
import csv
import dataclasses
import json
from dataclasses import dataclass
from typing import List, Optional, Union

from ...utils import is_tf_available, is_torch_available, logging


snake_case_      :					str				      =     logging.get_logger(__name__)





@dataclass
class 		__lowerCamelCase     :
							lowerCamelCase__:				str
							lowerCamelCase__:				str
							lowerCamelCase__:				Optional[str]      =							None
							lowerCamelCase__:				Optional[str]      =							None



							def     A__ (     self							)						->     int:






								"""simple docstring"""



								return json.dumps(dataclasses.asdict(self							)	,				indent=2							) + "\n"





@dataclass(frozen=_SCREAMING_SNAKE_CASE		)
class 		__lowerCamelCase     :
							lowerCamelCase__:				List[int]
							lowerCamelCase__:				Optional[List[int]]      =							None
							lowerCamelCase__:				Optional[List[int]]      =							None
							lowerCamelCase__:				Optional[Union[int, float]]      =							None



							def     A__ (     self							)						->     List[Any]:






								"""simple docstring"""



								return json.dumps(dataclasses.asdict(self							)							) + "\n"





class 		__lowerCamelCase     :



							def     A__ (     self	,				__snake_case							)						->     Optional[int]:






								"""simple docstring"""



								raise NotImplementedError()



							def     A__ (     self	,				__snake_case							)						->     Union[str, Any]:






								"""simple docstring"""



								raise NotImplementedError()



							def     A__ (     self	,				__snake_case							)						->     Optional[Any]:






								"""simple docstring"""



								raise NotImplementedError()



							def     A__ (     self	,				__snake_case							)						->     List[str]:






								"""simple docstring"""



								raise NotImplementedError()



							def     A__ (     self							)						->     int:






								"""simple docstring"""



								raise NotImplementedError()



							def     A__ (     self	,				__snake_case							)						->     Dict:






								"""simple docstring"""



								if len(self.get_labels()							) > 1:
									UpperCAmelCase:					Union[str, Any] 						=     self.get_labels()[int(example.label							)]
								return example




							@classmethod
							def     A__ (     cls	,				__snake_case	,				__snake_case=None							)						->     Union[str, Any]:






								"""simple docstring"""



								with open(_SCREAMING_SNAKE_CASE	,				"r"	,				encoding="utf-8-sig"							) as f:
									return list(csv.reader(_SCREAMING_SNAKE_CASE	,				delimiter="\t"	,				quotechar=_SCREAMING_SNAKE_CASE							)							)





class 		__lowerCamelCase     (   _SCREAMING_SNAKE_CASE		):



							def __init__(     self	,				__snake_case=None	,				__snake_case=None	,				__snake_case="classification"	,				__snake_case=False							)						->     Optional[Any]:






								"""simple docstring"""



								UpperCAmelCase:					str 						=     [] if labels is None else labels
								UpperCAmelCase:					str 						=     [] if examples is None else examples
								UpperCAmelCase:					Tuple 						=     mode
								UpperCAmelCase:					List[Any] 						=     verbose



							def __len__(     self							)						->     Dict:






								"""simple docstring"""



								return len(self.examples							)



							def __getitem__(     self	,				__snake_case							)						->     Optional[int]:






								"""simple docstring"""



								if isinstance(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE							):
									return SingleSentenceClassificationProcessor(labels=self.labels	,				examples=self.examples[idx]							)
								return self.examples[idx]



							@classmethod
							def     A__ (     cls	,				__snake_case	,				__snake_case=""	,				__snake_case=0	,				__snake_case=1	,				__snake_case=None	,				__snake_case=False	,				**__snake_case							)						->     Optional[Any]:






								"""simple docstring"""



								UpperCAmelCase:					str 						=     cls(**_SCREAMING_SNAKE_CASE							)
								processor.add_examples_from_csv(
								    _SCREAMING_SNAKE_CASE	,				split_name=_SCREAMING_SNAKE_CASE	,				column_label=_SCREAMING_SNAKE_CASE	,				column_text=_SCREAMING_SNAKE_CASE	,				column_id=_SCREAMING_SNAKE_CASE	,				skip_first_row=_SCREAMING_SNAKE_CASE	,				overwrite_labels=_SCREAMING_SNAKE_CASE	,				overwrite_examples=_SCREAMING_SNAKE_CASE	,				)
								return processor



							@classmethod
							def     A__ (     cls	,				__snake_case	,				__snake_case=None	,				**__snake_case							)						->     Union[str, Any]:






								"""simple docstring"""



								UpperCAmelCase:					Dict 						=     cls(**_SCREAMING_SNAKE_CASE							)
								processor.add_examples(_SCREAMING_SNAKE_CASE	,				labels=_SCREAMING_SNAKE_CASE							)
								return processor



							def     A__ (     self	,				__snake_case	,				__snake_case=""	,				__snake_case=0	,				__snake_case=1	,				__snake_case=None	,				__snake_case=False	,				__snake_case=False	,				__snake_case=False	,				)						->     int:






								"""simple docstring"""



								UpperCAmelCase:					Tuple 						=     self._read_tsv(_SCREAMING_SNAKE_CASE							)
								if skip_first_row:
									UpperCAmelCase:					str 						=     lines[1:]
								UpperCAmelCase:					Tuple 						=     []
								UpperCAmelCase:					str 						=     []
								UpperCAmelCase:					Tuple 						=     []
								for i, line in enumerate(_SCREAMING_SNAKE_CASE							):
									texts.append(line[column_text]							)
									labels.append(line[column_label]							)
									if column_id is not None:
										ids.append(line[column_id]							)
									else:
										UpperCAmelCase:					Optional[Any] 						=     F'{split_name}-{i}' if split_name else str(_SCREAMING_SNAKE_CASE							)
										ids.append(_SCREAMING_SNAKE_CASE							)

								return self.add_examples(
								    _SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE	,				overwrite_labels=_SCREAMING_SNAKE_CASE	,				overwrite_examples=_SCREAMING_SNAKE_CASE							)



							def     A__ (     self	,				__snake_case	,				__snake_case=None	,				__snake_case=None	,				__snake_case=False	,				__snake_case=False							)						->     List[Any]:






								"""simple docstring"""



								if labels is not None and len(_SCREAMING_SNAKE_CASE							) != len(_SCREAMING_SNAKE_CASE							):
									raise ValueError(
									    F'Text and labels have mismatched lengths {len(_SCREAMING_SNAKE_CASE							)} and {len(_SCREAMING_SNAKE_CASE							)}'							)
								if ids is not None and len(_SCREAMING_SNAKE_CASE							) != len(_SCREAMING_SNAKE_CASE							):
									raise ValueError(F'Text and ids have mismatched lengths {len(_SCREAMING_SNAKE_CASE							)} and {len(_SCREAMING_SNAKE_CASE							)}'							)
								if ids is None:
									UpperCAmelCase:					Any 						=     [None] * len(_SCREAMING_SNAKE_CASE							)
								if labels is None:
									UpperCAmelCase:					Tuple 						=     [None] * len(_SCREAMING_SNAKE_CASE							)
								UpperCAmelCase:					Union[str, Any] 						=     []
								UpperCAmelCase:					Optional[Any] 						=     set()
								for text_or_text_and_label, label, guid in zip(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE							):
									if isinstance(_SCREAMING_SNAKE_CASE	,				(tuple, list)							) and label is None:
										UpperCAmelCase						,   UpperCAmelCase:					int 						=     text_or_text_and_label
									else:
										UpperCAmelCase:					Tuple 						=     text_or_text_and_label
									added_labels.add(_SCREAMING_SNAKE_CASE							)
									examples.append(InputExample(guid=_SCREAMING_SNAKE_CASE	,				text_a=_SCREAMING_SNAKE_CASE	,				text_b=_SCREAMING_SNAKE_CASE	,				label=_SCREAMING_SNAKE_CASE							)							)

								# Update examples
								if overwrite_examples:
									UpperCAmelCase:					Union[str, Any] 						=     examples
								else:
									self.examples.extend(_SCREAMING_SNAKE_CASE							)

								# Update labels
								if overwrite_labels:
									UpperCAmelCase:					List[str] 						=     list(_SCREAMING_SNAKE_CASE							)
								else:
									UpperCAmelCase:					Optional[Any] 						=     list(set(self.labels							).union(_SCREAMING_SNAKE_CASE							)							)

								return self.examples




							def     A__ (     self	,				__snake_case	,				__snake_case=None	,				__snake_case=False	,				__snake_case=0	,				__snake_case=True	,				__snake_case=None	,				)						->     str:






								"""simple docstring"""



								if max_length is None:
									UpperCAmelCase:					Tuple 						=     tokenizer.max_len

								UpperCAmelCase:					Tuple 						=     {label: i for i, label in enumerate(self.labels							)}

								UpperCAmelCase:					List[Any] 						=     []
								for ex_index, example in enumerate(self.examples							):
									if ex_index % 1_0_0_0_0 == 0:
										logger.info(F'Tokenizing example {ex_index}'							)

									UpperCAmelCase:					Optional[int] 						=     tokenizer.encode(
									    example.text_a	,				add_special_tokens=_SCREAMING_SNAKE_CASE	,				max_length=min(_SCREAMING_SNAKE_CASE	,				tokenizer.max_len							)	,				)
									all_input_ids.append(_SCREAMING_SNAKE_CASE							)

								UpperCAmelCase:					Any 						=     max(len(_SCREAMING_SNAKE_CASE							) for input_ids in all_input_ids							)

								UpperCAmelCase:					List[str] 						=     []
								for ex_index, (input_ids, example) in enumerate(zip(_SCREAMING_SNAKE_CASE	,				self.examples							)							):
									if ex_index % 1_0_0_0_0 == 0:
										logger.info(F'Writing example {ex_index}/{len(self.examples							)}'							)
									# The mask has 1 for real tokens and 0 for padding tokens. Only real
									# tokens are attended to.
									UpperCAmelCase:					Tuple 						=     [1 if mask_padding_with_zero else 0] * len(_SCREAMING_SNAKE_CASE							)

									# Zero-pad up to the sequence length.
									UpperCAmelCase:					List[Any] 						=     batch_length - len(_SCREAMING_SNAKE_CASE							)
									if pad_on_left:
										UpperCAmelCase:					List[Any] 						=     ([pad_token] * padding_length) + input_ids
										UpperCAmelCase:					Optional[Any] 						=     ([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
									else:
										UpperCAmelCase:					Optional[Any] 						=     input_ids + ([pad_token] * padding_length)
										UpperCAmelCase:					Any 						=     attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)

									if len(_SCREAMING_SNAKE_CASE							) != batch_length:
										raise ValueError(F'Error with input length {len(_SCREAMING_SNAKE_CASE							)} vs {batch_length}'							)
									if len(_SCREAMING_SNAKE_CASE							) != batch_length:
										raise ValueError(F'Error with input length {len(_SCREAMING_SNAKE_CASE							)} vs {batch_length}'							)

									if self.mode == "classification":
										UpperCAmelCase:					Tuple 						=     label_map[example.label]
									elif self.mode == "regression":
										UpperCAmelCase:					List[Any] 						=     float(example.label							)
									else:
										raise ValueError(self.mode							)

									if ex_index < 5 and self.verbose:
										logger.info("*** Example ***"							)
										logger.info(F'guid: {example.guid}'							)
										logger.info(F'input_ids: {" ".join([str(_SCREAMING_SNAKE_CASE							) for x in input_ids]							)}'							)
										logger.info(F'attention_mask: {" ".join([str(_SCREAMING_SNAKE_CASE							) for x in attention_mask]							)}'							)
										logger.info(F'label: {example.label} (id = {label})'							)

									features.append(InputFeatures(input_ids=_SCREAMING_SNAKE_CASE	,				attention_mask=_SCREAMING_SNAKE_CASE	,				label=_SCREAMING_SNAKE_CASE							)							)

								if return_tensors is None:
									return features
								elif return_tensors == "tf":
									if not is_tf_available():
										raise RuntimeError("return_tensors set to 'tf' but TensorFlow 2.0 can't be imported"							)
									import tensorflow as tf

									def gen():
										for ex in features:
											yield ({"input_ids": ex.input_ids, "attention_mask": ex.attention_mask}, ex.label)

									UpperCAmelCase:					int 						=     tf.data.Dataset.from_generator(
									    _SCREAMING_SNAKE_CASE	,				({"input_ids": tf.intaa, "attention_mask": tf.intaa}, tf.intaa)	,				({"input_ids": tf.TensorShape([None]							), "attention_mask": tf.TensorShape([None]							)}, tf.TensorShape([]							))	,				)
									return dataset
								elif return_tensors == "pt":
									if not is_torch_available():
										raise RuntimeError("return_tensors set to 'pt' but PyTorch can't be imported"							)
									import torch
									from torch.utils.data import TensorDataset

									UpperCAmelCase:					str 						=     torch.tensor([f.input_ids for f in features]	,				dtype=torch.long							)
									UpperCAmelCase:					Tuple 						=     torch.tensor([f.attention_mask for f in features]	,				dtype=torch.long							)
									if self.mode == "classification":
										UpperCAmelCase:					Tuple 						=     torch.tensor([f.label for f in features]	,				dtype=torch.long							)
									elif self.mode == "regression":
										UpperCAmelCase:					Tuple 						=     torch.tensor([f.label for f in features]	,				dtype=torch.float							)

									UpperCAmelCase:					Optional[int] 						=     TensorDataset(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE							)
									return dataset
								else:
									raise ValueError("return_tensors should be one of 'tf' or 'pt'"							)




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


_A							     =       random.Random()

def 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      List[Any]     ,					UpperCamelCase		:      Any=1.0     ,					UpperCamelCase		:      Union[str, Any]=None     ,					UpperCamelCase		:      Union[str, Any]=None	)     ->	List[Any]:



	"""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 			lowerCamelCase_					(    unittest.TestCase    ):





							def   __magic_name__      (							self    ):
								a_     			=   """facebook/musicgen-small"""
								a_     			=   tempfile.mkdtemp()





							def   __magic_name__      (							self			,       **_SCREAMING_SNAKE_CASE    ):
								return TaTokenizer.from_pretrained(self.checkpoint			,       **_SCREAMING_SNAKE_CASE    )





							def   __magic_name__      (							self			,       **_SCREAMING_SNAKE_CASE    ):
								return EncodecFeatureExtractor.from_pretrained(self.checkpoint			,       **_SCREAMING_SNAKE_CASE    )





							def   __magic_name__      (							self    ):
								shutil.rmtree(self.tmpdirname    )





							def   __magic_name__      (							self    ):
								a_     			=   self.get_tokenizer()
								a_     			=   self.get_feature_extractor()

								a_     			=   MusicgenProcessor(tokenizer=_SCREAMING_SNAKE_CASE			,       feature_extractor=_SCREAMING_SNAKE_CASE    )

								processor.save_pretrained(self.tmpdirname    )
								a_     			=   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   __magic_name__      (							self    ):
								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=_SCREAMING_SNAKE_CASE			,       padding_value=1.0    )

								a_     			=   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   __magic_name__      (							self    ):
								a_     			=   self.get_feature_extractor()
								a_     			=   self.get_tokenizer()

								a_     			=   MusicgenProcessor(tokenizer=_SCREAMING_SNAKE_CASE			,       feature_extractor=_SCREAMING_SNAKE_CASE    )

								a_     			=   floats_list((3, 1000)    )

								a_     			=   feature_extractor(_SCREAMING_SNAKE_CASE			,       return_tensors="""np"""    )
								a_     			=   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   __magic_name__      (							self    ):
								a_     			=   self.get_feature_extractor()
								a_     			=   self.get_tokenizer()

								a_     			=   MusicgenProcessor(tokenizer=_SCREAMING_SNAKE_CASE			,       feature_extractor=_SCREAMING_SNAKE_CASE    )

								a_     			=   """This is a test string"""

								a_     			=   processor(text=_SCREAMING_SNAKE_CASE    )

								a_     			=   tokenizer(_SCREAMING_SNAKE_CASE    )

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





							def   __magic_name__      (							self    ):
								a_     			=   self.get_feature_extractor()
								a_     			=   self.get_tokenizer()

								a_     			=   MusicgenProcessor(tokenizer=_SCREAMING_SNAKE_CASE			,       feature_extractor=_SCREAMING_SNAKE_CASE    )

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

								a_     			=   processor.batch_decode(sequences=_SCREAMING_SNAKE_CASE    )
								a_     			=   tokenizer.batch_decode(_SCREAMING_SNAKE_CASE    )

								self.assertListEqual(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE    )





							def   __magic_name__      (							self    ):
								a_     			=   self.get_feature_extractor()
								a_     			=   self.get_tokenizer()

								a_     			=   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   __magic_name__      (							self    ):
								a_     			=   self.get_feature_extractor(padding_side="""left"""    )
								a_     			=   self.get_tokenizer()

								a_     			=   MusicgenProcessor(tokenizer=_SCREAMING_SNAKE_CASE			,       feature_extractor=_SCREAMING_SNAKE_CASE    )

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

								a_     			=   np.asarray(floats_list((3, 20)    )    )[:, None, :]
								a_     			=   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)    )