



"""simple docstring"""





import argparse
import collections

import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints

from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__   ):
      return params[f"""{prefix}/{prefix}/relpos_bias/rel_embedding"""][:, i, :]
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__="attention"   ):
      UpperCAmelCase_    				=							UpperCAmelCase_    				=							np.ascontiguousarray(params[f"""{prefix}/{prefix}/{layer_name}/key/kernel"""][:, i, :, :]   )
      UpperCAmelCase_    				=							k_tmp.reshape(k_tmp.shape[0]			,    k_tmp.shape[1] * k_tmp.shape[2]   )
      UpperCAmelCase_    				=							np.ascontiguousarray(params[f"""{prefix}/{prefix}/{layer_name}/out/kernel"""][:, i, :, :]   )
      UpperCAmelCase_    				=							o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1]			,    o_tmp.shape[2]   )
      UpperCAmelCase_    				=							np.ascontiguousarray(params[f"""{prefix}/{prefix}/{layer_name}/query/kernel"""][:, i, :, :]   )
      UpperCAmelCase_    				=							q_tmp.reshape(q_tmp.shape[0]			,    q_tmp.shape[1] * q_tmp.shape[2]   )
      UpperCAmelCase_    				=							np.ascontiguousarray(params[f"""{prefix}/{prefix}/{layer_name}/value/kernel"""][:, i, :, :]   )
      UpperCAmelCase_    				=							v_tmp.reshape(v_tmp.shape[0]			,    v_tmp.shape[1] * v_tmp.shape[2]   )
      return k, o, q, v
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__=False   ):
      if split_mlp_wi:
            UpperCAmelCase_    				=							params[f"""{prefix}/{prefix}/mlp/wi_0/kernel"""][:, i, :]
            UpperCAmelCase_    				=							params[f"""{prefix}/{prefix}/mlp/wi_1/kernel"""][:, i, :]
            UpperCAmelCase_    				=							(wi_a, wi_a)
      else:
            UpperCAmelCase_    				=							params[f"""{prefix}/{prefix}/mlp/wi/kernel"""][:, i, :]

      UpperCAmelCase_    				=							params[f"""{prefix}/{prefix}/mlp/wo/kernel"""][:, i, :]
      return wi, wo
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__   ):
      return params[f"""{prefix}/{prefix}/{layer_name}/scale"""][:, i]
def      a__			(   lowerCAmelCase__			,    *, lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__ = False   ):
      UpperCAmelCase_    				=							traverse_util.flatten_dict(variables["target"]   )
      UpperCAmelCase_    				=							{"/".join(lowerCAmelCase__   ): 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/encoder/mlp/wi_0/kernel" in old
      print("Split MLP:"			,    lowerCAmelCase__   )

      UpperCAmelCase_    				=							collections.OrderedDict()

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

      # Encoder.
      for i in range(lowerCAmelCase__   ):
            # Block i, layer 0 (Self Attention).
            UpperCAmelCase_    				=							tax_layer_norm_lookup(lowerCAmelCase__			,    lowerCAmelCase__			,    "encoder"			,    "pre_attention_layer_norm"   )
            UpperCAmelCase_				,					UpperCAmelCase_				,					UpperCAmelCase_				,					UpperCAmelCase_    				=							tax_attention_lookup(lowerCAmelCase__			,    lowerCAmelCase__			,    "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(lowerCAmelCase__			,    lowerCAmelCase__			,    "encoder"			,    "pre_mlp_layer_norm"   )
            UpperCAmelCase_				,					UpperCAmelCase_    				=							tax_mlp_lookup(lowerCAmelCase__			,    lowerCAmelCase__			,    "encoder"			,    lowerCAmelCase__   )
            UpperCAmelCase_    				=							layer_norm
            if split_mlp_wi:
                  UpperCAmelCase_    				=							wi[0].T
                  UpperCAmelCase_    				=							wi[1].T
            else:
                  UpperCAmelCase_    				=							wi.T
            UpperCAmelCase_    				=							wo.T
            if scalable_attention:
                  # convert the rel_embedding of each layer
                  UpperCAmelCase_    				=							tax_relpos_bias_lookup(
                      lowerCAmelCase__			,    lowerCAmelCase__			,    "encoder"   ).T

      UpperCAmelCase_    				=							old["encoder/encoder_norm/scale"]

      if not scalable_attention:
            UpperCAmelCase_    				=							tax_relpos_bias_lookup(
                lowerCAmelCase__			,    0			,    "encoder"   ).T
            UpperCAmelCase_    				=							tax_relpos_bias_lookup(
                lowerCAmelCase__			,    0			,    "decoder"   ).T

      if not is_encoder_only:
            # Decoder.
            for i in range(lowerCAmelCase__   ):
                  # Block i, layer 0 (Self Attention).
                  UpperCAmelCase_    				=							tax_layer_norm_lookup(lowerCAmelCase__			,    lowerCAmelCase__			,    "decoder"			,    "pre_self_attention_layer_norm"   )
                  UpperCAmelCase_				,					UpperCAmelCase_				,					UpperCAmelCase_				,					UpperCAmelCase_    				=							tax_attention_lookup(lowerCAmelCase__			,    lowerCAmelCase__			,    "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(lowerCAmelCase__			,    lowerCAmelCase__			,    "decoder"			,    "pre_cross_attention_layer_norm"   )
                  UpperCAmelCase_				,					UpperCAmelCase_				,					UpperCAmelCase_				,					UpperCAmelCase_    				=							tax_attention_lookup(lowerCAmelCase__			,    lowerCAmelCase__			,    "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(lowerCAmelCase__			,    lowerCAmelCase__			,    "decoder"			,    "pre_mlp_layer_norm"   )
                  UpperCAmelCase_				,					UpperCAmelCase_    				=							tax_mlp_lookup(lowerCAmelCase__			,    lowerCAmelCase__			,    "decoder"			,    lowerCAmelCase__   )
                  UpperCAmelCase_    				=							layer_norm
                  if split_mlp_wi:
                        UpperCAmelCase_    				=							wi[0].T
                        UpperCAmelCase_    				=							wi[1].T
                  else:
                        UpperCAmelCase_    				=							wi.T
                  UpperCAmelCase_    				=							wo.T

                  if scalable_attention:
                        # convert the rel_embedding of each layer
                        UpperCAmelCase_    				=							tax_relpos_bias_lookup(lowerCAmelCase__			,    lowerCAmelCase__			,    "decoder"   ).T

            UpperCAmelCase_    				=							old["decoder/decoder_norm/scale"]

            # 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__			,    lowerCAmelCase__   ):
      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__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__   ):
      UpperCAmelCase_    				=							checkpoints.load_tax_checkpoint(lowerCAmelCase__   )
      UpperCAmelCase_    				=							convert_tax_to_pytorch(
          lowerCAmelCase__			,    num_layers=config.num_layers			,    is_encoder_only=lowerCAmelCase__			,    scalable_attention=lowerCAmelCase__   )
      UpperCAmelCase_    				=							make_state_dict(lowerCAmelCase__			,    lowerCAmelCase__   )
      model.load_state_dict(lowerCAmelCase__			,    strict=lowerCAmelCase__   )
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__ = False			,    lowerCAmelCase__ = False			,    ):
      UpperCAmelCase_    				=							MTaConfig.from_json_file(lowerCAmelCase__   )
      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_    				=							UMTaEncoderModel(lowerCAmelCase__   )
      else:
            UpperCAmelCase_    				=							UMTaForConditionalGeneration(lowerCAmelCase__   )

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

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

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


if __name__ == "__main__":
  lowerCamelCase      =		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
  )
  parser.add_argument(
      """--scalable_attention""",
      action="""store_true""",
      help="""Whether the model uses scaled attention (umt5 model)""",
      default=False,
  )
  lowerCamelCase      =		parser.parse_args()
  convert_tax_checkpoint_to_pytorch(
      args.tax_checkpoint_path,
      args.config_file,
      args.pytorch_dump_path,
      args.is_encoder_only,
      args.scalable_attention,
  )









"""simple docstring"""





from __future__ import annotations

import math
def      a__			(   lowerCAmelCase__   ):

      if 1 < number < 4:
            # 2 and 3 are primes
            return True
      elif number < 2 or number % 2 == 0 or number % 3 == 0:
            # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
            return False

      # All primes number are in format of 6k +/- 1
      for i in range(5			,    int(math.sqrt(lowerCAmelCase__   ) + 1   )			,    6   ):
            if number % i == 0 or number % (i + 2) == 0:
                  return False
      return True


lowerCamelCase      =		[num for num in range(3, 100_001, 2) if not is_prime(num)]
def      a__			(   lowerCAmelCase__   ):
      if not isinstance(lowerCAmelCase__			,    lowerCAmelCase__   ):
            raise ValueError("n must be an integer"   )
      if n <= 0:
            raise ValueError("n must be >= 0"   )

      UpperCAmelCase_    				=							[]
      for num in range(len(lowerCAmelCase__   )   ):
            UpperCAmelCase_    				=							0
            while 2 * i * i <= odd_composites[num]:
                  UpperCAmelCase_    				=							odd_composites[num] - 2 * i * i
                  if is_prime(lowerCAmelCase__   ):
                        break
                  i += 1
            else:
                  list_nums.append(odd_composites[num]   )
                  if len(lowerCAmelCase__   ) == n:
                        return list_nums

      return []
def      a__			(   ):
      return compute_nums(1   )[0]


if __name__ == "__main__":
  print(F"{solution() = }")




