
"""simple docstring"""




import unittest

import numpy as np

from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
						import jax.numpy as jnp

						from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
						    FlaxRobertaPreLayerNormForCausalLM,
						    FlaxRobertaPreLayerNormForMaskedLM,
						    FlaxRobertaPreLayerNormForMultipleChoice,
						    FlaxRobertaPreLayerNormForQuestionAnswering,
						    FlaxRobertaPreLayerNormForSequenceClassification,
						    FlaxRobertaPreLayerNormForTokenClassification,
						    FlaxRobertaPreLayerNormModel,
						)





class  snake_case     (							unittest.TestCase   ):

							def __init__(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_=13       ,       SCREAMING_SNAKE_CASE_=7       ,       SCREAMING_SNAKE_CASE_=True       ,       SCREAMING_SNAKE_CASE_=True       ,       SCREAMING_SNAKE_CASE_=True       ,       SCREAMING_SNAKE_CASE_=True       ,       SCREAMING_SNAKE_CASE_=99       ,       SCREAMING_SNAKE_CASE_=32       ,       SCREAMING_SNAKE_CASE_=5       ,       SCREAMING_SNAKE_CASE_=4       ,       SCREAMING_SNAKE_CASE_=37       ,       SCREAMING_SNAKE_CASE_="gelu"       ,       SCREAMING_SNAKE_CASE_=0.1       ,       SCREAMING_SNAKE_CASE_=0.1       ,       SCREAMING_SNAKE_CASE_=5_12       ,       SCREAMING_SNAKE_CASE_=16       ,       SCREAMING_SNAKE_CASE_=2       ,       SCREAMING_SNAKE_CASE_=0.02       ,       SCREAMING_SNAKE_CASE_=4       ,       ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = parent
									SCREAMING_SNAKE_CASE_						 = batch_size
									SCREAMING_SNAKE_CASE_						 = seq_length
									SCREAMING_SNAKE_CASE_						 = is_training
									SCREAMING_SNAKE_CASE_						 = use_attention_mask
									SCREAMING_SNAKE_CASE_						 = use_token_type_ids
									SCREAMING_SNAKE_CASE_						 = use_labels
									SCREAMING_SNAKE_CASE_						 = vocab_size
									SCREAMING_SNAKE_CASE_						 = hidden_size
									SCREAMING_SNAKE_CASE_						 = num_hidden_layers
									SCREAMING_SNAKE_CASE_						 = num_attention_heads
									SCREAMING_SNAKE_CASE_						 = intermediate_size
									SCREAMING_SNAKE_CASE_						 = hidden_act
									SCREAMING_SNAKE_CASE_						 = hidden_dropout_prob
									SCREAMING_SNAKE_CASE_						 = attention_probs_dropout_prob
									SCREAMING_SNAKE_CASE_						 = max_position_embeddings
									SCREAMING_SNAKE_CASE_						 = type_vocab_size
									SCREAMING_SNAKE_CASE_						 = type_sequence_label_size
									SCREAMING_SNAKE_CASE_						 = initializer_range
									SCREAMING_SNAKE_CASE_						 = num_choices

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = ids_tensor([self.batch_size, self.seq_length]       ,       self.vocab_size    )

									SCREAMING_SNAKE_CASE_						 = None
									if self.use_attention_mask:
											SCREAMING_SNAKE_CASE_						 = random_attention_mask([self.batch_size, self.seq_length]    )

									SCREAMING_SNAKE_CASE_						 = None
									if self.use_token_type_ids:
											SCREAMING_SNAKE_CASE_						 = ids_tensor([self.batch_size, self.seq_length]       ,       self.type_vocab_size    )

									SCREAMING_SNAKE_CASE_						 = RobertaPreLayerNormConfig(
									    vocab_size=self.vocab_size       ,       hidden_size=self.hidden_size       ,       num_hidden_layers=self.num_hidden_layers       ,       num_attention_heads=self.num_attention_heads       ,       intermediate_size=self.intermediate_size       ,       hidden_act=self.hidden_act       ,       hidden_dropout_prob=self.hidden_dropout_prob       ,       attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,       max_position_embeddings=self.max_position_embeddings       ,       type_vocab_size=self.type_vocab_size       ,       is_decoder=lowerCamelCase__       ,       initializer_range=self.initializer_range       ,       )

									return config, input_ids, token_type_ids, attention_mask

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.prepare_config_and_inputs()
									SCREAMING_SNAKE_CASE_						 = config_and_inputs
									SCREAMING_SNAKE_CASE_						 = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
									return config, inputs_dict







							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.prepare_config_and_inputs()
									SCREAMING_SNAKE_CASE_						 = config_and_inputs

									SCREAMING_SNAKE_CASE_						 = True
									SCREAMING_SNAKE_CASE_						 = floats_tensor([self.batch_size, self.seq_length, self.hidden_size]    )
									SCREAMING_SNAKE_CASE_						 = ids_tensor([self.batch_size, self.seq_length]       ,       vocab_size=2    )

									return (
									    config,
									    input_ids,
									    token_type_ids,
									    encoder_hidden_states,
									    encoder_attention_mask,
									)







@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class  snake_case     (							__lowerCAmelCase       ,			unittest.TestCase   ):
							UpperCAmelCase__						     =      True

							UpperCAmelCase__						     =      (
							    (
							        FlaxRobertaPreLayerNormModel,
							        FlaxRobertaPreLayerNormForCausalLM,
							        FlaxRobertaPreLayerNormForMaskedLM,
							        FlaxRobertaPreLayerNormForSequenceClassification,
							        FlaxRobertaPreLayerNormForTokenClassification,
							        FlaxRobertaPreLayerNormForMultipleChoice,
							        FlaxRobertaPreLayerNormForQuestionAnswering,
							    )
							    if is_flax_available()
							    else ()
							)

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = FlaxRobertaPreLayerNormModelTester(self    )







							@slow
							def _lowercase					(self    ):

									"""simple docstring"""







									for model_class_name in self.all_model_classes:
											SCREAMING_SNAKE_CASE_						 = model_class_name.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''       ,       from_pt=lowerCamelCase__    )
											SCREAMING_SNAKE_CASE_						 = model(np.ones((1, 1)    )    )
											self.assertIsNotNone(lowerCamelCase__    )







@require_flax
class  snake_case     (							unittest.TestCase   ):

							@slow
							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''       ,       from_pt=lowerCamelCase__    )

									SCREAMING_SNAKE_CASE_						 = np.array([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]       ,       dtype=jnp.intaa    )
									SCREAMING_SNAKE_CASE_						 = model(lowerCamelCase__    )[0]
									SCREAMING_SNAKE_CASE_						 = [1, 11, 5_02_65]
									self.assertEqual(list(output.shape    )       ,       lowerCamelCase__    )
									# compare the actual values for a slice.
									SCREAMING_SNAKE_CASE_						 = np.array(
									    [[[40.48_80, 18.01_99, -5.23_67], [-1.88_77, -4.08_85, 10.70_85], [-2.26_13, -5.61_10, 7.26_65]]]       ,       dtype=np.floataa    )
									self.assertTrue(np.allclose(output[:, :3, :3]       ,       lowerCamelCase__       ,       atol=1e-4    )    )







							@slow
							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = FlaxRobertaPreLayerNormModel.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''       ,       from_pt=lowerCamelCase__    )

									SCREAMING_SNAKE_CASE_						 = np.array([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]       ,       dtype=jnp.intaa    )
									SCREAMING_SNAKE_CASE_						 = model(lowerCamelCase__    )[0]
									# compare the actual values for a slice.
									SCREAMING_SNAKE_CASE_						 = np.array(
									    [[[0.02_08, -0.03_56, 0.02_37], [-0.15_69, -0.04_11, -0.26_26], [0.18_79, 0.01_25, -0.00_89]]]       ,       dtype=np.floataa    )
									self.assertTrue(np.allclose(output[:, :3, :3]       ,       lowerCamelCase__       ,       atol=1e-4    )    )

"""simple docstring"""




import json
import os
import unittest

from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin





class  snake_case     (							__lowercase       ,			unittest.TestCase   ):
							UpperCAmelCase__						     =      CTRLTokenizer
							UpperCAmelCase__						     =      False
							UpperCAmelCase__						     =      False

							def _lowercase					(self    ):

									"""simple docstring"""







									super().setUp()

									# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
									SCREAMING_SNAKE_CASE_						 = ['''adapt''', '''re@@''', '''a@@''', '''apt''', '''c@@''', '''t''', '''<unk>''']
									SCREAMING_SNAKE_CASE_						 = dict(zip(SCREAMING_SNAKE_CASE_       ,       range(len(SCREAMING_SNAKE_CASE_    )    )    )    )
									SCREAMING_SNAKE_CASE_						 = ['''#version: 0.2''', '''a p''', '''ap t</w>''', '''r e''', '''a d''', '''ad apt</w>''', '''''']
									SCREAMING_SNAKE_CASE_						 = {'''unk_token''': '''<unk>'''}

									SCREAMING_SNAKE_CASE_						 = os.path.join(self.tmpdirname       ,       VOCAB_FILES_NAMES['''vocab_file''']    )
									SCREAMING_SNAKE_CASE_						 = os.path.join(self.tmpdirname       ,       VOCAB_FILES_NAMES['''merges_file''']    )
									with open(self.vocab_file       ,       '''w'''       ,       encoding='''utf-8'''    ) as fp:
											fp.write(json.dumps(SCREAMING_SNAKE_CASE_    ) + '''\n'''    )
									with open(self.merges_file       ,       '''w'''       ,       encoding='''utf-8'''    ) as fp:
											fp.write('''\n'''.join(SCREAMING_SNAKE_CASE_    )    )

							def _lowercase					(self       ,       **SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									kwargs.update(self.special_tokens_map    )
									return CTRLTokenizer.from_pretrained(self.tmpdirname       ,       **SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = '''adapt react readapt apt'''
									SCREAMING_SNAKE_CASE_						 = '''adapt react readapt apt'''
									return input_text, output_text







							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = CTRLTokenizer(self.vocab_file       ,       self.merges_file       ,       **self.special_tokens_map    )
									SCREAMING_SNAKE_CASE_						 = '''adapt react readapt apt'''
									SCREAMING_SNAKE_CASE_						 = '''adapt re@@ a@@ c@@ t re@@ adapt apt'''.split()
									SCREAMING_SNAKE_CASE_						 = tokenizer.tokenize(SCREAMING_SNAKE_CASE_    )
									self.assertListEqual(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_						 = tokens + [tokenizer.unk_token]

									SCREAMING_SNAKE_CASE_						 = [0, 1, 2, 4, 5, 1, 0, 3, 6]
									self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_    )       ,       SCREAMING_SNAKE_CASE_    )