






"""simple docstring"""
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import BatchEncoding, MarianTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import is_sentencepiece_available, is_tf_available, is_torch_available


if is_sentencepiece_available():
			from transformers.models.marian.tokenization_marian import VOCAB_FILES_NAMES, save_json

from ...test_tokenization_common import TokenizerTesterMixin


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

lowerCamelCase_							      = {'''target_lang''': '''fi''', '''source_lang''': '''en'''}
lowerCamelCase_							      = '''>>zh<<'''
lowerCamelCase_							      = '''Helsinki-NLP/'''

if is_torch_available():
			lowerCamelCase_							      = '''pt'''
elif is_tf_available():
			lowerCamelCase_							      = '''tf'''
else:
			lowerCamelCase_							      = '''jax'''







@require_sentencepiece
class      UpperCamelCase_			(__A							,		unittest.TestCase	):
	__magic_name__       =							MarianTokenizer
	__magic_name__       =							False
	__magic_name__       =							True





	def 	_SCREAMING_SNAKE_CASE ( self   :					str		)		->     Optional[Any]:
				super().setUp()
				UpperCAmelCase_	:					List[Any]     	=   ["</s>", "<unk>", "▁This", "▁is", "▁a", "▁t", "est", "\u0120", "<pad>"]
				UpperCAmelCase_	:					Tuple     	=   dict(zip(lowerCAmelCase_      ,      range(len(lowerCAmelCase_		)		)		)		)
				UpperCAmelCase_	:					Optional[Any]     	=   Path(self.tmpdirname		)
				save_json(lowerCAmelCase_      ,      save_dir / VOCAB_FILES_NAMES["vocab"]		)
				save_json(lowerCAmelCase_      ,      save_dir / VOCAB_FILES_NAMES["tokenizer_config_file"]		)
				if not (save_dir / VOCAB_FILES_NAMES["source_spm"]).exists():
							copyfile(lowerCAmelCase_      ,      save_dir / VOCAB_FILES_NAMES["source_spm"]		)
							copyfile(lowerCAmelCase_      ,      save_dir / VOCAB_FILES_NAMES["target_spm"]		)

				UpperCAmelCase_	:					str     	=   MarianTokenizer.from_pretrained(self.tmpdirname		)
				tokenizer.save_pretrained(self.tmpdirname		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Tuple      ,      **lowerCAmelCase_   :					Tuple		)		->     MarianTokenizer:
				return MarianTokenizer.from_pretrained(self.tmpdirname      ,      **lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					List[Any]      ,      lowerCAmelCase_   :					Optional[Any]		)		->     List[Any]:
				return (
				    "This is a test",
				    "This is a test",
				)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Dict		)		->     List[Any]:
				UpperCAmelCase_	:					List[str]     	=   "</s>"
				UpperCAmelCase_	:					Tuple     	=   0

				self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCAmelCase_		)      ,      lowerCAmelCase_		)
				self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCAmelCase_		)      ,      lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Any		)		->     List[Any]:
				UpperCAmelCase_	:					Union[str, Any]     	=   list(self.get_tokenizer().get_vocab().keys()		)

				self.assertEqual(vocab_keys[0]      ,      "</s>"		)
				self.assertEqual(vocab_keys[1]      ,      "<unk>"		)
				self.assertEqual(vocab_keys[-1]      ,      "<pad>"		)
				self.assertEqual(len(lowerCAmelCase_		)      ,      9		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					List[str]		)		->     int:
				self.assertEqual(self.get_tokenizer().vocab_size      ,      9		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[int]		)		->     Any:
				UpperCAmelCase_	:					List[str]     	=   MarianTokenizer.from_pretrained(f"""{ORG_NAME}opus-mt-en-de"""		)
				UpperCAmelCase_	:					Optional[int]     	=   en_de_tokenizer(["I am a small frog"]      ,      return_tensors=lowerCAmelCase_		)
				self.assertIsInstance(lowerCAmelCase_      ,      lowerCAmelCase_		)
				UpperCAmelCase_	:					Any     	=   [38, 121, 14, 697, 38_848, 0]
				self.assertListEqual(lowerCAmelCase_      ,      batch.input_ids[0]		)

				UpperCAmelCase_	:					Optional[Any]     	=   tempfile.mkdtemp()
				en_de_tokenizer.save_pretrained(lowerCAmelCase_		)
				UpperCAmelCase_	:					Tuple     	=   [x.name for x in Path(lowerCAmelCase_		).glob("*"		)]
				self.assertIn("source.spm"      ,      lowerCAmelCase_		)
				MarianTokenizer.from_pretrained(lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Dict		)		->     Dict:
				UpperCAmelCase_	:					Optional[int]     	=   self.get_tokenizer()

				UpperCAmelCase_	:					Tuple     	=   tok(
				    ["I am a small frog" * 1_000, "I am a small frog"]      ,      padding=lowerCAmelCase_      ,      truncation=lowerCAmelCase_      ,      return_tensors=lowerCAmelCase_		)
				self.assertIsInstance(lowerCAmelCase_      ,      lowerCAmelCase_		)
				self.assertEqual(batch.input_ids.shape      ,      (2, 512)		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Dict		)		->     Dict:
				UpperCAmelCase_	:					List[Any]     	=   self.get_tokenizer()
				UpperCAmelCase_	:					Any     	=   tok(["I am a tiny frog", "I am a small frog"]      ,      padding=lowerCAmelCase_      ,      return_tensors=lowerCAmelCase_		)
				self.assertIsInstance(lowerCAmelCase_      ,      lowerCAmelCase_		)
				self.assertEqual(batch_smaller.input_ids.shape      ,      (2, 10)		)





	@slow
	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[int]		)		->     List[str]:
				# fmt: off
				UpperCAmelCase_	:					int     	=   {"input_ids": [[43_495, 462, 20, 42_164, 1_369, 52, 464, 132, 1_703, 492, 13, 7_491, 38_999, 6, 8, 464, 132, 1_703, 492, 13, 4_669, 37_867, 13, 7_525, 27, 1_593, 988, 13, 33_972, 7_029, 6, 20, 8_251, 383, 2, 270, 5_866, 3_788, 2, 2_353, 8_251, 12_338, 2, 13_958, 387, 2, 3_629, 6_953, 188, 2_900, 2, 13_958, 8_011, 11_501, 23, 8_460, 4_073, 34_009, 20, 435, 11_439, 27, 8, 8_460, 4_073, 6_004, 20, 9_988, 375, 27, 33, 266, 1_945, 1_076, 1_350, 37_867, 3_288, 5, 577, 1_076, 4_374, 8, 5_082, 5, 26_453, 257, 556, 403, 2, 242, 132, 383, 316, 492, 8, 10_767, 6, 316, 304, 4_239, 3, 0], [148, 15_722, 19, 1_839, 12, 1_350, 13, 22_327, 5_082, 5_418, 47_567, 35_938, 59, 318, 19_552, 108, 2_183, 54, 14_976, 4_835, 32, 547, 1_114, 8, 315, 2_417, 5, 92, 19_088, 3, 0, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100], [36, 6_395, 12_570, 39_147, 11_597, 6, 266, 4, 45_405, 7_296, 3, 0, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100]], "attention_mask": [[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, 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, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
				# fmt: on

				self.tokenizer_integration_test_util(
				    expected_encoding=lowerCAmelCase_      ,      model_name="Helsinki-NLP/opus-mt-en-de"      ,      revision="1a8c2263da11e68e50938f97e10cd57820bd504c"      ,      decode_kwargs={"use_source_tokenizer": True}      ,      )





	def 	_SCREAMING_SNAKE_CASE ( self   :					int		)		->     Union[str, Any]:
				UpperCAmelCase_	:					int     	=   MarianTokenizer.from_pretrained("hf-internal-testing/test-marian-two-vocabs"		)

				UpperCAmelCase_	:					Dict     	=   "Tämä on testi"
				UpperCAmelCase_	:					Optional[Any]     	=   "This is a test"

				UpperCAmelCase_	:					Union[str, Any]     	=   [76, 7, 2_047, 2]
				UpperCAmelCase_	:					Optional[int]     	=   [69, 12, 11, 940, 2]

				UpperCAmelCase_	:					int     	=   tokenizer(lowerCAmelCase_		).input_ids
				self.assertListEqual(lowerCAmelCase_      ,      lowerCAmelCase_		)

				UpperCAmelCase_	:					Union[str, Any]     	=   tokenizer(text_target=lowerCAmelCase_		).input_ids
				self.assertListEqual(lowerCAmelCase_      ,      lowerCAmelCase_		)

				UpperCAmelCase_	:					List[Any]     	=   tokenizer.decode(lowerCAmelCase_      ,      skip_special_tokens=lowerCAmelCase_		)
				self.assertEqual(lowerCAmelCase_      ,      lowerCAmelCase_		)










'''simple docstring'''


import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest

class  lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase    :				Any								=       (DDPMParallelScheduler,)




 def 				_snake_case (						self      ,  **_lowerCAmelCase		)     ->				int:
     _lowerCAmelCase						      =   {
         "num_train_timesteps": 1000,
         "beta_start": 0.0001,
         "beta_end": 0.02,
         "beta_schedule": "linear",
         "variance_type": "fixed_small",
         "clip_sample": True,
     }

     config.update(**_lowerCAmelCase		)
     return config




 def 				_snake_case (						self		)     ->				List[Any]:
     for timesteps in [1, 5, 100, 1000]:
         self.check_over_configs(num_train_timesteps=_lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				List[Any]:
     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 				_snake_case (						self		)     ->				Any:
     for schedule in ["linear", "squaredcos_cap_v2"]:
         self.check_over_configs(beta_schedule=_lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				Optional[Any]:
     for variance in ["fixed_small", "fixed_large", "other"]:
         self.check_over_configs(variance_type=_lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				Optional[int]:
     for clip_sample in [True, False]:
         self.check_over_configs(clip_sample=_lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				List[str]:
     self.check_over_configs(thresholding=_lowerCAmelCase		)
     for threshold in [0.5, 1.0, 2.0]:
         for prediction_type in ["epsilon", "sample", "v_prediction"]:
             self.check_over_configs(
                 thresholding=_lowerCAmelCase      ,  prediction_type=_lowerCAmelCase      ,  sample_max_value=_lowerCAmelCase      ,  )




 def 				_snake_case (						self		)     ->				int:
     for prediction_type in ["epsilon", "sample", "v_prediction"]:
         self.check_over_configs(prediction_type=_lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				Dict:
     for t in [0, 500, 999]:
         self.check_over_forward(time_step=_lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				Union[str, Any]:
     _lowerCAmelCase						      =   self.scheduler_classes[0]
     _lowerCAmelCase						      =   self.get_scheduler_config()
     _lowerCAmelCase						      =   scheduler_class(**_lowerCAmelCase		)

     assert torch.sum(torch.abs(scheduler._get_variance(0		) - 0.0		)		) < 1E-5
     assert torch.sum(torch.abs(scheduler._get_variance(487		) - 0.00979		)		) < 1E-5
     assert torch.sum(torch.abs(scheduler._get_variance(999		) - 0.02		)		) < 1E-5




 def 				_snake_case (						self		)     ->				Tuple:
     _lowerCAmelCase						      =   self.scheduler_classes[0]
     _lowerCAmelCase						      =   self.get_scheduler_config()
     _lowerCAmelCase						      =   scheduler_class(**_lowerCAmelCase		)

     _lowerCAmelCase						      =   len(_lowerCAmelCase		)

     _lowerCAmelCase						      =   self.dummy_model()
     _lowerCAmelCase						      =   self.dummy_sample_deter
     _lowerCAmelCase						      =   self.dummy_sample_deter + 0.1
     _lowerCAmelCase						      =   self.dummy_sample_deter - 0.1

     _lowerCAmelCase						      =   samplea.shape[0]
     _lowerCAmelCase						      =   torch.stack([samplea, samplea, samplea]      ,  dim=0		)
     _lowerCAmelCase						      =   torch.arange(_lowerCAmelCase		)[0:3, None].repeat(1      ,  _lowerCAmelCase		)

     _lowerCAmelCase						      =   model(samples.flatten(0      ,  1		)      ,  timesteps.flatten(0      ,  1		)		)
     _lowerCAmelCase						      =   scheduler.batch_step_no_noise(_lowerCAmelCase      ,  timesteps.flatten(0      ,  1		)      ,  samples.flatten(0      ,  1		)		)

     _lowerCAmelCase						      =   torch.sum(torch.abs(_lowerCAmelCase		)		)
     _lowerCAmelCase						      =   torch.mean(torch.abs(_lowerCAmelCase		)		)

     assert abs(result_sum.item() - 1153.1833		) < 1E-2
     assert abs(result_mean.item() - 0.5005		) < 1E-3




 def 				_snake_case (						self		)     ->				Dict:
     _lowerCAmelCase						      =   self.scheduler_classes[0]
     _lowerCAmelCase						      =   self.get_scheduler_config()
     _lowerCAmelCase						      =   scheduler_class(**_lowerCAmelCase		)

     _lowerCAmelCase						      =   len(_lowerCAmelCase		)

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

     for t in reversed(range(_lowerCAmelCase		)		):
         # 1. predict noise residual
         _lowerCAmelCase						      =   model(_lowerCAmelCase      ,  _lowerCAmelCase		)

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

         _lowerCAmelCase						      =   pred_prev_sample

     _lowerCAmelCase						      =   torch.sum(torch.abs(_lowerCAmelCase		)		)
     _lowerCAmelCase						      =   torch.mean(torch.abs(_lowerCAmelCase		)		)

     assert abs(result_sum.item() - 258.9606		) < 1E-2
     assert abs(result_mean.item() - 0.3372		) < 1E-3




 def 				_snake_case (						self		)     ->				Optional[Any]:
     _lowerCAmelCase						      =   self.scheduler_classes[0]
     _lowerCAmelCase						      =   self.get_scheduler_config(prediction_type="v_prediction"		)
     _lowerCAmelCase						      =   scheduler_class(**_lowerCAmelCase		)

     _lowerCAmelCase						      =   len(_lowerCAmelCase		)

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

     for t in reversed(range(_lowerCAmelCase		)		):
         # 1. predict noise residual
         _lowerCAmelCase						      =   model(_lowerCAmelCase      ,  _lowerCAmelCase		)

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

         _lowerCAmelCase						      =   pred_prev_sample

     _lowerCAmelCase						      =   torch.sum(torch.abs(_lowerCAmelCase		)		)
     _lowerCAmelCase						      =   torch.mean(torch.abs(_lowerCAmelCase		)		)

     assert abs(result_sum.item() - 202.0296		) < 1E-2
     assert abs(result_mean.item() - 0.2631		) < 1E-3




 def 				_snake_case (						self		)     ->				Dict:
     _lowerCAmelCase						      =   self.scheduler_classes[0]
     _lowerCAmelCase						      =   self.get_scheduler_config()
     _lowerCAmelCase						      =   scheduler_class(**_lowerCAmelCase		)

     _lowerCAmelCase						      =   [100, 87, 50, 1, 0]

     scheduler.set_timesteps(timesteps=_lowerCAmelCase		)

     _lowerCAmelCase						      =   scheduler.timesteps

     for i, timestep in enumerate(_lowerCAmelCase		):
         if i == len(_lowerCAmelCase		) - 1:
             _lowerCAmelCase						      =   -1
         else:
             _lowerCAmelCase						      =   timesteps[i + 1]

         _lowerCAmelCase						      =   scheduler.previous_timestep(_lowerCAmelCase		)
         _lowerCAmelCase						      =   prev_t.item()

         self.assertEqual(_lowerCAmelCase      ,  _lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				Any:
     _lowerCAmelCase						      =   self.scheduler_classes[0]
     _lowerCAmelCase						      =   self.get_scheduler_config()
     _lowerCAmelCase						      =   scheduler_class(**_lowerCAmelCase		)

     _lowerCAmelCase						      =   [100, 87, 50, 51, 0]

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




 def 				_snake_case (						self		)     ->				Union[str, Any]:
     _lowerCAmelCase						      =   self.scheduler_classes[0]
     _lowerCAmelCase						      =   self.get_scheduler_config()
     _lowerCAmelCase						      =   scheduler_class(**_lowerCAmelCase		)

     _lowerCAmelCase						      =   [100, 87, 50, 1, 0]
     _lowerCAmelCase						      =   len(_lowerCAmelCase		)

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





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

     _lowerCAmelCase						      =   [scheduler.config.num_train_timesteps]

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


