




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

_UpperCAmelCase	:						List[str]			=     random.Random()



def       UpperCAmelCase__  (     lowerCamelCase,			lowerCamelCase=1.0,			lowerCamelCase=None,			lowerCamelCase=None   ):
  if rng is None:
    lowercase    :Union[str, Any]      	=						global_rng

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

  return values




class    __lowerCAmelCase       (							unittest.TestCase):






       def __init__(   self:					Optional[Any]       ,					_lowerCAmelCase:					Tuple       ,					_lowerCAmelCase:					Union[str, Any]=7       ,					_lowerCAmelCase:					str=4_00       ,					_lowerCAmelCase:					List[str]=20_00       ,					_lowerCAmelCase:					Dict=20_48       ,					_lowerCAmelCase:					Any=1_28       ,					_lowerCAmelCase:					Any=1       ,					_lowerCAmelCase:					List[Any]=5_12       ,					_lowerCAmelCase:					Optional[int]=30       ,					_lowerCAmelCase:					List[Any]=4_41_00       ,					):
         lowercase    :Any      	=						parent
         lowercase    :Any      	=						batch_size
         lowercase    :Optional[int]      	=						min_seq_length
         lowercase    :Optional[Any]      	=						max_seq_length
         lowercase    :Dict      	=						(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
         lowercase    :List[str]      	=						spectrogram_length
         lowercase    :int      	=						feature_size
         lowercase    :Union[str, Any]      	=						num_audio_channels
         lowercase    :Optional[int]      	=						hop_length
         lowercase    :str      	=						chunk_length
         lowercase    :List[str]      	=						sampling_rate






       def 					SCREAMING_SNAKE_CASE    (   self:					str				):
         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 					SCREAMING_SNAKE_CASE    (   self:					int       ,					_lowerCAmelCase:					int=False       ,					_lowerCAmelCase:					int=False				):
         def _flatten(_lowerCAmelCase:					Optional[Any]				):
           return list(itertools.chain(*_lowerCAmelCase				)				)

         if equal_length:
           lowercase    :Any      	=						[floats_list((self.max_seq_length, self.feature_size)				) for _ in range(self.batch_size				)]
         else:
           # make sure that inputs increase in size
           lowercase    :Tuple      	=						[
               floats_list((x, self.feature_size)				)
               for x in range(self.min_seq_length       ,					self.max_seq_length       ,					self.seq_length_diff				)
           ]
         if numpify:
           lowercase    :Optional[int]      	=						[np.asarray(_lowerCAmelCase				) for x in speech_inputs]
         return speech_inputs







@require_torch
@require_torchaudio
class    __lowerCAmelCase       (							lowerCAmelCase      , unittest.TestCase):
       _a 					=						TvltFeatureExtractor






       def 					SCREAMING_SNAKE_CASE    (   self:					Tuple				):
         lowercase    :Dict      	=						TvltFeatureExtractionTester(self				)






       def 					SCREAMING_SNAKE_CASE    (   self:					str				):
         lowercase    :List[Any]      	=						self.feature_extraction_class(**self.feat_extract_dict				)
         self.assertTrue(hasattr(_lowerCAmelCase       ,					"spectrogram_length"				)				)
         self.assertTrue(hasattr(_lowerCAmelCase       ,					"feature_size"				)				)
         self.assertTrue(hasattr(_lowerCAmelCase       ,					"num_audio_channels"				)				)
         self.assertTrue(hasattr(_lowerCAmelCase       ,					"hop_length"				)				)
         self.assertTrue(hasattr(_lowerCAmelCase       ,					"chunk_length"				)				)
         self.assertTrue(hasattr(_lowerCAmelCase       ,					"sampling_rate"				)				)






       def 					SCREAMING_SNAKE_CASE    (   self:					str				):
         lowercase    :Any      	=						self.feature_extraction_class(**self.feat_extract_dict				)

         with tempfile.TemporaryDirectory() as tmpdirname:
           lowercase    :Tuple      	=						feat_extract_first.save_pretrained(_lowerCAmelCase				)[0]
           check_json_file_has_correct_format(_lowerCAmelCase				)
           lowercase    :Tuple      	=						self.feature_extraction_class.from_pretrained(_lowerCAmelCase				)

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






       def 					SCREAMING_SNAKE_CASE    (   self:					Tuple				):
         lowercase    :Union[str, Any]      	=						self.feature_extraction_class(**self.feat_extract_dict				)

         with tempfile.TemporaryDirectory() as tmpdirname:
           lowercase    :Any      	=						os.path.join(_lowerCAmelCase       ,					"feat_extract.json"				)
           feat_extract_first.to_json_file(_lowerCAmelCase				)
           lowercase    :List[Any]      	=						self.feature_extraction_class.from_json_file(_lowerCAmelCase				)

         lowercase    :Tuple      	=						feat_extract_first.to_dict()
         lowercase    :int      	=						feat_extract_second.to_dict()
         lowercase    :Tuple      	=						dict_first.pop("mel_filters"				)
         lowercase    :Tuple      	=						dict_second.pop("mel_filters"				)
         self.assertTrue(np.allclose(_lowerCAmelCase       ,					_lowerCAmelCase				)				)
         self.assertEqual(_lowerCAmelCase       ,					_lowerCAmelCase				)






       def 					SCREAMING_SNAKE_CASE    (   self:					int				):
         # Initialize feature_extractor
         lowercase    :int      	=						self.feature_extraction_class(**self.feat_extract_dict				)

         # create three inputs of length 800, 1000, and 1200
         lowercase    :List[Any]      	=						[floats_list((1, x)				)[0] for x in range(8_00       ,					14_00       ,					2_00				)]
         lowercase    :Union[str, Any]      	=						[np.asarray(_lowerCAmelCase				) for speech_input in speech_inputs]

         # Test not batched input
         lowercase    :Any      	=						feature_extractor(np_speech_inputs[0]       ,					return_tensors="np"       ,					sampling_rate=4_41_00				).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
         lowercase    :Optional[int]      	=						feature_extractor(_lowerCAmelCase       ,					return_tensors="np"       ,					sampling_rate=4_41_00				).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
         lowercase    :Dict      	=						feature_extractor(
             _lowerCAmelCase       ,					return_tensors="np"       ,					sampling_rate=4_41_00       ,					mask_audio=_lowerCAmelCase				).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.
         lowercase    :Optional[int]      	=						[floats_list((1, x)				)[0] for x in (8_00, 8_00, 8_00)]
         lowercase    :Union[str, Any]      	=						np.asarray(_lowerCAmelCase				)
         lowercase    :Dict      	=						feature_extractor(_lowerCAmelCase       ,					return_tensors="np"       ,					sampling_rate=4_41_00				).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 					SCREAMING_SNAKE_CASE    (   self:					Optional[int]       ,					_lowerCAmelCase:					Optional[Any]				):
         lowercase    :str      	=						load_dataset("hf-internal-testing/librispeech_asr_dummy"       ,					"clean"       ,					split="validation"				)
         # automatic decoding with librispeech
         lowercase    :Any      	=						ds.sort("id"				).select(range(_lowerCAmelCase				)				)[:num_samples]["audio"]

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






       def 					SCREAMING_SNAKE_CASE    (   self:					Dict				):
         lowercase    :Optional[int]      	=						self._load_datasamples(1				)
         lowercase    :Union[str, Any]      	=						TvltFeatureExtractor()
         lowercase    :str      	=						feature_extractor(_lowerCAmelCase       ,					return_tensors="pt"				).audio_values

         self.assertEquals(audio_values.shape       ,					(1, 1, 1_92, 1_28)				)

         lowercase    :Optional[int]      	=						torch.tensor([[-0.30_32, -0.27_08], [-0.44_34, -0.40_07]]				)
         self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]       ,					_lowerCAmelCase       ,					atol=1e-4				)				)









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_UpperCAmelCase	:						List[Any]			=     {
    "configuration_swinv2": ["SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Swinv2Config"],
}


try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCAmelCase	:						Union[str, Any]			=     [
         "SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST",
         "Swinv2ForImageClassification",
         "Swinv2ForMaskedImageModeling",
         "Swinv2Model",
         "Swinv2PreTrainedModel",
     ]


if TYPE_CHECKING:
     from .configuration_swinva import SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinvaConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_swinva import (
              SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST,
              SwinvaForImageClassification,
              SwinvaForMaskedImageModeling,
              SwinvaModel,
              SwinvaPreTrainedModel,
          )


else:
     import sys

     _UpperCAmelCase	:						Tuple			=     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



