






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        a       :



      """simple docstring"""


      def __init__( self   :     Tuple						, __lowercase   :     Any						, __lowercase   :     Union[str, Any]=3						, __lowercase   :     str=32						, __lowercase   :     List[str]=3						, __lowercase   :     Union[str, Any]=10						, __lowercase   :     int=[10, 20, 30, 40]						, __lowercase   :     Any=[1, 1, 2, 1]						, __lowercase   :     Union[str, Any]=True						, __lowercase   :     Optional[Any]=True						, __lowercase   :     str="relu"						, __lowercase   :     int=3						, __lowercase   :     Any=None						, )				-> Optional[int]:
        __UpperCAmelCase				:      Tuple	 =    parent
        __UpperCAmelCase				:      Optional[Any]	 =    batch_size
        __UpperCAmelCase				:      Union[str, Any]	 =    image_size
        __UpperCAmelCase				:      List[str]	 =    num_channels
        __UpperCAmelCase				:      str	 =    embeddings_size
        __UpperCAmelCase				:      Union[str, Any]	 =    hidden_sizes
        __UpperCAmelCase				:      str	 =    depths
        __UpperCAmelCase				:      Any	 =    is_training
        __UpperCAmelCase				:      Optional[Any]	 =    use_labels
        __UpperCAmelCase				:      str	 =    hidden_act
        __UpperCAmelCase				:      Optional[int]	 =    num_labels
        __UpperCAmelCase				:      Dict	 =    scope
        __UpperCAmelCase				:      Any	 =    len(__lowercase   )
      def       UpperCAmelCase ( self   :     List[str]   )				-> List[Any]:
        __UpperCAmelCase				:      Union[str, Any]	 =    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

        __UpperCAmelCase				:      Any	 =    None
        if self.use_labels:
          __UpperCAmelCase				:      Dict	 =    ids_tensor([self.batch_size]						, self.num_labels   )

        __UpperCAmelCase				:      List[Any]	 =    self.get_config()

        return config, pixel_values, labels
      def       UpperCAmelCase ( self   :     str   )				-> Optional[int]:
        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       UpperCAmelCase ( self   :     int						, __lowercase   :     Dict						, __lowercase   :     Union[str, Any]						, __lowercase   :     Optional[Any]   )				-> Optional[Any]:
        __UpperCAmelCase				:      Optional[int]	 =    TFResNetModel(config=__lowercase   )
        __UpperCAmelCase				:      Any	 =    model(__lowercase   )
        # 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 // 32, self.image_size // 32)						, )
      def       UpperCAmelCase ( self   :     Any						, __lowercase   :     Optional[int]						, __lowercase   :     Optional[int]						, __lowercase   :     Any   )				-> List[Any]:
        __UpperCAmelCase				:      Optional[Any]	 =    self.num_labels
        __UpperCAmelCase				:      List[Any]	 =    TFResNetForImageClassification(__lowercase   )
        __UpperCAmelCase				:      Dict	 =    model(__lowercase						, labels=__lowercase   )
        self.parent.assertEqual(result.logits.shape						, (self.batch_size, self.num_labels)   )




      def       UpperCAmelCase ( self   :     List[Any]   )				-> str:
        __UpperCAmelCase				:      Tuple	 =    self.prepare_config_and_inputs()
        __UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase				:      Tuple	 =    config_and_inputs
        __UpperCAmelCase				:      int	 =    {"""pixel_values""": pixel_values}
        return config, inputs_dict


@require_tf
class        a       (					lowercase__				,			lowercase__				,			unittest.TestCase  ):



      """simple docstring"""


      a    :					Tuple							=      (TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
      a    :					Union[str, Any]							=      (
          {'feature-extraction': TFResNetModel, 'image-classification': TFResNetForImageClassification}
          if is_tf_available()
          else {}
      )

      a    :					List[str]							=      False
      a    :					List[Any]							=      False
      a    :					Tuple							=      False
      a    :					List[Any]							=      False
      a    :					Union[str, Any]							=      False
      def       UpperCAmelCase ( self   :     List[Any]   )				-> Optional[int]:
        __UpperCAmelCase				:      Union[str, Any]	 =    TFResNetModelTester(self   )
        __UpperCAmelCase				:      Optional[Any]	 =    ConfigTester(self						, config_class=__lowercase						, has_text_modality=__lowercase   )
      def       UpperCAmelCase ( self   :     Union[str, Any]   )				-> Optional[int]:
        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       UpperCAmelCase ( self   :     List[Any]   )				-> Tuple:
        return
      @unittest.skip(reason="""ResNet does not use inputs_embeds"""   )
      def       UpperCAmelCase ( self   :     Tuple   )				-> List[str]:
        pass
      @unittest.skip(reason="""ResNet does not support input and output embeddings"""   )
      def       UpperCAmelCase ( self   :     List[str]   )				-> Optional[int]:
        pass
      def       UpperCAmelCase ( self   :     Union[str, Any]   )				-> List[str]:
        __UpperCAmelCase ,					__UpperCAmelCase				:      str	 =    self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
          __UpperCAmelCase				:      str	 =    model_class(__lowercase   )
          __UpperCAmelCase				:      Optional[Any]	 =    inspect.signature(model.call   )
          # signature.parameters is an OrderedDict => so arg_names order is deterministic
          __UpperCAmelCase				:      Optional[Any]	 =    [*signature.parameters.keys()]

          __UpperCAmelCase				:      Optional[int]	 =    ["""pixel_values"""]
          self.assertListEqual(arg_names[:1]						, __lowercase   )
      def       UpperCAmelCase ( self   :     Tuple   )				-> str:
        __UpperCAmelCase				:      Dict	 =    self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__lowercase   )
      def       UpperCAmelCase ( self   :     Any   )				-> List[Any]:
        def check_hidden_states_output(__lowercase   :     List[Any]						, __lowercase   :     List[Any]						, __lowercase   :     Tuple   ):
          __UpperCAmelCase				:      Optional[int]	 =    model_class(__lowercase   )
          __UpperCAmelCase				:      str	 =    model(**self._prepare_for_class(__lowercase						, __lowercase   )   )

          __UpperCAmelCase				:      int	 =    outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

          __UpperCAmelCase				:      Dict	 =    self.model_tester.num_stages
          self.assertEqual(len(__lowercase   )						, 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]						, )

        __UpperCAmelCase ,					__UpperCAmelCase				:      Union[str, Any]	 =    self.model_tester.prepare_config_and_inputs_for_common()
        __UpperCAmelCase				:      List[str]	 =    ["""basic""", """bottleneck"""]
        for model_class in self.all_model_classes:
          for layer_type in layers_type:
            __UpperCAmelCase				:      Any	 =    layer_type
            __UpperCAmelCase				:      Optional[Any]	 =    True
            check_hidden_states_output(__lowercase						, __lowercase						, __lowercase   )

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

            check_hidden_states_output(__lowercase						, __lowercase						, __lowercase   )
      def       UpperCAmelCase ( self   :     Optional[Any]   )				-> Tuple:
        __UpperCAmelCase				:      Dict	 =    self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*__lowercase   )




      @slow
      def       UpperCAmelCase ( self   :     List[Any]   )				-> Union[str, Any]:
        for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          __UpperCAmelCase				:      Optional[int]	 =    TFResNetModel.from_pretrained(__lowercase   )
          self.assertIsNotNone(__lowercase   )






def 							lowerCamelCase__ (       ):
  __UpperCAmelCase				:      str	 =    Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""						)
  return image







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



      """simple docstring"""


      @cached_property
      def       UpperCAmelCase ( self   :     Any   )				-> int:
        return (
            AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]   )
            if is_vision_available()
            else None
        )




      @slow
      def       UpperCAmelCase ( self   :     Union[str, Any]   )				-> Optional[Any]:
        __UpperCAmelCase				:      Any	 =    TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]   )

        __UpperCAmelCase				:      int	 =    self.default_image_processor
        __UpperCAmelCase				:      Optional[Any]	 =    prepare_img()
        __UpperCAmelCase				:      List[str]	 =    image_processor(images=__lowercase						, return_tensors="""tf"""   )

        # forward pass
        __UpperCAmelCase				:      Tuple	 =    model(**__lowercase   )

        # verify the logits
        __UpperCAmelCase				:      Optional[int]	 =    tf.TensorShape((1, 1000)   )
        self.assertEqual(outputs.logits.shape						, __lowercase   )

        __UpperCAmelCase				:      Union[str, Any]	 =    tf.constant([-11.1_069, -9.7_877, -8.3_777]   )

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



import warnings
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch

from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


_lowercase:	List[str]       			=		logging.get_logger(__name__)  # pylint: disable=invalid-name






def _lowerCamelCase  (		snake_case  ):
							warnings.warn(
							    'The preprocess method is deprecated and will be removed in a future version. Please'
							    ' use VaeImageProcessor.preprocess instead'     , snake_case     , )
							if isinstance(snake_case     , torch.Tensor  ):
														return image
							elif isinstance(snake_case     , PIL.Image.Image  ):
														_lowerCAmelCase							=							[image]

							if isinstance(image[0]     , PIL.Image.Image  ):
														_lowerCAmelCase			,		_lowerCAmelCase							=							image[0].size
														_lowerCAmelCase			,		_lowerCAmelCase							=							(x - x % 8 for x in (w, h))  # resize to integer multiple of 8

														_lowerCAmelCase							=							[np.array(i.resize((w, h)     , resample=PIL_INTERPOLATION['lanczos']  )  )[None, :] for i in image]
														_lowerCAmelCase							=							np.concatenate(snake_case     , axis=0  )
														_lowerCAmelCase							=							np.array(snake_case  ).astype(np.floataa  ) / 2_55.0
														_lowerCAmelCase							=							image.transpose(0     , 3     , 1     , 2  )
														_lowerCAmelCase							=							2.0 * image - 1.0
														_lowerCAmelCase							=							torch.from_numpy(snake_case  )
							elif isinstance(image[0]     , torch.Tensor  ):
														_lowerCAmelCase							=							torch.cat(snake_case     , dim=0  )
							return image






def _lowerCamelCase  (		snake_case  ):
							if isinstance(snake_case     , torch.Tensor  ):
														return mask
							elif isinstance(snake_case     , PIL.Image.Image  ):
														_lowerCAmelCase							=							[mask]

							if isinstance(mask[0]     , PIL.Image.Image  ):
														_lowerCAmelCase			,		_lowerCAmelCase							=							mask[0].size
														_lowerCAmelCase			,		_lowerCAmelCase							=							(x - x % 32 for x in (w, h))  # resize to integer multiple of 32
														_lowerCAmelCase							=							[np.array(m.convert('L'  ).resize((w, h)     , resample=PIL_INTERPOLATION['nearest']  )  )[None, :] for m in mask]
														_lowerCAmelCase							=							np.concatenate(snake_case     , axis=0  )
														_lowerCAmelCase							=							mask.astype(np.floataa  ) / 2_55.0
														_lowerCAmelCase							=							0
														_lowerCAmelCase							=							1
														_lowerCAmelCase							=							torch.from_numpy(snake_case  )
							elif isinstance(mask[0]     , torch.Tensor  ):
														_lowerCAmelCase							=							torch.cat(snake_case     , dim=0  )
							return mask



class        lowerCamelCase__				(   UpperCAmelCase				):
			UpperCamelCase__    							=42
			UpperCamelCase__    							=42





			def __init__(       self	:	int  ,  lowercase__	:	List[Any]  ,  lowercase__	:	Optional[Any]						):
										super().__init__()
										self.register_modules(unet=lowercase__  ,  scheduler=lowercase__						)





			@torch.no_grad()
			def __call__(       self	:	Any  ,  lowercase__	:	Union[torch.Tensor, PIL.Image.Image]  ,  lowercase__	:	Union[torch.Tensor, PIL.Image.Image]  ,  lowercase__	:	int = 2_50  ,  lowercase__	:	float = 0.0  ,  lowercase__	:	int = 10  ,  lowercase__	:	int = 10  ,  lowercase__	:	Optional[Union[torch.Generator, List[torch.Generator]]] = None  ,  lowercase__	:	Optional[str] = "pil"  ,  lowercase__	:	bool = True  ,  ):
										_lowerCAmelCase							=							image

										_lowerCAmelCase							=							_preprocess_image(lowercase__						)
										_lowerCAmelCase							=							original_image.to(device=self.device  ,  dtype=self.unet.dtype						)
										_lowerCAmelCase							=							_preprocess_mask(lowercase__						)
										_lowerCAmelCase							=							mask_image.to(device=self.device  ,  dtype=self.unet.dtype						)

										_lowerCAmelCase							=							original_image.shape[0]

										# sample gaussian noise to begin the loop
										if isinstance(lowercase__  ,  lowercase__						) and len(lowercase__						) != batch_size:
																	raise ValueError(
																	    f'You have passed a list of generators of length {len(lowercase__						)}, but requested an effective batch'
																	    f' size of {batch_size}. Make sure the batch size matches the length of the generators.'						)

										_lowerCAmelCase							=							original_image.shape
										_lowerCAmelCase							=							randn_tensor(lowercase__  ,  generator=lowercase__  ,  device=self.device  ,  dtype=self.unet.dtype						)

										# set step values
										self.scheduler.set_timesteps(lowercase__  ,  lowercase__  ,  lowercase__  ,  self.device						)
										_lowerCAmelCase							=							eta

										_lowerCAmelCase							=							self.scheduler.timesteps[0] + 1
										_lowerCAmelCase							=							generator[0] if isinstance(lowercase__  ,  lowercase__						) else generator
										for i, t in enumerate(self.progress_bar(self.scheduler.timesteps						)						):
																	if t < t_last:
																								# predict the noise residual
																								_lowerCAmelCase							=							self.unet(lowercase__  ,  lowercase__						).sample
																								# compute previous image: x_t -> x_t-1
																								_lowerCAmelCase							=							self.scheduler.step(lowercase__  ,  lowercase__  ,  lowercase__  ,  lowercase__  ,  lowercase__  ,  lowercase__						).prev_sample

																	else:
																								# compute the reverse: x_t-1 -> x_t
																								_lowerCAmelCase							=							self.scheduler.undo_step(lowercase__  ,  lowercase__  ,  lowercase__						)
																	_lowerCAmelCase							=							t

										_lowerCAmelCase							=							(image / 2 + 0.5).clamp(0  ,  1						)
										_lowerCAmelCase							=							image.cpu().permute(0  ,  2  ,  3  ,  1						).numpy()
										if output_type == "pil":
																	_lowerCAmelCase							=							self.numpy_to_pil(lowercase__						)

										if not return_dict:
																	return (image,)

										return ImagePipelineOutput(images=lowercase__						)






