


'''simple docstring'''






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()
lowercase							=logging.get_logger()






@dataclass
class  __magic_name__							:
       UpperCAmelCase										=42
       UpperCAmelCase										=field(default_factory=lowerCAmelCase    )
       UpperCAmelCase										=field(default_factory=lowerCAmelCase    )






       def       lowerCAmelCase		(      self  ,							snake_case  ,							snake_case  ,							snake_case)						->  List[Any]:




          '''simple docstring'''
          _UpperCAmelCase       :       Union[str, Any]							   =len(list(m.modules())) == 1 or isinstance(snake_case  ,							nn.Convad) or isinstance(snake_case  ,							nn.BatchNormad)
          if has_not_submodules:
             self.traced.append(snake_case)






       def __call__(      self  ,							snake_case)						->  str:




          '''simple docstring'''
          for m in self.module.modules():
             self.handles.append(m.register_forward_hook(self._forward_hook))
          self.module(snake_case)
          [x.remove() for x in self.handles]
          return self






       @property
       def       lowerCAmelCase		(      self)						->  Tuple:




          '''simple docstring'''
          # check the len of the state_dict keys to see if we have learnable params
          return list(filter(lambda snake_case: len(list(x.state_dict().keys())) > 0  ,							self.traced))






@dataclass
class  __magic_name__							:
       UpperCAmelCase										=42
       UpperCAmelCase										=42
       UpperCAmelCase										=1
       UpperCAmelCase										=field(default_factory=lowerCAmelCase    )
       UpperCAmelCase										=field(default_factory=lowerCAmelCase    )
       UpperCAmelCase										=True






       def __call__(      self  ,							snake_case)						->  Optional[Any]:




          '''simple docstring'''
          _UpperCAmelCase       :       Dict							   =Tracker(self.dest)(snake_case).parametrized
          _UpperCAmelCase       :       Dict							   =Tracker(self.src)(snake_case).parametrized

          _UpperCAmelCase       :       Optional[Any]							   =list(filter(lambda snake_case: type(snake_case) not in self.src_skip  ,							snake_case))
          _UpperCAmelCase       :       Union[str, Any]							   =list(filter(lambda snake_case: type(snake_case) not in self.dest_skip  ,							snake_case))

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

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






class  __magic_name__							(		nn.Module    ):






       def __init__(      self  ,							snake_case)						->  Optional[Any]:




          '''simple docstring'''
          super().__init__()

          _UpperCAmelCase       :       List[Tuple[str, nn.Module]]							   =[]
          # - 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}"
             _UpperCAmelCase       :       List[str]							   =len(snake_case) + 1
             feature_blocks.append((f"res{block_index}", v))

          _UpperCAmelCase       :       Optional[Any]							   =nn.ModuleDict(snake_case)






       def       lowerCAmelCase		(      self  ,							snake_case)						->  Optional[int]:




          '''simple docstring'''
          return get_trunk_forward_outputs(
              snake_case  ,							out_feat_keys=snake_case  ,							feature_blocks=self._feature_blocks  ,							)






class  __magic_name__							(		lowerCAmelCase    ):






       def       lowerCAmelCase		(      self  ,							snake_case)						->  str:




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






       def __getitem__(      self  ,							snake_case)						->  Callable[[], Tuple[nn.Module, Dict]]:




          '''simple docstring'''
          # default to timm!
          if x not in self:
             _UpperCAmelCase       :       int							   =self.convert_name_to_timm(snake_case)
             _UpperCAmelCase       :       Tuple							   =partial(lambda: (timm.create_model(snake_case  ,							pretrained=snake_case).eval(), None))

          else:
             _UpperCAmelCase       :       Tuple							   =super().__getitem__(snake_case)

          return val






class  __magic_name__							(		lowerCAmelCase    ):






       def __getitem__(      self  ,							snake_case)						->  Callable[[], nn.Module]:




          '''simple docstring'''
          if "seer" in x and "in1k" not in x:
             _UpperCAmelCase       :       Union[str, Any]							   =RegNetModel
          else:
             _UpperCAmelCase       :       Tuple							   =RegNetForImageClassification
          return val






def lowerCamelCase__      (						__lowerCamelCase	:							Optional[Any]	,   __lowerCamelCase	:							Optional[int]	,   __lowerCamelCase	:							List[Tuple[str, str]]							):







   '''simple docstring'''




   for from_key, to_key in keys:
      _UpperCAmelCase       :       Optional[int]							   =from_state_dict[from_key].clone()
      print(f"Copied key={from_key} to={to_key}"							)
   return to_state_dict





def lowerCamelCase__      (						__lowerCamelCase	:							str	,   __lowerCamelCase	:							Callable[[], nn.Module]	,   __lowerCamelCase	:							Callable[[], nn.Module]	,   __lowerCamelCase	:							RegNetConfig	,   __lowerCamelCase	:							Path	,   __lowerCamelCase	:							bool = True	,   ):







   '''simple docstring'''




   print(f"Converting {name}..."							)
   with torch.no_grad():
      _UpperCAmelCase   ,	_UpperCAmelCase       :       List[str]							   =from_model_func()
      _UpperCAmelCase       :       Any							   =our_model_func(__lowerCamelCase							).eval()
      _UpperCAmelCase       :       List[Any]							   =ModuleTransfer(src=__lowerCamelCase	,   dest=__lowerCamelCase	,   raise_if_mismatch=__lowerCamelCase							)
      _UpperCAmelCase       :       Dict							   =torch.randn((1, 3, 2_2_4, 2_2_4)							)
      module_transfer(__lowerCamelCase							)

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

   _UpperCAmelCase       :       Union[str, Any]							   =our_model(__lowerCamelCase	,   output_hidden_states=__lowerCamelCase							)
   _UpperCAmelCase       :       Union[str, Any]							   =(
       our_outputs.logits if isinstance(__lowerCamelCase	,   __lowerCamelCase							) else our_outputs.last_hidden_state
   )

   _UpperCAmelCase       :       Dict							   =from_model(__lowerCamelCase							)
   _UpperCAmelCase       :       Any							   =from_output[-1] if type(__lowerCamelCase							) 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:
      _UpperCAmelCase       :       Optional[Any]							   =our_outputs.hidden_states[-1]

   assert torch.allclose(__lowerCamelCase	,   __lowerCamelCase							), "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=__lowerCamelCase	,   )

      _UpperCAmelCase       :       List[Any]							   =2_2_4 if 'seer' not in name else 3_8_4
      # we can use the convnext one
      _UpperCAmelCase       :       str							   =AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'	,   size=__lowerCamelCase							)
      image_processor.push_to_hub(
          repo_path_or_name=save_directory / name	,   commit_message='Add image processor'	,   use_temp_dir=__lowerCamelCase	,   )

      print(f"Pushed {name}"							)





def lowerCamelCase__      (						__lowerCamelCase	:							Path	,   __lowerCamelCase	:							str = None	,   __lowerCamelCase	:							bool = True							):







   '''simple docstring'''




   _UpperCAmelCase       :       Union[str, Any]							   ='imagenet-1k-id2label.json'
   _UpperCAmelCase       :       Tuple							   =1_0_0_0
   _UpperCAmelCase       :       Optional[Any]							   =(1, num_labels)

   _UpperCAmelCase       :       Any							   ='huggingface/label-files'
   _UpperCAmelCase       :       Union[str, Any]							   =num_labels
   _UpperCAmelCase       :       Optional[Any]							   =json.load(open(cached_download(hf_hub_url(__lowerCamelCase	,   __lowerCamelCase	,   repo_type='dataset'							)							)	,   'r'							)							)
   _UpperCAmelCase       :       List[str]							   ={int(__lowerCamelCase							): v for k, v in idalabel.items()}

   _UpperCAmelCase       :       Union[str, Any]							   =idalabel
   _UpperCAmelCase       :       List[Any]							   ={v: k for k, v in idalabel.items()}

   _UpperCAmelCase       :       Any							   =partial(__lowerCamelCase	,   num_labels=__lowerCamelCase	,   idalabel=__lowerCamelCase	,   labelaid=__lowerCamelCase							)

   _UpperCAmelCase       :       Union[str, Any]							   ={
       'regnet-x-002': ImageNetPreTrainedConfig(
           depths=[1, 1, 4, 7]	,   hidden_sizes=[2_4, 5_6, 1_5_2, 3_6_8]	,   groups_width=8	,   layer_type='x'							),
       'regnet-x-004': ImageNetPreTrainedConfig(
           depths=[1, 2, 7, 1_2]	,   hidden_sizes=[3_2, 6_4, 1_6_0, 3_8_4]	,   groups_width=1_6	,   layer_type='x'							),
       'regnet-x-006': ImageNetPreTrainedConfig(
           depths=[1, 3, 5, 7]	,   hidden_sizes=[4_8, 9_6, 2_4_0, 5_2_8]	,   groups_width=2_4	,   layer_type='x'							),
       'regnet-x-008': ImageNetPreTrainedConfig(
           depths=[1, 3, 7, 5]	,   hidden_sizes=[6_4, 1_2_8, 2_8_8, 6_7_2]	,   groups_width=1_6	,   layer_type='x'							),
       'regnet-x-016': ImageNetPreTrainedConfig(
           depths=[2, 4, 1_0, 2]	,   hidden_sizes=[7_2, 1_6_8, 4_0_8, 9_1_2]	,   groups_width=2_4	,   layer_type='x'							),
       'regnet-x-032': ImageNetPreTrainedConfig(
           depths=[2, 6, 1_5, 2]	,   hidden_sizes=[9_6, 1_9_2, 4_3_2, 1_0_0_8]	,   groups_width=4_8	,   layer_type='x'							),
       'regnet-x-040': ImageNetPreTrainedConfig(
           depths=[2, 5, 1_4, 2]	,   hidden_sizes=[8_0, 2_4_0, 5_6_0, 1_3_6_0]	,   groups_width=4_0	,   layer_type='x'							),
       'regnet-x-064': ImageNetPreTrainedConfig(
           depths=[2, 4, 1_0, 1]	,   hidden_sizes=[1_6_8, 3_9_2, 7_8_4, 1_6_2_4]	,   groups_width=5_6	,   layer_type='x'							),
       'regnet-x-080': ImageNetPreTrainedConfig(
           depths=[2, 5, 1_5, 1]	,   hidden_sizes=[8_0, 2_4_0, 7_2_0, 1_9_2_0]	,   groups_width=1_2_0	,   layer_type='x'							),
       'regnet-x-120': ImageNetPreTrainedConfig(
           depths=[2, 5, 1_1, 1]	,   hidden_sizes=[2_2_4, 4_4_8, 8_9_6, 2_2_4_0]	,   groups_width=1_1_2	,   layer_type='x'							),
       'regnet-x-160': ImageNetPreTrainedConfig(
           depths=[2, 6, 1_3, 1]	,   hidden_sizes=[2_5_6, 5_1_2, 8_9_6, 2_0_4_8]	,   groups_width=1_2_8	,   layer_type='x'							),
       'regnet-x-320': ImageNetPreTrainedConfig(
           depths=[2, 7, 1_3, 1]	,   hidden_sizes=[3_3_6, 6_7_2, 1_3_4_4, 2_5_2_0]	,   groups_width=1_6_8	,   layer_type='x'							),
       # y variant
       'regnet-y-002': ImageNetPreTrainedConfig(depths=[1, 1, 4, 7]	,   hidden_sizes=[2_4, 5_6, 1_5_2, 3_6_8]	,   groups_width=8							),
       'regnet-y-004': ImageNetPreTrainedConfig(
           depths=[1, 3, 6, 6]	,   hidden_sizes=[4_8, 1_0_4, 2_0_8, 4_4_0]	,   groups_width=8							),
       'regnet-y-006': ImageNetPreTrainedConfig(
           depths=[1, 3, 7, 4]	,   hidden_sizes=[4_8, 1_1_2, 2_5_6, 6_0_8]	,   groups_width=1_6							),
       'regnet-y-008': ImageNetPreTrainedConfig(
           depths=[1, 3, 8, 2]	,   hidden_sizes=[6_4, 1_2_8, 3_2_0, 7_6_8]	,   groups_width=1_6							),
       'regnet-y-016': ImageNetPreTrainedConfig(
           depths=[2, 6, 1_7, 2]	,   hidden_sizes=[4_8, 1_2_0, 3_3_6, 8_8_8]	,   groups_width=2_4							),
       'regnet-y-032': ImageNetPreTrainedConfig(
           depths=[2, 5, 1_3, 1]	,   hidden_sizes=[7_2, 2_1_6, 5_7_6, 1_5_1_2]	,   groups_width=2_4							),
       'regnet-y-040': ImageNetPreTrainedConfig(
           depths=[2, 6, 1_2, 2]	,   hidden_sizes=[1_2_8, 1_9_2, 5_1_2, 1_0_8_8]	,   groups_width=6_4							),
       'regnet-y-064': ImageNetPreTrainedConfig(
           depths=[2, 7, 1_4, 2]	,   hidden_sizes=[1_4_4, 2_8_8, 5_7_6, 1_2_9_6]	,   groups_width=7_2							),
       'regnet-y-080': ImageNetPreTrainedConfig(
           depths=[2, 4, 1_0, 1]	,   hidden_sizes=[1_6_8, 4_4_8, 8_9_6, 2_0_1_6]	,   groups_width=5_6							),
       'regnet-y-120': ImageNetPreTrainedConfig(
           depths=[2, 5, 1_1, 1]	,   hidden_sizes=[2_2_4, 4_4_8, 8_9_6, 2_2_4_0]	,   groups_width=1_1_2							),
       'regnet-y-160': ImageNetPreTrainedConfig(
           depths=[2, 4, 1_1, 1]	,   hidden_sizes=[2_2_4, 4_4_8, 1_2_3_2, 3_0_2_4]	,   groups_width=1_1_2							),
       'regnet-y-320': ImageNetPreTrainedConfig(
           depths=[2, 5, 1_2, 1]	,   hidden_sizes=[2_3_2, 6_9_6, 1_3_9_2, 3_7_1_2]	,   groups_width=2_3_2							),
       # models created by SEER -> https://arxiv.org/abs/2202.08360
       'regnet-y-320-seer': RegNetConfig(depths=[2, 5, 1_2, 1]	,   hidden_sizes=[2_3_2, 6_9_6, 1_3_9_2, 3_7_1_2]	,   groups_width=2_3_2							),
       'regnet-y-640-seer': RegNetConfig(depths=[2, 5, 1_2, 1]	,   hidden_sizes=[3_2_8, 9_8_4, 1_9_6_8, 4_9_2_0]	,   groups_width=3_2_8							),
       'regnet-y-1280-seer': RegNetConfig(
           depths=[2, 7, 1_7, 1]	,   hidden_sizes=[5_2_8, 1_0_5_6, 2_9_0_4, 7_3_9_2]	,   groups_width=2_6_4							),
       'regnet-y-2560-seer': RegNetConfig(
           depths=[3, 7, 1_6, 1]	,   hidden_sizes=[6_4_0, 1_6_9_6, 2_5_4_4, 5_0_8_8]	,   groups_width=6_4_0							),
       'regnet-y-10b-seer': ImageNetPreTrainedConfig(
           depths=[2, 7, 1_7, 1]	,   hidden_sizes=[2_0_2_0, 4_0_4_0, 1_1_1_1_0, 2_8_2_8_0]	,   groups_width=1_0_1_0							),
       # finetuned on imagenet
       'regnet-y-320-seer-in1k': ImageNetPreTrainedConfig(
           depths=[2, 5, 1_2, 1]	,   hidden_sizes=[2_3_2, 6_9_6, 1_3_9_2, 3_7_1_2]	,   groups_width=2_3_2							),
       'regnet-y-640-seer-in1k': ImageNetPreTrainedConfig(
           depths=[2, 5, 1_2, 1]	,   hidden_sizes=[3_2_8, 9_8_4, 1_9_6_8, 4_9_2_0]	,   groups_width=3_2_8							),
       'regnet-y-1280-seer-in1k': ImageNetPreTrainedConfig(
           depths=[2, 7, 1_7, 1]	,   hidden_sizes=[5_2_8, 1_0_5_6, 2_9_0_4, 7_3_9_2]	,   groups_width=2_6_4							),
       'regnet-y-2560-seer-in1k': ImageNetPreTrainedConfig(
           depths=[3, 7, 1_6, 1]	,   hidden_sizes=[6_4_0, 1_6_9_6, 2_5_4_4, 5_0_8_8]	,   groups_width=6_4_0							),
       'regnet-y-10b-seer-in1k': ImageNetPreTrainedConfig(
           depths=[2, 7, 1_7, 1]	,   hidden_sizes=[2_0_2_0, 4_0_4_0, 1_1_1_1_0, 2_8_2_8_0]	,   groups_width=1_0_1_0							),
   }

   _UpperCAmelCase       :       Optional[int]							   =NameToOurModelFuncMap()
   _UpperCAmelCase       :       Tuple							   =NameToFromModelFuncMap()
   # add seer weights logic

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

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

   _UpperCAmelCase       :       Union[str, Any]							   =partial(
       __lowerCamelCase	,   'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch'	,   lambda: FakeRegNetVisslWrapper(RegNetYaagf()							)	,   )

   _UpperCAmelCase       :       Dict							   =partial(
       __lowerCamelCase	,   '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()							)	,   )

   _UpperCAmelCase       :       Union[str, Any]							   =partial(
       __lowerCamelCase	,   'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch'	,   lambda: FakeRegNetVisslWrapper(
           RegNet(RegNetParams(depth=2_7	,   group_width=1_0_1_0	,   w_a=1_7_4_4	,   w_a=6_20.83	,   w_m=2.52							)							)							)	,   )

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

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

   _UpperCAmelCase       :       List[Any]							   =partial(
       __lowerCamelCase	,   'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch'	,   lambda: FakeRegNetVisslWrapper(RegNetYaaagf()							)	,   )

   _UpperCAmelCase       :       Dict							   =partial(
       __lowerCamelCase	,   'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch'	,   lambda: FakeRegNetVisslWrapper(
           RegNet(RegNetParams(depth=2_7	,   group_width=1_0_1_0	,   w_a=1_7_4_4	,   w_a=6_20.83	,   w_m=2.52							)							)							)	,   )

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


if __name__ == "__main__":
 lowercase							=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.',
 )

 lowercase							=parser.parse_args()

 lowercase							=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)





'''simple docstring'''






import jax.numpy as jnp

from ...utils import logging
from ..ta.modeling_flax_ta import FlaxTaEncoderModel, FlaxTaForConditionalGeneration, FlaxTaModel
from .configuration_mta import MTaConfig


lowercase							=logging.get_logger(__name__)

lowercase							='T5Config'





def lowerCamelCase__      (						__lowerCamelCase	:							jnp.array	,   __lowerCamelCase	:							int	,   __lowerCamelCase	:							int							):







   '''simple docstring'''




   _UpperCAmelCase       :       List[Any]							   =jnp.zeros_like(__lowerCamelCase							)
   _UpperCAmelCase       :       Union[str, Any]							   =shifted_input_ids.at[:, 1:].set(input_ids[:, :-1]							)
   _UpperCAmelCase       :       List[Any]							   =shifted_input_ids.at[:, 0].set(__lowerCamelCase							)

   _UpperCAmelCase       :       str							   =jnp.where(shifted_input_ids == -1_0_0	,   __lowerCamelCase	,   __lowerCamelCase							)
   return shifted_input_ids






class  __magic_name__							(		lowerCAmelCase    ):
       UpperCAmelCase										="mt5"
       UpperCAmelCase										=MTaConfig






class  __magic_name__							(		lowerCAmelCase    ):
       UpperCAmelCase										="mt5"
       UpperCAmelCase										=MTaConfig






class  __magic_name__							(		lowerCAmelCase    ):
       UpperCAmelCase										="mt5"
       UpperCAmelCase										=MTaConfig

