


'''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()
__lowerCamelCase												=						logging.get_logger()




@dataclass
class 				UpperCAmelCase			:
 UpperCAmelCase				=					42
 UpperCAmelCase				=					field(default_factory=_snake_case						)
 UpperCAmelCase				=					field(default_factory=_snake_case						)


 def        __SCREAMING_SNAKE_CASE					(      self							:	Union[str, Any]			,       __lowerCamelCase							:	Optional[int]			,       __lowerCamelCase							:	Tensor			,       __lowerCamelCase							:	Tensor ):
    UpperCAmelCase__	:List[Any]      =      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							:	Tuple			,       __lowerCamelCase							:	Tensor ):
    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        __SCREAMING_SNAKE_CASE					(      self							:	str ):
    # check the len of the state_dict keys to see if we have learnable params
    return list(filter(lambda __lowerCamelCase : len(list(x.state_dict().keys() ) ) > 0			,       self.traced ) )




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


 def __call__(      self							:	Any			,       __lowerCamelCase							:	Tensor ):
    UpperCAmelCase__	:Union[str, Any]      =      Tracker(self.dest )(__lowerCamelCase ).parametrized
    UpperCAmelCase__	:str      =      Tracker(self.src )(__lowerCamelCase ).parametrized

    UpperCAmelCase__	:Optional[Any]      =      list(filter(lambda __lowerCamelCase : type(__lowerCamelCase ) not in self.src_skip			,       __lowerCamelCase ) )
    UpperCAmelCase__	:List[Any]      =      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 				UpperCAmelCase			(			nn.Module						):


 def __init__(      self							:	int			,       __lowerCamelCase							:	nn.Module ):
    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__	:Tuple      =      len(__lowerCamelCase ) + 1
       feature_blocks.append((f'''res{block_index}''', v) )

    UpperCAmelCase__	:Optional[Any]      =      nn.ModuleDict(__lowerCamelCase )





 def        __SCREAMING_SNAKE_CASE					(      self							:	Optional[Any]			,       __lowerCamelCase							:	Tensor ):
    return get_trunk_forward_outputs(
        __lowerCamelCase			,       out_feat_keys=__lowerCamelCase			,       feature_blocks=self._feature_blocks			,       )




class 				UpperCAmelCase			(			_snake_case						):


 def        __SCREAMING_SNAKE_CASE					(      self							:	Dict			,       __lowerCamelCase							:	str ):
    UpperCAmelCase__	:Optional[int]      =      x.split('''-''' )
    return x_split[0] + x_split[1] + "_" + "".join(x_split[2:] )





 def __getitem__(      self							:	Optional[Any]			,       __lowerCamelCase							:	str ):
    # default to timm!
    if x not in self:
       UpperCAmelCase__	:Tuple      =      self.convert_name_to_timm(__lowerCamelCase )
       UpperCAmelCase__	:Any      =      partial(lambda: (timm.create_model(__lowerCamelCase			,       pretrained=__lowerCamelCase ).eval(), None) )

    else:
       UpperCAmelCase__	:Optional[int]      =      super().__getitem__(__lowerCamelCase )

    return val




class 				UpperCAmelCase			(			_snake_case						):


 def __getitem__(      self							:	Optional[Any]			,       __lowerCamelCase							:	str ):
    if "seer" in x and "in1k" not in x:
       UpperCAmelCase__	:Dict      =      RegNetModel
    else:
       UpperCAmelCase__	:int      =      RegNetForImageClassification
    return val


def        a__     (					UpperCamelCase_						:      Union[str, Any],   UpperCamelCase_						:      Union[str, Any],   UpperCamelCase_						:      List[Tuple[str, str]]						):
   for from_key, to_key in keys:
      UpperCAmelCase__	:str      =      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,   ):
   print(F'''Converting {name}...'''						)
   with torch.no_grad():
      UpperCAmelCase__   ,						UpperCAmelCase__	:Union[str, Any]      =      from_model_func()
      UpperCAmelCase__	:Any      =      our_model_func(UpperCamelCase_						).eval()
      UpperCAmelCase__	:Optional[int]      =      ModuleTransfer(src=UpperCamelCase_,   dest=UpperCamelCase_,   raise_if_mismatch=UpperCamelCase_						)
      UpperCAmelCase__	:Tuple      =      torch.randn((1, 3, 224, 224)						)
      module_transfer(UpperCamelCase_						)

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

   UpperCAmelCase__	:Optional[Any]      =      our_model(UpperCamelCase_,   output_hidden_states=UpperCamelCase_						)
   UpperCAmelCase__	:List[str]      =      (
       our_outputs.logits if isinstance(UpperCamelCase_,   UpperCamelCase_						) else our_outputs.last_hidden_state
   )

   UpperCAmelCase__	:Optional[Any]      =      from_model(UpperCamelCase_						)
   UpperCAmelCase__	:List[Any]      =      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:
      UpperCAmelCase__	:Tuple      =      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_,   )

      UpperCAmelCase__	:Dict      =      224 if '''seer''' not in name else 384
      # we can use the convnext one
      UpperCAmelCase__	:Any      =      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						):
   UpperCAmelCase__	:int      =      '''imagenet-1k-id2label.json'''
   UpperCAmelCase__	:Dict      =      1_000
   UpperCAmelCase__	:Tuple      =      (1, num_labels)

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

   UpperCAmelCase__	:Dict      =      idalabel
   UpperCAmelCase__	:List[Any]      =      {v: k for k, v in idalabel.items()}

   UpperCAmelCase__	:Dict      =      partial(UpperCamelCase_,   num_labels=UpperCamelCase_,   idalabel=UpperCamelCase_,   labelaid=UpperCamelCase_						)

   UpperCAmelCase__	:str      =      {
       '''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, 1_008],   groups_width=48,   layer_type='''x'''						),
       '''regnet-x-040''': ImageNetPreTrainedConfig(
           depths=[2, 5, 14, 2],   hidden_sizes=[80, 240, 560, 1_360],   groups_width=40,   layer_type='''x'''						),
       '''regnet-x-064''': ImageNetPreTrainedConfig(
           depths=[2, 4, 10, 1],   hidden_sizes=[168, 392, 784, 1_624],   groups_width=56,   layer_type='''x'''						),
       '''regnet-x-080''': ImageNetPreTrainedConfig(
           depths=[2, 5, 15, 1],   hidden_sizes=[80, 240, 720, 1_920],   groups_width=120,   layer_type='''x'''						),
       '''regnet-x-120''': ImageNetPreTrainedConfig(
           depths=[2, 5, 11, 1],   hidden_sizes=[224, 448, 896, 2_240],   groups_width=112,   layer_type='''x'''						),
       '''regnet-x-160''': ImageNetPreTrainedConfig(
           depths=[2, 6, 13, 1],   hidden_sizes=[256, 512, 896, 2_048],   groups_width=128,   layer_type='''x'''						),
       '''regnet-x-320''': ImageNetPreTrainedConfig(
           depths=[2, 7, 13, 1],   hidden_sizes=[336, 672, 1_344, 2_520],   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, 1_512],   groups_width=24						),
       '''regnet-y-040''': ImageNetPreTrainedConfig(
           depths=[2, 6, 12, 2],   hidden_sizes=[128, 192, 512, 1_088],   groups_width=64						),
       '''regnet-y-064''': ImageNetPreTrainedConfig(
           depths=[2, 7, 14, 2],   hidden_sizes=[144, 288, 576, 1_296],   groups_width=72						),
       '''regnet-y-080''': ImageNetPreTrainedConfig(
           depths=[2, 4, 10, 1],   hidden_sizes=[168, 448, 896, 2_016],   groups_width=56						),
       '''regnet-y-120''': ImageNetPreTrainedConfig(
           depths=[2, 5, 11, 1],   hidden_sizes=[224, 448, 896, 2_240],   groups_width=112						),
       '''regnet-y-160''': ImageNetPreTrainedConfig(
           depths=[2, 4, 11, 1],   hidden_sizes=[224, 448, 1_232, 3_024],   groups_width=112						),
       '''regnet-y-320''': ImageNetPreTrainedConfig(
           depths=[2, 5, 12, 1],   hidden_sizes=[232, 696, 1_392, 3_712],   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, 1_392, 3_712],   groups_width=232						),
       '''regnet-y-640-seer''': RegNetConfig(depths=[2, 5, 12, 1],   hidden_sizes=[328, 984, 1_968, 4_920],   groups_width=328						),
       '''regnet-y-1280-seer''': RegNetConfig(
           depths=[2, 7, 17, 1],   hidden_sizes=[528, 1_056, 2_904, 7_392],   groups_width=264						),
       '''regnet-y-2560-seer''': RegNetConfig(
           depths=[3, 7, 16, 1],   hidden_sizes=[640, 1_696, 2_544, 5_088],   groups_width=640						),
       '''regnet-y-10b-seer''': ImageNetPreTrainedConfig(
           depths=[2, 7, 17, 1],   hidden_sizes=[2_020, 4_040, 11_110, 28_280],   groups_width=1_010						),
       # finetuned on imagenet
       '''regnet-y-320-seer-in1k''': ImageNetPreTrainedConfig(
           depths=[2, 5, 12, 1],   hidden_sizes=[232, 696, 1_392, 3_712],   groups_width=232						),
       '''regnet-y-640-seer-in1k''': ImageNetPreTrainedConfig(
           depths=[2, 5, 12, 1],   hidden_sizes=[328, 984, 1_968, 4_920],   groups_width=328						),
       '''regnet-y-1280-seer-in1k''': ImageNetPreTrainedConfig(
           depths=[2, 7, 17, 1],   hidden_sizes=[528, 1_056, 2_904, 7_392],   groups_width=264						),
       '''regnet-y-2560-seer-in1k''': ImageNetPreTrainedConfig(
           depths=[3, 7, 16, 1],   hidden_sizes=[640, 1_696, 2_544, 5_088],   groups_width=640						),
       '''regnet-y-10b-seer-in1k''': ImageNetPreTrainedConfig(
           depths=[2, 7, 17, 1],   hidden_sizes=[2_020, 4_040, 11_110, 28_280],   groups_width=1_010						),
   }

   UpperCAmelCase__	:int      =      NameToOurModelFuncMap()
   UpperCAmelCase__	:Tuple      =      NameToFromModelFuncMap()
   # add seer weights logic

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

   # pretrained
   UpperCAmelCase__	:Optional[Any]      =      partial(
       UpperCamelCase_,   '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch''',   lambda: FakeRegNetVisslWrapper(RegNetYaagf()						),   )

   UpperCAmelCase__	:List[str]      =      partial(
       UpperCamelCase_,   '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch''',   lambda: FakeRegNetVisslWrapper(RegNetYaagf()						),   )

   UpperCAmelCase__	:List[str]      =      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()						),   )

   UpperCAmelCase__	:int      =      partial(
       UpperCamelCase_,   '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch''',   lambda: FakeRegNetVisslWrapper(
           RegNet(RegNetParams(depth=27,   group_width=1_010,   w_a=1_744,   w_a=620.83,   w_m=2.52						)						)						),   )

   # IN1K finetuned
   UpperCAmelCase__	:List[str]      =      partial(
       UpperCamelCase_,   '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch''',   lambda: FakeRegNetVisslWrapper(RegNetYaagf()						),   )

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

   UpperCAmelCase__	:List[Any]      =      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=1_010,   w_a=1_744,   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__":
    __lowerCamelCase												=						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.''',
    )

    __lowerCamelCase												=						parser.parse_args()

    __lowerCamelCase												=						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'''





from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
    from ... import FeatureExtractionMixin, TensorType

__lowerCamelCase												=						logging.get_logger(__name__)

__lowerCamelCase												=						{
    '''openai/imagegpt-small''': '''''',
    '''openai/imagegpt-medium''': '''''',
    '''openai/imagegpt-large''': '''''',
}




class 				UpperCAmelCase			(			_snake_case						):
 UpperCAmelCase				=					"imagegpt"
 UpperCAmelCase				=					["past_key_values"]
 UpperCAmelCase				=					{
     "hidden_size": "n_embd",
     "max_position_embeddings": "n_positions",
     "num_attention_heads": "n_head",
     "num_hidden_layers": "n_layer",
 }


 def __init__(      self							:	List[Any]			,       __lowerCamelCase							:	List[Any]=5_1_2 + 1			,       __lowerCamelCase							:	Dict=3_2 * 3_2			,       __lowerCamelCase							:	List[str]=5_1_2			,       __lowerCamelCase							:	List[Any]=2_4			,       __lowerCamelCase							:	Any=8			,       __lowerCamelCase							:	Tuple=None			,       __lowerCamelCase							:	Any="quick_gelu"			,       __lowerCamelCase							:	Optional[int]=0.1			,       __lowerCamelCase							:	str=0.1			,       __lowerCamelCase							:	int=0.1			,       __lowerCamelCase							:	Tuple=1e-5			,       __lowerCamelCase							:	Dict=0.02			,       __lowerCamelCase							:	Tuple=True			,       __lowerCamelCase							:	str=True			,       __lowerCamelCase							:	Union[str, Any]=False			,       __lowerCamelCase							:	Union[str, Any]=False			,       __lowerCamelCase							:	Dict=False			,       **__lowerCamelCase							:	str			,       ):
    UpperCAmelCase__	:Dict      =      vocab_size
    UpperCAmelCase__	:str      =      n_positions
    UpperCAmelCase__	:Tuple      =      n_embd
    UpperCAmelCase__	:Dict      =      n_layer
    UpperCAmelCase__	:List[Any]      =      n_head
    UpperCAmelCase__	:str      =      n_inner
    UpperCAmelCase__	:Optional[Any]      =      activation_function
    UpperCAmelCase__	:str      =      resid_pdrop
    UpperCAmelCase__	:Optional[Any]      =      embd_pdrop
    UpperCAmelCase__	:Tuple      =      attn_pdrop
    UpperCAmelCase__	:int      =      layer_norm_epsilon
    UpperCAmelCase__	:List[Any]      =      initializer_range
    UpperCAmelCase__	:List[Any]      =      scale_attn_weights
    UpperCAmelCase__	:List[str]      =      use_cache
    UpperCAmelCase__	:Tuple      =      scale_attn_by_inverse_layer_idx
    UpperCAmelCase__	:Union[str, Any]      =      reorder_and_upcast_attn
    UpperCAmelCase__	:List[Any]      =      tie_word_embeddings

    super().__init__(tie_word_embeddings=__lowerCamelCase			,       **__lowerCamelCase )




class 				UpperCAmelCase			(			_snake_case						):


 @property
 def        __SCREAMING_SNAKE_CASE					(      self							:	str ):
    return OrderedDict(
        [
            ('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
        ] )





 def        __SCREAMING_SNAKE_CASE					(      self							:	Dict			,       __lowerCamelCase							:	"FeatureExtractionMixin"			,       __lowerCamelCase							:	int = 1			,       __lowerCamelCase							:	int = -1			,       __lowerCamelCase							:	bool = False			,       __lowerCamelCase							:	Optional["TensorType"] = None			,       __lowerCamelCase							:	int = 3			,       __lowerCamelCase							:	int = 3_2			,       __lowerCamelCase							:	int = 3_2			,       ):
    UpperCAmelCase__	:Tuple      =      self._generate_dummy_images(__lowerCamelCase			,       __lowerCamelCase			,       __lowerCamelCase			,       __lowerCamelCase )
    UpperCAmelCase__	:Dict      =      dict(preprocessor(images=__lowerCamelCase			,       return_tensors=__lowerCamelCase ) )

    return inputs



