'''simple docstring'''

from collections import OrderedDict
from typing import Mapping

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


_lowerCamelCase :				str														= logging.get_logger(__name__)

_lowerCamelCase :				Optional[Any]														= {
    """google/electra-small-generator""": """https://huggingface.co/google/electra-small-generator/resolve/main/config.json""",
    """google/electra-base-generator""": """https://huggingface.co/google/electra-base-generator/resolve/main/config.json""",
    """google/electra-large-generator""": """https://huggingface.co/google/electra-large-generator/resolve/main/config.json""",
    """google/electra-small-discriminator""": (
        """https://huggingface.co/google/electra-small-discriminator/resolve/main/config.json"""
    ),
    """google/electra-base-discriminator""": (
        """https://huggingface.co/google/electra-base-discriminator/resolve/main/config.json"""
    ),
    """google/electra-large-discriminator""": (
        """https://huggingface.co/google/electra-large-discriminator/resolve/main/config.json"""
    ),
}




class    lowerCamelCase__  (   __snake_case     ):
   __UpperCAmelCase           =						"""electra"""



   def __init__(    self					,      lowerCAmelCase__=30_522					,      lowerCAmelCase__=128					,      lowerCAmelCase__=256					,      lowerCAmelCase__=12					,      lowerCAmelCase__=4					,      lowerCAmelCase__=1_024					,      lowerCAmelCase__="gelu"					,      lowerCAmelCase__=0.1					,      lowerCAmelCase__=0.1					,      lowerCAmelCase__=512					,      lowerCAmelCase__=2					,      lowerCAmelCase__=0.02					,      lowerCAmelCase__=1e-12					,      lowerCAmelCase__="first"					,      lowerCAmelCase__=True					,      lowerCAmelCase__="gelu"					,      lowerCAmelCase__=0.1					,      lowerCAmelCase__=0					,      lowerCAmelCase__="absolute"					,      lowerCAmelCase__=True					,      lowerCAmelCase__=None					,      **lowerCAmelCase__					,      )				->     Optional[int]:
        """simple docstring"""





        super().__init__(pad_token_id=lowerCAmelCase__					,      **lowerCAmelCase__     )

        _UpperCamelCase  :int       =vocab_size
        _UpperCamelCase  :List[str]       =embedding_size
        _UpperCamelCase  :Tuple       =hidden_size
        _UpperCamelCase  :Optional[int]       =num_hidden_layers
        _UpperCamelCase  :Any       =num_attention_heads
        _UpperCamelCase  :Dict       =intermediate_size
        _UpperCamelCase  :Union[str, Any]       =hidden_act
        _UpperCamelCase  :Optional[Any]       =hidden_dropout_prob
        _UpperCamelCase  :Tuple       =attention_probs_dropout_prob
        _UpperCamelCase  :List[str]       =max_position_embeddings
        _UpperCamelCase  :Any       =type_vocab_size
        _UpperCamelCase  :List[Any]       =initializer_range
        _UpperCamelCase  :str       =layer_norm_eps

        _UpperCamelCase  :Optional[int]       =summary_type
        _UpperCamelCase  :List[str]       =summary_use_proj
        _UpperCamelCase  :Union[str, Any]       =summary_activation
        _UpperCamelCase  :List[str]       =summary_last_dropout
        _UpperCamelCase  :List[str]       =position_embedding_type
        _UpperCamelCase  :Optional[Any]       =use_cache
        _UpperCamelCase  :int       =classifier_dropout




class    lowerCamelCase__  (   __snake_case     ):



   @property
   def  _UpperCamelCase		(    self     )				->     Mapping[str, Mapping[int, str]]:
        """simple docstring"""





        if self.task == "multiple-choice":
             _UpperCamelCase  :Tuple       ={0: """batch""", 1: """choice""", 2: """sequence"""}
        else:
             _UpperCamelCase  :Dict       ={0: """batch""", 1: """sequence"""}
        return OrderedDict(
            [
                ("""input_ids""", dynamic_axis),
                ("""attention_mask""", dynamic_axis),
                ("""token_type_ids""", dynamic_axis),
            ]     )



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


def   a						(				SCREAMING_SNAKE_CASE_   :		Optional[Any]				,      SCREAMING_SNAKE_CASE_   :		Optional[Any]=1.0				,      SCREAMING_SNAKE_CASE_   :		Optional[Any]=None				,      SCREAMING_SNAKE_CASE_   :		Union[str, Any]=None							):






       """simple docstring"""





       if rng is None:
              UpperCamelCase     :						str 						=		global_rng

       UpperCamelCase     :						Union[str, 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  UpperCAmelCase_						(				unittest.TestCase):
       '''simple docstring'''






       def 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase     :						int 						=		'''facebook/musicgen-small'''
              UpperCamelCase     :						str 						=		tempfile.mkdtemp()






       def 			_lowercase (  self			,       **__SCREAMING_SNAKE_CASE    ):




              """simple docstring"""




              return TaTokenizer.from_pretrained(self.checkpoint			,       **__SCREAMING_SNAKE_CASE    )






       def 			_lowercase (  self			,       **__SCREAMING_SNAKE_CASE    ):




              """simple docstring"""




              return EncodecFeatureExtractor.from_pretrained(self.checkpoint			,       **__SCREAMING_SNAKE_CASE    )






       def 			_lowercase (  self    ):




              """simple docstring"""




              shutil.rmtree(self.tmpdirname    )






       def 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase     :						Union[str, Any] 						=		self.get_tokenizer()
              UpperCamelCase     :						Any 						=		self.get_feature_extractor()

              UpperCamelCase     :						Optional[int] 						=		MusicgenProcessor(tokenizer=__SCREAMING_SNAKE_CASE			,       feature_extractor=__SCREAMING_SNAKE_CASE    )

              processor.save_pretrained(self.tmpdirname    )
              UpperCamelCase     :						int 						=		MusicgenProcessor.from_pretrained(self.tmpdirname    )

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

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






       def 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase     :						str 						=		MusicgenProcessor(tokenizer=self.get_tokenizer()			,       feature_extractor=self.get_feature_extractor()    )
              processor.save_pretrained(self.tmpdirname    )

              UpperCamelCase     :						Any 						=		self.get_tokenizer(bos_token='''(BOS)'''			,       eos_token='''(EOS)'''    )
              UpperCamelCase     :						Union[str, Any] 						=		self.get_feature_extractor(do_normalize=__SCREAMING_SNAKE_CASE			,       padding_value=1.0    )

              UpperCamelCase     :						Union[str, Any] 						=		MusicgenProcessor.from_pretrained(
                  self.tmpdirname			,       bos_token='''(BOS)'''			,       eos_token='''(EOS)'''			,       do_normalize=__SCREAMING_SNAKE_CASE			,       padding_value=1.0    )

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

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






       def 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase     :						int 						=		self.get_feature_extractor()
              UpperCamelCase     :						Tuple 						=		self.get_tokenizer()

              UpperCamelCase     :						Any 						=		MusicgenProcessor(tokenizer=__SCREAMING_SNAKE_CASE			,       feature_extractor=__SCREAMING_SNAKE_CASE    )

              UpperCamelCase     :						Union[str, Any] 						=		floats_list((3, 1_000)    )

              UpperCamelCase     :						Tuple 						=		feature_extractor(__SCREAMING_SNAKE_CASE			,       return_tensors='''np'''    )
              UpperCamelCase     :						Optional[Any] 						=		processor(__SCREAMING_SNAKE_CASE			,       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 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase     :						int 						=		self.get_feature_extractor()
              UpperCamelCase     :						List[str] 						=		self.get_tokenizer()

              UpperCamelCase     :						Optional[int] 						=		MusicgenProcessor(tokenizer=__SCREAMING_SNAKE_CASE			,       feature_extractor=__SCREAMING_SNAKE_CASE    )

              UpperCamelCase     :						Tuple 						=		'''This is a test string'''

              UpperCamelCase     :						Dict 						=		processor(text=__SCREAMING_SNAKE_CASE    )

              UpperCamelCase     :						Union[str, Any] 						=		tokenizer(__SCREAMING_SNAKE_CASE    )

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






       def 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase     :						Any 						=		self.get_feature_extractor()
              UpperCamelCase     :						int 						=		self.get_tokenizer()

              UpperCamelCase     :						Optional[int] 						=		MusicgenProcessor(tokenizer=__SCREAMING_SNAKE_CASE			,       feature_extractor=__SCREAMING_SNAKE_CASE    )

              UpperCamelCase     :						int 						=		[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

              UpperCamelCase     :						int 						=		processor.batch_decode(sequences=__SCREAMING_SNAKE_CASE    )
              UpperCamelCase     :						Union[str, Any] 						=		tokenizer.batch_decode(__SCREAMING_SNAKE_CASE    )

              self.assertListEqual(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    )






       def 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase     :						Dict 						=		self.get_feature_extractor()
              UpperCamelCase     :						Union[str, Any] 						=		self.get_tokenizer()

              UpperCamelCase     :						str 						=		MusicgenProcessor(tokenizer=__SCREAMING_SNAKE_CASE			,       feature_extractor=__SCREAMING_SNAKE_CASE    )

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






       def 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase     :						str 						=		self.get_feature_extractor(padding_side='''left'''    )
              UpperCamelCase     :						Optional[Any] 						=		self.get_tokenizer()

              UpperCamelCase     :						List[Any] 						=		MusicgenProcessor(tokenizer=__SCREAMING_SNAKE_CASE			,       feature_extractor=__SCREAMING_SNAKE_CASE    )

              UpperCamelCase     :						List[str] 						=		[floats_list((1, x)    )[0] for x in range(5			,       20			,       5    )]
              UpperCamelCase     :						Tuple 						=		processor(__SCREAMING_SNAKE_CASE    ).padding_mask

              UpperCamelCase     :						List[str] 						=		np.asarray(floats_list((3, 20)    )    )[:, None, :]
              UpperCamelCase     :						Optional[int] 						=		processor.batch_decode(__SCREAMING_SNAKE_CASE			,       padding_mask=__SCREAMING_SNAKE_CASE    )

              self.assertIsInstance(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    )

              for audio in decoded_audios:
                     self.assertIsInstance(__SCREAMING_SNAKE_CASE			,       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)    )

