



'''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 					UpperCAmelCase__	(		_UpperCAmelCase							,       unittest.TestCase ):
					a      :     int        =							DebertaTokenizer
					a      :     Optional[int]        =							True
					a      :     Tuple        =							DebertaTokenizerFast



					def        UpperCAmelCase_ (    self     )     ->			Tuple:
						super().setUp()

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

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



					def        UpperCAmelCase_ (    self			,				**UpperCamelCase     )     ->			Optional[Any]:
						kwargs.update(self.special_tokens_map     )
						return self.tokenizer_class.from_pretrained(self.tmpdirname			,				**lowercase__     )



					def        UpperCAmelCase_ (    self			,				UpperCamelCase     )     ->			int:
						__lowerCAmelCase												=  """lower newer"""
						__lowerCAmelCase												=  """lower newer"""
						return input_text, output_text



					def        UpperCAmelCase_ (    self     )     ->			Optional[Any]:
						__lowerCAmelCase												=  self.get_tokenizer()
						__lowerCAmelCase												=  """lower newer"""
						__lowerCAmelCase												=  ["""l""", """o""", """w""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
						__lowerCAmelCase												=  tokenizer.tokenize(lowercase__     )
						self.assertListEqual(lowercase__			,				lowercase__     )

						__lowerCAmelCase												=  tokens + [tokenizer.unk_token]
						__lowerCAmelCase												=  [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
						self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase__     )			,				lowercase__     )



					def        UpperCAmelCase_ (    self     )     ->			Tuple:
						__lowerCAmelCase												=  self.get_tokenizer()
						__lowerCAmelCase												=  tokenizer("Hello"			,				"World"     )
						__lowerCAmelCase												=  [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
						self.assertListEqual(tokd["token_type_ids"]			,				lowercase__     )



					@slow
					def        UpperCAmelCase_ (    self     )     ->			Union[str, Any]:
						__lowerCAmelCase												=  self.tokenizer_class.from_pretrained("microsoft/deberta-base"     )

						__lowerCAmelCase												=  tokenizer.encode("sequence builders"			,				add_special_tokens=lowercase__     )
						__lowerCAmelCase												=  tokenizer.encode("multi-sequence build"			,				add_special_tokens=lowercase__     )

						__lowerCAmelCase												=  tokenizer.encode(
						    "sequence builders"			,				add_special_tokens=lowercase__			,				add_prefix_space=lowercase__     )
						__lowerCAmelCase												=  tokenizer.encode(
						    "sequence builders"			,				"multi-sequence build"			,				add_special_tokens=lowercase__			,				add_prefix_space=lowercase__     )

						__lowerCAmelCase												=  tokenizer.build_inputs_with_special_tokens(lowercase__     )
						__lowerCAmelCase												=  tokenizer.build_inputs_with_special_tokens(lowercase__			,				lowercase__     )

						assert encoded_sentence == encoded_text_from_decode
						assert encoded_pair == encoded_pair_from_decode



					@slow
					def        UpperCAmelCase_ (    self     )     ->			str:
						__lowerCAmelCase												=  [self.tokenizer_class]
						if self.test_rust_tokenizer:
							tokenizer_classes.append(self.rust_tokenizer_class     )

						for tokenizer_class in tokenizer_classes:
							__lowerCAmelCase												=  tokenizer_class.from_pretrained("microsoft/deberta-base"     )

							__lowerCAmelCase												=  [
							    """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.""",
							]

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

							# fmt: off
							__lowerCAmelCase												=  {
							    """input_ids""": [
							        [1, 2118, 1_1126, 565, 35, 83, 2_5191, 163, 1_8854, 13, 1_2156, 12, 1_6101, 2_5376, 1_3807, 9, 2_2205, 2_7893, 1635, 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, 2118, 1_1126, 565, 2_4536, 80, 4_3797, 4878, 7373, 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, 3724, 1538, 3_3183, 1_1303, 4_3797, 1938, 4, 870, 2_4165, 2_9105, 5, 739, 3_2644, 3_3183, 1_1303, 3_6173, 88, 80, 650, 7821, 4_5940, 6, 52, 2559, 5, 1836, 9, 5, 7397, 1_3171, 31, 5, 1836, 9, 3_2644, 3_3183, 1_1303, 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

							__lowerCAmelCase												=  [
							    """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			,				lowercase__     )

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




'''simple docstring'''



import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest






class 					UpperCAmelCase__	(		UpperCamelCase__ ):
					a      :     List[str]        =							(CMStochasticIterativeScheduler,)
					a      :     str        =							1_0



					def        UpperCAmelCase_ (    self			,				**UpperCamelCase     )     ->			str:
						__lowerCAmelCase												=  {
						    "num_train_timesteps": 201,
						    "sigma_min": 0.0_02,
						    "sigma_max": 80.0,
						}

						config.update(**UpperCamelCase     )
						return config



					def        UpperCAmelCase_ (    self     )     ->			List[Any]:
						__lowerCAmelCase												=  10

						__lowerCAmelCase												=  self.get_scheduler_config()
						__lowerCAmelCase												=  self.scheduler_classes[0](**UpperCamelCase     )

						scheduler.set_timesteps(UpperCamelCase     )

						__lowerCAmelCase												=  scheduler.timesteps[0]
						__lowerCAmelCase												=  scheduler.timesteps[1]

						__lowerCAmelCase												=  self.dummy_sample
						__lowerCAmelCase												=  0.1 * sample

						__lowerCAmelCase												=  scheduler.step(UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase     ).prev_sample
						__lowerCAmelCase												=  scheduler.step(UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase     ).prev_sample

						self.assertEqual(output_a.shape			,				sample.shape     )
						self.assertEqual(output_a.shape			,				output_a.shape     )



					def        UpperCAmelCase_ (    self     )     ->			Any:
						for timesteps in [10, 50, 100, 1000]:
							self.check_over_configs(num_train_timesteps=UpperCamelCase     )



					def        UpperCAmelCase_ (    self     )     ->			Union[str, Any]:
						for clip_denoised in [True, False]:
							self.check_over_configs(clip_denoised=UpperCamelCase     )



					def        UpperCAmelCase_ (    self     )     ->			List[str]:
						__lowerCAmelCase												=  self.scheduler_classes[0]
						__lowerCAmelCase												=  self.get_scheduler_config()
						__lowerCAmelCase												=  scheduler_class(**UpperCamelCase     )

						__lowerCAmelCase												=  1
						scheduler.set_timesteps(UpperCamelCase     )
						__lowerCAmelCase												=  scheduler.timesteps

						__lowerCAmelCase												=  torch.manual_seed(0     )

						__lowerCAmelCase												=  self.dummy_model()
						__lowerCAmelCase												=  self.dummy_sample_deter * scheduler.init_noise_sigma

						for i, t in enumerate(UpperCamelCase     ):
							# 1. scale model input
							__lowerCAmelCase												=  scheduler.scale_model_input(UpperCamelCase			,				UpperCamelCase     )

							# 2. predict noise residual
							__lowerCAmelCase												=  model(UpperCamelCase			,				UpperCamelCase     )

							# 3. predict previous sample x_t-1
							__lowerCAmelCase												=  scheduler.step(UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				generator=UpperCamelCase     ).prev_sample

							__lowerCAmelCase												=  pred_prev_sample

						__lowerCAmelCase												=  torch.sum(torch.abs(UpperCamelCase     )     )
						__lowerCAmelCase												=  torch.mean(torch.abs(UpperCamelCase     )     )

						assert abs(result_sum.item() - 1_92.76_14     ) < 1E-2
						assert abs(result_mean.item() - 0.25_10     ) < 1E-3



					def        UpperCAmelCase_ (    self     )     ->			Optional[int]:
						__lowerCAmelCase												=  self.scheduler_classes[0]
						__lowerCAmelCase												=  self.get_scheduler_config()
						__lowerCAmelCase												=  scheduler_class(**UpperCamelCase     )

						__lowerCAmelCase												=  [106, 0]
						scheduler.set_timesteps(timesteps=UpperCamelCase     )
						__lowerCAmelCase												=  scheduler.timesteps

						__lowerCAmelCase												=  torch.manual_seed(0     )

						__lowerCAmelCase												=  self.dummy_model()
						__lowerCAmelCase												=  self.dummy_sample_deter * scheduler.init_noise_sigma

						for t in timesteps:
							# 1. scale model input
							__lowerCAmelCase												=  scheduler.scale_model_input(UpperCamelCase			,				UpperCamelCase     )

							# 2. predict noise residual
							__lowerCAmelCase												=  model(UpperCamelCase			,				UpperCamelCase     )

							# 3. predict previous sample x_t-1
							__lowerCAmelCase												=  scheduler.step(UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				generator=UpperCamelCase     ).prev_sample

							__lowerCAmelCase												=  pred_prev_sample

						__lowerCAmelCase												=  torch.sum(torch.abs(UpperCamelCase     )     )
						__lowerCAmelCase												=  torch.mean(torch.abs(UpperCamelCase     )     )

						assert abs(result_sum.item() - 3_47.63_57     ) < 1E-2
						assert abs(result_mean.item() - 0.45_27     ) < 1E-3



					def        UpperCAmelCase_ (    self     )     ->			Any:
						__lowerCAmelCase												=  self.scheduler_classes[0]
						__lowerCAmelCase												=  self.get_scheduler_config()
						__lowerCAmelCase												=  scheduler_class(**UpperCamelCase     )

						__lowerCAmelCase												=  [39, 30, 12, 15, 0]

						with self.assertRaises(UpperCamelCase			,				msg="`timesteps` must be in descending order."     ):
							scheduler.set_timesteps(timesteps=UpperCamelCase     )



					def        UpperCAmelCase_ (    self     )     ->			Optional[int]:
						__lowerCAmelCase												=  self.scheduler_classes[0]
						__lowerCAmelCase												=  self.get_scheduler_config()
						__lowerCAmelCase												=  scheduler_class(**UpperCamelCase     )

						__lowerCAmelCase												=  [39, 30, 12, 1, 0]
						__lowerCAmelCase												=  len(UpperCamelCase     )

						with self.assertRaises(UpperCamelCase			,				msg="Can only pass one of `num_inference_steps` or `timesteps`."     ):
							scheduler.set_timesteps(num_inference_steps=UpperCamelCase			,				timesteps=UpperCamelCase     )



					def        UpperCAmelCase_ (    self     )     ->			Tuple:
						__lowerCAmelCase												=  self.scheduler_classes[0]
						__lowerCAmelCase												=  self.get_scheduler_config()
						__lowerCAmelCase												=  scheduler_class(**UpperCamelCase     )

						__lowerCAmelCase												=  [scheduler.config.num_train_timesteps]

						with self.assertRaises(
						    UpperCamelCase			,				msg="`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"			,				):
							scheduler.set_timesteps(timesteps=UpperCamelCase     )