




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()





from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase__					:  str      							=				logging.get_logger(__name__)

lowerCamelCase__					:  Tuple      							=				{
    'funnel-transformer/small': 'https://huggingface.co/funnel-transformer/small/resolve/main/config.json',
    'funnel-transformer/small-base': 'https://huggingface.co/funnel-transformer/small-base/resolve/main/config.json',
    'funnel-transformer/medium': 'https://huggingface.co/funnel-transformer/medium/resolve/main/config.json',
    'funnel-transformer/medium-base': 'https://huggingface.co/funnel-transformer/medium-base/resolve/main/config.json',
    'funnel-transformer/intermediate': (
        'https://huggingface.co/funnel-transformer/intermediate/resolve/main/config.json'
    ),
    'funnel-transformer/intermediate-base': (
        'https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/config.json'
    ),
    'funnel-transformer/large': 'https://huggingface.co/funnel-transformer/large/resolve/main/config.json',
    'funnel-transformer/large-base': 'https://huggingface.co/funnel-transformer/large-base/resolve/main/config.json',
    'funnel-transformer/xlarge': 'https://huggingface.co/funnel-transformer/xlarge/resolve/main/config.json',
    'funnel-transformer/xlarge-base': 'https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/config.json',
}





class 			lowerCamelCase_				( _SCREAMING_SNAKE_CASE   ):


 '''simple docstring'''







 lowercase_							       =       "funnel"
 lowercase_							       =       {
     "hidden_size": "d_model",
     "num_attention_heads": "n_head",
 }



 def __init__(				self    :						int							,							_lowerCAmelCase    :						Optional[int]=30_522							,							_lowerCAmelCase    :						List[str]=[4, 4, 4]							,							_lowerCAmelCase    :						Tuple=None							,							_lowerCAmelCase    :						Optional[int]=2							,							_lowerCAmelCase    :						int=768							,							_lowerCAmelCase    :						Optional[Any]=12							,							_lowerCAmelCase    :						Optional[Any]=64							,							_lowerCAmelCase    :						Optional[Any]=3_072							,							_lowerCAmelCase    :						List[str]="gelu_new"							,							_lowerCAmelCase    :						Any=0.1							,							_lowerCAmelCase    :						int=0.1							,							_lowerCAmelCase    :						Tuple=0.0							,							_lowerCAmelCase    :						List[Any]=0.1							,							_lowerCAmelCase    :						Dict=None							,							_lowerCAmelCase    :						str=1E-9							,							_lowerCAmelCase    :						Any="mean"							,							_lowerCAmelCase    :						Union[str, Any]="relative_shift"							,							_lowerCAmelCase    :						Optional[Any]=True							,							_lowerCAmelCase    :						Dict=True							,							_lowerCAmelCase    :						Tuple=True							,							**_lowerCAmelCase    :						Optional[Any]							,							):
       SCREAMING_SNAKE_CASE_      		=     vocab_size
       SCREAMING_SNAKE_CASE_      		=     block_sizes
       SCREAMING_SNAKE_CASE_      		=     [1] * len(_lowerCAmelCase		) if block_repeats is None else block_repeats
       assert len(_lowerCAmelCase		) == len(
           self.block_repeats		), "`block_sizes` and `block_repeats` should have the same length."
       SCREAMING_SNAKE_CASE_      		=     num_decoder_layers
       SCREAMING_SNAKE_CASE_      		=     d_model
       SCREAMING_SNAKE_CASE_      		=     n_head
       SCREAMING_SNAKE_CASE_      		=     d_head
       SCREAMING_SNAKE_CASE_      		=     d_inner
       SCREAMING_SNAKE_CASE_      		=     hidden_act
       SCREAMING_SNAKE_CASE_      		=     hidden_dropout
       SCREAMING_SNAKE_CASE_      		=     attention_dropout
       SCREAMING_SNAKE_CASE_      		=     activation_dropout
       SCREAMING_SNAKE_CASE_      		=     initializer_range
       SCREAMING_SNAKE_CASE_      		=     initializer_std
       SCREAMING_SNAKE_CASE_      		=     layer_norm_eps
       assert pooling_type in [
           "mean",
           "max",
       ], F"Got {pooling_type} for `pooling_type` but only 'mean' and 'max' are supported."
       SCREAMING_SNAKE_CASE_      		=     pooling_type
       assert attention_type in [
           "relative_shift",
           "factorized",
       ], F"Got {attention_type} for `attention_type` but only 'relative_shift' and 'factorized' are supported."
       SCREAMING_SNAKE_CASE_      		=     attention_type
       SCREAMING_SNAKE_CASE_      		=     separate_cls
       SCREAMING_SNAKE_CASE_      		=     truncate_seq
       SCREAMING_SNAKE_CASE_      		=     pool_q_only

       super().__init__(**_lowerCAmelCase		)



 @property
 def  lowerCAmelCase_    (				self    :						Optional[int]		):
       return sum(self.block_sizes		)



 @num_hidden_layers.setter
 def  lowerCAmelCase_    (				self    :						int							,							_lowerCAmelCase    :						List[Any]		):
       raise NotImplementedError(
           'This model does not support the setting of `num_hidden_layers`. Please set `block_sizes`.'		)



 @property
 def  lowerCAmelCase_    (				self    :						List[Any]		):
       return len(self.block_sizes		)




 @num_blocks.setter
 def  lowerCAmelCase_    (				self    :						Dict							,							_lowerCAmelCase    :						Union[str, Any]		):
       raise NotImplementedError('This model does not support the setting of `num_blocks`. Please set `block_sizes`.'		)