


import gc
import unittest

import numpy as np
import torch

from diffusers import DDPMScheduler, MidiProcessor, SpectrogramDiffusionPipeline
from diffusers.pipelines.spectrogram_diffusion import SpectrogramContEncoder, SpectrogramNotesEncoder, TaFilmDecoder
from diffusers.utils import require_torch_gpu, skip_mps, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_note_seq, require_onnxruntime

from ..pipeline_params import TOKENS_TO_AUDIO_GENERATION_BATCH_PARAMS, TOKENS_TO_AUDIO_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()


_A							     =       './tests/fixtures/elise_format0.mid'

@unittest.skip("""The note-seq package currently throws an error on import"""    )
class 			lowerCamelCase_					(    _SCREAMING_SNAKE_CASE   ,		unittest.TestCase    ):
							_lowerCamelCase				: List[str]       =					SpectrogramDiffusionPipeline
							_lowerCamelCase				: str       =					PipelineTesterMixin.required_optional_params - {
							    """callback""",
							    """latents""",
							    """callback_steps""",
							    """output_type""",
							    """num_images_per_prompt""",
							}
							_lowerCamelCase				: Optional[Any]       =					False
							_lowerCamelCase				: List[Any]       =					False
							_lowerCamelCase				: Optional[int]       =					TOKENS_TO_AUDIO_GENERATION_PARAMS
							_lowerCamelCase				: Optional[int]       =					TOKENS_TO_AUDIO_GENERATION_BATCH_PARAMS





							def   __magic_name__      (							self    ):
								torch.manual_seed(0    )
								a_     			=   SpectrogramNotesEncoder(
								    max_length=2048			,       vocab_size=1536			,       d_model=768			,       dropout_rate=0.1			,       num_layers=1			,       num_heads=1			,       d_kv=4			,       d_ff=2048			,       feed_forward_proj="""gated-gelu"""			,       )

								a_     			=   SpectrogramContEncoder(
								    input_dims=128			,       targets_context_length=256			,       d_model=768			,       dropout_rate=0.1			,       num_layers=1			,       num_heads=1			,       d_kv=4			,       d_ff=2048			,       feed_forward_proj="""gated-gelu"""			,       )

								a_     			=   TaFilmDecoder(
								    input_dims=128			,       targets_length=256			,       max_decoder_noise_time=2_0000.0			,       d_model=768			,       num_layers=1			,       num_heads=1			,       d_kv=4			,       d_ff=2048			,       dropout_rate=0.1			,       )

								a_     			=   DDPMScheduler()

								a_     			=   {
								    """notes_encoder""": notes_encoder.eval(),
								    """continuous_encoder""": continuous_encoder.eval(),
								    """decoder""": decoder.eval(),
								    """scheduler""": scheduler,
								    """melgan""": None,
								}
								return components





							def   __magic_name__      (							self			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE=0    ):



								if str(lowercase__    ).startswith("""mps"""    ):
									a_     			=   torch.manual_seed(lowercase__    )
								else:
									a_     			=   torch.Generator(device=lowercase__    ).manual_seed(lowercase__    )
								a_     			=   {
								    """input_tokens""": [
								        [1134, 90, 1135, 1133, 1080, 112, 1132, 1080, 1133, 1079, 133, 1132, 1079, 1133, 1] + [0] * 2033
								    ],
								    """generator""": generator,
								    """num_inference_steps""": 4,
								    """output_type""": """mel""",
								}
								return inputs





							def   __magic_name__      (							self    ):
								a_     			=   """cpu"""  # ensure determinism for the device-dependent torch.Generator
								a_     			=   self.get_dummy_components()
								a_     			=   SpectrogramDiffusionPipeline(**lowercase__    )
								a_     			=   pipe.to(lowercase__    )
								pipe.set_progress_bar_config(disable=lowercase__    )

								a_     			=   self.get_dummy_inputs(lowercase__    )
								a_     			=   pipe(**lowercase__    )
								a_     			=   output.audios

								a_     			=   mel[0, -3:, -3:]

								assert mel_slice.shape == (3, 3)
								a_     			=   np.array(
								    [-1_1.5_1_2_9_2_5, -4.7_8_8_2_1_5, -0.4_6_1_7_2_9_0_5, -2.0_5_1_7_1_5, -1_0.5_3_9_1_4_7, -1_0.9_7_0_9_6_3, -9.0_9_1_6_3_4, 4.0, 4.0]    )
								assert np.abs(mel_slice.flatten() - expected_slice    ).max() < 1E-2





							@skip_mps
							def   __magic_name__      (							self    ):
								return super().test_save_load_local()





							@skip_mps
							def   __magic_name__      (							self    ):
								return super().test_dict_tuple_outputs_equivalent()





							@skip_mps
							def   __magic_name__      (							self    ):
								return super().test_save_load_optional_components()





							@skip_mps
							def   __magic_name__      (							self    ):
								return super().test_attention_slicing_forward_pass()





							def   __magic_name__      (							self    ):
								pass





							def   __magic_name__      (							self    ):
								pass





							@skip_mps
							def   __magic_name__      (							self    ):
								return super().test_progress_bar()







@slow
@require_torch_gpu
@require_onnxruntime
@require_note_seq
class 			lowerCamelCase_					(    unittest.TestCase    ):





							def   __magic_name__      (							self    ):
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()





							def   __magic_name__      (							self    ):
								a_     			=   torch_device

								a_     			=   SpectrogramDiffusionPipeline.from_pretrained("""google/music-spectrogram-diffusion"""    )
								a_     			=   pipe.melgan
								a_     			=   None

								a_     			=   pipe.to(lowercase__    )
								pipe.set_progress_bar_config(disable=lowercase__    )

								def callback(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE    ):
									# decode mel to audio
									a_     			=   melgan(input_features=mel_output.astype(np.floataa    )    )[0]
									assert len(audio[0]    ) == 8_1920 * (step + 1)
									# simulate that audio is played
									return audio

								a_     			=   MidiProcessor()
								a_     			=   processor(lowercase__    )

								a_     			=   input_tokens[:3]
								a_     			=   torch.manual_seed(0    )
								pipe(lowercase__			,       num_inference_steps=5			,       generator=lowercase__			,       callback=lowercase__			,       output_type="""mel"""    )





							def   __magic_name__      (							self    ):
								a_     			=   torch_device

								a_     			=   SpectrogramDiffusionPipeline.from_pretrained("""google/music-spectrogram-diffusion"""    )
								a_     			=   pipe.to(lowercase__    )
								pipe.set_progress_bar_config(disable=lowercase__    )
								a_     			=   MidiProcessor()

								a_     			=   processor(lowercase__    )
								# just run two denoising loops
								a_     			=   input_tokens[:2]

								a_     			=   torch.manual_seed(0    )
								a_     			=   pipe(lowercase__			,       num_inference_steps=2			,       generator=lowercase__    )

								a_     			=   output.audios[0]

								assert abs(np.abs(lowercase__    ).sum() - 3612.841    ) < 1E-1





							def   __magic_name__      (							self    ):
								a_     			=   torch_device

								a_     			=   SpectrogramDiffusionPipeline.from_pretrained("""google/music-spectrogram-diffusion"""    )
								a_     			=   pipe.to(lowercase__    )
								pipe.set_progress_bar_config(disable=lowercase__    )

								a_     			=   MidiProcessor()

								a_     			=   processor(lowercase__    )

								# just run 4 denoising loops
								a_     			=   input_tokens[:4]

								a_     			=   torch.manual_seed(0    )
								a_     			=   pipe(lowercase__			,       num_inference_steps=100			,       generator=lowercase__    )

								a_     			=   output.audios[0]
								assert abs(np.abs(lowercase__    ).sum() - 9389.1111    ) < 5E-2
import math
from typing import Optional

import numpy as np
import torch
from torch import nn

from .activations import get_activation






def  __lowerCamelCase						(		lowerCAmelCase__      ,    lowerCAmelCase__      ,    lowerCAmelCase__ = False      ,    lowerCAmelCase__ = 1      ,    lowerCAmelCase__ = 1      ,    lowerCAmelCase__ = 1_0_0_0_0      ,    ):
       assert len(timesteps.shape      ) == 1, "Timesteps should be a 1d-array"

       lowerCAmelCase__	      =   embedding_dim // 2
       lowerCAmelCase__	      =   -math.log(lowerCAmelCase__      ) * torch.arange(
           start=0      ,    end=lowerCAmelCase__      ,    dtype=torch.floataa      ,    device=timesteps.device      )
       lowerCAmelCase__	      =   exponent / (half_dim - downscale_freq_shift)

       lowerCAmelCase__	      =   torch.exp(lowerCAmelCase__      )
       lowerCAmelCase__	      =   timesteps[:, None].float() * emb[None, :]

       # scale embeddings
       lowerCAmelCase__	      =   scale * emb

       # concat sine and cosine embeddings
       lowerCAmelCase__	      =   torch.cat([torch.sin(lowerCAmelCase__      ), torch.cos(lowerCAmelCase__      )]      ,    dim=-1      )

       # flip sine and cosine embeddings
       if flip_sin_to_cos:
              lowerCAmelCase__	      =   torch.cat([emb[:, half_dim:], emb[:, :half_dim]]      ,    dim=-1      )

       # zero pad
       if embedding_dim % 2 == 1:
              lowerCAmelCase__	      =   torch.nn.functional.pad(lowerCAmelCase__      ,    (0, 1, 0, 0)      )
       return emb






def  __lowerCamelCase						(		lowerCAmelCase__      ,    lowerCAmelCase__      ,    lowerCAmelCase__=False      ,    lowerCAmelCase__=0      ):
       lowerCAmelCase__	      =   np.arange(lowerCAmelCase__      ,    dtype=np.floataa      )
       lowerCAmelCase__	      =   np.arange(lowerCAmelCase__      ,    dtype=np.floataa      )
       lowerCAmelCase__	      =   np.meshgrid(lowerCAmelCase__      ,    lowerCAmelCase__      )  # here w goes first
       lowerCAmelCase__	      =   np.stack(lowerCAmelCase__      ,    axis=0      )

       lowerCAmelCase__	      =   grid.reshape([2, 1, grid_size, grid_size]      )
       lowerCAmelCase__	      =   get_ad_sincos_pos_embed_from_grid(lowerCAmelCase__      ,    lowerCAmelCase__      )
       if cls_token and extra_tokens > 0:
              lowerCAmelCase__	      =   np.concatenate([np.zeros([extra_tokens, embed_dim]      ), pos_embed]      ,    axis=0      )
       return pos_embed






def  __lowerCamelCase						(		lowerCAmelCase__      ,    lowerCAmelCase__      ):
       if embed_dim % 2 != 0:
              raise ValueError('embed_dim must be divisible by 2'      )

       # use half of dimensions to encode grid_h
       lowerCAmelCase__	      =   get_ad_sincos_pos_embed_from_grid(embed_dim // 2      ,    grid[0]      )  # (H*W, D/2)
       lowerCAmelCase__	      =   get_ad_sincos_pos_embed_from_grid(embed_dim // 2      ,    grid[1]      )  # (H*W, D/2)

       lowerCAmelCase__	      =   np.concatenate([emb_h, emb_w]      ,    axis=1      )  # (H*W, D)
       return emb






def  __lowerCamelCase						(		lowerCAmelCase__      ,    lowerCAmelCase__      ):
       if embed_dim % 2 != 0:
              raise ValueError('embed_dim must be divisible by 2'      )

       lowerCAmelCase__	      =   np.arange(embed_dim // 2      ,    dtype=np.floataa      )
       omega /= embed_dim / 2.0
       lowerCAmelCase__	      =   1.0 / 1_0_0_0_0**omega  # (D/2,)

       lowerCAmelCase__	      =   pos.reshape(-1      )  # (M,)
       lowerCAmelCase__	      =   np.einsum('m,d->md'      ,    lowerCAmelCase__      ,    lowerCAmelCase__      )  # (M, D/2), outer product

       lowerCAmelCase__	      =   np.sin(lowerCAmelCase__      )  # (M, D/2)
       lowerCAmelCase__	      =   np.cos(lowerCAmelCase__      )  # (M, D/2)

       lowerCAmelCase__	      =   np.concatenate([emb_sin, emb_cos]      ,    axis=1      )  # (M, D)
       return emb
class 							a_					(		nn.Module		):






 '''simple docstring'''






 def __init__(     self   :    Optional[int]     ,       lowercase__   :    Any=224     ,       lowercase__   :    Any=224     ,       lowercase__   :    int=16     ,       lowercase__   :    List[Any]=3     ,       lowercase__   :    Dict=768     ,       lowercase__   :    Tuple=False     ,       lowercase__   :    Any=True     ,       lowercase__   :    List[str]=True     ,       ):







        '''simple docstring'''



        super().__init__()

        lowerCAmelCase__	      =   (height // patch_size) * (width // patch_size)
        lowerCAmelCase__	      =   flatten
        lowerCAmelCase__	      =   layer_norm

        lowerCAmelCase__	      =   nn.Convad(
            lowercase__     ,       lowercase__     ,       kernel_size=(patch_size, patch_size)     ,       stride=lowercase__     ,       bias=lowercase__)
        if layer_norm:
               lowerCAmelCase__	      =   nn.LayerNorm(lowercase__     ,       elementwise_affine=lowercase__     ,       eps=1e-6)
        else:
               lowerCAmelCase__	      =   None

        lowerCAmelCase__	      =   get_ad_sincos_pos_embed(lowercase__     ,       int(num_patches**0.5))
        self.register_buffer('pos_embed'     ,       torch.from_numpy(lowercase__).float().unsqueeze(0)     ,       persistent=lowercase__)




 def 						__snake_case		(     self   :    Union[str, Any]     ,       lowercase__   :    List[str]):







        '''simple docstring'''



        lowerCAmelCase__	      =   self.proj(lowercase__)
        if self.flatten:
               lowerCAmelCase__	      =   latent.flatten(2).transpose(1     ,       2)  # BCHW -> BNC
        if self.layer_norm:
               lowerCAmelCase__	      =   self.norm(lowercase__)
        return latent + self.pos_embed






class 							a_					(		nn.Module		):






 '''simple docstring'''






 def __init__(     self   :    str     ,       lowercase__   :    int     ,       lowercase__   :    int     ,       lowercase__   :    str = "silu"     ,       lowercase__   :    int = None     ,       lowercase__   :    Optional[str] = None     ,       lowercase__   :    List[str]=None     ,       ):







        '''simple docstring'''



        super().__init__()

        lowerCAmelCase__	      =   nn.Linear(lowercase__     ,       lowercase__)

        if cond_proj_dim is not None:
               lowerCAmelCase__	      =   nn.Linear(lowercase__     ,       lowercase__     ,       bias=lowercase__)
        else:
               lowerCAmelCase__	      =   None

        lowerCAmelCase__	      =   get_activation(lowercase__)

        if out_dim is not None:
               lowerCAmelCase__	      =   out_dim
        else:
               lowerCAmelCase__	      =   time_embed_dim
        lowerCAmelCase__	      =   nn.Linear(lowercase__     ,       lowercase__)

        if post_act_fn is None:
               lowerCAmelCase__	      =   None
        else:
               lowerCAmelCase__	      =   get_activation(lowercase__)




 def 						__snake_case		(     self   :    Union[str, Any]     ,       lowercase__   :    Tuple     ,       lowercase__   :    List[Any]=None):







        '''simple docstring'''



        if condition is not None:
               lowerCAmelCase__	      =   sample + self.cond_proj(lowercase__)
        lowerCAmelCase__	      =   self.linear_a(lowercase__)

        if self.act is not None:
               lowerCAmelCase__	      =   self.act(lowercase__)

        lowerCAmelCase__	      =   self.linear_a(lowercase__)

        if self.post_act is not None:
               lowerCAmelCase__	      =   self.post_act(lowercase__)
        return sample






class 							a_					(		nn.Module		):






 '''simple docstring'''






 def __init__(     self   :    Optional[int]     ,       lowercase__   :    int     ,       lowercase__   :    bool     ,       lowercase__   :    float):







        '''simple docstring'''



        super().__init__()
        lowerCAmelCase__	      =   num_channels
        lowerCAmelCase__	      =   flip_sin_to_cos
        lowerCAmelCase__	      =   downscale_freq_shift




 def 						__snake_case		(     self   :    int     ,       lowercase__   :    Union[str, Any]):







        '''simple docstring'''



        lowerCAmelCase__	      =   get_timestep_embedding(
            lowercase__     ,       self.num_channels     ,       flip_sin_to_cos=self.flip_sin_to_cos     ,       downscale_freq_shift=self.downscale_freq_shift     ,       )
        return t_emb






class 							a_					(		nn.Module		):






 '''simple docstring'''






 def __init__(     self   :    List[str]     ,       lowercase__   :    int = 256     ,       lowercase__   :    float = 1.0     ,       lowercase__   :    str=True     ,       lowercase__   :    List[str]=True     ,       lowercase__   :    Any=False):







        '''simple docstring'''



        super().__init__()
        lowerCAmelCase__	      =   nn.Parameter(torch.randn(lowercase__) * scale     ,       requires_grad=lowercase__)
        lowerCAmelCase__	      =   log
        lowerCAmelCase__	      =   flip_sin_to_cos

        if set_W_to_weight:
               # to delete later
               lowerCAmelCase__	      =   nn.Parameter(torch.randn(lowercase__) * scale     ,       requires_grad=lowercase__)

               lowerCAmelCase__	      =   self.W




 def 						__snake_case		(     self   :    Dict     ,       lowercase__   :    List[str]):







        '''simple docstring'''



        if self.log:
               lowerCAmelCase__	      =   torch.log(lowercase__)

        lowerCAmelCase__	      =   x[:, None] * self.weight[None, :] * 2 * np.pi

        if self.flip_sin_to_cos:
               lowerCAmelCase__	      =   torch.cat([torch.cos(lowercase__), torch.sin(lowercase__)]     ,       dim=-1)
        else:
               lowerCAmelCase__	      =   torch.cat([torch.sin(lowercase__), torch.cos(lowercase__)]     ,       dim=-1)
        return out






class 							a_					(		nn.Module		):






 '''simple docstring'''






 def __init__(     self   :    List[str]     ,       lowercase__   :    int     ,       lowercase__   :    int     ,       lowercase__   :    int     ,       lowercase__   :    int     ,       ):







        '''simple docstring'''



        super().__init__()

        lowerCAmelCase__	      =   height
        lowerCAmelCase__	      =   width
        lowerCAmelCase__	      =   num_embed
        lowerCAmelCase__	      =   embed_dim

        lowerCAmelCase__	      =   nn.Embedding(self.num_embed     ,       lowercase__)
        lowerCAmelCase__	      =   nn.Embedding(self.height     ,       lowercase__)
        lowerCAmelCase__	      =   nn.Embedding(self.width     ,       lowercase__)




 def 						__snake_case		(     self   :    Dict     ,       lowercase__   :    Any):







        '''simple docstring'''



        lowerCAmelCase__	      =   self.emb(lowercase__)

        lowerCAmelCase__	      =   self.height_emb(torch.arange(self.height     ,       device=index.device).view(1     ,       self.height))

        # 1 x H x D -> 1 x H x 1 x D
        lowerCAmelCase__	      =   height_emb.unsqueeze(2)

        lowerCAmelCase__	      =   self.width_emb(torch.arange(self.width     ,       device=index.device).view(1     ,       self.width))

        # 1 x W x D -> 1 x 1 x W x D
        lowerCAmelCase__	      =   width_emb.unsqueeze(1)

        lowerCAmelCase__	      =   height_emb + width_emb

        # 1 x H x W x D -> 1 x L xD
        lowerCAmelCase__	      =   pos_emb.view(1     ,       self.height * self.width     ,       -1)

        lowerCAmelCase__	      =   emb + pos_emb[:, : emb.shape[1], :]

        return emb






class 							a_					(		nn.Module		):






 '''simple docstring'''






 def __init__(     self   :    Any     ,       lowercase__   :    int     ,       lowercase__   :    Dict     ,       lowercase__   :    Any):







        '''simple docstring'''



        super().__init__()
        lowerCAmelCase__	      =   dropout_prob > 0
        lowerCAmelCase__	      =   nn.Embedding(num_classes + use_cfg_embedding     ,       lowercase__)
        lowerCAmelCase__	      =   num_classes
        lowerCAmelCase__	      =   dropout_prob




 def 						__snake_case		(     self   :    Any     ,       lowercase__   :    List[Any]     ,       lowercase__   :    List[Any]=None):







        '''simple docstring'''



        if force_drop_ids is None:
               lowerCAmelCase__	      =   torch.rand(labels.shape[0]     ,       device=labels.device) < self.dropout_prob
        else:
               lowerCAmelCase__	      =   torch.tensor(force_drop_ids == 1)
        lowerCAmelCase__	      =   torch.where(lowercase__     ,       self.num_classes     ,       lowercase__)
        return labels




 def 						__snake_case		(     self   :    Union[str, Any]     ,       lowercase__   :    torch.LongTensor     ,       lowercase__   :    str=None):







        '''simple docstring'''



        lowerCAmelCase__	      =   self.dropout_prob > 0
        if (self.training and use_dropout) or (force_drop_ids is not None):
               lowerCAmelCase__	      =   self.token_drop(lowercase__     ,       lowercase__)
        lowerCAmelCase__	      =   self.embedding_table(lowercase__)
        return embeddings






class 							a_					(		nn.Module		):






 '''simple docstring'''






 def __init__(     self   :    Optional[Any]     ,       lowercase__   :    int = 1_024     ,       lowercase__   :    int = 768     ,       lowercase__   :    int = 768     ,       lowercase__   :    int = 10     ,       ):







        '''simple docstring'''



        super().__init__()

        lowerCAmelCase__	      =   num_image_text_embeds
        lowerCAmelCase__	      =   nn.Linear(lowercase__     ,       self.num_image_text_embeds * cross_attention_dim)
        lowerCAmelCase__	      =   nn.Linear(lowercase__     ,       lowercase__)




 def 						__snake_case		(     self   :    List[Any]     ,       lowercase__   :    torch.FloatTensor     ,       lowercase__   :    torch.FloatTensor):







        '''simple docstring'''



        lowerCAmelCase__	      =   text_embeds.shape[0]

        # image
        lowerCAmelCase__	      =   self.image_embeds(lowercase__)
        lowerCAmelCase__	      =   image_text_embeds.reshape(lowercase__     ,       self.num_image_text_embeds     ,       -1)

        # text
        lowerCAmelCase__	      =   self.text_proj(lowercase__)

        return torch.cat([image_text_embeds, text_embeds]     ,       dim=1)






class 							a_					(		nn.Module		):






 '''simple docstring'''






 def __init__(     self   :    str     ,       lowercase__   :    int = 768     ,       lowercase__   :    int = 768     ,       lowercase__   :    int = 32     ,       ):







        '''simple docstring'''



        super().__init__()

        lowerCAmelCase__	      =   num_image_text_embeds
        lowerCAmelCase__	      =   nn.Linear(lowercase__     ,       self.num_image_text_embeds * cross_attention_dim)
        lowerCAmelCase__	      =   nn.LayerNorm(lowercase__)




 def 						__snake_case		(     self   :    Union[str, Any]     ,       lowercase__   :    torch.FloatTensor):







        '''simple docstring'''



        lowerCAmelCase__	      =   image_embeds.shape[0]

        # image
        lowerCAmelCase__	      =   self.image_embeds(lowercase__)
        lowerCAmelCase__	      =   image_embeds.reshape(lowercase__     ,       self.num_image_text_embeds     ,       -1)
        lowerCAmelCase__	      =   self.norm(lowercase__)
        return image_embeds






class 							a_					(		nn.Module		):






 '''simple docstring'''






 def __init__(     self   :    str     ,       lowercase__   :    Optional[int]     ,       lowercase__   :    Dict     ,       lowercase__   :    Tuple=0.1):







        '''simple docstring'''



        super().__init__()

        lowerCAmelCase__	      =   Timesteps(num_channels=256     ,       flip_sin_to_cos=lowercase__     ,       downscale_freq_shift=1)
        lowerCAmelCase__	      =   TimestepEmbedding(in_channels=256     ,       time_embed_dim=lowercase__)
        lowerCAmelCase__	      =   LabelEmbedding(lowercase__     ,       lowercase__     ,       lowercase__)




 def 						__snake_case		(     self   :    List[Any]     ,       lowercase__   :    List[str]     ,       lowercase__   :    Dict     ,       lowercase__   :    Dict=None):







        '''simple docstring'''



        lowerCAmelCase__	      =   self.time_proj(lowercase__)
        lowerCAmelCase__	      =   self.timestep_embedder(timesteps_proj.to(dtype=lowercase__))  # (N, D)

        lowerCAmelCase__	      =   self.class_embedder(lowercase__)  # (N, D)

        lowerCAmelCase__	      =   timesteps_emb + class_labels  # (N, D)

        return conditioning






class 							a_					(		nn.Module		):






 '''simple docstring'''






 def __init__(     self   :    Optional[Any]     ,       lowercase__   :    int     ,       lowercase__   :    int     ,       lowercase__   :    int = 64):







        '''simple docstring'''



        super().__init__()
        lowerCAmelCase__	      =   nn.LayerNorm(lowercase__)
        lowerCAmelCase__	      =   AttentionPooling(lowercase__     ,       lowercase__)
        lowerCAmelCase__	      =   nn.Linear(lowercase__     ,       lowercase__)
        lowerCAmelCase__	      =   nn.LayerNorm(lowercase__)




 def 						__snake_case		(     self   :    Optional[int]     ,       lowercase__   :    Any):







        '''simple docstring'''



        lowerCAmelCase__	      =   self.norma(lowercase__)
        lowerCAmelCase__	      =   self.pool(lowercase__)
        lowerCAmelCase__	      =   self.proj(lowercase__)
        lowerCAmelCase__	      =   self.norma(lowercase__)
        return hidden_states






class 							a_					(		nn.Module		):






 '''simple docstring'''






 def __init__(     self   :    List[str]     ,       lowercase__   :    int = 768     ,       lowercase__   :    int = 768     ,       lowercase__   :    int = 1_536):







        '''simple docstring'''



        super().__init__()
        lowerCAmelCase__	      =   nn.Linear(lowercase__     ,       lowercase__)
        lowerCAmelCase__	      =   nn.LayerNorm(lowercase__)
        lowerCAmelCase__	      =   nn.Linear(lowercase__     ,       lowercase__)




 def 						__snake_case		(     self   :    Union[str, Any]     ,       lowercase__   :    torch.FloatTensor     ,       lowercase__   :    torch.FloatTensor):







        '''simple docstring'''



        lowerCAmelCase__	      =   self.text_proj(lowercase__)
        lowerCAmelCase__	      =   self.text_norm(lowercase__)

        # image
        lowerCAmelCase__	      =   self.image_proj(lowercase__)

        return time_image_embeds + time_text_embeds






class 							a_					(		nn.Module		):






 '''simple docstring'''






 def __init__(     self   :    List[Any]     ,       lowercase__   :    int = 768     ,       lowercase__   :    int = 1_536):







        '''simple docstring'''



        super().__init__()
        lowerCAmelCase__	      =   nn.Linear(lowercase__     ,       lowercase__)
        lowerCAmelCase__	      =   nn.LayerNorm(lowercase__)




 def 						__snake_case		(     self   :    Union[str, Any]     ,       lowercase__   :    torch.FloatTensor):







        '''simple docstring'''



        lowerCAmelCase__	      =   self.image_proj(lowercase__)
        lowerCAmelCase__	      =   self.image_norm(lowercase__)
        return time_image_embeds






class 							a_					(		nn.Module		):






 '''simple docstring'''






 def __init__(     self   :    Optional[int]     ,       lowercase__   :    int = 768     ,       lowercase__   :    int = 1_536):







        '''simple docstring'''



        super().__init__()
        lowerCAmelCase__	      =   nn.Linear(lowercase__     ,       lowercase__)
        lowerCAmelCase__	      =   nn.LayerNorm(lowercase__)
        lowerCAmelCase__	      =   nn.Sequential(
            nn.Convad(3     ,       16     ,       3     ,       padding=1)     ,       nn.SiLU()     ,       nn.Convad(16     ,       16     ,       3     ,       padding=1)     ,       nn.SiLU()     ,       nn.Convad(16     ,       32     ,       3     ,       padding=1     ,       stride=2)     ,       nn.SiLU()     ,       nn.Convad(32     ,       32     ,       3     ,       padding=1)     ,       nn.SiLU()     ,       nn.Convad(32     ,       96     ,       3     ,       padding=1     ,       stride=2)     ,       nn.SiLU()     ,       nn.Convad(96     ,       96     ,       3     ,       padding=1)     ,       nn.SiLU()     ,       nn.Convad(96     ,       256     ,       3     ,       padding=1     ,       stride=2)     ,       nn.SiLU()     ,       nn.Convad(256     ,       4     ,       3     ,       padding=1)     ,       )




 def 						__snake_case		(     self   :    List[Any]     ,       lowercase__   :    torch.FloatTensor     ,       lowercase__   :    torch.FloatTensor):







        '''simple docstring'''



        lowerCAmelCase__	      =   self.image_proj(lowercase__)
        lowerCAmelCase__	      =   self.image_norm(lowercase__)
        lowerCAmelCase__	      =   self.input_hint_block(lowercase__)
        return time_image_embeds, hint






class 							a_					(		nn.Module		):






 '''simple docstring'''






 def __init__(     self   :    Union[str, Any]     ,       lowercase__   :    Optional[Any]     ,       lowercase__   :    Optional[int]     ,       lowercase__   :    Optional[int]=None):







        '''simple docstring'''



        super().__init__()
        lowerCAmelCase__	      =   dtype
        lowerCAmelCase__	      =   nn.Parameter(torch.randn(1     ,       lowercase__) / embed_dim**0.5)
        lowerCAmelCase__	      =   nn.Linear(lowercase__     ,       lowercase__     ,       dtype=self.dtype)
        lowerCAmelCase__	      =   nn.Linear(lowercase__     ,       lowercase__     ,       dtype=self.dtype)
        lowerCAmelCase__	      =   nn.Linear(lowercase__     ,       lowercase__     ,       dtype=self.dtype)
        lowerCAmelCase__	      =   num_heads
        lowerCAmelCase__	      =   embed_dim // self.num_heads




 def 						__snake_case		(     self   :    List[Any]     ,       lowercase__   :    List[Any]):







        '''simple docstring'''



        lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__	      =   x.size()

        def shape(lowercase__   :    Dict):
               # (bs, length, width) --> (bs, length, n_heads, dim_per_head)
               lowerCAmelCase__	      =   x.view(lowercase__     ,       -1     ,       self.num_heads     ,       self.dim_per_head)
               # (bs, length, n_heads, dim_per_head) --> (bs, n_heads, length, dim_per_head)
               lowerCAmelCase__	      =   x.transpose(1     ,       2)
               # (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head)
               lowerCAmelCase__	      =   x.reshape(bs * self.num_heads     ,       -1     ,       self.dim_per_head)
               # (bs*n_heads, length, dim_per_head) --> (bs*n_heads, dim_per_head, length)
               lowerCAmelCase__	      =   x.transpose(1     ,       2)
               return x

        lowerCAmelCase__	      =   x.mean(dim=1     ,       keepdim=lowercase__) + self.positional_embedding.to(x.dtype)
        lowerCAmelCase__	      =   torch.cat([class_token, x]     ,       dim=1)  # (bs, length+1, width)

        # (bs*n_heads, class_token_length, dim_per_head)
        lowerCAmelCase__	      =   shape(self.q_proj(lowercase__))
        # (bs*n_heads, length+class_token_length, dim_per_head)
        lowerCAmelCase__	      =   shape(self.k_proj(lowercase__))
        lowerCAmelCase__	      =   shape(self.v_proj(lowercase__))

        # (bs*n_heads, class_token_length, length+class_token_length):
        lowerCAmelCase__	      =   1 / math.sqrt(math.sqrt(self.dim_per_head))
        lowerCAmelCase__	      =   torch.einsum('bct,bcs->bts'     ,       q * scale     ,       k * scale)  # More stable with f16 than dividing afterwards
        lowerCAmelCase__	      =   torch.softmax(weight.float()     ,       dim=-1).type(weight.dtype)

        # (bs*n_heads, dim_per_head, class_token_length)
        lowerCAmelCase__	      =   torch.einsum('bts,bcs->bct'     ,       lowercase__     ,       lowercase__)

        # (bs, length+1, width)
        lowerCAmelCase__	      =   a.reshape(lowercase__     ,       -1     ,       1).transpose(1     ,       2)

        return a[:, 0, :]  # cls_token
