






'''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()
__UpperCamelCase   :							List[str]         =   logging.get_logger()
@dataclass
class 				__SCREAMING_SNAKE_CASE							:
      __a	       =42
      __a	       =field(default_factory=_lowerCAmelCase  )
      __a	       =field(default_factory=_lowerCAmelCase  )




      def 			__UpperCamelCase				(	self			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase   )				->str:



         '''simple docstring'''
         __a					  =     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			, lowerCamelCase   )				->str:



         '''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   )				->Union[str, Any]:



         '''simple docstring'''
         # 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 				__SCREAMING_SNAKE_CASE							:
      __a	       =42
      __a	       =42
      __a	       =1
      __a	       =field(default_factory=_lowerCAmelCase  )
      __a	       =field(default_factory=_lowerCAmelCase  )
      __a	       =True




      def __call__(	self			, lowerCamelCase   )				->Optional[int]:



         '''simple docstring'''
         __a					  =     Tracker(self.dest   )(lowerCamelCase   ).parametrized
         __a					  =     Tracker(self.src   )(lowerCamelCase   ).parametrized

         __a					  =     list(filter(lambda lowerCamelCase   : type(lowerCamelCase   ) not in self.src_skip			, lowerCamelCase   )   )
         __a					  =     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 				__SCREAMING_SNAKE_CASE							(				nn.Module  ):




      def __init__(	self			, lowerCamelCase   )				->List[Any]:



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

         __a					  =     []
         # - 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}"""
            __a					  =     len(lowerCamelCase   ) + 1
            feature_blocks.append((F"""res{block_index}""", v)   )

         __a					  =     nn.ModuleDict(lowerCamelCase   )






      def 			__UpperCamelCase				(	self			, lowerCamelCase   )				->Tuple:



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







class 				__SCREAMING_SNAKE_CASE							(				_lowerCAmelCase  ):




      def 			__UpperCamelCase				(	self			, lowerCamelCase   )				->str:



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






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



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

         else:
            __a					  =     super().__getitem__(lowerCamelCase   )

         return val







class 				__SCREAMING_SNAKE_CASE							(				_lowerCAmelCase  ):




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



         '''simple docstring'''
         if "seer" in x and "in1k" not in x:
            __a					  =     RegNetModel
         else:
            __a					  =     RegNetForImageClassification
         return val







def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						List[Any],       SCREAMING_SNAKE_CASE__:						Any,       SCREAMING_SNAKE_CASE__:						List[Tuple[str, str]]     )	->      List[Any]:

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



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						str,       SCREAMING_SNAKE_CASE__:						Callable[[], nn.Module],       SCREAMING_SNAKE_CASE__:						Callable[[], nn.Module],       SCREAMING_SNAKE_CASE__:						RegNetConfig,       SCREAMING_SNAKE_CASE__:						Path,       SCREAMING_SNAKE_CASE__:						bool = True,       )	->      Union[str, Any]:

   """simple docstring"""
   print(f"""Converting {name}..."""     )
   with torch.no_grad():
      __a  ,      __a					  =     from_model_func()
      __a					  =     our_model_func(SCREAMING_SNAKE_CASE__     ).eval()
      __a					  =     ModuleTransfer(src=SCREAMING_SNAKE_CASE__,       dest=SCREAMING_SNAKE_CASE__,       raise_if_mismatch=SCREAMING_SNAKE_CASE__     )
      __a					  =     torch.randn((1, 3, 224, 224)     )
      module_transfer(SCREAMING_SNAKE_CASE__     )

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

   __a					  =     our_model(SCREAMING_SNAKE_CASE__,       output_hidden_states=SCREAMING_SNAKE_CASE__     )
   __a					  =     (
       our_outputs.logits if isinstance(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     ) else our_outputs.last_hidden_state
   )

   __a					  =     from_model(SCREAMING_SNAKE_CASE__     )
   __a					  =     from_output[-1] if type(SCREAMING_SNAKE_CASE__     ) 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:
      __a					  =     our_outputs.hidden_states[-1]

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

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

      print(f"""Pushed {name}"""     )



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						Path,       SCREAMING_SNAKE_CASE__:						str = None,       SCREAMING_SNAKE_CASE__:						bool = True     )	->      Optional[Any]:

   """simple docstring"""
   __a					  =     'imagenet-1k-id2label.json'
   __a					  =     1000
   __a					  =     (1, num_labels)

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

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

   __a					  =     partial(SCREAMING_SNAKE_CASE__,       num_labels=SCREAMING_SNAKE_CASE__,       idalabel=SCREAMING_SNAKE_CASE__,       labelaid=SCREAMING_SNAKE_CASE__     )

   __a					  =     {
       '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, 11110, 28280],       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, 11110, 28280],       groups_width=1010     ),
   }

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

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

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

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

   __a					  =     partial(
       SCREAMING_SNAKE_CASE__,       '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()     ),       )

   __a					  =     partial(
       SCREAMING_SNAKE_CASE__,       '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=6_2_0.8_3,       w_m=2.5_2     )     )     ),       )

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

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

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

   __a					  =     partial(
       SCREAMING_SNAKE_CASE__,       '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=6_2_0.8_3,       w_m=2.5_2     )     )     ),       )

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


if __name__ == "__main__":
 __UpperCamelCase   :							str         =   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   :							Any         =   parser.parse_args()

 __UpperCamelCase   :							Path         =   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, Union

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


if TYPE_CHECKING:
 from ...feature_extraction_utils import FeatureExtractionMixin
 from ...tokenization_utils_base import PreTrainedTokenizerBase
 from ...utils import TensorType

__UpperCamelCase   :							List[str]         =   logging.get_logger(__name__)

__UpperCamelCase   :							Optional[int]         =   {
    """openai/whisper-base""": """https://huggingface.co/openai/whisper-base/resolve/main/config.json""",
}

# fmt: off
__UpperCamelCase   :							Optional[Any]         =   [
    1, 2, 7, 8, 9, 10, 14, 25,
    26, 27, 28, 29, 31, 58, 59, 60, 61, 62,
    63, 90, 91, 92, 93, 357, 366, 438, 532, 685,
    705, 796, 930, 1058, 1220, 1267, 1279, 1303, 1343, 1377,
    1391, 1635, 1782, 1875, 2162, 2361, 2488, 3467, 4008, 4211,
    4600, 4808, 5299, 5855, 6329, 7203, 9609, 9959, 10563, 10786,
    11420, 11709, 11907, 13163, 13697, 13700, 14808, 15306, 16410, 16791,
    17992, 19203, 19510, 20724, 22305, 22935, 27007, 30109, 30420, 33409,
    34949, 40283, 40493, 40549, 47282, 49146, 50257, 50359, 50360, 50361
]
__UpperCamelCase   :							List[str]         =   [
    1, 2, 7, 8, 9, 10, 14, 25,
    26, 27, 28, 29, 31, 58, 59, 60, 61, 62,
    63, 90, 91, 92, 93, 359, 503, 522, 542, 873,
    893, 902, 918, 922, 931, 1350, 1853, 1982, 2460, 2627,
    3246, 3253, 3268, 3536, 3846, 3961, 4183, 4667, 6585, 6647,
    7273, 9061, 9383, 10428, 10929, 11938, 12033, 12331, 12562, 13793,
    14157, 14635, 15265, 15618, 16553, 16604, 18362, 18956, 20075, 21675,
    22520, 26130, 26161, 26435, 28279, 29464, 31650, 32302, 32470, 36865,
    42863, 47425, 49870, 50254, 50258, 50360, 50361, 50362
]
class 				__SCREAMING_SNAKE_CASE							(				_lowerCAmelCase  ):
      __a	       ="whisper"
      __a	       =["past_key_values"]
      __a	       ={"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}




      def __init__(	self			, lowerCamelCase=5_1865			, lowerCamelCase=80			, lowerCamelCase=6			, lowerCamelCase=4			, lowerCamelCase=6			, lowerCamelCase=4			, lowerCamelCase=1536			, lowerCamelCase=1536			, lowerCamelCase=0.0			, lowerCamelCase=0.0			, lowerCamelCase=5_0257			, lowerCamelCase=True			, lowerCamelCase=True			, lowerCamelCase="gelu"			, lowerCamelCase=256			, lowerCamelCase=0.0			, lowerCamelCase=0.0			, lowerCamelCase=0.0			, lowerCamelCase=0.02			, lowerCamelCase=False			, lowerCamelCase=1500			, lowerCamelCase=448			, lowerCamelCase=5_0256			, lowerCamelCase=5_0256			, lowerCamelCase=5_0256			, lowerCamelCase=None			, lowerCamelCase=[220, 5_0256]			, lowerCamelCase=False			, lowerCamelCase=256			, lowerCamelCase=False			, lowerCamelCase=0.05			, lowerCamelCase=10			, lowerCamelCase=2			, lowerCamelCase=0.0			, lowerCamelCase=10			, lowerCamelCase=0			, lowerCamelCase=7			, **lowerCamelCase			, )				->Any:



         '''simple docstring'''
         __a					  =     vocab_size
         __a					  =     num_mel_bins
         __a					  =     d_model
         __a					  =     encoder_layers
         __a					  =     encoder_attention_heads
         __a					  =     decoder_layers
         __a					  =     decoder_attention_heads
         __a					  =     decoder_ffn_dim
         __a					  =     encoder_ffn_dim
         __a					  =     dropout
         __a					  =     attention_dropout
         __a					  =     activation_dropout
         __a					  =     activation_function
         __a					  =     init_std
         __a					  =     encoder_layerdrop
         __a					  =     decoder_layerdrop
         __a					  =     use_cache
         __a					  =     encoder_layers
         __a					  =     scale_embedding  # scale factor will be sqrt(d_model) if True
         __a					  =     max_source_positions
         __a					  =     max_target_positions

         # Audio Classification-specific parameters. Feel free to ignore for other classes.
         __a					  =     classifier_proj_size
         __a					  =     use_weighted_layer_sum

         # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
         __a					  =     apply_spec_augment
         __a					  =     mask_time_prob
         __a					  =     mask_time_length
         __a					  =     mask_time_min_masks
         __a					  =     mask_feature_prob
         __a					  =     mask_feature_length
         __a					  =     mask_feature_min_masks

         __a					  =     median_filter_width

         super().__init__(
             pad_token_id=lowerCamelCase			, bos_token_id=lowerCamelCase			, eos_token_id=lowerCamelCase			, is_encoder_decoder=lowerCamelCase			, decoder_start_token_id=lowerCamelCase			, suppress_tokens=lowerCamelCase			, begin_suppress_tokens=lowerCamelCase			, **lowerCamelCase			, )







class 				__SCREAMING_SNAKE_CASE							(				_lowerCAmelCase  ):




      @property
      def 			__UpperCamelCase				(	self   )				->Mapping[str, Mapping[int, str]]:



         '''simple docstring'''
         __a					  =     OrderedDict(
             [
                 ('input_features', {0: 'batch', 1: 'feature_size', 2: 'encoder_sequence'}),
             ]   )
         if self.use_past:
            __a					  =     {0: 'batch'}
         else:
            __a					  =     {0: 'batch', 1: 'decoder_sequence'}

         if self.use_past:
            self.fill_with_past_key_values_(lowerCamelCase			, direction='inputs'   )

         return common_inputs




      def 			__UpperCamelCase				(	self			, lowerCamelCase			, lowerCamelCase = -1			, lowerCamelCase = -1			, lowerCamelCase = False			, lowerCamelCase = None			, lowerCamelCase = 2_2050			, lowerCamelCase = 5.0			, lowerCamelCase = 220			, )				->Mapping[str, Any]:



         '''simple docstring'''
         __a					  =     OrderedDict()
         __a					  =     OnnxConfig.generate_dummy_inputs(
             self			, preprocessor=preprocessor.feature_extractor			, batch_size=lowerCamelCase			, framework=lowerCamelCase			, sampling_rate=lowerCamelCase			, time_duration=lowerCamelCase			, frequency=lowerCamelCase			, )
         __a					  =     encoder_inputs['input_features'].shape[2]
         __a					  =     encoder_sequence_length // 2 if self.use_past else seq_length

         __a					  =     super().generate_dummy_inputs(
             preprocessor.tokenizer			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase   )

         __a					  =     encoder_inputs.pop('input_features'   )
         __a					  =     decoder_inputs.pop('decoder_input_ids'   )

         if "past_key_values" in decoder_inputs:
            __a					  =     decoder_inputs.pop('past_key_values'   )

         return dummy_inputs






      @property
      def 			__UpperCamelCase				(	self   )				->float:



         '''simple docstring'''
         return 1e-3