




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_				( _SCREAMING_SNAKE_CASE			,  _SCREAMING_SNAKE_CASE			,  unittest.TestCase   ):


 '''simple docstring'''







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



 def  lowerCAmelCase_    (				self    :						str		):
       return self._get_dummy_components()



 def  lowerCAmelCase_    (				self    :						Any							,							_lowerCAmelCase    :						Union[str, Any]							,							_lowerCAmelCase    :						str=0		):
       if str(_lowerCAmelCase		).startswith('mps'		):
             SCREAMING_SNAKE_CASE_      		=     torch.manual_seed(_lowerCAmelCase		)
       else:
             SCREAMING_SNAKE_CASE_      		=     torch.Generator(device=_lowerCAmelCase		).manual_seed(_lowerCAmelCase		)

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

       return inputs



 def  lowerCAmelCase_    (				self    :						str		):
       self._test_save_load_optional_components()



 @unittest.skipIf(torch_device != 'cuda'							,							reason='float16 requires CUDA'		)
 def  lowerCAmelCase_    (				self    :						Optional[Any]		):
       # 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  lowerCAmelCase_    (				self    :						Dict		):
       self._test_attention_slicing_forward_pass(expected_max_diff=1E-2		)



 def  lowerCAmelCase_    (				self    :						List[Any]		):
       self._test_save_load_local()



 def  lowerCAmelCase_    (				self    :						Union[str, Any]		):
       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  lowerCAmelCase_    (				self    :						Dict		):
       self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3		)





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


 '''simple docstring'''







 def  lowerCAmelCase_    (				self    :						Optional[int]		):
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()



 def  lowerCAmelCase_    (				self    :						Any		):
       # if

       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=_lowerCAmelCase							,							tokenizer=_lowerCAmelCase		)

       # 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(_lowerCAmelCase							,							_lowerCAmelCase							,							_lowerCAmelCase							,							_lowerCAmelCase		)

       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(_lowerCAmelCase							,							_lowerCAmelCase							,							_lowerCAmelCase							,							_lowerCAmelCase		)

       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(_lowerCAmelCase							,							_lowerCAmelCase							,							_lowerCAmelCase							,							_lowerCAmelCase		)



 def  lowerCAmelCase_    (				self    :						Optional[Any]							,							_lowerCAmelCase    :						Union[str, Any]							,							_lowerCAmelCase    :						Tuple							,							_lowerCAmelCase    :						Optional[int]							,							_lowerCAmelCase    :						Optional[Any]		):
       # pipeline 1

       _start_torch_memory_measurement()

       SCREAMING_SNAKE_CASE_      		=     torch.Generator(device='cpu'		).manual_seed(0		)
       SCREAMING_SNAKE_CASE_      		=     pipe_a(
           prompt_embeds=_lowerCAmelCase							,							negative_prompt_embeds=_lowerCAmelCase							,							num_inference_steps=2							,							generator=_lowerCAmelCase							,							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(_lowerCAmelCase							,							_lowerCAmelCase		)

       # 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(_lowerCAmelCase		)

       SCREAMING_SNAKE_CASE_      		=     pipe_a(
           prompt_embeds=_lowerCAmelCase							,							negative_prompt_embeds=_lowerCAmelCase							,							image=_lowerCAmelCase							,							generator=_lowerCAmelCase							,							num_inference_steps=2							,							output_type='np'							,							)

       SCREAMING_SNAKE_CASE_      		=     output.images[0]

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



 def  lowerCAmelCase_    (				self    :						Tuple							,							_lowerCAmelCase    :						Tuple							,							_lowerCAmelCase    :						str							,							_lowerCAmelCase    :						Union[str, Any]							,							_lowerCAmelCase    :						int		):
       # pipeline 1

       _start_torch_memory_measurement()

       SCREAMING_SNAKE_CASE_      		=     floats_tensor((1, 3, 64, 64)							,							rng=random.Random(0		)		).to(_lowerCAmelCase		)

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

       SCREAMING_SNAKE_CASE_      		=     pipe_a(
           prompt_embeds=_lowerCAmelCase							,							negative_prompt_embeds=_lowerCAmelCase							,							image=_lowerCAmelCase							,							num_inference_steps=2							,							generator=_lowerCAmelCase							,							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(_lowerCAmelCase							,							_lowerCAmelCase		)

       # pipeline 2

       _start_torch_memory_measurement()

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

       SCREAMING_SNAKE_CASE_      		=     floats_tensor((1, 3, 256, 256)							,							rng=random.Random(0		)		).to(_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     floats_tensor((1, 3, 64, 64)							,							rng=random.Random(0		)		).to(_lowerCAmelCase		)

       SCREAMING_SNAKE_CASE_      		=     pipe_a(
           prompt_embeds=_lowerCAmelCase							,							negative_prompt_embeds=_lowerCAmelCase							,							image=_lowerCAmelCase							,							original_image=_lowerCAmelCase							,							generator=_lowerCAmelCase							,							num_inference_steps=2							,							output_type='np'							,							)

       SCREAMING_SNAKE_CASE_      		=     output.images[0]

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




 def  lowerCAmelCase_    (				self    :						List[Any]							,							_lowerCAmelCase    :						Dict							,							_lowerCAmelCase    :						List[Any]							,							_lowerCAmelCase    :						Dict							,							_lowerCAmelCase    :						int		):
       # pipeline 1

       _start_torch_memory_measurement()

       SCREAMING_SNAKE_CASE_      		=     floats_tensor((1, 3, 64, 64)							,							rng=random.Random(0		)		).to(_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     floats_tensor((1, 3, 64, 64)							,							rng=random.Random(1		)		).to(_lowerCAmelCase		)

       SCREAMING_SNAKE_CASE_      		=     torch.Generator(device='cpu'		).manual_seed(0		)
       SCREAMING_SNAKE_CASE_      		=     pipe_a(
           prompt_embeds=_lowerCAmelCase							,							negative_prompt_embeds=_lowerCAmelCase							,							image=_lowerCAmelCase							,							mask_image=_lowerCAmelCase							,							num_inference_steps=2							,							generator=_lowerCAmelCase							,							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(_lowerCAmelCase							,							_lowerCAmelCase		)

       # 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(_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     floats_tensor((1, 3, 256, 256)							,							rng=random.Random(0		)		).to(_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     floats_tensor((1, 3, 256, 256)							,							rng=random.Random(1		)		).to(_lowerCAmelCase		)

       SCREAMING_SNAKE_CASE_      		=     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'							,							)

       SCREAMING_SNAKE_CASE_      		=     output.images[0]

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


def    UpperCAmelCase_				( )   ->					Optional[Any]:
      torch.cuda.empty_cache()
      torch.cuda.reset_max_memory_allocated()
      torch.cuda.reset_peak_memory_stats()

"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__SCREAMING_SNAKE_CASE							=     {
    'configuration_whisper': ['WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'WhisperConfig', 'WhisperOnnxConfig'],
    'feature_extraction_whisper': ['WhisperFeatureExtractor'],
    'processing_whisper': ['WhisperProcessor'],
    'tokenization_whisper': ['WhisperTokenizer'],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __SCREAMING_SNAKE_CASE							=     ['WhisperTokenizerFast']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __SCREAMING_SNAKE_CASE							=     [
     'WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST',
     'WhisperForConditionalGeneration',
     'WhisperModel',
     'WhisperPreTrainedModel',
     'WhisperForAudioClassification',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __SCREAMING_SNAKE_CASE							=     [
     'TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST',
     'TFWhisperForConditionalGeneration',
     'TFWhisperModel',
     'TFWhisperPreTrainedModel',
 ]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __SCREAMING_SNAKE_CASE							=     [
     'FlaxWhisperForConditionalGeneration',
     'FlaxWhisperModel',
     'FlaxWhisperPreTrainedModel',
     'FlaxWhisperForAudioClassification',
 ]


if TYPE_CHECKING:
 from .configuration_whisper import WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP, WhisperConfig, WhisperOnnxConfig
 from .feature_extraction_whisper import WhisperFeatureExtractor
 from .processing_whisper import WhisperProcessor
 from .tokenization_whisper import WhisperTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_whisper_fast import WhisperTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_whisper import (
      WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
      WhisperForAudioClassification,
      WhisperForConditionalGeneration,
      WhisperModel,
      WhisperPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_whisper import (
      TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFWhisperForConditionalGeneration,
      TFWhisperModel,
      TFWhisperPreTrainedModel,
  )

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_whisper import (
      FlaxWhisperForAudioClassification,
      FlaxWhisperForConditionalGeneration,
      FlaxWhisperModel,
      FlaxWhisperPreTrainedModel,
  )

else:
 import sys

 __SCREAMING_SNAKE_CASE							=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
