




"""simple docstring"""

import unittest

from transformers import BigBirdConfig, 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

       from transformers.models.big_bird.modeling_flax_big_bird import (
           FlaxBigBirdForCausalLM,
           FlaxBigBirdForMaskedLM,
           FlaxBigBirdForMultipleChoice,
           FlaxBigBirdForPreTraining,
           FlaxBigBirdForQuestionAnswering,
           FlaxBigBirdForSequenceClassification,
           FlaxBigBirdForTokenClassification,
           FlaxBigBirdModel,
       )





class 							lowercase_			(	unittest.TestCase				):



 '''simple docstring'''

 def __init__(					self			:			List[str]       ,				_UpperCAmelCase			:			int       ,				_UpperCAmelCase			:			List[str]=2       ,				_UpperCAmelCase			:			int=56       ,				_UpperCAmelCase			:			List[Any]=True       ,				_UpperCAmelCase			:			Any=True       ,				_UpperCAmelCase			:			Dict=True       ,				_UpperCAmelCase			:			List[Any]=True       ,				_UpperCAmelCase			:			Any=99       ,				_UpperCAmelCase			:			Tuple=32       ,				_UpperCAmelCase			:			str=2       ,				_UpperCAmelCase			:			Any=2       ,				_UpperCAmelCase			:			List[Any]=7       ,				_UpperCAmelCase			:			Union[str, Any]="gelu_new"       ,				_UpperCAmelCase			:			Any=0.1       ,				_UpperCAmelCase			:			Optional[int]=0.1       ,				_UpperCAmelCase			:			Union[str, Any]=512       ,				_UpperCAmelCase			:			str=16       ,				_UpperCAmelCase			:			Dict=2       ,				_UpperCAmelCase			:			Optional[Any]=0.02       ,				_UpperCAmelCase			:			Optional[int]=4       ,				_UpperCAmelCase			:			int="block_sparse"       ,				_UpperCAmelCase			:			Dict=True       ,				_UpperCAmelCase			:			Union[str, Any]=False       ,				_UpperCAmelCase			:			Union[str, Any]=2       ,				_UpperCAmelCase			:			Union[str, Any]=3       ,				):
       _A     =  parent
       _A     =  batch_size
       _A     =  seq_length
       _A     =  is_training
       _A     =  use_attention_mask
       _A     =  use_token_type_ids
       _A     =  use_labels
       _A     =  vocab_size
       _A     =  hidden_size
       _A     =  num_hidden_layers
       _A     =  num_attention_heads
       _A     =  intermediate_size
       _A     =  hidden_act
       _A     =  hidden_dropout_prob
       _A     =  attention_probs_dropout_prob
       _A     =  max_position_embeddings
       _A     =  type_vocab_size
       _A     =  type_sequence_label_size
       _A     =  initializer_range
       _A     =  num_choices

       _A     =  rescale_embeddings
       _A     =  attention_type
       _A     =  use_bias
       _A     =  block_size
       _A     =  num_random_blocks

 def      lowerCAmelCase_				(					self			:			str						):
       _A     =  ids_tensor([self.batch_size, self.seq_length]       ,				self.vocab_size						)

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

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

       _A     =  BigBirdConfig(
           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       ,				attention_type=self.attention_type       ,				block_size=self.block_size       ,				num_random_blocks=self.num_random_blocks       ,				use_bias=self.use_bias       ,				rescale_embeddings=self.rescale_embeddings       ,				)

       return config, input_ids, token_type_ids, attention_mask



 def      lowerCAmelCase_				(					self			:			Optional[Any]						):
       _A     =  self.prepare_config_and_inputs()
       _A  ,       _A  ,       _A  ,       _A     =  config_and_inputs
       _A     =  {
           'input_ids': input_ids,
           'token_type_ids': token_type_ids,
           'attention_mask': attention_mask,
       }
       return config, inputs_dict





@require_flax
class 							lowercase_			(	__lowerCAmelCase     ,      unittest.TestCase				):



 '''simple docstring'''
 UpperCAmelCase       :	Optional[Any]     =      (
     (
         FlaxBigBirdForCausalLM,
         FlaxBigBirdModel,
         FlaxBigBirdForPreTraining,
         FlaxBigBirdForMaskedLM,
         FlaxBigBirdForMultipleChoice,
         FlaxBigBirdForQuestionAnswering,
         FlaxBigBirdForSequenceClassification,
         FlaxBigBirdForTokenClassification,
     )
     if is_flax_available()
     else ()
 )

 UpperCAmelCase       :	Union[str, Any]     =      False
 UpperCAmelCase       :	int     =      False

 def      lowerCAmelCase_				(					self			:			Any						):
       _A     =  FlaxBigBirdModelTester(self						)

 @slow
 # copied from `test_modeling_flax_common` because it takes much longer than other models
 def      lowerCAmelCase_				(					self			:			Any						):
       super().test_from_pretrained_save_pretrained()

 @slow
 # copied from `test_modeling_flax_common` because it takes much longer than other models
 def      lowerCAmelCase_				(					self			:			List[str]						):
       super().test_from_pretrained_with_no_automatic_init()

 @slow
 # copied from `test_modeling_flax_common` because it takes much longer than other models
 def      lowerCAmelCase_				(					self			:			List[Any]						):
       super().test_no_automatic_init()

 @slow
 # copied from `test_modeling_flax_common` because it takes much longer than other models
 def      lowerCAmelCase_				(					self			:			Any						):
       super().test_hidden_states_output()

 @slow
 def      lowerCAmelCase_				(					self			:			Optional[Any]						):
       for model_class_name in self.all_model_classes:
             _A     =  model_class_name.from_pretrained('google/bigbird-roberta-base'						)
             self.assertIsNotNone(__a						)

 def      lowerCAmelCase_				(					self			:			Tuple						):
       if self.test_attn_probs:
             super().test_attention_outputs()

 @slow
 # copied from `test_modeling_flax_common` because it takes much longer than other models
 def      lowerCAmelCase_				(					self			:			Dict						):
       _A  ,       _A     =  self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
             with self.subTest(model_class.__name__						):
                   _A     =  self._prepare_for_class(__a       ,				__a						)
                   _A     =  model_class(__a						)

                   @jax.jit
                   def model_jitted(_UpperCAmelCase			:			Dict       ,				_UpperCAmelCase			:			str=None       ,				**_UpperCAmelCase			:			Optional[Any]						):
                         return model(input_ids=__a       ,				attention_mask=__a       ,				**__a						)

                   with self.subTest('JIT Enabled'						):
                         _A     =  model_jitted(**__a						).to_tuple()

                   with self.subTest('JIT Disabled'						):
                         with jax.disable_jit():
                               _A     =  model_jitted(**__a						).to_tuple()

                   self.assertEqual(len(__a						)       ,				len(__a						)						)
                   for jitted_output, output in zip(__a       ,				__a						):
                         self.assertEqual(jitted_output.shape       ,				output.shape						)



 def      lowerCAmelCase_				(					self			:			Optional[int]       ,				_UpperCAmelCase			:			List[str]       ,				_UpperCAmelCase			:			List[str]       ,				_UpperCAmelCase			:			Any       ,				_UpperCAmelCase			:			Any=1E-5       ,				_UpperCAmelCase			:			int="outputs"       ,				_UpperCAmelCase			:			Optional[int]=None						):
       # `bigbird_block_sparse_attention` in `FlaxBigBird` returns `attention_probs = None`, while in PyTorch version,
       # an effort was done to return `attention_probs` (yet to be verified).
       if name.startswith('outputs.attentions'						):
             return
       else:
             super().check_pt_flax_outputs(__a       ,				__a       ,				__a       ,				__a       ,				__a       ,				__a						)


'''simple docstring'''


from typing import List, Optional, Union

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


__lowerCAmelCase						:							Union[str, Any]    							=						logging.get_logger(__name__)

__lowerCAmelCase						:							Optional[int]    							=						{
    "huggingface/time-series-transformer-tourism-monthly": (
        "https://huggingface.co/huggingface/time-series-transformer-tourism-monthly/resolve/main/config.json"
    ),
    # See all TimeSeriesTransformer models at https://huggingface.co/models?filter=time_series_transformer
}






class     A ( UpperCAmelCase ):
							a_										=   '''time_series_transformer'''
							a_										=   {
							    '''hidden_size''': '''d_model''',
							    '''num_attention_heads''': '''encoder_attention_heads''',
							    '''num_hidden_layers''': '''encoder_layers''',
							}





							def __init__(   self					:				str  ,		__a					:				Optional[int] = None  ,		__a					:				Optional[int] = None  ,		__a					:				str = "student_t"  ,		__a					:				str = "nll"  ,		__a					:				int = 1  ,		__a					:				List[int] = [1, 2, 3, 4, 5, 6, 7]  ,		__a					:				Optional[Union[str, bool]] = "mean"  ,		__a					:				int = 0  ,		__a					:				int = 0  ,		__a					:				int = 0  ,		__a					:				int = 0  ,		__a					:				Optional[List[int]] = None  ,		__a					:				Optional[List[int]] = None  ,		__a					:				int = 3_2  ,		__a					:				int = 3_2  ,		__a					:				int = 2  ,		__a					:				int = 2  ,		__a					:				int = 2  ,		__a					:				int = 2  ,		__a					:				bool = True  ,		__a					:				str = "gelu"  ,		__a					:				int = 6_4  ,		__a					:				float = 0.1  ,		__a					:				float = 0.1  ,		__a					:				float = 0.1  ,		__a					:				float = 0.1  ,		__a					:				float = 0.1  ,		__a					:				int = 1_0_0  ,		__a					:				float = 0.0_2  ,		__a					:				Optional[int]=True  ,		**__a					:				str  ,		)				->						int:
								# time series specific configuration
								__UpperCAmelCase      					=      prediction_length
								__UpperCAmelCase      					=      context_length or prediction_length
								__UpperCAmelCase      					=      distribution_output
								__UpperCAmelCase      					=      loss
								__UpperCAmelCase      					=      input_size
								__UpperCAmelCase      					=      num_time_features
								__UpperCAmelCase      					=      lags_sequence
								__UpperCAmelCase      					=      scaling
								__UpperCAmelCase      					=      num_dynamic_real_features
								__UpperCAmelCase      					=      num_static_real_features
								__UpperCAmelCase      					=      num_static_categorical_features
								if cardinality 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      					=      cardinality
								else:
									__UpperCAmelCase      					=      [0]
								if embedding_dimension 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      					=      embedding_dimension
								else:
									__UpperCAmelCase      					=      [min(5_0  ,		(cat + 1) // 2						) for cat in self.cardinality]
								__UpperCAmelCase      					=      num_parallel_samples

								# Transformer architecture configuration
								__UpperCAmelCase      					=      input_size * len(__a						) + self._number_of_features
								__UpperCAmelCase      					=      d_model
								__UpperCAmelCase      					=      encoder_attention_heads
								__UpperCAmelCase      					=      decoder_attention_heads
								__UpperCAmelCase      					=      encoder_ffn_dim
								__UpperCAmelCase      					=      decoder_ffn_dim
								__UpperCAmelCase      					=      encoder_layers
								__UpperCAmelCase      					=      decoder_layers

								__UpperCAmelCase      					=      dropout
								__UpperCAmelCase      					=      attention_dropout
								__UpperCAmelCase      					=      activation_dropout
								__UpperCAmelCase      					=      encoder_layerdrop
								__UpperCAmelCase      					=      decoder_layerdrop

								__UpperCAmelCase      					=      activation_function
								__UpperCAmelCase      					=      init_std

								__UpperCAmelCase      					=      use_cache

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






							@property
							def    snake_case__    (   self					:				List[Any]						)				->						int:
								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
								)
