from typing import Dict, List, Optional, Union

import numpy as np

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,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
 import PIL


lowerCAmelCase_:					Tuple     =					logging.get_logger(__name__)






class    a__    (   _a	):
   snake_case_							=      ["pixel_values"]


   def __init__(     self,				_UpperCAmelCase = True,				_UpperCAmelCase = None,				_UpperCAmelCase = None,				_UpperCAmelCase = PILImageResampling.BILINEAR,				_UpperCAmelCase = True,				_UpperCAmelCase = 1 / 255,				_UpperCAmelCase = True,				_UpperCAmelCase = None,				_UpperCAmelCase = None,				**_UpperCAmelCase,				):
        '''simple docstring'''



        super().__init__(**_UpperCAmelCase  )
        lowercase__				 =							size if size is not None else {"shortest_edge": 384}
        lowercase__				 =							get_size_dict(_UpperCAmelCase,				default_to_square=_UpperCAmelCase  )

        lowercase__				 =							do_resize
        lowercase__				 =							size
        # Default value set here for backwards compatibility where the value in config is None
        lowercase__				 =							crop_pct if crop_pct is not None else 224 / 256
        lowercase__				 =							resample
        lowercase__				 =							do_rescale
        lowercase__				 =							rescale_factor
        lowercase__				 =							do_normalize
        lowercase__				 =							image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
        lowercase__				 =							image_std if image_std is not None else IMAGENET_STANDARD_STD


   def 	snake_case__	(     self,				_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase = PILImageResampling.BICUBIC,				_UpperCAmelCase = None,				**_UpperCAmelCase,				):
        '''simple docstring'''



        lowercase__				 =							get_size_dict(_UpperCAmelCase,				default_to_square=_UpperCAmelCase  )
        if "shortest_edge" not in size:
             raise ValueError(F'''Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}'''  )
        lowercase__				 =							size["shortest_edge"]

        if shortest_edge < 384:
             # maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
             lowercase__				 =							int(shortest_edge / crop_pct  )
             lowercase__				 =							get_resize_output_image_size(_UpperCAmelCase,				size=_UpperCAmelCase,				default_to_square=_UpperCAmelCase  )
             lowercase__				 =							resize(image=_UpperCAmelCase,				size=_UpperCAmelCase,				resample=_UpperCAmelCase,				data_format=_UpperCAmelCase,				**_UpperCAmelCase  )
             # then crop to (shortest_edge, shortest_edge)
             return center_crop(image=_UpperCAmelCase,				size=(shortest_edge, shortest_edge),				data_format=_UpperCAmelCase,				**_UpperCAmelCase  )
        else:
             # warping (no cropping) when evaluated at 384 or larger
             return resize(
                 _UpperCAmelCase,				size=(shortest_edge, shortest_edge),				resample=_UpperCAmelCase,				data_format=_UpperCAmelCase,				**_UpperCAmelCase  )


   def 	snake_case__	(     self,				_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase = None,				**_UpperCAmelCase,				):
        '''simple docstring'''



        return rescale(_UpperCAmelCase,				scale=_UpperCAmelCase,				data_format=_UpperCAmelCase,				**_UpperCAmelCase  )


   def 	snake_case__	(     self,				_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase = None,				**_UpperCAmelCase,				):
        '''simple docstring'''



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


   def 	snake_case__	(     self,				_UpperCAmelCase,				_UpperCAmelCase = None,				_UpperCAmelCase = None,				_UpperCAmelCase = None,				_UpperCAmelCase = None,				_UpperCAmelCase = None,				_UpperCAmelCase = None,				_UpperCAmelCase = None,				_UpperCAmelCase = None,				_UpperCAmelCase = None,				_UpperCAmelCase = None,				_UpperCAmelCase = ChannelDimension.FIRST,				**_UpperCAmelCase,				):
        '''simple docstring'''



        lowercase__				 =							do_resize if do_resize is not None else self.do_resize
        lowercase__				 =							crop_pct if crop_pct is not None else self.crop_pct
        lowercase__				 =							resample if resample is not None else self.resample
        lowercase__				 =							do_rescale if do_rescale is not None else self.do_rescale
        lowercase__				 =							rescale_factor if rescale_factor is not None else self.rescale_factor
        lowercase__				 =							do_normalize if do_normalize is not None else self.do_normalize
        lowercase__				 =							image_mean if image_mean is not None else self.image_mean
        lowercase__				 =							image_std if image_std is not None else self.image_std

        lowercase__				 =							size if size is not None else self.size
        lowercase__				 =							get_size_dict(_UpperCAmelCase,				default_to_square=_UpperCAmelCase  )

        lowercase__				 =							make_list_of_images(_UpperCAmelCase  )

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

        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_resize and size["shortest_edge"] < 384 and crop_pct is None:
             raise ValueError("crop_pct must be specified if size < 384."  )

        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."  )

        # All transformations expect numpy arrays.
        lowercase__				 =							[to_numpy_array(_UpperCAmelCase  ) for image in images]

        if do_resize:
             lowercase__				 =							[self.resize(image=_UpperCAmelCase,				size=_UpperCAmelCase,				crop_pct=_UpperCAmelCase,				resample=_UpperCAmelCase  ) for image in images]

        if do_rescale:
             lowercase__				 =							[self.rescale(image=_UpperCAmelCase,				scale=_UpperCAmelCase  ) for image in images]

        if do_normalize:
             lowercase__				 =							[self.normalize(image=_UpperCAmelCase,				mean=_UpperCAmelCase,				std=_UpperCAmelCase  ) for image in images]

        lowercase__				 =							[to_channel_dimension_format(_UpperCAmelCase,				_UpperCAmelCase  ) for image in images]

        lowercase__				 =							{"pixel_values": images}
        return BatchFeature(data=_UpperCAmelCase,				tensor_type=_UpperCAmelCase  )





"""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    a__    (   _a					,					unittest.TestCase	):
   snake_case_							=      PriorTransformer
   snake_case_							=      "hidden_states"


   @property
   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							4
        lowercase__				 =							8
        lowercase__				 =							7

        lowercase__				 =							floats_tensor((batch_size, embedding_dim)  ).to(_UpperCAmelCase  )

        lowercase__				 =							floats_tensor((batch_size, embedding_dim)  ).to(_UpperCAmelCase  )
        lowercase__				 =							floats_tensor((batch_size, num_embeddings, embedding_dim)  ).to(_UpperCAmelCase  )

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


   def 	snake_case__	(     self,				_UpperCAmelCase=0  ):
        '''simple docstring'''



        torch.manual_seed(_UpperCAmelCase  )
        lowercase__				 =							4
        lowercase__				 =							8
        lowercase__				 =							7

        lowercase__				 =							torch.randn((batch_size, embedding_dim)  ).to(_UpperCAmelCase  )

        lowercase__				 =							torch.randn((batch_size, embedding_dim)  ).to(_UpperCAmelCase  )
        lowercase__				 =							torch.randn((batch_size, num_embeddings, embedding_dim)  ).to(_UpperCAmelCase  )

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


   @property
   def 	snake_case__	(     self  ):
        '''simple docstring'''



        return (4, 8)


   @property
   def 	snake_case__	(     self  ):
        '''simple docstring'''



        return (4, 8)


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							{
            "num_attention_heads": 2,
            "attention_head_dim": 4,
            "num_layers": 2,
            "embedding_dim": 8,
            "num_embeddings": 7,
            "additional_embeddings": 4,
        }
        lowercase__				 =							self.dummy_input
        return init_dict, inputs_dict


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__ ,						lowercase__				 =							PriorTransformer.from_pretrained(
            "hf-internal-testing/prior-dummy",				output_loading_info=_UpperCAmelCase  )
        self.assertIsNotNone(_UpperCAmelCase  )
        self.assertEqual(len(loading_info["missing_keys"]  ),				0  )

        model.to(_UpperCAmelCase  )
        lowercase__				 =							model(**self.dummy_input  )[0]

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


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__ ,						lowercase__				 =							self.prepare_init_args_and_inputs_for_common()

        lowercase__				 =							self.model_class(**_UpperCAmelCase  )
        lowercase__				 =							inspect.signature(model.forward  )
        # signature.parameters is an OrderedDict => so arg_names order is deterministic
        lowercase__				 =							[*signature.parameters.keys()]

        lowercase__				 =							["hidden_states", "timestep"]
        self.assertListEqual(arg_names[:2],				_UpperCAmelCase  )


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							PriorTransformer.from_pretrained("hf-internal-testing/prior-dummy"  )
        lowercase__				 =							model.to(_UpperCAmelCase  )

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

        lowercase__				 =							self.get_dummy_seed_input()

        with torch.no_grad():
             lowercase__				 =							model(**_UpperCAmelCase  )[0]

        lowercase__				 =							output[0, :5].flatten().cpu()
        print(_UpperCAmelCase  )

        # 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.
        lowercase__				 =							torch.tensor([-1.3_436, -0.2_870, 0.7_538, 0.4_368, -0.0_239]  )
        self.assertTrue(torch_all_close(_UpperCAmelCase,				_UpperCAmelCase,				rtol=1E-2  )  )






@slow
class    a__    (   unittest.TestCase	):


   def 	snake_case__	(     self,				_UpperCAmelCase=1,				_UpperCAmelCase=768,				_UpperCAmelCase=77,				_UpperCAmelCase=0  ):
        '''simple docstring'''



        torch.manual_seed(_UpperCAmelCase  )
        lowercase__				 =							batch_size
        lowercase__				 =							embedding_dim
        lowercase__				 =							num_embeddings

        lowercase__				 =							torch.randn((batch_size, embedding_dim)  ).to(_UpperCAmelCase  )

        lowercase__				 =							torch.randn((batch_size, embedding_dim)  ).to(_UpperCAmelCase  )
        lowercase__				 =							torch.randn((batch_size, num_embeddings, embedding_dim)  ).to(_UpperCAmelCase  )

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


   def 	snake_case__	(     self  ):
        '''simple docstring'''



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


   @parameterized.expand(
       [
           # fmt: off
           [13, [-0.5_861,  0.1_283, -0.0_931,  0.0_882,  0.4_476,  0.1_329, -0.0_498,  0.0_640]],
           [37, [-0.4_913,  0.0_110, -0.0_483,  0.0_541,  0.4_954, -0.0_170,  0.0_354,  0.1_651]],
           # fmt: on
       ]  )
   def 	snake_case__	(     self,				_UpperCAmelCase,				_UpperCAmelCase  ):
        '''simple docstring'''



        lowercase__				 =							PriorTransformer.from_pretrained("kandinsky-community/kandinsky-2-1-prior",				subfolder="prior"  )
        model.to(_UpperCAmelCase  )
        lowercase__				 =							self.get_dummy_seed_input(seed=_UpperCAmelCase  )

        with torch.no_grad():
             lowercase__				 =							model(**_UpperCAmelCase  )[0]

        assert list(sample.shape  ) == [1, 768]

        lowercase__				 =							sample[0, :8].flatten().cpu()
        print(_UpperCAmelCase  )
        lowercase__				 =							torch.tensor(_UpperCAmelCase  )

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




