





import torch

from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde

from .test_schedulers import SchedulerCommonTest



@require_torchsde
class       a					(       lowercase__      ):

     '''simple docstring'''





     lowerCAmelCase    :	Union[str, Any]       		=  (DPMSolverSDEScheduler,)
     lowerCAmelCase    :	Dict       		=  10
     def   lowerCamelCase_       (     self       :      Union[str, Any] ,   **__snake_case       :      Optional[Any]    ):
         UpperCAmelCase_										=					{
             """num_train_timesteps""": 11_00,
             """beta_start""": 0.0_001,
             """beta_end""": 0.02,
             """beta_schedule""": """linear""",
             """noise_sampler_seed""": 0,
         }

         config.update(**_UpperCamelCase    )
         return config
     def   lowerCamelCase_       (     self       :      int    ):
         for timesteps in [10, 50, 1_00, 10_00]:
             self.check_over_configs(num_train_timesteps=_UpperCamelCase    )
     def   lowerCamelCase_       (     self       :      Tuple    ):
         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=_UpperCamelCase ,   beta_end=_UpperCamelCase    )
     def   lowerCamelCase_       (     self       :      Optional[int]    ):
         for schedule in ["linear", "scaled_linear"]:
             self.check_over_configs(beta_schedule=_UpperCamelCase    )
     def   lowerCamelCase_       (     self       :      Optional[int]    ):
         for prediction_type in ["epsilon", "v_prediction"]:
             self.check_over_configs(prediction_type=_UpperCamelCase    )
     def   lowerCamelCase_       (     self       :      Optional[Any]    ):
         UpperCAmelCase_										=					self.scheduler_classes[0]
         UpperCAmelCase_										=					self.get_scheduler_config()
         UpperCAmelCase_										=					scheduler_class(**_UpperCamelCase    )

         scheduler.set_timesteps(self.num_inference_steps    )

         UpperCAmelCase_										=					self.dummy_model()
         UpperCAmelCase_										=					self.dummy_sample_deter * scheduler.init_noise_sigma
         UpperCAmelCase_										=					sample.to(_UpperCamelCase    )

         for i, t in enumerate(scheduler.timesteps    ):
             UpperCAmelCase_										=					scheduler.scale_model_input(_UpperCamelCase ,   _UpperCamelCase    )

             UpperCAmelCase_										=					model(_UpperCamelCase ,   _UpperCamelCase    )

             UpperCAmelCase_										=					scheduler.step(_UpperCamelCase ,   _UpperCamelCase ,   _UpperCamelCase    )
             UpperCAmelCase_										=					output.prev_sample

         UpperCAmelCase_										=					torch.sum(torch.abs(_UpperCamelCase    )    )
         UpperCAmelCase_										=					torch.mean(torch.abs(_UpperCamelCase    )    )

         if torch_device in ["mps"]:
             assert abs(result_sum.item() - 1_67.47_82_10_44_92_18_75    ) < 1E-2
             assert abs(result_mean.item() - 0.2_178_705_964_565_277    ) < 1E-3
         elif torch_device in ["cuda"]:
             assert abs(result_sum.item() - 1_71.59_35_21_11_81_64_06    ) < 1E-2
             assert abs(result_mean.item() - 0.22_342_906_892_299_652    ) < 1E-3
         else:
             assert abs(result_sum.item() - 1_62.52_38_34_22_85_15_62    ) < 1E-2
             assert abs(result_mean.item() - 0.211_619_570_851_326    ) < 1E-3
     def   lowerCamelCase_       (     self       :      str    ):
         UpperCAmelCase_										=					self.scheduler_classes[0]
         UpperCAmelCase_										=					self.get_scheduler_config(prediction_type='''v_prediction'''    )
         UpperCAmelCase_										=					scheduler_class(**_UpperCamelCase    )

         scheduler.set_timesteps(self.num_inference_steps    )

         UpperCAmelCase_										=					self.dummy_model()
         UpperCAmelCase_										=					self.dummy_sample_deter * scheduler.init_noise_sigma
         UpperCAmelCase_										=					sample.to(_UpperCamelCase    )

         for i, t in enumerate(scheduler.timesteps    ):
             UpperCAmelCase_										=					scheduler.scale_model_input(_UpperCamelCase ,   _UpperCamelCase    )

             UpperCAmelCase_										=					model(_UpperCamelCase ,   _UpperCamelCase    )

             UpperCAmelCase_										=					scheduler.step(_UpperCamelCase ,   _UpperCamelCase ,   _UpperCamelCase    )
             UpperCAmelCase_										=					output.prev_sample

         UpperCAmelCase_										=					torch.sum(torch.abs(_UpperCamelCase    )    )
         UpperCAmelCase_										=					torch.mean(torch.abs(_UpperCamelCase    )    )

         if torch_device in ["mps"]:
             assert abs(result_sum.item() - 1_24.77_14_92_00_43_94_53    ) < 1E-2
             assert abs(result_mean.item() - 0.16_226_289_014_816_284    ) < 1E-3
         elif torch_device in ["cuda"]:
             assert abs(result_sum.item() - 1_28.1_66_33_60_59_57_03    ) < 1E-2
             assert abs(result_mean.item() - 0.16_688_326_001_167_297    ) < 1E-3
         else:
             assert abs(result_sum.item() - 1_19.8_48_75_48_82_81_25    ) < 1E-2
             assert abs(result_mean.item() - 0.1_560_530_662_536_621    ) < 1E-3
     def   lowerCamelCase_       (     self       :      Any    ):
         UpperCAmelCase_										=					self.scheduler_classes[0]
         UpperCAmelCase_										=					self.get_scheduler_config()
         UpperCAmelCase_										=					scheduler_class(**_UpperCamelCase    )

         scheduler.set_timesteps(self.num_inference_steps ,   device=_UpperCamelCase    )

         UpperCAmelCase_										=					self.dummy_model()
         UpperCAmelCase_										=					self.dummy_sample_deter.to(_UpperCamelCase    ) * scheduler.init_noise_sigma

         for t in scheduler.timesteps:
             UpperCAmelCase_										=					scheduler.scale_model_input(_UpperCamelCase ,   _UpperCamelCase    )

             UpperCAmelCase_										=					model(_UpperCamelCase ,   _UpperCamelCase    )

             UpperCAmelCase_										=					scheduler.step(_UpperCamelCase ,   _UpperCamelCase ,   _UpperCamelCase    )
             UpperCAmelCase_										=					output.prev_sample

         UpperCAmelCase_										=					torch.sum(torch.abs(_UpperCamelCase    )    )
         UpperCAmelCase_										=					torch.mean(torch.abs(_UpperCamelCase    )    )

         if torch_device in ["mps"]:
             assert abs(result_sum.item() - 1_67.46_95_73_97_46_09_38    ) < 1E-2
             assert abs(result_mean.item() - 0.21_805_934_607_982_635    ) < 1E-3
         elif torch_device in ["cuda"]:
             assert abs(result_sum.item() - 1_71.59_35_36_37_69_53_12    ) < 1E-2
             assert abs(result_mean.item() - 0.22_342_908_382_415_771    ) < 1E-3
         else:
             assert abs(result_sum.item() - 1_62.52_38_34_22_85_15_62    ) < 1E-2
             assert abs(result_mean.item() - 0.211_619_570_851_326    ) < 1E-3




     def   lowerCamelCase_       (     self       :      Optional[Any]    ):
         UpperCAmelCase_										=					self.scheduler_classes[0]
         UpperCAmelCase_										=					self.get_scheduler_config()
         UpperCAmelCase_										=					scheduler_class(**_UpperCamelCase ,   use_karras_sigmas=_UpperCamelCase    )

         scheduler.set_timesteps(self.num_inference_steps ,   device=_UpperCamelCase    )

         UpperCAmelCase_										=					self.dummy_model()
         UpperCAmelCase_										=					self.dummy_sample_deter.to(_UpperCamelCase    ) * scheduler.init_noise_sigma
         UpperCAmelCase_										=					sample.to(_UpperCamelCase    )

         for t in scheduler.timesteps:
             UpperCAmelCase_										=					scheduler.scale_model_input(_UpperCamelCase ,   _UpperCamelCase    )

             UpperCAmelCase_										=					model(_UpperCamelCase ,   _UpperCamelCase    )

             UpperCAmelCase_										=					scheduler.step(_UpperCamelCase ,   _UpperCamelCase ,   _UpperCamelCase    )
             UpperCAmelCase_										=					output.prev_sample

         UpperCAmelCase_										=					torch.sum(torch.abs(_UpperCamelCase    )    )
         UpperCAmelCase_										=					torch.mean(torch.abs(_UpperCamelCase    )    )

         if torch_device in ["mps"]:
             assert abs(result_sum.item() - 1_76.66_97_41_35_74_21_88    ) < 1E-2
             assert abs(result_mean.item() - 0.23_003_872_730_981_811    ) < 1E-2
         elif torch_device in ["cuda"]:
             assert abs(result_sum.item() - 1_77.63_65_35_64_45_31_25    ) < 1E-2
             assert abs(result_mean.item() - 0.23_003_872_730_981_811    ) < 1E-2
         else:
             assert abs(result_sum.item() - 1_70.3_13_52_23_38_86_72    ) < 1E-2
             assert abs(result_mean.item() - 0.23_003_872_730_981_811    ) < 1E-2











import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Tuple	,					__UpperCamelCase      :						List[str]	,					__UpperCamelCase      :						str				)  ->							str:
    UpperCAmelCase_										=					AutoConfig.from_pretrained(__UpperCamelCase				)
    UpperCAmelCase_										=					FlaxAutoModelForSeqaSeqLM.from_config(config=__UpperCamelCase				)
    UpperCAmelCase_										=					checkpoints.load_tax_checkpoint(__UpperCamelCase				)

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

    if config.model_type == "t5":
        UpperCAmelCase_										=					'''SelfAttention'''
    if config.model_type == "longt5" and config.encoder_attention_type == "local":
        UpperCAmelCase_										=					'''LocalSelfAttention'''
    elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
        UpperCAmelCase_										=					'''TransientGlobalSelfAttention'''
    else:
        raise ValueError(
            '''Given config is expected to have `model_type=\'t5\'`, or `model_type=\'longt5` with `encoder_attention_type`'''
            ''' attribute with a value from [\'local\', \'transient-global].'''				)

    # Encoder
    for layer_index in range(config.num_layers				):
        UpperCAmelCase_										=					f'layers_{str(__UpperCamelCase				)}'

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

        # Global input layer norm
        if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
            UpperCAmelCase_										=					tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''T5LayerNorm_0''']['''scale''']

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

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

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

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

        # Assigning
        UpperCAmelCase_										=					flax_model.params['''encoder''']['''block'''][str(__UpperCamelCase				)]['''layer''']
        UpperCAmelCase_										=					tax_attention_key
        UpperCAmelCase_										=					tax_attention_out
        UpperCAmelCase_										=					tax_attention_query
        UpperCAmelCase_										=					tax_attention_value

        UpperCAmelCase_										=					tax_attention_layer_norm

        # Global input layer norm
        if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
            UpperCAmelCase_										=					tax_global_layer_norm

        if split_mlp_wi:
            UpperCAmelCase_										=					tax_mlp_wi_a
            UpperCAmelCase_										=					tax_mlp_wi_a
        else:
            UpperCAmelCase_										=					tax_mlp_wi

        UpperCAmelCase_										=					tax_mlp_wo
        UpperCAmelCase_										=					tax_mlp_layer_norm

        UpperCAmelCase_										=					flax_model_encoder_layer_block

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

    # Side/global relative position_bias + layer norm
    if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
        UpperCAmelCase_										=					tax_model['''target''']['''encoder''']['''side_relpos_bias''']['''rel_embedding'''].T
        UpperCAmelCase_										=					tax_encoder_global_rel_embedding

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

    # Decoder
    for layer_index in range(config.num_layers				):
        UpperCAmelCase_										=					f'layers_{str(__UpperCamelCase				)}'

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

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

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

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

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

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

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

        # Assigning
        UpperCAmelCase_										=					flax_model.params['''decoder''']['''block'''][str(__UpperCamelCase				)]['''layer''']
        UpperCAmelCase_										=					tax_attention_key
        UpperCAmelCase_										=					tax_attention_out
        UpperCAmelCase_										=					tax_attention_query
        UpperCAmelCase_										=					tax_attention_value

        UpperCAmelCase_										=					tax_pre_attention_layer_norm

        UpperCAmelCase_										=					tax_enc_dec_attention_key
        UpperCAmelCase_										=					tax_enc_dec_attention_out
        UpperCAmelCase_										=					tax_enc_dec_attention_query
        UpperCAmelCase_										=					tax_enc_dec_attention_value

        UpperCAmelCase_										=					tax_cross_layer_norm

        if split_mlp_wi:
            UpperCAmelCase_										=					tax_mlp_wi_a
            UpperCAmelCase_										=					tax_mlp_wi_a
        else:
            UpperCAmelCase_										=					tax_mlp_wi

        UpperCAmelCase_										=					tax_mlp_wo

        UpperCAmelCase_										=					txa_mlp_layer_norm

        UpperCAmelCase_										=					flax_model_decoder_layer_block

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

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

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

    # LM Head (only in v1.1 and LongT5 checkpoints)
    if "logits_dense" in tax_model["target"]["decoder"]:
        UpperCAmelCase_										=					tax_model['''target''']['''decoder''']['''logits_dense''']['''kernel''']

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


if __name__ == "__main__":
 _lowerCamelCase      			=						argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '--t5x_checkpoint_path', default=None, type=str, required=True, help='Path the T5X checkpoint.'
 )
 parser.add_argument('--config_name', default=None, type=str, required=True, help='Config name of LongT5/T5 model.')
 parser.add_argument(
     '--flax_dump_folder_path', default=None, type=str, required=True, help='Path to the output FLAX model.'
 )
 _lowerCamelCase      			=						parser.parse_args()
 convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)





