




'''simple docstring'''




import json
import os
import unittest

from transformers.models.gptsan_japanese.tokenization_gptsan_japanese import (
    VOCAB_FILES_NAMES,
    GPTSanJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class 			SCREAMING_SNAKE_CASE   (a__ ,		unittest.TestCase							):
     lowerCAmelCase		      =	GPTSanJapaneseTokenizer
     lowerCAmelCase		      =	False
     lowerCAmelCase		      =	{'''do_clean_text''': False, '''add_prefix_space''': False}


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          super().setUp()

          # fmt: off
          __A						:			str								=	['こん', 'こんに', 'にちは', 'ばんは', '世界,㔺界', '、', '。', '<BR>', '<SP>', '<TAB>', '<URL>', '<EMAIL>', '<TEL>', '<DATE>', '<PRICE>', '<BLOCK>', '<KIGOU>', '<U2000U2BFF>', '<|emoji1|>', '<unk>', '<|bagoftoken|>', '<|endoftext|>']
          # fmt: on
          __A						:			Optional[int]								=	{'emoji': {'\ud83d\ude00': '<|emoji1|>'}, 'emoji_inv': {'<|emoji1|>': '\ud83d\ude00'}}  # 😀
          __A						:			Any								=	{'unk_token': '<unk>'}

          __A						:			Optional[Any]								=	os.path.join(self.tmpdirname						,     VOCAB_FILES_NAMES['vocab_file'])
          __A						:			Dict								=	os.path.join(self.tmpdirname						,     VOCAB_FILES_NAMES['emoji_file'])
          with open(self.vocab_file						,     'w'						,     encoding='utf-8') as vocab_writer:
               vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]))
          with open(self.emoji_file						,     'w') as emoji_writer:
               emoji_writer.write(json.dumps(_UpperCAmelCase))


     def 							SCREAMING_SNAKE_CASE  (						self						,     **_UpperCAmelCase):

          '''simple docstring'''






          kwargs.update(self.special_tokens_map)
          return GPTSanJapaneseTokenizer.from_pretrained(self.tmpdirname						,     **_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase):

          '''simple docstring'''






          __A						:			List[str]								=	'こんにちは、世界。 \nこんばんは、㔺界。😀'
          __A						:			Any								=	'こんにちは、世界。 \nこんばんは、世界。😀'
          return input_text, output_text


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase):

          '''simple docstring'''






          __A						:			List[Any]								=	self.get_input_output_texts(_UpperCAmelCase)
          __A						:			Union[str, Any]								=	tokenizer.encode(_UpperCAmelCase						,     add_special_tokens=_UpperCAmelCase)
          __A						:			List[Any]								=	tokenizer.decode(_UpperCAmelCase						,     clean_up_tokenization_spaces=_UpperCAmelCase)
          return text, ids


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          pass  # TODO add if relevant


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          pass  # TODO add if relevant


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          pass  # TODO add if relevant


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Dict								=	self.get_tokenizer()

          # Testing tokenization
          __A						:			List[str]								=	'こんにちは、世界。　こんばんは、㔺界。'
          __A						:			str								=	['こん', 'にちは', '、', '世界', '。', '<SP>', 'こん', 'ばんは', '、', '㔺界', '。']
          __A						:			List[str]								=	tokenizer.tokenize(_UpperCAmelCase)
          self.assertListEqual(_UpperCAmelCase						,     _UpperCAmelCase)

          # Testing conversion to ids without special tokens
          __A						:			Optional[int]								=	[0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6]
          __A						:			str								=	tokenizer.convert_tokens_to_ids(_UpperCAmelCase)
          self.assertListEqual(_UpperCAmelCase						,     _UpperCAmelCase)

          # Testing conversion to ids with special tokens
          __A						:			Optional[int]								=	tokens + [tokenizer.unk_token]
          __A						:			Optional[int]								=	[0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 19]
          __A						:			Tuple								=	tokenizer.convert_tokens_to_ids(_UpperCAmelCase)
          self.assertListEqual(_UpperCAmelCase						,     _UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Optional[int]								=	self.get_tokenizer()

          # Testing tokenization
          __A						:			int								=	'こんにちは、<|bagoftoken|>世界。こんばんは、<|bagoftoken|>㔺界。'
          __A						:			Tuple								=	'こんにちは、、、、世界。こんばんは、、、、世界。'
          __A						:			int								=	tokenizer.encode(_UpperCAmelCase)
          __A						:			Optional[int]								=	tokenizer.decode(_UpperCAmelCase)
          self.assertEqual(_UpperCAmelCase						,     _UpperCAmelCase)


     @slow
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Optional[Any]								=	self.tokenizer_class.from_pretrained('Tanrei/GPTSAN-japanese')

          # Testing tokenization
          __A						:			Tuple								=	'こんにちは、世界。'
          __A						:			List[Any]								=	'こんばんは、㔺界。😀'
          __A						:			str								=	'こんにちは、世界。こんばんは、世界。😀'
          __A						:			Any								=	tokenizer.encode(prefix_text + input_text)
          __A						:			Optional[int]								=	tokenizer.encode(''						,     prefix_text=prefix_text + input_text)
          __A						:			Tuple								=	tokenizer.encode(_UpperCAmelCase						,     prefix_text=_UpperCAmelCase)
          __A						:			str								=	tokenizer.decode(_UpperCAmelCase)
          __A						:			Dict								=	tokenizer.decode(_UpperCAmelCase)
          __A						:			Union[str, Any]								=	tokenizer.decode(_UpperCAmelCase)
          self.assertEqual(_UpperCAmelCase						,     _UpperCAmelCase)
          self.assertEqual(_UpperCAmelCase						,     _UpperCAmelCase)
          self.assertEqual(_UpperCAmelCase						,     _UpperCAmelCase)


     @slow
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Union[str, Any]								=	self.tokenizer_class.from_pretrained('Tanrei/GPTSAN-japanese')

          # Testing tokenization
          __A						:			str								=	'こんにちは、世界。'
          __A						:			Tuple								=	'こんばんは、㔺界。😀'

          __A						:			Tuple								=	len(tokenizer.encode(_UpperCAmelCase)) - 2
          __A						:			str								=	len(tokenizer.encode(_UpperCAmelCase)) - 2

          __A						:			str								=	[1] + [0] * (len_prefix + len_text + 1)
          __A						:			List[Any]								=	[1] * (len_prefix + len_text + 1) + [0]
          __A						:			Dict								=	[1] + [1] * (len_prefix) + [0] * (len_text + 1)

          __A						:			int								=	tokenizer(prefix_text + input_text).token_type_ids
          __A						:			int								=	tokenizer(''						,     prefix_text=prefix_text + input_text).token_type_ids
          __A						:			Any								=	tokenizer(_UpperCAmelCase						,     prefix_text=_UpperCAmelCase).token_type_ids
          self.assertListEqual(_UpperCAmelCase						,     _UpperCAmelCase)
          self.assertListEqual(_UpperCAmelCase						,     _UpperCAmelCase)
          self.assertListEqual(_UpperCAmelCase						,     _UpperCAmelCase)


     @slow
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			str								=	self.tokenizer_class.from_pretrained('Tanrei/GPTSAN-japanese')

          __A						:			Any								=	tokenizer.encode('あンいワ')
          __A						:			Union[str, Any]								=	tokenizer.encode(''						,     prefix_text='あンいワ')
          __A						:			Optional[Any]								=	tokenizer.encode('いワ'						,     prefix_text='あン')

          self.assertEqual(tokenizer.decode(_UpperCAmelCase)						,     tokenizer.decode(_UpperCAmelCase))
          self.assertEqual(tokenizer.decode(_UpperCAmelCase)						,     tokenizer.decode(_UpperCAmelCase))
          self.assertNotEqual(_UpperCAmelCase						,     _UpperCAmelCase)
          self.assertNotEqual(_UpperCAmelCase						,     _UpperCAmelCase)
          self.assertEqual(x_token_a[1]						,     x_token_a[-1])  # SEG token
          self.assertEqual(x_token_a[1]						,     x_token_a[3])  # SEG token


     @slow
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Dict								=	self.tokenizer_class.from_pretrained('Tanrei/GPTSAN-japanese')

          __A						:			Dict								=	[['武田信玄', 'は、'], ['織田信長', 'の配下の、']]
          __A						:			str								=	tokenizer(_UpperCAmelCase						,     padding=_UpperCAmelCase)
          __A						:			Union[str, Any]								=	tokenizer.batch_encode_plus(_UpperCAmelCase						,     padding=_UpperCAmelCase)

          # fmt: off
          __A						:			Union[str, Any]								=	[[3_5993, 8640, 2_5948, 3_5998, 3_0647, 3_5675, 3_5999, 3_5999], [3_5993, 1_0382, 9868, 3_5998, 3_0646, 9459, 3_0646, 3_5675]]
          __A						:			Tuple								=	[[1, 1, 1, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0]]
          __A						:			int								=	[[1, 1, 1, 1, 1, 1, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1]]
          # fmt: on
          self.assertListEqual(x_token.input_ids						,     _UpperCAmelCase)
          self.assertListEqual(x_token.token_type_ids						,     _UpperCAmelCase)
          self.assertListEqual(x_token.attention_mask						,     _UpperCAmelCase)
          self.assertListEqual(x_token_a.input_ids						,     _UpperCAmelCase)
          self.assertListEqual(x_token_a.token_type_ids						,     _UpperCAmelCase)
          self.assertListEqual(x_token_a.attention_mask						,     _UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          pass


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          pass






'''simple docstring'''




from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
  if not (is_transformers_available() and is_torch_available()):
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from ...utils.dummy_torch_and_transformers_objects import ShapEPipeline
else:
  from .camera import create_pan_cameras
  from .pipeline_shap_e import ShapEPipeline
  from .pipeline_shap_e_img2img import ShapEImgaImgPipeline
  from .renderer import (
      BoundingBoxVolume,
      ImportanceRaySampler,
      MLPNeRFModelOutput,
      MLPNeRSTFModel,
      ShapEParamsProjModel,
      ShapERenderer,
      StratifiedRaySampler,
      VoidNeRFModel,
  )