"""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     :		Union[str, Any]											=					logging.get_logger()


@dataclass
class   UpperCAmelCase_			:
      lowerCamelCase__	:					List[str]										=						4_2
      lowerCamelCase__	:					Any										=						field(default_factory=UpperCamelCase_)
      lowerCamelCase__	:					Optional[int]										=						field(default_factory=UpperCamelCase_)


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a							) ->      List[Any]:
             lowercase__    :	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	,    a							) ->      List[Any]:
             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 	_UpperCAmelCase (     self							) ->      Union[str, Any]:
             # check the len of the state_dict keys to see if we have learnable params
             return list(filter(lambda a							: len(list(x.state_dict().keys()							)							) > 0	,    self.traced							)							)





@dataclass
class   UpperCAmelCase_			:
      lowerCamelCase__	:					str										=						4_2
      lowerCamelCase__	:					List[Any]										=						4_2
      lowerCamelCase__	:					int										=						1
      lowerCamelCase__	:					List[str]										=						field(default_factory=UpperCamelCase_)
      lowerCamelCase__	:					List[str]										=						field(default_factory=UpperCamelCase_)
      lowerCamelCase__	:					Optional[Any]										=						True


      def __call__(     self	,    a							) ->      Dict:
             lowercase__    :	List[str]     					=			Tracker(self.dest							)(a							).parametrized
             lowercase__    :	Any     					=			Tracker(self.src							)(a							).parametrized

             lowercase__    :	Any     					=			list(filter(lambda a							: type(a							) not in self.src_skip	,    a							)							)
             lowercase__    :	Tuple     					=			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   UpperCAmelCase_			(					nn.Module):


      def __init__(     self	,    a							) ->      List[Any]:
             super().__init__()

             lowercase__    :	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}"""
                    lowercase__    :	int     					=			len(a							) + 1
                    feature_blocks.append((f"""res{block_index}""", v)							)

             lowercase__    :	Any     					=			nn.ModuleDict(a							)


      def 	_UpperCAmelCase (     self	,    a							) ->      Any:
             return get_trunk_forward_outputs(
                 a	,    out_feat_keys=a	,    feature_blocks=self._feature_blocks	,    )





class   UpperCAmelCase_			(					UpperCamelCase_):


      def 	_UpperCAmelCase (     self	,    a							) ->      str:
             lowercase__    :	List[str]     					=			x.split('-'							)
             return x_split[0] + x_split[1] + "_" + "".join(x_split[2:]							)


      def __getitem__(     self	,    a							) ->      Callable[[], Tuple[nn.Module, Dict]]:
             # default to timm!
             if x not in self:
                    lowercase__    :	List[Any]     					=			self.convert_name_to_timm(a							)
                    lowercase__    :	Optional[Any]     					=			partial(lambda: (timm.create_model(a	,    pretrained=a							).eval(), None)							)

             else:
                    lowercase__    :	int     					=			super().__getitem__(a							)

             return val





class   UpperCAmelCase_			(					UpperCamelCase_):


      def __getitem__(     self	,    a							) ->      Callable[[], nn.Module]:
             if "seer" in x and "in1k" not in x:
                    lowercase__    :	int     					=			RegNetModel
             else:
                    lowercase__    :	Optional[Any]     					=			RegNetForImageClassification
             return val





def        a_  (   _lowerCAmelCase						:				List[Any]     ,				_lowerCAmelCase						:				Any     ,				_lowerCAmelCase						:				List[Tuple[str, str]]  ):





       '''simple docstring'''

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





def        a_  (   _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():
              lowercase__    :	Union[str, Any]     					=			from_model_func()
              lowercase__    :	Any     					=			our_model_func(snake_case_  ).eval()
              lowercase__    :	Optional[Any]     					=			ModuleTransfer(src=snake_case_     ,				dest=snake_case_     ,				raise_if_mismatch=snake_case_  )
              lowercase__    :	List[str]     					=			torch.randn((1, 3, 224, 224)  )
              module_transfer(snake_case_  )

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

       lowercase__    :	Tuple     					=			our_model(snake_case_     ,				output_hidden_states=snake_case_  )
       lowercase__    :	List[str]     					=			(
           our_outputs.logits if isinstance(snake_case_     ,				snake_case_  ) else our_outputs.last_hidden_state
       )

       lowercase__    :	str     					=			from_model(snake_case_  )
       lowercase__    :	List[str]     					=			from_output[-1] if type(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:
              lowercase__    :	Tuple     					=			our_outputs.hidden_states[-1]

       assert torch.allclose(snake_case_     ,				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=snake_case_     ,				)

              lowercase__    :	List[str]     					=			224 if '''seer''' not in name else 384
              # we can use the convnext one
              lowercase__    :	Union[str, Any]     					=			AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'     ,				size=snake_case_  )
              image_processor.push_to_hub(
                  repo_path_or_name=save_directory / name     ,				commit_message='Add image processor'     ,				use_temp_dir=snake_case_     ,				)

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





def        a_  (   _lowerCAmelCase						:				Path     ,				_lowerCAmelCase						:				str = None     ,				_lowerCAmelCase						:				bool = True  ):





       '''simple docstring'''

       lowercase__    :	List[str]     					=			'''imagenet-1k-id2label.json'''
       lowercase__    :	int     					=			1000
       lowercase__    :	List[Any]     					=			(1, num_labels)

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

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

       lowercase__    :	List[Any]     					=			partial(snake_case_     ,				num_labels=snake_case_     ,				idalabel=snake_case_     ,				labelaid=snake_case_  )

       lowercase__    :	Tuple     					=			{
           '''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, 1_1110, 2_8280]     ,				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, 1_1110, 2_8280]     ,				groups_width=1010  ),
       }

       lowercase__    :	List[str]     					=			NameToOurModelFuncMap()
       lowercase__    :	Tuple     					=			NameToFromModelFuncMap()
       # add seer weights logic

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

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

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

       lowercase__    :	Tuple     					=			partial(
           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()  )     ,				)

       lowercase__    :	Any     					=			partial(
           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=2.5_2  )  )  )     ,				)

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

       lowercase__    :	Optional[Any]     					=			partial(
           snake_case_     ,				'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch'     ,				lambda: FakeRegNetVisslWrapper(RegNetYaagf()  )     ,				)

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

       lowercase__    :	int     					=			partial(
           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=2.5_2  )  )  )     ,				)

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


if __name__ == "__main__":
       _UpperCamelCase     :		Any											=					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     :		Optional[int]											=					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"""





import unittest

import numpy as np

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

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
       import jax.numpy as jnp

       from transformers.models.albert.modeling_flax_albert import (
           FlaxAlbertForMaskedLM,
           FlaxAlbertForMultipleChoice,
           FlaxAlbertForPreTraining,
           FlaxAlbertForQuestionAnswering,
           FlaxAlbertForSequenceClassification,
           FlaxAlbertForTokenClassification,
           FlaxAlbertModel,
       )


class   UpperCAmelCase_			(					unittest.TestCase):


      def __init__(     self	,    a	,    a=1_3	,    a=7	,    a=True	,    a=True	,    a=True	,    a=True	,    a=9_9	,    a=3_2	,    a=5	,    a=4	,    a=3_7	,    a="gelu"	,    a=0.1	,    a=0.1	,    a=5_1_2	,    a=1_6	,    a=2	,    a=0.02	,    a=4	,    ) ->      Dict:
             lowercase__    :	Optional[Any]     					=			parent
             lowercase__    :	Dict     					=			batch_size
             lowercase__    :	List[Any]     					=			seq_length
             lowercase__    :	int     					=			is_training
             lowercase__    :	str     					=			use_attention_mask
             lowercase__    :	Dict     					=			use_token_type_ids
             lowercase__    :	Optional[int]     					=			use_labels
             lowercase__    :	Tuple     					=			vocab_size
             lowercase__    :	List[str]     					=			hidden_size
             lowercase__    :	Union[str, Any]     					=			num_hidden_layers
             lowercase__    :	int     					=			num_attention_heads
             lowercase__    :	Dict     					=			intermediate_size
             lowercase__    :	List[str]     					=			hidden_act
             lowercase__    :	Dict     					=			hidden_dropout_prob
             lowercase__    :	Tuple     					=			attention_probs_dropout_prob
             lowercase__    :	List[str]     					=			max_position_embeddings
             lowercase__    :	int     					=			type_vocab_size
             lowercase__    :	List[str]     					=			type_sequence_label_size
             lowercase__    :	Union[str, Any]     					=			initializer_range
             lowercase__    :	Optional[int]     					=			num_choices


      def 	_UpperCAmelCase (     self							) ->      Tuple:
             lowercase__    :	List[Any]     					=			ids_tensor([self.batch_size, self.seq_length]	,    self.vocab_size							)

             lowercase__    :	str     					=			None
             if self.use_attention_mask:
                    lowercase__    :	Optional[Any]     					=			random_attention_mask([self.batch_size, self.seq_length]							)

             lowercase__    :	List[str]     					=			None
             if self.use_token_type_ids:
                    lowercase__    :	Optional[int]     					=			ids_tensor([self.batch_size, self.seq_length]	,    self.type_vocab_size							)

             lowercase__    :	Any     					=			AlbertConfig(
                 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=a	,    initializer_range=self.initializer_range	,    )

             return config, input_ids, token_type_ids, attention_mask


      def 	_UpperCAmelCase (     self							) ->      Any:
             lowercase__    :	Optional[int]     					=			self.prepare_config_and_inputs()
             lowercase__					,   lowercase__					,   lowercase__					,   lowercase__    :	Tuple     					=			config_and_inputs
             lowercase__    :	Union[str, Any]     					=			{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
             return config, inputs_dict





@require_flax
class   UpperCAmelCase_			(					_a				,							unittest.TestCase):
      lowerCamelCase__	:					Tuple										=						(
          (
              FlaxAlbertModel,
              FlaxAlbertForPreTraining,
              FlaxAlbertForMaskedLM,
              FlaxAlbertForMultipleChoice,
              FlaxAlbertForQuestionAnswering,
              FlaxAlbertForSequenceClassification,
              FlaxAlbertForTokenClassification,
              FlaxAlbertForQuestionAnswering,
          )
          if is_flax_available()
          else ()
      )


      def 	_UpperCAmelCase (     self							) ->      List[Any]:
             lowercase__    :	Union[str, Any]     					=			FlaxAlbertModelTester(self							)


      @slow
      def 	_UpperCAmelCase (     self							) ->      str:
             for model_class_name in self.all_model_classes:
                    lowercase__    :	str     					=			model_class_name.from_pretrained('albert-base-v2'							)
                    lowercase__    :	Tuple     					=			model(np.ones((1, 1)							)							)
                    self.assertIsNotNone(a							)





@require_flax
class   UpperCAmelCase_			(					unittest.TestCase):


      @slow
      def 	_UpperCAmelCase (     self							) ->      Tuple:
             lowercase__    :	str     					=			FlaxAlbertModel.from_pretrained('albert-base-v2'							)
             lowercase__    :	Optional[int]     					=			np.array([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]]							)
             lowercase__    :	Optional[Any]     					=			np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]							)
             lowercase__    :	Any     					=			model(a	,    attention_mask=a							)[0]
             lowercase__    :	Tuple     					=			(1, 1_1, 7_6_8)
             self.assertEqual(output.shape	,    a							)
             lowercase__    :	Optional[Any]     					=			np.array(
                 [[[-0.6_513, 1.5_035, -0.2_766], [-0.6_515, 1.5_046, -0.2_780], [-0.6_512, 1.5_049, -0.2_784]]]							)

             self.assertTrue(jnp.allclose(output[:, 1:4, 1:4]	,    a	,    atol=1e-4							)							)

