




import torch
from torch import nn







class 						A(		nn.Module     ):



 '''simple docstring'''


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

       """simple docstring"""






       super().__init__()

       lowerCamelCase_	  =					n_token
       lowerCamelCase_	  =					d_embed
       lowerCamelCase_	  =					d_proj

       lowerCamelCase_	  =					cutoffs + [n_token]
       lowerCamelCase_	  =					[0] + self.cutoffs
       lowerCamelCase_	  =					div_val

       lowerCamelCase_	  =					self.cutoffs[0]
       lowerCamelCase_	  =					len(self.cutoffs						) - 1
       lowerCamelCase_	  =					self.shortlist_size + self.n_clusters

       if self.n_clusters > 0:
             lowerCamelCase_	  =					nn.Parameter(torch.zeros(self.n_clusters		,							self.d_embed						)						)
             lowerCamelCase_	  =					nn.Parameter(torch.zeros(self.n_clusters						)						)

       lowerCamelCase_	  =					nn.ModuleList()
       lowerCamelCase_	  =					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(__lowerCAmelCase		,							__lowerCAmelCase						)						)						)
                   else:
                         self.out_projs.append(__lowerCAmelCase						)

             self.out_layers.append(nn.Linear(__lowerCAmelCase		,							__lowerCAmelCase						)						)
       else:
             for i in range(len(self.cutoffs						)						):
                   lowerCamelCase_  ,       lowerCamelCase_	  =					self.cutoff_ends[i], self.cutoff_ends[i + 1]
                   lowerCamelCase_	  =					d_embed // (div_val**i)

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

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

       lowerCamelCase_	  =					keep_order

 def        a__				(       self      :						List[str]		,							A_      :						List[Any]		,							A_      :						int		,							A_      :						Any		,							A_      :						Dict						)     ->			Optional[Any]:

       """simple docstring"""






       if proj is None:
             lowerCamelCase_	  =					nn.functional.linear(__lowerCAmelCase		,							__lowerCAmelCase		,							bias=__lowerCAmelCase						)
       else:
             # if CUDA_MAJOR <= 9 and CUDA_MINOR <= 1:
             lowerCamelCase_	  =					nn.functional.linear(__lowerCAmelCase		,							proj.t().contiguous()						)
             lowerCamelCase_	  =					nn.functional.linear(__lowerCAmelCase		,							__lowerCAmelCase		,							bias=__lowerCAmelCase						)
             # 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      :						Union[str, Any]		,							A_      :						Tuple		,							A_      :						int=None		,							A_      :						Optional[Any]=False						)     ->			Optional[int]:

       """simple docstring"""






       if labels is not None:
             # Shift so that tokens < n predict n
             lowerCamelCase_	  =					hidden[..., :-1, :].contiguous()
             lowerCamelCase_	  =					labels[..., 1:].contiguous()
             lowerCamelCase_	  =					hidden.view(-1		,							hidden.size(-1						)						)
             lowerCamelCase_	  =					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:
             lowerCamelCase_	  =					hidden.view(-1		,							hidden.size(-1						)						)

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

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

                   weights.append(__lowerCAmelCase						)
                   biases.append(__lowerCAmelCase						)

             lowerCamelCase_  ,       lowerCamelCase_  ,       lowerCamelCase_	  =					weights[0], biases[0], self.out_projs[0]

             lowerCamelCase_	  =					self._compute_logit(__lowerCAmelCase		,							__lowerCAmelCase		,							__lowerCAmelCase		,							__lowerCAmelCase						)
             lowerCamelCase_	  =					nn.functional.log_softmax(__lowerCAmelCase		,							dim=1						)

             if labels is None:
                   lowerCamelCase_	  =					hidden.new_empty((head_logit.size(0						), self.n_token)						)
             else:
                   lowerCamelCase_	  =					torch.zeros_like(__lowerCAmelCase		,							dtype=hidden.dtype		,							device=hidden.device						)

             lowerCamelCase_	  =					0
             lowerCamelCase_	  =					[0] + self.cutoffs
             for i in range(len(__lowerCAmelCase						) - 1						):
                   lowerCamelCase_  ,       lowerCamelCase_	  =					cutoff_values[i], cutoff_values[i + 1]

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

                         if indices_i.numel() == 0:
                               continue

                         lowerCamelCase_	  =					labels.index_select(0		,							__lowerCAmelCase						) - l_idx
                         lowerCamelCase_	  =					head_logprob.index_select(0		,							__lowerCAmelCase						)
                         lowerCamelCase_	  =					hidden.index_select(0		,							__lowerCAmelCase						)
                   else:
                         lowerCamelCase_	  =					hidden

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

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

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

       return out





 def        a__				(       self      :						int		,							A_      :						Union[str, Any]						)     ->			int:

       """simple docstring"""






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

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

                   weights.append(__lowerCAmelCase						)
                   biases.append(__lowerCAmelCase						)

             lowerCamelCase_  ,       lowerCamelCase_  ,       lowerCamelCase_	  =					weights[0], biases[0], self.out_projs[0]
             lowerCamelCase_	  =					self._compute_logit(__lowerCAmelCase		,							__lowerCAmelCase		,							__lowerCAmelCase		,							__lowerCAmelCase						)

             lowerCamelCase_	  =					hidden.new_empty((head_logit.size(0						), self.n_token)						)
             lowerCamelCase_	  =					nn.functional.log_softmax(__lowerCAmelCase		,							dim=1						)

             lowerCamelCase_	  =					[0] + self.cutoffs
             for i in range(len(__lowerCAmelCase						) - 1						):
                   lowerCamelCase_  ,       lowerCamelCase_	  =					cutoff_values[i], cutoff_values[i + 1]

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

                         lowerCamelCase_	  =					self._compute_logit(__lowerCAmelCase		,							__lowerCAmelCase		,							__lowerCAmelCase		,							__lowerCAmelCase						)
                         lowerCamelCase_	  =					nn.functional.log_softmax(__lowerCAmelCase		,							dim=1						)

                         lowerCamelCase_	  =					head_logprob[:, -i] + tail_logprob_i
                         lowerCamelCase_	  =					logprob_i

             return out









import os
import re
import shutil
import sys
import tempfile
import unittest

import black


lowerCamelCase       :	List[Any]									=     os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If DDPMSchedulerOutput is changed in scheduling_ddpm.py, this code needs to be manually updated.
lowerCamelCase       :	Tuple									=     "    \"\"\"\n    Output class for the scheduler's step function output.\n\n    Args:\n        prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n            Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the\n            denoising loop.\n        pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n            The predicted denoised sample (x_{0}) based on the model output from the current timestep.\n            `pred_original_sample` can be used to preview progress or for guidance.\n    \"\"\"\n\n    prev_sample: torch.FloatTensor\n    pred_original_sample: Optional[torch.FloatTensor] = None\n"







class 						A(		unittest.TestCase     ):



 '''simple docstring'''


 def        a__				(       self      :						Union[str, Any]						)     ->			List[str]:

       """simple docstring"""






       lowerCamelCase_	  =					tempfile.mkdtemp()
       os.makedirs(os.path.join(self.diffusers_dir		,							'schedulers/'						)						)
       lowerCamelCase_	  =					self.diffusers_dir
       shutil.copy(
           os.path.join(A_		,							'src/diffusers/schedulers/scheduling_ddpm.py'						)		,							os.path.join(self.diffusers_dir		,							'schedulers/scheduling_ddpm.py'						)		,							)

 def        a__				(       self      :						Tuple						)     ->			List[str]:

       """simple docstring"""






       lowerCamelCase_	  =					'src/diffusers'
       shutil.rmtree(self.diffusers_dir						)

 def        a__				(       self      :						str		,							A_      :						Optional[Any]		,							A_      :						Optional[int]		,							A_      :						str		,							A_      :						Optional[Any]=None						)     ->			int:

       """simple docstring"""






       lowerCamelCase_	  =					comment + f"""\nclass {class_name}(nn.Module):\n""" + class_code
       if overwrite_result is not None:
             lowerCamelCase_	  =					comment + f"""\nclass {class_name}(nn.Module):\n""" + overwrite_result
       lowerCamelCase_	  =					black.Mode(target_versions={black.TargetVersion.PYaa}		,							line_length=119						)
       lowerCamelCase_	  =					black.format_str(A_		,							mode=A_						)
       lowerCamelCase_	  =					os.path.join(self.diffusers_dir		,							'new_code.py'						)
       with open(A_		,							'w'		,							newline='\n'						) as f:
             f.write(A_						)
       if overwrite_result is None:
             self.assertTrue(len(check_copies.is_copy_consistent(A_						)						) == 0						)
       else:
             check_copies.is_copy_consistent(f.name		,							overwrite=A_						)
             with open(A_		,							'r'						) as f:
                   self.assertTrue(f.read()		,							A_						)

 def        a__				(       self      :						Optional[int]						)     ->			Dict:

       """simple docstring"""






       lowerCamelCase_	  =					check_copies.find_code_in_diffusers('schedulers.scheduling_ddpm.DDPMSchedulerOutput'						)
       self.assertEqual(A_		,							A_						)





 def        a__				(       self      :						Any						)     ->			Dict:

       """simple docstring"""






       self.check_copy_consistency(
           '# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput'		,							'DDPMSchedulerOutput'		,							REFERENCE_CODE + '\n'		,							)

       # With no empty line at the end
       self.check_copy_consistency(
           '# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput'		,							'DDPMSchedulerOutput'		,							A_		,							)

       # Copy consistency with rename
       self.check_copy_consistency(
           '# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test'		,							'TestSchedulerOutput'		,							re.sub('DDPM'		,							'Test'		,							A_						)		,							)

       # Copy consistency with a really long name
       lowerCamelCase_	  =					'TestClassWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason'
       self.check_copy_consistency(
           f"""# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->{long_class_name}"""		,							f"""{long_class_name}SchedulerOutput"""		,							re.sub('Bert'		,							A_		,							A_						)		,							)

       # Copy consistency with overwrite
       self.check_copy_consistency(
           '# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test'		,							'TestSchedulerOutput'		,							A_		,							overwrite_result=re.sub('DDPM'		,							'Test'		,							A_						)		,							)



