
import json
import os
import unittest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class       _lowerCAmelCase   (      __a   , unittest.TestCase			):
				_lowercase     						=MgpstrTokenizer
				_lowercase     						=False
				_lowercase     						={}
				_lowercase     						=False




				def 	__a		(   self	)							-> Optional[int]:
							super().setUp()

							# fmt: off
							lowerCAmelCase_         =      ["[GO]", "[s]", "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m", "n", "o", "p", "q", "r", "s", "t", "u", "v", "w", "x", "y", "z"]
							# fmt: on
							lowerCAmelCase_         =      dict(zip(_UpperCamelCase							,							range(len(_UpperCamelCase	)	)	)	)

							lowerCAmelCase_         =      os.path.join(self.tmpdirname							,							VOCAB_FILES_NAMES["vocab_file"]	)
							with open(self.vocab_file							,							"w"							,							encoding="utf-8"	) as fp:
										fp.write(json.dumps(_UpperCamelCase	) + "\n"	)




				def 	__a		(   self							,							**_UpperCamelCase	)							-> List[str]:
							return MgpstrTokenizer.from_pretrained(self.tmpdirname							,							**_UpperCamelCase	)




				def 	__a		(   self							,							_UpperCamelCase	)							-> List[Any]:
							lowerCAmelCase_         =      "tester"
							lowerCAmelCase_         =      "tester"
							return input_text, output_text




				@unittest.skip("MGP-STR always lower cases letters."	)
				def 	__a		(   self	)							-> Tuple:
							pass




				def 	__a		(   self	)							-> Optional[Any]:
							lowerCAmelCase_         =      self.get_tokenizers(do_lower_case=_UpperCamelCase	)
							for tokenizer in tokenizers:
										with self.subTest(f"""{tokenizer.__class__.__name__}"""	):
													lowerCAmelCase_         =      "[SPECIAL_TOKEN]"

													tokenizer.add_special_tokens({"cls_token": special_token}	)
													lowerCAmelCase_         =      tokenizer.encode([special_token]							,							add_special_tokens=_UpperCamelCase	)
													self.assertEqual(len(_UpperCamelCase	)							,							1	)

													lowerCAmelCase_         =      tokenizer.decode(_UpperCamelCase							,							skip_special_tokens=_UpperCamelCase	)
													self.assertTrue(special_token not in decoded	)




				def 	__a		(   self	)							-> List[str]:
							lowerCAmelCase_         =      self.get_tokenizers()
							for tokenizer in tokenizers:
										with self.subTest(f"""{tokenizer.__class__.__name__}"""	):
													lowerCAmelCase_      ,    lowerCAmelCase_         =      self.get_input_output_texts(_UpperCamelCase	)

													lowerCAmelCase_         =      tokenizer.tokenize(_UpperCamelCase	)
													lowerCAmelCase_         =      tokenizer.convert_tokens_to_ids(_UpperCamelCase	)
													lowerCAmelCase_         =      tokenizer.encode(_UpperCamelCase							,							add_special_tokens=_UpperCamelCase	)
													self.assertListEqual(_UpperCamelCase							,							_UpperCamelCase	)

													lowerCAmelCase_         =      tokenizer.convert_ids_to_tokens(_UpperCamelCase	)
													self.assertNotEqual(len(_UpperCamelCase	)							,							0	)
													lowerCAmelCase_         =      tokenizer.decode(_UpperCamelCase	)
													self.assertIsInstance(_UpperCamelCase							,							_UpperCamelCase	)

													self.assertEqual(text_a.replace(" "							,							""	)							,							_UpperCamelCase	)




				@unittest.skip("MGP-STR tokenizer only handles one sequence."	)
				def 	__a		(   self	)							-> Any:
							pass




				@unittest.skip("inputs cannot be pretokenized in MgpstrTokenizer"	)
				def 	__a		(   self	)							-> Dict:
							pass





from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available





@dataclass
class       _lowerCAmelCase   (      __a			):
				_lowercase     						=42
				_lowercase     						=42


if is_transformers_available() and is_torch_available():
			from .pipeline_semantic_stable_diffusion import SemanticStableDiffusionPipeline



