

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		:       Any						    =	"""gpt2"""

if is_tf_available():







					class 			lowercase    (	tf.Module      ):




									def __init__( self				,       snake_case	):
																super().__init__()
																snake_case_          =   tokenizer
																snake_case_          =   AutoConfig.from_pretrained(snake_case	)
																snake_case_          =   TFGPTaLMHeadModel.from_config(snake_case	)




									@tf.function(input_signature=(tf.TensorSpec((None,)				,       tf.string				,       name='text'	),)	)
									def    a							( self				,       snake_case	):
																snake_case_          =   self.tokenizer(snake_case	)
																snake_case_          =   tokenized['input_ids'].to_tensor()

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

																snake_case_          =   self.model(input_ids=snake_case				,       attention_mask=snake_case	)['logits']

																return outputs







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




				def    a							( self	):
											super().setUp()

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

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




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

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

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




				@slow
				def    a							( self	):
											for tf_tokenizer in self.tf_tokenizers:
																		snake_case_          =   tf.function(snake_case	)
																		for test_inputs in self.test_sentences:
																									snake_case_          =   tf.constant(snake_case	)
																									snake_case_          =   compiled_tokenizer(snake_case	)
																									snake_case_          =   tf_tokenizer(snake_case	)

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




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

																		snake_case_          =   tf_tokenizer.get_config()
																		snake_case_          =   TFGPTaTokenizer.from_config(snake_case	)
																		snake_case_          =   model_from_config(snake_case	)

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




				@slow
				def    a							( self	):
											for tf_tokenizer in self.tf_tokenizers:
																		# for the test to run
																		snake_case_          =   12_3123

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

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

																									assert out_length == max_length







from __future__ import annotations





def  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__				):







							'''simple docstring'''






							snake_case_          =   []
							create_all_state(1       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							[]       ,							UpperCamelCase__				)
							return result





def  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							):







							'''simple docstring'''






							if level == 0:
														total_list.append(current_list[:]				)
														return

							for i in range(UpperCamelCase__       ,							total_number - level + 2				):
														current_list.append(UpperCamelCase__				)
														create_all_state(i + 1       ,							UpperCamelCase__       ,							level - 1       ,							UpperCamelCase__       ,							UpperCamelCase__				)
														current_list.pop()





def  __lowerCamelCase     (			UpperCamelCase__				):







							'''simple docstring'''






							for i in total_list:
														print(*UpperCamelCase__				)


if __name__ == "__main__":
					_UpperCAmelCase		:       str						    =	4
					_UpperCAmelCase		:       Tuple						    =	2
					_UpperCAmelCase		:       Optional[int]						    =	generate_all_combinations(n, k)
					print_all_state(total_list)




