



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					(   lowerCAmelCase_       : Optional[int]      ,lowerCAmelCase_       : List[Any]      ,lowerCAmelCase_       : Any      ,lowerCAmelCase_       : Tuple="attention"    )				->			Dict:






      '''simple docstring'''



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



def 	__a					(   lowerCAmelCase_       : List[str]      ,lowerCAmelCase_       : Dict      ,lowerCAmelCase_       : List[str]      ,lowerCAmelCase_       : str=False    )				->			int:






      '''simple docstring'''



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

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



def 	__a					(   lowerCAmelCase_       : Optional[int]      ,lowerCAmelCase_       : Dict      ,lowerCAmelCase_       : Optional[Any]      ,lowerCAmelCase_       : Tuple    )				->			int:






      '''simple docstring'''



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



def 	__a					(   lowerCAmelCase_       : dict      ,*, lowerCAmelCase_       : int      ,lowerCAmelCase_       : bool    )				->			Optional[int]:






      '''simple docstring'''



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

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

      UpperCAmelCase_=       collections.OrderedDict()

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

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

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

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

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

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

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

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

      return new



def 	__a					(   lowerCAmelCase_       : Union[str, Any]      ,lowerCAmelCase_       : bool    )				->			Any:






      '''simple docstring'''



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

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

      return state_dict



def 	__a					(   lowerCAmelCase_       : Dict      ,lowerCAmelCase_       : Optional[int]      ,lowerCAmelCase_       : Union[str, Any]      ,lowerCAmelCase_       : List[str]    )				->			Tuple:






      '''simple docstring'''



      UpperCAmelCase_=       checkpoints.load_tax_checkpoint(lowercase__    )
      UpperCAmelCase_=       convert_tax_to_pytorch(lowercase__      ,num_layers=config.num_layers      ,is_encoder_only=lowercase__    )
      UpperCAmelCase_=       make_state_dict(lowercase__      ,lowercase__    )
      model.load_state_dict(lowercase__      ,strict=lowercase__    )



def 	__a					(   lowerCAmelCase_       : str      ,lowerCAmelCase_       : Optional[Any]      ,lowerCAmelCase_       : List[Any]      ,lowerCAmelCase_       : bool = False    )				->			int:






      '''simple docstring'''



      UpperCAmelCase_=       TaConfig.from_json_file(lowercase__    )
      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:
            UpperCAmelCase_=       TaEncoderModel(lowercase__    )
      else:
            UpperCAmelCase_=       TaForConditionalGeneration(lowercase__    )

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

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

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


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









from typing import Optional

import pyspark

from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader






class 		lowercase						(			snake_case__):






	"""simple docstring"""


	def __init__(     self     :						int						,  __UpperCAmelCase     :						pyspark.sql.DataFrame						,  __UpperCAmelCase     :						Optional[NamedSplit] = None						,  __UpperCAmelCase     :						Optional[Features] = None						,  __UpperCAmelCase     :						bool = True						,  __UpperCAmelCase     :						str = None						,  __UpperCAmelCase     :						bool = False						,  __UpperCAmelCase     :						str = None						,  __UpperCAmelCase     :						bool = True						,  __UpperCAmelCase     :						str = "arrow"						,  **__UpperCAmelCase     :						str						,  )      ->			Dict:
							super().__init__(
							    split=__UpperCAmelCase						,  features=__UpperCAmelCase						,  cache_dir=__UpperCAmelCase						,  keep_in_memory=__UpperCAmelCase						,  streaming=__UpperCAmelCase						,  **__UpperCAmelCase						,  )
							UpperCAmelCase_=       load_from_cache_file
							UpperCAmelCase_=       file_format
							UpperCAmelCase_=       Spark(
							    df=__UpperCAmelCase						,  features=__UpperCAmelCase						,  cache_dir=__UpperCAmelCase						,  working_dir=__UpperCAmelCase						,  **__UpperCAmelCase						,  )




	def   _SCREAMING_SNAKE_CASE		(     self     :						Any					)      ->			Dict:
							if self.streaming:
													return self.builder.as_streaming_dataset(split=self.split					)
							UpperCAmelCase_=       None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
							self.builder.download_and_prepare(
							    download_mode=__UpperCAmelCase						,  file_format=self._file_format						,  )
							return self.builder.as_dataset(split=self.split					)




