

'''simple docstring'''



import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest



class 			a       (	_SCREAMING_SNAKE_CASE ):


     """simple docstring"""






     SCREAMING_SNAKE_CASE :				Dict							     =     (DDPMScheduler,)

     def 				lowerCamelCase__				(       self  :	Optional[Any]							,				**snake_case  :	Any    )		->							Dict:
          __UpperCAmelCase			:				Any													=    {
              '''num_train_timesteps''': 1000,
              '''beta_start''': 0.0_001,
              '''beta_end''': 0.02,
              '''beta_schedule''': '''linear''',
              '''variance_type''': '''fixed_small''',
              '''clip_sample''': True,
          }

          config.update(**A_    )
          return config

     def 				lowerCamelCase__				(       self  :	Any    )		->							List[Any]:
          for timesteps in [1, 5, 100, 1000]:
               self.check_over_configs(num_train_timesteps=A_    )

     def 				lowerCamelCase__				(       self  :	Optional[Any]    )		->							str:
          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=A_							,				beta_end=A_    )

     def 				lowerCamelCase__				(       self  :	Optional[Any]    )		->							Any:
          for schedule in ["linear", "squaredcos_cap_v2"]:
               self.check_over_configs(beta_schedule=A_    )

     def 				lowerCamelCase__				(       self  :	Any    )		->							Tuple:
          for variance in ["fixed_small", "fixed_large", "other"]:
               self.check_over_configs(variance_type=A_    )

     def 				lowerCamelCase__				(       self  :	Tuple    )		->							Union[str, Any]:
          for clip_sample in [True, False]:
               self.check_over_configs(clip_sample=A_    )

     def 				lowerCamelCase__				(       self  :	Dict    )		->							Optional[Any]:
          self.check_over_configs(thresholding=A_    )
          for threshold in [0.5, 1.0, 2.0]:
               for prediction_type in ["epsilon", "sample", "v_prediction"]:
                    self.check_over_configs(
                        thresholding=A_							,				prediction_type=A_							,				sample_max_value=A_							,				)

     def 				lowerCamelCase__				(       self  :	Any    )		->							Optional[int]:
          for prediction_type in ["epsilon", "sample", "v_prediction"]:
               self.check_over_configs(prediction_type=A_    )

     def 				lowerCamelCase__				(       self  :	Optional[int]    )		->							Optional[Any]:
          for t in [0, 500, 999]:
               self.check_over_forward(time_step=A_    )

     def 				lowerCamelCase__				(       self  :	List[Any]    )		->							List[str]:
          __UpperCAmelCase			:				List[str]													=    self.scheduler_classes[0]
          __UpperCAmelCase			:				Optional[int]													=    self.get_scheduler_config()
          __UpperCAmelCase			:				Optional[int]													=    scheduler_class(**A_    )

          assert torch.sum(torch.abs(scheduler._get_variance(0    ) - 0.0    )    ) < 1E-5
          assert torch.sum(torch.abs(scheduler._get_variance(487    ) - 0.00_979    )    ) < 1E-5
          assert torch.sum(torch.abs(scheduler._get_variance(999    ) - 0.02    )    ) < 1E-5

     def 				lowerCamelCase__				(       self  :	Optional[int]    )		->							List[Any]:
          __UpperCAmelCase			:				Optional[Any]													=    self.scheduler_classes[0]
          __UpperCAmelCase			:				Union[str, Any]													=    self.get_scheduler_config()
          __UpperCAmelCase			:				Optional[Any]													=    scheduler_class(**A_    )

          __UpperCAmelCase			:				Dict													=    len(A_    )

          __UpperCAmelCase			:				Union[str, Any]													=    self.dummy_model()
          __UpperCAmelCase			:				Optional[Any]													=    self.dummy_sample_deter
          __UpperCAmelCase			:				Dict													=    torch.manual_seed(0    )

          for t in reversed(range(A_    )    ):
               # 1. predict noise residual
               __UpperCAmelCase			:				str													=    model(A_							,				A_    )

               # 2. predict previous mean of sample x_t-1
               __UpperCAmelCase			:				int													=    scheduler.step(A_							,				A_							,				A_							,				generator=A_    ).prev_sample

               # if t > 0:
               #     noise = self.dummy_sample_deter
               #     variance = scheduler.get_variance(t) ** (0.5) * noise
               #
               # sample = pred_prev_sample + variance
               __UpperCAmelCase			:				List[Any]													=    pred_prev_sample

          __UpperCAmelCase			:				Optional[Any]													=    torch.sum(torch.abs(A_    )    )
          __UpperCAmelCase			:				Union[str, Any]													=    torch.mean(torch.abs(A_    )    )

          assert abs(result_sum.item() - 258.9_606    ) < 1E-2
          assert abs(result_mean.item() - 0.3_372    ) < 1E-3

     def 				lowerCamelCase__				(       self  :	int    )		->							Tuple:
          __UpperCAmelCase			:				List[str]													=    self.scheduler_classes[0]
          __UpperCAmelCase			:				Dict													=    self.get_scheduler_config(prediction_type='''v_prediction'''    )
          __UpperCAmelCase			:				Dict													=    scheduler_class(**A_    )

          __UpperCAmelCase			:				Optional[int]													=    len(A_    )

          __UpperCAmelCase			:				List[str]													=    self.dummy_model()
          __UpperCAmelCase			:				Any													=    self.dummy_sample_deter
          __UpperCAmelCase			:				Optional[int]													=    torch.manual_seed(0    )

          for t in reversed(range(A_    )    ):
               # 1. predict noise residual
               __UpperCAmelCase			:				Union[str, Any]													=    model(A_							,				A_    )

               # 2. predict previous mean of sample x_t-1
               __UpperCAmelCase			:				Dict													=    scheduler.step(A_							,				A_							,				A_							,				generator=A_    ).prev_sample

               # if t > 0:
               #     noise = self.dummy_sample_deter
               #     variance = scheduler.get_variance(t) ** (0.5) * noise
               #
               # sample = pred_prev_sample + variance
               __UpperCAmelCase			:				List[Any]													=    pred_prev_sample

          __UpperCAmelCase			:				Optional[int]													=    torch.sum(torch.abs(A_    )    )
          __UpperCAmelCase			:				List[Any]													=    torch.mean(torch.abs(A_    )    )

          assert abs(result_sum.item() - 202.0_296    ) < 1E-2
          assert abs(result_mean.item() - 0.2_631    ) < 1E-3

     def 				lowerCamelCase__				(       self  :	Tuple    )		->							Union[str, Any]:
          __UpperCAmelCase			:				Dict													=    self.scheduler_classes[0]
          __UpperCAmelCase			:				int													=    self.get_scheduler_config()
          __UpperCAmelCase			:				Optional[Any]													=    scheduler_class(**A_    )

          __UpperCAmelCase			:				Dict													=    [100, 87, 50, 1, 0]

          scheduler.set_timesteps(timesteps=A_    )

          __UpperCAmelCase			:				str													=    scheduler.timesteps

          for i, timestep in enumerate(A_    ):
               if i == len(A_    ) - 1:
                    __UpperCAmelCase			:				int													=    -1
               else:
                    __UpperCAmelCase			:				Dict													=    timesteps[i + 1]

               __UpperCAmelCase			:				Optional[int]													=    scheduler.previous_timestep(A_    )
               __UpperCAmelCase			:				Any													=    prev_t.item()

               self.assertEqual(A_							,				A_    )

     def 				lowerCamelCase__				(       self  :	int    )		->							Tuple:
          __UpperCAmelCase			:				Any													=    self.scheduler_classes[0]
          __UpperCAmelCase			:				Tuple													=    self.get_scheduler_config()
          __UpperCAmelCase			:				Dict													=    scheduler_class(**A_    )

          __UpperCAmelCase			:				Optional[int]													=    [100, 87, 50, 51, 0]

          with self.assertRaises(A_							,				msg='''`custom_timesteps` must be in descending order.'''    ):
               scheduler.set_timesteps(timesteps=A_    )

     def 				lowerCamelCase__				(       self  :	List[str]    )		->							Union[str, Any]:
          __UpperCAmelCase			:				Union[str, Any]													=    self.scheduler_classes[0]
          __UpperCAmelCase			:				Optional[int]													=    self.get_scheduler_config()
          __UpperCAmelCase			:				str													=    scheduler_class(**A_    )

          __UpperCAmelCase			:				Tuple													=    [100, 87, 50, 1, 0]
          __UpperCAmelCase			:				Tuple													=    len(A_    )

          with self.assertRaises(A_							,				msg='''Can only pass one of `num_inference_steps` or `custom_timesteps`.'''    ):
               scheduler.set_timesteps(num_inference_steps=A_							,				timesteps=A_    )






     def 				lowerCamelCase__				(       self  :	Union[str, Any]    )		->							int:
          __UpperCAmelCase			:				Dict													=    self.scheduler_classes[0]
          __UpperCAmelCase			:				List[Any]													=    self.get_scheduler_config()
          __UpperCAmelCase			:				Dict													=    scheduler_class(**A_    )

          __UpperCAmelCase			:				List[Any]													=    [scheduler.config.num_train_timesteps]

          with self.assertRaises(
              A_							,				msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'''							,				):
               scheduler.set_timesteps(timesteps=A_    )



'''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


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

if is_tf_available():



      class 			a       (	tf.keras.Model ):


           """simple docstring"""






           def __init__(       self  :	List[str]							,				snake_case  :	List[str]    )		->							str:
                super().__init__()
                __UpperCAmelCase			:				List[str]													=    tokenizer
                __UpperCAmelCase			:				List[Any]													=    AutoConfig.from_pretrained(snake_case    )
                __UpperCAmelCase			:				int													=    TFAutoModel.from_config(snake_case    )






           def 				lowerCamelCase__				(       self  :	List[Any]							,				snake_case  :	Optional[int]    )		->							Optional[Any]:
                __UpperCAmelCase			:				List[Any]													=    self.tokenizer(snake_case    )
                __UpperCAmelCase			:				Optional[Any]													=    self.bert(**snake_case    )
                return out["pooler_output"]



@require_tf
@require_tensorflow_text
class 			a       (	unittest.TestCase ):


     """simple docstring"""






     def 				lowerCamelCase__				(       self  :	Optional[int]    )		->							List[str]:
          super().setUp()

          __UpperCAmelCase			:				Tuple													=    [
              BertTokenizer.from_pretrained(snake_case    ) for checkpoint in (TOKENIZER_CHECKPOINTS * 2)
          ]  # repeat for when fast_bert_tokenizer=false
          __UpperCAmelCase			:				Any													=    [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			:				Optional[int]													=    [
              '''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			:				Optional[int]													=    list(zip(self.test_sentences							,				self.test_sentences[::-1]    )    )

     def 				lowerCamelCase__				(       self  :	Optional[int]    )		->							Optional[Any]:
          for tokenizer, tf_tokenizer in zip(self.tokenizers							,				self.tf_tokenizers    ):
               for test_inputs in (self.test_sentences, self.paired_sentences):
                    __UpperCAmelCase			:				Any													=    tokenizer(snake_case							,				return_tensors='''tf'''							,				padding='''longest'''    )
                    __UpperCAmelCase			:				Optional[int]													=    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 				lowerCamelCase__				(       self  :	List[Any]    )		->							str:
          for tf_tokenizer in self.tf_tokenizers:
               __UpperCAmelCase			:				Any													=    tf_tokenizer(self.paired_sentences    )
               __UpperCAmelCase			:				Union[str, Any]													=    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 				lowerCamelCase__				(       self  :	str    )		->							Union[str, Any]:
          for tf_tokenizer in self.tf_tokenizers:
               __UpperCAmelCase			:				Optional[int]													=    tf.function(snake_case    )
               for test_inputs in (self.test_sentences, self.paired_sentences):
                    __UpperCAmelCase			:				int													=    tf.constant(snake_case    )
                    __UpperCAmelCase			:				Tuple													=    compiled_tokenizer(snake_case    )
                    __UpperCAmelCase			:				Optional[int]													=    tf_tokenizer(snake_case    )

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






     @slow
     def 				lowerCamelCase__				(       self  :	str    )		->							str:
          for tf_tokenizer in self.tf_tokenizers:
               __UpperCAmelCase			:				List[Any]													=    ModelToSave(tokenizer=snake_case    )
               __UpperCAmelCase			:				Union[str, Any]													=    tf.convert_to_tensor(self.test_sentences    )
               __UpperCAmelCase			:				Tuple													=    model(snake_case    )  # Build model with some sample inputs
               with TemporaryDirectory() as tempdir:
                    __UpperCAmelCase			:				Any													=    Path(snake_case    ) / '''saved.model'''
                    model.save(snake_case    )
                    __UpperCAmelCase			:				str													=    tf.keras.models.load_model(snake_case    )
               __UpperCAmelCase			:				Optional[int]													=    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    )