


import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest



class A__     ( _UpperCamelCase  ):



  """simple docstring"""



  _lowercase									=		(UnCLIPScheduler,)

  def    _UpperCamelCase( self			:  List[Any]				,				**lowerCamelCase__			:  List[Any]       ):
        a__	:  Tuple     =   {
            """num_train_timesteps""": 1_000,
            """variance_type""": """fixed_small_log""",
            """clip_sample""": True,
            """clip_sample_range""": 1.0,
            """prediction_type""": """epsilon""",
        }

        config.update(**lowerCamelCase__       )
        return config

  def    _UpperCamelCase( self			:  int       ):







        for timesteps in [1, 5, 100, 1_000]:
              self.check_over_configs(num_train_timesteps=lowerCamelCase__       )

  def    _UpperCamelCase( self			:  Tuple       ):







        for variance in ["fixed_small_log", "learned_range"]:
              self.check_over_configs(variance_type=lowerCamelCase__       )

  def    _UpperCamelCase( self			:  Any       ):







        for clip_sample in [True, False]:
              self.check_over_configs(clip_sample=lowerCamelCase__       )

  def    _UpperCamelCase( self			:  str       ):







        for clip_sample_range in [1, 5, 10, 20]:
              self.check_over_configs(clip_sample_range=lowerCamelCase__       )

  def    _UpperCamelCase( self			:  int       ):







        for prediction_type in ["epsilon", "sample"]:
              self.check_over_configs(prediction_type=lowerCamelCase__       )

  def    _UpperCamelCase( self			:  Optional[Any]       ):







        for time_step in [0, 500, 999]:
              for prev_timestep in [None, 5, 100, 250, 500, 750]:
                    if prev_timestep is not None and prev_timestep >= time_step:
                          continue

                    self.check_over_forward(time_step=lowerCamelCase__				,				prev_timestep=lowerCamelCase__       )

  def    _UpperCamelCase( self			:  Optional[Any]       ):
        a__	:  str     =   self.scheduler_classes[0]
        a__	:  Optional[int]     =   self.get_scheduler_config(variance_type="fixed_small_log"       )
        a__	:  int     =   scheduler_class(**lowerCamelCase__       )

        assert torch.sum(torch.abs(scheduler._get_variance(0       ) - 1.00_00E-10       )       ) < 1E-5
        assert torch.sum(torch.abs(scheduler._get_variance(487       ) - 0.054_9625       )       ) < 1E-5
        assert torch.sum(torch.abs(scheduler._get_variance(999       ) - 0.999_4987       )       ) < 1E-5

  def    _UpperCamelCase( self			:  Optional[Any]       ):
        a__	:  Any     =   self.scheduler_classes[0]
        a__	:  Dict     =   self.get_scheduler_config(variance_type="learned_range"       )
        a__	:  Any     =   scheduler_class(**lowerCamelCase__       )

        a__	:  List[str]     =   0.5

        assert scheduler._get_variance(1				,				predicted_variance=lowerCamelCase__       ) - -10.171_2790 < 1E-5
        assert scheduler._get_variance(487				,				predicted_variance=lowerCamelCase__       ) - -5.799_8052 < 1E-5
        assert scheduler._get_variance(999				,				predicted_variance=lowerCamelCase__       ) - -0.001_0011 < 1E-5

  def    _UpperCamelCase( self			:  List[str]       ):
        a__	:  Union[str, Any]     =   self.scheduler_classes[0]
        a__	:  Tuple     =   self.get_scheduler_config()
        a__	:  Dict     =   scheduler_class(**lowerCamelCase__       )

        a__	:  Union[str, Any]     =   scheduler.timesteps

        a__	:  Optional[int]     =   self.dummy_model()
        a__	:  str     =   self.dummy_sample_deter
        a__	:  Tuple     =   torch.manual_seed(0       )

        for i, t in enumerate(lowerCamelCase__       ):
              # 1. predict noise residual
              a__	:  List[Any]     =   model(lowerCamelCase__				,				lowerCamelCase__       )

              # 2. predict previous mean of sample x_t-1
              a__	:  Optional[int]     =   scheduler.step(lowerCamelCase__				,				lowerCamelCase__				,				lowerCamelCase__				,				generator=lowerCamelCase__       ).prev_sample

              a__	:  Tuple     =   pred_prev_sample

        a__	:  Union[str, Any]     =   torch.sum(torch.abs(lowerCamelCase__       )       )
        a__	:  Dict     =   torch.mean(torch.abs(lowerCamelCase__       )       )

        assert abs(result_sum.item() - 252.2_682_495       ) < 1E-2
        assert abs(result_mean.item() - 0.328_4743       ) < 1E-3

  def    _UpperCamelCase( self			:  Optional[int]       ):
        a__	:  Any     =   self.scheduler_classes[0]
        a__	:  Dict     =   self.get_scheduler_config()
        a__	:  str     =   scheduler_class(**lowerCamelCase__       )

        scheduler.set_timesteps(25       )

        a__	:  Optional[int]     =   scheduler.timesteps

        a__	:  Optional[Any]     =   self.dummy_model()
        a__	:  int     =   self.dummy_sample_deter
        a__	:  Union[str, Any]     =   torch.manual_seed(0       )

        for i, t in enumerate(lowerCamelCase__       ):
              # 1. predict noise residual
              a__	:  List[str]     =   model(lowerCamelCase__				,				lowerCamelCase__       )

              if i + 1 == timesteps.shape[0]:
                    a__	:  int     =   None
              else:
                    a__	:  Optional[Any]     =   timesteps[i + 1]

              # 2. predict previous mean of sample x_t-1
              a__	:  str     =   scheduler.step(
                  lowerCamelCase__				,				lowerCamelCase__				,				lowerCamelCase__				,				prev_timestep=lowerCamelCase__				,				generator=lowerCamelCase__       ).prev_sample

              a__	:  Dict     =   pred_prev_sample

        a__	:  Optional[Any]     =   torch.sum(torch.abs(lowerCamelCase__       )       )
        a__	:  Any     =   torch.mean(torch.abs(lowerCamelCase__       )       )

        assert abs(result_sum.item() - 258.2_044_983       ) < 1E-2
        assert abs(result_mean.item() - 0.336_2038       ) < 1E-3

  def    _UpperCamelCase( self			:  List[Any]       ):
        pass

  def    _UpperCamelCase( self			:  Any       ):
        pass







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    SCREAMING_SNAKE_CASE_				(       _UpperCamelCase	,  unittest.TestCase   ):



	"""simple docstring"""


	__magic_name__   :       str      	=		ProphetNetTokenizer
	__magic_name__   :       Dict      	=		False






	def 					lowerCamelCase__     (					self		:						Union[str, Any]	)	->       List[str]:







						"""simple docstring"""







						super().setUp()

						__UpperCamelCase					:	Optional[Any]             =      [
						    """[UNK]""",
						    """[CLS]""",
						    """[SEP]""",
						    """[PAD]""",
						    """[MASK]""",
						    """want""",
						    """##want""",
						    """##ed""",
						    """wa""",
						    """un""",
						    """runn""",
						    """##ing""",
						    """,""",
						    """low""",
						    """lowest""",
						]
						__UpperCamelCase					:	List[Any]             =      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 					lowerCamelCase__     (					self		:						Tuple					,     lowerCAmelCase		:						int	)	->       List[str]:







						"""simple docstring"""







						__UpperCamelCase					:	Union[str, Any]             =      """UNwant\u00E9d,running"""
						__UpperCamelCase					:	List[str]             =      """unwanted, running"""
						return input_text, output_text






	def 					lowerCamelCase__     (					self		:						List[Any]	)	->       int:







						"""simple docstring"""







						__UpperCamelCase					:	Optional[int]             =      self.tokenizer_class(self.vocab_file	)

						__UpperCamelCase					:	List[str]             =      tokenizer.tokenize("""UNwant\u00E9d,running"""	)
						self.assertListEqual(lowerCAmelCase					,     ["""un""", """##want""", """##ed""", """,""", """runn""", """##ing"""]	)
						self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase	)					,     [9, 6, 7, 12, 10, 11]	)






	def 					lowerCamelCase__     (					self		:						str	)	->       List[Any]:







						"""simple docstring"""







						__UpperCamelCase					:	List[str]             =      BasicTokenizer()

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






	def 					lowerCamelCase__     (					self		:						int	)	->       Tuple:







						"""simple docstring"""







						__UpperCamelCase					:	Any             =      BasicTokenizer(do_lower_case=lowerCAmelCase	)

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






	def 					lowerCamelCase__     (					self		:						Dict	)	->       List[str]:







						"""simple docstring"""







						__UpperCamelCase					:	List[Any]             =      BasicTokenizer(do_lower_case=lowerCAmelCase					,     strip_accents=lowerCAmelCase	)

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






	def 					lowerCamelCase__     (					self		:						Any	)	->       List[Any]:







						"""simple docstring"""







						__UpperCamelCase					:	Tuple             =      BasicTokenizer(do_lower_case=lowerCAmelCase					,     strip_accents=lowerCAmelCase	)

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






	def 					lowerCamelCase__     (					self		:						List[str]	)	->       Optional[int]:







						"""simple docstring"""







						__UpperCamelCase					:	Dict             =      BasicTokenizer(do_lower_case=lowerCAmelCase	)

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






	def 					lowerCamelCase__     (					self		:						List[Any]	)	->       Tuple:







						"""simple docstring"""







						__UpperCamelCase					:	List[str]             =      BasicTokenizer(do_lower_case=lowerCAmelCase	)

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






	def 					lowerCamelCase__     (					self		:						List[Any]	)	->       Tuple:







						"""simple docstring"""







						__UpperCamelCase					:	Tuple             =      BasicTokenizer(do_lower_case=lowerCAmelCase					,     strip_accents=lowerCAmelCase	)

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






	def 					lowerCamelCase__     (					self		:						Optional[int]	)	->       List[Any]:







						"""simple docstring"""







						__UpperCamelCase					:	List[Any]             =      BasicTokenizer(do_lower_case=lowerCAmelCase					,     strip_accents=lowerCAmelCase	)

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






	def 					lowerCamelCase__     (					self		:						Optional[Any]	)	->       str:







						"""simple docstring"""







						__UpperCamelCase					:	int             =      BasicTokenizer(do_lower_case=lowerCAmelCase					,     never_split=["""[UNK]"""]	)

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






	def 					lowerCamelCase__     (					self		:						Union[str, Any]	)	->       Optional[Any]:







						"""simple docstring"""







						__UpperCamelCase					:	int             =      ["""[UNK]""", """[CLS]""", """[SEP]""", """want""", """##want""", """##ed""", """wa""", """un""", """runn""", """##ing"""]

						__UpperCamelCase					:	List[Any]             =      {}
						for i, token in enumerate(lowerCAmelCase	):
											__UpperCamelCase					:	Dict             =      i
						__UpperCamelCase					:	Any             =      WordpieceTokenizer(vocab=lowerCAmelCase					,     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 					lowerCamelCase__     (					self		:						Optional[Any]	)	->       Dict:







						"""simple docstring"""







						__UpperCamelCase					:	str             =      self.tokenizer_class.from_pretrained("""microsoft/prophetnet-large-uncased"""	)

						__UpperCamelCase					:	int             =      ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
						__UpperCamelCase					:	Optional[int]             =      [1037, 2146, 20423, 2005, 7680, 7849, 3989, 1012, 102]
						__UpperCamelCase					:	Optional[int]             =      tokenizer(lowerCAmelCase					,     padding=lowerCAmelCase					,     return_tensors="""pt"""	)
						self.assertIsInstance(lowerCAmelCase					,     lowerCAmelCase	)
						__UpperCamelCase					:	int             =      list(batch.input_ids.numpy()[0]	)
						self.assertListEqual(lowerCAmelCase					,     lowerCAmelCase	)

						self.assertEqual((2, 9)					,     batch.input_ids.shape	)
						self.assertEqual((2, 9)					,     batch.attention_mask.shape	)






	def 					lowerCamelCase__     (					self		:						int	)	->       List[Any]:







						"""simple docstring"""







						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 					lowerCamelCase__     (					self		:						Dict	)	->       Optional[int]:







						"""simple docstring"""







						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 					lowerCamelCase__     (					self		:						Tuple	)	->       Optional[Any]:







						"""simple docstring"""







						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 					lowerCamelCase__     (					self		:						str	)	->       Tuple:







						"""simple docstring"""







						__UpperCamelCase					:	List[str]             =      self.tokenizer_class.from_pretrained("""microsoft/prophetnet-large-uncased"""	)

						__UpperCamelCase					:	Optional[int]             =      tokenizer.encode("""sequence builders"""					,     add_special_tokens=lowerCAmelCase	)
						__UpperCamelCase					:	List[Any]             =      tokenizer.encode("""multi-sequence build"""					,     add_special_tokens=lowerCAmelCase	)

						__UpperCamelCase					:	Optional[Any]             =      tokenizer.build_inputs_with_special_tokens(lowerCAmelCase	)
						__UpperCamelCase					:	Union[str, Any]             =      tokenizer.build_inputs_with_special_tokens(lowerCAmelCase					,     lowerCAmelCase	)

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






