
import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest







class   lowerCamelCase	(						_a						):
     UpperCAmelCase__       :							Optional[Any]     =	(PNDMScheduler,)
     UpperCAmelCase__       :							Optional[int]     =	(("""num_inference_steps""", 50),)





     def 	UpperCAmelCase(self   :					str       ,					**_A   :					Dict     )		->		Optional[int]:
         snake_case				  =      {
             "num_train_timesteps": 1_0_0_0,
             "beta_start": 0.00_01,
             "beta_end": 0.02,
             "beta_schedule": "linear",
         }

         config.update(**snake_case_     )
         return config





     def 	UpperCAmelCase(self   :					str       ,					_A   :					Optional[Any]=0       ,					**_A   :					Optional[Any]     )		->		int:
         snake_case				  =      dict(self.forward_default_kwargs     )
         snake_case				  =      kwargs.pop("num_inference_steps"       ,					snake_case_     )
         snake_case				  =      self.dummy_sample
         snake_case				  =      0.1 * sample
         snake_case				  =      [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

         for scheduler_class in self.scheduler_classes:
             snake_case				  =      self.get_scheduler_config(**snake_case_     )
             snake_case				  =      scheduler_class(**snake_case_     )
             scheduler.set_timesteps(snake_case_     )
             # copy over dummy past residuals
             snake_case				  =      dummy_past_residuals[:]

             with tempfile.TemporaryDirectory() as tmpdirname:
                 scheduler.save_config(snake_case_     )
                 snake_case				  =      scheduler_class.from_pretrained(snake_case_     )
                 new_scheduler.set_timesteps(snake_case_     )
                 # copy over dummy past residuals
                 snake_case				  =      dummy_past_residuals[:]

             snake_case				  =      scheduler.step_prk(snake_case_       ,					snake_case_       ,					snake_case_       ,					**snake_case_     ).prev_sample
             snake_case				  =      new_scheduler.step_prk(snake_case_       ,					snake_case_       ,					snake_case_       ,					**snake_case_     ).prev_sample

             assert torch.sum(torch.abs(output - new_output     )     ) < 1E-5, "Scheduler outputs are not identical"

             snake_case				  =      scheduler.step_plms(snake_case_       ,					snake_case_       ,					snake_case_       ,					**snake_case_     ).prev_sample
             snake_case				  =      new_scheduler.step_plms(snake_case_       ,					snake_case_       ,					snake_case_       ,					**snake_case_     ).prev_sample

             assert torch.sum(torch.abs(output - new_output     )     ) < 1E-5, "Scheduler outputs are not identical"





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





     def 	UpperCAmelCase(self   :					Union[str, Any]       ,					_A   :					int=0       ,					**_A   :					Union[str, Any]     )		->		List[str]:
         snake_case				  =      dict(self.forward_default_kwargs     )
         snake_case				  =      kwargs.pop("num_inference_steps"       ,					snake_case_     )
         snake_case				  =      self.dummy_sample
         snake_case				  =      0.1 * sample
         snake_case				  =      [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

         for scheduler_class in self.scheduler_classes:
             snake_case				  =      self.get_scheduler_config()
             snake_case				  =      scheduler_class(**snake_case_     )
             scheduler.set_timesteps(snake_case_     )

             # copy over dummy past residuals (must be after setting timesteps)
             snake_case				  =      dummy_past_residuals[:]

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

                 # copy over dummy past residual (must be after setting timesteps)
                 snake_case				  =      dummy_past_residuals[:]

             snake_case				  =      scheduler.step_prk(snake_case_       ,					snake_case_       ,					snake_case_       ,					**snake_case_     ).prev_sample
             snake_case				  =      new_scheduler.step_prk(snake_case_       ,					snake_case_       ,					snake_case_       ,					**snake_case_     ).prev_sample

             assert torch.sum(torch.abs(output - new_output     )     ) < 1E-5, "Scheduler outputs are not identical"

             snake_case				  =      scheduler.step_plms(snake_case_       ,					snake_case_       ,					snake_case_       ,					**snake_case_     ).prev_sample
             snake_case				  =      new_scheduler.step_plms(snake_case_       ,					snake_case_       ,					snake_case_       ,					**snake_case_     ).prev_sample

             assert torch.sum(torch.abs(output - new_output     )     ) < 1E-5, "Scheduler outputs are not identical"





     def 	UpperCAmelCase(self   :					Tuple       ,					**_A   :					Optional[int]     )		->		Union[str, Any]:
         snake_case				  =      self.scheduler_classes[0]
         snake_case				  =      self.get_scheduler_config(**snake_case_     )
         snake_case				  =      scheduler_class(**snake_case_     )

         snake_case				  =      1_0
         snake_case				  =      self.dummy_model()
         snake_case				  =      self.dummy_sample_deter
         scheduler.set_timesteps(snake_case_     )

         for i, t in enumerate(scheduler.prk_timesteps     ):
             snake_case				  =      model(snake_case_       ,					snake_case_     )
             snake_case				  =      scheduler.step_prk(snake_case_       ,					snake_case_       ,					snake_case_     ).prev_sample

         for i, t in enumerate(scheduler.plms_timesteps     ):
             snake_case				  =      model(snake_case_       ,					snake_case_     )
             snake_case				  =      scheduler.step_plms(snake_case_       ,					snake_case_       ,					snake_case_     ).prev_sample

         return sample





     def 	UpperCAmelCase(self   :					Tuple     )		->		Union[str, Any]:
         snake_case				  =      dict(self.forward_default_kwargs     )

         snake_case				  =      kwargs.pop("num_inference_steps"       ,					snake_case_     )

         for scheduler_class in self.scheduler_classes:
             snake_case				  =      self.get_scheduler_config()
             snake_case				  =      scheduler_class(**snake_case_     )

             snake_case				  =      self.dummy_sample
             snake_case				  =      0.1 * sample

             if num_inference_steps is not None and hasattr(snake_case_       ,					"set_timesteps"     ):
                 scheduler.set_timesteps(snake_case_     )
             elif num_inference_steps is not None and not hasattr(snake_case_       ,					"set_timesteps"     ):
                 snake_case				  =      num_inference_steps

             # copy over dummy past residuals (must be done after set_timesteps)
             snake_case				  =      [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
             snake_case				  =      dummy_past_residuals[:]

             snake_case				  =      scheduler.step_prk(snake_case_       ,					0       ,					snake_case_       ,					**snake_case_     ).prev_sample
             snake_case				  =      scheduler.step_prk(snake_case_       ,					1       ,					snake_case_       ,					**snake_case_     ).prev_sample

             self.assertEqual(output_a.shape       ,					sample.shape     )
             self.assertEqual(output_a.shape       ,					output_a.shape     )

             snake_case				  =      scheduler.step_plms(snake_case_       ,					0       ,					snake_case_       ,					**snake_case_     ).prev_sample
             snake_case				  =      scheduler.step_plms(snake_case_       ,					1       ,					snake_case_       ,					**snake_case_     ).prev_sample

             self.assertEqual(output_a.shape       ,					sample.shape     )
             self.assertEqual(output_a.shape       ,					output_a.shape     )





     def 	UpperCAmelCase(self   :					str     )		->		Dict:
         for timesteps in [1_0_0, 1_0_0_0]:
             self.check_over_configs(num_train_timesteps=snake_case_     )





     def 	UpperCAmelCase(self   :					Dict     )		->		Any:
         for steps_offset in [0, 1]:
             self.check_over_configs(steps_offset=snake_case_     )

         snake_case				  =      self.scheduler_classes[0]
         snake_case				  =      self.get_scheduler_config(steps_offset=1     )
         snake_case				  =      scheduler_class(**snake_case_     )
         scheduler.set_timesteps(1_0     )
         assert torch.equal(
             scheduler.timesteps       ,					torch.LongTensor(
                 [9_0_1, 8_5_1, 8_5_1, 8_0_1, 8_0_1, 7_5_1, 7_5_1, 7_0_1, 7_0_1, 6_5_1, 6_5_1, 6_0_1, 6_0_1, 5_0_1, 4_0_1, 3_0_1, 2_0_1, 1_0_1, 1]     )       ,					)





     def 	UpperCAmelCase(self   :					Tuple     )		->		Optional[Any]:
         for beta_start, beta_end in zip([0.00_01, 0.0_01]       ,					[0.0_02, 0.02]     ):
             self.check_over_configs(beta_start=snake_case_       ,					beta_end=snake_case_     )





     def 	UpperCAmelCase(self   :					str     )		->		str:
         for schedule in ["linear", "squaredcos_cap_v2"]:
             self.check_over_configs(beta_schedule=snake_case_     )





     def 	UpperCAmelCase(self   :					List[str]     )		->		Any:
         for prediction_type in ["epsilon", "v_prediction"]:
             self.check_over_configs(prediction_type=snake_case_     )





     def 	UpperCAmelCase(self   :					Dict     )		->		Union[str, Any]:
         for t in [1, 5, 1_0]:
             self.check_over_forward(time_step=snake_case_     )





     def 	UpperCAmelCase(self   :					str     )		->		Union[str, Any]:
         for t, num_inference_steps in zip([1, 5, 1_0]       ,					[1_0, 5_0, 1_0_0]     ):
             self.check_over_forward(num_inference_steps=snake_case_     )





     def 	UpperCAmelCase(self   :					Any     )		->		Optional[int]:
         # earlier version of set_timesteps() caused an error indexing alpha's with inference steps as power of 3
         snake_case				  =      2_7

         for scheduler_class in self.scheduler_classes:
             snake_case				  =      self.dummy_sample
             snake_case				  =      0.1 * sample

             snake_case				  =      self.get_scheduler_config()
             snake_case				  =      scheduler_class(**snake_case_     )

             scheduler.set_timesteps(snake_case_     )

             # before power of 3 fix, would error on first step, so we only need to do two
             for i, t in enumerate(scheduler.prk_timesteps[:2]     ):
                 snake_case				  =      scheduler.step_prk(snake_case_       ,					snake_case_       ,					snake_case_     ).prev_sample





     def 	UpperCAmelCase(self   :					str     )		->		List[str]:
         with self.assertRaises(snake_case_     ):
             snake_case				  =      self.scheduler_classes[0]
             snake_case				  =      self.get_scheduler_config()
             snake_case				  =      scheduler_class(**snake_case_     )

             scheduler.step_plms(self.dummy_sample       ,					1       ,					self.dummy_sample     ).prev_sample





     def 	UpperCAmelCase(self   :					Union[str, Any]     )		->		Dict:
         snake_case				  =      self.full_loop()
         snake_case				  =      torch.sum(torch.abs(snake_case_     )     )
         snake_case				  =      torch.mean(torch.abs(snake_case_     )     )

         assert abs(result_sum.item() - 1_98.13_18     ) < 1E-2
         assert abs(result_mean.item() - 0.25_80     ) < 1E-3





     def 	UpperCAmelCase(self   :					Tuple     )		->		Optional[Any]:
         snake_case				  =      self.full_loop(prediction_type="v_prediction"     )
         snake_case				  =      torch.sum(torch.abs(snake_case_     )     )
         snake_case				  =      torch.mean(torch.abs(snake_case_     )     )

         assert abs(result_sum.item() - 67.39_86     ) < 1E-2
         assert abs(result_mean.item() - 0.08_78     ) < 1E-3





     def 	UpperCAmelCase(self   :					Union[str, Any]     )		->		List[str]:
         # We specify different beta, so that the first alpha is 0.99
         snake_case				  =      self.full_loop(set_alpha_to_one=snake_case_       ,					beta_start=0.01     )
         snake_case				  =      torch.sum(torch.abs(snake_case_     )     )
         snake_case				  =      torch.mean(torch.abs(snake_case_     )     )

         assert abs(result_sum.item() - 2_30.03_99     ) < 1E-2
         assert abs(result_mean.item() - 0.29_95     ) < 1E-3





     def 	UpperCAmelCase(self   :					Tuple     )		->		Union[str, Any]:
         # We specify different beta, so that the first alpha is 0.99
         snake_case				  =      self.full_loop(set_alpha_to_one=snake_case_       ,					beta_start=0.01     )
         snake_case				  =      torch.sum(torch.abs(snake_case_     )     )
         snake_case				  =      torch.mean(torch.abs(snake_case_     )     )

         assert abs(result_sum.item() - 1_86.94_82     ) < 1E-2
         assert abs(result_mean.item() - 0.24_34     ) < 1E-3



import os
import unittest

from transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer

from ...test_tokenization_common import TokenizerTesterMixin







class   lowerCamelCase	(						A_							,				unittest.TestCase						):
     UpperCAmelCase__       :							str     =	PhobertTokenizer
     UpperCAmelCase__       :							List[Any]     =	False





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

         # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
         snake_case				  =      ["T@@", "i", "I", "R@@", "r", "e@@"]
         snake_case				  =      dict(zip(_A       ,					range(len(_A     )     )     )     )
         snake_case				  =      ["#version: 0.2", "l à</w>"]
         snake_case				  =      {"unk_token": "<unk>"}

         snake_case				  =      os.path.join(self.tmpdirname       ,					VOCAB_FILES_NAMES["vocab_file"]     )
         snake_case				  =      os.path.join(self.tmpdirname       ,					VOCAB_FILES_NAMES["merges_file"]     )

         with open(self.vocab_file       ,					"w"       ,					encoding="utf-8"     ) as fp:
             for token in vocab_tokens:
                 fp.write(f'{token} {vocab_tokens[token]}\n'     )
         with open(self.merges_file       ,					"w"       ,					encoding="utf-8"     ) as fp:
             fp.write("\n".join(_A     )     )





     def 	UpperCAmelCase(self   :					Optional[int]       ,					**_A   :					Optional[int]     )		->		Union[str, Any]:
         kwargs.update(self.special_tokens_map     )
         return PhobertTokenizer.from_pretrained(self.tmpdirname       ,					**_A     )





     def 	UpperCAmelCase(self   :					Tuple       ,					_A   :					Optional[Any]     )		->		str:
         snake_case				  =      "Tôi là VinAI Research"
         snake_case				  =      "T<unk> i <unk> <unk> <unk> <unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>"
         return input_text, output_text





     def 	UpperCAmelCase(self   :					Any     )		->		Union[str, Any]:
         snake_case				  =      PhobertTokenizer(self.vocab_file       ,					self.merges_file       ,					**self.special_tokens_map     )
         snake_case				  =      "Tôi là VinAI Research"
         snake_case				  =      "T@@ ô@@ i l@@ à V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h".split()
         snake_case				  =      tokenizer.tokenize(_A     )
         print(_A     )
         self.assertListEqual(_A       ,					_A     )

         snake_case				  =      tokens + [tokenizer.unk_token]

         snake_case				  =      [4, 3, 5, 3, 3, 3, 3, 3, 3, 6, 7, 9, 3, 9, 3, 3, 3, 3, 3]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(_A     )       ,					_A     )

