






"""simple docstring"""



import argparse

import torch

from transformers import MraConfig, MraForMaskedLM


def      SCREAMING_SNAKE_CASE__    (   snake_case    :      Any							)->						Any:






     '''simple docstring'''



     if "model" in orig_key:
          UpperCAmelCase__		:  Optional[int]				=				orig_key.replace("model."       , ""							)
     if "norm1" in orig_key:
          UpperCAmelCase__		:  List[Any]				=				orig_key.replace("norm1"       , "attention.output.LayerNorm"							)
     if "norm2" in orig_key:
          UpperCAmelCase__		:  Tuple				=				orig_key.replace("norm2"       , "output.LayerNorm"							)
     if "norm" in orig_key:
          UpperCAmelCase__		:  Union[str, Any]				=				orig_key.replace("norm"       , "LayerNorm"							)
     if "transformer" in orig_key:
          UpperCAmelCase__		:  int				=				orig_key.split("."							)[0].split("_"							)[-1]
          UpperCAmelCase__		:  str				=				orig_key.replace(f'transformer_{layer_num}'       , f'encoder.layer.{layer_num}'							)
     if "mha.attn" in orig_key:
          UpperCAmelCase__		:  Optional[int]				=				orig_key.replace("mha.attn"       , "attention.self"							)
     if "mha" in orig_key:
          UpperCAmelCase__		:  Dict				=				orig_key.replace("mha"       , "attention"							)
     if "W_q" in orig_key:
          UpperCAmelCase__		:  List[Any]				=				orig_key.replace("W_q"       , "self.query"							)
     if "W_k" in orig_key:
          UpperCAmelCase__		:  Optional[int]				=				orig_key.replace("W_k"       , "self.key"							)
     if "W_v" in orig_key:
          UpperCAmelCase__		:  Any				=				orig_key.replace("W_v"       , "self.value"							)
     if "ff.0" in orig_key:
          UpperCAmelCase__		:  str				=				orig_key.replace("ff.0"       , "intermediate.dense"							)
     if "ff.2" in orig_key:
          UpperCAmelCase__		:  List[Any]				=				orig_key.replace("ff.2"       , "output.dense"							)
     if "ff" in orig_key:
          UpperCAmelCase__		:  str				=				orig_key.replace("ff"       , "output.dense"							)
     if "mlm_class" in orig_key:
          UpperCAmelCase__		:  Optional[int]				=				orig_key.replace("mlm.mlm_class"       , "cls.predictions.decoder"							)
     if "mlm" in orig_key:
          UpperCAmelCase__		:  Any				=				orig_key.replace("mlm"       , "cls.predictions.transform"							)
     if "backbone.backbone.encoders" in orig_key:
          UpperCAmelCase__		:  List[Any]				=				orig_key.replace("backbone.backbone.encoders"       , "encoder.layer"							)
     if "cls" not in orig_key:
          UpperCAmelCase__		:  Tuple				=				"mra." + orig_key

     return orig_key


def      SCREAMING_SNAKE_CASE__    (   snake_case    :      List[str]       , snake_case    :      Union[str, Any]							)->						Optional[int]:






     '''simple docstring'''



     for key in orig_state_dict.copy().keys():
          UpperCAmelCase__		:  Dict				=				orig_state_dict.pop(snake_case							)

          if ("pooler" in key) or ("sen_class" in key):
               continue
          else:
               UpperCAmelCase__		:  str				=				val

     UpperCAmelCase__		:  Tuple				=				orig_state_dict["cls.predictions.decoder.bias"]
     UpperCAmelCase__		:  List[str]				=				torch.arange(snake_case							).expand((1, -1)							) + 2

     return orig_state_dict


def      SCREAMING_SNAKE_CASE__    (   snake_case    :      int       , snake_case    :      Union[str, Any]       , snake_case    :      List[Any]							)->						Any:






     '''simple docstring'''



     UpperCAmelCase__		:  Optional[int]				=				torch.load(snake_case       , map_location="cpu"							)["model_state_dict"]
     UpperCAmelCase__		:  Any				=				MraConfig.from_json_file(snake_case							)
     UpperCAmelCase__		:  List[Any]				=				MraForMaskedLM(snake_case							)

     UpperCAmelCase__		:  Dict				=				convert_checkpoint_helper(config.max_position_embeddings       , snake_case							)

     print(model.load_state_dict(snake_case							)							)
     model.eval()
     model.save_pretrained(snake_case							)

     print(f'Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'							)


if __name__ == "__main__":
 _lowerCAmelCase     :     Optional[Any]            =	argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     """--pytorch_model_path""", default=None, type=str, required=True, help="""Path to Mra pytorch checkpoint."""
 )
 parser.add_argument(
     """--config_file""",
     default=None,
     type=str,
     required=True,
     help="""The json file for Mra model config.""",
 )
 parser.add_argument(
     """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
 )
 _lowerCAmelCase     :     Any            =	parser.parse_args()
 convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)








'''simple docstring'''
import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMParallelScheduler,
    DDPMParallelScheduler,
    StableDiffusionParadigmsPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    require_torch_gpu,
)

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()






class        SCREAMING_SNAKE_CASE(				__A   ,      __A   ,      unittest.TestCase     ):
       snake_case_ :     str    =					StableDiffusionParadigmsPipeline
       snake_case_ :     Optional[Any]    =					TEXT_TO_IMAGE_PARAMS
       snake_case_ :     Tuple    =					TEXT_TO_IMAGE_BATCH_PARAMS
       snake_case_ :     Optional[Any]    =					TEXT_TO_IMAGE_IMAGE_PARAMS
       snake_case_ :     str    =					TEXT_TO_IMAGE_IMAGE_PARAMS

       def     snake_case__			(    self     )    ->					int:

           """simple docstring"""



           torch.manual_seed(0     )
           __lowercase							=						UNetaDConditionModel(
               block_out_channels=(32, 64)					,		layers_per_block=2					,		sample_size=32					,		in_channels=4					,		out_channels=4					,		down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")					,		up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")					,		cross_attention_dim=32					,		attention_head_dim=(2, 4)					,		use_linear_projection=lowerCamelCase__					,		)
           __lowercase							=						DDIMParallelScheduler(
               beta_start=0.0_00_85					,		beta_end=0.0_12					,		beta_schedule="""scaled_linear"""					,		clip_sample=lowerCamelCase__					,		set_alpha_to_one=lowerCamelCase__					,		)
           torch.manual_seed(0     )
           __lowercase							=						AutoencoderKL(
               block_out_channels=[32, 64]					,		in_channels=3					,		out_channels=3					,		down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]					,		up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]					,		latent_channels=4					,		sample_size=128					,		)
           torch.manual_seed(0     )
           __lowercase							=						CLIPTextConfig(
               bos_token_id=0					,		eos_token_id=2					,		hidden_size=32					,		intermediate_size=37					,		layer_norm_eps=1E-0_5					,		num_attention_heads=4					,		num_hidden_layers=5					,		pad_token_id=1					,		vocab_size=1000					,		hidden_act="""gelu"""					,		projection_dim=512					,		)
           __lowercase							=						CLIPTextModel(lowerCamelCase__     )
           __lowercase							=						CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""     )

           __lowercase							=						{
               """unet""": unet,
               """scheduler""": scheduler,
               """vae""": vae,
               """text_encoder""": text_encoder,
               """tokenizer""": tokenizer,
               """safety_checker""": None,
               """feature_extractor""": None,
           }
           return components

       def     snake_case__			(    self					,		lowerCamelCase__					,		lowerCamelCase__=0     )    ->					Any:

           """simple docstring"""



           if str(lowerCamelCase__     ).startswith("""mps"""     ):
               __lowercase							=						torch.manual_seed(lowerCamelCase__     )
           else:
               __lowercase							=						torch.Generator(device=lowerCamelCase__     ).manual_seed(lowerCamelCase__     )
           __lowercase							=						{
               """prompt""": """a photograph of an astronaut riding a horse""",
               """generator""": generator,
               """num_inference_steps""": 10,
               """guidance_scale""": 6.0,
               """output_type""": """numpy""",
               """parallel""": 3,
               """debug""": True,
           }
           return inputs

       def     snake_case__			(    self     )    ->					List[str]:

           """simple docstring"""



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

           __lowercase							=						self.get_dummy_inputs(lowerCamelCase__     )
           __lowercase							=						sd_pipe(**lowerCamelCase__     ).images
           __lowercase							=						image[0, -3:, -3:, -1]
           assert image.shape == (1, 64, 64, 3)

           __lowercase							=						np.array([0.47_73, 0.54_17, 0.47_23, 0.49_25, 0.56_31, 0.47_52, 0.52_40, 0.49_35, 0.50_23]     )

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

       def     snake_case__			(    self     )    ->					str:

           """simple docstring"""



           __lowercase							=						"""cpu"""  # ensure determinism for the device-dependent torch.Generator
           __lowercase							=						self.get_dummy_components()
           torch.manual_seed(0     )
           __lowercase							=						DDPMParallelScheduler()
           torch.manual_seed(0     )
           __lowercase							=						StableDiffusionParadigmsPipeline(**lowerCamelCase__     )
           __lowercase							=						sd_pipe.to(lowerCamelCase__     )
           sd_pipe.set_progress_bar_config(disable=lowerCamelCase__     )

           __lowercase							=						self.get_dummy_inputs(lowerCamelCase__     )
           __lowercase							=						sd_pipe(**lowerCamelCase__     ).images
           __lowercase							=						image[0, -3:, -3:, -1]
           assert image.shape == (1, 64, 64, 3)

           __lowercase							=						np.array([0.35_73, 0.44_20, 0.49_60, 0.47_99, 0.37_96, 0.38_79, 0.48_19, 0.43_65, 0.44_68]     )

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

       def     snake_case__			(    self     )    ->					Any:

           """simple docstring"""



           super().test_inference_batch_consistent(batch_sizes=[1, 2]     )

       def     snake_case__			(    self     )    ->					Optional[Any]:

           """simple docstring"""



           super().test_inference_batch_single_identical(batch_size=2					,		expected_max_diff=3E-3     )




       def     snake_case__			(    self     )    ->					str:

           """simple docstring"""



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

           __lowercase							=						self.get_dummy_inputs(lowerCamelCase__     )
           __lowercase							=						"""french fries"""
           __lowercase							=						sd_pipe(**lowerCamelCase__					,		negative_prompt=lowerCamelCase__     )
           __lowercase							=						output.images
           __lowercase							=						image[0, -3:, -3:, -1]

           assert image.shape == (1, 64, 64, 3)

           __lowercase							=						np.array([0.47_71, 0.54_20, 0.46_83, 0.49_18, 0.56_36, 0.47_25, 0.52_30, 0.49_23, 0.50_15]     )

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






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

       def     snake_case__			(    self     )    ->					str:

           """simple docstring"""



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

       def     snake_case__			(    self					,		lowerCamelCase__=0     )    ->					int:

           """simple docstring"""



           __lowercase							=						torch.Generator(device=lowerCamelCase__     ).manual_seed(lowerCamelCase__     )
           __lowercase							=						{
               """prompt""": """a photograph of an astronaut riding a horse""",
               """generator""": generator,
               """num_inference_steps""": 10,
               """guidance_scale""": 7.5,
               """output_type""": """numpy""",
               """parallel""": 3,
               """debug""": True,
           }
           return inputs




       def     snake_case__			(    self     )    ->					Optional[Any]:

           """simple docstring"""



           __lowercase							=						"""stabilityai/stable-diffusion-2-base"""
           __lowercase							=						DDIMParallelScheduler.from_pretrained(lowerCamelCase__					,		subfolder="""scheduler"""     )
           __lowercase							=						StableDiffusionParadigmsPipeline.from_pretrained(lowerCamelCase__					,		scheduler=lowerCamelCase__					,		safety_checker=lowerCamelCase__     )
           pipe.to(lowerCamelCase__     )
           pipe.set_progress_bar_config(disable=lowerCamelCase__     )
           pipe.enable_attention_slicing()

           __lowercase							=						self.get_inputs()
           __lowercase							=						pipe(**lowerCamelCase__     ).images
           __lowercase							=						image[0, -3:, -3:, -1].flatten()

           assert image.shape == (1, 512, 512, 3)

           __lowercase							=						np.array([0.96_22, 0.96_02, 0.97_48, 0.95_91, 0.96_30, 0.96_91, 0.96_61, 0.96_31, 0.97_41]     )

           assert np.abs(expected_slice - image_slice     ).max() < 1E-2




