

"""simple docstring"""

import collections
import os
from typing import List, Optional, Tuple

from transformers.utils import is_jieba_available, requires_backends


if is_jieba_available():
    import jieba

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_UpperCamelCase		     =    logging.get_logger(__name__)

_UpperCamelCase		     =    {"""vocab_file""": """vocab.txt"""}

_UpperCamelCase		     =    {
    """vocab_file""": {
        """openbmb/cpm-ant-10b""": """https://huggingface.co/openbmb/cpm-ant-10b/blob/main/vocab.txt""",
    },
}

_UpperCamelCase		     =    {
    """openbmb/cpm-ant-10b""": 1_0_2_4,
}



def       SCREAMING_SNAKE_CASE							( lowercase__   )			->						Optional[int]:
 lowerCAmelCase__   :				Union[str, Any] 			=				collections.OrderedDict()
 with open(lowercase__					,					"r"					,					encoding="utf-8"   ) as reader:
  lowerCAmelCase__   :				Optional[int] 			=				reader.readlines()
 for index, token in enumerate(lowercase__   ):
  lowerCAmelCase__   :				List[str] 			=				token.rstrip("\n"   )
  lowerCAmelCase__   :				Optional[Any] 			=				index
 return vocab






class 			__a					(						__magic_name__       ):





       """simple docstring"""


       def __init__(   self			,					snake_case			,					snake_case="<unk>"			,					snake_case=200   ):





        """simple docstring"""

        lowerCAmelCase__   :				Optional[Any] 			=				vocab
        lowerCAmelCase__   :				Optional[Any] 			=				unk_token
        lowerCAmelCase__   :				Dict 			=				max_input_chars_per_word






       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case   ):





        """simple docstring"""

        lowerCAmelCase__   :				Optional[int] 			=				list(snake_case   )
        if len(snake_case   ) > self.max_input_chars_per_word:
         return [self.unk_token]

        lowerCAmelCase__   :				str 			=				0
        lowerCAmelCase__   :				Any 			=				[]
        while start < len(snake_case   ):
         lowerCAmelCase__   :				int 			=				len(snake_case   )
         lowerCAmelCase__   :				Dict 			=				None
         while start < end:
          lowerCAmelCase__   :				List[str] 			=				"".join(chars[start:end]   )
          if substr in self.vocab:
           lowerCAmelCase__   :				Any 			=				substr
           break
          end -= 1
         if cur_substr is None:
          sub_tokens.append(self.unk_token   )
          start += 1
         else:
          sub_tokens.append(snake_case   )
          lowerCAmelCase__   :				Any 			=				end

        return sub_tokens






class 			__a					(						__magic_name__       ):





       """simple docstring"""


       __UpperCamelCase					:      Optional[int]    =						VOCAB_FILES_NAMES
       __UpperCamelCase					:      List[Any]    =						PRETRAINED_VOCAB_FILES_MAP
       __UpperCamelCase					:      Union[str, Any]    =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       __UpperCamelCase					:      Optional[Any]    =						['input_ids', 'attention_mask']
       __UpperCamelCase					:      Any    =						False






       def __init__(   self			,					snake_case			,					snake_case="<d>"			,					snake_case="</d>"			,					snake_case="<s>"			,					snake_case="</s>"			,					snake_case="<pad>"			,					snake_case="<unk>"			,					snake_case="</n>"			,					snake_case="</_>"			,					snake_case="left"			,					**snake_case			,					):





        """simple docstring"""

        requires_backends(self			,					["jieba"]   )
        super().__init__(
            bod_token=snake_case			,					eod_token=snake_case			,					bos_token=snake_case			,					eos_token=snake_case			,					pad_token=snake_case			,					unk_token=snake_case			,					line_token=snake_case			,					space_token=snake_case			,					padding_side=snake_case			,					**snake_case			,					)
        lowerCAmelCase__   :				int 			=				bod_token
        lowerCAmelCase__   :				Dict 			=				eod_token
        lowerCAmelCase__   :				str 			=				load_vocab(snake_case   )
        lowerCAmelCase__   :				Optional[Any] 			=				self.encoder[space_token]
        lowerCAmelCase__   :				Tuple 			=				self.encoder[line_token]

        del self.encoder[space_token]
        del self.encoder[line_token]

        lowerCAmelCase__   :				Optional[Any] 			=				collections.OrderedDict(sorted(self.encoder.items()			,					key=lambda snake_case   : x[1]   )   )
        lowerCAmelCase__   :				Any 			=				{v: k for k, v in self.encoder.items()}

        lowerCAmelCase__   :				List[Any] 			=				WordpieceTokenizer(vocab=self.encoder			,					unk_token=self.unk_token   )






       @property
       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        return self.encoder[self.bod_token]






       @property
       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        return self.encoder[self.eod_token]






       @property
       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        return self.encoder["\n"]






       @property
       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        return len(self.encoder   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        return dict(self.encoder			,					**self.added_tokens_encoder   )






       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case   ):





        """simple docstring"""

        lowerCAmelCase__   :				int 			=				[]
        for x in jieba.cut(snake_case			,					cut_all=snake_case   ):
         output_tokens.extend(self.wordpiece_tokenizer.tokenize(snake_case   )   )
        return output_tokens






       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case			,					**snake_case   ):





        """simple docstring"""

        lowerCAmelCase__   :				Optional[Any] 			=				[i for i in token_ids if i >= 0]
        lowerCAmelCase__   :				Optional[int] 			=				[
            x for x in token_ids if x != self.pad_token_id and x != self.eos_token_id and x != self.bos_token_id
        ]
        return super()._decode(snake_case			,					**snake_case   )






       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case   ):





        """simple docstring"""

        return token in self.encoder






       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case   ):





        """simple docstring"""

        return "".join(snake_case   )






       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case   ):





        """simple docstring"""

        return self.encoder.get(snake_case			,					self.encoder.get(self.unk_token   )   )






       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case   ):





        """simple docstring"""

        return self.decoder.get(snake_case			,					self.unk_token   )






       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case			,					snake_case = None   ):





        """simple docstring"""

        if os.path.isdir(snake_case   ):
         lowerCAmelCase__   :				Union[str, Any] 			=				os.path.join(
             snake_case			,					(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]   )
        else:
         lowerCAmelCase__   :				Union[str, Any] 			=				(filename_prefix + "-" if filename_prefix else "") + save_directory
        lowerCAmelCase__   :				Any 			=				0
        if " " in self.encoder:
         lowerCAmelCase__   :				str 			=				self.encoder[" "]
         del self.encoder[" "]
        if "\n" in self.encoder:
         lowerCAmelCase__   :				Any 			=				self.encoder["\n"]
         del self.encoder["\n"]
        lowerCAmelCase__   :				str 			=				collections.OrderedDict(sorted(self.encoder.items()			,					key=lambda snake_case   : x[1]   )   )
        with open(snake_case			,					"w"			,					encoding="utf-8"   ) as writer:
         for token, token_index in self.encoder.items():
          if index != token_index:
           logger.warning(
               F"""Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."""
               " Please check that the vocabulary is not corrupted!"   )
           lowerCAmelCase__   :				List[str] 			=				token_index
          writer.write(token + "\n"   )
          index += 1
        return (vocab_file,)






       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case			,					snake_case = None   ):





        """simple docstring"""

        if token_ids_a is None:
         return [self.bos_token_id] + token_ids_a
        return [self.bos_token_id] + token_ids_a + [self.bos_token_id] + token_ids_a






       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case			,					snake_case = None			,					snake_case = False   ):





        """simple docstring"""

        if already_has_special_tokens:
         return super().get_special_tokens_mask(
             token_ids_a=snake_case			,					token_ids_a=snake_case			,					already_has_special_tokens=snake_case   )

        if token_ids_a is not None:
         return [1] + ([0] * len(snake_case   )) + [1] + ([0] * len(snake_case   ))
        return [1] + ([0] * len(snake_case   ))









"""simple docstring"""

import itertools
import os
import random
import tempfile
import unittest

import numpy as np

from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
    import torch

if is_datasets_available():
    from datasets import load_dataset

_UpperCamelCase		     =    random.Random()



def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__=1.0					,					lowercase__=None					,					lowercase__=None   )			->						Optional[Any]:
 if rng is None:
  lowerCAmelCase__   :				Union[str, Any] 			=				global_rng

 lowerCAmelCase__   :				Dict 			=				[]
 for batch_idx in range(shape[0]   ):
  values.append([]   )
  for _ in range(shape[1]   ):
   values[-1].append(rng.random() * scale   )

 return values






class 			__a					(						unittest.TestCase       ):





       """simple docstring"""


       def __init__(   self			,					snake_case			,					snake_case=7			,					snake_case=400			,					snake_case=2_000			,					snake_case=2_048			,					snake_case=128			,					snake_case=1			,					snake_case=512			,					snake_case=30			,					snake_case=44_100			,					):





        """simple docstring"""

        lowerCAmelCase__   :				Optional[int] 			=				parent
        lowerCAmelCase__   :				Optional[int] 			=				batch_size
        lowerCAmelCase__   :				Optional[Any] 			=				min_seq_length
        lowerCAmelCase__   :				Optional[int] 			=				max_seq_length
        lowerCAmelCase__   :				Dict 			=				(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
        lowerCAmelCase__   :				Dict 			=				spectrogram_length
        lowerCAmelCase__   :				Any 			=				feature_size
        lowerCAmelCase__   :				int 			=				num_audio_channels
        lowerCAmelCase__   :				Optional[int] 			=				hop_length
        lowerCAmelCase__   :				List[str] 			=				chunk_length
        lowerCAmelCase__   :				Optional[Any] 			=				sampling_rate






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        return {
            "spectrogram_length": self.spectrogram_length,
            "feature_size": self.feature_size,
            "num_audio_channels": self.num_audio_channels,
            "hop_length": self.hop_length,
            "chunk_length": self.chunk_length,
            "sampling_rate": self.sampling_rate,
        }






       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case=False			,					snake_case=False   ):





        """simple docstring"""

        def _flatten(snake_case   ):
         return list(itertools.chain(*snake_case   )   )

        if equal_length:
         lowerCAmelCase__   :				Dict 			=				[floats_list((self.max_seq_length, self.feature_size)   ) for _ in range(self.batch_size   )]
        else:
         # make sure that inputs increase in size
         lowerCAmelCase__   :				Tuple 			=				[
             floats_list((x, self.feature_size)   )
             for x in range(self.min_seq_length			,					self.max_seq_length			,					self.seq_length_diff   )
         ]
        if numpify:
         lowerCAmelCase__   :				Dict 			=				[np.asarray(snake_case   ) for x in speech_inputs]
        return speech_inputs






@require_torch
@require_torchaudio
class 			__a					(						__magic_name__       ,	unittest.TestCase       ):





       """simple docstring"""


       __UpperCamelCase					:      Optional[Any]    =						TvltFeatureExtractor






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				Optional[Any] 			=				TvltFeatureExtractionTester(self   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				List[str] 			=				self.feature_extraction_class(**self.feat_extract_dict   )
        self.assertTrue(hasattr(snake_case			,					"spectrogram_length"   )   )
        self.assertTrue(hasattr(snake_case			,					"feature_size"   )   )
        self.assertTrue(hasattr(snake_case			,					"num_audio_channels"   )   )
        self.assertTrue(hasattr(snake_case			,					"hop_length"   )   )
        self.assertTrue(hasattr(snake_case			,					"chunk_length"   )   )
        self.assertTrue(hasattr(snake_case			,					"sampling_rate"   )   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				int 			=				self.feature_extraction_class(**self.feat_extract_dict   )

        with tempfile.TemporaryDirectory() as tmpdirname:
         lowerCAmelCase__   :				Union[str, Any] 			=				feat_extract_first.save_pretrained(snake_case   )[0]
         check_json_file_has_correct_format(snake_case   )
         lowerCAmelCase__   :				Tuple 			=				self.feature_extraction_class.from_pretrained(snake_case   )

        lowerCAmelCase__   :				Union[str, Any] 			=				feat_extract_first.to_dict()
        lowerCAmelCase__   :				int 			=				feat_extract_second.to_dict()
        lowerCAmelCase__   :				Union[str, Any] 			=				dict_first.pop("mel_filters"   )
        lowerCAmelCase__   :				List[Any] 			=				dict_second.pop("mel_filters"   )
        self.assertTrue(np.allclose(snake_case			,					snake_case   )   )
        self.assertEqual(snake_case			,					snake_case   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				Any 			=				self.feature_extraction_class(**self.feat_extract_dict   )

        with tempfile.TemporaryDirectory() as tmpdirname:
         lowerCAmelCase__   :				List[Any] 			=				os.path.join(snake_case			,					"feat_extract.json"   )
         feat_extract_first.to_json_file(snake_case   )
         lowerCAmelCase__   :				List[Any] 			=				self.feature_extraction_class.from_json_file(snake_case   )

        lowerCAmelCase__   :				Optional[Any] 			=				feat_extract_first.to_dict()
        lowerCAmelCase__   :				Dict 			=				feat_extract_second.to_dict()
        lowerCAmelCase__   :				Tuple 			=				dict_first.pop("mel_filters"   )
        lowerCAmelCase__   :				Dict 			=				dict_second.pop("mel_filters"   )
        self.assertTrue(np.allclose(snake_case			,					snake_case   )   )
        self.assertEqual(snake_case			,					snake_case   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				Optional[int] 			=				self.feature_extraction_class(**self.feat_extract_dict   )

        # create three inputs of length 800, 1000, and 1200
        lowerCAmelCase__   :				Union[str, Any] 			=				[floats_list((1, x)   )[0] for x in range(800			,					1_400			,					200   )]
        lowerCAmelCase__   :				Optional[int] 			=				[np.asarray(snake_case   ) for speech_input in speech_inputs]

        # Test not batched input
        lowerCAmelCase__   :				Tuple 			=				feature_extractor(np_speech_inputs[0]			,					return_tensors="np"			,					sampling_rate=44_100   ).audio_values

        self.assertTrue(encoded_audios.ndim == 4   )
        self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size   )
        self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length   )
        self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels   )

        # Test batched
        lowerCAmelCase__   :				Optional[int] 			=				feature_extractor(snake_case			,					return_tensors="np"			,					sampling_rate=44_100   ).audio_values

        self.assertTrue(encoded_audios.ndim == 4   )
        self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size   )
        self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length   )
        self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels   )

        # Test audio masking
        lowerCAmelCase__   :				List[str] 			=				feature_extractor(
            snake_case			,					return_tensors="np"			,					sampling_rate=44_100			,					mask_audio=snake_case   ).audio_values

        self.assertTrue(encoded_audios.ndim == 4   )
        self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size   )
        self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length   )
        self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels   )

        # Test 2-D numpy arrays are batched.
        lowerCAmelCase__   :				List[str] 			=				[floats_list((1, x)   )[0] for x in (800, 800, 800)]
        lowerCAmelCase__   :				Tuple 			=				np.asarray(snake_case   )
        lowerCAmelCase__   :				List[Any] 			=				feature_extractor(snake_case			,					return_tensors="np"			,					sampling_rate=44_100   ).audio_values
        self.assertTrue(encoded_audios.ndim == 4   )
        self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size   )
        self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length   )
        self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels   )






       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case   ):





        """simple docstring"""

        lowerCAmelCase__   :				str 			=				load_dataset("hf-internal-testing/librispeech_asr_dummy"			,					"clean"			,					split="validation"   )
        # automatic decoding with librispeech
        lowerCAmelCase__   :				int 			=				ds.sort("id"   ).select(range(snake_case   )   )[:num_samples]["audio"]

        return [x["array"] for x in speech_samples]






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				str 			=				self._load_datasamples(1   )
        lowerCAmelCase__   :				Optional[Any] 			=				TvltFeatureExtractor()
        lowerCAmelCase__   :				Dict 			=				feature_extractor(snake_case			,					return_tensors="pt"   ).audio_values

        self.assertEquals(audio_values.shape			,					(1, 1, 192, 128)   )

        lowerCAmelCase__   :				Dict 			=				torch.tensor([[-0.3_032, -0.2_708], [-0.4_434, -0.4_007]]   )
        self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]			,					snake_case			,					atol=1e-4   )   )






