




'''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(							SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_="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 			lowerCamelCase(							SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_=False     )		->		List[Any]:
			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 			lowerCamelCase(							SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_     )		->		Tuple:
			return params[F'{prefix}/layers_{i}/{layer_name}/scale']





def 			lowerCamelCase(							SCREAMING_SNAKE_CASE_       ,*, SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_     )		->		str:
			A_									= traverse_util.flatten_dict(variables['target']     )
			A_									= {'/'.join(SCREAMING_SNAKE_CASE_     ): 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:'       ,SCREAMING_SNAKE_CASE_     )

			A_									= collections.OrderedDict()

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

			# Encoder.
			for i in range(SCREAMING_SNAKE_CASE_     ):
						# Block i, layer 0 (Self Attention).
						A_									= tax_layer_norm_lookup(SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,'encoder'       ,'pre_attention_layer_norm'     )
						A_		, A_		, A_		, A_									= tax_attention_lookup(SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,'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(SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,'encoder'       ,'pre_mlp_layer_norm'     )
						A_		, A_									= tax_mlp_lookup(SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,'encoder'       ,SCREAMING_SNAKE_CASE_     )
						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(SCREAMING_SNAKE_CASE_     ):
									# Block i, layer 0 (Self Attention).
									A_									= tax_layer_norm_lookup(SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,'decoder'       ,'pre_self_attention_layer_norm'     )
									A_		, A_		, A_		, A_									= tax_attention_lookup(SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,'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(SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,'decoder'       ,'pre_cross_attention_layer_norm'     )
									A_		, A_		, A_		, A_									= tax_attention_lookup(SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,'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(SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,'decoder'       ,'pre_mlp_layer_norm'     )
									A_		, A_									= tax_mlp_lookup(SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,'decoder'       ,SCREAMING_SNAKE_CASE_     )
									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 			lowerCamelCase(							SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_     )		->		Dict:
			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 			lowerCamelCase(							SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_     )		->		List[str]:
			A_									= checkpoints.load_tax_checkpoint(SCREAMING_SNAKE_CASE_     )
			A_									= convert_tax_to_pytorch(SCREAMING_SNAKE_CASE_       ,num_layers=config.num_layers       ,is_encoder_only=SCREAMING_SNAKE_CASE_     )
			A_									= make_state_dict(SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_     )
			model.load_state_dict(SCREAMING_SNAKE_CASE_       ,strict=SCREAMING_SNAKE_CASE_     )





def 			lowerCamelCase(							SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_ = False     )		->		Optional[Any]:
			A_									= TaConfig.from_json_file(SCREAMING_SNAKE_CASE_     )
			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(SCREAMING_SNAKE_CASE_     )
			else:
						A_									= TaForConditionalGeneration(SCREAMING_SNAKE_CASE_     )

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

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

			# Verify that we can load the checkpoint.
			model.from_pretrained(SCREAMING_SNAKE_CASE_     )
			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
							)










'''simple docstring'''
import math
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
class   __UpperCAmelCase				(     lowerCAmelCase			,lowerCAmelCase				):



					'''simple docstring'''





					_UpperCamelCase							=		1



					@register_to_config
					def __init__(	self     :						str      ,       _lowercase     :						int = 1_000      ,       _lowercase     :						Optional[Union[np.ndarray, List[float]]] = None)    ->							List[str]:
								# set `betas`, `alphas`, `timesteps`
								self.set_timesteps(_lowercase)

								# standard deviation of the initial noise distribution
								A_									= 1.0

								# For now we only support F-PNDM, i.e. the runge-kutta method
								# For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
								# mainly at formula (9), (12), (13) and the Algorithm 2.
								A_									= 4

								# running values
								A_									= []



					def     __snake_case						(	self     :						int      ,       _lowercase     :						int      ,       _lowercase     :						Union[str, torch.device] = None)    ->							Any:
								A_									= num_inference_steps
								A_									= torch.linspace(1      ,       0      ,       num_inference_steps + 1)[:-1]
								A_									= torch.cat([steps, torch.tensor([0.0])])

								if self.config.trained_betas is not None:
											A_									= torch.tensor(self.config.trained_betas      ,       dtype=torch.floataa)
								else:
											A_									= torch.sin(steps * math.pi / 2) ** 2

								A_									= (1.0 - self.betas**2) ** 0.5

								A_									= (torch.atana(self.betas      ,       self.alphas) / math.pi * 2)[:-1]
								A_									= timesteps.to(_lowercase)

								A_									= []



					def     __snake_case						(	self     :						Dict      ,       _lowercase     :						torch.FloatTensor      ,       _lowercase     :						int      ,       _lowercase     :						torch.FloatTensor      ,       _lowercase     :						bool = True      ,       )    ->							Union[SchedulerOutput, Tuple]:
								if self.num_inference_steps is None:
											raise ValueError(
											    'Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler')

								A_									= (self.timesteps == timestep).nonzero().item()
								A_									= timestep_index + 1

								A_									= sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
								self.ets.append(_lowercase)

								if len(self.ets) == 1:
											A_									= self.ets[-1]
								elif len(self.ets) == 2:
											A_									= (3 * self.ets[-1] - self.ets[-2]) / 2
								elif len(self.ets) == 3:
											A_									= (23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
								else:
											A_									= (1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])

								A_									= self._get_prev_sample(_lowercase      ,       _lowercase      ,       _lowercase      ,       _lowercase)

								if not return_dict:
											return (prev_sample,)

								return SchedulerOutput(prev_sample=_lowercase)



					def     __snake_case						(	self     :						Dict      ,       _lowercase     :						torch.FloatTensor      ,       *_lowercase     :						Optional[Any]      ,       **_lowercase     :						int)    ->							torch.FloatTensor:
								return sample



					def     __snake_case						(	self     :						Optional[int]      ,       _lowercase     :						Optional[Any]      ,       _lowercase     :						Dict      ,       _lowercase     :						Union[str, Any]      ,       _lowercase     :						Union[str, Any])    ->							Union[str, Any]:
								A_									= self.alphas[timestep_index]
								A_									= self.betas[timestep_index]

								A_									= self.alphas[prev_timestep_index]
								A_									= self.betas[prev_timestep_index]

								A_									= (sample - sigma * ets) / max(_lowercase      ,       1E-8)
								A_									= next_alpha * pred + ets * next_sigma

								return prev_sample



					def __len__(	self     :						List[str])    ->							Union[str, Any]:
								return self.config.num_train_timesteps




