

import argparse
import json

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

from transformers import ConvNextConfig, SegformerImageProcessor, UperNetConfig, UperNetForSemanticSegmentation



def    __UpperCAmelCase (	__a      :			str	)			->     List[str]:

					"""simple docstring"""

					_a	:    Union[str, Any]         =		384
					if "tiny" in model_name:
										_a	:    Dict         =		[3, 3, 9, 3]
										_a	:    Optional[int]         =		[96, 192, 384, 768]
					if "small" in model_name:
										_a	:    Optional[int]         =		[3, 3, 27, 3]
										_a	:    List[Any]         =		[96, 192, 384, 768]
					if "base" in model_name:
										_a	:    Optional[int]         =		[3, 3, 27, 3]
										_a	:    Any         =		[128, 256, 512, 1_024]
										_a	:    Optional[int]         =		512
					if "large" in model_name:
										_a	:    List[str]         =		[3, 3, 27, 3]
										_a	:    Tuple         =		[192, 384, 768, 1_536]
										_a	:    List[Any]         =		768
					if "xlarge" in model_name:
										_a	:    str         =		[3, 3, 27, 3]
										_a	:    str         =		[256, 512, 1_024, 2_048]
										_a	:    str         =		1_024

					# set label information
					_a	:    Dict         =		150
					_a	:    str         =		'''huggingface/label-files'''
					_a	:    Optional[Any]         =		'''ade20k-id2label.json'''
					_a	:    Any         =		json.load(open(hf_hub_download(__a		,__a		,repo_type='''dataset'''	)		,'''r'''	)	)
					_a	:    Tuple         =		{int(__a	): v for k, v in idalabel.items()}
					_a	:    int         =		{v: k for k, v in idalabel.items()}

					_a	:    Tuple         =		ConvNextConfig(
					    depths=__a		,hidden_sizes=__a		,out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']	)
					_a	:    Union[str, Any]         =		UperNetConfig(
					    backbone_config=__a		,auxiliary_in_channels=__a		,num_labels=__a		,idalabel=__a		,labelaid=__a		,)

					return config



def    __UpperCAmelCase (	__a      :			List[Any]	)			->     List[str]:

					"""simple docstring"""

					_a	:    Union[str, Any]         =		[]

					# fmt: off
					# stem
					rename_keys.append(('''backbone.downsample_layers.0.0.weight''', '''backbone.embeddings.patch_embeddings.weight''')	)
					rename_keys.append(('''backbone.downsample_layers.0.0.bias''', '''backbone.embeddings.patch_embeddings.bias''')	)
					rename_keys.append(('''backbone.downsample_layers.0.1.weight''', '''backbone.embeddings.layernorm.weight''')	)
					rename_keys.append(('''backbone.downsample_layers.0.1.bias''', '''backbone.embeddings.layernorm.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.stages.{i}.{j}.gamma""", F"""backbone.encoder.stages.{i}.layers.{j}.layer_scale_parameter""")	)
															rename_keys.append((F"""backbone.stages.{i}.{j}.depthwise_conv.weight""", F"""backbone.encoder.stages.{i}.layers.{j}.dwconv.weight""")	)
															rename_keys.append((F"""backbone.stages.{i}.{j}.depthwise_conv.bias""", F"""backbone.encoder.stages.{i}.layers.{j}.dwconv.bias""")	)
															rename_keys.append((F"""backbone.stages.{i}.{j}.norm.weight""", F"""backbone.encoder.stages.{i}.layers.{j}.layernorm.weight""")	)
															rename_keys.append((F"""backbone.stages.{i}.{j}.norm.bias""", F"""backbone.encoder.stages.{i}.layers.{j}.layernorm.bias""")	)
															rename_keys.append((F"""backbone.stages.{i}.{j}.pointwise_conv1.weight""", F"""backbone.encoder.stages.{i}.layers.{j}.pwconv1.weight""")	)
															rename_keys.append((F"""backbone.stages.{i}.{j}.pointwise_conv1.bias""", F"""backbone.encoder.stages.{i}.layers.{j}.pwconv1.bias""")	)
															rename_keys.append((F"""backbone.stages.{i}.{j}.pointwise_conv2.weight""", F"""backbone.encoder.stages.{i}.layers.{j}.pwconv2.weight""")	)
															rename_keys.append((F"""backbone.stages.{i}.{j}.pointwise_conv2.bias""", F"""backbone.encoder.stages.{i}.layers.{j}.pwconv2.bias""")	)
										if i > 0:
															rename_keys.append((F"""backbone.downsample_layers.{i}.0.weight""", F"""backbone.encoder.stages.{i}.downsampling_layer.0.weight""")	)
															rename_keys.append((F"""backbone.downsample_layers.{i}.0.bias""", F"""backbone.encoder.stages.{i}.downsampling_layer.0.bias""")	)
															rename_keys.append((F"""backbone.downsample_layers.{i}.1.weight""", F"""backbone.encoder.stages.{i}.downsampling_layer.1.weight""")	)
															rename_keys.append((F"""backbone.downsample_layers.{i}.1.bias""", F"""backbone.encoder.stages.{i}.downsampling_layer.1.bias""")	)

										rename_keys.append((F"""backbone.norm{i}.weight""", F"""backbone.hidden_states_norms.stage{i+1}.weight""")	)
										rename_keys.append((F"""backbone.norm{i}.bias""", F"""backbone.hidden_states_norms.stage{i+1}.bias""")	)

					# decode head
					rename_keys.extend(
					    [
					        ('''decode_head.conv_seg.weight''', '''decode_head.classifier.weight'''),
					        ('''decode_head.conv_seg.bias''', '''decode_head.classifier.bias'''),
					        ('''auxiliary_head.conv_seg.weight''', '''auxiliary_head.classifier.weight'''),
					        ('''auxiliary_head.conv_seg.bias''', '''auxiliary_head.classifier.bias'''),
					    ]	)
					# fmt: on

					return rename_keys



def    __UpperCAmelCase (	__a      :			Optional[Any]		,__a      :			List[Any]		,__a      :			str	)			->     Any:

					"""simple docstring"""

					_a	:    Union[str, Any]         =		dct.pop(__a	)
					_a	:    Tuple         =		val



def    __UpperCAmelCase (	__a      :			Union[str, Any]		,__a      :			str		,__a      :			Any	)			->     Any:

					"""simple docstring"""

					_a	:    str         =		{
					    '''upernet-convnext-tiny''': '''https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_tiny_fp16_512x512_160k_ade20k/upernet_convnext_tiny_fp16_512x512_160k_ade20k_20220227_124553-cad485de.pth''',
					    '''upernet-convnext-small''': '''https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_small_fp16_512x512_160k_ade20k/upernet_convnext_small_fp16_512x512_160k_ade20k_20220227_131208-1b1e394f.pth''',
					    '''upernet-convnext-base''': '''https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_base_fp16_512x512_160k_ade20k/upernet_convnext_base_fp16_512x512_160k_ade20k_20220227_181227-02a24fc6.pth''',
					    '''upernet-convnext-large''': '''https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_large_fp16_640x640_160k_ade20k/upernet_convnext_large_fp16_640x640_160k_ade20k_20220226_040532-e57aa54d.pth''',
					    '''upernet-convnext-xlarge''': '''https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_xlarge_fp16_640x640_160k_ade20k/upernet_convnext_xlarge_fp16_640x640_160k_ade20k_20220226_080344-95fc38c2.pth''',
					}
					_a	:    Any         =		model_name_to_url[model_name]
					_a	:    Union[str, Any]         =		torch.hub.load_state_dict_from_url(__a		,map_location='''cpu'''	)['''state_dict''']

					_a	:    List[Any]         =		get_upernet_config(__a	)
					_a	:    Optional[int]         =		UperNetForSemanticSegmentation(__a	)
					model.eval()

					# replace "bn" => "batch_norm"
					for key in state_dict.copy().keys():
										_a	:    List[str]         =		state_dict.pop(__a	)
										if "bn" in key:
															_a	:    Dict         =		key.replace('''bn'''		,'''batch_norm'''	)
										_a	:    Any         =		val

					# rename keys
					_a	:    List[Any]         =		create_rename_keys(__a	)
					for src, dest in rename_keys:
										rename_key(__a		,__a		,__a	)

					model.load_state_dict(__a	)

					# verify on image
					_a	:    int         =		'''https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg'''
					_a	:    List[str]         =		Image.open(requests.get(__a		,stream=__a	).raw	).convert('''RGB'''	)

					_a	:    List[str]         =		SegformerImageProcessor()
					_a	:    Optional[Any]         =		processor(__a		,return_tensors='''pt'''	).pixel_values

					with torch.no_grad():
										_a	:    Optional[Any]         =		model(__a	)

					if model_name == "upernet-convnext-tiny":
										_a	:    List[Any]         =		torch.tensor(
										    [[-8.81_10, -8.81_10, -8.65_21], [-8.81_10, -8.81_10, -8.65_21], [-8.77_46, -8.77_46, -8.61_30]]	)
					elif model_name == "upernet-convnext-small":
										_a	:    List[Any]         =		torch.tensor(
										    [[-8.82_36, -8.82_36, -8.67_71], [-8.82_36, -8.82_36, -8.67_71], [-8.76_38, -8.76_38, -8.62_40]]	)
					elif model_name == "upernet-convnext-base":
										_a	:    int         =		torch.tensor(
										    [[-8.85_58, -8.85_58, -8.69_05], [-8.85_58, -8.85_58, -8.69_05], [-8.76_69, -8.76_69, -8.60_21]]	)
					elif model_name == "upernet-convnext-large":
										_a	:    str         =		torch.tensor(
										    [[-8.66_60, -8.66_60, -8.62_10], [-8.66_60, -8.66_60, -8.62_10], [-8.63_10, -8.63_10, -8.59_64]]	)
					elif model_name == "upernet-convnext-xlarge":
										_a	:    List[Any]         =		torch.tensor(
										    [[-8.49_80, -8.49_80, -8.39_77], [-8.49_80, -8.49_80, -8.39_77], [-8.43_79, -8.43_79, -8.34_12]]	)
					print('''Logits:'''		,outputs.logits[0, 0, :3, :3]	)
					assert torch.allclose(outputs.logits[0, 0, :3, :3]		,__a		,atol=1E-4	)
					print('''Looks ok!'''	)

					if pytorch_dump_folder_path is not None:
										print(F"""Saving model {model_name} to {pytorch_dump_folder_path}"""	)
										model.save_pretrained(__a	)
										print(F"""Saving processor to {pytorch_dump_folder_path}"""	)
										processor.save_pretrained(__a	)

					if push_to_hub:
										print(F"""Pushing model and processor for {model_name} to hub"""	)
										model.push_to_hub(F"""openmmlab/{model_name}"""	)
										processor.push_to_hub(F"""openmmlab/{model_name}"""	)


if __name__ == "__main__":
			a__				=		argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '''--model_name''',
			    default='''upernet-convnext-tiny''',
			    type=str,
			    choices=[f'''upernet-convnext-{size}''' for size in ['''tiny''', '''small''', '''base''', '''large''', '''xlarge''']],
			    help='''Name of the ConvNext UperNet model you\'d like to convert.''',
			)
			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__				=		parser.parse_args()
			convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






import json
import os
import unittest

from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class 				UpperCAmelCase_		(			__lowercase				,       unittest.TestCase   ):
							"""simple docstring"""







							UpperCAmelCase__       :						int	    =    GPTaTokenizer
							UpperCAmelCase__       :						str	    =    GPTaTokenizerFast
							UpperCAmelCase__       :						Tuple	    =    True
							UpperCAmelCase__       :						List[Any]	    =    {"add_prefix_space": True}
							UpperCAmelCase__       :						int	    =    False


							def 				__lowercase				(			self       )    ->    int:
												super().setUp()

												# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
												_a	:    str         =		[
												    '''l''',
												    '''o''',
												    '''w''',
												    '''e''',
												    '''r''',
												    '''s''',
												    '''t''',
												    '''i''',
												    '''d''',
												    '''n''',
												    '''\u0120''',
												    '''\u0120l''',
												    '''\u0120n''',
												    '''\u0120lo''',
												    '''\u0120low''',
												    '''er''',
												    '''\u0120lowest''',
												    '''\u0120newer''',
												    '''\u0120wider''',
												    '''<unk>''',
												    '''<|endoftext|>''',
												]
												_a	:    List[Any]         =		dict(zip(_a       ,							range(len(_a       )       )       )       )
												_a	:    Tuple         =		['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
												_a	:    List[str]         =		{'''unk_token''': '''<unk>'''}

												_a	:    List[str]         =		os.path.join(self.tmpdirname       ,							VOCAB_FILES_NAMES['''vocab_file''']       )
												_a	:    Tuple         =		os.path.join(self.tmpdirname       ,							VOCAB_FILES_NAMES['''merges_file''']       )
												with open(self.vocab_file       ,							'''w'''       ,							encoding='''utf-8'''       ) as fp:
																	fp.write(json.dumps(_a       ) + '''\n'''       )
												with open(self.merges_file       ,							'''w'''       ,							encoding='''utf-8'''       ) as fp:
																	fp.write('''\n'''.join(_a       )       )


							def 				__lowercase				(			self       ,							**_a       )    ->    List[str]:
												kwargs.update(self.special_tokens_map       )
												return GPTaTokenizer.from_pretrained(self.tmpdirname       ,							**_a       )


							def 				__lowercase				(			self       ,							**_a       )    ->    List[Any]:
												kwargs.update(self.special_tokens_map       )
												return GPTaTokenizerFast.from_pretrained(self.tmpdirname       ,							**_a       )


							def 				__lowercase				(			self       ,							_a       )    ->    Optional[Any]:
												_a	:    Tuple         =		'''lower newer'''
												_a	:    Tuple         =		'''lower newer'''
												return input_text, output_text


							def 				__lowercase				(			self       )    ->    Any:
												_a	:    Any         =		GPTaTokenizer(self.vocab_file       ,							self.merges_file       ,							**self.special_tokens_map       )
												_a	:    int         =		'''lower newer'''
												_a	:    int         =		['''\u0120low''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
												_a	:    List[str]         =		tokenizer.tokenize(_a       ,							add_prefix_space=_a       )
												self.assertListEqual(_a       ,							_a       )

												_a	:    Optional[int]         =		tokens + [tokenizer.unk_token]
												_a	:    List[Any]         =		[1_4, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
												self.assertListEqual(tokenizer.convert_tokens_to_ids(_a       )       ,							_a       )


							def 				__lowercase				(			self       )    ->    Optional[int]:
												if not self.test_rust_tokenizer:
																	return

												_a	:    int         =		self.get_tokenizer()
												_a	:    Tuple         =		self.get_rust_tokenizer(add_prefix_space=_a       )

												_a	:    Tuple         =		'''lower newer'''

												# Testing tokenization
												_a	:    List[str]         =		tokenizer.tokenize(_a       ,							add_prefix_space=_a       )
												_a	:    Optional[Any]         =		rust_tokenizer.tokenize(_a       )
												self.assertListEqual(_a       ,							_a       )

												# Testing conversion to ids without special tokens
												_a	:    List[Any]         =		tokenizer.encode(_a       ,							add_special_tokens=_a       ,							add_prefix_space=_a       )
												_a	:    List[Any]         =		rust_tokenizer.encode(_a       ,							add_special_tokens=_a       )
												self.assertListEqual(_a       ,							_a       )

												# Testing conversion to ids with special tokens
												_a	:    List[str]         =		self.get_rust_tokenizer(add_prefix_space=_a       )
												_a	:    List[str]         =		tokenizer.encode(_a       ,							add_prefix_space=_a       )
												_a	:    Tuple         =		rust_tokenizer.encode(_a       )
												self.assertListEqual(_a       ,							_a       )

												# Testing the unknown token
												_a	:    Optional[Any]         =		tokens + [rust_tokenizer.unk_token]
												_a	:    str         =		[1_4, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
												self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(_a       )       ,							_a       )


							def 				__lowercase				(			self       ,							*_a       ,							**_a       )    ->    int:
												# It's very difficult to mix/test pretokenization with byte-level
												# And get both GPT2 and Roberta to work at the same time (mostly an issue of adding a space before the string)
												pass


							def 				__lowercase				(			self       ,							_a=1_5       )    ->    str:
												for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																	with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""       ):
																						_a	:    Optional[int]         =		self.rust_tokenizer_class.from_pretrained(_a       ,							**_a       )

																						# Simple input
																						_a	:    List[str]         =		'''This is a simple input'''
																						_a	:    Optional[Any]         =		['''This is a simple input 1''', '''This is a simple input 2''']
																						_a	:    Tuple         =		('''This is a simple input''', '''This is a pair''')
																						_a	:    Optional[Any]         =		[
																						    ('''This is a simple input 1''', '''This is a simple input 2'''),
																						    ('''This is a simple pair 1''', '''This is a simple pair 2'''),
																						]

																						# Simple input tests
																						self.assertRaises(_a       ,							tokenizer_r.encode       ,							_a       ,							max_length=_a       ,							padding='''max_length'''       )

																						# Simple input
																						self.assertRaises(_a       ,							tokenizer_r.encode_plus       ,							_a       ,							max_length=_a       ,							padding='''max_length'''       )

																						# Simple input
																						self.assertRaises(
																						    _a       ,							tokenizer_r.batch_encode_plus       ,							_a       ,							max_length=_a       ,							padding='''max_length'''       ,							)

																						# Pair input
																						self.assertRaises(_a       ,							tokenizer_r.encode       ,							_a       ,							max_length=_a       ,							padding='''max_length'''       )

																						# Pair input
																						self.assertRaises(_a       ,							tokenizer_r.encode_plus       ,							_a       ,							max_length=_a       ,							padding='''max_length'''       )

																						# Pair input
																						self.assertRaises(
																						    _a       ,							tokenizer_r.batch_encode_plus       ,							_a       ,							max_length=_a       ,							padding='''max_length'''       ,							)


							def 				__lowercase				(			self       )    ->    List[Any]:
												_a	:    int         =		GPTaTokenizer.from_pretrained(self.tmpdirname       ,							pad_token='''<pad>'''       )

												# Simple input
												_a	:    int         =		'''This is a simple input'''
												_a	:    int         =		['''This is a simple input looooooooong''', '''This is a simple input''']
												_a	:    Any         =		('''This is a simple input''', '''This is a pair''')
												_a	:    Dict         =		[
												    ('''This is a simple input loooooong''', '''This is a simple input'''),
												    ('''This is a simple pair loooooong''', '''This is a simple pair'''),
												]

												_a	:    Optional[int]         =		tokenizer.pad_token_id

												_a	:    List[Any]         =		tokenizer(_a       ,							padding='''max_length'''       ,							max_length=3_0       ,							return_tensors='''np'''       )
												_a	:    Optional[int]         =		tokenizer(_a       ,							padding=_a       ,							truncate=_a       ,							return_tensors='''np'''       )
												_a	:    int         =		tokenizer(*_a       ,							padding='''max_length'''       ,							max_length=6_0       ,							return_tensors='''np'''       )
												_a	:    Union[str, Any]         =		tokenizer(_a       ,							padding=_a       ,							truncate=_a       ,							return_tensors='''np'''       )

												# s
												# test single string max_length padding
												self.assertEqual(out_s['''input_ids'''].shape[-1]       ,							3_0       )
												self.assertTrue(pad_token_id in out_s['''input_ids''']       )
												self.assertTrue(0 in out_s['''attention_mask''']       )

												# s2
												# test automatic padding
												self.assertEqual(out_sa['''input_ids'''].shape[-1]       ,							3_3       )
												# long slice doesn't have padding
												self.assertFalse(pad_token_id in out_sa['''input_ids'''][0]       )
												self.assertFalse(0 in out_sa['''attention_mask'''][0]       )
												# short slice does have padding
												self.assertTrue(pad_token_id in out_sa['''input_ids'''][1]       )
												self.assertTrue(0 in out_sa['''attention_mask'''][1]       )

												# p
												# test single pair max_length padding
												self.assertEqual(out_p['''input_ids'''].shape[-1]       ,							6_0       )
												self.assertTrue(pad_token_id in out_p['''input_ids''']       )
												self.assertTrue(0 in out_p['''attention_mask''']       )

												# p2
												# test automatic padding pair
												self.assertEqual(out_pa['''input_ids'''].shape[-1]       ,							5_2       )
												# long slice pair doesn't have padding
												self.assertFalse(pad_token_id in out_pa['''input_ids'''][0]       )
												self.assertFalse(0 in out_pa['''attention_mask'''][0]       )
												# short slice pair does have padding
												self.assertTrue(pad_token_id in out_pa['''input_ids'''][1]       )
												self.assertTrue(0 in out_pa['''attention_mask'''][1]       )


							def 				__lowercase				(			self       )    ->    Any:
												_a	:    Union[str, Any]         =		'''$$$'''
												_a	:    List[Any]         =		GPTaTokenizer.from_pretrained(self.tmpdirname       ,							bos_token=_a       ,							add_bos_token=_a       )

												_a	:    Any         =		'''This is a simple input'''
												_a	:    str         =		['''This is a simple input 1''', '''This is a simple input 2''']

												_a	:    Tuple         =		tokenizer.bos_token_id

												_a	:    Optional[Any]         =		tokenizer(_a       )
												_a	:    str         =		tokenizer(_a       )

												self.assertEqual(out_s.input_ids[0]       ,							_a       )
												self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids       )       )

												_a	:    str         =		tokenizer.decode(out_s.input_ids       )
												_a	:    Optional[Any]         =		tokenizer.batch_decode(out_sa.input_ids       )

												self.assertEqual(decode_s.split()[0]       ,							_a       )
												self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa       )       )


							def 				__lowercase				(			self       )    ->    str:
												pass




							def 				__lowercase				(			self       )    ->    Dict:
												# TODO: change to self.get_tokenizers() when the fast version is implemented
												_a	:    Optional[int]         =		[self.get_tokenizer(do_lower_case=_a       ,							add_bos_token=_a       )]
												for tokenizer in tokenizers:
																	with self.subTest(F"""{tokenizer.__class__.__name__}"""       ):
																						_a	:    Tuple         =		'''Encode this.'''
																						_a	:    Optional[Any]         =		'''This one too please.'''
																						_a	:    List[Any]         =		tokenizer.encode(_a       ,							add_special_tokens=_a       )
																						encoded_sequence += tokenizer.encode(_a       ,							add_special_tokens=_a       )
																						_a	:    List[str]         =		tokenizer.encode_plus(
																						    _a       ,							_a       ,							add_special_tokens=_a       ,							return_special_tokens_mask=_a       ,							)
																						_a	:    int         =		encoded_sequence_dict['''input_ids''']
																						_a	:    int         =		encoded_sequence_dict['''special_tokens_mask''']
																						self.assertEqual(len(_a       )       ,							len(_a       )       )

																						_a	:    List[Any]         =		[
																						    (x if not special_tokens_mask[i] else None) for i, x in enumerate(_a       )
																						]
																						_a	:    str         =		[x for x in filtered_sequence if x is not None]
																						self.assertEqual(_a       ,							_a       )



@require_tokenizers
class 				UpperCAmelCase_		(			unittest.TestCase   ):
							"""simple docstring"""







							def 				__lowercase				(			self       )    ->    List[Any]:
												# More context:
												# https://huggingface.co/wjmcat/opt-350m-paddle/discussions/1
												# https://huggingface.slack.com/archives/C01N44FJDHT/p1653511495183519
												# https://github.com/huggingface/transformers/pull/17088#discussion_r871246439

												_a	:    Any         =		AutoTokenizer.from_pretrained('''facebook/opt-350m'''       ,							from_slow=_a       )
												_a	:    int         =		'''A photo of a cat'''

												_a	:    List[Any]         =		tokenizer.encode(
												    _a       ,							)
												self.assertEqual(_a       ,							[2, 2_5_0, 1_3_4_5, 9, 1_0, 4_7_5_8]       )
												tokenizer.save_pretrained('''test_opt'''       )

												_a	:    Union[str, Any]         =		AutoTokenizer.from_pretrained('''./test_opt'''       )
												_a	:    Any         =		tokenizer.encode(
												    _a       ,							)
												self.assertEqual(_a       ,							[2, 2_5_0, 1_3_4_5, 9, 1_0, 4_7_5_8]       )


							def 				__lowercase				(			self       )    ->    int:
												_a	:    Union[str, Any]         =		AutoTokenizer.from_pretrained('''facebook/opt-350m'''       ,							use_slow=_a       )
												_a	:    Any         =		'''A photo of a cat'''

												_a	:    Optional[int]         =		tokenizer.encode(
												    _a       ,							)
												# Same as above
												self.assertEqual(_a       ,							[2, 2_5_0, 1_3_4_5, 9, 1_0, 4_7_5_8]       )




							@unittest.skip('''This test is failing because of a bug in the fast tokenizer'''       )
							def 				__lowercase				(			self       )    ->    Any:
												_a	:    Union[str, Any]         =		AutoTokenizer.from_pretrained('''facebook/opt-350m'''       ,							from_slow=_a       )

												_a	:    Optional[Any]         =		'''bos'''
												_a	:    Optional[Any]         =		tokenizer.get_vocab()['''bos''']

												_a	:    str         =		'''A photo of a cat'''
												_a	:    int         =		tokenizer.encode(
												    _a       ,							)
												# We changed the bos token
												self.assertEqual(_a       ,							[3_1_9_5_7, 2_5_0, 1_3_4_5, 9, 1_0, 4_7_5_8]       )
												tokenizer.save_pretrained('''./tok'''       )
												_a	:    Dict         =		AutoTokenizer.from_pretrained('''./tok'''       )
												self.assertTrue(tokenizer.is_fast       )
												_a	:    Union[str, Any]         =		tokenizer.encode(
												    _a       ,							)
												self.assertEqual(_a       ,							[3_1_9_5_7, 2_5_0, 1_3_4_5, 9, 1_0, 4_7_5_8]       )



