



'''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__:   Union[str, Any]								=			["bert-base-uncased", "bert-base-cased"]
UpperCamelCase__:   Optional[Any]								=			"hf-internal-testing/tiny-bert-tf-only"

if is_tf_available():

						class 				SCREAMING_SNAKE_CASE(							tf.keras.Model			):







							"""simple docstring"""






							def __init__(						self			:     Union[str, Any]		,			__snake_case			:     Tuple		)			->		Dict:
														super().__init__()
														UpperCAmelCase :       Union[str, Any] 							=		tokenizer
														UpperCAmelCase :       str 							=		AutoConfig.from_pretrained(__snake_case		)
														UpperCAmelCase :       Any 							=		TFAutoModel.from_config(__snake_case		)







							def      A	(						self			:     List[Any]		,			__snake_case			:     Optional[Any]		)			->		Optional[Any]:
														UpperCAmelCase :       List[Any] 							=		self.tokenizer(__snake_case		)
														UpperCAmelCase :       Optional[int] 							=		self.bert(**__snake_case		)
														return out["pooler_output"]

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







	"""simple docstring"""






	def      A	(						self			:     int		)			->		Union[str, Any]:
								super().setUp()

								UpperCAmelCase :       Dict 							=		[
								    BertTokenizer.from_pretrained(__snake_case		) for checkpoint in (TOKENIZER_CHECKPOINTS * 2)
								]  # repeat for when fast_bert_tokenizer=false
								UpperCAmelCase :       str 							=		[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 :       str 							=		[
								    '''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[Any] 							=		list(zip(self.test_sentences		,			self.test_sentences[::-1]		)		)







	def      A	(						self			:     Dict		)			->		str:
								for tokenizer, tf_tokenizer in zip(self.tokenizers		,			self.tf_tokenizers		):
															for test_inputs in (self.test_sentences, self.paired_sentences):
																						UpperCAmelCase :       List[str] 							=		tokenizer(__snake_case		,			return_tensors='''tf'''		,			padding='''longest'''		)
																						UpperCAmelCase :       Dict 							=		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			:     Union[str, Any]		)			->		Union[str, Any]:
								for tf_tokenizer in self.tf_tokenizers:
															UpperCAmelCase :       Optional[int] 							=		tf_tokenizer(self.paired_sentences		)
															UpperCAmelCase :       Optional[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      A	(						self			:     str		)			->		Any:
								for tf_tokenizer in self.tf_tokenizers:
															UpperCAmelCase :       str 							=		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 :       List[Any] 							=		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[int]		)			->		int:
								for tf_tokenizer in self.tf_tokenizers:
															UpperCAmelCase :       Tuple 							=		ModelToSave(tokenizer=__snake_case		)
															UpperCAmelCase :       Any 							=		tf.convert_to_tensor(self.test_sentences		)
															UpperCAmelCase :       Optional[Any] 							=		model(__snake_case		)  # Build model with some sample inputs
															with TemporaryDirectory() as tempdir:
																						UpperCAmelCase :       Optional[Any] 							=		Path(__snake_case		) / '''saved.model'''
																						model.save(__snake_case		)
																						UpperCAmelCase :       Tuple 							=		tf.keras.models.load_model(__snake_case		)
															UpperCAmelCase :       List[Any] 							=		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		)









import unittest

import numpy as np
import torch

from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device

from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()






class   _lowerCamelCase (					UpperCamelCase					,							unittest.TestCase			):




       """simple docstring"""
       snake_case      =    DDIMPipeline
       snake_case      =    UNCONDITIONAL_IMAGE_GENERATION_PARAMS
       snake_case      =    PipelineTesterMixin.required_optional_params - {
           "num_images_per_prompt",
           "latents",
           "callback",
           "callback_steps",
       }
       snake_case      =    UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
       snake_case      =    False



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




         '''simple docstring'''





         torch.manual_seed(0		)
         A_      :				List[str]		     =					UNetaDModel(
             block_out_channels=(32, 64)				,       layers_per_block=2				,       sample_size=32				,       in_channels=3				,       out_channels=3				,       down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')				,       up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')				,       )
         A_      :				Optional[Any]		     =					DDIMScheduler()
         A_      :				str		     =					{'''unet''': unet, '''scheduler''': scheduler}
         return components



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE=0		)->Optional[Any]:




         '''simple docstring'''





         if str(_SCREAMING_SNAKE_CASE		).startswith('''mps'''		):
           A_      :				Any		     =					torch.manual_seed(_SCREAMING_SNAKE_CASE		)
         else:
           A_      :				Optional[int]		     =					torch.Generator(device=_SCREAMING_SNAKE_CASE		).manual_seed(_SCREAMING_SNAKE_CASE		)
         A_      :				Any		     =					{
             '''batch_size''': 1,
             '''generator''': generator,
             '''num_inference_steps''': 2,
             '''output_type''': '''numpy''',
         }
         return inputs



       def        _snake_case       (      self		)->List[Any]:




         '''simple docstring'''





         A_      :				Optional[int]		     =					'''cpu'''

         A_      :				Dict		     =					self.get_dummy_components()
         A_      :				str		     =					self.pipeline_class(**_SCREAMING_SNAKE_CASE		)
         pipe.to(_SCREAMING_SNAKE_CASE		)
         pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE		)

         A_      :				str		     =					self.get_dummy_inputs(_SCREAMING_SNAKE_CASE		)
         A_      :				Any		     =					pipe(**_SCREAMING_SNAKE_CASE		).images
         A_      :				int		     =					image[0, -3:, -3:, -1]

         self.assertEqual(image.shape				,       (1, 32, 32, 3)		)
         A_      :				List[Any]		     =					np.array(
             [1.000e00, 5.717e-01, 4.717e-01, 1.000e00, 0.000e00, 1.000e00, 3.000e-04, 0.000e00, 9.000e-04]		)
         A_      :				str		     =					np.abs(image_slice.flatten() - expected_slice		).max()
         self.assertLessEqual(_SCREAMING_SNAKE_CASE				,       1e-3		)



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




         '''simple docstring'''





         super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3		)



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




         '''simple docstring'''





         super().test_save_load_local(expected_max_difference=3e-3		)



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




         '''simple docstring'''





         super().test_save_load_optional_components(expected_max_difference=3e-3		)







       def        _snake_case       (      self		)->Any:




         '''simple docstring'''





         super().test_inference_batch_single_identical(expected_max_diff=3e-3		)






@slow
@require_torch_gpu
class   _lowerCamelCase (					unittest.TestCase			):




       """simple docstring"""



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




         '''simple docstring'''





         A_      :				int		     =					'''google/ddpm-cifar10-32'''

         A_      :				Tuple		     =					UNetaDModel.from_pretrained(_SCREAMING_SNAKE_CASE		)
         A_      :				str		     =					DDIMScheduler()

         A_      :				str		     =					DDIMPipeline(unet=_SCREAMING_SNAKE_CASE				,       scheduler=_SCREAMING_SNAKE_CASE		)
         ddim.to(_SCREAMING_SNAKE_CASE		)
         ddim.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE		)

         A_      :				Optional[int]		     =					torch.manual_seed(0		)
         A_      :				Any		     =					ddim(generator=_SCREAMING_SNAKE_CASE				,       eta=0.0				,       output_type='''numpy'''		).images

         A_      :				Any		     =					image[0, -3:, -3:, -1]

         assert image.shape == (1, 32, 32, 3)
         A_      :				Any		     =					np.array([0.1_7_2_3, 0.1_6_1_7, 0.1_6_0_0, 0.1_6_2_6, 0.1_4_9_7, 0.1_5_1_3, 0.1_5_0_5, 0.1_4_4_2, 0.1_4_5_3]		)

         assert np.abs(image_slice.flatten() - expected_slice		).max() < 1e-2







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




         '''simple docstring'''





         A_      :				Tuple		     =					'''google/ddpm-ema-bedroom-256'''

         A_      :				int		     =					UNetaDModel.from_pretrained(_SCREAMING_SNAKE_CASE		)
         A_      :				Any		     =					DDIMScheduler.from_pretrained(_SCREAMING_SNAKE_CASE		)

         A_      :				Optional[Any]		     =					DDIMPipeline(unet=_SCREAMING_SNAKE_CASE				,       scheduler=_SCREAMING_SNAKE_CASE		)
         ddpm.to(_SCREAMING_SNAKE_CASE		)
         ddpm.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE		)

         A_      :				Dict		     =					torch.manual_seed(0		)
         A_      :				List[str]		     =					ddpm(generator=_SCREAMING_SNAKE_CASE				,       output_type='''numpy'''		).images

         A_      :				Any		     =					image[0, -3:, -3:, -1]

         assert image.shape == (1, 256, 256, 3)
         A_      :				Tuple		     =					np.array([0.0_0_6_0, 0.0_2_0_1, 0.0_3_4_4, 0.0_0_2_4, 0.0_0_1_8, 0.0_0_0_2, 0.0_0_2_2, 0.0_0_0_0, 0.0_0_6_9]		)

         assert np.abs(image_slice.flatten() - expected_slice		).max() < 1e-2




