

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_    (  lowercase	,     lowercase	,     unittest.TestCase		):
							__lowercase				:	List[Any]        =				IFPipeline
							__lowercase				:	Tuple        =				TEXT_TO_IMAGE_PARAMS - {"width", "height", "latents"}
							__lowercase				:	Tuple        =				TEXT_TO_IMAGE_BATCH_PARAMS
							__lowercase				:	List[str]        =				PipelineTesterMixin.required_optional_params - {"latents"}


							def  lowercase   ( self						)							->   List[Any]:



								"""simple docstring"""

								return self._get_dummy_components()


							def  lowercase   ( self      ,				lowerCamelCase_      ,				lowerCamelCase_=0						)							->   List[str]:



								"""simple docstring"""

								if str(A_						).startswith("mps"						):
									_UpperCamelCase			=   torch.manual_seed(A_						)
								else:
									_UpperCamelCase			=   torch.Generator(device=A_						).manual_seed(A_						)

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

								return inputs


							def  lowercase   ( self						)							->   Union[str, Any]:



								"""simple docstring"""

								self._test_save_load_optional_components()


							@unittest.skipIf(torch_device != "cuda"      ,				reason="float16 requires CUDA"						)
							def  lowercase   ( self						)							->   List[str]:



								"""simple docstring"""

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


							def  lowercase   ( self						)							->   Any:



								"""simple docstring"""

								self._test_attention_slicing_forward_pass(expected_max_diff=1E-2						)


							def  lowercase   ( self						)							->   Any:



								"""simple docstring"""

								self._test_save_load_local()


							def  lowercase   ( 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  lowercase   ( self						)							->   Any:



								"""simple docstring"""

								self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3						)






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


							def  lowercase   ( self						)							->   Tuple:



								"""simple docstring"""

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


							def  lowercase   ( self						)							->   List[Any]:



								"""simple docstring"""

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

								_UpperCamelCase			=   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"						)

								_UpperCamelCase			=   pipe_a.encode_prompt("anime turtle"      ,				device="cuda"						)

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

								_UpperCamelCase			=   None
								_UpperCamelCase			=   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

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

								_UpperCamelCase			=   IFInpaintingPipeline(**pipe_a.components						)
								_UpperCamelCase			=   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  lowercase   ( self      ,				lowerCamelCase_      ,				lowerCamelCase_      ,				lowerCamelCase_      ,				lowerCamelCase_						)							->   Optional[Any]:



								"""simple docstring"""

								_start_torch_memory_measurement()

								_UpperCamelCase			=   torch.Generator(device="cpu"						).manual_seed(0						)
								_UpperCamelCase			=   pipe_a(
								    prompt_embeds=A_      ,				negative_prompt_embeds=A_      ,				num_inference_steps=2      ,				generator=A_      ,				output_type="np"      ,				)

								_UpperCamelCase			=   output.images[0]

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

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

								_UpperCamelCase			=   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()

								_UpperCamelCase			=   torch.Generator(device="cpu"						).manual_seed(0						)

								_UpperCamelCase			=   floats_tensor((1, 3, 64, 64)      ,				rng=random.Random(0						)						).to(A_						)

								_UpperCamelCase			=   pipe_a(
								    prompt_embeds=A_      ,				negative_prompt_embeds=A_      ,				image=A_      ,				generator=A_      ,				num_inference_steps=2      ,				output_type="np"      ,				)

								_UpperCamelCase			=   output.images[0]

								assert image.shape == (2_56, 2_56, 3)

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

								_UpperCamelCase			=   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  lowercase   ( self      ,				lowerCamelCase_      ,				lowerCamelCase_      ,				lowerCamelCase_      ,				lowerCamelCase_						)							->   Optional[Any]:



								"""simple docstring"""

								_start_torch_memory_measurement()

								_UpperCamelCase			=   floats_tensor((1, 3, 64, 64)      ,				rng=random.Random(0						)						).to(A_						)

								_UpperCamelCase			=   torch.Generator(device="cpu"						).manual_seed(0						)

								_UpperCamelCase			=   pipe_a(
								    prompt_embeds=A_      ,				negative_prompt_embeds=A_      ,				image=A_      ,				num_inference_steps=2      ,				generator=A_      ,				output_type="np"      ,				)

								_UpperCamelCase			=   output.images[0]

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

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

								_UpperCamelCase			=   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()

								_UpperCamelCase			=   torch.Generator(device="cpu"						).manual_seed(0						)

								_UpperCamelCase			=   floats_tensor((1, 3, 2_56, 2_56)      ,				rng=random.Random(0						)						).to(A_						)
								_UpperCamelCase			=   floats_tensor((1, 3, 64, 64)      ,				rng=random.Random(0						)						).to(A_						)

								_UpperCamelCase			=   pipe_a(
								    prompt_embeds=A_      ,				negative_prompt_embeds=A_      ,				image=A_      ,				original_image=A_      ,				generator=A_      ,				num_inference_steps=2      ,				output_type="np"      ,				)

								_UpperCamelCase			=   output.images[0]

								assert image.shape == (2_56, 2_56, 3)

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

								_UpperCamelCase			=   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  lowercase   ( self      ,				lowerCamelCase_      ,				lowerCamelCase_      ,				lowerCamelCase_      ,				lowerCamelCase_						)							->   Dict:



								"""simple docstring"""

								_start_torch_memory_measurement()

								_UpperCamelCase			=   floats_tensor((1, 3, 64, 64)      ,				rng=random.Random(0						)						).to(A_						)
								_UpperCamelCase			=   floats_tensor((1, 3, 64, 64)      ,				rng=random.Random(1						)						).to(A_						)

								_UpperCamelCase			=   torch.Generator(device="cpu"						).manual_seed(0						)
								_UpperCamelCase			=   pipe_a(
								    prompt_embeds=A_      ,				negative_prompt_embeds=A_      ,				image=A_      ,				mask_image=A_      ,				num_inference_steps=2      ,				generator=A_      ,				output_type="np"      ,				)

								_UpperCamelCase			=   output.images[0]

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

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

								_UpperCamelCase			=   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()

								_UpperCamelCase			=   torch.Generator(device="cpu"						).manual_seed(0						)

								_UpperCamelCase			=   floats_tensor((1, 3, 64, 64)      ,				rng=random.Random(0						)						).to(A_						)
								_UpperCamelCase			=   floats_tensor((1, 3, 2_56, 2_56)      ,				rng=random.Random(0						)						).to(A_						)
								_UpperCamelCase			=   floats_tensor((1, 3, 2_56, 2_56)      ,				rng=random.Random(1						)						).to(A_						)

								_UpperCamelCase			=   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"      ,				)

								_UpperCamelCase			=   output.images[0]

								assert image.shape == (2_56, 2_56, 3)

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

								_UpperCamelCase			=   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    _lowercase			(	)							->					List[str]:





	"""simple docstring"""


	torch.cuda.empty_cache()
	torch.cuda.reset_max_memory_allocated()
	torch.cuda.reset_peak_memory_stats()








from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


SCREAMING_SNAKE_CASE__					:     Optional[int]       =     logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__					:     List[str]       =     {
    'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/config.json',
    'umberto-commoncrawl-cased-v1': (
        'https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json'
    ),
    'umberto-wikipedia-uncased-v1': (
        'https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json'
    ),
}


class 		_SCREAMING_SNAKE_CASE     (						A     ):
     __SCREAMING_SNAKE_CASE     					=    '''camembert'''


     def __init__(  self							,					A_=3_05_22							,					A_=7_68							,					A_=12							,					A_=12							,					A_=30_72							,					A_="gelu"							,					A_=0.1							,					A_=0.1							,					A_=5_12							,					A_=2							,					A_=0.0_2							,					A_=1e-12							,					A_=1							,					A_=0							,					A_=2							,					A_="absolute"							,					A_=True							,					A_=None							,					**A_							,					):
       super().__init__(pad_token_id=A_							,					bos_token_id=A_							,					eos_token_id=A_							,					**A_    )

       _UpperCAmelCase		:							Optional[Any]							=     vocab_size
       _UpperCAmelCase		:							int							=     hidden_size
       _UpperCAmelCase		:							Any							=     num_hidden_layers
       _UpperCAmelCase		:							Optional[Any]							=     num_attention_heads
       _UpperCAmelCase		:							Dict							=     hidden_act
       _UpperCAmelCase		:							str							=     intermediate_size
       _UpperCAmelCase		:							str							=     hidden_dropout_prob
       _UpperCAmelCase		:							List[Any]							=     attention_probs_dropout_prob
       _UpperCAmelCase		:							List[Any]							=     max_position_embeddings
       _UpperCAmelCase		:							Union[str, Any]							=     type_vocab_size
       _UpperCAmelCase		:							List[Any]							=     initializer_range
       _UpperCAmelCase		:							Optional[Any]							=     layer_norm_eps
       _UpperCAmelCase		:							int							=     position_embedding_type
       _UpperCAmelCase		:							Tuple							=     use_cache
       _UpperCAmelCase		:							Any							=     classifier_dropout


class 		_SCREAMING_SNAKE_CASE     (						A     ):


     @property
     def        __snake_case(  self    ):
       if self.task == "multiple-choice":
         _UpperCAmelCase		:							Any							=     {0: """batch""", 1: """choice""", 2: """sequence"""}
       else:
         _UpperCAmelCase		:							List[Any]							=     {0: """batch""", 1: """sequence"""}
       return OrderedDict(
           [
               ("""input_ids""", dynamic_axis),
               ("""attention_mask""", dynamic_axis),
           ]    )





