




import json
import os
import unittest

from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import (
    VOCAB_FILES_NAMES,
    GPTNeoXJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class 		__UpperCAmelCase(		lowercase__							,						unittest.TestCase			):





     """simple docstring"""



     __UpperCAmelCase						:			Any							=     GPTNeoXJapaneseTokenizer
     __UpperCAmelCase						:			Tuple							=     False
     __UpperCAmelCase						:			Dict							=     {'do_clean_text': False, 'add_prefix_space': False}

     def 					__SCREAMING_SNAKE_CASE		(   self			:							Dict     )      ->      str:






         """simple docstring"""
         super().setUp()

         a__  			=							[
             """こん""",
             """こんに""",
             """にちは""",
             """ばんは""",
             """世界,㔺界""",
             """、""",
             """。""",
             """<BR>""",
             """<SP>""",
             """<TAB>""",
             """<URL>""",
             """<EMAIL>""",
             """<TEL>""",
             """<DATE>""",
             """<PRICE>""",
             """<BLOCK>""",
             """<KIGOU>""",
             """<U2000U2BFF>""",
             """<|emoji1|>""",
             """<unk>""",
             """<|startoftext|>""",
             """<|endoftext|>""",
         ]
         a__  			=							{"""emoji""": {"""\ud83d\ude00""": """<|emoji1|>"""}, """emoji_inv""": {"""<|emoji1|>""": """\ud83d\ude00"""}}  # 😀
         a__  			=							{"""unk_token""": """<unk>"""}

         a__  			=							os.path.join(self.tmpdirname    ,					VOCAB_FILES_NAMES["""vocab_file"""]     )
         a__  			=							os.path.join(self.tmpdirname    ,					VOCAB_FILES_NAMES["""emoji_file"""]     )
         with open(self.vocab_file    ,					"""w"""    ,					encoding="""utf-8"""     ) as vocab_writer:
             vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]     )     )
         with open(self.emoji_file    ,					"""w"""     ) as emoji_writer:
             emoji_writer.write(json.dumps(_UpperCAmelCase     )     )

     def 					__SCREAMING_SNAKE_CASE		(   self			:							Dict    ,					**A			:							Any     )      ->      List[str]:






         """simple docstring"""
         kwargs.update(self.special_tokens_map     )
         return GPTNeoXJapaneseTokenizer.from_pretrained(self.tmpdirname    ,					**_UpperCAmelCase     )

     def 					__SCREAMING_SNAKE_CASE		(   self			:							Optional[Any]    ,					A			:							List[Any]     )      ->      List[Any]:






         """simple docstring"""
         a__  			=							"""こんにちは、世界。 \nこんばんは、㔺界。😀"""
         a__  			=							"""こんにちは、世界。 \nこんばんは、世界。😀"""
         return input_text, output_text

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






         """simple docstring"""
         a__			,		a__  			=							self.get_input_output_texts(_UpperCAmelCase     )
         a__  			=							tokenizer.encode(_UpperCAmelCase    ,					add_special_tokens=_UpperCAmelCase     )
         a__  			=							tokenizer.decode(_UpperCAmelCase    ,					clean_up_tokenization_spaces=_UpperCAmelCase     )
         return text, ids

     def 					__SCREAMING_SNAKE_CASE		(   self			:							Any     )      ->      Optional[int]:






         """simple docstring"""
         pass  # TODO add if relevant

     def 					__SCREAMING_SNAKE_CASE		(   self			:							Optional[Any]     )      ->      int:






         """simple docstring"""
         pass  # TODO add if relevant

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






         """simple docstring"""
         pass  # TODO add if relevant

     def 					__SCREAMING_SNAKE_CASE		(   self			:							List[Any]     )      ->      List[str]:






         """simple docstring"""
         a__  			=							self.get_tokenizer()

         # Testing tokenization
         a__  			=							"""こんにちは、世界。　こんばんは、㔺界。"""
         a__  			=							["""こん""", """にちは""", """、""", """世界""", """。""", """<SP>""", """こん""", """ばんは""", """、""", """㔺界""", """。"""]
         a__  			=							tokenizer.tokenize(_UpperCAmelCase     )
         self.assertListEqual(_UpperCAmelCase    ,					_UpperCAmelCase     )

         # Testing conversion to ids without special tokens
         a__  			=							[0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6]
         a__  			=							tokenizer.convert_tokens_to_ids(_UpperCAmelCase     )
         self.assertListEqual(_UpperCAmelCase    ,					_UpperCAmelCase     )

         # Testing conversion to ids with special tokens
         a__  			=							tokens + [tokenizer.unk_token]
         a__  			=							[0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 19]
         a__  			=							tokenizer.convert_tokens_to_ids(_UpperCAmelCase     )
         self.assertListEqual(_UpperCAmelCase    ,					_UpperCAmelCase     )

     @slow
     def 					__SCREAMING_SNAKE_CASE		(   self			:							str     )      ->      Tuple:






         """simple docstring"""
         a__  			=							self.tokenizer_class.from_pretrained("""abeja/gpt-neox-japanese-2.7b"""     )

         a__  			=							tokenizer.encode("""ありがとう。"""    ,					add_special_tokens=_UpperCAmelCase     )
         a__  			=							tokenizer.encode("""どういたしまして。"""    ,					add_special_tokens=_UpperCAmelCase     )

         a__  			=							tokenizer.build_inputs_with_special_tokens(_UpperCAmelCase     )
         a__  			=							tokenizer.build_inputs_with_special_tokens(_UpperCAmelCase    ,					_UpperCAmelCase     )

         assert encoded_sentence == ids_a
         assert encoded_pair == ids_a + ids_a

     def 					__SCREAMING_SNAKE_CASE		(   self			:							int     )      ->      List[str]:






         """simple docstring"""
         # Intentionally convert some words to accommodate character fluctuations unique to Japanese
         pass






     def 					__SCREAMING_SNAKE_CASE		(   self			:							Optional[Any]     )      ->      int:






         """simple docstring"""
         # tokenizer has no padding token
         pass








import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class 			UpperCamelCase_				(  A     ):




      '''simple docstring'''







      a		:Any						 =      (LMSDiscreteScheduler,)
      a		:Dict						 =      10







      def lowercase__		( self					,					**_UpperCAmelCase):
            lowerCAmelCase_						=      {
                '''num_train_timesteps''': 1_100,
                '''beta_start''': 0.0001,
                '''beta_end''': 0.02,
                '''beta_schedule''': '''linear''',
            }

            config.update(**_UpperCAmelCase)
            return config







      def lowercase__		( self):
            for timesteps in [10, 50, 100, 1_000]:
                  self.check_over_configs(num_train_timesteps=_UpperCAmelCase)







      def lowercase__		( self):
            for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]					,					[0.0002, 0.002, 0.02]):
                  self.check_over_configs(beta_start=_UpperCAmelCase					,					beta_end=_UpperCAmelCase)







      def lowercase__		( self):
            for schedule in ["linear", "scaled_linear"]:
                  self.check_over_configs(beta_schedule=_UpperCAmelCase)







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







      def lowercase__		( self):
            for t in [0, 500, 800]:
                  self.check_over_forward(time_step=_UpperCAmelCase)







      def lowercase__		( self):
            lowerCAmelCase_						=      self.scheduler_classes[0]
            lowerCAmelCase_						=      self.get_scheduler_config()
            lowerCAmelCase_						=      scheduler_class(**_UpperCAmelCase)

            scheduler.set_timesteps(self.num_inference_steps)

            lowerCAmelCase_						=      self.dummy_model()
            lowerCAmelCase_						=      self.dummy_sample_deter * scheduler.init_noise_sigma

            for i, t in enumerate(scheduler.timesteps):
                  lowerCAmelCase_						=      scheduler.scale_model_input(_UpperCAmelCase					,					_UpperCAmelCase)

                  lowerCAmelCase_						=      model(_UpperCAmelCase					,					_UpperCAmelCase)

                  lowerCAmelCase_						=      scheduler.step(_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase)
                  lowerCAmelCase_						=      output.prev_sample

            lowerCAmelCase_						=      torch.sum(torch.abs(_UpperCAmelCase))
            lowerCAmelCase_						=      torch.mean(torch.abs(_UpperCAmelCase))

            assert abs(result_sum.item() - 1006.388) < 1E-2
            assert abs(result_mean.item() - 1.31) < 1E-3







      def lowercase__		( self):
            lowerCAmelCase_						=      self.scheduler_classes[0]
            lowerCAmelCase_						=      self.get_scheduler_config(prediction_type='''v_prediction''')
            lowerCAmelCase_						=      scheduler_class(**_UpperCAmelCase)

            scheduler.set_timesteps(self.num_inference_steps)

            lowerCAmelCase_						=      self.dummy_model()
            lowerCAmelCase_						=      self.dummy_sample_deter * scheduler.init_noise_sigma

            for i, t in enumerate(scheduler.timesteps):
                  lowerCAmelCase_						=      scheduler.scale_model_input(_UpperCAmelCase					,					_UpperCAmelCase)

                  lowerCAmelCase_						=      model(_UpperCAmelCase					,					_UpperCAmelCase)

                  lowerCAmelCase_						=      scheduler.step(_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase)
                  lowerCAmelCase_						=      output.prev_sample

            lowerCAmelCase_						=      torch.sum(torch.abs(_UpperCAmelCase))
            lowerCAmelCase_						=      torch.mean(torch.abs(_UpperCAmelCase))

            assert abs(result_sum.item() - 0.0017) < 1E-2
            assert abs(result_mean.item() - 2.2676E-06) < 1E-3







      def lowercase__		( self):
            lowerCAmelCase_						=      self.scheduler_classes[0]
            lowerCAmelCase_						=      self.get_scheduler_config()
            lowerCAmelCase_						=      scheduler_class(**_UpperCAmelCase)

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

            lowerCAmelCase_						=      self.dummy_model()
            lowerCAmelCase_						=      self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
            lowerCAmelCase_						=      sample.to(_UpperCAmelCase)

            for i, t in enumerate(scheduler.timesteps):
                  lowerCAmelCase_						=      scheduler.scale_model_input(_UpperCAmelCase					,					_UpperCAmelCase)

                  lowerCAmelCase_						=      model(_UpperCAmelCase					,					_UpperCAmelCase)

                  lowerCAmelCase_						=      scheduler.step(_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase)
                  lowerCAmelCase_						=      output.prev_sample

            lowerCAmelCase_						=      torch.sum(torch.abs(_UpperCAmelCase))
            lowerCAmelCase_						=      torch.mean(torch.abs(_UpperCAmelCase))

            assert abs(result_sum.item() - 1006.388) < 1E-2
            assert abs(result_mean.item() - 1.31) < 1E-3







      def lowercase__		( self):
            lowerCAmelCase_						=      self.scheduler_classes[0]
            lowerCAmelCase_						=      self.get_scheduler_config()
            lowerCAmelCase_						=      scheduler_class(**_UpperCAmelCase					,					use_karras_sigmas=_UpperCAmelCase)

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

            lowerCAmelCase_						=      self.dummy_model()
            lowerCAmelCase_						=      self.dummy_sample_deter.to(_UpperCAmelCase) * scheduler.init_noise_sigma
            lowerCAmelCase_						=      sample.to(_UpperCAmelCase)

            for t in scheduler.timesteps:
                  lowerCAmelCase_						=      scheduler.scale_model_input(_UpperCAmelCase					,					_UpperCAmelCase)

                  lowerCAmelCase_						=      model(_UpperCAmelCase					,					_UpperCAmelCase)

                  lowerCAmelCase_						=      scheduler.step(_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase)
                  lowerCAmelCase_						=      output.prev_sample

            lowerCAmelCase_						=      torch.sum(torch.abs(_UpperCAmelCase))
            lowerCAmelCase_						=      torch.mean(torch.abs(_UpperCAmelCase))

            assert abs(result_sum.item() - 3812.9927) < 2E-2
            assert abs(result_mean.item() - 4.9648) < 1E-3



