

import os
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():
       import torch

       from transformers import GPTaLMHeadModel



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

  @slow
  @unittest.skip('Model is not available.')
  def   lowerCAmelCase__   (    self):
      SCREAMING_SNAKE_CASE_													=  'nvidia/megatron-gpt2-345m/'
      if "MYDIR" in os.environ:
          SCREAMING_SNAKE_CASE_													=  os.path.join(os.environ['MYDIR']  ,						_A)
      SCREAMING_SNAKE_CASE_													=  GPTaLMHeadModel.from_pretrained(_A)
      model.to(_A)
      model.half()

      SCREAMING_SNAKE_CASE_													=  torch.tensor(
          [[101, 7110, 1005, 1056, 2023, 11333, 17413, 1029, 102]]  ,						device=_A  ,						dtype=torch.long  ,						)

      with torch.no_grad():
          SCREAMING_SNAKE_CASE_													=  model(_A).logits

      SCREAMING_SNAKE_CASE_													=  torch.Size((1, 9, 50257))
      self.assertEqual(output.shape  ,						_A)

      SCREAMING_SNAKE_CASE_													=  torch.tensor(
          [
              4.9_4_1_4,
              -0.2_9_2_0,
              -1.2_1_4_8,
              -4.0_2_7_3,
              -0.5_1_6_1,
              -5.2_1_0_9,
              -1.2_4_1_2,
              -1.8_3_0_1,
              -1.7_7_3_4,
              -4.7_1_4_8,
              -0.2_3_1_7,
              -1.0_8_1_1,
              -2.1_7_7_7,
              0.4_1_4_1,
              -3.7_9_6_9,
              -4.0_5_8_6,
              -2.5_3_3_2,
              -3.3_8_0_9,
              4.3_8_6_7,
          ]  ,						device=_A  ,						dtype=torch.half  ,						)

      for i in range(19):
          SCREAMING_SNAKE_CASE_							,	SCREAMING_SNAKE_CASE_													=  8 * i // 17, 2792 * i  # along the diagonal
          SCREAMING_SNAKE_CASE_							,	SCREAMING_SNAKE_CASE_													=  output[0, r, c], expected_diag[i]
          SCREAMING_SNAKE_CASE_													=  f"""row={r} col={c} computed={computed} expected={expected}"""
          self.assertAlmostEqual(_A  ,						_A  ,						delta=1E-4  ,						msg=_A)





import argparse
import json

from transformers import GPTNeoConfig, GPTNeoForCausalLM, load_tf_weights_in_gpt_neo
from transformers.utils import logging


logging.set_verbosity_info()


def     _UpperCAmelCase				(     _SCREAMING_SNAKE_CASE    :						int       ,      _SCREAMING_SNAKE_CASE    :						Tuple       ,      _SCREAMING_SNAKE_CASE    :						Union[str, Any]				):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_													=  json.load(open(_SCREAMING_SNAKE_CASE       ,      'r'				)				)
    SCREAMING_SNAKE_CASE_													=  GPTNeoConfig(
        hidden_size=config_json['n_embd']       ,      num_layers=config_json['n_layer']       ,      num_heads=config_json['n_head']       ,      attention_types=config_json['attention_types']       ,      max_position_embeddings=config_json['n_positions']       ,      resid_dropout=config_json['res_dropout']       ,      embed_dropout=config_json['embed_dropout']       ,      attention_dropout=config_json['attn_dropout']       ,      )
    print(f"""Building PyTorch model from configuration: {config}"""				)
    SCREAMING_SNAKE_CASE_													=  GPTNeoForCausalLM(_SCREAMING_SNAKE_CASE				)

    # Load weights from tf checkpoint
    load_tf_weights_in_gpt_neo(_SCREAMING_SNAKE_CASE       ,      _SCREAMING_SNAKE_CASE       ,      _SCREAMING_SNAKE_CASE				)

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


if __name__ == "__main__":
       UpperCamelCase__     :					Tuple								=			argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
       )
       parser.add_argument(
           "--config_file",
           default=None,
           type=str,
           required=True,
           help=(
               "The config json file corresponding to the pre-trained mesh-tf model. \n"
               "This specifies the model architecture."
           ),
       )
       parser.add_argument(
           "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
       )
       UpperCamelCase__     :					Any								=			parser.parse_args()
       convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)


