




'''simple docstring'''
import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   lowercase_	:    Any           =     AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE_							)
   lowercase_	:    int           =     FlaxAutoModelForSeqaSeqLM.from_config(config=SCREAMING_SNAKE_CASE_							)
   lowercase_	:    Optional[Any]           =     checkpoints.load_tax_checkpoint(SCREAMING_SNAKE_CASE_							)

   lowercase_	:    int           =     "wi_0" in tax_model["target"]["encoder"]["layers_0"]["mlp"]

   if config.model_type == "t5":
      lowercase_	:    List[Any]           =     "SelfAttention"
   if config.model_type == "longt5" and config.encoder_attention_type == "local":
      lowercase_	:    int           =     "LocalSelfAttention"
   elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
      lowercase_	:    Optional[Any]           =     "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							):
      lowercase_	:    Tuple           =     F"""layers_{str(SCREAMING_SNAKE_CASE_							)}"""

      # Self-Attention
      lowercase_	:    List[Any]           =     tax_model["target"]["encoder"][layer_name]["attention"]["key"]["kernel"]
      lowercase_	:    Dict           =     tax_model["target"]["encoder"][layer_name]["attention"]["out"]["kernel"]
      lowercase_	:    List[Any]           =     tax_model["target"]["encoder"][layer_name]["attention"]["query"]["kernel"]
      lowercase_	:    Any           =     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":
         lowercase_	:    int           =     tax_model["target"]["encoder"][layer_name]["attention"]["T5LayerNorm_0"]["scale"]

      # Layer Normalization
      lowercase_	:    Any           =     tax_model["target"]["encoder"][layer_name]["pre_attention_layer_norm"]["scale"]

      if split_mlp_wi:
         lowercase_	:    Union[str, Any]           =     tax_model["target"]["encoder"][layer_name]["mlp"]["wi_0"]["kernel"]
         lowercase_	:    Tuple           =     tax_model["target"]["encoder"][layer_name]["mlp"]["wi_1"]["kernel"]
      else:
         lowercase_	:    List[Any]           =     tax_model["target"]["encoder"][layer_name]["mlp"]["wi"]["kernel"]

      lowercase_	:    Union[str, Any]           =     tax_model["target"]["encoder"][layer_name]["mlp"]["wo"]["kernel"]

      # Layer Normalization
      lowercase_	:    Optional[int]           =     tax_model["target"]["encoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

      # Assigning
      lowercase_	:    Dict           =     flax_model.params["encoder"]["block"][str(SCREAMING_SNAKE_CASE_							)]["layer"]
      lowercase_	:    List[str]           =     tax_attention_key
      lowercase_	:    Tuple           =     tax_attention_out
      lowercase_	:    List[Any]           =     tax_attention_query
      lowercase_	:    int           =     tax_attention_value

      lowercase_	:    List[Any]           =     tax_attention_layer_norm

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

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

      lowercase_	:    str           =     tax_mlp_wo
      lowercase_	:    Dict           =     tax_mlp_layer_norm

      lowercase_	:    Optional[Any]           =     flax_model_encoder_layer_block

   # Only for layer 0:
   lowercase_	:    Optional[Any]           =     tax_model["target"]["encoder"]["relpos_bias"]["rel_embedding"].T
   lowercase_	:    int           =     tax_encoder_rel_embedding

   # Side/global relative position_bias + layer norm
   if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
      lowercase_	:    Tuple           =     tax_model["target"]["encoder"]["side_relpos_bias"]["rel_embedding"].T
      lowercase_	:    Tuple           =     tax_encoder_global_rel_embedding

   # Assigning
   lowercase_	:    List[str]           =     tax_model["target"]["encoder"]["encoder_norm"]["scale"]
   lowercase_	:    Optional[int]           =     tax_encoder_norm

   # Decoder
   for layer_index in range(config.num_layers							):
      lowercase_	:    int           =     F"""layers_{str(SCREAMING_SNAKE_CASE_							)}"""

      # Self-Attention
      lowercase_	:    Optional[Any]           =     tax_model["target"]["decoder"][layer_name]["self_attention"]["key"]["kernel"]
      lowercase_	:    int           =     tax_model["target"]["decoder"][layer_name]["self_attention"]["out"]["kernel"]
      lowercase_	:    Optional[Any]           =     tax_model["target"]["decoder"][layer_name]["self_attention"]["query"]["kernel"]
      lowercase_	:    int           =     tax_model["target"]["decoder"][layer_name]["self_attention"]["value"]["kernel"]

      # Layer Normalization
      lowercase_	:    Dict           =     tax_model["target"]["decoder"][layer_name]["pre_self_attention_layer_norm"][
          "scale"
      ]

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

      # Layer Normalization
      lowercase_	:    Union[str, Any]           =     tax_model["target"]["decoder"][layer_name]["pre_cross_attention_layer_norm"]["scale"]

      # MLP
      if split_mlp_wi:
         lowercase_	:    Optional[Any]           =     tax_model["target"]["decoder"][layer_name]["mlp"]["wi_0"]["kernel"]
         lowercase_	:    List[Any]           =     tax_model["target"]["decoder"][layer_name]["mlp"]["wi_1"]["kernel"]
      else:
         lowercase_	:    Dict           =     tax_model["target"]["decoder"][layer_name]["mlp"]["wi"]["kernel"]

      lowercase_	:    Optional[int]           =     tax_model["target"]["decoder"][layer_name]["mlp"]["wo"]["kernel"]

      # Layer Normalization
      lowercase_	:    Union[str, Any]           =     tax_model["target"]["decoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

      # Assigning
      lowercase_	:    Dict           =     flax_model.params["decoder"]["block"][str(SCREAMING_SNAKE_CASE_							)]["layer"]
      lowercase_	:    str           =     tax_attention_key
      lowercase_	:    Union[str, Any]           =     tax_attention_out
      lowercase_	:    List[Any]           =     tax_attention_query
      lowercase_	:    str           =     tax_attention_value

      lowercase_	:    List[Any]           =     tax_pre_attention_layer_norm

      lowercase_	:    int           =     tax_enc_dec_attention_key
      lowercase_	:    List[Any]           =     tax_enc_dec_attention_out
      lowercase_	:    List[str]           =     tax_enc_dec_attention_query
      lowercase_	:    str           =     tax_enc_dec_attention_value

      lowercase_	:    Tuple           =     tax_cross_layer_norm

      if split_mlp_wi:
         lowercase_	:    int           =     tax_mlp_wi_a
         lowercase_	:    str           =     tax_mlp_wi_a
      else:
         lowercase_	:    List[str]           =     tax_mlp_wi

      lowercase_	:    List[Any]           =     tax_mlp_wo

      lowercase_	:    List[str]           =     txa_mlp_layer_norm

      lowercase_	:    Union[str, Any]           =     flax_model_decoder_layer_block

   # Decoder Normalization
   lowercase_	:    Tuple           =     tax_model["target"]["decoder"]["decoder_norm"]["scale"]
   lowercase_	:    int           =     txa_decoder_norm

   # Only for layer 0:
   lowercase_	:    str           =     tax_model["target"]["decoder"]["relpos_bias"]["rel_embedding"].T
   lowercase_	:    Tuple           =     tax_decoder_rel_embedding

   # Token Embeddings
   lowercase_	:    int           =     tax_model["target"]["token_embedder"]["embedding"]
   lowercase_	:    Optional[int]           =     txa_token_embeddings

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

   flax_model.save_pretrained(SCREAMING_SNAKE_CASE_							)
   print("T5X Model was sucessfully converted!"							)


if __name__ == "__main__":
 UpperCamelCase__ =			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.'
 )
 UpperCamelCase__ =			parser.parse_args()
 convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)












'''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__ =			logging.get_logger(__name__)


class        _UpperCAmelCase						(		snake_case			):
    __lowerCamelCase:   Tuple		       =    'linear'
    __lowerCamelCase:   Any		       =    'cosine'
    __lowerCamelCase:   Optional[Any]		       =    'cosine_with_restarts'
    __lowerCamelCase:   Tuple		       =    'polynomial'
    __lowerCamelCase:   int		       =    'constant'
    __lowerCamelCase:   Optional[Any]		       =    'constant_with_warmup'
    __lowerCamelCase:   List[str]		       =    'piecewise_constant'




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase = -1							):


   """simple docstring"""




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




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase = -1							):


   """simple docstring"""




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

   return LambdaLR(_UpperCamelCase						,		_UpperCamelCase						,		last_epoch=_UpperCamelCase							)




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase = -1							):


   """simple docstring"""




   lowercase_	:    List[Any]           =     {}
   lowercase_	:    Dict           =     step_rules.split(","							)
   for rule_str in rule_list[:-1]:
      lowercase_    ,		lowercase_	:    Any           =     rule_str.split(":"							)
      lowercase_	:    List[Any]           =     int(_UpperCamelCase							)
      lowercase_	:    int           =     float(_UpperCamelCase							)
      lowercase_	:    Optional[int]           =     value
   lowercase_	:    Union[str, Any]           =     float(rule_list[-1]							)

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

      return rule_func

   lowercase_	:    Optional[int]           =     create_rules_function(_UpperCamelCase						,		_UpperCamelCase							)

   return LambdaLR(_UpperCamelCase						,		_UpperCamelCase						,		last_epoch=_UpperCamelCase							)




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase=-1							):


   """simple docstring"""




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

   return LambdaLR(_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							)




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase = 0.5						,		_UpperCamelCase = -1							):


   """simple docstring"""




   def lr_lambda(_UpperCamelCase							):
      if current_step < num_warmup_steps:
         return float(_UpperCamelCase							) / float(max(1						,		_UpperCamelCase							)							)
      lowercase_	:    List[str]           =     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(_UpperCamelCase							) * 2.0 * progress							))							)

   return LambdaLR(_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							)




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase = 1						,		_UpperCamelCase = -1							):


   """simple docstring"""




   def lr_lambda(_UpperCamelCase							):
      if current_step < num_warmup_steps:
         return float(_UpperCamelCase							) / float(max(1						,		_UpperCamelCase							)							)
      lowercase_	:    Dict           =     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(_UpperCamelCase							) * progress) % 1.0)							))							)

   return LambdaLR(_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							)




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase=1e-7						,		_UpperCamelCase=1.0						,		_UpperCamelCase=-1							):


   """simple docstring"""




   lowercase_	:    Dict           =     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(_UpperCamelCase							):
      if current_step < num_warmup_steps:
         return float(_UpperCamelCase							) / float(max(1						,		_UpperCamelCase							)							)
      elif current_step > num_training_steps:
         return lr_end / lr_init  # as LambdaLR multiplies by lr_init
      else:
         lowercase_	:    int           =     lr_init - lr_end
         lowercase_	:    Optional[int]           =     num_training_steps - num_warmup_steps
         lowercase_	:    Optional[Any]           =     1 - (current_step - num_warmup_steps) / decay_steps
         lowercase_	:    List[Any]           =     lr_range * pct_remaining**power + lr_end
         return decay / lr_init  # as LambdaLR multiplies by lr_init

   return LambdaLR(_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							)


UpperCamelCase__ =			{
    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 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase = None						,		_UpperCamelCase = None						,		_UpperCamelCase = None						,		_UpperCamelCase = 1						,		_UpperCamelCase = 1.0						,		_UpperCamelCase = -1						,		):


   """simple docstring"""




   lowercase_	:    Any           =     SchedulerType(_UpperCamelCase							)
   lowercase_	:    List[Any]           =     TYPE_TO_SCHEDULER_FUNCTION[name]
   if name == SchedulerType.CONSTANT:
      return schedule_func(_UpperCamelCase						,		last_epoch=_UpperCamelCase							)

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

   # 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(_UpperCamelCase						,		num_warmup_steps=_UpperCamelCase						,		last_epoch=_UpperCamelCase							)

   # 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(
          _UpperCamelCase						,		num_warmup_steps=_UpperCamelCase						,		num_training_steps=_UpperCamelCase						,		num_cycles=_UpperCamelCase						,		last_epoch=_UpperCamelCase						,		)

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

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




