
import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest






class    UpperCAmelCase_				(	__lowercase					):
 UpperCAmelCase_		    =						(DDIMParallelScheduler,)
 UpperCAmelCase_		    =						(('eta', 0.0), ('num_inference_steps', 5_0))





 def        snake_case__		(    self ,       **lowercase_):
     snake_case_ :    int     			=						{
         "num_train_timesteps": 10_00,
         "beta_start": 0.0_001,
         "beta_end": 0.02,
         "beta_schedule": "linear",
         "clip_sample": True,
     }

     config.update(**__A)
     return config





 def        snake_case__		(    self ,       **lowercase_):
     snake_case_ :    List[Any]     			=						self.scheduler_classes[0]
     snake_case_ :    Optional[int]     			=						self.get_scheduler_config(**__A)
     snake_case_ :    Optional[Any]     			=						scheduler_class(**__A)

     snake_case_			, snake_case_ :    Union[str, Any]     			=						10, 0.0

     snake_case_ :    List[str]     			=						self.dummy_model()
     snake_case_ :    List[str]     			=						self.dummy_sample_deter

     scheduler.set_timesteps(__A)

     for t in scheduler.timesteps:
         snake_case_ :    Tuple     			=						model(__A ,       __A)
         snake_case_ :    Tuple     			=						scheduler.step(__A ,       __A ,       __A ,       __A).prev_sample

     return sample





 def        snake_case__		(    self):
     for timesteps in [1_00, 5_00, 10_00]:
         self.check_over_configs(num_train_timesteps=__A)





 def        snake_case__		(    self):
     for steps_offset in [0, 1]:
         self.check_over_configs(steps_offset=__A)

     snake_case_ :    Optional[Any]     			=						self.scheduler_classes[0]
     snake_case_ :    Optional[int]     			=						self.get_scheduler_config(steps_offset=1)
     snake_case_ :    List[Any]     			=						scheduler_class(**__A)
     scheduler.set_timesteps(5)
     assert torch.equal(scheduler.timesteps ,       torch.LongTensor([8_01, 6_01, 4_01, 2_01, 1]))





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





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





 def        snake_case__		(    self):
     for clip_sample in [True, False]:
         self.check_over_configs(clip_sample=__A)





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





 def        snake_case__		(    self):
     for rescale_betas_zero_snr in [True, False]:
         self.check_over_configs(rescale_betas_zero_snr=__A)





 def        snake_case__		(    self):
     self.check_over_configs(thresholding=__A)
     for threshold in [0.5, 1.0, 2.0]:
         for prediction_type in ["epsilon", "v_prediction"]:
             self.check_over_configs(
                 thresholding=__A ,       prediction_type=__A ,       sample_max_value=__A ,       )





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





 def        snake_case__		(    self):
     for t, num_inference_steps in zip([1, 10, 50] ,       [10, 50, 5_00]):
         self.check_over_forward(time_step=__A ,       num_inference_steps=__A)





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





 def        snake_case__		(    self):
     snake_case_ :    Optional[int]     			=						self.scheduler_classes[0]
     snake_case_ :    Tuple     			=						self.get_scheduler_config()
     snake_case_ :    Union[str, Any]     			=						scheduler_class(**__A)

     assert torch.sum(torch.abs(scheduler._get_variance(0 ,       0) - 0.0)) < 1E-5
     assert torch.sum(torch.abs(scheduler._get_variance(4_20 ,       4_00) - 0.14_771)) < 1E-5
     assert torch.sum(torch.abs(scheduler._get_variance(9_80 ,       9_60) - 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(4_87 ,       4_86) - 0.00_979)) < 1E-5
     assert torch.sum(torch.abs(scheduler._get_variance(9_99 ,       9_98) - 0.02)) < 1E-5





 def        snake_case__		(    self):
     snake_case_ :    Optional[Any]     			=						self.scheduler_classes[0]
     snake_case_ :    List[Any]     			=						self.get_scheduler_config()
     snake_case_ :    Optional[Any]     			=						scheduler_class(**__A)

     snake_case_			, snake_case_ :    List[Any]     			=						10, 0.0
     scheduler.set_timesteps(__A)

     snake_case_ :    int     			=						self.dummy_model()
     snake_case_ :    Union[str, Any]     			=						self.dummy_sample_deter
     snake_case_ :    Any     			=						self.dummy_sample_deter + 0.1
     snake_case_ :    Union[str, Any]     			=						self.dummy_sample_deter - 0.1

     snake_case_ :    Dict     			=						samplea.shape[0]
     snake_case_ :    Optional[Any]     			=						torch.stack([samplea, samplea, samplea] ,       dim=0)
     snake_case_ :    Union[str, Any]     			=						torch.arange(__A)[0:3, None].repeat(1 ,       __A)

     snake_case_ :    str     			=						model(samples.flatten(0 ,       1) ,       timesteps.flatten(0 ,       1))
     snake_case_ :    Union[str, Any]     			=						scheduler.batch_step_no_noise(__A ,       timesteps.flatten(0 ,       1) ,       samples.flatten(0 ,       1) ,       __A)

     snake_case_ :    List[str]     			=						torch.sum(torch.abs(__A))
     snake_case_ :    Tuple     			=						torch.mean(torch.abs(__A))

     assert abs(result_sum.item() - 11_47.79_04) < 1E-2
     assert abs(result_mean.item() - 0.4_982) < 1E-3





 def        snake_case__		(    self):
     snake_case_ :    List[Any]     			=						self.full_loop()

     snake_case_ :    List[str]     			=						torch.sum(torch.abs(__A))
     snake_case_ :    List[Any]     			=						torch.mean(torch.abs(__A))

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





 def        snake_case__		(    self):
     snake_case_ :    Any     			=						self.full_loop(prediction_type="v_prediction")

     snake_case_ :    Any     			=						torch.sum(torch.abs(__A))
     snake_case_ :    Optional[int]     			=						torch.mean(torch.abs(__A))

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





 def        snake_case__		(    self):
     # We specify different beta, so that the first alpha is 0.99
     snake_case_ :    Dict     			=						self.full_loop(set_alpha_to_one=__A ,       beta_start=0.01)
     snake_case_ :    Union[str, Any]     			=						torch.sum(torch.abs(__A))
     snake_case_ :    List[str]     			=						torch.mean(torch.abs(__A))

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





 def        snake_case__		(    self):
     # We specify different beta, so that the first alpha is 0.99
     snake_case_ :    Union[str, Any]     			=						self.full_loop(set_alpha_to_one=__A ,       beta_start=0.01)
     snake_case_ :    List[str]     			=						torch.sum(torch.abs(__A))
     snake_case_ :    Dict     			=						torch.mean(torch.abs(__A))

     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_											=						["bert-base-uncased", "bert-base-cased"]
a_											=						"hf-internal-testing/tiny-bert-tf-only"

if is_tf_available():






  class    UpperCAmelCase_				(	tf.keras.Model					):





   def __init__(    self ,       lowercase_):
       super().__init__()
       snake_case_ :    Union[str, Any]     			=						tokenizer
       snake_case_ :    List[str]     			=						AutoConfig.from_pretrained(lowercase_)
       snake_case_ :    List[str]     			=						TFAutoModel.from_config(lowercase_)





   def        snake_case__		(    self ,       lowercase_):
       snake_case_ :    List[str]     			=						self.tokenizer(lowercase_)
       snake_case_ :    str     			=						self.bert(**lowercase_)
       return out["pooler_output"]






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





 def        snake_case__		(    self):
     super().setUp()

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

     snake_case_ :    Dict     			=						[
         "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",
     ]
     snake_case_ :    str     			=						list(zip(self.test_sentences ,       self.test_sentences[::-1]))





 def        snake_case__		(    self):
     for tokenizer, tf_tokenizer in zip(self.tokenizers ,       self.tf_tokenizers):
         for test_inputs in (self.test_sentences, self.paired_sentences):
             snake_case_ :    Any     			=						tokenizer(lowercase_ ,       return_tensors="tf" ,       padding="longest")
             snake_case_ :    Union[str, Any]     			=						tf_tokenizer(lowercase_)

             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        snake_case__		(    self):
     for tf_tokenizer in self.tf_tokenizers:
         snake_case_ :    Union[str, Any]     			=						tf_tokenizer(self.paired_sentences)
         snake_case_ :    str     			=						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        snake_case__		(    self):
     for tf_tokenizer in self.tf_tokenizers:
         snake_case_ :    int     			=						tf.function(lowercase_)
         for test_inputs in (self.test_sentences, self.paired_sentences):
             snake_case_ :    Optional[int]     			=						tf.constant(lowercase_)
             snake_case_ :    Tuple     			=						compiled_tokenizer(lowercase_)
             snake_case_ :    Any     			=						tf_tokenizer(lowercase_)

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





 @slow
 def        snake_case__		(    self):
     for tf_tokenizer in self.tf_tokenizers:
         snake_case_ :    List[Any]     			=						ModelToSave(tokenizer=lowercase_)
         snake_case_ :    Any     			=						tf.convert_to_tensor(self.test_sentences)
         snake_case_ :    List[Any]     			=						model(lowercase_)  # Build model with some sample inputs
         with TemporaryDirectory() as tempdir:
             snake_case_ :    Optional[Any]     			=						Path(lowercase_) / "saved.model"
             model.save(lowercase_)
             snake_case_ :    Tuple     			=						tf.keras.models.load_model(lowercase_)
         snake_case_ :    str     			=						loaded_model(lowercase_)
         # 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)




