'''simple docstring'''




import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch

def 	UpperCamelCase_							(					_UpperCAmelCase  :      str    )    ->    Optional[int]:


       """simple docstring"""







       _UpperCAmelCase     :  Optional[int]							      =    os.path.join(args.tf_model_dir			,	"parameters.json"    )
       _UpperCAmelCase     :  Any							      =    json.loads(open(_UpperCAmelCase    ).read()    )
       if not params:
              raise ValueError(
                  F"""It seems that the json file at {parameter_file} is empty. Make sure you have a correct json file."""    )
       if not args.output.endswith(".pt"    ):
              _UpperCAmelCase     :  Optional[Any]							      =    args.output + ".pt"
       _UpperCAmelCase     :  Union[str, Any]							      =    OrderedDict()
       with tf.device("/CPU:0"    ):
              _UpperCAmelCase     :  List[Any]							      =    tf.train.load_checkpoint(args.tf_model_dir    )
              _UpperCAmelCase     :  Dict							      =    reader.get_variable_to_shape_map()
              for key_name in shapes.keys():
                     _UpperCAmelCase     :  int							      =    reader.get_tensor(_UpperCAmelCase    ).astype(np.floataa    )
                     if key_name.endswith("/adam_m"    ) or key_name.endswith("/adam_v"    ):
                            continue
                     if key_name.startswith("pasts/"    ):
                            if key_name.startswith("pasts/mlp"    ):
                                   _UpperCAmelCase     :  Union[str, Any]							      =    int(key_name[9]    )
                            elif key_name.startswith("pasts/out"    ):
                                   _UpperCAmelCase     :  List[str]							      =    8
                            _UpperCAmelCase     :  Union[str, Any]							      =    "model.sqout.%d.weight" % (player * 2)  # enter to nn.Sequencial with Tanh, so 2 at a time
                            _UpperCAmelCase     :  Dict							      =    vnp.transpose([1, 0]    ).copy()  # Mesh-Tensorflow is a diagonal matrix
                            _UpperCAmelCase     :  Optional[Any]							      =    torch.tensor(_UpperCAmelCase    )
                     elif key_name.startswith("model/moe"    ):
                            _UpperCAmelCase     :  Optional[Any]							      =    int(key_name[9:].split("/"    )[0]    )
                            if key_name.endswith("/switch_gating/kernel"    ):
                                   _UpperCAmelCase     :  str							      =    "model.blocks.%d.feed_forward.mlp.router.classifier.weight" % player
                                   _UpperCAmelCase     :  Union[str, Any]							      =    vnp.transpose([1, 0]    ).copy()  # Mesh-Tensorflow is a diagonal matrix
                                   _UpperCAmelCase     :  Any							      =    torch.tensor(_UpperCAmelCase    )
                            elif key_name.endswith("/softmlp/kernel"    ):
                                   _UpperCAmelCase     :  Optional[Any]							      =    "model.blocks.%d.feed_forward.soft_bypass_mlp.weight" % player
                                   _UpperCAmelCase     :  Tuple							      =    vnp.transpose([1, 0]    ).copy()  # Mesh-Tensorflow is a diagonal matrix
                                   _UpperCAmelCase     :  Optional[int]							      =    torch.tensor(_UpperCAmelCase    )
                            elif key_name.endswith("/wo/kernel"    ) or key_name.endswith("/wi/kernel"    ):
                                   _UpperCAmelCase     :  Any							      =    key_name[-9:-7]
                                   for i in range(16    ):
                                          _UpperCAmelCase     :  int							      =    "model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight" % (player, i, nlayer)
                                          _UpperCAmelCase     :  Union[str, Any]							      =    (
                                              vnp[i].transpose([1, 0]    ).copy()
                                          )  # In Mesh-Tensorflow, it is one array, so it is divided
                                          _UpperCAmelCase     :  List[str]							      =    torch.tensor(_UpperCAmelCase    )
                     elif key_name.startswith("model/mlp"    ):
                            _UpperCAmelCase     :  List[Any]							      =    int(key_name[9:].split("/"    )[0]    )
                            if key_name.endswith("/p1/kernel"    ):
                                   _UpperCAmelCase     :  Dict							      =    "model.blocks.%d.feed_forward.mlp.wi.weight" % player
                                   _UpperCAmelCase     :  Optional[Any]							      =    vnp.transpose([1, 0]    ).copy()  # Mesh-Tensorflow is a diagonal matrix
                                   _UpperCAmelCase     :  Optional[Any]							      =    torch.tensor(_UpperCAmelCase    )
                            elif key_name.endswith("/p1/bias"    ):
                                   _UpperCAmelCase     :  List[str]							      =    "model.blocks.%d.feed_forward.mlp.wi.bias" % player
                                   _UpperCAmelCase     :  Any							      =    vnp.copy()  # same because it is one dimensional
                                   _UpperCAmelCase     :  int							      =    torch.tensor(_UpperCAmelCase    )
                            elif key_name.endswith("/p2/kernel"    ):
                                   _UpperCAmelCase     :  Tuple							      =    "model.blocks.%d.feed_forward.mlp.wo.weight" % player
                                   _UpperCAmelCase     :  Dict							      =    vnp.transpose([1, 0]    ).copy()  # Mesh-Tensorflow is a diagonal matrix
                                   _UpperCAmelCase     :  Union[str, Any]							      =    torch.tensor(_UpperCAmelCase    )
                            elif key_name.endswith("/p2/bias"    ):
                                   _UpperCAmelCase     :  Optional[int]							      =    "model.blocks.%d.feed_forward.mlp.wo.bias" % player
                                   _UpperCAmelCase     :  Union[str, Any]							      =    vnp.copy()  # same because it is one dimensional
                                   _UpperCAmelCase     :  int							      =    torch.tensor(_UpperCAmelCase    )
                     elif key_name.startswith("model/ln"    ):
                            _UpperCAmelCase     :  Any							      =    int(key_name[8:].split("/"    )[0]    )
                            if key_name.endswith("/b"    ):
                                   _UpperCAmelCase     :  int							      =    "model.blocks.%d.feed_forward.norm.bias" % player
                                   _UpperCAmelCase     :  Tuple							      =    vnp.copy()  # same because it is one dimensional
                                   _UpperCAmelCase     :  Union[str, Any]							      =    torch.tensor(_UpperCAmelCase    )
                            elif key_name.endswith("/g"    ):
                                   _UpperCAmelCase     :  int							      =    "model.blocks.%d.feed_forward.norm.weight" % player
                                   _UpperCAmelCase     :  List[Any]							      =    vnp.copy()  # same because it is one dimensional
                                   _UpperCAmelCase     :  Any							      =    torch.tensor(_UpperCAmelCase    )
                     elif key_name.startswith("model/att"    ):
                            _UpperCAmelCase     :  int							      =    int(key_name[9:].split("/"    )[0]    )
                            if key_name.endswith("/qkv/kernel"    ):
                                   _UpperCAmelCase     :  List[Any]							      =    vnp.copy()  # Compute same dimension as Mesh-tensorflow using einsum
                                   _UpperCAmelCase     :  Dict							      =    state[:, 0, :, :]
                                   _UpperCAmelCase     :  Tuple							      =    state[:, 1, :, :]
                                   _UpperCAmelCase     :  Dict							      =    state[:, 2, :, :]
                                   _UpperCAmelCase     :  str							      =    (
                                       state_q.reshape([state_q.shape[0], state_q.shape[1] * state_q.shape[2]]    )
                                       .transpose([1, 0]    )
                                       .copy()
                                   )  # Mesh-Tensorflow is a diagonal matrix
                                   _UpperCAmelCase     :  Union[str, Any]							      =    (
                                       state_k.reshape([state_k.shape[0], state_k.shape[1] * state_k.shape[2]]    )
                                       .transpose([1, 0]    )
                                       .copy()
                                   )  # Mesh-Tensorflow is a diagonal matrix
                                   _UpperCAmelCase     :  Dict							      =    (
                                       state_v.reshape([state_v.shape[0], state_v.shape[1] * state_v.shape[2]]    )
                                       .transpose([1, 0]    )
                                       .copy()
                                   )  # Mesh-Tensorflow is a diagonal matrix
                                   _UpperCAmelCase     :  Tuple							      =    "model.blocks.%d.self_attn.self_attn.q_proj.weight" % player
                                   _UpperCAmelCase     :  Union[str, Any]							      =    torch.tensor(_UpperCAmelCase    )
                                   _UpperCAmelCase     :  Optional[int]							      =    "model.blocks.%d.self_attn.self_attn.k_proj.weight" % player
                                   _UpperCAmelCase     :  Union[str, Any]							      =    torch.tensor(_UpperCAmelCase    )
                                   _UpperCAmelCase     :  int							      =    "model.blocks.%d.self_attn.self_attn.v_proj.weight" % player
                                   _UpperCAmelCase     :  Optional[Any]							      =    torch.tensor(_UpperCAmelCase    )
                            elif key_name.endswith("/o/kernel"    ):
                                   _UpperCAmelCase     :  Dict							      =    "model.blocks.%d.self_attn.self_attn.out_proj.weight" % player
                                   _UpperCAmelCase     :  Tuple							      =    (
                                       vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]]    ).transpose([1, 0]    ).copy()
                                   )  # Mesh-Tensorflow is a diagonal matrix
                                   _UpperCAmelCase     :  Dict							      =    torch.tensor(_UpperCAmelCase    )
                     elif key_name.startswith("model/an"    ):
                            _UpperCAmelCase     :  int							      =    int(key_name[8:].split("/"    )[0]    )
                            if key_name.endswith("/b"    ):
                                   _UpperCAmelCase     :  List[Any]							      =    "model.blocks.%d.self_attn.norm.bias" % player
                                   _UpperCAmelCase     :  Dict							      =    vnp.copy()  # same because it is one dimensional
                                   _UpperCAmelCase     :  Dict							      =    torch.tensor(_UpperCAmelCase    )
                            elif key_name.endswith("/g"    ):
                                   _UpperCAmelCase     :  Union[str, Any]							      =    "model.blocks.%d.self_attn.norm.weight" % player
                                   _UpperCAmelCase     :  Tuple							      =    vnp.copy()  # same because it is one dimensional
                                   _UpperCAmelCase     :  List[Any]							      =    torch.tensor(_UpperCAmelCase    )
                     elif (
                         key_name.startswith("model/wte"    )
                         or key_name.startswith("model/wpe"    )
                         or key_name.startswith("model/ete"    )
                     ):
                            _UpperCAmelCase     :  Union[str, Any]							      =    {"wte": "embed_tokens", "wpe": "position_embeddings", "ete": "extra_position_embeddings"}[
                                key_name[-3:]
                            ]
                            _UpperCAmelCase     :  str							      =    "model.%s.weight" % nlayer
                            _UpperCAmelCase     :  int							      =    vnp.copy()  # same in embedded
                            _UpperCAmelCase     :  List[str]							      =    torch.tensor(_UpperCAmelCase    )
                            if key_name.startswith("model/wte"    ):
                                   _UpperCAmelCase     :  Any							      =    "lm_head.weight"
                                   _UpperCAmelCase     :  List[Any]							      =    vnp.copy()  # same in embedded
                                   _UpperCAmelCase     :  Optional[Any]							      =    torch.tensor(_UpperCAmelCase    )
                     elif key_name.startswith("model/wob"    ):
                            _UpperCAmelCase     :  Union[str, Any]							      =    "final_logits_bias"
                            _UpperCAmelCase     :  List[str]							      =    vnp.copy()  # same in embedded
                            _UpperCAmelCase     :  List[str]							      =    state.reshape((1, -1)    )
                            _UpperCAmelCase     :  Union[str, Any]							      =    torch.tensor(_UpperCAmelCase    )
                     elif key_name == "model/dense/kernel":
                            _UpperCAmelCase     :  int							      =    "model.last_project.weight"
                            _UpperCAmelCase     :  Optional[Any]							      =    vnp.transpose([1, 0]    ).copy()  # Mesh-Tensorflow is a diagonal matrix
                            _UpperCAmelCase     :  Optional[int]							      =    torch.tensor(_UpperCAmelCase    )
                     elif key_name == "model/dense_1/bias":
                            _UpperCAmelCase     :  Dict							      =    "model.last_project.bias"
                            _UpperCAmelCase     :  Optional[int]							      =    vnp.copy()  # same because it is one dimensional
                            _UpperCAmelCase     :  Optional[int]							      =    torch.tensor(_UpperCAmelCase    )
       torch.save(_UpperCAmelCase			,	args.output    )


if __name__ == "__main__":
  __SCREAMING_SNAKE_CASE       :     Union[str, Any]     =  argparse.ArgumentParser(
      description="""model converter.""", formatter_class=argparse.ArgumentDefaultsHelpFormatter
  )
  parser.add_argument("""--tf_model_dir""", metavar="""PATH""", type=str, required=True, help="""import model""")
  parser.add_argument("""--output""", metavar="""PATH""", type=str, required=True, help="""output model""")
  __SCREAMING_SNAKE_CASE       :     List[str]     =  parser.parse_args()
  convert_tf_gptsan_to_pt(args)


'''simple docstring'''




import math
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput

class 	lowerCamelCase_	(snake_case__							, snake_case__    ):
      '''simple docstring'''

      __UpperCamelCase:  Optional[Any]						=						1




      @register_to_config
      def __init__(       self						:				Optional[int]						,					A						:				int = 1000						,					A						:				Optional[Union[np.ndarray, List[float]]] = None       ):
             # set `betas`, `alphas`, `timesteps`
             self.set_timesteps(A       )

             # standard deviation of the initial noise distribution
             _UpperCAmelCase     :  int							      =    1.0

             # For now we only support F-PNDM, i.e. the runge-kutta method
             # For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
             # mainly at formula (9), (12), (13) and the Algorithm 2.
             _UpperCAmelCase     :  int							      =    4

             # running values
             _UpperCAmelCase     :  Dict							      =    []




      def      _A	(       self						:				Optional[int]						,					A						:				int						,					A						:				Union[str, torch.device] = None       ):
             _UpperCAmelCase     :  int							      =    num_inference_steps
             _UpperCAmelCase     :  Union[str, Any]							      =    torch.linspace(1						,					0						,					num_inference_steps + 1       )[:-1]
             _UpperCAmelCase     :  Any							      =    torch.cat([steps, torch.tensor([0.0]       )]       )

             if self.config.trained_betas is not None:
                    _UpperCAmelCase     :  str							      =    torch.tensor(self.config.trained_betas						,					dtype=torch.floataa       )
             else:
                    _UpperCAmelCase     :  Dict							      =    torch.sin(steps * math.pi / 2       ) ** 2

             _UpperCAmelCase     :  List[Any]							      =    (1.0 - self.betas**2) ** 0.5

             _UpperCAmelCase     :  List[str]							      =    (torch.atana(self.betas						,					self.alphas       ) / math.pi * 2)[:-1]
             _UpperCAmelCase     :  Dict							      =    timesteps.to(A       )

             _UpperCAmelCase     :  Dict							      =    []




      def      _A	(       self						:				Optional[int]						,					A						:				torch.FloatTensor						,					A						:				int						,					A						:				torch.FloatTensor						,					A						:				bool = True						,					):
             if self.num_inference_steps is None:
                    raise ValueError(
                        "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"       )

             _UpperCAmelCase     :  Tuple							      =    (self.timesteps == timestep).nonzero().item()
             _UpperCAmelCase     :  Optional[Any]							      =    timestep_index + 1

             _UpperCAmelCase     :  int							      =    sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
             self.ets.append(A       )

             if len(self.ets       ) == 1:
                    _UpperCAmelCase     :  List[Any]							      =    self.ets[-1]
             elif len(self.ets       ) == 2:
                    _UpperCAmelCase     :  str							      =    (3 * self.ets[-1] - self.ets[-2]) / 2
             elif len(self.ets       ) == 3:
                    _UpperCAmelCase     :  Tuple							      =    (23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
             else:
                    _UpperCAmelCase     :  Union[str, Any]							      =    (1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])

             _UpperCAmelCase     :  Union[str, Any]							      =    self._get_prev_sample(A						,					A						,					A						,					A       )

             if not return_dict:
                    return (prev_sample,)

             return SchedulerOutput(prev_sample=A       )




      def      _A	(       self						:				Union[str, Any]						,					A						:				torch.FloatTensor						,					*A						:				Union[str, Any]						,					**A						:				Dict       ):
             return sample




      def      _A	(       self						:				Optional[Any]						,					A						:				Optional[int]						,					A						:				int						,					A						:				Optional[Any]						,					A						:				List[str]       ):
             _UpperCAmelCase     :  List[str]							      =    self.alphas[timestep_index]
             _UpperCAmelCase     :  List[Any]							      =    self.betas[timestep_index]

             _UpperCAmelCase     :  Optional[Any]							      =    self.alphas[prev_timestep_index]
             _UpperCAmelCase     :  Dict							      =    self.betas[prev_timestep_index]

             _UpperCAmelCase     :  Tuple							      =    (sample - sigma * ets) / max(A						,					1E-8       )
             _UpperCAmelCase     :  List[str]							      =    next_alpha * pred + ets * next_sigma

             return prev_sample





      def __len__(       self						:				Union[str, Any]       ):
             return self.config.num_train_timesteps

