

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

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

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


logging.set_verbosity_info()
__UpperCAmelCase    				=     logging.get_logger()





@dataclass
class A__    :



 """simple docstring"""







 _lowercase     :					nn.Module
 _lowercase     :					List[nn.Module]      =						field(default_factory=A )
 _lowercase     :					list      =						field(default_factory=A )

 def    __magic_name__       (		self :	int     ,						A_ :	Optional[int]     ,						A_ :	Tensor     ,						A_ :	Tensor       ):






      '''simple docstring'''




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

 def __call__(		self :	Optional[Any]     ,						A_ :	Tensor       ):






      '''simple docstring'''




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


 @property
 def    __magic_name__       (		self :	List[str]       ):






      '''simple docstring'''




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





@dataclass
class A__    :



 """simple docstring"""







 _lowercase     :					nn.Module
 _lowercase     :					nn.Module
 _lowercase     :					int      =						1
 _lowercase     :					List      =						field(default_factory=A )
 _lowercase     :					List      =						field(default_factory=A )
 _lowercase     :					bool      =						True

 def __call__(		self :	Union[str, Any]     ,						A_ :	Tensor       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[int]        =    Tracker(self.dest       )(A_       ).parametrized
      _lowerCAmelCase   :					str        =    Tracker(self.src       )(A_       ).parametrized

      _lowerCAmelCase   :					Any        =    list(filter(lambda A_       : type(A_       ) not in self.src_skip     ,						A_       )       )
      _lowerCAmelCase   :					List[Any]        =    list(filter(lambda A_       : type(A_       ) not in self.dest_skip     ,						A_       )       )

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

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





class A__    ( nn.Module ):



 """simple docstring"""







 def __init__(		self :	Optional[Any]     ,						A_ :	nn.Module       ):






      '''simple docstring'''




      super().__init__()

      _lowerCAmelCase   :					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}'''
           _lowerCAmelCase   :					Any        =    len(A_       ) + 1
           feature_blocks.append((F'''res{block_index}''', v)       )

      _lowerCAmelCase   :					Dict        =    nn.ModuleDict(A_       )


 def    __magic_name__       (		self :	Optional[int]     ,						A_ :	Tensor       ):






      '''simple docstring'''




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





class A__    ( A ):



 """simple docstring"""







 def    __magic_name__       (		self :	Union[str, Any]     ,						A_ :	str       ):






      '''simple docstring'''




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


 def __getitem__(		self :	Dict     ,						A_ :	str       ):






      '''simple docstring'''




      if x not in self:
           _lowerCAmelCase   :					List[Any]        =    self.convert_name_to_timm(A_       )
           _lowerCAmelCase   :					int        =    partial(lambda: (timm.create_model(A_     ,						pretrained=A_       ).eval(), None)       )

      else:
           _lowerCAmelCase   :					List[str]        =    super().__getitem__(A_       )

      return val





class A__    ( A ):



 """simple docstring"""







 def __getitem__(		self :	int     ,						A_ :	str       ):






      '''simple docstring'''




      if "seer" in x and "in1k" not in x:
           _lowerCAmelCase   :					str        =    RegNetModel
      else:
           _lowerCAmelCase   :					Tuple        =    RegNetForImageClassification
      return val







def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      List[Any]:

     """simple docstring"""







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







def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE = True		,							)       ->      Dict:

     """simple docstring"""







     print(f'''Converting {name}...'''				)
     with torch.no_grad():
          _lowerCAmelCase      ,			_lowerCAmelCase   :					Optional[Any]        =    from_model_func()
          _lowerCAmelCase   :					Optional[int]        =    our_model_func(SCREAMING_SNAKE_CASE				).eval()
          _lowerCAmelCase   :					Any        =    ModuleTransfer(src=SCREAMING_SNAKE_CASE		,							dest=SCREAMING_SNAKE_CASE		,							raise_if_mismatch=SCREAMING_SNAKE_CASE				)
          _lowerCAmelCase   :					Optional[int]        =    torch.randn((1, 3, 224, 224)				)
          module_transfer(SCREAMING_SNAKE_CASE				)

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

     _lowerCAmelCase   :					Optional[int]        =    our_model(SCREAMING_SNAKE_CASE		,							output_hidden_states=SCREAMING_SNAKE_CASE				)
     _lowerCAmelCase   :					Optional[Any]        =    (
         our_outputs.logits if isinstance(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				) else our_outputs.last_hidden_state
     )

     _lowerCAmelCase   :					List[Any]        =    from_model(SCREAMING_SNAKE_CASE				)
     _lowerCAmelCase   :					Optional[Any]        =    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:
          _lowerCAmelCase   :					List[str]        =    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		,							)

          _lowerCAmelCase   :					Tuple        =    224 if "seer" not in name else 384
          # we can use the convnext one
          _lowerCAmelCase   :					Optional[int]        =    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 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE = None		,							SCREAMING_SNAKE_CASE = True				)       ->      Any:

     """simple docstring"""







     _lowerCAmelCase   :					Optional[Any]        =    "imagenet-1k-id2label.json"
     _lowerCAmelCase   :					int        =    1000
     _lowerCAmelCase   :					int        =    (1, num_labels)

     _lowerCAmelCase   :					Tuple        =    "huggingface/label-files"
     _lowerCAmelCase   :					Optional[int]        =    num_labels
     _lowerCAmelCase   :					Tuple        =    json.load(open(cached_download(hf_hub_url(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							repo_type="dataset"				)				)		,							"r"				)				)
     _lowerCAmelCase   :					int        =    {int(SCREAMING_SNAKE_CASE				): v for k, v in idalabel.items()}

     _lowerCAmelCase   :					List[Any]        =    idalabel
     _lowerCAmelCase   :					Optional[int]        =    {v: k for k, v in idalabel.items()}

     _lowerCAmelCase   :					Optional[Any]        =    partial(SCREAMING_SNAKE_CASE		,							num_labels=SCREAMING_SNAKE_CASE		,							idalabel=SCREAMING_SNAKE_CASE		,							labelaid=SCREAMING_SNAKE_CASE				)

     _lowerCAmelCase   :					int        =    {
         "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				),
     }

     _lowerCAmelCase   :					Tuple        =    NameToOurModelFuncMap()
     _lowerCAmelCase   :					Optional[Any]        =    NameToFromModelFuncMap()
     # add seer weights logic

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

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

     _lowerCAmelCase   :					Optional[int]        =    partial(
         SCREAMING_SNAKE_CASE		,							"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch"		,							lambda: FakeRegNetVisslWrapper(RegNetYaagf()				)		,							)

     _lowerCAmelCase   :					List[Any]        =    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()				)		,							)

     _lowerCAmelCase   :					Any        =    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=620.83		,							w_m=2.52				)				)				)		,							)

     # IN1K finetuned
     _lowerCAmelCase   :					str        =    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()				)		,							)

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

     _lowerCAmelCase   :					Dict        =    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()				)		,							)

     _lowerCAmelCase   :					Optional[int]        =    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=620.83		,							w_m=2.52				)				)				)		,							)

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

  __UpperCAmelCase    				=     parser.parse_args()

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









import unittest

from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import (
      SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      SqueezeBertForMaskedLM,
      SqueezeBertForMultipleChoice,
      SqueezeBertForQuestionAnswering,
      SqueezeBertForSequenceClassification,
      SqueezeBertForTokenClassification,
      SqueezeBertModel,
  )





class A__    ( A ):



 """simple docstring"""







 def __init__(		self :	Tuple     ,						A_ :	Optional[Any]     ,						A_ :	Dict=1_3     ,						A_ :	str=7     ,						A_ :	Union[str, Any]=True     ,						A_ :	int=True     ,						A_ :	Any=False     ,						A_ :	str=True     ,						A_ :	int=9_9     ,						A_ :	int=3_2     ,						A_ :	Optional[int]=5     ,						A_ :	List[str]=4     ,						A_ :	int=6_4     ,						A_ :	Optional[int]="gelu"     ,						A_ :	List[Any]=0.1     ,						A_ :	int=0.1     ,						A_ :	List[str]=5_1_2     ,						A_ :	Optional[Any]=1_6     ,						A_ :	int=2     ,						A_ :	Optional[int]=0.02     ,						A_ :	Any=3     ,						A_ :	Optional[Any]=4     ,						A_ :	Union[str, Any]=None     ,						A_ :	Union[str, Any]=2     ,						A_ :	Tuple=2     ,						A_ :	Optional[int]=2     ,						A_ :	List[Any]=2     ,						A_ :	List[str]=4     ,						A_ :	Union[str, Any]=1     ,						):






      '''simple docstring'''




      _lowerCAmelCase   :					List[str]        =    parent
      _lowerCAmelCase   :					List[str]        =    batch_size
      _lowerCAmelCase   :					str        =    seq_length
      _lowerCAmelCase   :					int        =    is_training
      _lowerCAmelCase   :					Union[str, Any]        =    use_input_mask
      _lowerCAmelCase   :					Union[str, Any]        =    use_token_type_ids
      _lowerCAmelCase   :					List[Any]        =    use_labels
      _lowerCAmelCase   :					Optional[int]        =    vocab_size
      _lowerCAmelCase   :					Union[str, Any]        =    hidden_size
      _lowerCAmelCase   :					int        =    num_hidden_layers
      _lowerCAmelCase   :					Optional[int]        =    num_attention_heads
      _lowerCAmelCase   :					Optional[Any]        =    intermediate_size
      _lowerCAmelCase   :					Any        =    hidden_act
      _lowerCAmelCase   :					Tuple        =    hidden_dropout_prob
      _lowerCAmelCase   :					Optional[int]        =    attention_probs_dropout_prob
      _lowerCAmelCase   :					List[Any]        =    max_position_embeddings
      _lowerCAmelCase   :					List[Any]        =    type_vocab_size
      _lowerCAmelCase   :					int        =    type_sequence_label_size
      _lowerCAmelCase   :					List[str]        =    initializer_range
      _lowerCAmelCase   :					Tuple        =    num_labels
      _lowerCAmelCase   :					Dict        =    num_choices
      _lowerCAmelCase   :					Optional[Any]        =    scope
      _lowerCAmelCase   :					Union[str, Any]        =    q_groups
      _lowerCAmelCase   :					Tuple        =    k_groups
      _lowerCAmelCase   :					str        =    v_groups
      _lowerCAmelCase   :					Tuple        =    post_attention_groups
      _lowerCAmelCase   :					Tuple        =    intermediate_groups
      _lowerCAmelCase   :					List[Any]        =    output_groups

 def    __magic_name__       (		self :	Union[str, Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					int        =    ids_tensor([self.batch_size, self.seq_length]     ,						self.vocab_size       )

      _lowerCAmelCase   :					int        =    None
      if self.use_input_mask:
           _lowerCAmelCase   :					Union[str, Any]        =    random_attention_mask([self.batch_size, self.seq_length]       )

      _lowerCAmelCase   :					Dict        =    None
      _lowerCAmelCase   :					Any        =    None
      _lowerCAmelCase   :					List[Any]        =    None
      if self.use_labels:
           _lowerCAmelCase   :					List[Any]        =    ids_tensor([self.batch_size]     ,						self.type_sequence_label_size       )
           _lowerCAmelCase   :					Any        =    ids_tensor([self.batch_size, self.seq_length]     ,						self.num_labels       )
           _lowerCAmelCase   :					Tuple        =    ids_tensor([self.batch_size]     ,						self.num_choices       )

      _lowerCAmelCase   :					Any        =    self.get_config()

      return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels

 def    __magic_name__       (		self :	Any       ):






      '''simple docstring'''




      return SqueezeBertConfig(
          embedding_size=self.hidden_size     ,						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     ,						attention_probs_dropout_prob=self.hidden_dropout_prob     ,						attention_dropout=self.attention_probs_dropout_prob     ,						max_position_embeddings=self.max_position_embeddings     ,						initializer_range=self.initializer_range     ,						q_groups=self.q_groups     ,						k_groups=self.k_groups     ,						v_groups=self.v_groups     ,						post_attention_groups=self.post_attention_groups     ,						intermediate_groups=self.intermediate_groups     ,						output_groups=self.output_groups     ,						)

 def    __magic_name__       (		self :	List[str]     ,						A_ :	Dict     ,						A_ :	Union[str, Any]     ,						A_ :	List[str]     ,						A_ :	Optional[int]     ,						A_ :	str     ,						A_ :	Optional[int]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[Any]        =    SqueezeBertModel(config=A_       )
      model.to(A_       )
      model.eval()
      _lowerCAmelCase   :					List[Any]        =    model(A_     ,						A_       )
      _lowerCAmelCase   :					Tuple        =    model(A_       )
      self.parent.assertEqual(result.last_hidden_state.shape     ,						(self.batch_size, self.seq_length, self.hidden_size)       )

 def    __magic_name__       (		self :	Optional[int]     ,						A_ :	int     ,						A_ :	Dict     ,						A_ :	Any     ,						A_ :	List[Any]     ,						A_ :	List[Any]     ,						A_ :	Optional[int]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[Any]        =    SqueezeBertForMaskedLM(config=A_       )
      model.to(A_       )
      model.eval()
      _lowerCAmelCase   :					int        =    model(A_     ,						attention_mask=A_     ,						labels=A_       )
      self.parent.assertEqual(result.logits.shape     ,						(self.batch_size, self.seq_length, self.vocab_size)       )

 def    __magic_name__       (		self :	Optional[int]     ,						A_ :	Union[str, Any]     ,						A_ :	List[Any]     ,						A_ :	List[Any]     ,						A_ :	List[Any]     ,						A_ :	List[str]     ,						A_ :	int       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[Any]        =    SqueezeBertForQuestionAnswering(config=A_       )
      model.to(A_       )
      model.eval()
      _lowerCAmelCase   :					List[Any]        =    model(
          A_     ,						attention_mask=A_     ,						start_positions=A_     ,						end_positions=A_       )
      self.parent.assertEqual(result.start_logits.shape     ,						(self.batch_size, self.seq_length)       )
      self.parent.assertEqual(result.end_logits.shape     ,						(self.batch_size, self.seq_length)       )

 def    __magic_name__       (		self :	Tuple     ,						A_ :	Optional[int]     ,						A_ :	Dict     ,						A_ :	str     ,						A_ :	Tuple     ,						A_ :	List[Any]     ,						A_ :	List[Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					int        =    self.num_labels
      _lowerCAmelCase   :					int        =    SqueezeBertForSequenceClassification(A_       )
      model.to(A_       )
      model.eval()
      _lowerCAmelCase   :					List[Any]        =    model(A_     ,						attention_mask=A_     ,						labels=A_       )
      self.parent.assertEqual(result.logits.shape     ,						(self.batch_size, self.num_labels)       )

 def    __magic_name__       (		self :	Optional[Any]     ,						A_ :	List[Any]     ,						A_ :	List[Any]     ,						A_ :	Tuple     ,						A_ :	List[Any]     ,						A_ :	List[Any]     ,						A_ :	List[Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					List[Any]        =    self.num_labels
      _lowerCAmelCase   :					Any        =    SqueezeBertForTokenClassification(config=A_       )
      model.to(A_       )
      model.eval()

      _lowerCAmelCase   :					Any        =    model(A_     ,						attention_mask=A_     ,						labels=A_       )
      self.parent.assertEqual(result.logits.shape     ,						(self.batch_size, self.seq_length, self.num_labels)       )

 def    __magic_name__       (		self :	Tuple     ,						A_ :	Tuple     ,						A_ :	Tuple     ,						A_ :	Union[str, Any]     ,						A_ :	int     ,						A_ :	List[Any]     ,						A_ :	int       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[int]        =    self.num_choices
      _lowerCAmelCase   :					Dict        =    SqueezeBertForMultipleChoice(config=A_       )
      model.to(A_       )
      model.eval()
      _lowerCAmelCase   :					List[str]        =    input_ids.unsqueeze(1       ).expand(-1     ,						self.num_choices     ,						-1       ).contiguous()
      _lowerCAmelCase   :					Dict        =    input_mask.unsqueeze(1       ).expand(-1     ,						self.num_choices     ,						-1       ).contiguous()
      _lowerCAmelCase   :					str        =    model(
          A_     ,						attention_mask=A_     ,						labels=A_     ,						)
      self.parent.assertEqual(result.logits.shape     ,						(self.batch_size, self.num_choices)       )


 def    __magic_name__       (		self :	Optional[int]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[int]        =    self.prepare_config_and_inputs()
      ((_lowerCAmelCase)      ,			(_lowerCAmelCase)      ,			(_lowerCAmelCase)      ,			(_lowerCAmelCase)      ,			(_lowerCAmelCase)      ,			(_lowerCAmelCase))   :					Optional[int]        =    config_and_inputs
      _lowerCAmelCase   :					Optional[Any]        =    {"input_ids": input_ids, "attention_mask": input_mask}
      return config, inputs_dict





@require_torch
class A__    ( A  ,		A  ,		unittest.TestCase ):



 """simple docstring"""







 _lowercase     :					Tuple      =						(
     (
         SqueezeBertModel,
         SqueezeBertForMaskedLM,
         SqueezeBertForMultipleChoice,
         SqueezeBertForQuestionAnswering,
         SqueezeBertForSequenceClassification,
         SqueezeBertForTokenClassification,
     )
     if is_torch_available()
     else None
 )
 _lowercase     :					Optional[Any]      =						(
     {
         '''feature-extraction''': SqueezeBertModel,
         '''fill-mask''': SqueezeBertForMaskedLM,
         '''question-answering''': SqueezeBertForQuestionAnswering,
         '''text-classification''': SqueezeBertForSequenceClassification,
         '''token-classification''': SqueezeBertForTokenClassification,
         '''zero-shot''': SqueezeBertForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )
 _lowercase     :					Union[str, Any]      =						False
 _lowercase     :					int      =						True
 _lowercase     :					List[str]      =						False

 def    __magic_name__       (		self :	Optional[Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					int        =    SqueezeBertModelTester(self       )
      _lowerCAmelCase   :					Tuple        =    ConfigTester(self     ,						config_class=A_     ,						dim=3_7       )

 def    __magic_name__       (		self :	int       ):






      '''simple docstring'''




      self.config_tester.run_common_tests()

 def    __magic_name__       (		self :	List[str]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					str        =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_squeezebert_model(*A_       )

 def    __magic_name__       (		self :	Optional[Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Tuple        =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_squeezebert_for_masked_lm(*A_       )

 def    __magic_name__       (		self :	List[Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					str        =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_squeezebert_for_question_answering(*A_       )

 def    __magic_name__       (		self :	List[str]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					int        =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_squeezebert_for_sequence_classification(*A_       )

 def    __magic_name__       (		self :	Dict       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Dict        =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_squeezebert_for_token_classification(*A_       )

 def    __magic_name__       (		self :	List[str]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					List[Any]        =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_squeezebert_for_multiple_choice(*A_       )


 @slow
 def    __magic_name__       (		self :	Optional[int]       ):






      '''simple docstring'''




      for model_name in SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           _lowerCAmelCase   :					int        =    SqueezeBertModel.from_pretrained(A_       )
           self.assertIsNotNone(A_       )





@require_sentencepiece
@require_tokenizers
@require_torch
class A__    ( unittest.TestCase ):



 """simple docstring"""







 @slow
 def    __magic_name__       (		self :	List[str]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[Any]        =    SqueezeBertForSequenceClassification.from_pretrained("squeezebert/squeezebert-mnli"       )

      _lowerCAmelCase   :					Optional[int]        =    torch.tensor([[1, 2_9_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 1_3, 1_5_8_8, 2]]       )
      _lowerCAmelCase   :					List[str]        =    model(A_       )[0]
      _lowerCAmelCase   :					Any        =    torch.Size((1, 3)       )
      self.assertEqual(output.shape     ,						A_       )
      _lowerCAmelCase   :					Any        =    torch.tensor([[0.6401, -0.0349, -0.6041]]       )
      self.assertTrue(torch.allclose(A_     ,						A_     ,						atol=1E-4       )       )






