





"""simple docstring"""





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 						SCREAMING_SNAKE_CASE_			(			self   :List[str]						):
       __SCREAMING_SNAKE_CASE :			List[Any]												=       '''nvidia/megatron-gpt2-345m/'''
       if "MYDIR" in os.environ:
        __SCREAMING_SNAKE_CASE :			Tuple												=       os.path.join(os.environ['''MYDIR''']						,							_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       GPTaLMHeadModel.from_pretrained(_lowerCamelCase						)
       model.to(_lowerCamelCase						)
       model.half()

       __SCREAMING_SNAKE_CASE :			Optional[int]												=       torch.tensor(
           [[1_0_1, 7_1_1_0, 1_0_0_5, 1_0_5_6, 2_0_2_3, 1_1_3_3_3, 1_7_4_1_3, 1_0_2_9, 1_0_2]]						,							device=_lowerCamelCase						,							dtype=torch.long						,							)

       with torch.no_grad():
        __SCREAMING_SNAKE_CASE :			Dict												=       model(_lowerCamelCase						).logits

       __SCREAMING_SNAKE_CASE :			Any												=       torch.Size((1, 9, 5_0_2_5_7)						)
       self.assertEqual(output.shape						,							_lowerCamelCase						)

       __SCREAMING_SNAKE_CASE :			Dict												=       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=_lowerCamelCase						,							dtype=torch.half						,							)

       for i in range(1_9						):
        __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE :			List[Any]												=       8 * i // 1_7, 2_7_9_2 * i  # along the diagonal
        __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE :			Dict												=       output[0, r, c], expected_diag[i]
        __SCREAMING_SNAKE_CASE :			Dict												=       f'''row={r} col={c} computed={computed} expected={expected}'''
        self.assertAlmostEqual(_lowerCamelCase						,							_lowerCamelCase						,							delta=1e-4						,							msg=_lowerCamelCase						)








"""simple docstring"""





import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()




def 		lowerCAmelCase_	(  lowercase_   :							List[str] ,      lowercase_   :							List[str] ,      lowercase_   :							Dict		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE :			Tuple												=       TaConfig.from_json_file(lowercase_		)
 print(F'''Building PyTorch model from configuration: {config}'''		)
 __SCREAMING_SNAKE_CASE :			List[str]												=       TaForConditionalGeneration(lowercase_		)

 # Load weights from tf checkpoint
 load_tf_weights_in_ta(lowercase_ ,      lowercase_ ,      lowercase_		)

 # Save pytorch-model
 print(F'''Save PyTorch model to {pytorch_dump_path}'''		)
 model.save_pretrained(lowercase_		)


if __name__ == "__main__":
    _lowerCamelCase       =	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 T5 model. \nThis specifies the model architecture.'''
        ),
    )
    parser.add_argument(
        '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
    )
    _lowerCamelCase       =	parser.parse_args()
    convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)

