


"""simple docstring"""







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    					=      logging.get_logger(__name__)  # pylint: disable=invalid-name


def      lowercase			(				A_       )->      List[Any]:
  '''simple docstring'''







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

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

    a						:		str      							=			[np.array(i.resize((w, h)  ,   resample=PIL_INTERPOLATION["lanczos"]       )       )[None, :] for i in image]
    a						:		Optional[int]      							=			np.concatenate(A_  ,   axis=0       )
    a						:		Tuple      							=			np.array(A_       ).astype(np.floataa       ) / 2_5_5.0
    a						:		Any      							=			image.transpose(0  ,   3  ,   1  ,   2       )
    a						:		Any      							=			2.0 * image - 1.0
    a						:		Union[str, Any]      							=			torch.from_numpy(A_       )
  elif isinstance(image[0]  ,   torch.Tensor       ):
    a						:		int      							=			torch.cat(A_  ,   dim=0       )
  return image


def      lowercase			(				A_       )->      str:
  '''simple docstring'''







  if isinstance(A_  ,   torch.Tensor       ):
    return mask
  elif isinstance(A_  ,   PIL.Image.Image       ):
    a						:		Dict      							=			[mask]

  if isinstance(mask[0]  ,   PIL.Image.Image       ):
    a							,		a						:		str      							=			mask[0].size
    a							,		a						:		Any      							=			(x - x % 32 for x in (w, h))  # resize to integer multiple of 32
    a						:		List[str]      							=			[np.array(m.convert("L"       ).resize((w, h)  ,   resample=PIL_INTERPOLATION["nearest"]       )       )[None, :] for m in mask]
    a						:		List[Any]      							=			np.concatenate(A_  ,   axis=0       )
    a						:		Optional[int]      							=			mask.astype(np.floataa       ) / 2_5_5.0
    a						:		Optional[Any]      							=			0
    a						:		Dict      							=			1
    a						:		List[str]      							=			torch.from_numpy(A_       )
  elif isinstance(mask[0]  ,   torch.Tensor       ):
    a						:		int      							=			torch.cat(A_  ,   dim=0       )
  return mask







class 						_A  (  _a  ):







    """simple docstring"""




    UpperCAmelCase       :							UNetaDModel
    UpperCAmelCase       :							RePaintScheduler






    def __init__(					self				:    int				, __UpperCAmelCase				:    str				, __UpperCAmelCase				:    Dict):
      super().__init__()
      self.register_modules(unet=__UpperCAmelCase				, scheduler=__UpperCAmelCase)






    @torch.no_grad()
    def __call__(					self				:    List[str]				, __UpperCAmelCase				:    Union[torch.Tensor, PIL.Image.Image]				, __UpperCAmelCase				:    Union[torch.Tensor, PIL.Image.Image]				, __UpperCAmelCase				:    int = 250				, __UpperCAmelCase				:    float = 0.0				, __UpperCAmelCase				:    int = 10				, __UpperCAmelCase				:    int = 10				, __UpperCAmelCase				:    Optional[Union[torch.Generator, List[torch.Generator]]] = None				, __UpperCAmelCase				:    Optional[str] = "pil"				, __UpperCAmelCase				:    bool = True				, ):
      a						:		str      							=			image

      a						:		List[Any]      							=			_preprocess_image(__UpperCAmelCase)
      a						:		Tuple      							=			original_image.to(device=self.device				, dtype=self.unet.dtype)
      a						:		Optional[Any]      							=			_preprocess_mask(__UpperCAmelCase)
      a						:		Union[str, Any]      							=			mask_image.to(device=self.device				, dtype=self.unet.dtype)

      a						:		List[str]      							=			original_image.shape[0]

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

      a						:		Any      							=			original_image.shape
      a						:		List[str]      							=			randn_tensor(__UpperCAmelCase				, generator=__UpperCAmelCase				, device=self.device				, dtype=self.unet.dtype)

      # set step values
      self.scheduler.set_timesteps(__UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase				, self.device)
      a						:		List[str]      							=			eta

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

        else:
          # compute the reverse: x_t-1 -> x_t
          a						:		str      							=			self.scheduler.undo_step(__UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase)
        a						:		Any      							=			t

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

      if not return_dict:
        return (image,)

      return ImagePipelineOutput(images=__UpperCAmelCase)














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 __lowerCamelCase :





	"""simple docstring"""






	def __init__(							self       ,    UpperCAmelCase       ,    UpperCAmelCase=3       ,    UpperCAmelCase=32       ,    UpperCAmelCase=3       ,    UpperCAmelCase=10       ,    UpperCAmelCase=[10, 20, 30, 40]       ,    UpperCAmelCase=[1, 1, 2, 1]       ,    UpperCAmelCase=True       ,    UpperCAmelCase=True       ,    UpperCAmelCase="relu"       ,    UpperCAmelCase=3       ,    UpperCAmelCase=None       ,    ):
							"""simple docstring"""







							_UpperCAmelCase									=			parent
							_UpperCAmelCase									=			batch_size
							_UpperCAmelCase									=			image_size
							_UpperCAmelCase									=			num_channels
							_UpperCAmelCase									=			embeddings_size
							_UpperCAmelCase									=			hidden_sizes
							_UpperCAmelCase									=			depths
							_UpperCAmelCase									=			is_training
							_UpperCAmelCase									=			use_labels
							_UpperCAmelCase									=			hidden_act
							_UpperCAmelCase									=			num_labels
							_UpperCAmelCase									=			scope
							_UpperCAmelCase									=			len(UpperCAmelCase    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

							_UpperCAmelCase									=			None
							if self.use_labels:
													_UpperCAmelCase									=			ids_tensor([self.batch_size]       ,    self.num_labels    )

							_UpperCAmelCase									=			self.get_config()

							return config, pixel_values, labels

	def        UpperCamelCase    (							self    ):
							"""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        UpperCamelCase    (							self       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase    ):
							"""simple docstring"""







							_UpperCAmelCase									=			TFResNetModel(config=UpperCAmelCase    )
							_UpperCAmelCase									=			model(UpperCAmelCase    )
							# 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       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase    ):
							"""simple docstring"""







							_UpperCAmelCase									=			self.num_labels
							_UpperCAmelCase									=			TFResNetForImageClassification(UpperCAmelCase    )
							_UpperCAmelCase									=			model(UpperCAmelCase       ,    labels=UpperCAmelCase    )
							self.parent.assertEqual(result.logits.shape       ,    (self.batch_size, self.num_labels)    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			self.prepare_config_and_inputs()
							_UpperCAmelCase					, _UpperCAmelCase					, _UpperCAmelCase									=			config_and_inputs
							_UpperCAmelCase									=			{'pixel_values': pixel_values}
							return config, inputs_dict





@require_tf
class __lowerCamelCase (      snake_case__     ,       snake_case__     ,       unittest.TestCase):





	"""simple docstring"""






	UpperCamelCase__								=      (TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
	UpperCamelCase__								=      (
	    {"feature-extraction": TFResNetModel, "image-classification": TFResNetForImageClassification}
	    if is_tf_available()
	    else {}
	)

	UpperCamelCase__								=      False
	UpperCamelCase__								=      False
	UpperCamelCase__								=      False
	UpperCamelCase__								=      False
	UpperCamelCase__								=      False

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			TFResNetModelTester(self    )
							_UpperCAmelCase									=			ConfigTester(self       ,    config_class=UpperCAmelCase       ,    has_text_modality=UpperCAmelCase    )

	def        UpperCamelCase    (							self    ):
							"""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        UpperCamelCase    (							self    ):
							"""simple docstring"""







							return

	@unittest.skip(reason='ResNet does not use inputs_embeds'    )
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							pass

	@unittest.skip(reason='ResNet does not support input and output embeddings'    )
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							pass

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase					, _UpperCAmelCase									=			self.model_tester.prepare_config_and_inputs_for_common()

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

													_UpperCAmelCase									=			['pixel_values']
													self.assertListEqual(arg_names[:1]       ,    UpperCAmelCase    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*UpperCAmelCase    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							def check_hidden_states_output(UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase    ):
													_UpperCAmelCase									=			model_class(UpperCAmelCase    )
													_UpperCAmelCase									=			model(**self._prepare_for_class(UpperCAmelCase       ,    UpperCAmelCase    )    )

													_UpperCAmelCase									=			outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

													_UpperCAmelCase									=			self.model_tester.num_stages
													self.assertEqual(len(UpperCAmelCase    )       ,    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									=			self.model_tester.prepare_config_and_inputs_for_common()
							_UpperCAmelCase									=			['basic', 'bottleneck']
							for model_class in self.all_model_classes:
													for layer_type in layers_type:
																			_UpperCAmelCase									=			layer_type
																			_UpperCAmelCase									=			True
																			check_hidden_states_output(UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase    )

																			# check that output_hidden_states also work using config
																			del inputs_dict["output_hidden_states"]
																			_UpperCAmelCase									=			True

																			check_hidden_states_output(UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase    )

	@slow
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													_UpperCAmelCase									=			TFResNetModel.from_pretrained(UpperCAmelCase    )
													self.assertIsNotNone(UpperCAmelCase    )



def __A    (      )->  Optional[Any]:





						"""simple docstring"""



						_UpperCAmelCase									=			Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'	)
						return image





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





	"""simple docstring"""






	@cached_property
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







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

	@slow
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]    )

							_UpperCAmelCase									=			self.default_image_processor
							_UpperCAmelCase									=			prepare_img()
							_UpperCAmelCase									=			image_processor(images=UpperCAmelCase       ,    return_tensors='tf'    )

							# forward pass
							_UpperCAmelCase									=			model(**UpperCAmelCase    )

							# verify the logits
							_UpperCAmelCase									=			tf.TensorShape((1, 1000)    )
							self.assertEqual(outputs.logits.shape       ,    UpperCAmelCase    )

							_UpperCAmelCase									=			tf.constant([-11.10_69, -9.78_77, -8.37_77]    )

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