




import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig

def      lowerCAmelCase_     (					lowerCamelCase:    Tuple       ,			lowerCamelCase:    Union[str, Any]       ,			lowerCamelCase:    Optional[int]  )   ->     Any:


      '''simple docstring'''




      lowerCamelCase__ 					=			TaConfig.from_pretrained(lowerCamelCase  )
      lowerCamelCase__ 					=			FlaxTaForConditionalGeneration(config=lowerCamelCase  )
      lowerCamelCase__ 					=			checkpoints.load_tax_checkpoint(lowerCamelCase  )

      lowerCamelCase__ 					=			'wi_0' in tax_model['target']['encoder']['layers_0']['mlp']

      # Encoder
      for layer_index in range(config.num_layers  ):
            lowerCamelCase__ 					=			f"""layers_{str(lowerCamelCase  )}"""

            # Self-Attention
            lowerCamelCase__ 					=			tax_model['target']['encoder'][layer_name]['attention']['key']['kernel']
            lowerCamelCase__ 					=			tax_model['target']['encoder'][layer_name]['attention']['out']['kernel']
            lowerCamelCase__ 					=			tax_model['target']['encoder'][layer_name]['attention']['query']['kernel']
            lowerCamelCase__ 					=			tax_model['target']['encoder'][layer_name]['attention']['value']['kernel']

            # Layer Normalization
            lowerCamelCase__ 					=			tax_model['target']['encoder'][layer_name]['pre_attention_layer_norm']['scale']

            if split_mlp_wi:
                  lowerCamelCase__ 					=			tax_model['target']['encoder'][layer_name]['mlp']['wi_0']['kernel']
                  lowerCamelCase__ 					=			tax_model['target']['encoder'][layer_name]['mlp']['wi_1']['kernel']
            else:
                  lowerCamelCase__ 					=			tax_model['target']['encoder'][layer_name]['mlp']['wi']['kernel']

            lowerCamelCase__ 					=			tax_model['target']['encoder'][layer_name]['mlp']['wo']['kernel']

            # Layer Normalization
            lowerCamelCase__ 					=			tax_model['target']['encoder'][layer_name]['pre_mlp_layer_norm']['scale']

            # Assigning
            lowerCamelCase__ 					=			tax_attention_key
            lowerCamelCase__ 					=			tax_attention_out
            lowerCamelCase__ 					=			tax_attention_query
            lowerCamelCase__ 					=			tax_attention_value

            lowerCamelCase__ 					=			tax_attention_layer_norm

            if split_mlp_wi:
                  lowerCamelCase__ 					=			tax_mlp_wi_a
                  lowerCamelCase__ 					=			tax_mlp_wi_a
            else:
                  lowerCamelCase__ 					=			tax_mlp_wi

            lowerCamelCase__ 					=			tax_mlp_wo
            lowerCamelCase__ 					=			tax_mlp_layer_norm

      # Only for layer 0:
      lowerCamelCase__ 					=			tax_model['target']['encoder']['relpos_bias']['rel_embedding'].T
      lowerCamelCase__ 					=			tax_encoder_rel_embedding

      # Assigning
      lowerCamelCase__ 					=			tax_model['target']['encoder']['encoder_norm']['scale']
      lowerCamelCase__ 					=			tax_encoder_norm

      # Decoder
      for layer_index in range(config.num_decoder_layers  ):
            lowerCamelCase__ 					=			f"""layers_{str(lowerCamelCase  )}"""

            # Self-Attention
            lowerCamelCase__ 					=			tax_model['target']['decoder'][layer_name]['self_attention']['key']['kernel']
            lowerCamelCase__ 					=			tax_model['target']['decoder'][layer_name]['self_attention']['out']['kernel']
            lowerCamelCase__ 					=			tax_model['target']['decoder'][layer_name]['self_attention']['query']['kernel']
            lowerCamelCase__ 					=			tax_model['target']['decoder'][layer_name]['self_attention']['value']['kernel']

            # Layer Normalization
            lowerCamelCase__ 					=			tax_model['target']['decoder'][layer_name]['pre_self_attention_layer_norm'][
                'scale'
            ]

            # Encoder-Decoder-Attention
            lowerCamelCase__ 					=			tax_model['target']['decoder'][layer_name]['encoder_decoder_attention']['key'][
                'kernel'
            ]
            lowerCamelCase__ 					=			tax_model['target']['decoder'][layer_name]['encoder_decoder_attention']['out'][
                'kernel'
            ]
            lowerCamelCase__ 					=			tax_model['target']['decoder'][layer_name]['encoder_decoder_attention']['query'][
                'kernel'
            ]
            lowerCamelCase__ 					=			tax_model['target']['decoder'][layer_name]['encoder_decoder_attention']['value'][
                'kernel'
            ]

            # Layer Normalization
            lowerCamelCase__ 					=			tax_model['target']['decoder'][layer_name]['pre_cross_attention_layer_norm']['scale']

            # MLP
            if split_mlp_wi:
                  lowerCamelCase__ 					=			tax_model['target']['decoder'][layer_name]['mlp']['wi_0']['kernel']
                  lowerCamelCase__ 					=			tax_model['target']['decoder'][layer_name]['mlp']['wi_1']['kernel']
            else:
                  lowerCamelCase__ 					=			tax_model['target']['decoder'][layer_name]['mlp']['wi']['kernel']

            lowerCamelCase__ 					=			tax_model['target']['decoder'][layer_name]['mlp']['wo']['kernel']

            # Layer Normalization
            lowerCamelCase__ 					=			tax_model['target']['decoder'][layer_name]['pre_mlp_layer_norm']['scale']

            # Assigning
            lowerCamelCase__ 					=			tax_attention_key
            lowerCamelCase__ 					=			tax_attention_out
            lowerCamelCase__ 					=			tax_attention_query
            lowerCamelCase__ 					=			tax_attention_value

            lowerCamelCase__ 					=			tax_pre_attention_layer_norm

            lowerCamelCase__ 					=			tax_enc_dec_attention_key
            lowerCamelCase__ 					=			tax_enc_dec_attention_out
            lowerCamelCase__ 					=			tax_enc_dec_attention_query
            lowerCamelCase__ 					=			tax_enc_dec_attention_value

            lowerCamelCase__ 					=			tax_cross_layer_norm

            if split_mlp_wi:
                  lowerCamelCase__ 					=			tax_mlp_wi_a
                  lowerCamelCase__ 					=			tax_mlp_wi_a
            else:
                  lowerCamelCase__ 					=			tax_mlp_wi

            lowerCamelCase__ 					=			tax_mlp_wo

            lowerCamelCase__ 					=			txa_mlp_layer_norm

      # Decoder Normalization
      lowerCamelCase__ 					=			tax_model['target']['decoder']['decoder_norm']['scale']
      lowerCamelCase__ 					=			txa_decoder_norm

      # Only for layer 0:
      lowerCamelCase__ 					=			tax_model['target']['decoder']['relpos_bias']['rel_embedding'].T
      lowerCamelCase__ 					=			tax_decoder_rel_embedding

      # Token Embeddings
      lowerCamelCase__ 					=			tax_model['target']['token_embedder']['embedding']
      lowerCamelCase__ 					=			txa_token_embeddings

      # LM Head (only in v1.1 checkpoints)
      if "logits_dense" in tax_model["target"]["decoder"]:
            lowerCamelCase__ 					=			tax_model['target']['decoder']['logits_dense']['kernel']

      flax_model.save_pretrained(lowerCamelCase  )
      print('T5X Model was sucessfully converted!'  )


if __name__ == "__main__":
      __SCREAMING_SNAKE_CASE      :				int				      =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."""
      )
      __SCREAMING_SNAKE_CASE      :				Optional[int]				      =parser.parse_args()
      convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)





import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest






class      A     (						_lowerCAmelCase						):
     """simple docstring"""

     _snake_case:		Optional[int]       =(DPMSolverMultistepScheduler,)
     _snake_case:		Union[str, Any]       =(("num_inference_steps", 25),)




     def 	__snake_case				(      self					:				str		,					**A__					:				Union[str, Any]     )			->    str:
           lowerCamelCase__ 					=			{
               'num_train_timesteps': 1_0_0_0,
               'beta_start': 0.0001,
               'beta_end': 0.02,
               'beta_schedule': 'linear',
               'solver_order': 2,
               'prediction_type': 'epsilon',
               'thresholding': False,
               'sample_max_value': 1.0,
               'algorithm_type': 'dpmsolver++',
               'solver_type': 'midpoint',
               'lower_order_final': False,
               'lambda_min_clipped': -float('inf'     ),
               'variance_type': None,
           }

           config.update(**A__     )
           return config




     def 	__snake_case				(      self					:				Dict		,					A__					:				Dict=0		,					**A__					:				Dict     )			->    Union[str, Any]:
           lowerCamelCase__ 					=			dict(self.forward_default_kwargs     )
           lowerCamelCase__ 					=			kwargs.pop('num_inference_steps'		,					A__     )
           lowerCamelCase__ 					=			self.dummy_sample
           lowerCamelCase__ 					=			0.1 * sample
           lowerCamelCase__ 					=			[residual + 0.2, residual + 0.15, residual + 0.10]

           for scheduler_class in self.scheduler_classes:
                 lowerCamelCase__ 					=			self.get_scheduler_config(**A__     )
                 lowerCamelCase__ 					=			scheduler_class(**A__     )
                 scheduler.set_timesteps(A__     )
                 # copy over dummy past residuals
                 lowerCamelCase__ 					=			dummy_past_residuals[: scheduler.config.solver_order]

                 with tempfile.TemporaryDirectory() as tmpdirname:
                       scheduler.save_config(A__     )
                       lowerCamelCase__ 					=			scheduler_class.from_pretrained(A__     )
                       new_scheduler.set_timesteps(A__     )
                       # copy over dummy past residuals
                       lowerCamelCase__ 					=			dummy_past_residuals[: new_scheduler.config.solver_order]

                 lowerCamelCase__					,						lowerCamelCase__ 					=			sample, sample
                 for t in range(A__		,					time_step + scheduler.config.solver_order + 1     ):
                       lowerCamelCase__ 					=			scheduler.step(A__		,					A__		,					A__		,					**A__     ).prev_sample
                       lowerCamelCase__ 					=			new_scheduler.step(A__		,					A__		,					A__		,					**A__     ).prev_sample

                       assert torch.sum(torch.abs(output - new_output     )     ) < 1E-5, "Scheduler outputs are not identical"




     def 	__snake_case				(      self					:				List[str]     )			->    Union[str, Any]:
           pass




     def 	__snake_case				(      self					:				Optional[Any]		,					A__					:				List[str]=0		,					**A__					:				Optional[Any]     )			->    Optional[Any]:
           lowerCamelCase__ 					=			dict(self.forward_default_kwargs     )
           lowerCamelCase__ 					=			kwargs.pop('num_inference_steps'		,					A__     )
           lowerCamelCase__ 					=			self.dummy_sample
           lowerCamelCase__ 					=			0.1 * sample
           lowerCamelCase__ 					=			[residual + 0.2, residual + 0.15, residual + 0.10]

           for scheduler_class in self.scheduler_classes:
                 lowerCamelCase__ 					=			self.get_scheduler_config()
                 lowerCamelCase__ 					=			scheduler_class(**A__     )
                 scheduler.set_timesteps(A__     )

                 # copy over dummy past residuals (must be after setting timesteps)
                 lowerCamelCase__ 					=			dummy_past_residuals[: scheduler.config.solver_order]

                 with tempfile.TemporaryDirectory() as tmpdirname:
                       scheduler.save_config(A__     )
                       lowerCamelCase__ 					=			scheduler_class.from_pretrained(A__     )
                       # copy over dummy past residuals
                       new_scheduler.set_timesteps(A__     )

                       # copy over dummy past residual (must be after setting timesteps)
                       lowerCamelCase__ 					=			dummy_past_residuals[: new_scheduler.config.solver_order]

                 lowerCamelCase__ 					=			scheduler.step(A__		,					A__		,					A__		,					**A__     ).prev_sample
                 lowerCamelCase__ 					=			new_scheduler.step(A__		,					A__		,					A__		,					**A__     ).prev_sample

                 assert torch.sum(torch.abs(output - new_output     )     ) < 1E-5, "Scheduler outputs are not identical"




     def 	__snake_case				(      self					:				Tuple		,					A__					:				Any=None		,					**A__					:				Any     )			->    List[str]:
           if scheduler is None:
                 lowerCamelCase__ 					=			self.scheduler_classes[0]
                 lowerCamelCase__ 					=			self.get_scheduler_config(**A__     )
                 lowerCamelCase__ 					=			scheduler_class(**A__     )

           lowerCamelCase__ 					=			1_0
           lowerCamelCase__ 					=			self.dummy_model()
           lowerCamelCase__ 					=			self.dummy_sample_deter
           scheduler.set_timesteps(A__     )

           for i, t in enumerate(scheduler.timesteps     ):
                 lowerCamelCase__ 					=			model(A__		,					A__     )
                 lowerCamelCase__ 					=			scheduler.step(A__		,					A__		,					A__     ).prev_sample

           return sample




     def 	__snake_case				(      self					:				Dict     )			->    Any:
           lowerCamelCase__ 					=			dict(self.forward_default_kwargs     )

           lowerCamelCase__ 					=			kwargs.pop('num_inference_steps'		,					A__     )

           for scheduler_class in self.scheduler_classes:
                 lowerCamelCase__ 					=			self.get_scheduler_config()
                 lowerCamelCase__ 					=			scheduler_class(**A__     )

                 lowerCamelCase__ 					=			self.dummy_sample
                 lowerCamelCase__ 					=			0.1 * sample

                 if num_inference_steps is not None and hasattr(A__		,					'set_timesteps'     ):
                       scheduler.set_timesteps(A__     )
                 elif num_inference_steps is not None and not hasattr(A__		,					'set_timesteps'     ):
                       lowerCamelCase__ 					=			num_inference_steps

                 # copy over dummy past residuals (must be done after set_timesteps)
                 lowerCamelCase__ 					=			[residual + 0.2, residual + 0.15, residual + 0.10]
                 lowerCamelCase__ 					=			dummy_past_residuals[: scheduler.config.solver_order]

                 lowerCamelCase__ 					=			scheduler.timesteps[5]
                 lowerCamelCase__ 					=			scheduler.timesteps[6]

                 lowerCamelCase__ 					=			scheduler.step(A__		,					A__		,					A__		,					**A__     ).prev_sample
                 lowerCamelCase__ 					=			scheduler.step(A__		,					A__		,					A__		,					**A__     ).prev_sample

                 self.assertEqual(output_a.shape		,					sample.shape     )
                 self.assertEqual(output_a.shape		,					output_a.shape     )




     def 	__snake_case				(      self					:				Dict     )			->    Tuple:
           for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
                 self.check_over_configs(num_train_timesteps=A__     )




     def 	__snake_case				(      self					:				int     )			->    str:
           self.check_over_configs(thresholding=A__     )
           for order in [1, 2, 3]:
                 for solver_type in ["midpoint", "heun"]:
                       for threshold in [0.5, 1.0, 2.0]:
                             for prediction_type in ["epsilon", "sample"]:
                                   self.check_over_configs(
                                       thresholding=A__		,					prediction_type=A__		,					sample_max_value=A__		,					algorithm_type='dpmsolver++'		,					solver_order=A__		,					solver_type=A__		,					)




     def 	__snake_case				(      self					:				Any     )			->    Optional[Any]:
           for prediction_type in ["epsilon", "v_prediction"]:
                 self.check_over_configs(prediction_type=A__     )




     def 	__snake_case				(      self					:				Union[str, Any]     )			->    str:
           for algorithm_type in ["dpmsolver", "dpmsolver++", "sde-dpmsolver", "sde-dpmsolver++"]:
                 for solver_type in ["midpoint", "heun"]:
                       for order in [1, 2, 3]:
                             for prediction_type in ["epsilon", "sample"]:
                                   if algorithm_type in ["sde-dpmsolver", "sde-dpmsolver++"]:
                                         if order == 3:
                                               continue
                                   else:
                                         self.check_over_configs(
                                             solver_order=A__		,					solver_type=A__		,					prediction_type=A__		,					algorithm_type=A__		,					)
                                   lowerCamelCase__ 					=			self.full_loop(
                                       solver_order=A__		,					solver_type=A__		,					prediction_type=A__		,					algorithm_type=A__		,					)
                                   assert not torch.isnan(A__     ).any(), "Samples have nan numbers"




     def 	__snake_case				(      self					:				List[Any]     )			->    int:
           self.check_over_configs(lower_order_final=A__     )
           self.check_over_configs(lower_order_final=A__     )




     def 	__snake_case				(      self					:				List[Any]     )			->    Dict:
           self.check_over_configs(lambda_min_clipped=-float('inf'     )     )
           self.check_over_configs(lambda_min_clipped=-5.1     )




     def 	__snake_case				(      self					:				Optional[Any]     )			->    int:
           self.check_over_configs(variance_type=A__     )
           self.check_over_configs(variance_type='learned_range'     )




     def 	__snake_case				(      self					:				Optional[Any]     )			->    List[str]:
           for num_inference_steps in [1, 2, 3, 5, 1_0, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
                 self.check_over_forward(num_inference_steps=A__		,					time_step=0     )




     def 	__snake_case				(      self					:				List[Any]     )			->    str:
           lowerCamelCase__ 					=			self.full_loop()
           lowerCamelCase__ 					=			torch.mean(torch.abs(A__     )     )

           assert abs(result_mean.item() - 0.3301     ) < 1E-3




     def 	__snake_case				(      self					:				Tuple     )			->    int:
           lowerCamelCase__ 					=			self.full_loop(thresholding=A__		,					dynamic_thresholding_ratio=0.87		,					sample_max_value=0.5     )
           lowerCamelCase__ 					=			torch.mean(torch.abs(A__     )     )

           assert abs(result_mean.item() - 1.1364     ) < 1E-3




     def 	__snake_case				(      self					:				Optional[int]     )			->    List[Any]:
           lowerCamelCase__ 					=			self.full_loop(prediction_type='v_prediction'     )
           lowerCamelCase__ 					=			torch.mean(torch.abs(A__     )     )

           assert abs(result_mean.item() - 0.2251     ) < 1E-3




     def 	__snake_case				(      self					:				Tuple     )			->    Optional[Any]:
           lowerCamelCase__ 					=			self.full_loop(prediction_type='v_prediction'		,					use_karras_sigmas=A__     )
           lowerCamelCase__ 					=			torch.mean(torch.abs(A__     )     )

           assert abs(result_mean.item() - 0.2096     ) < 1E-3




     def 	__snake_case				(      self					:				Any     )			->    Optional[int]:
           # make sure that iterating over schedulers with same config names gives same results
           # for defaults
           lowerCamelCase__ 					=			DPMSolverMultistepScheduler(**self.get_scheduler_config()     )
           lowerCamelCase__ 					=			self.full_loop(scheduler=A__     )
           lowerCamelCase__ 					=			torch.mean(torch.abs(A__     )     )

           assert abs(result_mean.item() - 0.3301     ) < 1E-3

           lowerCamelCase__ 					=			DPMSolverSinglestepScheduler.from_config(scheduler.config     )
           lowerCamelCase__ 					=			UniPCMultistepScheduler.from_config(scheduler.config     )
           lowerCamelCase__ 					=			DEISMultistepScheduler.from_config(scheduler.config     )
           lowerCamelCase__ 					=			DPMSolverMultistepScheduler.from_config(scheduler.config     )

           lowerCamelCase__ 					=			self.full_loop(scheduler=A__     )
           lowerCamelCase__ 					=			torch.mean(torch.abs(A__     )     )

           assert abs(result_mean.item() - 0.3301     ) < 1E-3




     def 	__snake_case				(      self					:				int     )			->    Optional[int]:
           lowerCamelCase__ 					=			self.scheduler_classes[0]
           lowerCamelCase__ 					=			self.get_scheduler_config(thresholding=A__		,					dynamic_thresholding_ratio=0     )
           lowerCamelCase__ 					=			scheduler_class(**A__     )

           lowerCamelCase__ 					=			1_0
           lowerCamelCase__ 					=			self.dummy_model()
           lowerCamelCase__ 					=			self.dummy_sample_deter.half()
           scheduler.set_timesteps(A__     )

           for i, t in enumerate(scheduler.timesteps     ):
                 lowerCamelCase__ 					=			model(A__		,					A__     )
                 lowerCamelCase__ 					=			scheduler.step(A__		,					A__		,					A__     ).prev_sample

           assert sample.dtype == torch.floataa




     def 	__snake_case				(      self					:				List[str]		,					**A__					:				int     )			->    Optional[int]:
           for scheduler_class in self.scheduler_classes:
                 lowerCamelCase__ 					=			self.get_scheduler_config(**A__     )
                 lowerCamelCase__ 					=			scheduler_class(**A__     )

                 scheduler.set_timesteps(scheduler.config.num_train_timesteps     )
                 assert len(scheduler.timesteps.unique()     ) == scheduler.num_inference_steps