




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():
  _lowerCAmelCase						:   str		=   "pt"
elif is_tf_available():
  _lowerCAmelCase						:   int		=   "tf"
else:
  _lowerCAmelCase						:   int		=   "jax"
class 			_UpperCamelCase      (				lowerCAmelCase	,       unittest.TestCase		):
      UpperCAmelCase_        =		PerceiverTokenizer
      UpperCAmelCase_        =		False
      def 	UpperCAmelCase_						(			self   :Union[str, Any]      )     ->			List[str]:
       super().setUp()
       UpperCAmelCase__							 =						PerceiverTokenizer()
       tokenizer.save_pretrained(self.tmpdirname      )
      @cached_property
      def 	UpperCAmelCase_						(			self   :List[str]      )     ->			int:
       return PerceiverTokenizer.from_pretrained("deepmind/language-perceiver"      )
      def 	UpperCAmelCase_						(			self   :List[Any] ,      **lowerCamelCase   :Optional[int]      )     ->			PerceiverTokenizer:
       return self.tokenizer_class.from_pretrained(self.tmpdirname ,      **lowerCamelCase      )
      def 	UpperCAmelCase_						(			self   :Optional[Any] ,      lowerCamelCase   :Optional[Any] ,      lowerCamelCase   :Union[str, Any]=False ,      lowerCamelCase   :Optional[Any]=20 ,      lowerCamelCase   :Dict=5      )     ->			Tuple[str, list]:
       # 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__							 =						[]
       for i in range(len(lowerCamelCase      )      ):
        try:
         UpperCAmelCase__							 =						tokenizer.decode([i] ,      clean_up_tokenization_spaces=lowerCamelCase      )
        except UnicodeDecodeError:
         pass
        toks.append((i, tok)      )

       UpperCAmelCase__							 =						list(filter(lambda lowerCamelCase      : re.match(r"^[ a-zA-Z]+$" ,      t[1]      ) ,      lowerCamelCase      )      )
       UpperCAmelCase__							 =						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__							 =						toks[:max_length]
       if min_length is not None and len(lowerCamelCase      ) < min_length and len(lowerCamelCase      ) > 0:
        while len(lowerCamelCase      ) < min_length:
         UpperCAmelCase__							 =						toks + toks
        # toks_str = [t[1] for t in toks]
       UpperCAmelCase__							 =						[t[0] for t in toks]

       # Ensure consistency
       UpperCAmelCase__							 =						tokenizer.decode(lowerCamelCase ,      clean_up_tokenization_spaces=lowerCamelCase      )
       if " " not in output_txt and len(lowerCamelCase      ) > 1:
        UpperCAmelCase__							 =						(
            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__							 =						" " + output_txt
       UpperCAmelCase__							 =						tokenizer.encode(lowerCamelCase ,      add_special_tokens=lowerCamelCase      )
       return output_txt, output_ids
      def 	UpperCAmelCase_						(			self   :Optional[int]      )     ->			Any:
       UpperCAmelCase__							 =						self.perceiver_tokenizer
       UpperCAmelCase__							 =						"Unicode €."
       UpperCAmelCase__							 =						tokenizer(lowerCamelCase      )
       UpperCAmelCase__							 =						[4, 91, 116, 111, 105, 117, 106, 107, 38, 232, 136, 178, 52, 5]
       self.assertEqual(encoded["input_ids"] ,      lowerCamelCase      )

       # decoding
       UpperCAmelCase__							 =						tokenizer.decode(lowerCamelCase      )
       self.assertEqual(lowerCamelCase ,      "[CLS]Unicode €.[SEP]"      )

       UpperCAmelCase__							 =						tokenizer("e è é ê ë"      )
       UpperCAmelCase__							 =						[4, 107, 38, 201, 174, 38, 201, 175, 38, 201, 176, 38, 201, 177, 5]
       self.assertEqual(encoded["input_ids"] ,      lowerCamelCase      )
       # decoding
       UpperCAmelCase__							 =						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   :Optional[int]      )     ->			Union[str, Any]:
       UpperCAmelCase__							 =						self.perceiver_tokenizer
       UpperCAmelCase__							 =						["A long paragraph for summarization.", "Another paragraph for summarization."]
       # fmt: off
       UpperCAmelCase__							 =						[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
       UpperCAmelCase__							 =						tokenizer(lowerCamelCase ,      padding=lowerCamelCase ,      return_tensors=lowerCamelCase      )
       self.assertIsInstance(lowerCamelCase ,      lowerCamelCase      )

       if FRAMEWORK != "jax":
        UpperCAmelCase__							 =						list(batch.input_ids.numpy()[0]      )
       else:
        UpperCAmelCase__							 =						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   :Union[str, Any]      )     ->			Optional[Any]:
       UpperCAmelCase__							 =						self.perceiver_tokenizer
       UpperCAmelCase__							 =						["A long paragraph for summarization.", "Another paragraph for summarization."]
       UpperCAmelCase__							 =						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   :List[str]      )     ->			List[Any]:
       UpperCAmelCase__							 =						self.perceiver_tokenizer
       UpperCAmelCase__							 =						[
           "Summary of the text.",
           "Another summary.",
       ]
       UpperCAmelCase__							 =						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   :int      )     ->			Tuple:
       # safety check on max_len default value so we are sure the test works
       UpperCAmelCase__							 =						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__							 =						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__							 =						tempfile.mkdtemp()

         UpperCAmelCase__							 =						" He is very happy, UNwant\u00E9d,running"
         UpperCAmelCase__							 =						tokenizer.encode(lowerCamelCase ,      add_special_tokens=lowerCamelCase      )
         tokenizer.save_pretrained(lowerCamelCase      )

         UpperCAmelCase__							 =						tokenizer.__class__.from_pretrained(lowerCamelCase      )
         UpperCAmelCase__							 =						after_tokenizer.encode(lowerCamelCase ,      add_special_tokens=lowerCamelCase      )
         self.assertListEqual(lowerCamelCase ,      lowerCamelCase      )

         shutil.rmtree(lowerCamelCase      )

       UpperCAmelCase__							 =						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__							 =						tempfile.mkdtemp()

         UpperCAmelCase__							 =						" He is very happy, UNwant\u00E9d,running"
         tokenizer.add_tokens(["bim", "bambam"]      )
         UpperCAmelCase__							 =						tokenizer.additional_special_tokens
         additional_special_tokens.append("new_additional_special_token"      )
         tokenizer.add_special_tokens({"additional_special_tokens": additional_special_tokens}      )
         UpperCAmelCase__							 =						tokenizer.encode(lowerCamelCase ,      add_special_tokens=lowerCamelCase      )
         tokenizer.save_pretrained(lowerCamelCase      )

         UpperCAmelCase__							 =						tokenizer.__class__.from_pretrained(lowerCamelCase      )
         UpperCAmelCase__							 =						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__							 =						tokenizer.__class__.from_pretrained(lowerCamelCase ,      model_max_length=43      )
         self.assertEqual(tokenizer.model_max_length ,      43      )

         shutil.rmtree(lowerCamelCase      )
      def 	UpperCAmelCase_						(			self   :Dict      )     ->			Optional[Any]:
       UpperCAmelCase__							 =						[]
       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__							 =						json.load(lowerCamelCase      )

         with open(os.path.join(lowerCamelCase ,      "tokenizer_config.json"      ) ,      encoding="utf-8"      ) as json_file:
          UpperCAmelCase__							 =						json.load(lowerCamelCase      )

         UpperCAmelCase__							 =						[f'''<extra_id_{i}>''' for i in range(125      )]

         UpperCAmelCase__							 =						added_tokens_extra_ids + [
             "an_additional_special_token"
         ]
         UpperCAmelCase__							 =						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__							 =						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__							 =						added_tokens_extra_ids + [AddedToken("a_new_additional_special_token" ,      lstrip=lowerCamelCase      )]
         UpperCAmelCase__							 =						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   :int      )     ->			Union[str, Any]:
       UpperCAmelCase__							 =						self.perceiver_tokenizer
       self.assertEqual(tokenizer.decode([178]      ) ,      "�"      )
      def 	UpperCAmelCase_						(			self   :Dict      )     ->			Union[str, Any]:
       pass
      def 	UpperCAmelCase_						(			self   :Union[str, Any]      )     ->			Union[str, Any]:
       pass
      def 	UpperCAmelCase_						(			self   :Union[str, Any]      )     ->			Union[str, Any]:
       pass
      def 	UpperCAmelCase_						(			self   :Optional[Any]      )     ->			Optional[int]:
       pass







      def 	UpperCAmelCase_						(			self   :Optional[int]      )     ->			Dict:
       # The default common tokenizer tests uses invalid tokens for Perceiver that can only accept one-character
       # strings and special added tokens as tokens
       UpperCAmelCase__							 =						self.get_tokenizers(fast=lowerCamelCase ,      do_lower_case=lowerCamelCase      )
       for tokenizer in tokenizers:
        with self.subTest(f'''{tokenizer.__class__.__name__}'''      ):
         UpperCAmelCase__							 =						["[CLS]", "t", "h", "i", "s", " ", "i", "s", " ", "a", " ", "t", "e", "s", "t", "[SEP]"]
         UpperCAmelCase__							 =						tokenizer.convert_tokens_to_string(lowerCamelCase      )

         self.assertIsInstance(lowerCamelCase ,      lowerCamelCase      )











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


_lowerCAmelCase						:   Any		=   logging.get_logger(__name__)
class 			_UpperCamelCase      (				lowerCAmelCase		):
      UpperCAmelCase_        =		["""input_values""", """padding_mask"""]
      def __init__(			self   :Dict ,      lowerCamelCase   :int = 1 ,      lowerCamelCase   :int = 2_4000 ,      lowerCamelCase   :float = 0.0 ,      lowerCamelCase   :float = None ,      lowerCamelCase   :float = None ,      **lowerCamelCase   :Optional[Any] ,      )     ->			str:
       super().__init__(feature_size=lowerCamelCase ,      sampling_rate=lowerCamelCase ,      padding_value=lowerCamelCase ,      **lowerCamelCase      )
       UpperCAmelCase__							 =						chunk_length_s
       UpperCAmelCase__							 =						overlap
      @property
      def 	UpperCAmelCase_						(			self   :Optional[Any]      )     ->			Optional[int]:
       if self.chunk_length_s is None:
        return None
       else:
        return int(self.chunk_length_s * self.sampling_rate      )
      @property
      def 	UpperCAmelCase_						(			self   :str      )     ->			Optional[int]:
       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   :List[Any] ,      lowerCamelCase   :Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] ,      lowerCamelCase   :Optional[Union[bool, str, PaddingStrategy]] = None ,      lowerCamelCase   :Optional[bool] = False ,      lowerCamelCase   :Optional[int] = None ,      lowerCamelCase   :Optional[Union[str, TensorType]] = None ,      lowerCamelCase   :Optional[int] = None ,      )     ->			BatchFeature:
       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__							 =						True

       UpperCAmelCase__							 =						bool(
           isinstance(lowerCamelCase ,      (list, tuple)      ) and (isinstance(raw_audio[0] ,      (np.ndarray, tuple, list)      ))      )

       if is_batched:
        UpperCAmelCase__							 =						[np.asarray(lowerCamelCase ,      dtype=np.floataa      ).T for audio in raw_audio]
       elif not is_batched and not isinstance(lowerCamelCase ,      np.ndarray      ):
        UpperCAmelCase__							 =						np.asarray(lowerCamelCase ,      dtype=np.floataa      )
       elif isinstance(lowerCamelCase ,      np.ndarray      ) and raw_audio.dtype is np.dtype(np.floataa      ):
        UpperCAmelCase__							 =						raw_audio.astype(np.floataa      )

       # always return batch
       if not is_batched:
        UpperCAmelCase__							 =						[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__							 =						None
       UpperCAmelCase__							 =						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__							 =						min(array.shape[0] for array in raw_audio      )
         UpperCAmelCase__							 =						int(np.floor(max_length / self.chunk_stride      )      )
         UpperCAmelCase__							 =						(nb_step - 1) * self.chunk_stride + self.chunk_length
        elif padding:
         UpperCAmelCase__							 =						max(array.shape[0] for array in raw_audio      )
         UpperCAmelCase__							 =						int(np.ceil(max_length / self.chunk_stride      )      )
         UpperCAmelCase__							 =						(nb_step - 1) * self.chunk_stride + self.chunk_length
         UpperCAmelCase__							 =						"max_length"
        else:
         UpperCAmelCase__							 =						input_values

        # normal padding on batch
       if padded_inputs is None:
        UpperCAmelCase__							 =						self.pad(
            lowerCamelCase ,      max_length=lowerCamelCase ,      truncation=lowerCamelCase ,      padding=lowerCamelCase ,      return_attention_mask=lowerCamelCase ,      )
        if padding:
         UpperCAmelCase__							 =						padded_inputs.pop("attention_mask"      )

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

       UpperCAmelCase__							 =						input_values
       if return_tensors is not None:
        UpperCAmelCase__							 =						padded_inputs.convert_to_tensors(lowerCamelCase      )

       return padded_inputs





