





import argparse
import collections

import torch
from flax import traverse_util
from tax import checkpoints

from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :List[Any]			,							snake_case__  :Tuple			,							snake_case__  :Tuple			,							snake_case__  :Tuple="attention"     )      ->						List[str]:
  _lowercase							= params[F"""{prefix}/layers_{i}/{layer_name}/key/kernel"""]
  _lowercase							= params[F"""{prefix}/layers_{i}/{layer_name}/out/kernel"""]
  _lowercase							= params[F"""{prefix}/layers_{i}/{layer_name}/query/kernel"""]
  _lowercase							= params[F"""{prefix}/layers_{i}/{layer_name}/value/kernel"""]
  return k, o, q, v

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :Tuple			,							snake_case__  :List[Any]			,							snake_case__  :Optional[Any]			,							snake_case__  :Optional[Any]=False     )      ->						Tuple:
  if split_mlp_wi:
    _lowercase							= params[F"""{prefix}/layers_{i}/mlp/wi_0/kernel"""]
    _lowercase							= params[F"""{prefix}/layers_{i}/mlp/wi_1/kernel"""]
    _lowercase							= (wi_a, wi_a)
  else:
    _lowercase							= params[F"""{prefix}/layers_{i}/mlp/wi/kernel"""]

  _lowercase							= params[F"""{prefix}/layers_{i}/mlp/wo/kernel"""]
  return wi, wo

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :Tuple			,							snake_case__  :Any			,							snake_case__  :Optional[Any]			,							snake_case__  :Dict     )      ->						str:
  return params[F"""{prefix}/layers_{i}/{layer_name}/scale"""]

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :dict			,							*, snake_case__  :int			,							snake_case__  :bool     )      ->						List[str]:
  _lowercase							= traverse_util.flatten_dict(variables['target']     )
  _lowercase							= {'/'.join(snake_case__     ): v for k, v in old.items()}

  # v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
  _lowercase							= 'encoder/layers_0/mlp/wi_0/kernel' in old
  print('Split MLP:'			,							snake_case__     )

  _lowercase							= collections.OrderedDict()

  # Shared embeddings.
  _lowercase							= old['token_embedder/embedding']

  # Encoder.
  for i in range(snake_case__     ):
    # Block i, layer 0 (Self Attention).
    _lowercase							= tax_layer_norm_lookup(snake_case__			,							snake_case__			,							'encoder'			,							'pre_attention_layer_norm'     )
    _lowercase						,		_lowercase						,		_lowercase						,		_lowercase							= tax_attention_lookup(snake_case__			,							snake_case__			,							'encoder'			,							'attention'     )
    _lowercase							= layer_norm
    _lowercase							= k.T
    _lowercase							= o.T
    _lowercase							= q.T
    _lowercase							= v.T

    # Block i, layer 1 (MLP).
    _lowercase							= tax_layer_norm_lookup(snake_case__			,							snake_case__			,							'encoder'			,							'pre_mlp_layer_norm'     )
    _lowercase						,		_lowercase							= tax_mlp_lookup(snake_case__			,							snake_case__			,							'encoder'			,							snake_case__     )
    _lowercase							= layer_norm
    if split_mlp_wi:
      _lowercase							= wi[0].T
      _lowercase							= wi[1].T
    else:
      _lowercase							= wi.T
    _lowercase							= wo.T

  _lowercase							= old[
      'encoder/relpos_bias/rel_embedding'
  ].T
  _lowercase							= old['encoder/encoder_norm/scale']

  if not is_encoder_only:
    # Decoder.
    for i in range(snake_case__     ):
      # Block i, layer 0 (Self Attention).
      _lowercase							= tax_layer_norm_lookup(snake_case__			,							snake_case__			,							'decoder'			,							'pre_self_attention_layer_norm'     )
      _lowercase						,		_lowercase						,		_lowercase						,		_lowercase							= tax_attention_lookup(snake_case__			,							snake_case__			,							'decoder'			,							'self_attention'     )
      _lowercase							= layer_norm
      _lowercase							= k.T
      _lowercase							= o.T
      _lowercase							= q.T
      _lowercase							= v.T

      # Block i, layer 1 (Cross Attention).
      _lowercase							= tax_layer_norm_lookup(snake_case__			,							snake_case__			,							'decoder'			,							'pre_cross_attention_layer_norm'     )
      _lowercase						,		_lowercase						,		_lowercase						,		_lowercase							= tax_attention_lookup(snake_case__			,							snake_case__			,							'decoder'			,							'encoder_decoder_attention'     )
      _lowercase							= layer_norm
      _lowercase							= k.T
      _lowercase							= o.T
      _lowercase							= q.T
      _lowercase							= v.T

      # Block i, layer 2 (MLP).
      _lowercase							= tax_layer_norm_lookup(snake_case__			,							snake_case__			,							'decoder'			,							'pre_mlp_layer_norm'     )
      _lowercase						,		_lowercase							= tax_mlp_lookup(snake_case__			,							snake_case__			,							'decoder'			,							snake_case__     )
      _lowercase							= layer_norm
      if split_mlp_wi:
        _lowercase							= wi[0].T
        _lowercase							= wi[1].T
      else:
        _lowercase							= wi.T
      _lowercase							= wo.T

    _lowercase							= old['decoder/decoder_norm/scale']
    _lowercase							= old[
        'decoder/relpos_bias/rel_embedding'
    ].T

    # LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
    if "decoder/logits_dense/kernel" in old:
      _lowercase							= old['decoder/logits_dense/kernel'].T

  return new

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :Dict			,							snake_case__  :bool     )      ->						Union[str, Any]:
  _lowercase							= collections.OrderedDict([(k, torch.from_numpy(v.copy()     )) for (k, v) in converted_params.items()]     )

  # Add what is missing.
  if "encoder.embed_tokens.weight" not in state_dict:
    _lowercase							= state_dict['shared.weight']

  if not is_encoder_only:
    if "decoder.embed_tokens.weight" not in state_dict:
      _lowercase							= state_dict['shared.weight']

    if "lm_head.weight" not in state_dict:  # For old 1.0 models.
      print('Using shared word embeddings as lm_head.'     )
      _lowercase							= state_dict['shared.weight']

  return state_dict

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :List[str]			,							snake_case__  :Tuple			,							snake_case__  :Tuple			,							snake_case__  :Optional[int]     )      ->						List[Any]:
  _lowercase							= checkpoints.load_tax_checkpoint(snake_case__     )
  _lowercase							= convert_tax_to_pytorch(snake_case__			,							num_layers=config.num_layers			,							is_encoder_only=snake_case__     )
  _lowercase							= make_state_dict(snake_case__			,							snake_case__     )
  model.load_state_dict(snake_case__			,							strict=snake_case__     )

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :Tuple			,							snake_case__  :Any			,							snake_case__  :int			,							snake_case__  :bool = False     )      ->						Optional[int]:
  _lowercase							= TaConfig.from_json_file(snake_case__     )
  print(F"""Building PyTorch model from configuration: {config}"""     )
  # Non-v1.1 checkpoints could also use T5Model, but this works for all.
  # The v1.0 checkpoints will simply have an LM head that is the word embeddings.
  if is_encoder_only:
    _lowercase							= TaEncoderModel(snake_case__     )
  else:
    _lowercase							= TaForConditionalGeneration(snake_case__     )

  # Load weights from tf checkpoint
  load_tax_weights_in_ta(snake_case__			,							snake_case__			,							snake_case__			,							snake_case__     )

  # Save pytorch-model
  print(F"""Save PyTorch model to {pytorch_dump_path}"""     )
  model.save_pretrained(snake_case__     )

  # Verify that we can load the checkpoint.
  model.from_pretrained(snake_case__     )
  print('Done'     )


if __name__ == "__main__":
      snake_case 				=       argparse.ArgumentParser(description="""Converts a native T5X checkpoint into a PyTorch checkpoint.""")
      # Required parameters
      parser.add_argument(
          """--t5x_checkpoint_path""", default=None, type=str, required=True, help="""Path to the T5X checkpoint."""
      )
      parser.add_argument(
          """--config_file""",
          default=None,
          type=str,
          required=True,
          help="""The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.""",
      )
      parser.add_argument(
          """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
      )
      parser.add_argument(
          """--is_encoder_only""", action="""store_true""", help="""Check if the model is encoder-decoder model""", default=False
      )
      snake_case 				=       parser.parse_args()
      convert_tax_checkpoint_to_pytorch(
          args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
      )






import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest





class 				A_				(      UpperCAmelCase	):







       """simple docstring"""





       SCREAMING_SNAKE_CASE_      : int      =  (DEISMultistepScheduler,)
       SCREAMING_SNAKE_CASE_      : List[Any]      =  (('''num_inference_steps''', 2_5),)







       def    __UpperCAmelCase (    self	:	List[Any]						,**__A	:	int  )						->					List[Any]:
         _lowercase							= {
             'num_train_timesteps': 1000,
             'beta_start': 0.0001,
             'beta_end': 0.02,
             'beta_schedule': 'linear',
             'solver_order': 2,
         }

         config.update(**__A  )
         return config







       def    __UpperCAmelCase (    self	:	Dict						,__A	:	str=0						,**__A	:	Optional[int]  )						->					Tuple:
         _lowercase							= dict(self.forward_default_kwargs  )
         _lowercase							= kwargs.pop('num_inference_steps'						,__A  )
         _lowercase							= self.dummy_sample
         _lowercase							= 0.1 * sample
         _lowercase							= [residual + 0.2, residual + 0.15, residual + 0.10]

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

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

           _lowercase						,		_lowercase							= sample, sample
           for t in range(__A						,time_step + scheduler.config.solver_order + 1  ):
             _lowercase							= scheduler.step(__A						,__A						,__A						,**__A  ).prev_sample
             _lowercase							= 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    __UpperCAmelCase (    self	:	Dict  )						->					Optional[int]:
         pass







       def    __UpperCAmelCase (    self	:	int						,__A	:	Dict=0						,**__A	:	Optional[Any]  )						->					Dict:
         _lowercase							= dict(self.forward_default_kwargs  )
         _lowercase							= kwargs.pop('num_inference_steps'						,__A  )
         _lowercase							= self.dummy_sample
         _lowercase							= 0.1 * sample
         _lowercase							= [residual + 0.2, residual + 0.15, residual + 0.10]

         for scheduler_class in self.scheduler_classes:
           _lowercase							= self.get_scheduler_config()
           _lowercase							= scheduler_class(**__A  )
           scheduler.set_timesteps(__A  )

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

           with tempfile.TemporaryDirectory() as tmpdirname:
             scheduler.save_config(__A  )
             _lowercase							= 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)
             _lowercase							= dummy_past_residuals[: new_scheduler.config.solver_order]

           _lowercase							= scheduler.step(__A						,__A						,__A						,**__A  ).prev_sample
           _lowercase							= 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    __UpperCAmelCase (    self	:	Tuple						,__A	:	Tuple=None						,**__A	:	Optional[Any]  )						->					Tuple:
         if scheduler is None:
           _lowercase							= self.scheduler_classes[0]
           _lowercase							= self.get_scheduler_config(**__A  )
           _lowercase							= scheduler_class(**__A  )

         _lowercase							= self.scheduler_classes[0]
         _lowercase							= self.get_scheduler_config(**__A  )
         _lowercase							= scheduler_class(**__A  )

         _lowercase							= 10
         _lowercase							= self.dummy_model()
         _lowercase							= self.dummy_sample_deter
         scheduler.set_timesteps(__A  )

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

         return sample







       def    __UpperCAmelCase (    self	:	Optional[Any]  )						->					Any:
         _lowercase							= dict(self.forward_default_kwargs  )

         _lowercase							= kwargs.pop('num_inference_steps'						,__A  )

         for scheduler_class in self.scheduler_classes:
           _lowercase							= self.get_scheduler_config()
           _lowercase							= scheduler_class(**__A  )

           _lowercase							= self.dummy_sample
           _lowercase							= 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'  ):
             _lowercase							= num_inference_steps

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

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

           _lowercase							= scheduler.step(__A						,__A						,__A						,**__A  ).prev_sample
           _lowercase							= scheduler.step(__A						,__A						,__A						,**__A  ).prev_sample

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







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

         assert abs(result_mean.item() - 0.23916  ) < 1e-3

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

         _lowercase							= self.full_loop(scheduler=__A  )
         _lowercase							= torch.mean(torch.abs(__A  )  )

         assert abs(result_mean.item() - 0.23916  ) < 1e-3







       def    __UpperCAmelCase (    self	:	Optional[int]  )						->					Optional[int]:
         for timesteps in [25, 50, 100, 999, 1000]:
           self.check_over_configs(num_train_timesteps=__A  )







       def    __UpperCAmelCase (    self	:	int  )						->					str:
         self.check_over_configs(thresholding=__A  )
         for order in [1, 2, 3]:
           for solver_type in ["logrho"]:
             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='deis'						,solver_order=__A						,solver_type=__A						,)







       def    __UpperCAmelCase (    self	:	Tuple  )						->					Tuple:
         for prediction_type in ["epsilon", "v_prediction"]:
           self.check_over_configs(prediction_type=__A  )







       def    __UpperCAmelCase (    self	:	Dict  )						->					Optional[int]:
         for algorithm_type in ["deis"]:
           for solver_type in ["logrho"]:
             for order in [1, 2, 3]:
               for prediction_type in ["epsilon", "sample"]:
                 self.check_over_configs(
                     solver_order=__A						,solver_type=__A						,prediction_type=__A						,algorithm_type=__A						,)
                 _lowercase							= 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    __UpperCAmelCase (    self	:	Dict  )						->					int:
         self.check_over_configs(lower_order_final=__A  )
         self.check_over_configs(lower_order_final=__A  )







       def    __UpperCAmelCase (    self	:	str  )						->					Tuple:
         for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
           self.check_over_forward(num_inference_steps=__A						,time_step=0  )







       def    __UpperCAmelCase (    self	:	Tuple  )						->					Any:
         _lowercase							= self.full_loop()
         _lowercase							= torch.mean(torch.abs(__A  )  )

         assert abs(result_mean.item() - 0.23916  ) < 1e-3







       def    __UpperCAmelCase (    self	:	Any  )						->					List[str]:
         _lowercase							= self.full_loop(prediction_type='v_prediction'  )
         _lowercase							= torch.mean(torch.abs(__A  )  )

         assert abs(result_mean.item() - 0.091  ) < 1e-3







       def    __UpperCAmelCase (    self	:	Optional[int]  )						->					List[Any]:
         _lowercase							= self.scheduler_classes[0]
         _lowercase							= self.get_scheduler_config(thresholding=__A						,dynamic_thresholding_ratio=0  )
         _lowercase							= scheduler_class(**__A  )

         _lowercase							= 10
         _lowercase							= self.dummy_model()
         _lowercase							= self.dummy_sample_deter.half()
         scheduler.set_timesteps(__A  )

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

         assert sample.dtype == torch.floataa