
import gc
import unittest

import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    PriorTransformer,
    StableUnCLIPPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.testing_utils import enable_full_determinism, load_numpy, require_torch_gpu, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
    assert_mean_pixel_difference,
)


enable_full_determinism()







class 					lowerCamelCase		(  A_							,			A_							,			A_							,			unittest.TestCase  ):
     UpperCAmelCase__    :							Optional[int]									=		StableUnCLIPPipeline
     UpperCAmelCase__    :							Dict									=		TEXT_TO_IMAGE_PARAMS
     UpperCAmelCase__    :							Union[str, Any]									=		TEXT_TO_IMAGE_BATCH_PARAMS
     UpperCAmelCase__    :							Any									=		TEXT_TO_IMAGE_IMAGE_PARAMS
     UpperCAmelCase__    :							str									=		TEXT_TO_IMAGE_IMAGE_PARAMS

     # TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false
     UpperCAmelCase__    :							Union[str, Any]									=		False





     def 		UpperCAmelCase(self				:   Tuple       )						->						Union[str, Any]:
         snake_case								= 3_2
         snake_case								= embedder_hidden_size

         # prior components

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

         torch.manual_seed(0       )
         snake_case								= CLIPTextModelWithProjection(
             CLIPTextConfig(
                 bos_token_id=0    ,   eos_token_id=2    ,   hidden_size=_A    ,   projection_dim=_A    ,   intermediate_size=3_7    ,   layer_norm_eps=1E-05    ,   num_attention_heads=4    ,   num_hidden_layers=5    ,   pad_token_id=1    ,   vocab_size=1_0_0_0    ,   )       )

         torch.manual_seed(0       )
         snake_case								= PriorTransformer(
             num_attention_heads=2    ,   attention_head_dim=1_2    ,   embedding_dim=_A    ,   num_layers=1    ,   )

         torch.manual_seed(0       )
         snake_case								= DDPMScheduler(
             variance_type="fixed_small_log"    ,   prediction_type="sample"    ,   num_train_timesteps=1_0_0_0    ,   clip_sample=_A    ,   clip_sample_range=5.0    ,   beta_schedule="squaredcos_cap_v2"    ,   )

         # regular denoising components

         torch.manual_seed(0       )
         snake_case								= StableUnCLIPImageNormalizer(embedding_dim=_A       )
         snake_case								= DDPMScheduler(beta_schedule="squaredcos_cap_v2"       )

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

         torch.manual_seed(0       )
         snake_case								= CLIPTextModel(
             CLIPTextConfig(
                 bos_token_id=0    ,   eos_token_id=2    ,   hidden_size=_A    ,   projection_dim=3_2    ,   intermediate_size=3_7    ,   layer_norm_eps=1E-05    ,   num_attention_heads=4    ,   num_hidden_layers=5    ,   pad_token_id=1    ,   vocab_size=1_0_0_0    ,   )       )

         torch.manual_seed(0       )
         snake_case								= UNetaDConditionModel(
             sample_size=3_2    ,   in_channels=4    ,   out_channels=4    ,   down_block_types=("CrossAttnDownBlock2D", "DownBlock2D")    ,   up_block_types=("UpBlock2D", "CrossAttnUpBlock2D")    ,   block_out_channels=(3_2, 6_4)    ,   attention_head_dim=(2, 4)    ,   class_embed_type="projection"    ,   projection_class_embeddings_input_dim=embedder_projection_dim * 2    ,   cross_attention_dim=_A    ,   layers_per_block=1    ,   upcast_attention=_A    ,   use_linear_projection=_A    ,   )

         torch.manual_seed(0       )
         snake_case								= DDIMScheduler(
             beta_schedule="scaled_linear"    ,   beta_start=0.0_00_85    ,   beta_end=0.0_12    ,   prediction_type="v_prediction"    ,   set_alpha_to_one=_A    ,   steps_offset=1    ,   )

         torch.manual_seed(0       )
         snake_case								= AutoencoderKL()

         snake_case								= {
             # prior components
             "prior_tokenizer": prior_tokenizer,
             "prior_text_encoder": prior_text_encoder,
             "prior": prior,
             "prior_scheduler": prior_scheduler,
             # image noising components
             "image_normalizer": image_normalizer,
             "image_noising_scheduler": image_noising_scheduler,
             # regular denoising components
             "tokenizer": tokenizer,
             "text_encoder": text_encoder,
             "unet": unet,
             "scheduler": scheduler,
             "vae": vae,
         }

         return components





     def 		UpperCAmelCase(self				:   Union[str, Any]    ,   _A				:   List[Any]    ,   _A				:   str=0       )						->						List[Any]:
         if str(_A       ).startswith("mps"       ):
             snake_case								= torch.manual_seed(_A       )
         else:
             snake_case								= torch.Generator(device=_A       ).manual_seed(_A       )
         snake_case								= {
             "prompt": "A painting of a squirrel eating a burger",
             "generator": generator,
             "num_inference_steps": 2,
             "prior_num_inference_steps": 2,
             "output_type": "numpy",
         }
         return inputs





     def 		UpperCAmelCase(self				:   Tuple       )						->						str:
         snake_case								= torch_device == "cpu"

         self._test_attention_slicing_forward_pass(test_max_difference=_A       )





     def 		UpperCAmelCase(self				:   Any       )						->						Dict:
         snake_case								= torch_device in ["cpu", "mps"]

         self._test_inference_batch_single_identical(test_max_difference=_A       )







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





     def 		UpperCAmelCase(self				:   Union[str, Any]       )						->						Tuple:
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()





     def 		UpperCAmelCase(self				:   str       )						->						Optional[Any]:
         snake_case								= load_numpy(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy"       )

         snake_case								= StableUnCLIPPipeline.from_pretrained("fusing/stable-unclip-2-1-l"    ,   torch_dtype=torch.floataa       )
         pipe.to(_A       )
         pipe.set_progress_bar_config(disable=_A       )
         # 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()

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

         snake_case								= output.images[0]

         assert image.shape == (7_6_8, 7_6_8, 3)

         assert_mean_pixel_difference(_A    ,   _A       )





     def 		UpperCAmelCase(self				:   Union[str, Any]       )						->						str:
         torch.cuda.empty_cache()
         torch.cuda.reset_max_memory_allocated()
         torch.cuda.reset_peak_memory_stats()

         snake_case								= StableUnCLIPPipeline.from_pretrained("fusing/stable-unclip-2-1-l"    ,   torch_dtype=torch.floataa       )
         snake_case								= pipe.to(_A       )
         pipe.set_progress_bar_config(disable=_A       )
         pipe.enable_attention_slicing()
         pipe.enable_sequential_cpu_offload()

         snake_case								= pipe(
             "anime turtle"    ,   prior_num_inference_steps=2    ,   num_inference_steps=2    ,   output_type="np"    ,   )

         snake_case								= torch.cuda.max_memory_allocated()
         # make sure that less than 7 GB is allocated
         assert mem_bytes < 7 * 1_0**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()
_A   =			logging.get_logger(__name__)



def 						lowercase_      (       A__			,		A__		)							->							int:
    """simple docstring"""

    snake_case								= RobertaPreLayerNormConfig.from_pretrained(
        A__			,		architectures=["RobertaPreLayerNormForMaskedLM"]		)

    # convert state_dict
    snake_case								= torch.load(hf_hub_download(repo_id=A__			,		filename="pytorch_model.bin"		)		)
    snake_case								= {}
    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."		):
            snake_case								= "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

        snake_case								= tensor_value

    snake_case								= RobertaPreLayerNormForMaskedLM.from_pretrained(
        pretrained_model_name_or_path=A__			,		config=A__			,		state_dict=A__		)
    model.save_pretrained(A__		)

    # convert tokenizer
    snake_case								= AutoTokenizer.from_pretrained(A__		)
    tokenizer.save_pretrained(A__		)


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

