
'''simple docstring'''






from __future__ import annotations

import unittest

import numpy as np

from transformers import OPTConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
					import tensorflow as tf

					from transformers import GPTaTokenizer, TFOPTForCausalLM, TFOPTModel





def    UpperCAmelCase							(  UpperCAmelCase__							:							Dict       ,							UpperCAmelCase__							:							Dict       ,							UpperCAmelCase__							:							Optional[int]=None       ,							UpperCAmelCase__							:							Tuple=None):

				if attention_mask is None:
								lowerCamelCase					:			List[Any]									= tf.cast(tf.math.not_equal(UpperCAmelCase__       ,							config.pad_token_id)       ,							tf.inta)
				return {"input_ids": input_ids, "attention_mask": attention_mask}




@require_tf
class     __snake_case		:
					_lowerCAmelCase			     =	OPTConfig
					_lowerCAmelCase			     =	{}
					_lowerCAmelCase			     =	'''gelu'''
					def __init__(     self,    A,    A=13,    A=7,    A=True,    A=False,    A=99,    A=16,    A=2,    A=4,    A=4,    A="gelu",    A=0.1,    A=0.1,    A=20,    A=2,    A=1,    A=0,    A=16,    A=16,    ):

									"""simple docstring"""


									lowerCamelCase					:			Optional[Any]									= parent
									lowerCamelCase					:			Optional[int]									= batch_size
									lowerCamelCase					:			Tuple									= seq_length
									lowerCamelCase					:			Tuple									= is_training
									lowerCamelCase					:			int									= use_labels
									lowerCamelCase					:			List[str]									= vocab_size
									lowerCamelCase					:			Dict									= hidden_size
									lowerCamelCase					:			Optional[int]									= num_hidden_layers
									lowerCamelCase					:			Any									= num_attention_heads
									lowerCamelCase					:			Any									= intermediate_size
									lowerCamelCase					:			Tuple									= hidden_act
									lowerCamelCase					:			str									= hidden_dropout_prob
									lowerCamelCase					:			Optional[Any]									= attention_probs_dropout_prob
									lowerCamelCase					:			List[Any]									= max_position_embeddings
									lowerCamelCase					:			Any									= eos_token_id
									lowerCamelCase					:			Any									= pad_token_id
									lowerCamelCase					:			str									= bos_token_id
									lowerCamelCase					:			Optional[Any]									= embed_dim
									lowerCamelCase					:			Optional[int]									= word_embed_proj_dim
									lowerCamelCase					:			int									= False
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			int									= ids_tensor([self.batch_size, self.seq_length - 1],    self.vocab_size      )
									lowerCamelCase					:			Optional[int]									= tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size      ),    1      )
									lowerCamelCase					:			List[str]									= tf.concat([input_ids, eos_tensor],    axis=1      )

									lowerCamelCase					:			str									= self.config_cls(
									    vocab_size=self.vocab_size,    hidden_size=self.hidden_size,    num_hidden_layers=self.num_hidden_layers,    num_attention_heads=self.num_attention_heads,    ffn_dim=self.intermediate_size,    dropout=self.hidden_dropout_prob,    attention_dropout=self.attention_probs_dropout_prob,    max_position_embeddings=self.max_position_embeddings,    eos_token_id=self.eos_token_id,    bos_token_id=self.bos_token_id,    pad_token_id=self.pad_token_id,    embed_dim=self.embed_dim,    word_embed_proj_dim=self.word_embed_proj_dim,    is_encoder_decoder=__lowerCAmelCase,    **self.config_updates,    )
									lowerCamelCase					:			Tuple									= prepare_opt_inputs_dict(__lowerCAmelCase,    __lowerCAmelCase      )
									return config, inputs_dict




					def     UpperCAmelCase_							(     self,    A,    A      ):

									"""simple docstring"""


									lowerCamelCase					:			Any									= TFOPTModel(config=__lowerCAmelCase      )
									lowerCamelCase					:			Any									= inputs_dict['input_ids']

									lowerCamelCase					:			str									= input_ids[:1, :]
									lowerCamelCase					:			str									= inputs_dict['attention_mask'][:1, :]
									lowerCamelCase					:			str									= 1

									# first forward pass
									lowerCamelCase					:			Optional[Any]									= model(__lowerCAmelCase,    attention_mask=__lowerCAmelCase,    use_cache=__lowerCAmelCase      )

									lowerCamelCase			, lowerCamelCase					:			int									= outputs.to_tuple()

									# create hypothetical next token and extent to next_input_ids
									lowerCamelCase					:			Optional[Any]									= ids_tensor((self.batch_size, 3),    config.vocab_size      )
									lowerCamelCase					:			Tuple									= tf.cast(ids_tensor((self.batch_size, 3),    2      ),    tf.inta      )

									# append to next input_ids and
									lowerCamelCase					:			str									= tf.concat([input_ids, next_tokens],    axis=-1      )
									lowerCamelCase					:			str									= tf.concat([attention_mask, next_attn_mask],    axis=-1      )

									lowerCamelCase					:			Tuple									= model(__lowerCAmelCase,    attention_mask=__lowerCAmelCase      )[0]
									lowerCamelCase					:			Optional[int]									= model(__lowerCAmelCase,    attention_mask=__lowerCAmelCase,    past_key_values=__lowerCAmelCase      )[0]

									self.parent.assertEqual(next_tokens.shape[1],    output_from_past.shape[1]      )

									# select random slice
									lowerCamelCase					:			Optional[int]									= int(ids_tensor((1,),    output_from_past.shape[-1]      )      )
									lowerCamelCase					:			List[Any]									= output_from_no_past[:, -3:, random_slice_idx]
									lowerCamelCase					:			int									= output_from_past[:, :, random_slice_idx]

									# test that outputs are equal for slice
									tf.debugging.assert_near(__lowerCAmelCase,    __lowerCAmelCase,    rtol=1e-3      )





@require_tf
class     __snake_case		(   UpperCAmelCase__    ,   UpperCAmelCase__    ,   unittest.TestCase):
					_lowerCAmelCase			     =	(TFOPTModel, TFOPTForCausalLM) if is_tf_available() else ()
					_lowerCAmelCase			     =	(TFOPTForCausalLM,) if is_tf_available() else ()
					_lowerCAmelCase			     =	(
					    {'''feature-extraction''': TFOPTModel, '''text-generation''': TFOPTForCausalLM} if is_tf_available() else {}
					)
					_lowerCAmelCase			     =	False
					_lowerCAmelCase			     =	False
					_lowerCAmelCase			     =	False
					_lowerCAmelCase			     =	10
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			int									= TFOPTModelTester(self      )
									lowerCamelCase					:			Tuple									= ConfigTester(self,    config_class=__lowerCAmelCase      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									self.config_tester.run_common_tests()
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Optional[int]									= self.model_tester.prepare_config_and_inputs_for_common()
									self.model_tester.check_decoder_model_past_large_inputs(*__lowerCAmelCase      )




					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase			, lowerCamelCase					:			List[str]									= self.model_tester.prepare_config_and_inputs_for_common()

									def _get_word_embedding_weight(A,    A      ):
													if hasattr(__lowerCAmelCase,    'weight'      ):
																	return embedding_layer.weight
													else:
																	# Here we build the word embeddings weights if not exists.
																	# And then we retry to get the attribute once built.
																	model.build()
																	if hasattr(__lowerCAmelCase,    'weight'      ):
																					return embedding_layer.weight
																	else:
																					return None

									for model_class in self.all_model_classes:
													for size in [config.vocab_size - 10, config.vocab_size + 10]:
																	# build the embeddings
																	lowerCamelCase					:			Union[str, Any]									= model_class(config=__lowerCAmelCase      )
																	lowerCamelCase					:			str									= _get_word_embedding_weight(__lowerCAmelCase,    model.get_input_embeddings()      )
																	lowerCamelCase					:			int									= _get_word_embedding_weight(__lowerCAmelCase,    model.get_output_embeddings()      )

																	# reshape the embeddings
																	model.resize_token_embeddings(__lowerCAmelCase      )
																	lowerCamelCase					:			List[Any]									= _get_word_embedding_weight(__lowerCAmelCase,    model.get_input_embeddings()      )
																	lowerCamelCase					:			List[str]									= _get_word_embedding_weight(__lowerCAmelCase,    model.get_output_embeddings()      )

																	# check that the resized embeddings size matches the desired size.
																	lowerCamelCase					:			List[Any]									= size if size is not None else config.vocab_size

																	self.assertEqual(new_input_embeddings.shape[0],    __lowerCAmelCase      )

																	# check that weights remain the same after resizing
																	lowerCamelCase					:			Dict									= True
																	for pa, pa in zip(old_input_embeddings.value(),    new_input_embeddings.value()      ):
																					if tf.math.reduce_sum(tf.math.abs(pa - pa      )      ) > 0:
																									lowerCamelCase					:			List[str]									= False
																	self.assertTrue(__lowerCAmelCase      )

																	if old_output_embeddings is not None and new_output_embeddings is not None:
																					self.assertEqual(new_output_embeddings.shape[0],    __lowerCAmelCase      )

																					lowerCamelCase					:			Tuple									= True
																					for pa, pa in zip(old_output_embeddings.value(),    new_output_embeddings.value()      ):
																									if tf.math.reduce_sum(tf.math.abs(pa - pa      )      ) > 0:
																													lowerCamelCase					:			Tuple									= False
																					self.assertTrue(__lowerCAmelCase      )





def    UpperCAmelCase							(  UpperCAmelCase__							:							Union[str, Any]):
				return tf.constant(UpperCAmelCase__       ,							dtype=tf.intaa)




@require_tf
class     __snake_case		(   unittest.TestCase):
					_lowerCAmelCase			     =	99
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Any									= tf.ones((4, 1),    dtype=tf.intaa      ) * 2
									lowerCamelCase					:			Optional[Any]									= tf.concat([ids_tensor((4, 6),    self.vocab_size - 3      ) + 3, eos_column_vector],    axis=1      )
									lowerCamelCase					:			Tuple									= input_ids.shape[0]
									lowerCamelCase					:			Optional[Any]									= OPTConfig(
									    vocab_size=self.vocab_size,    hidden_size=24,    num_hidden_layers=2,    num_attention_heads=2,    ffn_dim=32,    max_position_embeddings=48,    eos_token_id=2,    pad_token_id=1,    bos_token_id=0,    )
									return config, input_ids, batch_size





@require_sentencepiece
@require_tf
class     __snake_case		(   unittest.TestCase):
					@slow
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			int									= TFOPTModel.from_pretrained('facebook/opt-350m'      )
									lowerCamelCase					:			Any									= _long_tensor([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]      )
									lowerCamelCase					:			Union[str, Any]									= tf.not_equal(__lowerCAmelCase,    model.config.pad_token_id      )
									with tf.GradientTape():
													lowerCamelCase					:			List[Any]									= model(input_ids=__lowerCAmelCase,    attention_mask=__lowerCAmelCase      ).last_hidden_state
									lowerCamelCase					:			Dict									= (1, 11, 512)
									self.assertEqual(output.shape,    __lowerCAmelCase      )
									lowerCamelCase					:			Dict									= tf.constant(
									    [[-0.2873, -1.9218, -0.3033], [-1.2710, -0.1338, -0.1902], [0.4095, 0.1214, -1.3121]]      )
									self.assertTrue(np.allclose(output[:, :3, :3],    __lowerCAmelCase,    atol=4e-3      )      )

									lowerCamelCase					:			List[Any]									= tf.function(__lowerCAmelCase,    jit_compile=__lowerCAmelCase      )
									lowerCamelCase					:			Tuple									= xla_generate(__lowerCAmelCase,    __lowerCAmelCase      )[0]
									self.assertTrue(np.allclose(output[:, :3, :3],    __lowerCAmelCase,    atol=4e-2      )      )





@require_tf
@slow
class     __snake_case		(   unittest.TestCase):
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									super().setUp()
									lowerCamelCase					:			Tuple									= 'facebook/opt-350m'




					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			List[str]									= TFOPTForCausalLM.from_pretrained(self.path_model      )
									lowerCamelCase					:			Dict									= GPTaTokenizer.from_pretrained(self.path_model      )

									lowerCamelCase					:			int									= [
									    'Today is a beautiful day and I want to',
									    'In the city of',
									    'Paris is the capital of France and',
									    'Computers and mobile phones have taken',
									]
									# verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
									lowerCamelCase					:			List[str]									= tokenizer(__lowerCAmelCase,    return_tensors='tf',    padding=__lowerCAmelCase,    add_special_tokens=__lowerCAmelCase      )
									lowerCamelCase					:			Any									= tf.math.reduce_mean(model(inputs.input_ids,    attention_mask=inputs.attention_mask      )[0],    axis=-1      )
									lowerCamelCase					:			List[str]									= tf.constant(
									    [
									        [1.3851, -13.8923, -10.5229, -10.7533, -0.2309, -10.2384, -0.5365, -9.0947, -5.1670],
									        [-4.7073, -10.6276, -3.9415, -21.5242, -0.2822, -0.2822, -0.2822, -0.2822, -0.2822],
									        [0.6247, -3.4229, -8.9179, -1.4297, -14.1650, 1.4146, -9.0218, -0.2703, -0.2703],
									        [6.4783, -1.9913, -10.7926, -2.3336, 1.5092, -0.9974, -6.8213, 1.3477, 1.3477],
									    ]      )
									self.assertTrue(np.allclose(__lowerCAmelCase,    __lowerCAmelCase,    atol=1e-4      )      )

									lowerCamelCase					:			int									= tf.function(__lowerCAmelCase,    jit_compile=__lowerCAmelCase      )
									lowerCamelCase					:			List[str]									= tf.math.reduce_mean(xla_generate(inputs.input_ids,    attention_mask=inputs.attention_mask      )[0],    axis=-1      )
									self.assertTrue(np.allclose(__lowerCAmelCase,    __lowerCAmelCase,    atol=1e-4      )      )





@require_tf
@slow
class     __snake_case		(   unittest.TestCase):
					@property
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									return [
									    "Today is a beautiful day and I want",
									    "In the city of",
									    "Paris is the capital of France and",
									    "Computers and mobile phones have taken",
									]
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Any									= 'facebook/opt-125m'

									lowerCamelCase					:			Dict									= [
									    'Today is a beautiful day and I want to',
									    'In the city of New York, the city',
									    'Paris is the capital of France and the capital',
									    'Computers and mobile phones have taken over the',
									]

									lowerCamelCase					:			Union[str, Any]									= []
									lowerCamelCase					:			Union[str, Any]									= GPTaTokenizer.from_pretrained(__lowerCAmelCase      )
									lowerCamelCase					:			int									= TFOPTForCausalLM.from_pretrained(__lowerCAmelCase      )

									for prompt in self.prompts:
													lowerCamelCase					:			Tuple									= tokenizer(__lowerCAmelCase,    return_tensors='tf'      ).input_ids

													lowerCamelCase					:			int									= model.generate(__lowerCAmelCase,    max_length=10      )

													lowerCamelCase					:			Optional[Any]									= tokenizer.batch_decode(__lowerCAmelCase,    skip_special_tokens=__lowerCAmelCase      )
													predicted_outputs += generated_string

									self.assertListEqual(__lowerCAmelCase,    __lowerCAmelCase      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Any									= 'facebook/opt-350m'

									lowerCamelCase					:			List[str]									= GPTaTokenizer.from_pretrained(__lowerCAmelCase      )
									lowerCamelCase					:			Union[str, Any]									= TFOPTForCausalLM.from_pretrained(__lowerCAmelCase      )

									lowerCamelCase					:			Optional[int]									= 'left'

									# use different length sentences to test batching
									lowerCamelCase					:			Union[str, Any]									= [
									    'Hello, my dog is a little',
									    'Today, I',
									]

									lowerCamelCase					:			Dict									= tokenizer(__lowerCAmelCase,    return_tensors='tf',    padding=__lowerCAmelCase      )
									lowerCamelCase					:			int									= inputs['input_ids']

									lowerCamelCase					:			Tuple									= model.generate(input_ids=__lowerCAmelCase,    attention_mask=inputs['attention_mask']      )

									lowerCamelCase					:			Optional[int]									= tokenizer(sentences[0],    return_tensors='tf'      ).input_ids
									lowerCamelCase					:			Tuple									= model.generate(input_ids=__lowerCAmelCase      )

									lowerCamelCase					:			Dict									= inputs_non_padded.shape[-1] - tf.math.reduce_sum(
									    tf.cast(inputs['attention_mask'][-1],    tf.intaa      )      )
									lowerCamelCase					:			List[str]									= tokenizer(sentences[1],    return_tensors='tf'      ).input_ids
									lowerCamelCase					:			Tuple									= model.generate(input_ids=__lowerCAmelCase,    max_length=model.config.max_length - num_paddings      )

									lowerCamelCase					:			Union[str, Any]									= tokenizer.batch_decode(__lowerCAmelCase,    skip_special_tokens=__lowerCAmelCase      )
									lowerCamelCase					:			Optional[Any]									= tokenizer.decode(output_non_padded[0],    skip_special_tokens=__lowerCAmelCase      )
									lowerCamelCase					:			List[str]									= tokenizer.decode(output_padded[0],    skip_special_tokens=__lowerCAmelCase      )

									lowerCamelCase					:			Union[str, Any]									= [
									    'Hello, my dog is a little bit of a dork.\nI\'m a little bit',
									    'Today, I was in the middle of a conversation with a friend about the',
									]
									self.assertListEqual(__lowerCAmelCase,    __lowerCAmelCase      )
									self.assertListEqual(__lowerCAmelCase,    [non_padded_sentence, padded_sentence]      )




					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			int									= 'facebook/opt-350m'

									lowerCamelCase					:			Optional[int]									= [
									    'Today is a beautiful day and I want to',
									    'In the city of San Francisco, the city',
									    'Paris is the capital of France and the capital',
									    'Computers and mobile phones have taken over the',
									]

									lowerCamelCase					:			Union[str, Any]									= []
									lowerCamelCase					:			int									= GPTaTokenizer.from_pretrained(__lowerCAmelCase      )
									lowerCamelCase					:			Optional[int]									= TFOPTForCausalLM.from_pretrained(__lowerCAmelCase      )

									for prompt in self.prompts:
													lowerCamelCase					:			Optional[Any]									= tokenizer(__lowerCAmelCase,    return_tensors='tf'      ).input_ids

													lowerCamelCase					:			int									= model.generate(__lowerCAmelCase,    max_length=10      )

													lowerCamelCase					:			Union[str, Any]									= tokenizer.batch_decode(__lowerCAmelCase,    skip_special_tokens=__lowerCAmelCase      )
													predicted_outputs += generated_string

									self.assertListEqual(__lowerCAmelCase,    __lowerCAmelCase      )


'''simple docstring'''






from __future__ import annotations

A			      =      '#'




class     __snake_case		:
					def __init__(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			dict									= {}
					def     UpperCAmelCase_							(     self,    A      ):

									"""simple docstring"""


									lowerCamelCase					:			int									= self._trie
									for char in text:
													if char not in trie:
																	lowerCamelCase					:			Dict									= {}
													lowerCamelCase					:			Optional[int]									= trie[char]
									lowerCamelCase					:			Optional[Any]									= True
					def     UpperCAmelCase_							(     self,    A      ):

									"""simple docstring"""


									lowerCamelCase					:			Dict									= self._trie
									for char in prefix:
													if char in trie:
																	lowerCamelCase					:			int									= trie[char]
													else:
																	return []
									return self._elements(A      )




					def     UpperCAmelCase_							(     self,    A      ):

									"""simple docstring"""


									lowerCamelCase					:			Optional[Any]									= []
									for c, v in d.items():
													lowerCamelCase					:			Optional[Any]									= [' '] if c == END else [(c + s) for s in self._elements(A      )]
													result.extend(A      )
									return tuple(A      )





A			      =      Trie()
A			      =      ('depart', 'detergent', 'daring', 'dog', 'deer', 'deal')
for word in words:
					trie.insert_word(word)





def    UpperCAmelCase							(  UpperCAmelCase__							:							str):
				lowerCamelCase					:			Any									= trie.find_word(UpperCAmelCase__)
				return tuple(string + word for word in suffixes)





def    UpperCAmelCase							(  ):
				print(autocomplete_using_trie('de'))


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					main()
