


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


SCREAMING_SNAKE_CASE	:Union[str, Any]  		=     random.Random()



def 			_lowerCAmelCase						(							lowerCAmelCase_							:Union[str, Any]		,						lowerCAmelCase_							:Optional[int]=1.0		,						lowerCAmelCase_							:List[str]=None		,						lowerCAmelCase_							:str=None							)->Union[str, Any]:
   '''simple docstring'''






   if rng is None:
      snake_case_					    =					global_rng

   snake_case_					    =					[]
   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     __lowerCAmelCase     ( unittest.TestCase					):






  """simple docstring"""





  def 					lowerCAmelCase__  (     self       :       List[str]  )   -> List[Any]:




     """simple docstring"""
     snake_case_					    =					"facebook/musicgen-small"
     snake_case_					    =					tempfile.mkdtemp()



  def 					lowerCAmelCase__  (     self       :       Tuple    ,				**_lowerCAmelCase       :       Tuple  )   -> Optional[int]:




     """simple docstring"""
     return TaTokenizer.from_pretrained(self.checkpoint    ,				**_lowerCAmelCase  )



  def 					lowerCAmelCase__  (     self       :       Union[str, Any]    ,				**_lowerCAmelCase       :       Union[str, Any]  )   -> int:




     """simple docstring"""
     return EncodecFeatureExtractor.from_pretrained(self.checkpoint    ,				**_lowerCAmelCase  )



  def 					lowerCAmelCase__  (     self       :       Optional[Any]  )   -> Optional[int]:




     """simple docstring"""
     shutil.rmtree(self.tmpdirname  )



  def 					lowerCAmelCase__  (     self       :       int  )   -> List[Any]:




     """simple docstring"""
     snake_case_					    =					self.get_tokenizer()
     snake_case_					    =					self.get_feature_extractor()

     snake_case_					    =					MusicgenProcessor(tokenizer=_lowerCAmelCase    ,				feature_extractor=_lowerCAmelCase  )

     processor.save_pretrained(self.tmpdirname  )
     snake_case_					    =					MusicgenProcessor.from_pretrained(self.tmpdirname  )

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

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



  def 					lowerCAmelCase__  (     self       :       Optional[Any]  )   -> Tuple:




     """simple docstring"""
     snake_case_					    =					MusicgenProcessor(tokenizer=self.get_tokenizer()    ,				feature_extractor=self.get_feature_extractor()  )
     processor.save_pretrained(self.tmpdirname  )

     snake_case_					    =					self.get_tokenizer(bos_token="(BOS)"    ,				eos_token="(EOS)"  )
     snake_case_					    =					self.get_feature_extractor(do_normalize=_lowerCAmelCase    ,				padding_value=1.0  )

     snake_case_					    =					MusicgenProcessor.from_pretrained(
         self.tmpdirname    ,				bos_token="(BOS)"    ,				eos_token="(EOS)"    ,				do_normalize=_lowerCAmelCase    ,				padding_value=1.0  )

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

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



  def 					lowerCAmelCase__  (     self       :       List[str]  )   -> Optional[Any]:




     """simple docstring"""
     snake_case_					    =					self.get_feature_extractor()
     snake_case_					    =					self.get_tokenizer()

     snake_case_					    =					MusicgenProcessor(tokenizer=_lowerCAmelCase    ,				feature_extractor=_lowerCAmelCase  )

     snake_case_					    =					floats_list((3, 1_0_0_0)  )

     snake_case_					    =					feature_extractor(_lowerCAmelCase    ,				return_tensors="np"  )
     snake_case_					    =					processor(_lowerCAmelCase    ,				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 					lowerCAmelCase__  (     self       :       Tuple  )   -> List[Any]:




     """simple docstring"""
     snake_case_					    =					self.get_feature_extractor()
     snake_case_					    =					self.get_tokenizer()

     snake_case_					    =					MusicgenProcessor(tokenizer=_lowerCAmelCase    ,				feature_extractor=_lowerCAmelCase  )

     snake_case_					    =					"This is a test string"

     snake_case_					    =					processor(text=_lowerCAmelCase  )

     snake_case_					    =					tokenizer(_lowerCAmelCase  )

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



  def 					lowerCAmelCase__  (     self       :       Any  )   -> str:




     """simple docstring"""
     snake_case_					    =					self.get_feature_extractor()
     snake_case_					    =					self.get_tokenizer()

     snake_case_					    =					MusicgenProcessor(tokenizer=_lowerCAmelCase    ,				feature_extractor=_lowerCAmelCase  )

     snake_case_					    =					[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

     snake_case_					    =					processor.batch_decode(sequences=_lowerCAmelCase  )
     snake_case_					    =					tokenizer.batch_decode(_lowerCAmelCase  )

     self.assertListEqual(_lowerCAmelCase    ,				_lowerCAmelCase  )



  def 					lowerCAmelCase__  (     self       :       Optional[int]  )   -> Dict:




     """simple docstring"""
     snake_case_					    =					self.get_feature_extractor()
     snake_case_					    =					self.get_tokenizer()

     snake_case_					    =					MusicgenProcessor(tokenizer=_lowerCAmelCase    ,				feature_extractor=_lowerCAmelCase  )

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



  def 					lowerCAmelCase__  (     self       :       Tuple  )   -> int:




     """simple docstring"""
     snake_case_					    =					self.get_feature_extractor(padding_side="left"  )
     snake_case_					    =					self.get_tokenizer()

     snake_case_					    =					MusicgenProcessor(tokenizer=_lowerCAmelCase    ,				feature_extractor=_lowerCAmelCase  )

     snake_case_					    =					[floats_list((1, x)  )[0] for x in range(5    ,				2_0    ,				5  )]
     snake_case_					    =					processor(_lowerCAmelCase  ).padding_mask

     snake_case_					    =					np.asarray(floats_list((3, 2_0)  )  )[:, None, :]
     snake_case_					    =					processor.batch_decode(_lowerCAmelCase    ,				padding_mask=_lowerCAmelCase  )

     self.assertIsInstance(_lowerCAmelCase    ,				_lowerCAmelCase  )

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

     self.assertTrue(decoded_audios[0].shape == (1, 1_0)  )
     self.assertTrue(decoded_audios[1].shape == (1, 1_5)  )
     self.assertTrue(decoded_audios[2].shape == (1, 2_0)  )








from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


SCREAMING_SNAKE_CASE	:int  		=     logging.get_logger(__name__)

SCREAMING_SNAKE_CASE	:Optional[Any]  		=     {
    '''google/long-t5-local-base''': '''https://huggingface.co/google/long-t5-local-base/blob/main/config.json''',
    '''google/long-t5-local-large''': '''https://huggingface.co/google/long-t5-local-large/blob/main/config.json''',
    '''google/long-t5-tglobal-base''': '''https://huggingface.co/google/long-t5-tglobal-base/blob/main/config.json''',
    '''google/long-t5-tglobal-large''': '''https://huggingface.co/google/long-t5-tglobal-large/blob/main/config.json''',
}


class     __lowerCAmelCase     ( a					):






  """simple docstring"""





  _SCREAMING_SNAKE_CASE		    =       'longt5'
  _SCREAMING_SNAKE_CASE		    =       ['past_key_values']
  _SCREAMING_SNAKE_CASE		    =       {'hidden_size': 'd_model', 'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}



  def __init__(     self       :       int    ,				_lowerCAmelCase       :       str=3_2_1_2_8    ,				_lowerCAmelCase       :       Optional[Any]=5_1_2    ,				_lowerCAmelCase       :       List[str]=6_4    ,				_lowerCAmelCase       :       Optional[int]=2_0_4_8    ,				_lowerCAmelCase       :       Any=6    ,				_lowerCAmelCase       :       Any=None    ,				_lowerCAmelCase       :       str=8    ,				_lowerCAmelCase       :       List[str]=1_2_7    ,				_lowerCAmelCase       :       int=1_6    ,				_lowerCAmelCase       :       List[str]=3_2    ,				_lowerCAmelCase       :       Any=1_2_8    ,				_lowerCAmelCase       :       Union[str, Any]=0.1    ,				_lowerCAmelCase       :       Union[str, Any]=1e-6    ,				_lowerCAmelCase       :       str=1.0    ,				_lowerCAmelCase       :       Tuple="relu"    ,				_lowerCAmelCase       :       List[str]=True    ,				_lowerCAmelCase       :       Tuple="local"    ,				_lowerCAmelCase       :       Union[str, Any]=True    ,				_lowerCAmelCase       :       Optional[int]=0    ,				_lowerCAmelCase       :       str=1    ,				**_lowerCAmelCase       :       int    ,				)   -> Optional[int]:




     """simple docstring"""
     snake_case_					    =					vocab_size
     snake_case_					    =					d_model
     snake_case_					    =					d_kv
     snake_case_					    =					d_ff
     snake_case_					    =					num_layers
     # default = symmetry
     snake_case_					    =					num_decoder_layers if num_decoder_layers is not None else self.num_layers
     snake_case_					    =					num_heads
     snake_case_					    =					local_radius
     snake_case_					    =					global_block_size
     snake_case_					    =					relative_attention_num_buckets
     snake_case_					    =					relative_attention_max_distance
     snake_case_					    =					dropout_rate
     snake_case_					    =					layer_norm_epsilon
     snake_case_					    =					initializer_factor
     snake_case_					    =					feed_forward_proj
     snake_case_					    =					encoder_attention_type
     snake_case_					    =					use_cache

     snake_case_					    =					self.feed_forward_proj.split("-"  )
     snake_case_					    =					act_info[-1]
     snake_case_					    =					act_info[0] == "gated"

     if len(_lowerCAmelCase  ) > 1 and act_info[0] != "gated" or len(_lowerCAmelCase  ) > 2:
        raise ValueError(
            F'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
            "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
            "'gated-gelu' or 'relu'"  )

     # for backwards compatibility
     if feed_forward_proj == "gated-gelu":
        snake_case_					    =					"gelu_new"

     super().__init__(
         pad_token_id=_lowerCAmelCase    ,				eos_token_id=_lowerCAmelCase    ,				is_encoder_decoder=_lowerCAmelCase    ,				**_lowerCAmelCase    ,				)






class     __lowerCAmelCase     ( a					):






  """simple docstring"""





  @property
  def 					lowerCAmelCase__  (     self       :       str  )   -> Mapping[str, Mapping[int, str]]:




     """simple docstring"""
     snake_case_					    =					{
         "input_ids": {0: "batch", 1: "encoder_sequence"},
         "attention_mask": {0: "batch", 1: "encoder_sequence"},
     }
     if self.use_past:
        snake_case_					    =					"past_encoder_sequence + sequence"
        snake_case_					    =					{0: "batch"}
        snake_case_					    =					{0: "batch", 1: "past_decoder_sequence + sequence"}
     else:
        snake_case_					    =					{0: "batch", 1: "decoder_sequence"}
        snake_case_					    =					{0: "batch", 1: "decoder_sequence"}

     if self.use_past:
        self.fill_with_past_key_values_(_lowerCAmelCase    ,				direction="inputs"  )

     return common_inputs



  @property
  def 					lowerCAmelCase__  (     self       :       List[Any]  )   -> int:




     """simple docstring"""
     return 1_3




