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 					lowercase__						(   UpperCamelCase_	,							unittest.TestCase):
						UpperCamelCase_     =		GPTaTokenizer
						UpperCamelCase_     =		GPTaTokenizerFast
						UpperCamelCase_     =		True
						UpperCamelCase_     =		{"""add_prefix_space""": True}
						UpperCamelCase_     =		False


						def        __A       (       self	:				List[str]						):





												'''simple docstring'''





												super().setUp()

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

												SCREAMING_SNAKE_CASE							:    List[Any]							=							os.path.join(self.tmpdirname    ,		VOCAB_FILES_NAMES['''vocab_file''']						)
												SCREAMING_SNAKE_CASE							:    List[Any]							=							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        __A       (       self	:				List[str]    ,		**UpperCamelCase__	:				List[str]						):





												'''simple docstring'''





												kwargs.update(self.special_tokens_map						)
												return GPTaTokenizer.from_pretrained(self.tmpdirname    ,		**UpperCamelCase__						)


						def        __A       (       self	:				int    ,		**UpperCamelCase__	:				List[str]						):





												'''simple docstring'''





												kwargs.update(self.special_tokens_map						)
												return GPTaTokenizerFast.from_pretrained(self.tmpdirname    ,		**UpperCamelCase__						)


						def        __A       (       self	:				str    ,		UpperCamelCase__	:				int						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Tuple							=							'''lower newer'''
												SCREAMING_SNAKE_CASE							:    Tuple							=							'''lower newer'''
												return input_text, output_text


						def        __A       (       self	:				int						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Dict							=							GPTaTokenizer(self.vocab_file    ,		self.merges_file    ,		**self.special_tokens_map						)
												SCREAMING_SNAKE_CASE							:    List[str]							=							'''lower newer'''
												SCREAMING_SNAKE_CASE							:    Any							=							['''\u0120low''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							tokenizer.tokenize(UpperCamelCase__    ,		add_prefix_space=UpperCamelCase__						)
												self.assertListEqual(UpperCamelCase__    ,		UpperCamelCase__						)

												SCREAMING_SNAKE_CASE							:    List[Any]							=							tokens + [tokenizer.unk_token]
												SCREAMING_SNAKE_CASE							:    List[Any]							=							[14, 15, 10, 9, 3, 2, 15, 19]
												self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase__						)    ,		UpperCamelCase__						)


						def        __A       (       self	:				Tuple						):





												'''simple docstring'''





												if not self.test_rust_tokenizer:
																		return

												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							self.get_tokenizer()
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							self.get_rust_tokenizer(add_prefix_space=UpperCamelCase__						)

												SCREAMING_SNAKE_CASE							:    Optional[int]							=							'''lower newer'''

												# Testing tokenization
												SCREAMING_SNAKE_CASE							:    int							=							tokenizer.tokenize(UpperCamelCase__    ,		add_prefix_space=UpperCamelCase__						)
												SCREAMING_SNAKE_CASE							:    List[str]							=							rust_tokenizer.tokenize(UpperCamelCase__						)
												self.assertListEqual(UpperCamelCase__    ,		UpperCamelCase__						)

												# Testing conversion to ids without special tokens
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							tokenizer.encode(UpperCamelCase__    ,		add_special_tokens=UpperCamelCase__    ,		add_prefix_space=UpperCamelCase__						)
												SCREAMING_SNAKE_CASE							:    List[str]							=							rust_tokenizer.encode(UpperCamelCase__    ,		add_special_tokens=UpperCamelCase__						)
												self.assertListEqual(UpperCamelCase__    ,		UpperCamelCase__						)

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

												# Testing the unknown token
												SCREAMING_SNAKE_CASE							:    List[str]							=							tokens + [rust_tokenizer.unk_token]
												SCREAMING_SNAKE_CASE							:    str							=							[14, 15, 10, 9, 3, 2, 15, 19]
												self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(UpperCamelCase__						)    ,		UpperCamelCase__						)


						def        __A       (       self	:				str    ,		*UpperCamelCase__	:				int    ,		**UpperCamelCase__	:				Optional[Any]						):





												'''simple docstring'''





												pass


						def        __A       (       self	:				Optional[int]    ,		UpperCamelCase__	:				Any=15						):





												'''simple docstring'''





												for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																		with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""						):
																								SCREAMING_SNAKE_CASE							:    Any							=							self.rust_tokenizer_class.from_pretrained(UpperCamelCase__    ,		**UpperCamelCase__						)

																								# Simple input
																								SCREAMING_SNAKE_CASE							:    Tuple							=							'''This is a simple input'''
																								SCREAMING_SNAKE_CASE							:    Dict							=							['''This is a simple input 1''', '''This is a simple input 2''']
																								SCREAMING_SNAKE_CASE							:    Tuple							=							('''This is a simple input''', '''This is a pair''')
																								SCREAMING_SNAKE_CASE							:    List[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(UpperCamelCase__    ,		tokenizer_r.encode    ,		UpperCamelCase__    ,		max_length=UpperCamelCase__    ,		padding='''max_length'''						)

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

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

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

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

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


						def        __A       (       self	:				Optional[int]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Tuple							=							GPTaTokenizer.from_pretrained(self.tmpdirname    ,		pad_token='''<pad>'''						)

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

												SCREAMING_SNAKE_CASE							:    str							=							tokenizer.pad_token_id

												SCREAMING_SNAKE_CASE							:    Tuple							=							tokenizer(UpperCamelCase__    ,		padding='''max_length'''    ,		max_length=30    ,		return_tensors='''np'''						)
												SCREAMING_SNAKE_CASE							:    Dict							=							tokenizer(UpperCamelCase__    ,		padding=UpperCamelCase__    ,		truncate=UpperCamelCase__    ,		return_tensors='''np'''						)
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							tokenizer(*UpperCamelCase__    ,		padding='''max_length'''    ,		max_length=60    ,		return_tensors='''np'''						)
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							tokenizer(UpperCamelCase__    ,		padding=UpperCamelCase__    ,		truncate=UpperCamelCase__    ,		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        __A       (       self	:				Dict						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Optional[int]							=							'''$$$'''
												SCREAMING_SNAKE_CASE							:    List[str]							=							GPTaTokenizer.from_pretrained(self.tmpdirname    ,		bos_token=UpperCamelCase__    ,		add_bos_token=UpperCamelCase__						)

												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							'''This is a simple input'''
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							['''This is a simple input 1''', '''This is a simple input 2''']

												SCREAMING_SNAKE_CASE							:    List[Any]							=							tokenizer.bos_token_id

												SCREAMING_SNAKE_CASE							:    Dict							=							tokenizer(UpperCamelCase__						)
												SCREAMING_SNAKE_CASE							:    Tuple							=							tokenizer(UpperCamelCase__						)

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

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

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


						def        __A       (       self	:				Dict						):





												'''simple docstring'''





												pass



						def        __A       (       self	:				List[Any]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    List[str]							=							[self.get_tokenizer(do_lower_case=UpperCamelCase__    ,		add_bos_token=UpperCamelCase__						)]
												for tokenizer in tokenizers:
																		with self.subTest(f"""{tokenizer.__class__.__name__}"""						):
																								SCREAMING_SNAKE_CASE							:    Dict							=							'''Encode this.'''
																								SCREAMING_SNAKE_CASE							:    Tuple							=							'''This one too please.'''
																								SCREAMING_SNAKE_CASE							:    Any							=							tokenizer.encode(UpperCamelCase__    ,		add_special_tokens=UpperCamelCase__						)
																								encoded_sequence += tokenizer.encode(UpperCamelCase__    ,		add_special_tokens=UpperCamelCase__						)
																								SCREAMING_SNAKE_CASE							:    str							=							tokenizer.encode_plus(
																								    UpperCamelCase__    ,		UpperCamelCase__    ,		add_special_tokens=UpperCamelCase__    ,		return_special_tokens_mask=UpperCamelCase__    ,		)
																								SCREAMING_SNAKE_CASE							:    List[Any]							=							encoded_sequence_dict['''input_ids''']
																								SCREAMING_SNAKE_CASE							:    Dict							=							encoded_sequence_dict['''special_tokens_mask''']
																								self.assertEqual(len(UpperCamelCase__						)    ,		len(UpperCamelCase__						)						)

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






@require_tokenizers
class 					lowercase__						(   unittest.TestCase):


						def        __A       (       self	:				Optional[int]						):





												'''simple docstring'''





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

												SCREAMING_SNAKE_CASE							:    Dict							=							tokenizer.encode(
												    UpperCamelCase__    ,		)
												self.assertEqual(UpperCamelCase__    ,		[2, 250, 1345, 9, 10, 4758]						)
												tokenizer.save_pretrained('''test_opt'''						)

												SCREAMING_SNAKE_CASE							:    Dict							=							AutoTokenizer.from_pretrained('''./test_opt'''						)
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							tokenizer.encode(
												    UpperCamelCase__    ,		)
												self.assertEqual(UpperCamelCase__    ,		[2, 250, 1345, 9, 10, 4758]						)


						def        __A       (       self	:				List[Any]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Optional[int]							=							AutoTokenizer.from_pretrained('''facebook/opt-350m'''    ,		use_slow=UpperCamelCase__						)
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							'''A photo of a cat'''

												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							tokenizer.encode(
												    UpperCamelCase__    ,		)
												# Same as above
												self.assertEqual(UpperCamelCase__    ,		[2, 250, 1345, 9, 10, 4758]						)



						@unittest.skip('''This test is failing because of a bug in the fast tokenizer'''						)
						def        __A       (       self	:				List[str]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Any							=							AutoTokenizer.from_pretrained('''facebook/opt-350m'''    ,		from_slow=UpperCamelCase__						)

												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							'''bos'''
												SCREAMING_SNAKE_CASE							:    List[str]							=							tokenizer.get_vocab()['''bos''']

												SCREAMING_SNAKE_CASE							:    Optional[int]							=							'''A photo of a cat'''
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							tokenizer.encode(
												    UpperCamelCase__    ,		)
												# We changed the bos token
												self.assertEqual(UpperCamelCase__    ,		[3_1957, 250, 1345, 9, 10, 4758]						)
												tokenizer.save_pretrained('''./tok'''						)
												SCREAMING_SNAKE_CASE							:    List[Any]							=							AutoTokenizer.from_pretrained('''./tok'''						)
												self.assertTrue(tokenizer.is_fast						)
												SCREAMING_SNAKE_CASE							:    Dict							=							tokenizer.encode(
												    UpperCamelCase__    ,		)
												self.assertEqual(UpperCamelCase__    ,		[3_1957, 250, 1345, 9, 10, 4758]						)






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


__UpperCamelCase		:       Tuple									=     {
    'configuration_swiftformer': [
        'SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'SwiftFormerConfig',
        'SwiftFormerOnnxConfig',
    ]
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__UpperCamelCase		:       Optional[Any]									=     [
					    'SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'SwiftFormerForImageClassification',
					    'SwiftFormerModel',
					    'SwiftFormerPreTrainedModel',
					]

if TYPE_CHECKING:
					from .configuration_swiftformer import (
					    SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    SwiftFormerConfig,
					    SwiftFormerOnnxConfig,
					)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_swiftformer import (
										    SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    SwiftFormerForImageClassification,
										    SwiftFormerModel,
										    SwiftFormerPreTrainedModel,
										)

else:
					import sys

					__UpperCamelCase		:       List[Any]									=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





