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__ (		__UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase="attention"				):
      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__ (		__UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase=False				):
      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__ (		__UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase				):
      return params[F'''{prefix}/layers_{i}/{layer_name}/scale''']


def      a__ (		__UpperCamelCase     ,      *, __UpperCamelCase     ,      __UpperCamelCase				):
      SCREAMING_SNAKE_CASE_          =  traverse_util.flatten_dict(variables["target"]				)
      SCREAMING_SNAKE_CASE_          =  {"/".join(__UpperCamelCase				): 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:"     ,      __UpperCamelCase				)

      SCREAMING_SNAKE_CASE_          =  collections.OrderedDict()

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

      # Encoder.
      for i in range(__UpperCamelCase				):
            # Block i, layer 0 (Self Attention).
            SCREAMING_SNAKE_CASE_          =  tax_layer_norm_lookup(__UpperCamelCase     ,      __UpperCamelCase     ,      "encoder"     ,      "pre_attention_layer_norm"				)
            SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  tax_attention_lookup(__UpperCamelCase     ,      __UpperCamelCase     ,      "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(__UpperCamelCase     ,      __UpperCamelCase     ,      "encoder"     ,      "pre_mlp_layer_norm"				)
            SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  tax_mlp_lookup(__UpperCamelCase     ,      __UpperCamelCase     ,      "encoder"     ,      __UpperCamelCase				)
            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(__UpperCamelCase				):
                  # Block i, layer 0 (Self Attention).
                  SCREAMING_SNAKE_CASE_          =  tax_layer_norm_lookup(__UpperCamelCase     ,      __UpperCamelCase     ,      "decoder"     ,      "pre_self_attention_layer_norm"				)
                  SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  tax_attention_lookup(__UpperCamelCase     ,      __UpperCamelCase     ,      "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(__UpperCamelCase     ,      __UpperCamelCase     ,      "decoder"     ,      "pre_cross_attention_layer_norm"				)
                  SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  tax_attention_lookup(__UpperCamelCase     ,      __UpperCamelCase     ,      "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(__UpperCamelCase     ,      __UpperCamelCase     ,      "decoder"     ,      "pre_mlp_layer_norm"				)
                  SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  tax_mlp_lookup(__UpperCamelCase     ,      __UpperCamelCase     ,      "decoder"     ,      __UpperCamelCase				)
                  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__ (		__UpperCamelCase     ,      __UpperCamelCase				):
      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__ (		__UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase				):
      SCREAMING_SNAKE_CASE_          =  checkpoints.load_tax_checkpoint(__UpperCamelCase				)
      SCREAMING_SNAKE_CASE_          =  convert_tax_to_pytorch(__UpperCamelCase     ,      num_layers=config.num_layers     ,      is_encoder_only=__UpperCamelCase				)
      SCREAMING_SNAKE_CASE_          =  make_state_dict(__UpperCamelCase     ,      __UpperCamelCase				)
      model.load_state_dict(__UpperCamelCase     ,      strict=__UpperCamelCase				)


def      a__ (		__UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase = False				):
      SCREAMING_SNAKE_CASE_          =  TaConfig.from_json_file(__UpperCamelCase				)
      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(__UpperCamelCase				)
      else:
            SCREAMING_SNAKE_CASE_          =  TaForConditionalGeneration(__UpperCamelCase				)

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

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

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


if __name__ == "__main__":
      A     :  List[str]					     =       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     :  List[Any]					     =       parser.parse_args()
      convert_tax_checkpoint_to_pytorch(
          args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
      )



# Author: OMKAR PATHAK, Nwachukwu Chidiebere

# Use a Python dictionary to construct the graph.
from __future__ import annotations

from pprint import pformat
from typing import Generic, TypeVar

A     :  Dict					     =       TypeVar("T")


class   lowerCamelCase					(Generic[T]					):
  """simple docstring"""







  def __init__(							self :       Optional[int]  ,     __magic_name__ :       bool = True   )  ->	None:
        SCREAMING_SNAKE_CASE_          =  {}  # dictionary of lists
        SCREAMING_SNAKE_CASE_          =  directed







  def    __A     (							self :       Dict  ,     __magic_name__ :       T  ,     __magic_name__ :       T   )  ->	GraphAdjacencyList[T]:

        if not self.directed:  # For undirected graphs
              # if both source vertex and destination vertex are both present in the
              # adjacency list, add destination vertex to source vertex list of adjacent
              # vertices and add source vertex to destination vertex list of adjacent
              # vertices.
              if source_vertex in self.adj_list and destination_vertex in self.adj_list:
                    self.adj_list[source_vertex].append(__magic_name__   )
                    self.adj_list[destination_vertex].append(__magic_name__   )
              # if only source vertex is present in adjacency list, add destination vertex
              # to source vertex list of adjacent vertices, then create a new vertex with
              # destination vertex as key and assign a list containing the source vertex
              # as it's first adjacent vertex.
              elif source_vertex in self.adj_list:
                    self.adj_list[source_vertex].append(__magic_name__   )
                    SCREAMING_SNAKE_CASE_          =  [source_vertex]
              # if only destination vertex is present in adjacency list, add source vertex
              # to destination vertex list of adjacent vertices, then create a new vertex
              # with source vertex as key and assign a list containing the source vertex
              # as it's first adjacent vertex.
              elif destination_vertex in self.adj_list:
                    self.adj_list[destination_vertex].append(__magic_name__   )
                    SCREAMING_SNAKE_CASE_          =  [destination_vertex]
              # if both source vertex and destination vertex are not present in adjacency
              # list, create a new vertex with source vertex as key and assign a list
              # containing the destination vertex as it's first adjacent vertex also
              # create a new vertex with destination vertex as key and assign a list
              # containing the source vertex as it's first adjacent vertex.
              else:
                    SCREAMING_SNAKE_CASE_          =  [destination_vertex]
                    SCREAMING_SNAKE_CASE_          =  [source_vertex]
        else:  # For directed graphs
              # if both source vertex and destination vertex are present in adjacency
              # list, add destination vertex to source vertex list of adjacent vertices.
              if source_vertex in self.adj_list and destination_vertex in self.adj_list:
                    self.adj_list[source_vertex].append(__magic_name__   )
              # if only source vertex is present in adjacency list, add destination
              # vertex to source vertex list of adjacent vertices and create a new vertex
              # with destination vertex as key, which has no adjacent vertex
              elif source_vertex in self.adj_list:
                    self.adj_list[source_vertex].append(__magic_name__   )
                    SCREAMING_SNAKE_CASE_          =  []
              # if only destination vertex is present in adjacency list, create a new
              # vertex with source vertex as key and assign a list containing destination
              # vertex as first adjacent vertex
              elif destination_vertex in self.adj_list:
                    SCREAMING_SNAKE_CASE_          =  [destination_vertex]
              # if both source vertex and destination vertex are not present in adjacency
              # list, create a new vertex with source vertex as key and a list containing
              # destination vertex as it's first adjacent vertex. Then create a new vertex
              # with destination vertex as key, which has no adjacent vertex
              else:
                    SCREAMING_SNAKE_CASE_          =  [destination_vertex]
                    SCREAMING_SNAKE_CASE_          =  []

        return self







  def __repr__(							self :       int   )  ->	str:
        return pformat(self.adj_list   )


