



'''simple docstring'''




import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors






@require_tokenizers
class 		_UpperCamelCase						(							__lowercase					,      unittest.TestCase					):


			'''simple docstring'''

			_A   :	Optional[int]    					=	MvpTokenizer
			_A   :	List[str]    					=	MvpTokenizerFast
			_A   :	str    					=	True
			_A   :	List[Any]    					=	filter_roberta_detectors

			def UpperCamelCase__      (							self  :				Union[str, Any]		):





					"""simple docstring"""






					super().setUp()
					__SCREAMING_SNAKE_CASE :	int					 =	[
					    """l""",
					    """o""",
					    """w""",
					    """e""",
					    """r""",
					    """s""",
					    """t""",
					    """i""",
					    """d""",
					    """n""",
					    """\u0120""",
					    """\u0120l""",
					    """\u0120n""",
					    """\u0120lo""",
					    """\u0120low""",
					    """er""",
					    """\u0120lowest""",
					    """\u0120newer""",
					    """\u0120wider""",
					    """<unk>""",
					]
					__SCREAMING_SNAKE_CASE :	int					 =	dict(zip(_A							,  range(len(_A		)		)		)		)
					__SCREAMING_SNAKE_CASE :	Any					 =	["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
					__SCREAMING_SNAKE_CASE :	Dict					 =	{"""unk_token""": """<unk>"""}

					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	os.path.join(self.tmpdirname							,  VOCAB_FILES_NAMES["""vocab_file"""]		)
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	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(_A		) + """\n"""		)
					with open(self.merges_file							,  """w"""							,  encoding="""utf-8"""		) as fp:
							fp.write("""\n""".join(_A		)		)

			def UpperCamelCase__      (							self  :				int							,  **lowerCAmelCase__  :				Any		):





					"""simple docstring"""






					kwargs.update(self.special_tokens_map		)
					return self.tokenizer_class.from_pretrained(self.tmpdirname							,  **_A		)

			def UpperCamelCase__      (							self  :				List[str]							,  **lowerCAmelCase__  :				Tuple		):





					"""simple docstring"""






					kwargs.update(self.special_tokens_map		)
					return self.rust_tokenizer_class.from_pretrained(self.tmpdirname							,  **_A		)

			def UpperCamelCase__      (							self  :				Optional[int]							,  lowerCAmelCase__  :				Any		):





					"""simple docstring"""






					return "lower newer", "lower newer"

			@cached_property
			def UpperCamelCase__      (							self  :				Optional[int]		):





					"""simple docstring"""






					return MvpTokenizer.from_pretrained("""RUCAIBox/mvp"""		)

			@cached_property
			def UpperCamelCase__      (							self  :				Any		):





					"""simple docstring"""






					return MvpTokenizerFast.from_pretrained("""RUCAIBox/mvp"""		)

			@require_torch
			def UpperCamelCase__      (							self  :				Any		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Tuple					 =	["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
					__SCREAMING_SNAKE_CASE :	Any					 =	[0, 2_5_0, 2_5_1, 1_7_8_1_8, 1_3, 3_9_1_8_6, 1_9_3_8, 4, 2]

					for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
							__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	tokenizer(_A							,  max_length=len(_A		)							,  padding=_A							,  return_tensors="""pt"""		)
							self.assertIsInstance(_A							,  _A		)

							self.assertEqual((2, 9)							,  batch.input_ids.shape		)
							self.assertEqual((2, 9)							,  batch.attention_mask.shape		)
							__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	batch.input_ids.tolist()[0]
							self.assertListEqual(_A							,  _A		)
							# Test that special tokens are reset

			@require_torch
			def UpperCamelCase__      (							self  :				str		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	List[str]					 =	["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
					for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
							__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	tokenizer(_A							,  padding=_A							,  return_tensors="""pt"""		)
							# check if input_ids are returned and no labels
							self.assertIn("""input_ids"""							,  _A		)
							self.assertIn("""attention_mask"""							,  _A		)
							self.assertNotIn("""labels"""							,  _A		)
							self.assertNotIn("""decoder_attention_mask"""							,  _A		)

			@require_torch
			def UpperCamelCase__      (							self  :				int		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	List[str]					 =	[
					    """Summary of the text.""",
					    """Another summary.""",
					]
					for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
							__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	tokenizer(text_target=_A							,  max_length=3_2							,  padding="""max_length"""							,  return_tensors="""pt"""		)
							self.assertEqual(3_2							,  targets["""input_ids"""].shape[1]		)

			@require_torch
			def UpperCamelCase__      (							self  :				List[str]		):





					"""simple docstring"""






					for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
							__SCREAMING_SNAKE_CASE :	Dict					 =	tokenizer(
							    ["""I am a small frog""" * 1_0_2_4, """I am a small frog"""]							,  padding=_A							,  truncation=_A							,  return_tensors="""pt"""		)
							self.assertIsInstance(_A							,  _A		)
							self.assertEqual(batch.input_ids.shape							,  (2, 1_0_2_4)		)

			@require_torch
			def UpperCamelCase__      (							self  :				List[str]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	List[Any]					 =	["""A long paragraph for summarization."""]
					__SCREAMING_SNAKE_CASE :	List[str]					 =	[
					    """Summary of the text.""",
					]
					for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
							__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	tokenizer(_A							,  text_target=_A							,  return_tensors="""pt"""		)
							__SCREAMING_SNAKE_CASE :	List[str]					 =	inputs["""input_ids"""]
							__SCREAMING_SNAKE_CASE :	Tuple					 =	inputs["""labels"""]
							self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()		)
							self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()		)
							self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()		)
							self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()		)

			def UpperCamelCase__      (							self  :				Dict		):





					"""simple docstring"""






					pass



			def UpperCamelCase__      (							self  :				Dict		):





					"""simple docstring"""






					for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
							with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"		):
									__SCREAMING_SNAKE_CASE :	int					 =	self.rust_tokenizer_class.from_pretrained(_A							,  **_A		)
									__SCREAMING_SNAKE_CASE :	List[str]					 =	self.tokenizer_class.from_pretrained(_A							,  **_A		)
									__SCREAMING_SNAKE_CASE :	Dict					 =	"""A, <mask> AllenNLP sentence."""
									__SCREAMING_SNAKE_CASE :	str					 =	tokenizer_r.encode_plus(_A							,  add_special_tokens=_A							,  return_token_type_ids=_A		)
									__SCREAMING_SNAKE_CASE :	int					 =	tokenizer_p.encode_plus(_A							,  add_special_tokens=_A							,  return_token_type_ids=_A		)

									# token_type_ids should put 0 everywhere
									self.assertEqual(sum(tokens_r["""token_type_ids"""]		)							,  sum(tokens_p["""token_type_ids"""]		)		)

									# attention_mask should put 1 everywhere, so sum over length should be 1
									self.assertEqual(
									    sum(tokens_r["""attention_mask"""]		) / len(tokens_r["""attention_mask"""]		)							,  sum(tokens_p["""attention_mask"""]		) / len(tokens_p["""attention_mask"""]		)							,  )

									__SCREAMING_SNAKE_CASE :	Optional[int]					 =	tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]		)
									__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	tokenizer_p.convert_ids_to_tokens(tokens_p["""input_ids"""]		)

									# Rust correctly handles the space before the mask while python doesnt
									self.assertSequenceEqual(tokens_p["""input_ids"""]							,  [0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]		)
									self.assertSequenceEqual(tokens_r["""input_ids"""]							,  [0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]		)

									self.assertSequenceEqual(
									    _A							,  ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]		)
									self.assertSequenceEqual(
									    _A							,  ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]		)






'''simple docstring'''




import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest






class 		_UpperCamelCase						(							lowerCamelCase__					):


			'''simple docstring'''

			_A   :	Optional[int]    					=	(KDPMaDiscreteScheduler,)
			_A   :	Dict    					=	10

			def UpperCamelCase__      (							self  :				Optional[int]							,  **lowerCAmelCase__  :				List[Any]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Any					 =	{
					    """num_train_timesteps""": 1_1_0_0,
					    """beta_start""": 0.00_01,
					    """beta_end""": 0.02,
					    """beta_schedule""": """linear""",
					}

					config.update(**lowerCAmelCase__		)
					return config

			def UpperCamelCase__      (							self  :				int		):





					"""simple docstring"""






					for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
							self.check_over_configs(num_train_timesteps=lowerCAmelCase__		)

			def UpperCamelCase__      (							self  :				str		):





					"""simple docstring"""






					for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01]							,  [0.00_02, 0.0_02, 0.02]		):
							self.check_over_configs(beta_start=lowerCAmelCase__							,  beta_end=lowerCAmelCase__		)

			def UpperCamelCase__      (							self  :				Tuple		):





					"""simple docstring"""






					for schedule in ["linear", "scaled_linear"]:
							self.check_over_configs(beta_schedule=lowerCAmelCase__		)

			def UpperCamelCase__      (							self  :				int		):





					"""simple docstring"""






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

			def UpperCamelCase__      (							self  :				List[str]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Any					 =	self.scheduler_classes[0]
					__SCREAMING_SNAKE_CASE :	Tuple					 =	self.get_scheduler_config(prediction_type="""v_prediction"""		)
					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	scheduler_class(**lowerCAmelCase__		)

					scheduler.set_timesteps(self.num_inference_steps		)

					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	self.dummy_model()
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	self.dummy_sample_deter * scheduler.init_noise_sigma
					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	sample.to(lowerCAmelCase__		)

					for i, t in enumerate(scheduler.timesteps		):
							__SCREAMING_SNAKE_CASE :	List[Any]					 =	scheduler.scale_model_input(lowerCAmelCase__							,  lowerCAmelCase__		)

							__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	model(lowerCAmelCase__							,  lowerCAmelCase__		)

							__SCREAMING_SNAKE_CASE :	Dict					 =	scheduler.step(lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__		)
							__SCREAMING_SNAKE_CASE :	List[str]					 =	output.prev_sample

					__SCREAMING_SNAKE_CASE :	List[Any]					 =	torch.sum(torch.abs(lowerCAmelCase__		)		)
					__SCREAMING_SNAKE_CASE :	List[Any]					 =	torch.mean(torch.abs(lowerCAmelCase__		)		)

					if torch_device in ["cpu", "mps"]:
							assert abs(result_sum.item() - 4.69_34E-07		) < 1E-2
							assert abs(result_mean.item() - 6.11_12E-10		) < 1E-3
					else:
							# CUDA
							assert abs(result_sum.item() - 4.6_93_42_86_50_17_09_72E-07		) < 1E-2
							assert abs(result_mean.item() - 0.00_02		) < 1E-3

			def UpperCamelCase__      (							self  :				Optional[int]		):





					"""simple docstring"""






					if torch_device == "mps":
							return
					__SCREAMING_SNAKE_CASE :	List[Any]					 =	self.scheduler_classes[0]
					__SCREAMING_SNAKE_CASE :	str					 =	self.get_scheduler_config()
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	scheduler_class(**lowerCAmelCase__		)

					scheduler.set_timesteps(self.num_inference_steps		)

					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	self.dummy_model()
					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	self.dummy_sample_deter * scheduler.init_noise_sigma
					__SCREAMING_SNAKE_CASE :	str					 =	sample.to(lowerCAmelCase__		)

					for i, t in enumerate(scheduler.timesteps		):
							__SCREAMING_SNAKE_CASE :	Tuple					 =	scheduler.scale_model_input(lowerCAmelCase__							,  lowerCAmelCase__		)

							__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	model(lowerCAmelCase__							,  lowerCAmelCase__		)

							__SCREAMING_SNAKE_CASE :	Any					 =	scheduler.step(lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__		)
							__SCREAMING_SNAKE_CASE :	List[Any]					 =	output.prev_sample

					__SCREAMING_SNAKE_CASE :	int					 =	torch.sum(torch.abs(lowerCAmelCase__		)		)
					__SCREAMING_SNAKE_CASE :	int					 =	torch.mean(torch.abs(lowerCAmelCase__		)		)

					if torch_device in ["cpu", "mps"]:
							assert abs(result_sum.item() - 20.41_25		) < 1E-2
							assert abs(result_mean.item() - 0.02_66		) < 1E-3
					else:
							# CUDA
							assert abs(result_sum.item() - 20.41_25		) < 1E-2
							assert abs(result_mean.item() - 0.02_66		) < 1E-3



			def UpperCamelCase__      (							self  :				List[str]		):





					"""simple docstring"""






					if torch_device == "mps":
							return
					__SCREAMING_SNAKE_CASE :	Dict					 =	self.scheduler_classes[0]
					__SCREAMING_SNAKE_CASE :	Dict					 =	self.get_scheduler_config()
					__SCREAMING_SNAKE_CASE :	List[Any]					 =	scheduler_class(**lowerCAmelCase__		)

					scheduler.set_timesteps(self.num_inference_steps							,  device=lowerCAmelCase__		)

					__SCREAMING_SNAKE_CASE :	List[Any]					 =	self.dummy_model()
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	self.dummy_sample_deter.to(lowerCAmelCase__		) * scheduler.init_noise_sigma

					for t in scheduler.timesteps:
							__SCREAMING_SNAKE_CASE :	Dict					 =	scheduler.scale_model_input(lowerCAmelCase__							,  lowerCAmelCase__		)

							__SCREAMING_SNAKE_CASE :	Any					 =	model(lowerCAmelCase__							,  lowerCAmelCase__		)

							__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	scheduler.step(lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__		)
							__SCREAMING_SNAKE_CASE :	str					 =	output.prev_sample

					__SCREAMING_SNAKE_CASE :	Dict					 =	torch.sum(torch.abs(lowerCAmelCase__		)		)
					__SCREAMING_SNAKE_CASE :	int					 =	torch.mean(torch.abs(lowerCAmelCase__		)		)

					if str(lowerCAmelCase__		).startswith("""cpu"""		):
							# The following sum varies between 148 and 156 on mps. Why?
							assert abs(result_sum.item() - 20.41_25		) < 1E-2
							assert abs(result_mean.item() - 0.02_66		) < 1E-3
					else:
							# CUDA
							assert abs(result_sum.item() - 20.41_25		) < 1E-2
							assert abs(result_mean.item() - 0.02_66		) < 1E-3