





'''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








'''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)

