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


UpperCamelCase    				=		logging.get_logger(__name__)



@dataclass
class     snake_case_			:
   __A      :						str
   __A      :						str
   __A      :						Optional[str]   =							None
   __A      :						Optional[str]   =							None






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



@dataclass(frozen=__A			)
class     snake_case_			:
   __A      :						List[int]
   __A      :						Optional[List[int]]   =							None
   __A      :						Optional[List[int]]   =							None
   __A      :						Optional[Union[int, float]]   =							None






   def  __UpperCamelCase	( self							:	Optional[Any]    )   ->		Optional[int]:
       return json.dumps(dataclasses.asdict(self    )    ) + "\n"



class     snake_case_			:






   def  __UpperCamelCase	( self							:	Any      ,		lowercase_							:	List[str]    )   ->		List[Any]:
       raise NotImplementedError()






   def  __UpperCamelCase	( self							:	str      ,		lowercase_							:	Union[str, Any]    )   ->		Optional[int]:
       raise NotImplementedError()






   def  __UpperCamelCase	( self							:	Optional[Any]      ,		lowercase_							:	Tuple    )   ->		Any:
       raise NotImplementedError()






   def  __UpperCamelCase	( self							:	List[str]      ,		lowercase_							:	Optional[int]    )   ->		int:
       raise NotImplementedError()






   def  __UpperCamelCase	( self							:	str    )   ->		Optional[int]:
       raise NotImplementedError()






   def  __UpperCamelCase	( self							:	List[str]      ,		lowercase_							:	List[str]    )   ->		Optional[Any]:
       if len(self.get_labels()    ) > 1:
           lowercase__					:  Tuple          =							self.get_labels()[int(example.label    )]
       return example







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



class     snake_case_			(						__A			):






   def __init__( self							:	Dict      ,		lowercase_							:	Optional[Any]=None      ,		lowercase_							:	Any=None      ,		lowercase_							:	Dict="classification"      ,		lowercase_							:	Dict=False    )   ->		Optional[int]:
       lowercase__					:  List[Any]          =							[] if labels is None else labels
       lowercase__					:  Tuple          =							[] if examples is None else examples
       lowercase__					:  Optional[Any]          =							mode
       lowercase__					:  int          =							verbose






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






   def __getitem__( self							:	Tuple      ,		lowercase_							:	Optional[int]    )   ->		Union[str, Any]:
       if isinstance(lowercase_      ,		lowercase_    ):
           return SingleSentenceClassificationProcessor(labels=self.labels      ,		examples=self.examples[idx]    )
       return self.examples[idx]






   @classmethod
   def  __UpperCamelCase	( cls							:	Any      ,		lowercase_							:	int      ,		lowercase_							:	str=""      ,		lowercase_							:	Dict=0      ,		lowercase_							:	Dict=1      ,		lowercase_							:	str=None      ,		lowercase_							:	Any=False      ,		**lowercase_							:	Dict    )   ->		str:
       lowercase__					:  List[Any]          =							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  __UpperCamelCase	( cls							:	Optional[Any]      ,		lowercase_							:	Any      ,		lowercase_							:	List[Any]=None      ,		**lowercase_							:	Dict    )   ->		Any:
       lowercase__					:  Optional[Any]          =							cls(**lowercase_    )
       processor.add_examples(lowercase_      ,		labels=lowercase_    )
       return processor






   def  __UpperCamelCase	( self							:	Any      ,		lowercase_							:	Dict      ,		lowercase_							:	int=""      ,		lowercase_							:	Union[str, Any]=0      ,		lowercase_							:	Optional[int]=1      ,		lowercase_							:	str=None      ,		lowercase_							:	Tuple=False      ,		lowercase_							:	Any=False      ,		lowercase_							:	Dict=False      ,		)   ->		Dict:
       lowercase__					:  List[Any]          =							self._read_tsv(lowercase_    )
       if skip_first_row:
           lowercase__					:  str          =							lines[1:]
       lowercase__					:  List[str]          =							[]
       lowercase__					:  Optional[int]          =							[]
       lowercase__					:  Any          =							[]
       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:
               lowercase__					:  int          =							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  __UpperCamelCase	( self							:	str      ,		lowercase_							:	Dict      ,		lowercase_							:	Union[str, Any]=None      ,		lowercase_							:	Any=None      ,		lowercase_							:	Optional[int]=False      ,		lowercase_							:	Dict=False    )   ->		Tuple:
       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:
           lowercase__					:  List[Any]          =							[None] * len(lowercase_    )
       if labels is None:
           lowercase__					:  Any          =							[None] * len(lowercase_    )
       lowercase__					:  List[str]          =							[]
       lowercase__					:  Optional[int]          =							set()
       for text_or_text_and_label, label, guid in zip(lowercase_      ,		lowercase_      ,		lowercase_    ):
           if isinstance(lowercase_      ,		(tuple, list)    ) and label is None:
               lowercase__   ,  lowercase__					:  List[Any]          =							text_or_text_and_label
           else:
               lowercase__					:  Any          =							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:
           lowercase__					:  List[Any]          =							examples
       else:
           self.examples.extend(lowercase_    )

       # Update labels
       if overwrite_labels:
           lowercase__					:  Dict          =							list(lowercase_    )
       else:
           lowercase__					:  Optional[int]          =							list(set(self.labels    ).union(lowercase_    )    )

       return self.examples







   def  __UpperCamelCase	( self							:	Optional[int]      ,		lowercase_							:	Optional[Any]      ,		lowercase_							:	Tuple=None      ,		lowercase_							:	Optional[int]=False      ,		lowercase_							:	Any=0      ,		lowercase_							:	str=True      ,		lowercase_							:	List[Any]=None      ,		)   ->		Tuple:
       if max_length is None:
           lowercase__					:  List[Any]          =							tokenizer.max_len

       lowercase__					:  Union[str, Any]          =							{label: i for i, label in enumerate(self.labels    )}

       lowercase__					:  Dict          =							[]
       for ex_index, example in enumerate(self.examples    ):
           if ex_index % 1_00_00 == 0:
               logger.info(F'''Tokenizing example {ex_index}'''    )

           lowercase__					:  List[str]          =							tokenizer.encode(
               example.text_a      ,		add_special_tokens=lowercase_      ,		max_length=min(lowercase_      ,		tokenizer.max_len    )      ,		)
           all_input_ids.append(lowercase_    )

       lowercase__					:  Optional[Any]          =							max(len(lowercase_    ) for input_ids in all_input_ids    )

       lowercase__					:  Union[str, Any]          =							[]
       for ex_index, (input_ids, example) in enumerate(zip(lowercase_      ,		self.examples    )    ):
           if ex_index % 1_00_00 == 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.
           lowercase__					:  Tuple          =							[1 if mask_padding_with_zero else 0] * len(lowercase_    )

           # Zero-pad up to the sequence length.
           lowercase__					:  Any          =							batch_length - len(lowercase_    )
           if pad_on_left:
               lowercase__					:  List[str]          =							([pad_token] * padding_length) + input_ids
               lowercase__					:  Any          =							([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
           else:
               lowercase__					:  int          =							input_ids + ([pad_token] * padding_length)
               lowercase__					:  int          =							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":
               lowercase__					:  List[Any]          =							label_map[example.label]
           elif self.mode == "regression":
               lowercase__					:  Tuple          =							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)

           lowercase__					:  List[Any]          =							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

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

           lowercase__					:  List[Any]          =							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)    )



