






import os
import unittest

from transformers import BatchEncoding
from transformers.models.bert.tokenization_bert import (
    BasicTokenizer,
    WordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.models.prophetnet.tokenization_prophetnet import VOCAB_FILES_NAMES, ProphetNetTokenizer
from transformers.testing_utils import require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin







class 			UpperCAmelCase_						(					snake_case_,	unittest.TestCase ):



      """simple docstring"""
      UpperCamelCase_				:					Tuple           =ProphetNetTokenizer
      UpperCamelCase_				:					Optional[Any]           =False
      def     UpperCAmelCase						(				self )				->      str:
          super().setUp()

          UpperCamelCase       :str								=   [
              """[UNK]""",
              """[CLS]""",
              """[SEP]""",
              """[PAD]""",
              """[MASK]""",
              """want""",
              """##want""",
              """##ed""",
              """wa""",
              """un""",
              """runn""",
              """##ing""",
              """,""",
              """low""",
              """lowest""",
          ]
          UpperCamelCase       :int								=   os.path.join(self.tmpdirname    ,				VOCAB_FILES_NAMES['''vocab_file'''] )
          with open(self.vocab_file    ,				'''w'''    ,				encoding='''utf-8''' ) as vocab_writer:
              vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens] ) )
      def     UpperCAmelCase						(				self    ,				SCREAMING_SNAKE_CASE_ )				->      Optional[Any]:
          UpperCamelCase       :str								=   """UNwant\u00E9d,running"""
          UpperCamelCase       :Union[str, Any]								=   """unwanted, running"""
          return input_text, output_text
      def     UpperCAmelCase						(				self )				->      Any:
          UpperCamelCase       :int								=   self.tokenizer_class(self.vocab_file )

          UpperCamelCase       :List[str]								=   tokenizer.tokenize('''UNwant\u00E9d,running''' )
          self.assertListEqual(UpperCAmelCase__    ,				['''un''', '''##want''', '''##ed''', ''',''', '''runn''', '''##ing'''] )
          self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase__ )    ,				[9, 6, 7, 12, 10, 11] )
      def     UpperCAmelCase						(				self )				->      List[Any]:
          UpperCamelCase       :List[str]								=   BasicTokenizer()

          self.assertListEqual(tokenizer.tokenize('''ah\u535A\u63A8zz''' )    ,				['''ah''', '''\u535A''', '''\u63A8''', '''zz'''] )
      def     UpperCAmelCase						(				self )				->      int:
          UpperCamelCase       :Union[str, Any]								=   BasicTokenizer(do_lower_case=UpperCAmelCase__ )

          self.assertListEqual(
              tokenizer.tokenize(''' \tHeLLo!how  \n Are yoU?  ''' )    ,				['''hello''', '''!''', '''how''', '''are''', '''you''', '''?'''] )
          self.assertListEqual(tokenizer.tokenize('''H\u00E9llo''' )    ,				['''hello'''] )
      def     UpperCAmelCase						(				self )				->      List[str]:
          UpperCamelCase       :List[Any]								=   BasicTokenizer(do_lower_case=UpperCAmelCase__    ,				strip_accents=UpperCAmelCase__ )

          self.assertListEqual(
              tokenizer.tokenize(''' \tHäLLo!how  \n Are yoU?  ''' )    ,				['''hällo''', '''!''', '''how''', '''are''', '''you''', '''?'''] )
          self.assertListEqual(tokenizer.tokenize('''H\u00E9llo''' )    ,				['''h\u00E9llo'''] )
      def     UpperCAmelCase						(				self )				->      Dict:
          UpperCamelCase       :Union[str, Any]								=   BasicTokenizer(do_lower_case=UpperCAmelCase__    ,				strip_accents=UpperCAmelCase__ )

          self.assertListEqual(
              tokenizer.tokenize(''' \tHäLLo!how  \n Are yoU?  ''' )    ,				['''hallo''', '''!''', '''how''', '''are''', '''you''', '''?'''] )
          self.assertListEqual(tokenizer.tokenize('''H\u00E9llo''' )    ,				['''hello'''] )
      def     UpperCAmelCase						(				self )				->      Tuple:
          UpperCamelCase       :Optional[int]								=   BasicTokenizer(do_lower_case=UpperCAmelCase__ )

          self.assertListEqual(
              tokenizer.tokenize(''' \tHäLLo!how  \n Are yoU?  ''' )    ,				['''hallo''', '''!''', '''how''', '''are''', '''you''', '''?'''] )
          self.assertListEqual(tokenizer.tokenize('''H\u00E9llo''' )    ,				['''hello'''] )
      def     UpperCAmelCase						(				self )				->      Union[str, Any]:
          UpperCamelCase       :List[str]								=   BasicTokenizer(do_lower_case=UpperCAmelCase__ )

          self.assertListEqual(
              tokenizer.tokenize(''' \tHeLLo!how  \n Are yoU?  ''' )    ,				['''HeLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?'''] )
      def     UpperCAmelCase						(				self )				->      Optional[Any]:
          UpperCamelCase       :Tuple								=   BasicTokenizer(do_lower_case=UpperCAmelCase__    ,				strip_accents=UpperCAmelCase__ )

          self.assertListEqual(
              tokenizer.tokenize(''' \tHäLLo!how  \n Are yoU?  ''' )    ,				['''HäLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?'''] )
      def     UpperCAmelCase						(				self )				->      Union[str, Any]:
          UpperCamelCase       :int								=   BasicTokenizer(do_lower_case=UpperCAmelCase__    ,				strip_accents=UpperCAmelCase__ )

          self.assertListEqual(
              tokenizer.tokenize(''' \tHäLLo!how  \n Are yoU?  ''' )    ,				['''HaLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?'''] )
      def     UpperCAmelCase						(				self )				->      Any:
          UpperCamelCase       :Tuple								=   BasicTokenizer(do_lower_case=UpperCAmelCase__    ,				never_split=['''[UNK]'''] )

          self.assertListEqual(
              tokenizer.tokenize(''' \tHeLLo!how  \n Are yoU? [UNK]''' )    ,				['''HeLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?''', '''[UNK]'''] )
      def     UpperCAmelCase						(				self )				->      Union[str, Any]:
          UpperCamelCase       :List[Any]								=   ["""[UNK]""", """[CLS]""", """[SEP]""", """want""", """##want""", """##ed""", """wa""", """un""", """runn""", """##ing"""]

          UpperCamelCase       :int								=   {}
          for i, token in enumerate(UpperCAmelCase__ ):
              UpperCamelCase       :Optional[Any]								=   i
          UpperCamelCase       :Optional[int]								=   WordpieceTokenizer(vocab=UpperCAmelCase__    ,				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'''] )
      @require_torch
      def     UpperCAmelCase						(				self )				->      Optional[int]:
          UpperCamelCase       :List[Any]								=   self.tokenizer_class.from_pretrained('''microsoft/prophetnet-large-uncased''' )

          UpperCamelCase       :Any								=   ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
          UpperCamelCase       :Optional[Any]								=   [1037, 2146, 2_0423, 2005, 7680, 7849, 3989, 1012, 102]
          UpperCamelCase       :str								=   tokenizer(UpperCAmelCase__    ,				padding=UpperCAmelCase__    ,				return_tensors='''pt''' )
          self.assertIsInstance(UpperCAmelCase__    ,				UpperCAmelCase__ )
          UpperCamelCase       :Optional[Any]								=   list(batch.input_ids.numpy()[0] )
          self.assertListEqual(UpperCAmelCase__    ,				UpperCAmelCase__ )

          self.assertEqual((2, 9)    ,				batch.input_ids.shape )
          self.assertEqual((2, 9)    ,				batch.attention_mask.shape )
      def     UpperCAmelCase						(				self )				->      Dict:
          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     UpperCAmelCase						(				self )				->      str:
          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     UpperCAmelCase						(				self )				->      Optional[int]:
          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(''' ''' ) )




      @slow
      def     UpperCAmelCase						(				self )				->      str:
          UpperCamelCase       :Union[str, Any]								=   self.tokenizer_class.from_pretrained('''microsoft/prophetnet-large-uncased''' )

          UpperCamelCase       :Optional[Any]								=   tokenizer.encode('''sequence builders'''    ,				add_special_tokens=UpperCAmelCase__ )
          UpperCamelCase       :Optional[int]								=   tokenizer.encode('''multi-sequence build'''    ,				add_special_tokens=UpperCAmelCase__ )

          UpperCamelCase       :List[str]								=   tokenizer.build_inputs_with_special_tokens(UpperCAmelCase__ )
          UpperCamelCase       :int								=   tokenizer.build_inputs_with_special_tokens(UpperCAmelCase__    ,				UpperCAmelCase__ )

          assert encoded_sentence == text + [102]
          assert encoded_pair == text + [102] + text_a + [102]













"""simple docstring"""




import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


_snake_case          =   False






class        UpperCamelCase			(	unittest.TestCase      ):
 pass







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


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


 def _lowercase	(     self		:						Tuple							)					->       List[Any]:
   _a  :       Tuple             =	VersatileDiffusionPipeline.from_pretrained("""shi-labs/versatile-diffusion"""							,						torch_dtype=torch.floataa							)
   pipe.to(UpperCAmelCase__							)
   pipe.set_progress_bar_config(disable=UpperCAmelCase__							)

   _a  :       List[str]             =	load_image(
       """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"""							)

   _a  :       Optional[Any]             =	torch.manual_seed(0							)
   _a  :       Union[str, Any]             =	pipe.dual_guided(
       prompt="""first prompt"""							,						image=UpperCAmelCase__							,						text_to_image_strength=0.7_5							,						generator=UpperCAmelCase__							,						guidance_scale=7.5							,						num_inference_steps=2							,						output_type="""numpy"""							,						).images

   with tempfile.TemporaryDirectory() as tmpdirname:
     pipe.save_pretrained(UpperCAmelCase__							)
     _a  :       Dict             =	VersatileDiffusionPipeline.from_pretrained(UpperCAmelCase__							,						torch_dtype=torch.floataa							)
   pipe.to(UpperCAmelCase__							)
   pipe.set_progress_bar_config(disable=UpperCAmelCase__							)

   _a  :       Optional[Any]             =	generator.manual_seed(0							)
   _a  :       str             =	pipe.dual_guided(
       prompt="""first prompt"""							,						image=UpperCAmelCase__							,						text_to_image_strength=0.7_5							,						generator=UpperCAmelCase__							,						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		:						Optional[int]							)					->       Optional[int]:
   _a  :       Optional[int]             =	VersatileDiffusionPipeline.from_pretrained("""shi-labs/versatile-diffusion"""							,						torch_dtype=torch.floataa							)
   pipe.to(UpperCAmelCase__							)
   pipe.set_progress_bar_config(disable=UpperCAmelCase__							)

   _a  :       int             =	"""cyberpunk 2077"""
   _a  :       Tuple             =	load_image(
       """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"""							)
   _a  :       Tuple             =	torch.manual_seed(0							)
   _a  :       Any             =	pipe.dual_guided(
       prompt=UpperCAmelCase__							,						image=UpperCAmelCase__							,						text_to_image_strength=0.7_5							,						generator=UpperCAmelCase__							,						guidance_scale=7.5							,						num_inference_steps=50							,						output_type="""numpy"""							,						).images

   _a  :       List[str]             =	image[0, 253:256, 253:256, -1]

   assert image.shape == (1, 512, 512, 3)
   _a  :       Optional[int]             =	np.array([0.1_4_4_8, 0.1_6_1_9, 0.1_7_4_1, 0.1_0_8_6, 0.1_1_4_7, 0.1_1_2_8, 0.1_1_9_9, 0.1_1_6_5, 0.1_0_0_1]							)

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

   _a  :       int             =	"""A painting of a squirrel eating a burger """
   _a  :       Tuple             =	torch.manual_seed(0							)
   _a  :       Union[str, Any]             =	pipe.text_to_image(
       prompt=UpperCAmelCase__							,						generator=UpperCAmelCase__							,						guidance_scale=7.5							,						num_inference_steps=50							,						output_type="""numpy"""							).images

   _a  :       int             =	image[0, 253:256, 253:256, -1]

   assert image.shape == (1, 512, 512, 3)
   _a  :       int             =	np.array([0.3_3_6_7, 0.3_1_6_9, 0.2_6_5_6, 0.3_8_7_0, 0.4_7_9_0, 0.3_7_9_6, 0.4_0_0_9, 0.4_8_7_8, 0.4_7_7_8]							)

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

   _a  :       str             =	pipe.image_variation(UpperCAmelCase__							,						generator=UpperCAmelCase__							,						output_type="""numpy"""							).images

   _a  :       str             =	image[0, 253:256, 253:256, -1]

   assert image.shape == (1, 512, 512, 3)
   _a  :       Optional[Any]             =	np.array([0.3_0_7_6, 0.3_1_2_3, 0.3_2_8_4, 0.3_7_8_2, 0.3_7_7_0, 0.3_8_9_4, 0.4_2_9_7, 0.4_3_3_1, 0.4_4_5_6]							)

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

