






"""simple docstring"""
import os
import sys
import unittest


lowerCAmelCase__    						=			os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))

import get_test_info  # noqa: E402
from get_test_info import (  # noqa: E402
    get_model_to_test_mapping,
    get_model_to_tester_mapping,
    get_test_to_tester_mapping,
)


lowerCAmelCase__    						=			os.path.join('tests', 'models', 'bert', 'test_modeling_bert.py')
lowerCAmelCase__    						=			os.path.join('tests', 'models', 'blip', 'test_modeling_blip.py')


class _lowerCAmelCase						(    unittest.TestCase       ):







						def 			A      (    self							)		->							List[str]:
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    get_test_to_tester_mapping(lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    get_test_to_tester_mapping(lowerCAmelCase_							)

										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    {'BertModelTest': 'BertModelTester'}

										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    {
										    'BlipModelTest': 'BlipModelTester',
										    'BlipTextImageModelTest': 'BlipTextImageModelsModelTester',
										    'BlipTextModelTest': 'BlipTextModelTester',
										    'BlipTextRetrievalModelTest': 'BlipTextRetrievalModelTester',
										    'BlipVQAModelTest': 'BlipVQAModelTester',
										    'BlipVisionModelTest': 'BlipVisionModelTester',
										}

										self.assertEqual(get_test_info.to_json(lowerCAmelCase_							)   ,				lowerCAmelCase_							)
										self.assertEqual(get_test_info.to_json(lowerCAmelCase_							)   ,				lowerCAmelCase_							)







						def 			A      (    self							)		->							Tuple:
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    get_model_to_test_mapping(lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		Dict       		=    get_model_to_test_mapping(lowerCAmelCase_							)

										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    {
										    'BertForMaskedLM': ['BertModelTest'],
										    'BertForMultipleChoice': ['BertModelTest'],
										    'BertForNextSentencePrediction': ['BertModelTest'],
										    'BertForPreTraining': ['BertModelTest'],
										    'BertForQuestionAnswering': ['BertModelTest'],
										    'BertForSequenceClassification': ['BertModelTest'],
										    'BertForTokenClassification': ['BertModelTest'],
										    'BertLMHeadModel': ['BertModelTest'],
										    'BertModel': ['BertModelTest'],
										}

										_SCREAMING_SNAKE_CASE   :		Dict       		=    {
										    'BlipForConditionalGeneration': ['BlipTextImageModelTest'],
										    'BlipForImageTextRetrieval': ['BlipTextRetrievalModelTest'],
										    'BlipForQuestionAnswering': ['BlipVQAModelTest'],
										    'BlipModel': ['BlipModelTest'],
										    'BlipTextModel': ['BlipTextModelTest'],
										    'BlipVisionModel': ['BlipVisionModelTest'],
										}

										self.assertEqual(get_test_info.to_json(lowerCAmelCase_							)   ,				lowerCAmelCase_							)
										self.assertEqual(get_test_info.to_json(lowerCAmelCase_							)   ,				lowerCAmelCase_							)







						def 			A      (    self							)		->							str:
										_SCREAMING_SNAKE_CASE   :		str       		=    get_model_to_tester_mapping(lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		int       		=    get_model_to_tester_mapping(lowerCAmelCase_							)

										_SCREAMING_SNAKE_CASE   :		List[str]       		=    {
										    'BertForMaskedLM': ['BertModelTester'],
										    'BertForMultipleChoice': ['BertModelTester'],
										    'BertForNextSentencePrediction': ['BertModelTester'],
										    'BertForPreTraining': ['BertModelTester'],
										    'BertForQuestionAnswering': ['BertModelTester'],
										    'BertForSequenceClassification': ['BertModelTester'],
										    'BertForTokenClassification': ['BertModelTester'],
										    'BertLMHeadModel': ['BertModelTester'],
										    'BertModel': ['BertModelTester'],
										}

										_SCREAMING_SNAKE_CASE   :		Dict       		=    {
										    'BlipForConditionalGeneration': ['BlipTextImageModelsModelTester'],
										    'BlipForImageTextRetrieval': ['BlipTextRetrievalModelTester'],
										    'BlipForQuestionAnswering': ['BlipVQAModelTester'],
										    'BlipModel': ['BlipModelTester'],
										    'BlipTextModel': ['BlipTextModelTester'],
										    'BlipVisionModel': ['BlipVisionModelTester'],
										}

										self.assertEqual(get_test_info.to_json(lowerCAmelCase_							)   ,				lowerCAmelCase_							)
										self.assertEqual(get_test_info.to_json(lowerCAmelCase_							)   ,				lowerCAmelCase_							)









"""simple docstring"""
from typing import List, Optional, Union

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


lowerCAmelCase__    						=			logging.get_logger(__name__)

lowerCAmelCase__    						=			{
    '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 _lowerCAmelCase						(    __UpperCAmelCase       ):
						SCREAMING_SNAKE_CASE_:					Any 					=				'time_series_transformer'
						SCREAMING_SNAKE_CASE_:					List[str] 					=				{
						    'hidden_size': 'd_model',
						    'num_attention_heads': 'encoder_attention_heads',
						    'num_hidden_layers': 'encoder_layers',
						}







						def __init__(    self   ,				lowerCAmelCase_ = None   ,				lowerCAmelCase_ = None   ,				lowerCAmelCase_ = "student_t"   ,				lowerCAmelCase_ = "nll"   ,				lowerCAmelCase_ = 1   ,				lowerCAmelCase_ = [1, 2, 3, 4, 5, 6, 7]   ,				lowerCAmelCase_ = "mean"   ,				lowerCAmelCase_ = 0   ,				lowerCAmelCase_ = 0   ,				lowerCAmelCase_ = 0   ,				lowerCAmelCase_ = 0   ,				lowerCAmelCase_ = None   ,				lowerCAmelCase_ = None   ,				lowerCAmelCase_ = 3_2   ,				lowerCAmelCase_ = 3_2   ,				lowerCAmelCase_ = 2   ,				lowerCAmelCase_ = 2   ,				lowerCAmelCase_ = 2   ,				lowerCAmelCase_ = 2   ,				lowerCAmelCase_ = True   ,				lowerCAmelCase_ = "gelu"   ,				lowerCAmelCase_ = 6_4   ,				lowerCAmelCase_ = 0.1   ,				lowerCAmelCase_ = 0.1   ,				lowerCAmelCase_ = 0.1   ,				lowerCAmelCase_ = 0.1   ,				lowerCAmelCase_ = 0.1   ,				lowerCAmelCase_ = 1_0_0   ,				lowerCAmelCase_ = 0.02   ,				lowerCAmelCase_=True   ,				**lowerCAmelCase_   ,				)		->							str:
										# time series specific configuration
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    prediction_length
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    context_length or prediction_length
										_SCREAMING_SNAKE_CASE   :		Any       		=    distribution_output
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    loss
										_SCREAMING_SNAKE_CASE   :		Any       		=    input_size
										_SCREAMING_SNAKE_CASE   :		Any       		=    num_time_features
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    lags_sequence
										_SCREAMING_SNAKE_CASE   :		Dict       		=    scaling
										_SCREAMING_SNAKE_CASE   :		Any       		=    num_dynamic_real_features
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    num_static_real_features
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    num_static_categorical_features
										if cardinality and num_static_categorical_features > 0:
														if len(lowerCAmelCase_							) != num_static_categorical_features:
																		raise ValueError(
																		    'The cardinality should be a list of the same length as `num_static_categorical_features`'							)
														_SCREAMING_SNAKE_CASE   :		List[str]       		=    cardinality
										else:
														_SCREAMING_SNAKE_CASE   :		Dict       		=    [0]
										if embedding_dimension and num_static_categorical_features > 0:
														if len(lowerCAmelCase_							) != num_static_categorical_features:
																		raise ValueError(
																		    'The embedding dimension should be a list of the same length as `num_static_categorical_features`'							)
														_SCREAMING_SNAKE_CASE   :		int       		=    embedding_dimension
										else:
														_SCREAMING_SNAKE_CASE   :		List[str]       		=    [min(5_0   ,				(cat + 1) // 2							) for cat in self.cardinality]
										_SCREAMING_SNAKE_CASE   :		str       		=    num_parallel_samples

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

										_SCREAMING_SNAKE_CASE   :		Dict       		=    dropout
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    attention_dropout
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    activation_dropout
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    encoder_layerdrop
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    decoder_layerdrop

										_SCREAMING_SNAKE_CASE   :		Dict       		=    activation_function
										_SCREAMING_SNAKE_CASE   :		str       		=    init_std

										_SCREAMING_SNAKE_CASE   :		Tuple       		=    use_cache

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







						@property
						def 			A      (    self							)		->							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
										)

