"""simple docstring"""





import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path

import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized

from transformers import AutoProcessor
from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.testing_utils import require_pyctcdecode, require_torch, require_torchaudio, slow
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_pyctcdecode_available, is_torch_available

from ..wavaveca.test_feature_extraction_wavaveca import floats_list


if is_pyctcdecode_available():
       from huggingface_hub import snapshot_download
       from pyctcdecode import BeamSearchDecoderCTC

       from transformers.models.wavaveca_with_lm import WavaVecaProcessorWithLM
       from transformers.models.wavaveca_with_lm.processing_wavaveca_with_lm import WavaVecaDecoderWithLMOutput

if is_torch_available():
       from transformers import WavaVecaForCTC


@require_pyctcdecode
class      UpperCAmelCase_	(    unittest.TestCase):


      def 		_UpperCAmelCase       (   self							)       ->					Dict:
             lowercase__			:		Optional[Any]       =	'| <pad> <unk> <s> </s> a b c d e f g h i j k'.split()
             lowercase__			:		Dict       =	dict(zip(a		,			range(len(a							)							)							)							)

             lowercase__			:		Union[str, Any]       =	{
                 'unk_token': '<unk>',
                 'bos_token': '<s>',
                 'eos_token': '</s>',
             }
             lowercase__			:		List[Any]       =	{
                 'feature_size': 1,
                 'padding_value': 0.0,
                 'sampling_rate': 1_6_0_0_0,
                 'return_attention_mask': False,
                 'do_normalize': True,
             }

             lowercase__			:		Dict       =	tempfile.mkdtemp()
             lowercase__			:		Optional[int]       =	os.path.join(self.tmpdirname		,			VOCAB_FILES_NAMES['vocab_file']							)
             lowercase__			:		List[Any]       =	os.path.join(self.tmpdirname		,			a							)
             with open(self.vocab_file		,			'w'		,			encoding='utf-8'							) as fp:
                    fp.write(json.dumps(a							) + '\n'							)

             with open(self.feature_extraction_file		,			'w'		,			encoding='utf-8'							) as fp:
                    fp.write(json.dumps(a							) + '\n'							)

             # load decoder from hub
             lowercase__			:		Union[str, Any]       =	'hf-internal-testing/ngram-beam-search-decoder'


      def 		_UpperCAmelCase       (   self		,			**a							)       ->					Any:
             lowercase__			:		Union[str, Any]       =	self.add_kwargs_tokens_map.copy()
             kwargs.update(a							)
             return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname		,			**a							)


      def 		_UpperCAmelCase       (   self		,			**a							)       ->					Union[str, Any]:
             return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname		,			**a							)


      def 		_UpperCAmelCase       (   self		,			**a							)       ->					Optional[Any]:
             return BeamSearchDecoderCTC.load_from_hf_hub(self.decoder_name		,			**a							)


      def 		_UpperCAmelCase       (   self							)       ->					List[Any]:
             shutil.rmtree(self.tmpdirname							)


      def 		_UpperCAmelCase       (   self							)       ->					int:
             lowercase__			:		Dict       =	self.get_tokenizer()
             lowercase__			:		Union[str, Any]       =	self.get_feature_extractor()
             lowercase__			:		Any       =	self.get_decoder()

             lowercase__			:		Union[str, Any]       =	WavaVecaProcessorWithLM(tokenizer=a		,			feature_extractor=a		,			decoder=a							)

             processor.save_pretrained(self.tmpdirname							)
             lowercase__			:		List[Any]       =	WavaVecaProcessorWithLM.from_pretrained(self.tmpdirname							)

             # tokenizer
             self.assertEqual(processor.tokenizer.get_vocab()		,			tokenizer.get_vocab()							)
             self.assertIsInstance(processor.tokenizer		,			a							)

             # feature extractor
             self.assertEqual(processor.feature_extractor.to_json_string()		,			feature_extractor.to_json_string()							)
             self.assertIsInstance(processor.feature_extractor		,			a							)

             # decoder
             self.assertEqual(processor.decoder._alphabet.labels		,			decoder._alphabet.labels							)
             self.assertEqual(
                 processor.decoder.model_container[decoder._model_key]._unigram_set		,			decoder.model_container[decoder._model_key]._unigram_set		,			)
             self.assertIsInstance(processor.decoder		,			a							)


      def 		_UpperCAmelCase       (   self							)       ->					List[str]:
             lowercase__			:		Dict       =	WavaVecaProcessorWithLM(
                 tokenizer=self.get_tokenizer()		,			feature_extractor=self.get_feature_extractor()		,			decoder=self.get_decoder()							)
             processor.save_pretrained(self.tmpdirname							)

             # make sure that error is thrown when decoder alphabet doesn't match
             lowercase__			:		Dict       =	WavaVecaProcessorWithLM.from_pretrained(
                 self.tmpdirname		,			alpha=5.0		,			beta=3.0		,			score_boundary=-7.0		,			unk_score_offset=3							)

             # decoder
             self.assertEqual(processor.language_model.alpha		,			5.0							)
             self.assertEqual(processor.language_model.beta		,			3.0							)
             self.assertEqual(processor.language_model.score_boundary		,			-7.0							)
             self.assertEqual(processor.language_model.unk_score_offset		,			3							)


      def 		_UpperCAmelCase       (   self							)       ->					int:
             lowercase__			:		Dict       =	self.get_tokenizer()
             # add token to trigger raise
             tokenizer.add_tokens(['xx']							)
             with self.assertRaisesRegex(a		,			'include'							):
                    WavaVecaProcessorWithLM(
                        tokenizer=a		,			feature_extractor=self.get_feature_extractor()		,			decoder=self.get_decoder()							)


      def 		_UpperCAmelCase       (   self							)       ->					List[Any]:
             lowercase__			:		int       =	self.get_feature_extractor()
             lowercase__			:		List[str]       =	self.get_tokenizer()
             lowercase__			:		Optional[Any]       =	self.get_decoder()

             lowercase__			:		Any       =	WavaVecaProcessorWithLM(tokenizer=a		,			feature_extractor=a		,			decoder=a							)

             lowercase__			:		Optional[int]       =	floats_list((3, 1_0_0_0)							)

             lowercase__			:		List[Any]       =	feature_extractor(a		,			return_tensors='np'							)
             lowercase__			:		Any       =	processor(a		,			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 		_UpperCAmelCase       (   self							)       ->					str:
             lowercase__			:		Any       =	self.get_feature_extractor()
             lowercase__			:		List[str]       =	self.get_tokenizer()
             lowercase__			:		List[str]       =	self.get_decoder()

             lowercase__			:		Union[str, Any]       =	WavaVecaProcessorWithLM(tokenizer=a		,			feature_extractor=a		,			decoder=a							)

             lowercase__			:		str       =	'This is a test string'

             lowercase__			:		Any       =	processor(text=a							)

             lowercase__			:		List[str]       =	tokenizer(a							)

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


      def 		_UpperCAmelCase       (   self		,			a=(2, 1_0, 1_6)		,			a=7_7							)       ->					Optional[int]:
             np.random.seed(a							)
             return np.random.rand(*a							)


      def 		_UpperCAmelCase       (   self							)       ->					Any:
             lowercase__			:		List[str]       =	self.get_feature_extractor()
             lowercase__			:		Optional[Any]       =	self.get_tokenizer()
             lowercase__			:		Any       =	self.get_decoder()

             lowercase__			:		str       =	WavaVecaProcessorWithLM(tokenizer=a		,			feature_extractor=a		,			decoder=a							)

             lowercase__			:		List[Any]       =	self._get_dummy_logits(shape=(1_0, 1_6)		,			seed=1_3							)

             lowercase__			:		Dict       =	processor.decode(a							)

             lowercase__			:		Optional[Any]       =	decoder.decode_beams(a							)[0]

             self.assertEqual(decoded_decoder[0]		,			decoded_processor.text							)
             self.assertEqual('</s> <s> </s>'		,			decoded_processor.text							)
             self.assertEqual(decoded_decoder[-2]		,			decoded_processor.logit_score							)
             self.assertEqual(decoded_decoder[-1]		,			decoded_processor.lm_score							)


      @parameterized.expand([[None], ['fork'], ['spawn']]							)
      def 		_UpperCAmelCase       (   self		,			a							)       ->					Any:
             lowercase__			:		Any       =	self.get_feature_extractor()
             lowercase__			:		int       =	self.get_tokenizer()
             lowercase__			:		Optional[Any]       =	self.get_decoder()

             lowercase__			:		Optional[int]       =	WavaVecaProcessorWithLM(tokenizer=a		,			feature_extractor=a		,			decoder=a							)

             lowercase__			:		List[str]       =	self._get_dummy_logits()

             # note: pool should be instantiated *after* Wav2Vec2ProcessorWithLM.
             #       otherwise, the LM won't be available to the pool's sub-processes.
             # manual logic used to allow parameterized test for both pool=None and pool=Pool(...)
             if pool_context is None:
                    lowercase__			:		List[Any]       =	processor.batch_decode(a							)
             else:
                    with get_context(a							).Pool() as pool:
                           lowercase__			:		int       =	processor.batch_decode(a		,			a							)

             lowercase__			:		Tuple       =	list(a							)

             with get_context('fork'							).Pool() as p:
                    lowercase__			:		str       =	decoder.decode_beams_batch(a		,			a							)

             lowercase__    ,      lowercase__    ,      lowercase__			:		Optional[int]       =	[], [], []
             for beams in decoded_beams:
                    texts_decoder.append(beams[0][0]							)
                    logit_scores_decoder.append(beams[0][-2]							)
                    lm_scores_decoder.append(beams[0][-1]							)

             self.assertListEqual(a		,			decoded_processor.text							)
             self.assertListEqual(['<s> <s> </s>', '<s> <s> <s>']		,			decoded_processor.text							)
             self.assertListEqual(a		,			decoded_processor.logit_score							)
             self.assertListEqual(a		,			decoded_processor.lm_score							)


      def 		_UpperCAmelCase       (   self							)       ->					List[Any]:
             lowercase__			:		List[Any]       =	self.get_feature_extractor()
             lowercase__			:		Optional[Any]       =	self.get_tokenizer()
             lowercase__			:		Any       =	self.get_decoder()

             lowercase__			:		List[str]       =	WavaVecaProcessorWithLM(tokenizer=a		,			feature_extractor=a		,			decoder=a							)

             lowercase__			:		List[str]       =	self._get_dummy_logits()

             lowercase__			:		int       =	1_5
             lowercase__			:		Tuple       =	-20.0
             lowercase__			:		Optional[Any]       =	-4.0

             lowercase__			:		Dict       =	processor.batch_decode(
                 a		,			beam_width=a		,			beam_prune_logp=a		,			token_min_logp=a		,			)
             lowercase__			:		Dict       =	decoded_processor_out.text

             lowercase__			:		Union[str, Any]       =	list(a							)

             with get_context('fork'							).Pool() as pool:
                    lowercase__			:		Union[str, Any]       =	decoder.decode_beams_batch(
                        a		,			a		,			beam_width=a		,			beam_prune_logp=a		,			token_min_logp=a		,			)

             lowercase__			:		List[Any]       =	[d[0][0] for d in decoded_decoder_out]
             lowercase__			:		List[Any]       =	[d[0][2] for d in decoded_decoder_out]
             lowercase__			:		Tuple       =	[d[0][3] for d in decoded_decoder_out]

             self.assertListEqual(a		,			a							)
             self.assertListEqual(['</s> <s> <s>', '<s> <s> <s>']		,			a							)

             self.assertTrue(np.array_equal(a		,			decoded_processor_out.logit_score							)							)
             self.assertTrue(np.allclose([-20.054, -18.447]		,			a		,			atol=1e-3							)							)

             self.assertTrue(np.array_equal(a		,			decoded_processor_out.lm_score							)							)
             self.assertTrue(np.allclose([-15.554, -13.9_474]		,			a		,			atol=1e-3							)							)


      def 		_UpperCAmelCase       (   self							)       ->					str:
             lowercase__			:		List[Any]       =	self.get_feature_extractor()
             lowercase__			:		str       =	self.get_tokenizer()
             lowercase__			:		int       =	self.get_decoder()

             lowercase__			:		str       =	WavaVecaProcessorWithLM(tokenizer=a		,			feature_extractor=a		,			decoder=a							)

             lowercase__			:		Dict       =	self._get_dummy_logits()

             lowercase__			:		Optional[int]       =	2.0
             lowercase__			:		Dict       =	5.0
             lowercase__			:		List[Any]       =	-20.0
             lowercase__			:		Optional[Any]       =	True

             lowercase__			:		Optional[int]       =	processor.batch_decode(
                 a		,			alpha=a		,			beta=a		,			unk_score_offset=a		,			lm_score_boundary=a		,			)
             lowercase__			:		Optional[Any]       =	decoded_processor_out.text

             lowercase__			:		Union[str, Any]       =	list(a							)
             decoder.reset_params(
                 alpha=a		,			beta=a		,			unk_score_offset=a		,			lm_score_boundary=a		,			)

             with get_context('fork'							).Pool() as pool:
                    lowercase__			:		int       =	decoder.decode_beams_batch(
                        a		,			a		,			)

             lowercase__			:		str       =	[d[0][0] for d in decoded_decoder_out]

             self.assertListEqual(a		,			a							)
             self.assertListEqual(['<s> </s> <s> </s> </s>', '</s> </s> <s> </s> </s>']		,			a							)
             lowercase__			:		Union[str, Any]       =	processor.decoder.model_container[processor.decoder._model_key]
             self.assertEqual(lm_model.alpha		,			2.0							)
             self.assertEqual(lm_model.beta		,			5.0							)
             self.assertEqual(lm_model.unk_score_offset		,			-20.0							)
             self.assertEqual(lm_model.score_boundary		,			a							)


      def 		_UpperCAmelCase       (   self							)       ->					Tuple:
             lowercase__			:		Optional[Any]       =	WavaVecaProcessorWithLM.from_pretrained('hf-internal-testing/processor_with_lm'							)

             lowercase__			:		int       =	processor.decoder.model_container[processor.decoder._model_key]
             lowercase__			:		List[str]       =	Path(language_model._kenlm_model.path.decode('utf-8'							)							).parent.parent.absolute()

             lowercase__			:		List[str]       =	os.listdir(a							)
             lowercase__			:		Dict       =	['alphabet.json', 'language_model']

             downloaded_decoder_files.sort()
             expected_decoder_files.sort()

             # test that only decoder relevant files from
             # https://huggingface.co/hf-internal-testing/processor_with_lm/tree/main
             # are downloaded and none of the rest (e.g. README.md, ...)
             self.assertListEqual(a		,			a							)


      def 		_UpperCAmelCase       (   self							)       ->					Optional[int]:
             lowercase__			:		Optional[int]       =	snapshot_download('hf-internal-testing/processor_with_lm'							)

             lowercase__			:		str       =	WavaVecaProcessorWithLM.from_pretrained(a							)

             lowercase__			:		int       =	processor.decoder.model_container[processor.decoder._model_key]
             lowercase__			:		Union[str, Any]       =	Path(language_model._kenlm_model.path.decode('utf-8'							)							).parent.parent.absolute()

             lowercase__			:		Union[str, Any]       =	os.listdir(a							)
             lowercase__			:		Dict       =	os.listdir(a							)

             local_decoder_files.sort()
             expected_decoder_files.sort()

             # test that both decoder form hub and local files in cache are the same
             self.assertListEqual(a		,			a							)


      def 		_UpperCAmelCase       (   self							)       ->					Optional[int]:
             lowercase__			:		Optional[Any]       =	WavaVecaProcessorWithLM.from_pretrained('hf-internal-testing/processor_with_lm'							)
             lowercase__			:		Any       =	AutoProcessor.from_pretrained('hf-internal-testing/processor_with_lm'							)

             lowercase__			:		Dict       =	floats_list((3, 1_0_0_0)							)

             lowercase__			:		Any       =	processor_wavaveca(a		,			return_tensors='np'							)
             lowercase__			:		Optional[Any]       =	processor_auto(a		,			return_tensors='np'							)

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

             lowercase__			:		Dict       =	self._get_dummy_logits()

             lowercase__			:		Optional[Any]       =	processor_wavaveca.batch_decode(a							)
             lowercase__			:		int       =	processor_auto.batch_decode(a							)

             self.assertListEqual(decoded_wavaveca.text		,			decoded_auto.text							)


      def 		_UpperCAmelCase       (   self							)       ->					List[Any]:
             lowercase__			:		int       =	self.get_feature_extractor()
             lowercase__			:		Optional[Any]       =	self.get_tokenizer()
             lowercase__			:		List[Any]       =	self.get_decoder()

             lowercase__			:		Any       =	WavaVecaProcessorWithLM(tokenizer=a		,			feature_extractor=a		,			decoder=a							)

             self.assertListEqual(
                 processor.model_input_names		,			feature_extractor.model_input_names		,			msg='`processor` and `feature_extractor` model input names do not match'		,			)


      @staticmethod
      def 		_UpperCAmelCase       (   a		,			a							)       ->					List[str]:
             lowercase__			:		Union[str, Any]       =	[d[key] for d in offsets]
             return retrieved_list


      def 		_UpperCAmelCase       (   self							)       ->					Any:
             lowercase__			:		Optional[int]       =	WavaVecaProcessorWithLM.from_pretrained('hf-internal-testing/processor_with_lm'							)
             lowercase__			:		Optional[int]       =	self._get_dummy_logits()[0]

             lowercase__			:		Any       =	processor.decode(a		,			output_word_offsets=a							)
             # check Wav2Vec2CTCTokenizerOutput keys for word
             self.assertEqual(len(outputs.keys()							)		,			4							)
             self.assertTrue('text' in outputs							)
             self.assertTrue('word_offsets' in outputs							)
             self.assertTrue(isinstance(a		,			a							)							)

             self.assertEqual(' '.join(self.get_from_offsets(outputs['word_offsets']		,			'word'							)							)		,			outputs.text							)
             self.assertListEqual(self.get_from_offsets(outputs['word_offsets']		,			'word'							)		,			['<s>', '<s>', '</s>']							)
             self.assertListEqual(self.get_from_offsets(outputs['word_offsets']		,			'start_offset'							)		,			[0, 2, 4]							)
             self.assertListEqual(self.get_from_offsets(outputs['word_offsets']		,			'end_offset'							)		,			[1, 3, 5]							)


      def 		_UpperCAmelCase       (   self							)       ->					int:
             lowercase__			:		Dict       =	WavaVecaProcessorWithLM.from_pretrained('hf-internal-testing/processor_with_lm'							)
             lowercase__			:		Dict       =	self._get_dummy_logits()

             lowercase__			:		Dict       =	processor.batch_decode(a		,			output_word_offsets=a							)

             # check Wav2Vec2CTCTokenizerOutput keys for word
             self.assertEqual(len(outputs.keys()							)		,			4							)
             self.assertTrue('text' in outputs							)
             self.assertTrue('word_offsets' in outputs							)
             self.assertTrue(isinstance(a		,			a							)							)

             self.assertListEqual(
                 [' '.join(self.get_from_offsets(a		,			'word'							)							) for o in outputs['word_offsets']]		,			outputs.text							)
             self.assertListEqual(self.get_from_offsets(outputs['word_offsets'][0]		,			'word'							)		,			['<s>', '<s>', '</s>']							)
             self.assertListEqual(self.get_from_offsets(outputs['word_offsets'][0]		,			'start_offset'							)		,			[0, 2, 4]							)
             self.assertListEqual(self.get_from_offsets(outputs['word_offsets'][0]		,			'end_offset'							)		,			[1, 3, 5]							)


      @slow
      @require_torch
      @require_torchaudio
      def 		_UpperCAmelCase       (   self							)       ->					str:
             import torch

             lowercase__			:		Dict       =	load_dataset('common_voice'		,			'en'		,			split='train'		,			streaming=a							)
             lowercase__			:		Optional[Any]       =	ds.cast_column('audio'		,			datasets.Audio(sampling_rate=1_6_0_0_0							)							)
             lowercase__			:		str       =	iter(a							)
             lowercase__			:		str       =	next(a							)

             lowercase__			:		Optional[int]       =	AutoProcessor.from_pretrained('patrickvonplaten/wav2vec2-base-100h-with-lm'							)
             lowercase__			:		Any       =	WavaVecaForCTC.from_pretrained('patrickvonplaten/wav2vec2-base-100h-with-lm'							)

             # compare to filename `common_voice_en_100038.mp3` of dataset viewer on https://huggingface.co/datasets/common_voice/viewer/en/train
             lowercase__			:		List[Any]       =	processor(sample['audio']['array']		,			return_tensors='pt'							).input_values

             with torch.no_grad():
                    lowercase__			:		int       =	model(a							).logits.cpu().numpy()

             lowercase__			:		List[str]       =	processor.decode(logits[0]		,			output_word_offsets=a							)

             lowercase__			:		Optional[Any]       =	model.config.inputs_to_logits_ratio / processor.feature_extractor.sampling_rate
             lowercase__			:		Tuple       =	[
                 {
                     'start_time': d['start_offset'] * time_offset,
                     'end_time': d['end_offset'] * time_offset,
                     'word': d['word'],
                 }
                 for d in output['word_offsets']
             ]

             lowercase__			:		List[Any]       =	'WHY DOES MILISANDRA LOOK LIKE SHE WANTS TO CONSUME JOHN SNOW ON THE RIVER AT THE WALL'

             # output words
             self.assertEqual(' '.join(self.get_from_offsets(a		,			'word'							)							)		,			a							)
             self.assertEqual(' '.join(self.get_from_offsets(a		,			'word'							)							)		,			output.text							)

             # output times
             lowercase__			:		Any       =	torch.tensor(self.get_from_offsets(a		,			'start_time'							)							)
             lowercase__			:		Dict       =	torch.tensor(self.get_from_offsets(a		,			'end_time'							)							)

             # fmt: off
             lowercase__			:		Tuple       =	torch.tensor([1.4_199, 1.6_599, 2.2_599, 3.0, 3.24, 3.5_999, 3.7_999, 4.0_999, 4.26, 4.94, 5.28, 5.6_599, 5.78, 5.94, 6.32, 6.5_399, 6.6_599]							)
             lowercase__			:		int       =	torch.tensor([1.5_399, 1.8_999, 2.9, 3.16, 3.5_399, 3.72, 4.0_199, 4.1_799, 4.76, 5.1_599, 5.5_599, 5.6_999, 5.86, 6.1_999, 6.38, 6.6_199, 6.94]							)
             # fmt: on

             self.assertTrue(torch.allclose(a		,			a		,			atol=0.01							)							)
             self.assertTrue(torch.allclose(a		,			a		,			atol=0.01							)							)



import warnings

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


UpperCAmelCase	      = logging.get_logger(__name__)

UpperCAmelCase	      = {
    '''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        A_   (			__lowerCamelCase   ):




	'''simple docstring'''


	_UpperCamelCase     :     Tuple							 =       """xlnet"""
	_UpperCamelCase     :     Optional[Any]							 =       ["""mems"""]
	_UpperCamelCase     :     Tuple							 =       {
	    """n_token""": """vocab_size""",  # Backward compatibility
	    """hidden_size""": """d_model""",
	    """num_attention_heads""": """n_head""",
	    """num_hidden_layers""": """n_layer""",
	}





	def __init__(       self		,    snake_case=3_2000		,    snake_case=1024		,    snake_case=24		,    snake_case=16		,    snake_case=4096		,    snake_case="gelu"		,    snake_case=True		,    snake_case="bi"		,    snake_case=0.02		,    snake_case=1E-12		,    snake_case=0.1		,    snake_case=512		,    snake_case=None		,    snake_case=True		,    snake_case=False		,    snake_case=False		,    snake_case=-1		,    snake_case=False		,    snake_case="last"		,    snake_case=True		,    snake_case="tanh"		,    snake_case=0.1		,    snake_case=5		,    snake_case=5		,    snake_case=5		,    snake_case=1		,    snake_case=2		,    **snake_case		,    ):
		lowercase       =    vocab_size
		lowercase       =    d_model
		lowercase       =    n_layer
		lowercase       =    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})'''       )
		lowercase       =    d_model // n_head
		lowercase       =    ff_activation
		lowercase       =    d_inner
		lowercase       =    untie_r
		lowercase       =    attn_type

		lowercase       =    initializer_range
		lowercase       =    layer_norm_eps

		lowercase       =    dropout
		lowercase       =    mem_len
		lowercase       =    reuse_len
		lowercase       =    bi_data
		lowercase       =    clamp_len
		lowercase       =    same_length

		lowercase       =    summary_type
		lowercase       =    summary_use_proj
		lowercase       =    summary_activation
		lowercase       =    summary_last_dropout
		lowercase       =    start_n_top
		lowercase       =    end_n_top

		lowercase       =    bos_token_id
		lowercase       =    pad_token_id
		lowercase       =    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.'		,    snake_case		,    )
			lowercase       =    kwargs['use_cache']

		lowercase       =    use_mems_eval
		lowercase       =    use_mems_train
		super().__init__(pad_token_id=snake_case		,    bos_token_id=snake_case		,    eos_token_id=snake_case		,    **snake_case       )





	@property
	def 	SCREAMING_SNAKE_CASE__						(       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 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case       ):
		# 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.'''       )

