




import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple

import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams, RegNetYaagf, RegNetYaagf, RegNetYaaagf
from huggingface_hub import cached_download, hf_hub_url
from torch import Tensor
from vissl.models.model_helpers import get_trunk_forward_outputs

from transformers import AutoImageProcessor, RegNetConfig, RegNetForImageClassification, RegNetModel
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase__				     = logging.get_logger()







@dataclass
class a :
						"""simple docstring"""

						UpperCamelCase_	:       nn.Module
						UpperCamelCase_	:       List[nn.Module]       	=		field(default_factory=__SCREAMING_SNAKE_CASE  )
						UpperCamelCase_	:       list       	=		field(default_factory=__SCREAMING_SNAKE_CASE  )






						def     UpperCAmelCase_      ( self :     str				,							lowerCamelCase__ :     Optional[Any]				,							lowerCamelCase__ :     Tensor				,							lowerCamelCase__ :     Tensor				)    ->						int:
													"""simple docstring"""


													__lowercase      =    len(list(m.modules()				)				) == 1 or isinstance(lowerCamelCase__				,							nn.Convad				) or isinstance(lowerCamelCase__				,							nn.BatchNormad				)
													if has_not_submodules:
																				self.traced.append(lowerCamelCase__				)






						def __call__( self :     Optional[int]				,							lowerCamelCase__ :     Tensor				)    ->						Union[str, Any]:
													"""simple docstring"""


													for m in self.module.modules():
																				self.handles.append(m.register_forward_hook(self._forward_hook				)				)
													self.module(lowerCamelCase__				)
													[x.remove() for x in self.handles]
													return self






						@property
						def     UpperCAmelCase_      ( self :     Optional[Any]				)    ->						str:
													"""simple docstring"""


													return list(filter(lambda lowerCamelCase__				: len(list(x.state_dict().keys()				)				) > 0				,							self.traced				)				)







@dataclass
class a :
						"""simple docstring"""

						UpperCamelCase_	:       nn.Module
						UpperCamelCase_	:       nn.Module
						UpperCamelCase_	:       int       	=		1
						UpperCamelCase_	:       List       	=		field(default_factory=__SCREAMING_SNAKE_CASE  )
						UpperCamelCase_	:       List       	=		field(default_factory=__SCREAMING_SNAKE_CASE  )
						UpperCamelCase_	:       bool       	=		True






						def __call__( self :     List[Any]				,							lowerCamelCase__ :     Tensor				)    ->						Union[str, Any]:
													"""simple docstring"""


													__lowercase      =    Tracker(self.dest				)(lowerCamelCase__				).parametrized
													__lowercase      =    Tracker(self.src				)(lowerCamelCase__				).parametrized

													__lowercase      =    list(filter(lambda lowerCamelCase__				: type(lowerCamelCase__				) not in self.src_skip				,							lowerCamelCase__				)				)
													__lowercase      =    list(filter(lambda lowerCamelCase__				: type(lowerCamelCase__				) not in self.dest_skip				,							lowerCamelCase__				)				)

													if len(lowerCamelCase__				) != len(lowerCamelCase__				) and self.raise_if_mismatch:
																				raise Exception(
																				    f'Numbers of operations are different. Source module has {len(lowerCamelCase__				)} operations while'
																				    f' destination module has {len(lowerCamelCase__				)}.'				)

													for dest_m, src_m in zip(lowerCamelCase__				,							lowerCamelCase__				):
																				dest_m.load_state_dict(src_m.state_dict()				)
																				if self.verbose == 1:
																											print(f'Transfered from={src_m} to={dest_m}'				)







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

						def __init__( self :     Tuple				,							lowerCamelCase__ :     nn.Module				)    ->						Any:
													"""simple docstring"""


													super().__init__()

													__lowercase      =    []
													# - get the stem
													feature_blocks.append(('''conv1''', model.stem)				)
													# - get all the feature blocks
													for k, v in model.trunk_output.named_children():
																				assert k.startswith('''block'''				), f'Unexpected layer name {k}'
																				__lowercase      =    len(lowerCamelCase__				) + 1
																				feature_blocks.append((f'res{block_index}', v)				)

													__lowercase      =    nn.ModuleDict(lowerCamelCase__				)






						def     UpperCAmelCase_      ( self :     Tuple				,							lowerCamelCase__ :     Tensor				)    ->						List[Any]:
													"""simple docstring"""


													return get_trunk_forward_outputs(
													    lowerCamelCase__				,							out_feat_keys=lowerCamelCase__				,							feature_blocks=self._feature_blocks				,							)







class a (     __SCREAMING_SNAKE_CASE  ):
						"""simple docstring"""

						def     UpperCAmelCase_      ( self :     int				,							lowerCamelCase__ :     str				)    ->						str:
													"""simple docstring"""


													__lowercase      =    x.split('''-'''				)
													return x_split[0] + x_split[1] + "_" + "".join(x_split[2:]				)






						def __getitem__( self :     List[Any]				,							lowerCamelCase__ :     str				)    ->						Callable[[], Tuple[nn.Module, Dict]]:
													"""simple docstring"""


													if x not in self:
																				__lowercase      =    self.convert_name_to_timm(lowerCamelCase__				)
																				__lowercase      =    partial(lambda: (timm.create_model(lowerCamelCase__				,							pretrained=lowerCamelCase__				).eval(), None)				)

													else:
																				__lowercase      =    super().__getitem__(lowerCamelCase__				)

													return val







class a (     __SCREAMING_SNAKE_CASE  ):
						"""simple docstring"""

						def __getitem__( self :     int				,							lowerCamelCase__ :     str				)    ->						Callable[[], nn.Module]:
													"""simple docstring"""


													if "seer" in x and "in1k" not in x:
																				__lowercase      =    RegNetModel
													else:
																				__lowercase      =    RegNetForImageClassification
													return val







def 		_A(       UpperCamelCase__							:			int				,       UpperCamelCase__							:			Union[str, Any]				,       UpperCamelCase__							:			List[Tuple[str, str]]				)    -> List[Any]:
							'''simple docstring'''


							for from_key, to_key in keys:
														__lowercase      =    from_state_dict[from_key].clone()
														print(F'Copied key={from_key} to={to_key}'				)
							return to_state_dict







def 		_A(       UpperCamelCase__							:			str				,       UpperCamelCase__							:			Callable[[], nn.Module]				,       UpperCamelCase__							:			Callable[[], nn.Module]				,       UpperCamelCase__							:			RegNetConfig				,       UpperCamelCase__							:			Path				,       UpperCamelCase__							:			bool = True				,       )    -> Tuple:
							'''simple docstring'''


							print(F'Converting {name}...'				)
							with torch.no_grad():
														__lowercase     ,     __lowercase      =    from_model_func()
														__lowercase      =    our_model_func(UpperCamelCase__				).eval()
														__lowercase      =    ModuleTransfer(src=UpperCamelCase__				,       dest=UpperCamelCase__				,       raise_if_mismatch=UpperCamelCase__				)
														__lowercase      =    torch.randn((1, 3, 224, 224)				)
														module_transfer(UpperCamelCase__				)

							if from_state_dict is not None:
														__lowercase      =    []
														# for seer - in1k finetuned we have to manually copy the head
														if "seer" in name and "in1k" in name:
																					__lowercase      =    [('''0.clf.0.weight''', '''classifier.1.weight'''), ('''0.clf.0.bias''', '''classifier.1.bias''')]
														__lowercase      =    manually_copy_vissl_head(UpperCamelCase__				,       our_model.state_dict()				,       UpperCamelCase__				)
														our_model.load_state_dict(UpperCamelCase__				)

							__lowercase      =    our_model(UpperCamelCase__				,       output_hidden_states=UpperCamelCase__				)
							__lowercase      =    (
							    our_outputs.logits if isinstance(UpperCamelCase__				,       UpperCamelCase__				) else our_outputs.last_hidden_state
							)

							__lowercase      =    from_model(UpperCamelCase__				)
							__lowercase      =    from_output[-1] if type(UpperCamelCase__				) is list else from_output

							# now since I don't want to use any config files, vissl seer model doesn't actually have an head, so let's just check the last hidden state
							if "seer" in name and "in1k" in name:
														__lowercase      =    our_outputs.hidden_states[-1]

							assert torch.allclose(UpperCamelCase__				,       UpperCamelCase__				), "The model logits don't match the original one."

							if push_to_hub:
														our_model.push_to_hub(
														    repo_path_or_name=save_directory / name				,       commit_message='''Add model'''				,       use_temp_dir=UpperCamelCase__				,       )

														__lowercase      =    224 if '''seer''' not in name else 384
														# we can use the convnext one
														__lowercase      =    AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k'''				,       size=UpperCamelCase__				)
														image_processor.push_to_hub(
														    repo_path_or_name=save_directory / name				,       commit_message='''Add image processor'''				,       use_temp_dir=UpperCamelCase__				,       )

														print(F'Pushed {name}'				)







def 		_A(       UpperCamelCase__							:			Path				,       UpperCamelCase__							:			str = None				,       UpperCamelCase__							:			bool = True				)    -> Dict:
							'''simple docstring'''


							__lowercase      =    '''imagenet-1k-id2label.json'''
							__lowercase      =    1000
							__lowercase      =    (1, num_labels)

							__lowercase      =    '''huggingface/label-files'''
							__lowercase      =    num_labels
							__lowercase      =    json.load(open(cached_download(hf_hub_url(UpperCamelCase__				,       UpperCamelCase__				,       repo_type='''dataset'''				)				)				,       '''r'''				)				)
							__lowercase      =    {int(UpperCamelCase__				): v for k, v in idalabel.items()}

							__lowercase      =    idalabel
							__lowercase      =    {v: k for k, v in idalabel.items()}

							__lowercase      =    partial(UpperCamelCase__				,       num_labels=UpperCamelCase__				,       idalabel=UpperCamelCase__				,       labelaid=UpperCamelCase__				)

							__lowercase      =    {
							    '''regnet-x-002''': ImageNetPreTrainedConfig(
							        depths=[1, 1, 4, 7]				,       hidden_sizes=[24, 56, 152, 368]				,       groups_width=8				,       layer_type='''x'''				),
							    '''regnet-x-004''': ImageNetPreTrainedConfig(
							        depths=[1, 2, 7, 12]				,       hidden_sizes=[32, 64, 160, 384]				,       groups_width=16				,       layer_type='''x'''				),
							    '''regnet-x-006''': ImageNetPreTrainedConfig(
							        depths=[1, 3, 5, 7]				,       hidden_sizes=[48, 96, 240, 528]				,       groups_width=24				,       layer_type='''x'''				),
							    '''regnet-x-008''': ImageNetPreTrainedConfig(
							        depths=[1, 3, 7, 5]				,       hidden_sizes=[64, 128, 288, 672]				,       groups_width=16				,       layer_type='''x'''				),
							    '''regnet-x-016''': ImageNetPreTrainedConfig(
							        depths=[2, 4, 10, 2]				,       hidden_sizes=[72, 168, 408, 912]				,       groups_width=24				,       layer_type='''x'''				),
							    '''regnet-x-032''': ImageNetPreTrainedConfig(
							        depths=[2, 6, 15, 2]				,       hidden_sizes=[96, 192, 432, 1008]				,       groups_width=48				,       layer_type='''x'''				),
							    '''regnet-x-040''': ImageNetPreTrainedConfig(
							        depths=[2, 5, 14, 2]				,       hidden_sizes=[80, 240, 560, 1360]				,       groups_width=40				,       layer_type='''x'''				),
							    '''regnet-x-064''': ImageNetPreTrainedConfig(
							        depths=[2, 4, 10, 1]				,       hidden_sizes=[168, 392, 784, 1624]				,       groups_width=56				,       layer_type='''x'''				),
							    '''regnet-x-080''': ImageNetPreTrainedConfig(
							        depths=[2, 5, 15, 1]				,       hidden_sizes=[80, 240, 720, 1920]				,       groups_width=120				,       layer_type='''x'''				),
							    '''regnet-x-120''': ImageNetPreTrainedConfig(
							        depths=[2, 5, 11, 1]				,       hidden_sizes=[224, 448, 896, 2240]				,       groups_width=112				,       layer_type='''x'''				),
							    '''regnet-x-160''': ImageNetPreTrainedConfig(
							        depths=[2, 6, 13, 1]				,       hidden_sizes=[256, 512, 896, 2048]				,       groups_width=128				,       layer_type='''x'''				),
							    '''regnet-x-320''': ImageNetPreTrainedConfig(
							        depths=[2, 7, 13, 1]				,       hidden_sizes=[336, 672, 1344, 2520]				,       groups_width=168				,       layer_type='''x'''				),
							    # y variant
							    '''regnet-y-002''': ImageNetPreTrainedConfig(depths=[1, 1, 4, 7]				,       hidden_sizes=[24, 56, 152, 368]				,       groups_width=8				),
							    '''regnet-y-004''': ImageNetPreTrainedConfig(
							        depths=[1, 3, 6, 6]				,       hidden_sizes=[48, 104, 208, 440]				,       groups_width=8				),
							    '''regnet-y-006''': ImageNetPreTrainedConfig(
							        depths=[1, 3, 7, 4]				,       hidden_sizes=[48, 112, 256, 608]				,       groups_width=16				),
							    '''regnet-y-008''': ImageNetPreTrainedConfig(
							        depths=[1, 3, 8, 2]				,       hidden_sizes=[64, 128, 320, 768]				,       groups_width=16				),
							    '''regnet-y-016''': ImageNetPreTrainedConfig(
							        depths=[2, 6, 17, 2]				,       hidden_sizes=[48, 120, 336, 888]				,       groups_width=24				),
							    '''regnet-y-032''': ImageNetPreTrainedConfig(
							        depths=[2, 5, 13, 1]				,       hidden_sizes=[72, 216, 576, 1512]				,       groups_width=24				),
							    '''regnet-y-040''': ImageNetPreTrainedConfig(
							        depths=[2, 6, 12, 2]				,       hidden_sizes=[128, 192, 512, 1088]				,       groups_width=64				),
							    '''regnet-y-064''': ImageNetPreTrainedConfig(
							        depths=[2, 7, 14, 2]				,       hidden_sizes=[144, 288, 576, 1296]				,       groups_width=72				),
							    '''regnet-y-080''': ImageNetPreTrainedConfig(
							        depths=[2, 4, 10, 1]				,       hidden_sizes=[168, 448, 896, 2016]				,       groups_width=56				),
							    '''regnet-y-120''': ImageNetPreTrainedConfig(
							        depths=[2, 5, 11, 1]				,       hidden_sizes=[224, 448, 896, 2240]				,       groups_width=112				),
							    '''regnet-y-160''': ImageNetPreTrainedConfig(
							        depths=[2, 4, 11, 1]				,       hidden_sizes=[224, 448, 1232, 3024]				,       groups_width=112				),
							    '''regnet-y-320''': ImageNetPreTrainedConfig(
							        depths=[2, 5, 12, 1]				,       hidden_sizes=[232, 696, 1392, 3712]				,       groups_width=232				),
							    # models created by SEER -> https://arxiv.org/abs/2202.08360
							    '''regnet-y-320-seer''': RegNetConfig(depths=[2, 5, 12, 1]				,       hidden_sizes=[232, 696, 1392, 3712]				,       groups_width=232				),
							    '''regnet-y-640-seer''': RegNetConfig(depths=[2, 5, 12, 1]				,       hidden_sizes=[328, 984, 1968, 4920]				,       groups_width=328				),
							    '''regnet-y-1280-seer''': RegNetConfig(
							        depths=[2, 7, 17, 1]				,       hidden_sizes=[528, 1056, 2904, 7392]				,       groups_width=264				),
							    '''regnet-y-2560-seer''': RegNetConfig(
							        depths=[3, 7, 16, 1]				,       hidden_sizes=[640, 1696, 2544, 5088]				,       groups_width=640				),
							    '''regnet-y-10b-seer''': ImageNetPreTrainedConfig(
							        depths=[2, 7, 17, 1]				,       hidden_sizes=[2020, 4040, 1_1110, 2_8280]				,       groups_width=1010				),
							    # finetuned on imagenet
							    '''regnet-y-320-seer-in1k''': ImageNetPreTrainedConfig(
							        depths=[2, 5, 12, 1]				,       hidden_sizes=[232, 696, 1392, 3712]				,       groups_width=232				),
							    '''regnet-y-640-seer-in1k''': ImageNetPreTrainedConfig(
							        depths=[2, 5, 12, 1]				,       hidden_sizes=[328, 984, 1968, 4920]				,       groups_width=328				),
							    '''regnet-y-1280-seer-in1k''': ImageNetPreTrainedConfig(
							        depths=[2, 7, 17, 1]				,       hidden_sizes=[528, 1056, 2904, 7392]				,       groups_width=264				),
							    '''regnet-y-2560-seer-in1k''': ImageNetPreTrainedConfig(
							        depths=[3, 7, 16, 1]				,       hidden_sizes=[640, 1696, 2544, 5088]				,       groups_width=640				),
							    '''regnet-y-10b-seer-in1k''': ImageNetPreTrainedConfig(
							        depths=[2, 7, 17, 1]				,       hidden_sizes=[2020, 4040, 1_1110, 2_8280]				,       groups_width=1010				),
							}

							__lowercase      =    NameToOurModelFuncMap()
							__lowercase      =    NameToFromModelFuncMap()
							# add seer weights logic

							def load_using_classy_vision(UpperCamelCase__							:			str				,       UpperCamelCase__							:			Callable[[], nn.Module]				) -> Tuple[nn.Module, Dict]:
														__lowercase      =    torch.hub.load_state_dict_from_url(UpperCamelCase__				,       model_dir=str(UpperCamelCase__				)				,       map_location='''cpu'''				)
														__lowercase      =    model_func()
														# check if we have a head, if yes add it
														__lowercase      =    files['''classy_state_dict''']['''base_model''']['''model''']
														__lowercase      =    model_state_dict['''trunk''']
														model.load_state_dict(UpperCamelCase__				)
														return model.eval(), model_state_dict["heads"]

							# pretrained
							__lowercase      =    partial(
							    UpperCamelCase__				,       '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch'''				,       lambda: FakeRegNetVisslWrapper(RegNetYaagf()				)				,       )

							__lowercase      =    partial(
							    UpperCamelCase__				,       '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch'''				,       lambda: FakeRegNetVisslWrapper(RegNetYaagf()				)				,       )

							__lowercase      =    partial(
							    UpperCamelCase__				,       '''https://dl.fbaipublicfiles.com/vissl/model_zoo/swav_ig1b_regnet128Gf_cnstant_bs32_node16_sinkhorn10_proto16k_syncBN64_warmup8k/model_final_checkpoint_phase0.torch'''				,       lambda: FakeRegNetVisslWrapper(RegNetYaaagf()				)				,       )

							__lowercase      =    partial(
							    UpperCamelCase__				,       '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch'''				,       lambda: FakeRegNetVisslWrapper(
							        RegNet(RegNetParams(depth=27				,       group_width=1010				,       w_a=1744				,       w_a=620.83				,       w_m=2.52				)				)				)				,       )

							# IN1K finetuned
							__lowercase      =    partial(
							    UpperCamelCase__				,       '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch'''				,       lambda: FakeRegNetVisslWrapper(RegNetYaagf()				)				,       )

							__lowercase      =    partial(
							    UpperCamelCase__				,       '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch'''				,       lambda: FakeRegNetVisslWrapper(RegNetYaagf()				)				,       )

							__lowercase      =    partial(
							    UpperCamelCase__				,       '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch'''				,       lambda: FakeRegNetVisslWrapper(RegNetYaaagf()				)				,       )

							__lowercase      =    partial(
							    UpperCamelCase__				,       '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch'''				,       lambda: FakeRegNetVisslWrapper(
							        RegNet(RegNetParams(depth=27				,       group_width=1010				,       w_a=1744				,       w_a=620.83				,       w_m=2.52				)				)				)				,       )

							if model_name:
														convert_weight_and_push(
														    UpperCamelCase__				,       names_to_from_model_map[model_name]				,       names_to_ours_model_map[model_name]				,       names_to_config[model_name]				,       UpperCamelCase__				,       UpperCamelCase__				,       )
							else:
														for model_name, config in names_to_config.items():
																					convert_weight_and_push(
																					    UpperCamelCase__				,       names_to_from_model_map[model_name]				,       names_to_ours_model_map[model_name]				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				,       )
							return config, expected_shape


if __name__ == "__main__":
			UpperCAmelCase__				     = argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    "--model_name",
			    default=None,
			    type=str,
			    help=(
			        "The name of the model you wish to convert, it must be one of the supported regnet* architecture,"
			        " currently: regnetx-*, regnety-*. If `None`, all of them will the converted."
			    ),
			)
			parser.add_argument(
			    "--pytorch_dump_folder_path",
			    default=None,
			    type=Path,
			    required=True,
			    help="Path to the output PyTorch model directory.",
			)
			parser.add_argument(
			    "--push_to_hub",
			    default=True,
			    type=bool,
			    required=False,
			    help="If True, push model and image processor to the hub.",
			)

			UpperCAmelCase__				     = parser.parse_args()

			UpperCAmelCase__				     = args.pytorch_dump_folder_path
			pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
			convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)









import tensorflow as tf

from ...tf_utils import shape_list







class a (     tf.keras.layers.Layer  ):
						"""simple docstring"""

						def __init__( self :     int				,							lowerCamelCase__ :     Union[str, Any]				,							lowerCamelCase__ :     Tuple				,							lowerCamelCase__ :     List[Any]				,							lowerCamelCase__ :     Optional[Any]				,							lowerCamelCase__ :     Tuple=1				,							lowerCamelCase__ :     Optional[Any]=False				,							**lowerCamelCase__ :     Optional[int]				)    ->						str:
													"""simple docstring"""


													super().__init__(**lowerCamelCase__				)

													__lowercase      =    vocab_size
													__lowercase      =    d_embed
													__lowercase      =    d_proj

													__lowercase      =    cutoffs + [vocab_size]
													__lowercase      =    [0] + self.cutoffs
													__lowercase      =    div_val

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

													__lowercase      =    []
													__lowercase      =    []






						def     UpperCAmelCase_      ( self :     Optional[int]				,							lowerCamelCase__ :     Tuple				)    ->						Tuple:
													"""simple docstring"""


													if self.n_clusters > 0:
																				__lowercase      =    self.add_weight(
																				    shape=(self.n_clusters, self.d_embed)				,							initializer='''zeros'''				,							trainable=lowerCamelCase__				,							name='''cluster_weight'''				)
																				__lowercase      =    self.add_weight(
																				    shape=(self.n_clusters,)				,							initializer='''zeros'''				,							trainable=lowerCamelCase__				,							name='''cluster_bias'''				)

													if self.div_val == 1:
																				for i in range(len(self.cutoffs				)				):
																											if self.d_proj != self.d_embed:
																																		__lowercase      =    self.add_weight(
																																		    shape=(self.d_embed, self.d_proj)				,							initializer='''zeros'''				,							trainable=lowerCamelCase__				,							name=f'out_projs_._{i}'				,							)
																																		self.out_projs.append(lowerCamelCase__				)
																											else:
																																		self.out_projs.append(lowerCamelCase__				)
																											__lowercase      =    self.add_weight(
																											    shape=(self.vocab_size, self.d_embed)				,							initializer='''zeros'''				,							trainable=lowerCamelCase__				,							name=f'out_layers_._{i}_._weight'				,							)
																											__lowercase      =    self.add_weight(
																											    shape=(self.vocab_size,)				,							initializer='''zeros'''				,							trainable=lowerCamelCase__				,							name=f'out_layers_._{i}_._bias'				,							)
																											self.out_layers.append((weight, bias)				)
													else:
																				for i in range(len(self.cutoffs				)				):
																											__lowercase     ,     __lowercase      =    self.cutoff_ends[i], self.cutoff_ends[i + 1]
																											__lowercase      =    self.d_embed // (self.div_val**i)

																											__lowercase      =    self.add_weight(
																											    shape=(d_emb_i, self.d_proj)				,							initializer='''zeros'''				,							trainable=lowerCamelCase__				,							name=f'out_projs_._{i}'				)
																											self.out_projs.append(lowerCamelCase__				)
																											__lowercase      =    self.add_weight(
																											    shape=(r_idx - l_idx, d_emb_i)				,							initializer='''zeros'''				,							trainable=lowerCamelCase__				,							name=f'out_layers_._{i}_._weight'				,							)
																											__lowercase      =    self.add_weight(
																											    shape=(r_idx - l_idx,)				,							initializer='''zeros'''				,							trainable=lowerCamelCase__				,							name=f'out_layers_._{i}_._bias'				,							)
																											self.out_layers.append((weight, bias)				)
													super().build(lowerCamelCase__				)






						@staticmethod
						def     UpperCAmelCase_      ( lowerCamelCase__ :     List[str]				,							lowerCamelCase__ :     int				,							lowerCamelCase__ :     List[Any]				,							lowerCamelCase__ :     List[str]=None				)    ->						Tuple:
													"""simple docstring"""


													__lowercase      =    x
													if proj is not None:
																				__lowercase      =    tf.einsum('''ibd,ed->ibe'''				,							lowerCamelCase__				,							lowerCamelCase__				)
													return tf.einsum('''ibd,nd->ibn'''				,							lowerCamelCase__				,							lowerCamelCase__				) + b






						@staticmethod
						def     UpperCAmelCase_      ( lowerCamelCase__ :     Tuple				,							lowerCamelCase__ :     List[str]				)    ->						str:
													"""simple docstring"""


													__lowercase      =    shape_list(lowerCamelCase__				)
													__lowercase      =    tf.range(lp_size[0]				,							dtype=target.dtype				)
													__lowercase      =    tf.stack([r, target]				,							1				)
													return tf.gather_nd(lowerCamelCase__				,							lowerCamelCase__				)






						def     UpperCAmelCase_      ( self :     Optional[int]				,							lowerCamelCase__ :     Tuple				,							lowerCamelCase__ :     str				,							lowerCamelCase__ :     Optional[int]=True				,							lowerCamelCase__ :     Optional[int]=False				)    ->						List[str]:
													"""simple docstring"""


													__lowercase      =    0
													if self.n_clusters == 0:
																				__lowercase      =    self._logit(lowerCamelCase__				,							self.out_layers[0][0]				,							self.out_layers[0][1]				,							self.out_projs[0]				)
																				if target is not None:
																											__lowercase      =    tf.nn.sparse_softmax_cross_entropy_with_logits(labels=lowerCamelCase__				,							logits=lowerCamelCase__				)
																				__lowercase      =    tf.nn.log_softmax(lowerCamelCase__				,							axis=-1				)
													else:
																				__lowercase      =    shape_list(lowerCamelCase__				)
																				__lowercase      =    []
																				__lowercase      =    tf.zeros(hidden_sizes[:2]				)
																				for i in range(len(self.cutoffs				)				):
																											__lowercase     ,     __lowercase      =    self.cutoff_ends[i], self.cutoff_ends[i + 1]
																											if target is not None:
																																		__lowercase      =    (target >= l_idx) & (target < r_idx)
																																		__lowercase      =    tf.where(lowerCamelCase__				)
																																		__lowercase      =    tf.boolean_mask(lowerCamelCase__				,							lowerCamelCase__				) - l_idx

																											if self.div_val == 1:
																																		__lowercase      =    self.out_layers[0][0][l_idx:r_idx]
																																		__lowercase      =    self.out_layers[0][1][l_idx:r_idx]
																											else:
																																		__lowercase      =    self.out_layers[i][0]
																																		__lowercase      =    self.out_layers[i][1]

																											if i == 0:
																																		__lowercase      =    tf.concat([cur_W, self.cluster_weight]				,							0				)
																																		__lowercase      =    tf.concat([cur_b, self.cluster_bias]				,							0				)

																																		__lowercase      =    self._logit(lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				,							self.out_projs[0]				)
																																		__lowercase      =    tf.nn.log_softmax(lowerCamelCase__				)
																																		out.append(head_logprob[..., : self.cutoffs[0]]				)
																																		if target is not None:
																																									__lowercase      =    tf.boolean_mask(lowerCamelCase__				,							lowerCamelCase__				)
																																									__lowercase      =    self._gather_logprob(lowerCamelCase__				,							lowerCamelCase__				)
																											else:
																																		__lowercase      =    self._logit(lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				,							self.out_projs[i]				)
																																		__lowercase      =    tf.nn.log_softmax(lowerCamelCase__				)
																																		__lowercase      =    self.cutoffs[0] + i - 1  # No probability for the head cluster
																																		__lowercase      =    head_logprob[..., cluster_prob_idx, None] + tail_logprob
																																		out.append(lowerCamelCase__				)
																																		if target is not None:
																																									__lowercase      =    tf.boolean_mask(lowerCamelCase__				,							lowerCamelCase__				)
																																									__lowercase      =    tf.boolean_mask(lowerCamelCase__				,							lowerCamelCase__				)
																																									__lowercase      =    self._gather_logprob(lowerCamelCase__				,							lowerCamelCase__				)
																																									cur_logprob += cur_head_logprob[:, self.cutoff_ends[1] + i - 1]
																											if target is not None:
																																		loss += tf.scatter_nd(lowerCamelCase__				,							-cur_logprob				,							shape_list(lowerCamelCase__				)				)
																				__lowercase      =    tf.concat(lowerCamelCase__				,							axis=-1				)

													if target is not None:
																				if return_mean:
																											__lowercase      =    tf.reduce_mean(lowerCamelCase__				)
																				# Add the training-time loss value to the layer using `self.add_loss()`.
																				self.add_loss(lowerCamelCase__				)

																				# Log the loss as a metric (we could log arbitrary metrics,
																				# including different metrics for training and inference.
																				self.add_metric(lowerCamelCase__				,							name=self.name				,							aggregation='''mean''' if return_mean else ''''''				)

													return out






