




'''simple docstring'''



import unittest

import numpy as np

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

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
     import jax.numpy as jnp

     from transformers.models.roformer.modeling_flax_roformer import (
         FlaxRoFormerForMaskedLM,
         FlaxRoFormerForMultipleChoice,
         FlaxRoFormerForQuestionAnswering,
         FlaxRoFormerForSequenceClassification,
         FlaxRoFormerForTokenClassification,
         FlaxRoFormerModel,
     )





class      lowerCAmelCase (			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=512     ,	__SCREAMING_SNAKE_CASE=16     ,	__SCREAMING_SNAKE_CASE=2     ,	__SCREAMING_SNAKE_CASE=0.02     ,	__SCREAMING_SNAKE_CASE=4     ,	)   ->	Any:



            '''simple docstring'''




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


       def    lowerCAmelCase   ( self	)   ->	Union[str, Any]:



            '''simple docstring'''




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

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

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

            __snake_case            =	RoFormerConfig(
                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=__SCREAMING_SNAKE_CASE     ,	initializer_range=self.initializer_range     ,	)

            return config, input_ids, token_type_ids, attention_mask




       def    lowerCAmelCase   ( self	)   ->	int:



            '''simple docstring'''




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





@require_flax
class      lowerCAmelCase (			__lowerCAmelCase					, unittest.TestCase):
       __lowercase	:   Tuple										=      True

       __lowercase	:   Optional[int]										=      (
           (
               FlaxRoFormerModel,
               FlaxRoFormerForMaskedLM,
               FlaxRoFormerForSequenceClassification,
               FlaxRoFormerForTokenClassification,
               FlaxRoFormerForMultipleChoice,
               FlaxRoFormerForQuestionAnswering,
           )
           if is_flax_available()
           else ()
       )


       def    lowerCAmelCase   ( self	)   ->	int:



            '''simple docstring'''




            __snake_case            =	FlaxRoFormerModelTester(self	)




       @slow
       def    lowerCAmelCase   ( self	)   ->	List[Any]:



            '''simple docstring'''




            for model_class_name in self.all_model_classes:
                 __snake_case            =	model_class_name.from_pretrained('''junnyu/roformer_chinese_small'''     ,	from_pt=__SCREAMING_SNAKE_CASE	)
                 __snake_case            =	model(np.ones((1, 1)	)	)
                 self.assertIsNotNone(__SCREAMING_SNAKE_CASE	)





@require_flax
class      lowerCAmelCase (			unittest.TestCase):


       @slow
       def    lowerCAmelCase   ( self	)   ->	Tuple:



            '''simple docstring'''




            __snake_case            =	FlaxRoFormerForMaskedLM.from_pretrained('''junnyu/roformer_chinese_base'''	)
            __snake_case            =	jnp.array([[0, 1, 2, 3, 4, 5]]	)
            __snake_case            =	model(__SCREAMING_SNAKE_CASE	)[0]

            __snake_case            =	5_0000

            __snake_case            =	(1, 6, vocab_size)
            self.assertEqual(output.shape     ,	__SCREAMING_SNAKE_CASE	)

            __snake_case            =	jnp.array(
                [[[-0.1_205, -1.0_265, 0.2_922], [-1.5_134, 0.1_974, 0.1_519], [-5.0_135, -3.9_003, -0.8_404]]]	)

            self.assertTrue(jnp.allclose(output[:, :3, :3]     ,	__SCREAMING_SNAKE_CASE     ,	atol=1E-4	)	)










from typing import List, Optional

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


lowerCAmelCase       		=    logging.get_logger(__name__)

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




class 							lowerCamelCase   (  _UpperCamelCase ):
	_lowerCAmelCase     :		Optional[int]										=						'''autoformer'''
	_lowerCAmelCase     :		Any										=						{
	    '''hidden_size''': '''d_model''',
	    '''num_attention_heads''': '''encoder_attention_heads''',
	    '''num_hidden_layers''': '''encoder_layers''',
	}


	def __init__(    self				,     lowercase__ = None				,     lowercase__ = None				,     lowercase__ = "student_t"				,     lowercase__ = "nll"				,     lowercase__ = 1				,     lowercase__ = [1, 2, 3, 4, 5, 6, 7]				,     lowercase__ = True				,     lowercase__ = 0				,     lowercase__ = 0				,     lowercase__ = 0				,     lowercase__ = 0				,     lowercase__ = None				,     lowercase__ = None				,     lowercase__ = 6_4				,     lowercase__ = 2				,     lowercase__ = 2				,     lowercase__ = 2				,     lowercase__ = 2				,     lowercase__ = 3_2				,     lowercase__ = 3_2				,     lowercase__ = "gelu"				,     lowercase__ = 0.1				,     lowercase__ = 0.1				,     lowercase__ = 0.1				,     lowercase__ = 0.1				,     lowercase__ = 0.1				,     lowercase__ = 1_0_0				,     lowercase__ = 0.0_2				,     lowercase__ = True				,     lowercase__=True				,     lowercase__ = 1_0				,     lowercase__ = 2_5				,     lowercase__ = 3				,     **lowercase__				,     ):
							# time series specific configuration
							__UpperCAmelCase			:							Optional[int]	    =	prediction_length
							__UpperCAmelCase			:							Tuple	    =	context_length if context_length is not None else prediction_length
							__UpperCAmelCase			:							Tuple	    =	distribution_output
							__UpperCAmelCase			:							Tuple	    =	loss
							__UpperCAmelCase			:							Dict	    =	input_size
							__UpperCAmelCase			:							str	    =	num_time_features
							__UpperCAmelCase			:							str	    =	lags_sequence
							__UpperCAmelCase			:							int	    =	scaling
							__UpperCAmelCase			:							int	    =	num_dynamic_real_features
							__UpperCAmelCase			:							List[str]	    =	num_static_real_features
							__UpperCAmelCase			:							List[str]	    =	num_static_categorical_features
							if cardinality is not None and num_static_categorical_features > 0:
													if len(lowercase__) != 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			:							List[Any]	    =	[0]
							if embedding_dimension is not None and num_static_categorical_features > 0:
													if len(lowercase__) != num_static_categorical_features:
																			raise ValueError(
																			    '''The embedding dimension should be a list of the same length as `num_static_categorical_features`''')
													__UpperCAmelCase			:							Union[str, Any]	    =	embedding_dimension
							else:
													__UpperCAmelCase			:							int	    =	[min(5_0				,     (cat + 1) // 2) for cat in self.cardinality]
							__UpperCAmelCase			:							Tuple	    =	num_parallel_samples

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

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

							__UpperCAmelCase			:							int	    =	activation_function
							__UpperCAmelCase			:							Optional[int]	    =	init_std

							__UpperCAmelCase			:							Optional[int]	    =	use_cache

							# Autoformer
							__UpperCAmelCase			:							str	    =	label_length
							__UpperCAmelCase			:							int	    =	moving_average
							__UpperCAmelCase			:							Optional[int]	    =	autocorrelation_factor

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


	@property
	def  A(    self):
							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
							)

