


'''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

a_						:       str      			=				random.Random()





def __snake_case      (   UpperCAmelCase_		:		Any      ,       UpperCAmelCase_		:		Any=1.0      ,       UpperCAmelCase_		:		List[str]=None      ,       UpperCAmelCase_		:		Optional[int]=None	):
 if rng is None:
  lowerCamelCase_  				=						global_rng

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

 return values





class   snake_case					(					unittest.TestCase    ):





  """simple docstring"""




  def __init__(					self    ,						UpperCamelCase    ,						UpperCamelCase=7    ,						UpperCamelCase=400    ,						UpperCamelCase=2000    ,						UpperCamelCase=2048    ,						UpperCamelCase=128    ,						UpperCamelCase=1    ,						UpperCamelCase=512    ,						UpperCamelCase=30    ,						UpperCamelCase=4_4100    ,						):







   """simple docstring"""
   lowerCamelCase_  				=						parent
   lowerCamelCase_  				=						batch_size
   lowerCamelCase_  				=						min_seq_length
   lowerCamelCase_  				=						max_seq_length
   lowerCamelCase_  				=						(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
   lowerCamelCase_  				=						spectrogram_length
   lowerCamelCase_  				=						feature_size
   lowerCamelCase_  				=						num_audio_channels
   lowerCamelCase_  				=						hop_length
   lowerCamelCase_  				=						chunk_length
   lowerCamelCase_  				=						sampling_rate






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







   """simple docstring"""
   def _flatten(UpperCamelCase  ):
    return list(itertools.chain(*UpperCamelCase  )  )

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





@require_torch
@require_torchaudio
class   snake_case					(					lowercase    ,		unittest.TestCase    ):





  """simple docstring"""




  _lowerCamelCase						=						TvltFeatureExtractor






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						TvltFeatureExtractionTester(self  )






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						self.feature_extraction_class(**self.feat_extract_dict  )
   self.assertTrue(hasattr(UpperCamelCase    ,						"spectrogram_length"  )  )
   self.assertTrue(hasattr(UpperCamelCase    ,						"feature_size"  )  )
   self.assertTrue(hasattr(UpperCamelCase    ,						"num_audio_channels"  )  )
   self.assertTrue(hasattr(UpperCamelCase    ,						"hop_length"  )  )
   self.assertTrue(hasattr(UpperCamelCase    ,						"chunk_length"  )  )
   self.assertTrue(hasattr(UpperCamelCase    ,						"sampling_rate"  )  )






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						self.feature_extraction_class(**self.feat_extract_dict  )

   with tempfile.TemporaryDirectory() as tmpdirname:
    lowerCamelCase_  				=						feat_extract_first.save_pretrained(UpperCamelCase  )[0]
    check_json_file_has_correct_format(UpperCamelCase  )
    lowerCamelCase_  				=						self.feature_extraction_class.from_pretrained(UpperCamelCase  )

   lowerCamelCase_  				=						feat_extract_first.to_dict()
   lowerCamelCase_  				=						feat_extract_second.to_dict()
   lowerCamelCase_  				=						dict_first.pop("mel_filters"  )
   lowerCamelCase_  				=						dict_second.pop("mel_filters"  )
   self.assertTrue(np.allclose(UpperCamelCase    ,						UpperCamelCase  )  )
   self.assertEqual(UpperCamelCase    ,						UpperCamelCase  )






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						self.feature_extraction_class(**self.feat_extract_dict  )

   with tempfile.TemporaryDirectory() as tmpdirname:
    lowerCamelCase_  				=						os.path.join(UpperCamelCase    ,						"feat_extract.json"  )
    feat_extract_first.to_json_file(UpperCamelCase  )
    lowerCamelCase_  				=						self.feature_extraction_class.from_json_file(UpperCamelCase  )

   lowerCamelCase_  				=						feat_extract_first.to_dict()
   lowerCamelCase_  				=						feat_extract_second.to_dict()
   lowerCamelCase_  				=						dict_first.pop("mel_filters"  )
   lowerCamelCase_  				=						dict_second.pop("mel_filters"  )
   self.assertTrue(np.allclose(UpperCamelCase    ,						UpperCamelCase  )  )
   self.assertEqual(UpperCamelCase    ,						UpperCamelCase  )






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						self.feature_extraction_class(**self.feat_extract_dict  )

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

   # Test not batched input
   lowerCamelCase_  				=						feature_extractor(np_speech_inputs[0]    ,						return_tensors="np"    ,						sampling_rate=4_4100  ).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_  				=						feature_extractor(UpperCamelCase    ,						return_tensors="np"    ,						sampling_rate=4_4100  ).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_  				=						feature_extractor(
       UpperCamelCase    ,						return_tensors="np"    ,						sampling_rate=4_4100    ,						mask_audio=UpperCamelCase  ).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_  				=						[floats_list((1, x)  )[0] for x in (800, 800, 800)]
   lowerCamelCase_  				=						np.asarray(UpperCamelCase  )
   lowerCamelCase_  				=						feature_extractor(UpperCamelCase    ,						return_tensors="np"    ,						sampling_rate=4_4100  ).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 						snake_case	(					self    ,						UpperCamelCase  ):







   """simple docstring"""
   lowerCamelCase_  				=						load_dataset("hf-internal-testing/librispeech_asr_dummy"    ,						"clean"    ,						split="validation"  )
   # automatic decoding with librispeech
   lowerCamelCase_  				=						ds.sort("id"  ).select(range(UpperCamelCase  )  )[:num_samples]["audio"]

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







  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						self._load_datasamples(1  )
   lowerCamelCase_  				=						TvltFeatureExtractor()
   lowerCamelCase_  				=						feature_extractor(UpperCamelCase    ,						return_tensors="pt"  ).audio_values

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

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







'''simple docstring'''

from dataclasses import dataclass
from typing import Optional, Tuple

import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput





@dataclass
class   snake_case					(					lowercase    ):





  """simple docstring"""




  _lowerCamelCase						=						None
  _lowerCamelCase						=						None
  _lowerCamelCase						=						None
  _lowerCamelCase						=						None





class   snake_case					(					lowercase    ):





  """simple docstring"""




  def __init__(					self    ,						UpperCamelCase=1    ,						UpperCamelCase=0    ,						UpperCamelCase=2    ,						UpperCamelCase=512    ,						UpperCamelCase="cls"    ,						UpperCamelCase=False    ,						UpperCamelCase=True    ,						**UpperCamelCase    ,						):







   """simple docstring"""
   super().__init__(pad_token_id=UpperCamelCase    ,						bos_token_id=UpperCamelCase    ,						eos_token_id=UpperCamelCase    ,						**UpperCamelCase  )
   lowerCamelCase_  				=						project_dim
   lowerCamelCase_  				=						pooler_fn
   lowerCamelCase_  				=						learn_encoder
   lowerCamelCase_  				=						use_attention_mask





class   snake_case					(					lowercase    ):





  """simple docstring"""




  _lowerCamelCase						=						[r"pooler", r"logit_scale"]
  _lowerCamelCase						=						[r"position_ids", r"predictions.decoder.bias"]
  _lowerCamelCase						=						"roberta"
  _lowerCamelCase						=						RobertaSeriesConfig






  def __init__(					self    ,						UpperCamelCase  ):







   """simple docstring"""
   super().__init__(UpperCamelCase  )
   lowerCamelCase_  				=						XLMRobertaModel(UpperCamelCase  )
   lowerCamelCase_  				=						nn.Linear(config.hidden_size    ,						config.project_dim  )
   lowerCamelCase_  				=						getattr(UpperCamelCase    ,						"has_pre_transformation"    ,						UpperCamelCase  )
   if self.has_pre_transformation:
    lowerCamelCase_  				=						nn.Linear(config.hidden_size    ,						config.project_dim  )
    lowerCamelCase_  				=						nn.LayerNorm(config.hidden_size    ,						eps=config.layer_norm_eps  )
   self.post_init()







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







   """simple docstring"""
   lowerCamelCase_  				=						return_dict if return_dict is not None else self.config.use_return_dict

   lowerCamelCase_  				=						self.base_model(
       input_ids=UpperCamelCase    ,						attention_mask=UpperCamelCase    ,						token_type_ids=UpperCamelCase    ,						position_ids=UpperCamelCase    ,						head_mask=UpperCamelCase    ,						inputs_embeds=UpperCamelCase    ,						encoder_hidden_states=UpperCamelCase    ,						encoder_attention_mask=UpperCamelCase    ,						output_attentions=UpperCamelCase    ,						output_hidden_states=True if self.has_pre_transformation else output_hidden_states    ,						return_dict=UpperCamelCase    ,						)

   if self.has_pre_transformation:
    lowerCamelCase_  				=						outputs["hidden_states"][-2]
    lowerCamelCase_  				=						self.pre_LN(UpperCamelCase  )
    lowerCamelCase_  				=						self.transformation_pre(UpperCamelCase  )

    return TransformationModelOutput(
        projection_state=UpperCamelCase    ,						last_hidden_state=outputs.last_hidden_state    ,						hidden_states=outputs.hidden_states    ,						attentions=outputs.attentions    ,						)
   else:
    lowerCamelCase_  				=						self.transformation(outputs.last_hidden_state  )
    return TransformationModelOutput(
        projection_state=UpperCamelCase    ,						last_hidden_state=outputs.last_hidden_state    ,						hidden_states=outputs.hidden_states    ,						attentions=outputs.attentions    ,						)



