





'''simple docstring'''


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


_lowerCamelCase					:						Optional[int]					     =							logging.get_logger(__name__)



@dataclass
class 				__UpperCAmelCase	:







		'''simple docstring'''







		__lowerCAmelCase								=     42
		__lowerCAmelCase								=     42
		__lowerCAmelCase								=     None
		__lowerCAmelCase								=     None







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






@dataclass(frozen=__A    )
class 				__UpperCAmelCase	:







		'''simple docstring'''







		__lowerCAmelCase								=     42
		__lowerCAmelCase								=     None
		__lowerCAmelCase								=     None
		__lowerCAmelCase								=     None







		def 							A  (self    :   Optional[Any]   ):
			return json.dumps(dataclasses.asdict(self   )   ) + "\n"






class 				__UpperCAmelCase	:







		'''simple docstring'''







		def 							A  (self    :   Any   ,   _lowerCAmelCase    :   List[str]   ):
			raise NotImplementedError()







		def 							A  (self    :   str   ,   _lowerCAmelCase    :   Union[str, Any]   ):
			raise NotImplementedError()







		def 							A  (self    :   Optional[Any]   ,   _lowerCAmelCase    :   Tuple   ):
			raise NotImplementedError()







		def 							A  (self    :   List[str]   ,   _lowerCAmelCase    :   Optional[int]   ):
			raise NotImplementedError()







		def 							A  (self    :   str   ):
			raise NotImplementedError()







		def 							A  (self    :   List[str]   ,   _lowerCAmelCase    :   List[str]   ):
			if len(self.get_labels()   ) > 1:
				A					=   self.get_labels()[int(example.label   )]
			return example







		@classmethod
		def 							A  (cls    :   Optional[int]   ,   _lowerCAmelCase    :   List[str]   ,   _lowerCAmelCase    :   int=None   ):
			with open(lowercase_   ,   """r"""   ,   encoding="""utf-8-sig"""   ) as f:
				return list(csv.reader(lowercase_   ,   delimiter="""\t"""   ,   quotechar=lowercase_   )   )






class 				__UpperCAmelCase	(							__A    ):







		'''simple docstring'''







		def __init__(self    :   Dict   ,   _lowerCAmelCase    :   Optional[Any]=None   ,   _lowerCAmelCase    :   Any=None   ,   _lowerCAmelCase    :   Dict="classification"   ,   _lowerCAmelCase    :   Dict=False   ):
			A					=   [] if labels is None else labels
			A					=   [] if examples is None else examples
			A					=   mode
			A					=   verbose







		def __len__(self    :   Optional[Any]   ):
			return len(self.examples   )







		def __getitem__(self    :   Tuple   ,   _lowerCAmelCase    :   Optional[int]   ):
			if isinstance(lowercase_   ,   lowercase_   ):
				return SingleSentenceClassificationProcessor(labels=self.labels   ,   examples=self.examples[idx]   )
			return self.examples[idx]







		@classmethod
		def 							A  (cls    :   Any   ,   _lowerCAmelCase    :   int   ,   _lowerCAmelCase    :   str=""   ,   _lowerCAmelCase    :   Dict=0   ,   _lowerCAmelCase    :   Dict=1   ,   _lowerCAmelCase    :   str=None   ,   _lowerCAmelCase    :   Any=False   ,   **_lowerCAmelCase    :   Dict   ):
			A					=   cls(**lowercase_   )
			processor.add_examples_from_csv(
			    lowercase_   ,   split_name=lowercase_   ,   column_label=lowercase_   ,   column_text=lowercase_   ,   column_id=lowercase_   ,   skip_first_row=lowercase_   ,   overwrite_labels=lowercase_   ,   overwrite_examples=lowercase_   ,   )
			return processor







		@classmethod
		def 							A  (cls    :   Optional[Any]   ,   _lowerCAmelCase    :   Any   ,   _lowerCAmelCase    :   List[Any]=None   ,   **_lowerCAmelCase    :   Dict   ):
			A					=   cls(**lowercase_   )
			processor.add_examples(lowercase_   ,   labels=lowercase_   )
			return processor







		def 							A  (self    :   Any   ,   _lowerCAmelCase    :   Dict   ,   _lowerCAmelCase    :   int=""   ,   _lowerCAmelCase    :   Union[str, Any]=0   ,   _lowerCAmelCase    :   Optional[int]=1   ,   _lowerCAmelCase    :   str=None   ,   _lowerCAmelCase    :   Tuple=False   ,   _lowerCAmelCase    :   Any=False   ,   _lowerCAmelCase    :   Dict=False   ,   ):
			A					=   self._read_tsv(lowercase_   )
			if skip_first_row:
				A					=   lines[1:]
			A					=   []
			A					=   []
			A					=   []
			for i, line in enumerate(lowercase_   ):
				texts.append(line[column_text]   )
				labels.append(line[column_label]   )
				if column_id is not None:
					ids.append(line[column_id]   )
				else:
					A					=   F"""{split_name}-{i}""" if split_name else str(lowercase_   )
					ids.append(lowercase_   )

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







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

			# Update examples
			if overwrite_examples:
				A					=   examples
			else:
				self.examples.extend(lowercase_   )

			# Update labels
			if overwrite_labels:
				A					=   list(lowercase_   )
			else:
				A					=   list(set(self.labels   ).union(lowercase_   )   )

			return self.examples







		def 							A  (self    :   Optional[int]   ,   _lowerCAmelCase    :   Optional[Any]   ,   _lowerCAmelCase    :   Tuple=None   ,   _lowerCAmelCase    :   Optional[int]=False   ,   _lowerCAmelCase    :   Any=0   ,   _lowerCAmelCase    :   str=True   ,   _lowerCAmelCase    :   List[Any]=None   ,   ):
			if max_length is None:
				A					=   tokenizer.max_len

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

			A					=   []
			for ex_index, example in enumerate(self.examples   ):
				if ex_index % 1_0000 == 0:
					logger.info(F"""Tokenizing example {ex_index}"""   )

				A					=   tokenizer.encode(
				    example.text_a   ,   add_special_tokens=lowercase_   ,   max_length=min(lowercase_   ,   tokenizer.max_len   )   ,   )
				all_input_ids.append(lowercase_   )

			A					=   max(len(lowercase_   ) for input_ids in all_input_ids   )

			A					=   []
			for ex_index, (input_ids, example) in enumerate(zip(lowercase_   ,   self.examples   )   ):
				if ex_index % 1_0000 == 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.
				A					=   [1 if mask_padding_with_zero else 0] * len(lowercase_   )

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

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

				if self.mode == "classification":
					A					=   label_map[example.label]
				elif self.mode == "regression":
					A					=   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(lowercase_   ) for x in input_ids]   )}"""   )
					logger.info(F"""attention_mask: {" ".join([str(lowercase_   ) for x in attention_mask]   )}"""   )
					logger.info(F"""label: {example.label} (id = {label})"""   )

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

			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)

				A					=   tf.data.Dataset.from_generator(
				    lowercase_   ,   ({"""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

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

				A					=   TensorDataset(lowercase_   ,   lowercase_   ,   lowercase_   )
				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


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)    )



