

import gc
import random
import unittest

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

from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

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 ):
 _UpperCamelCase			  =  CycleDiffusionPipeline
 _UpperCamelCase			  =  TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
     "negative_prompt",
     "height",
     "width",
     "negative_prompt_embeds",
 }
 _UpperCamelCase			  =  PipelineTesterMixin.required_optional_params - {"latents"}
 _UpperCamelCase			  =  TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"""source_prompt"""} )
 _UpperCamelCase			  =  IMAGE_TO_IMAGE_IMAGE_PARAMS
 _UpperCamelCase			  =  IMAGE_TO_IMAGE_IMAGE_PARAMS
 def 	UpperCamelCase__  (							self					)					->List[str]:



       '''simple docstring'''





       torch.manual_seed(0					)
       __lowerCAmelCase					:						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''')      ,							cross_attention_dim=32      ,							)
       __lowerCAmelCase					:						str					=					DDIMScheduler(
           beta_start=0.00_085      ,							beta_end=0.012      ,							beta_schedule='''scaled_linear'''      ,							num_train_timesteps=1000      ,							clip_sample=lowerCamelCase__      ,							set_alpha_to_one=lowerCamelCase__      ,							)
       torch.manual_seed(0					)
       __lowerCAmelCase					:						Union[str, Any]					=					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      ,							)
       torch.manual_seed(0					)
       __lowerCAmelCase					:						Union[str, Any]					=					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      ,							)
       __lowerCAmelCase					:						Tuple					=					CLIPTextModel(lowerCamelCase__					)
       __lowerCAmelCase					:						Dict					=					CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''					)

       __lowerCAmelCase					:						int					=					{
           '''unet''': unet,
           '''scheduler''': scheduler,
           '''vae''': vae,
           '''text_encoder''': text_encoder,
           '''tokenizer''': tokenizer,
           '''safety_checker''': None,
           '''feature_extractor''': None,
       }
       return components
 def 	UpperCamelCase__  (							self      ,							A_      ,							A_=0					)					->Union[str, Any]:



       '''simple docstring'''





       __lowerCAmelCase					:						Optional[Any]					=					floats_tensor((1, 3, 32, 32)      ,							rng=random.Random(lowerCamelCase__					)					).to(lowerCamelCase__					)
       __lowerCAmelCase					:						Tuple					=					image / 2 + 0.5
       if str(lowerCamelCase__					).startswith('''mps'''					):
             __lowerCAmelCase					:						Optional[Any]					=					torch.manual_seed(lowerCamelCase__					)
       else:
             __lowerCAmelCase					:						Any					=					torch.Generator(device=lowerCamelCase__					).manual_seed(lowerCamelCase__					)
       __lowerCAmelCase					:						str					=					{
           '''prompt''': '''An astronaut riding an elephant''',
           '''source_prompt''': '''An astronaut riding a horse''',
           '''image''': image,
           '''generator''': generator,
           '''num_inference_steps''': 2,
           '''eta''': 0.1,
           '''strength''': 0.8,
           '''guidance_scale''': 3,
           '''source_guidance_scale''': 1,
           '''output_type''': '''numpy''',
       }
       return inputs
 def 	UpperCamelCase__  (							self					)					->str:



       '''simple docstring'''





       __lowerCAmelCase					:						int					=					'''cpu'''  # ensure determinism for the device-dependent torch.Generator

       __lowerCAmelCase					:						Optional[Any]					=					self.get_dummy_components()
       __lowerCAmelCase					:						Any					=					CycleDiffusionPipeline(**lowerCamelCase__					)
       __lowerCAmelCase					:						Optional[int]					=					pipe.to(lowerCamelCase__					)
       pipe.set_progress_bar_config(disable=lowerCamelCase__					)

       __lowerCAmelCase					:						Union[str, Any]					=					self.get_dummy_inputs(lowerCamelCase__					)
       __lowerCAmelCase					:						Any					=					pipe(**lowerCamelCase__					)
       __lowerCAmelCase					:						Tuple					=					output.images

       __lowerCAmelCase					:						Union[str, Any]					=					images[0, -3:, -3:, -1]

       assert images.shape == (1, 32, 32, 3)
       __lowerCAmelCase					:						Optional[Any]					=					np.array([0.4_459, 0.4_943, 0.4_544, 0.6_643, 0.5_474, 0.4_327, 0.5_701, 0.5_959, 0.5_179]					)

       assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-2
 @unittest.skipIf(torch_device != '''cuda'''      ,							'''This test requires a GPU'''					)
 def 	UpperCamelCase__  (							self					)					->Tuple:



       '''simple docstring'''





       __lowerCAmelCase					:						List[str]					=					self.get_dummy_components()
       for name, module in components.items():
             if hasattr(lowerCamelCase__      ,							'''half'''					):
                   __lowerCAmelCase					:						Any					=					module.half()
       __lowerCAmelCase					:						Tuple					=					CycleDiffusionPipeline(**lowerCamelCase__					)
       __lowerCAmelCase					:						List[Any]					=					pipe.to(lowerCamelCase__					)
       pipe.set_progress_bar_config(disable=lowerCamelCase__					)

       __lowerCAmelCase					:						Tuple					=					self.get_dummy_inputs(lowerCamelCase__					)
       __lowerCAmelCase					:						Optional[Any]					=					pipe(**lowerCamelCase__					)
       __lowerCAmelCase					:						str					=					output.images

       __lowerCAmelCase					:						Optional[int]					=					images[0, -3:, -3:, -1]

       assert images.shape == (1, 32, 32, 3)
       __lowerCAmelCase					:						List[Any]					=					np.array([0.3_506, 0.4_543, 0.446, 0.4_575, 0.5_195, 0.4_155, 0.5_273, 0.518, 0.4_116]					)

       assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-2
 @skip_mps
 def 	UpperCamelCase__  (							self					)					->Optional[int]:



       '''simple docstring'''





       return super().test_save_load_local()
 @unittest.skip('''non-deterministic pipeline'''					)
 def 	UpperCamelCase__  (							self					)					->List[Any]:



       '''simple docstring'''





       return super().test_inference_batch_single_identical()
 @skip_mps
 def 	UpperCamelCase__  (							self					)					->List[Any]:



       '''simple docstring'''





       return super().test_dict_tuple_outputs_equivalent()
 @skip_mps
 def 	UpperCamelCase__  (							self					)					->Optional[Any]:



       '''simple docstring'''





       return super().test_save_load_optional_components()





 @skip_mps
 def 	UpperCamelCase__  (							self					)					->Optional[Any]:



       '''simple docstring'''





       return super().test_attention_slicing_forward_pass()







@slow
@require_torch_gpu
class   __lowercase      (unittest.TestCase ):
 def 	UpperCamelCase__  (							self					)					->Union[str, Any]:



       '''simple docstring'''





       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()
 def 	UpperCamelCase__  (							self					)					->int:



       '''simple docstring'''





       __lowerCAmelCase					:						Tuple					=					load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/cycle-diffusion/black_colored_car.png'''					)
       __lowerCAmelCase					:						Union[str, Any]					=					load_numpy(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy'''					)
       __lowerCAmelCase					:						Union[str, Any]					=					init_image.resize((512, 512)					)

       __lowerCAmelCase					:						str					=					'''CompVis/stable-diffusion-v1-4'''
       __lowerCAmelCase					:						str					=					DDIMScheduler.from_pretrained(lowerCamelCase__      ,							subfolder='''scheduler'''					)
       __lowerCAmelCase					:						Dict					=					CycleDiffusionPipeline.from_pretrained(
           lowerCamelCase__      ,							scheduler=lowerCamelCase__      ,							safety_checker=lowerCamelCase__      ,							torch_dtype=torch.floataa      ,							revision='''fp16'''					)

       pipe.to(lowerCamelCase__					)
       pipe.set_progress_bar_config(disable=lowerCamelCase__					)
       pipe.enable_attention_slicing()

       __lowerCAmelCase					:						Optional[Any]					=					'''A black colored car'''
       __lowerCAmelCase					:						Tuple					=					'''A blue colored car'''

       __lowerCAmelCase					:						Optional[Any]					=					torch.manual_seed(0					)
       __lowerCAmelCase					:						List[str]					=					pipe(
           prompt=lowerCamelCase__      ,							source_prompt=lowerCamelCase__      ,							image=lowerCamelCase__      ,							num_inference_steps=100      ,							eta=0.1      ,							strength=0.85      ,							guidance_scale=3      ,							source_guidance_scale=1      ,							generator=lowerCamelCase__      ,							output_type='''np'''      ,							)
       __lowerCAmelCase					:						str					=					output.images

       # the values aren't exactly equal, but the images look the same visually
       assert np.abs(image - expected_image					).max() < 5e-1





 def 	UpperCamelCase__  (							self					)					->List[str]:



       '''simple docstring'''





       __lowerCAmelCase					:						List[str]					=					load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/cycle-diffusion/black_colored_car.png'''					)
       __lowerCAmelCase					:						Tuple					=					load_numpy(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy'''					)
       __lowerCAmelCase					:						List[Any]					=					init_image.resize((512, 512)					)

       __lowerCAmelCase					:						Union[str, Any]					=					'''CompVis/stable-diffusion-v1-4'''
       __lowerCAmelCase					:						str					=					DDIMScheduler.from_pretrained(lowerCamelCase__      ,							subfolder='''scheduler'''					)
       __lowerCAmelCase					:						str					=					CycleDiffusionPipeline.from_pretrained(lowerCamelCase__      ,							scheduler=lowerCamelCase__      ,							safety_checker=lowerCamelCase__					)

       pipe.to(lowerCamelCase__					)
       pipe.set_progress_bar_config(disable=lowerCamelCase__					)
       pipe.enable_attention_slicing()

       __lowerCAmelCase					:						Tuple					=					'''A black colored car'''
       __lowerCAmelCase					:						Union[str, Any]					=					'''A blue colored car'''

       __lowerCAmelCase					:						Dict					=					torch.manual_seed(0					)
       __lowerCAmelCase					:						Any					=					pipe(
           prompt=lowerCamelCase__      ,							source_prompt=lowerCamelCase__      ,							image=lowerCamelCase__      ,							num_inference_steps=100      ,							eta=0.1      ,							strength=0.85      ,							guidance_scale=3      ,							source_guidance_scale=1      ,							generator=lowerCamelCase__      ,							output_type='''np'''      ,							)
       __lowerCAmelCase					:						Tuple					=					output.images

       assert np.abs(image - expected_image					).max() < 2e-2









from typing import List

from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE_						   =       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_						   =       {
    """snap-research/efficientformer-l1-300""": (
        """https://huggingface.co/snap-research/efficientformer-l1-300/resolve/main/config.json"""
    ),
}

class 		UpperCamelCase__ (      lowerCAmelCase_   ):



     '''simple docstring'''

     __snake_case    :	int			=			"efficientformer"






     def __init__(							self						:    Optional[int]						,lowerCamelCase__						:    List[int] = [3, 2, 6, 4]						,lowerCamelCase__						:    List[int] = [48, 96, 224, 448]						,lowerCamelCase__						:    List[bool] = [True, True, True, True]						,lowerCamelCase__						:    int = 448						,lowerCamelCase__						:    int = 32						,lowerCamelCase__						:    int = 4						,lowerCamelCase__						:    int = 7						,lowerCamelCase__						:    int = 5						,lowerCamelCase__						:    int = 8						,lowerCamelCase__						:    int = 4						,lowerCamelCase__						:    float = 0.0						,lowerCamelCase__						:    int = 16						,lowerCamelCase__						:    int = 3						,lowerCamelCase__						:    int = 3						,lowerCamelCase__						:    int = 3						,lowerCamelCase__						:    int = 2						,lowerCamelCase__						:    int = 1						,lowerCamelCase__						:    float = 0.0						,lowerCamelCase__						:    int = 1						,lowerCamelCase__						:    bool = True						,lowerCamelCase__						:    bool = True						,lowerCamelCase__						:    float = 1e-5						,lowerCamelCase__						:    str = "gelu"						,lowerCamelCase__						:    float = 0.02						,lowerCamelCase__						:    float = 1e-1_2						,lowerCamelCase__						:    int = 224						,lowerCamelCase__						:    float = 1e-0_5						,**lowerCamelCase__						:    str						,)     ->		None:




           '''simple docstring'''


           super().__init__(**lowerCamelCase__       )

           SCREAMING_SNAKE_CASE		=						hidden_act
           SCREAMING_SNAKE_CASE		=						hidden_dropout_prob
           SCREAMING_SNAKE_CASE		=						hidden_sizes
           SCREAMING_SNAKE_CASE		=						num_hidden_layers
           SCREAMING_SNAKE_CASE		=						num_attention_heads
           SCREAMING_SNAKE_CASE		=						initializer_range
           SCREAMING_SNAKE_CASE		=						layer_norm_eps
           SCREAMING_SNAKE_CASE		=						patch_size
           SCREAMING_SNAKE_CASE		=						num_channels
           SCREAMING_SNAKE_CASE		=						depths
           SCREAMING_SNAKE_CASE		=						mlp_expansion_ratio
           SCREAMING_SNAKE_CASE		=						downsamples
           SCREAMING_SNAKE_CASE		=						dim
           SCREAMING_SNAKE_CASE		=						key_dim
           SCREAMING_SNAKE_CASE		=						attention_ratio
           SCREAMING_SNAKE_CASE		=						resolution
           SCREAMING_SNAKE_CASE		=						pool_size
           SCREAMING_SNAKE_CASE		=						downsample_patch_size
           SCREAMING_SNAKE_CASE		=						downsample_stride
           SCREAMING_SNAKE_CASE		=						downsample_pad
           SCREAMING_SNAKE_CASE		=						drop_path_rate
           SCREAMING_SNAKE_CASE		=						num_metaad_blocks
           SCREAMING_SNAKE_CASE		=						distillation
           SCREAMING_SNAKE_CASE		=						use_layer_scale
           SCREAMING_SNAKE_CASE		=						layer_scale_init_value
           SCREAMING_SNAKE_CASE		=						image_size
           SCREAMING_SNAKE_CASE		=						batch_norm_eps


