




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    lowerCamelCase    (		_lowerCamelCase				,unittest.TestCase						):






			'''simple docstring'''





			UpperCamelCase__								=GPTaTokenizer
			UpperCamelCase__								=GPTaTokenizerFast
			UpperCamelCase__								=True
			UpperCamelCase__								={'''add_prefix_space''': True}
			UpperCamelCase__								=False

			def 							UpperCAmelCase__       ( self			:   Union[str, Any]  )						->   int:
								super().setUp()

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

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

			def 							UpperCAmelCase__       ( self			:   Optional[int]       ,						**lowerCamelCase_			:   Dict  )						->   List[Any]:
								kwargs.update(self.special_tokens_map  )
								return GPTaTokenizer.from_pretrained(self.tmpdirname       ,						**lowerCamelCase_  )

			def 							UpperCAmelCase__       ( self			:   List[Any]       ,						**lowerCamelCase_			:   Optional[Any]  )						->   List[str]:
								kwargs.update(self.special_tokens_map  )
								return GPTaTokenizerFast.from_pretrained(self.tmpdirname       ,						**lowerCamelCase_  )

			def 							UpperCAmelCase__       ( self			:   Any       ,						lowerCamelCase_			:   Tuple  )						->   List[str]:
								__magic_name__							:	List[Any]    							=							'''lower newer'''
								__magic_name__							:	Dict    							=							'''lower newer'''
								return input_text, output_text

			def 							UpperCAmelCase__       ( self			:   Any  )						->   Tuple:
								__magic_name__							:	List[str]    							=							GPTaTokenizer(self.vocab_file       ,						self.merges_file       ,						**self.special_tokens_map  )
								__magic_name__							:	Dict    							=							'''lower newer'''
								__magic_name__							:	List[Any]    							=							['''\u0120low''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
								__magic_name__							:	Union[str, Any]    							=							tokenizer.tokenize(lowerCamelCase_       ,						add_prefix_space=lowerCamelCase_  )
								self.assertListEqual(lowerCamelCase_       ,						lowerCamelCase_  )

								__magic_name__							:	Union[str, Any]    							=							tokens + [tokenizer.unk_token]
								__magic_name__							:	str    							=							[14, 15, 10, 9, 3, 2, 15, 19]
								self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase_  )       ,						lowerCamelCase_  )

			def 							UpperCAmelCase__       ( self			:   str  )						->   List[Any]:
								if not self.test_rust_tokenizer:
													return

								__magic_name__							:	Optional[int]    							=							self.get_tokenizer()
								__magic_name__							:	Optional[Any]    							=							self.get_rust_tokenizer(add_prefix_space=lowerCamelCase_  )

								__magic_name__							:	Any    							=							'''lower newer'''

								# Testing tokenization
								__magic_name__							:	Dict    							=							tokenizer.tokenize(lowerCamelCase_       ,						add_prefix_space=lowerCamelCase_  )
								__magic_name__							:	Optional[Any]    							=							rust_tokenizer.tokenize(lowerCamelCase_  )
								self.assertListEqual(lowerCamelCase_       ,						lowerCamelCase_  )

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

								# Testing conversion to ids with special tokens
								__magic_name__							:	Tuple    							=							self.get_rust_tokenizer(add_prefix_space=lowerCamelCase_  )
								__magic_name__							:	int    							=							tokenizer.encode(lowerCamelCase_       ,						add_prefix_space=lowerCamelCase_  )
								__magic_name__							:	Optional[Any]    							=							rust_tokenizer.encode(lowerCamelCase_  )
								self.assertListEqual(lowerCamelCase_       ,						lowerCamelCase_  )

								# Testing the unknown token
								__magic_name__							:	Optional[int]    							=							tokens + [rust_tokenizer.unk_token]
								__magic_name__							:	Dict    							=							[14, 15, 10, 9, 3, 2, 15, 19]
								self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(lowerCamelCase_  )       ,						lowerCamelCase_  )

			def 							UpperCAmelCase__       ( self			:   List[Any]       ,						*lowerCamelCase_			:   List[str]       ,						**lowerCamelCase_			:   List[Any]  )						->   str:
								# 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 							UpperCAmelCase__       ( self			:   Dict       ,						lowerCamelCase_			:   Dict=15  )						->   List[str]:
								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
													with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''  ):
																		__magic_name__							:	Any    							=							self.rust_tokenizer_class.from_pretrained(lowerCamelCase_       ,						**lowerCamelCase_  )

																		# Simple input
																		__magic_name__							:	str    							=							'''This is a simple input'''
																		__magic_name__							:	Any    							=							['''This is a simple input 1''', '''This is a simple input 2''']
																		__magic_name__							:	Union[str, Any]    							=							('''This is a simple input''', '''This is a pair''')
																		__magic_name__							:	Tuple    							=							[
																		    ('''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(lowerCamelCase_       ,						tokenizer_r.encode       ,						lowerCamelCase_       ,						max_length=lowerCamelCase_       ,						padding='''max_length'''  )

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

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

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

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

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

			def 							UpperCAmelCase__       ( self			:   Union[str, Any]  )						->   Dict:
								__magic_name__							:	Optional[Any]    							=							GPTaTokenizer.from_pretrained(self.tmpdirname       ,						pad_token='''<pad>'''  )

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

								__magic_name__							:	str    							=							tokenizer.pad_token_id

								__magic_name__							:	Any    							=							tokenizer(lowerCamelCase_       ,						padding='''max_length'''       ,						max_length=30       ,						return_tensors='''np'''  )
								__magic_name__							:	Any    							=							tokenizer(lowerCamelCase_       ,						padding=lowerCamelCase_       ,						truncate=lowerCamelCase_       ,						return_tensors='''np'''  )
								__magic_name__							:	int    							=							tokenizer(*lowerCamelCase_       ,						padding='''max_length'''       ,						max_length=60       ,						return_tensors='''np'''  )
								__magic_name__							:	List[str]    							=							tokenizer(lowerCamelCase_       ,						padding=lowerCamelCase_       ,						truncate=lowerCamelCase_       ,						return_tensors='''np'''  )

								# s
								# test single string max_length padding
								self.assertEqual(out_s['''input_ids'''].shape[-1]       ,						30  )
								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]       ,						33  )
								# 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]       ,						60  )
								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]       ,						52  )
								# 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 							UpperCAmelCase__       ( self			:   Any  )						->   Optional[int]:
								__magic_name__							:	Tuple    							=							'''$$$'''
								__magic_name__							:	Dict    							=							GPTaTokenizer.from_pretrained(self.tmpdirname       ,						bos_token=lowerCamelCase_       ,						add_bos_token=lowerCamelCase_  )

								__magic_name__							:	Dict    							=							'''This is a simple input'''
								__magic_name__							:	Dict    							=							['''This is a simple input 1''', '''This is a simple input 2''']

								__magic_name__							:	Optional[Any]    							=							tokenizer.bos_token_id

								__magic_name__							:	str    							=							tokenizer(lowerCamelCase_  )
								__magic_name__							:	List[Any]    							=							tokenizer(lowerCamelCase_  )

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

								__magic_name__							:	Dict    							=							tokenizer.decode(out_s.input_ids  )
								__magic_name__							:	Union[str, Any]    							=							tokenizer.batch_decode(out_sa.input_ids  )

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

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


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

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






@require_tokenizers
class    lowerCamelCase    (		unittest.TestCase						):






			'''simple docstring'''





			def 							UpperCAmelCase__       ( self			:   int  )						->   int:
								# 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

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

								__magic_name__							:	Any    							=							tokenizer.encode(
								    lowerCamelCase_       ,						)
								self.assertEqual(lowerCamelCase_       ,						[2, 250, 1345, 9, 10, 4758]  )
								tokenizer.save_pretrained('''test_opt'''  )

								__magic_name__							:	List[str]    							=							AutoTokenizer.from_pretrained('''./test_opt'''  )
								__magic_name__							:	Dict    							=							tokenizer.encode(
								    lowerCamelCase_       ,						)
								self.assertEqual(lowerCamelCase_       ,						[2, 250, 1345, 9, 10, 4758]  )

			def 							UpperCAmelCase__       ( self			:   Optional[Any]  )						->   Union[str, Any]:
								__magic_name__							:	List[str]    							=							AutoTokenizer.from_pretrained('''facebook/opt-350m'''       ,						use_slow=lowerCamelCase_  )
								__magic_name__							:	Tuple    							=							'''A photo of a cat'''

								__magic_name__							:	Optional[int]    							=							tokenizer.encode(
								    lowerCamelCase_       ,						)
								# Same as above
								self.assertEqual(lowerCamelCase_       ,						[2, 250, 1345, 9, 10, 4758]  )


			@unittest.skip('''This test is failing because of a bug in the fast tokenizer'''  )
			def 							UpperCAmelCase__       ( self			:   List[str]  )						->   Optional[int]:
								__magic_name__							:	List[str]    							=							AutoTokenizer.from_pretrained('''facebook/opt-350m'''       ,						from_slow=lowerCamelCase_  )

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

								__magic_name__							:	Dict    							=							'''A photo of a cat'''
								__magic_name__							:	Any    							=							tokenizer.encode(
								    lowerCamelCase_       ,						)
								# We changed the bos token
								self.assertEqual(lowerCamelCase_       ,						[31957, 250, 1345, 9, 10, 4758]  )
								tokenizer.save_pretrained('''./tok'''  )
								__magic_name__							:	List[Any]    							=							AutoTokenizer.from_pretrained('''./tok'''  )
								self.assertTrue(tokenizer.is_fast  )
								__magic_name__							:	Optional[Any]    							=							tokenizer.encode(
								    lowerCamelCase_       ,						)
								self.assertEqual(lowerCamelCase_       ,						[31957, 250, 1345, 9, 10, 4758]  )









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


__lowerCamelCase   :						Optional[int]				=			logging.get_logger(__name__)

__lowerCamelCase   :						Tuple				=			{
    '''shi-labs/nat-mini-in1k-224''': '''https://huggingface.co/shi-labs/nat-mini-in1k-224/resolve/main/config.json''',
    # See all Nat models at https://huggingface.co/models?filter=nat
}


class    lowerCamelCase    (		_lowerCamelCase				,_lowerCamelCase						):






			'''simple docstring'''





			UpperCamelCase__								='''nat'''

			UpperCamelCase__								={
			    '''num_attention_heads''': '''num_heads''',
			    '''num_hidden_layers''': '''num_layers''',
			}

			def __init__( self			:   Union[str, Any]       ,						lowerCamelCase_			:   Dict=4       ,						lowerCamelCase_			:   Union[str, Any]=3       ,						lowerCamelCase_			:   List[str]=64       ,						lowerCamelCase_			:   Union[str, Any]=[3, 4, 6, 5]       ,						lowerCamelCase_			:   List[Any]=[2, 4, 8, 16]       ,						lowerCamelCase_			:   List[Any]=7       ,						lowerCamelCase_			:   Union[str, Any]=3.0       ,						lowerCamelCase_			:   int=True       ,						lowerCamelCase_			:   str=0.0       ,						lowerCamelCase_			:   int=0.0       ,						lowerCamelCase_			:   Dict=0.1       ,						lowerCamelCase_			:   Optional[int]="gelu"       ,						lowerCamelCase_			:   Tuple=0.0_2       ,						lowerCamelCase_			:   Tuple=1E-5       ,						lowerCamelCase_			:   List[Any]=0.0       ,						lowerCamelCase_			:   str=None       ,						lowerCamelCase_			:   Tuple=None       ,						**lowerCamelCase_			:   Any       ,						)						->   Any:
								super().__init__(**lowerCamelCase_  )

								__magic_name__							:	List[Any]    							=							patch_size
								__magic_name__							:	str    							=							num_channels
								__magic_name__							:	Union[str, Any]    							=							embed_dim
								__magic_name__							:	Dict    							=							depths
								__magic_name__							:	str    							=							len(lowerCamelCase_  )
								__magic_name__							:	List[str]    							=							num_heads
								__magic_name__							:	Optional[int]    							=							kernel_size
								__magic_name__							:	Any    							=							mlp_ratio
								__magic_name__							:	Any    							=							qkv_bias
								__magic_name__							:	Union[str, Any]    							=							hidden_dropout_prob
								__magic_name__							:	Any    							=							attention_probs_dropout_prob
								__magic_name__							:	List[str]    							=							drop_path_rate
								__magic_name__							:	str    							=							hidden_act
								__magic_name__							:	Tuple    							=							layer_norm_eps
								__magic_name__							:	Any    							=							initializer_range
								# we set the hidden_size attribute in order to make Nat work with VisionEncoderDecoderModel
								# this indicates the channel dimension after the last stage of the model
								__magic_name__							:	str    							=							int(embed_dim * 2 ** (len(lowerCamelCase_  ) - 1)  )
								__magic_name__							:	str    							=							layer_scale_init_value
								__magic_name__							:	Dict    							=							['''stem'''] + [F'''stage{idx}''' for idx in range(1       ,						len(lowerCamelCase_  ) + 1  )]
								__magic_name__							,    __magic_name__							:	int    							=							get_aligned_output_features_output_indices(
								    out_features=lowerCamelCase_       ,						out_indices=lowerCamelCase_       ,						stage_names=self.stage_names  )



