

'''simple docstring'''



import argparse
import json
from collections import OrderedDict
from pathlib import Path

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

from transformers import (
    SegformerConfig,
    SegformerForImageClassification,
    SegformerForSemanticSegmentation,
    SegformerImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
__UpperCAmelCase    :int				       =     logging.get_logger(__name__)




def 			_a			(		_lowercase      :			Any    ,      _lowercase      :			str=False						):





     '''simple docstring'''






     __UpperCAmelCase			:				Optional[Any]													=    OrderedDict()
     for key, value in state_dict.items():
          if encoder_only and not key.startswith('''head'''						):
               __UpperCAmelCase			:				Tuple													=    '''segformer.encoder.''' + key
          if key.startswith('''backbone'''						):
               __UpperCAmelCase			:				List[str]													=    key.replace('''backbone'''    ,      '''segformer.encoder'''						)
          if "patch_embed" in key:
               # replace for example patch_embed1 by patch_embeddings.0
               __UpperCAmelCase			:				Tuple													=    key[key.find('''patch_embed'''						) + len('''patch_embed'''						)]
               __UpperCAmelCase			:				Any													=    key.replace(F'patch_embed{idx}'    ,      F'patch_embeddings.{int(_lowercase						)-1}'						)
          if "norm" in key:
               __UpperCAmelCase			:				Tuple													=    key.replace('''norm'''    ,      '''layer_norm'''						)
          if "segformer.encoder.layer_norm" in key:
               # replace for example layer_norm1 by layer_norm.0
               __UpperCAmelCase			:				List[Any]													=    key[key.find('''segformer.encoder.layer_norm'''						) + len('''segformer.encoder.layer_norm'''						)]
               __UpperCAmelCase			:				Optional[int]													=    key.replace(F'layer_norm{idx}'    ,      F'layer_norm.{int(_lowercase						)-1}'						)
          if "layer_norm1" in key:
               __UpperCAmelCase			:				Optional[Any]													=    key.replace('''layer_norm1'''    ,      '''layer_norm_1'''						)
          if "layer_norm2" in key:
               __UpperCAmelCase			:				Union[str, Any]													=    key.replace('''layer_norm2'''    ,      '''layer_norm_2'''						)
          if "block" in key:
               # replace for example block1 by block.0
               __UpperCAmelCase			:				Any													=    key[key.find('''block'''						) + len('''block'''						)]
               __UpperCAmelCase			:				List[Any]													=    key.replace(F'block{idx}'    ,      F'block.{int(_lowercase						)-1}'						)
          if "attn.q" in key:
               __UpperCAmelCase			:				List[Any]													=    key.replace('''attn.q'''    ,      '''attention.self.query'''						)
          if "attn.proj" in key:
               __UpperCAmelCase			:				str													=    key.replace('''attn.proj'''    ,      '''attention.output.dense'''						)
          if "attn" in key:
               __UpperCAmelCase			:				Optional[Any]													=    key.replace('''attn'''    ,      '''attention.self'''						)
          if "fc1" in key:
               __UpperCAmelCase			:				Dict													=    key.replace('''fc1'''    ,      '''dense1'''						)
          if "fc2" in key:
               __UpperCAmelCase			:				Any													=    key.replace('''fc2'''    ,      '''dense2'''						)
          if "linear_pred" in key:
               __UpperCAmelCase			:				Optional[int]													=    key.replace('''linear_pred'''    ,      '''classifier'''						)
          if "linear_fuse" in key:
               __UpperCAmelCase			:				Union[str, Any]													=    key.replace('''linear_fuse.conv'''    ,      '''linear_fuse'''						)
               __UpperCAmelCase			:				Dict													=    key.replace('''linear_fuse.bn'''    ,      '''batch_norm'''						)
          if "linear_c" in key:
               # replace for example linear_c4 by linear_c.3
               __UpperCAmelCase			:				Optional[Any]													=    key[key.find('''linear_c'''						) + len('''linear_c'''						)]
               __UpperCAmelCase			:				Optional[Any]													=    key.replace(F'linear_c{idx}'    ,      F'linear_c.{int(_lowercase						)-1}'						)
          if key.startswith('''head'''						):
               __UpperCAmelCase			:				List[str]													=    key.replace('''head'''    ,      '''classifier'''						)
          __UpperCAmelCase			:				Union[str, Any]													=    value

     return new_state_dict




def 			_a			(		_lowercase      :			str    ,      _lowercase      :			List[str]						):





     '''simple docstring'''






     for i in range(config.num_encoder_blocks						):
          for j in range(config.depths[i]						):
               # read in weights + bias of keys and values (which is a single matrix in the original implementation)
               __UpperCAmelCase			:				Union[str, Any]													=    state_dict.pop(F'segformer.encoder.block.{i}.{j}.attention.self.kv.weight'						)
               __UpperCAmelCase			:				Optional[Any]													=    state_dict.pop(F'segformer.encoder.block.{i}.{j}.attention.self.kv.bias'						)
               # next, add keys and values (in that order) to the state dict
               __UpperCAmelCase			:				str													=    kv_weight[
                   : config.hidden_sizes[i], :
               ]
               __UpperCAmelCase			:				str													=    kv_bias[: config.hidden_sizes[i]]
               __UpperCAmelCase			:				Union[str, Any]													=    kv_weight[
                   config.hidden_sizes[i] :, :
               ]
               __UpperCAmelCase			:				int													=    kv_bias[
                   config.hidden_sizes[i] :
               ]




def 			_a			(		):





     '''simple docstring'''






     __UpperCAmelCase			:				List[str]													=    '''http://images.cocodataset.org/val2017/000000039769.jpg'''
     __UpperCAmelCase			:				int													=    Image.open(requests.get(_lowercase    ,      stream=_lowercase						).raw						)

     return image




@torch.no_grad()
def 			_a			(		_lowercase      :			str    ,      _lowercase      :			str    ,      _lowercase      :			Union[str, Any]						):





     '''simple docstring'''






     __UpperCAmelCase			:				Union[str, Any]													=    SegformerConfig()
     __UpperCAmelCase			:				List[str]													=    False

     # set attributes based on model_name
     __UpperCAmelCase			:				List[Any]													=    '''huggingface/label-files'''
     if "segformer" in model_name:
          __UpperCAmelCase			:				Any													=    model_name[len('''segformer.'''						) : len('''segformer.'''						) + 2]
          if "ade" in model_name:
               __UpperCAmelCase			:				Any													=    150
               __UpperCAmelCase			:				List[Any]													=    '''ade20k-id2label.json'''
               __UpperCAmelCase			:				Any													=    (1, 150, 128, 128)
          elif "city" in model_name:
               __UpperCAmelCase			:				Optional[int]													=    19
               __UpperCAmelCase			:				List[Any]													=    '''cityscapes-id2label.json'''
               __UpperCAmelCase			:				int													=    (1, 19, 128, 128)
          else:
               raise ValueError(F'Model {model_name} not supported'						)
     elif "mit" in model_name:
          __UpperCAmelCase			:				Optional[Any]													=    True
          __UpperCAmelCase			:				Any													=    model_name[4:6]
          __UpperCAmelCase			:				List[Any]													=    1000
          __UpperCAmelCase			:				int													=    '''imagenet-1k-id2label.json'''
          __UpperCAmelCase			:				Any													=    (1, 1000)
     else:
          raise ValueError(F'Model {model_name} not supported'						)

     # set config attributes
     __UpperCAmelCase			:				Optional[int]													=    json.load(open(hf_hub_download(_lowercase    ,      _lowercase    ,      repo_type='''dataset'''						)    ,      '''r'''						)						)
     __UpperCAmelCase			:				List[Any]													=    {int(_lowercase						): v for k, v in idalabel.items()}
     __UpperCAmelCase			:				Union[str, Any]													=    idalabel
     __UpperCAmelCase			:				str													=    {v: k for k, v in idalabel.items()}
     if size == "b0":
          pass
     elif size == "b1":
          __UpperCAmelCase			:				Dict													=    [64, 128, 320, 512]
          __UpperCAmelCase			:				str													=    256
     elif size == "b2":
          __UpperCAmelCase			:				Tuple													=    [64, 128, 320, 512]
          __UpperCAmelCase			:				Union[str, Any]													=    768
          __UpperCAmelCase			:				List[Any]													=    [3, 4, 6, 3]
     elif size == "b3":
          __UpperCAmelCase			:				int													=    [64, 128, 320, 512]
          __UpperCAmelCase			:				List[str]													=    768
          __UpperCAmelCase			:				Any													=    [3, 4, 18, 3]
     elif size == "b4":
          __UpperCAmelCase			:				List[str]													=    [64, 128, 320, 512]
          __UpperCAmelCase			:				str													=    768
          __UpperCAmelCase			:				List[str]													=    [3, 8, 27, 3]
     elif size == "b5":
          __UpperCAmelCase			:				str													=    [64, 128, 320, 512]
          __UpperCAmelCase			:				Optional[int]													=    768
          __UpperCAmelCase			:				Dict													=    [3, 6, 40, 3]
     else:
          raise ValueError(F'Size {size} not supported'						)

     # load image processor (only resize + normalize)
     __UpperCAmelCase			:				Tuple													=    SegformerImageProcessor(
         image_scale=(512, 512)    ,      keep_ratio=_lowercase    ,      align=_lowercase    ,      do_random_crop=_lowercase						)

     # prepare image
     __UpperCAmelCase			:				List[str]													=    prepare_img()
     __UpperCAmelCase			:				List[str]													=    image_processor(images=_lowercase    ,      return_tensors='''pt'''						).pixel_values

     logger.info(F'Converting model {model_name}...'						)

     # load original state dict
     if encoder_only:
          __UpperCAmelCase			:				Union[str, Any]													=    torch.load(_lowercase    ,      map_location=torch.device('''cpu'''						)						)
     else:
          __UpperCAmelCase			:				Tuple													=    torch.load(_lowercase    ,      map_location=torch.device('''cpu'''						)						)['''state_dict''']

     # rename keys
     __UpperCAmelCase			:				Any													=    rename_keys(_lowercase    ,      encoder_only=_lowercase						)
     if not encoder_only:
          del state_dict["decode_head.conv_seg.weight"]
          del state_dict["decode_head.conv_seg.bias"]

     # key and value matrices need special treatment
     read_in_k_v(_lowercase    ,      _lowercase						)

     # create HuggingFace model and load state dict
     if encoder_only:
          __UpperCAmelCase			:				str													=    False
          __UpperCAmelCase			:				Any													=    SegformerForImageClassification(_lowercase						)
     else:
          __UpperCAmelCase			:				Tuple													=    SegformerForSemanticSegmentation(_lowercase						)
     model.load_state_dict(_lowercase						)
     model.eval()

     # forward pass
     __UpperCAmelCase			:				Union[str, Any]													=    model(_lowercase						)
     __UpperCAmelCase			:				List[str]													=    outputs.logits

     # set expected_slice based on model name
     # ADE20k checkpoints
     if model_name == "segformer.b0.512x512.ade.160k":
          __UpperCAmelCase			:				int													=    torch.tensor(
              [
                  [[-4.6310, -5.5232, -6.2356], [-5.1921, -6.1444, -6.5996], [-5.4424, -6.2790, -6.7574]],
                  [[-12.1391, -13.3122, -13.9554], [-12.8732, -13.9352, -14.3563], [-12.9438, -13.8226, -14.2513]],
                  [[-12.5134, -13.4686, -14.4915], [-12.8669, -14.4343, -14.7758], [-13.2523, -14.5819, -15.0694]],
              ]						)
     elif model_name == "segformer.b1.512x512.ade.160k":
          __UpperCAmelCase			:				str													=    torch.tensor(
              [
                  [[-7.5820, -8.7231, -8.3215], [-8.0600, -10.3529, -10.0304], [-7.5208, -9.4103, -9.6239]],
                  [[-12.6918, -13.8994, -13.7137], [-13.3196, -15.7523, -15.4789], [-12.9343, -14.8757, -14.9689]],
                  [[-11.1911, -11.9421, -11.3243], [-11.3342, -13.6839, -13.3581], [-10.3909, -12.1832, -12.4858]],
              ]						)
     elif model_name == "segformer.b2.512x512.ade.160k":
          __UpperCAmelCase			:				List[Any]													=    torch.tensor(
              [
                  [[-11.8173, -14.3850, -16.3128], [-14.5648, -16.5804, -18.6568], [-14.7223, -15.7387, -18.4218]],
                  [[-15.7290, -17.9171, -19.4423], [-18.3105, -19.9448, -21.4661], [-17.9296, -18.6497, -20.7910]],
                  [[-15.0783, -17.0336, -18.2789], [-16.8771, -18.6870, -20.1612], [-16.2454, -17.1426, -19.5055]],
              ]						)
     elif model_name == "segformer.b3.512x512.ade.160k":
          __UpperCAmelCase			:				List[Any]													=    torch.tensor(
              [
                  [[-9.0878, -10.2081, -10.1891], [-9.3144, -10.7941, -10.9843], [-9.2294, -10.3855, -10.5704]],
                  [[-12.2316, -13.9068, -13.6102], [-12.9161, -14.3702, -14.3235], [-12.5233, -13.7174, -13.7932]],
                  [[-14.6275, -15.2490, -14.9727], [-14.3400, -15.9687, -16.2827], [-14.1484, -15.4033, -15.8937]],
              ]						)
     elif model_name == "segformer.b4.512x512.ade.160k":
          __UpperCAmelCase			:				int													=    torch.tensor(
              [
                  [[-12.3144, -13.2447, -14.0802], [-13.3614, -14.5816, -15.6117], [-13.3340, -14.4433, -16.2219]],
                  [[-19.2781, -20.4128, -20.7506], [-20.6153, -21.6566, -22.0998], [-19.9800, -21.0430, -22.1494]],
                  [[-18.8739, -19.7804, -21.1834], [-20.1233, -21.6765, -23.2944], [-20.0315, -21.2641, -23.6944]],
              ]						)
     elif model_name == "segformer.b5.640x640.ade.160k":
          __UpperCAmelCase			:				Union[str, Any]													=    torch.tensor(
              [
                  [[-9.5524, -12.0835, -11.7348], [-10.5229, -13.6446, -14.5662], [-9.5842, -12.8851, -13.9414]],
                  [[-15.3432, -17.5323, -17.0818], [-16.3330, -18.9255, -19.2101], [-15.1340, -17.7848, -18.3971]],
                  [[-12.6072, -14.9486, -14.6631], [-13.7629, -17.0907, -17.7745], [-12.7899, -16.1695, -17.1671]],
              ]						)
     # Cityscapes checkpoints
     elif model_name == "segformer.b0.1024x1024.city.160k":
          __UpperCAmelCase			:				Optional[int]													=    torch.tensor(
              [
                  [[-11.9295, -13.4057, -14.8106], [-13.3431, -14.8179, -15.3781], [-14.2836, -15.5942, -16.1588]],
                  [[-11.4906, -12.8067, -13.6564], [-13.1189, -14.0500, -14.1543], [-13.8748, -14.5136, -14.8789]],
                  [[0.5374, 0.1067, -0.4742], [0.1141, -0.2255, -0.7099], [-0.3000, -0.5924, -1.3105]],
              ]						)
     elif model_name == "segformer.b0.512x1024.city.160k":
          __UpperCAmelCase			:				int													=    torch.tensor(
              [
                  [[-7.8217, -9.8767, -10.1717], [-9.4438, -10.9058, -11.4047], [-9.7939, -12.3495, -12.1079]],
                  [[-7.1514, -9.5336, -10.0860], [-9.7776, -11.6822, -11.8439], [-10.1411, -12.7655, -12.8972]],
                  [[0.3021, 0.0805, -0.2310], [-0.0328, -0.1605, -0.2714], [-0.1408, -0.5477, -0.6976]],
              ]						)
     elif model_name == "segformer.b0.640x1280.city.160k":
          __UpperCAmelCase			:				Optional[Any]													=    torch.tensor(
              [
                  [
                      [-1.13_72E01, -1.27_87E01, -1.34_77E01],
                      [-1.25_36E01, -1.41_94E01, -1.44_09E01],
                      [-1.32_17E01, -1.48_88E01, -1.53_27E01],
                  ],
                  [
                      [-1.47_91E01, -1.71_22E01, -1.82_77E01],
                      [-1.71_63E01, -1.91_92E01, -1.95_33E01],
                      [-1.78_97E01, -1.99_91E01, -2.03_15E01],
                  ],
                  [
                      [7.67_23E-01, 4.19_21E-01, -7.78_78E-02],
                      [4.77_72E-01, 9.55_57E-03, -2.80_82E-01],
                      [3.60_32E-01, -2.48_26E-01, -5.11_68E-01],
                  ],
              ]						)
     elif model_name == "segformer.b0.768x768.city.160k":
          __UpperCAmelCase			:				Optional[int]													=    torch.tensor(
              [
                  [[-9.4959, -11.3087, -11.7479], [-11.0025, -12.6540, -12.3319], [-11.4064, -13.0487, -12.9905]],
                  [[-9.8905, -11.3084, -12.0854], [-11.1726, -12.7698, -12.9583], [-11.5985, -13.3278, -14.1774]],
                  [[0.2213, 0.0192, -0.2466], [-0.1731, -0.4213, -0.4874], [-0.3126, -0.6541, -1.1389]],
              ]						)
     elif model_name == "segformer.b1.1024x1024.city.160k":
          __UpperCAmelCase			:				Dict													=    torch.tensor(
              [
                  [[-13.5748, -13.9111, -12.6500], [-14.3500, -15.3683, -14.2328], [-14.7532, -16.0424, -15.6087]],
                  [[-17.1651, -15.8725, -12.9653], [-17.2580, -17.3718, -14.8223], [-16.6058, -16.8783, -16.7452]],
                  [[-3.6456, -3.0209, -1.4203], [-3.0797, -3.1959, -2.0000], [-1.8757, -1.9217, -1.6997]],
              ]						)
     elif model_name == "segformer.b2.1024x1024.city.160k":
          __UpperCAmelCase			:				List[str]													=    torch.tensor(
              [
                  [[-16.0976, -16.4856, -17.3962], [-16.6234, -19.0342, -19.7685], [-16.0900, -18.0661, -19.1180]],
                  [[-18.4750, -18.8488, -19.5074], [-19.4030, -22.1570, -22.5977], [-19.1191, -20.8486, -22.3783]],
                  [[-4.5178, -5.5037, -6.5109], [-5.0884, -7.2174, -8.0334], [-4.4156, -5.8117, -7.2970]],
              ]						)
     elif model_name == "segformer.b3.1024x1024.city.160k":
          __UpperCAmelCase			:				Union[str, Any]													=    torch.tensor(
              [
                  [[-14.2081, -14.4732, -14.1977], [-14.5867, -16.4423, -16.6356], [-13.4441, -14.9685, -16.8696]],
                  [[-14.4576, -14.7073, -15.0451], [-15.0816, -17.6237, -17.9873], [-14.4213, -16.0199, -18.5992]],
                  [[-4.7349, -4.9588, -5.0966], [-4.3210, -6.9325, -7.2591], [-3.4312, -4.7484, -7.1917]],
              ]						)
     elif model_name == "segformer.b4.1024x1024.city.160k":
          __UpperCAmelCase			:				List[str]													=    torch.tensor(
              [
                  [[-11.7737, -11.9526, -11.3273], [-13.6692, -14.4574, -13.8878], [-13.8937, -14.6924, -15.9345]],
                  [[-14.6706, -14.5330, -14.1306], [-16.1502, -16.8180, -16.4269], [-16.8338, -17.8939, -20.1746]],
                  [[1.0491, 0.8289, 1.0310], [1.1044, 0.5219, 0.8055], [1.0899, 0.6926, 0.5590]],
              ]						)
     elif model_name == "segformer.b5.1024x1024.city.160k":
          __UpperCAmelCase			:				Dict													=    torch.tensor(
              [
                  [[-12.5641, -13.4777, -13.0684], [-13.9587, -15.8983, -16.6557], [-13.3109, -15.7350, -16.3141]],
                  [[-14.7074, -15.4352, -14.5944], [-16.6353, -18.1663, -18.6120], [-15.1702, -18.0329, -18.1547]],
                  [[-1.7990, -2.0951, -1.7784], [-2.6397, -3.8245, -3.9686], [-1.5264, -2.8126, -2.9316]],
              ]						)
     else:
          __UpperCAmelCase			:				Dict													=    logits.argmax(-1						).item()
          print('''Predicted class:'''    ,      model.config.idalabel[predicted_class_idx]						)

     # verify logits
     if not encoder_only:
          assert logits.shape == expected_shape
          assert torch.allclose(logits[0, :3, :3, :3]    ,      _lowercase    ,      atol=1E-2						)

     # finally, save model and image processor
     logger.info(F'Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'						)
     Path(_lowercase						).mkdir(exist_ok=_lowercase						)
     model.save_pretrained(_lowercase						)
     image_processor.save_pretrained(_lowercase						)


if __name__ == "__main__":
      __UpperCAmelCase    :Tuple				       =     argparse.ArgumentParser()

      parser.add_argument(
          "--model_name",
          default="segformer.b0.512x512.ade.160k",
          type=str,
          help="Name of the model you'd like to convert.",
      )
      parser.add_argument(
          "--checkpoint_path", default=None, type=str, help="Path to the original PyTorch checkpoint (.pth file)."
      )
      parser.add_argument(
          "--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
      )
      __UpperCAmelCase    :Dict				       =     parser.parse_args()
      convert_segformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)



'''simple docstring'''



import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require_tensorflow_text, require_tf, slow


if is_tf_available():
      import tensorflow as tf

if is_tensorflow_text_available():
      from transformers.models.bert import TFBertTokenizer


__UpperCAmelCase    :Optional[int]				       =     ["bert-base-uncased", "bert-base-cased"]
__UpperCAmelCase    :str				       =     "hf-internal-testing/tiny-bert-tf-only"

if is_tf_available():



      class 			a       (	tf.keras.Model ):


           """simple docstring"""






           def __init__(       self  :	List[str]							,				snake_case  :	List[str]    )		->							str:
                super().__init__()
                __UpperCAmelCase			:				List[str]													=    tokenizer
                __UpperCAmelCase			:				List[Any]													=    AutoConfig.from_pretrained(snake_case    )
                __UpperCAmelCase			:				int													=    TFAutoModel.from_config(snake_case    )






           def 				lowerCamelCase__				(       self  :	List[Any]							,				snake_case  :	Optional[int]    )		->							Optional[Any]:
                __UpperCAmelCase			:				List[Any]													=    self.tokenizer(snake_case    )
                __UpperCAmelCase			:				Optional[Any]													=    self.bert(**snake_case    )
                return out["pooler_output"]



@require_tf
@require_tensorflow_text
class 			a       (	unittest.TestCase ):


     """simple docstring"""






     def 				lowerCamelCase__				(       self  :	Optional[int]    )		->							List[str]:
          super().setUp()

          __UpperCAmelCase			:				Tuple													=    [
              BertTokenizer.from_pretrained(snake_case    ) for checkpoint in (TOKENIZER_CHECKPOINTS * 2)
          ]  # repeat for when fast_bert_tokenizer=false
          __UpperCAmelCase			:				Any													=    [TFBertTokenizer.from_pretrained(snake_case    ) for checkpoint in TOKENIZER_CHECKPOINTS] + [
              TFBertTokenizer.from_pretrained(snake_case							,				use_fast_bert_tokenizer=snake_case    )
              for checkpoint in TOKENIZER_CHECKPOINTS
          ]
          assert len(self.tokenizers    ) == len(self.tf_tokenizers    )

          __UpperCAmelCase			:				Optional[int]													=    [
              '''This is a straightforward English test sentence.''',
              '''This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.''',
              '''Now we\'re going to add some Chinese: 一 二 三 一二三''',
              '''And some much more rare Chinese: 齉 堃 齉堃''',
              '''Je vais aussi écrire en français pour tester les accents''',
              '''Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ''',
          ]
          __UpperCAmelCase			:				Optional[int]													=    list(zip(self.test_sentences							,				self.test_sentences[::-1]    )    )

     def 				lowerCamelCase__				(       self  :	Optional[int]    )		->							Optional[Any]:
          for tokenizer, tf_tokenizer in zip(self.tokenizers							,				self.tf_tokenizers    ):
               for test_inputs in (self.test_sentences, self.paired_sentences):
                    __UpperCAmelCase			:				Any													=    tokenizer(snake_case							,				return_tensors='''tf'''							,				padding='''longest'''    )
                    __UpperCAmelCase			:				Optional[int]													=    tf_tokenizer(snake_case    )

                    for key in python_outputs.keys():
                         self.assertTrue(tf.reduce_all(python_outputs[key].shape == tf_outputs[key].shape    )    )
                         self.assertTrue(tf.reduce_all(tf.cast(python_outputs[key]							,				tf.intaa    ) == tf_outputs[key]    )    )

     @slow
     def 				lowerCamelCase__				(       self  :	List[Any]    )		->							str:
          for tf_tokenizer in self.tf_tokenizers:
               __UpperCAmelCase			:				Any													=    tf_tokenizer(self.paired_sentences    )
               __UpperCAmelCase			:				Union[str, Any]													=    tf_tokenizer(
                   text=[sentence[0] for sentence in self.paired_sentences]							,				text_pair=[sentence[1] for sentence in self.paired_sentences]							,				)
               for key in merged_outputs.keys():
                    self.assertTrue(tf.reduce_all(tf.cast(merged_outputs[key]							,				tf.intaa    ) == separated_outputs[key]    )    )

     @slow
     def 				lowerCamelCase__				(       self  :	str    )		->							Union[str, Any]:
          for tf_tokenizer in self.tf_tokenizers:
               __UpperCAmelCase			:				Optional[int]													=    tf.function(snake_case    )
               for test_inputs in (self.test_sentences, self.paired_sentences):
                    __UpperCAmelCase			:				int													=    tf.constant(snake_case    )
                    __UpperCAmelCase			:				Tuple													=    compiled_tokenizer(snake_case    )
                    __UpperCAmelCase			:				Optional[int]													=    tf_tokenizer(snake_case    )

                    for key in eager_outputs.keys():
                         self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]    )    )






     @slow
     def 				lowerCamelCase__				(       self  :	str    )		->							str:
          for tf_tokenizer in self.tf_tokenizers:
               __UpperCAmelCase			:				List[Any]													=    ModelToSave(tokenizer=snake_case    )
               __UpperCAmelCase			:				Union[str, Any]													=    tf.convert_to_tensor(self.test_sentences    )
               __UpperCAmelCase			:				Tuple													=    model(snake_case    )  # Build model with some sample inputs
               with TemporaryDirectory() as tempdir:
                    __UpperCAmelCase			:				Any													=    Path(snake_case    ) / '''saved.model'''
                    model.save(snake_case    )
                    __UpperCAmelCase			:				str													=    tf.keras.models.load_model(snake_case    )
               __UpperCAmelCase			:				Optional[int]													=    loaded_model(snake_case    )
               # We may see small differences because the loaded model is compiled, so we need an epsilon for the test
               self.assertLessEqual(tf.reduce_max(tf.abs(out - loaded_output    )    )							,				1E-5    )