



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			(       lowercase__			,      lowercase__			,      lowercase__			,      unittest.TestCase     ):
     UpperCamelCase__		    =				AltDiffusionPipeline
     UpperCamelCase__		    =				TEXT_TO_IMAGE_PARAMS
     UpperCamelCase__		    =				TEXT_TO_IMAGE_BATCH_PARAMS
     UpperCamelCase__		    =				TEXT_TO_IMAGE_IMAGE_PARAMS
     UpperCamelCase__		    =				TEXT_TO_IMAGE_IMAGE_PARAMS




     def 		snake_case_		(				self):
         torch.manual_seed(0)
         A__		 =					UNetaDConditionModel(
             block_out_channels=(3_2, 6_4) ,						layers_per_block=2 ,						sample_size=3_2 ,						in_channels=4 ,						out_channels=4 ,						down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''') ,						up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''') ,						cross_attention_dim=3_2 ,						)
         A__		 =					DDIMScheduler(
             beta_start=0.0_0_0_8_5 ,						beta_end=0.0_1_2 ,						beta_schedule='''scaled_linear''' ,						clip_sample=a__ ,						set_alpha_to_one=a__ ,						)
         torch.manual_seed(0)
         A__		 =					AutoencoderKL(
             block_out_channels=[3_2, 6_4] ,						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=3_2 ,						projection_dim=3_2 ,						intermediate_size=3_7 ,						layer_norm_eps=1e-05 ,						num_attention_heads=4 ,						num_hidden_layers=5 ,						pad_token_id=1 ,						vocab_size=5_0_0_2 ,						)
         A__		 =					CLIPTextModel(a__)

         A__		 =					XLMRobertaTokenizer.from_pretrained('''hf-internal-testing/tiny-xlm-roberta''')
         A__		 =					7_7

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




     def 		snake_case_		(				self ,						a__ ,						a__=0):
         if str(a__).startswith('''mps'''):
             A__		 =					torch.manual_seed(a__)
         else:
             A__		 =					torch.Generator(device=a__).manual_seed(a__)
         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 		snake_case_		(				self):
         super().test_attention_slicing_forward_pass(expected_max_diff=3e-3)




     def 		snake_case_		(				self):
         super().test_inference_batch_single_identical(expected_max_diff=3e-3)




     def 		snake_case_		(				self):
         A__		 =					'''cpu'''  # ensure determinism for the device-dependent torch.Generator

         A__		 =					self.get_dummy_components()
         torch.manual_seed(0)
         A__		 =					RobertaSeriesConfig(
             hidden_size=3_2 ,						project_dim=3_2 ,						intermediate_size=3_7 ,						layer_norm_eps=1e-05 ,						num_attention_heads=4 ,						num_hidden_layers=5 ,						vocab_size=5_0_0_2 ,						)
         # TODO: remove after fixing the non-deterministic text encoder
         A__		 =					RobertaSeriesModelWithTransformation(a__)
         A__		 =					text_encoder

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

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

         assert image.shape == (1, 6_4, 6_4, 3)
         A__		 =					np.array(
             [0.5_7_4_8_1_6_2, 0.6_0_4_4_7_1_4_5, 0.4_8_8_2_1_2_1_7, 0.5_0_1_0_0_6_3_6, 0.5_4_3_1_1_8_5, 0.4_5_7_6_3_6_8_3, 0.4_9_6_5_7_6_9_6, 0.4_8_1_3_2_7_3_3, 0.4_7_5_7_3_0_9_3])

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




     def 		snake_case_		(				self):
         A__		 =					'''cpu'''  # ensure determinism for the device-dependent torch.Generator

         A__		 =					self.get_dummy_components()
         A__		 =					PNDMScheduler(skip_prk_steps=a__)
         torch.manual_seed(0)
         A__		 =					RobertaSeriesConfig(
             hidden_size=3_2 ,						project_dim=3_2 ,						intermediate_size=3_7 ,						layer_norm_eps=1e-05 ,						num_attention_heads=4 ,						num_hidden_layers=5 ,						vocab_size=5_0_0_2 ,						)
         # TODO: remove after fixing the non-deterministic text encoder
         A__		 =					RobertaSeriesModelWithTransformation(a__)
         A__		 =					text_encoder
         A__		 =					AltDiffusionPipeline(**a__)
         A__		 =					alt_pipe.to(a__)
         alt_pipe.set_progress_bar_config(disable=a__)

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

         assert image.shape == (1, 6_4, 6_4, 3)
         A__		 =					np.array(
             [0.5_1_6_0_5_0_9_3, 0.5_7_0_7_2_4_1, 0.4_7_3_6_5_5_0_7, 0.5_0_5_7_8_8_8_6, 0.5_6_3_3_8_7_7, 0.4_6_4_2_5_0_3, 0.5_1_8_2_0_8_1, 0.4_8_7_6_3_4_8_4, 0.4_9_0_8_4_2_3_7])

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







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




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




     def 		snake_case_		(				self):
         # make sure here that pndm scheduler skips prk
         A__		 =					AltDiffusionPipeline.from_pretrained('''BAAI/AltDiffusion''' ,						safety_checker=a__)
         A__		 =					alt_pipe.to(a__)
         alt_pipe.set_progress_bar_config(disable=a__)

         A__		 =					'''A painting of a squirrel eating a burger'''
         A__		 =					torch.manual_seed(0)
         A__		 =					alt_pipe([prompt] ,						generator=a__ ,						guidance_scale=6.0 ,						num_inference_steps=2_0 ,						output_type='''np''')

         A__		 =					output.images

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

         assert image.shape == (1, 5_1_2, 5_1_2, 3)
         A__		 =					np.array([0.1_0_1_0, 0.0_8_0_0, 0.0_7_9_4, 0.0_8_8_5, 0.0_8_4_3, 0.0_7_6_2, 0.0_7_6_9, 0.0_7_2_9, 0.0_5_8_6])

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




     def 		snake_case_		(				self):
         A__		 =					DDIMScheduler.from_pretrained('''BAAI/AltDiffusion''' ,						subfolder='''scheduler''')

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

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

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

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

         assert image.shape == (1, 5_1_2, 5_1_2, 3)
         A__		 =					np.array([0.4_0_1_9, 0.4_0_5_2, 0.3_8_1_0, 0.4_1_1_9, 0.3_9_1_6, 0.3_9_8_2, 0.4_6_5_1, 0.4_1_9_5, 0.5_3_2_3])

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





def 		lowerCAmelCase__    (					UpperCamelCase_				:       float ,       UpperCamelCase_				:       int	)->  float:
    if digit_amount > 0:
        return round(number - int(UpperCamelCase_	) ,       UpperCamelCase_	)
    return number - int(UpperCamelCase_	)


if __name__ == "__main__":
   print(decimal_isolate(1.53, 0))
   print(decimal_isolate(35.345, 1))
   print(decimal_isolate(35.345, 2))
   print(decimal_isolate(35.345, 3))
   print(decimal_isolate(-14.789, 3))
   print(decimal_isolate(0, 2))
   print(decimal_isolate(-14.123, 1))
   print(decimal_isolate(-14.123, 2))
   print(decimal_isolate(-14.123, 3))
