



'''simple docstring'''

import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union

import flax
import jax.numpy as jnp

from ..utils import BaseOutput


a							   =				'scheduler_config.json'






class 						a_		(     snake_case    ):
							UpperCAmelCase			:		List[Any]        =				1
							UpperCAmelCase			:		str        =				2
							UpperCAmelCase			:		Optional[Any]        =				3
							UpperCAmelCase			:		Tuple        =				4
							UpperCAmelCase			:		List[Any]        =				5






@dataclass
class 						a_		(     snake_case    ):
							UpperCAmelCase			:		jnp.ndarray






class 						a_		:
							UpperCAmelCase			:		List[str]        =				SCHEDULER_CONFIG_NAME
							UpperCAmelCase			:		Dict        =				["""dtype"""]
							UpperCAmelCase			:		List[Any]        =				[]
							UpperCAmelCase			:		List[Any]        =				True


							@classmethod
							def    UpperCamelCase     (				cls   :						Union[str, Any]      ,						a_   :						Dict[str, Any] = None      ,						a_   :						Optional[str] = None      ,						a_   :						List[str]=False      ,						**a_   :						Tuple      ,						)			->					Dict:
										snake_case							,  snake_case:    str      							=cls.load_config(
										    pretrained_model_name_or_path=a_      ,						subfolder=a_      ,						return_unused_kwargs=a_      ,						**a_      ,						)
										snake_case							,  snake_case:    Any      							=cls.from_config(a_      ,						return_unused_kwargs=a_      ,						**a_			)

										if hasattr(a_      ,						'create_state'			) and getattr(a_      ,						'has_state'      ,						a_			):
													snake_case:    Dict      							=scheduler.create_state()

										if return_unused_kwargs:
													return scheduler, state, unused_kwargs

										return scheduler, state


							def    UpperCamelCase     (				self   :						str      ,						a_   :						Union[str, os.PathLike]      ,						a_   :						bool = False      ,						**a_   :						Tuple			)			->					Union[str, Any]:
										self.save_config(save_directory=a_      ,						push_to_hub=a_      ,						**a_			)


							@property
							def    UpperCamelCase     (				self   :						Tuple			)			->					str:
										return self._get_compatibles()




							@classmethod
							def    UpperCamelCase     (				cls   :						Tuple			)			->					Any:
										snake_case:    Optional[int]      							=list(set([cls.__name__] + cls._compatibles			)			)
										snake_case:    List[Any]      							=importlib.import_module(__name__.split('.'			)[0]			)
										snake_case:    Union[str, Any]      							=[
										    getattr(a_      ,						a_			) for c in compatible_classes_str if hasattr(a_      ,						a_			)
										]
										return compatible_classes







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase					)				->					jnp.ndarray:





			"""simple docstring"""







			assert len(__UpperCAmelCase					) >= x.ndim
			return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(__UpperCAmelCase					) - x.ndim)					)				,							__UpperCAmelCase					)







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase=0.999				,							__UpperCAmelCase=jnp.floataa					)				->					jnp.ndarray:





			"""simple docstring"""







			def alpha_bar(__UpperCAmelCase					):
						return math.cos((time_step + 0.008) / 1.008 * math.pi / 2					) ** 2

			snake_case:    List[str]      							=[]
			for i in range(__UpperCAmelCase					):
						snake_case:    Any      							=i / num_diffusion_timesteps
						snake_case:    Union[str, Any]      							=(i + 1) / num_diffusion_timesteps
						betas.append(min(1 - alpha_bar(__UpperCAmelCase					) / alpha_bar(__UpperCAmelCase					)				,							__UpperCAmelCase					)					)
			return jnp.array(__UpperCAmelCase				,							dtype=__UpperCAmelCase					)






@flax.struct.dataclass
class 						a_		:
							UpperCAmelCase			:		jnp.ndarray
							UpperCAmelCase			:		jnp.ndarray
							UpperCAmelCase			:		jnp.ndarray


							@classmethod
							def    UpperCamelCase     (				cls   :						Any      ,						a_   :						Optional[Any]			)			->					List[Any]:
										snake_case:    str      							=scheduler.config

										if config.trained_betas is not None:
													snake_case:    str      							=jnp.asarray(config.trained_betas      ,						dtype=scheduler.dtype			)
										elif config.beta_schedule == "linear":
													snake_case:    List[Any]      							=jnp.linspace(config.beta_start      ,						config.beta_end      ,						config.num_train_timesteps      ,						dtype=scheduler.dtype			)
										elif config.beta_schedule == "scaled_linear":
													# this schedule is very specific to the latent diffusion model.
													snake_case:    str      							=(
													    jnp.linspace(
													        config.beta_start**0.5      ,						config.beta_end**0.5      ,						config.num_train_timesteps      ,						dtype=scheduler.dtype			)
													    ** 2
													)
										elif config.beta_schedule == "squaredcos_cap_v2":
													# Glide cosine schedule
													snake_case:    str      							=betas_for_alpha_bar(config.num_train_timesteps      ,						dtype=scheduler.dtype			)
										else:
													raise NotImplementedError(
													    F'''beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}'''			)

										snake_case:    List[str]      							=1.0 - betas

										snake_case:    Dict      							=jnp.cumprod(a_      ,						axis=0			)

										return cls(
										    alphas=a_      ,						betas=a_      ,						alphas_cumprod=a_      ,						)







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase					)				->					int:





			"""simple docstring"""







			snake_case:    Dict      							=state.alphas_cumprod

			snake_case:    Dict      							=alphas_cumprod[timesteps] ** 0.5
			snake_case:    Optional[Any]      							=sqrt_alpha_prod.flatten()
			snake_case:    str      							=broadcast_to_shape_from_left(__UpperCAmelCase				,							original_samples.shape					)

			snake_case:    List[Any]      							=(1 - alphas_cumprod[timesteps]) ** 0.5
			snake_case:    Optional[Any]      							=sqrt_one_minus_alpha_prod.flatten()
			snake_case:    Dict      							=broadcast_to_shape_from_left(__UpperCAmelCase				,							original_samples.shape					)

			return sqrt_alpha_prod, sqrt_one_minus_alpha_prod







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase					)				->					int:





			"""simple docstring"""







			snake_case							,  snake_case:    Dict      							=get_sqrt_alpha_prod(__UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase					)
			snake_case:    str      							=sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
			return noisy_samples







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase					)				->					Optional[Any]:





			"""simple docstring"""







			snake_case							,  snake_case:    List[str]      							=get_sqrt_alpha_prod(__UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase					)
			snake_case:    Tuple      							=sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
			return velocity









'''simple docstring'''

import argparse
import os
import re

import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints

from transformers import (
    AutoTokenizer,
    PixaStructConfig,
    PixaStructForConditionalGeneration,
    PixaStructImageProcessor,
    PixaStructProcessor,
    PixaStructTextConfig,
    PixaStructVisionConfig,
)







def a_     (    __UpperCAmelCase					)				->					int:





			"""simple docstring"""







			snake_case:    List[str]      							=checkpoints.load_tax_checkpoint(__UpperCAmelCase					)
			snake_case:    str      							=flatten_dict(__UpperCAmelCase					)
			return flax_params







def a_     (    __UpperCAmelCase					)				->					Optional[Any]:





			"""simple docstring"""







			snake_case:    int      							={}

			snake_case:    Any      							={
			    'token_embedder': 'embeddings',
			    'encoder_norm': 'layernorm',
			    'kernel': 'weight',
			    '.out': '.output',
			    'scale': 'weight',
			    'embedders_0.pos_embedding': 'row_embedder.weight',
			    'embedders_1.pos_embedding': 'column_embedder.weight',
			}

			snake_case:    Union[str, Any]      							={
			    'query': 'attention.query',
			    'key': 'attention.key',
			    'value': 'attention.value',
			    'output.dense': 'output',
			    'encoder_decoder_attention.o': 'encoder_decoder_attention.attention.o',
			    'pre_self_attention_layer_norm': 'self_attention.layer_norm',
			    'pre_cross_attention_layer_norm': 'encoder_decoder_attention.layer_norm',
			    'mlp.': 'mlp.DenseReluDense.',
			    'pre_mlp_layer_norm': 'mlp.layer_norm',
			    'self_attention.o': 'self_attention.attention.o',
			    'decoder.embeddings.embedding': 'decoder.embed_tokens.weight',
			    'decoder.relpos_bias.rel_embedding': 'decoder.layer.0.self_attention.attention.relative_attention_bias.weight',
			    'decoder.decoder_norm.weight': 'decoder.final_layer_norm.weight',
			    'decoder.logits_dense.weight': 'decoder.lm_head.weight',
			}

			for key in flax_dict.keys():
						if "target" in key:
									# remove the first prefix from the key
									snake_case:    Dict      							='.'.join(key[1:]					)

									# rename the key
									for old, new in CONVERSION_MAPPING.items():
												snake_case:    Union[str, Any]      							=new_key.replace(__UpperCAmelCase				,							__UpperCAmelCase					)

									if "decoder" in new_key:
												for old, new in DECODER_CONVERSION_MAPPING.items():
															snake_case:    Optional[Any]      							=new_key.replace(__UpperCAmelCase				,							__UpperCAmelCase					)

									if "layers" in new_key and "decoder" not in new_key:
												# use regex to replace the layer number
												snake_case:    Any      							=re.sub(R'layers_(\d+)'				,							R'layer.\1'				,							__UpperCAmelCase					)
												snake_case:    Optional[int]      							=new_key.replace('encoder'				,							'encoder.encoder'					)

									elif "layers" in new_key and "decoder" in new_key:
												# use regex to replace the layer number
												snake_case:    Optional[Any]      							=re.sub(R'layers_(\d+)'				,							R'layer.\1'				,							__UpperCAmelCase					)

									snake_case:    Optional[int]      							=flax_dict[key]

			snake_case:    Optional[int]      							={}
			# convert converted_dict into torch format
			for key in converted_dict.keys():
						if ("embed_tokens" not in key) and ("embedder" not in key):
									snake_case:    Union[str, Any]      							=torch.from_numpy(converted_dict[key].T					)
						else:
									snake_case:    Union[str, Any]      							=torch.from_numpy(converted_dict[key]					)

			return converted_torch_dict







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase=False				,							__UpperCAmelCase=False					)				->					Tuple:





			"""simple docstring"""







			snake_case:    Any      							=get_flax_param(__UpperCAmelCase					)

			if not use_large:
						snake_case:    Optional[int]      							=PixaStructVisionConfig()
						snake_case:    int      							=PixaStructTextConfig()
			else:
						snake_case:    Tuple      							=PixaStructVisionConfig(
						    hidden_size=15_36				,							d_ff=39_68				,							num_attention_heads=24				,							num_hidden_layers=18					)
						snake_case:    List[Any]      							=PixaStructTextConfig(hidden_size=15_36				,							d_ff=39_68				,							num_heads=24				,							num_layers=18					)
			snake_case:    Any      							=PixaStructConfig(
			    vision_config=encoder_config.to_dict()				,							text_config=decoder_config.to_dict()				,							is_vqa=__UpperCAmelCase					)

			snake_case:    List[Any]      							=PixaStructForConditionalGeneration(__UpperCAmelCase					)

			snake_case:    str      							=rename_and_convert_flax_params(__UpperCAmelCase					)
			model.load_state_dict(__UpperCAmelCase					)

			snake_case:    List[Any]      							=AutoTokenizer.from_pretrained('ybelkada/test-pix2struct-tokenizer'					)
			snake_case:    Optional[Any]      							=PixaStructImageProcessor()
			snake_case:    Optional[int]      							=PixaStructProcessor(image_processor=__UpperCAmelCase				,							tokenizer=__UpperCAmelCase					)

			if use_large:
						snake_case:    Optional[Any]      							=40_96

			snake_case:    str      							=True

			# mkdir if needed
			os.makedirs(__UpperCAmelCase				,							exist_ok=__UpperCAmelCase					)

			model.save_pretrained(__UpperCAmelCase					)
			processor.save_pretrained(__UpperCAmelCase					)

			print('Model saved in {}'.format(__UpperCAmelCase					)					)


if __name__ == "__main__":
	a							   =				argparse.ArgumentParser()
	parser.add_argument('--t5x_checkpoint_path', default=None, type=str, help='Path to the original T5x checkpoint.')
	parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
	parser.add_argument('--use_large', action='store_true', help='Use large model.')
	parser.add_argument('--is_vqa', action='store_true', help='Use large model.')
	a							   =				parser.parse_args()

	convert_pixastruct_original_pytorch_checkpoint_to_hf(
	    args.tax_checkpoint_path, args.pytorch_dump_folder_path, args.use_large
	)




