





"""simple docstring"""
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCamelCase_        =						logging.get_logger(__name__)

lowerCamelCase_        =						{
    "microsoft/beit-base-patch16-224-pt22k": (
        "https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json"
    ),
    # See all BEiT models at https://huggingface.co/models?filter=beit
}






class    _SCREAMING_SNAKE_CASE(  _a    ):
       SCREAMING_SNAKE_CASE_  : Optional[int]      		=    """beit"""




       def __init__(		self						,SCREAMING_SNAKE_CASE__=81_92						,SCREAMING_SNAKE_CASE__=7_68						,SCREAMING_SNAKE_CASE__=12						,SCREAMING_SNAKE_CASE__=12						,SCREAMING_SNAKE_CASE__=30_72						,SCREAMING_SNAKE_CASE__="gelu"						,SCREAMING_SNAKE_CASE__=0.0						,SCREAMING_SNAKE_CASE__=0.0						,SCREAMING_SNAKE_CASE__=0.0_2						,SCREAMING_SNAKE_CASE__=1E-12						,SCREAMING_SNAKE_CASE__=2_24						,SCREAMING_SNAKE_CASE__=16						,SCREAMING_SNAKE_CASE__=3						,SCREAMING_SNAKE_CASE__=False						,SCREAMING_SNAKE_CASE__=False						,SCREAMING_SNAKE_CASE__=False						,SCREAMING_SNAKE_CASE__=False						,SCREAMING_SNAKE_CASE__=0.1						,SCREAMING_SNAKE_CASE__=0.1						,SCREAMING_SNAKE_CASE__=True						,SCREAMING_SNAKE_CASE__=[3, 5, 7, 11]						,SCREAMING_SNAKE_CASE__=[1, 2, 3, 6]						,SCREAMING_SNAKE_CASE__=True						,SCREAMING_SNAKE_CASE__=0.4						,SCREAMING_SNAKE_CASE__=2_56						,SCREAMING_SNAKE_CASE__=1						,SCREAMING_SNAKE_CASE__=False						,SCREAMING_SNAKE_CASE__=2_55						,**SCREAMING_SNAKE_CASE__						,)     -> str:





             """simple docstring"""






             super().__init__(**A_				)

             __SCREAMING_SNAKE_CASE  :int													=    vocab_size
             __SCREAMING_SNAKE_CASE  :List[Any]													=    hidden_size
             __SCREAMING_SNAKE_CASE  :Tuple													=    num_hidden_layers
             __SCREAMING_SNAKE_CASE  :str													=    num_attention_heads
             __SCREAMING_SNAKE_CASE  :Dict													=    intermediate_size
             __SCREAMING_SNAKE_CASE  :Optional[Any]													=    hidden_act
             __SCREAMING_SNAKE_CASE  :Optional[Any]													=    hidden_dropout_prob
             __SCREAMING_SNAKE_CASE  :Optional[Any]													=    attention_probs_dropout_prob
             __SCREAMING_SNAKE_CASE  :Union[str, Any]													=    initializer_range
             __SCREAMING_SNAKE_CASE  :Any													=    layer_norm_eps

             __SCREAMING_SNAKE_CASE  :Any													=    image_size
             __SCREAMING_SNAKE_CASE  :Union[str, Any]													=    patch_size
             __SCREAMING_SNAKE_CASE  :str													=    num_channels
             __SCREAMING_SNAKE_CASE  :Optional[int]													=    use_mask_token
             __SCREAMING_SNAKE_CASE  :int													=    use_absolute_position_embeddings
             __SCREAMING_SNAKE_CASE  :Any													=    use_relative_position_bias
             __SCREAMING_SNAKE_CASE  :str													=    use_shared_relative_position_bias
             __SCREAMING_SNAKE_CASE  :str													=    layer_scale_init_value
             __SCREAMING_SNAKE_CASE  :Optional[Any]													=    drop_path_rate
             __SCREAMING_SNAKE_CASE  :str													=    use_mean_pooling
             # decode head attributes (semantic segmentation)
             __SCREAMING_SNAKE_CASE  :str													=    out_indices
             __SCREAMING_SNAKE_CASE  :Any													=    pool_scales
             # auxiliary head attributes (semantic segmentation)
             __SCREAMING_SNAKE_CASE  :Dict													=    use_auxiliary_head
             __SCREAMING_SNAKE_CASE  :Optional[int]													=    auxiliary_loss_weight
             __SCREAMING_SNAKE_CASE  :Tuple													=    auxiliary_channels
             __SCREAMING_SNAKE_CASE  :int													=    auxiliary_num_convs
             __SCREAMING_SNAKE_CASE  :int													=    auxiliary_concat_input
             __SCREAMING_SNAKE_CASE  :Union[str, Any]													=    semantic_loss_ignore_index






class    _SCREAMING_SNAKE_CASE(  _a    ):
       SCREAMING_SNAKE_CASE_  : int      		=    version.parse('''1.11'''    )




       @property
       def        _UpperCamelCase	(		self				)     -> Any:





             """simple docstring"""






             return OrderedDict(
                 [
                     ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
                 ]				)




       @property
       def        _UpperCamelCase	(		self				)     -> int:





             """simple docstring"""






             return 1E-4



"""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  lowerCamelCase__    (		_a      , _a      , unittest.TestCase     ):
      a			:  Any           =		IFPipeline
      a			:  str           =		TEXT_TO_IMAGE_PARAMS - {"""width""", """height""", """latents"""}
      a			:  Dict           =		TEXT_TO_IMAGE_BATCH_PARAMS
      a			:  int           =		PipelineTesterMixin.required_optional_params - {"""latents"""}
      def SCREAMING_SNAKE_CASE_						(		self						:						List[Any]				):






       '''simple docstring'''


       return self._get_dummy_components()
      def SCREAMING_SNAKE_CASE_						(		self						:						Any   ,							A_						:						int   ,							A_						:						Dict=0				):






       '''simple docstring'''


       if str(A_				).startswith("""mps"""				):
        __lowercase						=						torch.manual_seed(A_				)
       else:
        __lowercase						=						torch.Generator(device=A_				).manual_seed(A_				)

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

       return inputs
      def SCREAMING_SNAKE_CASE_						(		self						:						Union[str, Any]				):






       '''simple docstring'''


       self._test_save_load_optional_components()
      @unittest.skipIf(torch_device != """cuda"""   ,							reason="""float16 requires CUDA"""				)
      def SCREAMING_SNAKE_CASE_						(		self						:						List[str]				):






       '''simple docstring'''


       super().test_save_load_floataa(expected_max_diff=1e-1				)
      def SCREAMING_SNAKE_CASE_						(		self						:						List[Any]				):






       '''simple docstring'''


       self._test_attention_slicing_forward_pass(expected_max_diff=1e-2				)
      def SCREAMING_SNAKE_CASE_						(		self						:						List[str]				):






       '''simple docstring'''


       self._test_save_load_local()
      def SCREAMING_SNAKE_CASE_						(		self						:						Union[str, Any]				):






       '''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 SCREAMING_SNAKE_CASE_						(		self						:						Tuple				):






       '''simple docstring'''


       self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3				)






@slow
@require_torch_gpu
class  lowerCamelCase__    (		unittest.TestCase     ):
      def SCREAMING_SNAKE_CASE_						(		self						:						str				):






       '''simple docstring'''


       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()
      def SCREAMING_SNAKE_CASE_						(		self						:						Union[str, Any]				):






       '''simple docstring'''


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

       __lowercase						=						IFSuperResolutionPipeline.from_pretrained(
           """DeepFloyd/IF-II-L-v1.0"""   ,							variant="""fp16"""   ,							torch_dtype=torch.floataa   ,							text_encoder=A_   ,							tokenizer=A_				)

       # pre compute text embeddings and remove T5 to save memory

       pipe_a.text_encoder.to("""cuda"""				)

       __lowercase				,		__lowercase						=						pipe_a.encode_prompt("""anime turtle"""   ,							device="""cuda"""				)

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

       __lowercase						=						None
       __lowercase						=						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(A_   ,							A_   ,							A_   ,							A_				)

       pipe_a.remove_all_hooks()
       pipe_a.remove_all_hooks()

       # img2img

       __lowercase						=						IFImgaImgPipeline(**pipe_a.components				)
       __lowercase						=						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(A_   ,							A_   ,							A_   ,							A_				)

       pipe_a.remove_all_hooks()
       pipe_a.remove_all_hooks()

       # inpainting

       __lowercase						=						IFInpaintingPipeline(**pipe_a.components				)
       __lowercase						=						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(A_   ,							A_   ,							A_   ,							A_				)
      def SCREAMING_SNAKE_CASE_						(		self						:						Optional[int]   ,							A_						:						Any   ,							A_						:						int   ,							A_						:						str   ,							A_						:						Dict				):






       '''simple docstring'''


       _start_torch_memory_measurement()

       __lowercase						=						torch.Generator(device="""cpu"""				).manual_seed(0				)
       __lowercase						=						pipe_a(
           prompt_embeds=A_   ,							negative_prompt_embeds=A_   ,							num_inference_steps=2   ,							generator=A_   ,							output_type="""np"""   ,							)

       __lowercase						=						output.images[0]

       assert image.shape == (6_4, 6_4, 3)

       __lowercase						=						torch.cuda.max_memory_allocated()
       assert mem_bytes < 1_3 * 1_0**9

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

       # pipeline 2

       _start_torch_memory_measurement()

       __lowercase						=						torch.Generator(device="""cpu"""				).manual_seed(0				)

       __lowercase						=						floats_tensor((1, 3, 6_4, 6_4)   ,							rng=random.Random(0				)				).to(A_				)

       __lowercase						=						pipe_a(
           prompt_embeds=A_   ,							negative_prompt_embeds=A_   ,							image=A_   ,							generator=A_   ,							num_inference_steps=2   ,							output_type="""np"""   ,							)

       __lowercase						=						output.images[0]

       assert image.shape == (2_5_6, 2_5_6, 3)

       __lowercase						=						torch.cuda.max_memory_allocated()
       assert mem_bytes < 4 * 1_0**9

       __lowercase						=						load_numpy(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_superresolution_stage_II.npy"""				)
       assert_mean_pixel_difference(A_   ,							A_				)
      def SCREAMING_SNAKE_CASE_						(		self						:						Optional[Any]   ,							A_						:						Tuple   ,							A_						:						List[Any]   ,							A_						:						List[Any]   ,							A_						:						Any				):






       '''simple docstring'''


       _start_torch_memory_measurement()

       __lowercase						=						floats_tensor((1, 3, 6_4, 6_4)   ,							rng=random.Random(0				)				).to(A_				)

       __lowercase						=						torch.Generator(device="""cpu"""				).manual_seed(0				)

       __lowercase						=						pipe_a(
           prompt_embeds=A_   ,							negative_prompt_embeds=A_   ,							image=A_   ,							num_inference_steps=2   ,							generator=A_   ,							output_type="""np"""   ,							)

       __lowercase						=						output.images[0]

       assert image.shape == (6_4, 6_4, 3)

       __lowercase						=						torch.cuda.max_memory_allocated()
       assert mem_bytes < 1_0 * 1_0**9

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

       # pipeline 2

       _start_torch_memory_measurement()

       __lowercase						=						torch.Generator(device="""cpu"""				).manual_seed(0				)

       __lowercase						=						floats_tensor((1, 3, 2_5_6, 2_5_6)   ,							rng=random.Random(0				)				).to(A_				)
       __lowercase						=						floats_tensor((1, 3, 6_4, 6_4)   ,							rng=random.Random(0				)				).to(A_				)

       __lowercase						=						pipe_a(
           prompt_embeds=A_   ,							negative_prompt_embeds=A_   ,							image=A_   ,							original_image=A_   ,							generator=A_   ,							num_inference_steps=2   ,							output_type="""np"""   ,							)

       __lowercase						=						output.images[0]

       assert image.shape == (2_5_6, 2_5_6, 3)

       __lowercase						=						torch.cuda.max_memory_allocated()
       assert mem_bytes < 4 * 1_0**9

       __lowercase						=						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(A_   ,							A_				)






      def SCREAMING_SNAKE_CASE_						(		self						:						List[str]   ,							A_						:						List[Any]   ,							A_						:						str   ,							A_						:						List[Any]   ,							A_						:						List[Any]				):






       '''simple docstring'''


       _start_torch_memory_measurement()

       __lowercase						=						floats_tensor((1, 3, 6_4, 6_4)   ,							rng=random.Random(0				)				).to(A_				)
       __lowercase						=						floats_tensor((1, 3, 6_4, 6_4)   ,							rng=random.Random(1				)				).to(A_				)

       __lowercase						=						torch.Generator(device="""cpu"""				).manual_seed(0				)
       __lowercase						=						pipe_a(
           prompt_embeds=A_   ,							negative_prompt_embeds=A_   ,							image=A_   ,							mask_image=A_   ,							num_inference_steps=2   ,							generator=A_   ,							output_type="""np"""   ,							)

       __lowercase						=						output.images[0]

       assert image.shape == (6_4, 6_4, 3)

       __lowercase						=						torch.cuda.max_memory_allocated()
       assert mem_bytes < 1_0 * 1_0**9

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

       # pipeline 2

       _start_torch_memory_measurement()

       __lowercase						=						torch.Generator(device="""cpu"""				).manual_seed(0				)

       __lowercase						=						floats_tensor((1, 3, 6_4, 6_4)   ,							rng=random.Random(0				)				).to(A_				)
       __lowercase						=						floats_tensor((1, 3, 2_5_6, 2_5_6)   ,							rng=random.Random(0				)				).to(A_				)
       __lowercase						=						floats_tensor((1, 3, 2_5_6, 2_5_6)   ,							rng=random.Random(1				)				).to(A_				)

       __lowercase						=						pipe_a(
           prompt_embeds=A_   ,							negative_prompt_embeds=A_   ,							image=A_   ,							mask_image=A_   ,							original_image=A_   ,							generator=A_   ,							num_inference_steps=2   ,							output_type="""np"""   ,							)

       __lowercase						=						output.images[0]

       assert image.shape == (2_5_6, 2_5_6, 3)

       __lowercase						=						torch.cuda.max_memory_allocated()
       assert mem_bytes < 4 * 1_0**9

       __lowercase						=						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(A_   ,							A_				)






def lowerCAmelCase_   (					):






 """simple docstring"""







 torch.cuda.empty_cache()
 torch.cuda.reset_max_memory_allocated()
 torch.cuda.reset_peak_memory_stats()
