


"""simple docstring"""






import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError

from transformers import AutoFeatureExtractor, WavaVecaFeatureExtractor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test


sys.path.append(str(Path(__file__).parent.parent / """utils"""))

from test_module.custom_feature_extraction import CustomFeatureExtractor  # noqa E402


UpperCamelCase__				:int			   =	get_tests_dir("""fixtures""")






class      A(  unittest.TestCase		):

		"""simple docstring"""





		def   _UpperCamelCase( self   )      ->	Optional[int]:

					"""simple docstring"""



					_UpperCamelCase					:List[Any]					     =	mock.Mock()
					_UpperCamelCase					:Tuple					     =	5_00
					_UpperCamelCase					:Optional[int]					     =	{}
					_UpperCamelCase					:Any					     =	HTTPError
					_UpperCamelCase					:Tuple					     =	{}

					# Download this model to make sure it's in the cache.
					_UpperCamelCase					:Tuple					     =	WavaVecaFeatureExtractor.from_pretrained('''hf-internal-testing/tiny-random-wav2vec2'''   )
					# Under the mock environment we get a 500 error when trying to reach the model.
					with mock.patch('''requests.Session.request'''					,				return_value=__lowerCamelCase   ) as mock_head:
								_UpperCamelCase					:str					     =	WavaVecaFeatureExtractor.from_pretrained('''hf-internal-testing/tiny-random-wav2vec2'''   )
								# This check we did call the fake head request
								mock_head.assert_called()





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

					"""simple docstring"""



					_UpperCamelCase					:Union[str, Any]					     =	WavaVecaFeatureExtractor.from_pretrained(
					    '''https://huggingface.co/hf-internal-testing/tiny-random-wav2vec2/resolve/main/preprocessor_config.json'''   )






@is_staging_test
class      A(  unittest.TestCase		):

		"""simple docstring"""





		@classmethod
		def   _UpperCamelCase( cls   )      ->	Optional[int]:

					"""simple docstring"""



					_UpperCamelCase					:Tuple					     =	TOKEN
					HfFolder.save_token(__lowerCamelCase   )
		@classmethod
		def   _UpperCamelCase( cls   )      ->	Union[str, Any]:

					"""simple docstring"""



					try:
								delete_repo(token=cls._token					,				repo_id='''test-feature-extractor'''   )
					except HTTPError:
								pass

					try:
								delete_repo(token=cls._token					,				repo_id='''valid_org/test-feature-extractor-org'''   )
					except HTTPError:
								pass

					try:
								delete_repo(token=cls._token					,				repo_id='''test-dynamic-feature-extractor'''   )
					except HTTPError:
								pass
		def   _UpperCamelCase( self   )      ->	Union[str, Any]:

					"""simple docstring"""



					_UpperCamelCase					:Tuple					     =	WavaVecaFeatureExtractor.from_pretrained(__lowerCamelCase   )
					feature_extractor.push_to_hub('''test-feature-extractor'''					,				use_auth_token=self._token   )

					_UpperCamelCase					:str					     =	WavaVecaFeatureExtractor.from_pretrained(f"{USER}/test-feature-extractor"   )
					for k, v in feature_extractor.__dict__.items():
								self.assertEqual(__lowerCamelCase					,				getattr(__lowerCamelCase					,				__lowerCamelCase   )   )

					# Reset repo
					delete_repo(token=self._token					,				repo_id='''test-feature-extractor'''   )

					# Push to hub via save_pretrained
					with tempfile.TemporaryDirectory() as tmp_dir:
								feature_extractor.save_pretrained(
								    __lowerCamelCase					,				repo_id='''test-feature-extractor'''					,				push_to_hub=__lowerCamelCase					,				use_auth_token=self._token   )

					_UpperCamelCase					:Any					     =	WavaVecaFeatureExtractor.from_pretrained(f"{USER}/test-feature-extractor"   )
					for k, v in feature_extractor.__dict__.items():
								self.assertEqual(__lowerCamelCase					,				getattr(__lowerCamelCase					,				__lowerCamelCase   )   )
		def   _UpperCamelCase( self   )      ->	Dict:

					"""simple docstring"""



					_UpperCamelCase					:str					     =	WavaVecaFeatureExtractor.from_pretrained(__lowerCamelCase   )
					feature_extractor.push_to_hub('''valid_org/test-feature-extractor'''					,				use_auth_token=self._token   )

					_UpperCamelCase					:str					     =	WavaVecaFeatureExtractor.from_pretrained('''valid_org/test-feature-extractor'''   )
					for k, v in feature_extractor.__dict__.items():
								self.assertEqual(__lowerCamelCase					,				getattr(__lowerCamelCase					,				__lowerCamelCase   )   )

					# Reset repo
					delete_repo(token=self._token					,				repo_id='''valid_org/test-feature-extractor'''   )

					# Push to hub via save_pretrained
					with tempfile.TemporaryDirectory() as tmp_dir:
								feature_extractor.save_pretrained(
								    __lowerCamelCase					,				repo_id='''valid_org/test-feature-extractor-org'''					,				push_to_hub=__lowerCamelCase					,				use_auth_token=self._token   )

					_UpperCamelCase					:Dict					     =	WavaVecaFeatureExtractor.from_pretrained('''valid_org/test-feature-extractor-org'''   )
					for k, v in feature_extractor.__dict__.items():
								self.assertEqual(__lowerCamelCase					,				getattr(__lowerCamelCase					,				__lowerCamelCase   )   )





		def   _UpperCamelCase( self   )      ->	str:

					"""simple docstring"""



					CustomFeatureExtractor.register_for_auto_class()
					_UpperCamelCase					:Dict					     =	CustomFeatureExtractor.from_pretrained(__lowerCamelCase   )

					feature_extractor.push_to_hub('''test-dynamic-feature-extractor'''					,				use_auth_token=self._token   )

					# This has added the proper auto_map field to the config
					self.assertDictEqual(
					    feature_extractor.auto_map					,				{'''AutoFeatureExtractor''': '''custom_feature_extraction.CustomFeatureExtractor'''}					,				)

					_UpperCamelCase					:Tuple					     =	AutoFeatureExtractor.from_pretrained(
					    f"{USER}/test-dynamic-feature-extractor"					,				trust_remote_code=__lowerCamelCase   )
					# Can't make an isinstance check because the new_feature_extractor is from the CustomFeatureExtractor class of a dynamic module
					self.assertEqual(new_feature_extractor.__class__.__name__					,				'''CustomFeatureExtractor'''   )





'''simple docstring'''





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


 def __init__(      self							:	List[str]			,       __lowerCamelCase							:	Optional[int] = None			,       __lowerCamelCase							:	Optional[int] = None			,       __lowerCamelCase							:	str = "student_t"			,       __lowerCamelCase							:	str = "nll"			,       __lowerCamelCase							:	int = 1			,       __lowerCamelCase							:	List[int] = [1, 2, 3, 4, 5, 6, 7]			,       __lowerCamelCase							:	bool = True			,       __lowerCamelCase							:	int = 0			,       __lowerCamelCase							:	int = 0			,       __lowerCamelCase							:	int = 0			,       __lowerCamelCase							:	int = 0			,       __lowerCamelCase							:	Optional[List[int]] = None			,       __lowerCamelCase							:	Optional[List[int]] = None			,       __lowerCamelCase							:	int = 6_4			,       __lowerCamelCase							:	int = 2			,       __lowerCamelCase							:	int = 2			,       __lowerCamelCase							:	int = 2			,       __lowerCamelCase							:	int = 2			,       __lowerCamelCase							:	int = 3_2			,       __lowerCamelCase							:	int = 3_2			,       __lowerCamelCase							:	str = "gelu"			,       __lowerCamelCase							:	float = 0.1			,       __lowerCamelCase							:	float = 0.1			,       __lowerCamelCase							:	float = 0.1			,       __lowerCamelCase							:	float = 0.1			,       __lowerCamelCase							:	float = 0.1			,       __lowerCamelCase							:	int = 1_0_0			,       __lowerCamelCase							:	float = 0.02			,       __lowerCamelCase							:	bool = True			,       __lowerCamelCase							:	Optional[int]=True			,       __lowerCamelCase							:	int = 1_0			,       __lowerCamelCase							:	int = 2_5			,       __lowerCamelCase							:	int = 3			,       **__lowerCamelCase							:	int			,       ):
    # time series specific configuration
    UpperCAmelCase__	:Any      =      prediction_length
    UpperCAmelCase__	:Optional[int]      =      context_length if context_length is not None else prediction_length
    UpperCAmelCase__	:int      =      distribution_output
    UpperCAmelCase__	:str      =      loss
    UpperCAmelCase__	:Tuple      =      input_size
    UpperCAmelCase__	:Optional[int]      =      num_time_features
    UpperCAmelCase__	:List[Any]      =      lags_sequence
    UpperCAmelCase__	:Any      =      scaling
    UpperCAmelCase__	:Union[str, Any]      =      num_dynamic_real_features
    UpperCAmelCase__	:Dict      =      num_static_real_features
    UpperCAmelCase__	:Optional[int]      =      num_static_categorical_features
    if cardinality is not None 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`''' )
       UpperCAmelCase__	:Any      =      cardinality
    else:
       UpperCAmelCase__	:Dict      =      [0]
    if embedding_dimension is not None 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`''' )
       UpperCAmelCase__	:List[str]      =      embedding_dimension
    else:
       UpperCAmelCase__	:int      =      [min(5_0			,       (cat + 1) // 2 ) for cat in self.cardinality]
    UpperCAmelCase__	:Optional[Any]      =      num_parallel_samples

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

    UpperCAmelCase__	:Optional[Any]      =      dropout
    UpperCAmelCase__	:str      =      attention_dropout
    UpperCAmelCase__	:int      =      activation_dropout
    UpperCAmelCase__	:Optional[Any]      =      encoder_layerdrop
    UpperCAmelCase__	:Dict      =      decoder_layerdrop

    UpperCAmelCase__	:Union[str, Any]      =      activation_function
    UpperCAmelCase__	:List[Any]      =      init_std

    UpperCAmelCase__	:List[str]      =      use_cache

    # Autoformer
    UpperCAmelCase__	:List[str]      =      label_length
    UpperCAmelCase__	:Dict      =      moving_average
    UpperCAmelCase__	:str      =      autocorrelation_factor

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





 @property
 def        __SCREAMING_SNAKE_CASE					(      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
    )



