



'''simple docstring'''







import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()





class 						_SCREAMING_SNAKE_CASE      ( unittest.TestCase						):






			"""simple docstring"""







			@property
			def 						_lowerCamelCase       (   self    ):







				"""simple docstring"""



				torch.manual_seed(0    )
				__lowerCamelCase		       =		UNetaDModel(
				    block_out_channels=(32, 64)       , layers_per_block=2       , sample_size=32       , in_channels=3       , out_channels=3       , down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')       , up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')       , )
				return model


			def 						_lowerCamelCase       (   self    ):







				"""simple docstring"""



				__lowerCamelCase		       =		self.dummy_uncond_unet
				__lowerCamelCase		       =		ScoreSdeVeScheduler()

				__lowerCamelCase		       =		ScoreSdeVePipeline(unet=__lowerCamelCase       , scheduler=__lowerCamelCase    )
				sde_ve.to(__lowerCamelCase    )
				sde_ve.set_progress_bar_config(disable=__lowerCamelCase    )

				__lowerCamelCase		       =		torch.manual_seed(0    )
				__lowerCamelCase		       =		sde_ve(num_inference_steps=2       , output_type='''numpy'''       , generator=__lowerCamelCase    ).images

				__lowerCamelCase		       =		torch.manual_seed(0    )
				__lowerCamelCase		       =		sde_ve(num_inference_steps=2       , output_type='''numpy'''       , generator=__lowerCamelCase       , return_dict=__lowerCamelCase    )[
				    0
				]

				__lowerCamelCase		       =		image[0, -3:, -3:, -1]
				__lowerCamelCase		       =		image_from_tuple[0, -3:, -3:, -1]

				assert image.shape == (1, 32, 32, 3)
				__lowerCamelCase		       =		np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]    )

				assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2
				assert np.abs(image_from_tuple_slice.flatten() - expected_slice    ).max() < 1E-2





@slow
@require_torch
class 						_SCREAMING_SNAKE_CASE      ( unittest.TestCase						):






			"""simple docstring"""







			def 						_lowerCamelCase       (   self    ):







				"""simple docstring"""



				__lowerCamelCase		       =		'''google/ncsnpp-church-256'''
				__lowerCamelCase		       =		UNetaDModel.from_pretrained(__lowerCamelCase    )

				__lowerCamelCase		       =		ScoreSdeVeScheduler.from_pretrained(__lowerCamelCase    )

				__lowerCamelCase		       =		ScoreSdeVePipeline(unet=__lowerCamelCase       , scheduler=__lowerCamelCase    )
				sde_ve.to(__lowerCamelCase    )
				sde_ve.set_progress_bar_config(disable=__lowerCamelCase    )

				__lowerCamelCase		       =		torch.manual_seed(0    )
				__lowerCamelCase		       =		sde_ve(num_inference_steps=10       , output_type='''numpy'''       , generator=__lowerCamelCase    ).images

				__lowerCamelCase		       =		image[0, -3:, -3:, -1]

				assert image.shape == (1, 2_56, 2_56, 3)

				__lowerCamelCase		       =		np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0]    )

				assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2





'''simple docstring'''





from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
    from ... import FeatureExtractionMixin, TensorType

__lowerCamelCase												=						logging.get_logger(__name__)

__lowerCamelCase												=						{
    '''openai/imagegpt-small''': '''''',
    '''openai/imagegpt-medium''': '''''',
    '''openai/imagegpt-large''': '''''',
}




class 				UpperCAmelCase			(			_snake_case						):
 UpperCAmelCase				=					"imagegpt"
 UpperCAmelCase				=					["past_key_values"]
 UpperCAmelCase				=					{
     "hidden_size": "n_embd",
     "max_position_embeddings": "n_positions",
     "num_attention_heads": "n_head",
     "num_hidden_layers": "n_layer",
 }


 def __init__(      self							:	List[Any]			,       __lowerCamelCase							:	List[Any]=5_1_2 + 1			,       __lowerCamelCase							:	Dict=3_2 * 3_2			,       __lowerCamelCase							:	List[str]=5_1_2			,       __lowerCamelCase							:	List[Any]=2_4			,       __lowerCamelCase							:	Any=8			,       __lowerCamelCase							:	Tuple=None			,       __lowerCamelCase							:	Any="quick_gelu"			,       __lowerCamelCase							:	Optional[int]=0.1			,       __lowerCamelCase							:	str=0.1			,       __lowerCamelCase							:	int=0.1			,       __lowerCamelCase							:	Tuple=1e-5			,       __lowerCamelCase							:	Dict=0.02			,       __lowerCamelCase							:	Tuple=True			,       __lowerCamelCase							:	str=True			,       __lowerCamelCase							:	Union[str, Any]=False			,       __lowerCamelCase							:	Union[str, Any]=False			,       __lowerCamelCase							:	Dict=False			,       **__lowerCamelCase							:	str			,       ):
    UpperCAmelCase__	:Dict      =      vocab_size
    UpperCAmelCase__	:str      =      n_positions
    UpperCAmelCase__	:Tuple      =      n_embd
    UpperCAmelCase__	:Dict      =      n_layer
    UpperCAmelCase__	:List[Any]      =      n_head
    UpperCAmelCase__	:str      =      n_inner
    UpperCAmelCase__	:Optional[Any]      =      activation_function
    UpperCAmelCase__	:str      =      resid_pdrop
    UpperCAmelCase__	:Optional[Any]      =      embd_pdrop
    UpperCAmelCase__	:Tuple      =      attn_pdrop
    UpperCAmelCase__	:int      =      layer_norm_epsilon
    UpperCAmelCase__	:List[Any]      =      initializer_range
    UpperCAmelCase__	:List[Any]      =      scale_attn_weights
    UpperCAmelCase__	:List[str]      =      use_cache
    UpperCAmelCase__	:Tuple      =      scale_attn_by_inverse_layer_idx
    UpperCAmelCase__	:Union[str, Any]      =      reorder_and_upcast_attn
    UpperCAmelCase__	:List[Any]      =      tie_word_embeddings

    super().__init__(tie_word_embeddings=__lowerCamelCase			,       **__lowerCamelCase )




class 				UpperCAmelCase			(			_snake_case						):


 @property
 def        __SCREAMING_SNAKE_CASE					(      self							:	str ):
    return OrderedDict(
        [
            ('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
        ] )





 def        __SCREAMING_SNAKE_CASE					(      self							:	Dict			,       __lowerCamelCase							:	"FeatureExtractionMixin"			,       __lowerCamelCase							:	int = 1			,       __lowerCamelCase							:	int = -1			,       __lowerCamelCase							:	bool = False			,       __lowerCamelCase							:	Optional["TensorType"] = None			,       __lowerCamelCase							:	int = 3			,       __lowerCamelCase							:	int = 3_2			,       __lowerCamelCase							:	int = 3_2			,       ):
    UpperCAmelCase__	:Tuple      =      self._generate_dummy_images(__lowerCamelCase			,       __lowerCamelCase			,       __lowerCamelCase			,       __lowerCamelCase )
    UpperCAmelCase__	:Dict      =      dict(preprocessor(images=__lowerCamelCase			,       return_tensors=__lowerCamelCase ) )

    return inputs



