




import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import require_keras_nlp, require_tf, slow


if is_tf_available():
     import tensorflow as tf

if is_keras_nlp_available():
     from transformers.models.gpta import TFGPTaTokenizer


_UpperCAmelCase	:						Optional[Any]			=     ["gpt2"]
_UpperCAmelCase	:						Optional[Any]			=     "gpt2"

if is_tf_available():




     class    __lowerCAmelCase       (							tf.Module):






            def __init__(   self:					Dict       ,					_lowerCAmelCase:					List[str]				):
              super().__init__()
              lowercase    :List[str]      	=						tokenizer
              lowercase    :Any      	=						AutoConfig.from_pretrained(_lowerCAmelCase				)
              lowercase    :List[str]      	=						TFGPTaLMHeadModel.from_config(_lowerCAmelCase				)






            @tf.function(input_signature=(tf.TensorSpec((None,)       ,					tf.string       ,					name="text"				),)				)
            def 					SCREAMING_SNAKE_CASE    (   self:					List[Any]       ,					_lowerCAmelCase:					str				):
              lowercase    :Any      	=						self.tokenizer(_lowerCAmelCase				)
              lowercase    :Tuple      	=						tokenized["input_ids"].to_tensor()

              lowercase    :List[Any]      	=						tf.cast(input_ids_dense > 0       ,					tf.intaa				)
              # input_mask = tf.reshape(input_mask, [-1, MAX_SEQ_LEN])

              lowercase    :Union[str, Any]      	=						self.model(input_ids=_lowerCAmelCase       ,					attention_mask=_lowerCAmelCase				)["logits"]

              return outputs




@require_tf
@require_keras_nlp
class    __lowerCAmelCase       (							unittest.TestCase):






       def 					SCREAMING_SNAKE_CASE    (   self:					str				):
         super().setUp()

         lowercase    :List[str]      	=						[GPTaTokenizer.from_pretrained(_lowerCAmelCase				) for checkpoint in (TOKENIZER_CHECKPOINTS)]
         lowercase    :Tuple      	=						[TFGPTaTokenizer.from_pretrained(_lowerCAmelCase				) for checkpoint in TOKENIZER_CHECKPOINTS]
         assert len(self.tokenizers				) == len(self.tf_tokenizers				)

         lowercase    :Optional[Any]      	=						[
             "This is a straightforward English test sentence.",
             "This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.",
             "Now we're going to add some Chinese: 一 二 三 一二三",
             "And some much more rare Chinese: 齉 堃 齉堃",
             "Je vais aussi écrire en français pour tester les accents",
             "Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ",
         ]
         lowercase    :Optional[Any]      	=						list(zip(self.test_sentences       ,					self.test_sentences[::-1]				)				)






       def 					SCREAMING_SNAKE_CASE    (   self:					Dict				):
         for tokenizer, tf_tokenizer in zip(self.tokenizers       ,					self.tf_tokenizers				):
           for test_inputs in self.test_sentences:
             lowercase    :Any      	=						tokenizer([test_inputs]       ,					return_tensors="tf"				)
             lowercase    :List[str]      	=						tf_tokenizer([test_inputs]				)

             for key in python_outputs.keys():
               # convert them to numpy to avoid messing with ragged tensors
               lowercase    :List[Any]      	=						python_outputs[key].numpy()
               lowercase    :str      	=						tf_outputs[key].numpy()

               self.assertTrue(tf.reduce_all(python_outputs_values.shape == tf_outputs_values.shape				)				)
               self.assertTrue(tf.reduce_all(tf.cast(_lowerCAmelCase       ,					tf.intaa				) == tf_outputs_values				)				)






       @slow
       def 					SCREAMING_SNAKE_CASE    (   self:					Dict				):
         for tf_tokenizer in self.tf_tokenizers:
           lowercase    :Any      	=						tf.function(_lowerCAmelCase				)
           for test_inputs in self.test_sentences:
             lowercase    :Dict      	=						tf.constant(_lowerCAmelCase				)
             lowercase    :str      	=						compiled_tokenizer(_lowerCAmelCase				)
             lowercase    :Optional[Any]      	=						tf_tokenizer(_lowerCAmelCase				)

             for key in eager_outputs.keys():
               self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]				)				)






       @slow
       def 					SCREAMING_SNAKE_CASE    (   self:					Dict				):
         for tf_tokenizer in self.tf_tokenizers:
           lowercase    :List[str]      	=						ModelToSave(tokenizer=_lowerCAmelCase				)
           lowercase    :Tuple      	=						tf.convert_to_tensor([self.test_sentences[0]]				)
           lowercase    :Optional[Any]      	=						model.serving(_lowerCAmelCase				)  # Build model with some sample inputs
           with TemporaryDirectory() as tempdir:
             lowercase    :Optional[Any]      	=						Path(_lowerCAmelCase				) / "saved.model"
             tf.saved_model.save(_lowerCAmelCase       ,					_lowerCAmelCase       ,					signatures={"serving_default": model.serving}				)
             lowercase    :Optional[Any]      	=						tf.saved_model.load(_lowerCAmelCase				)
           lowercase    :List[str]      	=						loaded_model.signatures["serving_default"](_lowerCAmelCase				)["output_0"]
           # We may see small differences because the loaded model is compiled, so we need an epsilon for the test
           self.assertTrue(tf.reduce_all(out == loaded_output				)				)






       @slow
       def 					SCREAMING_SNAKE_CASE    (   self:					Dict				):
         for tf_tokenizer in self.tf_tokenizers:
           lowercase    :Dict      	=						tf.convert_to_tensor([self.test_sentences[0]]				)
           lowercase    :Optional[int]      	=						tf_tokenizer(_lowerCAmelCase				)  # Build model with some sample inputs

           lowercase    :List[str]      	=						tf_tokenizer.get_config()
           lowercase    :Any      	=						TFGPTaTokenizer.from_config(_lowerCAmelCase				)
           lowercase    :Optional[int]      	=						model_from_config(_lowerCAmelCase				)

           for key in from_config_output.keys():
             self.assertTrue(tf.reduce_all(from_config_output[key] == out[key]				)				)






       @slow
       def 					SCREAMING_SNAKE_CASE    (   self:					str				):
         for tf_tokenizer in self.tf_tokenizers:
           # for the test to run
           lowercase    :List[str]      	=						12_31_23

           for max_length in [3, 5, 10_24]:
             lowercase    :Optional[int]      	=						tf.convert_to_tensor([self.test_sentences[0]]				)
             lowercase    :str      	=						tf_tokenizer(_lowerCAmelCase       ,					max_length=_lowerCAmelCase				)

             lowercase    :str      	=						out["input_ids"].numpy().shape[1]

             assert out_length == max_length









import argparse
import shlex

import runhouse as rh


if __name__ == "__main__":
     # Refer to https://runhouse-docs.readthedocs-hosted.com/en/latest/api/python/cluster.html#hardware-setup for cloud access
     # setup instructions, if using on-demand hardware

     # If user passes --user <user> --host <host> --key_path <key_path> <example> <args>, fill them in as BYO cluster
     # If user passes --instance <instance> --provider <provider> <example> <args>, fill them in as on-demand cluster
     # Throw an error if user passes both BYO and on-demand cluster args
     # Otherwise, use default values
     _UpperCAmelCase	:						Optional[Any]			=     argparse.ArgumentParser()
     parser.add_argument("--user", type=str, default="ubuntu")
     parser.add_argument("--host", type=str, default="localhost")
     parser.add_argument("--key_path", type=str, default=None)
     parser.add_argument("--instance", type=str, default="V100:1")
     parser.add_argument("--provider", type=str, default="cheapest")
     parser.add_argument("--use_spot", type=bool, default=False)
     parser.add_argument("--example", type=str, default="pytorch/text-generation/run_generation.py")
     _UpperCAmelCase			,			_UpperCAmelCase	:						Dict			=     parser.parse_known_args()
     if args.host != "localhost":
          if args.instance != "V100:1" or args.provider != "cheapest":
               raise ValueError("Cannot specify both BYO and on-demand cluster args")
          _UpperCAmelCase	:						List[Any]			=     rh.cluster(
              name="rh-cluster", ips=[args.host], ssh_creds={"ssh_user": args.user, "ssh_private_key": args.key_path}
          )
     else:
          _UpperCAmelCase	:						Tuple			=     rh.cluster(
              name="rh-cluster", instance_type=args.instance, provider=args.provider, use_spot=args.use_spot
          )
     _UpperCAmelCase	:						Any			=     args.example.rsplit("/", 1)[0]

     # Set up remote environment
     cluster.install_packages(["pip:./"])  # Installs transformers from local source
     # Note transformers is copied into the home directory on the remote machine, so we can install from there
     cluster.run([f'''pip install -r transformers/examples/{example_dir}/requirements.txt'''])
     cluster.run(["pip install torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117"])

     # Run example. You can bypass the CLI wrapper and paste your own code here.
     cluster.run([f'''python transformers/examples/{args.example} {" ".join(shlex.quote(arg) for arg in unknown)}'''])

     # Alternatively, we can just import and run a training function (especially if there's no wrapper CLI):
     # from my_script... import train
     # reqs = ['pip:./', 'torch', 'datasets', 'accelerate', 'evaluate', 'tqdm', 'scipy', 'scikit-learn', 'tensorboard']
     # launch_train_gpu = rh.function(fn=train,
     #                                system=gpu,
     #                                reqs=reqs,
     #                                name='train_bert_glue')
     #
     # We can pass in arguments just like we would to a function:
     # launch_train_gpu(num_epochs = 3, lr = 2e-5, seed = 42, batch_size = 16
     #                  stream_logs=True)



