




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase_					    = {
    """configuration_longformer""": [
        """LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """LongformerConfig""",
        """LongformerOnnxConfig""",
    ],
    """tokenization_longformer""": ["""LongformerTokenizer"""],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCAmelCase_					    = ["""LongformerTokenizerFast"""]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCAmelCase_					    = [
         """LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """LongformerForMaskedLM""",
         """LongformerForMultipleChoice""",
         """LongformerForQuestionAnswering""",
         """LongformerForSequenceClassification""",
         """LongformerForTokenClassification""",
         """LongformerModel""",
         """LongformerPreTrainedModel""",
         """LongformerSelfAttention""",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCAmelCase_					    = [
         """TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """TFLongformerForMaskedLM""",
         """TFLongformerForMultipleChoice""",
         """TFLongformerForQuestionAnswering""",
         """TFLongformerForSequenceClassification""",
         """TFLongformerForTokenClassification""",
         """TFLongformerModel""",
         """TFLongformerPreTrainedModel""",
         """TFLongformerSelfAttention""",
     ]


if TYPE_CHECKING:
     from .configuration_longformer import (
         LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
         LongformerConfig,
         LongformerOnnxConfig,
     )
     from .tokenization_longformer import LongformerTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_longformer_fast import LongformerTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_longformer import (
              LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
              LongformerForMaskedLM,
              LongformerForMultipleChoice,
              LongformerForQuestionAnswering,
              LongformerForSequenceClassification,
              LongformerForTokenClassification,
              LongformerModel,
              LongformerPreTrainedModel,
              LongformerSelfAttention,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_longformer import (
              TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFLongformerForMaskedLM,
              TFLongformerForMultipleChoice,
              TFLongformerForQuestionAnswering,
              TFLongformerForSequenceClassification,
              TFLongformerForTokenClassification,
              TFLongformerModel,
              TFLongformerPreTrainedModel,
              TFLongformerSelfAttention,
          )

else:
     import sys

     lowerCAmelCase_					    = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)







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

