


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 (
    ConditionalDetrConfig,
    ConditionalDetrForObjectDetection,
    ConditionalDetrForSegmentation,
    ConditionalDetrImageProcessor,
)
from transformers.utils import logging


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

# here we list all keys to be renamed (original name on the left, our name on the right)
snake_case							:       Optional[int]										=	[]
for i in range(6):
     # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
     rename_keys.append(
         (F"""transformer.encoder.layers.{i}.self_attn.out_proj.weight""", F"""encoder.layers.{i}.self_attn.out_proj.weight""")
     )
     rename_keys.append(
         (F"""transformer.encoder.layers.{i}.self_attn.out_proj.bias""", F"""encoder.layers.{i}.self_attn.out_proj.bias""")
     )
     rename_keys.append((F"""transformer.encoder.layers.{i}.linear1.weight""", F"""encoder.layers.{i}.fc1.weight"""))
     rename_keys.append((F"""transformer.encoder.layers.{i}.linear1.bias""", F"""encoder.layers.{i}.fc1.bias"""))
     rename_keys.append((F"""transformer.encoder.layers.{i}.linear2.weight""", F"""encoder.layers.{i}.fc2.weight"""))
     rename_keys.append((F"""transformer.encoder.layers.{i}.linear2.bias""", F"""encoder.layers.{i}.fc2.bias"""))
     rename_keys.append(
         (F"""transformer.encoder.layers.{i}.norm1.weight""", F"""encoder.layers.{i}.self_attn_layer_norm.weight""")
     )
     rename_keys.append((F"""transformer.encoder.layers.{i}.norm1.bias""", F"""encoder.layers.{i}.self_attn_layer_norm.bias"""))
     rename_keys.append((F"""transformer.encoder.layers.{i}.norm2.weight""", F"""encoder.layers.{i}.final_layer_norm.weight"""))
     rename_keys.append((F"""transformer.encoder.layers.{i}.norm2.bias""", F"""encoder.layers.{i}.final_layer_norm.bias"""))
     # decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
     rename_keys.append(
         (F"""transformer.decoder.layers.{i}.self_attn.out_proj.weight""", F"""decoder.layers.{i}.self_attn.out_proj.weight""")
     )
     rename_keys.append(
         (F"""transformer.decoder.layers.{i}.self_attn.out_proj.bias""", F"""decoder.layers.{i}.self_attn.out_proj.bias""")
     )
     rename_keys.append(
         (
             F"""transformer.decoder.layers.{i}.cross_attn.out_proj.weight""",
             F"""decoder.layers.{i}.encoder_attn.out_proj.weight""",
         )
     )
     rename_keys.append(
         (
             F"""transformer.decoder.layers.{i}.cross_attn.out_proj.bias""",
             F"""decoder.layers.{i}.encoder_attn.out_proj.bias""",
         )
     )
     rename_keys.append((F"""transformer.decoder.layers.{i}.linear1.weight""", F"""decoder.layers.{i}.fc1.weight"""))
     rename_keys.append((F"""transformer.decoder.layers.{i}.linear1.bias""", F"""decoder.layers.{i}.fc1.bias"""))
     rename_keys.append((F"""transformer.decoder.layers.{i}.linear2.weight""", F"""decoder.layers.{i}.fc2.weight"""))
     rename_keys.append((F"""transformer.decoder.layers.{i}.linear2.bias""", F"""decoder.layers.{i}.fc2.bias"""))
     rename_keys.append(
         (F"""transformer.decoder.layers.{i}.norm1.weight""", F"""decoder.layers.{i}.self_attn_layer_norm.weight""")
     )
     rename_keys.append((F"""transformer.decoder.layers.{i}.norm1.bias""", F"""decoder.layers.{i}.self_attn_layer_norm.bias"""))
     rename_keys.append(
         (F"""transformer.decoder.layers.{i}.norm2.weight""", F"""decoder.layers.{i}.encoder_attn_layer_norm.weight""")
     )
     rename_keys.append(
         (F"""transformer.decoder.layers.{i}.norm2.bias""", F"""decoder.layers.{i}.encoder_attn_layer_norm.bias""")
     )
     rename_keys.append((F"""transformer.decoder.layers.{i}.norm3.weight""", F"""decoder.layers.{i}.final_layer_norm.weight"""))
     rename_keys.append((F"""transformer.decoder.layers.{i}.norm3.bias""", F"""decoder.layers.{i}.final_layer_norm.bias"""))

     # q, k, v projections in self/cross-attention in decoder for conditional DETR
     rename_keys.append(
         (F"""transformer.decoder.layers.{i}.sa_qcontent_proj.weight""", F"""decoder.layers.{i}.sa_qcontent_proj.weight""")
     )
     rename_keys.append(
         (F"""transformer.decoder.layers.{i}.sa_kcontent_proj.weight""", F"""decoder.layers.{i}.sa_kcontent_proj.weight""")
     )
     rename_keys.append(
         (F"""transformer.decoder.layers.{i}.sa_qpos_proj.weight""", F"""decoder.layers.{i}.sa_qpos_proj.weight""")
     )
     rename_keys.append(
         (F"""transformer.decoder.layers.{i}.sa_kpos_proj.weight""", F"""decoder.layers.{i}.sa_kpos_proj.weight""")
     )
     rename_keys.append((F"""transformer.decoder.layers.{i}.sa_v_proj.weight""", F"""decoder.layers.{i}.sa_v_proj.weight"""))
     rename_keys.append(
         (F"""transformer.decoder.layers.{i}.ca_qcontent_proj.weight""", F"""decoder.layers.{i}.ca_qcontent_proj.weight""")
     )
     # rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.weight", f"decoder.layers.{i}.ca_qpos_proj.weight"))
     rename_keys.append(
         (F"""transformer.decoder.layers.{i}.ca_kcontent_proj.weight""", F"""decoder.layers.{i}.ca_kcontent_proj.weight""")
     )
     rename_keys.append(
         (F"""transformer.decoder.layers.{i}.ca_kpos_proj.weight""", F"""decoder.layers.{i}.ca_kpos_proj.weight""")
     )
     rename_keys.append((F"""transformer.decoder.layers.{i}.ca_v_proj.weight""", F"""decoder.layers.{i}.ca_v_proj.weight"""))
     rename_keys.append(
         (F"""transformer.decoder.layers.{i}.ca_qpos_sine_proj.weight""", F"""decoder.layers.{i}.ca_qpos_sine_proj.weight""")
     )

     rename_keys.append(
         (F"""transformer.decoder.layers.{i}.sa_qcontent_proj.bias""", F"""decoder.layers.{i}.sa_qcontent_proj.bias""")
     )
     rename_keys.append(
         (F"""transformer.decoder.layers.{i}.sa_kcontent_proj.bias""", F"""decoder.layers.{i}.sa_kcontent_proj.bias""")
     )
     rename_keys.append((F"""transformer.decoder.layers.{i}.sa_qpos_proj.bias""", F"""decoder.layers.{i}.sa_qpos_proj.bias"""))
     rename_keys.append((F"""transformer.decoder.layers.{i}.sa_kpos_proj.bias""", F"""decoder.layers.{i}.sa_kpos_proj.bias"""))
     rename_keys.append((F"""transformer.decoder.layers.{i}.sa_v_proj.bias""", F"""decoder.layers.{i}.sa_v_proj.bias"""))
     rename_keys.append(
         (F"""transformer.decoder.layers.{i}.ca_qcontent_proj.bias""", F"""decoder.layers.{i}.ca_qcontent_proj.bias""")
     )
     # rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.bias", f"decoder.layers.{i}.ca_qpos_proj.bias"))
     rename_keys.append(
         (F"""transformer.decoder.layers.{i}.ca_kcontent_proj.bias""", F"""decoder.layers.{i}.ca_kcontent_proj.bias""")
     )
     rename_keys.append((F"""transformer.decoder.layers.{i}.ca_kpos_proj.bias""", F"""decoder.layers.{i}.ca_kpos_proj.bias"""))
     rename_keys.append((F"""transformer.decoder.layers.{i}.ca_v_proj.bias""", F"""decoder.layers.{i}.ca_v_proj.bias"""))
     rename_keys.append(
         (F"""transformer.decoder.layers.{i}.ca_qpos_sine_proj.bias""", F"""decoder.layers.{i}.ca_qpos_sine_proj.bias""")
     )

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
# for conditional DETR, also convert reference point head and query scale MLP
rename_keys.extend(
    [
        ('''input_proj.weight''', '''input_projection.weight'''),
        ('''input_proj.bias''', '''input_projection.bias'''),
        ('''query_embed.weight''', '''query_position_embeddings.weight'''),
        ('''transformer.decoder.norm.weight''', '''decoder.layernorm.weight'''),
        ('''transformer.decoder.norm.bias''', '''decoder.layernorm.bias'''),
        ('''class_embed.weight''', '''class_labels_classifier.weight'''),
        ('''class_embed.bias''', '''class_labels_classifier.bias'''),
        ('''bbox_embed.layers.0.weight''', '''bbox_predictor.layers.0.weight'''),
        ('''bbox_embed.layers.0.bias''', '''bbox_predictor.layers.0.bias'''),
        ('''bbox_embed.layers.1.weight''', '''bbox_predictor.layers.1.weight'''),
        ('''bbox_embed.layers.1.bias''', '''bbox_predictor.layers.1.bias'''),
        ('''bbox_embed.layers.2.weight''', '''bbox_predictor.layers.2.weight'''),
        ('''bbox_embed.layers.2.bias''', '''bbox_predictor.layers.2.bias'''),
        ('''transformer.decoder.ref_point_head.layers.0.weight''', '''decoder.ref_point_head.layers.0.weight'''),
        ('''transformer.decoder.ref_point_head.layers.0.bias''', '''decoder.ref_point_head.layers.0.bias'''),
        ('''transformer.decoder.ref_point_head.layers.1.weight''', '''decoder.ref_point_head.layers.1.weight'''),
        ('''transformer.decoder.ref_point_head.layers.1.bias''', '''decoder.ref_point_head.layers.1.bias'''),
        ('''transformer.decoder.query_scale.layers.0.weight''', '''decoder.query_scale.layers.0.weight'''),
        ('''transformer.decoder.query_scale.layers.0.bias''', '''decoder.query_scale.layers.0.bias'''),
        ('''transformer.decoder.query_scale.layers.1.weight''', '''decoder.query_scale.layers.1.weight'''),
        ('''transformer.decoder.query_scale.layers.1.bias''', '''decoder.query_scale.layers.1.bias'''),
        ('''transformer.decoder.layers.0.ca_qpos_proj.weight''', '''decoder.layers.0.ca_qpos_proj.weight'''),
        ('''transformer.decoder.layers.0.ca_qpos_proj.bias''', '''decoder.layers.0.ca_qpos_proj.bias'''),
    ]
)
def 							__lowerCamelCase		(					UpperCAmelCase_		:						Optional[Any]					,    UpperCAmelCase_		:						str					,    UpperCAmelCase_		:						Dict		):




      """simple docstring"""





      a		:Tuple									=			state_dict.pop(UpperCAmelCase_		)
      a		:Union[str, Any]									=			val
def 							__lowerCamelCase		(					UpperCAmelCase_		:						Dict		):




      """simple docstring"""





      a		:str									=			OrderedDict()
      for key, value in state_dict.items():
            if "backbone.0.body" in key:
                  a		:List[Any]									=			key.replace('''backbone.0.body'''					,    '''backbone.conv_encoder.model'''		)
                  a		:Any									=			value
            else:
                  a		:Tuple									=			value

      return new_state_dict
def 							__lowerCamelCase		(					UpperCAmelCase_		:						Any					,    UpperCAmelCase_		:						Tuple=False		):




      """simple docstring"""





      a		:List[Any]									=			''''''
      if is_panoptic:
            a		:Union[str, Any]									=			'''conditional_detr.'''

      # first: transformer encoder
      for i in range(6		):
            # read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
            a		:Dict									=			state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''		)
            a		:List[str]									=			state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.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[:256, :]
            a		:str									=			in_proj_bias[:256]
            a		:int									=			in_proj_weight[256:512, :]
            a		:int									=			in_proj_bias[256:512]
            a		:Tuple									=			in_proj_weight[-256:, :]
            a		:List[Any]									=			in_proj_bias[-256:]
def 							__lowerCamelCase		(					):




      """simple docstring"""





      a		:str									=			'''http://images.cocodataset.org/val2017/000000039769.jpg'''
      a		:Tuple									=			Image.open(requests.get(UpperCAmelCase_					,    stream=UpperCAmelCase_		).raw		)

      return im
@torch.no_grad()
def 							__lowerCamelCase		(					UpperCAmelCase_		:						Optional[int]					,    UpperCAmelCase_		:						Tuple		):




      """simple docstring"""





      a		:str									=			ConditionalDetrConfig()
      # set backbone and dilation attributes
      if "resnet101" in model_name:
            a		:Dict									=			'''resnet101'''
      if "dc5" in model_name:
            a		:Optional[int]									=			True
      a		:Union[str, Any]									=			'''panoptic''' in model_name
      if is_panoptic:
            a		:Dict									=			250
      else:
            a		:Union[str, Any]									=			91
            a		:Dict									=			'''huggingface/label-files'''
            a		:str									=			'''coco-detection-id2label.json'''
            a		:Dict									=			json.load(open(hf_hub_download(UpperCAmelCase_					,    UpperCAmelCase_					,    repo_type='''dataset'''		)					,    '''r'''		)		)
            a		:Union[str, Any]									=			{int(UpperCAmelCase_		): v for k, v in idalabel.items()}
            a		:Union[str, Any]									=			idalabel
            a		:Union[str, Any]									=			{v: k for k, v in idalabel.items()}

      # load image processor
      a		:List[Any]									=			'''coco_panoptic''' if is_panoptic else '''coco_detection'''
      a		:List[Any]									=			ConditionalDetrImageProcessor(format=UpperCAmelCase_		)

      # prepare image
      a		:Union[str, Any]									=			prepare_img()
      a		:str									=			image_processor(images=UpperCAmelCase_					,    return_tensors='''pt'''		)
      a		:Tuple									=			encoding['''pixel_values''']

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

      # load original model from torch hub
      a		:Optional[Any]									=			torch.hub.load('''DeppMeng/ConditionalDETR'''					,    UpperCAmelCase_					,    pretrained=UpperCAmelCase_		).eval()
      a		:Dict									=			conditional_detr.state_dict()
      # rename keys
      for src, dest in rename_keys:
            if is_panoptic:
                  a		:int									=			'''conditional_detr.''' + src
            rename_key(UpperCAmelCase_					,    UpperCAmelCase_					,    UpperCAmelCase_		)
      a		:int									=			rename_backbone_keys(UpperCAmelCase_		)
      # query, key and value matrices need special treatment
      read_in_q_k_v(UpperCAmelCase_					,    is_panoptic=UpperCAmelCase_		)
      # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
      a		:Optional[Any]									=			'''conditional_detr.model.''' if is_panoptic else '''model.'''
      for key in state_dict.copy().keys():
            if is_panoptic:
                  if (
                      key.startswith('''conditional_detr'''		)
                      and not key.startswith('''class_labels_classifier'''		)
                      and not key.startswith('''bbox_predictor'''		)
                  ):
                        a		:int									=			state_dict.pop(UpperCAmelCase_		)
                        a		:str									=			val
                  elif "class_labels_classifier" in key or "bbox_predictor" in key:
                        a		:Optional[Any]									=			state_dict.pop(UpperCAmelCase_		)
                        a		:List[str]									=			val
                  elif key.startswith('''bbox_attention'''		) or key.startswith('''mask_head'''		):
                        continue
                  else:
                        a		:Optional[Any]									=			state_dict.pop(UpperCAmelCase_		)
                        a		:Any									=			val
            else:
                  if not key.startswith('''class_labels_classifier'''		) and not key.startswith('''bbox_predictor'''		):
                        a		:Any									=			state_dict.pop(UpperCAmelCase_		)
                        a		:Tuple									=			val
    # finally, create HuggingFace model and load state dict
      a		:Any									=			ConditionalDetrForSegmentation(UpperCAmelCase_		) if is_panoptic else ConditionalDetrForObjectDetection(UpperCAmelCase_		)
      model.load_state_dict(UpperCAmelCase_		)
      model.eval()
      model.push_to_hub(repo_id=UpperCAmelCase_					,    organization='''DepuMeng'''					,    commit_message='''Add model'''		)
      # verify our conversion
      a		:Optional[Any]									=			conditional_detr(UpperCAmelCase_		)
      a		:List[str]									=			model(UpperCAmelCase_		)
      assert torch.allclose(outputs.logits					,    original_outputs['''pred_logits''']					,    atol=1E-4		)
      assert torch.allclose(outputs.pred_boxes					,    original_outputs['''pred_boxes''']					,    atol=1E-4		)
      if is_panoptic:
            assert torch.allclose(outputs.pred_masks					,    original_outputs['''pred_masks''']					,    atol=1E-4		)

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


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

     parser.add_argument(
         '''--model_name''',
         default='''conditional_detr_resnet50''',
         type=str,
         help='''Name of the CONDITIONAL_DETR model you\'d like to convert.''',
     )
     parser.add_argument(
         '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
     )
     snake_case							:       int										=	parser.parse_args()
     convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)









import json
import os
import shutil
import tempfile
import unittest

from transformers import BatchEncoding, CanineTokenizer
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.tokenization_utils import AddedToken
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin







class 						_snake_case    ( _snake_case		,       unittest.TestCase				):
 SCREAMING_SNAKE_CASE__ =			CanineTokenizer
 SCREAMING_SNAKE_CASE__ =			False




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       super().setUp()
       a		:List[str]									=			CanineTokenizer()
       tokenizer.save_pretrained(self.tmpdirname       )




 @cached_property
 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       return CanineTokenizer.from_pretrained('''google/canine-s'''       )




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    **_lowerCamelCase       ):
       a		:Dict									=			self.tokenizer_class.from_pretrained(self.tmpdirname   ,    **_lowerCamelCase       )
       a		:List[Any]									=			1024
       return tokenizer




 @require_torch
 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:Any									=			self.canine_tokenizer
       a		:Tuple									=			['''Life is like a box of chocolates.''', '''You never know what you\'re gonna get.''']
       # fmt: off
       a		:Any									=			[5_7344, 76, 105, 102, 101, 32, 105, 115, 32, 108, 105, 107, 101, 32, 97, 32, 98, 111, 120, 32, 111, 102, 32, 99, 104, 111, 99, 111, 108, 97, 116, 101, 115, 46, 5_7345, 0, 0, 0, 0]
       # fmt: on
       a		:Dict									=			tokenizer(_lowerCamelCase   ,    padding=_lowerCamelCase   ,    return_tensors='''pt'''       )
       self.assertIsInstance(_lowerCamelCase   ,    _lowerCamelCase       )

       a		:str									=			list(batch.input_ids.numpy()[0]       )

       self.assertListEqual(_lowerCamelCase   ,    _lowerCamelCase       )

       self.assertEqual((2, 39)   ,    batch.input_ids.shape       )
       self.assertEqual((2, 39)   ,    batch.attention_mask.shape       )




 @require_torch
 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:List[Any]									=			self.canine_tokenizer
       a		:Optional[int]									=			['''Once there was a man.''', '''He wrote a test in HuggingFace Tranformers.''']
       a		:Dict									=			tokenizer(_lowerCamelCase   ,    padding=_lowerCamelCase   ,    return_tensors='''pt'''       )
       # check if input_ids, attention_mask and token_type_ids are returned
       self.assertIn('''input_ids'''   ,    _lowerCamelCase       )
       self.assertIn('''attention_mask'''   ,    _lowerCamelCase       )
       self.assertIn('''token_type_ids'''   ,    _lowerCamelCase       )




 @require_torch
 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:Any									=			self.canine_tokenizer
       a		:str									=			[
           '''What\'s the weater?''',
           '''It\'s about 25 degrees.''',
       ]
       a		:List[str]									=			tokenizer(
           text_target=_lowerCamelCase   ,    max_length=32   ,    padding='''max_length'''   ,    truncation=_lowerCamelCase   ,    return_tensors='''pt'''       )
       self.assertEqual(32   ,    targets['''input_ids'''].shape[1]       )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       # safety check on max_len default value so we are sure the test works
       a		:Tuple									=			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		:Tuple									=			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		:List[str]									=			tempfile.mkdtemp()

                   a		:Any									=			''' He is very happy, UNwant\u00E9d,running'''
                   a		:Dict									=			tokenizer.encode(_lowerCamelCase   ,    add_special_tokens=_lowerCamelCase       )
                   tokenizer.save_pretrained(_lowerCamelCase       )

                   a		:Optional[Any]									=			tokenizer.__class__.from_pretrained(_lowerCamelCase       )
                   a		:str									=			after_tokenizer.encode(_lowerCamelCase   ,    add_special_tokens=_lowerCamelCase       )
                   self.assertListEqual(_lowerCamelCase   ,    _lowerCamelCase       )

                   shutil.rmtree(_lowerCamelCase       )

       a		:str									=			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		:Any									=			tempfile.mkdtemp()

                   a		:Optional[Any]									=			''' He is very happy, UNwant\u00E9d,running'''

                   a		:Optional[int]									=			tokenizer.additional_special_tokens

                   # We can add a new special token for Canine as follows:
                   a		:str									=			chr(0Xe_0_0_7       )
                   additional_special_tokens.append(_lowerCamelCase       )
                   tokenizer.add_special_tokens({'''additional_special_tokens''': additional_special_tokens}       )
                   a		:Tuple									=			tokenizer.encode(_lowerCamelCase   ,    add_special_tokens=_lowerCamelCase       )
                   tokenizer.save_pretrained(_lowerCamelCase       )

                   a		:int									=			tokenizer.__class__.from_pretrained(_lowerCamelCase       )
                   a		:Dict									=			after_tokenizer.encode(_lowerCamelCase   ,    add_special_tokens=_lowerCamelCase       )
                   self.assertListEqual(_lowerCamelCase   ,    _lowerCamelCase       )
                   self.assertIn(_lowerCamelCase   ,    after_tokenizer.additional_special_tokens       )
                   self.assertEqual(after_tokenizer.model_max_length   ,    42       )

                   a		:str									=			tokenizer.__class__.from_pretrained(_lowerCamelCase   ,    model_max_length=43       )
                   self.assertEqual(tokenizer.model_max_length   ,    43       )

                   shutil.rmtree(_lowerCamelCase       )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:Tuple									=			self.get_tokenizers(do_lower_case=_lowerCamelCase       )
       for tokenizer in tokenizers:
             with self.subTest(F'''{tokenizer.__class__.__name__}'''       ):
                   a				,							a		:Tuple									=			self.get_clean_sequence(_lowerCamelCase       )

                   # a special token for Canine can be defined as follows:
                   a		:Tuple									=			0Xe_0_0_5
                   a		:Optional[int]									=			chr(_lowerCamelCase       )

                   tokenizer.add_special_tokens({'''cls_token''': special_token}       )
                   a		:Optional[int]									=			tokenizer.encode(_lowerCamelCase   ,    add_special_tokens=_lowerCamelCase       )
                   self.assertEqual(len(_lowerCamelCase       )   ,    1       )

                   a		:List[Any]									=			tokenizer.decode(ids + encoded_special_token   ,    clean_up_tokenization_spaces=_lowerCamelCase       )
                   a		:List[str]									=			tokenizer.encode(_lowerCamelCase   ,    add_special_tokens=_lowerCamelCase       )

                   a		:Tuple									=			tokenizer.encode(_lowerCamelCase   ,    add_special_tokens=_lowerCamelCase       )
                   a		:Optional[Any]									=			tokenizer.encode(_lowerCamelCase   ,    add_special_tokens=_lowerCamelCase       )
                   self.assertEqual(_lowerCamelCase   ,    input_encoded + special_token_id       )

                   a		:Any									=			tokenizer.decode(_lowerCamelCase   ,    skip_special_tokens=_lowerCamelCase       )
                   self.assertTrue(special_token not in decoded       )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:int									=			self.get_tokenizers(do_lower_case=_lowerCamelCase       )
       for tokenizer in tokenizers:
             with self.subTest(F'''{tokenizer.__class__.__name__}'''       ):
                   a		:int									=			chr(0Xe_0_0_5       )
                   a		:str									=			chr(0Xe_0_0_6       )

                   # `add_tokens` method stores special tokens only in `tokenizer.unique_no_split_tokens`. (in tokenization_utils.py)
                   tokenizer.add_tokens([SPECIAL_TOKEN_1]   ,    special_tokens=_lowerCamelCase       )
                   # `add_special_tokens` method stores special tokens in `tokenizer.additional_special_tokens`,
                   # which also occur in `tokenizer.all_special_tokens`. (in tokenization_utils_base.py)
                   tokenizer.add_special_tokens({'''additional_special_tokens''': [SPECIAL_TOKEN_2]}       )

                   a		:Optional[int]									=			tokenizer.tokenize(_lowerCamelCase       )
                   a		:Optional[Any]									=			tokenizer.tokenize(_lowerCamelCase       )

                   self.assertEqual(len(_lowerCamelCase       )   ,    1       )
                   self.assertEqual(len(_lowerCamelCase       )   ,    1       )
                   self.assertEqual(token_a[0]   ,    _lowerCamelCase       )
                   self.assertEqual(token_a[0]   ,    _lowerCamelCase       )




 @require_tokenizers
 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:Optional[Any]									=			self.get_tokenizers(do_lower_case=_lowerCamelCase       )
       for tokenizer in tokenizers:
             with self.subTest(F'''{tokenizer.__class__.__name__}'''       ):
                   # a special token for Canine can be defined as follows:
                   a		:Optional[int]									=			0Xe_0_0_6
                   a		:List[str]									=			chr(_lowerCamelCase       )

                   a		:Optional[int]									=			AddedToken(_lowerCamelCase   ,    lstrip=_lowerCamelCase       )
                   tokenizer.add_special_tokens({'''additional_special_tokens''': [new_token]}       )

                   with tempfile.TemporaryDirectory() as tmp_dir_name:
                         tokenizer.save_pretrained(_lowerCamelCase       )
                         tokenizer.from_pretrained(_lowerCamelCase       )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:int									=			[]
       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		:Optional[Any]									=			json.load(_lowerCamelCase       )

                   with open(os.path.join(_lowerCamelCase   ,    '''tokenizer_config.json'''       )   ,    encoding='''utf-8'''       ) as json_file:
                         a		:Tuple									=			json.load(_lowerCamelCase       )

                   # a special token for Canine can be defined as follows:
                   a		:int									=			0Xe_0_0_6
                   a		:Optional[Any]									=			chr(_lowerCamelCase       )

                   a		:Union[str, Any]									=			[new_token_a]
                   a		:Optional[int]									=			[new_token_a]

                   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		:str									=			tokenizer_class.from_pretrained(_lowerCamelCase   ,    extra_ids=0       )
                   self.assertIn(_lowerCamelCase   ,    tokenizer_without_change_in_init.additional_special_tokens       )
                   # self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
                   self.assertEqual(
                       [new_token_a]   ,    tokenizer_without_change_in_init.convert_ids_to_tokens(
                           tokenizer_without_change_in_init.convert_tokens_to_ids([new_token_a]       )       )   ,    )

                   a		:Optional[int]									=			0Xe_0_0_7
                   a		:Any									=			chr(_lowerCamelCase       )
                   # Now we test that we can change the value of additional_special_tokens in the from_pretrained
                   a		:Tuple									=			[AddedToken(_lowerCamelCase   ,    lstrip=_lowerCamelCase       )]
                   a		:Optional[Any]									=			tokenizer_class.from_pretrained(
                       _lowerCamelCase   ,    additional_special_tokens=_lowerCamelCase   ,    extra_ids=0       )

                   self.assertIn(_lowerCamelCase   ,    tokenizer.additional_special_tokens       )
                   # self.assertIn(new_token_2,tokenizer.get_vocab()) # ByT5Tokenization no vocab
                   self.assertEqual(
                       [new_token_a]   ,    tokenizer.convert_ids_to_tokens(tokenizer.convert_tokens_to_ids([new_token_a]       )       )       )




 @require_tokenizers
 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:List[Any]									=			self.get_tokenizers(do_lower_case=_lowerCamelCase       )
       for tokenizer in tokenizers:
             with self.subTest(F'''{tokenizer.__class__.__name__}'''       ):
                   a		:List[str]									=			'''hello world'''
                   if self.space_between_special_tokens:
                         a		:Optional[Any]									=			'''[CLS] hello world [SEP]'''
                   else:
                         a		:Tuple									=			input
                   a		:Any									=			tokenizer.encode(_lowerCamelCase   ,    add_special_tokens=_lowerCamelCase       )
                   a		:List[Any]									=			tokenizer.decode(_lowerCamelCase   ,    spaces_between_special_tokens=self.space_between_special_tokens       )
                   self.assertIn(_lowerCamelCase   ,    [output, output.lower()]       )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:int									=			self.get_tokenizers()
       for tokenizer in tokenizers:
             with self.subTest(F'''{tokenizer.__class__.__name__}'''       ):
                   a		:Any									=			[
                       '''bos_token''',
                       '''eos_token''',
                       '''unk_token''',
                       '''sep_token''',
                       '''pad_token''',
                       '''cls_token''',
                       '''mask_token''',
                   ]

       a		:str									=			'''a'''
       a		:List[Any]									=			ord(_lowerCamelCase       )

       for attr in attributes_list:
             setattr(_lowerCamelCase   ,    attr + '''_id'''   ,    _lowerCamelCase       )
             self.assertEqual(getattr(_lowerCamelCase   ,    _lowerCamelCase       )   ,    _lowerCamelCase       )
             self.assertEqual(getattr(_lowerCamelCase   ,    attr + '''_id'''       )   ,    _lowerCamelCase       )

             setattr(_lowerCamelCase   ,    attr + '''_id'''   ,    _lowerCamelCase       )
             self.assertEqual(getattr(_lowerCamelCase   ,    _lowerCamelCase       )   ,    _lowerCamelCase       )
             self.assertEqual(getattr(_lowerCamelCase   ,    attr + '''_id'''       )   ,    _lowerCamelCase       )

       setattr(_lowerCamelCase   ,    '''additional_special_tokens_ids'''   ,    []       )
       self.assertListEqual(getattr(_lowerCamelCase   ,    '''additional_special_tokens'''       )   ,    []       )
       self.assertListEqual(getattr(_lowerCamelCase   ,    '''additional_special_tokens_ids'''       )   ,    []       )

       a		:List[Any]									=			0Xe_0_0_6
       a		:str									=			chr(_lowerCamelCase       )
       setattr(_lowerCamelCase   ,    '''additional_special_tokens_ids'''   ,    [additional_special_token_id]       )
       self.assertListEqual(getattr(_lowerCamelCase   ,    '''additional_special_tokens'''       )   ,    [additional_special_token]       )
       self.assertListEqual(getattr(_lowerCamelCase   ,    '''additional_special_tokens_ids'''       )   ,    [additional_special_token_id]       )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       pass




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       pass




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       pass




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       pass




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       pass




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       pass




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       pass




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       pass





