


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():
    A      :    Dict		=					"pt"
elif is_tf_available():
    A      :    List[Any]		=					"tf"
else:
    A      :    Optional[Any]		=					"jax"







class 			_lowercase (							__a							,  unittest.TestCase):


       """simple docstring"""




       A__											=			PerceiverTokenizer
       A__											=			False





       def 						lowerCAmelCase     (   self							:			Union[str, Any]       ):


             '''simple docstring'''



             super().setUp()
             lowerCamelCase__		:				str    =  PerceiverTokenizer()
             tokenizer.save_pretrained(self.tmpdirname       )





       @cached_property
       def 						lowerCAmelCase     (   self							:			Optional[int]       ):


             '''simple docstring'''



             return PerceiverTokenizer.from_pretrained("deepmind/language-perceiver"       )





       def 						lowerCAmelCase     (   self							:			Union[str, Any]					,      **__lowerCamelCase							:			int       ):


             '''simple docstring'''



             return self.tokenizer_class.from_pretrained(self.tmpdirname					,      **__lowerCamelCase       )





       def 						lowerCAmelCase     (   self							:			Tuple					,      __lowerCamelCase							:			str					,      __lowerCamelCase							:			List[str]=False					,      __lowerCamelCase							:			Union[str, Any]=20					,      __lowerCamelCase							:			Union[str, Any]=5       ):


             '''simple docstring'''



             lowerCamelCase__		:				int    =  []
             for i in range(len(__lowerCamelCase       )       ):
                   try:
                         lowerCamelCase__		:				Any    =  tokenizer.decode([i]					,      clean_up_tokenization_spaces=__lowerCamelCase       )
                   except UnicodeDecodeError:
                         pass
                   toks.append((i, tok)       )

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

             # Ensure consistency
             lowerCamelCase__		:				int    =  tokenizer.decode(__lowerCamelCase					,      clean_up_tokenization_spaces=__lowerCamelCase       )
             if " " not in output_txt and len(__lowerCamelCase       ) > 1:
                   lowerCamelCase__		:				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:
                   lowerCamelCase__		:				int    =  " " + output_txt
             lowerCamelCase__		:				Any    =  tokenizer.encode(__lowerCamelCase					,      add_special_tokens=__lowerCamelCase       )
             return output_txt, output_ids





       def 						lowerCAmelCase     (   self							:			Optional[int]       ):


             '''simple docstring'''



             lowerCamelCase__		:				List[Any]    =  self.perceiver_tokenizer
             lowerCamelCase__		:				Tuple    =  "Unicode €."
             lowerCamelCase__		:				Optional[Any]    =  tokenizer(__lowerCamelCase       )
             lowerCamelCase__		:				Tuple    =  [4, 91, 116, 111, 105, 117, 106, 107, 38, 232, 136, 178, 52, 5]
             self.assertEqual(encoded["input_ids"]					,      __lowerCamelCase       )

             # decoding
             lowerCamelCase__		:				Tuple    =  tokenizer.decode(__lowerCamelCase       )
             self.assertEqual(__lowerCamelCase					,      "[CLS]Unicode €.[SEP]"       )

             lowerCamelCase__		:				str    =  tokenizer("e è é ê ë"       )
             lowerCamelCase__		:				Any    =  [4, 107, 38, 201, 174, 38, 201, 175, 38, 201, 176, 38, 201, 177, 5]
             self.assertEqual(encoded["input_ids"]					,      __lowerCamelCase       )
             # decoding
             lowerCamelCase__		:				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 						lowerCAmelCase     (   self							:			str       ):


             '''simple docstring'''



             lowerCamelCase__		:				Optional[Any]    =  self.perceiver_tokenizer
             lowerCamelCase__		:				Optional[int]    =  ["A long paragraph for summarization.", "Another paragraph for summarization."]
             # fmt: off
             lowerCamelCase__		:				str    =  [4, 71, 38, 114, 117, 116, 109, 38, 118, 103, 120, 103, 109, 120, 103, 118, 110, 38, 108, 117, 120, 38, 121, 123, 115, 115, 103, 120, 111, 128, 103, 122, 111, 117, 116, 52, 5, 0]
             # fmt: on
             lowerCamelCase__		:				Optional[Any]    =  tokenizer(__lowerCamelCase					,      padding=__lowerCamelCase					,      return_tensors=__lowerCamelCase       )
             self.assertIsInstance(__lowerCamelCase					,      __lowerCamelCase       )

             if FRAMEWORK != "jax":
                   lowerCamelCase__		:				List[str]    =  list(batch.input_ids.numpy()[0]       )
             else:
                   lowerCamelCase__		:				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 						lowerCAmelCase     (   self							:			str       ):


             '''simple docstring'''



             lowerCamelCase__		:				Tuple    =  self.perceiver_tokenizer
             lowerCamelCase__		:				Optional[Any]    =  ["A long paragraph for summarization.", "Another paragraph for summarization."]
             lowerCamelCase__		:				Union[str, Any]    =  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 						lowerCAmelCase     (   self							:			Dict       ):


             '''simple docstring'''



             lowerCamelCase__		:				List[Any]    =  self.perceiver_tokenizer
             lowerCamelCase__		:				Optional[int]    =  [
                 "Summary of the text.",
                 "Another summary.",
             ]
             lowerCamelCase__		:				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 						lowerCAmelCase     (   self							:			int       ):


             '''simple docstring'''



             lowerCamelCase__		:				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
             lowerCamelCase__		:				Any    =  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
                         lowerCamelCase__		:				List[Any]    =  tempfile.mkdtemp()

                         lowerCamelCase__		:				str    =  " He is very happy, UNwant\u00E9d,running"
                         lowerCamelCase__		:				Optional[int]    =  tokenizer.encode(__lowerCamelCase					,      add_special_tokens=__lowerCamelCase       )
                         tokenizer.save_pretrained(__lowerCamelCase       )

                         lowerCamelCase__		:				Dict    =  tokenizer.__class__.from_pretrained(__lowerCamelCase       )
                         lowerCamelCase__		:				Tuple    =  after_tokenizer.encode(__lowerCamelCase					,      add_special_tokens=__lowerCamelCase       )
                         self.assertListEqual(__lowerCamelCase					,      __lowerCamelCase       )

                         shutil.rmtree(__lowerCamelCase       )

             lowerCamelCase__		:				str    =  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
                         lowerCamelCase__		:				str    =  tempfile.mkdtemp()

                         lowerCamelCase__		:				List[Any]    =  " He is very happy, UNwant\u00E9d,running"
                         tokenizer.add_tokens(["bim", "bambam"]       )
                         lowerCamelCase__		:				Optional[Any]    =  tokenizer.additional_special_tokens
                         additional_special_tokens.append("new_additional_special_token"       )
                         tokenizer.add_special_tokens({"additional_special_tokens": additional_special_tokens}       )
                         lowerCamelCase__		:				Optional[int]    =  tokenizer.encode(__lowerCamelCase					,      add_special_tokens=__lowerCamelCase       )
                         tokenizer.save_pretrained(__lowerCamelCase       )

                         lowerCamelCase__		:				int    =  tokenizer.__class__.from_pretrained(__lowerCamelCase       )
                         lowerCamelCase__		:				str    =  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       )

                         lowerCamelCase__		:				Dict    =  tokenizer.__class__.from_pretrained(__lowerCamelCase					,      model_max_length=43       )
                         self.assertEqual(tokenizer.model_max_length					,      43       )

                         shutil.rmtree(__lowerCamelCase       )





       def 						lowerCAmelCase     (   self							:			Optional[int]       ):


             '''simple docstring'''



             lowerCamelCase__		:				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:
                               lowerCamelCase__		:				Optional[Any]    =  json.load(__lowerCamelCase       )

                         with open(os.path.join(__lowerCamelCase					,      "tokenizer_config.json"       )					,      encoding="utf-8"       ) as json_file:
                               lowerCamelCase__		:				Optional[Any]    =  json.load(__lowerCamelCase       )

                         lowerCamelCase__		:				int    =  [f"<extra_id_{i}>" for i in range(125       )]

                         lowerCamelCase__		:				Optional[int]    =  added_tokens_extra_ids + [
                             "an_additional_special_token"
                         ]
                         lowerCamelCase__		:				str    =  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
                         lowerCamelCase__		:				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
                         lowerCamelCase__		:				Any    =  added_tokens_extra_ids + [AddedToken("a_new_additional_special_token"					,      lstrip=__lowerCamelCase       )]
                         lowerCamelCase__		:				int    =  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 						lowerCAmelCase     (   self							:			Optional[int]       ):


             '''simple docstring'''



             lowerCamelCase__		:				Union[str, Any]    =  self.perceiver_tokenizer
             self.assertEqual(tokenizer.decode([178]       )					,      "�"       )





       def 						lowerCAmelCase     (   self							:			Union[str, Any]       ):


             '''simple docstring'''



             pass





       def 						lowerCAmelCase     (   self							:			Any       ):


             '''simple docstring'''



             pass





       def 						lowerCAmelCase     (   self							:			Dict       ):


             '''simple docstring'''



             pass





       def 						lowerCAmelCase     (   self							:			str       ):


             '''simple docstring'''



             pass





       def 						lowerCAmelCase     (   self							:			List[str]       ):


             '''simple docstring'''



             lowerCamelCase__		:				List[Any]    =  self.get_tokenizers(fast=__lowerCamelCase					,      do_lower_case=__lowerCamelCase       )
             for tokenizer in tokenizers:
                   with self.subTest(f"{tokenizer.__class__.__name__}"       ):
                         lowerCamelCase__		:				Optional[Any]    =  ["[CLS]", "t", "h", "i", "s", " ", "i", "s", " ", "a", " ", "t", "e", "s", "t", "[SEP]"]
                         lowerCamelCase__		:				Optional[int]    =  tokenizer.convert_tokens_to_string(__lowerCamelCase       )

                         self.assertIsInstance(__lowerCamelCase					,      __lowerCamelCase       )








'''simple docstring'''







import math
from typing import Optional

import numpy as np

from ...configuration_utils import PretrainedConfig
from ...utils import logging


A     =logging.get_logger(__name__)

A     ={
    'facebook/encodec_24khz': 'https://huggingface.co/facebook/encodec_24khz/resolve/main/config.json',
    'facebook/encodec_48khz': 'https://huggingface.co/facebook/encodec_48khz/resolve/main/config.json',
}


class 			_a  (	__a ):
       __a				:     Union[str, Any]   				=					"""encodec"""
       def __init__(	self						:	Tuple   ,				lowercase						:	List[str]=[1.5, 3.0, 6.0, 12.0, 24.0]   ,				lowercase						:	Any=24_000   ,				lowercase						:	str=1   ,				lowercase						:	Optional[int]=False   ,				lowercase						:	Optional[Any]=None   ,				lowercase						:	str=None   ,				lowercase						:	Tuple=128   ,				lowercase						:	Union[str, Any]=32   ,				lowercase						:	Union[str, Any]=1   ,				lowercase						:	Optional[Any]=[8, 5, 4, 2]   ,				lowercase						:	Any="weight_norm"   ,				lowercase						:	Tuple=7   ,				lowercase						:	int=7   ,				lowercase						:	Dict=3   ,				lowercase						:	List[Any]=2   ,				lowercase						:	str=True   ,				lowercase						:	List[str]="reflect"   ,				lowercase						:	List[Any]=2   ,				lowercase						:	Optional[Any]=2   ,				lowercase						:	int=1.0   ,				lowercase						:	Dict=1_024   ,				lowercase						:	str=None   ,				lowercase						:	Union[str, Any]=True   ,				**lowercase						:	Optional[int]   ,				):



           '''simple docstring'''

           UpperCAmelCase			       =		target_bandwidths
           UpperCAmelCase			       =		sampling_rate
           UpperCAmelCase			       =		audio_channels
           UpperCAmelCase			       =		normalize
           UpperCAmelCase			       =		chunk_length_s
           UpperCAmelCase			       =		overlap
           UpperCAmelCase			       =		hidden_size
           UpperCAmelCase			       =		num_filters
           UpperCAmelCase			       =		num_residual_layers
           UpperCAmelCase			       =		upsampling_ratios
           UpperCAmelCase			       =		norm_type
           UpperCAmelCase			       =		kernel_size
           UpperCAmelCase			       =		last_kernel_size
           UpperCAmelCase			       =		residual_kernel_size
           UpperCAmelCase			       =		dilation_growth_rate
           UpperCAmelCase			       =		use_causal_conv
           UpperCAmelCase			       =		pad_mode
           UpperCAmelCase			       =		compress
           UpperCAmelCase			       =		num_lstm_layers
           UpperCAmelCase			       =		trim_right_ratio
           UpperCAmelCase			       =		codebook_size
           UpperCAmelCase			       =		codebook_dim if codebook_dim is not None else hidden_size
           UpperCAmelCase			       =		use_conv_shortcut

           if self.norm_type not in ["weight_norm", "time_group_norm"]:
               raise ValueError(
                   f"self.norm_type must be one of `\"weight_norm\"`, `\"time_group_norm\"`), got {self.norm_type}"				)

           super().__init__(**lowercase				)
       @property
       def     A      (	self						:	Dict				):



           '''simple docstring'''

           if self.chunk_length_s is None:
               return None
           else:
               return int(self.chunk_length_s * self.sampling_rate				)
       @property
       def     A      (	self						:	Union[str, Any]				):



           '''simple docstring'''

           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				)				)
       @property
       def     A      (	self						:	Any				):



           '''simple docstring'''

           UpperCAmelCase			       =		np.prod(self.upsampling_ratios				)
           return math.ceil(self.sampling_rate / hop_length				)







       @property
       def     A      (	self						:	Optional[int]				):



           '''simple docstring'''

           return int(1_000 * self.target_bandwidths[-1] // (self.frame_rate * 10)				)




