






'''simple docstring'''





from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch

from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, require_torch


@require_torch
@require_tf
class  snake_case__      (					snake_case_):




					def 		A      (  self  :		Optional[Any]      )    ->							Tuple:
								UpperCAmelCase_      :       int          =       SMALL_MODEL_IDENTIFIER
								UpperCAmelCase_      :       int          =       "pt"
								UpperCAmelCase_      :       Any          =       "tf"




					def 		A      (  self  :		Union[str, Any]				, _A  :		List[str]      )    ->							int:
								UpperCAmelCase_      :       List[str]          =       AutoModel.from_pretrained(self.test_model      )
								model_pt.save_pretrained(UpperCamelCase__      )




					def 		A      (  self  :		List[str]				, _A  :		Optional[int]      )    ->							Any:
								UpperCAmelCase_      :       str          =       TFAutoModel.from_pretrained(self.test_model				, from_pt=UpperCamelCase__      )
								model_tf.save_pretrained(UpperCamelCase__      )




					def 		A      (  self  :		Any      )    ->							List[str]:
								UpperCAmelCase_      :       str          =       "mock_framework"

								# Framework provided - return whatever the user provides
								UpperCAmelCase_      :       Optional[Any]          =       FeaturesManager.determine_framework(self.test_model				, UpperCamelCase__      )
								self.assertEqual(UpperCamelCase__				, UpperCamelCase__      )

								# Local checkpoint and framework provided - return provided framework
								# PyTorch checkpoint
								with TemporaryDirectory() as local_pt_ckpt:
											self._setup_pt_ckpt(UpperCamelCase__      )
											UpperCAmelCase_      :       Optional[int]          =       FeaturesManager.determine_framework(UpperCamelCase__				, UpperCamelCase__      )
											self.assertEqual(UpperCamelCase__				, UpperCamelCase__      )

								# TensorFlow checkpoint
								with TemporaryDirectory() as local_tf_ckpt:
											self._setup_tf_ckpt(UpperCamelCase__      )
											UpperCAmelCase_      :       int          =       FeaturesManager.determine_framework(UpperCamelCase__				, UpperCamelCase__      )
											self.assertEqual(UpperCamelCase__				, UpperCamelCase__      )




					def 		A      (  self  :		Optional[Any]      )    ->							List[Any]:




								with TemporaryDirectory() as local_pt_ckpt:
											self._setup_pt_ckpt(UpperCamelCase__      )
											UpperCAmelCase_      :       Any          =       FeaturesManager.determine_framework(UpperCamelCase__      )
											self.assertEqual(UpperCamelCase__				, self.framework_pt      )

								# TensorFlow checkpoint
								with TemporaryDirectory() as local_tf_ckpt:
											self._setup_tf_ckpt(UpperCamelCase__      )
											UpperCAmelCase_      :       Dict          =       FeaturesManager.determine_framework(UpperCamelCase__      )
											self.assertEqual(UpperCamelCase__				, self.framework_tf      )

								# Invalid local checkpoint
								with TemporaryDirectory() as local_invalid_ckpt:
											with self.assertRaises(UpperCamelCase__      ):
														UpperCAmelCase_      :       Optional[int]          =       FeaturesManager.determine_framework(UpperCamelCase__      )





					def 		A      (  self  :		Union[str, Any]      )    ->							Union[str, Any]:
								UpperCAmelCase_      :       List[Any]          =       MagicMock(return_value=UpperCamelCase__      )
								with patch('''transformers.onnx.features.is_tf_available'''				, UpperCamelCase__      ):
											UpperCAmelCase_      :       int          =       FeaturesManager.determine_framework(self.test_model      )
											self.assertEqual(UpperCamelCase__				, self.framework_pt      )

								# PyTorch not in environment -> use TensorFlow
								UpperCAmelCase_      :       Union[str, Any]          =       MagicMock(return_value=UpperCamelCase__      )
								with patch('''transformers.onnx.features.is_torch_available'''				, UpperCamelCase__      ):
											UpperCAmelCase_      :       List[Any]          =       FeaturesManager.determine_framework(self.test_model      )
											self.assertEqual(UpperCamelCase__				, self.framework_tf      )

								# Both in environment -> use PyTorch
								UpperCAmelCase_      :       Optional[Any]          =       MagicMock(return_value=UpperCamelCase__      )
								UpperCAmelCase_      :       Dict          =       MagicMock(return_value=UpperCamelCase__      )
								with patch('''transformers.onnx.features.is_tf_available'''				, UpperCamelCase__      ), patch(
								    '''transformers.onnx.features.is_torch_available'''				, UpperCamelCase__      ):
											UpperCAmelCase_      :       List[str]          =       FeaturesManager.determine_framework(self.test_model      )
											self.assertEqual(UpperCamelCase__				, self.framework_pt      )

								# Both not in environment -> raise error
								UpperCAmelCase_      :       List[Any]          =       MagicMock(return_value=UpperCamelCase__      )
								UpperCAmelCase_      :       List[Any]          =       MagicMock(return_value=UpperCamelCase__      )
								with patch('''transformers.onnx.features.is_tf_available'''				, UpperCamelCase__      ), patch(
								    '''transformers.onnx.features.is_torch_available'''				, UpperCamelCase__      ):
											with self.assertRaises(UpperCamelCase__      ):
														UpperCAmelCase_      :       Optional[int]          =       FeaturesManager.determine_framework(self.test_model      )






"""simple docstring"""



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

import numpy as np
import PIL
import torch
import torch.utils.checkpoint

from ...models import UNetaDModel, VQModel
from ...schedulers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







def     __lowerCAmelCase		(		lowercase    :    Optional[int]					)      ->		Union[str, Any]:


    """simple docstring"""





    snake_case   ,snake_case       : List[str]    =       image.size
    snake_case   ,snake_case       : int    =       (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
    snake_case       : Any    =       image.resize((w, h)		,				resample=PIL_INTERPOLATION["lanczos"]					)
    snake_case       : Dict    =       np.array(lowercase					).astype(np.floataa					) / 255.0
    snake_case       : List[str]    =       image[None].transpose(0		,				3		,				1		,				2					)
    snake_case       : List[Any]    =       torch.from_numpy(lowercase					)
    return 2.0 * image - 1.0







class _lowerCAmelCase							( snake_case_      ):







   def __init__(      self		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		,      )      ->						Optional[Any]:

       '''simple docstring'''




       super().__init__()
       self.register_modules(vqvae=UpperCamelCase__		,      unet=UpperCamelCase__		,      scheduler=UpperCamelCase__		)







   @torch.no_grad()
   def __call__(      self		,      UpperCamelCase__ = None		,      UpperCamelCase__ = 1		,      UpperCamelCase__ = 100		,      UpperCamelCase__ = 0.0		,      UpperCamelCase__ = None		,      UpperCamelCase__ = "pil"		,      UpperCamelCase__ = True		,      )      ->						Union[Tuple, ImagePipelineOutput]:

       '''simple docstring'''




       if isinstance(UpperCamelCase__		,      PIL.Image.Image		):
           snake_case       : Tuple    =       1
       elif isinstance(UpperCamelCase__		,      torch.Tensor		):
           snake_case       : str    =       image.shape[0]
       else:
           raise ValueError(F'`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(UpperCamelCase__		)}'		)

       if isinstance(UpperCamelCase__		,      PIL.Image.Image		):
           snake_case       : Optional[int]    =       preprocess(UpperCamelCase__		)

       snake_case   ,snake_case       : List[str]    =       image.shape[-2:]

       # in_channels should be 6: 3 for latents, 3 for low resolution image
       snake_case       : Any    =       (batch_size, self.unet.config.in_channels // 2, height, width)
       snake_case       : List[str]    =       next(self.unet.parameters()		).dtype

       snake_case       : int    =       randn_tensor(UpperCamelCase__		,      generator=UpperCamelCase__		,      device=self.device		,      dtype=UpperCamelCase__		)

       snake_case       : List[Any]    =       image.to(device=self.device		,      dtype=UpperCamelCase__		)

       # set timesteps and move to the correct device
       self.scheduler.set_timesteps(UpperCamelCase__		,      device=self.device		)
       snake_case       : Optional[Any]    =       self.scheduler.timesteps

       # scale the initial noise by the standard deviation required by the scheduler
       snake_case       : Optional[int]    =       latents * self.scheduler.init_noise_sigma

       # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
       # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
       # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
       # and should be between [0, 1]
       snake_case       : Optional[int]    =       "eta" in set(inspect.signature(self.scheduler.step		).parameters.keys()		)
       snake_case       : Union[str, Any]    =       {}
       if accepts_eta:
           snake_case       : List[str]    =       eta

       for t in self.progress_bar(UpperCamelCase__		):
           # concat latents and low resolution image in the channel dimension.
           snake_case       : int    =       torch.cat([latents, image]		,      dim=1		)
           snake_case       : List[Any]    =       self.scheduler.scale_model_input(UpperCamelCase__		,      UpperCamelCase__		)
           # predict the noise residual
           snake_case       : int    =       self.unet(UpperCamelCase__		,      UpperCamelCase__		).sample
           # compute the previous noisy sample x_t -> x_t-1
           snake_case       : List[Any]    =       self.scheduler.step(UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		,      **UpperCamelCase__		).prev_sample

       # decode the image latents with the VQVAE
       snake_case       : Optional[int]    =       self.vqvae.decode(UpperCamelCase__		).sample
       snake_case       : Tuple    =       torch.clamp(UpperCamelCase__		,      -1.0		,      1.0		)
       snake_case       : Union[str, Any]    =       image / 2 + 0.5
       snake_case       : Optional[Any]    =       image.cpu().permute(0		,      2		,      3		,      1		).numpy()

       if output_type == "pil":
           snake_case       : Union[str, Any]    =       self.numpy_to_pil(UpperCamelCase__		)

       if not return_dict:
           return (image,)

       return ImagePipelineOutput(images=UpperCamelCase__		)





