






import gc
import random
import unittest

import numpy as np
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModel,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableUnCLIPImgaImgPipeline, UNetaDConditionModel
from diffusers.pipelines.pipeline_utils import DiffusionPipeline
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    floats_tensor,
    load_image,
    load_numpy,
    require_torch_gpu,
    skip_mps,
    slow,
    torch_device,
)

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
    assert_mean_pixel_difference,
)


enable_full_determinism()


class        __UpperCamelCase      (     _lowerCAmelCase   ,			_lowerCAmelCase   ,			_lowerCAmelCase   ,			unittest.TestCase   ):
	__snake_case	:Any   =       StableUnCLIPImgaImgPipeline
	__snake_case	:List[str]   =       TEXT_GUIDED_IMAGE_VARIATION_PARAMS
	__snake_case	:Tuple   =       TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
	__snake_case	:List[Any]   =       frozenset(
	    []   )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
	__snake_case	:Dict   =       frozenset([]   )







	def  _a   (  self      :							Optional[Any]			)		-> str:
		"""simple docstring"""







		__lowercase             =  32
		__lowercase             =  embedder_hidden_size

		# image encoding components

		__lowercase             =  CLIPImageProcessor(crop_size=32       ,				size=32			)

		torch.manual_seed(0			)
		__lowercase             =  CLIPVisionModelWithProjection(
		    CLIPVisionConfig(
		        hidden_size=_lowerCAmelCase       ,				projection_dim=_lowerCAmelCase       ,				num_hidden_layers=5       ,				num_attention_heads=4       ,				image_size=32       ,				intermediate_size=37       ,				patch_size=1       ,				)			)

		# regular denoising components

		torch.manual_seed(0			)
		__lowercase             =  StableUnCLIPImageNormalizer(embedding_dim=_lowerCAmelCase			)
		__lowercase             =  DDPMScheduler(beta_schedule="""squaredcos_cap_v2"""			)

		torch.manual_seed(0			)
		__lowercase             =  CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""			)

		torch.manual_seed(0			)
		__lowercase             =  CLIPTextModel(
		    CLIPTextConfig(
		        bos_token_id=0       ,				eos_token_id=2       ,				hidden_size=_lowerCAmelCase       ,				projection_dim=32       ,				intermediate_size=37       ,				layer_norm_eps=1e-05       ,				num_attention_heads=4       ,				num_hidden_layers=5       ,				pad_token_id=1       ,				vocab_size=1000       ,				)			)

		torch.manual_seed(0			)
		__lowercase             =  UNetaDConditionModel(
		    sample_size=32       ,				in_channels=4       ,				out_channels=4       ,				down_block_types=("""CrossAttnDownBlock2D""", """DownBlock2D""")       ,				up_block_types=("""UpBlock2D""", """CrossAttnUpBlock2D""")       ,				block_out_channels=(32, 64)       ,				attention_head_dim=(2, 4)       ,				class_embed_type="""projection"""       ,				projection_class_embeddings_input_dim=embedder_projection_dim * 2       ,				cross_attention_dim=_lowerCAmelCase       ,				layers_per_block=1       ,				upcast_attention=_lowerCAmelCase       ,				use_linear_projection=_lowerCAmelCase       ,				)

		torch.manual_seed(0			)
		__lowercase             =  DDIMScheduler(
		    beta_schedule="""scaled_linear"""       ,				beta_start=0.00_085       ,				beta_end=0.012       ,				prediction_type="""v_prediction"""       ,				set_alpha_to_one=_lowerCAmelCase       ,				steps_offset=1       ,				)

		torch.manual_seed(0			)
		__lowercase             =  AutoencoderKL()

		__lowercase             =  {
		    # image encoding components
		    """feature_extractor""": feature_extractor,
		    """image_encoder""": image_encoder.eval(),
		    # image noising components
		    """image_normalizer""": image_normalizer.eval(),
		    """image_noising_scheduler""": image_noising_scheduler,
		    # regular denoising components
		    """tokenizer""": tokenizer,
		    """text_encoder""": text_encoder.eval(),
		    """unet""": unet.eval(),
		    """scheduler""": scheduler,
		    """vae""": vae.eval(),
		}

		return components







	def  _a   (  self      :							List[Any]       ,				_lowerCAmelCase      :							int       ,				_lowerCAmelCase      :							Union[str, Any]=0       ,				_lowerCAmelCase      :							Optional[int]=True			)		-> Tuple:
		"""simple docstring"""







		if str(_lowerCAmelCase			).startswith("""mps"""			):
			__lowercase             =  torch.manual_seed(_lowerCAmelCase			)
		else:
			__lowercase             =  torch.Generator(device=_lowerCAmelCase			).manual_seed(_lowerCAmelCase			)

		__lowercase             =  floats_tensor((1, 3, 32, 32)       ,				rng=random.Random(_lowerCAmelCase			)			).to(_lowerCAmelCase			)

		if pil_image:
			__lowercase             =  input_image * 0.5 + 0.5
			__lowercase             =  input_image.clamp(0       ,				1			)
			__lowercase             =  input_image.cpu().permute(0       ,				2       ,				3       ,				1			).float().numpy()
			__lowercase             =  DiffusionPipeline.numpy_to_pil(_lowerCAmelCase			)[0]

		return {
		    "prompt": "An anime racoon running a marathon",
		    "image": input_image,
		    "generator": generator,
		    "num_inference_steps": 2,
		    "output_type": "np",
		}







	@skip_mps
	def  _a   (  self      :							str			)		-> Any:
		"""simple docstring"""







		__lowercase             =  """cpu"""  # ensure determinism for the device-dependent torch.Generator
		__lowercase             =  self.get_dummy_components()
		__lowercase             =  StableUnCLIPImgaImgPipeline(**_lowerCAmelCase			)
		__lowercase             =  sd_pipe.to(_lowerCAmelCase			)
		sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase			)

		__lowercase             =  self.get_dummy_inputs(_lowerCAmelCase			)
		inputs.update({"""image_embeds""": None}			)
		__lowercase             =  sd_pipe(**_lowerCAmelCase			).images
		__lowercase             =  image[0, -3:, -3:, -1]

		assert image.shape == (1, 32, 32, 3)
		__lowercase             =  np.array([0.3_872, 0.7_224, 0.5_601, 0.4_741, 0.6_872, 0.5_814, 0.4_636, 0.3_867, 0.5_078]			)

		assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-3







	def  _a   (  self      :							List[str]			)		-> Optional[Any]:
		"""simple docstring"""







		__lowercase             =  torch_device in ["""cpu""", """mps"""]

		self._test_attention_slicing_forward_pass(test_max_difference=_lowerCAmelCase			)







	def  _a   (  self      :							Tuple			)		-> Optional[Any]:
		"""simple docstring"""







		__lowercase             =  torch_device in ["""cpu""", """mps"""]

		self._test_inference_batch_single_identical(test_max_difference=_lowerCAmelCase			)







	@unittest.skipIf(
	    torch_device != """cuda""" or not is_xformers_available()       ,				reason="""XFormers attention is only available with CUDA and `xformers` installed"""       ,				)
	def  _a   (  self      :							int			)		-> Tuple:
		"""simple docstring"""







		self._test_xformers_attention_forwardGenerator_pass(test_max_difference=_lowerCAmelCase			)


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







	def  _a   (  self      :							int			)		-> List[Any]:
		"""simple docstring"""







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







	def  _a   (  self      :							List[str]			)		-> int:
		"""simple docstring"""







		__lowercase             =  load_image(
		    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png"""			)

		__lowercase             =  load_numpy(
		    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_img2img_anime_turtle_fp16.npy"""			)

		__lowercase             =  StableUnCLIPImgaImgPipeline.from_pretrained(
		    """fusing/stable-unclip-2-1-l-img2img"""       ,				torch_dtype=torch.floataa			)
		pipe.to(_lowerCAmelCase			)
		pipe.set_progress_bar_config(disable=_lowerCAmelCase			)
		# stable unclip will oom when integration tests are run on a V100,
		# so turn on memory savings
		pipe.enable_attention_slicing()
		pipe.enable_sequential_cpu_offload()

		__lowercase             =  torch.Generator(device="""cpu"""			).manual_seed(0			)
		__lowercase             =  pipe(_lowerCAmelCase       ,				"""anime turle"""       ,				generator=_lowerCAmelCase       ,				output_type="""np"""			)

		__lowercase             =  output.images[0]

		assert image.shape == (768, 768, 3)

		assert_mean_pixel_difference(_lowerCAmelCase       ,				_lowerCAmelCase			)







	def  _a   (  self      :							str			)		-> Tuple:
		"""simple docstring"""







		__lowercase             =  load_image(
		    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png"""			)

		__lowercase             =  load_numpy(
		    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_h_img2img_anime_turtle_fp16.npy"""			)

		__lowercase             =  StableUnCLIPImgaImgPipeline.from_pretrained(
		    """fusing/stable-unclip-2-1-h-img2img"""       ,				torch_dtype=torch.floataa			)
		pipe.to(_lowerCAmelCase			)
		pipe.set_progress_bar_config(disable=_lowerCAmelCase			)
		# stable unclip will oom when integration tests are run on a V100,
		# so turn on memory savings
		pipe.enable_attention_slicing()
		pipe.enable_sequential_cpu_offload()

		__lowercase             =  torch.Generator(device="""cpu"""			).manual_seed(0			)
		__lowercase             =  pipe(_lowerCAmelCase       ,				"""anime turle"""       ,				generator=_lowerCAmelCase       ,				output_type="""np"""			)

		__lowercase             =  output.images[0]

		assert image.shape == (768, 768, 3)

		assert_mean_pixel_difference(_lowerCAmelCase       ,				_lowerCAmelCase			)







	def  _a   (  self      :							Dict			)		-> List[str]:
		"""simple docstring"""







		__lowercase             =  load_image(
		    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png"""			)

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

		__lowercase             =  StableUnCLIPImgaImgPipeline.from_pretrained(
		    """fusing/stable-unclip-2-1-h-img2img"""       ,				torch_dtype=torch.floataa			)
		__lowercase             =  pipe.to(_lowerCAmelCase			)
		pipe.set_progress_bar_config(disable=_lowerCAmelCase			)
		pipe.enable_attention_slicing()
		pipe.enable_sequential_cpu_offload()

		__lowercase             =  pipe(
		    _lowerCAmelCase       ,				"""anime turtle"""       ,				num_inference_steps=2       ,				output_type="""np"""       ,				)

		__lowercase             =  torch.cuda.max_memory_allocated()
		# make sure that less than 7 GB is allocated
		assert mem_bytes < 7 * 10**9









import argparse

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging


logging.set_verbosity_info()
__UpperCamelCase      :      str     						=						logging.get_logger(__name__)





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase					):




	'''simple docstring'''







	__lowercase             =  RobertaPreLayerNormConfig.from_pretrained(
	    lowerCamelCase				,  architectures=["""RobertaPreLayerNormForMaskedLM"""]					)

	# convert state_dict
	__lowercase             =  torch.load(hf_hub_download(repo_id=lowerCamelCase				,  filename="""pytorch_model.bin"""					)					)
	__lowercase             =  {}
	for tensor_key, tensor_value in original_state_dict.items():
		# The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
		if tensor_key.startswith("""roberta."""					):
			__lowercase             =  """roberta_prelayernorm.""" + tensor_key[len("""roberta."""					) :]

		# The original implementation contains weights which are not used, remove them from the state_dict
		if tensor_key.endswith(""".self.LayerNorm.weight"""					) or tensor_key.endswith(""".self.LayerNorm.bias"""					):
			continue

		__lowercase             =  tensor_value

	__lowercase             =  RobertaPreLayerNormForMaskedLM.from_pretrained(
	    pretrained_model_name_or_path=lowerCamelCase				,  config=lowerCamelCase				,  state_dict=lowerCamelCase					)
	model.save_pretrained(lowerCamelCase					)

	# convert tokenizer
	__lowercase             =  AutoTokenizer.from_pretrained(lowerCamelCase					)
	tokenizer.save_pretrained(lowerCamelCase					)


if __name__ == "__main__":
						__UpperCamelCase      :      List[Any]     						=						argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    """--checkpoint-repo""",
						    default=None,
						    type=str,
						    required=True,
						    help="""Path the official PyTorch dump, e.g. 'andreasmadsen/efficient_mlm_m0.40'.""",
						)
						parser.add_argument(
						    """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
						)
						__UpperCamelCase      :      Dict     						=						parser.parse_args()
						convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)

