






"""simple docstring"""
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   SCREAMING_SNAKE_CASE	(					_SCREAMING_SNAKE_CASE							,							_SCREAMING_SNAKE_CASE							,							unittest.TestCase    ):





	"""simple docstring"""




	_A							:					Optional[int] 			=					StableDiffusionXLImgaImgPipeline
	_A							:					List[str] 			=					TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""height""", """width"""}
	_A							:					Optional[int] 			=					PipelineTesterMixin.required_optional_params - {"""latents"""}
	_A							:					List[str] 			=					TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
	_A							:					Optional[Any] 			=					IMAGE_TO_IMAGE_IMAGE_PARAMS
	_A							:					Optional[Any] 			=					IMAGE_TO_IMAGE_IMAGE_PARAMS





	def  lowerCamelCase(self				):
		torch.manual_seed(0				)
		A_		:    Dict						     = 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=lowerCAmelCase_  ,  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_		:    Dict						     = EulerDiscreteScheduler(
		    beta_start=0.00085  ,  beta_end=0.012  ,  steps_offset=1  ,  beta_schedule="""scaled_linear"""  ,  timestep_spacing="""leading"""  ,  )
		torch.manual_seed(0				)
		A_		:    str						     = 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_		:    str						     = 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  ,  hidden_act="""gelu"""  ,  projection_dim=32  ,  )
		A_		:    int						     = CLIPTextModel(lowerCAmelCase_				)
		A_		:    Union[str, Any]						     = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""  ,  local_files_only=lowerCAmelCase_				)

		A_		:    Dict						     = CLIPTextModelWithProjection(lowerCAmelCase_				)
		A_		:    List[Any]						     = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""  ,  local_files_only=lowerCAmelCase_				)

		A_		:    Tuple						     = {
		    """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  lowerCamelCase(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_=0				):
		A_		:    Dict						     = floats_tensor((1, 3, 32, 32)  ,  rng=random.Random(lowerCAmelCase_				)				).to(lowerCAmelCase_				)
		A_		:    Optional[int]						     = image / 2 + 0.5
		if str(lowerCAmelCase_				).startswith("""mps"""				):
			A_		:    Optional[Any]						     = torch.manual_seed(lowerCAmelCase_				)
		else:
			A_		:    List[Any]						     = torch.Generator(device=lowerCAmelCase_				).manual_seed(lowerCAmelCase_				)
		A_		:    Any						     = {
		    """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  lowerCamelCase(self				):
		A_		:    Union[str, Any]						     = """cpu"""  # ensure determinism for the device-dependent torch.Generator
		A_		:    Optional[int]						     = self.get_dummy_components()
		A_		:    Union[str, Any]						     = StableDiffusionXLImgaImgPipeline(**lowerCAmelCase_				)
		A_		:    Any						     = sd_pipe.to(lowerCAmelCase_				)
		sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_				)

		A_		:    Dict						     = self.get_dummy_inputs(lowerCAmelCase_				)
		A_		:    Any						     = sd_pipe(**lowerCAmelCase_				).images
		A_		:    int						     = image[0, -3:, -3:, -1]

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

		A_		:    Optional[int]						     = np.array([0.4656, 0.4840, 0.4439, 0.6698, 0.5574, 0.4524, 0.5799, 0.5943, 0.5165]				)

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





	def  lowerCamelCase(self				):
		super().test_attention_slicing_forward_pass(expected_max_diff=3e-3				)





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





	def  lowerCamelCase(self				):
		pass





	def  lowerCamelCase(self				):
		A_		:    Optional[int]						     = self.get_dummy_components()
		A_		:    List[Any]						     = StableDiffusionXLImgaImgPipeline(**lowerCAmelCase_				)
		A_		:    List[str]						     = sd_pipe.to(lowerCAmelCase_				)
		A_		:    Union[str, Any]						     = sd_pipe.to(lowerCAmelCase_				)
		sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_				)

		# forward without prompt embeds
		A_		:    List[str]						     = self.get_dummy_inputs(lowerCAmelCase_				)
		A_		:    List[str]						     = 3 * ["""this is a negative prompt"""]
		A_		:    Tuple						     = negative_prompt
		A_		:    Dict						     = 3 * [inputs["""prompt"""]]

		A_		:    List[Any]						     = sd_pipe(**lowerCAmelCase_				)
		A_		:    Tuple						     = output.images[0, -3:, -3:, -1]

		# forward with prompt embeds
		A_		:    Optional[int]						     = self.get_dummy_inputs(lowerCAmelCase_				)
		A_		:    Tuple						     = 3 * ["""this is a negative prompt"""]
		A_		:    Tuple						     = 3 * [inputs.pop("""prompt"""				)]

		(
		    (
		    A_
		)     ,  (
		    A_
		)     ,  (
		    A_
		)     ,  (
		    A_
		)     ,  
		)		:    List[str]						     = sd_pipe.encode_prompt(lowerCAmelCase_  ,  negative_prompt=lowerCAmelCase_				)

		A_		:    Union[str, Any]						     = sd_pipe(
		    **lowerCAmelCase_  ,  prompt_embeds=lowerCAmelCase_  ,  negative_prompt_embeds=lowerCAmelCase_  ,  pooled_prompt_embeds=lowerCAmelCase_  ,  negative_pooled_prompt_embeds=lowerCAmelCase_  ,  )
		A_		:    List[str]						     = 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   SCREAMING_SNAKE_CASE	(					unittest.TestCase    ):





	"""simple docstring"""




	def  lowerCamelCase(self				):
		super().tearDown()
		gc.collect()
		torch.cuda.empty_cache()





	def  lowerCamelCase(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_="cpu"  ,  lowerCAmelCase_=torch.floataa  ,  lowerCAmelCase_=0				):
		A_		:    Optional[Any]						     = torch.Generator(device=lowerCAmelCase_				).manual_seed(lowerCAmelCase_				)
		A_		:    Union[str, Any]						     = np.random.RandomState(lowerCAmelCase_				).standard_normal((1, 4, 64, 64)				)
		A_		:    Tuple						     = torch.from_numpy(lowerCAmelCase_				).to(device=lowerCAmelCase_  ,  dtype=lowerCAmelCase_				)
		A_		:    Optional[Any]						     = {
		    """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  lowerCamelCase(self				):
		A_		:    Any						     = DiffusionPipeline.from_pretrained("""stabilityai/stable-diffusion-2-base"""				)
		pipe.to(lowerCAmelCase_				)
		pipe.set_progress_bar_config(disable=lowerCAmelCase_				)

		A_		:    Tuple						     = self.get_inputs(lowerCAmelCase_				)
		A_		:    Union[str, Any]						     = pipe(**lowerCAmelCase_				).images
		A_		:    str						     = image[0, -3:, -3:, -1].flatten()

		assert image.shape == (1, 512, 512, 3)
		A_		:    str						     = np.array([0.49493, 0.47896, 0.40798, 0.54214, 0.53212, 0.48202, 0.47656, 0.46329, 0.48506]				)
		assert np.abs(image_slice - expected_slice				).max() < 7e-3









"""simple docstring"""
import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin



class   SCREAMING_SNAKE_CASE	(					_SCREAMING_SNAKE_CASE							,							_SCREAMING_SNAKE_CASE							,							_SCREAMING_SNAKE_CASE    ):





	"""simple docstring"""




	@register_to_config
	def __init__(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_ = False  ,  ):
		super().__init__()

		A_		:    Tuple						     = nn.Embedding(lowerCAmelCase_  ,  lowerCAmelCase_				)

		A_		:    List[str]						     = nn.Embedding(lowerCAmelCase_  ,  lowerCAmelCase_				)
		A_		:    Any						     = False

		A_		:    Tuple						     = nn.Dropout(p=lowerCAmelCase_				)

		A_		:    List[str]						     = TaConfig(
		    vocab_size=lowerCAmelCase_  ,  d_model=lowerCAmelCase_  ,  num_heads=lowerCAmelCase_  ,  d_kv=lowerCAmelCase_  ,  d_ff=lowerCAmelCase_  ,  dropout_rate=lowerCAmelCase_  ,  feed_forward_proj=lowerCAmelCase_  ,  is_decoder=lowerCAmelCase_  ,  is_encoder_decoder=lowerCAmelCase_  ,  )

		A_		:    Optional[Any]						     = nn.ModuleList()
		for lyr_num in range(lowerCAmelCase_				):
			A_		:    Tuple						     = TaBlock(lowerCAmelCase_				)
			self.encoders.append(lowerCAmelCase_				)

		A_		:    Any						     = TaLayerNorm(lowerCAmelCase_				)
		A_		:    Union[str, Any]						     = nn.Dropout(p=lowerCAmelCase_				)





	def  lowerCamelCase(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_				):
		A_		:    List[Any]						     = self.token_embedder(lowerCAmelCase_				)

		A_		:    Optional[Any]						     = encoder_input_tokens.shape[1]
		A_		:    Any						     = torch.arange(lowerCAmelCase_  ,  device=encoder_input_tokens.device				)
		x += self.position_encoding(lowerCAmelCase_				)

		A_		:    Optional[Any]						     = self.dropout_pre(lowerCAmelCase_				)

		# inverted the attention mask
		A_		:    int						     = encoder_input_tokens.size()
		A_		:    Optional[Any]						     = self.get_extended_attention_mask(lowerCAmelCase_  ,  lowerCAmelCase_				)

		for lyr in self.encoders:
			A_		:    int						     = lyr(lowerCAmelCase_  ,  lowerCAmelCase_				)[0]
		A_		:    List[str]						     = self.layer_norm(lowerCAmelCase_				)

		return self.dropout_post(lowerCAmelCase_				), encoder_inputs_mask

