


'''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

__magic_name__			=      logging.get_logger(__name__)

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



class 	__lowerCAmelCase		(							__SCREAMING_SNAKE_CASE    ):


  '''simple docstring'''







  a_  					=							"""imagegpt"""
  a_  					=							["""past_key_values"""]
  a_  					=							{
      """hidden_size""": """n_embd""",
      """max_position_embeddings""": """n_positions""",
      """num_attention_heads""": """n_head""",
      """num_hidden_layers""": """n_layer""",
  }






  def __init__(							self :       str	,_a :       int=512 + 1	,_a :       List[str]=32 * 32	,_a :       int=512	,_a :       Optional[int]=24	,_a :       List[str]=8	,_a :       Dict=None	,_a :       Optional[Any]="quick_gelu"	,_a :       str=0.1	,_a :       Optional[Any]=0.1	,_a :       List[str]=0.1	,_a :       str=1e-5	,_a :       Union[str, Any]=0.02	,_a :       int=True	,_a :       Optional[int]=True	,_a :       Optional[Any]=False	,_a :       List[Any]=False	,_a :       str=False	,**_a :       Tuple	,):

        '''simple docstring'''




        A_						:  Optional[int]          =				vocab_size
        A_						:  List[Any]          =				n_positions
        A_						:  Any          =				n_embd
        A_						:  List[Any]          =				n_layer
        A_						:  Optional[int]          =				n_head
        A_						:  Union[str, Any]          =				n_inner
        A_						:  Tuple          =				activation_function
        A_						:  Union[str, Any]          =				resid_pdrop
        A_						:  Union[str, Any]          =				embd_pdrop
        A_						:  Tuple          =				attn_pdrop
        A_						:  List[Any]          =				layer_norm_epsilon
        A_						:  Union[str, Any]          =				initializer_range
        A_						:  Any          =				scale_attn_weights
        A_						:  int          =				use_cache
        A_						:  Tuple          =				scale_attn_by_inverse_layer_idx
        A_						:  Optional[Any]          =				reorder_and_upcast_attn
        A_						:  Dict          =				tie_word_embeddings

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







class 	__lowerCAmelCase		(							__SCREAMING_SNAKE_CASE    ):


  '''simple docstring'''







  @property
  def        _a							(							self :       List[str] ):

        '''simple docstring'''




        return OrderedDict(
            [
                ("""input_ids""", {0: """batch""", 1: """sequence"""}),
            ] )






  def        _a							(							self :       int	,_a :       "FeatureExtractionMixin"	,_a :       int = 1	,_a :       int = -1	,_a :       bool = False	,_a :       Optional["TensorType"] = None	,_a :       int = 3	,_a :       int = 32	,_a :       int = 32	,):

        '''simple docstring'''




        A_						:  Any          =				self._generate_dummy_images(_a	,_a	,_a	,_a )
        A_						:  List[Any]          =				dict(preprocessor(images=_a	,return_tensors=_a ) )

        return inputs




'''simple docstring'''


import gc
import random
import unittest

import numpy as np
import torch

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()



class 	__lowerCAmelCase		(							__SCREAMING_SNAKE_CASE  , unittest.TestCase    ):


  '''simple docstring'''







  a_  					=							KandinskyVaaControlnetPipeline
  a_  					=							["""image_embeds""", """negative_image_embeds""", """hint"""]
  a_  					=							["""image_embeds""", """negative_image_embeds""", """hint"""]
  a_  					=							[
      """generator""",
      """height""",
      """width""",
      """latents""",
      """guidance_scale""",
      """num_inference_steps""",
      """return_dict""",
      """guidance_scale""",
      """num_images_per_prompt""",
      """output_type""",
      """return_dict""",
  ]
  a_  					=							False






  @property
  def        _a							(							self :       Any ):

        '''simple docstring'''




        return 32






  @property
  def        _a							(							self :       Tuple ):

        '''simple docstring'''




        return 32






  @property
  def        _a							(							self :       Tuple ):

        '''simple docstring'''




        return self.time_input_dim






  @property
  def        _a							(							self :       str ):

        '''simple docstring'''




        return self.time_input_dim * 4






  @property
  def        _a							(							self :       Optional[Any] ):

        '''simple docstring'''




        return 100






  @property
  def        _a							(							self :       List[Any] ):

        '''simple docstring'''




        torch.manual_seed(0 )

        A_						:  List[Any]          =				{
            """in_channels""": 8,
            # Out channels is double in channels because predicts mean and variance
            """out_channels""": 8,
            """addition_embed_type""": """image_hint""",
            """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
            """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
            """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
            """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
            """layers_per_block""": 1,
            """encoder_hid_dim""": self.text_embedder_hidden_size,
            """encoder_hid_dim_type""": """image_proj""",
            """cross_attention_dim""": self.cross_attention_dim,
            """attention_head_dim""": 4,
            """resnet_time_scale_shift""": """scale_shift""",
            """class_embed_type""": None,
        }

        A_						:  Tuple          =				UNetaDConditionModel(**_a )
        return model






  @property
  def        _a							(							self :       List[str] ):

        '''simple docstring'''




        return {
            "block_out_channels": [32, 32, 64, 64],
            "down_block_types": [
                "DownEncoderBlock2D",
                "DownEncoderBlock2D",
                "DownEncoderBlock2D",
                "AttnDownEncoderBlock2D",
            ],
            "in_channels": 3,
            "latent_channels": 4,
            "layers_per_block": 1,
            "norm_num_groups": 8,
            "norm_type": "spatial",
            "num_vq_embeddings": 12,
            "out_channels": 3,
            "up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
            "vq_embed_dim": 4,
        }






  @property
  def        _a							(							self :       Optional[int] ):

        '''simple docstring'''




        torch.manual_seed(0 )
        A_						:  int          =				VQModel(**self.dummy_movq_kwargs )
        return model






  def        _a							(							self :       List[str] ):

        '''simple docstring'''




        A_						:  Optional[Any]          =				self.dummy_unet
        A_						:  int          =				self.dummy_movq

        A_						:  Tuple          =				DDIMScheduler(
            num_train_timesteps=1000	,beta_schedule="""linear"""	,beta_start=0.00085	,beta_end=0.012	,clip_sample=_a	,set_alpha_to_one=_a	,steps_offset=1	,prediction_type="""epsilon"""	,thresholding=_a	,)

        A_						:  int          =				{
            """unet""": unet,
            """scheduler""": scheduler,
            """movq""": movq,
        }
        return components






  def        _a							(							self :       Dict	,_a :       str	,_a :       Union[str, Any]=0 ):

        '''simple docstring'''




        A_						:  Dict          =				floats_tensor((1, self.text_embedder_hidden_size)	,rng=random.Random(_a ) ).to(_a )
        A_						:  int          =				floats_tensor((1, self.text_embedder_hidden_size)	,rng=random.Random(seed + 1 ) ).to(
            _a )

        # create hint
        A_						:  List[Any]          =				floats_tensor((1, 3, 64, 64)	,rng=random.Random(_a ) ).to(_a )

        if str(_a ).startswith("""mps""" ):
              A_						:  Optional[Any]          =				torch.manual_seed(_a )
        else:
              A_						:  str          =				torch.Generator(device=_a ).manual_seed(_a )
        A_						:  List[Any]          =				{
            """image_embeds""": image_embeds,
            """negative_image_embeds""": negative_image_embeds,
            """hint""": hint,
            """generator""": generator,
            """height""": 64,
            """width""": 64,
            """guidance_scale""": 4.0,
            """num_inference_steps""": 2,
            """output_type""": """np""",
        }
        return inputs






  def        _a							(							self :       Dict ):

        '''simple docstring'''




        A_						:  List[Any]          =				"""cpu"""

        A_						:  List[str]          =				self.get_dummy_components()

        A_						:  Tuple          =				self.pipeline_class(**_a )
        A_						:  Dict          =				pipe.to(_a )

        pipe.set_progress_bar_config(disable=_a )

        A_						:  Tuple          =				pipe(**self.get_dummy_inputs(_a ) )
        A_						:  Tuple          =				output.images

        A_						:  Optional[Any]          =				pipe(
            **self.get_dummy_inputs(_a )	,return_dict=_a	,)[0]

        A_						:  Tuple          =				image[0, -3:, -3:, -1]
        A_						:  Any          =				image_from_tuple[0, -3:, -3:, -1]

        assert image.shape == (1, 64, 64, 3)

        A_						:  List[Any]          =				np.array(
            [0.6959826, 0.868279, 0.7558092, 0.68769467, 0.85805804, 0.65977496, 0.44885302, 0.5959111, 0.4251595] )

        assert (
            np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
        ), f' expected_slice {expected_slice}, but got {image_slice.flatten()}'

        assert (
            np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
        ), f' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'







@slow
@require_torch_gpu
class 	__lowerCAmelCase		(							unittest.TestCase    ):


  '''simple docstring'''







  def        _a							(							self :       Tuple ):

        '''simple docstring'''




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






  def        _a							(							self :       Any ):

        '''simple docstring'''




        A_						:  Tuple          =				load_numpy(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
            """/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy""" )

        A_						:  Optional[int]          =				load_image(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
            """/kandinskyv22/hint_image_cat.png""" )
        A_						:  Optional[int]          =				torch.from_numpy(np.array(_a ) ).float() / 255.0
        A_						:  List[Any]          =				hint.permute(2	,0	,1 ).unsqueeze(0 )

        A_						:  List[Any]          =				KandinskyVaaPriorPipeline.from_pretrained(
            """kandinsky-community/kandinsky-2-2-prior"""	,torch_dtype=torch.floataa )
        pipe_prior.to(_a )

        A_						:  Union[str, Any]          =				KandinskyVaaControlnetPipeline.from_pretrained(
            """kandinsky-community/kandinsky-2-2-controlnet-depth"""	,torch_dtype=torch.floataa )
        A_						:  Union[str, Any]          =				pipeline.to(_a )
        pipeline.set_progress_bar_config(disable=_a )

        A_						:  Optional[Any]          =				"""A robot, 4k photo"""

        A_						:  Any          =				torch.Generator(device="""cuda""" ).manual_seed(0 )
        A_   ,					A_						:  List[str]          =				pipe_prior(
            _a	,generator=_a	,num_inference_steps=5	,negative_prompt=""""""	,).to_tuple()

        A_						:  int          =				torch.Generator(device="""cuda""" ).manual_seed(0 )
        A_						:  List[Any]          =				pipeline(
            image_embeds=_a	,negative_image_embeds=_a	,hint=_a	,generator=_a	,num_inference_steps=100	,output_type="""np"""	,)

        A_						:  Dict          =				output.images[0]

        assert image.shape == (512, 512, 3)

        assert_mean_pixel_difference(_a	,_a )
