




import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch



def      __lowerCAmelCase			(					UpperCamelCase      )     ->	Union[str, Any]:

  '''simple docstring'''





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


if __name__ == "__main__":
     lowerCAmelCase_					    = 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""")
     lowerCAmelCase_					    = parser.parse_args()
     convert_tf_gptsan_to_pt(args)







from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
     import numpy as np
     import tensorflow as tf

     from transformers import TFCamembertModel






@require_tf
@require_sentencepiece
@require_tokenizers
class  _lowerCAmelCase     (  unittest.TestCase	):

      @slow
      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  Optional[Any]		       =     TFCamembertModel.from_pretrained('''jplu/tf-camembert-base'''		)

        lowerCAmelCase__				:  List[str]		       =     tf.convert_to_tensor(
            [[5, 121, 11, 660, 16, 730, 2_5543, 110, 83, 6]]      ,				dtype=tf.intaa      ,				)  # J'aime le camembert !"

        lowerCAmelCase__				:  str		       =     model(__UpperCAmelCase		)['''last_hidden_state''']
        lowerCAmelCase__				:  List[Any]		       =     tf.TensorShape((1, 10, 768)		)
        self.assertEqual(output.shape      ,				__UpperCAmelCase		)
        # compare the actual values for a slice.
        lowerCAmelCase__				:  int		       =     tf.convert_to_tensor(
            [[[-0.0254, 0.0235, 0.1027], [0.0606, -0.1811, -0.0418], [-0.1561, -0.1127, 0.2687]]]      ,				dtype=tf.floataa      ,				)
        # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
        # camembert.eval()
        # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

        self.assertTrue(np.allclose(output[:, :3, :3].numpy()      ,				expected_slice.numpy()      ,				atol=1e-4		)		)

