






'''simple docstring'''

from typing import List, Optional, Union

import numpy as np

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


UpperCamelCase							=logging.get_logger(__name__)




class     A    (      SCREAMING_SNAKE_CASE__						):






 """simple docstring"""





 __a			:						int      =						['''input_values''', '''padding_mask''']
 def __init__(							self			,     __lowerCAmelCase = 1			,     __lowerCAmelCase = 2_40_00			,     __lowerCAmelCase = 0.0			,     __lowerCAmelCase = None			,     __lowerCAmelCase = None			,     **__lowerCAmelCase			,     ):
       super().__init__(feature_size=__lowerCAmelCase			,     sampling_rate=__lowerCAmelCase			,     padding_value=__lowerCAmelCase			,     **__lowerCAmelCase )
       UpperCamelCase_				:   Optional[int]					    =							chunk_length_s
       UpperCamelCase_				:   Optional[Any]					    =							overlap
 @property
 def   _UpperCAmelCase       (							self ):
       if self.chunk_length_s is None:
             return None
       else:
             return int(self.chunk_length_s * self.sampling_rate )
 @property
 def   _UpperCAmelCase       (							self ):
       if self.chunk_length_s is None or self.overlap is None:
             return None
       else:
             return max(1			,     int((1.0 - self.overlap) * self.chunk_length ) )





 def __call__(							self			,     __lowerCAmelCase			,     __lowerCAmelCase = None			,     __lowerCAmelCase = False			,     __lowerCAmelCase = None			,     __lowerCAmelCase = None			,     __lowerCAmelCase = None			,     ):
       if sampling_rate is not None:
             if sampling_rate != self.sampling_rate:
                   raise ValueError(
                       F"The model corresponding to this feature extractor: {self} was trained using a sampling rate of"
                       F" {self.sampling_rate}. Please make sure that the provided audio input was sampled with"
                       F" {self.sampling_rate} and not {sampling_rate}." )
       else:
             logger.warning(
                 """It is strongly recommended to pass the `sampling_rate` argument to this function. """
                 """Failing to do so can result in silent errors that might be hard to debug.""" )

       if padding and truncation:
             raise ValueError("""Both padding and truncation were set. Make sure you only set one.""" )
       elif padding is None:
             # by default let's pad the inputs
             UpperCamelCase_				:   Tuple					    =							True

       UpperCamelCase_				:   Union[str, Any]					    =							bool(
           isinstance(__lowerCAmelCase			,     (list, tuple) ) and (isinstance(raw_audio[0]			,     (np.ndarray, tuple, list) )) )

       if is_batched:
             UpperCamelCase_				:   Optional[int]					    =							[np.asarray(__lowerCAmelCase			,     dtype=np.floataa ).T for audio in raw_audio]
       elif not is_batched and not isinstance(__lowerCAmelCase			,     np.ndarray ):
             UpperCamelCase_				:   Union[str, Any]					    =							np.asarray(__lowerCAmelCase			,     dtype=np.floataa )
       elif isinstance(__lowerCAmelCase			,     np.ndarray ) and raw_audio.dtype is np.dtype(np.floataa ):
             UpperCamelCase_				:   List[Any]					    =							raw_audio.astype(np.floataa )

       # always return batch
       if not is_batched:
             UpperCamelCase_				:   List[Any]					    =							[np.asarray(__lowerCAmelCase ).T]

       # verify inputs are valid
       for idx, example in enumerate(__lowerCAmelCase ):
             if example.ndim > 2:
                   raise ValueError(F"Expected input shape (channels, length) but got shape {example.shape}" )
             if self.feature_size == 1 and example.ndim != 1:
                   raise ValueError(F"Expected mono audio but example has {example.shape[-1]} channels" )
             if self.feature_size == 2 and example.shape[-1] != 2:
                   raise ValueError(F"Expected stereo audio but example has {example.shape[-1]} channels" )

       UpperCamelCase_				:   int					    =							None
       UpperCamelCase_				:   List[Any]					    =							BatchFeature({"""input_values""": raw_audio} )
       if self.chunk_stride is not None and self.chunk_length is not None and max_length is None:
             if truncation:
                   UpperCamelCase_				:   Any					    =							min(array.shape[0] for array in raw_audio )
                   UpperCamelCase_				:   Dict					    =							int(np.floor(max_length / self.chunk_stride ) )
                   UpperCamelCase_				:   List[Any]					    =							(nb_step - 1) * self.chunk_stride + self.chunk_length
             elif padding:
                   UpperCamelCase_				:   List[Any]					    =							max(array.shape[0] for array in raw_audio )
                   UpperCamelCase_				:   List[Any]					    =							int(np.ceil(max_length / self.chunk_stride ) )
                   UpperCamelCase_				:   Dict					    =							(nb_step - 1) * self.chunk_stride + self.chunk_length
                   UpperCamelCase_				:   Dict					    =							"""max_length"""
             else:
                   UpperCamelCase_				:   Any					    =							input_values

        # normal padding on batch
       if padded_inputs is None:
             UpperCamelCase_				:   Optional[int]					    =							self.pad(
                 __lowerCAmelCase			,     max_length=__lowerCAmelCase			,     truncation=__lowerCAmelCase			,     padding=__lowerCAmelCase			,     return_attention_mask=__lowerCAmelCase			,     )
             if padding:
                   UpperCamelCase_				:   Optional[Any]					    =							padded_inputs.pop("""attention_mask""" )

       UpperCamelCase_				:   Optional[int]					    =							[]
       for example in padded_inputs.pop("""input_values""" ):
             if self.feature_size == 1:
                   UpperCamelCase_				:   Tuple					    =							example[..., None]
             input_values.append(example.T )

       UpperCamelCase_				:   List[str]					    =							input_values
       if return_tensors is not None:
             UpperCamelCase_				:   Optional[Any]					    =							padded_inputs.convert_to_tensors(__lowerCAmelCase )

       return padded_inputs














'''simple docstring'''

import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple

from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


if is_torch_available():
      UpperCamelCase							="pt"
elif is_tf_available():
      UpperCamelCase							="tf"
else:
      UpperCamelCase							="jax"




class     A    (      SCREAMING_SNAKE_CASE__,   unittest.TestCase						):






 """simple docstring"""





 __a			:						int      =						PerceiverTokenizer
 __a			:						List[str]      =						False
 def   _UpperCAmelCase       (							self ):
       super().setUp()
       UpperCamelCase_				:   Optional[int]					    =							PerceiverTokenizer()
       tokenizer.save_pretrained(self.tmpdirname )
 @cached_property
 def   _UpperCAmelCase       (							self ):
       return PerceiverTokenizer.from_pretrained("""deepmind/language-perceiver""" )
 def   _UpperCAmelCase       (							self			,     **__lowerCAmelCase ):
       return self.tokenizer_class.from_pretrained(self.tmpdirname			,     **__lowerCAmelCase )
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase			,     __lowerCAmelCase=False			,     __lowerCAmelCase=20			,     __lowerCAmelCase=5 ):
       # XXX The default common tokenizer tests assume that every ID is decodable on its own.
       # This assumption is invalid for Perceiver because single bytes might not be
       # valid utf-8 (byte 128 for instance).
       # Here we're overriding the smallest possible method to provide
       # a clean sequence without making the same assumption.

       UpperCamelCase_				:   str					    =							[]
       for i in range(len(__lowerCAmelCase ) ):
             try:
                   UpperCamelCase_				:   Tuple					    =							tokenizer.decode([i]			,     clean_up_tokenization_spaces=__lowerCAmelCase )
             except UnicodeDecodeError:
                   pass
             toks.append((i, tok) )

       UpperCamelCase_				:   Optional[Any]					    =							list(filter(lambda __lowerCAmelCase : re.match(R"""^[ a-zA-Z]+$"""			,     t[1] )			,     __lowerCAmelCase ) )
       UpperCamelCase_				:   int					    =							list(filter(lambda __lowerCAmelCase : [t[0]] == tokenizer.encode(t[1]			,     add_special_tokens=__lowerCAmelCase )			,     __lowerCAmelCase ) )
       if max_length is not None and len(__lowerCAmelCase ) > max_length:
             UpperCamelCase_				:   List[Any]					    =							toks[:max_length]
       if min_length is not None and len(__lowerCAmelCase ) < min_length and len(__lowerCAmelCase ) > 0:
             while len(__lowerCAmelCase ) < min_length:
                   UpperCamelCase_				:   Tuple					    =							toks + toks
        # toks_str = [t[1] for t in toks]
       UpperCamelCase_				:   List[Any]					    =							[t[0] for t in toks]

       # Ensure consistency
       UpperCamelCase_				:   str					    =							tokenizer.decode(__lowerCAmelCase			,     clean_up_tokenization_spaces=__lowerCAmelCase )
       if " " not in output_txt and len(__lowerCAmelCase ) > 1:
             UpperCamelCase_				:   Tuple					    =							(
                 tokenizer.decode([toks_ids[0]]			,     clean_up_tokenization_spaces=__lowerCAmelCase )
                 + """ """
                 + tokenizer.decode(toks_ids[1:]			,     clean_up_tokenization_spaces=__lowerCAmelCase )
             )
       if with_prefix_space:
             UpperCamelCase_				:   Tuple					    =							""" """ + output_txt
       UpperCamelCase_				:   List[str]					    =							tokenizer.encode(__lowerCAmelCase			,     add_special_tokens=__lowerCAmelCase )
       return output_txt, output_ids
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   Dict					    =							self.perceiver_tokenizer
       UpperCamelCase_				:   Optional[int]					    =							"""Unicode €."""
       UpperCamelCase_				:   Dict					    =							tokenizer(__lowerCAmelCase )
       UpperCamelCase_				:   Tuple					    =							[4, 91, 1_16, 1_11, 1_05, 1_17, 1_06, 1_07, 38, 2_32, 1_36, 1_78, 52, 5]
       self.assertEqual(encoded["""input_ids"""]			,     __lowerCAmelCase )

       # decoding
       UpperCamelCase_				:   str					    =							tokenizer.decode(__lowerCAmelCase )
       self.assertEqual(__lowerCAmelCase			,     """[CLS]Unicode €.[SEP]""" )

       UpperCamelCase_				:   str					    =							tokenizer("""e è é ê ë""" )
       UpperCamelCase_				:   List[str]					    =							[4, 1_07, 38, 2_01, 1_74, 38, 2_01, 1_75, 38, 2_01, 1_76, 38, 2_01, 1_77, 5]
       self.assertEqual(encoded["""input_ids"""]			,     __lowerCAmelCase )
       # decoding
       UpperCamelCase_				:   List[str]					    =							tokenizer.decode(__lowerCAmelCase )
       self.assertEqual(__lowerCAmelCase			,     """[CLS]e è é ê ë[SEP]""" )

       # encode/decode, but with `encode` instead of `__call__`
       self.assertEqual(tokenizer.decode(tokenizer.encode("""e è é ê ë""" ) )			,     """[CLS]e è é ê ë[SEP]""" )
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   Union[str, Any]					    =							self.perceiver_tokenizer
       UpperCamelCase_				:   Optional[Any]					    =							["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
       # fmt: off
       UpperCamelCase_				:   int					    =							[4, 71, 38, 1_14, 1_17, 1_16, 1_09, 38, 1_18, 1_03, 1_20, 1_03, 1_09, 1_20, 1_03, 1_18, 1_10, 38, 1_08, 1_17, 1_20, 38, 1_21, 1_23, 1_15, 1_15, 1_03, 1_20, 1_11, 1_28, 1_03, 1_22, 1_11, 1_17, 1_16, 52, 5, 0]
       # fmt: on
       UpperCamelCase_				:   Any					    =							tokenizer(__lowerCAmelCase			,     padding=__lowerCAmelCase			,     return_tensors=__lowerCAmelCase )
       self.assertIsInstance(__lowerCAmelCase			,     __lowerCAmelCase )

       if FRAMEWORK != "jax":
             UpperCamelCase_				:   Optional[Any]					    =							list(batch.input_ids.numpy()[0] )
       else:
             UpperCamelCase_				:   Optional[int]					    =							list(batch.input_ids.tolist()[0] )

       self.assertListEqual(__lowerCAmelCase			,     __lowerCAmelCase )

       self.assertEqual((2, 38)			,     batch.input_ids.shape )
       self.assertEqual((2, 38)			,     batch.attention_mask.shape )
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   Optional[int]					    =							self.perceiver_tokenizer
       UpperCamelCase_				:   Union[str, Any]					    =							["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
       UpperCamelCase_				:   int					    =							tokenizer(__lowerCAmelCase			,     padding=__lowerCAmelCase			,     return_tensors=__lowerCAmelCase )
       # check if input_ids are returned and no decoder_input_ids
       self.assertIn("""input_ids"""			,     __lowerCAmelCase )
       self.assertIn("""attention_mask"""			,     __lowerCAmelCase )
       self.assertNotIn("""decoder_input_ids"""			,     __lowerCAmelCase )
       self.assertNotIn("""decoder_attention_mask"""			,     __lowerCAmelCase )
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   int					    =							self.perceiver_tokenizer
       UpperCamelCase_				:   List[str]					    =							[
           """Summary of the text.""",
           """Another summary.""",
       ]
       UpperCamelCase_				:   Dict					    =							tokenizer(
           text_target=__lowerCAmelCase			,     max_length=32			,     padding="""max_length"""			,     truncation=__lowerCAmelCase			,     return_tensors=__lowerCAmelCase )
       self.assertEqual(32			,     targets["""input_ids"""].shape[1] )
 def   _UpperCAmelCase       (							self ):
       # safety check on max_len default value so we are sure the test works
       UpperCamelCase_				:   Tuple					    =							self.get_tokenizers()
       for tokenizer in tokenizers:
             with self.subTest(F"{tokenizer.__class__.__name__}" ):
                   self.assertNotEqual(tokenizer.model_max_length			,     42 )

        # Now let's start the test
       UpperCamelCase_				:   Tuple					    =							self.get_tokenizers()
       for tokenizer in tokenizers:
             with self.subTest(F"{tokenizer.__class__.__name__}" ):
                   # Isolate this from the other tests because we save additional tokens/etc
                   UpperCamelCase_				:   Tuple					    =							tempfile.mkdtemp()

                   UpperCamelCase_				:   Optional[int]					    =							""" He is very happy, UNwant\u00E9d,running"""
                   UpperCamelCase_				:   Dict					    =							tokenizer.encode(__lowerCAmelCase			,     add_special_tokens=__lowerCAmelCase )
                   tokenizer.save_pretrained(__lowerCAmelCase )

                   UpperCamelCase_				:   Tuple					    =							tokenizer.__class__.from_pretrained(__lowerCAmelCase )
                   UpperCamelCase_				:   Union[str, Any]					    =							after_tokenizer.encode(__lowerCAmelCase			,     add_special_tokens=__lowerCAmelCase )
                   self.assertListEqual(__lowerCAmelCase			,     __lowerCAmelCase )

                   shutil.rmtree(__lowerCAmelCase )

       UpperCamelCase_				:   Union[str, Any]					    =							self.get_tokenizers(model_max_length=42 )
       for tokenizer in tokenizers:
             with self.subTest(F"{tokenizer.__class__.__name__}" ):
                   # Isolate this from the other tests because we save additional tokens/etc
                   UpperCamelCase_				:   Tuple					    =							tempfile.mkdtemp()

                   UpperCamelCase_				:   List[str]					    =							""" He is very happy, UNwant\u00E9d,running"""
                   tokenizer.add_tokens(["""bim""", """bambam"""] )
                   UpperCamelCase_				:   Any					    =							tokenizer.additional_special_tokens
                   additional_special_tokens.append("""new_additional_special_token""" )
                   tokenizer.add_special_tokens({"""additional_special_tokens""": additional_special_tokens} )
                   UpperCamelCase_				:   Union[str, Any]					    =							tokenizer.encode(__lowerCAmelCase			,     add_special_tokens=__lowerCAmelCase )
                   tokenizer.save_pretrained(__lowerCAmelCase )

                   UpperCamelCase_				:   Tuple					    =							tokenizer.__class__.from_pretrained(__lowerCAmelCase )
                   UpperCamelCase_				:   List[Any]					    =							after_tokenizer.encode(__lowerCAmelCase			,     add_special_tokens=__lowerCAmelCase )
                   self.assertListEqual(__lowerCAmelCase			,     __lowerCAmelCase )
                   self.assertIn("""new_additional_special_token"""			,     after_tokenizer.additional_special_tokens )
                   self.assertEqual(after_tokenizer.model_max_length			,     42 )

                   UpperCamelCase_				:   int					    =							tokenizer.__class__.from_pretrained(__lowerCAmelCase			,     model_max_length=43 )
                   self.assertEqual(tokenizer.model_max_length			,     43 )

                   shutil.rmtree(__lowerCAmelCase )
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   str					    =							[]
       if self.test_slow_tokenizer:
             tokenizer_list.append((self.tokenizer_class, self.get_tokenizer()) )

       if self.test_rust_tokenizer:
             tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer()) )

       for tokenizer_class, tokenizer_utils in tokenizer_list:
             with tempfile.TemporaryDirectory() as tmp_dir:
                   tokenizer_utils.save_pretrained(__lowerCAmelCase )

                   with open(os.path.join(__lowerCAmelCase			,     """special_tokens_map.json""" )			,     encoding="""utf-8""" ) as json_file:
                         UpperCamelCase_				:   Union[str, Any]					    =							json.load(__lowerCAmelCase )

                   with open(os.path.join(__lowerCAmelCase			,     """tokenizer_config.json""" )			,     encoding="""utf-8""" ) as json_file:
                         UpperCamelCase_				:   List[str]					    =							json.load(__lowerCAmelCase )

                   UpperCamelCase_				:   Tuple					    =							[F"<extra_id_{i}>" for i in range(1_25 )]

                   UpperCamelCase_				:   Union[str, Any]					    =							added_tokens_extra_ids + [
                       """an_additional_special_token"""
                   ]
                   UpperCamelCase_				:   List[Any]					    =							added_tokens_extra_ids + [
                       """an_additional_special_token"""
                   ]

                   with open(os.path.join(__lowerCAmelCase			,     """special_tokens_map.json""" )			,     """w"""			,     encoding="""utf-8""" ) as outfile:
                         json.dump(__lowerCAmelCase			,     __lowerCAmelCase )
                   with open(os.path.join(__lowerCAmelCase			,     """tokenizer_config.json""" )			,     """w"""			,     encoding="""utf-8""" ) as outfile:
                         json.dump(__lowerCAmelCase			,     __lowerCAmelCase )

                   # the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
                   # into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
                   # "special_tokens_map.json" files
                   UpperCamelCase_				:   Optional[int]					    =							tokenizer_class.from_pretrained(
                       __lowerCAmelCase			,     )
                   self.assertIn(
                       """an_additional_special_token"""			,     tokenizer_without_change_in_init.additional_special_tokens )
                   self.assertEqual(
                       ["""an_additional_special_token"""]			,     tokenizer_without_change_in_init.convert_ids_to_tokens(
                           tokenizer_without_change_in_init.convert_tokens_to_ids(["""an_additional_special_token"""] ) )			,     )

                   # Now we test that we can change the value of additional_special_tokens in the from_pretrained
                   UpperCamelCase_				:   Optional[Any]					    =							added_tokens_extra_ids + [AddedToken("""a_new_additional_special_token"""			,     lstrip=__lowerCAmelCase )]
                   UpperCamelCase_				:   Union[str, Any]					    =							tokenizer_class.from_pretrained(
                       __lowerCAmelCase			,     additional_special_tokens=__lowerCAmelCase			,     )

                   self.assertIn("""a_new_additional_special_token"""			,     tokenizer.additional_special_tokens )
                   self.assertEqual(
                       ["""a_new_additional_special_token"""]			,     tokenizer.convert_ids_to_tokens(
                           tokenizer.convert_tokens_to_ids(["""a_new_additional_special_token"""] ) )			,     )
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   List[Any]					    =							self.perceiver_tokenizer
       self.assertEqual(tokenizer.decode([1_78] )			,     """�""" )
 def   _UpperCAmelCase       (							self ):
       pass
 def   _UpperCAmelCase       (							self ):
       pass
 def   _UpperCAmelCase       (							self ):
       pass
 def   _UpperCAmelCase       (							self ):
       pass





 def   _UpperCAmelCase       (							self ):
       # The default common tokenizer tests uses invalid tokens for Perceiver that can only accept one-character
       # strings and special added tokens as tokens
       UpperCamelCase_				:   Dict					    =							self.get_tokenizers(fast=__lowerCAmelCase			,     do_lower_case=__lowerCAmelCase )
       for tokenizer in tokenizers:
             with self.subTest(F"{tokenizer.__class__.__name__}" ):
                   UpperCamelCase_				:   str					    =							["""[CLS]""", """t""", """h""", """i""", """s""", """ """, """i""", """s""", """ """, """a""", """ """, """t""", """e""", """s""", """t""", """[SEP]"""]
                   UpperCamelCase_				:   Optional[Any]					    =							tokenizer.convert_tokens_to_string(__lowerCAmelCase )

                   self.assertIsInstance(__lowerCAmelCase			,     __lowerCAmelCase )






