




'''simple docstring'''







import json
import os
import unittest

from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin


class 			_a  (	SCREAMING_SNAKE_CASE__							,							unittest.TestCase ):
       __a				:     Union[str, Any]   				=					DebertaTokenizer
       __a				:     Any   				=					True
       __a				:     Tuple   				=					DebertaTokenizerFast
       def     A      (	self						:	Dict				):



           '''simple docstring'''

           super().setUp()

           # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
           UpperCAmelCase			       =		[
               """l""",
               """o""",
               """w""",
               """e""",
               """r""",
               """s""",
               """t""",
               """i""",
               """d""",
               """n""",
               """\u0120""",
               """\u0120l""",
               """\u0120n""",
               """\u0120lo""",
               """\u0120low""",
               """er""",
               """\u0120lowest""",
               """\u0120newer""",
               """\u0120wider""",
               """[UNK]""",
           ]
           UpperCAmelCase			       =		dict(zip(lowerCamelCase__   ,				range(len(lowerCamelCase__				)				)				)				)
           UpperCAmelCase			       =		["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
           UpperCAmelCase			       =		{"""unk_token""": """[UNK]"""}

           UpperCAmelCase			       =		os.path.join(self.tmpdirname   ,				VOCAB_FILES_NAMES['''vocab_file''']				)
           UpperCAmelCase			       =		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(lowerCamelCase__				) + '''\n'''				)
           with open(self.merges_file   ,				'''w'''   ,				encoding='''utf-8'''				) as fp:
               fp.write('''\n'''.join(lowerCamelCase__				)				)
       def     A      (	self						:	int   ,				**lowercase						:	Union[str, Any]				):



           '''simple docstring'''

           kwargs.update(self.special_tokens_map				)
           return self.tokenizer_class.from_pretrained(self.tmpdirname   ,				**lowerCamelCase__				)
       def     A      (	self						:	Tuple   ,				lowercase						:	Union[str, Any]				):



           '''simple docstring'''

           UpperCAmelCase			       =		"""lower newer"""
           UpperCAmelCase			       =		"""lower newer"""
           return input_text, output_text
       def     A      (	self						:	Optional[int]				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.get_tokenizer()
           UpperCAmelCase			       =		"""lower newer"""
           UpperCAmelCase			       =		["""l""", """o""", """w""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
           UpperCAmelCase			       =		tokenizer.tokenize(lowerCamelCase__				)
           self.assertListEqual(lowerCamelCase__   ,				lowerCamelCase__				)

           UpperCAmelCase			       =		tokens + [tokenizer.unk_token]
           UpperCAmelCase			       =		[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
           self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase__				)   ,				lowerCamelCase__				)
       def     A      (	self						:	Tuple				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.get_tokenizer()
           UpperCAmelCase			       =		tokenizer('''Hello'''   ,				'''World'''				)
           UpperCAmelCase			       =		[0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
           self.assertListEqual(tokd['''token_type_ids''']   ,				lowerCamelCase__				)
       @slow
       def     A      (	self						:	Optional[int]				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.tokenizer_class.from_pretrained('''microsoft/deberta-base'''				)

           UpperCAmelCase			       =		tokenizer.encode('''sequence builders'''   ,				add_special_tokens=lowerCamelCase__				)
           UpperCAmelCase			       =		tokenizer.encode('''multi-sequence build'''   ,				add_special_tokens=lowerCamelCase__				)

           UpperCAmelCase			       =		tokenizer.encode(
               '''sequence builders'''   ,				add_special_tokens=lowerCamelCase__   ,				add_prefix_space=lowerCamelCase__				)
           UpperCAmelCase			       =		tokenizer.encode(
               '''sequence builders'''   ,				'''multi-sequence build'''   ,				add_special_tokens=lowerCamelCase__   ,				add_prefix_space=lowerCamelCase__				)

           UpperCAmelCase			       =		tokenizer.build_inputs_with_special_tokens(lowerCamelCase__				)
           UpperCAmelCase			       =		tokenizer.build_inputs_with_special_tokens(lowerCamelCase__   ,				lowerCamelCase__				)

           assert encoded_sentence == encoded_text_from_decode
           assert encoded_pair == encoded_pair_from_decode







       @slow
       def     A      (	self						:	Tuple				):



           '''simple docstring'''

           UpperCAmelCase			       =		[self.tokenizer_class]
           if self.test_rust_tokenizer:
               tokenizer_classes.append(self.rust_tokenizer_class				)

           for tokenizer_class in tokenizer_classes:
               UpperCAmelCase			       =		tokenizer_class.from_pretrained('''microsoft/deberta-base'''				)

               UpperCAmelCase			       =		[
                   """ALBERT: A Lite BERT for Self-supervised Learning of Language Representations""",
                   """ALBERT incorporates two parameter reduction techniques""",
                   """The first one is a factorized embedding parameterization. By decomposing the large vocabulary"""
                   """ embedding matrix into two small matrices, we separate the size of the hidden layers from the size of"""
                   """ vocabulary embedding.""",
               ]

               UpperCAmelCase			       =		tokenizer(lowerCamelCase__   ,				padding=lowerCamelCase__				)
               UpperCAmelCase			       =		[tokenizer.decode(lowerCamelCase__   ,				skip_special_tokens=lowerCamelCase__				) for seq in encoding["""input_ids"""]]

               # fmt: off
               UpperCAmelCase			       =		{
                   """input_ids""": [
                       [1, 2_118, 11_126, 565, 35, 83, 25_191, 163, 18_854, 13, 12_156, 12, 16_101, 25_376, 13_807, 9, 22_205, 27_893, 1_635, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                       [1, 2_118, 11_126, 565, 24_536, 80, 43_797, 4_878, 7_373, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                       [1, 133, 78, 65, 16, 10, 3_724, 1_538, 33_183, 11_303, 43_797, 1_938, 4, 870, 24_165, 29_105, 5, 739, 32_644, 33_183, 11_303, 36_173, 88, 80, 650, 7_821, 45_940, 6, 52, 2_559, 5, 1_836, 9, 5, 7_397, 13_171, 31, 5, 1_836, 9, 32_644, 33_183, 11_303, 4, 2]
                   ],
                   """token_type_ids""": [
                       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
                   ],
                   """attention_mask""": [
                       [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                       [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                       [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
                   ]
               }
               # fmt: on

               UpperCAmelCase			       =		[
                   """ALBERT: A Lite BERT for Self-supervised Learning of Language Representations""",
                   """ALBERT incorporates two parameter reduction techniques""",
                   """The first one is a factorized embedding parameterization. By decomposing the large vocabulary"""
                   """ embedding matrix into two small matrices, we separate the size of the hidden layers from the size of"""
                   """ vocabulary embedding.""",
               ]

               self.assertDictEqual(encoding.data   ,				lowerCamelCase__				)

               for expected, decoded in zip(lowerCamelCase__   ,				lowerCamelCase__				):
                   self.assertEqual(lowerCamelCase__   ,				lowerCamelCase__				)









import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest






class SCREAMING_SNAKE_CASE__     (		SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    __lowerCamelCase			: List[str]					=      (DDIMParallelScheduler,)
    __lowerCamelCase			: int					=      (("eta", 0.0), ("num_inference_steps", 50))






    def 			_lowerCAmelCase					(       self,		**lowerCamelCase__		):
        A :       int          =   {
            """num_train_timesteps""": 1000,
            """beta_start""": 0.0001,
            """beta_end""": 0.02,
            """beta_schedule""": """linear""",
            """clip_sample""": True,
        }

        config.update(**lowerCamelCase__		)
        return config






    def 			_lowerCAmelCase					(       self,		**lowerCamelCase__		):
        A :       Dict          =   self.scheduler_classes[0]
        A :       str          =   self.get_scheduler_config(**lowerCamelCase__		)
        A :       Tuple          =   scheduler_class(**lowerCamelCase__		)

        A     ,							A :       Tuple          =   10, 0.0

        A :       Optional[int]          =   self.dummy_model()
        A :       List[str]          =   self.dummy_sample_deter

        scheduler.set_timesteps(lowerCamelCase__		)

        for t in scheduler.timesteps:
            A :       Optional[int]          =   model(lowerCamelCase__,		lowerCamelCase__		)
            A :       str          =   scheduler.step(lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		).prev_sample

        return sample






    def 			_lowerCAmelCase					(       self		):
        for timesteps in [100, 500, 1000]:
            self.check_over_configs(num_train_timesteps=lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self		):
        for steps_offset in [0, 1]:
            self.check_over_configs(steps_offset=lowerCamelCase__		)

        A :       Tuple          =   self.scheduler_classes[0]
        A :       Optional[Any]          =   self.get_scheduler_config(steps_offset=1		)
        A :       List[Any]          =   scheduler_class(**lowerCamelCase__		)
        scheduler.set_timesteps(5		)
        assert torch.equal(scheduler.timesteps,		torch.LongTensor([801, 601, 401, 201, 1]		)		)






    def 			_lowerCAmelCase					(       self		):
        for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1],		[0.002, 0.02, 0.2, 2]		):
            self.check_over_configs(beta_start=lowerCamelCase__,		beta_end=lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self		):
        for schedule in ["linear", "squaredcos_cap_v2"]:
            self.check_over_configs(beta_schedule=lowerCamelCase__		)






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






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






    def 			_lowerCAmelCase					(       self		):
        for timestep_spacing in ["trailing", "leading"]:
            self.check_over_configs(timestep_spacing=lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self		):
        for rescale_betas_zero_snr in [True, False]:
            self.check_over_configs(rescale_betas_zero_snr=lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self		):
        self.check_over_configs(thresholding=lowerCamelCase__		)
        for threshold in [0.5, 1.0, 2.0]:
            for prediction_type in ["epsilon", "v_prediction"]:
                self.check_over_configs(
                    thresholding=lowerCamelCase__,		prediction_type=lowerCamelCase__,		sample_max_value=lowerCamelCase__,		)






    def 			_lowerCAmelCase					(       self		):
        for t in [1, 10, 49]:
            self.check_over_forward(time_step=lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self		):
        for t, num_inference_steps in zip([1, 10, 50],		[10, 50, 500]		):
            self.check_over_forward(time_step=lowerCamelCase__,		num_inference_steps=lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self		):
        for t, eta in zip([1, 10, 49],		[0.0, 0.5, 1.0]		):
            self.check_over_forward(time_step=lowerCamelCase__,		eta=lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self		):
        A :       List[str]          =   self.scheduler_classes[0]
        A :       List[str]          =   self.get_scheduler_config()
        A :       Dict          =   scheduler_class(**lowerCamelCase__		)

        assert torch.sum(torch.abs(scheduler._get_variance(0,		0		) - 0.0		)		) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(420,		400		) - 0.1_4771		)		) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(980,		960		) - 0.3_2460		)		) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(0,		0		) - 0.0		)		) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(487,		486		) - 0.0_0979		)		) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(999,		998		) - 0.02		)		) < 1e-5






    def 			_lowerCAmelCase					(       self		):
        A :       int          =   self.scheduler_classes[0]
        A :       Any          =   self.get_scheduler_config()
        A :       Union[str, Any]          =   scheduler_class(**lowerCamelCase__		)

        A     ,							A :       List[str]          =   10, 0.0
        scheduler.set_timesteps(lowerCamelCase__		)

        A :       Any          =   self.dummy_model()
        A :       Dict          =   self.dummy_sample_deter
        A :       Dict          =   self.dummy_sample_deter + 0.1
        A :       Tuple          =   self.dummy_sample_deter - 0.1

        A :       Tuple          =   samplea.shape[0]
        A :       List[Any]          =   torch.stack([samplea, samplea, samplea],		dim=0		)
        A :       str          =   torch.arange(lowerCamelCase__		)[0:3, None].repeat(1,		lowerCamelCase__		)

        A :       Any          =   model(samples.flatten(0,		1		),		timesteps.flatten(0,		1		)		)
        A :       Any          =   scheduler.batch_step_no_noise(lowerCamelCase__,		timesteps.flatten(0,		1		),		samples.flatten(0,		1		),		lowerCamelCase__		)

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

        assert abs(result_sum.item() - 1147.7904		) < 1e-2
        assert abs(result_mean.item() - 0.4982		) < 1e-3






    def 			_lowerCAmelCase					(       self		):
        A :       Dict          =   self.full_loop()

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

        assert abs(result_sum.item() - 172.0067		) < 1e-2
        assert abs(result_mean.item() - 0.22_3967		) < 1e-3






    def 			_lowerCAmelCase					(       self		):
        A :       str          =   self.full_loop(prediction_type="""v_prediction"""		)

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

        assert abs(result_sum.item() - 52.5302		) < 1e-2
        assert abs(result_mean.item() - 0.0684		) < 1e-3






    def 			_lowerCAmelCase					(       self		):
        # We specify different beta, so that the first alpha is 0.99
        A :       Any          =   self.full_loop(set_alpha_to_one=lowerCamelCase__,		beta_start=0.01		)
        A :       Dict          =   torch.sum(torch.abs(lowerCamelCase__		)		)
        A :       str          =   torch.mean(torch.abs(lowerCamelCase__		)		)

        assert abs(result_sum.item() - 149.8295		) < 1e-2
        assert abs(result_mean.item() - 0.1951		) < 1e-3






    def 			_lowerCAmelCase					(       self		):
        # We specify different beta, so that the first alpha is 0.99
        A :       int          =   self.full_loop(set_alpha_to_one=lowerCamelCase__,		beta_start=0.01		)
        A :       List[Any]          =   torch.sum(torch.abs(lowerCamelCase__		)		)
        A :       List[Any]          =   torch.mean(torch.abs(lowerCamelCase__		)		)

        assert abs(result_sum.item() - 149.0784		) < 1e-2
        assert abs(result_mean.item() - 0.1941		) < 1e-3


