




"""simple docstring"""



import gc
import unittest

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

from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()




class      UpperCAmelCase_  (						__SCREAMING_SNAKE_CASE,       __SCREAMING_SNAKE_CASE,       __SCREAMING_SNAKE_CASE,       unittest.TestCase							):
    lowercase__						 =				AltDiffusionPipeline
    lowercase__						 =				TEXT_TO_IMAGE_PARAMS
    lowercase__						 =				TEXT_TO_IMAGE_BATCH_PARAMS
    lowercase__						 =				TEXT_TO_IMAGE_IMAGE_PARAMS
    lowercase__						 =				TEXT_TO_IMAGE_IMAGE_PARAMS




    def 							__magic_name__							(   self						:	int				)				->							str:




     '''simple docstring'''

     torch.manual_seed(0				)
     A__	=    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     ,			)
     A__	=    DDIMScheduler(
         beta_start=0.0_0085     ,			beta_end=0.012     ,			beta_schedule="scaled_linear"     ,			clip_sample=_snake_case     ,			set_alpha_to_one=_snake_case     ,			)
     torch.manual_seed(0				)
     A__	=    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     ,			)

     # TODO: address the non-deterministic text encoder (fails for save-load tests)
     # torch.manual_seed(0)
     # text_encoder_config = RobertaSeriesConfig(
     #     hidden_size=32,
     #     project_dim=32,
     #     intermediate_size=37,
     #     layer_norm_eps=1e-05,
     #     num_attention_heads=4,
     #     num_hidden_layers=5,
     #     vocab_size=5002,
     # )
     # text_encoder = RobertaSeriesModelWithTransformation(text_encoder_config)

     torch.manual_seed(0				)
     A__	=    CLIPTextConfig(
         bos_token_id=0     ,			eos_token_id=2     ,			hidden_size=32     ,			projection_dim=32     ,			intermediate_size=37     ,			layer_norm_eps=1e-05     ,			num_attention_heads=4     ,			num_hidden_layers=5     ,			pad_token_id=1     ,			vocab_size=5_002     ,			)
     A__	=    CLIPTextModel(_snake_case				)

     A__	=    XLMRobertaTokenizer.from_pretrained("hf-internal-testing/tiny-xlm-roberta"				)
     A__	=    77

     A__	=    {
         "unet": unet,
         "scheduler": scheduler,
         "vae": vae,
         "text_encoder": text_encoder,
         "tokenizer": tokenizer,
         "safety_checker": None,
         "feature_extractor": None,
     }
     return components




    def 							__magic_name__							(   self						:	List[str]     ,			snake_case_						:	Union[str, Any]     ,			snake_case_						:	Tuple=0				)				->							Dict:




     '''simple docstring'''

     if str(_snake_case				).startswith("mps"				):
      A__	=    torch.manual_seed(_snake_case				)
     else:
      A__	=    torch.Generator(device=_snake_case				).manual_seed(_snake_case				)
     A__	=    {
         "prompt": "A painting of a squirrel eating a burger",
         "generator": generator,
         "num_inference_steps": 2,
         "guidance_scale": 6.0,
         "output_type": "numpy",
     }
     return inputs




    def 							__magic_name__							(   self						:	List[Any]				)				->							Tuple:




     '''simple docstring'''

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




    def 							__magic_name__							(   self						:	int				)				->							Optional[Any]:




     '''simple docstring'''

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




    def 							__magic_name__							(   self						:	Tuple				)				->							Optional[Any]:




     '''simple docstring'''

     A__	=    "cpu"  # ensure determinism for the device-dependent torch.Generator

     A__	=    self.get_dummy_components()
     torch.manual_seed(0				)
     A__	=    RobertaSeriesConfig(
         hidden_size=32     ,			project_dim=32     ,			intermediate_size=37     ,			layer_norm_eps=1e-05     ,			num_attention_heads=4     ,			num_hidden_layers=5     ,			vocab_size=5_002     ,			)
     # TODO: remove after fixing the non-deterministic text encoder
     A__	=    RobertaSeriesModelWithTransformation(_snake_case				)
     A__	=    text_encoder

     A__	=    AltDiffusionPipeline(**_snake_case				)
     A__	=    alt_pipe.to(_snake_case				)
     alt_pipe.set_progress_bar_config(disable=_snake_case				)

     A__	=    self.get_dummy_inputs(_snake_case				)
     A__	=    "A photo of an astronaut"
     A__	=    alt_pipe(**_snake_case				)
     A__	=    output.images
     A__	=    image[0, -3:, -3:, -1]

     assert image.shape == (1, 64, 64, 3)
     A__	=    np.array(
         [0.574_8162, 0.6044_7145, 0.4882_1217, 0.5010_0636, 0.543_1185, 0.4576_3683, 0.4965_7696, 0.4813_2733, 0.4757_3093]				)

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




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




     '''simple docstring'''

     A__	=    "cpu"  # ensure determinism for the device-dependent torch.Generator

     A__	=    self.get_dummy_components()
     A__	=    PNDMScheduler(skip_prk_steps=_snake_case				)
     torch.manual_seed(0				)
     A__	=    RobertaSeriesConfig(
         hidden_size=32     ,			project_dim=32     ,			intermediate_size=37     ,			layer_norm_eps=1e-05     ,			num_attention_heads=4     ,			num_hidden_layers=5     ,			vocab_size=5_002     ,			)
     # TODO: remove after fixing the non-deterministic text encoder
     A__	=    RobertaSeriesModelWithTransformation(_snake_case				)
     A__	=    text_encoder
     A__	=    AltDiffusionPipeline(**_snake_case				)
     A__	=    alt_pipe.to(_snake_case				)
     alt_pipe.set_progress_bar_config(disable=_snake_case				)

     A__	=    self.get_dummy_inputs(_snake_case				)
     A__	=    alt_pipe(**_snake_case				)
     A__	=    output.images
     A__	=    image[0, -3:, -3:, -1]

     assert image.shape == (1, 64, 64, 3)
     A__	=    np.array(
         [0.5160_5093, 0.570_7241, 0.4736_5507, 0.5057_8886, 0.563_3877, 0.464_2503, 0.518_2081, 0.4876_3484, 0.4908_4237]				)

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






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




    def 							__magic_name__							(   self						:	Tuple				)				->							Dict:




     '''simple docstring'''

     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()




    def 							__magic_name__							(   self						:	Optional[int]				)				->							List[Any]:




     '''simple docstring'''

     A__	=    AltDiffusionPipeline.from_pretrained("BAAI/AltDiffusion"     ,			safety_checker=_snake_case				)
     A__	=    alt_pipe.to(_snake_case				)
     alt_pipe.set_progress_bar_config(disable=_snake_case				)

     A__	=    "A painting of a squirrel eating a burger"
     A__	=    torch.manual_seed(0				)
     A__	=    alt_pipe([prompt]     ,			generator=_snake_case     ,			guidance_scale=6.0     ,			num_inference_steps=20     ,			output_type="np"				)

     A__	=    output.images

     A__	=    image[0, -3:, -3:, -1]

     assert image.shape == (1, 512, 512, 3)
     A__	=    np.array([0.1010, 0.0800, 0.0794, 0.0885, 0.0843, 0.0762, 0.0769, 0.0729, 0.0586]				)

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




    def 							__magic_name__							(   self						:	int				)				->							Tuple:




     '''simple docstring'''

     A__	=    DDIMScheduler.from_pretrained("BAAI/AltDiffusion"     ,			subfolder="scheduler"				)

     A__	=    AltDiffusionPipeline.from_pretrained("BAAI/AltDiffusion"     ,			scheduler=_snake_case     ,			safety_checker=_snake_case				)
     A__	=    alt_pipe.to(_snake_case				)
     alt_pipe.set_progress_bar_config(disable=_snake_case				)

     A__	=    "A painting of a squirrel eating a burger"
     A__	=    torch.manual_seed(0				)

     A__	=    alt_pipe([prompt]     ,			generator=_snake_case     ,			num_inference_steps=2     ,			output_type="numpy"				)
     A__	=    output.images

     A__	=    image[0, -3:, -3:, -1]

     assert image.shape == (1, 512, 512, 3)
     A__	=    np.array([0.4019, 0.4052, 0.3810, 0.4119, 0.3916, 0.3982, 0.4651, 0.4195, 0.5323]				)

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







"""simple docstring"""



import unittest

from transformers.testing_utils import CaptureStdout
from transformers.tools.python_interpreter import evaluate







def   _SCREAMING_SNAKE_CASE       (							lowercase_						)						->		int:
 return x + 2






class      UpperCAmelCase_  (						unittest.TestCase							):




    def 							__magic_name__							(   self						:	Any				)				->							Any:




     '''simple docstring'''

     A__	=    "x = 3"
     A__	=    {}
     A__	=    evaluate(snake_case_     ,			{}     ,			state=snake_case_				)
     assert result == 3
     self.assertDictEqual(snake_case_     ,			{"x": 3}				)

     A__	=    "x = y"
     A__	=    {"y": 5}
     A__	=    evaluate(snake_case_     ,			{}     ,			state=snake_case_				)
     # evaluate returns the value of the last assignment.
     assert result == 5
     self.assertDictEqual(snake_case_     ,			{"x": 5, "y": 5}				)




    def 							__magic_name__							(   self						:	Optional[Any]				)				->							Any:




     '''simple docstring'''

     A__	=    "y = add_two(x)"
     A__	=    {"x": 3}
     A__	=    evaluate(snake_case_     ,			{"add_two": add_two}     ,			state=snake_case_				)
     assert result == 5
     self.assertDictEqual(snake_case_     ,			{"x": 3, "y": 5}				)

     # Won't work without the tool
     with CaptureStdout() as out:
      A__	=    evaluate(snake_case_     ,			{}     ,			state=snake_case_				)
     assert result is None
     assert "tried to execute add_two" in out.out




    def 							__magic_name__							(   self						:	Dict				)				->							List[str]:




     '''simple docstring'''

     A__	=    "x = 3"
     A__	=    {}
     A__	=    evaluate(snake_case_     ,			{}     ,			state=snake_case_				)
     assert result == 3
     self.assertDictEqual(snake_case_     ,			{"x": 3}				)




    def 							__magic_name__							(   self						:	Dict				)				->							Optional[Any]:




     '''simple docstring'''

     A__	=    "test_dict = {'x': x, 'y': add_two(x)}"
     A__	=    {"x": 3}
     A__	=    evaluate(snake_case_     ,			{"add_two": add_two}     ,			state=snake_case_				)
     self.assertDictEqual(snake_case_     ,			{"x": 3, "y": 5}				)
     self.assertDictEqual(snake_case_     ,			{"x": 3, "test_dict": {"x": 3, "y": 5}}				)




    def 							__magic_name__							(   self						:	int				)				->							str:




     '''simple docstring'''

     A__	=    "x = 3\ny = 5"
     A__	=    {}
     A__	=    evaluate(snake_case_     ,			{}     ,			state=snake_case_				)
     # evaluate returns the value of the last assignment.
     assert result == 5
     self.assertDictEqual(snake_case_     ,			{"x": 3, "y": 5}				)




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




     '''simple docstring'''

     A__	=    "text = f'This is x: {x}.'"
     A__	=    {"x": 3}
     A__	=    evaluate(snake_case_     ,			{}     ,			state=snake_case_				)
     # evaluate returns the value of the last assignment.
     assert result == "This is x: 3."
     self.assertDictEqual(snake_case_     ,			{"x": 3, "text": "This is x: 3."}				)




    def 							__magic_name__							(   self						:	str				)				->							Optional[int]:




     '''simple docstring'''

     A__	=    "if x <= 3:\n    y = 2\nelse:\n    y = 5"
     A__	=    {"x": 3}
     A__	=    evaluate(snake_case_     ,			{}     ,			state=snake_case_				)
     # evaluate returns the value of the last assignment.
     assert result == 2
     self.assertDictEqual(snake_case_     ,			{"x": 3, "y": 2}				)

     A__	=    {"x": 8}
     A__	=    evaluate(snake_case_     ,			{}     ,			state=snake_case_				)
     # evaluate returns the value of the last assignment.
     assert result == 5
     self.assertDictEqual(snake_case_     ,			{"x": 8, "y": 5}				)




    def 							__magic_name__							(   self						:	List[str]				)				->							Tuple:




     '''simple docstring'''

     A__	=    "test_list = [x, add_two(x)]"
     A__	=    {"x": 3}
     A__	=    evaluate(snake_case_     ,			{"add_two": add_two}     ,			state=snake_case_				)
     self.assertListEqual(snake_case_     ,			[3, 5]				)
     self.assertDictEqual(snake_case_     ,			{"x": 3, "test_list": [3, 5]}				)




    def 							__magic_name__							(   self						:	Tuple				)				->							int:




     '''simple docstring'''

     A__	=    "y = x"
     A__	=    {"x": 3}
     A__	=    evaluate(snake_case_     ,			{}     ,			state=snake_case_				)
     assert result == 3
     self.assertDictEqual(snake_case_     ,			{"x": 3, "y": 3}				)




    def 							__magic_name__							(   self						:	str				)				->							Dict:




     '''simple docstring'''

     A__	=    "test_list = [x, add_two(x)]\ntest_list[1]"
     A__	=    {"x": 3}
     A__	=    evaluate(snake_case_     ,			{"add_two": add_two}     ,			state=snake_case_				)
     assert result == 5
     self.assertDictEqual(snake_case_     ,			{"x": 3, "test_list": [3, 5]}				)

     A__	=    "test_dict = {'x': x, 'y': add_two(x)}\ntest_dict['y']"
     A__	=    {"x": 3}
     A__	=    evaluate(snake_case_     ,			{"add_two": add_two}     ,			state=snake_case_				)
     assert result == 5
     self.assertDictEqual(snake_case_     ,			{"x": 3, "test_dict": {"x": 3, "y": 5}}				)




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




     '''simple docstring'''

     A__	=    "x = 0\nfor i in range(3):\n    x = i"
     A__	=    {}
     A__	=    evaluate(snake_case_     ,			{"range": range}     ,			state=snake_case_				)
     assert result == 2
     self.assertDictEqual(snake_case_     ,			{"x": 2, "i": 2}				)





