


"""simple docstring"""







import torch
from torch import nn




class 				UpperCamelCase_			(				nn.Module):

	"""simple docstring"""





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

						__SCREAMING_SNAKE_CASE		     =       n_token
						__SCREAMING_SNAKE_CASE		     =       d_embed
						__SCREAMING_SNAKE_CASE		     =       d_proj

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

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

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

						__SCREAMING_SNAKE_CASE		     =       nn.ModuleList()
						__SCREAMING_SNAKE_CASE		     =       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     )     ):
																__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE		     =       self.cutoff_ends[i], self.cutoff_ends[i + 1]
																__SCREAMING_SNAKE_CASE		     =       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     )     )

						__SCREAMING_SNAKE_CASE		     =       keep_order




	def 						UpperCAmelCase_   (				self    :       List[Any]    ,    UpperCAmelCase__    :       int    ,    UpperCAmelCase__    :       str    ,    UpperCAmelCase__    :       str    ,    UpperCAmelCase__    :       List[Any]     ) ->			Tuple:
						if proj is None:
											__SCREAMING_SNAKE_CASE		     =       nn.functional.linear(UpperCAmelCase__    ,    UpperCAmelCase__    ,    bias=UpperCAmelCase__     )
						else:
											# if CUDA_MAJOR <= 9 and CUDA_MINOR <= 1:
											__SCREAMING_SNAKE_CASE		     =       nn.functional.linear(UpperCAmelCase__    ,    proj.t().contiguous()     )
											__SCREAMING_SNAKE_CASE		     =       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 						UpperCAmelCase_   (				self    :       str    ,    UpperCAmelCase__    :       List[str]    ,    UpperCAmelCase__    :       Union[str, Any]=None    ,    UpperCAmelCase__    :       Dict=False     ) ->			int:

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

						if self.n_clusters == 0:
											__SCREAMING_SNAKE_CASE		     =       self._compute_logit(UpperCAmelCase__    ,    self.out_layers[0].weight    ,    self.out_layers[0].bias    ,    self.out_projs[0]     )
											if labels is not None:
																__SCREAMING_SNAKE_CASE		     =       labels != -1_0_0
																__SCREAMING_SNAKE_CASE		     =       torch.zeros_like(UpperCAmelCase__    ,    dtype=hidden.dtype    ,    device=hidden.device     )
																__SCREAMING_SNAKE_CASE		     =       (
																    -nn.functional.log_softmax(UpperCAmelCase__    ,    dim=-1     )[mask].gather(1    ,    labels[mask].unsqueeze(1     )     ).squeeze(1     )
																)
											else:
																__SCREAMING_SNAKE_CASE		     =       nn.functional.log_softmax(UpperCAmelCase__    ,    dim=-1     )
						else:
											# construct weights and biases
											__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE		     =       [], []
											for i in range(len(self.cutoffs     )     ):
																if self.div_val == 1:
																					__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE		     =       self.cutoff_ends[i], self.cutoff_ends[i + 1]
																					__SCREAMING_SNAKE_CASE		     =       self.out_layers[0].weight[l_idx:r_idx]
																					__SCREAMING_SNAKE_CASE		     =       self.out_layers[0].bias[l_idx:r_idx]
																else:
																					__SCREAMING_SNAKE_CASE		     =       self.out_layers[i].weight
																					__SCREAMING_SNAKE_CASE		     =       self.out_layers[i].bias

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

																weights.append(UpperCAmelCase__     )
																biases.append(UpperCAmelCase__     )

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

											__SCREAMING_SNAKE_CASE		     =       self._compute_logit(UpperCAmelCase__    ,    UpperCAmelCase__    ,    UpperCAmelCase__    ,    UpperCAmelCase__     )
											__SCREAMING_SNAKE_CASE		     =       nn.functional.log_softmax(UpperCAmelCase__    ,    dim=1     )

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

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

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

																					if indices_i.numel() == 0:
																										continue

																					__SCREAMING_SNAKE_CASE		     =       labels.index_select(0    ,    UpperCAmelCase__     ) - l_idx
																					__SCREAMING_SNAKE_CASE		     =       head_logprob.index_select(0    ,    UpperCAmelCase__     )
																					__SCREAMING_SNAKE_CASE		     =       hidden.index_select(0    ,    UpperCAmelCase__     )
																else:
																					__SCREAMING_SNAKE_CASE		     =       hidden

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

																					__SCREAMING_SNAKE_CASE		     =       self._compute_logit(UpperCAmelCase__    ,    UpperCAmelCase__    ,    UpperCAmelCase__    ,    UpperCAmelCase__     )
																					__SCREAMING_SNAKE_CASE		     =       nn.functional.log_softmax(UpperCAmelCase__    ,    dim=1     )
																					__SCREAMING_SNAKE_CASE		     =       self.cutoffs[0] + i - 1  # No probability for the head cluster
																					if labels is not None:
																										__SCREAMING_SNAKE_CASE		     =       head_logprob_i[:, cluster_prob_idx] + tail_logprob_i.gather(
																										    1    ,    target_i[:, None]     ).squeeze(1     )
																					else:
																										__SCREAMING_SNAKE_CASE		     =       head_logprob[:, cluster_prob_idx, None] + tail_logprob_i
																										__SCREAMING_SNAKE_CASE		     =       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 						UpperCAmelCase_   (				self    :       Union[str, Any]    ,    UpperCAmelCase__    :       List[str]     ) ->			Union[str, Any]:
						if self.n_clusters == 0:
											__SCREAMING_SNAKE_CASE		     =       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
											__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE		     =       [], []
											for i in range(len(self.cutoffs     )     ):
																if self.div_val == 1:
																					__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE		     =       self.cutoff_ends[i], self.cutoff_ends[i + 1]
																					__SCREAMING_SNAKE_CASE		     =       self.out_layers[0].weight[l_idx:r_idx]
																					__SCREAMING_SNAKE_CASE		     =       self.out_layers[0].bias[l_idx:r_idx]
																else:
																					__SCREAMING_SNAKE_CASE		     =       self.out_layers[i].weight
																					__SCREAMING_SNAKE_CASE		     =       self.out_layers[i].bias

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

																weights.append(UpperCAmelCase__     )
																biases.append(UpperCAmelCase__     )

											__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE		     =       weights[0], biases[0], self.out_projs[0]
											__SCREAMING_SNAKE_CASE		     =       self._compute_logit(UpperCAmelCase__    ,    UpperCAmelCase__    ,    UpperCAmelCase__    ,    UpperCAmelCase__     )

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

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

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

																					__SCREAMING_SNAKE_CASE		     =       self._compute_logit(UpperCAmelCase__    ,    UpperCAmelCase__    ,    UpperCAmelCase__    ,    UpperCAmelCase__     )
																					__SCREAMING_SNAKE_CASE		     =       nn.functional.log_softmax(UpperCAmelCase__    ,    dim=1     )

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

											return out




"""simple docstring"""







import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand




def      UpperCAmelCase__ (lowerCAmelCase_     ):




					'''simple docstring'''


					return EnvironmentCommand()




class 				UpperCamelCase_			(				UpperCamelCase):

	"""simple docstring"""





	@staticmethod
	def 						UpperCAmelCase_   (				UpperCAmelCase__    :       ArgumentParser     ) ->			Optional[Any]:
						__SCREAMING_SNAKE_CASE		     =       parser.add_parser("env"     )
						download_parser.set_defaults(func=UpperCAmelCase__     )




	def 						UpperCAmelCase_   (				self    :       List[Any]     ) ->			List[str]:
						__SCREAMING_SNAKE_CASE		     =       huggingface_hub.__version__

						__SCREAMING_SNAKE_CASE		     =       "not installed"
						__SCREAMING_SNAKE_CASE		     =       "NA"
						if is_torch_available():
											import torch

											__SCREAMING_SNAKE_CASE		     =       torch.__version__
											__SCREAMING_SNAKE_CASE		     =       torch.cuda.is_available()

						__SCREAMING_SNAKE_CASE		     =       "not installed"
						if is_transformers_available():
											import transformers

											__SCREAMING_SNAKE_CASE		     =       transformers.__version__

						__SCREAMING_SNAKE_CASE		     =       "not installed"
						if is_accelerate_available():
											import accelerate

											__SCREAMING_SNAKE_CASE		     =       accelerate.__version__

						__SCREAMING_SNAKE_CASE		     =       "not installed"
						if is_xformers_available():
											import xformers

											__SCREAMING_SNAKE_CASE		     =       xformers.__version__

						__SCREAMING_SNAKE_CASE		     =       {
						    "`diffusers` version": version,
						    "Platform": platform.platform(),
						    "Python version": platform.python_version(),
						    "PyTorch version (GPU?)": F"""{pt_version} ({pt_cuda_available})""",
						    "Huggingface_hub version": hub_version,
						    "Transformers version": transformers_version,
						    "Accelerate version": accelerate_version,
						    "xFormers version": xformers_version,
						    "Using GPU in script?": "<fill in>",
						    "Using distributed or parallel set-up in script?": "<fill in>",
						}

						print("\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n"     )
						print(self.format_dict(UpperCAmelCase__     )     )

						return info




	@staticmethod
	def 						UpperCAmelCase_   (				UpperCAmelCase__    :       Optional[Any]     ) ->			str:
						return "\n".join([F"""- {prop}: {val}""" for prop, val in d.items()]     ) + "\n"
