




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


_A     =			['''gpt2''']
_A     =			'''gpt2'''

if is_tf_available():


						class  A		(  tf.Module     ):



								def __init__(     self,	UpperCamelCase__   ):







														"""simple docstring"""



														super().__init__()
														lowerCAmelCase_      			=  tokenizer
														lowerCAmelCase_      			=  AutoConfig.from_pretrained(a_   )
														lowerCAmelCase_      			=  TFGPTaLMHeadModel.from_config(a_   )







								@tf.function(input_signature=(tf.TensorSpec((None,),	tf.string,	name='''text'''   ),)   )
								def        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__   ):







														"""simple docstring"""



														lowerCAmelCase_      			=  self.tokenizer(a_   )
														lowerCAmelCase_      			=  tokenized['''input_ids'''].to_tensor()

														lowerCAmelCase_      			=  tf.cast(input_ids_dense > 0,	tf.intaa   )
														# input_mask = tf.reshape(input_mask, [-1, MAX_SEQ_LEN])

														lowerCAmelCase_      			=  self.model(input_ids=a_,	attention_mask=a_   )['''logits''']

														return outputs


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



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								super().setUp()

								lowerCAmelCase_      			=  [GPTaTokenizer.from_pretrained(a_   ) for checkpoint in (TOKENIZER_CHECKPOINTS)]
								lowerCAmelCase_      			=  [TFGPTaTokenizer.from_pretrained(a_   ) for checkpoint in TOKENIZER_CHECKPOINTS]
								assert len(self.tokenizers   ) == len(self.tf_tokenizers   )

								lowerCAmelCase_      			=  [
								    '''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''',
								]
								lowerCAmelCase_      			=  list(zip(self.test_sentences,	self.test_sentences[::-1]   )   )



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								for tokenizer, tf_tokenizer in zip(self.tokenizers,	self.tf_tokenizers   ):
														for test_inputs in self.test_sentences:
																				lowerCAmelCase_      			=  tokenizer([test_inputs],	return_tensors='''tf'''   )
																				lowerCAmelCase_      			=  tf_tokenizer([test_inputs]   )

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

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



		@slow
		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								for tf_tokenizer in self.tf_tokenizers:
														lowerCAmelCase_      			=  tf.function(a_   )
														for test_inputs in self.test_sentences:
																				lowerCAmelCase_      			=  tf.constant(a_   )
																				lowerCAmelCase_      			=  compiled_tokenizer(a_   )
																				lowerCAmelCase_      			=  tf_tokenizer(a_   )

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



		@slow
		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								for tf_tokenizer in self.tf_tokenizers:
														lowerCAmelCase_      			=  ModelToSave(tokenizer=a_   )
														lowerCAmelCase_      			=  tf.convert_to_tensor([self.test_sentences[0]]   )
														lowerCAmelCase_      			=  model.serving(a_   )  # Build model with some sample inputs
														with TemporaryDirectory() as tempdir:
																				lowerCAmelCase_      			=  Path(a_   ) / '''saved.model'''
																				tf.saved_model.save(a_,	a_,	signatures={'''serving_default''': model.serving}   )
																				lowerCAmelCase_      			=  tf.saved_model.load(a_   )
														lowerCAmelCase_      			=  loaded_model.signatures['''serving_default'''](a_   )['''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   ):







								"""simple docstring"""



								for tf_tokenizer in self.tf_tokenizers:
														lowerCAmelCase_      			=  tf.convert_to_tensor([self.test_sentences[0]]   )
														lowerCAmelCase_      			=  tf_tokenizer(a_   )  # Build model with some sample inputs

														lowerCAmelCase_      			=  tf_tokenizer.get_config()
														lowerCAmelCase_      			=  TFGPTaTokenizer.from_config(a_   )
														lowerCAmelCase_      			=  model_from_config(a_   )

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







		@slow
		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								for tf_tokenizer in self.tf_tokenizers:
														# for the test to run
														lowerCAmelCase_      			=  12_3123

														for max_length in [3, 5, 1024]:
																				lowerCAmelCase_      			=  tf.convert_to_tensor([self.test_sentences[0]]   )
																				lowerCAmelCase_      			=  tf_tokenizer(a_,	max_length=a_   )

																				lowerCAmelCase_      			=  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
				__A   			=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")
				__A			,			__A   			=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")
								__A   			=rh.cluster(
								    name="rh-cluster", ips=[args.host], ssh_creds={"ssh_user": args.user, "ssh_private_key": args.key_path}
								)
				else:
								__A   			=rh.cluster(
								    name="rh-cluster", instance_type=args.instance, provider=args.provider, use_spot=args.use_spot
								)
				__A   			=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)





