


import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig



def 			_a      (    __UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase			):
     a_     :  Dict				      =     TaConfig.from_pretrained(SCREAMING_SNAKE_CASE_			)
     a_     :  Optional[Any]				      =     FlaxTaForConditionalGeneration(config=SCREAMING_SNAKE_CASE_			)
     a_     :  Tuple				      =     checkpoints.load_tax_checkpoint(SCREAMING_SNAKE_CASE_			)

     a_     :  Dict				      =     '''wi_0''' in tax_model['''target''']['''encoder''']['''layers_0''']['''mlp''']

     # Encoder
     for layer_index in range(config.num_layers			):
          a_     :  Dict				      =     F'''layers_{str(SCREAMING_SNAKE_CASE_			)}'''

          # Self-Attention
          a_     :  Any				      =     tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''key''']['''kernel''']
          a_     :  Any				      =     tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''out''']['''kernel''']
          a_     :  Optional[Any]				      =     tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''query''']['''kernel''']
          a_     :  Union[str, Any]				      =     tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''value''']['''kernel''']

          # Layer Normalization
          a_     :  int				      =     tax_model['''target''']['''encoder'''][layer_name]['''pre_attention_layer_norm''']['''scale''']

          if split_mlp_wi:
               a_     :  Any				      =     tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
               a_     :  str				      =     tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
          else:
               a_     :  Dict				      =     tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

          a_     :  str				      =     tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

          # Layer Normalization
          a_     :  str				      =     tax_model['''target''']['''encoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

          # Assigning
          a_     :  List[str]				      =     tax_attention_key
          a_     :  List[str]				      =     tax_attention_out
          a_     :  Dict				      =     tax_attention_query
          a_     :  str				      =     tax_attention_value

          a_     :  Optional[int]				      =     tax_attention_layer_norm

          if split_mlp_wi:
               a_     :  Any				      =     tax_mlp_wi_a
               a_     :  Optional[Any]				      =     tax_mlp_wi_a
          else:
               a_     :  Any				      =     tax_mlp_wi

          a_     :  Dict				      =     tax_mlp_wo
          a_     :  Optional[int]				      =     tax_mlp_layer_norm

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

     # Assigning
     a_     :  List[str]				      =     tax_model['''target''']['''encoder''']['''encoder_norm''']['''scale''']
     a_     :  Any				      =     tax_encoder_norm

     # Decoder
     for layer_index in range(config.num_decoder_layers			):
          a_     :  List[str]				      =     F'''layers_{str(SCREAMING_SNAKE_CASE_			)}'''

          # Self-Attention
          a_     :  Tuple				      =     tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''key''']['''kernel''']
          a_     :  Tuple				      =     tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''out''']['''kernel''']
          a_     :  List[str]				      =     tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''query''']['''kernel''']
          a_     :  List[Any]				      =     tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''value''']['''kernel''']

          # Layer Normalization
          a_     :  Union[str, Any]				      =     tax_model['''target''']['''decoder'''][layer_name]['''pre_self_attention_layer_norm'''][
              '''scale'''
          ]

          # Encoder-Decoder-Attention
          a_     :  List[str]				      =     tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''key'''][
              '''kernel'''
          ]
          a_     :  Optional[int]				      =     tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''out'''][
              '''kernel'''
          ]
          a_     :  str				      =     tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''query'''][
              '''kernel'''
          ]
          a_     :  Any				      =     tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''value'''][
              '''kernel'''
          ]

          # Layer Normalization
          a_     :  List[str]				      =     tax_model['''target''']['''decoder'''][layer_name]['''pre_cross_attention_layer_norm''']['''scale''']

          # MLP
          if split_mlp_wi:
               a_     :  Any				      =     tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
               a_     :  Optional[int]				      =     tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
          else:
               a_     :  Optional[Any]				      =     tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

          a_     :  Any				      =     tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

          # Layer Normalization
          a_     :  Optional[Any]				      =     tax_model['''target''']['''decoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

          # Assigning
          a_     :  Union[str, Any]				      =     tax_attention_key
          a_     :  Any				      =     tax_attention_out
          a_     :  Optional[int]				      =     tax_attention_query
          a_     :  List[str]				      =     tax_attention_value

          a_     :  int				      =     tax_pre_attention_layer_norm

          a_     :  List[str]				      =     tax_enc_dec_attention_key
          a_     :  int				      =     tax_enc_dec_attention_out
          a_     :  Tuple				      =     tax_enc_dec_attention_query
          a_     :  Dict				      =     tax_enc_dec_attention_value

          a_     :  Optional[int]				      =     tax_cross_layer_norm

          if split_mlp_wi:
               a_     :  Optional[Any]				      =     tax_mlp_wi_a
               a_     :  str				      =     tax_mlp_wi_a
          else:
               a_     :  Optional[Any]				      =     tax_mlp_wi

          a_     :  Union[str, Any]				      =     tax_mlp_wo

          a_     :  Tuple				      =     txa_mlp_layer_norm

     # Decoder Normalization
     a_     :  Union[str, Any]				      =     tax_model['''target''']['''decoder''']['''decoder_norm''']['''scale''']
     a_     :  str				      =     txa_decoder_norm

     # Only for layer 0:
     a_     :  Union[str, Any]				      =     tax_model['''target''']['''decoder''']['''relpos_bias''']['''rel_embedding'''].T
     a_     :  List[str]				      =     tax_decoder_rel_embedding

     # Token Embeddings
     a_     :  Dict				      =     tax_model['''target''']['''token_embedder''']['''embedding''']
     a_     :  Any				      =     txa_token_embeddings

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

     flax_model.save_pretrained(SCREAMING_SNAKE_CASE_			)
     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 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   	=  parser.parse_args()
       convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)







import tempfile
import unittest

import torch

from diffusers import UNetaDConditionModel
from diffusers.training_utils import EMAModel
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps, torch_device


enable_full_determinism()



class  UpperCAmelCase_						(				unittest.TestCase):
       '''simple docstring'''
       __UpperCamelCase   :  Tuple				=       "hf-internal-testing/tiny-stable-diffusion-pipe"
       __UpperCamelCase   :  Optional[Any]				=       1
       __UpperCamelCase   :  str				=       77
       __UpperCamelCase   :  Tuple				=       32
       __UpperCamelCase   :  List[str]				=       4
       __UpperCamelCase   :  Optional[int]				=       64
       __UpperCamelCase   :  Union[str, Any]				=       torch.manual_seed(0)






       def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE=0.9_999    ):




              """simple docstring"""




              UpperCamelCase     :						Optional[int] 						=		UNetaDConditionModel.from_pretrained(self.model_id			,       subfolder='''unet'''    )
              UpperCamelCase     :						Any 						=		unet.to(__SCREAMING_SNAKE_CASE    )
              UpperCamelCase     :						Dict 						=		EMAModel(unet.parameters()			,       decay=__SCREAMING_SNAKE_CASE			,       model_cls=__SCREAMING_SNAKE_CASE			,       model_config=unet.config    )
              return unet, ema_unet






       def 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase     :						Tuple 						=		torch.randn(
                  self.batch_size			,       self.num_in_channels			,       self.latent_height			,       self.latent_width			,       generator=self.generator    ).to(__SCREAMING_SNAKE_CASE    )
              UpperCamelCase     :						Optional[int] 						=		torch.randint(0			,       1_000			,       size=(self.batch_size,)			,       generator=self.generator    ).to(__SCREAMING_SNAKE_CASE    )
              UpperCamelCase     :						List[Any] 						=		torch.randn(
                  self.batch_size			,       self.prompt_length			,       self.text_encoder_hidden_dim			,       generator=self.generator    ).to(__SCREAMING_SNAKE_CASE    )
              return noisy_latents, timesteps, encoder_hidden_states






       def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE    ):




              """simple docstring"""




              UpperCamelCase     :						Dict 						=		{}
              for k, param in unet.state_dict().items():
                     UpperCamelCase     :						int 						=		torch.randn_like(__SCREAMING_SNAKE_CASE    ) + (param * torch.randn_like(__SCREAMING_SNAKE_CASE    ))
                     updated_state_dict.update({k: updated_param}    )
              unet.load_state_dict(__SCREAMING_SNAKE_CASE    )
              return unet






       def 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase						,			UpperCamelCase     :						Optional[Any] 						=		self.get_models()
              # Take the first (hypothetical) EMA step.
              ema_unet.step(unet.parameters()    )
              assert ema_unet.optimization_step == 1

              # Take two more.
              for _ in range(2    ):
                     ema_unet.step(unet.parameters()    )
              assert ema_unet.optimization_step == 3






       def 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase						,			UpperCamelCase     :						List[Any] 						=		self.get_models()
              # Since the `unet` is not being updated (i.e., backprop'd)
              # there won't be any difference between the `params` of `unet`
              # and `ema_unet` even if we call `ema_unet.step(unet.parameters())`.
              ema_unet.step(unet.parameters()    )
              UpperCamelCase     :						int 						=		list(unet.parameters()    )
              for s_param, param in zip(ema_unet.shadow_params			,       __SCREAMING_SNAKE_CASE    ):
                     assert torch.allclose(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    )

              # The above holds true even if we call `ema.step()` multiple times since
              # `unet` params are still not being updated.
              for _ in range(4    ):
                     ema_unet.step(unet.parameters()    )
              for s_param, param in zip(ema_unet.shadow_params			,       __SCREAMING_SNAKE_CASE    ):
                     assert torch.allclose(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    )






       def 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase						,			UpperCamelCase     :						Any 						=		self.get_models()
              # Here we simulate the parameter updates for `unet`. Since there might
              # be some parameters which are initialized to zero we take extra care to
              # initialize their values to something non-zero before the multiplication.
              UpperCamelCase     :						Union[str, Any] 						=		self.simulate_backprop(__SCREAMING_SNAKE_CASE    )

              # Take the EMA step.
              ema_unet.step(unet_pseudo_updated_step_one.parameters()    )

              # Now the EMA'd parameters won't be equal to the original model parameters.
              UpperCamelCase     :						Any 						=		list(unet_pseudo_updated_step_one.parameters()    )
              for s_param, param in zip(ema_unet.shadow_params			,       __SCREAMING_SNAKE_CASE    ):
                     assert ~torch.allclose(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    )

              # Ensure this is the case when we take multiple EMA steps.
              for _ in range(4    ):
                     ema_unet.step(unet.parameters()    )
              for s_param, param in zip(ema_unet.shadow_params			,       __SCREAMING_SNAKE_CASE    ):
                     assert ~torch.allclose(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    )






       def 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase						,			UpperCamelCase     :						Optional[int] 						=		self.get_models()

              # First backprop + EMA
              UpperCamelCase     :						Dict 						=		self.simulate_backprop(__SCREAMING_SNAKE_CASE    )
              ema_unet.step(unet_step_one.parameters()    )
              UpperCamelCase     :						List[str] 						=		ema_unet.shadow_params

              # Second backprop + EMA
              UpperCamelCase     :						str 						=		self.simulate_backprop(__SCREAMING_SNAKE_CASE    )
              ema_unet.step(unet_step_two.parameters()    )
              UpperCamelCase     :						List[Any] 						=		ema_unet.shadow_params

              for step_one, step_two in zip(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    ):
                     assert ~torch.allclose(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    )






       def 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase						,			UpperCamelCase     :						int 						=		self.get_models(decay=0.0    )
              UpperCamelCase     :						Optional[int] 						=		self.simulate_backprop(__SCREAMING_SNAKE_CASE    )
              ema_unet.step(unet_step_one.parameters()    )
              UpperCamelCase     :						Union[str, Any] 						=		ema_unet.shadow_params

              UpperCamelCase     :						Any 						=		self.simulate_backprop(__SCREAMING_SNAKE_CASE    )
              ema_unet.step(unet_step_two.parameters()    )
              UpperCamelCase     :						int 						=		ema_unet.shadow_params

              for step_one, step_two in zip(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    ):
                     assert torch.allclose(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    )






       @skip_mps
       def 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase						,			UpperCamelCase     :						str 						=		self.get_models()
              UpperCamelCase						,			UpperCamelCase						,			UpperCamelCase     :						Union[str, Any] 						=		self.get_dummy_inputs()

              with tempfile.TemporaryDirectory() as tmpdir:
                     ema_unet.save_pretrained(__SCREAMING_SNAKE_CASE    )
                     UpperCamelCase     :						Optional[int] 						=		UNetaDConditionModel.from_pretrained(__SCREAMING_SNAKE_CASE			,       model_cls=__SCREAMING_SNAKE_CASE    )
                     UpperCamelCase     :						List[str] 						=		loaded_unet.to(unet.device    )

              # Since no EMA step has been performed the outputs should match.
              UpperCamelCase     :						Optional[Any] 						=		unet(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    ).sample
              UpperCamelCase     :						List[str] 						=		loaded_unet(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    ).sample

              assert torch.allclose(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE			,       atol=1e-4    )

