





"""simple docstring"""





import unittest

import numpy as np

from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
       import jax.numpy as jnp

       from transformers.models.albert.modeling_flax_albert import (
           FlaxAlbertForMaskedLM,
           FlaxAlbertForMultipleChoice,
           FlaxAlbertForPreTraining,
           FlaxAlbertForQuestionAnswering,
           FlaxAlbertForSequenceClassification,
           FlaxAlbertForTokenClassification,
           FlaxAlbertModel,
       )
class 		SCREAMING_SNAKE_CASE_							(    unittest.TestCase   ):
    """simple docstring"""




    def __init__(     self  :Dict						,    __lowercase  :Union[str, Any]						,    __lowercase  :Optional[Any]=13						,    __lowercase  :str=7						,    __lowercase  :int=True						,    __lowercase  :Optional[int]=True						,    __lowercase  :Dict=True						,    __lowercase  :Union[str, Any]=True						,    __lowercase  :Union[str, Any]=99						,    __lowercase  :Tuple=32						,    __lowercase  :Any=5						,    __lowercase  :Dict=4						,    __lowercase  :Any=37						,    __lowercase  :Optional[int]="gelu"						,    __lowercase  :List[str]=0.1						,    __lowercase  :Any=0.1						,    __lowercase  :Dict=512						,    __lowercase  :Tuple=16						,    __lowercase  :Union[str, Any]=2						,    __lowercase  :Tuple=0.02						,    __lowercase  :Dict=4						,    ):
           __lowerCamelCase      :      Dict         =parent
           __lowerCamelCase      :      Optional[int]         =batch_size
           __lowerCamelCase      :      Dict         =seq_length
           __lowerCamelCase      :      str         =is_training
           __lowerCamelCase      :      Optional[Any]         =use_attention_mask
           __lowerCamelCase      :      Optional[int]         =use_token_type_ids
           __lowerCamelCase      :      str         =use_labels
           __lowerCamelCase      :      Tuple         =vocab_size
           __lowerCamelCase      :      Union[str, Any]         =hidden_size
           __lowerCamelCase      :      List[Any]         =num_hidden_layers
           __lowerCamelCase      :      List[str]         =num_attention_heads
           __lowerCamelCase      :      str         =intermediate_size
           __lowerCamelCase      :      List[str]         =hidden_act
           __lowerCamelCase      :      Dict         =hidden_dropout_prob
           __lowerCamelCase      :      Optional[int]         =attention_probs_dropout_prob
           __lowerCamelCase      :      Optional[Any]         =max_position_embeddings
           __lowerCamelCase      :      Dict         =type_vocab_size
           __lowerCamelCase      :      List[Any]         =type_sequence_label_size
           __lowerCamelCase      :      int         =initializer_range
           __lowerCamelCase      :      List[str]         =num_choices




    def 					__lowercase  (     self  :List[Any]			):
           __lowerCamelCase      :      List[Any]         =ids_tensor([self.batch_size, self.seq_length]						,    self.vocab_size			)

           __lowerCamelCase      :      str         =None
           if self.use_attention_mask:
                  __lowerCamelCase      :      str         =random_attention_mask([self.batch_size, self.seq_length]			)

           __lowerCamelCase      :      Optional[Any]         =None
           if self.use_token_type_ids:
                  __lowerCamelCase      :      Optional[Any]         =ids_tensor([self.batch_size, self.seq_length]						,    self.type_vocab_size			)

           __lowerCamelCase      :      List[Any]         =AlbertConfig(
               vocab_size=self.vocab_size						,    hidden_size=self.hidden_size						,    num_hidden_layers=self.num_hidden_layers						,    num_attention_heads=self.num_attention_heads						,    intermediate_size=self.intermediate_size						,    hidden_act=self.hidden_act						,    hidden_dropout_prob=self.hidden_dropout_prob						,    attention_probs_dropout_prob=self.attention_probs_dropout_prob						,    max_position_embeddings=self.max_position_embeddings						,    type_vocab_size=self.type_vocab_size						,    is_decoder=__lowercase						,    initializer_range=self.initializer_range						,    )

           return config, input_ids, token_type_ids, attention_mask




    def 					__lowercase  (     self  :int			):
           __lowerCamelCase      :      Optional[Any]         =self.prepare_config_and_inputs()
           __lowerCamelCase      :      Optional[int]         =config_and_inputs
           __lowerCamelCase      :      Dict         ={'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
           return config, inputs_dict

@require_flax
class 		SCREAMING_SNAKE_CASE_							(    snake_case__       ,  unittest.TestCase   ):
    """simple docstring"""
    __snake_case   :       str	 =		(
        (
            FlaxAlbertModel,
            FlaxAlbertForPreTraining,
            FlaxAlbertForMaskedLM,
            FlaxAlbertForMultipleChoice,
            FlaxAlbertForQuestionAnswering,
            FlaxAlbertForSequenceClassification,
            FlaxAlbertForTokenClassification,
            FlaxAlbertForQuestionAnswering,
        )
        if is_flax_available()
        else ()
    )




    def 					__lowercase  (     self  :Optional[int]			):
           __lowerCamelCase      :      int         =FlaxAlbertModelTester(self			)




    @slow
    def 					__lowercase  (     self  :Dict			):
           for model_class_name in self.all_model_classes:
                  __lowerCamelCase      :      str         =model_class_name.from_pretrained('''albert-base-v2'''			)
                  __lowerCamelCase      :      str         =model(np.ones((1, 1)			)			)
                  self.assertIsNotNone(__lowercase			)

@require_flax
class 		SCREAMING_SNAKE_CASE_							(    unittest.TestCase   ):
    """simple docstring"""




    @slow
    def 					__lowercase  (     self  :Optional[int]			):
           __lowerCamelCase      :      Optional[Any]         =FlaxAlbertModel.from_pretrained('''albert-base-v2'''			)
           __lowerCamelCase      :      List[str]         =np.array([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]			)
           __lowerCamelCase      :      List[str]         =np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]			)
           __lowerCamelCase      :      List[str]         =model(__lowercase						,    attention_mask=__lowercase			)[0]
           __lowerCamelCase      :      Optional[Any]         =(1, 11, 768)
           self.assertEqual(output.shape						,    __lowercase			)
           __lowerCamelCase      :      Dict         =np.array(
               [[[-0.6513, 1.5035, -0.2766], [-0.6515, 1.5046, -0.2780], [-0.6512, 1.5049, -0.2784]]]			)

           self.assertTrue(jnp.allclose(output[:, 1:4, 1:4]						,    __lowercase						,    atol=1e-4			)			)











"""simple docstring"""





import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()
class 		SCREAMING_SNAKE_CASE_							(    snake_case__       ,  snake_case__       ,  unittest.TestCase   ):
				"""simple docstring"""
				__snake_case   :       Dict	 =		CycleDiffusionPipeline
				__snake_case   :       Optional[int]	 =		TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
				    """negative_prompt""",
				    """height""",
				    """width""",
				    """negative_prompt_embeds""",
				}
				__snake_case   :       Any	 =		PipelineTesterMixin.required_optional_params - {"""latents"""}
				__snake_case   :       int	 =		TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"""source_prompt"""}   )
				__snake_case   :       Optional[int]	 =		IMAGE_TO_IMAGE_IMAGE_PARAMS
				__snake_case   :       str	 =		IMAGE_TO_IMAGE_IMAGE_PARAMS




				def 					__lowercase  (     self  :Dict			):
											torch.manual_seed(0			)
											__lowerCamelCase      :      List[str]         =UNetaDConditionModel(
											    block_out_channels=(32, 64)						,    layers_per_block=2						,    sample_size=32						,    in_channels=4						,    out_channels=4						,    down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')						,    up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')						,    cross_attention_dim=32						,    )
											__lowerCamelCase      :      List[Any]         =DDIMScheduler(
											    beta_start=0.00085						,    beta_end=0.012						,    beta_schedule='''scaled_linear'''						,    num_train_timesteps=1000						,    clip_sample=__lowercase						,    set_alpha_to_one=__lowercase						,    )
											torch.manual_seed(0			)
											__lowerCamelCase      :      Optional[int]         =AutoencoderKL(
											    block_out_channels=[32, 64]						,    in_channels=3						,    out_channels=3						,    down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']						,    up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']						,    latent_channels=4						,    )
											torch.manual_seed(0			)
											__lowerCamelCase      :      int         =CLIPTextConfig(
											    bos_token_id=0						,    eos_token_id=2						,    hidden_size=32						,    intermediate_size=37						,    layer_norm_eps=1e-0_5						,    num_attention_heads=4						,    num_hidden_layers=5						,    pad_token_id=1						,    vocab_size=1000						,    )
											__lowerCamelCase      :      Optional[Any]         =CLIPTextModel(__lowercase			)
											__lowerCamelCase      :      Tuple         =CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''			)

											__lowerCamelCase      :      Optional[int]         ={
											    '''unet''': unet,
											    '''scheduler''': scheduler,
											    '''vae''': vae,
											    '''text_encoder''': text_encoder,
											    '''tokenizer''': tokenizer,
											    '''safety_checker''': None,
											    '''feature_extractor''': None,
											}
											return components




				def 					__lowercase  (     self  :Union[str, Any]						,    __lowercase  :Optional[int]						,    __lowercase  :str=0			):
											__lowerCamelCase      :      List[Any]         =floats_tensor((1, 3, 32, 32)						,    rng=random.Random(__lowercase			)			).to(__lowercase			)
											__lowerCamelCase      :      str         =image / 2 + 0.5
											if str(__lowercase			).startswith('''mps'''			):
																		__lowerCamelCase      :      Union[str, Any]         =torch.manual_seed(__lowercase			)
											else:
																		__lowerCamelCase      :      Any         =torch.Generator(device=__lowercase			).manual_seed(__lowercase			)
											__lowerCamelCase      :      Dict         ={
											    '''prompt''': '''An astronaut riding an elephant''',
											    '''source_prompt''': '''An astronaut riding a horse''',
											    '''image''': image,
											    '''generator''': generator,
											    '''num_inference_steps''': 2,
											    '''eta''': 0.1,
											    '''strength''': 0.8,
											    '''guidance_scale''': 3,
											    '''source_guidance_scale''': 1,
											    '''output_type''': '''numpy''',
											}
											return inputs




				def 					__lowercase  (     self  :Optional[int]			):
											__lowerCamelCase      :      int         ='''cpu'''  # ensure determinism for the device-dependent torch.Generator

											__lowerCamelCase      :      Tuple         =self.get_dummy_components()
											__lowerCamelCase      :      List[str]         =CycleDiffusionPipeline(**__lowercase			)
											__lowerCamelCase      :      Tuple         =pipe.to(__lowercase			)
											pipe.set_progress_bar_config(disable=__lowercase			)

											__lowerCamelCase      :      List[Any]         =self.get_dummy_inputs(__lowercase			)
											__lowerCamelCase      :      int         =pipe(**__lowercase			)
											__lowerCamelCase      :      Dict         =output.images

											__lowerCamelCase      :      Union[str, Any]         =images[0, -3:, -3:, -1]

											assert images.shape == (1, 32, 32, 3)
											__lowerCamelCase      :      Optional[Any]         =np.array([0.4459, 0.4943, 0.4544, 0.6643, 0.5474, 0.4327, 0.5701, 0.5959, 0.5179]			)

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




				@unittest.skipIf(torch_device != '''cuda'''						,    '''This test requires a GPU'''			)
				def 					__lowercase  (     self  :str			):
											__lowerCamelCase      :      str         =self.get_dummy_components()
											for name, module in components.items():
																		if hasattr(__lowercase						,    '''half'''			):
																									__lowerCamelCase      :      Union[str, Any]         =module.half()
											__lowerCamelCase      :      int         =CycleDiffusionPipeline(**__lowercase			)
											__lowerCamelCase      :      List[str]         =pipe.to(__lowercase			)
											pipe.set_progress_bar_config(disable=__lowercase			)

											__lowerCamelCase      :      Optional[int]         =self.get_dummy_inputs(__lowercase			)
											__lowerCamelCase      :      Dict         =pipe(**__lowercase			)
											__lowerCamelCase      :      List[str]         =output.images

											__lowerCamelCase      :      Dict         =images[0, -3:, -3:, -1]

											assert images.shape == (1, 32, 32, 3)
											__lowerCamelCase      :      str         =np.array([0.3506, 0.4543, 0.446, 0.4575, 0.5195, 0.4155, 0.5273, 0.518, 0.4116]			)

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




				@skip_mps
				def 					__lowercase  (     self  :Optional[Any]			):
											return super().test_save_load_local()




				@unittest.skip('''non-deterministic pipeline'''			)
				def 					__lowercase  (     self  :Dict			):
											return super().test_inference_batch_single_identical()




				@skip_mps
				def 					__lowercase  (     self  :Optional[Any]			):
											return super().test_dict_tuple_outputs_equivalent()




				@skip_mps
				def 					__lowercase  (     self  :str			):
											return super().test_save_load_optional_components()




				@skip_mps
				def 					__lowercase  (     self  :Dict			):
											return super().test_attention_slicing_forward_pass()

@slow
@require_torch_gpu
class 		SCREAMING_SNAKE_CASE_							(    unittest.TestCase   ):
				"""simple docstring"""




				def 					__lowercase  (     self  :Tuple			):
											# clean up the VRAM after each test
											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()




				def 					__lowercase  (     self  :Dict			):
											__lowerCamelCase      :      Dict         =load_image(
											    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
											    '''/cycle-diffusion/black_colored_car.png'''			)
											__lowerCamelCase      :      Union[str, Any]         =load_numpy(
											    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy'''			)
											__lowerCamelCase      :      Any         =init_image.resize((512, 512)			)

											__lowerCamelCase      :      Optional[Any]         ='''CompVis/stable-diffusion-v1-4'''
											__lowerCamelCase      :      Optional[Any]         =DDIMScheduler.from_pretrained(__lowercase						,    subfolder='''scheduler'''			)
											__lowerCamelCase      :      Optional[Any]         =CycleDiffusionPipeline.from_pretrained(
											    __lowercase						,    scheduler=__lowercase						,    safety_checker=__lowercase						,    torch_dtype=torch.floataa						,    revision='''fp16'''			)

											pipe.to(__lowercase			)
											pipe.set_progress_bar_config(disable=__lowercase			)
											pipe.enable_attention_slicing()

											__lowerCamelCase      :      Dict         ='''A black colored car'''
											__lowerCamelCase      :      Union[str, Any]         ='''A blue colored car'''

											__lowerCamelCase      :      Dict         =torch.manual_seed(0			)
											__lowerCamelCase      :      Tuple         =pipe(
											    prompt=__lowercase						,    source_prompt=__lowercase						,    image=__lowercase						,    num_inference_steps=100						,    eta=0.1						,    strength=0.85						,    guidance_scale=3						,    source_guidance_scale=1						,    generator=__lowercase						,    output_type='''np'''						,    )
											__lowerCamelCase      :      Tuple         =output.images

											# the values aren't exactly equal, but the images look the same visually
											assert np.abs(image - expected_image			).max() < 5e-1




				def 					__lowercase  (     self  :Any			):
											__lowerCamelCase      :      Tuple         =load_image(
											    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
											    '''/cycle-diffusion/black_colored_car.png'''			)
											__lowerCamelCase      :      List[Any]         =load_numpy(
											    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy'''			)
											__lowerCamelCase      :      Optional[Any]         =init_image.resize((512, 512)			)

											__lowerCamelCase      :      Any         ='''CompVis/stable-diffusion-v1-4'''
											__lowerCamelCase      :      List[Any]         =DDIMScheduler.from_pretrained(__lowercase						,    subfolder='''scheduler'''			)
											__lowerCamelCase      :      str         =CycleDiffusionPipeline.from_pretrained(__lowercase						,    scheduler=__lowercase						,    safety_checker=__lowercase			)

											pipe.to(__lowercase			)
											pipe.set_progress_bar_config(disable=__lowercase			)
											pipe.enable_attention_slicing()

											__lowerCamelCase      :      Any         ='''A black colored car'''
											__lowerCamelCase      :      int         ='''A blue colored car'''

											__lowerCamelCase      :      Tuple         =torch.manual_seed(0			)
											__lowerCamelCase      :      Union[str, Any]         =pipe(
											    prompt=__lowercase						,    source_prompt=__lowercase						,    image=__lowercase						,    num_inference_steps=100						,    eta=0.1						,    strength=0.85						,    guidance_scale=3						,    source_guidance_scale=1						,    generator=__lowercase						,    output_type='''np'''						,    )
											__lowerCamelCase      :      Dict         =output.images

											assert np.abs(image - expected_image			).max() < 2e-2




