




"""simple docstring"""

import os
import tempfile
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
       import torch
       from torch import nn

       from transformers import (
           Adafactor,
           AdamW,
           get_constant_schedule,
           get_constant_schedule_with_warmup,
           get_cosine_schedule_with_warmup,
           get_cosine_with_hard_restarts_schedule_with_warmup,
           get_inverse_sqrt_schedule,
           get_linear_schedule_with_warmup,
           get_polynomial_decay_schedule_with_warmup,
       )






def 		_snake_case	(   _snake_case				:   Optional[int]							,    _snake_case				:   Optional[Any]=10				)       ->   Optional[int]:






      '''simple docstring'''


      _A     =  []
      for _ in range(_snake_case				):
            lrs.append(scheduler.get_lr()[0]				)
            scheduler.step()
      return lrs






def 		_snake_case	(   _snake_case				:   Optional[Any]							,    _snake_case				:   Union[str, Any]=10				)       ->   List[str]:






      '''simple docstring'''


      _A     =  []
      for step in range(_snake_case				):
            lrs.append(scheduler.get_lr()[0]				)
            scheduler.step()
            if step == num_steps // 2:
                  with tempfile.TemporaryDirectory() as tmpdirname:
                        _A     =  os.path.join(_snake_case							,    'schedule.bin'				)
                        torch.save(scheduler.state_dict()							,    _snake_case				)

                        _A     =  torch.load(_snake_case				)
                        scheduler.load_state_dict(_snake_case				)
      return lrs





@require_torch
class 							lowercase_			(	unittest.TestCase				):



 '''simple docstring'''

 def      lowerCAmelCase_				(					self			:			List[Any]       ,				_UpperCAmelCase			:			List[Any]       ,				_UpperCAmelCase			:			Optional[Any]       ,				_UpperCAmelCase			:			Tuple						):
       self.assertEqual(len(_UpperCAmelCase						)       ,				len(_UpperCAmelCase						)						)
       for a, b in zip(_UpperCAmelCase       ,				_UpperCAmelCase						):
             self.assertAlmostEqual(_UpperCAmelCase       ,				_UpperCAmelCase       ,				delta=_UpperCAmelCase						)

 def      lowerCAmelCase_				(					self			:			Any						):
       _A     =  torch.tensor([0.1, -0.2, -0.1]       ,				requires_grad=_UpperCAmelCase						)
       _A     =  torch.tensor([0.4, 0.2, -0.5]						)
       _A     =  nn.MSELoss()
       # No warmup, constant schedule, no gradient clipping
       _A     =  AdamW(params=[w]       ,				lr=2E-1       ,				weight_decay=0.0						)
       for _ in range(100						):
             _A     =  criterion(_UpperCAmelCase       ,				_UpperCAmelCase						)
             loss.backward()
             optimizer.step()
             w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
             w.grad.zero_()
       self.assertListAlmostEqual(w.tolist()       ,				[0.4, 0.2, -0.5]       ,				tol=1E-2						)



 def      lowerCAmelCase_				(					self			:			int						):
       _A     =  torch.tensor([0.1, -0.2, -0.1]       ,				requires_grad=_UpperCAmelCase						)
       _A     =  torch.tensor([0.4, 0.2, -0.5]						)
       _A     =  nn.MSELoss()
       # No warmup, constant schedule, no gradient clipping
       _A     =  Adafactor(
           params=[w]       ,				lr=1E-2       ,				eps=(1E-3_0, 1E-3)       ,				clip_threshold=1.0       ,				decay_rate=-0.8       ,				betaa=_UpperCAmelCase       ,				weight_decay=0.0       ,				relative_step=_UpperCAmelCase       ,				scale_parameter=_UpperCAmelCase       ,				warmup_init=_UpperCAmelCase       ,				)
       for _ in range(1_000						):
             _A     =  criterion(_UpperCAmelCase       ,				_UpperCAmelCase						)
             loss.backward()
             optimizer.step()
             w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
             w.grad.zero_()
       self.assertListAlmostEqual(w.tolist()       ,				[0.4, 0.2, -0.5]       ,				tol=1E-2						)





@require_torch
class 							lowercase_			(	unittest.TestCase				):



 '''simple docstring'''
 UpperCAmelCase       :	List[str]     =      nn.Linear(50     ,      50				) if is_torch_available() else None
 UpperCAmelCase       :	Tuple     =      AdamW(m.parameters()     ,      lr=10.0				) if is_torch_available() else None
 UpperCAmelCase       :	Dict     =      10

 def      lowerCAmelCase_				(					self			:			Any       ,				_UpperCAmelCase			:			Optional[Any]       ,				_UpperCAmelCase			:			str       ,				_UpperCAmelCase			:			List[Any]       ,				_UpperCAmelCase			:			List[Any]=None						):
       self.assertEqual(len(_UpperCAmelCase						)       ,				len(_UpperCAmelCase						)						)
       for a, b in zip(_UpperCAmelCase       ,				_UpperCAmelCase						):
             self.assertAlmostEqual(_UpperCAmelCase       ,				_UpperCAmelCase       ,				delta=_UpperCAmelCase       ,				msg=_UpperCAmelCase						)



 def      lowerCAmelCase_				(					self			:			List[Any]						):
       _A     =  {'num_warmup_steps': 2, 'num_training_steps': 10}
       # schedulers doct format
       # function: (sched_args_dict, expected_learning_rates)
       _A     =  {
           get_constant_schedule: ({}, [10.0] * self.num_steps),
           get_constant_schedule_with_warmup: (
               {'num_warmup_steps': 4},
               [0.0, 2.5, 5.0, 7.5, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0],
           ),
           get_linear_schedule_with_warmup: (
               {**common_kwargs},
               [0.0, 5.0, 10.0, 8.75, 7.5, 6.25, 5.0, 3.75, 2.5, 1.25],
           ),
           get_cosine_schedule_with_warmup: (
               {**common_kwargs},
               [0.0, 5.0, 10.0, 9.61, 8.53, 6.91, 5.0, 3.08, 1.46, 0.38],
           ),
           get_cosine_with_hard_restarts_schedule_with_warmup: (
               {**common_kwargs, 'num_cycles': 2},
               [0.0, 5.0, 10.0, 8.53, 5.0, 1.46, 10.0, 8.53, 5.0, 1.46],
           ),
           get_polynomial_decay_schedule_with_warmup: (
               {**common_kwargs, 'power': 2.0, 'lr_end': 1E-7},
               [0.0, 5.0, 10.0, 7.656, 5.625, 3.906, 2.5, 1.406, 0.625, 0.156],
           ),
           get_inverse_sqrt_schedule: (
               {'num_warmup_steps': 2},
               [0.0, 5.0, 10.0, 8.165, 7.071, 6.325, 5.774, 5.345, 5.0, 4.714],
           ),
       }

       for scheduler_func, data in scheds.items():
             _A  ,       _A     =  data

             _A     =  scheduler_func(self.optimizer       ,				**_UpperCAmelCase						)
             self.assertEqual(len([scheduler.get_lr()[0]]						)       ,				1						)
             _A     =  unwrap_schedule(_UpperCAmelCase       ,				self.num_steps						)
             self.assertListAlmostEqual(
                 _UpperCAmelCase       ,				_UpperCAmelCase       ,				tol=1E-2       ,				msg=F'''failed for {scheduler_func} in normal scheduler'''       ,				)

             _A     =  scheduler_func(self.optimizer       ,				**_UpperCAmelCase						)
             if scheduler_func.__name__ != "get_constant_schedule":
                   LambdaScheduleWrapper.wrap_scheduler(_UpperCAmelCase						)  # wrap to test picklability of the schedule
             _A     =  unwrap_and_save_reload_schedule(_UpperCAmelCase       ,				self.num_steps						)
             self.assertListEqual(_UpperCAmelCase       ,				_UpperCAmelCase       ,				msg=F'''failed for {scheduler_func} in save and reload'''						)





class 							lowercase_			:



 '''simple docstring'''

 def __init__(					self			:			Union[str, Any]       ,				_UpperCAmelCase			:			Optional[int]						):
       _A     =  fn

 def __call__(					self			:			Tuple       ,				*_UpperCAmelCase			:			List[str]       ,				**_UpperCAmelCase			:			List[str]						):
       return self.fn(*_UpperCAmelCase       ,				**_UpperCAmelCase						)



 @classmethod
 def      lowerCAmelCase_				(					self			:			Union[str, Any]       ,				_UpperCAmelCase			:			Any						):
       _A     =  list(map(self       ,				scheduler.lr_lambdas						)						)






"""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


__snake_case				:  List[Any] =       get_tests_dir('fixtures/test_sentencepiece.model')

__snake_case				:  Union[str, Any] =       {'target_lang': 'fi', 'source_lang': 'en'}
__snake_case				:  Union[str, Any] =       '>>zh<<'
__snake_case				:  List[str] =       'Helsinki-NLP/'

if is_torch_available():
	__snake_case				:  Optional[int] =       'pt'
elif is_tf_available():
	__snake_case				:  List[Any] =       'tf'
else:
	__snake_case				:  Union[str, Any] =       'jax'



@require_sentencepiece
class      A__  (    __SCREAMING_SNAKE_CASE    ,						unittest.TestCase				):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE        =      MarianTokenizer
	SCREAMING_SNAKE_CASE        =      False
	SCREAMING_SNAKE_CASE        =      True

	def 				_SCREAMING_SNAKE_CASE  (	self:						int)							->			Tuple:

				"""simple docstring"""

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

				__lowerCAmelCase :    Tuple				=			MarianTokenizer.from_pretrained(self.tmpdirname)
				tokenizer.save_pretrained(self.tmpdirname)

	def 				_SCREAMING_SNAKE_CASE  (	self:						Dict			,     **_SCREAMING_SNAKE_CASE:						List[str])							->			MarianTokenizer:

				"""simple docstring"""

				return MarianTokenizer.from_pretrained(self.tmpdirname			,     **_SCREAMING_SNAKE_CASE)

	def 				_SCREAMING_SNAKE_CASE  (	self:						List[Any]			,     _SCREAMING_SNAKE_CASE:						List[str])							->			Dict:

				"""simple docstring"""

				return (
				    "This is a test",
				    "This is a test",
				)

	def 				_SCREAMING_SNAKE_CASE  (	self:						Optional[int])							->			int:

				"""simple docstring"""

				__lowerCAmelCase :    Union[str, Any]				=			"</s>"
				__lowerCAmelCase :    int				=			0

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

	def 				_SCREAMING_SNAKE_CASE  (	self:						Tuple)							->			List[Any]:

				"""simple docstring"""

				__lowerCAmelCase :    List[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(_SCREAMING_SNAKE_CASE)			,     9)

	def 				_SCREAMING_SNAKE_CASE  (	self:						Tuple)							->			Dict:

				"""simple docstring"""

				self.assertEqual(self.get_tokenizer().vocab_size			,     9)

	def 				_SCREAMING_SNAKE_CASE  (	self:						List[Any])							->			Any:

				"""simple docstring"""

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

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

	def 				_SCREAMING_SNAKE_CASE  (	self:						Union[str, Any])							->			List[Any]:

				"""simple docstring"""

				__lowerCAmelCase :    List[str]				=			self.get_tokenizer()

				__lowerCAmelCase :    int				=			tok(
				    ["I am a small frog" * 1000, "I am a small frog"]			,     padding=_SCREAMING_SNAKE_CASE			,     truncation=_SCREAMING_SNAKE_CASE			,     return_tensors=_SCREAMING_SNAKE_CASE)
				self.assertIsInstance(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)
				self.assertEqual(batch.input_ids.shape			,     (2, 512))

	def 				_SCREAMING_SNAKE_CASE  (	self:						List[Any])							->			Optional[Any]:

				"""simple docstring"""

				__lowerCAmelCase :    Any				=			self.get_tokenizer()
				__lowerCAmelCase :    str				=			tok(["I am a tiny frog", "I am a small frog"]			,     padding=_SCREAMING_SNAKE_CASE			,     return_tensors=_SCREAMING_SNAKE_CASE)
				self.assertIsInstance(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)
				self.assertEqual(batch_smaller.input_ids.shape			,     (2, 10))

	@slow
	def 				_SCREAMING_SNAKE_CASE  (	self:						Optional[Any])							->			Optional[Any]:

				"""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=_SCREAMING_SNAKE_CASE			,     model_name="Helsinki-NLP/opus-mt-en-de"			,     revision="1a8c2263da11e68e50938f97e10cd57820bd504c"			,     decode_kwargs={"use_source_tokenizer": True}			,     )







	def 				_SCREAMING_SNAKE_CASE  (	self:						Union[str, Any])							->			Any:

				"""simple docstring"""

				__lowerCAmelCase :    List[Any]				=			MarianTokenizer.from_pretrained("hf-internal-testing/test-marian-two-vocabs")

				__lowerCAmelCase :    List[str]				=			"Tämä on testi"
				__lowerCAmelCase :    int				=			"This is a test"

				__lowerCAmelCase :    Union[str, Any]				=			[76, 7, 2047, 2]
				__lowerCAmelCase :    Dict				=			[69, 12, 11, 940, 2]

				__lowerCAmelCase :    List[str]				=			tokenizer(_SCREAMING_SNAKE_CASE).input_ids
				self.assertListEqual(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)

				__lowerCAmelCase :    str				=			tokenizer(text_target=_SCREAMING_SNAKE_CASE).input_ids
				self.assertListEqual(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)

				__lowerCAmelCase :    Dict				=			tokenizer.decode(_SCREAMING_SNAKE_CASE			,     skip_special_tokens=_SCREAMING_SNAKE_CASE)
				self.assertEqual(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)