






'''simple docstring'''




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()
lowerCAmelCase__          =						logging.get_logger(__name__)







def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_)							->     Tuple:
		UpperCamelCase__				:					str					=					RobertaPreLayerNormConfig.from_pretrained(
		    lowerCamelCase_     ,      architectures=['RobertaPreLayerNormForMaskedLM'])

		# convert state_dict
		UpperCamelCase__				:					Tuple					=					torch.load(hf_hub_download(repo_id=lowerCamelCase_     ,      filename='pytorch_model.bin'))
		UpperCamelCase__				:					Union[str, Any]					=					{}
		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.'):
						UpperCamelCase__				:					Tuple					=					'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

				UpperCamelCase__				:					Tuple					=					tensor_value

		UpperCamelCase__				:					Union[str, Any]					=					RobertaPreLayerNormForMaskedLM.from_pretrained(
		    pretrained_model_name_or_path=lowerCamelCase_     ,      config=lowerCamelCase_     ,      state_dict=lowerCamelCase_)
		model.save_pretrained(lowerCamelCase_)

		# convert tokenizer
		UpperCamelCase__				:					Any					=					AutoTokenizer.from_pretrained(lowerCamelCase_)
		tokenizer.save_pretrained(lowerCamelCase_)


if __name__ == "__main__":
		lowerCAmelCase__          =						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.'
		)
		lowerCAmelCase__          =						parser.parse_args()
		convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)












'''simple docstring'''




import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings
from diffusers.utils import load_numpy, slow, torch_device
from diffusers.utils.testing_utils import require_torch_gpu


lowerCAmelCase__          =						False







class       __lowercase					(unittest.TestCase    ):

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

	@property
	def      __UpperCamelCase							(      self   :						int):
			return 12

	@property
	def      __UpperCamelCase							(      self   :						Tuple):
			return 12

	@property
	def      __UpperCamelCase							(      self   :						Dict):
			return 32

	@property
	def      __UpperCamelCase							(      self   :						Optional[int]):
			torch.manual_seed(0)
			UpperCamelCase__				:					List[Any]					=					VQModel(
			    block_out_channels=[32, 64]  ,					in_channels=3  ,					out_channels=3  ,					down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']  ,					up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']  ,					latent_channels=3  ,					num_vq_embeddings=self.num_embed  ,					vq_embed_dim=3  ,					)
			return model

	@property
	def      __UpperCamelCase							(      self   :						Optional[Any]):
			UpperCamelCase__				:					Any					=					CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip')
			return tokenizer

	@property
	def      __UpperCamelCase							(      self   :						List[str]):
			torch.manual_seed(0)
			UpperCamelCase__				:					Optional[int]					=					CLIPTextConfig(
			    bos_token_id=0  ,					eos_token_id=2  ,					hidden_size=self.text_embedder_hidden_size  ,					intermediate_size=37  ,					layer_norm_eps=1e-05  ,					num_attention_heads=4  ,					num_hidden_layers=5  ,					pad_token_id=1  ,					vocab_size=1_000  ,					)
			return CLIPTextModel(UpperCAmelCase_)

	@property
	def      __UpperCamelCase							(      self   :						Optional[int]):
			torch.manual_seed(0)

			UpperCamelCase__				:					List[Any]					=					12
			UpperCamelCase__				:					Dict					=					12

			UpperCamelCase__				:					Union[str, Any]					=					{
			    'attention_bias': True,
			    'cross_attention_dim': 32,
			    'attention_head_dim': height * width,
			    'num_attention_heads': 1,
			    'num_vector_embeds': self.num_embed,
			    'num_embeds_ada_norm': self.num_embeds_ada_norm,
			    'norm_num_groups': 32,
			    'sample_size': width,
			    'activation_fn': 'geglu-approximate',
			}

			UpperCamelCase__				:					Tuple					=					TransformeraDModel(**UpperCAmelCase_)
			return model

	def      __UpperCamelCase							(      self   :						int):
			UpperCamelCase__				:					List[Any]					=					'cpu'

			UpperCamelCase__				:					List[str]					=					self.dummy_vqvae
			UpperCamelCase__				:					List[str]					=					self.dummy_text_encoder
			UpperCamelCase__				:					Optional[int]					=					self.dummy_tokenizer
			UpperCamelCase__				:					List[str]					=					self.dummy_transformer
			UpperCamelCase__				:					Dict					=					VQDiffusionScheduler(self.num_embed)
			UpperCamelCase__				:					List[Any]					=					LearnedClassifierFreeSamplingEmbeddings(learnable=UpperCAmelCase_)

			UpperCamelCase__				:					int					=					VQDiffusionPipeline(
			    vqvae=UpperCAmelCase_  ,					text_encoder=UpperCAmelCase_  ,					tokenizer=UpperCAmelCase_  ,					transformer=UpperCAmelCase_  ,					scheduler=UpperCAmelCase_  ,					learned_classifier_free_sampling_embeddings=UpperCAmelCase_  ,					)
			UpperCamelCase__				:					Optional[Any]					=					pipe.to(UpperCAmelCase_)
			pipe.set_progress_bar_config(disable=UpperCAmelCase_)

			UpperCamelCase__				:					Optional[Any]					=					'teddy bear playing in the pool'

			UpperCamelCase__				:					Dict					=					torch.Generator(device=UpperCAmelCase_).manual_seed(0)
			UpperCamelCase__				:					Any					=					pipe([prompt]  ,					generator=UpperCAmelCase_  ,					num_inference_steps=2  ,					output_type='np')
			UpperCamelCase__				:					Optional[Any]					=					output.images

			UpperCamelCase__				:					int					=					torch.Generator(device=UpperCAmelCase_).manual_seed(0)
			UpperCamelCase__				:					Any					=					pipe(
			    [prompt]  ,					generator=UpperCAmelCase_  ,					output_type='np'  ,					return_dict=UpperCAmelCase_  ,					num_inference_steps=2)[0]

			UpperCamelCase__				:					Optional[Any]					=					image[0, -3:, -3:, -1]
			UpperCamelCase__				:					Any					=					image_from_tuple[0, -3:, -3:, -1]

			assert image.shape == (1, 24, 24, 3)

			UpperCamelCase__				:					Any					=					np.array([0.65_51, 0.61_68, 0.50_08, 0.56_76, 0.56_59, 0.42_95, 0.60_73, 0.55_99, 0.49_92])

			assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
			assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2





	def      __UpperCamelCase							(      self   :						Optional[int]):
			UpperCamelCase__				:					Optional[int]					=					'cpu'

			UpperCamelCase__				:					str					=					self.dummy_vqvae
			UpperCamelCase__				:					Any					=					self.dummy_text_encoder
			UpperCamelCase__				:					List[Any]					=					self.dummy_tokenizer
			UpperCamelCase__				:					Dict					=					self.dummy_transformer
			UpperCamelCase__				:					Optional[Any]					=					VQDiffusionScheduler(self.num_embed)
			UpperCamelCase__				:					Optional[Any]					=					LearnedClassifierFreeSamplingEmbeddings(
			    learnable=UpperCAmelCase_  ,					hidden_size=self.text_embedder_hidden_size  ,					length=tokenizer.model_max_length)

			UpperCamelCase__				:					str					=					VQDiffusionPipeline(
			    vqvae=UpperCAmelCase_  ,					text_encoder=UpperCAmelCase_  ,					tokenizer=UpperCAmelCase_  ,					transformer=UpperCAmelCase_  ,					scheduler=UpperCAmelCase_  ,					learned_classifier_free_sampling_embeddings=UpperCAmelCase_  ,					)
			UpperCamelCase__				:					str					=					pipe.to(UpperCAmelCase_)
			pipe.set_progress_bar_config(disable=UpperCAmelCase_)

			UpperCamelCase__				:					List[Any]					=					'teddy bear playing in the pool'

			UpperCamelCase__				:					Union[str, Any]					=					torch.Generator(device=UpperCAmelCase_).manual_seed(0)
			UpperCamelCase__				:					Any					=					pipe([prompt]  ,					generator=UpperCAmelCase_  ,					num_inference_steps=2  ,					output_type='np')
			UpperCamelCase__				:					int					=					output.images

			UpperCamelCase__				:					List[Any]					=					torch.Generator(device=UpperCAmelCase_).manual_seed(0)
			UpperCamelCase__				:					Optional[Any]					=					pipe(
			    [prompt]  ,					generator=UpperCAmelCase_  ,					output_type='np'  ,					return_dict=UpperCAmelCase_  ,					num_inference_steps=2)[0]

			UpperCamelCase__				:					Union[str, Any]					=					image[0, -3:, -3:, -1]
			UpperCamelCase__				:					Dict					=					image_from_tuple[0, -3:, -3:, -1]

			assert image.shape == (1, 24, 24, 3)

			UpperCamelCase__				:					str					=					np.array([0.66_93, 0.60_75, 0.49_59, 0.57_01, 0.55_83, 0.43_33, 0.61_71, 0.56_84, 0.49_88])

			assert np.abs(image_slice.flatten() - expected_slice).max() < 2.0
			assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2







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

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





	def      __UpperCamelCase							(      self   :						List[Any]):
			UpperCamelCase__				:					Optional[Any]					=					load_numpy(
			    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
			    '/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy')

			UpperCamelCase__				:					List[Any]					=					VQDiffusionPipeline.from_pretrained('microsoft/vq-diffusion-ithq')
			UpperCamelCase__				:					Any					=					pipeline.to(UpperCAmelCase_)
			pipeline.set_progress_bar_config(disable=UpperCAmelCase_)

			# requires GPU generator for gumbel softmax
			# don't use GPU generator in tests though
			UpperCamelCase__				:					Optional[int]					=					torch.Generator(device=UpperCAmelCase_).manual_seed(0)
			UpperCamelCase__				:					int					=					pipeline(
			    'teddy bear playing in the pool'  ,					num_images_per_prompt=1  ,					generator=UpperCAmelCase_  ,					output_type='np'  ,					)

			UpperCamelCase__				:					int					=					output.images[0]

			assert image.shape == (256, 256, 3)
			assert np.abs(expected_image - image).max() < 2.0




