




import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch


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


if __name__ == "__main__":
   snake_case__			:    Optional[int]			     =      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''')
   snake_case__			:    Dict			     =      parser.parse_args()
   convert_tf_gptsan_to_pt(args)







"""simple docstring"""
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
    from transformers import AutoModelForSeqaSeqLM, AutoTokenizer

@require_torch
@require_sentencepiece
@require_tokenizers
class       lowerCamelCase	(       unittest.TestCase   ):


  '''simple docstring'''







  @slow
  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							Dict							=    AutoModelForSeqaSeqLM.from_pretrained("""google/mt5-small"""							,       return_dict=_lowerCamelCase	).to(_lowerCamelCase	)
        UpperCAmelCase__			:							Optional[Any]							=    AutoTokenizer.from_pretrained("""google/mt5-small"""	)

        UpperCAmelCase__			:							Any							=    tokenizer("""Hello there"""							,       return_tensors="""pt"""	).input_ids
        UpperCAmelCase__			:							str							=    tokenizer("""Hi I am"""							,       return_tensors="""pt"""	).input_ids

        UpperCAmelCase__			:							Tuple							=    model(input_ids.to(_lowerCamelCase	)							,       labels=labels.to(_lowerCamelCase	)	).loss
        UpperCAmelCase__			:							Union[str, Any]							=    -(labels.shape[-1] * loss.item())

        UpperCAmelCase__			:							List[str]							=    -84.9_127
        self.assertTrue(abs(mtf_score - EXPECTED_SCORE	) < 1e-4	)

