
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


_A		     =    logging.get_logger(__name__)

class 						lowercase_				(				A__ ):

 def __init__(					self   ,		__UpperCamelCase   ,		__UpperCamelCase   ,		__UpperCamelCase   ,		**__UpperCamelCase   ):




        """simple docstring"""


        UpperCamelCase_											=				feature_size
        UpperCamelCase_											=				sampling_rate
        UpperCamelCase_											=				padding_value

        UpperCamelCase_											=				kwargs.pop("""padding_side"""   ,		"""right"""   )
        UpperCamelCase_											=				kwargs.pop("""return_attention_mask"""   ,		__snake_case   )

        super().__init__(**__snake_case   )

 def lowerCamelCase_ (					self   ,		__UpperCamelCase   ,		__UpperCamelCase = True   ,		__UpperCamelCase = None   ,		__UpperCamelCase = False   ,		__UpperCamelCase = None   ,		__UpperCamelCase = None   ,		__UpperCamelCase = None   ,		):




        """simple docstring"""


        if isinstance(__snake_case   ,		(list, tuple)   ) and isinstance(processed_features[0]   ,		(dict, BatchFeature)   ):
               UpperCamelCase_											=				{
                   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()   )}'''   )

        UpperCamelCase_											=				processed_features[self.model_input_names[0]]
        UpperCamelCase_											=				(
            return_attention_mask if return_attention_mask is not None else self.return_attention_mask
        )

        if len(__snake_case   ) == 0:
               if return_attention_mask:
                      UpperCamelCase_											=				[]
               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

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

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

        for key, value in processed_features.items():
               if isinstance(value[0]   ,		(int, float)   ):
                      UpperCamelCase_											=				to_numpy(__snake_case   )
               else:
                      UpperCamelCase_											=				[to_numpy(__snake_case   ) for v in value]

        # Convert padding_strategy in PaddingStrategy
        UpperCamelCase_											=				self._get_padding_strategies(padding=__snake_case   ,		max_length=__snake_case   )

        UpperCamelCase_											=				processed_features[self.model_input_names[0]]

        UpperCamelCase_											=				len(__snake_case   )
        if not all(len(__snake_case   ) == batch_size for v in processed_features.values()   ):
               raise ValueError("""Some items in the output dictionary have a different batch size than others."""   )

        UpperCamelCase_											=				[]
        for i in range(__snake_case   ):
               UpperCamelCase_											=				{k: v[i] for k, v in processed_features.items()}
               # truncation
               UpperCamelCase_											=				self._truncate(
                   __snake_case   ,		max_length=__snake_case   ,		pad_to_multiple_of=__snake_case   ,		truncation=__snake_case   ,		)
               truncated_inputs.append(__snake_case   )

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

        UpperCamelCase_											=				{}
        for i in range(__snake_case   ):
               # padding
               UpperCamelCase_											=				self._pad(
                   truncated_inputs[i]   ,		max_length=__snake_case   ,		padding_strategy=__snake_case   ,		pad_to_multiple_of=__snake_case   ,		return_attention_mask=__snake_case   ,		)

               for key, value in outputs.items():
                      if key not in batch_outputs:
                             UpperCamelCase_											=				[]
                      if value.dtype is np.dtype(np.floataa   ):
                             UpperCamelCase_											=				value.astype(np.floataa   )
                      batch_outputs[key].append(__snake_case   )

        return BatchFeature(__snake_case   ,		tensor_type=__snake_case   )

 def lowerCamelCase_ (					self   ,		__UpperCamelCase   ,		__UpperCamelCase = None   ,		__UpperCamelCase = PaddingStrategy.DO_NOT_PAD   ,		__UpperCamelCase = None   ,		__UpperCamelCase = None   ,		):




        """simple docstring"""


        UpperCamelCase_											=				processed_features[self.model_input_names[0]]

        if padding_strategy == PaddingStrategy.LONGEST:
               UpperCamelCase_											=				len(__snake_case   )

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

        UpperCamelCase_											=				padding_strategy != PaddingStrategy.DO_NOT_PAD and len(__snake_case   ) < max_length

        if return_attention_mask and "attention_mask" not in processed_features:
               UpperCamelCase_											=				np.ones(len(__snake_case   )   ,		dtype=np.intaa   )

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

        return processed_features

 def lowerCamelCase_ (					self   ,		__UpperCamelCase   ,		__UpperCamelCase = None   ,		__UpperCamelCase = None   ,		__UpperCamelCase = None   ,		):




        """simple docstring"""


        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."""   )

        UpperCamelCase_											=				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):
               UpperCamelCase_											=				((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

        UpperCamelCase_											=				len(__snake_case   ) > max_length

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

        return processed_features

 def lowerCamelCase_ (					self   ,		__UpperCamelCase=False   ,		__UpperCamelCase=None   ):




        """simple docstring"""


        if padding is not False:
               if padding is True:
                      UpperCamelCase_											=				PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
               elif not isinstance(__snake_case   ,		__snake_case   ):
                      UpperCamelCase_											=				PaddingStrategy(__snake_case   )
               elif isinstance(__snake_case   ,		__snake_case   ):
                      UpperCamelCase_											=				padding
        else:
               UpperCamelCase_											=				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'''






from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput

class 				SCREAMING_SNAKE_CASE(							A__			):







	"""simple docstring"""






	lowerCamelCase__			 =       42
	lowerCamelCase__			 =       42







	def __init__(						self			:     Union[str, Any]		,			__snake_case			:     UNetaDModel		,			__snake_case			:     ScoreSdeVeScheduler		)			->		int:
								super().__init__()
								self.register_modules(unet=__snake_case		,			scheduler=__snake_case		)







	@torch.no_grad()
	def __call__(						self			:     Optional[int]		,			__snake_case			:     int = 1		,			__snake_case			:     int = 2000		,			__snake_case			:     Optional[Union[torch.Generator, List[torch.Generator]]] = None		,			__snake_case			:     Optional[str] = "pil"		,			__snake_case			:     bool = True		,			**__snake_case			:     Optional[int]		,			)			->		Union[ImagePipelineOutput, Tuple]:
								UpperCAmelCase :       str 							=		self.unet.config.sample_size
								UpperCAmelCase :       Union[str, Any] 							=		(batch_size, 3, img_size, img_size)

								UpperCAmelCase :       int 							=		self.unet

								UpperCAmelCase :       Any 							=		randn_tensor(__snake_case		,			generator=__snake_case		) * self.scheduler.init_noise_sigma
								UpperCAmelCase :       List[Any] 							=		sample.to(self.device		)

								self.scheduler.set_timesteps(__snake_case		)
								self.scheduler.set_sigmas(__snake_case		)

								for i, t in enumerate(self.progress_bar(self.scheduler.timesteps		)		):
															UpperCAmelCase :       Any 							=		self.scheduler.sigmas[i] * torch.ones(shape[0]		,			device=self.device		)

															# correction step
															for _ in range(self.scheduler.config.correct_steps		):
																						UpperCAmelCase :       Union[str, Any] 							=		self.unet(__snake_case		,			__snake_case		).sample
																						UpperCAmelCase :       Optional[Any] 							=		self.scheduler.step_correct(__snake_case		,			__snake_case		,			generator=__snake_case		).prev_sample

															# prediction step
															UpperCAmelCase :       Optional[Any] 							=		model(__snake_case		,			__snake_case		).sample
															UpperCAmelCase :       List[str] 							=		self.scheduler.step_pred(__snake_case		,			__snake_case		,			__snake_case		,			generator=__snake_case		)

															UpperCAmelCase							,		UpperCAmelCase :       Optional[Any] 							=		output.prev_sample, output.prev_sample_mean

								UpperCAmelCase :       int 							=		sample_mean.clamp(0		,			1		)
								UpperCAmelCase :       Union[str, Any] 							=		sample.cpu().permute(0		,			2		,			3		,			1		).numpy()
								if output_type == "pil":
															UpperCAmelCase :       Optional[Any] 							=		self.numpy_to_pil(__snake_case		)

								if not return_dict:
															return (sample,)

								return ImagePipelineOutput(images=__snake_case		)





