
"""simple docstring"""






import argparse
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from PIL import Image

from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging


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






def 						_snake_case       (	lowercase__		:  Optional[Any]		)  ->				str:


			'''simple docstring'''





			lowerCAmelCase_    :str			     =  OrderedDict()
			for key, value in state_dict.items():
						if key.startswith("""module.encoder"""		):
									lowerCAmelCase_    :Union[str, Any]			     =  key.replace("""module.encoder"""      ,     """glpn.encoder"""		)
						if key.startswith("""module.decoder"""		):
									lowerCAmelCase_    :Any			     =  key.replace("""module.decoder"""      ,     """decoder.stages"""		)
						if "patch_embed" in key:
									# replace for example patch_embed1 by patch_embeddings.0
									lowerCAmelCase_    :List[str]			     =  key[key.find("""patch_embed"""		) + len("""patch_embed"""		)]
									lowerCAmelCase_    :Tuple			     =  key.replace(f"""patch_embed{idx}"""      ,     f"""patch_embeddings.{int(lowercase__		)-1}"""		)
						if "norm" in key:
									lowerCAmelCase_    :Dict			     =  key.replace("""norm"""      ,     """layer_norm"""		)
						if "glpn.encoder.layer_norm" in key:
									# replace for example layer_norm1 by layer_norm.0
									lowerCAmelCase_    :str			     =  key[key.find("""glpn.encoder.layer_norm"""		) + len("""glpn.encoder.layer_norm"""		)]
									lowerCAmelCase_    :str			     =  key.replace(f"""layer_norm{idx}"""      ,     f"""layer_norm.{int(lowercase__		)-1}"""		)
						if "layer_norm1" in key:
									lowerCAmelCase_    :Optional[Any]			     =  key.replace("""layer_norm1"""      ,     """layer_norm_1"""		)
						if "layer_norm2" in key:
									lowerCAmelCase_    :str			     =  key.replace("""layer_norm2"""      ,     """layer_norm_2"""		)
						if "block" in key:
									# replace for example block1 by block.0
									lowerCAmelCase_    :List[str]			     =  key[key.find("""block"""		) + len("""block"""		)]
									lowerCAmelCase_    :int			     =  key.replace(f"""block{idx}"""      ,     f"""block.{int(lowercase__		)-1}"""		)
						if "attn.q" in key:
									lowerCAmelCase_    :Tuple			     =  key.replace("""attn.q"""      ,     """attention.self.query"""		)
						if "attn.proj" in key:
									lowerCAmelCase_    :Optional[int]			     =  key.replace("""attn.proj"""      ,     """attention.output.dense"""		)
						if "attn" in key:
									lowerCAmelCase_    :str			     =  key.replace("""attn"""      ,     """attention.self"""		)
						if "fc1" in key:
									lowerCAmelCase_    :List[Any]			     =  key.replace("""fc1"""      ,     """dense1"""		)
						if "fc2" in key:
									lowerCAmelCase_    :Optional[Any]			     =  key.replace("""fc2"""      ,     """dense2"""		)
						if "linear_pred" in key:
									lowerCAmelCase_    :List[str]			     =  key.replace("""linear_pred"""      ,     """classifier"""		)
						if "linear_fuse" in key:
									lowerCAmelCase_    :str			     =  key.replace("""linear_fuse.conv"""      ,     """linear_fuse"""		)
									lowerCAmelCase_    :Any			     =  key.replace("""linear_fuse.bn"""      ,     """batch_norm"""		)
						if "linear_c" in key:
									# replace for example linear_c4 by linear_c.3
									lowerCAmelCase_    :str			     =  key[key.find("""linear_c"""		) + len("""linear_c"""		)]
									lowerCAmelCase_    :Optional[int]			     =  key.replace(f"""linear_c{idx}"""      ,     f"""linear_c.{int(lowercase__		)-1}"""		)
						if "bot_conv" in key:
									lowerCAmelCase_    :Union[str, Any]			     =  key.replace("""bot_conv"""      ,     """0.convolution"""		)
						if "skip_conv1" in key:
									lowerCAmelCase_    :int			     =  key.replace("""skip_conv1"""      ,     """1.convolution"""		)
						if "skip_conv2" in key:
									lowerCAmelCase_    :str			     =  key.replace("""skip_conv2"""      ,     """2.convolution"""		)
						if "fusion1" in key:
									lowerCAmelCase_    :Any			     =  key.replace("""fusion1"""      ,     """1.fusion"""		)
						if "fusion2" in key:
									lowerCAmelCase_    :List[str]			     =  key.replace("""fusion2"""      ,     """2.fusion"""		)
						if "fusion3" in key:
									lowerCAmelCase_    :Dict			     =  key.replace("""fusion3"""      ,     """3.fusion"""		)
						if "fusion" in key and "conv" in key:
									lowerCAmelCase_    :Any			     =  key.replace("""conv"""      ,     """convolutional_layer"""		)
						if key.startswith("""module.last_layer_depth"""		):
									lowerCAmelCase_    :Tuple			     =  key.replace("""module.last_layer_depth"""      ,     """head.head"""		)
						lowerCAmelCase_    :List[Any]			     =  value

			return new_state_dict






def 						_snake_case       (	lowercase__		:  str      ,     lowercase__		:  int		)  ->				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)
									lowerCAmelCase_    :Tuple			     =  state_dict.pop(f"""glpn.encoder.block.{i}.{j}.attention.self.kv.weight"""		)
									lowerCAmelCase_    :Tuple			     =  state_dict.pop(f"""glpn.encoder.block.{i}.{j}.attention.self.kv.bias"""		)
									# next, add keys and values (in that order) to the state dict
									lowerCAmelCase_    :Optional[Any]			     =  kv_weight[
									    : config.hidden_sizes[i], :
									]
									lowerCAmelCase_    :Union[str, Any]			     =  kv_bias[: config.hidden_sizes[i]]
									lowerCAmelCase_    :List[Any]			     =  kv_weight[
									    config.hidden_sizes[i] :, :
									]
									lowerCAmelCase_    :int			     =  kv_bias[config.hidden_sizes[i] :]






def 						_snake_case       (	)  ->				Any:


			'''simple docstring'''





			lowerCAmelCase_    :int			     =  """http://images.cocodataset.org/val2017/000000039769.jpg"""
			lowerCAmelCase_    :Optional[Any]			     =  Image.open(requests.get(lowercase__      ,     stream=lowercase__		).raw		)

			return image






@torch.no_grad()
def 						_snake_case       (	lowercase__		:  List[Any]      ,     lowercase__		:  str      ,     lowercase__		:  Dict=False      ,     lowercase__		:  List[Any]=None		)  ->				int:


			'''simple docstring'''





			lowerCAmelCase_    :int			     =  GLPNConfig(hidden_sizes=[6_4, 1_2_8, 3_2_0, 5_1_2]      ,     decoder_hidden_size=6_4      ,     depths=[3, 8, 2_7, 3]		)

			# load image processor (only resize + rescale)
			lowerCAmelCase_    :Union[str, Any]			     =  GLPNImageProcessor()

			# prepare image
			lowerCAmelCase_    :List[Any]			     =  prepare_img()
			lowerCAmelCase_    :int			     =  image_processor(images=lowercase__      ,     return_tensors="""pt"""		).pixel_values

			logger.info("""Converting model..."""		)

			# load original state dict
			lowerCAmelCase_    :Tuple			     =  torch.load(lowercase__      ,     map_location=torch.device("""cpu"""		)		)

			# rename keys
			lowerCAmelCase_    :Union[str, Any]			     =  rename_keys(lowercase__		)

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

			# create HuggingFace model and load state dict
			lowerCAmelCase_    :List[Any]			     =  GLPNForDepthEstimation(lowercase__		)
			model.load_state_dict(lowercase__		)
			model.eval()

			# forward pass
			lowerCAmelCase_    :Dict			     =  model(lowercase__		)
			lowerCAmelCase_    :Tuple			     =  outputs.predicted_depth

			# verify output
			if model_name is not None:
						if "nyu" in model_name:
									lowerCAmelCase_    :Optional[Any]			     =  torch.tensor(
									    [[4.4147, 4.0873, 4.0673], [3.7890, 3.2881, 3.1525], [3.7674, 3.5423, 3.4913]]		)
						elif "kitti" in model_name:
									lowerCAmelCase_    :Any			     =  torch.tensor(
									    [[3.4291, 2.7865, 2.5151], [3.2841, 2.7021, 2.3502], [3.1147, 2.4625, 2.2481]]		)
						else:
									raise ValueError(f"""Unknown model name: {model_name}"""		)

						lowerCAmelCase_    :Union[str, Any]			     =  torch.Size([1, 4_8_0, 6_4_0]		)

						assert predicted_depth.shape == expected_shape
						assert torch.allclose(predicted_depth[0, :3, :3]      ,     lowercase__      ,     atol=1E-4		)
						print("""Looks ok!"""		)

			# finally, push to hub if required
			if push_to_hub:
						logger.info("""Pushing model and image processor to the hub..."""		)
						model.push_to_hub(
						    repo_path_or_name=Path(lowercase__      ,     lowercase__		)      ,     organization="""nielsr"""      ,     commit_message="""Add model"""      ,     use_temp_dir=lowercase__      ,     )
						image_processor.push_to_hub(
						    repo_path_or_name=Path(lowercase__      ,     lowercase__		)      ,     organization="""nielsr"""      ,     commit_message="""Add image processor"""      ,     use_temp_dir=lowercase__      ,     )


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

				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.'
				)
				parser.add_argument(
				    '--push_to_hub', action='store_true', help='Whether to upload the model to the HuggingFace hub.'
				)
				parser.add_argument(
				    '--model_name',
				    default='glpn-kitti',
				    type=str,
				    help='Name of the model in case you\'re pushing to the hub.',
				)
				__UpperCAmelCase       =  parser.parse_args()
				convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)







import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple

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

from ...test_tokenization_common import TokenizerTesterMixin


if is_torch_available():
		__a  :     Tuple       =  """pt"""
elif is_tf_available():
		__a  :     int       =  """tf"""
else:
		__a  :     Tuple       =  """jax"""




class    _UpperCamelCase      (						_UpperCAmelCase						,unittest.TestCase ):


			"""simple docstring"""






			__a  : List[Any]			 =						ByTaTokenizer
			__a  : str			 =						False





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					List[Any]:

							'''simple docstring'''


							super().setUp()
							__lowercase 			=							ByTaTokenizer()
							tokenizer.save_pretrained(self.tmpdirname						)





			@cached_property
			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Any:

							'''simple docstring'''


							return ByTaTokenizer.from_pretrained('''google/byt5-small'''						)





			def        _SCREAMING_SNAKE_CASE			(   self      ,   **lowerCAmelCase__						)     ->					ByTaTokenizer:

							'''simple docstring'''


							return self.tokenizer_class.from_pretrained(self.tmpdirname      ,   **lowerCAmelCase__						)





			def        _SCREAMING_SNAKE_CASE			(   self      ,   lowerCAmelCase__      ,   lowerCAmelCase__=False      ,   lowerCAmelCase__=20      ,   lowerCAmelCase__=5						)     ->					Tuple[str, list]:

							'''simple docstring'''


							__lowercase 			=							[]
							for i in range(len(lowerCAmelCase__						)						):
											try:
															__lowercase 			=							tokenizer.decode([i]      ,   clean_up_tokenization_spaces=lowerCAmelCase__						)
											except UnicodeDecodeError:
															pass
											toks.append((i, tok)						)

							__lowercase 			=							list(filter(lambda lowerCAmelCase__						: re.match(R'''^[ a-zA-Z]+$'''      ,   t[1]						)      ,   lowerCAmelCase__						)						)
							__lowercase 			=							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:
											__lowercase 			=							toks[:max_length]
							if min_length is not None and len(lowerCAmelCase__						) < min_length and len(lowerCAmelCase__						) > 0:
											while len(lowerCAmelCase__						) < min_length:
															__lowercase 			=							toks + toks
        # toks_str = [t[1] for t in toks]
							__lowercase 			=							[t[0] for t in toks]

							# Ensure consistency
							__lowercase 			=							tokenizer.decode(lowerCAmelCase__      ,   clean_up_tokenization_spaces=lowerCAmelCase__						)
							if " " not in output_txt and len(lowerCAmelCase__						) > 1:
											__lowercase 			=							(
											    tokenizer.decode([toks_ids[0]]      ,   clean_up_tokenization_spaces=lowerCAmelCase__						)
											    + ''' '''
											    + tokenizer.decode(toks_ids[1:]      ,   clean_up_tokenization_spaces=lowerCAmelCase__						)
											)
							if with_prefix_space:
											__lowercase 			=							''' ''' + output_txt
							__lowercase 			=							tokenizer.encode(lowerCAmelCase__      ,   add_special_tokens=lowerCAmelCase__						)
							return output_txt, output_ids





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Tuple:

							'''simple docstring'''


							__lowercase 			=							self.ta_base_tokenizer
							__lowercase 			=							tokenizer(['''hi</s>''', '''I went to the gym</s>''', '''</s>''']						)
							__lowercase 			=							tokenizer(['''hi''', '''I went to the gym''', '''''']						)
							self.assertListEqual(batch_with_eos_added['''input_ids''']      ,   batch_without_eos_added['''input_ids''']						)





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Dict:

							'''simple docstring'''


							__lowercase 			=							self.ta_base_tokenizer
							__lowercase 			=							'''Unicode €.'''
							__lowercase 			=							tokenizer(lowerCAmelCase__						)
							__lowercase 			=							[88, 1_13, 1_08, 1_02, 1_14, 1_03, 1_04, 35, 2_29, 1_33, 1_75, 49, 1]
							self.assertEqual(encoded['''input_ids''']      ,   lowerCAmelCase__						)

							# decoding
							__lowercase 			=							tokenizer.decode(lowerCAmelCase__						)
							self.assertEqual(lowerCAmelCase__      ,   '''Unicode €.</s>'''						)

							__lowercase 			=							tokenizer('''e è é ê ë'''						)
							__lowercase 			=							[1_04, 35, 1_98, 1_71, 35, 1_98, 1_72, 35, 1_98, 1_73, 35, 1_98, 1_74, 1]
							self.assertEqual(encoded['''input_ids''']      ,   lowerCAmelCase__						)
							# decoding
							__lowercase 			=							tokenizer.decode(lowerCAmelCase__						)
							self.assertEqual(lowerCAmelCase__      ,   '''e è é ê ë</s>'''						)

							# encode/decode, but with `encode` instead of `__call__`
							self.assertEqual(tokenizer.decode(tokenizer.encode('''e è é ê ë'''						)						)      ,   '''e è é ê ë</s>'''						)





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					List[Any]:

							'''simple docstring'''


							__lowercase 			=							self.ta_base_tokenizer
							__lowercase 			=							['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
							# fmt: off
							__lowercase 			=							[68, 35, 1_11, 1_14, 1_13, 1_06, 35, 1_15, 1_00, 1_17, 1_00, 1_06, 1_17, 1_00, 1_15, 1_07, 35, 1_05, 1_14, 1_17, 35, 1_18, 1_20, 1_12, 1_12, 1_00, 1_17, 1_08, 1_25, 1_00, 1_19, 1_08, 1_14, 1_13, 49, 1, 0]
							# fmt: on
							__lowercase 			=							tokenizer(lowerCAmelCase__      ,   padding=lowerCAmelCase__      ,   return_tensors=lowerCAmelCase__						)
							self.assertIsInstance(lowerCAmelCase__      ,   lowerCAmelCase__						)

							if FRAMEWORK != "jax":
											__lowercase 			=							list(batch.input_ids.numpy()[0]						)
							else:
											__lowercase 			=							list(batch.input_ids.tolist()[0]						)

							self.assertListEqual(lowerCAmelCase__      ,   lowerCAmelCase__						)

							self.assertEqual((2, 37)      ,   batch.input_ids.shape						)
							self.assertEqual((2, 37)      ,   batch.attention_mask.shape						)





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					int:

							'''simple docstring'''


							__lowercase 			=							self.ta_base_tokenizer
							__lowercase 			=							['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
							__lowercase 			=							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        _SCREAMING_SNAKE_CASE			(   self						)     ->					Tuple:

							'''simple docstring'''


							__lowercase 			=							self.ta_base_tokenizer
							__lowercase 			=							[
							    '''Summary of the text.''',
							    '''Another summary.''',
							]
							__lowercase 			=							tokenizer(
							    text_target=lowerCAmelCase__      ,   max_length=32      ,   padding='''max_length'''      ,   truncation=lowerCAmelCase__      ,   return_tensors=lowerCAmelCase__						)
							self.assertEqual(32      ,   targets['''input_ids'''].shape[1]						)





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					int:

							'''simple docstring'''


							__lowercase 			=							self.ta_base_tokenizer
							__lowercase 			=							['''A long paragraph for summarization. </s>''']
							__lowercase 			=							['''Summary of the text. </s>''']
							# fmt: off
							__lowercase 			=							[68, 35, 1_11, 1_14, 1_13, 1_06, 35, 1_15, 1_00, 1_17, 1_00, 1_06, 1_17, 1_00, 1_15, 1_07, 35, 1_05, 1_14, 1_17, 35, 1_18, 1_20, 1_12, 1_12, 1_00, 1_17, 1_08, 1_25, 1_00, 1_19, 1_08, 1_14, 1_13, 49, 35, 1]
							__lowercase 			=							[86, 1_20, 1_12, 1_12, 1_00, 1_17, 1_24, 35, 1_14, 1_05, 35, 1_19, 1_07, 1_04, 35, 1_19, 1_04, 1_23, 1_19, 49, 35, 1]
							# fmt: on

							__lowercase 			=							tokenizer(lowerCAmelCase__      ,   text_target=lowerCAmelCase__						)

							self.assertEqual(lowerCAmelCase__      ,   batch['''input_ids'''][0]						)
							self.assertEqual(lowerCAmelCase__      ,   batch['''labels'''][0]						)





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					List[str]:

							'''simple docstring'''


							__lowercase 			=							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
							__lowercase 			=							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
															__lowercase 			=							tempfile.mkdtemp()

															__lowercase 			=							''' He is very happy, UNwant\u00E9d,running'''
															__lowercase 			=							tokenizer.encode(lowerCAmelCase__      ,   add_special_tokens=lowerCAmelCase__						)
															tokenizer.save_pretrained(lowerCAmelCase__						)

															__lowercase 			=							tokenizer.__class__.from_pretrained(lowerCAmelCase__						)
															__lowercase 			=							after_tokenizer.encode(lowerCAmelCase__      ,   add_special_tokens=lowerCAmelCase__						)
															self.assertListEqual(lowerCAmelCase__      ,   lowerCAmelCase__						)

															shutil.rmtree(lowerCAmelCase__						)

							__lowercase 			=							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
															__lowercase 			=							tempfile.mkdtemp()

															__lowercase 			=							''' He is very happy, UNwant\u00E9d,running'''
															tokenizer.add_tokens(['''bim''', '''bambam''']						)
															__lowercase 			=							tokenizer.additional_special_tokens
															additional_special_tokens.append('''new_additional_special_token'''						)
															tokenizer.add_special_tokens({'''additional_special_tokens''': additional_special_tokens}						)
															__lowercase 			=							tokenizer.encode(lowerCAmelCase__      ,   add_special_tokens=lowerCAmelCase__						)
															tokenizer.save_pretrained(lowerCAmelCase__						)

															__lowercase 			=							tokenizer.__class__.from_pretrained(lowerCAmelCase__						)
															__lowercase 			=							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						)

															__lowercase 			=							tokenizer.__class__.from_pretrained(lowerCAmelCase__      ,   model_max_length=43						)
															self.assertEqual(tokenizer.model_max_length      ,   43						)

															shutil.rmtree(lowerCAmelCase__						)





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Dict:

							'''simple docstring'''


							__lowercase 			=							[]
							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:
																			__lowercase 			=							json.load(lowerCAmelCase__						)

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

															__lowercase 			=							[F"<extra_id_{i}>" for i in range(1_25						)]

															__lowercase 			=							added_tokens_extra_ids + [
															    '''an_additional_special_token'''
															]
															__lowercase 			=							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
															__lowercase 			=							tokenizer_class.from_pretrained(
															    lowerCAmelCase__      ,   )
															self.assertIn(
															    '''an_additional_special_token'''      ,   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(
															    ['''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
															__lowercase 			=							added_tokens_extra_ids + [AddedToken('''a_new_additional_special_token'''      ,   lstrip=lowerCAmelCase__						)]
															__lowercase 			=							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        _SCREAMING_SNAKE_CASE			(   self						)     ->					List[str]:

							'''simple docstring'''


							__lowercase 			=							[]
							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__						)

															__lowercase 			=							tokenizer_class.from_pretrained(lowerCAmelCase__						)

															self.assertTrue(tokenizer.decode([2_55]						) == ''''''						)





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Optional[int]:

							'''simple docstring'''


							pass





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Union[str, Any]:

							'''simple docstring'''


							pass





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					int:

							'''simple docstring'''


							pass





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					str:

							'''simple docstring'''


							pass





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Optional[int]:

							'''simple docstring'''


							__lowercase 			=							self.get_tokenizers(fast=lowerCAmelCase__      ,   do_lower_case=lowerCAmelCase__						)
							for tokenizer in tokenizers:
											with self.subTest(F"{tokenizer.__class__.__name__}"						):
															__lowercase 			=							['''t''', '''h''', '''i''', '''s''', ''' ''', '''i''', '''s''', ''' ''', '''a''', ''' ''', '''t''', '''e''', '''x''', '''t''', '''</s>''']
															__lowercase 			=							tokenizer.convert_tokens_to_string(lowerCAmelCase__						)

															self.assertIsInstance(lowerCAmelCase__      ,   lowerCAmelCase__						)





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					int:

							'''simple docstring'''


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

															__lowercase 			=							0
															__lowercase 			=							tokenizer.convert_ids_to_tokens(
															    lowerCAmelCase__      ,   skip_special_tokens=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'''						)      ,   []						)

															setattr(lowerCAmelCase__      ,   '''additional_special_tokens_ids'''      ,   [token_id_to_test_setters]						)
															self.assertListEqual(getattr(lowerCAmelCase__      ,   '''additional_special_tokens'''						)      ,   [token_to_test_setters]						)
															self.assertListEqual(getattr(lowerCAmelCase__      ,   '''additional_special_tokens_ids'''						)      ,   [token_id_to_test_setters]						)