





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        _a    (    UpperCamelCase__						,			UpperCamelCase__						,			unittest.TestCase):

      """simple docstring"""


      UpperCamelCase__  		=       IFPipeline
      UpperCamelCase__  		=       TEXT_TO_IMAGE_PARAMS - {"""width""", """height""", """latents"""}
      UpperCamelCase__  		=       TEXT_TO_IMAGE_BATCH_PARAMS
      UpperCamelCase__  		=       PipelineTesterMixin.required_optional_params - {"""latents"""}






      def 			UpperCAmelCase_       (		self:			List[str]					):
          '''simple docstring'''


          return self._get_dummy_components()






      def 			UpperCAmelCase_       (		self:			Union[str, Any]	,    __lowerCamelCase:			List[str]	,    __lowerCamelCase:			List[Any]=0					):
          '''simple docstring'''


          if str(__lowerCamelCase					).startswith("mps"					):
              UpperCamelCase__:	Union[str, Any]					=     torch.manual_seed(__lowerCamelCase					)
          else:
              UpperCamelCase__:	Optional[int]					=     torch.Generator(device=__lowerCamelCase					).manual_seed(__lowerCamelCase					)

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

          return inputs






      def 			UpperCAmelCase_       (		self:			Any					):
          '''simple docstring'''


          self._test_save_load_optional_components()






      @unittest.skipIf(torch_device != "cuda"	,    reason="float16 requires CUDA"					)
      def 			UpperCAmelCase_       (		self:			List[Any]					):
          '''simple docstring'''


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






      def 			UpperCAmelCase_       (		self:			List[str]					):
          '''simple docstring'''


          self._test_attention_slicing_forward_pass(expected_max_diff=1e-2					)






      def 			UpperCAmelCase_       (		self:			int					):
          '''simple docstring'''


          self._test_save_load_local()






      def 			UpperCAmelCase_       (		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 			UpperCAmelCase_       (		self:			int					):
          '''simple docstring'''


          self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3					)




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

      """simple docstring"""


      def 			UpperCAmelCase_       (		self:			str					):
          '''simple docstring'''


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






      def 			UpperCAmelCase_       (		self:			Union[str, Any]					):
          '''simple docstring'''


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

          UpperCamelCase__:	Union[str, Any]					=     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"					)

          UpperCamelCase__				, UpperCamelCase__:	int					=     pipe_a.encode_prompt("anime turtle"	,    device="cuda"					)

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

          UpperCamelCase__:	Union[str, Any]					=     None
          UpperCamelCase__:	List[Any]					=     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

          UpperCamelCase__:	int					=     IFImgaImgPipeline(**pipe_a.components					)
          UpperCamelCase__:	Union[str, Any]					=     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

          UpperCamelCase__:	Optional[Any]					=     IFInpaintingPipeline(**pipe_a.components					)
          UpperCamelCase__:	List[Any]					=     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:			List[Any]	,    __lowerCamelCase:			int	,    __lowerCamelCase:			Dict	,    __lowerCamelCase:			List[str]	,    __lowerCamelCase:			Optional[int]					):
          '''simple docstring'''


          _start_torch_memory_measurement()

          UpperCamelCase__:	Tuple					=     torch.Generator(device="cpu"					).manual_seed(0					)
          UpperCamelCase__:	Tuple					=     pipe_a(
              prompt_embeds=__lowerCamelCase	,    negative_prompt_embeds=__lowerCamelCase	,    num_inference_steps=2	,    generator=__lowerCamelCase	,    output_type="np"	,    )

          UpperCamelCase__:	str					=     output.images[0]

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

          UpperCamelCase__:	Optional[int]					=     torch.cuda.max_memory_allocated()
          assert mem_bytes < 13 * 10**9

          UpperCamelCase__:	Any					=     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()

          UpperCamelCase__:	List[str]					=     torch.Generator(device="cpu"					).manual_seed(0					)

          UpperCamelCase__:	Optional[int]					=     floats_tensor((1, 3, 64, 64)	,    rng=random.Random(0					)					).to(__lowerCamelCase					)

          UpperCamelCase__:	Optional[Any]					=     pipe_a(
              prompt_embeds=__lowerCamelCase	,    negative_prompt_embeds=__lowerCamelCase	,    image=__lowerCamelCase	,    generator=__lowerCamelCase	,    num_inference_steps=2	,    output_type="np"	,    )

          UpperCamelCase__:	Optional[Any]					=     output.images[0]

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

          UpperCamelCase__:	str					=     torch.cuda.max_memory_allocated()
          assert mem_bytes < 4 * 10**9

          UpperCamelCase__:	Union[str, Any]					=     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:			Union[str, Any]	,    __lowerCamelCase:			Dict	,    __lowerCamelCase:			Union[str, Any]	,    __lowerCamelCase:			int	,    __lowerCamelCase:			Optional[int]					):
          '''simple docstring'''


          _start_torch_memory_measurement()

          UpperCamelCase__:	Union[str, Any]					=     floats_tensor((1, 3, 64, 64)	,    rng=random.Random(0					)					).to(__lowerCamelCase					)

          UpperCamelCase__:	int					=     torch.Generator(device="cpu"					).manual_seed(0					)

          UpperCamelCase__:	Union[str, Any]					=     pipe_a(
              prompt_embeds=__lowerCamelCase	,    negative_prompt_embeds=__lowerCamelCase	,    image=__lowerCamelCase	,    num_inference_steps=2	,    generator=__lowerCamelCase	,    output_type="np"	,    )

          UpperCamelCase__:	List[str]					=     output.images[0]

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

          UpperCamelCase__:	Dict					=     torch.cuda.max_memory_allocated()
          assert mem_bytes < 10 * 10**9

          UpperCamelCase__:	Any					=     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()

          UpperCamelCase__:	Union[str, Any]					=     torch.Generator(device="cpu"					).manual_seed(0					)

          UpperCamelCase__:	Dict					=     floats_tensor((1, 3, 256, 256)	,    rng=random.Random(0					)					).to(__lowerCamelCase					)
          UpperCamelCase__:	List[str]					=     floats_tensor((1, 3, 64, 64)	,    rng=random.Random(0					)					).to(__lowerCamelCase					)

          UpperCamelCase__:	str					=     pipe_a(
              prompt_embeds=__lowerCamelCase	,    negative_prompt_embeds=__lowerCamelCase	,    image=__lowerCamelCase	,    original_image=__lowerCamelCase	,    generator=__lowerCamelCase	,    num_inference_steps=2	,    output_type="np"	,    )

          UpperCamelCase__:	str					=     output.images[0]

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

          UpperCamelCase__:	str					=     torch.cuda.max_memory_allocated()
          assert mem_bytes < 4 * 10**9

          UpperCamelCase__:	str					=     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:			int	,    __lowerCamelCase:			Dict	,    __lowerCamelCase:			Tuple	,    __lowerCamelCase:			Any	,    __lowerCamelCase:			Union[str, Any]					):
          '''simple docstring'''


          _start_torch_memory_measurement()

          UpperCamelCase__:	Optional[int]					=     floats_tensor((1, 3, 64, 64)	,    rng=random.Random(0					)					).to(__lowerCamelCase					)
          UpperCamelCase__:	str					=     floats_tensor((1, 3, 64, 64)	,    rng=random.Random(1					)					).to(__lowerCamelCase					)

          UpperCamelCase__:	Union[str, Any]					=     torch.Generator(device="cpu"					).manual_seed(0					)
          UpperCamelCase__:	Tuple					=     pipe_a(
              prompt_embeds=__lowerCamelCase	,    negative_prompt_embeds=__lowerCamelCase	,    image=__lowerCamelCase	,    mask_image=__lowerCamelCase	,    num_inference_steps=2	,    generator=__lowerCamelCase	,    output_type="np"	,    )

          UpperCamelCase__:	Union[str, Any]					=     output.images[0]

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

          UpperCamelCase__:	Optional[Any]					=     torch.cuda.max_memory_allocated()
          assert mem_bytes < 10 * 10**9

          UpperCamelCase__:	Dict					=     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()

          UpperCamelCase__:	Tuple					=     torch.Generator(device="cpu"					).manual_seed(0					)

          UpperCamelCase__:	Any					=     floats_tensor((1, 3, 64, 64)	,    rng=random.Random(0					)					).to(__lowerCamelCase					)
          UpperCamelCase__:	Optional[Any]					=     floats_tensor((1, 3, 256, 256)	,    rng=random.Random(0					)					).to(__lowerCamelCase					)
          UpperCamelCase__:	Optional[int]					=     floats_tensor((1, 3, 256, 256)	,    rng=random.Random(1					)					).to(__lowerCamelCase					)

          UpperCamelCase__:	List[Any]					=     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"	,    )

          UpperCamelCase__:	Optional[Any]					=     output.images[0]

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

          UpperCamelCase__:	Dict					=     torch.cuda.max_memory_allocated()
          assert mem_bytes < 4 * 10**9

          UpperCamelCase__:	List[str]					=     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 				lowerCAmelCase_ (							):
    torch.cuda.empty_cache()
    torch.cuda.reset_max_memory_allocated()
    torch.cuda.reset_peak_memory_stats()








from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable


A__:    Any  	=	{
    '''configuration_gpt_neox_japanese''': ['''GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTNeoXJapaneseConfig'''],
    '''tokenization_gpt_neox_japanese''': ['''GPTNeoXJapaneseTokenizer'''],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A__:    Union[str, Any]  	=	[
       '''GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''GPTNeoXJapaneseForCausalLM''',
       '''GPTNeoXJapaneseLayer''',
       '''GPTNeoXJapaneseModel''',
       '''GPTNeoXJapanesePreTrainedModel''',
   ]


if TYPE_CHECKING:
   from .configuration_gpt_neox_japanese import GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXJapaneseConfig
   from .tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_gpt_neox_japanese import (
          GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
          GPTNeoXJapaneseForCausalLM,
          GPTNeoXJapaneseLayer,
          GPTNeoXJapaneseModel,
          GPTNeoXJapanesePreTrainedModel,
      )


else:
   import sys

   A__:    str  	=	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

