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 __snake_case       (		lowerCamelCase_     ,	lowerCamelCase_     ,	unittest.TestCase	):
   lowerCAmelCase_														=							IFPipeline
   lowerCAmelCase_														=							TEXT_TO_IMAGE_PARAMS - {"width", "height", "latents"}
   lowerCAmelCase_														=							TEXT_TO_IMAGE_BATCH_PARAMS
   lowerCAmelCase_														=							PipelineTesterMixin.required_optional_params - {"latents"}




   def 			__a		(					self    :       Any   ):



     """simple docstring"""






     return self._get_dummy_components()




   def 			__a		(					self    :       List[Any] ,			_lowercase    :       List[str] ,			_lowercase    :       Optional[Any]=0   ):



     """simple docstring"""






     if str(_lowercase   ).startswith("""mps"""   ):
       SCREAMING_SNAKE_CASE__    							=   torch.manual_seed(_lowercase   )
     else:
       SCREAMING_SNAKE_CASE__    							=   torch.Generator(device=_lowercase   ).manual_seed(_lowercase   )

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

     return inputs




   def 			__a		(					self    :       str   ):



     """simple docstring"""






     self._test_save_load_optional_components()




   @unittest.skipIf(torch_device != """cuda""" ,			reason="""float16 requires CUDA"""   )
   def 			__a		(					self    :       Dict   ):



     """simple docstring"""






     super().test_save_load_floataa(expected_max_diff=1E-1   )




   def 			__a		(					self    :       Dict   ):



     """simple docstring"""






     self._test_attention_slicing_forward_pass(expected_max_diff=1E-2   )




   def 			__a		(					self    :       Any   ):



     """simple docstring"""






     self._test_save_load_local()




   def 			__a		(					self    :       List[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 			__a		(					self    :       Union[str, Any]   ):



     """simple docstring"""






     self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3   )







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




   def 			__a		(					self    :       str   ):



     """simple docstring"""






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




   def 			__a		(					self    :       Dict   ):



     """simple docstring"""






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

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

     # pre compute text embeddings and remove T5 to save memory

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

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

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

     SCREAMING_SNAKE_CASE__    							=   None
     SCREAMING_SNAKE_CASE__    							=   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(_lowercase ,			_lowercase ,			_lowercase ,			_lowercase   )

     pipe_a.remove_all_hooks()
     pipe_a.remove_all_hooks()

     # img2img

     SCREAMING_SNAKE_CASE__    							=   IFImgaImgPipeline(**pipe_a.components   )
     SCREAMING_SNAKE_CASE__    							=   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(_lowercase ,			_lowercase ,			_lowercase ,			_lowercase   )

     pipe_a.remove_all_hooks()
     pipe_a.remove_all_hooks()

     # inpainting

     SCREAMING_SNAKE_CASE__    							=   IFInpaintingPipeline(**pipe_a.components   )
     SCREAMING_SNAKE_CASE__    							=   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(_lowercase ,			_lowercase ,			_lowercase ,			_lowercase   )




   def 			__a		(					self    :       Union[str, Any] ,			_lowercase    :       Any ,			_lowercase    :       List[str] ,			_lowercase    :       Dict ,			_lowercase    :       int   ):



     """simple docstring"""






     _start_torch_memory_measurement()

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

     SCREAMING_SNAKE_CASE__    							=   output.images[0]

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

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

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

     # pipeline 2

     _start_torch_memory_measurement()

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

     SCREAMING_SNAKE_CASE__    							=   floats_tensor((1, 3, 64, 64) ,			rng=random.Random(0   )   ).to(_lowercase   )

     SCREAMING_SNAKE_CASE__    							=   pipe_a(
         prompt_embeds=_lowercase ,			negative_prompt_embeds=_lowercase ,			image=_lowercase ,			generator=_lowercase ,			num_inference_steps=2 ,			output_type="""np""" ,			)

     SCREAMING_SNAKE_CASE__    							=   output.images[0]

     assert image.shape == (2_56, 2_56, 3)

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

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




   def 			__a		(					self    :       Dict ,			_lowercase    :       Any ,			_lowercase    :       str ,			_lowercase    :       List[Any] ,			_lowercase    :       Optional[int]   ):



     """simple docstring"""






     _start_torch_memory_measurement()

     SCREAMING_SNAKE_CASE__    							=   floats_tensor((1, 3, 64, 64) ,			rng=random.Random(0   )   ).to(_lowercase   )

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

     SCREAMING_SNAKE_CASE__    							=   pipe_a(
         prompt_embeds=_lowercase ,			negative_prompt_embeds=_lowercase ,			image=_lowercase ,			num_inference_steps=2 ,			generator=_lowercase ,			output_type="""np""" ,			)

     SCREAMING_SNAKE_CASE__    							=   output.images[0]

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

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

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

     # pipeline 2

     _start_torch_memory_measurement()

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

     SCREAMING_SNAKE_CASE__    							=   floats_tensor((1, 3, 2_56, 2_56) ,			rng=random.Random(0   )   ).to(_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   floats_tensor((1, 3, 64, 64) ,			rng=random.Random(0   )   ).to(_lowercase   )

     SCREAMING_SNAKE_CASE__    							=   pipe_a(
         prompt_embeds=_lowercase ,			negative_prompt_embeds=_lowercase ,			image=_lowercase ,			original_image=_lowercase ,			generator=_lowercase ,			num_inference_steps=2 ,			output_type="""np""" ,			)

     SCREAMING_SNAKE_CASE__    							=   output.images[0]

     assert image.shape == (2_56, 2_56, 3)

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

     SCREAMING_SNAKE_CASE__    							=   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(_lowercase ,			_lowercase   )




   def 			__a		(					self    :       Any ,			_lowercase    :       Union[str, Any] ,			_lowercase    :       List[Any] ,			_lowercase    :       Union[str, Any] ,			_lowercase    :       Union[str, Any]   ):



     """simple docstring"""






     _start_torch_memory_measurement()

     SCREAMING_SNAKE_CASE__    							=   floats_tensor((1, 3, 64, 64) ,			rng=random.Random(0   )   ).to(_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   floats_tensor((1, 3, 64, 64) ,			rng=random.Random(1   )   ).to(_lowercase   )

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

     SCREAMING_SNAKE_CASE__    							=   output.images[0]

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

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

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

     # pipeline 2

     _start_torch_memory_measurement()

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

     SCREAMING_SNAKE_CASE__    							=   floats_tensor((1, 3, 64, 64) ,			rng=random.Random(0   )   ).to(_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   floats_tensor((1, 3, 2_56, 2_56) ,			rng=random.Random(0   )   ).to(_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   floats_tensor((1, 3, 2_56, 2_56) ,			rng=random.Random(1   )   ).to(_lowercase   )

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

     SCREAMING_SNAKE_CASE__    							=   output.images[0]

     assert image.shape == (2_56, 2_56, 3)

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

     SCREAMING_SNAKE_CASE__    							=   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(_lowercase ,			_lowercase   )





def 			__SCREAMING_SNAKE_CASE						(      )							->      Optional[int]:







  """simple docstring"""





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

import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import ClassLabel, Features, Value
from .base import TaskTemplate







@dataclass(frozen=lowerCamelCase_	)
class __snake_case       (		lowerCamelCase_	):
   # `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON serialization
   lowerCAmelCase_														=							field(default="text-classification"     ,	metadata={"include_in_asdict_even_if_is_default": True}	)
   lowerCAmelCase_														=							Features({"text": Value("string"	)}	)
   lowerCAmelCase_														=							Features({"labels": ClassLabel}	)
   lowerCAmelCase_														=							"text"
   lowerCAmelCase_														=							"labels"




   def 			__a		(					self    :       Dict ,			_lowercase    :       List[Any]   ):



     """simple docstring"""






     if self.label_column not in features:
       raise ValueError(f"""Column {self.label_column} is not present in features."""   )
     if not isinstance(features[self.label_column] ,			_lowercase   ):
       raise ValueError(f"""Column {self.label_column} is not a ClassLabel."""   )
     SCREAMING_SNAKE_CASE__    							=   copy.deepcopy(self   )
     SCREAMING_SNAKE_CASE__    							=   self.label_schema.copy()
     SCREAMING_SNAKE_CASE__    							=   features[self.label_column]
     SCREAMING_SNAKE_CASE__    							=   label_schema
     return task_template




   @property
   def 			__a		(					self    :       List[Any]   ):



     """simple docstring"""






     return {
         self.text_column: "text",
         self.label_column: "labels",
     }
