





import argparse
import collections
import json
from pathlib import Path

import requests
import torch
import yaml
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileViTImageProcessor,
    MobileViTVaConfig,
    MobileViTVaForImageClassification,
    MobileViTVaForSemanticSegmentation,
)
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase_  							=  logging.get_logger(__name__)




def 	lowerCAmelCase_					(						__UpperCAmelCase:    Dict   )   ->			Union[str, Any]:
    print('''Loading config file...'''   )

    def flatten_yaml_as_dict(__UpperCAmelCase:    Optional[Any]	,     __UpperCAmelCase:    Optional[Any]=""	,     __UpperCAmelCase:    Union[str, Any]="."   ):
        UpperCamelCase__							:       List[Any]      =   []
        for k, v in d.items():
            UpperCamelCase__							:       List[str]      =   parent_key + sep + k if parent_key else k
            if isinstance(__UpperCAmelCase	,     collections.abc.MutableMapping   ):
                items.extend(flatten_yaml_as_dict(__UpperCAmelCase	,     __UpperCAmelCase	,     sep=__UpperCAmelCase   ).items()   )
            else:
                items.append((new_key, v)   )
        return dict(__UpperCAmelCase   )

    UpperCamelCase__							:       List[Any]      =   argparse.Namespace()
    with open(__UpperCAmelCase	,     '''r'''   ) as yaml_file:
        try:
            UpperCamelCase__							:       int      =   yaml.load(__UpperCAmelCase	,     Loader=yaml.FullLoader   )

            UpperCamelCase__							:       int      =   flatten_yaml_as_dict(__UpperCAmelCase   )
            for k, v in flat_cfg.items():
                setattr(__UpperCAmelCase	,     __UpperCAmelCase	,     __UpperCAmelCase   )
        except yaml.YAMLError as exc:
            logger.error('''Error while loading config file: {}. Error message: {}'''.format(__UpperCAmelCase	,     str(__UpperCAmelCase   )   )   )
    return config




def 	lowerCAmelCase_					(						__UpperCAmelCase:    Tuple	,     __UpperCAmelCase:    Any   )   ->			int:
    UpperCamelCase__							:       Optional[int]      =   MobileViTVaConfig()

    UpperCamelCase__							:       Optional[int]      =   False

    # dataset
    if task_name.startswith('''imagenet1k_'''   ):
        UpperCamelCase__							:       Union[str, Any]      =   1000
        if int(task_name.strip().split('''_'''   )[-1]   ) == 384:
            UpperCamelCase__							:       Tuple      =   384
        else:
            UpperCamelCase__							:       Tuple      =   256
        UpperCamelCase__							:       Union[str, Any]      =   '''imagenet-1k-id2label.json'''
    elif task_name.startswith('''imagenet21k_to_1k_'''   ):
        UpperCamelCase__							:       List[Any]      =   2_1000
        if int(task_name.strip().split('''_'''   )[-1]   ) == 384:
            UpperCamelCase__							:       int      =   384
        else:
            UpperCamelCase__							:       Optional[Any]      =   256
        UpperCamelCase__							:       Optional[Any]      =   '''imagenet-22k-id2label.json'''
    elif task_name.startswith('''ade20k_'''   ):
        UpperCamelCase__							:       Optional[Any]      =   151
        UpperCamelCase__							:       List[Any]      =   512
        UpperCamelCase__							:       int      =   '''ade20k-id2label.json'''
        UpperCamelCase__							:       str      =   True
    elif task_name.startswith('''voc_'''   ):
        UpperCamelCase__							:       Union[str, Any]      =   21
        UpperCamelCase__							:       List[str]      =   512
        UpperCamelCase__							:       Dict      =   '''pascal-voc-id2label.json'''
        UpperCamelCase__							:       Union[str, Any]      =   True

    # orig_config
    UpperCamelCase__							:       Dict      =   load_orig_config_file(__UpperCAmelCase   )
    assert getattr(__UpperCAmelCase	,     '''model.classification.name'''	,     -1   ) == "mobilevit_v2", "Invalid model"
    UpperCamelCase__							:       List[str]      =   getattr(__UpperCAmelCase	,     '''model.classification.mitv2.width_multiplier'''	,     1.0   )
    assert (
        getattr(__UpperCAmelCase	,     '''model.classification.mitv2.attn_norm_layer'''	,     -1   ) == "layer_norm_2d"
    ), "Norm layers other than layer_norm_2d is not supported"
    UpperCamelCase__							:       int      =   getattr(__UpperCAmelCase	,     '''model.classification.activation.name'''	,     '''swish'''   )
    # config.image_size == getattr(orig_config,  'sampler.bs.crop_size_width', 256)

    if is_segmentation_model:
        UpperCamelCase__							:       str      =   getattr(__UpperCAmelCase	,     '''model.segmentation.output_stride'''	,     16   )
        if "_deeplabv3" in task_name:
            UpperCamelCase__							:       List[Any]      =   getattr(__UpperCAmelCase	,     '''model.segmentation.deeplabv3.aspp_rates'''	,     [12, 24, 36]   )
            UpperCamelCase__							:       Dict      =   getattr(__UpperCAmelCase	,     '''model.segmentation.deeplabv3.aspp_out_channels'''	,     512   )
            UpperCamelCase__							:       Union[str, Any]      =   getattr(__UpperCAmelCase	,     '''model.segmentation.deeplabv3.aspp_dropout'''	,     0.1   )

    # id2label
    UpperCamelCase__							:       List[str]      =   '''huggingface/label-files'''
    UpperCamelCase__							:       Tuple      =   json.load(open(hf_hub_download(__UpperCAmelCase	,     __UpperCAmelCase	,     repo_type='''dataset'''   )	,     '''r'''   )   )
    UpperCamelCase__							:       Optional[int]      =   {int(__UpperCAmelCase   ): v for k, v in idalabel.items()}
    UpperCamelCase__							:       Optional[Any]      =   idalabel
    UpperCamelCase__							:       Any      =   {v: k for k, v in idalabel.items()}

    return config




def 	lowerCAmelCase_					(						__UpperCAmelCase:    int	,     __UpperCAmelCase:    Tuple	,     __UpperCAmelCase:    Optional[Any]   )   ->			int:
    UpperCamelCase__							:       List[str]      =   dct.pop(__UpperCAmelCase   )
    UpperCamelCase__							:       List[Any]      =   val




def 	lowerCAmelCase_					(						__UpperCAmelCase:    Dict	,     __UpperCAmelCase:    List[Any]=False   )   ->			str:
    if base_model:
        UpperCamelCase__							:       Union[str, Any]      =   ''''''
    else:
        UpperCamelCase__							:       str      =   '''mobilevitv2.'''

    UpperCamelCase__							:       Any      =   []
    for k in state_dict.keys():
        if k[:8] == "encoder.":
            UpperCamelCase__							:       Optional[Any]      =   k[8:]
        else:
            UpperCamelCase__							:       int      =   k

        if ".block." in k:
            UpperCamelCase__							:       Optional[Any]      =   k_new.replace('''.block.'''	,     '''.'''   )
        if ".conv." in k:
            UpperCamelCase__							:       str      =   k_new.replace('''.conv.'''	,     '''.convolution.'''   )
        if ".norm." in k:
            UpperCamelCase__							:       List[str]      =   k_new.replace('''.norm.'''	,     '''.normalization.'''   )

        if "conv_1." in k:
            UpperCamelCase__							:       List[Any]      =   k_new.replace('''conv_1.'''	,     f"{model_prefix}conv_stem."   )
        for i in [1, 2]:
            if f"layer_{i}." in k:
                UpperCamelCase__							:       Dict      =   k_new.replace(f"layer_{i}."	,     f"{model_prefix}encoder.layer.{i-1}.layer."   )
        if ".exp_1x1." in k:
            UpperCamelCase__							:       Optional[int]      =   k_new.replace('''.exp_1x1.'''	,     '''.expand_1x1.'''   )
        if ".red_1x1." in k:
            UpperCamelCase__							:       List[str]      =   k_new.replace('''.red_1x1.'''	,     '''.reduce_1x1.'''   )

        for i in [3, 4, 5]:
            if f"layer_{i}.0." in k:
                UpperCamelCase__							:       Union[str, Any]      =   k_new.replace(f"layer_{i}.0."	,     f"{model_prefix}encoder.layer.{i-1}.downsampling_layer."   )
            if f"layer_{i}.1.local_rep.0." in k:
                UpperCamelCase__							:       Optional[int]      =   k_new.replace(f"layer_{i}.1.local_rep.0."	,     f"{model_prefix}encoder.layer.{i-1}.conv_kxk."   )
            if f"layer_{i}.1.local_rep.1." in k:
                UpperCamelCase__							:       Union[str, Any]      =   k_new.replace(f"layer_{i}.1.local_rep.1."	,     f"{model_prefix}encoder.layer.{i-1}.conv_1x1."   )

        for i in [3, 4, 5]:
            if i == 3:
                UpperCamelCase__							:       List[Any]      =   [0, 1]
            elif i == 4:
                UpperCamelCase__							:       List[Any]      =   [0, 1, 2, 3]
            elif i == 5:
                UpperCamelCase__							:       Any      =   [0, 1, 2]

            for j in j_in:
                if f"layer_{i}.1.global_rep.{j}." in k:
                    UpperCamelCase__							:       Any      =   k_new.replace(
                        f"layer_{i}.1.global_rep.{j}."	,     f"{model_prefix}encoder.layer.{i-1}.transformer.layer.{j}."   )
            if f"layer_{i}.1.global_rep.{j+1}." in k:
                UpperCamelCase__							:       Any      =   k_new.replace(
                    f"layer_{i}.1.global_rep.{j+1}."	,     f"{model_prefix}encoder.layer.{i-1}.layernorm."   )

            if f"layer_{i}.1.conv_proj." in k:
                UpperCamelCase__							:       Optional[int]      =   k_new.replace(f"layer_{i}.1.conv_proj."	,     f"{model_prefix}encoder.layer.{i-1}.conv_projection."   )

        if "pre_norm_attn.0." in k:
            UpperCamelCase__							:       str      =   k_new.replace('''pre_norm_attn.0.'''	,     '''layernorm_before.'''   )
        if "pre_norm_attn.1." in k:
            UpperCamelCase__							:       Any      =   k_new.replace('''pre_norm_attn.1.'''	,     '''attention.'''   )
        if "pre_norm_ffn.0." in k:
            UpperCamelCase__							:       Any      =   k_new.replace('''pre_norm_ffn.0.'''	,     '''layernorm_after.'''   )
        if "pre_norm_ffn.1." in k:
            UpperCamelCase__							:       Any      =   k_new.replace('''pre_norm_ffn.1.'''	,     '''ffn.conv1.'''   )
        if "pre_norm_ffn.3." in k:
            UpperCamelCase__							:       Optional[int]      =   k_new.replace('''pre_norm_ffn.3.'''	,     '''ffn.conv2.'''   )

        if "classifier.1." in k:
            UpperCamelCase__							:       List[str]      =   k_new.replace('''classifier.1.'''	,     '''classifier.'''   )

        if "seg_head." in k:
            UpperCamelCase__							:       Tuple      =   k_new.replace('''seg_head.'''	,     '''segmentation_head.'''   )
        if ".aspp_layer." in k:
            UpperCamelCase__							:       Optional[int]      =   k_new.replace('''.aspp_layer.'''	,     '''.'''   )
        if ".aspp_pool." in k:
            UpperCamelCase__							:       int      =   k_new.replace('''.aspp_pool.'''	,     '''.'''   )

        rename_keys.append((k, k_new)   )
    return rename_keys




def 	lowerCAmelCase_					(						__UpperCAmelCase:    Optional[Any]   )   ->			Dict:
    UpperCamelCase__							:       Any      =   []
    for k in state_dict.keys():
        if k.startswith('''seg_head.aux_head.'''   ):
            keys_to_ignore.append(__UpperCAmelCase   )
    for k in keys_to_ignore:
        state_dict.pop(__UpperCAmelCase	,     __UpperCAmelCase   )




def 	lowerCAmelCase_					(						)   ->			Union[str, Any]:
    UpperCamelCase__							:       Any      =   '''http://images.cocodataset.org/val2017/000000039769.jpg'''
    # url = "https://cdn.britannica.com/86/141086-050-9D7C75EE/Gulfstream-G450-business-jet-passengers.jpg"
    UpperCamelCase__							:       Optional[Any]      =   Image.open(requests.get(__UpperCAmelCase	,     stream=__UpperCAmelCase   ).raw   )
    return im




@torch.no_grad()
def 	lowerCAmelCase_					(						__UpperCAmelCase:    int	,     __UpperCAmelCase:    Union[str, Any]	,     __UpperCAmelCase:    Union[str, Any]	,     __UpperCAmelCase:    Dict   )   ->			Optional[int]:
    UpperCamelCase__							:       Any      =   get_mobilevitva_config(__UpperCAmelCase	,     __UpperCAmelCase   )

    # load original state_dict
    UpperCamelCase__							:       int      =   torch.load(__UpperCAmelCase	,     map_location='''cpu'''   )

    # load huggingface model
    if task_name.startswith('''ade20k_'''   ) or task_name.startswith('''voc_'''   ):
        UpperCamelCase__							:       Optional[int]      =   MobileViTVaForSemanticSegmentation(__UpperCAmelCase   ).eval()
        UpperCamelCase__							:       Optional[int]      =   False
    else:
        UpperCamelCase__							:       str      =   MobileViTVaForImageClassification(__UpperCAmelCase   ).eval()
        UpperCamelCase__							:       Dict      =   False

    # remove and rename some keys of load the original model
    UpperCamelCase__							:       Optional[int]      =   checkpoint
    remove_unused_keys(__UpperCAmelCase   )
    UpperCamelCase__							:       Optional[int]      =   create_rename_keys(__UpperCAmelCase	,     base_model=__UpperCAmelCase   )
    for rename_key_src, rename_key_dest in rename_keys:
        rename_key(__UpperCAmelCase	,     __UpperCAmelCase	,     __UpperCAmelCase   )

    # load modified state_dict
    model.load_state_dict(__UpperCAmelCase   )

    # Check outputs on an image, prepared by MobileViTImageProcessor
    UpperCamelCase__							:       Optional[Any]      =   MobileViTImageProcessor(crop_size=config.image_size	,     size=config.image_size + 32   )
    UpperCamelCase__							:       Dict      =   image_processor(images=prepare_img()	,     return_tensors='''pt'''   )
    UpperCamelCase__							:       Optional[int]      =   model(**__UpperCAmelCase   )

    # verify classification model
    if task_name.startswith('''imagenet'''   ):
        UpperCamelCase__							:       Optional[Any]      =   outputs.logits
        UpperCamelCase__							:       str      =   logits.argmax(-1   ).item()
        print('''Predicted class:'''	,     model.config.idalabel[predicted_class_idx]   )
        if task_name.startswith('''imagenet1k_256'''   ) and config.width_multiplier == 1.0:
            # expected_logits for base variant
            UpperCamelCase__							:       Optional[Any]      =   torch.tensor([-1.6_336e00, -7.3_204e-02, -5.1_883e-01]   )
            assert torch.allclose(logits[0, :3]	,     __UpperCAmelCase	,     atol=1e-4   )

    Path(__UpperCAmelCase   ).mkdir(exist_ok=__UpperCAmelCase   )
    print(f"Saving model {task_name} to {pytorch_dump_folder_path}"   )
    model.save_pretrained(__UpperCAmelCase   )
    print(f"Saving image processor to {pytorch_dump_folder_path}"   )
    image_processor.save_pretrained(__UpperCAmelCase   )


if __name__ == "__main__":
  UpperCAmelCase_  							=  argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '--task',
      default='imagenet1k_256',
      type=str,
      help=(
          'Name of the task for which the MobileViTV2 model you\'d like to convert is trained on . '
          '\n                Classification (ImageNet-1k)\n                    - MobileViTV2 (256x256) : imagenet1k_256\n                    - MobileViTV2 (Trained on 256x256 and Finetuned on 384x384) : imagenet1k_384\n                    - MobileViTV2 (Trained on ImageNet-21k and Finetuned on ImageNet-1k 256x256) :\n                      imagenet21k_to_1k_256\n                    - MobileViTV2 (Trained on ImageNet-21k, Finetuned on ImageNet-1k 256x256, and Finetuned on\n                      ImageNet-1k 384x384) : imagenet21k_to_1k_384\n                Segmentation\n                    - ADE20K Dataset : ade20k_deeplabv3\n                    - Pascal VOC 2012 Dataset: voc_deeplabv3\n            '
      ),
      choices=[
          'imagenet1k_256',
          'imagenet1k_384',
          'imagenet21k_to_1k_256',
          'imagenet21k_to_1k_384',
          'ade20k_deeplabv3',
          'voc_deeplabv3',
      ],
  )

  parser.add_argument(
      '--orig_checkpoint_path', required=True, type=str, help='Path to the original state dict (.pt file).'
  )
  parser.add_argument('--orig_config_path', required=True, type=str, help='Path to the original config file.')
  parser.add_argument(
      '--pytorch_dump_folder_path', required=True, type=str, help='Path to the output PyTorch model directory.'
  )

  UpperCAmelCase_  							=  parser.parse_args()
  convert_mobilevitva_checkpoint(
      args.task, args.orig_checkpoint_path, args.orig_config_path, args.pytorch_dump_folder_path
  )








import unittest

from transformers import BigBirdConfig, 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

  from transformers.models.big_bird.modeling_flax_big_bird import (
      FlaxBigBirdForCausalLM,
      FlaxBigBirdForMaskedLM,
      FlaxBigBirdForMultipleChoice,
      FlaxBigBirdForPreTraining,
      FlaxBigBirdForQuestionAnswering,
      FlaxBigBirdForSequenceClassification,
      FlaxBigBirdForTokenClassification,
      FlaxBigBirdModel,
  )






class 					lowercase__ (			unittest.TestCase       ):

   '''simple docstring'''


   def __init__(					self,	__magic_name__,	__magic_name__=2,	__magic_name__=56,	__magic_name__=True,	__magic_name__=True,	__magic_name__=True,	__magic_name__=True,	__magic_name__=99,	__magic_name__=32,	__magic_name__=2,	__magic_name__=2,	__magic_name__=7,	__magic_name__="gelu_new",	__magic_name__=0.1,	__magic_name__=0.1,	__magic_name__=512,	__magic_name__=16,	__magic_name__=2,	__magic_name__=0.02,	__magic_name__=4,	__magic_name__="block_sparse",	__magic_name__=True,	__magic_name__=False,	__magic_name__=2,	__magic_name__=3,	)				->   Optional[int]:






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

       UpperCamelCase__							:       Dict      =   rescale_embeddings
       UpperCamelCase__							:       Tuple      =   attention_type
       UpperCamelCase__							:       Union[str, Any]      =   use_bias
       UpperCamelCase__							:       Union[str, Any]      =   block_size
       UpperCamelCase__							:       Optional[int]      =   num_random_blocks




   def    UpperCamelCase__						(					self							)				->   int:






       """simple docstring"""
       UpperCamelCase__							:       int      =   ids_tensor([self.batch_size, self.seq_length],	self.vocab_size							)

       UpperCamelCase__							:       int      =   None
       if self.use_attention_mask:
           UpperCamelCase__							:       List[str]      =   random_attention_mask([self.batch_size, self.seq_length]							)

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

       UpperCamelCase__							:       str      =   BigBirdConfig(
           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=__magic_name__,	initializer_range=self.initializer_range,	attention_type=self.attention_type,	block_size=self.block_size,	num_random_blocks=self.num_random_blocks,	use_bias=self.use_bias,	rescale_embeddings=self.rescale_embeddings,	)

       return config, input_ids, token_type_ids, attention_mask




   def    UpperCamelCase__						(					self							)				->   str:






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






@require_flax
class 					lowercase__ (			__lowerCamelCase    ,       unittest.TestCase       ):

   '''simple docstring'''


   a		:							Any     	=	(
       (
           FlaxBigBirdForCausalLM,
           FlaxBigBirdModel,
           FlaxBigBirdForPreTraining,
           FlaxBigBirdForMaskedLM,
           FlaxBigBirdForMultipleChoice,
           FlaxBigBirdForQuestionAnswering,
           FlaxBigBirdForSequenceClassification,
           FlaxBigBirdForTokenClassification,
       )
       if is_flax_available()
       else ()
   )

   a		:							List[str]     	=	False
   a		:							str     	=	False




   def    UpperCamelCase__						(					self							)				->   Any:






       """simple docstring"""
       UpperCamelCase__							:       Union[str, Any]      =   FlaxBigBirdModelTester(self							)




   @slow
   # copied from `test_modeling_flax_common` because it takes much longer than other models
   def    UpperCamelCase__						(					self							)				->   Dict:






       """simple docstring"""
       super().test_from_pretrained_save_pretrained()




   @slow
   # copied from `test_modeling_flax_common` because it takes much longer than other models
   def    UpperCamelCase__						(					self							)				->   Optional[Any]:






       """simple docstring"""
       super().test_from_pretrained_with_no_automatic_init()




   @slow
   # copied from `test_modeling_flax_common` because it takes much longer than other models
   def    UpperCamelCase__						(					self							)				->   Dict:






       """simple docstring"""
       super().test_no_automatic_init()




   @slow
   # copied from `test_modeling_flax_common` because it takes much longer than other models
   def    UpperCamelCase__						(					self							)				->   Optional[Any]:






       """simple docstring"""
       super().test_hidden_states_output()




   @slow
   def    UpperCamelCase__						(					self							)				->   List[str]:






       """simple docstring"""
       for model_class_name in self.all_model_classes:
           UpperCamelCase__							:       List[str]      =   model_class_name.from_pretrained('''google/bigbird-roberta-base'''							)
           self.assertIsNotNone(__magic_name__							)




   def    UpperCamelCase__						(					self							)				->   str:






       """simple docstring"""
       if self.test_attn_probs:
           super().test_attention_outputs()




   @slow
   # copied from `test_modeling_flax_common` because it takes much longer than other models
   def    UpperCamelCase__						(					self							)				->   Any:






       """simple docstring"""
       UpperCamelCase__     ,UpperCamelCase__							:       Dict      =   self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
           with self.subTest(model_class.__name__							):
               UpperCamelCase__							:       Union[str, Any]      =   self._prepare_for_class(__magic_name__,	__magic_name__							)
               UpperCamelCase__							:       Tuple      =   model_class(__magic_name__							)

               @jax.jit
               def model_jitted(__magic_name__,	__magic_name__=None,	**__magic_name__							):
                   return model(input_ids=__magic_name__,	attention_mask=__magic_name__,	**__magic_name__							)

               with self.subTest('''JIT Enabled'''							):
                   UpperCamelCase__							:       str      =   model_jitted(**__magic_name__							).to_tuple()

               with self.subTest('''JIT Disabled'''							):
                   with jax.disable_jit():
                       UpperCamelCase__							:       Dict      =   model_jitted(**__magic_name__							).to_tuple()

               self.assertEqual(len(__magic_name__							),	len(__magic_name__							)							)
               for jitted_output, output in zip(__magic_name__,	__magic_name__							):
                   self.assertEqual(jitted_output.shape,	output.shape							)




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__,	__magic_name__,	__magic_name__=1E-5,	__magic_name__="outputs",	__magic_name__=None							)				->   List[Any]:






       """simple docstring"""
       # `bigbird_block_sparse_attention` in `FlaxBigBird` returns `attention_probs = None`, while in PyTorch version,
       # an effort was done to return `attention_probs` (yet to be verified).
       if name.startswith('''outputs.attentions'''							):
           return
       else:
           super().check_pt_flax_outputs(__magic_name__,	__magic_name__,	__magic_name__,	__magic_name__,	__magic_name__,	__magic_name__							)

