
"""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  lowerCAmelCase_ (					_lowercase						,    unittest.TestCase			):



						'''simple docstring'''


						_lowerCamelCase:			Tuple					=			GPTaTokenizer
						_lowerCamelCase:			Tuple					=			GPTaTokenizerFast
						_lowerCamelCase:			str					=			True
						_lowerCamelCase:			Any					=			{'''add_prefix_space''': True}
						_lowerCamelCase:			List[Any]					=			False


						def   _SCREAMING_SNAKE_CASE					(       self   :					Dict					)   ->      List[Any]:
							super().setUp()

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

							A        =      os.path.join(self.tmpdirname     ,VOCAB_FILES_NAMES['vocab_file']					)
							A        =      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   _SCREAMING_SNAKE_CASE					(       self   :					Optional[Any]     ,**A_   :					Tuple					)   ->      Any:
							kwargs.update(self.special_tokens_map					)
							return GPTaTokenizer.from_pretrained(self.tmpdirname     ,**A_					)


						def   _SCREAMING_SNAKE_CASE					(       self   :					str     ,**A_   :					List[str]					)   ->      List[str]:
							kwargs.update(self.special_tokens_map					)
							return GPTaTokenizerFast.from_pretrained(self.tmpdirname     ,**A_					)


						def   _SCREAMING_SNAKE_CASE					(       self   :					List[str]     ,A_   :					List[Any]					)   ->      Union[str, Any]:
							A        =      'lower newer'
							A        =      'lower newer'
							return input_text, output_text


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

							A        =      tokens + [tokenizer.unk_token]
							A        =      [14, 15, 10, 9, 3, 2, 15, 19]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(A_					)     ,A_					)


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

							A        =      self.get_tokenizer()
							A        =      self.get_rust_tokenizer(add_prefix_space=A_					)

							A        =      'lower newer'

							# Testing tokenization
							A        =      tokenizer.tokenize(A_     ,add_prefix_space=A_					)
							A        =      rust_tokenizer.tokenize(A_					)
							self.assertListEqual(A_     ,A_					)

							# Testing conversion to ids without special tokens
							A        =      tokenizer.encode(A_     ,add_special_tokens=A_     ,add_prefix_space=A_					)
							A        =      rust_tokenizer.encode(A_     ,add_special_tokens=A_					)
							self.assertListEqual(A_     ,A_					)

							# Testing conversion to ids with special tokens
							A        =      self.get_rust_tokenizer(add_prefix_space=A_					)
							A        =      tokenizer.encode(A_     ,add_prefix_space=A_					)
							A        =      rust_tokenizer.encode(A_					)
							self.assertListEqual(A_     ,A_					)

							# Testing the unknown token
							A        =      tokens + [rust_tokenizer.unk_token]
							A        =      [14, 15, 10, 9, 3, 2, 15, 19]
							self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(A_					)     ,A_					)


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

									# Simple input
									A        =      'This is a simple input'
									A        =      ['This is a simple input 1', 'This is a simple input 2']
									A        =      ('This is a simple input', 'This is a pair')
									A        =      [
									    ('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   _SCREAMING_SNAKE_CASE					(       self   :					str					)   ->      List[Any]:
							A        =      GPTaTokenizer.from_pretrained(self.tmpdirname     ,pad_token='<pad>'					)

							# Simple input
							A        =      'This is a simple input'
							A        =      ['This is a simple input looooooooong', 'This is a simple input']
							A        =      ('This is a simple input', 'This is a pair')
							A        =      [
							    ('This is a simple input loooooong', 'This is a simple input'),
							    ('This is a simple pair loooooong', 'This is a simple pair'),
							]

							A        =      tokenizer.pad_token_id

							A        =      tokenizer(A_     ,padding='max_length'     ,max_length=30     ,return_tensors='np'					)
							A        =      tokenizer(A_     ,padding=A_     ,truncate=A_     ,return_tensors='np'					)
							A        =      tokenizer(*A_     ,padding='max_length'     ,max_length=60     ,return_tensors='np'					)
							A        =      tokenizer(A_     ,padding=A_     ,truncate=A_     ,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   _SCREAMING_SNAKE_CASE					(       self   :					Union[str, Any]					)   ->      Optional[int]:
							A        =      '$$$'
							A        =      GPTaTokenizer.from_pretrained(self.tmpdirname     ,bos_token=A_     ,add_bos_token=A_					)

							A        =      'This is a simple input'
							A        =      ['This is a simple input 1', 'This is a simple input 2']

							A        =      tokenizer.bos_token_id

							A        =      tokenizer(A_					)
							A        =      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        =      tokenizer.decode(out_s.input_ids					)
							A        =      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   _SCREAMING_SNAKE_CASE					(       self   :					Union[str, Any]					)   ->      int:
							pass




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

									A        =      [
									    (x if not special_tokens_mask[i] else None) for i, x in enumerate(A_					)
									]
									A        =      [x for x in filtered_sequence if x is not None]
									self.assertEqual(A_     ,A_					)







@require_tokenizers
class  lowerCAmelCase_ (					unittest.TestCase			):



						'''simple docstring'''


						def   _SCREAMING_SNAKE_CASE					(       self   :					Any					)   ->      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        =      AutoTokenizer.from_pretrained('facebook/opt-350m'     ,from_slow=A_					)
							A        =      'A photo of a cat'

							A        =      tokenizer.encode(
							    A_     ,)
							self.assertEqual(A_     ,[2, 250, 1345, 9, 10, 4758]					)
							tokenizer.save_pretrained('test_opt'					)

							A        =      AutoTokenizer.from_pretrained('./test_opt'					)
							A        =      tokenizer.encode(
							    A_     ,)
							self.assertEqual(A_     ,[2, 250, 1345, 9, 10, 4758]					)


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

							A        =      tokenizer.encode(
							    A_     ,)
							# Same as above
							self.assertEqual(A_     ,[2, 250, 1345, 9, 10, 4758]					)




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

							A        =      'bos'
							A        =      tokenizer.get_vocab()['bos']

							A        =      'A photo of a cat'
							A        =      tokenizer.encode(
							    A_     ,)
							# We changed the bos token
							self.assertEqual(A_     ,[3_1957, 250, 1345, 9, 10, 4758]					)
							tokenizer.save_pretrained('./tok'					)
							A        =      AutoTokenizer.from_pretrained('./tok'					)
							self.assertTrue(tokenizer.is_fast					)
							A        =      tokenizer.encode(
							    A_     ,)
							self.assertEqual(A_     ,[3_1957, 250, 1345, 9, 10, 4758]					)




"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowerCAmelCase    :int     =					{'configuration_vit_msn': ['VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMSNConfig']}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase    :Any     =					[
      'VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST',
      'ViTMSNModel',
      'ViTMSNForImageClassification',
      'ViTMSNPreTrainedModel',
  ]

if TYPE_CHECKING:
  from .configuration_vit_msn import VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMSNConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_vit_msn import (
        VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST,
        ViTMSNForImageClassification,
        ViTMSNModel,
        ViTMSNPreTrainedModel,
    )

else:
  import sys

  _lowerCAmelCase    :int     =					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


