
import tempfile
import unittest

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
    is_torch_available,
    require_optimum,
    require_torch,
    slow,
)


if is_torch_available():
							import torch







@require_torch
@require_optimum
@slow
class 				lowercase		(					unittest.TestCase		):




							def  A__      (				self):
												lowercase							=						'''hf-internal-testing/tiny-random-t5'''
												lowercase							=						AutoTokenizer.from_pretrained(lowerCAmelCase_)
												lowercase							=						AutoModelForSeqaSeqLM.from_pretrained(lowerCAmelCase_)

												lowercase							=						tokenizer('''This is me'''   ,return_tensors='''pt''')

												lowercase							=						model.to_bettertransformer()

												self.assertTrue(any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model.named_modules()))

												lowercase							=						model.generate(**lowerCAmelCase_)

												lowercase							=						model.reverse_bettertransformer()

												self.assertFalse(any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model.named_modules()))

												with tempfile.TemporaryDirectory() as tmpdirname:
																	model.save_pretrained(lowerCAmelCase_)

																	lowercase							=						AutoModelForSeqaSeqLM.from_pretrained(lowerCAmelCase_)

																	self.assertFalse(
																	    any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model_reloaded.named_modules()))

																	lowercase							=						model_reloaded.generate(**lowerCAmelCase_)
																	self.assertTrue(torch.allclose(lowerCAmelCase_   ,lowerCAmelCase_))





							def  A__      (				self):
												lowercase							=						'''hf-internal-testing/tiny-random-t5'''
												lowercase							=						AutoModelForSeqaSeqLM.from_pretrained(lowerCAmelCase_)

												lowercase							=						model.to_bettertransformer()

												with tempfile.TemporaryDirectory() as tmpdirname:
																	with self.assertRaises(lowerCAmelCase_):
																						model.save_pretrained(lowerCAmelCase_)

																	lowercase							=						model.reverse_bettertransformer()
																	model.save_pretrained(lowerCAmelCase_)





import argparse

import torch

from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert


logging.set_verbosity_info()






def  snake_case  (       snake_case__			:int   ,	snake_case__			:List[str]   ,	snake_case__			:Union[str, Any])						->   str:
       # Initialise PyTorch model
       _A        =  AlbertConfig.from_json_file(snake_case__)
       print(F'''Building PyTorch model from configuration: {config}''')
       _A        =  AlbertForPreTraining(snake_case__)

       # Load weights from tf checkpoint
       load_tf_weights_in_albert(snake_case__   ,	snake_case__   ,	snake_case__)

       # Save pytorch-model
       print(F'''Save PyTorch model to {pytorch_dump_path}''')
       torch.save(model.state_dict()   ,	snake_case__)


if __name__ == "__main__":
    _SCREAMING_SNAKE_CASE										=						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(
        '--albert_config_file',
        default=None,
        type=str,
        required=True,
        help=(
            'The config json file corresponding to the pre-trained ALBERT 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.'
    )
    _SCREAMING_SNAKE_CASE										=						parser.parse_args()
    convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)




