code: 
  
  
  
  
  
  '''simple docstring'''
  
  
  import torch
  
  from diffusers import KDPMaAncestralDiscreteScheduler
  from diffusers.utils import torch_device
  
  from .test_schedulers import SchedulerCommonTest
  
  
  
  class 							__UpperCAmelCase  (       A__						):
  
  
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		__lowerCAmelCase 				=    (KDPMaAncestralDiscreteScheduler,)
  		__lowerCAmelCase 				=    10
  
  
  
  
  
  
  
  		def        A     (self						:      Any      ,      **_lowerCAmelCase						:      List[Any]      ):
  					A							  =      {
  					    """num_train_timesteps""": 1100,
  					    """beta_start""": 0.0_001,
  					    """beta_end""": 0.02,
  					    """beta_schedule""": """linear""",
  					}
  
  					config.update(**_lowerCAmelCase      )
  					return config
  
  
  
  
  
  
  
  		def        A     (self						:      Dict      ):
  					for timesteps in [10, 50, 100, 1000]:
  								self.check_over_configs(num_train_timesteps=_lowerCAmelCase      )
  
  
  
  
  
  
  
  		def        A     (self						:      Optional[int]      ):
  					for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]      ,      [0.0_002, 0.002, 0.02]      ):
  								self.check_over_configs(beta_start=_lowerCAmelCase      ,      beta_end=_lowerCAmelCase      )
  
  
  
  
  
  
  
  		def        A     (self						:      Tuple      ):
  					for schedule in ["linear", "scaled_linear"]:
  								self.check_over_configs(beta_schedule=_lowerCAmelCase      )
  
  
  
  
  
  
  
  		def        A     (self						:      Dict      ):
  					if torch_device == "mps":
  								return
  					A							  =      self.scheduler_classes[0]
  					A							  =      self.get_scheduler_config()
  					A							  =      scheduler_class(**_lowerCAmelCase      )
  
  					scheduler.set_timesteps(self.num_inference_steps      )
  
  					A							  =      torch.manual_seed(0      )
  
  					A							  =      self.dummy_model()
  					A							  =      self.dummy_sample_deter * scheduler.init_noise_sigma
  					A							  =      sample.to(_lowerCAmelCase      )
  
  					for i, t in enumerate(scheduler.timesteps      ):
  								A							  =      scheduler.scale_model_input(_lowerCAmelCase      ,      _lowerCAmelCase      )
  
  								A							  =      model(_lowerCAmelCase      ,      _lowerCAmelCase      )
  
  								A							  =      scheduler.step(_lowerCAmelCase      ,      _lowerCAmelCase      ,      _lowerCAmelCase      ,      generator=_lowerCAmelCase      )
  								A							  =      output.prev_sample
  
  					A							  =      torch.sum(torch.abs(_lowerCAmelCase      )      )
  					A							  =      torch.mean(torch.abs(_lowerCAmelCase      )      )
  
  					assert abs(result_sum.item() - 1_3849.3877      ) < 1e-2
  					assert abs(result_mean.item() - 18.0_331      ) < 5e-3
  
  
  
  
  
  
  
  		def        A     (self						:      Union[str, Any]      ):
  					for prediction_type in ["epsilon", "v_prediction"]:
  								self.check_over_configs(prediction_type=_lowerCAmelCase      )
  
  
  
  
  
  
  
  		def        A     (self						:      Dict      ):
  					if torch_device == "mps":
  								return
  					A							  =      self.scheduler_classes[0]
  					A							  =      self.get_scheduler_config(prediction_type="""v_prediction"""      )
  					A							  =      scheduler_class(**_lowerCAmelCase      )
  
  					scheduler.set_timesteps(self.num_inference_steps      )
  
  					A							  =      self.dummy_model()
  					A							  =      self.dummy_sample_deter * scheduler.init_noise_sigma
  					A							  =      sample.to(_lowerCAmelCase      )
  
  					A							  =      torch.manual_seed(0      )
  
  					for i, t in enumerate(scheduler.timesteps      ):
  								A							  =      scheduler.scale_model_input(_lowerCAmelCase      ,      _lowerCAmelCase      )
  
  								A							  =      model(_lowerCAmelCase      ,      _lowerCAmelCase      )
  
  								A							  =      scheduler.step(_lowerCAmelCase      ,      _lowerCAmelCase      ,      _lowerCAmelCase      ,      generator=_lowerCAmelCase      )
  								A							  =      output.prev_sample
  
  					A							  =      torch.sum(torch.abs(_lowerCAmelCase      )      )
  					A							  =      torch.mean(torch.abs(_lowerCAmelCase      )      )
  
  					assert abs(result_sum.item() - 328.9_970      ) < 1e-2
  					assert abs(result_mean.item() - 0.4_284      ) < 1e-3
  
  
  
  
  
  
  
  		def        A     (self						:      Optional[int]      ):
  					if torch_device == "mps":
  								return
  					A							  =      self.scheduler_classes[0]
  					A							  =      self.get_scheduler_config()
  					A							  =      scheduler_class(**_lowerCAmelCase      )
  
  					scheduler.set_timesteps(self.num_inference_steps      ,      device=_lowerCAmelCase      )
  					A							  =      torch.manual_seed(0      )
  
  					A							  =      self.dummy_model()
  					A							  =      self.dummy_sample_deter.to(_lowerCAmelCase      ) * scheduler.init_noise_sigma
  
  					for t in scheduler.timesteps:
  								A							  =      scheduler.scale_model_input(_lowerCAmelCase      ,      _lowerCAmelCase      )
  
  								A							  =      model(_lowerCAmelCase      ,      _lowerCAmelCase      )
  
  								A							  =      scheduler.step(_lowerCAmelCase      ,      _lowerCAmelCase      ,      _lowerCAmelCase      ,      generator=_lowerCAmelCase      )
  								A							  =      output.prev_sample
  
  					A							  =      torch.sum(torch.abs(_lowerCAmelCase      )      )
  					A							  =      torch.mean(torch.abs(_lowerCAmelCase      )      )
  
  					assert abs(result_sum.item() - 1_3849.3818      ) < 1e-1
  					assert abs(result_mean.item() - 18.0_331      ) < 1e-3
  
  
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)
  
  