






'''simple docstring'''



import copy
import os
from typing import TYPE_CHECKING, List, Union


if TYPE_CHECKING:
 pass

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


__UpperCamelCase      :       List[str]      			=      logging.get_logger(__name__)

__UpperCamelCase      :       List[Any]      			=      {
    """kakaobrain/align-base""": """https://huggingface.co/kakaobrain/align-base/resolve/main/config.json""",
}
class 							__SCREAMING_SNAKE_CASE       (							_lowerCAmelCase     ):
      __a  					="align_text_model"




      def __init__(  self ,   lowerCamelCase=3_0522 ,   lowerCamelCase=768 ,   lowerCamelCase=12 ,   lowerCamelCase=12 ,   lowerCamelCase=3072 ,   lowerCamelCase="gelu" ,   lowerCamelCase=0.1 ,   lowerCamelCase=0.1 ,   lowerCamelCase=512 ,   lowerCamelCase=2 ,   lowerCamelCase=0.02 ,   lowerCamelCase=1e-12 ,   lowerCamelCase=0 ,   lowerCamelCase="absolute" ,   lowerCamelCase=True ,   **lowerCamelCase ,   )							->List[Any]:



         '''simple docstring'''
         super().__init__(**lowerCamelCase      )

         __a				     =    vocab_size
         __a				     =    hidden_size
         __a				     =    num_hidden_layers
         __a				     =    num_attention_heads
         __a				     =    hidden_act
         __a				     =    intermediate_size
         __a				     =    hidden_dropout_prob
         __a				     =    attention_probs_dropout_prob
         __a				     =    max_position_embeddings
         __a				     =    type_vocab_size
         __a				     =    initializer_range
         __a				     =    layer_norm_eps
         __a				     =    position_embedding_type
         __a				     =    use_cache
         __a				     =    pad_token_id






      @classmethod
      def  __UpperCamelCase							(  cls ,   lowerCamelCase ,   **lowerCamelCase      )							->"PretrainedConfig":



         '''simple docstring'''
         cls._set_token_in_kwargs(lowerCamelCase      )

         __a     ,			__a				     =    cls.get_config_dict(lowerCamelCase ,   **lowerCamelCase      )

         # get the text config dict if we are loading from AlignConfig
         if config_dict.get('model_type'      ) == "align":
            __a				     =    config_dict['text_config']

         if "model_type" in config_dict and hasattr(cls ,   'model_type'      ) and config_dict["model_type"] != cls.model_type:
            logger.warning(
                F"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
                F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""      )

         return cls.from_dict(lowerCamelCase ,   **lowerCamelCase      )







class 							__SCREAMING_SNAKE_CASE       (							_lowerCAmelCase     ):
      __a  					="align_vision_model"




      def __init__(  self ,   lowerCamelCase = 3 ,   lowerCamelCase = 600 ,   lowerCamelCase = 2.0 ,   lowerCamelCase = 3.1 ,   lowerCamelCase = 8 ,   lowerCamelCase = [3, 3, 5, 3, 5, 5, 3] ,   lowerCamelCase = [32, 16, 24, 40, 80, 112, 192] ,   lowerCamelCase = [16, 24, 40, 80, 112, 192, 320] ,   lowerCamelCase = [] ,   lowerCamelCase = [1, 2, 2, 2, 1, 2, 1] ,   lowerCamelCase = [1, 2, 2, 3, 3, 4, 1] ,   lowerCamelCase = [1, 6, 6, 6, 6, 6, 6] ,   lowerCamelCase = 0.25 ,   lowerCamelCase = "swish" ,   lowerCamelCase = 2560 ,   lowerCamelCase = "mean" ,   lowerCamelCase = 0.02 ,   lowerCamelCase = 0.001 ,   lowerCamelCase = 0.99 ,   lowerCamelCase = 0.2 ,   **lowerCamelCase ,   )							->Optional[int]:



         '''simple docstring'''
         super().__init__(**lowerCamelCase      )

         __a				     =    num_channels
         __a				     =    image_size
         __a				     =    width_coefficient
         __a				     =    depth_coefficient
         __a				     =    depth_divisor
         __a				     =    kernel_sizes
         __a				     =    in_channels
         __a				     =    out_channels
         __a				     =    depthwise_padding
         __a				     =    strides
         __a				     =    num_block_repeats
         __a				     =    expand_ratios
         __a				     =    squeeze_expansion_ratio
         __a				     =    hidden_act
         __a				     =    hidden_dim
         __a				     =    pooling_type
         __a				     =    initializer_range
         __a				     =    batch_norm_eps
         __a				     =    batch_norm_momentum
         __a				     =    drop_connect_rate
         __a				     =    sum(lowerCamelCase      ) * 4






      @classmethod
      def  __UpperCamelCase							(  cls ,   lowerCamelCase ,   **lowerCamelCase      )							->"PretrainedConfig":



         '''simple docstring'''
         cls._set_token_in_kwargs(lowerCamelCase      )

         __a     ,			__a				     =    cls.get_config_dict(lowerCamelCase ,   **lowerCamelCase      )

         # get the vision config dict if we are loading from AlignConfig
         if config_dict.get('model_type'      ) == "align":
            __a				     =    config_dict['vision_config']

         if "model_type" in config_dict and hasattr(cls ,   'model_type'      ) and config_dict["model_type"] != cls.model_type:
            logger.warning(
                F"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
                F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""      )

         return cls.from_dict(lowerCamelCase ,   **lowerCamelCase      )







class 							__SCREAMING_SNAKE_CASE       (							_lowerCAmelCase     ):
      __a  					="align"
      __a  					=True




      def __init__(  self ,   lowerCamelCase=None ,   lowerCamelCase=None ,   lowerCamelCase=640 ,   lowerCamelCase=1.0 ,   lowerCamelCase=0.02 ,   **lowerCamelCase ,   )							->List[str]:



         '''simple docstring'''
         super().__init__(**lowerCamelCase      )

         if text_config is None:
            __a				     =    {}
            logger.info('text_config is None. Initializing the AlignTextConfig with default values.'      )

         if vision_config is None:
            __a				     =    {}
            logger.info('vision_config is None. Initializing the AlignVisionConfig with default values.'      )

         __a				     =    AlignTextConfig(**lowerCamelCase      )
         __a				     =    AlignVisionConfig(**lowerCamelCase      )

         __a				     =    projection_dim
         __a				     =    temperature_init_value
         __a				     =    initializer_range




      @classmethod
      def  __UpperCamelCase							(  cls ,   lowerCamelCase ,   lowerCamelCase ,   **lowerCamelCase      )							->List[str]:



         '''simple docstring'''
         return cls(text_config=text_config.to_dict() ,   vision_config=vision_config.to_dict() ,   **lowerCamelCase      )






      def  __UpperCamelCase							(  self      )							->int:



         '''simple docstring'''
         __a				     =    copy.deepcopy(self.__dict__      )
         __a				     =    self.text_config.to_dict()
         __a				     =    self.vision_config.to_dict()
         __a				     =    self.__class__.model_type
         return output







'''simple docstring'''



import gc
import random
import unittest

import torch

from diffusers import (
    IFImgaImgPipeline,
    IFImgaImgSuperResolutionPipeline,
    IFInpaintingPipeline,
    IFInpaintingSuperResolutionPipeline,
    IFPipeline,
    IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import floats_tensor, load_numpy, require_torch_gpu, skip_mps, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
from . import IFPipelineTesterMixin
@skip_mps
class 							__SCREAMING_SNAKE_CASE       (							_lowerCAmelCase      ,      _lowerCAmelCase      ,      unittest.TestCase     ):
      __a  					=IFPipeline
      __a  					=TEXT_TO_IMAGE_PARAMS - {"width", "height", "latents"}
      __a  					=TEXT_TO_IMAGE_BATCH_PARAMS
      __a  					=PipelineTesterMixin.required_optional_params - {"latents"}




      def  __UpperCamelCase							(  self      )							->int:



         '''simple docstring'''
         return self._get_dummy_components()




      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase=0      )							->Tuple:



         '''simple docstring'''
         if str(lowerCamelCase      ).startswith('mps'      ):
            __a				     =    torch.manual_seed(lowerCamelCase      )
         else:
            __a				     =    torch.Generator(device=lowerCamelCase      ).manual_seed(lowerCamelCase      )

         __a				     =    {
             'prompt': 'A painting of a squirrel eating a burger',
             'generator': generator,
             'num_inference_steps': 2,
             'output_type': 'numpy',
         }

         return inputs




      def  __UpperCamelCase							(  self      )							->Optional[Any]:



         '''simple docstring'''
         self._test_save_load_optional_components()




      @unittest.skipIf(torch_device != 'cuda' ,   reason='float16 requires CUDA'      )
      def  __UpperCamelCase							(  self      )							->Optional[Any]:



         '''simple docstring'''
         # Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
         super().test_save_load_floataa(expected_max_diff=1e-1      )




      def  __UpperCamelCase							(  self      )							->Optional[int]:



         '''simple docstring'''
         self._test_attention_slicing_forward_pass(expected_max_diff=1e-2      )




      def  __UpperCamelCase							(  self      )							->Any:



         '''simple docstring'''
         self._test_save_load_local()




      def  __UpperCamelCase							(  self      )							->Tuple:



         '''simple docstring'''
         self._test_inference_batch_single_identical(
             expected_max_diff=1e-2 ,   )






      @unittest.skipIf(
          torch_device != 'cuda' or not is_xformers_available() ,   reason='XFormers attention is only available with CUDA and `xformers` installed' ,   )
      def  __UpperCamelCase							(  self      )							->List[str]:



         '''simple docstring'''
         self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3      )







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




      def  __UpperCamelCase							(  self      )							->Optional[Any]:



         '''simple docstring'''
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()




      def  __UpperCamelCase							(  self      )							->Any:



         '''simple docstring'''
         # if

         __a				     =    IFPipeline.from_pretrained('DeepFloyd/IF-I-XL-v1.0' ,   variant='fp16' ,   torch_dtype=torch.floataa      )

         __a				     =    IFSuperResolutionPipeline.from_pretrained(
             'DeepFloyd/IF-II-L-v1.0' ,   variant='fp16' ,   torch_dtype=torch.floataa ,   text_encoder=lowerCamelCase ,   tokenizer=lowerCamelCase      )

         # pre compute text embeddings and remove T5 to save memory

         pipe_a.text_encoder.to('cuda'      )

         __a     ,			__a				     =    pipe_a.encode_prompt('anime turtle' ,   device='cuda'      )

         del pipe_a.tokenizer
         del pipe_a.text_encoder
         gc.collect()

         __a				     =    None
         __a				     =    None

         pipe_a.enable_model_cpu_offload()
         pipe_a.enable_model_cpu_offload()

         pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()      )
         pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()      )

         self._test_if(lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase      )

         pipe_a.remove_all_hooks()
         pipe_a.remove_all_hooks()

         # img2img

         __a				     =    IFImgaImgPipeline(**pipe_a.components      )
         __a				     =    IFImgaImgSuperResolutionPipeline(**pipe_a.components      )

         pipe_a.enable_model_cpu_offload()
         pipe_a.enable_model_cpu_offload()

         pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()      )
         pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()      )

         self._test_if_imgaimg(lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase      )

         pipe_a.remove_all_hooks()
         pipe_a.remove_all_hooks()

         # inpainting

         __a				     =    IFInpaintingPipeline(**pipe_a.components      )
         __a				     =    IFInpaintingSuperResolutionPipeline(**pipe_a.components      )

         pipe_a.enable_model_cpu_offload()
         pipe_a.enable_model_cpu_offload()

         pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()      )
         pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()      )

         self._test_if_inpainting(lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase      )




      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase      )							->List[Any]:



         '''simple docstring'''
         # pipeline 1

         _start_torch_memory_measurement()

         __a				     =    torch.Generator(device='cpu'      ).manual_seed(0      )
         __a				     =    pipe_a(
             prompt_embeds=lowerCamelCase ,   negative_prompt_embeds=lowerCamelCase ,   num_inference_steps=2 ,   generator=lowerCamelCase ,   output_type='np' ,   )

         __a				     =    output.images[0]

         assert image.shape == (64, 64, 3)

         __a				     =    torch.cuda.max_memory_allocated()
         assert mem_bytes < 13 * 10**9

         __a				     =    load_numpy(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if.npy'      )
         assert_mean_pixel_difference(lowerCamelCase ,   lowerCamelCase      )

         # pipeline 2

         _start_torch_memory_measurement()

         __a				     =    torch.Generator(device='cpu'      ).manual_seed(0      )

         __a				     =    floats_tensor((1, 3, 64, 64) ,   rng=random.Random(0      )      ).to(lowerCamelCase      )

         __a				     =    pipe_a(
             prompt_embeds=lowerCamelCase ,   negative_prompt_embeds=lowerCamelCase ,   image=lowerCamelCase ,   generator=lowerCamelCase ,   num_inference_steps=2 ,   output_type='np' ,   )

         __a				     =    output.images[0]

         assert image.shape == (256, 256, 3)

         __a				     =    torch.cuda.max_memory_allocated()
         assert mem_bytes < 4 * 10**9

         __a				     =    load_numpy(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_superresolution_stage_II.npy'      )
         assert_mean_pixel_difference(lowerCamelCase ,   lowerCamelCase      )




      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase      )							->Any:



         '''simple docstring'''
         # pipeline 1

         _start_torch_memory_measurement()

         __a				     =    floats_tensor((1, 3, 64, 64) ,   rng=random.Random(0      )      ).to(lowerCamelCase      )

         __a				     =    torch.Generator(device='cpu'      ).manual_seed(0      )

         __a				     =    pipe_a(
             prompt_embeds=lowerCamelCase ,   negative_prompt_embeds=lowerCamelCase ,   image=lowerCamelCase ,   num_inference_steps=2 ,   generator=lowerCamelCase ,   output_type='np' ,   )

         __a				     =    output.images[0]

         assert image.shape == (64, 64, 3)

         __a				     =    torch.cuda.max_memory_allocated()
         assert mem_bytes < 10 * 10**9

         __a				     =    load_numpy(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img.npy'      )
         assert_mean_pixel_difference(lowerCamelCase ,   lowerCamelCase      )

         # pipeline 2

         _start_torch_memory_measurement()

         __a				     =    torch.Generator(device='cpu'      ).manual_seed(0      )

         __a				     =    floats_tensor((1, 3, 256, 256) ,   rng=random.Random(0      )      ).to(lowerCamelCase      )
         __a				     =    floats_tensor((1, 3, 64, 64) ,   rng=random.Random(0      )      ).to(lowerCamelCase      )

         __a				     =    pipe_a(
             prompt_embeds=lowerCamelCase ,   negative_prompt_embeds=lowerCamelCase ,   image=lowerCamelCase ,   original_image=lowerCamelCase ,   generator=lowerCamelCase ,   num_inference_steps=2 ,   output_type='np' ,   )

         __a				     =    output.images[0]

         assert image.shape == (256, 256, 3)

         __a				     =    torch.cuda.max_memory_allocated()
         assert mem_bytes < 4 * 10**9

         __a				     =    load_numpy(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img_superresolution_stage_II.npy'      )
         assert_mean_pixel_difference(lowerCamelCase ,   lowerCamelCase      )






      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase      )							->Optional[int]:



         '''simple docstring'''
         # pipeline 1

         _start_torch_memory_measurement()

         __a				     =    floats_tensor((1, 3, 64, 64) ,   rng=random.Random(0      )      ).to(lowerCamelCase      )
         __a				     =    floats_tensor((1, 3, 64, 64) ,   rng=random.Random(1      )      ).to(lowerCamelCase      )

         __a				     =    torch.Generator(device='cpu'      ).manual_seed(0      )
         __a				     =    pipe_a(
             prompt_embeds=lowerCamelCase ,   negative_prompt_embeds=lowerCamelCase ,   image=lowerCamelCase ,   mask_image=lowerCamelCase ,   num_inference_steps=2 ,   generator=lowerCamelCase ,   output_type='np' ,   )

         __a				     =    output.images[0]

         assert image.shape == (64, 64, 3)

         __a				     =    torch.cuda.max_memory_allocated()
         assert mem_bytes < 10 * 10**9

         __a				     =    load_numpy(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting.npy'      )
         assert_mean_pixel_difference(lowerCamelCase ,   lowerCamelCase      )

         # pipeline 2

         _start_torch_memory_measurement()

         __a				     =    torch.Generator(device='cpu'      ).manual_seed(0      )

         __a				     =    floats_tensor((1, 3, 64, 64) ,   rng=random.Random(0      )      ).to(lowerCamelCase      )
         __a				     =    floats_tensor((1, 3, 256, 256) ,   rng=random.Random(0      )      ).to(lowerCamelCase      )
         __a				     =    floats_tensor((1, 3, 256, 256) ,   rng=random.Random(1      )      ).to(lowerCamelCase      )

         __a				     =    pipe_a(
             prompt_embeds=lowerCamelCase ,   negative_prompt_embeds=lowerCamelCase ,   image=lowerCamelCase ,   mask_image=lowerCamelCase ,   original_image=lowerCamelCase ,   generator=lowerCamelCase ,   num_inference_steps=2 ,   output_type='np' ,   )

         __a				     =    output.images[0]

         assert image.shape == (256, 256, 3)

         __a				     =    torch.cuda.max_memory_allocated()
         assert mem_bytes < 4 * 10**9

         __a				     =    load_numpy(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting_superresolution_stage_II.npy'      )
         assert_mean_pixel_difference(lowerCamelCase ,   lowerCamelCase      )







def    __UpperCAmelCase	(				)  ->			Dict:

   """simple docstring"""
   torch.cuda.empty_cache()
   torch.cuda.reset_max_memory_allocated()
   torch.cuda.reset_peak_memory_stats()