






import itertools
import random
import unittest

import numpy as np

from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_speech_available():
  from transformers import SpeechaTextFeatureExtractor

__a:     Optional[int]   					=					random.Random()




def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case=1.0   ,      __snake_case=None   ,      __snake_case=None )				->						Any:
       if rng is None:
              _UpperCAmelCase							=    global_rng

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

       return values
@require_torch
@require_torchaudio
class 		SCREAMING_SNAKE_CASE__	(  unittest.TestCase     ):

  '''simple docstring'''







  def __init__(      self		:			Union[str, Any]       ,				lowerCamelCase		:			Optional[Any]       ,				lowerCamelCase		:			Optional[Any]=7       ,				lowerCamelCase		:			Optional[Any]=400       ,				lowerCamelCase		:			List[Any]=2000       ,				lowerCamelCase		:			List[str]=24       ,				lowerCamelCase		:			str=24       ,				lowerCamelCase		:			Tuple=0.0       ,				lowerCamelCase		:			List[str]=1_6000       ,				lowerCamelCase		:			List[str]=True       ,				lowerCamelCase		:			Dict=True       ,				)						->	Union[str, Any]:


         """simple docstring"""
         _UpperCAmelCase							=    parent
         _UpperCAmelCase							=    batch_size
         _UpperCAmelCase							=    min_seq_length
         _UpperCAmelCase							=    max_seq_length
         _UpperCAmelCase							=    (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
         _UpperCAmelCase							=    feature_size
         _UpperCAmelCase							=    num_mel_bins
         _UpperCAmelCase							=    padding_value
         _UpperCAmelCase							=    sampling_rate
         _UpperCAmelCase							=    return_attention_mask
         _UpperCAmelCase							=    do_normalize


  def  lowerCamelCase       (      self		:			Tuple							)						->	Optional[int]:


         """simple docstring"""
         return {
             "feature_size": self.feature_size,
             "num_mel_bins": self.num_mel_bins,
             "padding_value": self.padding_value,
             "sampling_rate": self.sampling_rate,
             "return_attention_mask": self.return_attention_mask,
             "do_normalize": self.do_normalize,
         }





  def  lowerCamelCase       (      self		:			List[Any]       ,				lowerCamelCase		:			List[Any]=False       ,				lowerCamelCase		:			Dict=False							)						->	Optional[int]:


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

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



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

  '''simple docstring'''







  _lowerCamelCase    =  SpeechaTextFeatureExtractor if is_speech_available() else None


  def  lowerCamelCase       (      self		:			str							)						->	Optional[Any]:


         """simple docstring"""
         _UpperCAmelCase							=    SpeechaTextFeatureExtractionTester(self							)


  def  lowerCamelCase       (      self		:			List[str]       ,				lowerCamelCase		:			str							)						->	List[str]:


         """simple docstring"""
         self.assertTrue(np.all(np.mean(lowerCamelCase       ,				axis=0							) < 1E-3							)							)
         self.assertTrue(np.all(np.abs(np.var(lowerCamelCase       ,				axis=0							) - 1							) < 1E-3							)							)


  def  lowerCamelCase       (      self		:			Optional[int]							)						->	Dict:


         """simple docstring"""
         # Tests that all call wrap to encode_plus and batch_encode_plus
         _UpperCAmelCase							=    self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
         # create three inputs of length 800, 1000, and 1200
         _UpperCAmelCase							=    [floats_list((1, x)							)[0] for x in range(800       ,				1400       ,				200							)]
         _UpperCAmelCase							=    [np.asarray(lowerCamelCase							) for speech_input in speech_inputs]

         # Test feature size
         _UpperCAmelCase							=    feature_extractor(lowerCamelCase       ,				padding=lowerCamelCase       ,				return_tensors="""np"""							).input_features
         self.assertTrue(input_features.ndim == 3							)
         self.assertTrue(input_features.shape[-1] == feature_extractor.feature_size							)

         # Test not batched input
         _UpperCAmelCase							=    feature_extractor(speech_inputs[0]       ,				return_tensors="""np"""							).input_features
         _UpperCAmelCase							=    feature_extractor(np_speech_inputs[0]       ,				return_tensors="""np"""							).input_features
         self.assertTrue(np.allclose(lowerCamelCase       ,				lowerCamelCase       ,				atol=1E-3							)							)

         # Test batched
         _UpperCAmelCase							=    feature_extractor(lowerCamelCase       ,				return_tensors="""np"""							).input_features
         _UpperCAmelCase							=    feature_extractor(lowerCamelCase       ,				return_tensors="""np"""							).input_features
         for enc_seq_a, enc_seq_a in zip(lowerCamelCase       ,				lowerCamelCase							):
                self.assertTrue(np.allclose(lowerCamelCase       ,				lowerCamelCase       ,				atol=1E-3							)							)

         # Test 2-D numpy arrays are batched.
         _UpperCAmelCase							=    [floats_list((1, x)							)[0] for x in (800, 800, 800)]
         _UpperCAmelCase							=    np.asarray(lowerCamelCase							)
         _UpperCAmelCase							=    feature_extractor(lowerCamelCase       ,				return_tensors="""np"""							).input_features
         _UpperCAmelCase							=    feature_extractor(lowerCamelCase       ,				return_tensors="""np"""							).input_features
         for enc_seq_a, enc_seq_a in zip(lowerCamelCase       ,				lowerCamelCase							):
                self.assertTrue(np.allclose(lowerCamelCase       ,				lowerCamelCase       ,				atol=1E-3							)							)


  def  lowerCamelCase       (      self		:			Any							)						->	Union[str, Any]:


         """simple docstring"""
         _UpperCAmelCase							=    self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
         _UpperCAmelCase							=    [floats_list((1, x)							)[0] for x in range(800       ,				1400       ,				200							)]

         _UpperCAmelCase							=    ["""longest""", """max_length""", """do_not_pad"""]
         _UpperCAmelCase							=    [None, 16, None]
         for max_length, padding in zip(lowerCamelCase       ,				lowerCamelCase							):
                _UpperCAmelCase							=    feature_extractor(
                    lowerCamelCase       ,				padding=lowerCamelCase       ,				max_length=lowerCamelCase       ,				return_attention_mask=lowerCamelCase							)
                _UpperCAmelCase							=    inputs.input_features
                _UpperCAmelCase							=    inputs.attention_mask
                _UpperCAmelCase							=    [np.sum(lowerCamelCase							) for x in attention_mask]

                self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]							)
                self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]							)
                self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]							)


  def  lowerCamelCase       (      self		:			List[Any]							)						->	Optional[int]:


         """simple docstring"""
         _UpperCAmelCase							=    self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
         _UpperCAmelCase							=    [floats_list((1, x)							)[0] for x in range(800       ,				1400       ,				200							)]

         _UpperCAmelCase							=    ["""longest""", """max_length""", """do_not_pad"""]
         _UpperCAmelCase							=    [None, 16, None]
         for max_length, padding in zip(lowerCamelCase       ,				lowerCamelCase							):
                _UpperCAmelCase							=    feature_extractor(
                    lowerCamelCase       ,				max_length=lowerCamelCase       ,				padding=lowerCamelCase       ,				return_tensors="""np"""       ,				return_attention_mask=lowerCamelCase							)
                _UpperCAmelCase							=    inputs.input_features
                _UpperCAmelCase							=    inputs.attention_mask
                _UpperCAmelCase							=    [np.sum(lowerCamelCase							) for x in attention_mask]

                self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]							)
                self.assertTrue(input_features[0][fbank_feat_lengths[0] :].sum() < 1E-6							)
                self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]							)
                self.assertTrue(input_features[0][fbank_feat_lengths[1] :].sum() < 1E-6							)
                self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]							)


  def  lowerCamelCase       (      self		:			List[Any]							)						->	List[Any]:


         """simple docstring"""
         _UpperCAmelCase							=    self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
         _UpperCAmelCase							=    [floats_list((1, x)							)[0] for x in range(800       ,				1400       ,				200							)]
         _UpperCAmelCase							=    feature_extractor(
             lowerCamelCase       ,				padding="""max_length"""       ,				max_length=4       ,				truncation=lowerCamelCase       ,				return_tensors="""np"""       ,				return_attention_mask=lowerCamelCase       ,				)
         _UpperCAmelCase							=    inputs.input_features
         _UpperCAmelCase							=    inputs.attention_mask
         _UpperCAmelCase							=    np.sum(attention_mask == 1       ,				axis=1							)

         self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]							)
         self._check_zero_mean_unit_variance(input_features[1]							)
         self._check_zero_mean_unit_variance(input_features[2]							)


  def  lowerCamelCase       (      self		:			Union[str, Any]							)						->	Optional[Any]:


         """simple docstring"""
         _UpperCAmelCase							=    self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
         _UpperCAmelCase							=    [floats_list((1, x)							)[0] for x in range(800       ,				1400       ,				200							)]
         _UpperCAmelCase							=    feature_extractor(
             lowerCamelCase       ,				padding="""longest"""       ,				max_length=4       ,				truncation=lowerCamelCase       ,				return_tensors="""np"""       ,				return_attention_mask=lowerCamelCase       ,				)
         _UpperCAmelCase							=    inputs.input_features
         _UpperCAmelCase							=    inputs.attention_mask
         _UpperCAmelCase							=    np.sum(attention_mask == 1       ,				axis=1							)

         self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]							)
         self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]							)
         self._check_zero_mean_unit_variance(input_features[2]							)

         # make sure that if max_length < longest -> then pad to max_length
         self.assertEqual(input_features.shape       ,				(3, 4, 24)							)

         _UpperCAmelCase							=    [floats_list((1, x)							)[0] for x in range(800       ,				1400       ,				200							)]
         _UpperCAmelCase							=    feature_extractor(
             lowerCamelCase       ,				padding="""longest"""       ,				max_length=16       ,				truncation=lowerCamelCase       ,				return_tensors="""np"""       ,				return_attention_mask=lowerCamelCase       ,				)
         _UpperCAmelCase							=    inputs.input_features
         _UpperCAmelCase							=    inputs.attention_mask
         _UpperCAmelCase							=    np.sum(attention_mask == 1       ,				axis=1							)

         self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]							)
         self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]							)
         self._check_zero_mean_unit_variance(input_features[2]							)

         # make sure that if max_length < longest -> then pad to max_length
         self.assertEqual(input_features.shape       ,				(3, 6, 24)							)


  def  lowerCamelCase       (      self		:			Dict							)						->	Union[str, Any]:


         """simple docstring"""
         import torch

         _UpperCAmelCase							=    self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
         _UpperCAmelCase							=    np.random.rand(100       ,				32							).astype(np.floataa							)
         _UpperCAmelCase							=    np_speech_inputs.tolist()

         for inputs in [py_speech_inputs, np_speech_inputs]:
                _UpperCAmelCase							=    feature_extractor.pad([{"""input_features""": inputs}]       ,				return_tensors="""np"""							)
                self.assertTrue(np_processed.input_features.dtype == np.floataa							)
                _UpperCAmelCase							=    feature_extractor.pad([{"""input_features""": inputs}]       ,				return_tensors="""pt"""							)
                self.assertTrue(pt_processed.input_features.dtype == torch.floataa							)


  def  lowerCamelCase       (      self		:			Tuple       ,				lowerCamelCase		:			Dict							)						->	Dict:


         """simple docstring"""
         from datasets import load_dataset

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

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





  def  lowerCamelCase       (      self		:			Tuple							)						->	Any:


         """simple docstring"""
         # fmt: off
         _UpperCAmelCase							=    np.array([
             -1.5745, -1.7713, -1.7020, -1.6069, -1.2250, -1.1105, -0.9072, -0.8241,
             -1.2310, -0.8098, -0.3320, -0.4101, -0.7985, -0.4996, -0.8213, -0.9128,
             -1.0420, -1.1286, -1.0440, -0.7999, -0.8405, -1.2275, -1.5443, -1.4625,
         ]							)
         # fmt: on

         _UpperCAmelCase							=    self._load_datasamples(1							)
         _UpperCAmelCase							=    self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
         _UpperCAmelCase							=    feature_extractor(lowerCamelCase       ,				return_tensors="""pt"""							).input_features
         self.assertEquals(input_features.shape       ,				(1, 584, 24)							)
         self.assertTrue(np.allclose(input_features[0, 0, :30]       ,				lowerCamelCase       ,				atol=1E-4							)							)







# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.multicontrolnet import MultiControlNetModel  # noqa: F401
from ..controlnet.pipeline_controlnet import StableDiffusionControlNetPipeline  # noqa: F401


deprecate(
    '''stable diffusion controlnet''',
    '''0.22.0''',
    '''Importing `StableDiffusionControlNetPipeline` or `MultiControlNetModel` from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet is deprecated. Please import `from diffusers import StableDiffusionControlNetPipeline` instead.''',
    standard_warn=False,
    stacklevel=3,
)