


from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
    import tensorflow as tf

    from transformers import TFResNetForImageClassification, TFResNetModel
    from transformers.models.resnet.modeling_tf_resnet import TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import AutoImageProcessor







class 							_UpperCamelCase :

   '''simple docstring'''







   def __init__(  self  : int       ,  _lowerCamelCase  : int       ,  _lowerCamelCase  : Any=3       ,  _lowerCamelCase  : List[Any]=3_2       ,  _lowerCamelCase  : Tuple=3       ,  _lowerCamelCase  : Any=1_0       ,  _lowerCamelCase  : str=[1_0, 2_0, 3_0, 4_0]       ,  _lowerCamelCase  : Dict=[1, 1, 2, 1]       ,  _lowerCamelCase  : List[Any]=True       ,  _lowerCamelCase  : Union[str, Any]=True       ,  _lowerCamelCase  : Any="relu"       ,  _lowerCamelCase  : Union[str, Any]=3       ,  _lowerCamelCase  : Optional[int]=None       ,  ):






         '''simple docstring'''



         __lowerCamelCase     :      Union[str, Any]       						=						parent
         __lowerCamelCase     :      Union[str, Any]       						=						batch_size
         __lowerCamelCase     :      Any       						=						image_size
         __lowerCamelCase     :      int       						=						num_channels
         __lowerCamelCase     :      Any       						=						embeddings_size
         __lowerCamelCase     :      List[str]       						=						hidden_sizes
         __lowerCamelCase     :      Dict       						=						depths
         __lowerCamelCase     :      Optional[int]       						=						is_training
         __lowerCamelCase     :      int       						=						use_labels
         __lowerCamelCase     :      Union[str, Any]       						=						hidden_act
         __lowerCamelCase     :      str       						=						num_labels
         __lowerCamelCase     :      Optional[int]       						=						scope
         __lowerCamelCase     :      List[str]       						=						len(_snake_case							)

   def   _snake_case    (  self  : List[str]							):






         '''simple docstring'''



         __lowerCamelCase     :      int       						=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

         __lowerCamelCase     :      Union[str, Any]       						=						None
         if self.use_labels:
               __lowerCamelCase     :      Any       						=						ids_tensor([self.batch_size]       ,  self.num_labels							)

         __lowerCamelCase     :      Tuple       						=						self.get_config()

         return config, pixel_values, labels

   def   _snake_case    (  self  : Any							):






         '''simple docstring'''



         return ResNetConfig(
             num_channels=self.num_channels       ,  embeddings_size=self.embeddings_size       ,  hidden_sizes=self.hidden_sizes       ,  depths=self.depths       ,  hidden_act=self.hidden_act       ,  num_labels=self.num_labels       ,  image_size=self.image_size       ,  )

   def   _snake_case    (  self  : Any       ,  _lowerCamelCase  : List[str]       ,  _lowerCamelCase  : List[str]       ,  _lowerCamelCase  : List[str]							):






         '''simple docstring'''



         __lowerCamelCase     :      List[str]       						=						TFResNetModel(config=_snake_case							)
         __lowerCamelCase     :      List[Any]       						=						model(_snake_case							)
         # expected last hidden states: B, C, H // 32, W // 32
         self.parent.assertEqual(
             result.last_hidden_state.shape       ,  (self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2)       ,  )

   def   _snake_case    (  self  : Any       ,  _lowerCamelCase  : Optional[int]       ,  _lowerCamelCase  : Tuple       ,  _lowerCamelCase  : List[Any]							):






         '''simple docstring'''



         __lowerCamelCase     :      List[Any]       						=						self.num_labels
         __lowerCamelCase     :      List[str]       						=						TFResNetForImageClassification(_snake_case							)
         __lowerCamelCase     :      Union[str, Any]       						=						model(_snake_case       ,  labels=_snake_case							)
         self.parent.assertEqual(result.logits.shape       ,  (self.batch_size, self.num_labels)							)


   def   _snake_case    (  self  : Dict							):






         '''simple docstring'''



         __lowerCamelCase     :      List[str]       						=						self.prepare_config_and_inputs()
         __lowerCamelCase						, __lowerCamelCase						, __lowerCamelCase     :      Any       						=						config_and_inputs
         __lowerCamelCase     :      Union[str, Any]       						=						{"""pixel_values""": pixel_values}
         return config, inputs_dict







@require_tf
class 							_UpperCamelCase (						UpperCAmelCase_,UpperCAmelCase_,unittest.TestCase						):

   '''simple docstring'''







   a_				:							Dict    = (TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
   a_				:							List[str]    = (
       {"feature-extraction": TFResNetModel, "image-classification": TFResNetForImageClassification}
       if is_tf_available()
       else {}
   )

   a_				:							Any    = False
   a_				:							Dict    = False
   a_				:							Optional[Any]    = False
   a_				:							str    = False
   a_				:							str    = False

   def   _snake_case    (  self  : Optional[int]							):






         '''simple docstring'''



         __lowerCamelCase     :      Dict       						=						TFResNetModelTester(self							)
         __lowerCamelCase     :      int       						=						ConfigTester(self       ,  config_class=_snake_case       ,  has_text_modality=_snake_case							)

   def   _snake_case    (  self  : Dict							):






         '''simple docstring'''



         self.create_and_test_config_common_properties()
         self.config_tester.create_and_test_config_to_json_string()
         self.config_tester.create_and_test_config_to_json_file()
         self.config_tester.create_and_test_config_from_and_save_pretrained()
         self.config_tester.create_and_test_config_with_num_labels()
         self.config_tester.check_config_can_be_init_without_params()
         self.config_tester.check_config_arguments_init()

   def   _snake_case    (  self  : Any							):






         '''simple docstring'''



         return

   @unittest.skip(reason="""ResNet does not use inputs_embeds"""							)
   def   _snake_case    (  self  : Union[str, Any]							):






         '''simple docstring'''



         pass

   @unittest.skip(reason="""ResNet does not support input and output embeddings"""							)
   def   _snake_case    (  self  : str							):






         '''simple docstring'''



         pass

   def   _snake_case    (  self  : str							):






         '''simple docstring'''



         __lowerCamelCase						, __lowerCamelCase     :      List[str]       						=						self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
               __lowerCamelCase     :      Tuple       						=						model_class(_snake_case							)
               __lowerCamelCase     :      str       						=						inspect.signature(model.call							)
               # signature.parameters is an OrderedDict => so arg_names order is deterministic
               __lowerCamelCase     :      Union[str, Any]       						=						[*signature.parameters.keys()]

               __lowerCamelCase     :      str       						=						["""pixel_values"""]
               self.assertListEqual(arg_names[:1]       ,  _snake_case							)

   def   _snake_case    (  self  : Dict							):






         '''simple docstring'''



         __lowerCamelCase     :      Optional[Any]       						=						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*_snake_case							)

   def   _snake_case    (  self  : str							):






         '''simple docstring'''



         def check_hidden_states_output(_lowerCamelCase  : Tuple       ,  _lowerCamelCase  : int       ,  _lowerCamelCase  : Optional[Any]							):
               __lowerCamelCase     :      Optional[int]       						=						model_class(_snake_case							)
               __lowerCamelCase     :      int       						=						model(**self._prepare_for_class(_snake_case       ,  _snake_case							)							)

               __lowerCamelCase     :      str       						=						outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

               __lowerCamelCase     :      Dict       						=						self.model_tester.num_stages
               self.assertEqual(len(_snake_case							)       ,  expected_num_stages + 1							)

               # ResNet's feature maps are of shape (batch_size, num_channels, height, width)
               self.assertListEqual(
                   list(hidden_states[0].shape[-2:]							)       ,  [self.model_tester.image_size // 4, self.model_tester.image_size // 4]       ,  )

         __lowerCamelCase						, __lowerCamelCase     :      Optional[int]       						=						self.model_tester.prepare_config_and_inputs_for_common()
         __lowerCamelCase     :      Optional[int]       						=						["""basic""", """bottleneck"""]
         for model_class in self.all_model_classes:
               for layer_type in layers_type:
                     __lowerCamelCase     :      Any       						=						layer_type
                     __lowerCamelCase     :      int       						=						True
                     check_hidden_states_output(_snake_case       ,  _snake_case       ,  _snake_case							)

                     # check that output_hidden_states also work using config
                     del inputs_dict["output_hidden_states"]
                     __lowerCamelCase     :      str       						=						True

                     check_hidden_states_output(_snake_case       ,  _snake_case       ,  _snake_case							)

   def   _snake_case    (  self  : List[str]							):






         '''simple docstring'''



         __lowerCamelCase     :      Any       						=						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*_snake_case							)


   @slow
   def   _snake_case    (  self  : int							):






         '''simple docstring'''



         for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               __lowerCamelCase     :      Any       						=						TFResNetModel.from_pretrained(_snake_case							)
               self.assertIsNotNone(_snake_case							)





def   _UpperCAmelCase       (     ):
      """simple docstring"""




      __lowerCamelCase     :      List[str]       						=						Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""      )
      return image







@require_tf
@require_vision
class 							_UpperCamelCase (						unittest.TestCase						):

   '''simple docstring'''







   @cached_property
   def   _snake_case    (  self  : Dict							):






         '''simple docstring'''



         return (
             AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]							)
             if is_vision_available()
             else None
         )


   @slow
   def   _snake_case    (  self  : int							):






         '''simple docstring'''



         __lowerCamelCase     :      Dict       						=						TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]							)

         __lowerCamelCase     :      Any       						=						self.default_image_processor
         __lowerCamelCase     :      List[str]       						=						prepare_img()
         __lowerCamelCase     :      str       						=						image_processor(images=_snake_case       ,  return_tensors="""tf"""							)

         # forward pass
         __lowerCamelCase     :      Tuple       						=						model(**_snake_case							)

         # verify the logits
         __lowerCamelCase     :      List[str]       						=						tf.TensorShape((1, 1_0_0_0)							)
         self.assertEqual(outputs.logits.shape       ,  _snake_case							)

         __lowerCamelCase     :      int       						=						tf.constant([-11.1_069, -9.7_877, -8.3_777]							)

         self.assertTrue(np.allclose(outputs.logits[0, :3].numpy()       ,  _snake_case       ,  atol=1E-4							)							)







import random
import unittest

import numpy as np
import torch

from diffusers import (
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionUpscalePipeline,
    PNDMScheduler,
)
from diffusers.utils import floats_tensor
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 							_UpperCamelCase (						A,unittest.TestCase						):

   '''simple docstring'''







   a_				:							Any    = "ssube/stable-diffusion-x4-upscaler-onnx"

   def   _snake_case    (  self  : Any       ,  _lowerCamelCase  : List[str]=0							):






         '''simple docstring'''



         __lowerCamelCase     :      Union[str, Any]       						=						floats_tensor((1, 3, 1_2_8, 1_2_8)       ,  rng=random.Random(_lowerCamelCase							)							)
         __lowerCamelCase     :      int       						=						torch.manual_seed(_lowerCamelCase							)
         __lowerCamelCase     :      Union[str, Any]       						=						{
             """prompt""": """A painting of a squirrel eating a burger""",
             """image""": image,
             """generator""": generator,
             """num_inference_steps""": 3,
             """guidance_scale""": 7.5,
             """output_type""": """numpy""",
         }
         return inputs

   def   _snake_case    (  self  : Optional[Any]							):






         '''simple docstring'''



         __lowerCamelCase     :      Union[str, Any]       						=						OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint       ,  provider="""CPUExecutionProvider"""							)
         pipe.set_progress_bar_config(disable=_lowerCamelCase							)

         __lowerCamelCase     :      Tuple       						=						self.get_dummy_inputs()
         __lowerCamelCase     :      Dict       						=						pipe(**_lowerCamelCase							).images
         __lowerCamelCase     :      Dict       						=						image[0, -3:, -3:, -1].flatten()

         # started as 128, should now be 512
         assert image.shape == (1, 5_1_2, 5_1_2, 3)
         __lowerCamelCase     :      str       						=						np.array(
             [0.6_974_782, 0.68_902_093, 0.70_135_885, 0.7_583_618, 0.7_804_545, 0.7_854_912, 0.78_667_426, 0.78_743_863, 0.78_070_223]							)
         assert np.abs(image_slice - expected_slice							).max() < 1E-1

   def   _snake_case    (  self  : Dict							):






         '''simple docstring'''



         __lowerCamelCase     :      Optional[Any]       						=						OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint       ,  provider="""CPUExecutionProvider"""							)
         __lowerCamelCase     :      str       						=						PNDMScheduler.from_config(pipe.scheduler.config       ,  skip_prk_steps=_lowerCamelCase							)
         pipe.set_progress_bar_config(disable=_lowerCamelCase							)

         __lowerCamelCase     :      Tuple       						=						self.get_dummy_inputs()
         __lowerCamelCase     :      Union[str, Any]       						=						pipe(**_lowerCamelCase							).images
         __lowerCamelCase     :      List[Any]       						=						image[0, -3:, -3:, -1]

         assert image.shape == (1, 5_1_2, 5_1_2, 3)
         __lowerCamelCase     :      int       						=						np.array(
             [0.6_898_892, 0.59_240_556, 0.52_499_527, 0.58_866_215, 0.52_258_235, 0.52_572_715, 0.62_414_473, 0.6_174_387, 0.6_214_964]							)
         assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-1

   def   _snake_case    (  self  : Tuple							):






         '''simple docstring'''



         __lowerCamelCase     :      Optional[int]       						=						OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint       ,  provider="""CPUExecutionProvider"""							)
         __lowerCamelCase     :      int       						=						DPMSolverMultistepScheduler.from_config(pipe.scheduler.config							)
         pipe.set_progress_bar_config(disable=_lowerCamelCase							)

         __lowerCamelCase     :      List[str]       						=						self.get_dummy_inputs()
         __lowerCamelCase     :      List[str]       						=						pipe(**_lowerCamelCase							).images
         __lowerCamelCase     :      Optional[Any]       						=						image[0, -3:, -3:, -1]

         assert image.shape == (1, 5_1_2, 5_1_2, 3)
         __lowerCamelCase     :      str       						=						np.array(
             [0.7_659_278, 0.76_437_664, 0.75_579_107, 0.7_691_116, 0.77_666_986, 0.7_727_672, 0.7_758_664, 0.7_812_226, 0.76_942_515]							)

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

   def   _snake_case    (  self  : str							):






         '''simple docstring'''



         __lowerCamelCase     :      Optional[int]       						=						OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint       ,  provider="""CPUExecutionProvider"""							)
         __lowerCamelCase     :      Optional[Any]       						=						EulerDiscreteScheduler.from_config(pipe.scheduler.config							)
         pipe.set_progress_bar_config(disable=_lowerCamelCase							)

         __lowerCamelCase     :      Dict       						=						self.get_dummy_inputs()
         __lowerCamelCase     :      Dict       						=						pipe(**_lowerCamelCase							).images
         __lowerCamelCase     :      List[str]       						=						image[0, -3:, -3:, -1]

         assert image.shape == (1, 5_1_2, 5_1_2, 3)
         __lowerCamelCase     :      List[str]       						=						np.array(
             [0.6_974_782, 0.68_902_093, 0.70_135_885, 0.7_583_618, 0.7_804_545, 0.7_854_912, 0.78_667_426, 0.78_743_863, 0.78_070_223]							)
         assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-1


   def   _snake_case    (  self  : Any							):






         '''simple docstring'''



         __lowerCamelCase     :      List[str]       						=						OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint       ,  provider="""CPUExecutionProvider"""							)
         __lowerCamelCase     :      Union[str, Any]       						=						EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config							)
         pipe.set_progress_bar_config(disable=_lowerCamelCase							)

         __lowerCamelCase     :      Optional[int]       						=						self.get_dummy_inputs()
         __lowerCamelCase     :      int       						=						pipe(**_lowerCamelCase							).images
         __lowerCamelCase     :      int       						=						image[0, -3:, -3:, -1]

         assert image.shape == (1, 5_1_2, 5_1_2, 3)
         __lowerCamelCase     :      Optional[int]       						=						np.array(
             [0.77_424_496, 0.773_601, 0.7_645_288, 0.7_769_598, 0.7_772_739, 0.7_738_688, 0.78_187_233, 0.77_879_584, 0.767_043]							)

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



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

   '''simple docstring'''







   @property
   def   _snake_case    (  self  : str							):






         '''simple docstring'''



         return (
             "CUDAExecutionProvider",
             {
                 "gpu_mem_limit": "15000000000",  # 15GB
                 "arena_extend_strategy": "kSameAsRequested",
             },
         )

   @property
   def   _snake_case    (  self  : Tuple							):






         '''simple docstring'''



         __lowerCamelCase     :      Any       						=						ort.SessionOptions()
         __lowerCamelCase     :      str       						=						False
         return options

   def   _snake_case    (  self  : Any							):






         '''simple docstring'''



         __lowerCamelCase     :      str       						=						load_image(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
             """/img2img/sketch-mountains-input.jpg"""							)
         __lowerCamelCase     :      Optional[Any]       						=						init_image.resize((1_2_8, 1_2_8)							)
         # using the PNDM scheduler by default
         __lowerCamelCase     :      List[Any]       						=						OnnxStableDiffusionUpscalePipeline.from_pretrained(
             """ssube/stable-diffusion-x4-upscaler-onnx"""       ,  provider=self.gpu_provider       ,  sess_options=self.gpu_options       ,  )
         pipe.set_progress_bar_config(disable=_lowerCamelCase							)

         __lowerCamelCase     :      List[Any]       						=						"""A fantasy landscape, trending on artstation"""

         __lowerCamelCase     :      str       						=						torch.manual_seed(0							)
         __lowerCamelCase     :      Any       						=						pipe(
             prompt=_lowerCamelCase       ,  image=_lowerCamelCase       ,  guidance_scale=7.5       ,  num_inference_steps=1_0       ,  generator=_lowerCamelCase       ,  output_type="""np"""       ,  )
         __lowerCamelCase     :      List[str]       						=						output.images
         __lowerCamelCase     :      Any       						=						images[0, 2_5_5:2_5_8, 3_8_3:3_8_6, -1]

         assert images.shape == (1, 5_1_2, 5_1_2, 3)
         __lowerCamelCase     :      int       						=						np.array([0.4_883, 0.4_947, 0.4_980, 0.4_975, 0.4_982, 0.4_980, 0.5_000, 0.5_006, 0.4_972]							)
         # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

         assert np.abs(image_slice.flatten() - expected_slice							).max() < 2E-2


   def   _snake_case    (  self  : Any							):






         '''simple docstring'''



         __lowerCamelCase     :      Tuple       						=						load_image(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
             """/img2img/sketch-mountains-input.jpg"""							)
         __lowerCamelCase     :      Union[str, Any]       						=						init_image.resize((1_2_8, 1_2_8)							)
         __lowerCamelCase     :      str       						=						LMSDiscreteScheduler.from_pretrained(
             """ssube/stable-diffusion-x4-upscaler-onnx"""       ,  subfolder="""scheduler"""							)
         __lowerCamelCase     :      str       						=						OnnxStableDiffusionUpscalePipeline.from_pretrained(
             """ssube/stable-diffusion-x4-upscaler-onnx"""       ,  scheduler=_lowerCamelCase       ,  provider=self.gpu_provider       ,  sess_options=self.gpu_options       ,  )
         pipe.set_progress_bar_config(disable=_lowerCamelCase							)

         __lowerCamelCase     :      str       						=						"""A fantasy landscape, trending on artstation"""

         __lowerCamelCase     :      Tuple       						=						torch.manual_seed(0							)
         __lowerCamelCase     :      Dict       						=						pipe(
             prompt=_lowerCamelCase       ,  image=_lowerCamelCase       ,  guidance_scale=7.5       ,  num_inference_steps=2_0       ,  generator=_lowerCamelCase       ,  output_type="""np"""       ,  )
         __lowerCamelCase     :      int       						=						output.images
         __lowerCamelCase     :      List[Any]       						=						images[0, 2_5_5:2_5_8, 3_8_3:3_8_6, -1]

         assert images.shape == (1, 5_1_2, 5_1_2, 3)
         __lowerCamelCase     :      List[str]       						=						np.array(
             [0.50_173_753, 0.50_223_356, 0.502_039, 0.50_233_036, 0.5_023_725, 0.5_022_601, 0.5_018_758, 0.50_234_085, 0.50_241_566]							)
         # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

         assert np.abs(image_slice.flatten() - expected_slice							).max() < 2E-2



