






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 			_UpperCamelCase(       __lowerCamelCase		,				unittest.TestCase		):
		__SCREAMING_SNAKE_CASE  :       List[Any]							=							LEDTokenizer
		__SCREAMING_SNAKE_CASE  :       List[str]							=							LEDTokenizerFast
		__SCREAMING_SNAKE_CASE  :       Optional[Any]							=							True

		def    __lowerCAmelCase  (					self		:    Optional[Any]    ):



					'''simple docstring'''



					super().setUp()
					__a						:  str									=     [
					    'l',
					    'o',
					    'w',
					    'e',
					    'r',
					    's',
					    't',
					    'i',
					    'd',
					    'n',
					    '\u0120',
					    '\u0120l',
					    '\u0120n',
					    '\u0120lo',
					    '\u0120low',
					    'er',
					    '\u0120lowest',
					    '\u0120newer',
					    '\u0120wider',
					    '<unk>',
					]
					__a						:  Union[str, Any]									=     dict(zip(SCREAMING_SNAKE_CASE__    ,       range(len(SCREAMING_SNAKE_CASE__    )    )    )    )
					__a						:  str									=     ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
					__a						:  Optional[int]									=     {'unk_token': '<unk>'}

					__a						:  List[str]									=     os.path.join(self.tmpdirname    ,       VOCAB_FILES_NAMES['vocab_file']    )
					__a						:  Optional[int]									=     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(SCREAMING_SNAKE_CASE__    ) + '\n'    )
					with open(self.merges_file    ,       'w'    ,       encoding='utf-8'    ) as fp:
								fp.write('\n'.join(SCREAMING_SNAKE_CASE__    )    )

		def    __lowerCAmelCase  (					self		:    Union[str, Any]    ,       **SCREAMING_SNAKE_CASE__		:    int    ):



					'''simple docstring'''



					kwargs.update(self.special_tokens_map    )
					return self.tokenizer_class.from_pretrained(self.tmpdirname    ,       **SCREAMING_SNAKE_CASE__    )

		def    __lowerCAmelCase  (					self		:    str    ,       **SCREAMING_SNAKE_CASE__		:    List[str]    ):



					'''simple docstring'''



					kwargs.update(self.special_tokens_map    )
					return self.rust_tokenizer_class.from_pretrained(self.tmpdirname    ,       **SCREAMING_SNAKE_CASE__    )

		def    __lowerCAmelCase  (					self		:    List[str]    ,       SCREAMING_SNAKE_CASE__		:    List[str]    ):



					'''simple docstring'''



					return "lower newer", "lower newer"

		@cached_property
		def    __lowerCAmelCase  (					self		:    str    ):



					'''simple docstring'''



					return LEDTokenizer.from_pretrained('allenai/led-base-16384'    )

		@cached_property
		def    __lowerCAmelCase  (					self		:    List[str]    ):



					'''simple docstring'''



					return LEDTokenizerFast.from_pretrained('allenai/led-base-16384'    )

		@require_torch
		def    __lowerCAmelCase  (					self		:    List[str]    ):



					'''simple docstring'''



					__a						:  str									=     ['A long paragraph for summarization.', 'Another paragraph for summarization.']
					__a						:  int									=     [0, 2_5_0, 2_5_1, 1_7_8_1_8, 1_3, 3_9_1_8_6, 1_9_3_8, 4, 2]

					for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
								__a						:  Optional[int]									=     tokenizer(SCREAMING_SNAKE_CASE__    ,       max_length=len(SCREAMING_SNAKE_CASE__    )    ,       padding=SCREAMING_SNAKE_CASE__    ,       return_tensors='pt'    )
								self.assertIsInstance(SCREAMING_SNAKE_CASE__    ,       SCREAMING_SNAKE_CASE__    )

								self.assertEqual((2, 9)    ,       batch.input_ids.shape    )
								self.assertEqual((2, 9)    ,       batch.attention_mask.shape    )
								__a						:  int									=     batch.input_ids.tolist()[0]
								self.assertListEqual(SCREAMING_SNAKE_CASE__    ,       SCREAMING_SNAKE_CASE__    )

		@require_torch
		def    __lowerCAmelCase  (					self		:    List[Any]    ):



					'''simple docstring'''



					__a						:  Any									=     ['A long paragraph for summarization.', 'Another paragraph for summarization.']
					for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
								__a						:  Dict									=     tokenizer(SCREAMING_SNAKE_CASE__    ,       padding=SCREAMING_SNAKE_CASE__    ,       return_tensors='pt'    )
								self.assertIn('input_ids'    ,       SCREAMING_SNAKE_CASE__    )
								self.assertIn('attention_mask'    ,       SCREAMING_SNAKE_CASE__    )
								self.assertNotIn('labels'    ,       SCREAMING_SNAKE_CASE__    )
								self.assertNotIn('decoder_attention_mask'    ,       SCREAMING_SNAKE_CASE__    )

		@require_torch
		def    __lowerCAmelCase  (					self		:    Dict    ):



					'''simple docstring'''



					__a						:  int									=     [
					    'Summary of the text.',
					    'Another summary.',
					]
					for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
								__a						:  Optional[int]									=     tokenizer(text_target=SCREAMING_SNAKE_CASE__    ,       max_length=3_2    ,       padding='max_length'    ,       return_tensors='pt'    )
								self.assertEqual(3_2    ,       targets['input_ids'].shape[1]    )

		@require_torch
		def    __lowerCAmelCase  (					self		:    Union[str, Any]    ):



					'''simple docstring'''



					for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
								__a						:  List[Any]									=     tokenizer(
								    ['I am a small frog' * 1_0_2_4, 'I am a small frog']    ,       padding=SCREAMING_SNAKE_CASE__    ,       truncation=SCREAMING_SNAKE_CASE__    ,       return_tensors='pt'    )
								self.assertIsInstance(SCREAMING_SNAKE_CASE__    ,       SCREAMING_SNAKE_CASE__    )
								self.assertEqual(batch.input_ids.shape    ,       (2, 5_1_2_2)    )

		@require_torch
		def    __lowerCAmelCase  (					self		:    Tuple    ):



					'''simple docstring'''



					__a						:  Dict									=     ['A long paragraph for summarization.']
					__a						:  List[Any]									=     [
					    'Summary of the text.',
					]
					for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
								__a						:  List[str]									=     tokenizer(SCREAMING_SNAKE_CASE__    ,       return_tensors='pt'    )
								__a						:  Union[str, Any]									=     tokenizer(text_target=SCREAMING_SNAKE_CASE__    ,       return_tensors='pt'    )
								__a						:  Tuple									=     inputs['input_ids']
								__a						:  List[str]									=     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    __lowerCAmelCase  (					self		:    Optional[int]    ):



					'''simple docstring'''



					for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
								__a						:  Tuple									=     ['Summary of the text.', 'Another summary.']
								__a						:  Tuple									=     [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

								__a						:  Union[str, Any]									=     tokenizer(SCREAMING_SNAKE_CASE__    ,       padding=SCREAMING_SNAKE_CASE__    )
								__a						:  Dict									=     [[0] * len(SCREAMING_SNAKE_CASE__    ) for x in encoded_output['input_ids']]
								__a						:  Optional[int]									=     tokenizer.pad(SCREAMING_SNAKE_CASE__    )
								self.assertSequenceEqual(outputs['global_attention_mask']    ,       SCREAMING_SNAKE_CASE__    )

		def    __lowerCAmelCase  (					self		:    str    ):



					'''simple docstring'''



					pass



		def    __lowerCAmelCase  (					self		:    int    ):



					'''simple docstring'''



					for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
								with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''    ):
											__a						:  Tuple									=     self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE__    ,       **SCREAMING_SNAKE_CASE__    )
											__a						:  str									=     self.tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE__    ,       **SCREAMING_SNAKE_CASE__    )
											__a						:  Optional[Any]									=     'A, <mask> AllenNLP sentence.'
											__a						:  Dict									=     tokenizer_r.encode_plus(SCREAMING_SNAKE_CASE__    ,       add_special_tokens=SCREAMING_SNAKE_CASE__    ,       return_token_type_ids=SCREAMING_SNAKE_CASE__    )
											__a						:  Dict									=     tokenizer_p.encode_plus(SCREAMING_SNAKE_CASE__    ,       add_special_tokens=SCREAMING_SNAKE_CASE__    ,       return_token_type_ids=SCREAMING_SNAKE_CASE__    )
											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						:  Dict									=     tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']    )
											__a						:  List[str]									=     tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids']    )
											self.assertSequenceEqual(tokens_p['input_ids']    ,       [0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]    )
											self.assertSequenceEqual(tokens_r['input_ids']    ,       [0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]    )

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











"""simple docstring"""

import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest





class 							lowercase_			(	__lowerCAmelCase				):



 '''simple docstring'''
 UpperCAmelCase       :	Optional[int]     =      (KDPMaDiscreteScheduler,)
 UpperCAmelCase       :	Any     =      10

 def      lowerCAmelCase_				(					self			:			Dict       ,				**_UpperCAmelCase			:			Optional[Any]						):
       _A     =  {
           'num_train_timesteps': 1_100,
           'beta_start': 0.0001,
           'beta_end': 0.02,
           'beta_schedule': 'linear',
       }

       config.update(**_UpperCAmelCase						)
       return config

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

 def      lowerCAmelCase_				(					self			:			Dict						):
       for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]       ,				[0.0002, 0.002, 0.02]						):
             self.check_over_configs(beta_start=_UpperCAmelCase       ,				beta_end=_UpperCAmelCase						)

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

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

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

       scheduler.set_timesteps(self.num_inference_steps						)

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

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

             _A     =  model(_UpperCAmelCase       ,				_UpperCAmelCase						)

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

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

       if torch_device in ["cpu", "mps"]:
             assert abs(result_sum.item() - 4.6_9_3_4E-0_7						) < 1E-2
             assert abs(result_mean.item() - 6.1_1_1_2E-1_0						) < 1E-3
       else:
             # CUDA
             assert abs(result_sum.item() - 4.6_9_3_4_2_8_6_5_0_1_7_0_9_7_2E-0_7						) < 1E-2
             assert abs(result_mean.item() - 0.0002						) < 1E-3

 def      lowerCAmelCase_				(					self			:			Optional[Any]						):
       if torch_device == "mps":
             return
       _A     =  self.scheduler_classes[0]
       _A     =  self.get_scheduler_config()
       _A     =  scheduler_class(**_UpperCAmelCase						)

       scheduler.set_timesteps(self.num_inference_steps						)

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

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

             _A     =  model(_UpperCAmelCase       ,				_UpperCAmelCase						)

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

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

       if torch_device in ["cpu", "mps"]:
             assert abs(result_sum.item() - 20.4125						) < 1E-2
             assert abs(result_mean.item() - 0.0266						) < 1E-3
       else:
             # CUDA
             assert abs(result_sum.item() - 20.4125						) < 1E-2
             assert abs(result_mean.item() - 0.0266						) < 1E-3



 def      lowerCAmelCase_				(					self			:			Any						):
       if torch_device == "mps":
             return
       _A     =  self.scheduler_classes[0]
       _A     =  self.get_scheduler_config()
       _A     =  scheduler_class(**_UpperCAmelCase						)

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

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

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

             _A     =  model(_UpperCAmelCase       ,				_UpperCAmelCase						)

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

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

       if str(_UpperCAmelCase						).startswith('cpu'						):
             # The following sum varies between 148 and 156 on mps. Why?
             assert abs(result_sum.item() - 20.4125						) < 1E-2
             assert abs(result_mean.item() - 0.0266						) < 1E-3
       else:
             # CUDA
             assert abs(result_sum.item() - 20.4125						) < 1E-2
             assert abs(result_mean.item() - 0.0266						) < 1E-3

