



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_keras_nlp_available,
    is_tensorflow_text_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase							=       {
    """configuration_gpt2""": ["""GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPT2Config""", """GPT2OnnxConfig"""],
    """tokenization_gpt2""": ["""GPT2Tokenizer"""],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase							=       ["""GPT2TokenizerFast"""]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase							=       [
	    """GPT2_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """GPT2DoubleHeadsModel""",
	    """GPT2ForQuestionAnswering""",
	    """GPT2ForSequenceClassification""",
	    """GPT2ForTokenClassification""",
	    """GPT2LMHeadModel""",
	    """GPT2Model""",
	    """GPT2PreTrainedModel""",
	    """load_tf_weights_in_gpt2""",
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase							=       [
	    """TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """TFGPT2DoubleHeadsModel""",
	    """TFGPT2ForSequenceClassification""",
	    """TFGPT2LMHeadModel""",
	    """TFGPT2MainLayer""",
	    """TFGPT2Model""",
	    """TFGPT2PreTrainedModel""",
	]

try:
	if not is_keras_nlp_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase							=       ["""TFGPT2Tokenizer"""]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase							=       ["""FlaxGPT2LMHeadModel""", """FlaxGPT2Model""", """FlaxGPT2PreTrainedModel"""]

if TYPE_CHECKING:
	from .configuration_gpta import GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTaConfig, GPTaOnnxConfig
	from .tokenization_gpta import GPTaTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_gpta_fast import GPTaTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_gpta import (
		    GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
		    GPTaDoubleHeadsModel,
		    GPTaForQuestionAnswering,
		    GPTaForSequenceClassification,
		    GPTaForTokenClassification,
		    GPTaLMHeadModel,
		    GPTaModel,
		    GPTaPreTrainedModel,
		    load_tf_weights_in_gpta,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_gpta import (
		    TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFGPTaDoubleHeadsModel,
		    TFGPTaForSequenceClassification,
		    TFGPTaLMHeadModel,
		    TFGPTaMainLayer,
		    TFGPTaModel,
		    TFGPTaPreTrainedModel,
		)

	try:
		if not is_keras_nlp_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_gpta_tf import TFGPTaTokenizer

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel, FlaxGPTaPreTrainedModel

else:
	import sys

	UpperCAmelCase							=       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


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)    )



