


'''simple docstring'''




import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch


def    UpperCAmelCase_  (       A      ):
   '''simple docstring'''





   _a					:  int						=  os.path.join(args.tf_model_dir		,	'parameters.json'      )
   _a					:  List[str]						=  json.loads(open(A      ).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'      ):
      _a					:  Union[str, Any]						=  args.output + '.pt'
   _a					:  str						=  OrderedDict()
   with tf.device('/CPU:0'      ):
      _a					:  Dict						=  tf.train.load_checkpoint(args.tf_model_dir      )
      _a					:  List[Any]						=  reader.get_variable_to_shape_map()
      for key_name in shapes.keys():
         _a					:  Any						=  reader.get_tensor(A      ).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'      ):
               _a					:  Optional[int]						=  int(key_name[9]      )
            elif key_name.startswith('pasts/out'      ):
               _a					:  Optional[Any]						=  8
            _a					:  List[str]						=  'model.sqout.%d.weight' % (player * 2)  # enter to nn.Sequencial with Tanh, so 2 at a time
            _a					:  List[str]						=  vnp.transpose([1, 0]      ).copy()  # Mesh-Tensorflow is a diagonal matrix
            _a					:  Optional[int]						=  torch.tensor(A      )
         elif key_name.startswith('model/moe'      ):
            _a					:  List[str]						=  int(key_name[9:].split('/'      )[0]      )
            if key_name.endswith('/switch_gating/kernel'      ):
               _a					:  str						=  'model.blocks.%d.feed_forward.mlp.router.classifier.weight' % player
               _a					:  List[str]						=  vnp.transpose([1, 0]      ).copy()  # Mesh-Tensorflow is a diagonal matrix
               _a					:  int						=  torch.tensor(A      )
            elif key_name.endswith('/softmlp/kernel'      ):
               _a					:  Optional[int]						=  'model.blocks.%d.feed_forward.soft_bypass_mlp.weight' % player
               _a					:  Dict						=  vnp.transpose([1, 0]      ).copy()  # Mesh-Tensorflow is a diagonal matrix
               _a					:  Tuple						=  torch.tensor(A      )
            elif key_name.endswith('/wo/kernel'      ) or key_name.endswith('/wi/kernel'      ):
               _a					:  Union[str, Any]						=  key_name[-9:-7]
               for i in range(1_6      ):
                  _a					:  List[Any]						=  'model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight' % (player, i, nlayer)
                  _a					:  Optional[int]						=  (
                      vnp[i].transpose([1, 0]      ).copy()
                  )  # In Mesh-Tensorflow, it is one array, so it is divided
                  _a					:  Any						=  torch.tensor(A      )
         elif key_name.startswith('model/mlp'      ):
            _a					:  Optional[Any]						=  int(key_name[9:].split('/'      )[0]      )
            if key_name.endswith('/p1/kernel'      ):
               _a					:  Optional[int]						=  'model.blocks.%d.feed_forward.mlp.wi.weight' % player
               _a					:  List[Any]						=  vnp.transpose([1, 0]      ).copy()  # Mesh-Tensorflow is a diagonal matrix
               _a					:  Tuple						=  torch.tensor(A      )
            elif key_name.endswith('/p1/bias'      ):
               _a					:  str						=  'model.blocks.%d.feed_forward.mlp.wi.bias' % player
               _a					:  Optional[int]						=  vnp.copy()  # same because it is one dimensional
               _a					:  Tuple						=  torch.tensor(A      )
            elif key_name.endswith('/p2/kernel'      ):
               _a					:  Tuple						=  'model.blocks.%d.feed_forward.mlp.wo.weight' % player
               _a					:  List[str]						=  vnp.transpose([1, 0]      ).copy()  # Mesh-Tensorflow is a diagonal matrix
               _a					:  Dict						=  torch.tensor(A      )
            elif key_name.endswith('/p2/bias'      ):
               _a					:  Tuple						=  'model.blocks.%d.feed_forward.mlp.wo.bias' % player
               _a					:  Optional[Any]						=  vnp.copy()  # same because it is one dimensional
               _a					:  Any						=  torch.tensor(A      )
         elif key_name.startswith('model/ln'      ):
            _a					:  str						=  int(key_name[8:].split('/'      )[0]      )
            if key_name.endswith('/b'      ):
               _a					:  Optional[int]						=  'model.blocks.%d.feed_forward.norm.bias' % player
               _a					:  List[str]						=  vnp.copy()  # same because it is one dimensional
               _a					:  Union[str, Any]						=  torch.tensor(A      )
            elif key_name.endswith('/g'      ):
               _a					:  Optional[int]						=  'model.blocks.%d.feed_forward.norm.weight' % player
               _a					:  str						=  vnp.copy()  # same because it is one dimensional
               _a					:  List[Any]						=  torch.tensor(A      )
         elif key_name.startswith('model/att'      ):
            _a					:  Optional[int]						=  int(key_name[9:].split('/'      )[0]      )
            if key_name.endswith('/qkv/kernel'      ):
               _a					:  Any						=  vnp.copy()  # Compute same dimension as Mesh-tensorflow using einsum
               _a					:  Dict						=  state[:, 0, :, :]
               _a					:  str						=  state[:, 1, :, :]
               _a					:  Dict						=  state[:, 2, :, :]
               _a					:  Optional[int]						=  (
                   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
               _a					:  Optional[int]						=  (
                   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
               _a					:  str						=  (
                   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
               _a					:  Union[str, Any]						=  'model.blocks.%d.self_attn.self_attn.q_proj.weight' % player
               _a					:  int						=  torch.tensor(A      )
               _a					:  List[Any]						=  'model.blocks.%d.self_attn.self_attn.k_proj.weight' % player
               _a					:  Dict						=  torch.tensor(A      )
               _a					:  Tuple						=  'model.blocks.%d.self_attn.self_attn.v_proj.weight' % player
               _a					:  List[Any]						=  torch.tensor(A      )
            elif key_name.endswith('/o/kernel'      ):
               _a					:  Tuple						=  'model.blocks.%d.self_attn.self_attn.out_proj.weight' % player
               _a					:  List[str]						=  (
                   vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]]      ).transpose([1, 0]      ).copy()
               )  # Mesh-Tensorflow is a diagonal matrix
               _a					:  Union[str, Any]						=  torch.tensor(A      )
         elif key_name.startswith('model/an'      ):
            _a					:  Union[str, Any]						=  int(key_name[8:].split('/'      )[0]      )
            if key_name.endswith('/b'      ):
               _a					:  Union[str, Any]						=  'model.blocks.%d.self_attn.norm.bias' % player
               _a					:  Optional[int]						=  vnp.copy()  # same because it is one dimensional
               _a					:  int						=  torch.tensor(A      )
            elif key_name.endswith('/g'      ):
               _a					:  List[str]						=  'model.blocks.%d.self_attn.norm.weight' % player
               _a					:  Union[str, Any]						=  vnp.copy()  # same because it is one dimensional
               _a					:  Dict						=  torch.tensor(A      )
         elif (
             key_name.startswith('model/wte'      )
             or key_name.startswith('model/wpe'      )
             or key_name.startswith('model/ete'      )
         ):
            _a					:  List[Any]						=  {'wte': 'embed_tokens', 'wpe': 'position_embeddings', 'ete': 'extra_position_embeddings'}[
                key_name[-3:]
            ]
            _a					:  Union[str, Any]						=  'model.%s.weight' % nlayer
            _a					:  str						=  vnp.copy()  # same in embedded
            _a					:  Tuple						=  torch.tensor(A      )
            if key_name.startswith('model/wte'      ):
               _a					:  str						=  'lm_head.weight'
               _a					:  Optional[int]						=  vnp.copy()  # same in embedded
               _a					:  Union[str, Any]						=  torch.tensor(A      )
         elif key_name.startswith('model/wob'      ):
            _a					:  Dict						=  'final_logits_bias'
            _a					:  List[Any]						=  vnp.copy()  # same in embedded
            _a					:  Optional[int]						=  state.reshape((1, -1)      )
            _a					:  int						=  torch.tensor(A      )
         elif key_name == "model/dense/kernel":
            _a					:  Optional[Any]						=  'model.last_project.weight'
            _a					:  Union[str, Any]						=  vnp.transpose([1, 0]      ).copy()  # Mesh-Tensorflow is a diagonal matrix
            _a					:  Any						=  torch.tensor(A      )
         elif key_name == "model/dense_1/bias":
            _a					:  Tuple						=  'model.last_project.bias'
            _a					:  Any						=  vnp.copy()  # same because it is one dimensional
            _a					:  Optional[Any]						=  torch.tensor(A      )
   torch.save(A		,	args.output      )


if __name__ == "__main__":
 UpperCAmelCase_      :		Tuple          =		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")
 UpperCAmelCase_      :		List[str]          =		parser.parse_args()
 convert_tf_gptsan_to_pt(args)






'''simple docstring'''




import warnings

from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor


UpperCAmelCase_      :		Tuple          =		logging.get_logger(__name__)







class 					a			(				snake_case__  ):
 '''simple docstring'''




 def __init__(    self		,      *lowerCamelCase_		,      **lowerCamelCase_      )      ->    None:
    warnings.warn(
        'The class PerceiverFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
        ' Please use PerceiverImageProcessor instead.'		,      lowerCamelCase_		,      )
    super().__init__(*lowerCamelCase_		,      **lowerCamelCase_      )


