






"""simple docstring"""

import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, PLBartTokenizer, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
)

from ...test_tokenization_common import TokenizerTesterMixin


lowercase    			=       get_tests_dir('''fixtures/test_sentencepiece.model''')


if is_torch_available():
			from transformers.models.plbart.modeling_plbart import shift_tokens_right

lowercase    			=       50003
lowercase    			=       50002

@require_sentencepiece
@require_tokenizers
class 						A_							(      snake_case_					,							unittest.TestCase  ):
				UpperCAmelCase__        =				PLBartTokenizer
				UpperCAmelCase__        =				None
				UpperCAmelCase__        =				False







				def     _snake_case					(							self							:						Union[str, Any]	)				->					int:
										super().setUp()

										# We have a SentencePiece fixture for testing
										__magic_name__            =     PLBartTokenizer(__lowerCamelCase						,    language_codes="base"						,    keep_accents=__lowerCamelCase	)
										tokenizer.save_pretrained(self.tmpdirname	)







				def     _snake_case					(							self							:						int	)				->					Dict:
										__magic_name__            =     PLBartTokenizer(__lowerCamelCase						,    language_codes="base"						,    keep_accents=__lowerCamelCase	)

										__magic_name__            =     tokenizer.tokenize("This is a test"	)
										self.assertListEqual(__lowerCamelCase						,    ["▁This", "▁is", "▁a", "▁t", "est"]	)

										self.assertListEqual(
										    tokenizer.convert_tokens_to_ids(__lowerCamelCase	)						,    [value + tokenizer.fairseq_offset for value in [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]]						,    )

										__magic_name__            =     tokenizer.tokenize("I was born in 92000, and this is falsé."	)
										self.assertListEqual(
										    __lowerCamelCase						,    [
										        SPIECE_UNDERLINE + "I",
										        SPIECE_UNDERLINE + "was",
										        SPIECE_UNDERLINE + "b",
										        "or",
										        "n",
										        SPIECE_UNDERLINE + "in",
										        SPIECE_UNDERLINE + "",
										        "9",
										        "2",
										        "0",
										        "0",
										        "0",
										        ",",
										        SPIECE_UNDERLINE + "and",
										        SPIECE_UNDERLINE + "this",
										        SPIECE_UNDERLINE + "is",
										        SPIECE_UNDERLINE + "f",
										        "al",
										        "s",
										        "é",
										        ".",
										    ]						,    )
										__magic_name__            =     tokenizer.convert_tokens_to_ids(__lowerCamelCase	)
										self.assertListEqual(
										    __lowerCamelCase						,    [
										        value + tokenizer.fairseq_offset
										        for value in [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 2, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 2, 4]
										    ]						,    )

										__magic_name__            =     tokenizer.convert_ids_to_tokens(__lowerCamelCase	)
										self.assertListEqual(
										    __lowerCamelCase						,    [
										        SPIECE_UNDERLINE + "I",
										        SPIECE_UNDERLINE + "was",
										        SPIECE_UNDERLINE + "b",
										        "or",
										        "n",
										        SPIECE_UNDERLINE + "in",
										        SPIECE_UNDERLINE + "",
										        "<unk>",
										        "2",
										        "0",
										        "0",
										        "0",
										        ",",
										        SPIECE_UNDERLINE + "and",
										        SPIECE_UNDERLINE + "this",
										        SPIECE_UNDERLINE + "is",
										        SPIECE_UNDERLINE + "f",
										        "al",
										        "s",
										        "<unk>",
										        ".",
										    ]						,    )

										__magic_name__            =     tokenizer.vocab_size
										__magic_name__            =     [tokenizer.convert_ids_to_tokens(__lowerCamelCase	) for x in range(end - 4						,    __lowerCamelCase	)]

										self.assertListEqual(__lowerCamelCase						,    ["__java__", "__python__", "__en_XX__", "<mask>"]	)

										__magic_name__            =     "java.lang.Exception, python.lang.Exception, javascript, php, ruby, go"
										__magic_name__            =     tokenizer(__lowerCamelCase	).input_ids
										self.assertEqual(
										    tokenizer.decode(__lowerCamelCase						,    skip_special_tokens=__lowerCamelCase						,    clean_up_tokenization_spaces=__lowerCamelCase	)						,    __lowerCamelCase						,    )







				def     _snake_case					(							self							:						Union[str, Any]	)				->					str:
										__magic_name__            =     PLBartTokenizer(__lowerCamelCase						,    language_codes="multi"						,    keep_accents=__lowerCamelCase	)

										__magic_name__            =     tokenizer.tokenize("This is a test"	)
										self.assertListEqual(__lowerCamelCase						,    ["▁This", "▁is", "▁a", "▁t", "est"]	)

										self.assertListEqual(
										    tokenizer.convert_tokens_to_ids(__lowerCamelCase	)						,    [value + tokenizer.fairseq_offset for value in [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]]						,    )

										__magic_name__            =     tokenizer.tokenize("I was born in 92000, and this is falsé."	)
										self.assertListEqual(
										    __lowerCamelCase						,    [
										        SPIECE_UNDERLINE + "I",
										        SPIECE_UNDERLINE + "was",
										        SPIECE_UNDERLINE + "b",
										        "or",
										        "n",
										        SPIECE_UNDERLINE + "in",
										        SPIECE_UNDERLINE + "",
										        "9",
										        "2",
										        "0",
										        "0",
										        "0",
										        ",",
										        SPIECE_UNDERLINE + "and",
										        SPIECE_UNDERLINE + "this",
										        SPIECE_UNDERLINE + "is",
										        SPIECE_UNDERLINE + "f",
										        "al",
										        "s",
										        "é",
										        ".",
										    ]						,    )
										__magic_name__            =     tokenizer.convert_tokens_to_ids(__lowerCamelCase	)
										self.assertListEqual(
										    __lowerCamelCase						,    [
										        value + tokenizer.fairseq_offset
										        for value in [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 2, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 2, 4]
										    ]						,    )

										__magic_name__            =     tokenizer.convert_ids_to_tokens(__lowerCamelCase	)
										self.assertListEqual(
										    __lowerCamelCase						,    [
										        SPIECE_UNDERLINE + "I",
										        SPIECE_UNDERLINE + "was",
										        SPIECE_UNDERLINE + "b",
										        "or",
										        "n",
										        SPIECE_UNDERLINE + "in",
										        SPIECE_UNDERLINE + "",
										        "<unk>",
										        "2",
										        "0",
										        "0",
										        "0",
										        ",",
										        SPIECE_UNDERLINE + "and",
										        SPIECE_UNDERLINE + "this",
										        SPIECE_UNDERLINE + "is",
										        SPIECE_UNDERLINE + "f",
										        "al",
										        "s",
										        "<unk>",
										        ".",
										    ]						,    )
										__magic_name__            =     tokenizer.vocab_size
										__magic_name__            =     [tokenizer.convert_ids_to_tokens(__lowerCamelCase	) for x in range(end - 7						,    __lowerCamelCase	)]

										self.assertListEqual(
										    __lowerCamelCase						,    ["__java__", "__python__", "__en_XX__", "__javascript__", "__php__", "__ruby__", "__go__"]	)
										__magic_name__            =     "java.lang.Exception, python.lang.Exception, javascript, php, ruby, go"
										__magic_name__            =     tokenizer(__lowerCamelCase	).input_ids
										self.assertEqual(
										    tokenizer.decode(__lowerCamelCase						,    skip_special_tokens=__lowerCamelCase						,    clean_up_tokenization_spaces=__lowerCamelCase	)						,    __lowerCamelCase						,    )





@require_torch
@require_sentencepiece
@require_tokenizers
class 						A_							(      unittest.TestCase  ):
				UpperCAmelCase__        =				'''uclanlp/plbart-python-en_XX'''
				UpperCAmelCase__        =				[
				    '''def maximum(a,b,c):NEW_LINE_INDENTreturn max([a,b,c])''',
				    '''def sum(a,b,c):NEW_LINE_INDENTreturn sum([a,b,c])''',
				]
				UpperCAmelCase__        =				[
				    '''Returns the maximum value of a b c.''',
				    '''Sums the values of a b c.''',
				]
				UpperCAmelCase__        =				[
				    1_3_4,
				    5_4_5_2,
				    3_3_4_6_0,
				    3_3_4_4_1,
				    3_3_4_6_3,
				    3_3_4_6_5,
				    3_3_4_6_3,
				    3_3_4_4_9,
				    9_8_8,
				    2_0,
				    3_3_4_5_6,
				    1_9,
				    3_3_4_5_6,
				    7_7_1,
				    3_9,
				    4_2_5_8,
				    8_8_9,
				    3_3_1_8,
				    3_3_4_4_1,
				    3_3_4_6_3,
				    3_3_4_6_5,
				    3_3_4_6_3,
				    3_3_4_4_9,
				    2_4_7_1,
				    2,
				    PYTHON_CODE,
				]







				@classmethod
				def     _snake_case					(							cls							:						List[str]	)				->					List[str]:
										__magic_name__            =     PLBartTokenizer.from_pretrained(
										    cls.checkpoint_name						,    language_codes="base"						,    src_lang="python"						,    tgt_lang="en_XX"	)
										__magic_name__            =     1
										return cls







				def     _snake_case					(							self							:						Union[str, Any]	)				->					Tuple:
										self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["__java__"]						,    5_0_0_0_1	)
										self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["__python__"]						,    5_0_0_0_2	)
										self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["__en_XX__"]						,    5_0_0_0_3	)







				def     _snake_case					(							self							:						List[Any]	)				->					Tuple:
										__magic_name__            =     self.tokenizer.batch_encode_plus(self.src_text	).input_ids[0]
										self.assertListEqual(self.expected_src_tokens						,    __lowerCamelCase	)







				def     _snake_case					(							self							:						Tuple	)				->					Dict:
										self.assertIn(__lowerCamelCase						,    self.tokenizer.all_special_ids	)
										__magic_name__            =     [EN_CODE, 9_0_3_7, 3_3_4_4_2, 5_7, 7_5_2, 1_5_3, 1_4, 5_6, 1_8, 9, 2]
										__magic_name__            =     self.tokenizer.decode(__lowerCamelCase						,    skip_special_tokens=__lowerCamelCase	)
										__magic_name__            =     self.tokenizer.decode(generated_ids[1:]						,    skip_special_tokens=__lowerCamelCase	)
										self.assertEqual(__lowerCamelCase						,    __lowerCamelCase	)
										self.assertNotIn(self.tokenizer.eos_token						,    __lowerCamelCase	)







				def     _snake_case					(							self							:						List[str]	)				->					Union[str, Any]:
										__magic_name__            =     ["def sum(a,b,c):NEW_LINE_INDENTreturn sum([a,b,c])" * 2_0]
										self.assertIsInstance(src_text[0]						,    __lowerCamelCase	)
										__magic_name__            =     1_0
										__magic_name__            =     self.tokenizer(__lowerCamelCase						,    max_length=__lowerCamelCase						,    truncation=__lowerCamelCase	).input_ids[0]
										self.assertEqual(ids[-2]						,    2	)
										self.assertEqual(ids[-1]						,    __lowerCamelCase	)
										self.assertEqual(len(__lowerCamelCase	)						,    __lowerCamelCase	)







				def     _snake_case					(							self							:						Any	)				->					Tuple:
										self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "__java__"]	)						,    [5_0_0_0_4, 5_0_0_0_1]	)







				def     _snake_case					(							self							:						Optional[int]	)				->					Any:
										__magic_name__            =     tempfile.mkdtemp()
										__magic_name__            =     self.tokenizer.fairseq_tokens_to_ids
										self.tokenizer.save_pretrained(__lowerCamelCase	)
										__magic_name__            =     PLBartTokenizer.from_pretrained(__lowerCamelCase	)
										self.assertDictEqual(new_tok.fairseq_tokens_to_ids						,    __lowerCamelCase	)







				@require_torch
				def     _snake_case					(							self							:						Union[str, Any]	)				->					Dict:
										__magic_name__            =     self.tokenizer(self.src_text						,    text_target=self.tgt_text						,    padding=__lowerCamelCase						,    return_tensors="pt"	)
										__magic_name__            =     shift_tokens_right(batch["labels"]						,    self.tokenizer.pad_token_id	)

										# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
										self.assertEqual(batch.input_ids[1][-2:].tolist()						,    [2, PYTHON_CODE]	)
										self.assertEqual(batch.decoder_input_ids[1][0]						,    __lowerCamelCase	)
										self.assertEqual(batch.decoder_input_ids[1][-1]						,    2	)
										self.assertEqual(batch.labels[1][-2:].tolist()						,    [2, EN_CODE]	)







				@require_torch
				def     _snake_case					(							self							:						str	)				->					Union[str, Any]:
										__magic_name__            =     self.tokenizer(
										    self.src_text						,    text_target=self.tgt_text						,    padding=__lowerCamelCase						,    truncation=__lowerCamelCase						,    max_length=len(self.expected_src_tokens	)						,    return_tensors="pt"						,    )
										__magic_name__            =     shift_tokens_right(batch["labels"]						,    self.tokenizer.pad_token_id	)

										self.assertIsInstance(__lowerCamelCase						,    __lowerCamelCase	)

										self.assertEqual((2, 2_6)						,    batch.input_ids.shape	)
										self.assertEqual((2, 2_6)						,    batch.attention_mask.shape	)
										__magic_name__            =     batch.input_ids.tolist()[0]
										self.assertListEqual(self.expected_src_tokens						,    __lowerCamelCase	)
										self.assertEqual(2						,    batch.decoder_input_ids[0, -1]	)  # EOS
										# Test that special tokens are reset
										self.assertEqual(self.tokenizer.prefix_tokens						,    []	)
										self.assertEqual(self.tokenizer.suffix_tokens						,    [self.tokenizer.eos_token_id, PYTHON_CODE]	)







				def     _snake_case					(							self							:						List[Any]	)				->					List[str]:
										__magic_name__            =     self.tokenizer(self.src_text						,    padding=__lowerCamelCase						,    truncation=__lowerCamelCase						,    max_length=3						,    return_tensors="pt"	)
										__magic_name__            =     self.tokenizer(
										    text_target=self.tgt_text						,    padding=__lowerCamelCase						,    truncation=__lowerCamelCase						,    max_length=1_0						,    return_tensors="pt"	)
										__magic_name__            =     targets["input_ids"]
										__magic_name__            =     shift_tokens_right(__lowerCamelCase						,    self.tokenizer.pad_token_id	)

										self.assertEqual(batch.input_ids.shape[1]						,    3	)
										self.assertEqual(batch.decoder_input_ids.shape[1]						,    1_0	)







				@require_torch
				def     _snake_case					(							self							:						Tuple	)				->					List[Any]:
										__magic_name__            =     self.tokenizer._build_translation_inputs(
										    "A test"						,    return_tensors="pt"						,    src_lang="en_XX"						,    tgt_lang="java"	)

										self.assertEqual(
										    nested_simplify(__lowerCamelCase	)						,    {
										        # A, test, EOS, en_XX
										        "input_ids": [[1_5_0, 2_4_2, 2, 5_0_0_0_3]],
										        "attention_mask": [[1, 1, 1, 1]],
										        # java
										        "forced_bos_token_id": 5_0_0_0_1,
										    }						,    )











"""simple docstring"""

import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest

class 						A_							(      snake_case_  ):
				UpperCAmelCase__        =				(UnCLIPScheduler,)







				def     _snake_case					(							self							:						Any						,    **__lowerCamelCase							:						List[Any]	)				->					str:
										__magic_name__            =     {
										    "num_train_timesteps": 1_0_0_0,
										    "variance_type": "fixed_small_log",
										    "clip_sample": True,
										    "clip_sample_range": 1.0,
										    "prediction_type": "epsilon",
										}

										config.update(**__lowerCamelCase	)
										return config







				def     _snake_case					(							self							:						int	)				->					Tuple:
										for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
																self.check_over_configs(num_train_timesteps=__lowerCamelCase	)







				def     _snake_case					(							self							:						int	)				->					Any:
										for variance in ["fixed_small_log", "learned_range"]:
																self.check_over_configs(variance_type=__lowerCamelCase	)







				def     _snake_case					(							self							:						List[Any]	)				->					Tuple:
										for clip_sample in [True, False]:
																self.check_over_configs(clip_sample=__lowerCamelCase	)







				def     _snake_case					(							self							:						Tuple	)				->					Optional[int]:
										for clip_sample_range in [1, 5, 1_0, 2_0]:
																self.check_over_configs(clip_sample_range=__lowerCamelCase	)







				def     _snake_case					(							self							:						List[Any]	)				->					Any:
										for prediction_type in ["epsilon", "sample"]:
																self.check_over_configs(prediction_type=__lowerCamelCase	)







				def     _snake_case					(							self							:						Union[str, Any]	)				->					List[str]:
										for time_step in [0, 5_0_0, 9_9_9]:
																for prev_timestep in [None, 5, 1_0_0, 2_5_0, 5_0_0, 7_5_0]:
																						if prev_timestep is not None and prev_timestep >= time_step:
																												continue

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







				def     _snake_case					(							self							:						Union[str, Any]	)				->					Tuple:
										__magic_name__            =     self.scheduler_classes[0]
										__magic_name__            =     self.get_scheduler_config(variance_type="fixed_small_log"	)
										__magic_name__            =     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(4_8_7	) - 0.054_9625	)	) < 1e-5
										assert torch.sum(torch.abs(scheduler._get_variance(9_9_9	) - 0.999_4987	)	) < 1e-5







				def     _snake_case					(							self							:						Union[str, Any]	)				->					Optional[Any]:
										__magic_name__            =     self.scheduler_classes[0]
										__magic_name__            =     self.get_scheduler_config(variance_type="learned_range"	)
										__magic_name__            =     scheduler_class(**__lowerCamelCase	)

										__magic_name__            =     0.5

										assert scheduler._get_variance(1						,    predicted_variance=__lowerCamelCase	) - -10.171_2790 < 1e-5
										assert scheduler._get_variance(4_8_7						,    predicted_variance=__lowerCamelCase	) - -5.799_8052 < 1e-5
										assert scheduler._get_variance(9_9_9						,    predicted_variance=__lowerCamelCase	) - -0.001_0011 < 1e-5







				def     _snake_case					(							self							:						Optional[Any]	)				->					str:
										__magic_name__            =     self.scheduler_classes[0]
										__magic_name__            =     self.get_scheduler_config()
										__magic_name__            =     scheduler_class(**__lowerCamelCase	)

										__magic_name__            =     scheduler.timesteps

										__magic_name__            =     self.dummy_model()
										__magic_name__            =     self.dummy_sample_deter
										__magic_name__            =     torch.manual_seed(0	)

										for i, t in enumerate(__lowerCamelCase	):
																# 1. predict noise residual
																__magic_name__            =     model(__lowerCamelCase						,    __lowerCamelCase	)

																# 2. predict previous mean of sample x_t-1
																__magic_name__            =     scheduler.step(__lowerCamelCase						,    __lowerCamelCase						,    __lowerCamelCase						,    generator=__lowerCamelCase	).prev_sample

																__magic_name__            =     pred_prev_sample

										__magic_name__            =     torch.sum(torch.abs(__lowerCamelCase	)	)
										__magic_name__            =     torch.mean(torch.abs(__lowerCamelCase	)	)

										assert abs(result_sum.item() - 252.268_2495	) < 1e-2
										assert abs(result_mean.item() - 0.328_4743	) < 1e-3







				def     _snake_case					(							self							:						int	)				->					int:
										__magic_name__            =     self.scheduler_classes[0]
										__magic_name__            =     self.get_scheduler_config()
										__magic_name__            =     scheduler_class(**__lowerCamelCase	)

										scheduler.set_timesteps(2_5	)

										__magic_name__            =     scheduler.timesteps

										__magic_name__            =     self.dummy_model()
										__magic_name__            =     self.dummy_sample_deter
										__magic_name__            =     torch.manual_seed(0	)

										for i, t in enumerate(__lowerCamelCase	):
																# 1. predict noise residual
																__magic_name__            =     model(__lowerCamelCase						,    __lowerCamelCase	)

																if i + 1 == timesteps.shape[0]:
																						__magic_name__            =     None
																else:
																						__magic_name__            =     timesteps[i + 1]

																# 2. predict previous mean of sample x_t-1
																__magic_name__            =     scheduler.step(
																    __lowerCamelCase						,    __lowerCamelCase						,    __lowerCamelCase						,    prev_timestep=__lowerCamelCase						,    generator=__lowerCamelCase	).prev_sample

																__magic_name__            =     pred_prev_sample

										__magic_name__            =     torch.sum(torch.abs(__lowerCamelCase	)	)
										__magic_name__            =     torch.mean(torch.abs(__lowerCamelCase	)	)

										assert abs(result_sum.item() - 258.204_4983	) < 1e-2
										assert abs(result_mean.item() - 0.336_2038	) < 1e-3







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







				def     _snake_case					(							self							:						int	)				->					Optional[int]:
										pass



