



'''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      SCREAMING_SNAKE_CASE				(				lowercase_    :      List[Any] ,  lowercase_    :      Union[str, Any] ,  lowercase_    :      int ,  lowercase_    :      int="attention"						):
      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				(				lowercase_    :      List[str] ,  lowercase_    :      int ,  lowercase_    :      Any ,  lowercase_    :      Optional[Any]=False						):
      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				(				lowercase_    :      Any ,  lowercase_    :      Union[str, Any] ,  lowercase_    :      Dict ,  lowercase_    :      int						):
      return params[F"""{prefix}/layers_{i}/{layer_name}/scale"""]







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      dict ,  *, lowercase_    :      int ,  lowercase_    :      bool						):
      lowercase        =							traverse_util.flatten_dict(variables["""target"""]						)
      lowercase        =							{"""/""".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
      lowercase        =							"""encoder/layers_0/mlp/wi_0/kernel""" in old
      print("""Split MLP:""" ,  lowercase_						)

      lowercase        =							collections.OrderedDict()

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

      # Encoder.
      for i in range(lowercase_						):
            # Block i, layer 0 (Self Attention).
            lowercase        =							tax_layer_norm_lookup(lowercase_ ,  lowercase_ ,  """encoder""" ,  """pre_attention_layer_norm"""						)
            lowercase     ,   lowercase     ,   lowercase     ,   lowercase        =							tax_attention_lookup(lowercase_ ,  lowercase_ ,  """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(lowercase_ ,  lowercase_ ,  """encoder""" ,  """pre_mlp_layer_norm"""						)
            lowercase     ,   lowercase        =							tax_mlp_lookup(lowercase_ ,  lowercase_ ,  """encoder""" ,  lowercase_						)
            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(lowercase_						):
                  # Block i, layer 0 (Self Attention).
                  lowercase        =							tax_layer_norm_lookup(lowercase_ ,  lowercase_ ,  """decoder""" ,  """pre_self_attention_layer_norm"""						)
                  lowercase     ,   lowercase     ,   lowercase     ,   lowercase        =							tax_attention_lookup(lowercase_ ,  lowercase_ ,  """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(lowercase_ ,  lowercase_ ,  """decoder""" ,  """pre_cross_attention_layer_norm"""						)
                  lowercase     ,   lowercase     ,   lowercase     ,   lowercase        =							tax_attention_lookup(lowercase_ ,  lowercase_ ,  """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(lowercase_ ,  lowercase_ ,  """decoder""" ,  """pre_mlp_layer_norm"""						)
                  lowercase     ,   lowercase        =							tax_mlp_lookup(lowercase_ ,  lowercase_ ,  """decoder""" ,  lowercase_						)
                  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				(				lowercase_    :      Dict ,  lowercase_    :      bool						):
      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				(				lowercase_    :      Optional[Any] ,  lowercase_    :      Tuple ,  lowercase_    :      int ,  lowercase_    :      Any						):
      lowercase        =							checkpoints.load_tax_checkpoint(lowercase_						)
      lowercase        =							convert_tax_to_pytorch(lowercase_ ,  num_layers=config.num_layers ,  is_encoder_only=lowercase_						)
      lowercase        =							make_state_dict(lowercase_ ,  lowercase_						)
      model.load_state_dict(lowercase_ ,  strict=lowercase_						)







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      List[Any] ,  lowercase_    :      str ,  lowercase_    :      List[Any] ,  lowercase_    :      bool = False						):
      lowercase        =							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:
            lowercase        =							TaEncoderModel(lowercase_						)
      else:
            lowercase        =							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__":
      lowercase_      :				Optional[int]   					=    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
      )
      lowercase_      :				Union[str, Any]   					=    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'''


def      SCREAMING_SNAKE_CASE				(				):
						lowercase        =							[]
						lowercase        =							1

						while len(lowercase_						) < 1E6:
												constant.append(str(lowercase_						)						)
												i += 1

						lowercase        =							"""""".join(lowercase_						)

						return (
						    int(constant[0]						)
						    * int(constant[9]						)
						    * int(constant[99]						)
						    * int(constant[999]						)
						    * int(constant[9999]						)
						    * int(constant[9_9999]						)
						    * int(constant[99_9999]						)
						)


if __name__ == "__main__":
						print(solution())


