





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__				(       _UpperCAmelCase						,      _UpperCAmelCase						,      unittest.TestCase  ):
      A_						:				List[str]							=    IFPipeline
      A_						:				Tuple							=    TEXT_TO_IMAGE_PARAMS - {'width', 'height', 'latents'}
      A_						:				int							=    TEXT_TO_IMAGE_BATCH_PARAMS
      A_						:				int							=    PipelineTesterMixin.required_optional_params - {'latents'}


      def    a (self       :      List[Any]      ):





         """simple docstring"""



         return self._get_dummy_components()


      def    a (self       :      Union[str, Any]				,			a__       :      Dict				,			a__       :      str=0      ):





         """simple docstring"""



         if str(a__      ).startswith('''mps'''      ):
            __snake_case							       =   torch.manual_seed(a__      )
         else:
            __snake_case							       =   torch.Generator(device=a__      ).manual_seed(a__      )

         __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       :      Tuple      ):





         """simple docstring"""



         self._test_save_load_optional_components()


      @unittest.skipIf(torch_device != '''cuda'''				,			reason='''float16 requires CUDA'''      )
      def    a (self       :      Union[str, Any]      ):





         """simple docstring"""



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


      def    a (self       :      List[str]      ):





         """simple docstring"""



         self._test_attention_slicing_forward_pass(expected_max_diff=1E-2      )


      def    a (self       :      Union[str, Any]      ):





         """simple docstring"""



         self._test_save_load_local()


      def    a (self       :      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       :      Any      ):





         """simple docstring"""



         self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3      )






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


      def    a (self       :      Union[str, Any]      ):





         """simple docstring"""



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


      def    a (self       :      int      ):





         """simple docstring"""



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

         __snake_case							       =   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'''      )

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

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

         __snake_case							       =   None
         __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(a__				,			a__				,			a__				,			a__      )

         pipe_a.remove_all_hooks()
         pipe_a.remove_all_hooks()

         # img2img

         __snake_case							       =   IFImgaImgPipeline(**pipe_a.components      )
         __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(a__				,			a__				,			a__				,			a__      )

         pipe_a.remove_all_hooks()
         pipe_a.remove_all_hooks()

         # inpainting

         __snake_case							       =   IFInpaintingPipeline(**pipe_a.components      )
         __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(a__				,			a__				,			a__				,			a__      )


      def    a (self       :      str				,			a__       :      Optional[Any]				,			a__       :      List[Any]				,			a__       :      Tuple				,			a__       :      Optional[int]      ):





         """simple docstring"""



         _start_torch_memory_measurement()

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

         __snake_case							       =   output.images[0]

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

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

         __snake_case							       =   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()

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

         __snake_case							       =   floats_tensor((1, 3, 64, 64)				,			rng=random.Random(0      )      ).to(a__      )

         __snake_case							       =   pipe_a(
             prompt_embeds=a__				,			negative_prompt_embeds=a__				,			image=a__				,			generator=a__				,			num_inference_steps=2				,			output_type='''np'''				,			)

         __snake_case							       =   output.images[0]

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

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

         __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(a__				,			a__      )


      def    a (self       :      str				,			a__       :      List[Any]				,			a__       :      Optional[Any]				,			a__       :      Tuple				,			a__       :      str      ):





         """simple docstring"""



         _start_torch_memory_measurement()

         __snake_case							       =   floats_tensor((1, 3, 64, 64)				,			rng=random.Random(0      )      ).to(a__      )

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

         __snake_case							       =   pipe_a(
             prompt_embeds=a__				,			negative_prompt_embeds=a__				,			image=a__				,			num_inference_steps=2				,			generator=a__				,			output_type='''np'''				,			)

         __snake_case							       =   output.images[0]

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

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

         __snake_case							       =   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()

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

         __snake_case							       =   floats_tensor((1, 3, 256, 256)				,			rng=random.Random(0      )      ).to(a__      )
         __snake_case							       =   floats_tensor((1, 3, 64, 64)				,			rng=random.Random(0      )      ).to(a__      )

         __snake_case							       =   pipe_a(
             prompt_embeds=a__				,			negative_prompt_embeds=a__				,			image=a__				,			original_image=a__				,			generator=a__				,			num_inference_steps=2				,			output_type='''np'''				,			)

         __snake_case							       =   output.images[0]

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

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

         __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(a__				,			a__      )


      def    a (self       :      List[str]				,			a__       :      List[str]				,			a__       :      int				,			a__       :      Union[str, Any]				,			a__       :      int      ):





         """simple docstring"""



         _start_torch_memory_measurement()

         __snake_case							       =   floats_tensor((1, 3, 64, 64)				,			rng=random.Random(0      )      ).to(a__      )
         __snake_case							       =   floats_tensor((1, 3, 64, 64)				,			rng=random.Random(1      )      ).to(a__      )

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

         __snake_case							       =   output.images[0]

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

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

         __snake_case							       =   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()

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

         __snake_case							       =   floats_tensor((1, 3, 64, 64)				,			rng=random.Random(0      )      ).to(a__      )
         __snake_case							       =   floats_tensor((1, 3, 256, 256)				,			rng=random.Random(0      )      ).to(a__      )
         __snake_case							       =   floats_tensor((1, 3, 256, 256)				,			rng=random.Random(1      )      ).to(a__      )

         __snake_case							       =   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'''				,			)

         __snake_case							       =   output.images[0]

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

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

         __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(a__				,			a__      )






def lowerCamelCase__    (							)      ->				List[Any]:
   torch.cuda.empty_cache()
   torch.cuda.reset_max_memory_allocated()
   torch.cuda.reset_peak_memory_stats()








import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding

class 		SCREAMING_SNAKE_CASE__				(       _UpperCAmelCase  ):
      A_						:				List[str]							=    ['image_processor', 'tokenizer']
      A_						:				Optional[Any]							=    'CLIPImageProcessor'
      A_						:				Any							=    ('XLMRobertaTokenizer', 'XLMRobertaTokenizerFast')


      def __init__(self       :      int				,			a__       :      int=None				,			a__       :      Dict=None				,			**a__       :      List[str]      ):





         """simple docstring"""



         __snake_case							       =   None
         if "feature_extractor" in kwargs:
            warnings.warn(
                '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
                ''' instead.'''				,			a__				,			)
            __snake_case							       =   kwargs.pop('''feature_extractor'''      )

         __snake_case							       =   image_processor if image_processor is not None else feature_extractor
         if image_processor is None:
            raise ValueError('''You need to specify an `image_processor`.'''      )
         if tokenizer is None:
            raise ValueError('''You need to specify a `tokenizer`.'''      )

         super().__init__(a__				,			a__      )


      def __call__(self       :      Any				,			a__       :      Dict=None				,			a__       :      List[str]=None				,			a__       :      Dict=None				,			**a__       :      Tuple      ):





         """simple docstring"""



         if text is None and images is None:
            raise ValueError('''You have to specify either text or images. Both cannot be none.'''      )

         if text is not None:
            __snake_case							       =   self.tokenizer(a__				,			return_tensors=a__				,			**a__      )

         if images is not None:
            __snake_case							       =   self.image_processor(a__				,			return_tensors=a__				,			**a__      )

         if text is not None and images is not None:
            __snake_case							       =   image_features.pixel_values
            return encoding
         elif text is not None:
            return encoding
         else:
            return BatchEncoding(data=dict(**a__      )				,			tensor_type=a__      )


      def    a (self       :      Union[str, Any]				,			*a__       :      int				,			**a__       :      List[Any]      ):





         """simple docstring"""



         return self.tokenizer.batch_decode(*a__				,			**a__      )


      def    a (self       :      Any				,			*a__       :      List[Any]				,			**a__       :      List[str]      ):





         """simple docstring"""



         return self.tokenizer.decode(*a__				,			**a__      )


      @property
      def    a (self       :      int      ):





         """simple docstring"""



         __snake_case							       =   self.tokenizer.model_input_names
         __snake_case							       =   self.image_processor.model_input_names
         return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names      )      )

