






'''simple docstring'''


import unittest

import numpy as np
import PIL
import torch

from diffusers.image_processor import VaeImageProcessor




class 							lowerCAmelCase					(							unittest.TestCase   ):







   """simple docstring"""







   @property
   def    __magic_name__    ( self			)    ->   Optional[Any]:
    __a    :	str									=						1
    __a    :	Optional[int]									=						3
    __a    :	List[str]									=						8
    __a    :	int									=						8

    __a    :	Optional[int]									=						torch.rand((batch_size, num_channels, height, width)			)

    return sample



   def    __magic_name__    ( self   ,   _A			)    ->   str:
    if isinstance(image[0]   ,   PIL.Image.Image			):
     return np.stack([np.array(_A			) for i in image]   ,   axis=0			)
    elif isinstance(_A   ,   torch.Tensor			):
     return image.cpu().numpy().transpose(0   ,   2   ,   3   ,   1			)
    return image



   def    __magic_name__    ( self			)    ->   Union[str, Any]:
    __a    :	Optional[int]									=						VaeImageProcessor(do_resize=_A   ,   do_normalize=_A			)

    __a    :	str									=						self.dummy_sample
    __a    :	List[Any]									=						self.to_np(_A			)

    for output_type in ["pt", "np", "pil"]:
     __a    :	List[str]									=						image_processor.postprocess(
         image_processor.preprocess(_A			)   ,   output_type=_A   ,   )
     __a    :	Optional[Any]									=						self.to_np(_A			)
     __a    :	Dict									=						(input_np * 255).round() if output_type == 'pil' else input_np
     assert (
         np.abs(in_np - out_np			).max() < 1E-6
     ), f'''decoded output does not match input for output_type {output_type}'''



   def    __magic_name__    ( self			)    ->   Any:
    __a    :	int									=						VaeImageProcessor(do_resize=_A   ,   do_normalize=_A			)
    __a    :	Tuple									=						self.dummy_sample.cpu().numpy().transpose(0   ,   2   ,   3   ,   1			)

    for output_type in ["pt", "np", "pil"]:
     __a    :	str									=						image_processor.postprocess(image_processor.preprocess(_A			)   ,   output_type=_A			)

     __a    :	int									=						self.to_np(_A			)
     __a    :	Any									=						(input_np * 255).round() if output_type == 'pil' else input_np
     assert (
         np.abs(in_np - out_np			).max() < 1E-6
     ), f'''decoded output does not match input for output_type {output_type}'''



   def    __magic_name__    ( self			)    ->   List[Any]:
    __a    :	List[Any]									=						VaeImageProcessor(do_resize=_A   ,   do_normalize=_A			)

    __a    :	List[Any]									=						self.dummy_sample.cpu().numpy().transpose(0   ,   2   ,   3   ,   1			)
    __a    :	Optional[int]									=						image_processor.numpy_to_pil(_A			)

    for output_type in ["pt", "np", "pil"]:
     __a    :	List[Any]									=						image_processor.postprocess(image_processor.preprocess(_A			)   ,   output_type=_A			)
     for i, o in zip(_A   ,   _A			):
      __a    :	List[Any]									=						np.array(_A			)
      __a    :	str									=						self.to_np(_A			) if output_type == 'pil' else (self.to_np(_A			) * 255).round()
      assert (
          np.abs(in_np - out_np			).max() < 1E-6
      ), f'''decoded output does not match input for output_type {output_type}'''



   def    __magic_name__    ( self			)    ->   Union[str, Any]:
    __a    :	List[Any]									=						VaeImageProcessor(do_resize=_A   ,   do_normalize=_A			)

    __a    :	str									=						self.dummy_sample
    __a    :	Union[str, Any]									=						input_pt_ad.squeeze(0			)

    __a    :	Dict									=						image_processor.postprocess(
        image_processor.preprocess(_A			)   ,   output_type='np'   ,   )
    __a    :	Optional[int]									=						image_processor.postprocess(
        image_processor.preprocess(_A			)   ,   output_type='np'   ,   )

    __a    :	Tuple									=						self.to_np(self.dummy_sample			)
    __a    :	Dict									=						input_np_ad.squeeze(0			)

    __a    :	Any									=						image_processor.postprocess(
        image_processor.preprocess(_A			)   ,   output_type='np'   ,   )
    __a    :	Any									=						image_processor.postprocess(
        image_processor.preprocess(_A			)   ,   output_type='np'   ,   )

    assert np.abs(out_pt_ad - out_pt_ad			).max() < 1E-6
    assert np.abs(out_np_ad - out_np_ad			).max() < 1E-6



   def    __magic_name__    ( self			)    ->   str:
    __a    :	Union[str, Any]									=						VaeImageProcessor(do_resize=_A   ,   do_normalize=_A			)

    __a    :	List[Any]									=						self.dummy_sample
    __a    :	Optional[Any]									=						list(_A			)

    __a    :	int									=						image_processor.postprocess(
        image_processor.preprocess(_A			)   ,   output_type='np'   ,   )

    __a    :	Optional[int]									=						image_processor.postprocess(
        image_processor.preprocess(_A			)   ,   output_type='np'   ,   )

    __a    :	int									=						self.to_np(self.dummy_sample			)
    list(_A			)

    __a    :	Tuple									=						image_processor.postprocess(
        image_processor.preprocess(_A			)   ,   output_type='np'   ,   )

    __a    :	str									=						image_processor.postprocess(
        image_processor.preprocess(_A			)   ,   output_type='np'   ,   )

    assert np.abs(out_pt_ad - out_pt_list			).max() < 1E-6
    assert np.abs(out_np_ad - out_np_list			).max() < 1E-6












'''simple docstring'''


from typing import List, Optional, Tuple, Union

from . import logging
from .import_utils import is_torch_available, is_torch_version


if is_torch_available():
  import torch

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

try:
  from torch._dynamo import allow_in_graph as maybe_allow_in_graph
except (ImportError, ModuleNotFoundError):






  def   lowerCAmelCase__			(					cls		):
   return cls






def   lowerCAmelCase__			(					SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__ = None       ,		SCREAMING_SNAKE_CASE__ = None       ,		SCREAMING_SNAKE_CASE__ = None       ,		SCREAMING_SNAKE_CASE__ = None       ,		):
 __a    :	int									=						device
 __a    :	Union[str, Any]									=						shape[0]

 __a    :	Union[str, Any]									=						layout or torch.strided
 __a    :	Any									=						device or torch.device('cpu'		)

 if generator is not None:
  __a    :	Optional[int]									=						generator.device.type if not isinstance(SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__		) else generator[0].device.type
  if gen_device_type != device.type and gen_device_type == "cpu":
   __a    :	int									=						'cpu'
   if device != "mps":
    logger.info(
        f'''The passed generator was created on \'cpu\' even though a tensor on {device} was expected.'''
        f''' Tensors will be created on \'cpu\' and then moved to {device}. Note that one can probably'''
        f''' slighly speed up this function by passing a generator that was created on the {device} device.'''		)
  elif gen_device_type != device.type and gen_device_type == "cuda":
   raise ValueError(f'''Cannot generate a {device} tensor from a generator of type {gen_device_type}.'''		)

 if isinstance(SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__		):
  __a    :	Optional[int]									=						(1,) + shape[1:]
  __a    :	Dict									=						[
      torch.randn(SCREAMING_SNAKE_CASE__       ,		generator=generator[i]       ,		device=SCREAMING_SNAKE_CASE__       ,		dtype=SCREAMING_SNAKE_CASE__       ,		layout=SCREAMING_SNAKE_CASE__		)
      for i in range(SCREAMING_SNAKE_CASE__		)
  ]
  __a    :	List[str]									=						torch.cat(SCREAMING_SNAKE_CASE__       ,		dim=0		).to(SCREAMING_SNAKE_CASE__		)
 else:
  __a    :	Tuple									=						torch.randn(SCREAMING_SNAKE_CASE__       ,		generator=SCREAMING_SNAKE_CASE__       ,		device=SCREAMING_SNAKE_CASE__       ,		dtype=SCREAMING_SNAKE_CASE__       ,		layout=SCREAMING_SNAKE_CASE__		).to(SCREAMING_SNAKE_CASE__		)

 return latents






def   lowerCAmelCase__			(					SCREAMING_SNAKE_CASE__		):
 if is_torch_version('<'       ,		'2.0.0'		) or not hasattr(SCREAMING_SNAKE_CASE__       ,		'_dynamo'		):
  return False
 return isinstance(SCREAMING_SNAKE_CASE__       ,		torch._dynamo.eval_frame.OptimizedModule		)




