'''simple docstring'''
import numpy as np

from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch

from .test_feature_extraction_common import FeatureExtractionSavingTestMixin






class 			__lowercase		(						_lowerCamelCase						):







     """simple docstring"""






     SCREAMING_SNAKE_CASE__		   =						None
     SCREAMING_SNAKE_CASE__		   =						None







     @property
     def lowerCAmelCase     (		self    ):
         return self.feat_extract_tester.prepare_feat_extract_dict()







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	int		    =  self.feature_extraction_class(**self.feat_extract_dict    )
         self.assertTrue(hasattr(_lowerCamelCase     ,			'feature_size'    )    )
         self.assertTrue(hasattr(_lowerCamelCase     ,			'sampling_rate'    )    )
         self.assertTrue(hasattr(_lowerCamelCase     ,			'padding_value'    )    )







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Tuple		    =  self.feat_extract_tester.prepare_inputs_for_common()
         __UpperCamelCase	:	List[str]		    =  self.feature_extraction_class(**self.feat_extract_dict    )
         __UpperCamelCase	:	Tuple		    =  feat_extract.model_input_names[0]

         __UpperCamelCase	:	Optional[Any]		    =  BatchFeature({input_name: speech_inputs}    )

         self.assertTrue(all(len(_lowerCamelCase    ) == len(_lowerCamelCase    ) for x, y in zip(_lowerCamelCase     ,			processed_features[input_name]    )    )    )

         __UpperCamelCase	:	List[str]		    =  self.feat_extract_tester.prepare_inputs_for_common(equal_length=_lowerCamelCase    )
         __UpperCamelCase	:	int		    =  BatchFeature({input_name: speech_inputs}     ,			tensor_type='np'    )

         __UpperCamelCase	:	Dict		    =  processed_features[input_name]

         if len(batch_features_input.shape    ) < 3:
             __UpperCamelCase	:	Optional[int]		    =  batch_features_input[:, :, None]

         self.assertTrue(
             batch_features_input.shape
             == (self.feat_extract_tester.batch_size, len(speech_inputs[0]    ), self.feat_extract_tester.feature_size)    )







     @require_torch
     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Union[str, Any]		    =  self.feat_extract_tester.prepare_inputs_for_common(equal_length=_lowerCamelCase    )
         __UpperCamelCase	:	Any		    =  self.feature_extraction_class(**self.feat_extract_dict    )
         __UpperCamelCase	:	Optional[Any]		    =  feat_extract.model_input_names[0]

         __UpperCamelCase	:	str		    =  BatchFeature({input_name: speech_inputs}     ,			tensor_type='pt'    )

         __UpperCamelCase	:	List[str]		    =  processed_features[input_name]

         if len(batch_features_input.shape    ) < 3:
             __UpperCamelCase	:	Tuple		    =  batch_features_input[:, :, None]

         self.assertTrue(
             batch_features_input.shape
             == (self.feat_extract_tester.batch_size, len(speech_inputs[0]    ), self.feat_extract_tester.feature_size)    )







     @require_tf
     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Tuple		    =  self.feat_extract_tester.prepare_inputs_for_common(equal_length=_lowerCamelCase    )
         __UpperCamelCase	:	Any		    =  self.feature_extraction_class(**self.feat_extract_dict    )
         __UpperCamelCase	:	List[str]		    =  feat_extract.model_input_names[0]

         __UpperCamelCase	:	int		    =  BatchFeature({input_name: speech_inputs}     ,			tensor_type='tf'    )

         __UpperCamelCase	:	Dict		    =  processed_features[input_name]

         if len(batch_features_input.shape    ) < 3:
             __UpperCamelCase	:	Optional[int]		    =  batch_features_input[:, :, None]

         self.assertTrue(
             batch_features_input.shape
             == (self.feat_extract_tester.batch_size, len(speech_inputs[0]    ), self.feat_extract_tester.feature_size)    )







     def lowerCAmelCase     (		self     ,			_lowerCamelCase=False    ):
         def _inputs_have_equal_length(_lowerCamelCase    ):
             __UpperCamelCase	:	Optional[int]		    =  len(input[0]    )
             for input_slice in input[1:]:
                 if len(_lowerCamelCase    ) != length:
                     return False
             return True

         def _inputs_are_equal(_lowerCamelCase     ,			_lowerCamelCase    ):
             if len(_lowerCamelCase    ) != len(_lowerCamelCase    ):
                 return False

             for input_slice_a, input_slice_a in zip(_lowerCamelCase     ,			_lowerCamelCase    ):
                 if not np.allclose(np.asarray(_lowerCamelCase    )     ,			np.asarray(_lowerCamelCase    )     ,			atol=1E-3    ):
                     return False
             return True

         __UpperCamelCase	:	Dict		    =  self.feature_extraction_class(**self.feat_extract_dict    )
         __UpperCamelCase	:	Dict		    =  self.feat_extract_tester.prepare_inputs_for_common(numpify=_lowerCamelCase    )
         __UpperCamelCase	:	Union[str, Any]		    =  feat_extract.model_input_names[0]

         __UpperCamelCase	:	Any		    =  BatchFeature({input_name: speech_inputs}    )

         __UpperCamelCase	:	Any		    =  self.feat_extract_tester.seq_length_diff
         __UpperCamelCase	:	List[Any]		    =  self.feat_extract_tester.max_seq_length + pad_diff
         __UpperCamelCase	:	Any		    =  self.feat_extract_tester.min_seq_length
         __UpperCamelCase	:	Tuple		    =  self.feat_extract_tester.batch_size
         __UpperCamelCase	:	str		    =  self.feat_extract_tester.feature_size

         # test padding for List[int] + numpy
         __UpperCamelCase	:	str		    =  feat_extract.pad(_lowerCamelCase     ,			padding=_lowerCamelCase    )
         __UpperCamelCase	:	List[Any]		    =  input_a[input_name]

         __UpperCamelCase	:	List[Any]		    =  feat_extract.pad(_lowerCamelCase     ,			padding='longest'    )
         __UpperCamelCase	:	List[str]		    =  input_a[input_name]

         __UpperCamelCase	:	Tuple		    =  feat_extract.pad(_lowerCamelCase     ,			padding='max_length'     ,			max_length=len(speech_inputs[-1]    )    )
         __UpperCamelCase	:	Optional[Any]		    =  input_a[input_name]

         __UpperCamelCase	:	Optional[int]		    =  feat_extract.pad(_lowerCamelCase     ,			padding='longest'     ,			return_tensors='np'    )
         __UpperCamelCase	:	int		    =  input_a[input_name]

         # max_length parameter has to be provided when setting `padding="max_length"`
         with self.assertRaises(_lowerCamelCase    ):
             feat_extract.pad(_lowerCamelCase     ,			padding='max_length'    )[input_name]

         __UpperCamelCase	:	Optional[int]		    =  feat_extract.pad(
             _lowerCamelCase     ,			padding='max_length'     ,			max_length=_lowerCamelCase     ,			return_tensors='np'    )
         __UpperCamelCase	:	Dict		    =  input_a[input_name]

         self.assertFalse(_inputs_have_equal_length(_lowerCamelCase    )    )
         self.assertTrue(_inputs_have_equal_length(_lowerCamelCase    )    )
         self.assertTrue(_inputs_have_equal_length(_lowerCamelCase    )    )
         self.assertTrue(_inputs_are_equal(_lowerCamelCase     ,			_lowerCamelCase    )    )
         self.assertTrue(len(input_a[0]    ) == pad_min_length    )
         self.assertTrue(len(input_a[1]    ) == pad_min_length + pad_diff    )
         self.assertTrue(input_a.shape[:2] == (batch_size, len(input_a[0]    ))    )
         self.assertTrue(input_a.shape[:2] == (batch_size, pad_max_length)    )

         if feature_size > 1:
             self.assertTrue(input_a.shape[2] == input_a.shape[2] == feature_size    )

         # test padding for `pad_to_multiple_of` for List[int] + numpy
         __UpperCamelCase	:	str		    =  feat_extract.pad(_lowerCamelCase     ,			pad_to_multiple_of=1_0    )
         __UpperCamelCase	:	Any		    =  input_a[input_name]

         __UpperCamelCase	:	Optional[Any]		    =  feat_extract.pad(_lowerCamelCase     ,			padding='longest'     ,			pad_to_multiple_of=1_0    )
         __UpperCamelCase	:	str		    =  input_a[input_name]

         __UpperCamelCase	:	Optional[int]		    =  feat_extract.pad(
             _lowerCamelCase     ,			padding='max_length'     ,			pad_to_multiple_of=1_0     ,			max_length=_lowerCamelCase    )
         __UpperCamelCase	:	str		    =  input_a[input_name]

         __UpperCamelCase	:	List[str]		    =  feat_extract.pad(
             _lowerCamelCase     ,			padding='max_length'     ,			pad_to_multiple_of=1_0     ,			max_length=_lowerCamelCase     ,			return_tensors='np'     ,			)
         __UpperCamelCase	:	Optional[Any]		    =  input_a[input_name]

         self.assertTrue(all(len(_lowerCamelCase    ) % 1_0 == 0 for x in input_a    )    )
         self.assertTrue(_inputs_are_equal(_lowerCamelCase     ,			_lowerCamelCase    )    )

         __UpperCamelCase	:	List[str]		    =  pad_max_length if pad_max_length % 1_0 == 0 else (pad_max_length // 1_0 + 1) * 1_0
         self.assertTrue(all(len(_lowerCamelCase    ) == expected_mult_pad_length for x in input_a    )    )
         self.assertEqual(input_a.shape[:2]     ,			(batch_size, expected_mult_pad_length)    )

         if feature_size > 1:
             self.assertTrue(input_a.shape[2] == feature_size    )

         # Check padding value is correct
         __UpperCamelCase	:	List[str]		    =  (np.ones(self.feat_extract_tester.feature_size    ) * feat_extract.padding_value).sum()
         self.assertTrue(
             abs(np.asarray(input_a[0]    )[pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)    )
             < 1E-3    )
         self.assertTrue(
             abs(
                 np.asarray(input_a[1]    )[pad_min_length + pad_diff :].sum()
                 - padding_vector_sum * (pad_max_length - pad_min_length - pad_diff)    )
             < 1E-3    )
         self.assertTrue(
             abs(
                 np.asarray(input_a[2]    )[pad_min_length + 2 * pad_diff :].sum()
                 - padding_vector_sum * (pad_max_length - pad_min_length - 2 * pad_diff)    )
             < 1E-3    )
         self.assertTrue(
             abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)    ) < 1E-3    )
         self.assertTrue(
             abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (expected_mult_pad_length - pad_min_length)    )
             < 1E-3    )







     def lowerCAmelCase     (		self     ,			_lowerCamelCase=False    ):
         def _inputs_have_equal_length(_lowerCamelCase    ):
             __UpperCamelCase	:	Any		    =  len(input[0]    )
             for input_slice in input[1:]:
                 if len(_lowerCamelCase    ) != length:
                     return False
             return True

         def _inputs_are_equal(_lowerCamelCase     ,			_lowerCamelCase    ):
             if len(_lowerCamelCase    ) != len(_lowerCamelCase    ):
                 return False

             for input_slice_a, input_slice_a in zip(_lowerCamelCase     ,			_lowerCamelCase    ):
                 if not np.allclose(np.asarray(_lowerCamelCase    )     ,			np.asarray(_lowerCamelCase    )     ,			atol=1E-3    ):
                     return False
             return True

         __UpperCamelCase	:	Optional[Any]		    =  self.feature_extraction_class(**self.feat_extract_dict    )
         __UpperCamelCase	:	int		    =  self.feat_extract_tester.prepare_inputs_for_common(numpify=_lowerCamelCase    )
         __UpperCamelCase	:	Union[str, Any]		    =  feat_extract.model_input_names[0]

         __UpperCamelCase	:	Tuple		    =  BatchFeature({input_name: speech_inputs}    )

         # truncate to smallest
         __UpperCamelCase	:	List[Any]		    =  feat_extract.pad(
             _lowerCamelCase     ,			padding='max_length'     ,			max_length=len(speech_inputs[0]    )     ,			truncation=_lowerCamelCase    )
         __UpperCamelCase	:	Any		    =  input_a[input_name]

         __UpperCamelCase	:	List[str]		    =  feat_extract.pad(_lowerCamelCase     ,			padding='max_length'     ,			max_length=len(speech_inputs[0]    )    )
         __UpperCamelCase	:	List[Any]		    =  input_a[input_name]

         self.assertTrue(_inputs_have_equal_length(_lowerCamelCase    )    )
         self.assertFalse(_inputs_have_equal_length(_lowerCamelCase    )    )

         # truncate to smallest with np
         __UpperCamelCase	:	Optional[int]		    =  feat_extract.pad(
             _lowerCamelCase     ,			padding='max_length'     ,			max_length=len(speech_inputs[0]    )     ,			return_tensors='np'     ,			truncation=_lowerCamelCase     ,			)
         __UpperCamelCase	:	Optional[int]		    =  input_a[input_name]

         __UpperCamelCase	:	Optional[Any]		    =  feat_extract.pad(
             _lowerCamelCase     ,			padding='max_length'     ,			max_length=len(speech_inputs[0]    )     ,			return_tensors='np'    )
         __UpperCamelCase	:	Optional[Any]		    =  input_a[input_name]

         self.assertTrue(_inputs_have_equal_length(_lowerCamelCase    )    )
         self.assertTrue(input_a.shape[1] == len(speech_inputs[0]    )    )

         # since truncation forces padding to be smaller than longest input
         # function can't return `np.ndarray`, but has to return list
         self.assertFalse(_inputs_have_equal_length(_lowerCamelCase    )    )

         # truncate to middle
         __UpperCamelCase	:	Tuple		    =  feat_extract.pad(
             _lowerCamelCase     ,			padding='max_length'     ,			max_length=len(speech_inputs[1]    )     ,			truncation=_lowerCamelCase     ,			return_tensors='np'     ,			)
         __UpperCamelCase	:	Dict		    =  input_a[input_name]

         __UpperCamelCase	:	List[Any]		    =  feat_extract.pad(
             _lowerCamelCase     ,			padding='max_length'     ,			max_length=len(speech_inputs[1]    )     ,			truncation=_lowerCamelCase    )
         __UpperCamelCase	:	List[str]		    =  input_a[input_name]

         __UpperCamelCase	:	List[str]		    =  feat_extract.pad(
             _lowerCamelCase     ,			padding='max_length'     ,			max_length=len(speech_inputs[1]    )     ,			return_tensors='np'    )
         __UpperCamelCase	:	Tuple		    =  input_a[input_name]

         self.assertTrue(input_a.shape[1] == len(speech_inputs[1]    )    )
         self.assertTrue(_inputs_have_equal_length(_lowerCamelCase    )    )
         self.assertTrue(_inputs_have_equal_length(_lowerCamelCase    )    )
         self.assertTrue(_inputs_are_equal(_lowerCamelCase     ,			_lowerCamelCase    )    )

         # since truncation forces padding to be smaller than longest input
         # function can't return `np.ndarray`, but has to return list
         self.assertFalse(_inputs_have_equal_length(_lowerCamelCase    )    )
         self.assertTrue(len(input_a[-1]    ) == len(speech_inputs[-1]    )    )

         # padding has to be max_length when setting `truncation=True`
         with self.assertRaises(_lowerCamelCase    ):
             feat_extract.pad(_lowerCamelCase     ,			truncation=_lowerCamelCase    )[input_name]

         # padding has to be max_length when setting `truncation=True`
         with self.assertRaises(_lowerCamelCase    ):
             feat_extract.pad(_lowerCamelCase     ,			padding='longest'     ,			truncation=_lowerCamelCase    )[input_name]

         # padding has to be max_length when setting `truncation=True`
         with self.assertRaises(_lowerCamelCase    ):
             feat_extract.pad(_lowerCamelCase     ,			padding='longest'     ,			truncation=_lowerCamelCase    )[input_name]

         # max_length parameter has to be provided when setting `truncation=True` and padding="max_length"
         with self.assertRaises(_lowerCamelCase    ):
             feat_extract.pad(_lowerCamelCase     ,			padding='max_length'     ,			truncation=_lowerCamelCase    )[input_name]

         # test truncation for `pad_to_multiple_of` for List[int] + numpy
         __UpperCamelCase	:	Union[str, Any]		    =  1_2
         __UpperCamelCase	:	Optional[Any]		    =  feat_extract.pad(
             _lowerCamelCase     ,			padding='max_length'     ,			max_length=len(speech_inputs[0]    )     ,			pad_to_multiple_of=_lowerCamelCase     ,			truncation=_lowerCamelCase     ,			)
         __UpperCamelCase	:	List[Any]		    =  input_a[input_name]

         __UpperCamelCase	:	Optional[Any]		    =  feat_extract.pad(
             _lowerCamelCase     ,			padding='max_length'     ,			max_length=len(speech_inputs[0]    )     ,			pad_to_multiple_of=_lowerCamelCase     ,			)
         __UpperCamelCase	:	List[Any]		    =  input_a[input_name]

         # retrieve expected_length as multiple of pad_to_multiple_of
         __UpperCamelCase	:	Optional[int]		    =  len(speech_inputs[0]    )
         if expected_length % pad_to_multiple_of != 0:
             __UpperCamelCase	:	Tuple		    =  ((len(speech_inputs[0]    ) // pad_to_multiple_of) + 1) * pad_to_multiple_of

         self.assertTrue(len(input_a[0]    ) == expected_length    )
         self.assertTrue(_inputs_have_equal_length(_lowerCamelCase    )    )
         self.assertFalse(_inputs_have_equal_length(_lowerCamelCase    )    )







     def lowerCAmelCase     (		self    ):
         self._check_padding(numpify=_lowerCamelCase    )







     def lowerCAmelCase     (		self    ):
         self._check_padding(numpify=_lowerCamelCase    )







     def lowerCAmelCase     (		self    ):
         self._check_truncation(numpify=_lowerCamelCase    )







     def lowerCAmelCase     (		self    ):
         self._check_truncation(numpify=_lowerCamelCase    )







     @require_torch
     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Optional[int]		    =  self.feature_extraction_class(**self.feat_extract_dict    )
         __UpperCamelCase	:	List[str]		    =  self.feat_extract_tester.prepare_inputs_for_common()
         __UpperCamelCase	:	Any		    =  feat_extract.model_input_names[0]

         __UpperCamelCase	:	Optional[Any]		    =  BatchFeature({input_name: speech_inputs}    )

         __UpperCamelCase	:	Union[str, Any]		    =  feat_extract.pad(_lowerCamelCase     ,			padding='longest'     ,			return_tensors='np'    )[input_name]
         __UpperCamelCase	:	Tuple		    =  feat_extract.pad(_lowerCamelCase     ,			padding='longest'     ,			return_tensors='pt'    )[input_name]

         self.assertTrue(abs(input_np.astype(np.floataa    ).sum() - input_pt.numpy().astype(np.floataa    ).sum()    ) < 1E-2    )







     @require_tf
     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Optional[int]		    =  self.feature_extraction_class(**self.feat_extract_dict    )
         __UpperCamelCase	:	Tuple		    =  self.feat_extract_tester.prepare_inputs_for_common()
         __UpperCamelCase	:	Any		    =  feat_extract.model_input_names[0]

         __UpperCamelCase	:	Dict		    =  BatchFeature({input_name: speech_inputs}    )

         __UpperCamelCase	:	Dict		    =  feat_extract.pad(_lowerCamelCase     ,			padding='longest'     ,			return_tensors='np'    )[input_name]
         __UpperCamelCase	:	int		    =  feat_extract.pad(_lowerCamelCase     ,			padding='longest'     ,			return_tensors='tf'    )[input_name]

         self.assertTrue(abs(input_np.astype(np.floataa    ).sum() - input_tf.numpy().astype(np.floataa    ).sum()    ) < 1E-2    )







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Any		    =  self.feat_extract_dict
         __UpperCamelCase	:	Optional[int]		    =  True
         __UpperCamelCase	:	Dict		    =  self.feature_extraction_class(**_lowerCamelCase    )
         __UpperCamelCase	:	Union[str, Any]		    =  self.feat_extract_tester.prepare_inputs_for_common()
         __UpperCamelCase	:	List[str]		    =  [len(_lowerCamelCase    ) for x in speech_inputs]
         __UpperCamelCase	:	Optional[int]		    =  feat_extract.model_input_names[0]

         __UpperCamelCase	:	Dict		    =  BatchFeature({input_name: speech_inputs}    )

         __UpperCamelCase	:	List[str]		    =  feat_extract.pad(_lowerCamelCase     ,			padding='longest'     ,			return_tensors='np'    )
         self.assertIn('attention_mask'     ,			_lowerCamelCase    )
         self.assertListEqual(list(processed.attention_mask.shape    )     ,			list(processed[input_name].shape[:2]    )    )
         self.assertListEqual(processed.attention_mask.sum(-1    ).tolist()     ,			_lowerCamelCase    )







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Optional[int]		    =  self.feat_extract_dict
         __UpperCamelCase	:	Tuple		    =  True
         __UpperCamelCase	:	Optional[int]		    =  self.feature_extraction_class(**_lowerCamelCase    )
         __UpperCamelCase	:	int		    =  self.feat_extract_tester.prepare_inputs_for_common()
         __UpperCamelCase	:	str		    =  [len(_lowerCamelCase    ) for x in speech_inputs]
         __UpperCamelCase	:	Optional[int]		    =  feat_extract.model_input_names[0]

         __UpperCamelCase	:	List[str]		    =  BatchFeature({input_name: speech_inputs}    )
         __UpperCamelCase	:	Any		    =  min(_lowerCamelCase    )

         __UpperCamelCase	:	List[Any]		    =  feat_extract.pad(
             _lowerCamelCase     ,			padding='max_length'     ,			max_length=_lowerCamelCase     ,			truncation=_lowerCamelCase     ,			return_tensors='np'    )
         self.assertIn('attention_mask'     ,			_lowerCamelCase    )
         self.assertListEqual(
             list(processed_pad.attention_mask.shape    )     ,			[processed_pad[input_name].shape[0], max_length]    )
         self.assertListEqual(
             processed_pad.attention_mask[:, :max_length].sum(-1    ).tolist()     ,			[max_length for x in speech_inputs]    )




'''simple docstring'''
import warnings

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


a=  logging.get_logger(__name__)

a=  {
    '''xlnet-base-cased''': '''https://huggingface.co/xlnet-base-cased/resolve/main/config.json''',
    '''xlnet-large-cased''': '''https://huggingface.co/xlnet-large-cased/resolve/main/config.json''',
}






class 			__lowercase		(						_lowerCamelCase						):







     """simple docstring"""






     SCREAMING_SNAKE_CASE__		   =						'''xlnet'''
     SCREAMING_SNAKE_CASE__		   =						['''mems''']
     SCREAMING_SNAKE_CASE__		   =						{
         '''n_token''': '''vocab_size''',  # Backward compatibility
         '''hidden_size''': '''d_model''',
         '''num_attention_heads''': '''n_head''',
         '''num_hidden_layers''': '''n_layer''',
     }







     def __init__(		self     ,			_lowerCamelCase=3_2_0_0_0     ,			_lowerCamelCase=1_0_2_4     ,			_lowerCamelCase=2_4     ,			_lowerCamelCase=1_6     ,			_lowerCamelCase=4_0_9_6     ,			_lowerCamelCase="gelu"     ,			_lowerCamelCase=True     ,			_lowerCamelCase="bi"     ,			_lowerCamelCase=0.0_2     ,			_lowerCamelCase=1E-1_2     ,			_lowerCamelCase=0.1     ,			_lowerCamelCase=5_1_2     ,			_lowerCamelCase=None     ,			_lowerCamelCase=True     ,			_lowerCamelCase=False     ,			_lowerCamelCase=False     ,			_lowerCamelCase=-1     ,			_lowerCamelCase=False     ,			_lowerCamelCase="last"     ,			_lowerCamelCase=True     ,			_lowerCamelCase="tanh"     ,			_lowerCamelCase=0.1     ,			_lowerCamelCase=5     ,			_lowerCamelCase=5     ,			_lowerCamelCase=5     ,			_lowerCamelCase=1     ,			_lowerCamelCase=2     ,			**_lowerCamelCase     ,			):
         __UpperCamelCase	:	List[str]		    =  vocab_size
         __UpperCamelCase	:	List[Any]		    =  d_model
         __UpperCamelCase	:	List[str]		    =  n_layer
         __UpperCamelCase	:	Optional[Any]		    =  n_head
         if d_model % n_head != 0:
             raise ValueError(f"""'d_model % n_head' ({d_model % n_head}) should be equal to 0"""    )
         if "d_head" in kwargs:
             if kwargs["d_head"] != d_model // n_head:
                 raise ValueError(
                     f"""`d_head` ({kwargs['d_head']}) should be equal to `d_model // n_head` ({d_model // n_head})"""    )
         __UpperCamelCase	:	str		    =  d_model // n_head
         __UpperCamelCase	:	Union[str, Any]		    =  ff_activation
         __UpperCamelCase	:	str		    =  d_inner
         __UpperCamelCase	:	Any		    =  untie_r
         __UpperCamelCase	:	List[Any]		    =  attn_type

         __UpperCamelCase	:	str		    =  initializer_range
         __UpperCamelCase	:	Optional[int]		    =  layer_norm_eps

         __UpperCamelCase	:	Union[str, Any]		    =  dropout
         __UpperCamelCase	:	List[str]		    =  mem_len
         __UpperCamelCase	:	Optional[int]		    =  reuse_len
         __UpperCamelCase	:	str		    =  bi_data
         __UpperCamelCase	:	Tuple		    =  clamp_len
         __UpperCamelCase	:	List[Any]		    =  same_length

         __UpperCamelCase	:	Optional[Any]		    =  summary_type
         __UpperCamelCase	:	List[str]		    =  summary_use_proj
         __UpperCamelCase	:	Union[str, Any]		    =  summary_activation
         __UpperCamelCase	:	str		    =  summary_last_dropout
         __UpperCamelCase	:	Union[str, Any]		    =  start_n_top
         __UpperCamelCase	:	str		    =  end_n_top

         __UpperCamelCase	:	Optional[int]		    =  bos_token_id
         __UpperCamelCase	:	Optional[Any]		    =  pad_token_id
         __UpperCamelCase	:	List[Any]		    =  eos_token_id

         if "use_cache" in kwargs:
             warnings.warn(
                 'The `use_cache` argument is deprecated and will be removed in a future version, use `use_mems_eval`'
                 ' instead.'     ,			_lowerCamelCase     ,			)
             __UpperCamelCase	:	Optional[int]		    =  kwargs['use_cache']

         __UpperCamelCase	:	Optional[int]		    =  use_mems_eval
         __UpperCamelCase	:	Tuple		    =  use_mems_train
         super().__init__(pad_token_id=_lowerCamelCase     ,			bos_token_id=_lowerCamelCase     ,			eos_token_id=_lowerCamelCase     ,			**_lowerCamelCase    )







     @property
     def lowerCAmelCase     (		self    ):
         logger.info(f"""The model {self.model_type} is one of the few models that has no sequence length limit."""    )
         return -1







     @max_position_embeddings.setter
     def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         # Message copied from Transformer-XL documentation
         raise NotImplementedError(
             f"""The model {self.model_type} is one of the few models that has no sequence length limit."""    )



