
'''simple docstring'''

from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
    import PIL

__snake_case:  Tuple								=			logging.get_logger(__name__)






def 	_snake_case						(      A_       :    Tuple   ):
  """simple docstring"""







  if isinstance(A_     ,		(list, tuple)   ) and isinstance(videos[0]     ,		(list, tuple)   ) and is_valid_image(videos[0][0]   ):
    return videos

  elif isinstance(A_     ,		(list, tuple)   ) and is_valid_image(videos[0]   ):
    return [videos]

  elif is_valid_image(A_   ):
    return [[videos]]

  raise ValueError(f'''Could not make batched video from {videos}'''   )




class  _UpperCAmelCase ( lowerCAmelCase__    ):

     """simple docstring"""

     a_    	=					["pixel_values"]


     def __init__(      self							,		lowerCAmelCase_ = True							,		lowerCAmelCase_ = None							,		lowerCAmelCase_ = PILImageResampling.BILINEAR							,		lowerCAmelCase_ = True							,		lowerCAmelCase_ = None							,		lowerCAmelCase_ = True							,		lowerCAmelCase_ = 1 / 2_55							,		lowerCAmelCase_ = True							,		lowerCAmelCase_ = True							,		lowerCAmelCase_ = None							,		lowerCAmelCase_ = None							,		**lowerCAmelCase_							,		):

       '''simple docstring'''

       super().__init__(**lowerCAmelCase_ )
       a_      :	List[str]      =							size if size is not None else {"""shortest_edge""": 2_56}
       a_      :	Any      =							get_size_dict(lowerCAmelCase_							,		default_to_square=lowerCAmelCase_ )
       a_      :	List[Any]      =							crop_size if crop_size is not None else {"""height""": 2_24, """width""": 2_24}
       a_      :	Union[str, Any]      =							get_size_dict(lowerCAmelCase_							,		param_name="""crop_size""" )

       a_      :	Optional[Any]      =							do_resize
       a_      :	Tuple      =							size
       a_      :	List[str]      =							do_center_crop
       a_      :	List[str]      =							crop_size
       a_      :	str      =							resample
       a_      :	List[Any]      =							do_rescale
       a_      :	Optional[int]      =							rescale_factor
       a_      :	Optional[int]      =							offset
       a_      :	str      =							do_normalize
       a_      :	List[str]      =							image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
       a_      :	Optional[Any]      =							image_std if image_std is not None else IMAGENET_STANDARD_STD


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_ = PILImageResampling.BILINEAR							,		lowerCAmelCase_ = None							,		**lowerCAmelCase_							,		):

       '''simple docstring'''

       a_      :	int      =							get_size_dict(lowerCAmelCase_							,		default_to_square=lowerCAmelCase_ )
       if "shortest_edge" in size:
         a_      :	Optional[Any]      =							get_resize_output_image_size(lowerCAmelCase_							,		size["""shortest_edge"""]							,		default_to_square=lowerCAmelCase_ )
       elif "height" in size and "width" in size:
         a_      :	str      =							(size["""height"""], size["""width"""])
       else:
         raise ValueError(f'''Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}''' )
       return resize(lowerCAmelCase_							,		size=lowerCAmelCase_							,		resample=lowerCAmelCase_							,		data_format=lowerCAmelCase_							,		**lowerCAmelCase_ )


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_ = None							,		**lowerCAmelCase_							,		):

       '''simple docstring'''

       a_      :	str      =							get_size_dict(lowerCAmelCase_ )
       if "height" not in size or "width" not in size:
         raise ValueError(f'''Size must have \'height\' and \'width\' as keys. Got {size.keys()}''' )
       return center_crop(lowerCAmelCase_							,		size=(size["""height"""], size["""width"""])							,		data_format=lowerCAmelCase_							,		**lowerCAmelCase_ )


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_ = True							,		lowerCAmelCase_ = None							,		**lowerCAmelCase_							,		):

       '''simple docstring'''

       a_      :	Any      =							image.astype(np.floataa )
       if offset:
         a_      :	List[str]      =							image - (scale / 2)
       return rescale(lowerCAmelCase_							,		scale=lowerCAmelCase_							,		data_format=lowerCAmelCase_							,		**lowerCAmelCase_ )


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_ = None							,		**lowerCAmelCase_							,		):

       '''simple docstring'''

       return normalize(lowerCAmelCase_							,		mean=lowerCAmelCase_							,		std=lowerCAmelCase_							,		data_format=lowerCAmelCase_							,		**lowerCAmelCase_ )


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_ = None							,		lowerCAmelCase_ = None							,		lowerCAmelCase_ = None							,		lowerCAmelCase_ = None							,		lowerCAmelCase_ = None							,		lowerCAmelCase_ = None							,		lowerCAmelCase_ = None							,		lowerCAmelCase_ = None							,		lowerCAmelCase_ = None							,		lowerCAmelCase_ = None							,		lowerCAmelCase_ = None							,		lowerCAmelCase_ = ChannelDimension.FIRST							,		):

       '''simple docstring'''

       if do_resize and size is None or resample is None:
         raise ValueError("""Size and resample must be specified if do_resize is True.""" )

       if do_center_crop and crop_size is None:
         raise ValueError("""Crop size must be specified if do_center_crop is True.""" )

       if do_rescale and rescale_factor is None:
         raise ValueError("""Rescale factor must be specified if do_rescale is True.""" )

       if do_normalize and (image_mean is None or image_std is None):
         raise ValueError("""Image mean and std must be specified if do_normalize is True.""" )

       if offset and not do_rescale:
         raise ValueError("""For offset, do_rescale must also be set to True.""" )

       # All transformations expect numpy arrays.
       a_      :	Optional[int]      =							to_numpy_array(lowerCAmelCase_ )

       if do_resize:
         a_      :	List[str]      =							self.resize(image=lowerCAmelCase_							,		size=lowerCAmelCase_							,		resample=lowerCAmelCase_ )

       if do_center_crop:
         a_      :	Optional[int]      =							self.center_crop(lowerCAmelCase_							,		size=lowerCAmelCase_ )

       if do_rescale:
         a_      :	List[Any]      =							self.rescale(image=lowerCAmelCase_							,		scale=lowerCAmelCase_							,		offset=lowerCAmelCase_ )

       if do_normalize:
         a_      :	List[str]      =							self.normalize(image=lowerCAmelCase_							,		mean=lowerCAmelCase_							,		std=lowerCAmelCase_ )

       a_      :	Tuple      =							to_channel_dimension_format(lowerCAmelCase_							,		lowerCAmelCase_ )
       return image






     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_ = None							,		lowerCAmelCase_ = None							,		lowerCAmelCase_ = None							,		lowerCAmelCase_ = None							,		lowerCAmelCase_ = None							,		lowerCAmelCase_ = None							,		lowerCAmelCase_ = None							,		lowerCAmelCase_ = None							,		lowerCAmelCase_ = None							,		lowerCAmelCase_ = None							,		lowerCAmelCase_ = None							,		lowerCAmelCase_ = None							,		lowerCAmelCase_ = ChannelDimension.FIRST							,		**lowerCAmelCase_							,		):

       '''simple docstring'''

       a_      :	List[Any]      =							do_resize if do_resize is not None else self.do_resize
       a_      :	int      =							resample if resample is not None else self.resample
       a_      :	List[Any]      =							do_center_crop if do_center_crop is not None else self.do_center_crop
       a_      :	Optional[int]      =							do_rescale if do_rescale is not None else self.do_rescale
       a_      :	Dict      =							rescale_factor if rescale_factor is not None else self.rescale_factor
       a_      :	Any      =							offset if offset is not None else self.offset
       a_      :	str      =							do_normalize if do_normalize is not None else self.do_normalize
       a_      :	Any      =							image_mean if image_mean is not None else self.image_mean
       a_      :	Optional[Any]      =							image_std if image_std is not None else self.image_std

       a_      :	List[Any]      =							size if size is not None else self.size
       a_      :	Any      =							get_size_dict(lowerCAmelCase_							,		default_to_square=lowerCAmelCase_ )
       a_      :	Any      =							crop_size if crop_size is not None else self.crop_size
       a_      :	Dict      =							get_size_dict(lowerCAmelCase_							,		param_name="""crop_size""" )

       if not valid_images(lowerCAmelCase_ ):
         raise ValueError(
             """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
             """torch.Tensor, tf.Tensor or jax.ndarray.""" )

       a_      :	Any      =							make_batched(lowerCAmelCase_ )

       a_      :	Union[str, Any]      =							[
           [
               self._preprocess_image(
                   image=lowerCAmelCase_							,		do_resize=lowerCAmelCase_							,		size=lowerCAmelCase_							,		resample=lowerCAmelCase_							,		do_center_crop=lowerCAmelCase_							,		crop_size=lowerCAmelCase_							,		do_rescale=lowerCAmelCase_							,		rescale_factor=lowerCAmelCase_							,		offset=lowerCAmelCase_							,		do_normalize=lowerCAmelCase_							,		image_mean=lowerCAmelCase_							,		image_std=lowerCAmelCase_							,		data_format=lowerCAmelCase_							,		)
               for img in video
           ]
           for video in videos
       ]

       a_      :	Dict      =							{"""pixel_values""": videos}
       return BatchFeature(data=lowerCAmelCase_							,		tensor_type=lowerCAmelCase_ )




'''simple docstring'''

import gc
import inspect
import unittest

import torch
from parameterized import parameterized

from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin


enable_full_determinism()


class  _UpperCAmelCase ( lowerCAmelCase__	,unittest.TestCase    ):

     """simple docstring"""

     a_    	=					PriorTransformer
     a_    	=					"hidden_states"


     @property
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Optional[int]      =							4
       a_      :	List[str]      =							8
       a_      :	List[str]      =							7

       a_      :	Tuple      =							floats_tensor((batch_size, embedding_dim) ).to(lowerCAmelCase_ )

       a_      :	List[Any]      =							floats_tensor((batch_size, embedding_dim) ).to(lowerCAmelCase_ )
       a_      :	List[Any]      =							floats_tensor((batch_size, num_embeddings, embedding_dim) ).to(lowerCAmelCase_ )

       return {
           "hidden_states": hidden_states,
           "timestep": 2,
           "proj_embedding": proj_embedding,
           "encoder_hidden_states": encoder_hidden_states,
       }


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_=0 ):

       '''simple docstring'''

       torch.manual_seed(lowerCAmelCase_ )
       a_      :	List[Any]      =							4
       a_      :	Union[str, Any]      =							8
       a_      :	Optional[int]      =							7

       a_      :	List[Any]      =							torch.randn((batch_size, embedding_dim) ).to(lowerCAmelCase_ )

       a_      :	Optional[Any]      =							torch.randn((batch_size, embedding_dim) ).to(lowerCAmelCase_ )
       a_      :	Optional[Any]      =							torch.randn((batch_size, num_embeddings, embedding_dim) ).to(lowerCAmelCase_ )

       return {
           "hidden_states": hidden_states,
           "timestep": 2,
           "proj_embedding": proj_embedding,
           "encoder_hidden_states": encoder_hidden_states,
       }


     @property
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       return (4, 8)


     @property
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       return (4, 8)


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Union[str, Any]      =							{
           """num_attention_heads""": 2,
           """attention_head_dim""": 4,
           """num_layers""": 2,
           """embedding_dim""": 8,
           """num_embeddings""": 7,
           """additional_embeddings""": 4,
       }
       a_      :	Optional[int]      =							self.dummy_input
       return init_dict, inputs_dict


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      , a_      :	Optional[Any]      =							PriorTransformer.from_pretrained(
           """hf-internal-testing/prior-dummy"""							,		output_loading_info=lowerCAmelCase_ )
       self.assertIsNotNone(lowerCAmelCase_ )
       self.assertEqual(len(loading_info["""missing_keys"""] )							,		0 )

       model.to(lowerCAmelCase_ )
       a_      :	List[Any]      =							model(**self.dummy_input )[0]

       assert hidden_states is not None, "Make sure output is not None"


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      , a_      :	Optional[int]      =							self.prepare_init_args_and_inputs_for_common()

       a_      :	str      =							self.model_class(**lowerCAmelCase_ )
       a_      :	Optional[Any]      =							inspect.signature(model.forward )
       # signature.parameters is an OrderedDict => so arg_names order is deterministic
       a_      :	str      =							[*signature.parameters.keys()]

       a_      :	str      =							["""hidden_states""", """timestep"""]
       self.assertListEqual(arg_names[:2]							,		lowerCAmelCase_ )






     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Optional[Any]      =							PriorTransformer.from_pretrained("""hf-internal-testing/prior-dummy""" )
       a_      :	Optional[Any]      =							model.to(lowerCAmelCase_ )

       if hasattr(lowerCAmelCase_							,		"""set_default_attn_processor""" ):
         model.set_default_attn_processor()

       a_      :	Tuple      =							self.get_dummy_seed_input()

       with torch.no_grad():
         a_      :	List[str]      =							model(**lowerCAmelCase_ )[0]

       a_      :	Optional[Any]      =							output[0, :5].flatten().cpu()
       print(lowerCAmelCase_ )

       # Since the VAE Gaussian prior's generator is seeded on the appropriate device,
       # the expected output slices are not the same for CPU and GPU.
       a_      :	List[str]      =							torch.tensor([-1.3436, -0.2870, 0.7538, 0.4368, -0.0239] )
       self.assertTrue(torch_all_close(lowerCAmelCase_							,		lowerCAmelCase_							,		rtol=1E-2 ) )







@slow
class  _UpperCAmelCase ( unittest.TestCase    ):

     """simple docstring"""

     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_=1							,		lowerCAmelCase_=7_68							,		lowerCAmelCase_=77							,		lowerCAmelCase_=0 ):

       '''simple docstring'''

       torch.manual_seed(lowerCAmelCase_ )
       a_      :	str      =							batch_size
       a_      :	Dict      =							embedding_dim
       a_      :	Tuple      =							num_embeddings

       a_      :	Optional[Any]      =							torch.randn((batch_size, embedding_dim) ).to(lowerCAmelCase_ )

       a_      :	List[str]      =							torch.randn((batch_size, embedding_dim) ).to(lowerCAmelCase_ )
       a_      :	List[Any]      =							torch.randn((batch_size, num_embeddings, embedding_dim) ).to(lowerCAmelCase_ )

       return {
           "hidden_states": hidden_states,
           "timestep": 2,
           "proj_embedding": proj_embedding,
           "encoder_hidden_states": encoder_hidden_states,
       }


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()






     @parameterized.expand(
         [
             # fmt: off
             [13, [-0.5861,  0.1283, -0.0931,  0.0882,  0.4476,  0.1329, -0.0498,  0.0640]],
             [37, [-0.4913,  0.0110, -0.0483,  0.0541,  0.4954, -0.0170,  0.0354,  0.1651]],
             # fmt: on
         ] )
     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_ ):

       '''simple docstring'''

       a_      :	Any      =							PriorTransformer.from_pretrained("""kandinsky-community/kandinsky-2-1-prior"""							,		subfolder="""prior""" )
       model.to(lowerCAmelCase_ )
       a_      :	Optional[int]      =							self.get_dummy_seed_input(seed=lowerCAmelCase_ )

       with torch.no_grad():
         a_      :	str      =							model(**lowerCAmelCase_ )[0]

       assert list(sample.shape ) == [1, 7_68]

       a_      :	int      =							sample[0, :8].flatten().cpu()
       print(lowerCAmelCase_ )
       a_      :	str      =							torch.tensor(lowerCAmelCase_ )

       assert torch_all_close(lowerCAmelCase_							,		lowerCAmelCase_							,		atol=1E-3 )


