code: 
  
  
  
  
  
  '''simple docstring'''
  
  
  import math
  from dataclasses import dataclass
  from typing import Optional, Tuple, Union
  
  import numpy as np
  import torch
  
  from ..configuration_utils import ConfigMixin, register_to_config
  from ..utils import BaseOutput, randn_tensor
  from .scheduling_utils import SchedulerMixin
  
  
  
  @dataclass
  class 							__UpperCAmelCase  (       A__						):
  
  
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		__lowerCAmelCase 				=    42
  		__lowerCAmelCase 				=    42
  
  
  
  
  
  
  def    __a (     UpperCAmelCase       ,			UpperCAmelCase=0.999       ,			UpperCAmelCase="cosine"       ,			)      ->Optional[int]:
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	if alpha_transform_type == "cosine":
  
  		def alpha_bar_fn(UpperCAmelCase    ):
  			return math.cos((t + 0.008) / 1.008 * math.pi / 2    ) ** 2
  
  	elif alpha_transform_type == "exp":
  
  		def alpha_bar_fn(UpperCAmelCase    ):
  			return math.exp(t * -12.0    )
  
  	else:
  		raise ValueError(f"""Unsupported alpha_tranform_type: {alpha_transform_type}"""    )
  
  	A							  =      []
  	for i in range(UpperCAmelCase    ):
  		A							  =      i / num_diffusion_timesteps
  		A							  =      (i + 1) / num_diffusion_timesteps
  		betas.append(min(1 - alpha_bar_fn(UpperCAmelCase    ) / alpha_bar_fn(UpperCAmelCase    )       ,			UpperCAmelCase    )    )
  	return torch.tensor(UpperCAmelCase       ,			dtype=torch.floataa    )
  
  
  
  class 							__UpperCAmelCase  (       A__		,					A__						):
  
  
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		__lowerCAmelCase 				=    1
  
  
  
  
  
  
  
  		@register_to_config
  		def __init__(self						:      str      ,      _lowerCAmelCase						:      int = 1000      ,      _lowerCAmelCase						:      float = 0.0_001      ,      _lowerCAmelCase						:      float = 0.02      ,      _lowerCAmelCase						:      str = "linear"      ,      _lowerCAmelCase						:      float = 0.0      ,      _lowerCAmelCase						:      Optional[np.ndarray] = None      ,      _lowerCAmelCase						:      bool = True      ,      ):
  			if trained_betas is not None:
  				A							  =      torch.from_numpy(_lowerCAmelCase      )
  			elif beta_schedule == "linear":
  				A							  =      torch.linspace(_lowerCAmelCase      ,      _lowerCAmelCase      ,      _lowerCAmelCase      ,      dtype=torch.floataa      )
  			elif beta_schedule == "scaled_linear":
  				# this schedule is very specific to the latent diffusion model.
  				A							  =      (
  				    torch.linspace(beta_start**0.5      ,      beta_end**0.5      ,      _lowerCAmelCase      ,      dtype=torch.floataa      ) ** 2
  				)
  			elif beta_schedule == "squaredcos_cap_v2":
  				# Glide cosine schedule
  				A							  =      betas_for_alpha_bar(_lowerCAmelCase      )
  			elif beta_schedule == "sigmoid":
  				# GeoDiff sigmoid schedule
  				A							  =      torch.linspace(-6      ,      6      ,      _lowerCAmelCase      )
  				A							  =      torch.sigmoid(_lowerCAmelCase      ) * (beta_end - beta_start) + beta_start
  			else:
  				raise NotImplementedError(F"""{beta_schedule} does is not implemented for {self.__class__}"""      )
  
  			A							  =      1.0 - self.betas
  			A							  =      torch.cumprod(self.alphas      ,      dim=0      )
  			A							  =      torch.tensor(1.0      )
  
  			A							  =      torch.tensor(1.0      )
  
  			# standard deviation of the initial noise distribution
  			A							  =      1.0
  
  			# setable values
  			A							  =      None
  			A							  =      torch.from_numpy(np.arange(0      ,      _lowerCAmelCase      )[::-1].copy()      )
  
  			A							  =      eta
  
  
  
  
  
  
  
  		def        A     (self						:      Any      ,      _lowerCAmelCase						:      torch.FloatTensor      ,      _lowerCAmelCase						:      Optional[int] = None      ):
  			return sample
  
  
  
  
  
  
  
  		def        A     (self						:      Union[str, Any]      ,      _lowerCAmelCase						:      int      ,      _lowerCAmelCase						:      int = 10      ,      _lowerCAmelCase						:      int = 10      ,      _lowerCAmelCase						:      Union[str, torch.device] = None      ,      ):
  			A							  =      min(self.config.num_train_timesteps      ,      _lowerCAmelCase      )
  			A							  =      num_inference_steps
  
  			A							  =      []
  
  			A							  =      {}
  			for j in range(0      ,      num_inference_steps - jump_length      ,      _lowerCAmelCase      ):
  				A							  =      jump_n_sample - 1
  
  			A							  =      num_inference_steps
  			while t >= 1:
  				A							  =      t - 1
  				timesteps.append(_lowerCAmelCase      )
  
  				if jumps.get(_lowerCAmelCase      ,      0      ) > 0:
  					A							  =      jumps[t] - 1
  					for _ in range(_lowerCAmelCase      ):
  						A							  =      t + 1
  						timesteps.append(_lowerCAmelCase      )
  
  			A							  =      np.array(_lowerCAmelCase      ) * (self.config.num_train_timesteps // self.num_inference_steps)
  			A							  =      torch.from_numpy(_lowerCAmelCase      ).to(_lowerCAmelCase      )
  
  
  
  
  
  
  
  		def        A     (self						:      Dict      ,      _lowerCAmelCase						:      Optional[Any]      ):
  			A							  =      t - self.config.num_train_timesteps // self.num_inference_steps
  
  			A							  =      self.alphas_cumprod[t]
  			A							  =      self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
  			A							  =      1 - alpha_prod_t
  			A							  =      1 - alpha_prod_t_prev
  
  			# For t > 0, compute predicted variance βt (see formula (6) and (7) from
  			# https://arxiv.org/pdf/2006.11239.pdf) and sample from it to get
  			# previous sample x_{t-1} ~ N(pred_prev_sample, variance) == add
  			# variance to pred_sample
  			# Is equivalent to formula (16) in https://arxiv.org/pdf/2010.02502.pdf
  			# without eta.
  			# variance = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * self.betas[t]
  			A							  =      (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)
  
  			return variance
  
  
  
  
  
  
  
  		def        A     (self						:      List[Any]      ,      _lowerCAmelCase						:      torch.FloatTensor      ,      _lowerCAmelCase						:      int      ,      _lowerCAmelCase						:      torch.FloatTensor      ,      _lowerCAmelCase						:      torch.FloatTensor      ,      _lowerCAmelCase						:      torch.FloatTensor      ,      _lowerCAmelCase						:      Optional[torch.Generator] = None      ,      _lowerCAmelCase						:      bool = True      ,      ):
  			A							  =      timestep
  			A							  =      timestep - self.config.num_train_timesteps // self.num_inference_steps
  
  			# 1. compute alphas, betas
  			A							  =      self.alphas_cumprod[t]
  			A							  =      self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
  			A							  =      1 - alpha_prod_t
  
  			# 2. compute predicted original sample from predicted noise also called
  			# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
  			A							  =      (sample - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5
  
  			# 3. Clip "predicted x_0"
  			if self.config.clip_sample:
  				A							  =      torch.clamp(_lowerCAmelCase      ,      -1      ,      1      )
  
  			# We choose to follow RePaint Algorithm 1 to get x_{t-1}, however we
  			# substitute formula (7) in the algorithm coming from DDPM paper
  			# (formula (4) Algorithm 2 - Sampling) with formula (12) from DDIM paper.
  			# DDIM schedule gives the same results as DDPM with eta = 1.0
  			# Noise is being reused in 7. and 8., but no impact on quality has
  			# been observed.
  
  			# 5. Add noise
  			A							  =      model_output.device
  			A							  =      randn_tensor(model_output.shape      ,      generator=_lowerCAmelCase      ,      device=_lowerCAmelCase      ,      dtype=model_output.dtype      )
  			A							  =      self.eta * self._get_variance(_lowerCAmelCase      ) ** 0.5
  
  			A							  =      0
  			if t > 0 and self.eta > 0:
  				A							  =      std_dev_t * noise
  
  			# 6. compute "direction pointing to x_t" of formula (12)
  			# from https://arxiv.org/pdf/2010.02502.pdf
  			A							  =      (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output
  
  			# 7. compute x_{t-1} of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
  			A							  =      alpha_prod_t_prev**0.5 * pred_original_sample + pred_sample_direction + variance
  
  			# 8. Algorithm 1 Line 5 https://arxiv.org/pdf/2201.09865.pdf
  			A							  =      (alpha_prod_t_prev**0.5) * original_image + ((1 - alpha_prod_t_prev) ** 0.5) * noise
  
  			# 9. Algorithm 1 Line 8 https://arxiv.org/pdf/2201.09865.pdf
  			A							  =      mask * prev_known_part + (1.0 - mask) * prev_unknown_part
  
  			if not return_dict:
  				return (
  				    pred_prev_sample,
  				    pred_original_sample,
  				)
  
  			return RePaintSchedulerOutput(prev_sample=_lowerCAmelCase      ,      pred_original_sample=_lowerCAmelCase      )
  
  
  
  
  
  
  
  		def        A     (self						:      int      ,      _lowerCAmelCase						:      Optional[Any]      ,      _lowerCAmelCase						:      Union[str, Any]      ,      _lowerCAmelCase						:      Optional[Any]=None      ):
  			A							  =      self.config.num_train_timesteps // self.num_inference_steps
  
  			for i in range(_lowerCAmelCase      ):
  				A							  =      self.betas[timestep + i]
  				if sample.device.type == "mps":
  					# randn does not work reproducibly on mps
  					A							  =      randn_tensor(sample.shape      ,      dtype=sample.dtype      ,      generator=_lowerCAmelCase      )
  					A							  =      noise.to(sample.device      )
  				else:
  					A							  =      randn_tensor(sample.shape      ,      generator=_lowerCAmelCase      ,      device=sample.device      ,      dtype=sample.dtype      )
  
  				# 10. Algorithm 1 Line 10 https://arxiv.org/pdf/2201.09865.pdf
  				A							  =      (1 - beta) ** 0.5 * sample + beta**0.5 * noise
  
  			return sample
  
  
  
  
  
  
  
  		def        A     (self						:      List[Any]      ,      _lowerCAmelCase						:      torch.FloatTensor      ,      _lowerCAmelCase						:      torch.FloatTensor      ,      _lowerCAmelCase						:      torch.IntTensor      ,      ):
  			raise NotImplementedError("""Use `DDPMScheduler.add_noise()` to train for sampling with RePaint."""      )
  
  
  
  
  
  
  
  		def __len__(self						:      List[Any]      ):
  			return self.config.num_train_timesteps
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  
  import argparse
  
  from tax import checkpoints
  
  from transformers import FlaxTaForConditionalGeneration, TaConfig
  
  
  
  
  def    __a (     UpperCAmelCase       ,			UpperCAmelCase       ,			UpperCAmelCase    )      ->List[Any]:
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	A							  =      TaConfig.from_pretrained(UpperCAmelCase    )
  	A							  =      FlaxTaForConditionalGeneration(config=UpperCAmelCase    )
  	A							  =      checkpoints.load_tax_checkpoint(UpperCAmelCase    )
  
  	A							  =      """wi_0""" in tax_model["""target"""]["""encoder"""]["""layers_0"""]["""mlp"""]
  
  	# Encoder
  	for layer_index in range(config.num_layers    ):
  		A							  =      f"""layers_{str(UpperCAmelCase    )}"""
  
  		# Self-Attention
  		A							  =      tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""key"""]["""kernel"""]
  		A							  =      tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""out"""]["""kernel"""]
  		A							  =      tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""query"""]["""kernel"""]
  		A							  =      tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""value"""]["""kernel"""]
  
  		# Layer Normalization
  		A							  =      tax_model["""target"""]["""encoder"""][layer_name]["""pre_attention_layer_norm"""]["""scale"""]
  
  		if split_mlp_wi:
  			A							  =      tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi_0"""]["""kernel"""]
  			A							  =      tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi_1"""]["""kernel"""]
  		else:
  			A							  =      tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi"""]["""kernel"""]
  
  		A							  =      tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]
  
  		# Layer Normalization
  		A							  =      tax_model["""target"""]["""encoder"""][layer_name]["""pre_mlp_layer_norm"""]["""scale"""]
  
  		# Assigning
  		A							  =      tax_attention_key
  		A							  =      tax_attention_out
  		A							  =      tax_attention_query
  		A							  =      tax_attention_value
  
  		A							  =      tax_attention_layer_norm
  
  		if split_mlp_wi:
  			A							  =      tax_mlp_wi_a
  			A							  =      tax_mlp_wi_a
  		else:
  			A							  =      tax_mlp_wi
  
  		A							  =      tax_mlp_wo
  		A							  =      tax_mlp_layer_norm
  
  	# Only for layer 0:
  	A							  =      tax_model["""target"""]["""encoder"""]["""relpos_bias"""]["""rel_embedding"""].T
  	A							  =      tax_encoder_rel_embedding
  
  	# Assigning
  	A							  =      tax_model["""target"""]["""encoder"""]["""encoder_norm"""]["""scale"""]
  	A							  =      tax_encoder_norm
  
  	# Decoder
  	for layer_index in range(config.num_decoder_layers    ):
  		A							  =      f"""layers_{str(UpperCAmelCase    )}"""
  
  		# Self-Attention
  		A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""key"""]["""kernel"""]
  		A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""out"""]["""kernel"""]
  		A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""query"""]["""kernel"""]
  		A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""value"""]["""kernel"""]
  
  		# Layer Normalization
  		A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""pre_self_attention_layer_norm"""][
  		    """scale"""
  		]
  
  		# Encoder-Decoder-Attention
  		A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""key"""][
  		    """kernel"""
  		]
  		A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""out"""][
  		    """kernel"""
  		]
  		A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""query"""][
  		    """kernel"""
  		]
  		A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""value"""][
  		    """kernel"""
  		]
  
  		# Layer Normalization
  		A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""pre_cross_attention_layer_norm"""]["""scale"""]
  
  		# MLP
  		if split_mlp_wi:
  			A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi_0"""]["""kernel"""]
  			A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi_1"""]["""kernel"""]
  		else:
  			A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi"""]["""kernel"""]
  
  		A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]
  
  		# Layer Normalization
  		A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""pre_mlp_layer_norm"""]["""scale"""]
  
  		# Assigning
  		A							  =      tax_attention_key
  		A							  =      tax_attention_out
  		A							  =      tax_attention_query
  		A							  =      tax_attention_value
  
  		A							  =      tax_pre_attention_layer_norm
  
  		A							  =      tax_enc_dec_attention_key
  		A							  =      tax_enc_dec_attention_out
  		A							  =      tax_enc_dec_attention_query
  		A							  =      tax_enc_dec_attention_value
  
  		A							  =      tax_cross_layer_norm
  
  		if split_mlp_wi:
  			A							  =      tax_mlp_wi_a
  			A							  =      tax_mlp_wi_a
  		else:
  			A							  =      tax_mlp_wi
  
  		A							  =      tax_mlp_wo
  
  		A							  =      txa_mlp_layer_norm
  
  	# Decoder Normalization
  	A							  =      tax_model["""target"""]["""decoder"""]["""decoder_norm"""]["""scale"""]
  	A							  =      txa_decoder_norm
  
  	# Only for layer 0:
  	A							  =      tax_model["""target"""]["""decoder"""]["""relpos_bias"""]["""rel_embedding"""].T
  	A							  =      tax_decoder_rel_embedding
  
  	# Token Embeddings
  	A							  =      tax_model["""target"""]["""token_embedder"""]["""embedding"""]
  	A							  =      txa_token_embeddings
  
  	# LM Head (only in v1.1 checkpoints)
  	if "logits_dense" in tax_model["target"]["decoder"]:
  		A							  =      tax_model["""target"""]["""decoder"""]["""logits_dense"""]["""kernel"""]
  
  	flax_model.save_pretrained(UpperCAmelCase    )
  	print("""T5X Model was sucessfully converted!"""    )
  
  
  if __name__ == "__main__":
  				_lowerCamelCase				:	Tuple				    =       argparse.ArgumentParser()
  				# Required parameters
  				parser.add_argument(
  				    '--t5x_checkpoint_path', default=None, type=str, required=True, help='Path the TX5 checkpoint.'
  				)
  				parser.add_argument('--config_name', default=None, type=str, required=True, help='Config name of T5 model.')
  				parser.add_argument(
  				    '--flax_dump_folder_path', default=None, type=str, required=True, help='Path to the output FLAX model.'
  				)
  				_lowerCamelCase				:	Union[str, Any]				    =       parser.parse_args()
  				convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)
  
  