






"""simple docstring"""





import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest
class    __lowerCamelCase    (  __snake_case  ):
   a      :				Optional[int] 						=(DDIMParallelScheduler,)
   a      :				Union[str, Any] 						=(("""eta""", 0.0), ("""num_inference_steps""", 5_0))

   def 						SCREAMING_SNAKE_CASE__ (					self ,				**snake_case_      )		->       Dict:
       UpperCamelCase__         =    {
           'num_train_timesteps': 1000,
           'beta_start': 0.0_001,
           'beta_end': 0.02,
           'beta_schedule': 'linear',
           'clip_sample': True,
       }

       config.update(**__UpperCamelCase      )
       return config

   def 						SCREAMING_SNAKE_CASE__ (					self ,				**snake_case_      )		->       Any:
       UpperCamelCase__         =    self.scheduler_classes[0]
       UpperCamelCase__         =    self.get_scheduler_config(**__UpperCamelCase      )
       UpperCamelCase__         =    scheduler_class(**__UpperCamelCase      )

       UpperCamelCase__       ,						UpperCamelCase__         =    10, 0.0

       UpperCamelCase__         =    self.dummy_model()
       UpperCamelCase__         =    self.dummy_sample_deter

       scheduler.set_timesteps(__UpperCamelCase      )

       for t in scheduler.timesteps:
           UpperCamelCase__         =    model(__UpperCamelCase ,				__UpperCamelCase      )
           UpperCamelCase__         =    scheduler.step(__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase      ).prev_sample

       return sample

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       int:


       for timesteps in [100, 500, 1000]:
           self.check_over_configs(num_train_timesteps=__UpperCamelCase      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       List[str]:


       for steps_offset in [0, 1]:
           self.check_over_configs(steps_offset=__UpperCamelCase      )

       UpperCamelCase__         =    self.scheduler_classes[0]
       UpperCamelCase__         =    self.get_scheduler_config(steps_offset=1      )
       UpperCamelCase__         =    scheduler_class(**__UpperCamelCase      )
       scheduler.set_timesteps(5      )
       assert torch.equal(scheduler.timesteps ,				torch.LongTensor([801, 601, 401, 201, 1]      )      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Optional[int]:


       for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1] ,				[0.002, 0.02, 0.2, 2]      ):
           self.check_over_configs(beta_start=__UpperCamelCase ,				beta_end=__UpperCamelCase      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Any:


       for schedule in ["linear", "squaredcos_cap_v2"]:
           self.check_over_configs(beta_schedule=__UpperCamelCase      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Any:


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

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Optional[Any]:


       for clip_sample in [True, False]:
           self.check_over_configs(clip_sample=__UpperCamelCase      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       List[str]:


       for timestep_spacing in ["trailing", "leading"]:
           self.check_over_configs(timestep_spacing=__UpperCamelCase      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Optional[int]:


       for rescale_betas_zero_snr in [True, False]:
           self.check_over_configs(rescale_betas_zero_snr=__UpperCamelCase      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Tuple:
       self.check_over_configs(thresholding=__UpperCamelCase      )
       for threshold in [0.5, 1.0, 2.0]:
           for prediction_type in ["epsilon", "v_prediction"]:
               self.check_over_configs(
                   thresholding=__UpperCamelCase ,				prediction_type=__UpperCamelCase ,				sample_max_value=__UpperCamelCase ,				)

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       int:


       for t in [1, 10, 49]:
           self.check_over_forward(time_step=__UpperCamelCase      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Tuple:


       for t, num_inference_steps in zip([1, 10, 50] ,				[10, 50, 500]      ):
           self.check_over_forward(time_step=__UpperCamelCase ,				num_inference_steps=__UpperCamelCase      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       int:


       for t, eta in zip([1, 10, 49] ,				[0.0, 0.5, 1.0]      ):
           self.check_over_forward(time_step=__UpperCamelCase ,				eta=__UpperCamelCase      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Any:
       UpperCamelCase__         =    self.scheduler_classes[0]
       UpperCamelCase__         =    self.get_scheduler_config()
       UpperCamelCase__         =    scheduler_class(**__UpperCamelCase      )

       assert torch.sum(torch.abs(scheduler._get_variance(0 ,				0      ) - 0.0      )      ) < 1E-5
       assert torch.sum(torch.abs(scheduler._get_variance(420 ,				400      ) - 0.14_771      )      ) < 1E-5
       assert torch.sum(torch.abs(scheduler._get_variance(980 ,				960      ) - 0.32_460      )      ) < 1E-5
       assert torch.sum(torch.abs(scheduler._get_variance(0 ,				0      ) - 0.0      )      ) < 1E-5
       assert torch.sum(torch.abs(scheduler._get_variance(487 ,				486      ) - 0.00_979      )      ) < 1E-5
       assert torch.sum(torch.abs(scheduler._get_variance(999 ,				998      ) - 0.02      )      ) < 1E-5

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       str:
       UpperCamelCase__         =    self.scheduler_classes[0]
       UpperCamelCase__         =    self.get_scheduler_config()
       UpperCamelCase__         =    scheduler_class(**__UpperCamelCase      )

       UpperCamelCase__       ,						UpperCamelCase__         =    10, 0.0
       scheduler.set_timesteps(__UpperCamelCase      )

       UpperCamelCase__         =    self.dummy_model()
       UpperCamelCase__         =    self.dummy_sample_deter
       UpperCamelCase__         =    self.dummy_sample_deter + 0.1
       UpperCamelCase__         =    self.dummy_sample_deter - 0.1

       UpperCamelCase__         =    samplea.shape[0]
       UpperCamelCase__         =    torch.stack([samplea, samplea, samplea] ,				dim=0      )
       UpperCamelCase__         =    torch.arange(__UpperCamelCase      )[0:3, None].repeat(1 ,				__UpperCamelCase      )

       UpperCamelCase__         =    model(samples.flatten(0 ,				1      ) ,				timesteps.flatten(0 ,				1      )      )
       UpperCamelCase__         =    scheduler.batch_step_no_noise(__UpperCamelCase ,				timesteps.flatten(0 ,				1      ) ,				samples.flatten(0 ,				1      ) ,				__UpperCamelCase      )

       UpperCamelCase__         =    torch.sum(torch.abs(__UpperCamelCase      )      )
       UpperCamelCase__         =    torch.mean(torch.abs(__UpperCamelCase      )      )

       assert abs(result_sum.item() - 1_147.7_904      ) < 1E-2
       assert abs(result_mean.item() - 0.4_982      ) < 1E-3

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       int:
       UpperCamelCase__         =    self.full_loop()

       UpperCamelCase__         =    torch.sum(torch.abs(__UpperCamelCase      )      )
       UpperCamelCase__         =    torch.mean(torch.abs(__UpperCamelCase      )      )

       assert abs(result_sum.item() - 172.0_067      ) < 1E-2
       assert abs(result_mean.item() - 0.223_967      ) < 1E-3

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Tuple:
       UpperCamelCase__         =    self.full_loop(prediction_type='v_prediction'      )

       UpperCamelCase__         =    torch.sum(torch.abs(__UpperCamelCase      )      )
       UpperCamelCase__         =    torch.mean(torch.abs(__UpperCamelCase      )      )

       assert abs(result_sum.item() - 52.5_302      ) < 1E-2
       assert abs(result_mean.item() - 0.0_684      ) < 1E-3

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       List[Any]:
       UpperCamelCase__         =    self.full_loop(set_alpha_to_one=__UpperCamelCase ,				beta_start=0.01      )
       UpperCamelCase__         =    torch.sum(torch.abs(__UpperCamelCase      )      )
       UpperCamelCase__         =    torch.mean(torch.abs(__UpperCamelCase      )      )

       assert abs(result_sum.item() - 149.8_295      ) < 1E-2
       assert abs(result_mean.item() - 0.1_951      ) < 1E-3







   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       List[Any]:
       UpperCamelCase__         =    self.full_loop(set_alpha_to_one=__UpperCamelCase ,				beta_start=0.01      )
       UpperCamelCase__         =    torch.sum(torch.abs(__UpperCamelCase      )      )
       UpperCamelCase__         =    torch.mean(torch.abs(__UpperCamelCase      )      )

       assert abs(result_sum.item() - 149.0_784      ) < 1E-2
       assert abs(result_mean.item() - 0.1_941      ) < 1E-3








"""simple docstring"""





import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require_tensorflow_text, require_tf, slow


if is_tf_available():
  import tensorflow as tf

if is_tensorflow_text_available():
  from transformers.models.bert import TFBertTokenizer


A__					: List[Any]=	["""bert-base-uncased""", """bert-base-cased"""]
A__					: Optional[int]=	"""hf-internal-testing/tiny-bert-tf-only"""

if is_tf_available():
  class    __lowerCamelCase    (  tf.keras.Model  ):

     def __init__(					self ,				snake_case_      )		->       Optional[int]:
         super().__init__()
         UpperCamelCase__         =    tokenizer
         UpperCamelCase__         =    AutoConfig.from_pretrained(snake_case_      )
         UpperCamelCase__         =    TFAutoModel.from_config(snake_case_      )







     def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       int:
         UpperCamelCase__         =    self.tokenizer(snake_case_      )
         UpperCamelCase__         =    self.bert(**snake_case_      )
         return out["pooler_output"]
@require_tf
@require_tensorflow_text
class    __lowerCamelCase    (  unittest.TestCase  ):

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       str:
       super().setUp()

       UpperCamelCase__         =    [
           BertTokenizer.from_pretrained(snake_case_      ) for checkpoint in (TOKENIZER_CHECKPOINTS * 2)
       ]  # repeat for when fast_bert_tokenizer=false
       UpperCamelCase__         =    [TFBertTokenizer.from_pretrained(snake_case_      ) for checkpoint in TOKENIZER_CHECKPOINTS] + [
           TFBertTokenizer.from_pretrained(snake_case_ ,				use_fast_bert_tokenizer=snake_case_      )
           for checkpoint in TOKENIZER_CHECKPOINTS
       ]
       assert len(self.tokenizers      ) == len(self.tf_tokenizers      )

       UpperCamelCase__         =    [
           'This is a straightforward English test sentence.',
           'This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.',
           'Now we\'re going to add some Chinese: 一 二 三 一二三',
           'And some much more rare Chinese: 齉 堃 齉堃',
           'Je vais aussi écrire en français pour tester les accents',
           'Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ',
       ]
       UpperCamelCase__         =    list(zip(self.test_sentences ,				self.test_sentences[::-1]      )      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Any:
       for tokenizer, tf_tokenizer in zip(self.tokenizers ,				self.tf_tokenizers      ):
           for test_inputs in (self.test_sentences, self.paired_sentences):
               UpperCamelCase__         =    tokenizer(snake_case_ ,				return_tensors='tf' ,				padding='longest'      )
               UpperCamelCase__         =    tf_tokenizer(snake_case_      )

               for key in python_outputs.keys():
                   self.assertTrue(tf.reduce_all(python_outputs[key].shape == tf_outputs[key].shape      )      )
                   self.assertTrue(tf.reduce_all(tf.cast(python_outputs[key] ,				tf.intaa      ) == tf_outputs[key]      )      )

   @slow
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       str:
       for tf_tokenizer in self.tf_tokenizers:
           UpperCamelCase__         =    tf_tokenizer(self.paired_sentences      )
           UpperCamelCase__         =    tf_tokenizer(
               text=[sentence[0] for sentence in self.paired_sentences] ,				text_pair=[sentence[1] for sentence in self.paired_sentences] ,				)
           for key in merged_outputs.keys():
               self.assertTrue(tf.reduce_all(tf.cast(merged_outputs[key] ,				tf.intaa      ) == separated_outputs[key]      )      )

   @slow
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Dict:
       for tf_tokenizer in self.tf_tokenizers:
           UpperCamelCase__         =    tf.function(snake_case_      )
           for test_inputs in (self.test_sentences, self.paired_sentences):
               UpperCamelCase__         =    tf.constant(snake_case_      )
               UpperCamelCase__         =    compiled_tokenizer(snake_case_      )
               UpperCamelCase__         =    tf_tokenizer(snake_case_      )

               for key in eager_outputs.keys():
                   self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]      )      )







   @slow
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       List[str]:
       for tf_tokenizer in self.tf_tokenizers:
           UpperCamelCase__         =    ModelToSave(tokenizer=snake_case_      )
           UpperCamelCase__         =    tf.convert_to_tensor(self.test_sentences      )
           UpperCamelCase__         =    model(snake_case_      )  # Build model with some sample inputs
           with TemporaryDirectory() as tempdir:
               UpperCamelCase__         =    Path(snake_case_      ) / 'saved.model'
               model.save(snake_case_      )
               UpperCamelCase__         =    tf.keras.models.load_model(snake_case_      )
           UpperCamelCase__         =    loaded_model(snake_case_      )
           # We may see small differences because the loaded model is compiled, so we need an epsilon for the test
           self.assertLessEqual(tf.reduce_max(tf.abs(out - loaded_output      )      ) ,				1E-5      )




