


import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors


@require_tokenizers
class 			_a				(     UpperCamelCase__	,      unittest.TestCase     ):
							_lowercase      :     Tuple     =					MvpTokenizer
							_lowercase      :     Tuple     =					MvpTokenizerFast
							_lowercase      :     Dict     =					True
							_lowercase      :     str     =					filter_roberta_detectors

							def  lowerCamelCase_					(				self:      Optional[Any]				)     ->  int:





									"""simple docstring"""







									super().setUp()
									lowercase__				= [
									    '''l''',
									    '''o''',
									    '''w''',
									    '''e''',
									    '''r''',
									    '''s''',
									    '''t''',
									    '''i''',
									    '''d''',
									    '''n''',
									    '''\u0120''',
									    '''\u0120l''',
									    '''\u0120n''',
									    '''\u0120lo''',
									    '''\u0120low''',
									    '''er''',
									    '''\u0120lowest''',
									    '''\u0120newer''',
									    '''\u0120wider''',
									    '''<unk>''',
									]
									lowercase__				= dict(zip(UpperCamelCase_       ,		range(len(UpperCamelCase_				)				)				)				)
									lowercase__				= ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
									lowercase__				= {'''unk_token''': '''<unk>'''}

									lowercase__				= os.path.join(self.tmpdirname       ,		VOCAB_FILES_NAMES['''vocab_file''']				)
									lowercase__				= 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(UpperCamelCase_				) + '''\n'''				)
									with open(self.merges_file       ,		'''w'''       ,		encoding='''utf-8'''				) as fp:
											fp.write('''\n'''.join(UpperCamelCase_				)				)

							def  lowerCamelCase_					(				self:      str       ,		**UpperCamelCase_:      Optional[Any]				)     ->  List[Any]:





									"""simple docstring"""







									kwargs.update(self.special_tokens_map				)
									return self.tokenizer_class.from_pretrained(self.tmpdirname       ,		**UpperCamelCase_				)

							def  lowerCamelCase_					(				self:      Optional[Any]       ,		**UpperCamelCase_:      int				)     ->  int:





									"""simple docstring"""







									kwargs.update(self.special_tokens_map				)
									return self.rust_tokenizer_class.from_pretrained(self.tmpdirname       ,		**UpperCamelCase_				)

							def  lowerCamelCase_					(				self:      str       ,		UpperCamelCase_:      Dict				)     ->  Tuple:





									"""simple docstring"""







									return "lower newer", "lower newer"

							@cached_property
							def  lowerCamelCase_					(				self:      int				)     ->  List[str]:





									"""simple docstring"""







									return MvpTokenizer.from_pretrained('''RUCAIBox/mvp'''				)

							@cached_property
							def  lowerCamelCase_					(				self:      Tuple				)     ->  Dict:





									"""simple docstring"""







									return MvpTokenizerFast.from_pretrained('''RUCAIBox/mvp'''				)

							@require_torch
							def  lowerCamelCase_					(				self:      Any				)     ->  Optional[Any]:





									"""simple docstring"""







									lowercase__				= ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
									lowercase__				= [0, 250, 251, 17_818, 13, 39_186, 1_938, 4, 2]

									for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											lowercase__				= tokenizer(UpperCamelCase_       ,		max_length=len(UpperCamelCase_				)       ,		padding=UpperCamelCase_       ,		return_tensors='''pt'''				)
											self.assertIsInstance(UpperCamelCase_       ,		UpperCamelCase_				)

											self.assertEqual((2, 9)       ,		batch.input_ids.shape				)
											self.assertEqual((2, 9)       ,		batch.attention_mask.shape				)
											lowercase__				= batch.input_ids.tolist()[0]
											self.assertListEqual(UpperCamelCase_       ,		UpperCamelCase_				)
											# Test that special tokens are reset

							@require_torch
							def  lowerCamelCase_					(				self:      Optional[Any]				)     ->  List[Any]:





									"""simple docstring"""







									lowercase__				= ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
									for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											lowercase__				= tokenizer(UpperCamelCase_       ,		padding=UpperCamelCase_       ,		return_tensors='''pt'''				)
											# check if input_ids are returned and no labels
											self.assertIn('''input_ids'''       ,		UpperCamelCase_				)
											self.assertIn('''attention_mask'''       ,		UpperCamelCase_				)
											self.assertNotIn('''labels'''       ,		UpperCamelCase_				)
											self.assertNotIn('''decoder_attention_mask'''       ,		UpperCamelCase_				)

							@require_torch
							def  lowerCamelCase_					(				self:      Union[str, Any]				)     ->  Dict:





									"""simple docstring"""







									lowercase__				= [
									    '''Summary of the text.''',
									    '''Another summary.''',
									]
									for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											lowercase__				= tokenizer(text_target=UpperCamelCase_       ,		max_length=32       ,		padding='''max_length'''       ,		return_tensors='''pt'''				)
											self.assertEqual(32       ,		targets['''input_ids'''].shape[1]				)

							@require_torch
							def  lowerCamelCase_					(				self:      Union[str, Any]				)     ->  Optional[int]:





									"""simple docstring"""







									for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											lowercase__				= tokenizer(
											    ['''I am a small frog''' * 1_024, '''I am a small frog''']       ,		padding=UpperCamelCase_       ,		truncation=UpperCamelCase_       ,		return_tensors='''pt'''				)
											self.assertIsInstance(UpperCamelCase_       ,		UpperCamelCase_				)
											self.assertEqual(batch.input_ids.shape       ,		(2, 1_024)				)

							@require_torch
							def  lowerCamelCase_					(				self:      int				)     ->  Tuple:





									"""simple docstring"""







									lowercase__				= ['''A long paragraph for summarization.''']
									lowercase__				= [
									    '''Summary of the text.''',
									]
									for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											lowercase__				= tokenizer(UpperCamelCase_       ,		text_target=UpperCamelCase_       ,		return_tensors='''pt'''				)
											lowercase__				= inputs['''input_ids''']
											lowercase__				= inputs['''labels''']
											self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()				)
											self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()				)
											self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()				)
											self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()				)

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





									"""simple docstring"""







									pass


							def  lowerCamelCase_					(				self:      int				)     ->  Any:





									"""simple docstring"""







									for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
											with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'				):
													lowercase__				= self.rust_tokenizer_class.from_pretrained(UpperCamelCase_       ,		**UpperCamelCase_				)
													lowercase__				= self.tokenizer_class.from_pretrained(UpperCamelCase_       ,		**UpperCamelCase_				)
													lowercase__				= '''A, <mask> AllenNLP sentence.'''
													lowercase__				= tokenizer_r.encode_plus(UpperCamelCase_       ,		add_special_tokens=UpperCamelCase_       ,		return_token_type_ids=UpperCamelCase_				)
													lowercase__				= tokenizer_p.encode_plus(UpperCamelCase_       ,		add_special_tokens=UpperCamelCase_       ,		return_token_type_ids=UpperCamelCase_				)

													# token_type_ids should put 0 everywhere
													self.assertEqual(sum(tokens_r['''token_type_ids''']				)       ,		sum(tokens_p['''token_type_ids''']				)				)

													# attention_mask should put 1 everywhere, so sum over length should be 1
													self.assertEqual(
													    sum(tokens_r['''attention_mask''']				) / len(tokens_r['''attention_mask''']				)       ,		sum(tokens_p['''attention_mask''']				) / len(tokens_p['''attention_mask''']				)       ,		)

													lowercase__				= tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']				)
													lowercase__				= tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids''']				)

													# Rust correctly handles the space before the mask while python doesnt
													self.assertSequenceEqual(tokens_p['''input_ids''']       ,		[0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]				)
													self.assertSequenceEqual(tokens_r['''input_ids''']       ,		[0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]				)

													self.assertSequenceEqual(
													    UpperCamelCase_       ,		['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']				)
													self.assertSequenceEqual(
													    UpperCamelCase_       ,		['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']				)









"""simple docstring"""


from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


__magic_name__       :					Any						=			logging.get_logger(__name__)

__magic_name__       :					int						=			{
    """facebook/convnextv2-tiny-1k-224""": """https://huggingface.co/facebook/convnextv2-tiny-1k-224/resolve/main/config.json""",
}






class 							lowercase__			(   __SCREAMING_SNAKE_CASE					,     __SCREAMING_SNAKE_CASE	):

			"""simple docstring"""
			__lowerCAmelCase     :				str										=  """convnextv2"""







			def __init__(      self						,   _A=3						,   _A=4						,   _A=4						,   _A=None						,   _A=None						,   _A="gelu"						,   _A=0.02						,   _A=1e-1_2						,   _A=0.0						,   _A=2_2_4						,   _A=None						,   _A=None						,   **_A						,   ):






									'''simple docstring'''







									super().__init__(**_A    )

									UpperCamelCase				:		Optional[Any]				   =			num_channels
									UpperCamelCase				:		int				   =			patch_size
									UpperCamelCase				:		Dict				   =			num_stages
									UpperCamelCase				:		Tuple				   =			[9_6, 1_9_2, 3_8_4, 7_6_8] if hidden_sizes is None else hidden_sizes
									UpperCamelCase				:		str				   =			[3, 3, 9, 3] if depths is None else depths
									UpperCamelCase				:		List[str]				   =			hidden_act
									UpperCamelCase				:		List[str]				   =			initializer_range
									UpperCamelCase				:		List[Any]				   =			layer_norm_eps
									UpperCamelCase				:		Any				   =			drop_path_rate
									UpperCamelCase				:		Any				   =			image_size
									UpperCamelCase				:		Union[str, Any]				   =			["""stem"""] + [f"""stage{idx}""" for idx in range(1						,   len(self.depths    ) + 1    )]
									UpperCamelCase   ,						UpperCamelCase				:		Optional[int]				   =			get_aligned_output_features_output_indices(
									    out_features=_A						,   out_indices=_A						,   stage_names=self.stage_names    )



