




"""simple docstring"""





from typing import List, Optional

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


A   =       logging.get_logger(__name__)

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



class      a__				(	__magic_name__   ):
			lowercase_    =	"autoformer"
			lowercase_    =	{
			    "hidden_size": "d_model",
			    "num_attention_heads": "encoder_attention_heads",
			    "num_hidden_layers": "encoder_layers",
			}


			def __init__(      self      :    int      ,  UpperCamelCase_      :    Optional[int] = None      ,  UpperCamelCase_      :    Optional[int] = None      ,  UpperCamelCase_      :    str = "student_t"      ,  UpperCamelCase_      :    str = "nll"      ,  UpperCamelCase_      :    int = 1      ,  UpperCamelCase_      :    List[int] = [1, 2, 3, 4, 5, 6, 7]      ,  UpperCamelCase_      :    bool = True      ,  UpperCamelCase_      :    int = 0      ,  UpperCamelCase_      :    int = 0      ,  UpperCamelCase_      :    int = 0      ,  UpperCamelCase_      :    int = 0      ,  UpperCamelCase_      :    Optional[List[int]] = None      ,  UpperCamelCase_      :    Optional[List[int]] = None      ,  UpperCamelCase_      :    int = 64      ,  UpperCamelCase_      :    int = 2      ,  UpperCamelCase_      :    int = 2      ,  UpperCamelCase_      :    int = 2      ,  UpperCamelCase_      :    int = 2      ,  UpperCamelCase_      :    int = 32      ,  UpperCamelCase_      :    int = 32      ,  UpperCamelCase_      :    str = "gelu"      ,  UpperCamelCase_      :    float = 0.1      ,  UpperCamelCase_      :    float = 0.1      ,  UpperCamelCase_      :    float = 0.1      ,  UpperCamelCase_      :    float = 0.1      ,  UpperCamelCase_      :    float = 0.1      ,  UpperCamelCase_      :    int = 100      ,  UpperCamelCase_      :    float = 0.02      ,  UpperCamelCase_      :    bool = True      ,  UpperCamelCase_      :    List[Any]=True      ,  UpperCamelCase_      :    int = 10      ,  UpperCamelCase_      :    int = 25      ,  UpperCamelCase_      :    int = 3      ,  **UpperCamelCase_      :    int      ,  ):


						"""simple docstring"""




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

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

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

						__UpperCAmelCase	: str		=					activation_function
						__UpperCAmelCase	: Optional[Any]		=					init_std

						__UpperCAmelCase	: int		=					use_cache

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

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


			@property
			def       a_     (      self      :    Dict):


						"""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
						)








"""simple docstring"""





import shutil
import tempfile
import unittest

from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio

from .test_feature_extraction_clap import floats_list



@require_torchaudio
@require_sentencepiece
class      a__				(	unittest.TestCase   ):


			def       a_     (      self      :    Any):


						"""simple docstring"""




						__UpperCAmelCase	: Tuple		=					"laion/clap-htsat-unfused"
						__UpperCAmelCase	: List[str]		=					tempfile.mkdtemp()


			def       a_     (      self      :    Optional[Any]      ,  **UpperCamelCase_      :    Union[str, Any]):


						"""simple docstring"""




						return RobertaTokenizer.from_pretrained(self.checkpoint      ,  **UpperCamelCase_)


			def       a_     (      self      :    Union[str, Any]      ,  **UpperCamelCase_      :    Optional[int]):


						"""simple docstring"""




						return ClapFeatureExtractor.from_pretrained(self.checkpoint      ,  **UpperCamelCase_)


			def       a_     (      self      :    List[Any]):


						"""simple docstring"""




						shutil.rmtree(self.tmpdirname)


			def       a_     (      self      :    Optional[int]):


						"""simple docstring"""




						__UpperCAmelCase	: Union[str, Any]		=					self.get_tokenizer()
						__UpperCAmelCase	: str		=					self.get_feature_extractor()

						__UpperCAmelCase	: List[Any]		=					ClapProcessor(tokenizer=UpperCamelCase_      ,  feature_extractor=UpperCamelCase_)

						processor.save_pretrained(self.tmpdirname)
						__UpperCAmelCase	: Union[str, Any]		=					ClapProcessor.from_pretrained(self.tmpdirname)

						self.assertEqual(processor.tokenizer.get_vocab()      ,  tokenizer.get_vocab())
						self.assertIsInstance(processor.tokenizer      ,  UpperCamelCase_)

						self.assertEqual(processor.feature_extractor.to_json_string()      ,  feature_extractor.to_json_string())
						self.assertIsInstance(processor.feature_extractor      ,  UpperCamelCase_)


			def       a_     (      self      :    Any):


						"""simple docstring"""




						__UpperCAmelCase	: Union[str, Any]		=					ClapProcessor(tokenizer=self.get_tokenizer()      ,  feature_extractor=self.get_feature_extractor())
						processor.save_pretrained(self.tmpdirname)

						__UpperCAmelCase	: int		=					self.get_tokenizer(bos_token="(BOS)"      ,  eos_token="(EOS)")
						__UpperCAmelCase	: Any		=					self.get_feature_extractor(do_normalize=UpperCamelCase_      ,  padding_value=1.0)

						__UpperCAmelCase	: Tuple		=					ClapProcessor.from_pretrained(
						    self.tmpdirname      ,  bos_token="(BOS)"      ,  eos_token="(EOS)"      ,  do_normalize=UpperCamelCase_      ,  padding_value=1.0)

						self.assertEqual(processor.tokenizer.get_vocab()      ,  tokenizer_add_kwargs.get_vocab())
						self.assertIsInstance(processor.tokenizer      ,  UpperCamelCase_)

						self.assertEqual(processor.feature_extractor.to_json_string()      ,  feature_extractor_add_kwargs.to_json_string())
						self.assertIsInstance(processor.feature_extractor      ,  UpperCamelCase_)


			def       a_     (      self      :    Any):


						"""simple docstring"""




						__UpperCAmelCase	: Tuple		=					self.get_feature_extractor()
						__UpperCAmelCase	: int		=					self.get_tokenizer()

						__UpperCAmelCase	: Any		=					ClapProcessor(tokenizer=UpperCamelCase_      ,  feature_extractor=UpperCamelCase_)

						__UpperCAmelCase	: Optional[int]		=					floats_list((3, 1000))

						__UpperCAmelCase	: Tuple		=					feature_extractor(UpperCamelCase_      ,  return_tensors="np")
						__UpperCAmelCase	: Any		=					processor(audios=UpperCamelCase_      ,  return_tensors="np")

						for key in input_feat_extract.keys():
									self.assertAlmostEqual(input_feat_extract[key].sum()      ,  input_processor[key].sum()      ,  delta=1e-2)


			def       a_     (      self      :    Optional[Any]):


						"""simple docstring"""




						__UpperCAmelCase	: Union[str, Any]		=					self.get_feature_extractor()
						__UpperCAmelCase	: Optional[int]		=					self.get_tokenizer()

						__UpperCAmelCase	: str		=					ClapProcessor(tokenizer=UpperCamelCase_      ,  feature_extractor=UpperCamelCase_)

						__UpperCAmelCase	: Any		=					"This is a test string"

						__UpperCAmelCase	: List[Any]		=					processor(text=UpperCamelCase_)

						__UpperCAmelCase	: Dict		=					tokenizer(UpperCamelCase_)

						for key in encoded_tok.keys():
									self.assertListEqual(encoded_tok[key]      ,  encoded_processor[key])


			def       a_     (      self      :    Tuple):


						"""simple docstring"""




						__UpperCAmelCase	: int		=					self.get_feature_extractor()
						__UpperCAmelCase	: List[str]		=					self.get_tokenizer()

						__UpperCAmelCase	: List[str]		=					ClapProcessor(tokenizer=UpperCamelCase_      ,  feature_extractor=UpperCamelCase_)

						__UpperCAmelCase	: Tuple		=					[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

						__UpperCAmelCase	: int		=					processor.batch_decode(UpperCamelCase_)
						__UpperCAmelCase	: Tuple		=					tokenizer.batch_decode(UpperCamelCase_)

						self.assertListEqual(UpperCamelCase_      ,  UpperCamelCase_)


			def       a_     (      self      :    Optional[int]):


						"""simple docstring"""




						__UpperCAmelCase	: Any		=					self.get_feature_extractor()
						__UpperCAmelCase	: int		=					self.get_tokenizer()

						__UpperCAmelCase	: int		=					ClapProcessor(tokenizer=UpperCamelCase_      ,  feature_extractor=UpperCamelCase_)

						self.assertListEqual(
						    processor.model_input_names[2:]      ,  feature_extractor.model_input_names      ,  msg="`processor` and `feature_extractor` model input names do not match"      ,  )


