


from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch

from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, require_torch

@require_torch
@require_tf
class        __magic_name__	(_lowerCAmelCase   ):




							"""simple docstring"""


							def 					__snake_case      (							self :						Optional[Any]       )			->					Optional[Any]:
														__snake_case	:    Tuple											=     SMALL_MODEL_IDENTIFIER
														__snake_case	:    int											=     "pt"
														__snake_case	:    int											=     "tf"

							def 					__snake_case      (							self :						Any					,			lowerCamelCase :						str       )			->					int:
														__snake_case	:    List[str]											=     AutoModel.from_pretrained(self.test_model       )
														model_pt.save_pretrained(lowerCamelCase       )

							def 					__snake_case      (							self :						str					,			lowerCamelCase :						List[str]       )			->					Tuple:
														__snake_case	:    str											=     TFAutoModel.from_pretrained(self.test_model					,			from_pt=lowerCamelCase       )
														model_tf.save_pretrained(lowerCamelCase       )

							def 					__snake_case      (							self :						Union[str, Any]       )			->					Any:
														__snake_case	:    List[str]											=     "mock_framework"

														# Framework provided - return whatever the user provides
														__snake_case	:    List[str]											=     FeaturesManager.determine_framework(self.test_model					,			lowerCamelCase       )
														self.assertEqual(lowerCamelCase					,			lowerCamelCase       )

														# Local checkpoint and framework provided - return provided framework
														# PyTorch checkpoint
														with TemporaryDirectory() as local_pt_ckpt:
																					self._setup_pt_ckpt(lowerCamelCase       )
																					__snake_case	:    List[Any]											=     FeaturesManager.determine_framework(lowerCamelCase					,			lowerCamelCase       )
																					self.assertEqual(lowerCamelCase					,			lowerCamelCase       )

														# TensorFlow checkpoint
														with TemporaryDirectory() as local_tf_ckpt:
																					self._setup_tf_ckpt(lowerCamelCase       )
																					__snake_case	:    List[Any]											=     FeaturesManager.determine_framework(lowerCamelCase					,			lowerCamelCase       )
																					self.assertEqual(lowerCamelCase					,			lowerCamelCase       )

							def 					__snake_case      (							self :						Tuple       )			->					Union[str, Any]:
														# PyTorch checkpoint
														with TemporaryDirectory() as local_pt_ckpt:
																					self._setup_pt_ckpt(lowerCamelCase       )
																					__snake_case	:    Union[str, Any]											=     FeaturesManager.determine_framework(lowerCamelCase       )
																					self.assertEqual(lowerCamelCase					,			self.framework_pt       )

														# TensorFlow checkpoint
														with TemporaryDirectory() as local_tf_ckpt:
																					self._setup_tf_ckpt(lowerCamelCase       )
																					__snake_case	:    Optional[int]											=     FeaturesManager.determine_framework(lowerCamelCase       )
																					self.assertEqual(lowerCamelCase					,			self.framework_tf       )

														# Invalid local checkpoint
														with TemporaryDirectory() as local_invalid_ckpt:
																					with self.assertRaises(lowerCamelCase       ):
																												__snake_case	:    Optional[Any]											=     FeaturesManager.determine_framework(lowerCamelCase       )






							def 					__snake_case      (							self :						str       )			->					List[Any]:
														__snake_case	:    List[Any]											=     MagicMock(return_value=lowerCamelCase       )
														with patch("transformers.onnx.features.is_tf_available"					,			lowerCamelCase       ):
																					__snake_case	:    Any											=     FeaturesManager.determine_framework(self.test_model       )
																					self.assertEqual(lowerCamelCase					,			self.framework_pt       )

														# PyTorch not in environment -> use TensorFlow
														__snake_case	:    Union[str, Any]											=     MagicMock(return_value=lowerCamelCase       )
														with patch("transformers.onnx.features.is_torch_available"					,			lowerCamelCase       ):
																					__snake_case	:    Dict											=     FeaturesManager.determine_framework(self.test_model       )
																					self.assertEqual(lowerCamelCase					,			self.framework_tf       )

														# Both in environment -> use PyTorch
														__snake_case	:    Optional[int]											=     MagicMock(return_value=lowerCamelCase       )
														__snake_case	:    List[Any]											=     MagicMock(return_value=lowerCamelCase       )
														with patch("transformers.onnx.features.is_tf_available"					,			lowerCamelCase       ), patch(
														    "transformers.onnx.features.is_torch_available"					,			lowerCamelCase       ):
																					__snake_case	:    str											=     FeaturesManager.determine_framework(self.test_model       )
																					self.assertEqual(lowerCamelCase					,			self.framework_pt       )

														# Both not in environment -> raise error
														__snake_case	:    Tuple											=     MagicMock(return_value=lowerCamelCase       )
														__snake_case	:    str											=     MagicMock(return_value=lowerCamelCase       )
														with patch("transformers.onnx.features.is_tf_available"					,			lowerCamelCase       ), patch(
														    "transformers.onnx.features.is_torch_available"					,			lowerCamelCase       ):
																					with self.assertRaises(lowerCamelCase       ):
																												__snake_case	:    List[str]											=     FeaturesManager.determine_framework(self.test_model       )





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        a	(_lowerCAmelCase  ,       _lowerCAmelCase  ,       unittest.TestCase   ):




							"""simple docstring"""


							__UpperCAmelCase					:							Optional[Any]  						=						StableDiffusionXLImgaImgPipeline
							__UpperCAmelCase					:							Any  						=						TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width"}
							__UpperCAmelCase					:							Dict  						=						PipelineTesterMixin.required_optional_params - {"latents"}
							__UpperCAmelCase					:							Union[str, Any]  						=						TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
							__UpperCAmelCase					:							Tuple  						=						IMAGE_TO_IMAGE_IMAGE_PARAMS
							__UpperCAmelCase					:							str  						=						IMAGE_TO_IMAGE_IMAGE_PARAMS

							def 					__snake_case      (							self :						Optional[Any]       )			->					Tuple:
														torch.manual_seed(0       )
														__snake_case	:    Optional[int]											=     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					,			)
														__snake_case	:    Tuple											=     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       )
														__snake_case	:    int											=     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       )
														__snake_case	:    Optional[int]											=     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					,			)
														__snake_case	:    List[str]											=     CLIPTextModel(lowerCamelCase       )
														__snake_case	:    Any											=     CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"					,			local_files_only=lowerCamelCase       )

														__snake_case	:    List[str]											=     CLIPTextModelWithProjection(lowerCamelCase       )
														__snake_case	:    List[Any]											=     CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"					,			local_files_only=lowerCamelCase       )

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

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

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

														__snake_case	:    str											=     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 					__snake_case      (							self :						str       )			->					Optional[Any]:
														super().test_attention_slicing_forward_pass(expected_max_diff=3E-3       )

							def 					__snake_case      (							self :						Any       )			->					Dict:
														super().test_inference_batch_single_identical(expected_max_diff=3E-3       )

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






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

														# forward without prompt embeds
														__snake_case	:    List[str]											=     self.get_dummy_inputs(lowerCamelCase       )
														__snake_case	:    str											=     3 * ["this is a negative prompt"]
														__snake_case	:    Any											=     negative_prompt
														__snake_case	:    Optional[Any]											=     3 * [inputs["prompt"]]

														__snake_case	:    int											=     sd_pipe(**lowerCamelCase       )
														__snake_case	:    List[Any]											=     output.images[0, -3:, -3:, -1]

														# forward with prompt embeds
														__snake_case	:    List[Any]											=     self.get_dummy_inputs(lowerCamelCase       )
														__snake_case	:    Optional[Any]											=     3 * ["this is a negative prompt"]
														__snake_case	:    int											=     3 * [inputs.pop("prompt"       )]

														(
														    (
														    __snake_case
														)				,    (
														    __snake_case
														)				,    (
														    __snake_case
														)				,    (
														    __snake_case
														)				,    
														)	:    Dict											=     sd_pipe.encode_prompt(lowerCamelCase					,			negative_prompt=lowerCamelCase       )

														__snake_case	:    Tuple											=     sd_pipe(
														    **lowerCamelCase					,			prompt_embeds=lowerCamelCase					,			negative_prompt_embeds=lowerCamelCase					,			pooled_prompt_embeds=lowerCamelCase					,			negative_pooled_prompt_embeds=lowerCamelCase					,			)
														__snake_case	:    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        a	(unittest.TestCase   ):




							"""simple docstring"""


							def 					__snake_case      (							self :						Optional[int]       )			->					Dict:
														super().tearDown()
														gc.collect()
														torch.cuda.empty_cache()

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

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

														assert image.shape == (1, 512, 512, 3)
														__snake_case	:    Optional[int]											=     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

