



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      _UpperCAmelCase      (							A__      ,   A__      ,   unittest.TestCase  ):
     UpperCamelCase__		     =					IFPipeline
     UpperCamelCase__		     =					TEXT_TO_IMAGE_PARAMS - {'''width''', '''height''', '''latents'''}
     UpperCamelCase__		     =					TEXT_TO_IMAGE_BATCH_PARAMS
     UpperCamelCase__		     =					PipelineTesterMixin.required_optional_params - {'''latents'''}




     def 		snake_case_		(					self):
         return self._get_dummy_components()




     def 		snake_case_		(					self			,    a__			,    a__=0):
         if str(a__).startswith('''mps'''):
             A__					=       torch.manual_seed(a__)
         else:
             A__					=       torch.Generator(device=a__).manual_seed(a__)

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

         return inputs




     def 		snake_case_		(					self):
         self._test_save_load_optional_components()




     @unittest.skipIf(torch_device != '''cuda'''			,    reason='''float16 requires CUDA''')
     def 		snake_case_		(					self):
         # 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 		snake_case_		(					self):
         self._test_attention_slicing_forward_pass(expected_max_diff=1e-2)




     def 		snake_case_		(					self):
         self._test_save_load_local()




     def 		snake_case_		(					self):
         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 		snake_case_		(					self):
         self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3)







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




     def 		snake_case_		(					self):
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()




     def 		snake_case_		(					self):
         # if

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

         A__					=       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''')

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

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

         A__					=       None
         A__					=       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

         A__					=       IFImgaImgPipeline(**pipe_a.components)
         A__					=       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

         A__					=       IFInpaintingPipeline(**pipe_a.components)
         A__					=       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 		snake_case_		(					self			,    a__			,    a__			,    a__			,    a__):
         # pipeline 1

         _start_torch_memory_measurement()

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

         A__					=       output.images[0]

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

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

         A__					=       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()

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

         A__					=       floats_tensor((1, 3, 6_4, 6_4)			,    rng=random.Random(0)).to(a__)

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

         A__					=       output.images[0]

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

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

         A__					=       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 		snake_case_		(					self			,    a__			,    a__			,    a__			,    a__):
         # pipeline 1

         _start_torch_memory_measurement()

         A__					=       floats_tensor((1, 3, 6_4, 6_4)			,    rng=random.Random(0)).to(a__)

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

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

         A__					=       output.images[0]

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

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

         A__					=       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()

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

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

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

         A__					=       output.images[0]

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

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

         A__					=       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 		snake_case_		(					self			,    a__			,    a__			,    a__			,    a__):
         # pipeline 1

         _start_torch_memory_measurement()

         A__					=       floats_tensor((1, 3, 6_4, 6_4)			,    rng=random.Random(0)).to(a__)
         A__					=       floats_tensor((1, 3, 6_4, 6_4)			,    rng=random.Random(1)).to(a__)

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

         A__					=       output.images[0]

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

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

         A__					=       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()

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

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

         A__					=       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'''			,    )

         A__					=       output.images[0]

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

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

         A__					=       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__     (       )->	Any:
    torch.cuda.empty_cache()
    torch.cuda.reset_max_memory_allocated()
    torch.cuda.reset_peak_memory_stats()





from typing import TYPE_CHECKING

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


_lowercase    	=		{
    "configuration_mctct": ["MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MCTCTConfig"],
    "feature_extraction_mctct": ["MCTCTFeatureExtractor"],
    "processing_mctct": ["MCTCTProcessor"],
}


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowercase    	=		[
       "MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST",
       "MCTCTForCTC",
       "MCTCTModel",
       "MCTCTPreTrainedModel",
   ]


if TYPE_CHECKING:
   from .configuration_mctct import MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP, MCTCTConfig
   from .feature_extraction_mctct import MCTCTFeatureExtractor
   from .processing_mctct import MCTCTProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_mctct import MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST, MCTCTForCTC, MCTCTModel, MCTCTPreTrainedModel

else:
   import sys

   _lowercase    	=		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
