



'''simple docstring'''






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







    def 			lowercase_       (      self :  Optional[Any]	):
      super().setUp()

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

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







    def 			lowercase_       (      self :  Any						,			**__snake_case :  Tuple	):
      kwargs.update(self.special_tokens_map	)
      return GPTaTokenizer.from_pretrained(self.tmpdirname						,			**__snake_case	)







    def 			lowercase_       (      self :  Union[str, Any]						,			**__snake_case :  Optional[int]	):
      kwargs.update(self.special_tokens_map	)
      return GPTaTokenizerFast.from_pretrained(self.tmpdirname						,			**__snake_case	)







    def 			lowercase_       (      self :  Tuple						,			__snake_case :  Optional[Any]	):
      a						:					List[Any]        =     '''lower newer'''
      a						:					List[str]        =     '''lower newer'''
      return input_text, output_text







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

      a						:					Tuple        =     tokens + [tokenizer.unk_token]
      a						:					Optional[int]        =     [14, 15, 10, 9, 3, 2, 15, 19]
      self.assertListEqual(tokenizer.convert_tokens_to_ids(__snake_case	)						,			__snake_case	)







    def 			lowercase_       (      self :  Union[str, Any]	):
      if not self.test_rust_tokenizer:
        return

      a						:					Union[str, Any]        =     self.get_tokenizer()
      a						:					Union[str, Any]        =     self.get_rust_tokenizer(add_prefix_space=__snake_case	)

      a						:					Any        =     '''lower newer'''

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

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

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

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







    def 			lowercase_       (      self :  Tuple						,			*__snake_case :  int						,			**__snake_case :  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 :  Tuple						,			__snake_case :  int=15	):
      for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
        with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""	):
          a						:					Tuple        =     self.rust_tokenizer_class.from_pretrained(__snake_case						,			**__snake_case	)

          # Simple input
          a						:					Optional[int]        =     '''This is a simple input'''
          a						:					Optional[Any]        =     ['''This is a simple input 1''', '''This is a simple input 2''']
          a						:					Dict        =     ('''This is a simple input''', '''This is a pair''')
          a						:					Optional[int]        =     [
              ('''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(__snake_case						,			tokenizer_r.encode						,			__snake_case						,			max_length=__snake_case						,			padding='max_length'	)

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

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

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

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

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







    def 			lowercase_       (      self :  str	):
      a						:					str        =     GPTaTokenizer.from_pretrained(self.tmpdirname						,			pad_token='<pad>'	)

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

      a						:					int        =     tokenizer.pad_token_id

      a						:					str        =     tokenizer(__snake_case						,			padding='max_length'						,			max_length=30						,			return_tensors='np'	)
      a						:					List[str]        =     tokenizer(__snake_case						,			padding=__snake_case						,			truncate=__snake_case						,			return_tensors='np'	)
      a						:					List[str]        =     tokenizer(*__snake_case						,			padding='max_length'						,			max_length=60						,			return_tensors='np'	)
      a						:					Any        =     tokenizer(__snake_case						,			padding=__snake_case						,			truncate=__snake_case						,			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 			lowercase_       (      self :  Any	):
      a						:					Tuple        =     '''$$$'''
      a						:					Tuple        =     GPTaTokenizer.from_pretrained(self.tmpdirname						,			bos_token=__snake_case						,			add_bos_token=__snake_case	)

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

      a						:					str        =     tokenizer.bos_token_id

      a						:					List[Any]        =     tokenizer(__snake_case	)
      a						:					Union[str, Any]        =     tokenizer(__snake_case	)

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

      a						:					Dict        =     tokenizer.decode(out_s.input_ids	)
      a						:					Tuple        =     tokenizer.batch_decode(out_sa.input_ids	)

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







    def 			lowercase_       (      self :  List[str]	):
      pass







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

          a						:					Union[str, Any]        =     [
              (x if not special_tokens_mask[i] else None) for i, x in enumerate(__snake_case	)
          ]
          a						:					Optional[int]        =     [x for x in filtered_sequence if x is not None]
          self.assertEqual(__snake_case						,			__snake_case	)

@require_tokenizers
class 							a__(					unittest.TestCase	):







    def 			lowercase_       (      self :  str	):
      # 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						:					Dict        =     AutoTokenizer.from_pretrained('facebook/opt-350m'						,			from_slow=__snake_case	)
      a						:					Tuple        =     '''A photo of a cat'''

      a						:					str        =     tokenizer.encode(
          __snake_case						,			)
      self.assertEqual(__snake_case						,			[2, 2_50, 13_45, 9, 10, 47_58]	)
      tokenizer.save_pretrained('test_opt'	)

      a						:					Union[str, Any]        =     AutoTokenizer.from_pretrained('./test_opt'	)
      a						:					Dict        =     tokenizer.encode(
          __snake_case						,			)
      self.assertEqual(__snake_case						,			[2, 2_50, 13_45, 9, 10, 47_58]	)







    def 			lowercase_       (      self :  str	):
      a						:					Dict        =     AutoTokenizer.from_pretrained('facebook/opt-350m'						,			use_slow=__snake_case	)
      a						:					Optional[Any]        =     '''A photo of a cat'''

      a						:					Union[str, Any]        =     tokenizer.encode(
          __snake_case						,			)
      # Same as above
      self.assertEqual(__snake_case						,			[2, 2_50, 13_45, 9, 10, 47_58]	)







    @unittest.skip('This test is failing because of a bug in the fast tokenizer'	)
    def 			lowercase_       (      self :  int	):
      a						:					int        =     AutoTokenizer.from_pretrained('facebook/opt-350m'						,			from_slow=__snake_case	)

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

      a						:					Tuple        =     '''A photo of a cat'''
      a						:					Optional[Any]        =     tokenizer.encode(
          __snake_case						,			)
      # We changed the bos token
      self.assertEqual(__snake_case						,			[3_19_57, 2_50, 13_45, 9, 10, 47_58]	)
      tokenizer.save_pretrained('./tok'	)
      a						:					List[str]        =     AutoTokenizer.from_pretrained('./tok'	)
      self.assertTrue(tokenizer.is_fast	)
      a						:					List[str]        =     tokenizer.encode(
          __snake_case						,			)
      self.assertEqual(__snake_case						,			[3_19_57, 2_50, 13_45, 9, 10, 47_58]	)




"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowerCAmelCase    :int     =					logging.get_logger(__name__)

_lowerCAmelCase    :Union[str, Any]     =					{
    'alibaba-damo/mgp-str-base': 'https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json',
}
class 							_UpperCAmelCase							( a      ):





     '''simple docstring'''



     a__									='''mgp-str'''





     def __init__(					self       ,	A=[3_2, 1_2_8]       ,	A=4       ,	A=3       ,	A=2_7       ,	A=3_8       ,	A=5_0_2_5_7       ,	A=3_0_5_2_2       ,	A=7_6_8       ,	A=1_2       ,	A=1_2       ,	A=4.0       ,	A=True       ,	A=False       ,	A=1E-5       ,	A=0.0       ,	A=0.0       ,	A=0.0       ,	A=False       ,	A=0.02       ,	**A       ,	)							->					Union[str, Any]:
         super().__init__(**A		)

         _UpperCAmelCase							:  Any											=    image_size
         _UpperCAmelCase							:  str											=    patch_size
         _UpperCAmelCase							:  Dict											=    num_channels
         _UpperCAmelCase							:  Dict											=    max_token_length
         _UpperCAmelCase							:  Optional[Any]											=    num_character_labels
         _UpperCAmelCase							:  int											=    num_bpe_labels
         _UpperCAmelCase							:  List[str]											=    num_wordpiece_labels
         _UpperCAmelCase							:  Optional[int]											=    hidden_size
         _UpperCAmelCase							:  Any											=    num_hidden_layers
         _UpperCAmelCase							:  List[Any]											=    num_attention_heads
         _UpperCAmelCase							:  List[Any]											=    mlp_ratio
         _UpperCAmelCase							:  List[str]											=    distilled
         _UpperCAmelCase							:  Optional[int]											=    layer_norm_eps
         _UpperCAmelCase							:  str											=    drop_rate
         _UpperCAmelCase							:  List[Any]											=    qkv_bias
         _UpperCAmelCase							:  List[str]											=    attn_drop_rate
         _UpperCAmelCase							:  Dict											=    drop_path_rate
         _UpperCAmelCase							:  Union[str, Any]											=    output_aa_attentions
         _UpperCAmelCase							:  List[str]											=    initializer_range


