





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


_SCREAMING_SNAKE_CASE					=    ["gpt2"]
_SCREAMING_SNAKE_CASE					=    "gpt2"

if is_tf_available():






	class 							__lowercase       (      tf.Module						):







		'''simple docstring'''

		def __init__(self     ,_lowerCamelCase )							->							Tuple:







					'''simple docstring'''





					super().__init__()
					__lowercase										=							tokenizer
					__lowercase										=							AutoConfig.from_pretrained(A_ )
					__lowercase										=							TFGPTaLMHeadModel.from_config(A_ )


		@tf.function(input_signature=(tf.TensorSpec((None,)     ,tf.string     ,name='''text''' ),) )
		def 						_UpperCAmelCase     (self     ,_lowerCamelCase )							->							Any:







					'''simple docstring'''





					__lowercase										=							self.tokenizer(A_ )
					__lowercase										=							tokenized['''input_ids'''].to_tensor()

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

					__lowercase										=							self.model(input_ids=A_     ,attention_mask=A_ )['''logits''']

					return outputs






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







	'''simple docstring'''

	def 						_UpperCAmelCase     (self )							->							Union[str, Any]:







				'''simple docstring'''





				super().setUp()

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

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


	def 						_UpperCAmelCase     (self )							->							Optional[int]:







				'''simple docstring'''





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

										for key in python_outputs.keys():
													# convert them to numpy to avoid messing with ragged tensors
													__lowercase										=							python_outputs[key].numpy()
													__lowercase										=							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 						_UpperCAmelCase     (self )							->							int:







				'''simple docstring'''





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

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


	@slow
	def 						_UpperCAmelCase     (self )							->							Dict:







				'''simple docstring'''





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







				'''simple docstring'''





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

							__lowercase										=							tf_tokenizer.get_config()
							__lowercase										=							TFGPTaTokenizer.from_config(A_ )
							__lowercase										=							model_from_config(A_ )

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


	@slow
	def 						_UpperCAmelCase     (self )							->							int:







				'''simple docstring'''





				for tf_tokenizer in self.tf_tokenizers:
							# for the test to run
							__lowercase										=							123123

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

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

										assert out_length == max_length












'''simple docstring'''



from argparse import ArgumentParser

from .env import EnvironmentCommand




def    _lowerCAmelCase						(				):
			__lowercase										=							ArgumentParser('''Diffusers CLI tool'''		,       usage='''diffusers-cli <command> [<args>]'''				)
			__lowercase										=							parser.add_subparsers(help='''diffusers-cli command helpers'''				)

			# Register commands
			EnvironmentCommand.register_subcommand(lowerCamelCase_				)

			# Let's go
			__lowercase										=							parser.parse_args()

			if not hasattr(lowerCamelCase_		,       '''func'''				):
						parser.print_help()
						exit(1				)

			# Run
			__lowercase										=							args.func(lowerCamelCase_				)
			service.run()


if __name__ == "__main__":
	main()





