
"""simple docstring"""




import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
)
from transformers.utils import is_sentencepiece_available


if is_sentencepiece_available():
  from transformers.models.mam_aaa.tokenization_mam_aaa import VOCAB_FILES_NAMES, save_json

from ...test_tokenization_common import TokenizerTesterMixin


if is_sentencepiece_available():
  A__   :					List[Any]							=   get_tests_dir('fixtures/test_sentencepiece.model')


if is_torch_available():
  from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right

A__   :					Tuple							=   1_2_8_0_2_2
A__   :					Union[str, Any]							=   1_2_8_0_2_8


@require_sentencepiece
class 			__magic_name__       (       SCREAMING_SNAKE_CASE__   ,					unittest.TestCase ):
       UpperCamelCase_   =		MaMaaaTokenizer
       UpperCamelCase_   =		False
       UpperCamelCase_   =		False
       UpperCamelCase_   =		True
       def 							lowercase_							(     self       )     ->	Union[str, Any]:





         """simple docstring"""




         super().setUp()

         _lowercase:			Optional[int]       =     ['''</s>''', '''<unk>''', '''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''', '''\u0120''', '''<pad>''']
         _lowercase:			Tuple       =     dict(zip(A_ ,				range(len(A_       )       )       )       )
         _lowercase:			Union[str, Any]       =     Path(self.tmpdirname       )
         save_json(A_ ,				save_dir / VOCAB_FILES_NAMES['''vocab_file''']       )
         if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
           copyfile(A_ ,				save_dir / VOCAB_FILES_NAMES['''spm_file''']       )

         _lowercase:			List[str]       =     MaMaaaTokenizer.from_pretrained(self.tmpdirname       )
         tokenizer.save_pretrained(self.tmpdirname       )
       def 							lowercase_							(     self ,				**A_       )     ->	Tuple:





         """simple docstring"""




         return MaMaaaTokenizer.from_pretrained(self.tmpdirname ,				**A_       )
       def 							lowercase_							(     self ,				A_       )     ->	Optional[int]:





         """simple docstring"""




         return (
             "This is a test",
             "This is a test",
         )
       def 							lowercase_							(     self       )     ->	Optional[int]:





         """simple docstring"""




         _lowercase:			List[str]       =     '''</s>'''
         _lowercase:			str       =     0

         self.assertEqual(self.get_tokenizer()._convert_token_to_id(A_       ) ,				A_       )
         self.assertEqual(self.get_tokenizer()._convert_id_to_token(A_       ) ,				A_       )
       def 							lowercase_							(     self       )     ->	str:





         """simple docstring"""




         _lowercase:			Optional[Any]       =     self.get_tokenizer()
         _lowercase:			Optional[int]       =     list(tokenizer.get_vocab().keys()       )

         self.assertEqual(vocab_keys[0] ,				'''</s>'''       )
         self.assertEqual(vocab_keys[1] ,				'''<unk>'''       )
         self.assertEqual(vocab_keys[-1] ,				'''<s>'''       )
         self.assertEqual(len(A_       ) ,				tokenizer.vocab_size + len(tokenizer.get_added_vocab()       )       )
       @unittest.skip('''Skip this test while all models are still to be uploaded.'''       )
       def 							lowercase_							(     self       )     ->	Dict:





         """simple docstring"""




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





         """simple docstring"""




         _lowercase:			str       =     self.get_tokenizer()

         _lowercase:			Dict       =     tokenizer.tokenize('''This is a test'''       )
         self.assertListEqual(A_ ,				['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']       )

         self.assertListEqual(
             tokenizer.convert_tokens_to_ids(A_       ) ,				[2, 3, 4, 5, 6] ,				)

         _lowercase:			Dict       =     tokenizer.convert_ids_to_tokens([2, 3, 4, 5, 6]       )
         self.assertListEqual(A_ ,				['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']       )

         _lowercase:			List[str]       =     tokenizer.convert_tokens_to_string(A_       )
         self.assertEqual(A_ ,				'''This is a test'''       )



       @slow
       def 							lowercase_							(     self       )     ->	int:





         """simple docstring"""




         _lowercase:			Any       =     {'''input_ids''': [[12_8022, 11_0108, 397, 11, 3_8272, 2247, 12_4811, 285, 1_8105, 1586, 207, 7, 3_9534, 4428, 397, 1019, 1_8105, 1586, 207, 7, 4_1337, 1_6786, 241, 7, 2_0214, 17, 12_5690, 1_0398, 7, 4_4378, 5_8069, 6_8342, 7798, 7343, 11, 299, 3_3310, 4, 158, 3_7350, 9_4077, 4569, 299, 3_3310, 90, 4, 5_2840, 290, 4, 3_1270, 112, 299, 682, 4, 5_2840, 3_9953, 1_4079, 193, 5_2519, 9_0894, 1_7894, 12_0697, 11, 4_0445, 551, 17, 1019, 5_2519, 9_0894, 1_7756, 963, 11, 4_0445, 480, 17, 9792, 1120, 5173, 1393, 6240, 1_6786, 241, 12_0996, 28, 1245, 1393, 11_8240, 1_1123, 1019, 9_3612, 2691, 1_0618, 9_8058, 12_0409, 1928, 279, 4, 4_0683, 367, 178, 207, 1019, 103, 10_3121, 506, 6_5296, 5, 2], [12_8022, 2_1217, 367, 117, 12_5450, 128, 719, 7, 7308, 40, 9_3612, 1_2669, 1116, 1_6704, 71, 1_7785, 3699, 1_5592, 35, 144, 9584, 241, 1_1943, 713, 950, 799, 2247, 8_8427, 150, 149, 11_8813, 12_0706, 1019, 10_6906, 8_1518, 28, 1224, 2_2799, 397, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [12_8022, 1658, 12_3311, 5155, 5578, 4722, 279, 1_4947, 2366, 1120, 1197, 14, 1348, 9232, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], '''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, 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], [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, 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='''facebook/m2m100_418M''' ,				revision='''c168bae485c864188cf9aa0e4108b0b6934dc91e''' ,				)


@require_torch
@require_sentencepiece
@require_tokenizers
class 			__magic_name__       (       unittest.TestCase ):
       UpperCamelCase_   =		'''facebook/m2m100_418M'''
       UpperCamelCase_   =		[
           '''In my opinion, there are two levels of response from the French government.''',
           '''NSA Affair Emphasizes Complete Lack of Debate on Intelligence''',
       ]
       UpperCamelCase_   =		[
           '''Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.''',
           '''L\'affaire NSA souligne l\'absence totale de débat sur le renseignement''',
       ]

       # fmt: off
       UpperCamelCase_   =		[EN_CODE, 593, 1949, 11_5781, 4, 7_1586, 4234, 6_0633, 12_6233, 432, 12_3808, 1_5592, 1197, 11_7132, 12_0618, 5, 2]
       @classmethod
       def 							lowercase_							(     cls       )     ->	List[str]:





         """simple docstring"""




         _lowercase:			MaMaaaTokenizer       =     MaMaaaTokenizer.from_pretrained(
             cls.checkpoint_name ,				src_lang='''en''' ,				tgt_lang='''fr'''       )
         _lowercase:			Optional[Any]       =     1
         return cls
       def 							lowercase_							(     self       )     ->	Optional[int]:





         """simple docstring"""




         self.assertEqual(self.tokenizer.get_lang_id('''ar'''       ) ,				12_8006       )
         self.assertEqual(self.tokenizer.get_lang_id('''en'''       ) ,				12_8022       )
         self.assertEqual(self.tokenizer.get_lang_id('''ro'''       ) ,				12_8076       )
         self.assertEqual(self.tokenizer.get_lang_id('''mr'''       ) ,				12_8063       )
       def 							lowercase_							(     self       )     ->	Optional[Any]:





         """simple docstring"""




         _lowercase:			List[Any]       =     self.tokenizer.get_vocab()
         self.assertEqual(len(A_       ) ,				self.tokenizer.vocab_size       )
         self.assertEqual(vocab['''<unk>'''] ,				3       )
         self.assertIn(self.tokenizer.get_lang_token('''en'''       ) ,				A_       )
       def 							lowercase_							(     self       )     ->	Union[str, Any]:





         """simple docstring"""




         _lowercase:			Tuple       =     '''en'''
         _lowercase:			int       =     self.tokenizer.batch_encode_plus(self.src_text       ).input_ids[0]
         self.assertListEqual(self.expected_src_tokens ,				A_       )
       def 							lowercase_							(     self       )     ->	Optional[Any]:





         """simple docstring"""




         self.assertIn(A_ ,				self.tokenizer.all_special_ids       )
         # fmt: off
         _lowercase:			Tuple       =     [FR_CODE, 5364, 82, 8642, 4, 294, 47, 8, 1_4028, 136, 3286, 9706, 6, 9_0797, 6, 14_4012, 162, 8_8128, 3_0061, 5, 2]
         # fmt: on
         _lowercase:			Any       =     self.tokenizer.decode(A_ ,				skip_special_tokens=A_       )
         _lowercase:			Dict       =     self.tokenizer.decode(generated_ids[1:] ,				skip_special_tokens=A_       )
         self.assertEqual(A_ ,				A_       )
         self.assertNotIn(self.tokenizer.eos_token ,				A_       )
       def 							lowercase_							(     self       )     ->	Optional[Any]:





         """simple docstring"""




         _lowercase:			List[Any]       =     tempfile.mkdtemp()
         _lowercase:			Any       =     self.tokenizer.lang_token_to_id
         self.tokenizer.save_pretrained(A_       )
         _lowercase:			List[str]       =     MaMaaaTokenizer.from_pretrained(A_       )
         self.assertDictEqual(new_tok.lang_token_to_id ,				A_       )
       @require_torch
       def 							lowercase_							(     self       )     ->	List[str]:





         """simple docstring"""




         _lowercase:			Tuple       =     '''en'''
         _lowercase:			Optional[int]       =     '''fr'''

         _lowercase:			str       =     self.tokenizer(self.src_text ,				text_target=self.tgt_text ,				padding=A_ ,				return_tensors='''pt'''       )

         _lowercase:			Optional[int]       =     shift_tokens_right(
             batch['''labels'''] ,				self.tokenizer.pad_token_id ,				self.tokenizer.eos_token_id       )

         for k in batch:
           _lowercase:			Optional[int]       =     batch[k].tolist()
         # batch = {k: v.tolist() for k,v in batch.items()}
         # fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
         # batch.decoder_inputs_ids[0][0] ==
         assert batch.input_ids[1][0] == EN_CODE
         assert batch.input_ids[1][-1] == 2
         assert batch.labels[1][0] == FR_CODE
         assert batch.labels[1][-1] == 2
         assert batch.decoder_input_ids[1][:2] == [2, FR_CODE]
       @require_torch
       def 							lowercase_							(     self       )     ->	Optional[int]:





         """simple docstring"""




         _lowercase:			Any       =     '''mr'''
         self.assertListEqual(self.tokenizer.prefix_tokens ,				[self.tokenizer.get_lang_id('''mr'''       )]       )
         self.assertListEqual(self.tokenizer.suffix_tokens ,				[self.tokenizer.eos_token_id]       )

         _lowercase:			Optional[Any]       =     '''zh'''
         self.assertListEqual(self.tokenizer.prefix_tokens ,				[self.tokenizer.get_lang_id('''zh'''       )]       )
         self.assertListEqual(self.tokenizer.suffix_tokens ,				[self.tokenizer.eos_token_id]       )
       @require_torch
       def 							lowercase_							(     self       )     ->	List[str]:





         """simple docstring"""




         _lowercase:			Optional[Any]       =     '''mr'''
         self.tokenizer._switch_to_target_mode()
         self.assertListEqual(self.tokenizer.prefix_tokens ,				[self.tokenizer.get_lang_id('''mr'''       )]       )
         self.assertListEqual(self.tokenizer.suffix_tokens ,				[self.tokenizer.eos_token_id]       )
         self.tokenizer._switch_to_input_mode()
         self.assertListEqual(self.tokenizer.prefix_tokens ,				[self.tokenizer.get_lang_id(self.tokenizer.src_lang       )]       )

         _lowercase:			Union[str, Any]       =     '''zh'''
         self.tokenizer._switch_to_target_mode()
         self.assertListEqual(self.tokenizer.prefix_tokens ,				[self.tokenizer.get_lang_id('''zh'''       )]       )
         self.assertListEqual(self.tokenizer.suffix_tokens ,				[self.tokenizer.eos_token_id]       )
         self.tokenizer._switch_to_input_mode()
         self.assertListEqual(self.tokenizer.prefix_tokens ,				[self.tokenizer.get_lang_id(self.tokenizer.src_lang       )]       )



       @require_torch
       def 							lowercase_							(     self       )     ->	Union[str, Any]:





         """simple docstring"""




         _lowercase:			List[Any]       =     self.tokenizer._build_translation_inputs('''A test''' ,				return_tensors='''pt''' ,				src_lang='''en''' ,				tgt_lang='''ar'''       )

         self.assertEqual(
             nested_simplify(A_       ) ,				{
                 # en_XX, A, test, EOS
                 '''input_ids''': [[12_8022, 58, 4183, 2]],
                 '''attention_mask''': [[1, 1, 1, 1]],
                 # ar_AR
                 '''forced_bos_token_id''': 12_8006,
             } ,				)


"""simple docstring"""




import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class 			__magic_name__       (       SCREAMING_SNAKE_CASE__ ):
       UpperCamelCase_   =		(KDPMaDiscreteScheduler,)
       UpperCamelCase_   =		10
       def 							lowercase_							(     self ,				**A_       )     ->	List[Any]:





         """simple docstring"""




         _lowercase:			Tuple       =     {
             '''num_train_timesteps''': 1100,
             '''beta_start''': 0.00_01,
             '''beta_end''': 0.02,
             '''beta_schedule''': '''linear''',
         }

         config.update(**A_       )
         return config
       def 							lowercase_							(     self       )     ->	List[str]:





         """simple docstring"""




         for timesteps in [10, 50, 100, 1000]:
           self.check_over_configs(num_train_timesteps=A_       )
       def 							lowercase_							(     self       )     ->	Tuple:





         """simple docstring"""




         for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01] ,				[0.00_02, 0.0_02, 0.02]       ):
           self.check_over_configs(beta_start=A_ ,				beta_end=A_       )
       def 							lowercase_							(     self       )     ->	int:





         """simple docstring"""




         for schedule in ["linear", "scaled_linear"]:
           self.check_over_configs(beta_schedule=A_       )
       def 							lowercase_							(     self       )     ->	str:





         """simple docstring"""




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





         """simple docstring"""




         _lowercase:			Optional[int]       =     self.scheduler_classes[0]
         _lowercase:			List[str]       =     self.get_scheduler_config(prediction_type='''v_prediction'''       )
         _lowercase:			List[Any]       =     scheduler_class(**A_       )

         scheduler.set_timesteps(self.num_inference_steps       )

         _lowercase:			List[str]       =     self.dummy_model()
         _lowercase:			Dict       =     self.dummy_sample_deter * scheduler.init_noise_sigma
         _lowercase:			List[Any]       =     sample.to(A_       )

         for i, t in enumerate(scheduler.timesteps       ):
           _lowercase:			Optional[int]       =     scheduler.scale_model_input(A_ ,				A_       )

           _lowercase:			Optional[Any]       =     model(A_ ,				A_       )

           _lowercase:			List[str]       =     scheduler.step(A_ ,				A_ ,				A_       )
           _lowercase:			str       =     output.prev_sample

         _lowercase:			Dict       =     torch.sum(torch.abs(A_       )       )
         _lowercase:			Optional[int]       =     torch.mean(torch.abs(A_       )       )

         if torch_device in ["cpu", "mps"]:
           assert abs(result_sum.item() - 4.69_34E-07       ) < 1E-2
           assert abs(result_mean.item() - 6.11_12E-10       ) < 1E-3
         else:
           # CUDA
           assert abs(result_sum.item() - 4.6_93_42_86_50_17_09_72E-07       ) < 1E-2
           assert abs(result_mean.item() - 0.00_02       ) < 1E-3
       def 							lowercase_							(     self       )     ->	str:





         """simple docstring"""




         if torch_device == "mps":
           return
         _lowercase:			List[str]       =     self.scheduler_classes[0]
         _lowercase:			Optional[int]       =     self.get_scheduler_config()
         _lowercase:			int       =     scheduler_class(**A_       )

         scheduler.set_timesteps(self.num_inference_steps       )

         _lowercase:			Tuple       =     self.dummy_model()
         _lowercase:			Tuple       =     self.dummy_sample_deter * scheduler.init_noise_sigma
         _lowercase:			Any       =     sample.to(A_       )

         for i, t in enumerate(scheduler.timesteps       ):
           _lowercase:			Any       =     scheduler.scale_model_input(A_ ,				A_       )

           _lowercase:			List[Any]       =     model(A_ ,				A_       )

           _lowercase:			Any       =     scheduler.step(A_ ,				A_ ,				A_       )
           _lowercase:			List[Any]       =     output.prev_sample

         _lowercase:			Optional[Any]       =     torch.sum(torch.abs(A_       )       )
         _lowercase:			Tuple       =     torch.mean(torch.abs(A_       )       )

         if torch_device in ["cpu", "mps"]:
           assert abs(result_sum.item() - 20.41_25       ) < 1E-2
           assert abs(result_mean.item() - 0.02_66       ) < 1E-3
         else:
           # CUDA
           assert abs(result_sum.item() - 20.41_25       ) < 1E-2
           assert abs(result_mean.item() - 0.02_66       ) < 1E-3



       def 							lowercase_							(     self       )     ->	Any:





         """simple docstring"""




         if torch_device == "mps":
           return
         _lowercase:			int       =     self.scheduler_classes[0]
         _lowercase:			Union[str, Any]       =     self.get_scheduler_config()
         _lowercase:			Optional[Any]       =     scheduler_class(**A_       )

         scheduler.set_timesteps(self.num_inference_steps ,				device=A_       )

         _lowercase:			List[str]       =     self.dummy_model()
         _lowercase:			Union[str, Any]       =     self.dummy_sample_deter.to(A_       ) * scheduler.init_noise_sigma

         for t in scheduler.timesteps:
           _lowercase:			Optional[Any]       =     scheduler.scale_model_input(A_ ,				A_       )

           _lowercase:			int       =     model(A_ ,				A_       )

           _lowercase:			Tuple       =     scheduler.step(A_ ,				A_ ,				A_       )
           _lowercase:			int       =     output.prev_sample

         _lowercase:			Union[str, Any]       =     torch.sum(torch.abs(A_       )       )
         _lowercase:			Dict       =     torch.mean(torch.abs(A_       )       )

         if str(A_       ).startswith('''cpu'''       ):
           # The following sum varies between 148 and 156 on mps. Why?
           assert abs(result_sum.item() - 20.41_25       ) < 1E-2
           assert abs(result_mean.item() - 0.02_66       ) < 1E-3
         else:
           # CUDA
           assert abs(result_sum.item() - 20.41_25       ) < 1E-2
           assert abs(result_mean.item() - 0.02_66       ) < 1E-3
