





import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device


_snake_case    :       Union[str, Any]		       =    False



class 							_UpperCAmelCase       (       unittest.TestCase       ):

  """simple docstring"""


  pass





@nightly
@require_torch_gpu
class 							_UpperCAmelCase       (       unittest.TestCase       ):

  """simple docstring"""


  def 							lowercase						(							self					:						Optional[int]	)				->		Optional[int]:
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()






  def 							lowercase						(							self					:						Optional[int]	)				->		List[str]:
     __lowerCAmelCase				      =      VersatileDiffusionTextToImagePipeline.from_pretrained('shi-labs/versatile-diffusion'	)
     # remove text_unet
     pipe.remove_unused_weights()
     pipe.to(lowerCAmelCase_	)
     pipe.set_progress_bar_config(disable=lowerCAmelCase_	)

     __lowerCAmelCase				      =      'A painting of a squirrel eating a burger '
     __lowerCAmelCase				      =      torch.manual_seed(0	)
     __lowerCAmelCase				      =      pipe(
         prompt=lowerCAmelCase_   ,    generator=lowerCAmelCase_   ,    guidance_scale=7.5   ,    num_inference_steps=2   ,    output_type='numpy'	).images

     with tempfile.TemporaryDirectory() as tmpdirname:
        pipe.save_pretrained(lowerCAmelCase_	)
        __lowerCAmelCase				      =      VersatileDiffusionTextToImagePipeline.from_pretrained(lowerCAmelCase_	)
     pipe.to(lowerCAmelCase_	)
     pipe.set_progress_bar_config(disable=lowerCAmelCase_	)

     __lowerCAmelCase				      =      generator.manual_seed(0	)
     __lowerCAmelCase				      =      pipe(
         prompt=lowerCAmelCase_   ,    generator=lowerCAmelCase_   ,    guidance_scale=7.5   ,    num_inference_steps=2   ,    output_type='numpy'	).images

     assert np.abs(image - new_image	).sum() < 1e-5, "Models don't have the same forward pass"






  def 							lowercase						(							self					:						Tuple	)				->		Optional[int]:
     __lowerCAmelCase				      =      VersatileDiffusionTextToImagePipeline.from_pretrained(
         'shi-labs/versatile-diffusion'   ,    torch_dtype=torch.floataa	)
     pipe.to(lowerCAmelCase_	)
     pipe.set_progress_bar_config(disable=lowerCAmelCase_	)

     __lowerCAmelCase				      =      'A painting of a squirrel eating a burger '
     __lowerCAmelCase				      =      torch.manual_seed(0	)
     __lowerCAmelCase				      =      pipe(
         prompt=lowerCAmelCase_   ,    generator=lowerCAmelCase_   ,    guidance_scale=7.5   ,    num_inference_steps=5_0   ,    output_type='numpy'	).images

     __lowerCAmelCase				      =      image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

     assert image.shape == (1, 5_1_2, 5_1_2, 3)
     __lowerCAmelCase				      =      np.array([0.33_67, 0.31_69, 0.26_56, 0.38_70, 0.47_90, 0.37_96, 0.40_09, 0.48_78, 0.47_78]	)

     assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-2




import json
import os
import unittest

from transformers.models.roc_bert.tokenization_roc_bert import (
    VOCAB_FILES_NAMES,
    RoCBertBasicTokenizer,
    RoCBertTokenizer,
    RoCBertWordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english



@require_tokenizers
class        _a		(__magic_name__						,  unittest.TestCase							):


					'''simple docstring'''






					UpperCAmelCase__:  List[str]    				=			RoCBertTokenizer
					UpperCAmelCase__:  Dict    				=			None
					UpperCAmelCase__:  Optional[Any]    				=			False
					UpperCAmelCase__:  Union[str, Any]    				=			True
					UpperCAmelCase__:  Union[str, Any]    				=			filter_non_english






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

						A__				:       Tuple	  =						["""[UNK]""", """[CLS]""", """[SEP]""", """[PAD]""", """[MASK]""", """你""", """好""", """是""", """谁""", """a""", """b""", """c""", """d"""]
						A__				:       Union[str, Any]	  =						{}
						A__				:       Dict	  =						{}
						for i, value in enumerate(A__     ):
							A__				:       Optional[int]	  =						i
							A__				:       Optional[int]	  =						i
						A__				:       Dict	  =						os.path.join(self.tmpdirname       ,			VOCAB_FILES_NAMES["""vocab_file"""]     )
						A__				:       Optional[Any]	  =						os.path.join(self.tmpdirname       ,			VOCAB_FILES_NAMES["""word_shape_file"""]     )
						A__				:       int	  =						os.path.join(self.tmpdirname       ,			VOCAB_FILES_NAMES["""word_pronunciation_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.word_shape_file       ,			"""w"""       ,			encoding="""utf-8"""     ) as word_shape_writer:
							json.dump(A__       ,			A__       ,			ensure_ascii=A__     )
						with open(self.word_pronunciation_file       ,			"""w"""       ,			encoding="""utf-8"""     ) as word_pronunciation_writer:
							json.dump(A__       ,			A__       ,			ensure_ascii=A__     )






					def     __A     (					self     ):
						A__				:       Dict	  =						self.tokenizer_class(self.vocab_file       ,			self.word_shape_file       ,			self.word_pronunciation_file     )

						A__				:       Optional[Any]	  =						tokenizer.tokenize("""你好[SEP]你是谁"""     )
						self.assertListEqual(A__       ,			["""你""", """好""", """[SEP]""", """你""", """是""", """谁"""]     )
						self.assertListEqual(tokenizer.convert_tokens_to_ids(A__     )       ,			[5, 6, 2, 5, 7, 8]     )
						self.assertListEqual(tokenizer.convert_tokens_to_shape_ids(A__     )       ,			[5, 6, 2, 5, 7, 8]     )
						self.assertListEqual(tokenizer.convert_tokens_to_pronunciation_ids(A__     )       ,			[5, 6, 2, 5, 7, 8]     )






					def     __A     (					self     ):
						A__				:       Dict	  =						RoCBertBasicTokenizer()

						self.assertListEqual(tokenizer.tokenize("""ah\u535A\u63A8zz"""     )       ,			["""ah""", """\u535A""", """\u63A8""", """zz"""]     )






					def     __A     (					self     ):
						A__				:       List[str]	  =						RoCBertBasicTokenizer(do_lower_case=A__     )

						self.assertListEqual(
						    tokenizer.tokenize(""" \tHeLLo!how  \n Are yoU?  """     )       ,			["""hello""", """!""", """how""", """are""", """you""", """?"""]     )
						self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""     )       ,			["""hello"""]     )






					def     __A     (					self     ):
						A__				:       int	  =						RoCBertBasicTokenizer(do_lower_case=A__       ,			strip_accents=A__     )

						self.assertListEqual(
						    tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """     )       ,			["""hällo""", """!""", """how""", """are""", """you""", """?"""]     )
						self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""     )       ,			["""h\u00E9llo"""]     )






					def     __A     (					self     ):
						A__				:       Optional[Any]	  =						RoCBertBasicTokenizer(do_lower_case=A__       ,			strip_accents=A__     )

						self.assertListEqual(
						    tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """     )       ,			["""hallo""", """!""", """how""", """are""", """you""", """?"""]     )
						self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""     )       ,			["""hello"""]     )






					def     __A     (					self     ):
						A__				:       Union[str, Any]	  =						RoCBertBasicTokenizer(do_lower_case=A__     )

						self.assertListEqual(
						    tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """     )       ,			["""hallo""", """!""", """how""", """are""", """you""", """?"""]     )
						self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""     )       ,			["""hello"""]     )






					def     __A     (					self     ):
						A__				:       Union[str, Any]	  =						RoCBertBasicTokenizer(do_lower_case=A__     )

						self.assertListEqual(
						    tokenizer.tokenize(""" \tHeLLo!how  \n Are yoU?  """     )       ,			["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?"""]     )






					def     __A     (					self     ):
						A__				:       Dict	  =						RoCBertBasicTokenizer(do_lower_case=A__       ,			strip_accents=A__     )

						self.assertListEqual(
						    tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """     )       ,			["""HäLLo""", """!""", """how""", """Are""", """yoU""", """?"""]     )






					def     __A     (					self     ):
						A__				:       Optional[Any]	  =						RoCBertBasicTokenizer(do_lower_case=A__       ,			strip_accents=A__     )

						self.assertListEqual(
						    tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """     )       ,			["""HaLLo""", """!""", """how""", """Are""", """yoU""", """?"""]     )






					def     __A     (					self     ):
						A__				:       Dict	  =						RoCBertBasicTokenizer(do_lower_case=A__       ,			never_split=["""[UNK]"""]     )

						self.assertListEqual(
						    tokenizer.tokenize(""" \tHeLLo!how  \n Are yoU? [UNK]"""     )       ,			["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?""", """[UNK]"""]     )






					def     __A     (					self     ):
						A__				:       List[Any]	  =						["""[UNK]""", """[CLS]""", """[SEP]""", """want""", """##want""", """##ed""", """wa""", """un""", """runn""", """##ing"""]

						A__				:       Any	  =						{}
						for i, token in enumerate(A__     ):
							A__				:       Optional[int]	  =						i
						A__				:       Optional[Any]	  =						RoCBertWordpieceTokenizer(vocab=A__       ,			unk_token="""[UNK]"""     )

						self.assertListEqual(tokenizer.tokenize(""""""     )       ,			[]     )

						self.assertListEqual(tokenizer.tokenize("""unwanted running"""     )       ,			["""un""", """##want""", """##ed""", """runn""", """##ing"""]     )

						self.assertListEqual(tokenizer.tokenize("""unwantedX running"""     )       ,			["""[UNK]""", """runn""", """##ing"""]     )






					def     __A     (					self     ):
						self.assertTrue(_is_whitespace(""" """     )     )
						self.assertTrue(_is_whitespace("""\t"""     )     )
						self.assertTrue(_is_whitespace("""\r"""     )     )
						self.assertTrue(_is_whitespace("""\n"""     )     )
						self.assertTrue(_is_whitespace("""\u00A0"""     )     )

						self.assertFalse(_is_whitespace("""A"""     )     )
						self.assertFalse(_is_whitespace("""-"""     )     )






					def     __A     (					self     ):
						self.assertTrue(_is_control("""\u0005"""     )     )

						self.assertFalse(_is_control("""A"""     )     )
						self.assertFalse(_is_control(""" """     )     )
						self.assertFalse(_is_control("""\t"""     )     )
						self.assertFalse(_is_control("""\r"""     )     )






					def     __A     (					self     ):
						self.assertTrue(_is_punctuation("""-"""     )     )
						self.assertTrue(_is_punctuation("""$"""     )     )
						self.assertTrue(_is_punctuation("""`"""     )     )
						self.assertTrue(_is_punctuation("""."""     )     )

						self.assertFalse(_is_punctuation("""A"""     )     )
						self.assertFalse(_is_punctuation(""" """     )     )






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

						# Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
						self.assertListEqual([tokenizer.tokenize(A__     ) for t in ["""Test""", """\xad""", """test"""]]       ,			[["""[UNK]"""], [], ["""[UNK]"""]]     )

						if self.test_rust_tokenizer:
							A__				:       Tuple	  =						self.get_rust_tokenizer()
							self.assertListEqual(
							    [rust_tokenizer.tokenize(A__     ) for t in ["""Test""", """\xad""", """test"""]]       ,			[["""[UNK]"""], [], ["""[UNK]"""]]     )






					def     __A     (					self     ):
						for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
							with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""     ):
								A__				:       Dict	  =						self.rust_tokenizer_class.from_pretrained(A__       ,			**A__     )

								A__				:       str	  =						F"""A, naïve {tokenizer_r.mask_token} AllenNLP sentence."""
								A__				:       Union[str, Any]	  =						tokenizer_r.encode_plus(
								    A__       ,			return_attention_mask=A__       ,			return_token_type_ids=A__       ,			return_offsets_mapping=A__       ,			add_special_tokens=A__       ,			)

								A__				:       Any	  =						tokenizer_r.do_lower_case if hasattr(A__       ,			"""do_lower_case"""     ) else False
								A__				:       List[str]	  =						(
								    [
								        ((0, 0), tokenizer_r.cls_token),
								        ((0, 1), """A"""),
								        ((1, 2), ""","""),
								        ((3, 5), """na"""),
								        ((5, 6), """##ï"""),
								        ((6, 8), """##ve"""),
								        ((9, 15), tokenizer_r.mask_token),
								        ((16, 21), """Allen"""),
								        ((21, 23), """##NL"""),
								        ((23, 24), """##P"""),
								        ((25, 33), """sentence"""),
								        ((33, 34), """."""),
								        ((0, 0), tokenizer_r.sep_token),
								    ]
								    if not do_lower_case
								    else [
								        ((0, 0), tokenizer_r.cls_token),
								        ((0, 1), """a"""),
								        ((1, 2), ""","""),
								        ((3, 8), """naive"""),
								        ((9, 15), tokenizer_r.mask_token),
								        ((16, 21), """allen"""),
								        ((21, 23), """##nl"""),
								        ((23, 24), """##p"""),
								        ((25, 33), """sentence"""),
								        ((33, 34), """."""),
								        ((0, 0), tokenizer_r.sep_token),
								    ]
								)

								self.assertEqual(
								    [e[1] for e in expected_results]       ,			tokenizer_r.convert_ids_to_tokens(tokens["""input_ids"""]     )     )
								self.assertEqual([e[0] for e in expected_results]       ,			tokens["""offset_mapping"""]     )






					def     __A     (					self     ):
						A__				:       Union[str, Any]	  =						["""的""", """人""", """有"""]
						A__				:       List[str]	  =						"""""".join(A__     )
						for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
							with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""     ):
								A__				:       Any	  =						True
								A__				:       int	  =						self.tokenizer_class.from_pretrained(A__       ,			**A__     )
								A__				:       Dict	  =						self.rust_tokenizer_class.from_pretrained(A__       ,			**A__     )

								A__				:       List[str]	  =						tokenizer_p.encode(A__       ,			add_special_tokens=A__     )
								A__				:       List[Any]	  =						tokenizer_r.encode(A__       ,			add_special_tokens=A__     )

								A__				:       Tuple	  =						tokenizer_r.convert_ids_to_tokens(A__     )
								A__				:       int	  =						tokenizer_p.convert_ids_to_tokens(A__     )

								# it is expected that each Chinese character is not preceded by "##"
								self.assertListEqual(A__       ,			A__     )
								self.assertListEqual(A__       ,			A__     )

								A__				:       Optional[int]	  =						False
								A__				:       Union[str, Any]	  =						self.rust_tokenizer_class.from_pretrained(A__       ,			**A__     )
								A__				:       str	  =						self.tokenizer_class.from_pretrained(A__       ,			**A__     )

								A__				:       Tuple	  =						tokenizer_r.encode(A__       ,			add_special_tokens=A__     )
								A__				:       List[str]	  =						tokenizer_p.encode(A__       ,			add_special_tokens=A__     )

								A__				:       Union[str, Any]	  =						tokenizer_r.convert_ids_to_tokens(A__     )
								A__				:       Tuple	  =						tokenizer_p.convert_ids_to_tokens(A__     )

								# it is expected that only the first Chinese character is not preceded by "##".
								A__				:       str	  =						[
								    F"""##{token}""" if idx != 0 else token for idx, token in enumerate(A__     )
								]
								self.assertListEqual(A__       ,			A__     )
								self.assertListEqual(A__       ,			A__     )






					@slow
					def     __A     (					self     ):
						A__				:       str	  =						self.tokenizer_class(self.vocab_file       ,			self.word_shape_file       ,			self.word_pronunciation_file     )

						A__				:       Optional[Any]	  =						tokenizer.encode("""你好"""       ,			add_special_tokens=A__     )
						A__				:       List[Any]	  =						tokenizer.encode("""你是谁"""       ,			add_special_tokens=A__     )

						A__				:       Any	  =						tokenizer.build_inputs_with_special_tokens(A__     )
						A__				:       Any	  =						tokenizer.build_inputs_with_special_tokens(A__       ,			A__     )

						assert encoded_sentence == [1] + text + [2]
						assert encoded_pair == [1] + text + [2] + text_a + [2]






					def     __A     (					self     ):
						A__				:       List[str]	  =						self.get_tokenizers(do_lower_case=A__     )
						for tokenizer in tokenizers:
							with self.subTest(F"""{tokenizer.__class__.__name__}"""     ):
								A__				:       Optional[int]	  =						"""你好，你是谁"""
								A__				:       List[str]	  =						tokenizer.tokenize(A__     )
								A__				:       Union[str, Any]	  =						tokenizer.convert_tokens_to_ids(A__     )
								A__				:       str	  =						tokenizer.convert_tokens_to_shape_ids(A__     )
								A__				:       Optional[int]	  =						tokenizer.convert_tokens_to_pronunciation_ids(A__     )
								A__				:       Union[str, Any]	  =						tokenizer.prepare_for_model(
								    A__       ,			A__       ,			A__       ,			add_special_tokens=A__     )

								A__				:       int	  =						tokenizer.encode_plus(A__       ,			add_special_tokens=A__     )

								self.assertEqual(A__       ,			A__     )



