





import json
import os
import unittest

from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin




@require_tokenizers
class     SCREAMING_SNAKE_CASE__ (						UpperCamelCase__				,					unittest.TestCase      ):
 __SCREAMING_SNAKE_CASE							       =	LEDTokenizer
 __SCREAMING_SNAKE_CASE							       =	LEDTokenizerFast
 __SCREAMING_SNAKE_CASE							       =	True



 def 				UpperCamelCase			(							self ):
        super().setUp()
        A__											=						[
            '''l''',
            '''o''',
            '''w''',
            '''e''',
            '''r''',
            '''s''',
            '''t''',
            '''i''',
            '''d''',
            '''n''',
            '''\u0120''',
            '''\u0120l''',
            '''\u0120n''',
            '''\u0120lo''',
            '''\u0120low''',
            '''er''',
            '''\u0120lowest''',
            '''\u0120newer''',
            '''\u0120wider''',
            '''<unk>''',
        ]
        A__											=						dict(zip(__lowerCamelCase,range(len(__lowerCamelCase ) ) ) )
        A__											=						['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
        A__											=						{'''unk_token''': '''<unk>'''}

        A__											=						os.path.join(self.tmpdirname,VOCAB_FILES_NAMES['''vocab_file'''] )
        A__											=						os.path.join(self.tmpdirname,VOCAB_FILES_NAMES['''merges_file'''] )
        with open(self.vocab_file,'''w''',encoding='''utf-8''' ) as fp:
               fp.write(json.dumps(__lowerCamelCase ) + '''\n''' )
        with open(self.merges_file,'''w''',encoding='''utf-8''' ) as fp:
               fp.write('''\n'''.join(__lowerCamelCase ) )



 def 				UpperCamelCase			(							self,**__lowerCamelCase ):
        kwargs.update(self.special_tokens_map )
        return self.tokenizer_class.from_pretrained(self.tmpdirname,**__lowerCamelCase )



 def 				UpperCamelCase			(							self,**__lowerCamelCase ):
        kwargs.update(self.special_tokens_map )
        return self.rust_tokenizer_class.from_pretrained(self.tmpdirname,**__lowerCamelCase )



 def 				UpperCamelCase			(							self,__lowerCamelCase ):
        return "lower newer", "lower newer"



 @cached_property
 def 				UpperCamelCase			(							self ):
        return LEDTokenizer.from_pretrained('''allenai/led-base-16384''' )



 @cached_property
 def 				UpperCamelCase			(							self ):
        return LEDTokenizerFast.from_pretrained('''allenai/led-base-16384''' )



 @require_torch
 def 				UpperCamelCase			(							self ):
        A__											=						['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
        A__											=						[0, 250, 251, 1_7818, 13, 3_9186, 1938, 4, 2]

        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
               A__											=						tokenizer(__lowerCamelCase,max_length=len(__lowerCamelCase ),padding=__lowerCamelCase,return_tensors='''pt''' )
               self.assertIsInstance(__lowerCamelCase,__lowerCamelCase )

               self.assertEqual((2, 9),batch.input_ids.shape )
               self.assertEqual((2, 9),batch.attention_mask.shape )
               A__											=						batch.input_ids.tolist()[0]
               self.assertListEqual(__lowerCamelCase,__lowerCamelCase )



 @require_torch
 def 				UpperCamelCase			(							self ):
        A__											=						['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
               A__											=						tokenizer(__lowerCamelCase,padding=__lowerCamelCase,return_tensors='''pt''' )
               self.assertIn('''input_ids''',__lowerCamelCase )
               self.assertIn('''attention_mask''',__lowerCamelCase )
               self.assertNotIn('''labels''',__lowerCamelCase )
               self.assertNotIn('''decoder_attention_mask''',__lowerCamelCase )



 @require_torch
 def 				UpperCamelCase			(							self ):
        A__											=						[
            '''Summary of the text.''',
            '''Another summary.''',
        ]
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
               A__											=						tokenizer(text_target=__lowerCamelCase,max_length=32,padding='''max_length''',return_tensors='''pt''' )
               self.assertEqual(32,targets['''input_ids'''].shape[1] )



 @require_torch
 def 				UpperCamelCase			(							self ):
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
               A__											=						tokenizer(
                   ['''I am a small frog''' * 1024, '''I am a small frog'''],padding=__lowerCamelCase,truncation=__lowerCamelCase,return_tensors='''pt''' )
               self.assertIsInstance(__lowerCamelCase,__lowerCamelCase )
               self.assertEqual(batch.input_ids.shape,(2, 5122) )



 @require_torch
 def 				UpperCamelCase			(							self ):
        A__											=						['''A long paragraph for summarization.''']
        A__											=						[
            '''Summary of the text.''',
        ]
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
               A__											=						tokenizer(__lowerCamelCase,return_tensors='''pt''' )
               A__											=						tokenizer(text_target=__lowerCamelCase,return_tensors='''pt''' )
               A__											=						inputs['''input_ids''']
               A__											=						targets['''input_ids''']
               self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item() )
               self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item() )
               self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item() )
               self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item() )



 @require_torch
 def 				UpperCamelCase			(							self ):
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
               A__											=						['''Summary of the text.''', '''Another summary.''']
               A__											=						[[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

               A__											=						tokenizer(__lowerCamelCase,padding=__lowerCamelCase )
               A__											=						[[0] * len(__lowerCamelCase ) for x in encoded_output['''input_ids''']]
               A__											=						tokenizer.pad(__lowerCamelCase )
               self.assertSequenceEqual(outputs['''global_attention_mask'''],__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        pass




 def 				UpperCamelCase			(							self ):
        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
               with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})" ):
                      A__											=						self.rust_tokenizer_class.from_pretrained(__lowerCamelCase,**__lowerCamelCase )
                      A__											=						self.tokenizer_class.from_pretrained(__lowerCamelCase,**__lowerCamelCase )
                      A__											=						'''A, <mask> AllenNLP sentence.'''
                      A__											=						tokenizer_r.encode_plus(__lowerCamelCase,add_special_tokens=__lowerCamelCase,return_token_type_ids=__lowerCamelCase )
                      A__											=						tokenizer_p.encode_plus(__lowerCamelCase,add_special_tokens=__lowerCamelCase,return_token_type_ids=__lowerCamelCase )
                      self.assertEqual(sum(tokens_r['''token_type_ids'''] ),sum(tokens_p['''token_type_ids'''] ) )
                      self.assertEqual(
                          sum(tokens_r['''attention_mask'''] ) / len(tokens_r['''attention_mask'''] ),sum(tokens_p['''attention_mask'''] ) / len(tokens_p['''attention_mask'''] ),)

                      A__											=						tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids'''] )
                      A__											=						tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids'''] )
                      self.assertSequenceEqual(tokens_p['''input_ids'''],[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2] )
                      self.assertSequenceEqual(tokens_r['''input_ids'''],[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2] )

                      self.assertSequenceEqual(
                          __lowerCamelCase,['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>'''] )
                      self.assertSequenceEqual(
                          __lowerCamelCase,['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>'''] )












import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest




class     SCREAMING_SNAKE_CASE__ (						UpperCamelCase__      ):
 __SCREAMING_SNAKE_CASE							       =	(DPMSolverSinglestepScheduler,)
 __SCREAMING_SNAKE_CASE							       =	(('''num_inference_steps''', 25),)



 def 				UpperCamelCase			(							self,**__lowerCamelCase ):
        A__											=						{
            '''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(**__lowerCamelCase )
        return config



 def 				UpperCamelCase			(							self,__lowerCamelCase=0,**__lowerCamelCase ):
        A__											=						dict(self.forward_default_kwargs )
        A__											=						kwargs.pop('''num_inference_steps''',__lowerCamelCase )
        A__											=						self.dummy_sample
        A__											=						0.1 * sample
        A__											=						[residual + 0.2, residual + 0.15, residual + 0.10]

        for scheduler_class in self.scheduler_classes:
               A__											=						self.get_scheduler_config(**__lowerCamelCase )
               A__											=						scheduler_class(**__lowerCamelCase )
               scheduler.set_timesteps(__lowerCamelCase )
               # copy over dummy past residuals
               A__											=						dummy_past_residuals[: scheduler.config.solver_order]

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

               A__					,	A__											=						sample, sample
               for t in range(__lowerCamelCase,time_step + scheduler.config.solver_order + 1 ):
                      A__											=						scheduler.step(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,**__lowerCamelCase ).prev_sample
                      A__											=						new_scheduler.step(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,**__lowerCamelCase ).prev_sample

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



 def 				UpperCamelCase			(							self ):
        pass



 def 				UpperCamelCase			(							self,__lowerCamelCase=0,**__lowerCamelCase ):
        A__											=						dict(self.forward_default_kwargs )
        A__											=						kwargs.pop('''num_inference_steps''',__lowerCamelCase )
        A__											=						self.dummy_sample
        A__											=						0.1 * sample
        A__											=						[residual + 0.2, residual + 0.15, residual + 0.10]

        for scheduler_class in self.scheduler_classes:
               A__											=						self.get_scheduler_config()
               A__											=						scheduler_class(**__lowerCamelCase )
               scheduler.set_timesteps(__lowerCamelCase )

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

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

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

               A__											=						scheduler.step(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,**__lowerCamelCase ).prev_sample
               A__											=						new_scheduler.step(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,**__lowerCamelCase ).prev_sample

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



 def 				UpperCamelCase			(							self,__lowerCamelCase=None,**__lowerCamelCase ):
        if scheduler is None:
               A__											=						self.scheduler_classes[0]
               A__											=						self.get_scheduler_config(**__lowerCamelCase )
               A__											=						scheduler_class(**__lowerCamelCase )

        A__											=						self.scheduler_classes[0]
        A__											=						self.get_scheduler_config(**__lowerCamelCase )
        A__											=						scheduler_class(**__lowerCamelCase )

        A__											=						10
        A__											=						self.dummy_model()
        A__											=						self.dummy_sample_deter
        scheduler.set_timesteps(__lowerCamelCase )

        for i, t in enumerate(scheduler.timesteps ):
               A__											=						model(__lowerCamelCase,__lowerCamelCase )
               A__											=						scheduler.step(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase ).prev_sample

        return sample



 def 				UpperCamelCase			(							self ):
        A__											=						DPMSolverSinglestepScheduler(**self.get_scheduler_config() )
        A__											=						50
        A__											=						self.dummy_model()
        A__											=						self.dummy_sample_deter
        scheduler.set_timesteps(__lowerCamelCase )

        # make sure that the first t is uneven
        for i, t in enumerate(scheduler.timesteps[3:] ):
               A__											=						model(__lowerCamelCase,__lowerCamelCase )
               A__											=						scheduler.step(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase ).prev_sample

        A__											=						torch.mean(torch.abs(__lowerCamelCase ) )

        assert abs(result_mean.item() - 0.2574 ) < 1E-3



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



 def 				UpperCamelCase			(							self ):
        # make sure that iterating over schedulers with same config names gives same results
        # for defaults
        A__											=						DPMSolverSinglestepScheduler(**self.get_scheduler_config() )
        A__											=						self.full_loop(scheduler=__lowerCamelCase )
        A__											=						torch.mean(torch.abs(__lowerCamelCase ) )

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

        A__											=						DEISMultistepScheduler.from_config(scheduler.config )
        A__											=						DPMSolverMultistepScheduler.from_config(scheduler.config )
        A__											=						UniPCMultistepScheduler.from_config(scheduler.config )
        A__											=						DPMSolverSinglestepScheduler.from_config(scheduler.config )

        A__											=						self.full_loop(scheduler=__lowerCamelCase )
        A__											=						torch.mean(torch.abs(__lowerCamelCase ) )

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



 def 				UpperCamelCase			(							self ):
        self.check_over_configs(thresholding=__lowerCamelCase )
        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=__lowerCamelCase,prediction_type=__lowerCamelCase,sample_max_value=__lowerCamelCase,algorithm_type='''dpmsolver++''',solver_order=__lowerCamelCase,solver_type=__lowerCamelCase,)



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



 def 				UpperCamelCase			(							self ):
        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=__lowerCamelCase,solver_type=__lowerCamelCase,prediction_type=__lowerCamelCase,algorithm_type=__lowerCamelCase,)
                                    A__											=						self.full_loop(
                                        solver_order=__lowerCamelCase,solver_type=__lowerCamelCase,prediction_type=__lowerCamelCase,algorithm_type=__lowerCamelCase,)
                                    assert not torch.isnan(__lowerCamelCase ).any(), "Samples have nan numbers"



 def 				UpperCamelCase			(							self ):
        self.check_over_configs(lower_order_final=__lowerCamelCase )
        self.check_over_configs(lower_order_final=__lowerCamelCase )



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



 def 				UpperCamelCase			(							self ):
        self.check_over_configs(variance_type=__lowerCamelCase )
        self.check_over_configs(variance_type='''learned_range''' )



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



 def 				UpperCamelCase			(							self ):
        A__											=						self.full_loop()
        A__											=						torch.mean(torch.abs(__lowerCamelCase ) )

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



 def 				UpperCamelCase			(							self ):
        A__											=						self.full_loop(use_karras_sigmas=__lowerCamelCase )
        A__											=						torch.mean(torch.abs(__lowerCamelCase ) )

        assert abs(result_mean.item() - 0.2248 ) < 1E-3



 def 				UpperCamelCase			(							self ):
        A__											=						self.full_loop(prediction_type='''v_prediction''' )
        A__											=						torch.mean(torch.abs(__lowerCamelCase ) )

        assert abs(result_mean.item() - 0.1453 ) < 1E-3



 def 				UpperCamelCase			(							self ):
        A__											=						self.full_loop(prediction_type='''v_prediction''',use_karras_sigmas=__lowerCamelCase )
        A__											=						torch.mean(torch.abs(__lowerCamelCase ) )

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




 def 				UpperCamelCase			(							self ):
        A__											=						self.scheduler_classes[0]
        A__											=						self.get_scheduler_config(thresholding=__lowerCamelCase,dynamic_thresholding_ratio=0 )
        A__											=						scheduler_class(**__lowerCamelCase )

        A__											=						10
        A__											=						self.dummy_model()
        A__											=						self.dummy_sample_deter.half()
        scheduler.set_timesteps(__lowerCamelCase )

        for i, t in enumerate(scheduler.timesteps ):
               A__											=						model(__lowerCamelCase,__lowerCamelCase )
               A__											=						scheduler.step(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase ).prev_sample

        assert sample.dtype == torch.floataa





