



"""simple docstring"""







import os
import re
import shutil
import sys
import tempfile
import unittest

import black


A    =			os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If DDPMSchedulerOutput is changed in scheduling_ddpm.py, this code needs to be manually updated.
A    =			'    \"""\n    Output class for the scheduler\'s step function output.\n\n    Args:\n        prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n            Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the\n            denoising loop.\n        pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n            The predicted denoised sample (x_{0}) based on the model output from the current timestep.\n            `pred_original_sample` can be used to preview progress or for guidance.\n    \"""\n\n    prev_sample: torch.FloatTensor\n    pred_original_sample: Optional[torch.FloatTensor] = None\n'






class      UpperCAmelCase__		(      unittest.TestCase     ):



		def 					A_		( self  :    Any				)			->						Union[str, Any]:






									'''simple docstring'''

									A         =					tempfile.mkdtemp()
									os.makedirs(os.path.join(self.diffusers_dir      ,							'schedulers/'				)				)
									A         =					self.diffusers_dir
									shutil.copy(
									    os.path.join(snake_case      ,							'src/diffusers/schedulers/scheduling_ddpm.py'				)      ,							os.path.join(self.diffusers_dir      ,							'schedulers/scheduling_ddpm.py'				)      ,							)



		def 					A_		( self  :    List[Any]				)			->						List[str]:






									'''simple docstring'''

									A         =					'src/diffusers'
									shutil.rmtree(self.diffusers_dir				)



		def 					A_		( self  :    Union[str, Any]      ,							snake_case  :    int      ,							snake_case  :    Optional[Any]      ,							snake_case  :    Tuple      ,							snake_case  :    Any=None				)			->						Optional[int]:






									'''simple docstring'''

									A         =					comment + f"""\nclass {class_name}(nn.Module):\n""" + class_code
									if overwrite_result is not None:
																A         =					comment + f"""\nclass {class_name}(nn.Module):\n""" + overwrite_result
									A         =					black.Mode(target_versions={black.TargetVersion.PYaa}      ,							line_length=119				)
									A         =					black.format_str(snake_case      ,							mode=snake_case				)
									A         =					os.path.join(self.diffusers_dir      ,							'new_code.py'				)
									with open(snake_case      ,							'w'      ,							newline='\n'				) as f:
																f.write(snake_case				)
									if overwrite_result is None:
																self.assertTrue(len(check_copies.is_copy_consistent(snake_case				)				) == 0				)
									else:
																check_copies.is_copy_consistent(f.name      ,							overwrite=snake_case				)
																with open(snake_case      ,							'r'				) as f:
																							self.assertTrue(f.read()      ,							snake_case				)



		def 					A_		( self  :    Tuple				)			->						Optional[int]:






									'''simple docstring'''

									A         =					check_copies.find_code_in_diffusers('schedulers.scheduling_ddpm.DDPMSchedulerOutput'				)
									self.assertEqual(snake_case      ,							snake_case				)



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






									'''simple docstring'''

									self.check_copy_consistency(
									    '# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput'      ,							'DDPMSchedulerOutput'      ,							REFERENCE_CODE + '\n'      ,							)

									# With no empty line at the end
									self.check_copy_consistency(
									    '# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput'      ,							'DDPMSchedulerOutput'      ,							snake_case      ,							)

									# Copy consistency with rename
									self.check_copy_consistency(
									    '# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test'      ,							'TestSchedulerOutput'      ,							re.sub('DDPM'      ,							'Test'      ,							snake_case				)      ,							)

									# Copy consistency with a really long name
									A         =					'TestClassWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason'
									self.check_copy_consistency(
									    f"""# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->{long_class_name}"""      ,							f"""{long_class_name}SchedulerOutput"""      ,							re.sub('Bert'      ,							snake_case      ,							snake_case				)      ,							)

									# Copy consistency with overwrite
									self.check_copy_consistency(
									    '# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test'      ,							'TestSchedulerOutput'      ,							snake_case      ,							overwrite_result=re.sub('DDPM'      ,							'Test'      ,							snake_case				)      ,							)








"""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  :    Tuple      ,							snake_case  :    Optional[int]				)			->						List[str]:






																'''simple docstring'''

																super().__init__()
																A         =					tokenizer
																A         =					AutoConfig.from_pretrained(snake_case				)
																A         =					TFAutoModel.from_config(snake_case				)



									def 					A_		( self  :    Any      ,							snake_case  :    Optional[int]				)			->						Dict:






																'''simple docstring'''

																A         =					self.tokenizer(snake_case				)
																A         =					self.bert(**snake_case				)
																return out["pooler_output"]






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



		def 					A_		( self  :    int				)			->						Tuple:






									'''simple docstring'''

									super().setUp()

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

									A         =					[
									    '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',
									]
									A         =					list(zip(self.test_sentences      ,							self.test_sentences[::-1]				)				)



		def 					A_		( self  :    List[Any]				)			->						Dict:






									'''simple docstring'''

									for tokenizer, tf_tokenizer in zip(self.tokenizers      ,							self.tf_tokenizers				):
																for test_inputs in (self.test_sentences, self.paired_sentences):
																							A         =					tokenizer(snake_case      ,							return_tensors='tf'      ,							padding='longest'				)
																							A         =					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 					A_		( self  :    Tuple				)			->						Tuple:






									'''simple docstring'''

									for tf_tokenizer in self.tf_tokenizers:
																A         =					tf_tokenizer(self.paired_sentences				)
																A         =					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 					A_		( self  :    Any				)			->						Tuple:






									'''simple docstring'''

									for tf_tokenizer in self.tf_tokenizers:
																A         =					tf.function(snake_case				)
																for test_inputs in (self.test_sentences, self.paired_sentences):
																							A         =					tf.constant(snake_case				)
																							A         =					compiled_tokenizer(snake_case				)
																							A         =					tf_tokenizer(snake_case				)

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



		@slow
		def 					A_		( self  :    Optional[Any]				)			->						Optional[Any]:






									'''simple docstring'''

									for tf_tokenizer in self.tf_tokenizers:
																A         =					ModelToSave(tokenizer=snake_case				)
																A         =					tf.convert_to_tensor(self.test_sentences				)
																A         =					model(snake_case				)  # Build model with some sample inputs
																with TemporaryDirectory() as tempdir:
																							A         =					Path(snake_case				) / 'saved.model'
																							model.save(snake_case				)
																							A         =					tf.keras.models.load_model(snake_case				)
																A         =					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				)



