
'''simple docstring'''
import argparse
import json
import pickle
from pathlib import Path

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

from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
A__   :		Tuple 		=logging.get_logger(__name__)







def        A_     (						__SCREAMING_SNAKE_CASE						:  str )				->       List[Any]:





  """simple docstring"""

  __A		:     Any												=		SwinConfig.from_pretrained(
      """microsoft/swin-tiny-patch4-window7-224"""			,     out_features=["""stage1""", """stage2""", """stage3""", """stage4"""] )
  __A		:     List[str]												=		MaskFormerConfig(backbone_config=__SCREAMING_SNAKE_CASE )

  __A		:     int												=		"""huggingface/label-files"""
  if "ade20k-full" in model_name:
    # this should be ok
    __A		:     str												=		847
    __A		:     Optional[Any]												=		"""maskformer-ade20k-full-id2label.json"""
  elif "ade" in model_name:
    # this should be ok
    __A		:     Tuple												=		150
    __A		:     Any												=		"""ade20k-id2label.json"""
  elif "coco-stuff" in model_name:
    # this should be ok
    __A		:     Optional[Any]												=		171
    __A		:     List[Any]												=		"""maskformer-coco-stuff-id2label.json"""
  elif "coco" in model_name:
    # TODO
    __A		:     Dict												=		133
    __A		:     Optional[Any]												=		"""coco-panoptic-id2label.json"""
  elif "cityscapes" in model_name:
    # this should be ok
    __A		:     Dict												=		19
    __A		:     str												=		"""cityscapes-id2label.json"""
  elif "vistas" in model_name:
    # this should be ok
    __A		:     List[Any]												=		65
    __A		:     Optional[Any]												=		"""mapillary-vistas-id2label.json"""

  __A		:     Tuple												=		json.load(open(hf_hub_download(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     repo_type="""dataset""" )			,     """r""" ) )
  __A		:     int												=		{int(__SCREAMING_SNAKE_CASE ): v for k, v in idalabel.items()}

  return config







def        A_     (						__SCREAMING_SNAKE_CASE						:  Tuple )				->       Optional[Any]:





  """simple docstring"""

  __A		:     Optional[Any]												=		[]
  # stem
  # fmt: off
  rename_keys.append(("""backbone.patch_embed.proj.weight""", """model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.weight""") )
  rename_keys.append(("""backbone.patch_embed.proj.bias""", """model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.bias""") )
  rename_keys.append(("""backbone.patch_embed.norm.weight""", """model.pixel_level_module.encoder.model.embeddings.norm.weight""") )
  rename_keys.append(("""backbone.patch_embed.norm.bias""", """model.pixel_level_module.encoder.model.embeddings.norm.bias""") )
  # stages
  for i in range(len(config.backbone_config.depths ) ):
    for j in range(config.backbone_config.depths[i] ):
      rename_keys.append((F"backbone.layers.{i}.blocks.{j}.norm1.weight", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight") )
      rename_keys.append((F"backbone.layers.{i}.blocks.{j}.norm1.bias", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias") )
      rename_keys.append((F"backbone.layers.{i}.blocks.{j}.attn.relative_position_bias_table", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table") )
      rename_keys.append((F"backbone.layers.{i}.blocks.{j}.attn.relative_position_index", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index") )
      rename_keys.append((F"backbone.layers.{i}.blocks.{j}.attn.proj.weight", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight") )
      rename_keys.append((F"backbone.layers.{i}.blocks.{j}.attn.proj.bias", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias") )
      rename_keys.append((F"backbone.layers.{i}.blocks.{j}.norm2.weight", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight") )
      rename_keys.append((F"backbone.layers.{i}.blocks.{j}.norm2.bias", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias") )
      rename_keys.append((F"backbone.layers.{i}.blocks.{j}.mlp.fc1.weight", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight") )
      rename_keys.append((F"backbone.layers.{i}.blocks.{j}.mlp.fc1.bias", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias") )
      rename_keys.append((F"backbone.layers.{i}.blocks.{j}.mlp.fc2.weight", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.weight") )
      rename_keys.append((F"backbone.layers.{i}.blocks.{j}.mlp.fc2.bias", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.bias") )

    if i < 3:
      rename_keys.append((F"backbone.layers.{i}.downsample.reduction.weight", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.reduction.weight") )
      rename_keys.append((F"backbone.layers.{i}.downsample.norm.weight", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.weight") )
      rename_keys.append((F"backbone.layers.{i}.downsample.norm.bias", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.bias") )
    rename_keys.append((F"backbone.norm{i}.weight", F"model.pixel_level_module.encoder.hidden_states_norms.{i}.weight") )
    rename_keys.append((F"backbone.norm{i}.bias", F"model.pixel_level_module.encoder.hidden_states_norms.{i}.bias") )

  # FPN
  rename_keys.append(("""sem_seg_head.layer_4.weight""", """model.pixel_level_module.decoder.fpn.stem.0.weight""") )
  rename_keys.append(("""sem_seg_head.layer_4.norm.weight""", """model.pixel_level_module.decoder.fpn.stem.1.weight""") )
  rename_keys.append(("""sem_seg_head.layer_4.norm.bias""", """model.pixel_level_module.decoder.fpn.stem.1.bias""") )
  for source_index, target_index in zip(range(3			,     0			,     -1 )			,     range(0			,     3 ) ):
    rename_keys.append((F"sem_seg_head.adapter_{source_index}.weight", F"model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.0.weight") )
    rename_keys.append((F"sem_seg_head.adapter_{source_index}.norm.weight", F"model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.weight") )
    rename_keys.append((F"sem_seg_head.adapter_{source_index}.norm.bias", F"model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.bias") )
    rename_keys.append((F"sem_seg_head.layer_{source_index}.weight", F"model.pixel_level_module.decoder.fpn.layers.{target_index}.block.0.weight") )
    rename_keys.append((F"sem_seg_head.layer_{source_index}.norm.weight", F"model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.weight") )
    rename_keys.append((F"sem_seg_head.layer_{source_index}.norm.bias", F"model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.bias") )
  rename_keys.append(("""sem_seg_head.mask_features.weight""", """model.pixel_level_module.decoder.mask_projection.weight""") )
  rename_keys.append(("""sem_seg_head.mask_features.bias""", """model.pixel_level_module.decoder.mask_projection.bias""") )

  # Transformer decoder
  for idx in range(config.decoder_config.decoder_layers ):
    # self-attention out projection
    rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.weight", F"model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.weight") )
    rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.bias", F"model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.bias") )
    # cross-attention out projection
    rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.weight", F"model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.weight") )
    rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.bias", F"model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.bias") )
    # MLP 1
    rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.weight", F"model.transformer_module.decoder.layers.{idx}.fc1.weight") )
    rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.bias", F"model.transformer_module.decoder.layers.{idx}.fc1.bias") )
    # MLP 2
    rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.weight", F"model.transformer_module.decoder.layers.{idx}.fc2.weight") )
    rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.bias", F"model.transformer_module.decoder.layers.{idx}.fc2.bias") )
    # layernorm 1 (self-attention layernorm)
    rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.weight", F"model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.weight") )
    rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.bias", F"model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.bias") )
    # layernorm 2 (cross-attention layernorm)
    rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.weight", F"model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.weight") )
    rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.bias", F"model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.bias") )
    # layernorm 3 (final layernorm)
    rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.weight", F"model.transformer_module.decoder.layers.{idx}.final_layer_norm.weight") )
    rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.bias", F"model.transformer_module.decoder.layers.{idx}.final_layer_norm.bias") )

  rename_keys.append(("""sem_seg_head.predictor.transformer.decoder.norm.weight""", """model.transformer_module.decoder.layernorm.weight""") )
  rename_keys.append(("""sem_seg_head.predictor.transformer.decoder.norm.bias""", """model.transformer_module.decoder.layernorm.bias""") )

  # heads on top
  rename_keys.append(("""sem_seg_head.predictor.query_embed.weight""", """model.transformer_module.queries_embedder.weight""") )

  rename_keys.append(("""sem_seg_head.predictor.input_proj.weight""", """model.transformer_module.input_projection.weight""") )
  rename_keys.append(("""sem_seg_head.predictor.input_proj.bias""", """model.transformer_module.input_projection.bias""") )

  rename_keys.append(("""sem_seg_head.predictor.class_embed.weight""", """class_predictor.weight""") )
  rename_keys.append(("""sem_seg_head.predictor.class_embed.bias""", """class_predictor.bias""") )

  for i in range(3 ):
    rename_keys.append((F"sem_seg_head.predictor.mask_embed.layers.{i}.weight", F"mask_embedder.{i}.0.weight") )
    rename_keys.append((F"sem_seg_head.predictor.mask_embed.layers.{i}.bias", F"mask_embedder.{i}.0.bias") )
  # fmt: on

  return rename_keys







def        A_     (						__SCREAMING_SNAKE_CASE						:  str			,     __SCREAMING_SNAKE_CASE						:  int			,     __SCREAMING_SNAKE_CASE						:  Union[str, Any] )				->       Optional[int]:





  """simple docstring"""

  __A		:     Union[str, Any]												=		dct.pop(__SCREAMING_SNAKE_CASE )
  __A		:     Any												=		val







def        A_     (						__SCREAMING_SNAKE_CASE						:  Optional[Any]			,     __SCREAMING_SNAKE_CASE						:  Any )				->       Optional[int]:





  """simple docstring"""

  __A		:     Tuple												=		[int(backbone_config.embed_dim * 2**i ) for i in range(len(backbone_config.depths ) )]
  for i in range(len(backbone_config.depths ) ):
    __A		:     Any												=		num_features[i]
    for j in range(backbone_config.depths[i] ):
      # fmt: off
      # read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
      __A		:     Optional[Any]												=		state_dict.pop(F"backbone.layers.{i}.blocks.{j}.attn.qkv.weight" )
      __A		:     Any												=		state_dict.pop(F"backbone.layers.{i}.blocks.{j}.attn.qkv.bias" )
      # next, add query, keys and values (in that order) to the state dict
      __A		:     Dict												=		in_proj_weight[:dim, :]
      __A		:     str												=		in_proj_bias[: dim]
      __A		:     Optional[int]												=		in_proj_weight[
          dim : dim * 2, :
      ]
      __A		:     Union[str, Any]												=		in_proj_bias[
          dim : dim * 2
      ]
      __A		:     List[Any]												=		in_proj_weight[
          -dim :, :
      ]
      __A		:     str												=		in_proj_bias[-dim :]
      # fmt: on







def        A_     (						__SCREAMING_SNAKE_CASE						:  int			,     __SCREAMING_SNAKE_CASE						:  Optional[int] )				->       Tuple:





  """simple docstring"""

  __A		:     Any												=		config.decoder_config.hidden_size
  for idx in range(config.decoder_config.decoder_layers ):
    # read in weights + bias of self-attention input projection layer (in the original implementation, this is a single matrix + bias)
    __A		:     Any												=		state_dict.pop(F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight" )
    __A		:     Optional[int]												=		state_dict.pop(F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_bias" )
    # next, add query, keys and values (in that order) to the state dict
    __A		:     Union[str, Any]												=		in_proj_weight[: hidden_size, :]
    __A		:     List[str]												=		in_proj_bias[:config.hidden_size]
    __A		:     List[Any]												=		in_proj_weight[hidden_size : hidden_size * 2, :]
    __A		:     Optional[Any]												=		in_proj_bias[hidden_size : hidden_size * 2]
    __A		:     Union[str, Any]												=		in_proj_weight[-hidden_size :, :]
    __A		:     Optional[int]												=		in_proj_bias[-hidden_size :]
    # read in weights + bias of cross-attention input projection layer (in the original implementation, this is a single matrix + bias)
    __A		:     Any												=		state_dict.pop(F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight" )
    __A		:     List[str]												=		state_dict.pop(F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_bias" )
    # next, add query, keys and values (in that order) to the state dict
    __A		:     Any												=		in_proj_weight[: hidden_size, :]
    __A		:     Union[str, Any]												=		in_proj_bias[:config.hidden_size]
    __A		:     str												=		in_proj_weight[hidden_size : hidden_size * 2, :]
    __A		:     List[str]												=		in_proj_bias[hidden_size : hidden_size * 2]
    __A		:     Tuple												=		in_proj_weight[-hidden_size :, :]
    __A		:     str												=		in_proj_bias[-hidden_size :]
  # fmt: on







def        A_     (						)				->       torch.Tensor:





  """simple docstring"""

  __A		:     str												=		"""http://images.cocodataset.org/val2017/000000039769.jpg"""
  __A		:     List[str]												=		Image.open(requests.get(__SCREAMING_SNAKE_CASE			,     stream=__SCREAMING_SNAKE_CASE ).raw )
  return im







@torch.no_grad()
def        A_     (						__SCREAMING_SNAKE_CASE						:  str			,     __SCREAMING_SNAKE_CASE						:  str			,     __SCREAMING_SNAKE_CASE						:  str			,     __SCREAMING_SNAKE_CASE						:  bool = False )				->       str:





  """simple docstring"""

  __A		:     Dict												=		get_maskformer_config(__SCREAMING_SNAKE_CASE )

  # load original state_dict
  with open(__SCREAMING_SNAKE_CASE			,     """rb""" ) as f:
    __A		:     List[str]												=		pickle.load(__SCREAMING_SNAKE_CASE )
  __A		:     List[Any]												=		data["""model"""]

  # for name, param in state_dict.items():
  #     print(name, param.shape)

  # rename keys
  __A		:     Optional[int]												=		create_rename_keys(__SCREAMING_SNAKE_CASE )
  for src, dest in rename_keys:
    rename_key(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )
  read_in_swin_q_k_v(__SCREAMING_SNAKE_CASE			,     config.backbone_config )
  read_in_decoder_q_k_v(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )

  # update to torch tensors
  for key, value in state_dict.items():
    __A		:     Any												=		torch.from_numpy(__SCREAMING_SNAKE_CASE )

  # load 🤗 model
  __A		:     int												=		MaskFormerForInstanceSegmentation(__SCREAMING_SNAKE_CASE )
  model.eval()

  for name, param in model.named_parameters():
    print(__SCREAMING_SNAKE_CASE			,     param.shape )

  __A							,					__A		:     Tuple												=		model.load_state_dict(__SCREAMING_SNAKE_CASE			,     strict=__SCREAMING_SNAKE_CASE )
  assert missing_keys == [
      "model.pixel_level_module.encoder.model.layernorm.weight",
      "model.pixel_level_module.encoder.model.layernorm.bias",
  ]
  assert len(__SCREAMING_SNAKE_CASE ) == 0, F"Unexpected keys: {unexpected_keys}"

  # verify results
  __A		:     Optional[int]												=		prepare_img()
  if "vistas" in model_name:
    __A		:     Tuple												=		65
  elif "cityscapes" in model_name:
    __A		:     Any												=		6_5535
  else:
    __A		:     Any												=		255
  __A		:     Dict												=		True if """ade""" in model_name else False
  __A		:     Tuple												=		MaskFormerImageProcessor(ignore_index=__SCREAMING_SNAKE_CASE			,     reduce_labels=__SCREAMING_SNAKE_CASE )

  __A		:     int												=		image_processor(__SCREAMING_SNAKE_CASE			,     return_tensors="""pt""" )

  __A		:     Union[str, Any]												=		model(**__SCREAMING_SNAKE_CASE )

  print("""Logits:"""			,     outputs.class_queries_logits[0, :3, :3] )

  if model_name == "maskformer-swin-tiny-ade":
    __A		:     int												=		torch.tensor(
        [[3.6_353, -4.4_770, -2.6_065], [0.5_081, -4.2_394, -3.5_343], [2.1_909, -5.0_353, -1.9_323]] )
  assert torch.allclose(outputs.class_queries_logits[0, :3, :3]			,     __SCREAMING_SNAKE_CASE			,     atol=1e-4 )
  print("""Looks ok!""" )

  if pytorch_dump_folder_path is not None:
    print(F"Saving model and image processor to {pytorch_dump_folder_path}" )
    Path(__SCREAMING_SNAKE_CASE ).mkdir(exist_ok=__SCREAMING_SNAKE_CASE )
    model.save_pretrained(__SCREAMING_SNAKE_CASE )
    image_processor.save_pretrained(__SCREAMING_SNAKE_CASE )

  if push_to_hub:
    print("""Pushing model and image processor to the hub...""" )
    model.push_to_hub(F"nielsr/{model_name}" )
    image_processor.push_to_hub(F"nielsr/{model_name}" )


if __name__ == "__main__":
     A__   :		str 		=argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '--model_name',
         default='maskformer-swin-tiny-ade',
         type=str,
         help=('Name of the MaskFormer model you\'d like to convert',),
     )
     parser.add_argument(
         '--checkpoint_path',
         default='/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl',
         type=str,
         help='Path to the original state dict (.pth file).',
     )
     parser.add_argument(
         '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
     )
     parser.add_argument(
         '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
     )

     A__   :		int 		=parser.parse_args()
     convert_maskformer_checkpoint(
         args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
     )





'''simple docstring'''
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple

from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


if is_torch_available():
     A__   :		List[str] 		='pt'
elif is_tf_available():
     A__   :		List[str] 		='tf'
else:
     A__   :		List[str] 		='jax'







class  __A				( _SCREAMING_SNAKE_CASE    ,						unittest.TestCase			):
 lowerCamelCase				       =PerceiverTokenizer
 lowerCamelCase				       =False
 def 		lowercase_(   self			:    int       ):



   """simple docstring"""





   super().setUp()
   __A		:     Optional[int]												=		PerceiverTokenizer()
   tokenizer.save_pretrained(self.tmpdirname       )
 @cached_property
 def 		lowercase_(   self			:    Any       ):



   """simple docstring"""





   return PerceiverTokenizer.from_pretrained("""deepmind/language-perceiver"""       )
 def 		lowercase_(   self			:    Optional[Any]				,   **lowerCamelCase			:    Union[str, Any]       ):



   """simple docstring"""





   return self.tokenizer_class.from_pretrained(self.tmpdirname				,   **lowerCamelCase       )
 def 		lowercase_(   self			:    Any				,   lowerCamelCase			:    List[Any]				,   lowerCamelCase			:    Any=False				,   lowerCamelCase			:    List[Any]=20				,   lowerCamelCase			:    List[str]=5       ):



   """simple docstring"""





   __A		:     Optional[Any]												=		[]
   for i in range(len(lowerCamelCase       )       ):
     try:
       __A		:     str												=		tokenizer.decode([i]				,   clean_up_tokenization_spaces=lowerCamelCase       )
     except UnicodeDecodeError:
       pass
     toks.append((i, tok)       )

   __A		:     List[str]												=		list(filter(lambda lowerCamelCase       : re.match(r"""^[ a-zA-Z]+$"""				,   t[1]       )				,   lowerCamelCase       )       )
   __A		:     List[str]												=		list(filter(lambda lowerCamelCase       : [t[0]] == tokenizer.encode(t[1]				,   add_special_tokens=lowerCamelCase       )				,   lowerCamelCase       )       )
   if max_length is not None and len(lowerCamelCase       ) > max_length:
     __A		:     Optional[Any]												=		toks[:max_length]
   if min_length is not None and len(lowerCamelCase       ) < min_length and len(lowerCamelCase       ) > 0:
     while len(lowerCamelCase       ) < min_length:
       __A		:     Dict												=		toks + toks
        # toks_str = [t[1] for t in toks]
   __A		:     Optional[int]												=		[t[0] for t in toks]

   # Ensure consistency
   __A		:     Optional[Any]												=		tokenizer.decode(lowerCamelCase				,   clean_up_tokenization_spaces=lowerCamelCase       )
   if " " not in output_txt and len(lowerCamelCase       ) > 1:
     __A		:     int												=		(
         tokenizer.decode([toks_ids[0]]				,   clean_up_tokenization_spaces=lowerCamelCase       )
         + """ """
         + tokenizer.decode(toks_ids[1:]				,   clean_up_tokenization_spaces=lowerCamelCase       )
     )
   if with_prefix_space:
     __A		:     Union[str, Any]												=		""" """ + output_txt
   __A		:     Any												=		tokenizer.encode(lowerCamelCase				,   add_special_tokens=lowerCamelCase       )
   return output_txt, output_ids
 def 		lowercase_(   self			:    Optional[Any]       ):



   """simple docstring"""





   __A		:     int												=		self.perceiver_tokenizer
   __A		:     List[str]												=		"""Unicode €."""
   __A		:     Dict												=		tokenizer(lowerCamelCase       )
   __A		:     Any												=		[4, 91, 1_16, 1_11, 1_05, 1_17, 1_06, 1_07, 38, 2_32, 1_36, 1_78, 52, 5]
   self.assertEqual(encoded["""input_ids"""]				,   lowerCamelCase       )

   # decoding
   __A		:     Dict												=		tokenizer.decode(lowerCamelCase       )
   self.assertEqual(lowerCamelCase				,   """[CLS]Unicode €.[SEP]"""       )

   __A		:     int												=		tokenizer("""e è é ê ë"""       )
   __A		:     str												=		[4, 1_07, 38, 2_01, 1_74, 38, 2_01, 1_75, 38, 2_01, 1_76, 38, 2_01, 1_77, 5]
   self.assertEqual(encoded["""input_ids"""]				,   lowerCamelCase       )
   # decoding
   __A		:     Optional[Any]												=		tokenizer.decode(lowerCamelCase       )
   self.assertEqual(lowerCamelCase				,   """[CLS]e è é ê ë[SEP]"""       )

   # encode/decode, but with `encode` instead of `__call__`
   self.assertEqual(tokenizer.decode(tokenizer.encode("""e è é ê ë"""       )       )				,   """[CLS]e è é ê ë[SEP]"""       )
 def 		lowercase_(   self			:    Union[str, Any]       ):



   """simple docstring"""





   __A		:     Optional[Any]												=		self.perceiver_tokenizer
   __A		:     int												=		["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
   # fmt: off
   __A		:     Any												=		[4, 71, 38, 1_14, 1_17, 1_16, 1_09, 38, 1_18, 1_03, 1_20, 1_03, 1_09, 1_20, 1_03, 1_18, 1_10, 38, 1_08, 1_17, 1_20, 38, 1_21, 1_23, 1_15, 1_15, 1_03, 1_20, 1_11, 1_28, 1_03, 1_22, 1_11, 1_17, 1_16, 52, 5, 0]
   # fmt: on
   __A		:     Optional[int]												=		tokenizer(lowerCamelCase				,   padding=lowerCamelCase				,   return_tensors=lowerCamelCase       )
   self.assertIsInstance(lowerCamelCase				,   lowerCamelCase       )

   if FRAMEWORK != "jax":
     __A		:     str												=		list(batch.input_ids.numpy()[0]       )
   else:
     __A		:     Optional[Any]												=		list(batch.input_ids.tolist()[0]       )

   self.assertListEqual(lowerCamelCase				,   lowerCamelCase       )

   self.assertEqual((2, 38)				,   batch.input_ids.shape       )
   self.assertEqual((2, 38)				,   batch.attention_mask.shape       )
 def 		lowercase_(   self			:    Optional[Any]       ):



   """simple docstring"""





   __A		:     str												=		self.perceiver_tokenizer
   __A		:     Any												=		["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
   __A		:     Optional[int]												=		tokenizer(lowerCamelCase				,   padding=lowerCamelCase				,   return_tensors=lowerCamelCase       )
   # check if input_ids are returned and no decoder_input_ids
   self.assertIn("""input_ids"""				,   lowerCamelCase       )
   self.assertIn("""attention_mask"""				,   lowerCamelCase       )
   self.assertNotIn("""decoder_input_ids"""				,   lowerCamelCase       )
   self.assertNotIn("""decoder_attention_mask"""				,   lowerCamelCase       )
 def 		lowercase_(   self			:    Optional[Any]       ):



   """simple docstring"""





   __A		:     Optional[int]												=		self.perceiver_tokenizer
   __A		:     Optional[Any]												=		[
       """Summary of the text.""",
       """Another summary.""",
   ]
   __A		:     Union[str, Any]												=		tokenizer(
       text_target=lowerCamelCase				,   max_length=32				,   padding="""max_length"""				,   truncation=lowerCamelCase				,   return_tensors=lowerCamelCase       )
   self.assertEqual(32				,   targets["""input_ids"""].shape[1]       )
 def 		lowercase_(   self			:    Optional[Any]       ):



   """simple docstring"""





   __A		:     int												=		self.get_tokenizers()
   for tokenizer in tokenizers:
     with self.subTest(f"{tokenizer.__class__.__name__}"       ):
       self.assertNotEqual(tokenizer.model_max_length				,   42       )

        # Now let's start the test
   __A		:     Optional[int]												=		self.get_tokenizers()
   for tokenizer in tokenizers:
     with self.subTest(f"{tokenizer.__class__.__name__}"       ):
       # Isolate this from the other tests because we save additional tokens/etc
       __A		:     Optional[int]												=		tempfile.mkdtemp()

       __A		:     List[str]												=		""" He is very happy, UNwant\u00E9d,running"""
       __A		:     str												=		tokenizer.encode(lowerCamelCase				,   add_special_tokens=lowerCamelCase       )
       tokenizer.save_pretrained(lowerCamelCase       )

       __A		:     Optional[int]												=		tokenizer.__class__.from_pretrained(lowerCamelCase       )
       __A		:     Optional[int]												=		after_tokenizer.encode(lowerCamelCase				,   add_special_tokens=lowerCamelCase       )
       self.assertListEqual(lowerCamelCase				,   lowerCamelCase       )

       shutil.rmtree(lowerCamelCase       )

   __A		:     Optional[Any]												=		self.get_tokenizers(model_max_length=42       )
   for tokenizer in tokenizers:
     with self.subTest(f"{tokenizer.__class__.__name__}"       ):
       # Isolate this from the other tests because we save additional tokens/etc
       __A		:     List[Any]												=		tempfile.mkdtemp()

       __A		:     Optional[Any]												=		""" He is very happy, UNwant\u00E9d,running"""
       tokenizer.add_tokens(["""bim""", """bambam"""]       )
       __A		:     str												=		tokenizer.additional_special_tokens
       additional_special_tokens.append("""new_additional_special_token"""       )
       tokenizer.add_special_tokens({"""additional_special_tokens""": additional_special_tokens}       )
       __A		:     List[Any]												=		tokenizer.encode(lowerCamelCase				,   add_special_tokens=lowerCamelCase       )
       tokenizer.save_pretrained(lowerCamelCase       )

       __A		:     Tuple												=		tokenizer.__class__.from_pretrained(lowerCamelCase       )
       __A		:     Union[str, Any]												=		after_tokenizer.encode(lowerCamelCase				,   add_special_tokens=lowerCamelCase       )
       self.assertListEqual(lowerCamelCase				,   lowerCamelCase       )
       self.assertIn("""new_additional_special_token"""				,   after_tokenizer.additional_special_tokens       )
       self.assertEqual(after_tokenizer.model_max_length				,   42       )

       __A		:     List[str]												=		tokenizer.__class__.from_pretrained(lowerCamelCase				,   model_max_length=43       )
       self.assertEqual(tokenizer.model_max_length				,   43       )

       shutil.rmtree(lowerCamelCase       )
 def 		lowercase_(   self			:    int       ):



   """simple docstring"""





   __A		:     str												=		[]
   if self.test_slow_tokenizer:
     tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())       )

   if self.test_rust_tokenizer:
     tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())       )

   for tokenizer_class, tokenizer_utils in tokenizer_list:
     with tempfile.TemporaryDirectory() as tmp_dir:
       tokenizer_utils.save_pretrained(lowerCamelCase       )

       with open(os.path.join(lowerCamelCase				,   """special_tokens_map.json"""       )				,   encoding="""utf-8"""       ) as json_file:
         __A		:     int												=		json.load(lowerCamelCase       )

       with open(os.path.join(lowerCamelCase				,   """tokenizer_config.json"""       )				,   encoding="""utf-8"""       ) as json_file:
         __A		:     Any												=		json.load(lowerCamelCase       )

       __A		:     str												=		[f"<extra_id_{i}>" for i in range(1_25       )]

       __A		:     int												=		added_tokens_extra_ids + [
           """an_additional_special_token"""
       ]
       __A		:     Any												=		added_tokens_extra_ids + [
           """an_additional_special_token"""
       ]

       with open(os.path.join(lowerCamelCase				,   """special_tokens_map.json"""       )				,   """w"""				,   encoding="""utf-8"""       ) as outfile:
         json.dump(lowerCamelCase				,   lowerCamelCase       )
       with open(os.path.join(lowerCamelCase				,   """tokenizer_config.json"""       )				,   """w"""				,   encoding="""utf-8"""       ) as outfile:
         json.dump(lowerCamelCase				,   lowerCamelCase       )

       # the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
       # into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
       # "special_tokens_map.json" files
       __A		:     int												=		tokenizer_class.from_pretrained(
           lowerCamelCase				,   )
       self.assertIn(
           """an_additional_special_token"""				,   tokenizer_without_change_in_init.additional_special_tokens       )
       self.assertEqual(
           ["""an_additional_special_token"""]				,   tokenizer_without_change_in_init.convert_ids_to_tokens(
               tokenizer_without_change_in_init.convert_tokens_to_ids(["""an_additional_special_token"""]       )       )				,   )

       # Now we test that we can change the value of additional_special_tokens in the from_pretrained
       __A		:     int												=		added_tokens_extra_ids + [AddedToken("""a_new_additional_special_token"""				,   lstrip=lowerCamelCase       )]
       __A		:     str												=		tokenizer_class.from_pretrained(
           lowerCamelCase				,   additional_special_tokens=lowerCamelCase				,   )

       self.assertIn("""a_new_additional_special_token"""				,   tokenizer.additional_special_tokens       )
       self.assertEqual(
           ["""a_new_additional_special_token"""]				,   tokenizer.convert_ids_to_tokens(
               tokenizer.convert_tokens_to_ids(["""a_new_additional_special_token"""]       )       )				,   )
 def 		lowercase_(   self			:    Union[str, Any]       ):



   """simple docstring"""





   __A		:     Dict												=		self.perceiver_tokenizer
   self.assertEqual(tokenizer.decode([1_78]       )				,   """�"""       )
 def 		lowercase_(   self			:    Optional[int]       ):



   """simple docstring"""





   pass
 def 		lowercase_(   self			:    Optional[Any]       ):



   """simple docstring"""





   pass
 def 		lowercase_(   self			:    Any       ):



   """simple docstring"""





   pass
 def 		lowercase_(   self			:    Union[str, Any]       ):



   """simple docstring"""





   pass






 def 		lowercase_(   self			:    Optional[int]       ):



   """simple docstring"""





   __A		:     Optional[int]												=		self.get_tokenizers(fast=lowerCamelCase				,   do_lower_case=lowerCamelCase       )
   for tokenizer in tokenizers:
     with self.subTest(f"{tokenizer.__class__.__name__}"       ):
       __A		:     Optional[int]												=		["""[CLS]""", """t""", """h""", """i""", """s""", """ """, """i""", """s""", """ """, """a""", """ """, """t""", """e""", """s""", """t""", """[SEP]"""]
       __A		:     List[Any]												=		tokenizer.convert_tokens_to_string(lowerCamelCase       )

       self.assertIsInstance(lowerCamelCase				,   lowerCamelCase       )



