'''simple docstring'''




from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


_lowercase		:					Union[str, Any]													=		logging.get_logger(__name__)

_lowercase		:					str													=		{
    '''facebook/mbart-large-cc25''': '''https://huggingface.co/facebook/mbart-large-cc25/resolve/main/config.json''',
    # See all MBART models at https://huggingface.co/models?filter=mbart
}





class        __magic_name__		(       _UpperCAmelCase):
			UpperCamelCase__							=			'mbart'
			UpperCamelCase__							=			['past_key_values']
			UpperCamelCase__							=			{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}







			def __init__(      self							:    List[Any]							,     lowercase_							:    Optional[int]=50265							,     lowercase_							:    Union[str, Any]=1024							,     lowercase_							:    str=12							,     lowercase_							:    List[Any]=4096							,     lowercase_							:    Tuple=16							,     lowercase_							:    Union[str, Any]=12							,     lowercase_							:    Union[str, Any]=4096							,     lowercase_							:    Tuple=16							,     lowercase_							:    Any=0.0							,     lowercase_							:    Optional[Any]=0.0							,     lowercase_							:    Optional[Any]=True							,     lowercase_							:    Optional[int]=True							,     lowercase_							:    Tuple="gelu"							,     lowercase_							:    int=1024							,     lowercase_							:    Any=0.1							,     lowercase_							:    Any=0.0							,     lowercase_							:    int=0.0							,     lowercase_							:    Any=0.02							,     lowercase_							:    int=0.0							,     lowercase_							:    List[str]=False							,     lowercase_							:    int=1							,     lowercase_							:    List[str]=0							,     lowercase_							:    List[Any]=2							,     lowercase_							:    Optional[int]=2							,     **lowercase_							:    Dict							,     ):
										lowercase_   :     List[Any]									=			vocab_size
										lowercase_   :     Dict									=			max_position_embeddings
										lowercase_   :     str									=			d_model
										lowercase_   :     List[Any]									=			encoder_ffn_dim
										lowercase_   :     Union[str, Any]									=			encoder_layers
										lowercase_   :     Optional[int]									=			encoder_attention_heads
										lowercase_   :     Optional[int]									=			decoder_ffn_dim
										lowercase_   :     Optional[int]									=			decoder_layers
										lowercase_   :     Dict									=			decoder_attention_heads
										lowercase_   :     str									=			dropout
										lowercase_   :     int									=			attention_dropout
										lowercase_   :     str									=			activation_dropout
										lowercase_   :     int									=			activation_function
										lowercase_   :     Dict									=			init_std
										lowercase_   :     Union[str, Any]									=			encoder_layerdrop
										lowercase_   :     Union[str, Any]									=			decoder_layerdrop
										lowercase_   :     int									=			classifier_dropout
										lowercase_   :     str									=			use_cache
										lowercase_   :     Optional[Any]									=			encoder_layers
										lowercase_   :     Optional[Any]									=			scale_embedding  # scale factor will be sqrt(d_model) if True
										super().__init__(
										    pad_token_id=lowercase_							,     bos_token_id=lowercase_							,     eos_token_id=lowercase_							,     is_encoder_decoder=lowercase_							,     forced_eos_token_id=lowercase_							,     **lowercase_							,     )





class        __magic_name__		(       _UpperCAmelCase):







			@property
			def       SCREAMING_SNAKE_CASE_	(      self							:    Dict       ):






										if self.task in ["default", "seq2seq-lm"]:
																	lowercase_   :     Tuple									=			OrderedDict(
																	    [
																	        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
																	        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
																	    ]       )

																	if self.use_past:
																								lowercase_   :     Any									=			{0: """batch"""}
																								lowercase_   :     Optional[int]									=			{0: """batch""", 1: """past_decoder_sequence + sequence"""}
																	else:
																								lowercase_   :     str									=			{0: """batch""", 1: """decoder_sequence"""}
																								lowercase_   :     Optional[int]									=			{0: """batch""", 1: """decoder_sequence"""}

																	if self.use_past:
																								self.fill_with_past_key_values_(lowercase_							,     direction="""inputs"""       )
										elif self.task == "causal-lm":
																	# TODO: figure this case out.
																	lowercase_   :     List[str]									=			OrderedDict(
																	    [
																	        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
																	        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
																	    ]       )
																	if self.use_past:
																								lowercase_		,		lowercase_   :     int									=			self.num_layers
																								for i in range(lowercase_       ):
																															lowercase_   :     Any									=			{0: """batch""", 2: """past_sequence + sequence"""}
																															lowercase_   :     Optional[int]									=			{0: """batch""", 2: """past_sequence + sequence"""}
										else:
																	lowercase_   :     str									=			OrderedDict(
																	    [
																	        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
																	        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
																	        ("""decoder_input_ids""", {0: """batch""", 1: """decoder_sequence"""}),
																	        ("""decoder_attention_mask""", {0: """batch""", 1: """decoder_sequence"""}),
																	    ]       )

										return common_inputs







			@property
			def       SCREAMING_SNAKE_CASE_	(      self							:    Tuple       ):






										if self.task in ["default", "seq2seq-lm"]:
																	lowercase_   :     Union[str, Any]									=			super().outputs
										else:
																	lowercase_   :     Optional[int]									=			super(lowercase_							,     self       ).outputs
																	if self.use_past:
																								lowercase_		,		lowercase_   :     Optional[Any]									=			self.num_layers
																								for i in range(lowercase_       ):
																															lowercase_   :     Optional[Any]									=			{0: """batch""", 2: """past_sequence + sequence"""}
																															lowercase_   :     Optional[Any]									=			{0: """batch""", 2: """past_sequence + sequence"""}
										return common_outputs







			def       SCREAMING_SNAKE_CASE_	(      self							:    str							,     lowercase_							:    PreTrainedTokenizer							,     lowercase_							:    int = -1							,     lowercase_							:    int = -1							,     lowercase_							:    bool = False							,     lowercase_							:    Optional[TensorType] = None							,     ):
										lowercase_   :     List[str]									=			self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
										    lowercase_							,     lowercase_							,     lowercase_							,     lowercase_							,     lowercase_       )

										# Generate decoder inputs
										lowercase_   :     Any									=			seq_length if not self.use_past else 1
										lowercase_   :     Optional[int]									=			self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
										    lowercase_							,     lowercase_							,     lowercase_							,     lowercase_							,     lowercase_       )
										lowercase_   :     Tuple									=			{f'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
										lowercase_   :     str									=			dict(**lowercase_							,     **lowercase_       )

										if self.use_past:
																	if not is_torch_available():
																								raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""       )
																	else:
																								import torch
																	lowercase_		,		lowercase_   :     Dict									=			common_inputs["""input_ids"""].shape
																	lowercase_   :     Union[str, Any]									=			common_inputs["""decoder_input_ids"""].shape[1]
																	lowercase_		,		lowercase_   :     Any									=			self.num_attention_heads
																	lowercase_   :     Optional[Any]									=			(
																	    batch,
																	    num_encoder_attention_heads,
																	    encoder_seq_length,
																	    self._config.hidden_size // num_encoder_attention_heads,
																	)
																	lowercase_   :     Any									=			decoder_seq_length + 3
																	lowercase_   :     Tuple									=			(
																	    batch,
																	    num_decoder_attention_heads,
																	    decoder_past_length,
																	    self._config.hidden_size // num_decoder_attention_heads,
																	)

																	lowercase_   :     List[Any]									=			torch.cat(
																	    [common_inputs["""decoder_attention_mask"""], torch.ones(lowercase_							,     lowercase_       )]							,     dim=1       )

																	lowercase_   :     List[str]									=			[]
																	# If the number of encoder and decoder layers are present in the model configuration, both are considered
																	lowercase_		,		lowercase_   :     List[str]									=			self.num_layers
																	lowercase_   :     Union[str, Any]									=			min(lowercase_							,     lowercase_       )
																	lowercase_   :     Any									=			max(lowercase_							,     lowercase_       ) - min_num_layers
																	lowercase_   :     Any									=			"""encoder""" if num_encoder_layers > num_decoder_layers else """decoder"""

																	for _ in range(lowercase_       ):
																								common_inputs["past_key_values"].append(
																								    (
																								        torch.zeros(lowercase_       ),
																								        torch.zeros(lowercase_       ),
																								        torch.zeros(lowercase_       ),
																								        torch.zeros(lowercase_       ),
																								    )       )
																	# TODO: test this.
																	lowercase_   :     Dict									=			encoder_shape if remaining_side_name == """encoder""" else decoder_shape
																	for _ in range(lowercase_							,     lowercase_       ):
																								common_inputs["past_key_values"].append((torch.zeros(lowercase_       ), torch.zeros(lowercase_       ))       )
										return common_inputs







			def       SCREAMING_SNAKE_CASE_	(      self							:    Any							,     lowercase_							:    PreTrainedTokenizer							,     lowercase_							:    int = -1							,     lowercase_							:    int = -1							,     lowercase_							:    bool = False							,     lowercase_							:    Optional[TensorType] = None							,     ):
										lowercase_   :     Any									=			self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
										    lowercase_							,     lowercase_							,     lowercase_							,     lowercase_							,     lowercase_       )

										if self.use_past:
																	if not is_torch_available():
																								raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""       )
																	else:
																								import torch
																	lowercase_		,		lowercase_   :     List[Any]									=			common_inputs["""input_ids"""].shape
																	# Not using the same length for past_key_values
																	lowercase_   :     List[str]									=			seqlen + 2
																	lowercase_		,		lowercase_   :     Optional[int]									=			self.num_layers
																	lowercase_		,		lowercase_   :     Dict									=			self.num_attention_heads
																	lowercase_   :     Optional[int]									=			(
																	    batch,
																	    num_encoder_attention_heads,
																	    past_key_values_length,
																	    self._config.hidden_size // num_encoder_attention_heads,
																	)

																	lowercase_   :     Tuple									=			common_inputs["""attention_mask"""].dtype
																	lowercase_   :     Optional[int]									=			torch.cat(
																	    [common_inputs["""attention_mask"""], torch.ones(lowercase_							,     lowercase_							,     dtype=lowercase_       )]							,     dim=1       )
																	lowercase_   :     List[Any]									=			[
																	    (torch.zeros(lowercase_       ), torch.zeros(lowercase_       )) for _ in range(lowercase_       )
																	]
										return common_inputs







			def       SCREAMING_SNAKE_CASE_	(      self							:    Any							,     lowercase_							:    PreTrainedTokenizer							,     lowercase_							:    int = -1							,     lowercase_							:    int = -1							,     lowercase_							:    bool = False							,     lowercase_							:    Optional[TensorType] = None							,     ):
										lowercase_   :     List[Any]									=			compute_effective_axis_dimension(
										    lowercase_							,     fixed_dimension=OnnxConfig.default_fixed_batch							,     num_token_to_add=0       )

										# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
										lowercase_   :     Optional[Any]									=			tokenizer.num_special_tokens_to_add(lowercase_       )
										lowercase_   :     Optional[Any]									=			compute_effective_axis_dimension(
										    lowercase_							,     fixed_dimension=OnnxConfig.default_fixed_sequence							,     num_token_to_add=lowercase_       )

										# Generate dummy inputs according to compute batch and sequence
										lowercase_   :     Union[str, Any]									=			[""" """.join([tokenizer.unk_token]       ) * seq_length] * batch_size
										lowercase_   :     str									=			dict(tokenizer(lowercase_							,     return_tensors=lowercase_       )       )
										return common_inputs







			def       SCREAMING_SNAKE_CASE_	(      self							:    Optional[int]							,     lowercase_							:    PreTrainedTokenizer							,     lowercase_							:    int = -1							,     lowercase_							:    int = -1							,     lowercase_							:    bool = False							,     lowercase_							:    Optional[TensorType] = None							,     ):






										if self.task in ["default", "seq2seq-lm"]:
																	lowercase_   :     Optional[int]									=			self._generate_dummy_inputs_for_default_and_seqaseq_lm(
																	    lowercase_							,     batch_size=lowercase_							,     seq_length=lowercase_							,     is_pair=lowercase_							,     framework=lowercase_       )

										elif self.task == "causal-lm":
																	lowercase_   :     Union[str, Any]									=			self._generate_dummy_inputs_for_causal_lm(
																	    lowercase_							,     batch_size=lowercase_							,     seq_length=lowercase_							,     is_pair=lowercase_							,     framework=lowercase_       )
										else:
																	lowercase_   :     Tuple									=			self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
																	    lowercase_							,     batch_size=lowercase_							,     seq_length=lowercase_							,     is_pair=lowercase_							,     framework=lowercase_       )

										return common_inputs







			def       SCREAMING_SNAKE_CASE_	(      self							:    Optional[int]							,     lowercase_							:    Dict							,     lowercase_							:    Tuple							,     lowercase_							:    Optional[Any]							,     lowercase_							:    List[Any]       ):






										if self.task in ["default", "seq2seq-lm"]:
																	lowercase_   :     Any									=			super()._flatten_past_key_values_(lowercase_							,     lowercase_							,     lowercase_							,     lowercase_       )
										else:
																	lowercase_   :     Optional[Any]									=			super(lowercase_							,     self       )._flatten_past_key_values_(
																	    lowercase_							,     lowercase_							,     lowercase_							,     lowercase_       )







"""simple docstring"""




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


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

def __snake_case (      UpperCamelCase						,					UpperCamelCase=1.0						,					UpperCamelCase=None						,					UpperCamelCase=None    )       ->    Tuple:


		"""simple docstring"""





		if rng is None:
				a__   		=     global_rng

		a__   		=     []
		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 SCREAMING_SNAKE_CASE       (      unittest.TestCase					):






	'''simple docstring'''







	def    _UpperCamelCase							(  self :Optional[int]			)			->   List[str]:

			'''simple docstring'''






			a__   		=     '''facebook/musicgen-small'''
			a__   		=     tempfile.mkdtemp()


	def    _UpperCamelCase							(  self :Union[str, Any]       ,    **__magic_name__ :Union[str, Any]			)			->   str:

			'''simple docstring'''






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


	def    _UpperCamelCase							(  self :Optional[int]       ,    **__magic_name__ :Any			)			->   Optional[int]:

			'''simple docstring'''






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


	def    _UpperCamelCase							(  self :Optional[int]			)			->   Optional[int]:

			'''simple docstring'''






			shutil.rmtree(self.tmpdirname			)


	def    _UpperCamelCase							(  self :Tuple			)			->   Any:

			'''simple docstring'''






			a__   		=     self.get_tokenizer()
			a__   		=     self.get_feature_extractor()

			a__   		=     MusicgenProcessor(tokenizer=__magic_name__       ,    feature_extractor=__magic_name__			)

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

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

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


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

			'''simple docstring'''






			a__   		=     MusicgenProcessor(tokenizer=self.get_tokenizer()       ,    feature_extractor=self.get_feature_extractor()			)
			processor.save_pretrained(self.tmpdirname			)

			a__   		=     self.get_tokenizer(bos_token='''(BOS)'''       ,    eos_token='''(EOS)'''			)
			a__   		=     self.get_feature_extractor(do_normalize=__magic_name__       ,    padding_value=1.0			)

			a__   		=     MusicgenProcessor.from_pretrained(
			    self.tmpdirname       ,    bos_token='''(BOS)'''       ,    eos_token='''(EOS)'''       ,    do_normalize=__magic_name__       ,    padding_value=1.0			)

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

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


	def    _UpperCamelCase							(  self :Any			)			->   Tuple:

			'''simple docstring'''






			a__   		=     self.get_feature_extractor()
			a__   		=     self.get_tokenizer()

			a__   		=     MusicgenProcessor(tokenizer=__magic_name__       ,    feature_extractor=__magic_name__			)

			a__   		=     floats_list((3, 1000)			)

			a__   		=     feature_extractor(__magic_name__       ,    return_tensors='''np'''			)
			a__   		=     processor(__magic_name__       ,    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 :Tuple			)			->   Dict:

			'''simple docstring'''






			a__   		=     self.get_feature_extractor()
			a__   		=     self.get_tokenizer()

			a__   		=     MusicgenProcessor(tokenizer=__magic_name__       ,    feature_extractor=__magic_name__			)

			a__   		=     '''This is a test string'''

			a__   		=     processor(text=__magic_name__			)

			a__   		=     tokenizer(__magic_name__			)

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


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

			'''simple docstring'''






			a__   		=     self.get_feature_extractor()
			a__   		=     self.get_tokenizer()

			a__   		=     MusicgenProcessor(tokenizer=__magic_name__       ,    feature_extractor=__magic_name__			)

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

			a__   		=     processor.batch_decode(sequences=__magic_name__			)
			a__   		=     tokenizer.batch_decode(__magic_name__			)

			self.assertListEqual(__magic_name__       ,    __magic_name__			)


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

			'''simple docstring'''






			a__   		=     self.get_feature_extractor()
			a__   		=     self.get_tokenizer()

			a__   		=     MusicgenProcessor(tokenizer=__magic_name__       ,    feature_extractor=__magic_name__			)

			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 :Any			)			->   List[str]:

			'''simple docstring'''






			a__   		=     self.get_feature_extractor(padding_side='''left'''			)
			a__   		=     self.get_tokenizer()

			a__   		=     MusicgenProcessor(tokenizer=__magic_name__       ,    feature_extractor=__magic_name__			)

			a__   		=     [floats_list((1, x)			)[0] for x in range(5       ,    20       ,    5			)]
			a__   		=     processor(__magic_name__			).padding_mask

			a__   		=     np.asarray(floats_list((3, 20)			)			)[:, None, :]
			a__   		=     processor.batch_decode(__magic_name__       ,    padding_mask=__magic_name__			)

			self.assertIsInstance(__magic_name__       ,    __magic_name__			)

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




