

from typing import Any, Dict, Optional

import torch
import torch.nn.functional as F
from torch import nn

from ..utils import maybe_allow_in_graph
from .activations import get_activation
from .attention_processor import Attention
from .embeddings import CombinedTimestepLabelEmbeddings






@maybe_allow_in_graph
class  UpperCAmelCase__	( nn.Module ):






  """simple docstring"""


  def __init__(							self		:	int			,     __lowerCamelCase		:	int			,     __lowerCamelCase		:	int			,     __lowerCamelCase		:	int			,     __lowerCamelCase		:	Any=0.0			,     __lowerCamelCase		:	Optional[int] = None			,     __lowerCamelCase		:	str = "geglu"			,     __lowerCamelCase		:	Optional[int] = None			,     __lowerCamelCase		:	bool = False			,     __lowerCamelCase		:	bool = False			,     __lowerCamelCase		:	bool = False			,     __lowerCamelCase		:	bool = False			,     __lowerCamelCase		:	bool = True			,     __lowerCamelCase		:	str = "layer_norm"			,     __lowerCamelCase		:	bool = False			,     )	->							str:
       super().__init__()
       SCREAMING_SNAKE_CASE__					=					only_cross_attention

       SCREAMING_SNAKE_CASE__					=					(num_embeds_ada_norm is not None) and norm_type == '''ada_norm_zero'''
       SCREAMING_SNAKE_CASE__					=					(num_embeds_ada_norm is not None) and norm_type == '''ada_norm'''

       if norm_type in ("ada_norm", "ada_norm_zero") and num_embeds_ada_norm is None:
            raise ValueError(
                f'''`norm_type` is set to {norm_type}, but `num_embeds_ada_norm` is not defined. Please make sure to'''
                f''' define `num_embeds_ada_norm` if setting `norm_type` to {norm_type}.'''      )

       # Define 3 blocks. Each block has its own normalization layer.
       # 1. Self-Attn
       if self.use_ada_layer_norm:
            SCREAMING_SNAKE_CASE__					=					AdaLayerNorm(__lowerCamelCase			,     __lowerCamelCase      )
       elif self.use_ada_layer_norm_zero:
            SCREAMING_SNAKE_CASE__					=					AdaLayerNormZero(__lowerCamelCase			,     __lowerCamelCase      )
       else:
            SCREAMING_SNAKE_CASE__					=					nn.LayerNorm(__lowerCamelCase			,     elementwise_affine=__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					Attention(
           query_dim=__lowerCamelCase			,     heads=__lowerCamelCase			,     dim_head=__lowerCamelCase			,     dropout=__lowerCamelCase			,     bias=__lowerCamelCase			,     cross_attention_dim=cross_attention_dim if only_cross_attention else None			,     upcast_attention=__lowerCamelCase			,     )

       # 2. Cross-Attn
       if cross_attention_dim is not None or double_self_attention:
            # We currently only use AdaLayerNormZero for self attention where there will only be one attention block.
            # I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during
            # the second cross attention block.
            SCREAMING_SNAKE_CASE__					=					(
                AdaLayerNorm(__lowerCamelCase			,     __lowerCamelCase      )
                if self.use_ada_layer_norm
                else nn.LayerNorm(__lowerCamelCase			,     elementwise_affine=__lowerCamelCase      )
            )
            SCREAMING_SNAKE_CASE__					=					Attention(
                query_dim=__lowerCamelCase			,     cross_attention_dim=cross_attention_dim if not double_self_attention else None			,     heads=__lowerCamelCase			,     dim_head=__lowerCamelCase			,     dropout=__lowerCamelCase			,     bias=__lowerCamelCase			,     upcast_attention=__lowerCamelCase			,     )  # is self-attn if encoder_hidden_states is none
       else:
            SCREAMING_SNAKE_CASE__					=					None
            SCREAMING_SNAKE_CASE__					=					None

       # 3. Feed-forward
       SCREAMING_SNAKE_CASE__					=					nn.LayerNorm(__lowerCamelCase			,     elementwise_affine=__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					FeedForward(__lowerCamelCase			,     dropout=__lowerCamelCase			,     activation_fn=__lowerCamelCase			,     final_dropout=__lowerCamelCase      )

       # let chunk size default to None
       SCREAMING_SNAKE_CASE__					=					None
       SCREAMING_SNAKE_CASE__					=					0






  def    lowercase_	(							self		:	int			,     __lowerCamelCase		:	Optional[int]			,     __lowerCamelCase		:	int      )	->							Union[str, Any]:
       # Sets chunk feed-forward
       SCREAMING_SNAKE_CASE__					=					chunk_size
       SCREAMING_SNAKE_CASE__					=					dim







  def    lowercase_	(							self		:	Union[str, Any]			,     __lowerCamelCase		:	torch.FloatTensor			,     __lowerCamelCase		:	Optional[torch.FloatTensor] = None			,     __lowerCamelCase		:	Optional[torch.FloatTensor] = None			,     __lowerCamelCase		:	Optional[torch.FloatTensor] = None			,     __lowerCamelCase		:	Optional[torch.LongTensor] = None			,     __lowerCamelCase		:	Dict[str, Any] = None			,     __lowerCamelCase		:	Optional[torch.LongTensor] = None			,     )	->							Optional[Any]:
       # Notice that normalization is always applied before the real computation in the following blocks.
       # 1. Self-Attention
       if self.use_ada_layer_norm:
            SCREAMING_SNAKE_CASE__					=					self.norma(__lowerCamelCase			,     __lowerCamelCase      )
       elif self.use_ada_layer_norm_zero:
            SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__					=					self.norma(
                __lowerCamelCase			,     __lowerCamelCase			,     __lowerCamelCase			,     hidden_dtype=hidden_states.dtype      )
       else:
            SCREAMING_SNAKE_CASE__					=					self.norma(__lowerCamelCase      )

       SCREAMING_SNAKE_CASE__					=					cross_attention_kwargs if cross_attention_kwargs is not None else {}

       SCREAMING_SNAKE_CASE__					=					self.attna(
           __lowerCamelCase			,     encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None			,     attention_mask=__lowerCamelCase			,     **__lowerCamelCase			,     )
       if self.use_ada_layer_norm_zero:
            SCREAMING_SNAKE_CASE__					=					gate_msa.unsqueeze(1      ) * attn_output
       SCREAMING_SNAKE_CASE__					=					attn_output + hidden_states

       # 2. Cross-Attention
       if self.attna is not None:
            SCREAMING_SNAKE_CASE__					=					(
                self.norma(__lowerCamelCase			,     __lowerCamelCase      ) if self.use_ada_layer_norm else self.norma(__lowerCamelCase      )
            )

            SCREAMING_SNAKE_CASE__					=					self.attna(
                __lowerCamelCase			,     encoder_hidden_states=__lowerCamelCase			,     attention_mask=__lowerCamelCase			,     **__lowerCamelCase			,     )
            SCREAMING_SNAKE_CASE__					=					attn_output + hidden_states

       # 3. Feed-forward
       SCREAMING_SNAKE_CASE__					=					self.norma(__lowerCamelCase      )

       if self.use_ada_layer_norm_zero:
            SCREAMING_SNAKE_CASE__					=					norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]

       if self._chunk_size is not None:
            # "feed_forward_chunk_size" can be used to save memory
            if norm_hidden_states.shape[self._chunk_dim] % self._chunk_size != 0:
                 raise ValueError(
                     f'''`hidden_states` dimension to be chunked: {norm_hidden_states.shape[self._chunk_dim]} has to be divisible by chunk size: {self._chunk_size}. Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`.'''      )

            SCREAMING_SNAKE_CASE__					=					norm_hidden_states.shape[self._chunk_dim] // self._chunk_size
            SCREAMING_SNAKE_CASE__					=					torch.cat(
                [self.ff(__lowerCamelCase      ) for hid_slice in norm_hidden_states.chunk(__lowerCamelCase			,     dim=self._chunk_dim      )]			,     dim=self._chunk_dim			,     )
       else:
            SCREAMING_SNAKE_CASE__					=					self.ff(__lowerCamelCase      )

       if self.use_ada_layer_norm_zero:
            SCREAMING_SNAKE_CASE__					=					gate_mlp.unsqueeze(1      ) * ff_output

       SCREAMING_SNAKE_CASE__					=					ff_output + hidden_states

       return hidden_states






class  UpperCAmelCase__	( nn.Module ):






  """simple docstring"""


  def __init__(							self		:	List[str]			,     __lowerCamelCase		:	int			,     __lowerCamelCase		:	Optional[int] = None			,     __lowerCamelCase		:	int = 4			,     __lowerCamelCase		:	float = 0.0			,     __lowerCamelCase		:	str = "geglu"			,     __lowerCamelCase		:	bool = False			,     )	->							Dict:
       super().__init__()
       SCREAMING_SNAKE_CASE__					=					int(dim * mult      )
       SCREAMING_SNAKE_CASE__					=					dim_out if dim_out is not None else dim

       if activation_fn == "gelu":
            SCREAMING_SNAKE_CASE__					=					GELU(__lowerCamelCase			,     __lowerCamelCase      )
       if activation_fn == "gelu-approximate":
            SCREAMING_SNAKE_CASE__					=					GELU(__lowerCamelCase			,     __lowerCamelCase			,     approximate='''tanh'''      )
       elif activation_fn == "geglu":
            SCREAMING_SNAKE_CASE__					=					GEGLU(__lowerCamelCase			,     __lowerCamelCase      )
       elif activation_fn == "geglu-approximate":
            SCREAMING_SNAKE_CASE__					=					ApproximateGELU(__lowerCamelCase			,     __lowerCamelCase      )

       SCREAMING_SNAKE_CASE__					=					nn.ModuleList([]      )
       # project in
       self.net.append(__lowerCamelCase      )
       # project dropout
       self.net.append(nn.Dropout(__lowerCamelCase      )      )
       # project out
       self.net.append(nn.Linear(__lowerCamelCase			,     __lowerCamelCase      )      )
       # FF as used in Vision Transformer, MLP-Mixer, etc. have a final dropout
       if final_dropout:
            self.net.append(nn.Dropout(__lowerCamelCase      )      )







  def    lowercase_	(							self		:	Optional[Any]			,     __lowerCamelCase		:	int      )	->							Optional[Any]:
       for module in self.net:
            SCREAMING_SNAKE_CASE__					=					module(__lowerCamelCase      )
       return hidden_states






class  UpperCAmelCase__	( nn.Module ):






  """simple docstring"""


  def __init__(							self		:	str			,     __lowerCamelCase		:	int			,     __lowerCamelCase		:	int			,     __lowerCamelCase		:	str = "none"      )	->							Optional[Any]:
       super().__init__()
       SCREAMING_SNAKE_CASE__					=					nn.Linear(__lowerCamelCase			,     __lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					approximate






  def    lowercase_	(							self		:	Union[str, Any]			,     __lowerCamelCase		:	Any      )	->							str:
       if gate.device.type != "mps":
            return F.gelu(__lowerCamelCase			,     approximate=self.approximate      )
       # mps: gelu is not implemented for float16
       return F.gelu(gate.to(dtype=torch.floataa      )			,     approximate=self.approximate      ).to(dtype=gate.dtype      )







  def    lowercase_	(							self		:	Union[str, Any]			,     __lowerCamelCase		:	List[str]      )	->							int:
       SCREAMING_SNAKE_CASE__					=					self.proj(__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					self.gelu(__lowerCamelCase      )
       return hidden_states






class  UpperCAmelCase__	( nn.Module ):






  """simple docstring"""


  def __init__(							self		:	Optional[int]			,     __lowerCamelCase		:	int			,     __lowerCamelCase		:	int      )	->							Tuple:
       super().__init__()
       SCREAMING_SNAKE_CASE__					=					nn.Linear(__lowerCamelCase			,     dim_out * 2      )






  def    lowercase_	(							self		:	int			,     __lowerCamelCase		:	Optional[Any]      )	->							str:
       if gate.device.type != "mps":
            return F.gelu(__lowerCamelCase      )
       # mps: gelu is not implemented for float16
       return F.gelu(gate.to(dtype=torch.floataa      )      ).to(dtype=gate.dtype      )







  def    lowercase_	(							self		:	List[str]			,     __lowerCamelCase		:	Dict      )	->							Dict:
       SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__					=					self.proj(__lowerCamelCase      ).chunk(2			,     dim=-1      )
       return hidden_states * self.gelu(__lowerCamelCase      )






class  UpperCAmelCase__	( nn.Module ):






  """simple docstring"""


  def __init__(							self		:	List[str]			,     __lowerCamelCase		:	int			,     __lowerCamelCase		:	int      )	->							Union[str, Any]:
       super().__init__()
       SCREAMING_SNAKE_CASE__					=					nn.Linear(__lowerCamelCase			,     __lowerCamelCase      )







  def    lowercase_	(							self		:	str			,     __lowerCamelCase		:	List[Any]      )	->							Dict:
       SCREAMING_SNAKE_CASE__					=					self.proj(__lowerCamelCase      )
       return x * torch.sigmoid(1.702 * x      )






class  UpperCAmelCase__	( nn.Module ):






  """simple docstring"""


  def __init__(							self		:	str			,     __lowerCamelCase		:	Any			,     __lowerCamelCase		:	Optional[Any]      )	->							Any:
       super().__init__()
       SCREAMING_SNAKE_CASE__					=					nn.Embedding(__lowerCamelCase			,     __lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					nn.SiLU()
       SCREAMING_SNAKE_CASE__					=					nn.Linear(__lowerCamelCase			,     embedding_dim * 2      )
       SCREAMING_SNAKE_CASE__					=					nn.LayerNorm(__lowerCamelCase			,     elementwise_affine=__lowerCamelCase      )







  def    lowercase_	(							self		:	Optional[int]			,     __lowerCamelCase		:	Optional[int]			,     __lowerCamelCase		:	int      )	->							Any:
       SCREAMING_SNAKE_CASE__					=					self.linear(self.silu(self.emb(__lowerCamelCase      )      )      )
       SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__					=					torch.chunk(__lowerCamelCase			,     2      )
       SCREAMING_SNAKE_CASE__					=					self.norm(__lowerCamelCase      ) * (1 + scale) + shift
       return x






class  UpperCAmelCase__	( nn.Module ):






  """simple docstring"""


  def __init__(							self		:	Optional[Any]			,     __lowerCamelCase		:	Optional[Any]			,     __lowerCamelCase		:	int      )	->							List[Any]:
       super().__init__()

       SCREAMING_SNAKE_CASE__					=					CombinedTimestepLabelEmbeddings(__lowerCamelCase			,     __lowerCamelCase      )

       SCREAMING_SNAKE_CASE__					=					nn.SiLU()
       SCREAMING_SNAKE_CASE__					=					nn.Linear(__lowerCamelCase			,     6 * embedding_dim			,     bias=__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					nn.LayerNorm(__lowerCamelCase			,     elementwise_affine=__lowerCamelCase			,     eps=1e-6      )







  def    lowercase_	(							self		:	Optional[int]			,     __lowerCamelCase		:	Union[str, Any]			,     __lowerCamelCase		:	Optional[int]			,     __lowerCamelCase		:	List[Any]			,     __lowerCamelCase		:	Tuple=None      )	->							List[Any]:
       SCREAMING_SNAKE_CASE__					=					self.linear(self.silu(self.emb(__lowerCamelCase			,     __lowerCamelCase			,     hidden_dtype=__lowerCamelCase      )      )      )
       SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__					=					emb.chunk(6			,     dim=1      )
       SCREAMING_SNAKE_CASE__					=					self.norm(__lowerCamelCase      ) * (1 + scale_msa[:, None]) + shift_msa[:, None]
       return x, gate_msa, shift_mlp, scale_mlp, gate_mlp






class  UpperCAmelCase__	( nn.Module ):






  """simple docstring"""


  def __init__(							self		:	str			,     __lowerCamelCase		:	int			,     __lowerCamelCase		:	int			,     __lowerCamelCase		:	int			,     __lowerCamelCase		:	Optional[str] = None			,     __lowerCamelCase		:	float = 1e-5      )	->							int:
       super().__init__()
       SCREAMING_SNAKE_CASE__					=					num_groups
       SCREAMING_SNAKE_CASE__					=					eps

       if act_fn is None:
            SCREAMING_SNAKE_CASE__					=					None
       else:
            SCREAMING_SNAKE_CASE__					=					get_activation(__lowerCamelCase      )

       SCREAMING_SNAKE_CASE__					=					nn.Linear(__lowerCamelCase			,     out_dim * 2      )







  def    lowercase_	(							self		:	Union[str, Any]			,     __lowerCamelCase		:	Optional[Any]			,     __lowerCamelCase		:	List[str]      )	->							Optional[int]:
       if self.act:
            SCREAMING_SNAKE_CASE__					=					self.act(__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					self.linear(__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					emb[:, :, None, None]
       SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__					=					emb.chunk(2			,     dim=1      )

       SCREAMING_SNAKE_CASE__					=					F.group_norm(__lowerCamelCase			,     self.num_groups			,     eps=self.eps      )
       SCREAMING_SNAKE_CASE__					=					x * (1 + scale) + shift
       return x





import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionPanoramaPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()






@skip_mps
class  UpperCAmelCase__	( A__		,  A__		,  unittest.TestCase ):






  """simple docstring"""


  a     =      StableDiffusionPanoramaPipeline
  a     =      TEXT_TO_IMAGE_PARAMS
  a     =      TEXT_TO_IMAGE_BATCH_PARAMS
  a     =      TEXT_TO_IMAGE_IMAGE_PARAMS
  a     =      TEXT_TO_IMAGE_IMAGE_PARAMS






  def    lowercase_	(							self		:	Union[str, Any]      )	->							List[str]:
       torch.manual_seed(0      )
       SCREAMING_SNAKE_CASE__					=					UNetaDConditionModel(
           block_out_channels=(32, 64)			,     layers_per_block=1			,     sample_size=32			,     in_channels=4			,     out_channels=4			,     down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')			,     up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')			,     cross_attention_dim=32			,     )
       SCREAMING_SNAKE_CASE__					=					DDIMScheduler()
       torch.manual_seed(0      )
       SCREAMING_SNAKE_CASE__					=					AutoencoderKL(
           block_out_channels=[32, 64]			,     in_channels=3			,     out_channels=3			,     down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']			,     up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']			,     latent_channels=4			,     )
       torch.manual_seed(0      )
       SCREAMING_SNAKE_CASE__					=					CLIPTextConfig(
           bos_token_id=0			,     eos_token_id=2			,     hidden_size=32			,     intermediate_size=37			,     layer_norm_eps=1e-05			,     num_attention_heads=4			,     num_hidden_layers=5			,     pad_token_id=1			,     vocab_size=1000			,     )
       SCREAMING_SNAKE_CASE__					=					CLIPTextModel(__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''      )

       SCREAMING_SNAKE_CASE__					=					{
           '''unet''': unet,
           '''scheduler''': scheduler,
           '''vae''': vae,
           '''text_encoder''': text_encoder,
           '''tokenizer''': tokenizer,
           '''safety_checker''': None,
           '''feature_extractor''': None,
       }
       return components






  def    lowercase_	(							self		:	Union[str, Any]			,     __lowerCamelCase		:	Union[str, Any]			,     __lowerCamelCase		:	List[str]=0      )	->							Any:
       SCREAMING_SNAKE_CASE__					=					torch.manual_seed(__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					{
           '''prompt''': '''a photo of the dolomites''',
           '''generator''': generator,
           # Setting height and width to None to prevent OOMs on CPU.
           '''height''': None,
           '''width''': None,
           '''num_inference_steps''': 1,
           '''guidance_scale''': 6.0,
           '''output_type''': '''numpy''',
       }
       return inputs






  def    lowercase_	(							self		:	Optional[int]      )	->							List[str]:
       SCREAMING_SNAKE_CASE__					=					'''cpu'''  # ensure determinism for the device-dependent torch.Generator
       SCREAMING_SNAKE_CASE__					=					self.get_dummy_components()
       SCREAMING_SNAKE_CASE__					=					StableDiffusionPanoramaPipeline(**__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					sd_pipe.to(__lowerCamelCase      )
       sd_pipe.set_progress_bar_config(disable=__lowerCamelCase      )

       SCREAMING_SNAKE_CASE__					=					self.get_dummy_inputs(__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					sd_pipe(**__lowerCamelCase      ).images
       SCREAMING_SNAKE_CASE__					=					image[0, -3:, -3:, -1]
       assert image.shape == (1, 64, 64, 3)

       SCREAMING_SNAKE_CASE__					=					np.array([0.6186, 0.5374, 0.4915, 0.4135, 0.4114, 0.4563, 0.5128, 0.4977, 0.4757]      )

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






  def    lowercase_	(							self		:	Any      )	->							Optional[Any]:
       super().test_inference_batch_consistent(batch_sizes=[1, 2]      )






  def    lowercase_	(							self		:	List[Any]      )	->							List[Any]:
       super().test_inference_batch_single_identical(batch_size=2			,     expected_max_diff=3.25e-3      )






  def    lowercase_	(							self		:	Union[str, Any]      )	->							str:
       SCREAMING_SNAKE_CASE__					=					'''cpu'''  # ensure determinism for the device-dependent torch.Generator
       SCREAMING_SNAKE_CASE__					=					self.get_dummy_components()
       SCREAMING_SNAKE_CASE__					=					StableDiffusionPanoramaPipeline(**__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					sd_pipe.to(__lowerCamelCase      )
       sd_pipe.set_progress_bar_config(disable=__lowerCamelCase      )

       SCREAMING_SNAKE_CASE__					=					self.get_dummy_inputs(__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					'''french fries'''
       SCREAMING_SNAKE_CASE__					=					sd_pipe(**__lowerCamelCase			,     negative_prompt=__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					output.images
       SCREAMING_SNAKE_CASE__					=					image[0, -3:, -3:, -1]

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

       SCREAMING_SNAKE_CASE__					=					np.array([0.6187, 0.5375, 0.4915, 0.4136, 0.4114, 0.4563, 0.5128, 0.4976, 0.4757]      )

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






  def    lowercase_	(							self		:	Tuple      )	->							Dict:
       SCREAMING_SNAKE_CASE__					=					'''cpu'''  # ensure determinism for the device-dependent torch.Generator
       SCREAMING_SNAKE_CASE__					=					self.get_dummy_components()
       SCREAMING_SNAKE_CASE__					=					StableDiffusionPanoramaPipeline(**__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					sd_pipe.to(__lowerCamelCase      )
       sd_pipe.set_progress_bar_config(disable=__lowerCamelCase      )

       SCREAMING_SNAKE_CASE__					=					self.get_dummy_inputs(__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					sd_pipe(**__lowerCamelCase			,     view_batch_size=2      )
       SCREAMING_SNAKE_CASE__					=					output.images
       SCREAMING_SNAKE_CASE__					=					image[0, -3:, -3:, -1]

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

       SCREAMING_SNAKE_CASE__					=					np.array([0.6187, 0.5375, 0.4915, 0.4136, 0.4114, 0.4563, 0.5128, 0.4976, 0.4757]      )

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






  def    lowercase_	(							self		:	Optional[Any]      )	->							Any:
       SCREAMING_SNAKE_CASE__					=					'''cpu'''  # ensure determinism for the device-dependent torch.Generator
       SCREAMING_SNAKE_CASE__					=					self.get_dummy_components()
       SCREAMING_SNAKE_CASE__					=					EulerAncestralDiscreteScheduler(
           beta_start=0.00085			,     beta_end=0.012			,     beta_schedule='''scaled_linear'''      )
       SCREAMING_SNAKE_CASE__					=					StableDiffusionPanoramaPipeline(**__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					sd_pipe.to(__lowerCamelCase      )
       sd_pipe.set_progress_bar_config(disable=__lowerCamelCase      )

       SCREAMING_SNAKE_CASE__					=					self.get_dummy_inputs(__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					sd_pipe(**__lowerCamelCase      ).images
       SCREAMING_SNAKE_CASE__					=					image[0, -3:, -3:, -1]

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

       SCREAMING_SNAKE_CASE__					=					np.array([0.4024, 0.6510, 0.4901, 0.5378, 0.5813, 0.5622, 0.4795, 0.4467, 0.4952]      )

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







  def    lowercase_	(							self		:	Optional[int]      )	->							List[str]:
       SCREAMING_SNAKE_CASE__					=					'''cpu'''  # ensure determinism for the device-dependent torch.Generator
       SCREAMING_SNAKE_CASE__					=					self.get_dummy_components()
       SCREAMING_SNAKE_CASE__					=					PNDMScheduler(
           beta_start=0.00085			,     beta_end=0.012			,     beta_schedule='''scaled_linear'''			,     skip_prk_steps=__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					StableDiffusionPanoramaPipeline(**__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					sd_pipe.to(__lowerCamelCase      )
       sd_pipe.set_progress_bar_config(disable=__lowerCamelCase      )

       SCREAMING_SNAKE_CASE__					=					self.get_dummy_inputs(__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					sd_pipe(**__lowerCamelCase      ).images
       SCREAMING_SNAKE_CASE__					=					image[0, -3:, -3:, -1]

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

       SCREAMING_SNAKE_CASE__					=					np.array([0.6391, 0.6291, 0.4861, 0.5134, 0.5552, 0.4578, 0.5032, 0.5023, 0.4539]      )

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






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






  """simple docstring"""


  def    lowercase_	(							self		:	Tuple      )	->							str:
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()






  def    lowercase_	(							self		:	Any			,     __lowerCamelCase		:	Dict=0      )	->							Dict:
       SCREAMING_SNAKE_CASE__					=					torch.manual_seed(__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					{
           '''prompt''': '''a photo of the dolomites''',
           '''generator''': generator,
           '''num_inference_steps''': 3,
           '''guidance_scale''': 7.5,
           '''output_type''': '''numpy''',
       }
       return inputs






  def    lowercase_	(							self		:	Union[str, Any]      )	->							List[Any]:
       SCREAMING_SNAKE_CASE__					=					'''stabilityai/stable-diffusion-2-base'''
       SCREAMING_SNAKE_CASE__					=					DDIMScheduler.from_pretrained(__lowerCamelCase			,     subfolder='''scheduler'''      )
       SCREAMING_SNAKE_CASE__					=					StableDiffusionPanoramaPipeline.from_pretrained(__lowerCamelCase			,     scheduler=__lowerCamelCase			,     safety_checker=__lowerCamelCase      )
       pipe.to(__lowerCamelCase      )
       pipe.set_progress_bar_config(disable=__lowerCamelCase      )
       pipe.enable_attention_slicing()

       SCREAMING_SNAKE_CASE__					=					self.get_inputs()
       SCREAMING_SNAKE_CASE__					=					pipe(**__lowerCamelCase      ).images
       SCREAMING_SNAKE_CASE__					=					image[0, -3:, -3:, -1].flatten()

       assert image.shape == (1, 512, 2048, 3)

       SCREAMING_SNAKE_CASE__					=					np.array(
           [
               0.36968392,
               0.27025372,
               0.32446766,
               0.28379387,
               0.36363274,
               0.30733347,
               0.27100027,
               0.27054125,
               0.25536096,
           ]      )

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






  def    lowercase_	(							self		:	Optional[Any]      )	->							Dict:
       SCREAMING_SNAKE_CASE__					=					StableDiffusionPanoramaPipeline.from_pretrained(
           '''stabilityai/stable-diffusion-2-base'''			,     safety_checker=__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					LMSDiscreteScheduler.from_config(pipe.scheduler.config      )
       pipe.to(__lowerCamelCase      )
       pipe.set_progress_bar_config(disable=__lowerCamelCase      )
       pipe.enable_attention_slicing()

       SCREAMING_SNAKE_CASE__					=					self.get_inputs()
       SCREAMING_SNAKE_CASE__					=					pipe(**__lowerCamelCase      ).images
       SCREAMING_SNAKE_CASE__					=					image[0, -3:, -3:, -1].flatten()

       assert image.shape == (1, 512, 2048, 3)

       SCREAMING_SNAKE_CASE__					=					np.array(
           [
               [
                   0.0,
                   0.0,
                   0.0,
                   0.0,
                   0.0,
                   0.0,
                   0.0,
                   0.0,
                   0.0,
               ]
           ]      )

       assert np.abs(expected_slice - image_slice      ).max() < 1e-3






  def    lowercase_	(							self		:	List[str]      )	->							List[str]:
       SCREAMING_SNAKE_CASE__					=					0

       def callback_fn(__lowerCamelCase		:	int			,     __lowerCamelCase		:	int			,     __lowerCamelCase		:	torch.FloatTensor      ) -> None:
            SCREAMING_SNAKE_CASE__					=					True
            nonlocal number_of_steps
            number_of_steps += 1
            if step == 1:
                 SCREAMING_SNAKE_CASE__					=					latents.detach().cpu().numpy()
                 assert latents.shape == (1, 4, 64, 256)
                 SCREAMING_SNAKE_CASE__					=					latents[0, -3:, -3:, -1]

                 SCREAMING_SNAKE_CASE__					=					np.array(
                     [
                         0.18681869,
                         0.33907816,
                         0.5361276,
                         0.14432865,
                         -0.02856611,
                         -0.73941123,
                         0.23397987,
                         0.47322682,
                         -0.37823164,
                     ]      )
                 assert np.abs(latents_slice.flatten() - expected_slice      ).max() < 5e-2
            elif step == 2:
                 SCREAMING_SNAKE_CASE__					=					latents.detach().cpu().numpy()
                 assert latents.shape == (1, 4, 64, 256)
                 SCREAMING_SNAKE_CASE__					=					latents[0, -3:, -3:, -1]

                 SCREAMING_SNAKE_CASE__					=					np.array(
                     [
                         0.18539645,
                         0.33987248,
                         0.5378559,
                         0.14437142,
                         -0.02455261,
                         -0.7338317,
                         0.23990755,
                         0.47356272,
                         -0.3786505,
                     ]      )

                 assert np.abs(latents_slice.flatten() - expected_slice      ).max() < 5e-2

       SCREAMING_SNAKE_CASE__					=					False

       SCREAMING_SNAKE_CASE__					=					'''stabilityai/stable-diffusion-2-base'''
       SCREAMING_SNAKE_CASE__					=					DDIMScheduler.from_pretrained(__lowerCamelCase			,     subfolder='''scheduler'''      )
       SCREAMING_SNAKE_CASE__					=					StableDiffusionPanoramaPipeline.from_pretrained(__lowerCamelCase			,     scheduler=__lowerCamelCase			,     safety_checker=__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					pipe.to(__lowerCamelCase      )
       pipe.set_progress_bar_config(disable=__lowerCamelCase      )
       pipe.enable_attention_slicing()

       SCREAMING_SNAKE_CASE__					=					self.get_inputs()
       pipe(**__lowerCamelCase			,     callback=__lowerCamelCase			,     callback_steps=1      )
       assert callback_fn.has_been_called
       assert number_of_steps == 3







  def    lowercase_	(							self		:	Tuple      )	->							Tuple:
       torch.cuda.empty_cache()
       torch.cuda.reset_max_memory_allocated()
       torch.cuda.reset_peak_memory_stats()

       SCREAMING_SNAKE_CASE__					=					'''stabilityai/stable-diffusion-2-base'''
       SCREAMING_SNAKE_CASE__					=					DDIMScheduler.from_pretrained(__lowerCamelCase			,     subfolder='''scheduler'''      )
       SCREAMING_SNAKE_CASE__					=					StableDiffusionPanoramaPipeline.from_pretrained(__lowerCamelCase			,     scheduler=__lowerCamelCase			,     safety_checker=__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					pipe.to(__lowerCamelCase      )
       pipe.set_progress_bar_config(disable=__lowerCamelCase      )
       pipe.enable_attention_slicing(1      )
       pipe.enable_sequential_cpu_offload()

       SCREAMING_SNAKE_CASE__					=					self.get_inputs()
       SCREAMING_SNAKE_CASE__					=					pipe(**__lowerCamelCase      )

       SCREAMING_SNAKE_CASE__					=					torch.cuda.max_memory_allocated()
       # make sure that less than 5.2 GB is allocated
       assert mem_bytes < 5.5 * 10**9


