






'''simple docstring'''



import torch
from torch import nn
class UpperCAmelCase_       (     nn.Module					):




      """simple docstring"""




      def __init__(			self      :		List[Any]  ,   UpperCAmelCase      :		Union[str, Any]  ,   UpperCAmelCase      :		Union[str, Any]  ,   UpperCAmelCase      :		str  ,   UpperCAmelCase      :		Union[str, Any]  ,   UpperCAmelCase      :		List[str]=1  ,   UpperCAmelCase      :		Union[str, Any]=False		)	->	Dict:


       '''simple docstring'''

       super().__init__()

       lowercase       : str						=n_token
       lowercase       : int						=d_embed
       lowercase       : Tuple						=d_proj

       lowercase       : Union[str, Any]						=cutoffs + [n_token]
       lowercase       : Dict						=[0] + self.cutoffs
       lowercase       : Union[str, Any]						=div_val

       lowercase       : Union[str, Any]						=self.cutoffs[0]
       lowercase       : Any						=len(self.cutoffs		) - 1
       lowercase       : Dict						=self.shortlist_size + self.n_clusters

       if self.n_clusters > 0:
        lowercase       : Tuple						=nn.Parameter(torch.zeros(self.n_clusters  ,   self.d_embed		)		)
        lowercase       : Union[str, Any]						=nn.Parameter(torch.zeros(self.n_clusters		)		)

       lowercase       : Tuple						=nn.ModuleList()
       lowercase       : Optional[Any]						=nn.ParameterList()

       if div_val == 1:
        for i in range(len(self.cutoffs		)		):
         if d_proj != d_embed:
          self.out_projs.append(nn.Parameter(torch.FloatTensor(UpperCAmelCase  ,   UpperCAmelCase		)		)		)
         else:
          self.out_projs.append(UpperCAmelCase		)

        self.out_layers.append(nn.Linear(UpperCAmelCase  ,   UpperCAmelCase		)		)
       else:
        for i in range(len(self.cutoffs		)		):
         lowercase	,		lowercase       : Union[str, Any]						=self.cutoff_ends[i], self.cutoff_ends[i + 1]
         lowercase       : Optional[int]						=d_embed // (div_val**i)

         self.out_projs.append(nn.Parameter(torch.FloatTensor(UpperCAmelCase  ,   UpperCAmelCase		)		)		)

         self.out_layers.append(nn.Linear(UpperCAmelCase  ,   r_idx - l_idx		)		)

       lowercase       : int						=keep_order






      def 					A__   (			self      :		List[str]  ,   UpperCAmelCase      :		str  ,   UpperCAmelCase      :		List[Any]  ,   UpperCAmelCase      :		Any  ,   UpperCAmelCase      :		List[str]		)	->	Optional[int]:


       '''simple docstring'''

       if proj is None:
        lowercase       : Optional[int]						=nn.functional.linear(UpperCAmelCase  ,   UpperCAmelCase  ,   bias=UpperCAmelCase		)
       else:
        # if CUDA_MAJOR <= 9 and CUDA_MINOR <= 1:
        lowercase       : List[Any]						=nn.functional.linear(UpperCAmelCase  ,   proj.t().contiguous()		)
        lowercase       : Optional[Any]						=nn.functional.linear(UpperCAmelCase  ,   UpperCAmelCase  ,   bias=UpperCAmelCase		)
        # else:
        #     logit = torch.einsum('bd,de,ev->bv', (hidden, proj, weight.t()))
        #     if bias is not None:
        #         logit = logit + bias

       return logit






      def 					A__   (			self      :		Dict  ,   UpperCAmelCase      :		Optional[int]  ,   UpperCAmelCase      :		Union[str, Any]=None  ,   UpperCAmelCase      :		Union[str, Any]=False		)	->	List[str]:


       '''simple docstring'''

       if labels is not None:
        # Shift so that tokens < n predict n
        lowercase       : int						=hidden[..., :-1, :].contiguous()
        lowercase       : Dict						=labels[..., 1:].contiguous()
        lowercase       : str						=hidden.view(-1  ,   hidden.size(-1		)		)
        lowercase       : List[Any]						=labels.view(-1		)
        if hidden.size(0		) != labels.size(0		):
         raise RuntimeError('''Input and labels should have the same size in the batch dimension.'''		)
       else:
        lowercase       : Union[str, Any]						=hidden.view(-1  ,   hidden.size(-1		)		)

       if self.n_clusters == 0:
        lowercase       : Any						=self._compute_logit(UpperCAmelCase  ,   self.out_layers[0].weight  ,   self.out_layers[0].bias  ,   self.out_projs[0]		)
        if labels is not None:
         lowercase       : Optional[Any]						=labels != -100
         lowercase       : Optional[Any]						=torch.zeros_like(UpperCAmelCase  ,   dtype=hidden.dtype  ,   device=hidden.device		)
         lowercase       : str						=(
             -nn.functional.log_softmax(UpperCAmelCase  ,   dim=-1		)[mask].gather(1  ,   labels[mask].unsqueeze(1		)		).squeeze(1		)
         )
        else:
         lowercase       : int						=nn.functional.log_softmax(UpperCAmelCase  ,   dim=-1		)
       else:
        # construct weights and biases
        lowercase	,		lowercase       : Optional[int]						=[], []
        for i in range(len(self.cutoffs		)		):
         if self.div_val == 1:
          lowercase	,		lowercase       : str						=self.cutoff_ends[i], self.cutoff_ends[i + 1]
          lowercase       : List[Any]						=self.out_layers[0].weight[l_idx:r_idx]
          lowercase       : Optional[int]						=self.out_layers[0].bias[l_idx:r_idx]
         else:
          lowercase       : int						=self.out_layers[i].weight
          lowercase       : str						=self.out_layers[i].bias

         if i == 0:
          lowercase       : int						=torch.cat([weight_i, self.cluster_weight]  ,   dim=0		)
          lowercase       : Tuple						=torch.cat([bias_i, self.cluster_bias]  ,   dim=0		)

         weights.append(UpperCAmelCase		)
         biases.append(UpperCAmelCase		)

        lowercase	,		lowercase	,		lowercase       : List[Any]						=weights[0], biases[0], self.out_projs[0]

        lowercase       : Any						=self._compute_logit(UpperCAmelCase  ,   UpperCAmelCase  ,   UpperCAmelCase  ,   UpperCAmelCase		)
        lowercase       : Tuple						=nn.functional.log_softmax(UpperCAmelCase  ,   dim=1		)

        if labels is None:
         lowercase       : Optional[Any]						=hidden.new_empty((head_logit.size(0		), self.n_token)		)
        else:
         lowercase       : List[str]						=torch.zeros_like(UpperCAmelCase  ,   dtype=hidden.dtype  ,   device=hidden.device		)

        lowercase       : Union[str, Any]						=0
        lowercase       : Optional[Any]						=[0] + self.cutoffs
        for i in range(len(UpperCAmelCase		) - 1		):
         lowercase	,		lowercase       : Union[str, Any]						=cutoff_values[i], cutoff_values[i + 1]

         if labels is not None:
          lowercase       : str						=(labels >= l_idx) & (labels < r_idx)
          lowercase       : Dict						=mask_i.nonzero().squeeze()

          if indices_i.numel() == 0:
           continue

          lowercase       : List[str]						=labels.index_select(0  ,   UpperCAmelCase		) - l_idx
          lowercase       : Optional[int]						=head_logprob.index_select(0  ,   UpperCAmelCase		)
          lowercase       : Union[str, Any]						=hidden.index_select(0  ,   UpperCAmelCase		)
         else:
          lowercase       : str						=hidden

         if i == 0:
          if labels is not None:
           lowercase       : Dict						=head_logprob_i.gather(1  ,   target_i[:, None]		).squeeze(1		)
          else:
           lowercase       : List[str]						=head_logprob[:, : self.cutoffs[0]]
         else:
          lowercase	,		lowercase	,		lowercase       : int						=weights[i], biases[i], self.out_projs[i]

          lowercase       : List[Any]						=self._compute_logit(UpperCAmelCase  ,   UpperCAmelCase  ,   UpperCAmelCase  ,   UpperCAmelCase		)
          lowercase       : List[Any]						=nn.functional.log_softmax(UpperCAmelCase  ,   dim=1		)
          lowercase       : Tuple						=self.cutoffs[0] + i - 1  # No probability for the head cluster
          if labels is not None:
           lowercase       : Any						=head_logprob_i[:, cluster_prob_idx] + tail_logprob_i.gather(
               1  ,   target_i[:, None]		).squeeze(1		)
          else:
           lowercase       : List[str]						=head_logprob[:, cluster_prob_idx, None] + tail_logprob_i
           lowercase       : Union[str, Any]						=logprob_i

         if labels is not None:
          if (hasattr(self  ,   '''keep_order'''		) and self.keep_order) or keep_order:
           out.index_copy_(0  ,   UpperCAmelCase  ,   -logprob_i		)
          else:
           out[offset : offset + logprob_i.size(0		)].copy_(-logprob_i		)
          offset += logprob_i.size(0		)

       return out






      def 					A__   (			self      :		Tuple  ,   UpperCAmelCase      :		List[Any]		)	->	Optional[Any]:


       '''simple docstring'''

       if self.n_clusters == 0:
        lowercase       : Optional[Any]						=self._compute_logit(UpperCAmelCase  ,   self.out_layers[0].weight  ,   self.out_layers[0].bias  ,   self.out_projs[0]		)
        return nn.functional.log_softmax(UpperCAmelCase  ,   dim=-1		)
       else:
        # construct weights and biases
        lowercase	,		lowercase       : Tuple						=[], []
        for i in range(len(self.cutoffs		)		):
         if self.div_val == 1:
          lowercase	,		lowercase       : Union[str, Any]						=self.cutoff_ends[i], self.cutoff_ends[i + 1]
          lowercase       : Tuple						=self.out_layers[0].weight[l_idx:r_idx]
          lowercase       : str						=self.out_layers[0].bias[l_idx:r_idx]
         else:
          lowercase       : Dict						=self.out_layers[i].weight
          lowercase       : Optional[Any]						=self.out_layers[i].bias

         if i == 0:
          lowercase       : Tuple						=torch.cat([weight_i, self.cluster_weight]  ,   dim=0		)
          lowercase       : Union[str, Any]						=torch.cat([bias_i, self.cluster_bias]  ,   dim=0		)

         weights.append(UpperCAmelCase		)
         biases.append(UpperCAmelCase		)

        lowercase	,		lowercase	,		lowercase       : List[Any]						=weights[0], biases[0], self.out_projs[0]
        lowercase       : List[Any]						=self._compute_logit(UpperCAmelCase  ,   UpperCAmelCase  ,   UpperCAmelCase  ,   UpperCAmelCase		)

        lowercase       : List[Any]						=hidden.new_empty((head_logit.size(0		), self.n_token)		)
        lowercase       : int						=nn.functional.log_softmax(UpperCAmelCase  ,   dim=1		)

        lowercase       : Tuple						=[0] + self.cutoffs
        for i in range(len(UpperCAmelCase		) - 1		):
         lowercase	,		lowercase       : Any						=cutoff_values[i], cutoff_values[i + 1]

         if i == 0:
          lowercase       : Any						=head_logprob[:, : self.cutoffs[0]]
         else:
          lowercase	,		lowercase	,		lowercase       : Any						=weights[i], biases[i], self.out_projs[i]

          lowercase       : List[str]						=self._compute_logit(UpperCAmelCase  ,   UpperCAmelCase  ,   UpperCAmelCase  ,   UpperCAmelCase		)
          lowercase       : List[str]						=nn.functional.log_softmax(UpperCAmelCase  ,   dim=1		)

          lowercase       : Tuple						=head_logprob[:, -i] + tail_logprob_i
          lowercase       : Optional[int]						=logprob_i

        return out








'''simple docstring'''

import unittest

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

from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()

class       __magic_name__		(       unittest.TestCase  ):







							@property
							def 				__lowercase     (				self   :			int						):
									torch.manual_seed(0						)
									_a	: int								=				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







							@property
							def 				__lowercase     (				self   :			Any						):
									torch.manual_seed(0						)
									_a	: int								=				VQModel(
									    block_out_channels=[32, 64]      ,in_channels=3      ,out_channels=3      ,down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']      ,up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']      ,latent_channels=3      ,)
									return model







							@property
							def 				__lowercase     (				self   :			Union[str, Any]						):
									torch.manual_seed(0						)
									_a	: Dict								=				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      ,)
									return CLIPTextModel(_UpperCAmelCase						)







							def 				__lowercase     (				self   :			Dict						):
									_a	: Union[str, Any]								=				self.dummy_uncond_unet
									_a	: Union[str, Any]								=				DDIMScheduler()
									_a	: Union[str, Any]								=				self.dummy_vq_model

									_a	: List[Any]								=				LDMPipeline(unet=_UpperCAmelCase      ,vqvae=_UpperCAmelCase      ,scheduler=_UpperCAmelCase						)
									ldm.to(_UpperCAmelCase						)
									ldm.set_progress_bar_config(disable=_UpperCAmelCase						)

									_a	: Dict								=				torch.manual_seed(0						)
									_a	: int								=				ldm(generator=_UpperCAmelCase      ,num_inference_steps=2      ,output_type='numpy'						).images

									_a	: Tuple								=				torch.manual_seed(0						)
									_a	: List[Any]								=				ldm(generator=_UpperCAmelCase      ,num_inference_steps=2      ,output_type='numpy'      ,return_dict=_UpperCAmelCase						)[0]

									_a	: Any								=				image[0, -3:, -3:, -1]
									_a	: List[Any]								=				image_from_tuple[0, -3:, -3:, -1]

									assert image.shape == (1, 64, 64, 3)
									_a	: Union[str, Any]								=				np.array([0.85_12, 0.8_18, 0.64_11, 0.68_08, 0.44_65, 0.56_18, 0.46, 0.62_31, 0.51_72]						)
									_a	: Any								=				1E-2 if torch_device != 'mps' else 3E-2

									assert np.abs(image_slice.flatten() - expected_slice						).max() < tolerance
									assert np.abs(image_from_tuple_slice.flatten() - expected_slice						).max() < tolerance

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







							def 				__lowercase     (				self   :			Optional[int]						):
									_a	: Dict								=				LDMPipeline.from_pretrained('CompVis/ldm-celebahq-256'						)
									ldm.to(_UpperCAmelCase						)
									ldm.set_progress_bar_config(disable=_UpperCAmelCase						)

									_a	: str								=				torch.manual_seed(0						)
									_a	: Optional[Any]								=				ldm(generator=_UpperCAmelCase      ,num_inference_steps=5      ,output_type='numpy'						).images

									_a	: Optional[Any]								=				image[0, -3:, -3:, -1]

									assert image.shape == (1, 256, 256, 3)
									_a	: Union[str, Any]								=				np.array([0.43_99, 0.4_49_75, 0.4_68_25, 0.4_74, 0.43_59, 0.45_81, 0.4_50_95, 0.43_41, 0.44_47]						)
									_a	: List[str]								=				1E-2 if torch_device != 'mps' else 3E-2

									assert np.abs(image_slice.flatten() - expected_slice						).max() < tolerance




