



'''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


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

if is_tf_available():






	class 					UpperCAmelCase__	(		tf.keras.Model ):



						def __init__(    self			,				UpperCamelCase     )     ->			str:
							super().__init__()
							__lowerCAmelCase												=  tokenizer
							__lowerCAmelCase												=  AutoConfig.from_pretrained(lowercase_     )
							__lowerCAmelCase												=  TFAutoModel.from_config(lowercase_     )



						def        UpperCAmelCase_ (    self			,				UpperCamelCase     )     ->			Optional[int]:
							__lowerCAmelCase												=  self.tokenizer(lowercase_     )
							__lowerCAmelCase												=  self.bert(**lowercase_     )
							return out["pooler_output"]






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



					def        UpperCAmelCase_ (    self     )     ->			Dict:
						super().setUp()

						__lowerCAmelCase												=  [
						    BertTokenizer.from_pretrained(lowercase_     ) for checkpoint in (TOKENIZER_CHECKPOINTS * 2)
						]  # repeat for when fast_bert_tokenizer=false
						__lowerCAmelCase												=  [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     )

						__lowerCAmelCase												=  [
						    "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",
						]
						__lowerCAmelCase												=  list(zip(self.test_sentences			,				self.test_sentences[::-1]     )     )



					def        UpperCAmelCase_ (    self     )     ->			Optional[Any]:
						for tokenizer, tf_tokenizer in zip(self.tokenizers			,				self.tf_tokenizers     ):
							for test_inputs in (self.test_sentences, self.paired_sentences):
								__lowerCAmelCase												=  tokenizer(lowercase_			,				return_tensors="tf"			,				padding="longest"     )
								__lowerCAmelCase												=  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        UpperCAmelCase_ (    self     )     ->			Union[str, Any]:
						for tf_tokenizer in self.tf_tokenizers:
							__lowerCAmelCase												=  tf_tokenizer(self.paired_sentences     )
							__lowerCAmelCase												=  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        UpperCAmelCase_ (    self     )     ->			List[str]:
						for tf_tokenizer in self.tf_tokenizers:
							__lowerCAmelCase												=  tf.function(lowercase_     )
							for test_inputs in (self.test_sentences, self.paired_sentences):
								__lowerCAmelCase												=  tf.constant(lowercase_     )
								__lowerCAmelCase												=  compiled_tokenizer(lowercase_     )
								__lowerCAmelCase												=  tf_tokenizer(lowercase_     )

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



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




'''simple docstring'''



import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest






class 					UpperCAmelCase__	(		UpperCamelCase__ ):
					a      :     List[str]        =							(CMStochasticIterativeScheduler,)
					a      :     str        =							1_0



					def        UpperCAmelCase_ (    self			,				**UpperCamelCase     )     ->			str:
						__lowerCAmelCase												=  {
						    "num_train_timesteps": 201,
						    "sigma_min": 0.0_02,
						    "sigma_max": 80.0,
						}

						config.update(**UpperCamelCase     )
						return config



					def        UpperCAmelCase_ (    self     )     ->			List[Any]:
						__lowerCAmelCase												=  10

						__lowerCAmelCase												=  self.get_scheduler_config()
						__lowerCAmelCase												=  self.scheduler_classes[0](**UpperCamelCase     )

						scheduler.set_timesteps(UpperCamelCase     )

						__lowerCAmelCase												=  scheduler.timesteps[0]
						__lowerCAmelCase												=  scheduler.timesteps[1]

						__lowerCAmelCase												=  self.dummy_sample
						__lowerCAmelCase												=  0.1 * sample

						__lowerCAmelCase												=  scheduler.step(UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase     ).prev_sample
						__lowerCAmelCase												=  scheduler.step(UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase     ).prev_sample

						self.assertEqual(output_a.shape			,				sample.shape     )
						self.assertEqual(output_a.shape			,				output_a.shape     )



					def        UpperCAmelCase_ (    self     )     ->			Any:
						for timesteps in [10, 50, 100, 1000]:
							self.check_over_configs(num_train_timesteps=UpperCamelCase     )



					def        UpperCAmelCase_ (    self     )     ->			Union[str, Any]:
						for clip_denoised in [True, False]:
							self.check_over_configs(clip_denoised=UpperCamelCase     )



					def        UpperCAmelCase_ (    self     )     ->			List[str]:
						__lowerCAmelCase												=  self.scheduler_classes[0]
						__lowerCAmelCase												=  self.get_scheduler_config()
						__lowerCAmelCase												=  scheduler_class(**UpperCamelCase     )

						__lowerCAmelCase												=  1
						scheduler.set_timesteps(UpperCamelCase     )
						__lowerCAmelCase												=  scheduler.timesteps

						__lowerCAmelCase												=  torch.manual_seed(0     )

						__lowerCAmelCase												=  self.dummy_model()
						__lowerCAmelCase												=  self.dummy_sample_deter * scheduler.init_noise_sigma

						for i, t in enumerate(UpperCamelCase     ):
							# 1. scale model input
							__lowerCAmelCase												=  scheduler.scale_model_input(UpperCamelCase			,				UpperCamelCase     )

							# 2. predict noise residual
							__lowerCAmelCase												=  model(UpperCamelCase			,				UpperCamelCase     )

							# 3. predict previous sample x_t-1
							__lowerCAmelCase												=  scheduler.step(UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				generator=UpperCamelCase     ).prev_sample

							__lowerCAmelCase												=  pred_prev_sample

						__lowerCAmelCase												=  torch.sum(torch.abs(UpperCamelCase     )     )
						__lowerCAmelCase												=  torch.mean(torch.abs(UpperCamelCase     )     )

						assert abs(result_sum.item() - 1_92.76_14     ) < 1E-2
						assert abs(result_mean.item() - 0.25_10     ) < 1E-3



					def        UpperCAmelCase_ (    self     )     ->			Optional[int]:
						__lowerCAmelCase												=  self.scheduler_classes[0]
						__lowerCAmelCase												=  self.get_scheduler_config()
						__lowerCAmelCase												=  scheduler_class(**UpperCamelCase     )

						__lowerCAmelCase												=  [106, 0]
						scheduler.set_timesteps(timesteps=UpperCamelCase     )
						__lowerCAmelCase												=  scheduler.timesteps

						__lowerCAmelCase												=  torch.manual_seed(0     )

						__lowerCAmelCase												=  self.dummy_model()
						__lowerCAmelCase												=  self.dummy_sample_deter * scheduler.init_noise_sigma

						for t in timesteps:
							# 1. scale model input
							__lowerCAmelCase												=  scheduler.scale_model_input(UpperCamelCase			,				UpperCamelCase     )

							# 2. predict noise residual
							__lowerCAmelCase												=  model(UpperCamelCase			,				UpperCamelCase     )

							# 3. predict previous sample x_t-1
							__lowerCAmelCase												=  scheduler.step(UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				generator=UpperCamelCase     ).prev_sample

							__lowerCAmelCase												=  pred_prev_sample

						__lowerCAmelCase												=  torch.sum(torch.abs(UpperCamelCase     )     )
						__lowerCAmelCase												=  torch.mean(torch.abs(UpperCamelCase     )     )

						assert abs(result_sum.item() - 3_47.63_57     ) < 1E-2
						assert abs(result_mean.item() - 0.45_27     ) < 1E-3



					def        UpperCAmelCase_ (    self     )     ->			Any:
						__lowerCAmelCase												=  self.scheduler_classes[0]
						__lowerCAmelCase												=  self.get_scheduler_config()
						__lowerCAmelCase												=  scheduler_class(**UpperCamelCase     )

						__lowerCAmelCase												=  [39, 30, 12, 15, 0]

						with self.assertRaises(UpperCamelCase			,				msg="`timesteps` must be in descending order."     ):
							scheduler.set_timesteps(timesteps=UpperCamelCase     )



					def        UpperCAmelCase_ (    self     )     ->			Optional[int]:
						__lowerCAmelCase												=  self.scheduler_classes[0]
						__lowerCAmelCase												=  self.get_scheduler_config()
						__lowerCAmelCase												=  scheduler_class(**UpperCamelCase     )

						__lowerCAmelCase												=  [39, 30, 12, 1, 0]
						__lowerCAmelCase												=  len(UpperCamelCase     )

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



					def        UpperCAmelCase_ (    self     )     ->			Tuple:
						__lowerCAmelCase												=  self.scheduler_classes[0]
						__lowerCAmelCase												=  self.get_scheduler_config()
						__lowerCAmelCase												=  scheduler_class(**UpperCamelCase     )

						__lowerCAmelCase												=  [scheduler.config.num_train_timesteps]

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