

'''simple docstring'''



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    a__       (  a__      ,    a__      ,    a__      ,    a__="attention"						):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     =      params[F'{prefix}/layers_{i}/{layer_name}/key/kernel']
     __SCREAMING_SNAKE_CASE     =      params[F'{prefix}/layers_{i}/{layer_name}/out/kernel']
     __SCREAMING_SNAKE_CASE     =      params[F'{prefix}/layers_{i}/{layer_name}/query/kernel']
     __SCREAMING_SNAKE_CASE     =      params[F'{prefix}/layers_{i}/{layer_name}/value/kernel']
     return k, o, q, v







def    a__       (  a__      ,    a__      ,    a__      ,    a__=False						):




     """simple docstring"""


     if split_mlp_wi:
          __SCREAMING_SNAKE_CASE     =      params[F'{prefix}/layers_{i}/mlp/wi_0/kernel']
          __SCREAMING_SNAKE_CASE     =      params[F'{prefix}/layers_{i}/mlp/wi_1/kernel']
          __SCREAMING_SNAKE_CASE     =      (wi_a, wi_a)
     else:
          __SCREAMING_SNAKE_CASE     =      params[F'{prefix}/layers_{i}/mlp/wi/kernel']

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







def    a__       (  a__      ,    a__      ,    a__      ,    a__						):




     """simple docstring"""


     return params[F'{prefix}/layers_{i}/{layer_name}/scale']







def    a__       (  a__      ,    *, a__      ,    a__						):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     =      traverse_util.flatten_dict(variables["""target"""]						)
     __SCREAMING_SNAKE_CASE     =      {"""/""".join(a__						): v for k, v in old.items()}

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

     __SCREAMING_SNAKE_CASE     =      collections.OrderedDict()

     # Shared embeddings.
     __SCREAMING_SNAKE_CASE     =      old["""token_embedder/embedding"""]

     # Encoder.
     for i in range(a__						):
          # Block i, layer 0 (Self Attention).
          __SCREAMING_SNAKE_CASE     =      tax_layer_norm_lookup(a__      ,    a__      ,    """encoder"""      ,    """pre_attention_layer_norm"""						)
          __SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE     =      tax_attention_lookup(a__      ,    a__      ,    """encoder"""      ,    """attention"""						)
          __SCREAMING_SNAKE_CASE     =      layer_norm
          __SCREAMING_SNAKE_CASE     =      k.T
          __SCREAMING_SNAKE_CASE     =      o.T
          __SCREAMING_SNAKE_CASE     =      q.T
          __SCREAMING_SNAKE_CASE     =      v.T

          # Block i, layer 1 (MLP).
          __SCREAMING_SNAKE_CASE     =      tax_layer_norm_lookup(a__      ,    a__      ,    """encoder"""      ,    """pre_mlp_layer_norm"""						)
          __SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE     =      tax_mlp_lookup(a__      ,    a__      ,    """encoder"""      ,    a__						)
          __SCREAMING_SNAKE_CASE     =      layer_norm
          if split_mlp_wi:
               __SCREAMING_SNAKE_CASE     =      wi[0].T
               __SCREAMING_SNAKE_CASE     =      wi[1].T
          else:
               __SCREAMING_SNAKE_CASE     =      wi.T
          __SCREAMING_SNAKE_CASE     =      wo.T

     __SCREAMING_SNAKE_CASE     =      old[
         """encoder/relpos_bias/rel_embedding"""
     ].T
     __SCREAMING_SNAKE_CASE     =      old["""encoder/encoder_norm/scale"""]

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

               # Block i, layer 1 (Cross Attention).
               __SCREAMING_SNAKE_CASE     =      tax_layer_norm_lookup(a__      ,    a__      ,    """decoder"""      ,    """pre_cross_attention_layer_norm"""						)
               __SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE     =      tax_attention_lookup(a__      ,    a__      ,    """decoder"""      ,    """encoder_decoder_attention"""						)
               __SCREAMING_SNAKE_CASE     =      layer_norm
               __SCREAMING_SNAKE_CASE     =      k.T
               __SCREAMING_SNAKE_CASE     =      o.T
               __SCREAMING_SNAKE_CASE     =      q.T
               __SCREAMING_SNAKE_CASE     =      v.T

               # Block i, layer 2 (MLP).
               __SCREAMING_SNAKE_CASE     =      tax_layer_norm_lookup(a__      ,    a__      ,    """decoder"""      ,    """pre_mlp_layer_norm"""						)
               __SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE     =      tax_mlp_lookup(a__      ,    a__      ,    """decoder"""      ,    a__						)
               __SCREAMING_SNAKE_CASE     =      layer_norm
               if split_mlp_wi:
                    __SCREAMING_SNAKE_CASE     =      wi[0].T
                    __SCREAMING_SNAKE_CASE     =      wi[1].T
               else:
                    __SCREAMING_SNAKE_CASE     =      wi.T
               __SCREAMING_SNAKE_CASE     =      wo.T

          __SCREAMING_SNAKE_CASE     =      old["""decoder/decoder_norm/scale"""]
          __SCREAMING_SNAKE_CASE     =      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:
               __SCREAMING_SNAKE_CASE     =      old["""decoder/logits_dense/kernel"""].T

     return new







def    a__       (  a__      ,    a__						):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     =      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:
          __SCREAMING_SNAKE_CASE     =      state_dict["""shared.weight"""]

     if not is_encoder_only:
          if "decoder.embed_tokens.weight" not in state_dict:
               __SCREAMING_SNAKE_CASE     =      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."""						)
               __SCREAMING_SNAKE_CASE     =      state_dict["""shared.weight"""]

     return state_dict







def    a__       (  a__      ,    a__      ,    a__      ,    a__						):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     =      checkpoints.load_tax_checkpoint(a__						)
     __SCREAMING_SNAKE_CASE     =      convert_tax_to_pytorch(a__      ,    num_layers=config.num_layers      ,    is_encoder_only=a__						)
     __SCREAMING_SNAKE_CASE     =      make_state_dict(a__      ,    a__						)
     model.load_state_dict(a__      ,    strict=a__						)







def    a__       (  a__      ,    a__      ,    a__      ,    a__ = False						):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     =      TaConfig.from_json_file(a__						)
     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:
          __SCREAMING_SNAKE_CASE     =      TaEncoderModel(a__						)
     else:
          __SCREAMING_SNAKE_CASE     =      TaForConditionalGeneration(a__						)

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

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

     # Verify that we can load the checkpoint.
     model.from_pretrained(a__						)
     print("""Done"""						)


if __name__ == "__main__":
  UpperCAmelCase :       Dict			   =     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
  )
  UpperCAmelCase :       Dict			   =     parser.parse_args()
  convert_tax_checkpoint_to_pytorch(
      args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
  )









'''simple docstring'''



import math
from enum import Enum
from typing import Optional, Union

from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR

from .utils import logging


UpperCAmelCase :       str			   =     logging.get_logger(__name__)







class 						lowerCAmelCase__   (      a					):



 """simple docstring"""



 lowerCAmelCase__  					=      "linear"
 lowerCAmelCase__  					=      "cosine"
 lowerCAmelCase__  					=      "cosine_with_restarts"
 lowerCAmelCase__  					=      "polynomial"
 lowerCAmelCase__  					=      "constant"
 lowerCAmelCase__  					=      "constant_with_warmup"
 lowerCAmelCase__  					=      "piecewise_constant"







def    a__       (  a__      ,    a__ = -1						):




     """simple docstring"""


     return LambdaLR(a__      ,    lambda a__						: 1      ,    last_epoch=a__						)







def    a__       (  a__      ,    a__      ,    a__ = -1						):




     """simple docstring"""


     def lr_lambda(a__						):
          if current_step < num_warmup_steps:
               return float(a__						) / float(max(1.0      ,    a__						)						)
          return 1.0

     return LambdaLR(a__      ,    a__      ,    last_epoch=a__						)







def    a__       (  a__      ,    a__      ,    a__ = -1						):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     =      {}
     __SCREAMING_SNAKE_CASE     =      step_rules.split(""","""						)
     for rule_str in rule_list[:-1]:
          __SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE     =      rule_str.split(""":"""						)
          __SCREAMING_SNAKE_CASE     =      int(a__						)
          __SCREAMING_SNAKE_CASE     =      float(a__						)
          __SCREAMING_SNAKE_CASE     =      value
     __SCREAMING_SNAKE_CASE     =      float(rule_list[-1]						)

     def create_rules_function(a__      ,    a__						):
          def rule_func(a__						) -> float:
               __SCREAMING_SNAKE_CASE     =      sorted(rules_dict.keys()						)
               for i, sorted_step in enumerate(a__						):
                    if steps < sorted_step:
                         return rules_dict[sorted_steps[i]]
               return last_lr_multiple

          return rule_func

     __SCREAMING_SNAKE_CASE     =      create_rules_function(a__      ,    a__						)

     return LambdaLR(a__      ,    a__      ,    last_epoch=a__						)







def    a__       (  a__      ,    a__      ,    a__      ,    a__=-1						):




     """simple docstring"""


     def lr_lambda(a__						):
          if current_step < num_warmup_steps:
               return float(a__						) / float(max(1      ,    a__						)						)
          return max(
              0.0      ,    float(num_training_steps - current_step						) / float(max(1      ,    num_training_steps - num_warmup_steps						)						)						)

     return LambdaLR(a__      ,    a__      ,    a__						)







def    a__       (  a__      ,    a__      ,    a__      ,    a__ = 0.5      ,    a__ = -1						):




     """simple docstring"""


     def lr_lambda(a__						):
          if current_step < num_warmup_steps:
               return float(a__						) / float(max(1      ,    a__						)						)
          __SCREAMING_SNAKE_CASE     =      float(current_step - num_warmup_steps						) / float(max(1      ,    num_training_steps - num_warmup_steps						)						)
          return max(0.0      ,    0.5 * (1.0 + math.cos(math.pi * float(a__						) * 2.0 * progress						))						)

     return LambdaLR(a__      ,    a__      ,    a__						)







def    a__       (  a__      ,    a__      ,    a__      ,    a__ = 1      ,    a__ = -1						):




     """simple docstring"""


     def lr_lambda(a__						):
          if current_step < num_warmup_steps:
               return float(a__						) / float(max(1      ,    a__						)						)
          __SCREAMING_SNAKE_CASE     =      float(current_step - num_warmup_steps						) / float(max(1      ,    num_training_steps - num_warmup_steps						)						)
          if progress >= 1.0:
               return 0.0
          return max(0.0      ,    0.5 * (1.0 + math.cos(math.pi * ((float(a__						) * progress) % 1.0)						))						)

     return LambdaLR(a__      ,    a__      ,    a__						)







def    a__       (  a__      ,    a__      ,    a__      ,    a__=1E-7      ,    a__=1.0      ,    a__=-1						):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     =      optimizer.defaults["""lr"""]
     if not (lr_init > lr_end):
          raise ValueError(F'lr_end ({lr_end}) must be be smaller than initial lr ({lr_init})'						)

     def lr_lambda(a__						):
          if current_step < num_warmup_steps:
               return float(a__						) / float(max(1      ,    a__						)						)
          elif current_step > num_training_steps:
               return lr_end / lr_init  # as LambdaLR multiplies by lr_init
          else:
               __SCREAMING_SNAKE_CASE     =      lr_init - lr_end
               __SCREAMING_SNAKE_CASE     =      num_training_steps - num_warmup_steps
               __SCREAMING_SNAKE_CASE     =      1 - (current_step - num_warmup_steps) / decay_steps
               __SCREAMING_SNAKE_CASE     =      lr_range * pct_remaining**power + lr_end
               return decay / lr_init  # as LambdaLR multiplies by lr_init

     return LambdaLR(a__      ,    a__      ,    a__						)


UpperCAmelCase :       Optional[Any]			   =     {
    SchedulerType.LINEAR: get_linear_schedule_with_warmup,
    SchedulerType.COSINE: get_cosine_schedule_with_warmup,
    SchedulerType.COSINE_WITH_RESTARTS: get_cosine_with_hard_restarts_schedule_with_warmup,
    SchedulerType.POLYNOMIAL: get_polynomial_decay_schedule_with_warmup,
    SchedulerType.CONSTANT: get_constant_schedule,
    SchedulerType.CONSTANT_WITH_WARMUP: get_constant_schedule_with_warmup,
    SchedulerType.PIECEWISE_CONSTANT: get_piecewise_constant_schedule,
}







def    a__       (  a__      ,    a__      ,    a__ = None      ,    a__ = None      ,    a__ = None      ,    a__ = 1      ,    a__ = 1.0      ,    a__ = -1      ,    ):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     =      SchedulerType(a__						)
     __SCREAMING_SNAKE_CASE     =      TYPE_TO_SCHEDULER_FUNCTION[name]
     if name == SchedulerType.CONSTANT:
          return schedule_func(a__      ,    last_epoch=a__						)

     if name == SchedulerType.PIECEWISE_CONSTANT:
          return schedule_func(a__      ,    step_rules=a__      ,    last_epoch=a__						)

     # All other schedulers require `num_warmup_steps`
     if num_warmup_steps is None:
          raise ValueError(F'{name} requires `num_warmup_steps`, please provide that argument.'						)

     if name == SchedulerType.CONSTANT_WITH_WARMUP:
          return schedule_func(a__      ,    num_warmup_steps=a__      ,    last_epoch=a__						)

     # All other schedulers require `num_training_steps`
     if num_training_steps is None:
          raise ValueError(F'{name} requires `num_training_steps`, please provide that argument.'						)

     if name == SchedulerType.COSINE_WITH_RESTARTS:
          return schedule_func(
              a__      ,    num_warmup_steps=a__      ,    num_training_steps=a__      ,    num_cycles=a__      ,    last_epoch=a__      ,    )

     if name == SchedulerType.POLYNOMIAL:
          return schedule_func(
              a__      ,    num_warmup_steps=a__      ,    num_training_steps=a__      ,    power=a__      ,    last_epoch=a__      ,    )

     return schedule_func(
         a__      ,    num_warmup_steps=a__      ,    num_training_steps=a__      ,    last_epoch=a__						)






