
'''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


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

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

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




@require_sentencepiece
class     __snake_case		(   a__    ,   unittest.TestCase):
					_lowerCAmelCase			     =	MarianTokenizer
					_lowerCAmelCase			     =	False
					_lowerCAmelCase			     =	True
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


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

									lowerCamelCase					:			Union[str, Any]									= MarianTokenizer.from_pretrained(self.tmpdirname      )
									tokenizer.save_pretrained(self.tmpdirname      )
					def     UpperCAmelCase_							(     self,    **A      ):

									"""simple docstring"""


									return MarianTokenizer.from_pretrained(self.tmpdirname,    **A      )
					def     UpperCAmelCase_							(     self,    A      ):

									"""simple docstring"""


									return (
									    "This is a test",
									    "This is a test",
									)
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			int									= '</s>'
									lowerCamelCase					:			Union[str, Any]									= 0

									self.assertEqual(self.get_tokenizer()._convert_token_to_id(A      ),    A      )
									self.assertEqual(self.get_tokenizer()._convert_id_to_token(A      ),    A      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			str									= 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(A      ),    9      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									self.assertEqual(self.get_tokenizer().vocab_size,    9      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			List[Any]									= MarianTokenizer.from_pretrained(F'''{ORG_NAME}opus-mt-en-de'''      )
									lowerCamelCase					:			Any									= en_de_tokenizer(['I am a small frog'],    return_tensors=A      )
									self.assertIsInstance(A,    A      )
									lowerCamelCase					:			List[Any]									= [38, 121, 14, 697, 3_8848, 0]
									self.assertListEqual(A,    batch.input_ids[0]      )

									lowerCamelCase					:			Tuple									= tempfile.mkdtemp()
									en_de_tokenizer.save_pretrained(A      )
									lowerCamelCase					:			str									= [x.name for x in Path(A      ).glob('*'      )]
									self.assertIn('source.spm',    A      )
									MarianTokenizer.from_pretrained(A      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Optional[Any]									= self.get_tokenizer()

									lowerCamelCase					:			Union[str, Any]									= tok(
									    ['I am a small frog' * 1000, 'I am a small frog'],    padding=A,    truncation=A,    return_tensors=A      )
									self.assertIsInstance(A,    A      )
									self.assertEqual(batch.input_ids.shape,    (2, 512)      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Optional[int]									= self.get_tokenizer()
									lowerCamelCase					:			Tuple									= tok(['I am a tiny frog', 'I am a small frog'],    padding=A,    return_tensors=A      )
									self.assertIsInstance(A,    A      )
									self.assertEqual(batch_smaller.input_ids.shape,    (2, 10)      )
					@slow
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Optional[int]									= {'input_ids': [[4_3495, 462, 20, 4_2164, 1369, 52, 464, 132, 1703, 492, 13, 7491, 3_8999, 6, 8, 464, 132, 1703, 492, 13, 4669, 3_7867, 13, 7525, 27, 1593, 988, 13, 3_3972, 7029, 6, 20, 8251, 383, 2, 270, 5866, 3788, 2, 2353, 8251, 1_2338, 2, 1_3958, 387, 2, 3629, 6953, 188, 2900, 2, 1_3958, 8011, 1_1501, 23, 8460, 4073, 3_4009, 20, 435, 1_1439, 27, 8, 8460, 4073, 6004, 20, 9988, 375, 27, 33, 266, 1945, 1076, 1350, 3_7867, 3288, 5, 577, 1076, 4374, 8, 5082, 5, 2_6453, 257, 556, 403, 2, 242, 132, 383, 316, 492, 8, 1_0767, 6, 316, 304, 4239, 3, 0], [148, 1_5722, 19, 1839, 12, 1350, 13, 2_2327, 5082, 5418, 4_7567, 3_5938, 59, 318, 1_9552, 108, 2183, 54, 1_4976, 4835, 32, 547, 1114, 8, 315, 2417, 5, 92, 1_9088, 3, 0, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100], [36, 6395, 1_2570, 3_9147, 1_1597, 6, 266, 4, 4_5405, 7296, 3, 0, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100]], '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=A,    model_name='Helsinki-NLP/opus-mt-en-de',    revision='1a8c2263da11e68e50938f97e10cd57820bd504c',    decode_kwargs={'use_source_tokenizer': True},    )




					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Optional[Any]									= MarianTokenizer.from_pretrained('hf-internal-testing/test-marian-two-vocabs'      )

									lowerCamelCase					:			List[str]									= 'Tämä on testi'
									lowerCamelCase					:			Tuple									= 'This is a test'

									lowerCamelCase					:			int									= [76, 7, 2047, 2]
									lowerCamelCase					:			int									= [69, 12, 11, 940, 2]

									lowerCamelCase					:			Union[str, Any]									= tokenizer(A      ).input_ids
									self.assertListEqual(A,    A      )

									lowerCamelCase					:			Optional[Any]									= tokenizer(text_target=A      ).input_ids
									self.assertListEqual(A,    A      )

									lowerCamelCase					:			List[str]									= tokenizer.decode(A,    skip_special_tokens=A      )
									self.assertEqual(A,    A      )


'''simple docstring'''






import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest




class     __snake_case		(   a__):
					_lowerCAmelCase			     =	(DPMSolverSinglestepScheduler,)
					_lowerCAmelCase			     =	(('''num_inference_steps''', 25),)
					def     UpperCAmelCase_							(     self,    **A      ):

									"""simple docstring"""


									lowerCamelCase					:			List[Any]									= {
									    'num_train_timesteps': 1000,
									    'beta_start': 0.0001,
									    'beta_end': 0.02,
									    'beta_schedule': 'linear',
									    'solver_order': 2,
									    'prediction_type': 'epsilon',
									    'thresholding': False,
									    'sample_max_value': 1.0,
									    'algorithm_type': 'dpmsolver++',
									    'solver_type': 'midpoint',
									    'lambda_min_clipped': -float('inf'      ),
									    'variance_type': None,
									}

									config.update(**A      )
									return config
					def     UpperCAmelCase_							(     self,    A=0,    **A      ):

									"""simple docstring"""


									lowerCamelCase					:			List[str]									= dict(self.forward_default_kwargs      )
									lowerCamelCase					:			Optional[Any]									= kwargs.pop('num_inference_steps',    A      )
									lowerCamelCase					:			Union[str, Any]									= self.dummy_sample
									lowerCamelCase					:			Dict									= 0.1 * sample
									lowerCamelCase					:			Dict									= [residual + 0.2, residual + 0.15, residual + 0.10]

									for scheduler_class in self.scheduler_classes:
													lowerCamelCase					:			Optional[Any]									= self.get_scheduler_config(**A      )
													lowerCamelCase					:			Dict									= scheduler_class(**A      )
													scheduler.set_timesteps(A      )
													# copy over dummy past residuals
													lowerCamelCase					:			str									= dummy_past_residuals[: scheduler.config.solver_order]

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

													lowerCamelCase			, lowerCamelCase					:			Optional[int]									= sample, sample
													for t in range(A,    time_step + scheduler.config.solver_order + 1      ):
																	lowerCamelCase					:			Dict									= scheduler.step(A,    A,    A,    **A      ).prev_sample
																	lowerCamelCase					:			Optional[int]									= new_scheduler.step(A,    A,    A,    **A      ).prev_sample

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

									"""simple docstring"""


									pass
					def     UpperCAmelCase_							(     self,    A=0,    **A      ):

									"""simple docstring"""


									lowerCamelCase					:			List[str]									= dict(self.forward_default_kwargs      )
									lowerCamelCase					:			str									= kwargs.pop('num_inference_steps',    A      )
									lowerCamelCase					:			Union[str, Any]									= self.dummy_sample
									lowerCamelCase					:			List[str]									= 0.1 * sample
									lowerCamelCase					:			List[str]									= [residual + 0.2, residual + 0.15, residual + 0.10]

									for scheduler_class in self.scheduler_classes:
													lowerCamelCase					:			Tuple									= self.get_scheduler_config()
													lowerCamelCase					:			Optional[Any]									= scheduler_class(**A      )
													scheduler.set_timesteps(A      )

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

													with tempfile.TemporaryDirectory() as tmpdirname:
																	scheduler.save_config(A      )
																	lowerCamelCase					:			Tuple									= scheduler_class.from_pretrained(A      )
																	# copy over dummy past residuals
																	new_scheduler.set_timesteps(A      )

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

													lowerCamelCase					:			int									= scheduler.step(A,    A,    A,    **A      ).prev_sample
													lowerCamelCase					:			Dict									= new_scheduler.step(A,    A,    A,    **A      ).prev_sample

													assert torch.sum(torch.abs(output - new_output      )      ) < 1e-5, "Scheduler outputs are not identical"
					def     UpperCAmelCase_							(     self,    A=None,    **A      ):

									"""simple docstring"""


									if scheduler is None:
													lowerCamelCase					:			Any									= self.scheduler_classes[0]
													lowerCamelCase					:			Optional[Any]									= self.get_scheduler_config(**A      )
													lowerCamelCase					:			Optional[int]									= scheduler_class(**A      )

									lowerCamelCase					:			List[Any]									= self.scheduler_classes[0]
									lowerCamelCase					:			Optional[Any]									= self.get_scheduler_config(**A      )
									lowerCamelCase					:			Optional[int]									= scheduler_class(**A      )

									lowerCamelCase					:			Any									= 10
									lowerCamelCase					:			Dict									= self.dummy_model()
									lowerCamelCase					:			Any									= self.dummy_sample_deter
									scheduler.set_timesteps(A      )

									for i, t in enumerate(scheduler.timesteps      ):
													lowerCamelCase					:			Dict									= model(A,    A      )
													lowerCamelCase					:			List[str]									= scheduler.step(A,    A,    A      ).prev_sample

									return sample
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Dict									= DPMSolverSinglestepScheduler(**self.get_scheduler_config()      )
									lowerCamelCase					:			Dict									= 50
									lowerCamelCase					:			Tuple									= self.dummy_model()
									lowerCamelCase					:			Optional[int]									= self.dummy_sample_deter
									scheduler.set_timesteps(A      )

									# make sure that the first t is uneven
									for i, t in enumerate(scheduler.timesteps[3:]      ):
													lowerCamelCase					:			Any									= model(A,    A      )
													lowerCamelCase					:			Optional[int]									= scheduler.step(A,    A,    A      ).prev_sample

									lowerCamelCase					:			Any									= torch.mean(torch.abs(A      )      )

									assert abs(result_mean.item() - 0.2574      ) < 1e-3
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									for timesteps in [25, 50, 100, 999, 1000]:
													self.check_over_configs(num_train_timesteps=A      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Dict									= DPMSolverSinglestepScheduler(**self.get_scheduler_config()      )
									lowerCamelCase					:			str									= self.full_loop(scheduler=A      )
									lowerCamelCase					:			Optional[int]									= torch.mean(torch.abs(A      )      )

									assert abs(result_mean.item() - 0.2791      ) < 1e-3

									lowerCamelCase					:			Dict									= DEISMultistepScheduler.from_config(scheduler.config      )
									lowerCamelCase					:			Optional[int]									= DPMSolverMultistepScheduler.from_config(scheduler.config      )
									lowerCamelCase					:			Any									= UniPCMultistepScheduler.from_config(scheduler.config      )
									lowerCamelCase					:			Optional[Any]									= DPMSolverSinglestepScheduler.from_config(scheduler.config      )

									lowerCamelCase					:			str									= self.full_loop(scheduler=A      )
									lowerCamelCase					:			Optional[int]									= torch.mean(torch.abs(A      )      )

									assert abs(result_mean.item() - 0.2791      ) < 1e-3
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									self.check_over_configs(thresholding=A      )
									for order in [1, 2, 3]:
													for solver_type in ["midpoint", "heun"]:
																	for threshold in [0.5, 1.0, 2.0]:
																					for prediction_type in ["epsilon", "sample"]:
																									self.check_over_configs(
																									    thresholding=A,    prediction_type=A,    sample_max_value=A,    algorithm_type='dpmsolver++',    solver_order=A,    solver_type=A,    )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


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

									"""simple docstring"""


									for algorithm_type in ["dpmsolver", "dpmsolver++"]:
													for solver_type in ["midpoint", "heun"]:
																	for order in [1, 2, 3]:
																					for prediction_type in ["epsilon", "sample"]:
																									self.check_over_configs(
																									    solver_order=A,    solver_type=A,    prediction_type=A,    algorithm_type=A,    )
																									lowerCamelCase					:			Optional[Any]									= self.full_loop(
																									    solver_order=A,    solver_type=A,    prediction_type=A,    algorithm_type=A,    )
																									assert not torch.isnan(A      ).any(), "Samples have nan numbers"
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									self.check_over_configs(lower_order_final=A      )
									self.check_over_configs(lower_order_final=A      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									self.check_over_configs(lambda_min_clipped=-float('inf'      )      )
									self.check_over_configs(lambda_min_clipped=-5.1      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									self.check_over_configs(variance_type=A      )
									self.check_over_configs(variance_type='learned_range'      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
													self.check_over_forward(num_inference_steps=A,    time_step=0      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Union[str, Any]									= self.full_loop()
									lowerCamelCase					:			str									= torch.mean(torch.abs(A      )      )

									assert abs(result_mean.item() - 0.2791      ) < 1e-3
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Union[str, Any]									= self.full_loop(use_karras_sigmas=A      )
									lowerCamelCase					:			Tuple									= torch.mean(torch.abs(A      )      )

									assert abs(result_mean.item() - 0.2248      ) < 1e-3
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			List[Any]									= self.full_loop(prediction_type='v_prediction'      )
									lowerCamelCase					:			Dict									= torch.mean(torch.abs(A      )      )

									assert abs(result_mean.item() - 0.1453      ) < 1e-3
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			List[Any]									= self.full_loop(prediction_type='v_prediction',    use_karras_sigmas=A      )
									lowerCamelCase					:			Optional[Any]									= torch.mean(torch.abs(A      )      )

									assert abs(result_mean.item() - 0.0649      ) < 1e-3




					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Optional[Any]									= self.scheduler_classes[0]
									lowerCamelCase					:			Dict									= self.get_scheduler_config(thresholding=A,    dynamic_thresholding_ratio=0      )
									lowerCamelCase					:			str									= scheduler_class(**A      )

									lowerCamelCase					:			List[Any]									= 10
									lowerCamelCase					:			List[str]									= self.dummy_model()
									lowerCamelCase					:			int									= self.dummy_sample_deter.half()
									scheduler.set_timesteps(A      )

									for i, t in enumerate(scheduler.timesteps      ):
													lowerCamelCase					:			str									= model(A,    A      )
													lowerCamelCase					:			Tuple									= scheduler.step(A,    A,    A      ).prev_sample

									assert sample.dtype == torch.floataa
