


'''simple docstring'''
import numpy as np

from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch

from .test_feature_extraction_common import FeatureExtractionSavingTestMixin

class     _lowerCamelCase       (     snake_case_					):




 '''simple docstring'''
 # to overwrite at feature extractactor specific tests
 __lowercase					:		List[Any]     				=    None
 __lowercase					:		Optional[int]     				=    None


 @property
 def 			snake_case__ (   self    ):

  """simple docstring"""







  return self.feat_extract_tester.prepare_feat_extract_dict()


 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A	:		Tuple	  =      self.feature_extraction_class(**self.feat_extract_dict    )
  self.assertTrue(hasattr(__lowercase   ,   'feature_size'    )    )
  self.assertTrue(hasattr(__lowercase   ,   'sampling_rate'    )    )
  self.assertTrue(hasattr(__lowercase   ,   'padding_value'    )    )


 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A	:		List[Any]	  =      self.feat_extract_tester.prepare_inputs_for_common()
  __A	:		Any	  =      self.feature_extraction_class(**self.feat_extract_dict    )
  __A	:		Dict	  =      feat_extract.model_input_names[0]

  __A	:		List[str]	  =      BatchFeature({input_name: speech_inputs}    )

  self.assertTrue(all(len(__lowercase    ) == len(__lowercase    ) for x, y in zip(__lowercase   ,   processed_features[input_name]    )    )    )

  __A	:		List[Any]	  =      self.feat_extract_tester.prepare_inputs_for_common(equal_length=__lowercase    )
  __A	:		str	  =      BatchFeature({input_name: speech_inputs}   ,   tensor_type='np'    )

  __A	:		List[Any]	  =      processed_features[input_name]

  if len(batch_features_input.shape    ) < 3:
   __A	:		str	  =      batch_features_input[:, :, None]

  self.assertTrue(
      batch_features_input.shape
      == (self.feat_extract_tester.batch_size, len(speech_inputs[0]    ), self.feat_extract_tester.feature_size)    )


 @require_torch
 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A	:		List[str]	  =      self.feat_extract_tester.prepare_inputs_for_common(equal_length=__lowercase    )
  __A	:		Tuple	  =      self.feature_extraction_class(**self.feat_extract_dict    )
  __A	:		List[Any]	  =      feat_extract.model_input_names[0]

  __A	:		Any	  =      BatchFeature({input_name: speech_inputs}   ,   tensor_type='pt'    )

  __A	:		Optional[int]	  =      processed_features[input_name]

  if len(batch_features_input.shape    ) < 3:
   __A	:		Any	  =      batch_features_input[:, :, None]

  self.assertTrue(
      batch_features_input.shape
      == (self.feat_extract_tester.batch_size, len(speech_inputs[0]    ), self.feat_extract_tester.feature_size)    )


 @require_tf
 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A	:		Dict	  =      self.feat_extract_tester.prepare_inputs_for_common(equal_length=__lowercase    )
  __A	:		Optional[int]	  =      self.feature_extraction_class(**self.feat_extract_dict    )
  __A	:		List[str]	  =      feat_extract.model_input_names[0]

  __A	:		List[Any]	  =      BatchFeature({input_name: speech_inputs}   ,   tensor_type='tf'    )

  __A	:		Optional[int]	  =      processed_features[input_name]

  if len(batch_features_input.shape    ) < 3:
   __A	:		Tuple	  =      batch_features_input[:, :, None]

  self.assertTrue(
      batch_features_input.shape
      == (self.feat_extract_tester.batch_size, len(speech_inputs[0]    ), self.feat_extract_tester.feature_size)    )


 def 			snake_case__ (   self   ,   __lowercase=False    ):

  """simple docstring"""







  def _inputs_have_equal_length(__lowercase    ):
   __A	:		int	  =      len(input[0]    )
   for input_slice in input[1:]:
    if len(__lowercase    ) != length:
     return False
   return True

  def _inputs_are_equal(__lowercase   ,   __lowercase    ):
   if len(__lowercase    ) != len(__lowercase    ):
    return False

   for input_slice_a, input_slice_a in zip(__lowercase   ,   __lowercase    ):
    if not np.allclose(np.asarray(__lowercase    )   ,   np.asarray(__lowercase    )   ,   atol=1E-3    ):
     return False
   return True

  __A	:		Any	  =      self.feature_extraction_class(**self.feat_extract_dict    )
  __A	:		List[Any]	  =      self.feat_extract_tester.prepare_inputs_for_common(numpify=__lowercase    )
  __A	:		Union[str, Any]	  =      feat_extract.model_input_names[0]

  __A	:		List[str]	  =      BatchFeature({input_name: speech_inputs}    )

  __A	:		Tuple	  =      self.feat_extract_tester.seq_length_diff
  __A	:		int	  =      self.feat_extract_tester.max_seq_length + pad_diff
  __A	:		List[str]	  =      self.feat_extract_tester.min_seq_length
  __A	:		List[str]	  =      self.feat_extract_tester.batch_size
  __A	:		Tuple	  =      self.feat_extract_tester.feature_size

  # test padding for List[int] + numpy
  __A	:		Any	  =      feat_extract.pad(__lowercase   ,   padding=__lowercase    )
  __A	:		Optional[Any]	  =      input_a[input_name]

  __A	:		Optional[int]	  =      feat_extract.pad(__lowercase   ,   padding='longest'    )
  __A	:		Dict	  =      input_a[input_name]

  __A	:		Any	  =      feat_extract.pad(__lowercase   ,   padding='max_length'   ,   max_length=len(speech_inputs[-1]    )    )
  __A	:		Optional[int]	  =      input_a[input_name]

  __A	:		Optional[Any]	  =      feat_extract.pad(__lowercase   ,   padding='longest'   ,   return_tensors='np'    )
  __A	:		Optional[Any]	  =      input_a[input_name]

  # max_length parameter has to be provided when setting `padding="max_length"`
  with self.assertRaises(__lowercase    ):
   feat_extract.pad(__lowercase   ,   padding='max_length'    )[input_name]

  __A	:		str	  =      feat_extract.pad(
      __lowercase   ,   padding='max_length'   ,   max_length=__lowercase   ,   return_tensors='np'    )
  __A	:		Any	  =      input_a[input_name]

  self.assertFalse(_inputs_have_equal_length(__lowercase    )    )
  self.assertTrue(_inputs_have_equal_length(__lowercase    )    )
  self.assertTrue(_inputs_have_equal_length(__lowercase    )    )
  self.assertTrue(_inputs_are_equal(__lowercase   ,   __lowercase    )    )
  self.assertTrue(len(input_a[0]    ) == pad_min_length    )
  self.assertTrue(len(input_a[1]    ) == pad_min_length + pad_diff    )
  self.assertTrue(input_a.shape[:2] == (batch_size, len(input_a[0]    ))    )
  self.assertTrue(input_a.shape[:2] == (batch_size, pad_max_length)    )

  if feature_size > 1:
   self.assertTrue(input_a.shape[2] == input_a.shape[2] == feature_size    )

  # test padding for `pad_to_multiple_of` for List[int] + numpy
  __A	:		Dict	  =      feat_extract.pad(__lowercase   ,   pad_to_multiple_of=10    )
  __A	:		int	  =      input_a[input_name]

  __A	:		Optional[int]	  =      feat_extract.pad(__lowercase   ,   padding='longest'   ,   pad_to_multiple_of=10    )
  __A	:		List[Any]	  =      input_a[input_name]

  __A	:		int	  =      feat_extract.pad(
      __lowercase   ,   padding='max_length'   ,   pad_to_multiple_of=10   ,   max_length=__lowercase    )
  __A	:		Optional[int]	  =      input_a[input_name]

  __A	:		str	  =      feat_extract.pad(
      __lowercase   ,   padding='max_length'   ,   pad_to_multiple_of=10   ,   max_length=__lowercase   ,   return_tensors='np'   ,   )
  __A	:		str	  =      input_a[input_name]

  self.assertTrue(all(len(__lowercase    ) % 10 == 0 for x in input_a    )    )
  self.assertTrue(_inputs_are_equal(__lowercase   ,   __lowercase    )    )

  __A	:		List[str]	  =      pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
  self.assertTrue(all(len(__lowercase    ) == expected_mult_pad_length for x in input_a    )    )
  self.assertEqual(input_a.shape[:2]   ,   (batch_size, expected_mult_pad_length)    )

  if feature_size > 1:
   self.assertTrue(input_a.shape[2] == feature_size    )

  # Check padding value is correct
  __A	:		str	  =      (np.ones(self.feat_extract_tester.feature_size    ) * feat_extract.padding_value).sum()
  self.assertTrue(
      abs(np.asarray(input_a[0]    )[pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)    )
      < 1E-3    )
  self.assertTrue(
      abs(
          np.asarray(input_a[1]    )[pad_min_length + pad_diff :].sum()
          - padding_vector_sum * (pad_max_length - pad_min_length - pad_diff)    )
      < 1E-3    )
  self.assertTrue(
      abs(
          np.asarray(input_a[2]    )[pad_min_length + 2 * pad_diff :].sum()
          - padding_vector_sum * (pad_max_length - pad_min_length - 2 * pad_diff)    )
      < 1E-3    )
  self.assertTrue(
      abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)    ) < 1E-3    )
  self.assertTrue(
      abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (expected_mult_pad_length - pad_min_length)    )
      < 1E-3    )


 def 			snake_case__ (   self   ,   __lowercase=False    ):

  """simple docstring"""







  def _inputs_have_equal_length(__lowercase    ):
   __A	:		str	  =      len(input[0]    )
   for input_slice in input[1:]:
    if len(__lowercase    ) != length:
     return False
   return True

  def _inputs_are_equal(__lowercase   ,   __lowercase    ):
   if len(__lowercase    ) != len(__lowercase    ):
    return False

   for input_slice_a, input_slice_a in zip(__lowercase   ,   __lowercase    ):
    if not np.allclose(np.asarray(__lowercase    )   ,   np.asarray(__lowercase    )   ,   atol=1E-3    ):
     return False
   return True

  __A	:		Optional[Any]	  =      self.feature_extraction_class(**self.feat_extract_dict    )
  __A	:		Dict	  =      self.feat_extract_tester.prepare_inputs_for_common(numpify=__lowercase    )
  __A	:		Dict	  =      feat_extract.model_input_names[0]

  __A	:		Union[str, Any]	  =      BatchFeature({input_name: speech_inputs}    )

  # truncate to smallest
  __A	:		Tuple	  =      feat_extract.pad(
      __lowercase   ,   padding='max_length'   ,   max_length=len(speech_inputs[0]    )   ,   truncation=__lowercase    )
  __A	:		Union[str, Any]	  =      input_a[input_name]

  __A	:		int	  =      feat_extract.pad(__lowercase   ,   padding='max_length'   ,   max_length=len(speech_inputs[0]    )    )
  __A	:		List[Any]	  =      input_a[input_name]

  self.assertTrue(_inputs_have_equal_length(__lowercase    )    )
  self.assertFalse(_inputs_have_equal_length(__lowercase    )    )

  # truncate to smallest with np
  __A	:		Optional[int]	  =      feat_extract.pad(
      __lowercase   ,   padding='max_length'   ,   max_length=len(speech_inputs[0]    )   ,   return_tensors='np'   ,   truncation=__lowercase   ,   )
  __A	:		str	  =      input_a[input_name]

  __A	:		Optional[Any]	  =      feat_extract.pad(
      __lowercase   ,   padding='max_length'   ,   max_length=len(speech_inputs[0]    )   ,   return_tensors='np'    )
  __A	:		Tuple	  =      input_a[input_name]

  self.assertTrue(_inputs_have_equal_length(__lowercase    )    )
  self.assertTrue(input_a.shape[1] == len(speech_inputs[0]    )    )

  # since truncation forces padding to be smaller than longest input
  # function can't return `np.ndarray`, but has to return list
  self.assertFalse(_inputs_have_equal_length(__lowercase    )    )

  # truncate to middle
  __A	:		Optional[int]	  =      feat_extract.pad(
      __lowercase   ,   padding='max_length'   ,   max_length=len(speech_inputs[1]    )   ,   truncation=__lowercase   ,   return_tensors='np'   ,   )
  __A	:		Optional[Any]	  =      input_a[input_name]

  __A	:		Optional[int]	  =      feat_extract.pad(
      __lowercase   ,   padding='max_length'   ,   max_length=len(speech_inputs[1]    )   ,   truncation=__lowercase    )
  __A	:		str	  =      input_a[input_name]

  __A	:		Dict	  =      feat_extract.pad(
      __lowercase   ,   padding='max_length'   ,   max_length=len(speech_inputs[1]    )   ,   return_tensors='np'    )
  __A	:		Optional[Any]	  =      input_a[input_name]

  self.assertTrue(input_a.shape[1] == len(speech_inputs[1]    )    )
  self.assertTrue(_inputs_have_equal_length(__lowercase    )    )
  self.assertTrue(_inputs_have_equal_length(__lowercase    )    )
  self.assertTrue(_inputs_are_equal(__lowercase   ,   __lowercase    )    )

  # since truncation forces padding to be smaller than longest input
  # function can't return `np.ndarray`, but has to return list
  self.assertFalse(_inputs_have_equal_length(__lowercase    )    )
  self.assertTrue(len(input_a[-1]    ) == len(speech_inputs[-1]    )    )

  # padding has to be max_length when setting `truncation=True`
  with self.assertRaises(__lowercase    ):
   feat_extract.pad(__lowercase   ,   truncation=__lowercase    )[input_name]

  # padding has to be max_length when setting `truncation=True`
  with self.assertRaises(__lowercase    ):
   feat_extract.pad(__lowercase   ,   padding='longest'   ,   truncation=__lowercase    )[input_name]

  # padding has to be max_length when setting `truncation=True`
  with self.assertRaises(__lowercase    ):
   feat_extract.pad(__lowercase   ,   padding='longest'   ,   truncation=__lowercase    )[input_name]

  # max_length parameter has to be provided when setting `truncation=True` and padding="max_length"
  with self.assertRaises(__lowercase    ):
   feat_extract.pad(__lowercase   ,   padding='max_length'   ,   truncation=__lowercase    )[input_name]

  # test truncation for `pad_to_multiple_of` for List[int] + numpy
  __A	:		List[str]	  =      12
  __A	:		Optional[Any]	  =      feat_extract.pad(
      __lowercase   ,   padding='max_length'   ,   max_length=len(speech_inputs[0]    )   ,   pad_to_multiple_of=__lowercase   ,   truncation=__lowercase   ,   )
  __A	:		int	  =      input_a[input_name]

  __A	:		Dict	  =      feat_extract.pad(
      __lowercase   ,   padding='max_length'   ,   max_length=len(speech_inputs[0]    )   ,   pad_to_multiple_of=__lowercase   ,   )
  __A	:		List[Any]	  =      input_a[input_name]

  # retrieve expected_length as multiple of pad_to_multiple_of
  __A	:		int	  =      len(speech_inputs[0]    )
  if expected_length % pad_to_multiple_of != 0:
   __A	:		str	  =      ((len(speech_inputs[0]    ) // pad_to_multiple_of) + 1) * pad_to_multiple_of

  self.assertTrue(len(input_a[0]    ) == expected_length    )
  self.assertTrue(_inputs_have_equal_length(__lowercase    )    )
  self.assertFalse(_inputs_have_equal_length(__lowercase    )    )


 def 			snake_case__ (   self    ):

  """simple docstring"""







  self._check_padding(numpify=__lowercase    )


 def 			snake_case__ (   self    ):

  """simple docstring"""







  self._check_padding(numpify=__lowercase    )


 def 			snake_case__ (   self    ):

  """simple docstring"""







  self._check_truncation(numpify=__lowercase    )


 def 			snake_case__ (   self    ):

  """simple docstring"""







  self._check_truncation(numpify=__lowercase    )


 @require_torch
 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A	:		List[str]	  =      self.feature_extraction_class(**self.feat_extract_dict    )
  __A	:		List[str]	  =      self.feat_extract_tester.prepare_inputs_for_common()
  __A	:		Dict	  =      feat_extract.model_input_names[0]

  __A	:		Dict	  =      BatchFeature({input_name: speech_inputs}    )

  __A	:		Tuple	  =      feat_extract.pad(__lowercase   ,   padding='longest'   ,   return_tensors='np'    )[input_name]
  __A	:		List[str]	  =      feat_extract.pad(__lowercase   ,   padding='longest'   ,   return_tensors='pt'    )[input_name]

  self.assertTrue(abs(input_np.astype(np.floataa    ).sum() - input_pt.numpy().astype(np.floataa    ).sum()    ) < 1E-2    )


 @require_tf
 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A	:		List[str]	  =      self.feature_extraction_class(**self.feat_extract_dict    )
  __A	:		Dict	  =      self.feat_extract_tester.prepare_inputs_for_common()
  __A	:		Union[str, Any]	  =      feat_extract.model_input_names[0]

  __A	:		List[str]	  =      BatchFeature({input_name: speech_inputs}    )

  __A	:		List[str]	  =      feat_extract.pad(__lowercase   ,   padding='longest'   ,   return_tensors='np'    )[input_name]
  __A	:		Tuple	  =      feat_extract.pad(__lowercase   ,   padding='longest'   ,   return_tensors='tf'    )[input_name]

  self.assertTrue(abs(input_np.astype(np.floataa    ).sum() - input_tf.numpy().astype(np.floataa    ).sum()    ) < 1E-2    )


 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A	:		Dict	  =      self.feat_extract_dict
  __A	:		Optional[int]	  =      True
  __A	:		Optional[Any]	  =      self.feature_extraction_class(**__lowercase    )
  __A	:		Dict	  =      self.feat_extract_tester.prepare_inputs_for_common()
  __A	:		int	  =      [len(__lowercase    ) for x in speech_inputs]
  __A	:		Union[str, Any]	  =      feat_extract.model_input_names[0]

  __A	:		str	  =      BatchFeature({input_name: speech_inputs}    )

  __A	:		str	  =      feat_extract.pad(__lowercase   ,   padding='longest'   ,   return_tensors='np'    )
  self.assertIn('attention_mask'   ,   __lowercase    )
  self.assertListEqual(list(processed.attention_mask.shape    )   ,   list(processed[input_name].shape[:2]    )    )
  self.assertListEqual(processed.attention_mask.sum(-1    ).tolist()   ,   __lowercase    )







 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A	:		Optional[int]	  =      self.feat_extract_dict
  __A	:		Tuple	  =      True
  __A	:		Tuple	  =      self.feature_extraction_class(**__lowercase    )
  __A	:		int	  =      self.feat_extract_tester.prepare_inputs_for_common()
  __A	:		Tuple	  =      [len(__lowercase    ) for x in speech_inputs]
  __A	:		List[str]	  =      feat_extract.model_input_names[0]

  __A	:		Dict	  =      BatchFeature({input_name: speech_inputs}    )
  __A	:		Optional[Any]	  =      min(__lowercase    )

  __A	:		List[Any]	  =      feat_extract.pad(
      __lowercase   ,   padding='max_length'   ,   max_length=__lowercase   ,   truncation=__lowercase   ,   return_tensors='np'    )
  self.assertIn('attention_mask'   ,   __lowercase    )
  self.assertListEqual(
      list(processed_pad.attention_mask.shape    )   ,   [processed_pad[input_name].shape[0], max_length]    )
  self.assertListEqual(
      processed_pad.attention_mask[:, :max_length].sum(-1    ).tolist()   ,   [max_length for x in speech_inputs]    )










'''simple docstring'''
import unittest
from typing import Tuple

import torch

from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch

@require_torch
class     _lowerCamelCase       :




 '''simple docstring'''


 @property
 def 			snake_case__ (   self    ):

  """simple docstring"""







  return self.get_dummy_input()


 @property
 def 			snake_case__ (   self    ):

  """simple docstring"""







  if self.block_type == "down":
   return (4, 32, 16, 16)
  elif self.block_type == "mid":
   return (4, 32, 32, 32)
  elif self.block_type == "up":
   return (4, 32, 64, 64)

  raise ValueError(F"""'{self.block_type}' is not a supported block_type. Set it to 'up', 'mid', or 'down'."""    )


 def 			snake_case__ (   self   ,   __lowercase=True   ,   __lowercase=False   ,   __lowercase=False   ,   __lowercase=False   ,   ):

  """simple docstring"""







  __A	:		List[str]	  =      4
  __A	:		List[Any]	  =      32
  __A	:		Optional[Any]	  =      (32, 32)

  __A	:		List[str]	  =      torch.manual_seed(0    )
  __A	:		Union[str, Any]	  =      torch.device(__lowercase    )
  __A	:		Optional[int]	  =      (batch_size, num_channels) + sizes
  __A	:		str	  =      randn_tensor(__lowercase   ,   generator=__lowercase   ,   device=__lowercase    )
  __A	:		int	  =      {'hidden_states': hidden_states}

  if include_temb:
   __A	:		Optional[Any]	  =      128
   __A	:		Any	  =      randn_tensor((batch_size, temb_channels)   ,   generator=__lowercase   ,   device=__lowercase    )

  if include_res_hidden_states_tuple:
   __A	:		Any	  =      torch.manual_seed(1    )
   __A	:		List[Any]	  =      (randn_tensor(__lowercase   ,   generator=__lowercase   ,   device=__lowercase    ),)

  if include_encoder_hidden_states:
   __A	:		str	  =      floats_tensor((batch_size, 32, 32)    ).to(__lowercase    )

  if include_skip_sample:
   __A	:		List[Any]	  =      randn_tensor(((batch_size, 3) + sizes)   ,   generator=__lowercase   ,   device=__lowercase    )

  return dummy_input


 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A	:		Tuple	  =      {
      'in_channels': 32,
      'out_channels': 32,
      'temb_channels': 128,
  }
  if self.block_type == "up":
   __A	:		int	  =      32

  if self.block_type == "mid":
   init_dict.pop('out_channels'    )

  __A	:		Optional[int]	  =      self.dummy_input
  return init_dict, inputs_dict


 def 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







  __A  ,__A	:		List[Any]	  =      self.prepare_init_args_and_inputs_for_common()
  __A	:		int	  =      self.block_class(**__lowercase    )
  unet_block.to(__lowercase    )
  unet_block.eval()

  with torch.no_grad():
   __A	:		List[Any]	  =      unet_block(**__lowercase    )

  if isinstance(__lowercase   ,   __lowercase    ):
   __A	:		Tuple	  =      output[0]

  self.assertEqual(output.shape   ,   self.output_shape    )

  __A	:		Any	  =      output[0, -1, -3:, -3:]
  __A	:		Dict	  =      torch.tensor(__lowercase    ).to(__lowercase    )
  assert torch_all_close(output_slice.flatten()   ,   __lowercase   ,   atol=5E-3    )







 @unittest.skipIf(torch_device == 'mps'   ,   'Training is not supported in mps'    )
 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A  ,__A	:		str	  =      self.prepare_init_args_and_inputs_for_common()
  __A	:		Optional[Any]	  =      self.block_class(**__lowercase    )
  model.to(__lowercase    )
  model.train()
  __A	:		Any	  =      model(**__lowercase    )

  if isinstance(__lowercase   ,   __lowercase    ):
   __A	:		Any	  =      output[0]

  __A	:		int	  =      torch.device(__lowercase    )
  __A	:		Dict	  =      randn_tensor(output.shape   ,   device=__lowercase    )
  __A	:		Dict	  =      torch.nn.functional.mse_loss(__lowercase   ,   __lowercase    )
  loss.backward()






