




'''simple docstring'''





import torch
from torch import nn




class 					snake_case__							(nn.Module     ):
				"""simple docstring"""



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

							a					=    n_token
							a					=    d_embed
							a					=    d_proj

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

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

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

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

							a					=    keep_order





				def 							__UpperCAmelCase							(   self       :  Dict      ,   __lowerCamelCase       :  Union[str, Any]      ,   __lowerCamelCase       :  List[str]      ,   __lowerCamelCase       :  int      ,   __lowerCamelCase       :  Union[str, Any]							)      ->  List[Any]:







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







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

							if self.n_clusters == 0:
										a					=    self._compute_logit(__lowerCAmelCase      ,   self.out_layers[0].weight      ,   self.out_layers[0].bias      ,   self.out_projs[0]							)
										if labels is not None:
													a					=    labels != -1_00
													a					=    torch.zeros_like(__lowerCAmelCase      ,   dtype=hidden.dtype      ,   device=hidden.device							)
													a					=    (
													    -nn.functional.log_softmax(__lowerCAmelCase      ,   dim=-1							)[mask].gather(1      ,   labels[mask].unsqueeze(1							)							).squeeze(1							)
													)
										else:
													a					=    nn.functional.log_softmax(__lowerCAmelCase      ,   dim=-1							)
							else:
										# construct weights and biases
										a					,	a					=    [], []
										for i in range(len(self.cutoffs							)							):
													if self.div_val == 1:
																a					,	a					=    self.cutoff_ends[i], self.cutoff_ends[i + 1]
																a					=    self.out_layers[0].weight[l_idx:r_idx]
																a					=    self.out_layers[0].bias[l_idx:r_idx]
													else:
																a					=    self.out_layers[i].weight
																a					=    self.out_layers[i].bias

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

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

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

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

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

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

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

																if indices_i.numel() == 0:
																			continue

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

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

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







							if self.n_clusters == 0:
										a					=    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
										a					,	a					=    [], []
										for i in range(len(self.cutoffs							)							):
													if self.div_val == 1:
																a					,	a					=    self.cutoff_ends[i], self.cutoff_ends[i + 1]
																a					=    self.out_layers[0].weight[l_idx:r_idx]
																a					=    self.out_layers[0].bias[l_idx:r_idx]
													else:
																a					=    self.out_layers[i].weight
																a					=    self.out_layers[i].bias

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

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

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

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

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

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

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

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

										return out












import argparse
import os
import re


__lowerCAmelCase	:       Union[str, Any]     =	'src/transformers/models/auto'


# re pattern that matches mapping introductions:
#    SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
__lowerCAmelCase	:       Dict     =	re.compile(r'[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict')
# re pattern that matches identifiers in mappings
__lowerCAmelCase	:       Any     =	re.compile(r'\s*\(\s*"(\S[^"]+)"')




def     __magic_name__       (				A	:      int,						A	:      bool = False     ):






			'''simple docstring'''







			with open(A,						"r",						encoding="utf-8"     ) as f:
						a					=    f.read()

			a					=    content.split("\n"     )
			a					=    []
			a					=    0
			while line_idx < len(A     ):
						if _re_intro_mapping.search(lines[line_idx]     ) is not None:
									a					=    len(re.search(R"^(\s*)\S",						lines[line_idx]     ).groups()[0]     ) + 8
									# Start of a new mapping!
									while not lines[line_idx].startswith(" " * indent + "("     ):
												new_lines.append(lines[line_idx]     )
												line_idx += 1

									a					=    []
									while lines[line_idx].strip() != "]":
												# Blocks either fit in one line or not
												if lines[line_idx].strip() == "(":
															a					=    line_idx
															while not lines[line_idx].startswith(" " * indent + ")"     ):
																		line_idx += 1
															blocks.append("\n".join(lines[start_idx : line_idx + 1]     )     )
												else:
															blocks.append(lines[line_idx]     )
												line_idx += 1

									# Sort blocks by their identifiers
									a					=    sorted(A,						key=lambda A     : _re_identifier.search(A     ).groups()[0]     )
									new_lines += blocks
						else:
									new_lines.append(lines[line_idx]     )
									line_idx += 1

			if overwrite:
						with open(A,						"w",						encoding="utf-8"     ) as f:
									f.write("\n".join(A     )     )
			elif "\n".join(A     ) != content:
						return True




def     __magic_name__       (				A	:      bool = False     ):






			'''simple docstring'''







			a					=    [os.path.join(A,						A     ) for f in os.listdir(A     ) if f.endswith(".py"     )]
			a					=    [sort_auto_mapping(A,						overwrite=A     ) for fname in fnames]

			if not overwrite and any(A     ):
						a					=    [f for f, d in zip(A,						A     ) if d]
						raise ValueError(
						    F"""The following files have auto mappings that need sorting: {", ".join(A     )}. Run `make style` to fix"""
						    " this."     )


if __name__ == "__main__":
		__lowerCAmelCase	:       Dict     =	argparse.ArgumentParser()
		parser.add_argument('--check_only', action='store_true', help='Whether to only check or fix style.')
		__lowerCAmelCase	:       Optional[Any]     =	parser.parse_args()

		sort_all_auto_mappings(not args.check_only)






