


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

A   :					int	=      random.Random()

def    UpperCamelCase__	(	_A    :					List[str]					,      _A    :					List[str]=1.0					,      _A    :					Union[str, Any]=None					,      _A    :					Dict=None		):

      """simple docstring"""




      if rng is None:
            lowerCamelCase__	:       int					=						global_rng

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

      return values







class     _lowercase				(							unittest.TestCase):


       """simple docstring"""




       def __init__(     self							:    str      ,   __lowerCamelCase							:    Any      ,   __lowerCamelCase							:    List[Any]=7      ,   __lowerCamelCase							:    Any=400      ,   __lowerCamelCase							:    str=2000      ,   __lowerCamelCase							:    List[Any]=2048      ,   __lowerCamelCase							:    int=128      ,   __lowerCamelCase							:    Optional[Any]=1      ,   __lowerCamelCase							:    List[str]=512      ,   __lowerCamelCase							:    str=30      ,   __lowerCamelCase							:    Optional[int]=44100      ,   ):


             '''simple docstring'''



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





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


             '''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 		lowerCAmelCase  (     self							:    List[str]      ,   __lowerCamelCase							:    Tuple=False      ,   __lowerCamelCase							:    Optional[int]=False ):


             '''simple docstring'''



             def _flatten(__lowerCamelCase							:    Optional[int] ):
                   return list(itertools.chain(*snake_case__ ) )

             if equal_length:
                   lowerCamelCase__	:       Tuple					=						[floats_list((self.max_seq_length, self.feature_size) ) for _ in range(self.batch_size )]
             else:
                   # make sure that inputs increase in size
                   lowerCamelCase__	:       str					=						[
                       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__	:       List[str]					=						[np.asarray(snake_case__ ) for x in speech_inputs]
             return speech_inputs







@require_torch
@require_torchaudio
class     _lowercase				(							SCREAMING_SNAKE_CASE__							,						unittest.TestCase):


       """simple docstring"""




       A__      		=    TvltFeatureExtractor





       def 		lowerCAmelCase  (     self							:    Optional[Any] ):


             '''simple docstring'''



             lowerCamelCase__	:       List[Any]					=						TvltFeatureExtractionTester(self )





       def 		lowerCAmelCase  (     self							:    Any ):


             '''simple docstring'''



             lowerCamelCase__	:       int					=						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 		lowerCAmelCase  (     self							:    List[Any] ):


             '''simple docstring'''



             lowerCamelCase__	:       Tuple					=						self.feature_extraction_class(**self.feat_extract_dict )

             with tempfile.TemporaryDirectory() as tmpdirname:
                   lowerCamelCase__	:       Tuple					=						feat_extract_first.save_pretrained(snake_case__ )[0]
                   check_json_file_has_correct_format(snake_case__ )
                   lowerCamelCase__	:       List[Any]					=						self.feature_extraction_class.from_pretrained(snake_case__ )

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





       def 		lowerCAmelCase  (     self							:    List[Any] ):


             '''simple docstring'''



             lowerCamelCase__	:       str					=						self.feature_extraction_class(**self.feat_extract_dict )

             with tempfile.TemporaryDirectory() as tmpdirname:
                   lowerCamelCase__	:       int					=						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__	:       int					=						feat_extract_first.to_dict()
             lowerCamelCase__	:       List[Any]					=						feat_extract_second.to_dict()
             lowerCamelCase__	:       Optional[Any]					=						dict_first.pop("mel_filters" )
             lowerCamelCase__	:       str					=						dict_second.pop("mel_filters" )
             self.assertTrue(np.allclose(snake_case__      ,   snake_case__ ) )
             self.assertEqual(snake_case__      ,   snake_case__ )





       def 		lowerCAmelCase  (     self							:    str ):


             '''simple docstring'''



             lowerCamelCase__	:       Tuple					=						self.feature_extraction_class(**self.feat_extract_dict )

             # create three inputs of length 800, 1000, and 1200
             lowerCamelCase__	:       List[Any]					=						[floats_list((1, x) )[0] for x in range(800      ,   1400      ,   200 )]
             lowerCamelCase__	:       List[Any]					=						[np.asarray(snake_case__ ) for speech_input in speech_inputs]

             # Test not batched input
             lowerCamelCase__	:       Optional[Any]					=						feature_extractor(np_speech_inputs[0]      ,   return_tensors="np"      ,   sampling_rate=44100 ).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__	:       List[Any]					=						feature_extractor(snake_case__      ,   return_tensors="np"      ,   sampling_rate=44100 ).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__	:       Optional[int]					=						feature_extractor(
                 snake_case__      ,   return_tensors="np"      ,   sampling_rate=44100      ,   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__	:       Union[str, Any]					=						[floats_list((1, x) )[0] for x in (800, 800, 800)]
             lowerCamelCase__	:       Optional[int]					=						np.asarray(snake_case__ )
             lowerCamelCase__	:       Any					=						feature_extractor(snake_case__      ,   return_tensors="np"      ,   sampling_rate=44100 ).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 		lowerCAmelCase  (     self							:    str      ,   __lowerCamelCase							:    Optional[int] ):


             '''simple docstring'''



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

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





       def 		lowerCAmelCase  (     self							:    List[Any] ):


             '''simple docstring'''



             lowerCamelCase__	:       Any					=						self._load_datasamples(1 )
             lowerCamelCase__	:       List[str]					=						TvltFeatureExtractor()
             lowerCamelCase__	:       Optional[int]					=						feature_extractor(snake_case__      ,   return_tensors="pt" ).audio_values

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

             lowerCamelCase__	:       str					=						torch.tensor([[-0.3_0_3_2, -0.2_7_0_8], [-0.4_4_3_4, -0.4_0_0_7]] )
             self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]      ,   snake_case__      ,   atol=1E-4 ) )






from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


A   :					Union[str, Any]	=      logging.get_logger(__name__)

A   :					Dict	=      {
    "kssteven/ibert-roberta-base": "https://huggingface.co/kssteven/ibert-roberta-base/resolve/main/config.json",
    "kssteven/ibert-roberta-large": "https://huggingface.co/kssteven/ibert-roberta-large/resolve/main/config.json",
    "kssteven/ibert-roberta-large-mnli": (
        "https://huggingface.co/kssteven/ibert-roberta-large-mnli/resolve/main/config.json"
    ),
}







class     _lowercase				(							lowercase__):


       """simple docstring"""




       A__      		=    "ibert"





       def __init__(     self							:    int      ,   __lowerCamelCase							:    List[str]=30522      ,   __lowerCamelCase							:    Optional[int]=768      ,   __lowerCamelCase							:    List[Any]=12      ,   __lowerCamelCase							:    str=12      ,   __lowerCamelCase							:    List[str]=3072      ,   __lowerCamelCase							:    Dict="gelu"      ,   __lowerCamelCase							:    Dict=0.1      ,   __lowerCamelCase							:    Optional[int]=0.1      ,   __lowerCamelCase							:    Any=512      ,   __lowerCamelCase							:    List[str]=2      ,   __lowerCamelCase							:    Union[str, Any]=0.0_2      ,   __lowerCamelCase							:    Any=1E-1_2      ,   __lowerCamelCase							:    int=1      ,   __lowerCamelCase							:    Optional[Any]=0      ,   __lowerCamelCase							:    int=2      ,   __lowerCamelCase							:    int="absolute"      ,   __lowerCamelCase							:    Tuple=False      ,   __lowerCamelCase							:    Dict="none"      ,   **__lowerCamelCase							:    Tuple      ,   ):


             '''simple docstring'''



             super().__init__(pad_token_id=__lowerCamelCase      ,   bos_token_id=__lowerCamelCase      ,   eos_token_id=__lowerCamelCase      ,   **__lowerCamelCase )

             lowerCamelCase__	:       Any					=						vocab_size
             lowerCamelCase__	:       Optional[Any]					=						hidden_size
             lowerCamelCase__	:       Optional[int]					=						num_hidden_layers
             lowerCamelCase__	:       int					=						num_attention_heads
             lowerCamelCase__	:       List[str]					=						hidden_act
             lowerCamelCase__	:       List[str]					=						intermediate_size
             lowerCamelCase__	:       Optional[int]					=						hidden_dropout_prob
             lowerCamelCase__	:       Any					=						attention_probs_dropout_prob
             lowerCamelCase__	:       Tuple					=						max_position_embeddings
             lowerCamelCase__	:       Any					=						type_vocab_size
             lowerCamelCase__	:       Optional[int]					=						initializer_range
             lowerCamelCase__	:       Tuple					=						layer_norm_eps
             lowerCamelCase__	:       int					=						position_embedding_type
             lowerCamelCase__	:       List[str]					=						quant_mode
             lowerCamelCase__	:       int					=						force_dequant







class     _lowercase				(							lowercase__):


       """simple docstring"""




       @property
       def 		lowerCAmelCase  (     self							:    List[Any] ):


             '''simple docstring'''



             if self.task == "multiple-choice":
                   lowerCamelCase__	:       Any					=						{0: "batch", 1: "choice", 2: "sequence"}
             else:
                   lowerCamelCase__	:       Any					=						{0: "batch", 1: "sequence"}
             return OrderedDict(
                 [
                     ("input_ids", dynamic_axis),
                     ("attention_mask", dynamic_axis),
                 ] )


