


'''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										=4_2
       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'''
          return list(filter(lambda snake_case: len(list(x.state_dict().keys())) > 0  ,							self.traced))






@dataclass
class  __magic_name__							:
       UpperCAmelCase										=4_2
       UpperCAmelCase										=4_2
       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       :       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_2_0.8_3	,   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_2_0.8_3	,   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 unittest

import numpy as np

from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
 import jax.numpy as jnp

 from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
     FlaxRobertaPreLayerNormForCausalLM,
     FlaxRobertaPreLayerNormForMaskedLM,
     FlaxRobertaPreLayerNormForMultipleChoice,
     FlaxRobertaPreLayerNormForQuestionAnswering,
     FlaxRobertaPreLayerNormForSequenceClassification,
     FlaxRobertaPreLayerNormForTokenClassification,
     FlaxRobertaPreLayerNormModel,
 )






class  __magic_name__							(		unittest.TestCase    ):






       def __init__(      self  ,							snake_case  ,							snake_case=1_3  ,							snake_case=7  ,							snake_case=True  ,							snake_case=True  ,							snake_case=True  ,							snake_case=True  ,							snake_case=9_9  ,							snake_case=3_2  ,							snake_case=5  ,							snake_case=4  ,							snake_case=3_7  ,							snake_case="gelu"  ,							snake_case=0.1  ,							snake_case=0.1  ,							snake_case=5_1_2  ,							snake_case=1_6  ,							snake_case=2  ,							snake_case=0.02  ,							snake_case=4  ,							)						->  Dict:




          '''simple docstring'''
          _UpperCAmelCase       :       Dict							   =parent
          _UpperCAmelCase       :       Dict							   =batch_size
          _UpperCAmelCase       :       List[Any]							   =seq_length
          _UpperCAmelCase       :       List[str]							   =is_training
          _UpperCAmelCase       :       Optional[int]							   =use_attention_mask
          _UpperCAmelCase       :       Dict							   =use_token_type_ids
          _UpperCAmelCase       :       Dict							   =use_labels
          _UpperCAmelCase       :       Optional[Any]							   =vocab_size
          _UpperCAmelCase       :       str							   =hidden_size
          _UpperCAmelCase       :       Dict							   =num_hidden_layers
          _UpperCAmelCase       :       Tuple							   =num_attention_heads
          _UpperCAmelCase       :       List[str]							   =intermediate_size
          _UpperCAmelCase       :       List[str]							   =hidden_act
          _UpperCAmelCase       :       int							   =hidden_dropout_prob
          _UpperCAmelCase       :       Optional[int]							   =attention_probs_dropout_prob
          _UpperCAmelCase       :       Optional[Any]							   =max_position_embeddings
          _UpperCAmelCase       :       Union[str, Any]							   =type_vocab_size
          _UpperCAmelCase       :       Dict							   =type_sequence_label_size
          _UpperCAmelCase       :       Union[str, Any]							   =initializer_range
          _UpperCAmelCase       :       Any							   =num_choices






       def       lowerCAmelCase		(      self)						->  Dict:




          '''simple docstring'''
          _UpperCAmelCase       :       Dict							   =ids_tensor([self.batch_size, self.seq_length]  ,							self.vocab_size)

          _UpperCAmelCase       :       str							   =None
          if self.use_attention_mask:
             _UpperCAmelCase       :       Dict							   =random_attention_mask([self.batch_size, self.seq_length])

          _UpperCAmelCase       :       Optional[Any]							   =None
          if self.use_token_type_ids:
             _UpperCAmelCase       :       Tuple							   =ids_tensor([self.batch_size, self.seq_length]  ,							self.type_vocab_size)

          _UpperCAmelCase       :       Union[str, Any]							   =RobertaPreLayerNormConfig(
              vocab_size=self.vocab_size  ,							hidden_size=self.hidden_size  ,							num_hidden_layers=self.num_hidden_layers  ,							num_attention_heads=self.num_attention_heads  ,							intermediate_size=self.intermediate_size  ,							hidden_act=self.hidden_act  ,							hidden_dropout_prob=self.hidden_dropout_prob  ,							attention_probs_dropout_prob=self.attention_probs_dropout_prob  ,							max_position_embeddings=self.max_position_embeddings  ,							type_vocab_size=self.type_vocab_size  ,							is_decoder=snake_case  ,							initializer_range=self.initializer_range  ,							)

          return config, input_ids, token_type_ids, attention_mask






       def       lowerCAmelCase		(      self)						->  str:




          '''simple docstring'''
          _UpperCAmelCase       :       Dict							   =self.prepare_config_and_inputs()
          _UpperCAmelCase   ,	_UpperCAmelCase   ,	_UpperCAmelCase   ,	_UpperCAmelCase       :       str							   =config_and_inputs
          _UpperCAmelCase       :       List[Any]							   ={'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
          return config, inputs_dict






       def       lowerCAmelCase		(      self)						->  Optional[Any]:




          '''simple docstring'''
          _UpperCAmelCase       :       Tuple							   =self.prepare_config_and_inputs()
          _UpperCAmelCase   ,	_UpperCAmelCase   ,	_UpperCAmelCase   ,	_UpperCAmelCase       :       Optional[int]							   =config_and_inputs

          _UpperCAmelCase       :       Tuple							   =True
          _UpperCAmelCase       :       Any							   =floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
          _UpperCAmelCase       :       Optional[Any]							   =ids_tensor([self.batch_size, self.seq_length]  ,							vocab_size=2)

          return (
              config,
              input_ids,
              token_type_ids,
              encoder_hidden_states,
              encoder_attention_mask,
          )






@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class  __magic_name__							(		lowerCAmelCase ,unittest.TestCase    ):
       UpperCAmelCase										=True

       UpperCAmelCase										=(
           (
               FlaxRobertaPreLayerNormModel,
               FlaxRobertaPreLayerNormForCausalLM,
               FlaxRobertaPreLayerNormForMaskedLM,
               FlaxRobertaPreLayerNormForSequenceClassification,
               FlaxRobertaPreLayerNormForTokenClassification,
               FlaxRobertaPreLayerNormForMultipleChoice,
               FlaxRobertaPreLayerNormForQuestionAnswering,
           )
           if is_flax_available()
           else ()
       )






       def       lowerCAmelCase		(      self)						->  List[str]:




          '''simple docstring'''
          _UpperCAmelCase       :       int							   =FlaxRobertaPreLayerNormModelTester(self)






       @slow
       def       lowerCAmelCase		(      self)						->  Optional[int]:




          '''simple docstring'''
          for model_class_name in self.all_model_classes:
             _UpperCAmelCase       :       List[str]							   =model_class_name.from_pretrained('andreasmadsen/efficient_mlm_m0.40'  ,							from_pt=snake_case)
             _UpperCAmelCase       :       Dict							   =model(np.ones((1, 1)))
             self.assertIsNotNone(snake_case)






@require_flax
class  __magic_name__							(		unittest.TestCase    ):






       @slow
       def       lowerCAmelCase		(      self)						->  Tuple:




          '''simple docstring'''
          _UpperCAmelCase       :       Tuple							   =FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('andreasmadsen/efficient_mlm_m0.40'  ,							from_pt=snake_case)

          _UpperCAmelCase       :       Optional[int]							   =np.array([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]  ,							dtype=jnp.intaa)
          _UpperCAmelCase       :       str							   =model(snake_case)[0]
          _UpperCAmelCase       :       int							   =[1, 1_1, 5_0_2_6_5]
          self.assertEqual(list(output.shape)  ,							snake_case)
          # compare the actual values for a slice.
          _UpperCAmelCase       :       List[str]							   =np.array(
              [[[40.48_80, 18.01_99, -5.23_67], [-1.88_77, -4.08_85, 10.70_85], [-2.26_13, -5.61_10, 7.26_65]]]  ,							dtype=np.floataa)
          self.assertTrue(np.allclose(output[:, :3, :3]  ,							snake_case  ,							atol=1E-4))






       @slow
       def       lowerCAmelCase		(      self)						->  Tuple:




          '''simple docstring'''
          _UpperCAmelCase       :       Dict							   =FlaxRobertaPreLayerNormModel.from_pretrained('andreasmadsen/efficient_mlm_m0.40'  ,							from_pt=snake_case)

          _UpperCAmelCase       :       List[str]							   =np.array([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]  ,							dtype=jnp.intaa)
          _UpperCAmelCase       :       Tuple							   =model(snake_case)[0]
          # compare the actual values for a slice.
          _UpperCAmelCase       :       List[str]							   =np.array(
              [[[0.02_08, -0.03_56, 0.02_37], [-0.15_69, -0.04_11, -0.26_26], [0.18_79, 0.01_25, -0.00_89]]]  ,							dtype=np.floataa)
          self.assertTrue(np.allclose(output[:, :3, :3]  ,							snake_case  ,							atol=1E-4))

