






"""simple docstring"""

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


lowercase    			=       ['''gpt2''']
lowercase    			=       '''gpt2'''

if is_tf_available():

			class 						A_							(      tf.Module  ):







							def __init__(							self							:						int						,    __lowerCamelCase							:						int	)				->					str:
													super().__init__()
													__magic_name__            =     tokenizer
													__magic_name__            =     AutoConfig.from_pretrained(UpperCAmelCase_	)
													__magic_name__            =     TFGPTaLMHeadModel.from_config(UpperCAmelCase_	)







							@tf.function(input_signature=(tf.TensorSpec((None,)						,    tf.string						,    name="text"	),)	)
							def     _snake_case					(							self							:						Any						,    __lowerCamelCase							:						int	)				->					List[str]:
													__magic_name__            =     self.tokenizer(UpperCAmelCase_	)
													__magic_name__            =     tokenized['input_ids'].to_tensor()

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

													__magic_name__            =     self.model(input_ids=UpperCAmelCase_						,    attention_mask=UpperCAmelCase_	)['logits']

													return outputs

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







				def     _snake_case					(							self							:						int	)				->					str:
										super().setUp()

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

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







				def     _snake_case					(							self							:						str	)				->					List[Any]:
										for tokenizer, tf_tokenizer in zip(self.tokenizers						,    self.tf_tokenizers	):
																for test_inputs in self.test_sentences:
																						__magic_name__            =     tokenizer([test_inputs]						,    return_tensors="tf"	)
																						__magic_name__            =     tf_tokenizer([test_inputs]	)

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

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







				@slow
				def     _snake_case					(							self							:						List[Any]	)				->					int:
										for tf_tokenizer in self.tf_tokenizers:
																__magic_name__            =     tf.function(UpperCAmelCase_	)
																for test_inputs in self.test_sentences:
																						__magic_name__            =     tf.constant(UpperCAmelCase_	)
																						__magic_name__            =     compiled_tokenizer(UpperCAmelCase_	)
																						__magic_name__            =     tf_tokenizer(UpperCAmelCase_	)

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







				@slow
				def     _snake_case					(							self							:						Union[str, Any]	)				->					List[Any]:
										for tf_tokenizer in self.tf_tokenizers:
																__magic_name__            =     ModelToSave(tokenizer=UpperCAmelCase_	)
																__magic_name__            =     tf.convert_to_tensor([self.test_sentences[0]]	)
																__magic_name__            =     model.serving(UpperCAmelCase_	)  # Build model with some sample inputs
																with TemporaryDirectory() as tempdir:
																						__magic_name__            =     Path(UpperCAmelCase_	) / 'saved.model'
																						tf.saved_model.save(UpperCAmelCase_						,    UpperCAmelCase_						,    signatures={"serving_default": model.serving}	)
																						__magic_name__            =     tf.saved_model.load(UpperCAmelCase_	)
																__magic_name__            =     loaded_model.signatures['serving_default'](UpperCAmelCase_	)['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     _snake_case					(							self							:						Dict	)				->					List[str]:
										for tf_tokenizer in self.tf_tokenizers:
																__magic_name__            =     tf.convert_to_tensor([self.test_sentences[0]]	)
																__magic_name__            =     tf_tokenizer(UpperCAmelCase_	)  # Build model with some sample inputs

																__magic_name__            =     tf_tokenizer.get_config()
																__magic_name__            =     TFGPTaTokenizer.from_config(UpperCAmelCase_	)
																__magic_name__            =     model_from_config(UpperCAmelCase_	)

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







				@slow
				def     _snake_case					(							self							:						List[Any]	)				->					List[Any]:
										for tf_tokenizer in self.tf_tokenizers:
																# for the test to run
																__magic_name__            =     1_2_3_1_2_3

																for max_length in [3, 5, 1_0_2_4]:
																						__magic_name__            =     tf.convert_to_tensor([self.test_sentences[0]]	)
																						__magic_name__            =     tf_tokenizer(UpperCAmelCase_						,    max_length=UpperCAmelCase_	)

																						__magic_name__            =     out['input_ids'].numpy().shape[1]

																						assert out_length == max_length














"""simple docstring"""

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



