
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   __lowercase						(						__snake_case   , unittest.TestCase       ):
	_A       =   MvpTokenizer
	_A       =   MvpTokenizerFast
	_A       =   True
	_A       =   filter_roberta_detectors






	def       _a(self				: Union[str, Any]    ) ->			int:
							super().setUp()
							_lowercase       :	int           =   [
							    "l",
							    "o",
							    "w",
							    "e",
							    "r",
							    "s",
							    "t",
							    "i",
							    "d",
							    "n",
							    "\u0120",
							    "\u0120l",
							    "\u0120n",
							    "\u0120lo",
							    "\u0120low",
							    "er",
							    "\u0120lowest",
							    "\u0120newer",
							    "\u0120wider",
							    "<unk>",
							]
							_lowercase       :	List[Any]           =   dict(zip(snake_case      ,	range(len(snake_case    )    )    )    )
							_lowercase       :	int           =   ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
							_lowercase       :	Tuple           =   {"unk_token": "<unk>"}

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






	def       _a(self				: List[str]      ,	**snake_case				: List[str]    ) ->			List[str]:
							kwargs.update(self.special_tokens_map    )
							return self.tokenizer_class.from_pretrained(self.tmpdirname      ,	**snake_case    )






	def       _a(self				: Tuple      ,	**snake_case				: str    ) ->			Any:
							kwargs.update(self.special_tokens_map    )
							return self.rust_tokenizer_class.from_pretrained(self.tmpdirname      ,	**snake_case    )






	def       _a(self				: str      ,	snake_case				: int    ) ->			Optional[Any]:
							return "lower newer", "lower newer"






	@cached_property
	def       _a(self				: Optional[Any]    ) ->			Dict:
							return MvpTokenizer.from_pretrained("RUCAIBox/mvp"    )






	@cached_property
	def       _a(self				: int    ) ->			Union[str, Any]:
							return MvpTokenizerFast.from_pretrained("RUCAIBox/mvp"    )






	@require_torch
	def       _a(self				: str    ) ->			Tuple:
							_lowercase       :	Optional[int]           =   ["A long paragraph for summarization.", "Another paragraph for summarization."]
							_lowercase       :	str           =   [0, 250, 251, 1_7818, 13, 3_9186, 1938, 4, 2]

							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
													_lowercase       :	str           =   tokenizer(snake_case      ,	max_length=len(snake_case    )      ,	padding=snake_case      ,	return_tensors="pt"    )
													self.assertIsInstance(snake_case      ,	snake_case    )

													self.assertEqual((2, 9)      ,	batch.input_ids.shape    )
													self.assertEqual((2, 9)      ,	batch.attention_mask.shape    )
													_lowercase       :	int           =   batch.input_ids.tolist()[0]
													self.assertListEqual(snake_case      ,	snake_case    )
													# Test that special tokens are reset






	@require_torch
	def       _a(self				: Tuple    ) ->			Any:
							_lowercase       :	List[str]           =   ["A long paragraph for summarization.", "Another paragraph for summarization."]
							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
													_lowercase       :	str           =   tokenizer(snake_case      ,	padding=snake_case      ,	return_tensors="pt"    )
													# check if input_ids are returned and no labels
													self.assertIn("input_ids"      ,	snake_case    )
													self.assertIn("attention_mask"      ,	snake_case    )
													self.assertNotIn("labels"      ,	snake_case    )
													self.assertNotIn("decoder_attention_mask"      ,	snake_case    )






	@require_torch
	def       _a(self				: Optional[Any]    ) ->			int:
							_lowercase       :	List[str]           =   [
							    "Summary of the text.",
							    "Another summary.",
							]
							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
													_lowercase       :	Optional[int]           =   tokenizer(text_target=snake_case      ,	max_length=32      ,	padding="max_length"      ,	return_tensors="pt"    )
													self.assertEqual(32      ,	targets["input_ids"].shape[1]    )






	@require_torch
	def       _a(self				: Any    ) ->			str:
							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
													_lowercase       :	List[str]           =   tokenizer(
													    ["I am a small frog" * 1024, "I am a small frog"]      ,	padding=snake_case      ,	truncation=snake_case      ,	return_tensors="pt"    )
													self.assertIsInstance(snake_case      ,	snake_case    )
													self.assertEqual(batch.input_ids.shape      ,	(2, 1024)    )






	@require_torch
	def       _a(self				: List[str]    ) ->			Tuple:
							_lowercase       :	Optional[int]           =   ["A long paragraph for summarization."]
							_lowercase       :	Any           =   [
							    "Summary of the text.",
							]
							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
													_lowercase       :	Dict           =   tokenizer(snake_case      ,	text_target=snake_case      ,	return_tensors="pt"    )
													_lowercase       :	Dict           =   inputs["input_ids"]
													_lowercase       :	int           =   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       _a(self				: Optional[int]    ) ->			Dict:
							pass






	def       _a(self				: Dict    ) ->			Tuple:
							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
													with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""    ):
																			_lowercase       :	Optional[int]           =   self.rust_tokenizer_class.from_pretrained(snake_case      ,	**snake_case    )
																			_lowercase       :	Any           =   self.tokenizer_class.from_pretrained(snake_case      ,	**snake_case    )
																			_lowercase       :	str           =   "A, <mask> AllenNLP sentence."
																			_lowercase       :	str           =   tokenizer_r.encode_plus(snake_case      ,	add_special_tokens=snake_case      ,	return_token_type_ids=snake_case    )
																			_lowercase       :	Any           =   tokenizer_p.encode_plus(snake_case      ,	add_special_tokens=snake_case      ,	return_token_type_ids=snake_case    )

																			# 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"]    )      ,	)

																			_lowercase       :	int           =   tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]    )
																			_lowercase       :	int           =   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, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]    )
																			self.assertSequenceEqual(tokens_r["input_ids"]      ,	[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]    )

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






import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest




class   __lowercase						(						__snake_case       ):
	_A       =   (DEISMultistepScheduler,)
	_A       =   (("num_inference_steps", 25),)






	def       _a(self				: Optional[int]      ,	**snake_case				: str    ) ->			Optional[Any]:
							_lowercase       :	str           =   {
							    "num_train_timesteps": 1000,
							    "beta_start": 0.00_01,
							    "beta_end": 0.02,
							    "beta_schedule": "linear",
							    "solver_order": 2,
							}

							config.update(**snake_case    )
							return config






	def       _a(self				: Optional[int]      ,	snake_case				: Optional[int]=0      ,	**snake_case				: List[Any]    ) ->			Dict:
							_lowercase       :	Dict           =   dict(self.forward_default_kwargs    )
							_lowercase       :	Optional[Any]           =   kwargs.pop("num_inference_steps"      ,	snake_case    )
							_lowercase       :	Optional[int]           =   self.dummy_sample
							_lowercase       :	Tuple           =   0.1 * sample
							_lowercase       :	Optional[Any]           =   [residual + 0.2, residual + 0.15, residual + 0.10]

							for scheduler_class in self.scheduler_classes:
													_lowercase       :	Dict           =   self.get_scheduler_config(**snake_case    )
													_lowercase       :	int           =   scheduler_class(**snake_case    )
													scheduler.set_timesteps(snake_case    )
													# copy over dummy past residuals
													_lowercase       :	Any           =   dummy_past_residuals[: scheduler.config.solver_order]

													with tempfile.TemporaryDirectory() as tmpdirname:
																			scheduler.save_config(snake_case    )
																			_lowercase       :	Any           =   scheduler_class.from_pretrained(snake_case    )
																			new_scheduler.set_timesteps(snake_case    )
																			# copy over dummy past residuals
																			_lowercase       :	int           =   dummy_past_residuals[: new_scheduler.config.solver_order]

													_lowercase      ,    _lowercase       :	Optional[Any]           =   sample, sample
													for t in range(snake_case      ,	time_step + scheduler.config.solver_order + 1    ):
																			_lowercase       :	List[Any]           =   scheduler.step(snake_case      ,	snake_case      ,	snake_case      ,	**snake_case    ).prev_sample
																			_lowercase       :	Union[str, Any]           =   new_scheduler.step(snake_case      ,	snake_case      ,	snake_case      ,	**snake_case    ).prev_sample

																			assert torch.sum(torch.abs(output - new_output    )    ) < 1e-5, "Scheduler outputs are not identical"






	def       _a(self				: str    ) ->			List[str]:
							pass






	def       _a(self				: Optional[Any]      ,	snake_case				: List[Any]=0      ,	**snake_case				: str    ) ->			Union[str, Any]:
							_lowercase       :	List[str]           =   dict(self.forward_default_kwargs    )
							_lowercase       :	Optional[int]           =   kwargs.pop("num_inference_steps"      ,	snake_case    )
							_lowercase       :	Dict           =   self.dummy_sample
							_lowercase       :	Union[str, Any]           =   0.1 * sample
							_lowercase       :	List[str]           =   [residual + 0.2, residual + 0.15, residual + 0.10]

							for scheduler_class in self.scheduler_classes:
													_lowercase       :	List[Any]           =   self.get_scheduler_config()
													_lowercase       :	int           =   scheduler_class(**snake_case    )
													scheduler.set_timesteps(snake_case    )

													# copy over dummy past residuals (must be after setting timesteps)
													_lowercase       :	Union[str, Any]           =   dummy_past_residuals[: scheduler.config.solver_order]

													with tempfile.TemporaryDirectory() as tmpdirname:
																			scheduler.save_config(snake_case    )
																			_lowercase       :	str           =   scheduler_class.from_pretrained(snake_case    )
																			# copy over dummy past residuals
																			new_scheduler.set_timesteps(snake_case    )

																			# copy over dummy past residual (must be after setting timesteps)
																			_lowercase       :	int           =   dummy_past_residuals[: new_scheduler.config.solver_order]

													_lowercase       :	Any           =   scheduler.step(snake_case      ,	snake_case      ,	snake_case      ,	**snake_case    ).prev_sample
													_lowercase       :	Dict           =   new_scheduler.step(snake_case      ,	snake_case      ,	snake_case      ,	**snake_case    ).prev_sample

													assert torch.sum(torch.abs(output - new_output    )    ) < 1e-5, "Scheduler outputs are not identical"






	def       _a(self				: Dict      ,	snake_case				: str=None      ,	**snake_case				: Dict    ) ->			Optional[Any]:
							if scheduler is None:
													_lowercase       :	Optional[Any]           =   self.scheduler_classes[0]
													_lowercase       :	Dict           =   self.get_scheduler_config(**snake_case    )
													_lowercase       :	Optional[int]           =   scheduler_class(**snake_case    )

							_lowercase       :	Tuple           =   self.scheduler_classes[0]
							_lowercase       :	str           =   self.get_scheduler_config(**snake_case    )
							_lowercase       :	Tuple           =   scheduler_class(**snake_case    )

							_lowercase       :	Optional[int]           =   10
							_lowercase       :	List[str]           =   self.dummy_model()
							_lowercase       :	List[Any]           =   self.dummy_sample_deter
							scheduler.set_timesteps(snake_case    )

							for i, t in enumerate(scheduler.timesteps    ):
													_lowercase       :	str           =   model(snake_case      ,	snake_case    )
													_lowercase       :	int           =   scheduler.step(snake_case      ,	snake_case      ,	snake_case    ).prev_sample

							return sample






	def       _a(self				: Any    ) ->			Tuple:
							_lowercase       :	Union[str, Any]           =   dict(self.forward_default_kwargs    )

							_lowercase       :	Optional[int]           =   kwargs.pop("num_inference_steps"      ,	snake_case    )

							for scheduler_class in self.scheduler_classes:
													_lowercase       :	List[Any]           =   self.get_scheduler_config()
													_lowercase       :	Optional[Any]           =   scheduler_class(**snake_case    )

													_lowercase       :	Tuple           =   self.dummy_sample
													_lowercase       :	List[Any]           =   0.1 * sample

													if num_inference_steps is not None and hasattr(snake_case      ,	"set_timesteps"    ):
																			scheduler.set_timesteps(snake_case    )
													elif num_inference_steps is not None and not hasattr(snake_case      ,	"set_timesteps"    ):
																			_lowercase       :	str           =   num_inference_steps

													# copy over dummy past residuals (must be done after set_timesteps)
													_lowercase       :	int           =   [residual + 0.2, residual + 0.15, residual + 0.10]
													_lowercase       :	Dict           =   dummy_past_residuals[: scheduler.config.solver_order]

													_lowercase       :	Tuple           =   scheduler.timesteps[5]
													_lowercase       :	Dict           =   scheduler.timesteps[6]

													_lowercase       :	Tuple           =   scheduler.step(snake_case      ,	snake_case      ,	snake_case      ,	**snake_case    ).prev_sample
													_lowercase       :	Union[str, Any]           =   scheduler.step(snake_case      ,	snake_case      ,	snake_case      ,	**snake_case    ).prev_sample

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






	def       _a(self				: Optional[int]    ) ->			List[Any]:
							# make sure that iterating over schedulers with same config names gives same results
							# for defaults
							_lowercase       :	Any           =   DEISMultistepScheduler(**self.get_scheduler_config()    )
							_lowercase       :	List[Any]           =   self.full_loop(scheduler=snake_case    )
							_lowercase       :	Tuple           =   torch.mean(torch.abs(snake_case    )    )

							assert abs(result_mean.item() - 0.2_39_16    ) < 1e-3

							_lowercase       :	str           =   DPMSolverSinglestepScheduler.from_config(scheduler.config    )
							_lowercase       :	Optional[int]           =   DPMSolverMultistepScheduler.from_config(scheduler.config    )
							_lowercase       :	List[Any]           =   UniPCMultistepScheduler.from_config(scheduler.config    )
							_lowercase       :	int           =   DEISMultistepScheduler.from_config(scheduler.config    )

							_lowercase       :	Union[str, Any]           =   self.full_loop(scheduler=snake_case    )
							_lowercase       :	Dict           =   torch.mean(torch.abs(snake_case    )    )

							assert abs(result_mean.item() - 0.2_39_16    ) < 1e-3






	def       _a(self				: Union[str, Any]    ) ->			Optional[int]:
							for timesteps in [25, 50, 100, 999, 1000]:
													self.check_over_configs(num_train_timesteps=snake_case    )






	def       _a(self				: Optional[Any]    ) ->			List[str]:
							self.check_over_configs(thresholding=snake_case    )
							for order in [1, 2, 3]:
													for solver_type in ["logrho"]:
																			for threshold in [0.5, 1.0, 2.0]:
																									for prediction_type in ["epsilon", "sample"]:
																															self.check_over_configs(
																															    thresholding=snake_case      ,	prediction_type=snake_case      ,	sample_max_value=snake_case      ,	algorithm_type="deis"      ,	solver_order=snake_case      ,	solver_type=snake_case      ,	)






	def       _a(self				: int    ) ->			Tuple:
							for prediction_type in ["epsilon", "v_prediction"]:
													self.check_over_configs(prediction_type=snake_case    )






	def       _a(self				: Tuple    ) ->			List[Any]:
							for algorithm_type in ["deis"]:
													for solver_type in ["logrho"]:
																			for order in [1, 2, 3]:
																									for prediction_type in ["epsilon", "sample"]:
																															self.check_over_configs(
																															    solver_order=snake_case      ,	solver_type=snake_case      ,	prediction_type=snake_case      ,	algorithm_type=snake_case      ,	)
																															_lowercase       :	Any           =   self.full_loop(
																															    solver_order=snake_case      ,	solver_type=snake_case      ,	prediction_type=snake_case      ,	algorithm_type=snake_case      ,	)
																															assert not torch.isnan(snake_case    ).any(), "Samples have nan numbers"






	def       _a(self				: int    ) ->			List[str]:
							self.check_over_configs(lower_order_final=snake_case    )
							self.check_over_configs(lower_order_final=snake_case    )






	def       _a(self				: Any    ) ->			List[Any]:
							for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
													self.check_over_forward(num_inference_steps=snake_case      ,	time_step=0    )






	def       _a(self				: Tuple    ) ->			Optional[Any]:
							_lowercase       :	Optional[Any]           =   self.full_loop()
							_lowercase       :	Tuple           =   torch.mean(torch.abs(snake_case    )    )

							assert abs(result_mean.item() - 0.2_39_16    ) < 1e-3






	def       _a(self				: Optional[int]    ) ->			Dict:
							_lowercase       :	int           =   self.full_loop(prediction_type="v_prediction"    )
							_lowercase       :	str           =   torch.mean(torch.abs(snake_case    )    )

							assert abs(result_mean.item() - 0.0_91    ) < 1e-3






	def       _a(self				: int    ) ->			Optional[int]:
							_lowercase       :	int           =   self.scheduler_classes[0]
							_lowercase       :	Tuple           =   self.get_scheduler_config(thresholding=snake_case      ,	dynamic_thresholding_ratio=0    )
							_lowercase       :	int           =   scheduler_class(**snake_case    )

							_lowercase       :	Optional[int]           =   10
							_lowercase       :	Union[str, Any]           =   self.dummy_model()
							_lowercase       :	Dict           =   self.dummy_sample_deter.half()
							scheduler.set_timesteps(snake_case    )

							for i, t in enumerate(scheduler.timesteps    ):
													_lowercase       :	Tuple           =   model(snake_case      ,	snake_case    )
													_lowercase       :	List[str]           =   scheduler.step(snake_case      ,	snake_case      ,	snake_case    ).prev_sample

							assert sample.dtype == torch.floataa




