



from typing import Dict, List, Optional, Union

import numpy as np

from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy


_lowerCAmelCase										=     logging.get_logger(__name__)






class 	lowerCAmelCase_(    lowercase__					):
  '''simple docstring'''




  def __init__(   self  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,**__UpperCAmelCase				)   ->		Any:
      lowerCAmelCase__			:  Union[str, Any]											= feature_size
      lowerCAmelCase__			:  Union[str, Any]											= sampling_rate
      lowerCAmelCase__			:  Optional[Any]											= padding_value

      lowerCAmelCase__			:  Tuple											= kwargs.pop("""padding_side"""  ,"""right"""				)
      lowerCAmelCase__			:  List[Any]											= kwargs.pop("""return_attention_mask"""  ,_a				)

      super().__init__(**_a				)




  def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase  ,__UpperCAmelCase = True  ,__UpperCAmelCase = None  ,__UpperCAmelCase = False  ,__UpperCAmelCase = None  ,__UpperCAmelCase = None  ,__UpperCAmelCase = None  ,)   ->		Any:






      if isinstance(_a  ,(list, tuple)				) and isinstance(processed_features[0]  ,(dict, BatchFeature)				):
          lowerCAmelCase__			:  List[Any]											= {
              key: [example[key] for example in processed_features] for key in processed_features[0].keys()
          }

      # The model's main input name, usually `input_values`, has be passed for padding
      if self.model_input_names[0] not in processed_features:
          raise ValueError(
              """You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`"""
              F""" to this method that includes {self.model_input_names[0]}, but you provided"""
              F""" {list(processed_features.keys()				)}"""				)

      lowerCAmelCase__			:  Any											= processed_features[self.model_input_names[0]]
      lowerCAmelCase__			:  List[str]											= (
          return_attention_mask if return_attention_mask is not None else self.return_attention_mask
      )

      if len(_a				) == 0:
          if return_attention_mask:
              lowerCAmelCase__			:  Tuple											= []
          return processed_features

      # If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
      # and rebuild them afterwards if no return_tensors is specified
      # Note that we lose the specific device the tensor may be on for PyTorch

      lowerCAmelCase__			:  List[Any]											= required_input[0]
      if isinstance(_a  ,(list, tuple)				):
          # first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
          lowerCAmelCase__			:  Optional[int]											= 0
          while len(required_input[index]				) == 0:
              index += 1
          if index < len(_a				):
              lowerCAmelCase__			:  str											= required_input[index][0]

      if return_tensors is None:
          if is_tf_tensor(_a				):
              lowerCAmelCase__			:  Any											= 'tf'
          elif is_torch_tensor(_a				):
              lowerCAmelCase__			:  int											= 'pt'
          elif isinstance(_a  ,(int, float, list, tuple, np.ndarray)				):
              lowerCAmelCase__			:  Tuple											= 'np'
          else:
              raise ValueError(
                  F"""type of {first_element} unknown: {type(_a				)}. """
                  """Should be one of a python, numpy, pytorch or tensorflow object."""				)

      for key, value in processed_features.items():
          if isinstance(value[0]  ,(int, float)				):
              lowerCAmelCase__			:  Optional[Any]											= to_numpy(_a				)
          else:
              lowerCAmelCase__			:  int											= [to_numpy(_a				) for v in value]

          # Convert padding_strategy in PaddingStrategy
      lowerCAmelCase__			:  Dict											= self._get_padding_strategies(padding=_a  ,max_length=_a				)

      lowerCAmelCase__			:  Optional[Any]											= processed_features[self.model_input_names[0]]

      lowerCAmelCase__			:  Optional[Any]											= len(_a				)
      if not all(len(_a				) == batch_size for v in processed_features.values()				):
          raise ValueError("""Some items in the output dictionary have a different batch size than others."""				)

      lowerCAmelCase__			:  str											= []
      for i in range(_a				):
          lowerCAmelCase__			:  Any											= {k: v[i] for k, v in processed_features.items()}
          # truncation
          lowerCAmelCase__			:  List[Any]											= self._truncate(
              _a  ,max_length=_a  ,pad_to_multiple_of=_a  ,truncation=_a  ,)
          truncated_inputs.append(_a				)

      if padding_strategy == PaddingStrategy.LONGEST:
          # make sure that `max_length` cannot be longer than the longest truncated length
          lowerCAmelCase__			:  Dict											= max(len(input_slice[self.model_input_names[0]]				) for input_slice in truncated_inputs				)
          lowerCAmelCase__			:  Union[str, Any]											= PaddingStrategy.MAX_LENGTH

      lowerCAmelCase__			:  List[str]											= {}
      for i in range(_a				):
          # padding
          lowerCAmelCase__			:  Optional[Any]											= self._pad(
              truncated_inputs[i]  ,max_length=_a  ,padding_strategy=_a  ,pad_to_multiple_of=_a  ,return_attention_mask=_a  ,)

          for key, value in outputs.items():
              if key not in batch_outputs:
                  lowerCAmelCase__			:  List[str]											= []
              if value.dtype is np.dtype(np.floataa				):
                  lowerCAmelCase__			:  Any											= value.astype(np.floataa				)
              batch_outputs[key].append(_a				)

      return BatchFeature(_a  ,tensor_type=_a				)




  def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase  ,__UpperCAmelCase = None  ,__UpperCAmelCase = PaddingStrategy.DO_NOT_PAD  ,__UpperCAmelCase = None  ,__UpperCAmelCase = None  ,)   ->		str:
      lowerCAmelCase__			:  Optional[int]											= processed_features[self.model_input_names[0]]

      if padding_strategy == PaddingStrategy.LONGEST:
          lowerCAmelCase__			:  int											= len(_a				)

      if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
          lowerCAmelCase__			:  Any											= ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

      lowerCAmelCase__			:  str											= padding_strategy != PaddingStrategy.DO_NOT_PAD and len(_a				) < max_length

      if return_attention_mask and "attention_mask" not in processed_features:
          lowerCAmelCase__			:  List[str]											= np.ones(len(_a				)  ,dtype=np.intaa				)

      if needs_to_be_padded:
          lowerCAmelCase__			:  Any											= max_length - len(_a				)
          if self.padding_side == "right":
              if return_attention_mask:
                  lowerCAmelCase__			:  Optional[int]											= np.pad(
                      processed_features["""attention_mask"""]  ,(0, difference)				)
              lowerCAmelCase__			:  int											= ((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
              lowerCAmelCase__			:  Dict											= np.pad(
                  _a  ,_a  ,"""constant"""  ,constant_values=self.padding_value				)
          elif self.padding_side == "left":
              if return_attention_mask:
                  lowerCAmelCase__			:  Any											= np.pad(
                      processed_features["""attention_mask"""]  ,(difference, 0)				)
              lowerCAmelCase__			:  str											= ((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
              lowerCAmelCase__			:  Union[str, Any]											= np.pad(
                  _a  ,_a  ,"""constant"""  ,constant_values=self.padding_value				)
          else:
              raise ValueError("""Invalid padding strategy:""" + str(self.padding_side				)				)

      return processed_features




  def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase  ,__UpperCAmelCase = None  ,__UpperCAmelCase = None  ,__UpperCAmelCase = None  ,)   ->		Optional[int]:






      if not truncation:
          return processed_features
      elif truncation and max_length is None:
          raise ValueError("""When setting ``truncation=True``, make sure that ``max_length`` is defined."""				)

      lowerCAmelCase__			:  str											= processed_features[self.model_input_names[0]]

      # find `max_length` that fits `pad_to_multiple_of`
      if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
          lowerCAmelCase__			:  str											= ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

      lowerCAmelCase__			:  Dict											= len(_a				) > max_length

      if needs_to_be_truncated:
          lowerCAmelCase__			:  str											= processed_features[self.model_input_names[0]][:max_length]
          if "attention_mask" in processed_features:
              lowerCAmelCase__			:  str											= processed_features['attention_mask'][:max_length]

      return processed_features






  def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase=False  ,__UpperCAmelCase=None				)   ->		int:






      if padding is not False:
          if padding is True:
              lowerCAmelCase__			:  int											= PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
          elif not isinstance(_a  ,_a				):
              lowerCAmelCase__			:  Tuple											= PaddingStrategy(_a				)
          elif isinstance(_a  ,_a				):
              lowerCAmelCase__			:  int											= padding
      else:
          lowerCAmelCase__			:  int											= PaddingStrategy.DO_NOT_PAD

      # Set max length if needed
      if max_length is None:
          if padding_strategy == PaddingStrategy.MAX_LENGTH:
              raise ValueError(
                  F"""When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined"""				)

          # Test if we have a padding value
      if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
          raise ValueError(
              """Asking to pad but the feature_extractor does not have a padding value. Please select a value to use"""
              """ as `padding_value`. For example: `feature_extractor.padding_value = 0.0`."""				)

      return padding_strategy







'''simple docstring'''





import unittest

import torch

from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()






class 	lowerCAmelCase_(    SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			unittest.TestCase					):
  '''simple docstring'''




  __lowercase							:				Optional[Any]           =				VQModel
  __lowercase							:				Tuple           =				'''sample'''




  @property
  def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase=(32, 32)				)   ->		List[Any]:
      lowerCAmelCase__			:  Dict											= 4
      lowerCAmelCase__			:  List[str]											= 3

      lowerCAmelCase__			:  Dict											= floats_tensor((batch_size, num_channels) + sizes				).to(__UpperCAmelCase				)

      return {"sample": image}




  @property
  def 							UpperCAmelCase_     (   self				)   ->		List[Any]:
      return (3, 32, 32)




  @property
  def 							UpperCAmelCase_     (   self				)   ->		Optional[Any]:
      return (3, 32, 32)




  def 							UpperCAmelCase_     (   self				)   ->		List[Any]:
      lowerCAmelCase__			:  Dict											= {
          """block_out_channels""": [32, 64],
          """in_channels""": 3,
          """out_channels""": 3,
          """down_block_types""": ["""DownEncoderBlock2D""", """DownEncoderBlock2D"""],
          """up_block_types""": ["""UpDecoderBlock2D""", """UpDecoderBlock2D"""],
          """latent_channels""": 3,
      }
      lowerCAmelCase__			:  Any											= self.dummy_input
      return init_dict, inputs_dict




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




  def 							UpperCAmelCase_     (   self				)   ->		Optional[int]:
      pass




  def 							UpperCAmelCase_     (   self				)   ->		Tuple:
      lowerCAmelCase__				,       lowerCAmelCase__			:  Union[str, Any]											= VQModel.from_pretrained("""fusing/vqgan-dummy"""  ,output_loading_info=__UpperCAmelCase				)
      self.assertIsNotNone(__UpperCAmelCase				)
      self.assertEqual(len(loading_info["""missing_keys"""]				)  ,0				)

      model.to(__UpperCAmelCase				)
      lowerCAmelCase__			:  List[Any]											= model(**self.dummy_input				)

      assert image is not None, "Make sure output is not None"






  def 							UpperCAmelCase_     (   self				)   ->		List[str]:
      lowerCAmelCase__			:  Tuple											= VQModel.from_pretrained("""fusing/vqgan-dummy"""				)
      model.to(__UpperCAmelCase				).eval()

      torch.manual_seed(0				)
      if torch.cuda.is_available():
          torch.cuda.manual_seed_all(0				)

      lowerCAmelCase__			:  List[str]											= torch.randn(1  ,model.config.in_channels  ,model.config.sample_size  ,model.config.sample_size				)
      lowerCAmelCase__			:  Optional[Any]											= image.to(__UpperCAmelCase				)
      with torch.no_grad():
          lowerCAmelCase__			:  Optional[Any]											= model(__UpperCAmelCase				).sample

      lowerCAmelCase__			:  Tuple											= output[0, -1, -3:, -3:].flatten().cpu()
      # fmt: off
      lowerCAmelCase__			:  Tuple											= torch.tensor([-0.0_1_5_3, -0.4_0_4_4, -0.1_8_8_0, -0.5_1_6_1, -0.2_4_1_8, -0.4_0_7_2, -0.1_6_1_2, -0.0_6_3_3, -0.0_1_4_3]				)
      # fmt: on
      self.assertTrue(torch.allclose(__UpperCAmelCase  ,__UpperCAmelCase  ,atol=1E-3				)				)


