
"""simple docstring"""




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__      :	List[Any]          =    logging.get_logger(__name__)







class   _lowercase						(       lowerCAmelCase_				):


     '''simple docstring'''





     def __init__(      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			**__UpperCamelCase  )->					Union[str, Any]:
          UpperCAmelCase__							: str 							=						feature_size
          UpperCAmelCase__							: List[str] 							=						sampling_rate
          UpperCAmelCase__							: List[str] 							=						padding_value

          UpperCAmelCase__							: Dict 							=						kwargs.pop("padding_side" ,			"right"  )
          UpperCAmelCase__							: Optional[int] 							=						kwargs.pop("return_attention_mask" ,			__UpperCamelCase  )

          super().__init__(**__UpperCamelCase  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase = True ,			__UpperCamelCase = None ,			__UpperCamelCase = False ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			)->					BatchFeature:
          # If we have a list of dicts, let's convert it in a dict of lists
          # We do this to allow using this method as a collate_fn function in PyTorch Dataloader
          if isinstance(__UpperCamelCase ,			(list, tuple)  ) and isinstance(processed_features[0] ,			(dict, BatchFeature)  ):
               UpperCAmelCase__							: 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()  )}"  )

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

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

          if return_tensors is None:
               if is_tf_tensor(__UpperCamelCase  ):
                    UpperCAmelCase__							: int 							=						"tf"
               elif is_torch_tensor(__UpperCamelCase  ):
                    UpperCAmelCase__							: Optional[Any] 							=						"pt"
               elif isinstance(__UpperCamelCase ,			(int, float, list, tuple, np.ndarray)  ):
                    UpperCAmelCase__							: Optional[int] 							=						"np"
               else:
                    raise ValueError(
                        F"type of {first_element} unknown: {type(__UpperCamelCase  )}. "
                        "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__							: List[Any] 							=						to_numpy(__UpperCamelCase  )
               else:
                    UpperCAmelCase__							: Tuple 							=						[to_numpy(__UpperCamelCase  ) for v in value]

        # Convert padding_strategy in PaddingStrategy
          UpperCAmelCase__							: str 							=						self._get_padding_strategies(padding=__UpperCamelCase ,			max_length=__UpperCamelCase  )

          UpperCAmelCase__							: Tuple 							=						processed_features[self.model_input_names[0]]

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

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

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

          UpperCAmelCase__							: List[str] 							=						{}
          for i in range(__UpperCamelCase  ):
               # padding
               UpperCAmelCase__							: int 							=						self._pad(
                   truncated_inputs[i] ,			max_length=__UpperCamelCase ,			padding_strategy=__UpperCamelCase ,			pad_to_multiple_of=__UpperCamelCase ,			return_attention_mask=__UpperCamelCase ,			)

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

          return BatchFeature(__UpperCamelCase ,			tensor_type=__UpperCamelCase  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase = None ,			__UpperCamelCase = PaddingStrategy.DO_NOT_PAD ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			)->					dict:
          UpperCAmelCase__							: str 							=						processed_features[self.model_input_names[0]]

          if padding_strategy == PaddingStrategy.LONGEST:
               UpperCAmelCase__							: Dict 							=						len(__UpperCamelCase  )

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

          UpperCAmelCase__							: List[str] 							=						padding_strategy != PaddingStrategy.DO_NOT_PAD and len(__UpperCamelCase  ) < max_length

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

          if needs_to_be_padded:
               UpperCAmelCase__							: List[Any] 							=						max_length - len(__UpperCamelCase  )
               if self.padding_side == "right":
                    if return_attention_mask:
                         UpperCAmelCase__							: Optional[Any] 							=						np.pad(
                             processed_features["attention_mask"] ,			(0, difference)  )
                    UpperCAmelCase__							: Any 							=						((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
                    UpperCAmelCase__							: List[Any] 							=						np.pad(
                        __UpperCamelCase ,			__UpperCamelCase ,			"constant" ,			constant_values=self.padding_value  )
               elif self.padding_side == "left":
                    if return_attention_mask:
                         UpperCAmelCase__							: List[str] 							=						np.pad(
                             processed_features["attention_mask"] ,			(difference, 0)  )
                    UpperCAmelCase__							: Dict 							=						((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
                    UpperCAmelCase__							: List[Any] 							=						np.pad(
                        __UpperCamelCase ,			__UpperCamelCase ,			"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 ,			)->					Optional[Any]:
          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__							: Optional[int] 							=						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__							: Tuple 							=						((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

          UpperCAmelCase__							: Optional[Any] 							=						len(__UpperCamelCase  ) > max_length

          if needs_to_be_truncated:
               UpperCAmelCase__							: Dict 							=						processed_features[self.model_input_names[0]][:max_length]
               if "attention_mask" in processed_features:
                    UpperCAmelCase__							: int 							=						processed_features["attention_mask"][:max_length]

          return processed_features



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase=False ,			__UpperCamelCase=None  )->					Union[str, Any]:

          # Get padding strategy
          if padding is not False:
               if padding is True:
                    UpperCAmelCase__							: Dict 							=						PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
               elif not isinstance(__UpperCamelCase ,			__UpperCamelCase  ):
                    UpperCAmelCase__							: Union[str, Any] 							=						PaddingStrategy(__UpperCamelCase  )
               elif isinstance(__UpperCamelCase ,			__UpperCamelCase  ):
                    UpperCAmelCase__							: Tuple 							=						padding
          else:
               UpperCAmelCase__							: Union[str, Any] 							=						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 numpy as np

from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
    import onnxruntime as ort







class   _lowercase						(       lowerCAmelCase_	,   unittest.TestCase				):


     '''simple docstring'''





     pass







@nightly
@require_onnxruntime
@require_torch_gpu
class   _lowercase						(       unittest.TestCase				):


     '''simple docstring'''





     @property
     def  lowerCAmelCase__       (      self  )->					int:
          return (
              "CUDAExecutionProvider",
              {
                  "gpu_mem_limit": "15000000000",  # 15GB
                  "arena_extend_strategy": "kSameAsRequested",
              },
          )



     @property
     def  lowerCAmelCase__       (      self  )->					Any:
          UpperCAmelCase__							: Tuple 							=						ort.SessionOptions()
          UpperCAmelCase__							: List[str] 							=						False
          return options



     def  lowerCAmelCase__       (      self  )->					List[str]:
          UpperCAmelCase__							: Union[str, Any] 							=						load_image(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
              "/in_paint/overture-creations-5sI6fQgYIuo.png"  )
          UpperCAmelCase__							: int 							=						load_image(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
              "/in_paint/overture-creations-5sI6fQgYIuo_mask.png"  )
          UpperCAmelCase__							: str 							=						OnnxStableDiffusionInpaintPipeline.from_pretrained(
              "runwayml/stable-diffusion-inpainting" ,			revision="onnx" ,			safety_checker=__UpperCamelCase ,			feature_extractor=__UpperCamelCase ,			provider=self.gpu_provider ,			sess_options=self.gpu_options ,			)
          pipe.set_progress_bar_config(disable=__UpperCamelCase  )

          UpperCAmelCase__							: int 							=						"A red cat sitting on a park bench"

          UpperCAmelCase__							: Tuple 							=						np.random.RandomState(0  )
          UpperCAmelCase__							: Any 							=						pipe(
              prompt=__UpperCamelCase ,			image=__UpperCamelCase ,			mask_image=__UpperCamelCase ,			guidance_scale=7.5 ,			num_inference_steps=10 ,			generator=__UpperCamelCase ,			output_type="np" ,			)
          UpperCAmelCase__							: Tuple 							=						output.images
          UpperCAmelCase__							: Dict 							=						images[0, 2_55:2_58, 2_55:2_58, -1]

          assert images.shape == (1, 5_12, 5_12, 3)
          UpperCAmelCase__							: Union[str, Any] 							=						np.array([0.2514, 0.3007, 0.3517, 0.1790, 0.2382, 0.3167, 0.1944, 0.2273, 0.2464]  )

          assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1E-3



     def  lowerCAmelCase__       (      self  )->					Tuple:
          UpperCAmelCase__							: Tuple 							=						load_image(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
              "/in_paint/overture-creations-5sI6fQgYIuo.png"  )
          UpperCAmelCase__							: Tuple 							=						load_image(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
              "/in_paint/overture-creations-5sI6fQgYIuo_mask.png"  )
          UpperCAmelCase__							: Optional[Any] 							=						LMSDiscreteScheduler.from_pretrained(
              "runwayml/stable-diffusion-inpainting" ,			subfolder="scheduler" ,			revision="onnx"  )
          UpperCAmelCase__							: Optional[Any] 							=						OnnxStableDiffusionInpaintPipeline.from_pretrained(
              "runwayml/stable-diffusion-inpainting" ,			revision="onnx" ,			scheduler=__UpperCamelCase ,			safety_checker=__UpperCamelCase ,			feature_extractor=__UpperCamelCase ,			provider=self.gpu_provider ,			sess_options=self.gpu_options ,			)
          pipe.set_progress_bar_config(disable=__UpperCamelCase  )

          UpperCAmelCase__							: int 							=						"A red cat sitting on a park bench"

          UpperCAmelCase__							: List[str] 							=						np.random.RandomState(0  )
          UpperCAmelCase__							: str 							=						pipe(
              prompt=__UpperCamelCase ,			image=__UpperCamelCase ,			mask_image=__UpperCamelCase ,			guidance_scale=7.5 ,			num_inference_steps=20 ,			generator=__UpperCamelCase ,			output_type="np" ,			)
          UpperCAmelCase__							: List[str] 							=						output.images
          UpperCAmelCase__							: List[Any] 							=						images[0, 2_55:2_58, 2_55:2_58, -1]

          assert images.shape == (1, 5_12, 5_12, 3)
          UpperCAmelCase__							: int 							=						np.array([0.0086, 0.0077, 0.0083, 0.0093, 0.0107, 0.0139, 0.0094, 0.0097, 0.0125]  )

          assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1E-3



