




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	):

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

        lowerCAmelCase__				:  Dict		       =     kwargs.pop('''padding_side'''      ,				'''right'''		)
        lowerCAmelCase__				:  List[str]		       =     kwargs.pop('''return_attention_mask'''      ,				__UpperCAmelCase		)

        super().__init__(**__UpperCAmelCase		)

      def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase = True      ,				__UpperCAmelCase = None      ,				__UpperCAmelCase = False      ,				__UpperCAmelCase = None      ,				__UpperCAmelCase = None      ,				__UpperCAmelCase = None      ,				):
        # 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)		):
          lowerCAmelCase__				:  str		       =     {
              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__				:  Dict		       =     processed_features[self.model_input_names[0]]
        lowerCAmelCase__				:  Optional[int]		       =     (
            return_attention_mask if return_attention_mask is not None else self.return_attention_mask
        )

        if len(__UpperCAmelCase		) == 0:
          if return_attention_mask:
            lowerCAmelCase__				:  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

        lowerCAmelCase__				:  Optional[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.
          lowerCAmelCase__				:  List[Any]		       =     0
          while len(required_input[index]		) == 0:
            index += 1
          if index < len(__UpperCAmelCase		):
            lowerCAmelCase__				:  List[str]		       =     required_input[index][0]

        if return_tensors is None:
          if is_tf_tensor(__UpperCAmelCase		):
            lowerCAmelCase__				:  int		       =     '''tf'''
          elif is_torch_tensor(__UpperCAmelCase		):
            lowerCAmelCase__				:  Any		       =     '''pt'''
          elif isinstance(__UpperCAmelCase      ,				(int, float, list, tuple, np.ndarray)		):
            lowerCAmelCase__				:  List[str]		       =     '''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)		):
            lowerCAmelCase__				:  Dict		       =     to_numpy(__UpperCAmelCase		)
          else:
            lowerCAmelCase__				:  int		       =     [to_numpy(__UpperCAmelCase		) for v in value]

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

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

        lowerCAmelCase__				:  List[Any]		       =     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.'''		)

        lowerCAmelCase__				:  int		       =     []
        for i in range(__UpperCAmelCase		):
          lowerCAmelCase__				:  Any		       =     {k: v[i] for k, v in processed_features.items()}
          # truncation
          lowerCAmelCase__				:  int		       =     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
          lowerCAmelCase__				:  Union[str, Any]		       =     max(len(input_slice[self.model_input_names[0]]		) for input_slice in truncated_inputs		)
          lowerCAmelCase__				:  Any		       =     PaddingStrategy.MAX_LENGTH

        lowerCAmelCase__				:  Any		       =     {}
        for i in range(__UpperCAmelCase		):
          # padding
          lowerCAmelCase__				:  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:
              lowerCAmelCase__				:  Union[str, Any]		       =     []
            if value.dtype is np.dtype(np.floataa		):
              lowerCAmelCase__				:  List[str]		       =     value.astype(np.floataa		)
            batch_outputs[key].append(__UpperCAmelCase		)

        return BatchFeature(__UpperCAmelCase      ,				tensor_type=__UpperCAmelCase		)

      def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase = None      ,				__UpperCAmelCase = PaddingStrategy.DO_NOT_PAD      ,				__UpperCAmelCase = None      ,				__UpperCAmelCase = None      ,				):
        lowerCAmelCase__				:  Dict		       =     processed_features[self.model_input_names[0]]

        if padding_strategy == PaddingStrategy.LONGEST:
          lowerCAmelCase__				:  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):
          lowerCAmelCase__				:  Optional[Any]		       =     ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

        lowerCAmelCase__				:  Tuple		       =     padding_strategy != PaddingStrategy.DO_NOT_PAD and len(__UpperCAmelCase		) < max_length

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

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

        return processed_features

      def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase = None      ,				__UpperCAmelCase = None      ,				__UpperCAmelCase = None      ,				):
        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__				:  Union[str, Any]		       =     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__				:  List[str]		       =     ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

        lowerCAmelCase__				:  List[Any]		       =     len(__UpperCAmelCase		) > max_length

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

        return processed_features







      def 	__magic_name__(   self      ,				__UpperCAmelCase=False      ,				__UpperCAmelCase=None		):

        # Get padding strategy
        if padding is not False:
          if padding is True:
            lowerCAmelCase__				:  Union[str, Any]		       =     PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
          elif not isinstance(__UpperCAmelCase      ,				__UpperCAmelCase		):
            lowerCAmelCase__				:  Union[str, Any]		       =     PaddingStrategy(__UpperCAmelCase		)
          elif isinstance(__UpperCAmelCase      ,				__UpperCAmelCase		):
            lowerCAmelCase__				:  Union[str, Any]		       =     padding
        else:
          lowerCAmelCase__				:  List[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







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  _lowerCAmelCase     (  _lowercase    ,			unittest.TestCase	):
      # FIXME: add fast tests
      pass






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

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

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

      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  Tuple		       =     load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
            '''/in_paint/overture-creations-5sI6fQgYIuo.png'''		)
        lowerCAmelCase__				:  Optional[int]		       =     load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
            '''/in_paint/overture-creations-5sI6fQgYIuo_mask.png'''		)
        lowerCAmelCase__				:  List[Any]		       =     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		)

        lowerCAmelCase__				:  Optional[int]		       =     '''A red cat sitting on a park bench'''

        lowerCAmelCase__				:  List[Any]		       =     np.random.RandomState(0		)
        lowerCAmelCase__				:  int		       =     pipe(
            prompt=__UpperCAmelCase      ,				image=__UpperCAmelCase      ,				mask_image=__UpperCAmelCase      ,				guidance_scale=7.5      ,				num_inference_steps=10      ,				generator=__UpperCAmelCase      ,				output_type='''np'''      ,				)
        lowerCAmelCase__				:  Any		       =     output.images
        lowerCAmelCase__				:  Any		       =     images[0, 255:258, 255:258, -1]

        assert images.shape == (1, 512, 512, 3)
        lowerCAmelCase__				:  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 	__magic_name__(   self		):
        lowerCAmelCase__				:  List[str]		       =     load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
            '''/in_paint/overture-creations-5sI6fQgYIuo.png'''		)
        lowerCAmelCase__				:  str		       =     load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
            '''/in_paint/overture-creations-5sI6fQgYIuo_mask.png'''		)
        lowerCAmelCase__				:  Union[str, Any]		       =     LMSDiscreteScheduler.from_pretrained(
            '''runwayml/stable-diffusion-inpainting'''      ,				subfolder='''scheduler'''      ,				revision='''onnx'''		)
        lowerCAmelCase__				:  Union[str, 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		)

        lowerCAmelCase__				:  str		       =     '''A red cat sitting on a park bench'''

        lowerCAmelCase__				:  Union[str, Any]		       =     np.random.RandomState(0		)
        lowerCAmelCase__				:  Optional[Any]		       =     pipe(
            prompt=__UpperCAmelCase      ,				image=__UpperCAmelCase      ,				mask_image=__UpperCAmelCase      ,				guidance_scale=7.5      ,				num_inference_steps=20      ,				generator=__UpperCAmelCase      ,				output_type='''np'''      ,				)
        lowerCAmelCase__				:  Tuple		       =     output.images
        lowerCAmelCase__				:  Optional[Any]		       =     images[0, 255:258, 255:258, -1]

        assert images.shape == (1, 512, 512, 3)
        lowerCAmelCase__				:  Optional[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

