





"""simple docstring"""




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


UpperCamelCase_	 =logging.get_logger(__name__)

UpperCamelCase_	 ={
    """google/reformer-crime-and-punishment""": (
        """https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/config.json"""
    ),
    """google/reformer-enwik8""": """https://huggingface.co/google/reformer-enwik8/resolve/main/config.json""",
}




class 					_a		( _lowerCAmelCase							):
				UpperCamelCase								=       '''reformer'''
				UpperCamelCase								=       ['''past_buckets_states''']
				UpperCamelCase								=       {}






				def __init__(					self						:       Optional[Any],					lowerCAmelCase__						:       str=6_4,					lowerCAmelCase__						:       Union[str, Any]=["local", "lsh", "local", "lsh", "local", "lsh"],					lowerCAmelCase__						:       Optional[int]=1.0,					lowerCAmelCase__						:       Union[str, Any]=True,					lowerCAmelCase__						:       int=[6_4, 6_4],					lowerCAmelCase__						:       List[Any]=[6_4, 1_9_2],					lowerCAmelCase__						:       str=0,					lowerCAmelCase__						:       List[Any]=2,					lowerCAmelCase__						:       Dict=5_1_2,					lowerCAmelCase__						:       List[Any]=None,					lowerCAmelCase__						:       int="relu",					lowerCAmelCase__						:       Optional[Any]=0.05,					lowerCAmelCase__						:       Union[str, Any]=2_5_6,					lowerCAmelCase__						:       Tuple=0.02,					lowerCAmelCase__						:       str=False,					lowerCAmelCase__						:       Any=1e-1_2,					lowerCAmelCase__						:       Tuple=1,					lowerCAmelCase__						:       str=0,					lowerCAmelCase__						:       List[str]=0.05,					lowerCAmelCase__						:       Optional[int]=6_4,					lowerCAmelCase__						:       Union[str, Any]=6_4,					lowerCAmelCase__						:       List[Any]=0.0,					lowerCAmelCase__						:       Optional[Any]=1,					lowerCAmelCase__						:       str=0,					lowerCAmelCase__						:       Optional[int]=4_0_9_6,					lowerCAmelCase__						:       int=1_2,					lowerCAmelCase__						:       Optional[int]=None,					lowerCAmelCase__						:       Tuple=1,					lowerCAmelCase__						:       str=0,					lowerCAmelCase__						:       int=3_2_0,					lowerCAmelCase__						:       List[str]=False,					lowerCAmelCase__						:       Optional[int]=True,					lowerCAmelCase__						:       List[Any]=None,					**lowerCAmelCase__						:       Any,					)      -> Optional[Any]:




										'''simple docstring'''



										_UpperCamelCase     :   Union[str, Any]      =       hash_seed
										_UpperCamelCase     :   Optional[Any]      =       vocab_size
										_UpperCamelCase     :   int      =       attention_head_size
										_UpperCamelCase     :   str      =       hidden_size
										_UpperCamelCase     :   int      =       num_attention_heads
										_UpperCamelCase     :   int      =       num_hashes
										_UpperCamelCase     :   Dict      =       len(lowerCAmelCase__				)
										_UpperCamelCase     :   Dict      =       tuple(lowerCAmelCase__				) if isinstance(lowerCAmelCase__,					lowerCAmelCase__				) else num_buckets
										_UpperCamelCase     :   Optional[Any]      =       lsh_attn_chunk_length
										_UpperCamelCase     :   Any      =       local_attn_chunk_length
										_UpperCamelCase     :   Any      =       lsh_num_chunks_after
										_UpperCamelCase     :   int      =       lsh_num_chunks_before
										_UpperCamelCase     :   Union[str, Any]      =       local_num_chunks_after
										_UpperCamelCase     :   Tuple      =       local_num_chunks_before
										_UpperCamelCase     :   Dict      =       hidden_act
										_UpperCamelCase     :   Optional[Any]      =       feed_forward_size
										_UpperCamelCase     :   Optional[Any]      =       hidden_dropout_prob
										_UpperCamelCase     :   str      =       lsh_attention_probs_dropout_prob
										_UpperCamelCase     :   Tuple      =       local_attention_probs_dropout_prob
										_UpperCamelCase     :   Optional[int]      =       max_position_embeddings
										_UpperCamelCase     :   Any      =       initializer_range
										_UpperCamelCase     :   str      =       layer_norm_eps
										_UpperCamelCase     :   Union[str, Any]      =       axial_pos_embds
										_UpperCamelCase     :   Optional[Any]      =       tuple(lowerCAmelCase__				)
										_UpperCamelCase     :   str      =       tuple(lowerCAmelCase__				)
										_UpperCamelCase     :   Dict      =       axial_norm_std
										_UpperCamelCase     :   Optional[int]      =       chunk_size_lm_head
										_UpperCamelCase     :   Dict      =       attn_layers
										_UpperCamelCase     :   int      =       use_cache
										_UpperCamelCase     :   List[Any]      =       classifier_dropout
										super().__init__(
										    pad_token_id=lowerCAmelCase__,					eos_token_id=lowerCAmelCase__,					is_decoder=lowerCAmelCase__,					tie_word_embeddings=lowerCAmelCase__,					**lowerCAmelCase__,					)




import random
import shutil
import tempfile
import unittest

import numpy as np

from transformers import TaTokenizer, TaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torch
from transformers.utils.import_utils import is_speech_available, is_torch_available


if is_torch_available():
      pass

if is_speech_available():
      from transformers import EncodecFeatureExtractor, MusicgenProcessor


UpperCamelCase    				=		random.Random()

def      lowercase_							(     _lowerCamelCase				:			Union[str, Any]						,      _lowerCamelCase				:			str=1.0						,      _lowerCamelCase				:			Optional[int]=None						,      _lowerCamelCase				:			int=None):
    if rng is None:
        lowercase__					:  Dict          =							global_rng

    lowercase__					:  Optional[Any]          =							[]
    for batch_idx in range(shape[0]):
        values.append([])
        for _ in range(shape[1]):
            values[-1].append(rng.random() * scale)

    return values



@require_torch
@require_sentencepiece
class     snake_case_			(						unittest.TestCase			):






   def  __UpperCamelCase	( self							:	Union[str, Any]    )   ->		Union[str, Any]:
       lowercase__					:  List[Any]          =							"facebook/musicgen-small"
       lowercase__					:  List[Any]          =							tempfile.mkdtemp()






   def  __UpperCamelCase	( self							:	Optional[Any]      ,		**lowercase_							:	Optional[Any]    )   ->		Optional[Any]:
       return TaTokenizer.from_pretrained(self.checkpoint      ,		**lowercase_    )






   def  __UpperCamelCase	( self							:	int      ,		**lowercase_							:	Any    )   ->		List[Any]:
       return EncodecFeatureExtractor.from_pretrained(self.checkpoint      ,		**lowercase_    )






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






   def  __UpperCamelCase	( self							:	Dict    )   ->		Union[str, Any]:
       lowercase__					:  List[str]          =							self.get_tokenizer()
       lowercase__					:  Optional[Any]          =							self.get_feature_extractor()

       lowercase__					:  Any          =							MusicgenProcessor(tokenizer=lowercase_      ,		feature_extractor=lowercase_    )

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

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

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






   def  __UpperCamelCase	( self							:	List[str]    )   ->		Union[str, Any]:
       lowercase__					:  Tuple          =							MusicgenProcessor(tokenizer=self.get_tokenizer()      ,		feature_extractor=self.get_feature_extractor()    )
       processor.save_pretrained(self.tmpdirname    )

       lowercase__					:  int          =							self.get_tokenizer(bos_token="(BOS)"      ,		eos_token="(EOS)"    )
       lowercase__					:  int          =							self.get_feature_extractor(do_normalize=lowercase_      ,		padding_value=1.0    )

       lowercase__					:  Optional[int]          =							MusicgenProcessor.from_pretrained(
           self.tmpdirname      ,		bos_token="(BOS)"      ,		eos_token="(EOS)"      ,		do_normalize=lowercase_      ,		padding_value=1.0    )

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

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






   def  __UpperCamelCase	( self							:	List[Any]    )   ->		Optional[Any]:
       lowercase__					:  str          =							self.get_feature_extractor()
       lowercase__					:  Union[str, Any]          =							self.get_tokenizer()

       lowercase__					:  str          =							MusicgenProcessor(tokenizer=lowercase_      ,		feature_extractor=lowercase_    )

       lowercase__					:  Tuple          =							floats_list((3, 10_00)    )

       lowercase__					:  int          =							feature_extractor(lowercase_      ,		return_tensors="np"    )
       lowercase__					:  int          =							processor(lowercase_      ,		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							:	Any    )   ->		int:
       lowercase__					:  List[Any]          =							self.get_feature_extractor()
       lowercase__					:  int          =							self.get_tokenizer()

       lowercase__					:  int          =							MusicgenProcessor(tokenizer=lowercase_      ,		feature_extractor=lowercase_    )

       lowercase__					:  Tuple          =							"This is a test string"

       lowercase__					:  Any          =							processor(text=lowercase_    )

       lowercase__					:  str          =							tokenizer(lowercase_    )

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






   def  __UpperCamelCase	( self							:	Tuple    )   ->		List[str]:
       lowercase__					:  Dict          =							self.get_feature_extractor()
       lowercase__					:  Optional[Any]          =							self.get_tokenizer()

       lowercase__					:  str          =							MusicgenProcessor(tokenizer=lowercase_      ,		feature_extractor=lowercase_    )

       lowercase__					:  Any          =							[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

       lowercase__					:  List[Any]          =							processor.batch_decode(sequences=lowercase_    )
       lowercase__					:  Optional[Any]          =							tokenizer.batch_decode(lowercase_    )

       self.assertListEqual(lowercase_      ,		lowercase_    )






   def  __UpperCamelCase	( self							:	Dict    )   ->		List[str]:
       lowercase__					:  List[Any]          =							self.get_feature_extractor()
       lowercase__					:  Union[str, Any]          =							self.get_tokenizer()

       lowercase__					:  str          =							MusicgenProcessor(tokenizer=lowercase_      ,		feature_extractor=lowercase_    )

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







   def  __UpperCamelCase	( self							:	Optional[Any]    )   ->		Optional[int]:
       lowercase__					:  List[str]          =							self.get_feature_extractor(padding_side="left"    )
       lowercase__					:  Tuple          =							self.get_tokenizer()

       lowercase__					:  Any          =							MusicgenProcessor(tokenizer=lowercase_      ,		feature_extractor=lowercase_    )

       lowercase__					:  Optional[Any]          =							[floats_list((1, x)    )[0] for x in range(5      ,		20      ,		5    )]
       lowercase__					:  Optional[int]          =							processor(lowercase_    ).padding_mask

       lowercase__					:  Union[str, Any]          =							np.asarray(floats_list((3, 20)    )    )[:, None, :]
       lowercase__					:  Dict          =							processor.batch_decode(lowercase_      ,		padding_mask=lowercase_    )

       self.assertIsInstance(lowercase_      ,		lowercase_    )

       for audio in decoded_audios:
           self.assertIsInstance(lowercase_      ,		np.ndarray    )

       self.assertTrue(decoded_audios[0].shape == (1, 10)    )
       self.assertTrue(decoded_audios[1].shape == (1, 15)    )
       self.assertTrue(decoded_audios[2].shape == (1, 20)    )



