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





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



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



     @property
     def 			a__  (					self       ):
            _A= ort.SessionOptions()
            _A= False
            return options



     def 			a__  (					self       ):
            _A= load_image(
                'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
                '/in_paint/overture-creations-5sI6fQgYIuo.png'       )
            _A= load_image(
                'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
                '/in_paint/overture-creations-5sI6fQgYIuo_mask.png'       )
            _A= OnnxStableDiffusionInpaintPipeline.from_pretrained(
                'runwayml/stable-diffusion-inpainting'				,      revision='onnx'				,      safety_checker=lowerCAmelCase__				,      feature_extractor=lowerCAmelCase__				,      provider=self.gpu_provider				,      sess_options=self.gpu_options				,      )
            pipe.set_progress_bar_config(disable=lowerCAmelCase__       )

            _A= 'A red cat sitting on a park bench'

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

            assert images.shape == (1, 512, 512, 3)
            _A= 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 			a__  (					self       ):
            _A= load_image(
                'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
                '/in_paint/overture-creations-5sI6fQgYIuo.png'       )
            _A= load_image(
                'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
                '/in_paint/overture-creations-5sI6fQgYIuo_mask.png'       )
            _A= LMSDiscreteScheduler.from_pretrained(
                'runwayml/stable-diffusion-inpainting'				,      subfolder='scheduler'				,      revision='onnx'       )
            _A= OnnxStableDiffusionInpaintPipeline.from_pretrained(
                'runwayml/stable-diffusion-inpainting'				,      revision='onnx'				,      scheduler=lowerCAmelCase__				,      safety_checker=lowerCAmelCase__				,      feature_extractor=lowerCAmelCase__				,      provider=self.gpu_provider				,      sess_options=self.gpu_options				,      )
            pipe.set_progress_bar_config(disable=lowerCAmelCase__       )

            _A= 'A red cat sitting on a park bench'

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

            assert images.shape == (1, 512, 512, 3)
            _A= 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
import time
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
      import torch

      from transformers.generation import (
          MaxLengthCriteria,
          MaxNewTokensCriteria,
          MaxTimeCriteria,
          StoppingCriteriaList,
          validate_stopping_criteria,
      )





@require_torch
class      lowerCAmelCase						(					unittest.TestCase			):



     def 			a__  (					self				,      lowerCAmelCase__       ):
            _A=	3
            _A=	250

            _A=	ids_tensor((batch_size, length)				,      lowerCAmelCase__       )
            _A=	torch.ones((batch_size, length)				,      device=lowerCAmelCase__				,      dtype=torch.float       ) / length
            return input_ids, scores



     def 			a__  (					self       ):
            _A,			_A=	self._get_tensors(5       )

            _A=	StoppingCriteriaList(
                [
                    MaxLengthCriteria(max_length=10       ),
                    MaxTimeCriteria(max_time=0.1       ),
                ]       )

            self.assertFalse(criteria(lowerCAmelCase__				,      lowerCAmelCase__       )       )

            _A,			_A=	self._get_tensors(9       )
            self.assertFalse(criteria(lowerCAmelCase__				,      lowerCAmelCase__       )       )

            _A,			_A=	self._get_tensors(10       )
            self.assertTrue(criteria(lowerCAmelCase__				,      lowerCAmelCase__       )       )



     def 			a__  (					self       ):
            _A=	MaxLengthCriteria(max_length=10       )

            _A,			_A=	self._get_tensors(5       )
            self.assertFalse(criteria(lowerCAmelCase__				,      lowerCAmelCase__       )       )

            _A,			_A=	self._get_tensors(9       )
            self.assertFalse(criteria(lowerCAmelCase__				,      lowerCAmelCase__       )       )

            _A,			_A=	self._get_tensors(10       )
            self.assertTrue(criteria(lowerCAmelCase__				,      lowerCAmelCase__       )       )



     def 			a__  (					self       ):
            _A=	MaxNewTokensCriteria(start_length=5				,      max_new_tokens=5       )

            _A,			_A=	self._get_tensors(5       )
            self.assertFalse(criteria(lowerCAmelCase__				,      lowerCAmelCase__       )       )

            _A,			_A=	self._get_tensors(9       )
            self.assertFalse(criteria(lowerCAmelCase__				,      lowerCAmelCase__       )       )

            _A,			_A=	self._get_tensors(10       )
            self.assertTrue(criteria(lowerCAmelCase__				,      lowerCAmelCase__       )       )

            _A=	StoppingCriteriaList([criteria]       )
            self.assertEqual(criteria_list.max_length				,      10       )



     def 			a__  (					self       ):
            _A,			_A=	self._get_tensors(5       )

            _A=	MaxTimeCriteria(max_time=0.1       )
            self.assertFalse(criteria(lowerCAmelCase__				,      lowerCAmelCase__       )       )

            _A=	MaxTimeCriteria(max_time=0.1				,      initial_timestamp=time.time() - 0.2       )
            self.assertTrue(criteria(lowerCAmelCase__				,      lowerCAmelCase__       )       )





     def 			a__  (					self       ):
            validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10       )]       )				,      10       )

            with self.assertWarns(lowerCAmelCase__       ):
                   validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10       )]       )				,      11       )

            _A=	validate_stopping_criteria(StoppingCriteriaList()				,      11       )

            self.assertEqual(len(lowerCAmelCase__       )				,      1       )