_sample=__snake_case				)







		def        lowercase       (    self ,						__snake_case ,						__snake_case ,						__snake_case ,						)      ->      torch.FloatTensor:


							'''simple docstring'''

							_snake_case       :					Union[str, Any]				       =   timesteps.to(original_samples.device				)
							_snake_case       :					Any				       =   self.discrete_sigmas.to(original_samples.device				)[timesteps]
							_snake_case       :					Optional[int]				       =   (
							    noise * sigmas[:, None, None, None]
							    if noise is not None
							    else torch.randn_like(__snake_case				) * sigmas[:, None, None, None]
							)
							_snake_case       :					Optional[Any]				       =   noise + original_samples
							return noisy_samples







		def __len__(    self				)      ->      Any:


							'''simple docstring'''

							return self.config.num_train_timesteps






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"				):
					_snake_case       :					Any				       =   params[F"""{prefix}/layers_{i}/{layer_name}/key/kernel"""]
					_snake_case       :					Tuple				       =   params[F"""{prefix}/layers_{i}/{layer_name}/out/kernel"""]
					_snake_case       :					str				       =   params[F"""{prefix}/layers_{i}/{layer_name}/query/kernel"""]
					_snake_case       :					List[Any]				       =   params[F"""{prefix}/layers_{i}/{layer_name}/value/kernel"""]
					return k, o, q, v







def    A				(							UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase=False				):
					if split_mlp_wi:
										_snake_case       :					List[Any]				       =   params[F"""{prefix}/layers_{i}/mlp/wi_0/kernel"""]
										_snake_case       :					Tuple				       =   params[F"""{prefix}/layers_{i}/mlp/wi_1/kernel"""]
										_snake_case       :					Union[str, Any]				       =   (wi_a, wi_a)
					else:
										_snake_case       :					Optional[int]				       =   params[F"""{prefix}/layers_{i}/mlp/wi/kernel"""]

					_snake_case       :					List[str]				       =   params[F"""{prefix}/layers_{i}/mlp/wo/kernel"""]
					return wi, wo







def    A				(							UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase				):
					return params[F"""{prefix}/layers_{i}/{layer_name}/scale"""]







def    A				(							UpperCAmelCase  ,  *, UpperCAmelCase  ,  UpperCAmelCase				):
					_snake_case       :					Any				       =   traverse_util.flatten_dict(variables["target"]				)
					_snake_case       :					str				       =   {"/".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
					_snake_case       :					Tuple				       =   "encoder/layers_0/mlp/wi_0/kernel" in old
					print("Split MLP:"  ,  UpperCAmelCase				)

					_snake_case       :					List[Any]				       =   collections.OrderedDict()

					# Shared embeddings.
					_snake_case       :					Tuple				       =   old["token_embedder/embedding"]

					# Encoder.
					for i in range(UpperCAmelCase				):
										# Block i, layer 0 (Self Attention).
										_snake_case       :					Optional[Any]				       =   tax_layer_norm_lookup(UpperCAmelCase  ,  UpperCAmelCase  ,  "encoder"  ,  "pre_attention_layer_norm"				)
										_snake_case       , _snake_case       , _snake_case       , _snake_case       :					Union[str, Any]				       =   tax_attention_lookup(UpperCAmelCase  ,  UpperCAmelCase  ,  "encoder"  ,  "attention"				)
										_snake_case       :					Optional[Any]				       =   layer_norm
										_snake_case       :					int				       =   k.T
										_snake_case       :					Optional[int]				       =   o.T
										_snake_case       :					Dict				       =   q.T
										_snake_case       :					Any				       =   v.T

										# Block i, layer 1 (MLP).
										_snake_case       :					Any				       =   tax_layer_norm_lookup(UpperCAmelCase  ,  UpperCAmelCase  ,  "encoder"  ,  "pre_mlp_layer_norm"				)
										_snake_case       , _snake_case       :					Tuple				       =   tax_mlp_lookup(UpperCAmelCase  ,  UpperCAmelCase  ,  "encoder"  ,  UpperCAmelCase				)
										_snake_case       :					Any				       =   layer_norm
										if split_mlp_wi:
															_snake_case       :					Union[str, Any]				       =   wi[0].T
															_snake_case       :					List[str]				       =   wi[1].T
										else:
															_snake_case       :					List[Any]				       =   wi.T
										_snake_case       :					Dict				       =   wo.T

					_snake_case       :					Dict				       =   old[
					    "encoder/relpos_bias/rel_embedding"
					].T
					_snake_case       :					Dict				       =   old["encoder/encoder_norm/scale"]

					if not is_encoder_only:
										# Decoder.
										for i in range(UpperCAmelCase				):
															# Block i, layer 0 (Self Attention).
															_snake_case       :					str				       =   tax_layer_norm_lookup(UpperCAmelCase  ,  UpperCAmelCase  ,  "decoder"  ,  "pre_self_attention_layer_norm"				)
															_snake_case       , _snake_case       , _snake_case       , _snake_case       :					Optional[Any]				       =   tax_attention_lookup(UpperCAmelCase  ,  UpperCAmelCase  ,  "decoder"  ,  "self_attention"				)
															_snake_case       :					Optional[Any]				       =   layer_norm
															_snake_case       :					Dict				       =   k.T
															_snake_case       :					List[str]				       =   o.T
															_snake_case       :					List[Any]				       =   q.T
															_snake_case       :					List[Any]				       =   v.T

															# Block i, layer 1 (Cross Attention).
															_snake_case       :					Tuple				       =   tax_layer_norm_lookup(UpperCAmelCase  ,  UpperCAmelCase  ,  "decoder"  ,  "pre_cross_attention_layer_norm"				)
															_snake_case       , _snake_case       , _snake_case       , _snake_case       :					Union[str, Any]				       =   tax_attention_lookup(UpperCAmelCase  ,  UpperCAmelCase  ,  "decoder"  ,  "encoder_decoder_attention"				)
															_snake_case       :					Tuple				       =   layer_norm
															_snake_case       :					Union[str, Any]				       =   k.T
															_snake_case       :					str				       =   o.T
															_snake_case       :					List[str]				       =   q.T
															_snake_case       :					List[Any]				       =   v.T

															# Block i, layer 2 (MLP).
															_snake_case       :					Dict				       =   tax_layer_norm_lookup(UpperCAmelCase  ,  UpperCAmelCase  ,  "decoder"  ,  "pre_mlp_layer_norm"				)
															_snake_case       , _snake_case       :					Union[str, Any]				       =   tax_mlp_lookup(UpperCAmelCase  ,  UpperCAmelCase  ,  "decoder"  ,  UpperCAmelCase				)
															_snake_case       :					Optional[Any]				       =   layer_norm
															if split_mlp_wi:
																				_snake_case       :					str				       =   wi[0].T
																				_snake_case       :					Union[str, Any]				       =   wi[1].T
															else:
																				_snake_case       :					Optional[Any]				       =   wi.T
															_snake_case       :					int				       =   wo.T

										_snake_case       :					Optional[int]				       =   old["decoder/decoder_norm/scale"]
										_snake_case       :					int				       =   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       :					Tuple				       =   old["decoder/logits_dense/kernel"].T

					return new







def    A				(							UpperCAmelCase  ,  UpperCAmelCase				):
					_snake_case       :					Tuple				       =   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       :					Tuple				       =   state_dict["shared.weight"]

					if not is_encoder_only:
										if "decoder.embed_tokens.weight" not in state_dict:
															_snake_case       :					List[Any]				       =   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       :					Optional[int]				       =   state_dict["shared.weight"]

					return state_dict







def    A				(							UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase				):
					_snake_case       :					List[str]				       =   checkpoints.load_tax_checkpoint(UpperCAmelCase				)
					_snake_case       :					Dict				       =   convert_tax_to_pytorch(UpperCAmelCase  ,  num_layers=config.num_layers  ,  is_encoder_only=UpperCAmelCase				)
					_snake_case       :					Dict				       =   make_state_dict(UpperCAmelCase  ,  UpperCAmelCase				)
					model.load_state_dict(UpperCAmelCase  ,  strict=UpperCAmelCase				)







def    A				(							UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase = False				):
					_snake_case       :					str				       =   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:
										_snake_case       :					List[Any]				       =   TaEncoderModel(UpperCAmelCase				)
					else:
										_snake_case       :					List[str]				       =   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__":
							__lowerCAmelCase :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
							)
							__lowerCAmelCase :Optional[int]     =    parser.parse_args()
							convert_tax_checkpoint_to_pytorch(
							    args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
							)