
'''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    :							Dict      =					["gpt2"]
lowercase    :							Any      =					"gpt2"

if is_tf_available():


					class 						__UpperCAmelCase      (					tf.Module						):


												def __init__(       self    ,   lowerCAmelCase_  ):





													"""simple docstring"""






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




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





													"""simple docstring"""






													_snake_case   						=		self.tokenizer(lowerCAmelCase_  )
													_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=lowerCAmelCase_    ,   attention_mask=lowerCAmelCase_  )['logits']

													return outputs


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


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								super().setUp()

								_snake_case   						=		[GPTaTokenizer.from_pretrained(lowerCAmelCase_  ) for checkpoint in (TOKENIZER_CHECKPOINTS)]
								_snake_case   						=		[TFGPTaTokenizer.from_pretrained(lowerCAmelCase_  ) 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  lowerCamelCase				(       self  ):





								"""simple docstring"""






								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(lowerCAmelCase_    ,   tf.intaa  ) == tf_outputs_values  )  )


							@slow
							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






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

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


							@slow
							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






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





								"""simple docstring"""






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

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

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




							@slow
							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								for tf_tokenizer in self.tf_tokenizers:
									# for the test to run
									_snake_case   						=		12_31_23

									for max_length in [3, 5, 10_24]:
										_snake_case   						=		tf.convert_to_tensor([self.test_sentences[0]]  )
										_snake_case   						=		tf_tokenizer(lowerCAmelCase_    ,   max_length=lowerCAmelCase_  )

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

										assert out_length == max_length



from collections import defaultdict

from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst





def 			lowerCamelCase_ (				):
				lowerCamelCase_	,    lowerCamelCase_						=			9, 1_4  # noqa: F841
				lowerCamelCase_						=			[
				    [0, 1, 4],
				    [0, 7, 8],
				    [1, 2, 8],
				    [7, 8, 7],
				    [7, 6, 1],
				    [2, 8, 2],
				    [8, 6, 6],
				    [2, 3, 7],
				    [2, 5, 4],
				    [6, 5, 2],
				    [3, 5, 1_4],
				    [3, 4, 9],
				    [5, 4, 1_0],
				    [1, 7, 1_1],
				]

				lowerCamelCase_						=			defaultdict(lowerCamelCase__    )
				for nodea, nodea, cost in edges:
								adjancency[nodea].append([nodea, cost]    )
								adjancency[nodea].append([nodea, cost]    )

				lowerCamelCase_						=			mst(lowerCamelCase__    )

				lowerCamelCase_						=			[
				    [7, 6, 1],
				    [2, 8, 2],
				    [6, 5, 2],
				    [0, 1, 4],
				    [2, 5, 4],
				    [2, 3, 7],
				    [0, 7, 8],
				    [3, 4, 9],
				]

				for answer in expected:
								lowerCamelCase_						=			tuple(answer[:2]    )
								lowerCamelCase_						=			tuple(edge[::-1]    )
								assert edge in result or reverse in result


