


import gc
import random
import unittest

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

from diffusers import (
    AutoencoderKL,
    DiffusionPipeline,
    EulerDiscreteScheduler,
    StableDiffusionXLImgaImgPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

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    _UpperCamelCase     (       __snake_case     ,		__snake_case     ,		unittest.TestCase					):

				"""simple docstring"""







				lowerCAmelCase					=			StableDiffusionXLImgaImgPipeline
				lowerCAmelCase					=			TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width'}
				lowerCAmelCase					=			PipelineTesterMixin.required_optional_params - {'latents'}
				lowerCAmelCase					=			TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
				lowerCAmelCase					=			IMAGE_TO_IMAGE_IMAGE_PARAMS
				lowerCAmelCase					=			IMAGE_TO_IMAGE_IMAGE_PARAMS

				def   _UpperCAmelCase       (    self )						->					Any:
					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""")					, attention_head_dim=(2, 4)					, use_linear_projection=a__					, addition_embed_type="""text_time"""					, addition_time_embed_dim=8					, transformer_layers_per_block=(1, 2)					, projection_class_embeddings_input_dim=80					, cross_attention_dim=64					, )
					A             =					EulerDiscreteScheduler(
					    beta_start=0.0_00_85					, beta_end=0.0_12					, steps_offset=1					, beta_schedule="""scaled_linear"""					, timestep_spacing="""leading"""					, )
					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					, sample_size=128					, )
					torch.manual_seed(0 )
					A             =					CLIPTextConfig(
					    bos_token_id=0					, eos_token_id=2					, hidden_size=32					, intermediate_size=37					, layer_norm_eps=1e-05					, num_attention_heads=4					, num_hidden_layers=5					, pad_token_id=1					, vocab_size=1000					, hidden_act="""gelu"""					, projection_dim=32					, )
					A             =					CLIPTextModel(a__ )
					A             =					CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""					, local_files_only=a__ )

					A             =					CLIPTextModelWithProjection(a__ )
					A             =					CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""					, local_files_only=a__ )

					A             =					{
					    """unet""": unet,
					    """scheduler""": scheduler,
					    """vae""": vae,
					    """text_encoder""": text_encoder,
					    """tokenizer""": tokenizer,
					    """text_encoder_2""": text_encoder_a,
					    """tokenizer_2""": tokenizer_a,
					    # "safety_checker": None,
					    # "feature_extractor": None,
					}
					return components

				def   _UpperCAmelCase       (    self					, a__					, a__=0 )						->					List[str]:
					A             =					floats_tensor((1, 3, 32, 32)					, rng=random.Random(a__ ) ).to(a__ )
					A             =					image / 2 + 0.5
					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""",
					    """image""": image,
					    """generator""": generator,
					    """num_inference_steps""": 2,
					    """guidance_scale""": 5.0,
					    """output_type""": """numpy""",
					    """strength""": 0.75,
					}
					return inputs

				def   _UpperCAmelCase       (    self )						->					Optional[Any]:
					A             =					"""cpu"""  # ensure determinism for the device-dependent torch.Generator
					A             =					self.get_dummy_components()
					A             =					StableDiffusionXLImgaImgPipeline(**a__ )
					A             =					sd_pipe.to(a__ )
					sd_pipe.set_progress_bar_config(disable=a__ )

					A             =					self.get_dummy_inputs(a__ )
					A             =					sd_pipe(**a__ ).images
					A             =					image[0, -3:, -3:, -1]

					assert image.shape == (1, 32, 32, 3)

					A             =					np.array([0.46_56, 0.48_40, 0.44_39, 0.66_98, 0.55_74, 0.45_24, 0.57_99, 0.59_43, 0.51_65] )

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

				def   _UpperCAmelCase       (    self )						->					Tuple:
					super().test_attention_slicing_forward_pass(expected_max_diff=3e-3 )

				def   _UpperCAmelCase       (    self )						->					int:
					super().test_inference_batch_single_identical(expected_max_diff=3e-3 )

				def   _UpperCAmelCase       (    self )						->					Optional[int]:
					pass

				def   _UpperCAmelCase       (    self )						->					str:
					A             =					self.get_dummy_components()
					A             =					StableDiffusionXLImgaImgPipeline(**a__ )
					A             =					sd_pipe.to(a__ )
					A             =					sd_pipe.to(a__ )
					sd_pipe.set_progress_bar_config(disable=a__ )

					# forward without prompt embeds
					A             =					self.get_dummy_inputs(a__ )
					A             =					3 * ["""this is a negative prompt"""]
					A             =					negative_prompt
					A             =					3 * [inputs["""prompt"""]]

					A             =					sd_pipe(**a__ )
					A             =					output.images[0, -3:, -3:, -1]

					# forward with prompt embeds
					A             =					self.get_dummy_inputs(a__ )
					A             =					3 * ["""this is a negative prompt"""]
					A             =					3 * [inputs.pop("""prompt""" )]

					(
					    (
					    A 
					)       ,      (
					    A 
					)       ,      (
					    A 
					)       ,      (
					    A 
					)       ,       
					)             =					sd_pipe.encode_prompt(a__					, negative_prompt=a__ )

					A             =					sd_pipe(
					    **a__					, prompt_embeds=a__					, negative_prompt_embeds=a__					, pooled_prompt_embeds=a__					, negative_pooled_prompt_embeds=a__					, )
					A             =					output.images[0, -3:, -3:, -1]

					# make sure that it's equal
					assert np.abs(image_slice_a.flatten() - image_slice_a.flatten() ).max() < 1e-4






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

				"""simple docstring"""







				def   _UpperCAmelCase       (    self )						->					Any:
					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()

				def   _UpperCAmelCase       (    self					, a__					, a__="cpu"					, a__=torch.floataa					, a__=0 )						->					List[Any]:
					A             =					torch.Generator(device=a__ ).manual_seed(a__ )
					A             =					np.random.RandomState(a__ ).standard_normal((1, 4, 64, 64) )
					A             =					torch.from_numpy(a__ ).to(device=a__					, dtype=a__ )
					A             =					{
					    """prompt""": """a photograph of an astronaut riding a horse""",
					    """latents""": latents,
					    """generator""": generator,
					    """num_inference_steps""": 3,
					    """guidance_scale""": 7.5,
					    """output_type""": """numpy""",
					}
					return inputs

				def   _UpperCAmelCase       (    self )						->					Tuple:
					A             =					DiffusionPipeline.from_pretrained("""stabilityai/stable-diffusion-2-base""" )
					pipe.to(a__ )
					pipe.set_progress_bar_config(disable=a__ )

					A             =					self.get_inputs(a__ )
					A             =					pipe(**a__ ).images
					A             =					image[0, -3:, -3:, -1].flatten()

					assert image.shape == (1, 512, 512, 3)
					A             =					np.array([0.4_94_93, 0.4_78_96, 0.4_07_98, 0.5_42_14, 0.5_32_12, 0.4_82_02, 0.4_76_56, 0.4_63_29, 0.4_85_06] )
					assert np.abs(image_slice - expected_slice ).max() < 7e-3





"""simple docstring"""


import os
from collections import namedtuple

import pytest

from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict


A_    							=					namedtuple(
    """_TestCommandArgs""",
    [
        """dataset""",
        """name""",
        """cache_dir""",
        """data_dir""",
        """all_configs""",
        """save_infos""",
        """ignore_verifications""",
        """force_redownload""",
        """clear_cache""",
    ],
    defaults=[None, None, None, False, False, False, False, False],
)






def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__						):
			return (abs(source - target						) / target) < 0.01






@pytest.mark.integration
def  lowercase						(	lowerCAmelCase__						):
			lowerCamelCase_					=  _TestCommandArgs(dataset=lowerCAmelCase__	,all_configs=lowerCAmelCase__	,save_infos=lowerCAmelCase__						)
			lowerCamelCase_					=  TestCommand(*lowerCAmelCase__						)
			test_command.run()
			lowerCamelCase_					=  os.path.join(lowerCAmelCase__	,'''README.md'''						)
			assert os.path.exists(lowerCAmelCase__						)
			lowerCamelCase_					=  DatasetInfosDict.from_directory(lowerCAmelCase__						)
			lowerCamelCase_					=  DatasetInfosDict(
			    {
			        '''default''': DatasetInfo(
			            features=Features(
			                {
			                    '''tokens''': Sequence(Value('''string'''						)						),
			                    '''ner_tags''': Sequence(
			                        ClassLabel(names=['''O''', '''B-PER''', '''I-PER''', '''B-ORG''', '''I-ORG''', '''B-LOC''', '''I-LOC''']						)						),
			                    '''langs''': Sequence(Value('''string'''						)						),
			                    '''spans''': Sequence(Value('''string'''						)						),
			                }						)	,splits=[
			                {
			                    '''name''': '''train''',
			                    '''num_bytes''': 2_351_563,
			                    '''num_examples''': 10_000,
			                },
			                {
			                    '''name''': '''validation''',
			                    '''num_bytes''': 238_418,
			                    '''num_examples''': 1_000,
			                },
			            ]	,download_size=3_940_680	,dataset_size=2_589_981	,)
			    }						)
			assert dataset_infos.keys() == expected_dataset_infos.keys()
			for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
						lowerCamelCase_		, lowerCamelCase_					=  getattr(dataset_infos['''default''']	,lowerCAmelCase__						), getattr(expected_dataset_infos['''default''']	,lowerCAmelCase__						)
						if key == "num_bytes":
									assert is_apercent_close(lowerCAmelCase__	,lowerCAmelCase__						)
						elif key == "splits":
									assert list(lowerCAmelCase__						) == list(lowerCAmelCase__						)
									for split in result:
												assert result[split].name == expected[split].name
												assert result[split].num_examples == expected[split].num_examples
												assert is_apercent_close(result[split].num_bytes	,expected[split].num_bytes						)
						else:
									result == expected



