"""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							(				SCREAMING_SNAKE_CASE_					,			unittest.TestCase	):
					_UpperCamelCase							:    Any	      =	GPTaTokenizer
					_UpperCamelCase							:    Optional[int]	      =	GPTaTokenizerFast
					_UpperCamelCase							:    Optional[int]	      =	True
					_UpperCamelCase							:    List[Any]	      =	{"add_prefix_space": True}
					_UpperCamelCase							:    List[Any]	      =	False






					def 					__A						(	self						):
							super().setUp()

							# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
							_lowerCAmelCase		: int						    =  [
							    """l""",
							    """o""",
							    """w""",
							    """e""",
							    """r""",
							    """s""",
							    """t""",
							    """i""",
							    """d""",
							    """n""",
							    """\u0120""",
							    """\u0120l""",
							    """\u0120n""",
							    """\u0120lo""",
							    """\u0120low""",
							    """er""",
							    """\u0120lowest""",
							    """\u0120newer""",
							    """\u0120wider""",
							    """<unk>""",
							    """<|endoftext|>""",
							]
							_lowerCAmelCase		: str						    =  dict(zip(a__       ,  range(len(a__						)						)						)						)
							_lowerCAmelCase		: List[str]						    =  ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
							_lowerCAmelCase		: Dict						    =  {"""unk_token""": """<unk>"""}

							_lowerCAmelCase		: str						    =  os.path.join(self.tmpdirname       ,  VOCAB_FILES_NAMES["""vocab_file"""]						)
							_lowerCAmelCase		: 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(a__						) + """\n"""						)
							with open(self.merges_file       ,  """w"""       ,  encoding="""utf-8"""						) as fp:
									fp.write("""\n""".join(a__						)						)






					def 					__A						(	self       ,  **a__						):
							kwargs.update(self.special_tokens_map						)
							return GPTaTokenizer.from_pretrained(self.tmpdirname       ,  **a__						)






					def 					__A						(	self       ,  **a__						):
							kwargs.update(self.special_tokens_map						)
							return GPTaTokenizerFast.from_pretrained(self.tmpdirname       ,  **a__						)






					def 					__A						(	self       ,  a__						):
							_lowerCAmelCase		: Any						    =  """lower newer"""
							_lowerCAmelCase		: Tuple						    =  """lower newer"""
							return input_text, output_text






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

							_lowerCAmelCase		: List[str]						    =  tokens + [tokenizer.unk_token]
							_lowerCAmelCase		: Tuple						    =  [14, 15, 10, 9, 3, 2, 15, 19]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(a__						)       ,  a__						)






					def 					__A						(	self						):
							if not self.test_rust_tokenizer:
									return

							_lowerCAmelCase		: List[str]						    =  self.get_tokenizer()
							_lowerCAmelCase		: int						    =  self.get_rust_tokenizer(add_prefix_space=a__						)

							_lowerCAmelCase		: Optional[int]						    =  """lower newer"""

							# Testing tokenization
							_lowerCAmelCase		: Optional[Any]						    =  tokenizer.tokenize(a__       ,  add_prefix_space=a__						)
							_lowerCAmelCase		: Dict						    =  rust_tokenizer.tokenize(a__						)
							self.assertListEqual(a__       ,  a__						)

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

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

							# Testing the unknown token
							_lowerCAmelCase		: int						    =  tokens + [rust_tokenizer.unk_token]
							_lowerCAmelCase		: Optional[Any]						    =  [14, 15, 10, 9, 3, 2, 15, 19]
							self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(a__						)       ,  a__						)






					def 					__A						(	self       ,  *a__       ,  **a__						):
							# 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 					__A						(	self       ,  a__=15						):
							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
									with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"						):
											_lowerCAmelCase		: str						    =  self.rust_tokenizer_class.from_pretrained(a__       ,  **a__						)

											# Simple input
											_lowerCAmelCase		: Optional[Any]						    =  """This is a simple input"""
											_lowerCAmelCase		: Any						    =  ["""This is a simple input 1""", """This is a simple input 2"""]
											_lowerCAmelCase		: Any						    =  ("""This is a simple input""", """This is a pair""")
											_lowerCAmelCase		: Tuple						    =  [
											    ("""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 					__A						(	self						):
							_lowerCAmelCase		: Tuple						    =  GPTaTokenizer.from_pretrained(self.tmpdirname       ,  pad_token="""<pad>"""						)

							# Simple input
							_lowerCAmelCase		: Any						    =  """This is a simple input"""
							_lowerCAmelCase		: List[Any]						    =  ["""This is a simple input looooooooong""", """This is a simple input"""]
							_lowerCAmelCase		: List[str]						    =  ("""This is a simple input""", """This is a pair""")
							_lowerCAmelCase		: Union[str, Any]						    =  [
							    ("""This is a simple input loooooong""", """This is a simple input"""),
							    ("""This is a simple pair loooooong""", """This is a simple pair"""),
							]

							_lowerCAmelCase		: Optional[Any]						    =  tokenizer.pad_token_id

							_lowerCAmelCase		: Tuple						    =  tokenizer(a__       ,  padding="""max_length"""       ,  max_length=30       ,  return_tensors="""np"""						)
							_lowerCAmelCase		: Optional[Any]						    =  tokenizer(a__       ,  padding=a__       ,  truncate=a__       ,  return_tensors="""np"""						)
							_lowerCAmelCase		: List[str]						    =  tokenizer(*a__       ,  padding="""max_length"""       ,  max_length=60       ,  return_tensors="""np"""						)
							_lowerCAmelCase		: Tuple						    =  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 					__A						(	self						):
							_lowerCAmelCase		: str						    =  """$$$"""
							_lowerCAmelCase		: Dict						    =  GPTaTokenizer.from_pretrained(self.tmpdirname       ,  bos_token=a__       ,  add_bos_token=a__						)

							_lowerCAmelCase		: List[str]						    =  """This is a simple input"""
							_lowerCAmelCase		: Optional[int]						    =  ["""This is a simple input 1""", """This is a simple input 2"""]

							_lowerCAmelCase		: Optional[int]						    =  tokenizer.bos_token_id

							_lowerCAmelCase		: Optional[int]						    =  tokenizer(a__						)
							_lowerCAmelCase		: Optional[Any]						    =  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						)						)

							_lowerCAmelCase		: Optional[Any]						    =  tokenizer.decode(out_s.input_ids						)
							_lowerCAmelCase		: List[Any]						    =  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 					__A						(	self						):
							pass






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

											_lowerCAmelCase		: Dict						    =  [
											    (x if not special_tokens_mask[i] else None) for i, x in enumerate(a__						)
											]
											_lowerCAmelCase		: List[str]						    =  [x for x in filtered_sequence if x is not None]
											self.assertEqual(a__       ,  a__						)







@require_tokenizers
class 		__A							(				unittest.TestCase	):






					def 					__A						(	self						):
							# 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

							_lowerCAmelCase		: Optional[int]						    =  AutoTokenizer.from_pretrained("""facebook/opt-350m"""       ,  from_slow=a__						)
							_lowerCAmelCase		: Tuple						    =  """A photo of a cat"""

							_lowerCAmelCase		: List[str]						    =  tokenizer.encode(
							    a__       ,  )
							self.assertEqual(a__       ,  [2, 250, 1345, 9, 10, 4758]						)
							tokenizer.save_pretrained("""test_opt"""						)

							_lowerCAmelCase		: Optional[int]						    =  AutoTokenizer.from_pretrained("""./test_opt"""						)
							_lowerCAmelCase		: List[Any]						    =  tokenizer.encode(
							    a__       ,  )
							self.assertEqual(a__       ,  [2, 250, 1345, 9, 10, 4758]						)






					def 					__A						(	self						):
							_lowerCAmelCase		: str						    =  AutoTokenizer.from_pretrained("""facebook/opt-350m"""       ,  use_slow=a__						)
							_lowerCAmelCase		: Any						    =  """A photo of a cat"""

							_lowerCAmelCase		: Dict						    =  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 					__A						(	self						):
							_lowerCAmelCase		: List[Any]						    =  AutoTokenizer.from_pretrained("""facebook/opt-350m"""       ,  from_slow=a__						)

							_lowerCAmelCase		: Dict						    =  """bos"""
							_lowerCAmelCase		: str						    =  tokenizer.get_vocab()["""bos"""]

							_lowerCAmelCase		: Any						    =  """A photo of a cat"""
							_lowerCAmelCase		: List[Any]						    =  tokenizer.encode(
							    a__       ,  )
							# We changed the bos token
							self.assertEqual(a__       ,  [31957, 250, 1345, 9, 10, 4758]						)
							tokenizer.save_pretrained("""./tok"""						)
							_lowerCAmelCase		: int						    =  AutoTokenizer.from_pretrained("""./tok"""						)
							self.assertTrue(tokenizer.is_fast						)
							_lowerCAmelCase		: Union[str, Any]						    =  tokenizer.encode(
							    a__       ,  )
							self.assertEqual(a__       ,  [31957, 250, 1345, 9, 10, 4758]						)





"""simple docstring"""




import warnings

from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline  # noqa F401


warnings.warn(
    'The `inpainting.py` script is outdated. Please use directly `from diffusers import'
    ' StableDiffusionInpaintPipeline` instead.'
)




