
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 						lowercase_				(				lowerCamelCase_							,		lowerCamelCase_							,		unittest.TestCase ):
 A__  : Optional[Any]      					=			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__  : int      					=			IMAGE_TO_IMAGE_IMAGE_PARAMS
 A__  : str      					=			IMAGE_TO_IMAGE_IMAGE_PARAMS

 def lowerCamelCase_ (					self   ):




        """simple docstring"""


        torch.manual_seed(0   )
        UpperCamelCase_											=				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""")   ,		attention_head_dim=(2, 4)   ,		use_linear_projection=__snake_case   ,		addition_embed_type="""text_time"""   ,		addition_time_embed_dim=8   ,		transformer_layers_per_block=(1, 2)   ,		projection_class_embeddings_input_dim=8_0   ,		cross_attention_dim=6_4   ,		)
        UpperCamelCase_											=				EulerDiscreteScheduler(
            beta_start=0.00_085   ,		beta_end=0.012   ,		steps_offset=1   ,		beta_schedule="""scaled_linear"""   ,		timestep_spacing="""leading"""   ,		)
        torch.manual_seed(0   )
        UpperCamelCase_											=				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   ,		sample_size=1_2_8   ,		)
        torch.manual_seed(0   )
        UpperCamelCase_											=				CLIPTextConfig(
            bos_token_id=0   ,		eos_token_id=2   ,		hidden_size=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=1_0_0_0   ,		hidden_act="""gelu"""   ,		projection_dim=3_2   ,		)
        UpperCamelCase_											=				CLIPTextModel(__snake_case   )
        UpperCamelCase_											=				CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""   ,		local_files_only=__snake_case   )

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

        UpperCamelCase_											=				{
            """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   ,		__UpperCamelCase   ,		__UpperCamelCase=0   ):




        """simple docstring"""


        UpperCamelCase_											=				floats_tensor((1, 3, 3_2, 3_2)   ,		rng=random.Random(__snake_case   )   ).to(__snake_case   )
        UpperCamelCase_											=				image / 2 + 0.5
        if str(__snake_case   ).startswith("""mps"""   ):
               UpperCamelCase_											=				torch.manual_seed(__snake_case   )
        else:
               UpperCamelCase_											=				torch.Generator(device=__snake_case   ).manual_seed(__snake_case   )
        UpperCamelCase_											=				{
            """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   ):




        """simple docstring"""


        UpperCamelCase_											=				"""cpu"""  # ensure determinism for the device-dependent torch.Generator
        UpperCamelCase_											=				self.get_dummy_components()
        UpperCamelCase_											=				StableDiffusionXLImgaImgPipeline(**__snake_case   )
        UpperCamelCase_											=				sd_pipe.to(__snake_case   )
        sd_pipe.set_progress_bar_config(disable=__snake_case   )

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

        assert image.shape == (1, 3_2, 3_2, 3)

        UpperCamelCase_											=				np.array([0.4_656, 0.4_840, 0.4_439, 0.6_698, 0.5_574, 0.4_524, 0.5_799, 0.5_943, 0.5_165]   )

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

 def lowerCamelCase_ (					self   ):




        """simple docstring"""


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

 def lowerCamelCase_ (					self   ):




        """simple docstring"""


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

 def lowerCamelCase_ (					self   ):




        """simple docstring"""


        pass

 def lowerCamelCase_ (					self   ):




        """simple docstring"""


        UpperCamelCase_											=				self.get_dummy_components()
        UpperCamelCase_											=				StableDiffusionXLImgaImgPipeline(**__snake_case   )
        UpperCamelCase_											=				sd_pipe.to(__snake_case   )
        UpperCamelCase_											=				sd_pipe.to(__snake_case   )
        sd_pipe.set_progress_bar_config(disable=__snake_case   )

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

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

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

        (
            (
            UpperCamelCase_					
        )				, (
            UpperCamelCase_					
        )				, (
            UpperCamelCase_					
        )				, (
            UpperCamelCase_					
        )				, 					
        )											=				sd_pipe.encode_prompt(__snake_case   ,		negative_prompt=__snake_case   )

        UpperCamelCase_											=				sd_pipe(
            **__snake_case   ,		prompt_embeds=__snake_case   ,		negative_prompt_embeds=__snake_case   ,		pooled_prompt_embeds=__snake_case   ,		negative_pooled_prompt_embeds=__snake_case   ,		)
        UpperCamelCase_											=				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 						lowercase_				(				unittest.TestCase ):

 def lowerCamelCase_ (					self   ):




        """simple docstring"""


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

 def lowerCamelCase_ (					self   ,		__UpperCamelCase   ,		__UpperCamelCase="cpu"   ,		__UpperCamelCase=torch.floataa   ,		__UpperCamelCase=0   ):




        """simple docstring"""


        UpperCamelCase_											=				torch.Generator(device=__snake_case   ).manual_seed(__snake_case   )
        UpperCamelCase_											=				np.random.RandomState(__snake_case   ).standard_normal((1, 4, 6_4, 6_4)   )
        UpperCamelCase_											=				torch.from_numpy(__snake_case   ).to(device=__snake_case   ,		dtype=__snake_case   )
        UpperCamelCase_											=				{
            """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   ):




        """simple docstring"""


        UpperCamelCase_											=				DiffusionPipeline.from_pretrained("""stabilityai/stable-diffusion-2-base"""   )
        pipe.to(__snake_case   )
        pipe.set_progress_bar_config(disable=__snake_case   )

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

        assert image.shape == (1, 5_1_2, 5_1_2, 3)
        UpperCamelCase_											=				np.array([0.49_493, 0.47_896, 0.40_798, 0.54_214, 0.53_212, 0.48_202, 0.47_656, 0.46_329, 0.48_506]   )
        assert np.abs(image_slice - expected_slice   ).max() < 7e-3







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


snake_case							:       str     			=      {
    '''configuration_rag''': ['''RagConfig'''],
    '''retrieval_rag''': ['''RagRetriever'''],
    '''tokenization_rag''': ['''RagTokenizer'''],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						snake_case							:       Any     			=      [
						    '''RagModel''',
						    '''RagPreTrainedModel''',
						    '''RagSequenceForGeneration''',
						    '''RagTokenForGeneration''',
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						snake_case							:       Optional[int]     			=      [
						    '''TFRagModel''',
						    '''TFRagPreTrainedModel''',
						    '''TFRagSequenceForGeneration''',
						    '''TFRagTokenForGeneration''',
						]


if TYPE_CHECKING:
						from .configuration_rag import RagConfig
						from .retrieval_rag import RagRetriever
						from .tokenization_rag import RagTokenizer

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_rag import (
												    TFRagModel,
												    TFRagPreTrainedModel,
												    TFRagSequenceForGeneration,
												    TFRagTokenForGeneration,
												)

else:
						import sys

						snake_case							:       int     			=      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




