

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 			a					(		unittest.TestCase			):


						def __init__(    self  ,   A_  ,   A_=13  ,   A_=7  ,   A_=True  ,   A_=True  ,   A_=True  ,   A_=True  ,   A_=99  ,   A_=32  ,   A_=5  ,   A_=4  ,   A_=37  ,   A_="gelu"  ,   A_=0.1  ,   A_=0.1  ,   A_=512  ,   A_=16  ,   A_=2  ,   A_=0.02  ,   A_=4  ,   ):







										'''simple docstring'''


										_UpperCAmelCase    :						List[str]							=      parent
										_UpperCAmelCase    :						Any							=      batch_size
										_UpperCAmelCase    :						List[Any]							=      seq_length
										_UpperCAmelCase    :						List[Any]							=      is_training
										_UpperCAmelCase    :						Dict							=      use_attention_mask
										_UpperCAmelCase    :						Union[str, Any]							=      use_token_type_ids
										_UpperCAmelCase    :						List[Any]							=      use_labels
										_UpperCAmelCase    :						List[str]							=      vocab_size
										_UpperCAmelCase    :						Dict							=      hidden_size
										_UpperCAmelCase    :						int							=      num_hidden_layers
										_UpperCAmelCase    :						Union[str, Any]							=      num_attention_heads
										_UpperCAmelCase    :						Optional[int]							=      intermediate_size
										_UpperCAmelCase    :						Dict							=      hidden_act
										_UpperCAmelCase    :						Optional[int]							=      hidden_dropout_prob
										_UpperCAmelCase    :						int							=      attention_probs_dropout_prob
										_UpperCAmelCase    :						List[str]							=      max_position_embeddings
										_UpperCAmelCase    :						Any							=      type_vocab_size
										_UpperCAmelCase    :						Union[str, Any]							=      type_sequence_label_size
										_UpperCAmelCase    :						Optional[Any]							=      initializer_range
										_UpperCAmelCase    :						List[str]							=      num_choices


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						int							=      ids_tensor([self.batch_size, self.seq_length]  ,   self.vocab_size		)

										_UpperCAmelCase    :						Union[str, Any]							=      None
										if self.use_attention_mask:
														_UpperCAmelCase    :						int							=      random_attention_mask([self.batch_size, self.seq_length]		)

										_UpperCAmelCase    :						Dict							=      None
										if self.use_token_type_ids:
														_UpperCAmelCase    :						List[Any]							=      ids_tensor([self.batch_size, self.seq_length]  ,   self.type_vocab_size		)

										_UpperCAmelCase    :						Optional[int]							=      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=A_  ,   initializer_range=self.initializer_range  ,   )

										return config, input_ids, token_type_ids, attention_mask


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Union[str, Any]							=      self.prepare_config_and_inputs()
										_UpperCAmelCase						,							_UpperCAmelCase						,							_UpperCAmelCase						,							_UpperCAmelCase    :						str							=      config_and_inputs
										_UpperCAmelCase    :						Any							=      {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
										return config, inputs_dict





						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Any							=      self.prepare_config_and_inputs()
										_UpperCAmelCase						,							_UpperCAmelCase						,							_UpperCAmelCase						,							_UpperCAmelCase    :						Dict							=      config_and_inputs

										_UpperCAmelCase    :						Dict							=      True
										_UpperCAmelCase    :						Tuple							=      floats_tensor([self.batch_size, self.seq_length, self.hidden_size]		)
										_UpperCAmelCase    :						Optional[Any]							=      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 			a					(		UpperCAmelCase							,	unittest.TestCase			):
						_lowercase        =     True

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


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Any							=      FlaxRobertaPreLayerNormModelTester(self		)





						@slow
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										for model_class_name in self.all_model_classes:
														_UpperCAmelCase    :						Any							=      model_class_name.from_pretrained("andreasmadsen/efficient_mlm_m0.40"  ,   from_pt=A_		)
														_UpperCAmelCase    :						List[str]							=      model(np.ones((1, 1)		)		)
														self.assertIsNotNone(A_		)







@require_flax
class 			a					(		unittest.TestCase			):


						@slow
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Any							=      FlaxRobertaPreLayerNormForMaskedLM.from_pretrained("andreasmadsen/efficient_mlm_m0.40"  ,   from_pt=A_		)

										_UpperCAmelCase    :						str							=      np.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]  ,   dtype=jnp.intaa		)
										_UpperCAmelCase    :						str							=      model(A_		)[0]
										_UpperCAmelCase    :						Tuple							=      [1, 11, 50265]
										self.assertEqual(list(output.shape		)  ,   A_		)
										# compare the actual values for a slice.
										_UpperCAmelCase    :						List[Any]							=      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]  ,   A_  ,   atol=1e-4		)		)





						@slow
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Optional[Any]							=      FlaxRobertaPreLayerNormModel.from_pretrained("andreasmadsen/efficient_mlm_m0.40"  ,   from_pt=A_		)

										_UpperCAmelCase    :						List[Any]							=      np.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]  ,   dtype=jnp.intaa		)
										_UpperCAmelCase    :						Optional[Any]							=      model(A_		)[0]
										# compare the actual values for a slice.
										_UpperCAmelCase    :						str							=      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]  ,   A_  ,   atol=1e-4		)		)





from typing import List, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE_            = logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_            = {
    'huggingface/autoformer-tourism-monthly': 'https://huggingface.co/huggingface/autoformer-tourism-monthly/resolve/main/config.json',
}

class 			a					(		UpperCAmelCase			):
						_lowercase        =     "autoformer"
						_lowercase        =     {
						    "hidden_size": "d_model",
						    "num_attention_heads": "encoder_attention_heads",
						    "num_hidden_layers": "encoder_layers",
						}


						def __init__(    self  ,   A_ = None  ,   A_ = None  ,   A_ = "student_t"  ,   A_ = "nll"  ,   A_ = 1  ,   A_ = [1, 2, 3, 4, 5, 6, 7]  ,   A_ = True  ,   A_ = 0  ,   A_ = 0  ,   A_ = 0  ,   A_ = 0  ,   A_ = None  ,   A_ = None  ,   A_ = 64  ,   A_ = 2  ,   A_ = 2  ,   A_ = 2  ,   A_ = 2  ,   A_ = 32  ,   A_ = 32  ,   A_ = "gelu"  ,   A_ = 0.1  ,   A_ = 0.1  ,   A_ = 0.1  ,   A_ = 0.1  ,   A_ = 0.1  ,   A_ = 100  ,   A_ = 0.02  ,   A_ = True  ,   A_=True  ,   A_ = 10  ,   A_ = 25  ,   A_ = 3  ,   **A_  ,   ):







										'''simple docstring'''


										_UpperCAmelCase    :						List[Any]							=      prediction_length
										_UpperCAmelCase    :						Dict							=      context_length if context_length is not None else prediction_length
										_UpperCAmelCase    :						Tuple							=      distribution_output
										_UpperCAmelCase    :						List[Any]							=      loss
										_UpperCAmelCase    :						Optional[Any]							=      input_size
										_UpperCAmelCase    :						int							=      num_time_features
										_UpperCAmelCase    :						str							=      lags_sequence
										_UpperCAmelCase    :						Union[str, Any]							=      scaling
										_UpperCAmelCase    :						Union[str, Any]							=      num_dynamic_real_features
										_UpperCAmelCase    :						int							=      num_static_real_features
										_UpperCAmelCase    :						int							=      num_static_categorical_features
										if cardinality is not None and num_static_categorical_features > 0:
														if len(A_		) != num_static_categorical_features:
																		raise ValueError(
																		    "The cardinality should be a list of the same length as `num_static_categorical_features`"		)
														_UpperCAmelCase    :						Union[str, Any]							=      cardinality
										else:
														_UpperCAmelCase    :						Any							=      [0]
										if embedding_dimension is not None and num_static_categorical_features > 0:
														if len(A_		) != num_static_categorical_features:
																		raise ValueError(
																		    "The embedding dimension should be a list of the same length as `num_static_categorical_features`"		)
														_UpperCAmelCase    :						int							=      embedding_dimension
										else:
														_UpperCAmelCase    :						int							=      [min(50  ,   (cat + 1) // 2		) for cat in self.cardinality]
										_UpperCAmelCase    :						Union[str, Any]							=      num_parallel_samples

										# Transformer architecture configuration
										_UpperCAmelCase    :						Union[str, Any]							=      input_size * len(self.lags_sequence		) + self._number_of_features
										_UpperCAmelCase    :						int							=      d_model
										_UpperCAmelCase    :						Any							=      encoder_attention_heads
										_UpperCAmelCase    :						str							=      decoder_attention_heads
										_UpperCAmelCase    :						Union[str, Any]							=      encoder_ffn_dim
										_UpperCAmelCase    :						Any							=      decoder_ffn_dim
										_UpperCAmelCase    :						Any							=      encoder_layers
										_UpperCAmelCase    :						Optional[int]							=      decoder_layers

										_UpperCAmelCase    :						Optional[Any]							=      dropout
										_UpperCAmelCase    :						Union[str, Any]							=      attention_dropout
										_UpperCAmelCase    :						Dict							=      activation_dropout
										_UpperCAmelCase    :						Dict							=      encoder_layerdrop
										_UpperCAmelCase    :						int							=      decoder_layerdrop

										_UpperCAmelCase    :						Union[str, Any]							=      activation_function
										_UpperCAmelCase    :						str							=      init_std

										_UpperCAmelCase    :						Tuple							=      use_cache

										# Autoformer
										_UpperCAmelCase    :						str							=      label_length
										_UpperCAmelCase    :						Any							=      moving_average
										_UpperCAmelCase    :						Optional[Any]							=      autocorrelation_factor

										super().__init__(is_encoder_decoder=A_  ,   **A_		)





						@property
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										return (
										    sum(self.embedding_dimension		)
										    + self.num_dynamic_real_features
										    + self.num_time_features
										    + self.num_static_real_features
										    + self.input_size * 2  # the log1p(abs(loc)) and log(scale) features
										)


