
'''simple docstring'''





import argparse

# utilities to import the model weights and config file
import os
import pickle as pkl

# PyTorch + new model classes
import torch
from torch import nn

from transformers import AutoTokenizer, MegaConfig, MegaForMaskedLM


# import the EncoderLayer class used to pretrain
# !! NOTE !! this requires the version of fairseq that is built when you install the Mega source
try:
 from fairseq.modules.mega_layer import MegaEncoderLayer
except ImportError:
 raise ImportError("You need to install the version of fairseq from the Mega repo!")


class 			UpperCamelCase__	(     nn.Module):






 def __init__(      self	:Union[str, Any]						,					_A	:List[Any]						,					_A	:Tuple						,					_A	:List[str]      )					->      int:







       '''simple docstring'''



       super().__init__()
       __A													=      mega_args
       __A													=      nn.Embedding(_A						,					self.mega_args.encoder_embed_dim      )
       __A													=      nn.ModuleList([MegaEncoderLayer(self.mega_args      ) for _ in range(_A      )]      )
       __A													=      depth






 def 						lowercase_    (      self	:Optional[Any]						,					_A	:int						,					_A	:Dict						,					_A	:int=True						,					_A	:Tuple=0      )					->      Tuple:







       '''simple docstring'''



       if batch_first:
             __A													=      input_ids.T

       # to make things more confusing, Mega expects the attention mask to
       # be (batch, time), but with values of 0 (normal token) and 1 (ignore token)
       # which is the opposite of what HF returns
       if ignore_mask_value == 0:
             __A													=      1 - attention_mask

       # get token embeddings from IDs
       __A													=      self.embedding_layer(_A      )

       # pass through the Mega layers
       # input is (time, batch, encoder dim) and output is the same
       for encoder in self.encoders:
             __A													=      encoder(_A						,					_A      )

       # return according to the shape specified
       if batch_first:
             # (T, B, H) --> (B, T, H)
             return torch.transpose(_A						,					0						,					1      )
       else:
             return embeds


class 			UpperCamelCase__	(     nn.Module):






 def __init__(      self	:List[str]						,					_A	:Optional[Any]						,					_A	:Tuple						,					_A	:str      )					->      Optional[Any]:







       '''simple docstring'''



       super().__init__()
       __A													=      MegaLM(_A						,					_A						,					_A      )
       __A													=      nn.Linear(mega_args.encoder_embed_dim						,					_A      )
       __A													=      nn.Dropout(p=0.1      )






 def 						lowercase_    (      self	:Optional[int]						,					_A	:Union[str, Any]						,					_A	:Any						,					_A	:Union[str, Any]=True						,					_A	:List[str]=0      )					->      Union[str, Any]:







       '''simple docstring'''



       __A													=      self.mega(_A						,					_A						,					_A						,					_A      )
       return self.mlm_head(self.dropout(_A      )      )





def 							snake_case				(		UpperCAmelCase   ,	UpperCAmelCase   ,	UpperCAmelCase						)->      Dict:







      """simple docstring"""
      with open(os.path.join(UpperCAmelCase   ,	'model_args.pkl'						)   ,	'rb'						) as f:
            __A													=      pkl.load(UpperCAmelCase						)

      # load the original encoder
      __A													=      OriginalMegaForMaskedLM(**UpperCAmelCase						).eval()

      # load its weights
      print(
          'Original Mega encoder:'   ,	original_mlm.mega.load_state_dict(
              torch.load(os.path.join(UpperCAmelCase   ,	'encoder_weights.pt'						)   ,	map_location='cpu'						)						)   ,	)
      print(
          'Original Mega MLM layer:'   ,	original_mlm.mlm_head.load_state_dict(
              torch.load(os.path.join(UpperCAmelCase   ,	'mlm_head_weights.pt'						)   ,	map_location='cpu'						)						)   ,	)

      # create a new config from the old one
      __A													=      MegaConfig(
          num_hidden_layers=mega_original_args['depth']   ,	vocab_size=mega_original_args['vocab_size']   ,	hidden_size=mega_original_args['mega_args'].encoder_embed_dim   ,	shared_representation_size=mega_original_args['mega_args'].encoder_z_dim   ,	intermediate_size=mega_original_args['mega_args'].encoder_hidden_dim   ,	ema_projection_size=mega_original_args['mega_args'].encoder_n_dim   ,	dropout_prob=mega_original_args['mega_args'].dropout   ,	attention_probs_dropout_prob=mega_original_args['mega_args'].attention_dropout   ,	hidden_dropout_prob=mega_original_args['mega_args'].hidden_dropout   ,	activation=mega_original_args['mega_args'].activation_fn   ,	attention_activation=mega_original_args['mega_args'].attention_activation_fn   ,	bidirectional=mega_original_args['mega_args'].bidirectional   ,	use_chunking=mega_original_args['mega_args'].encoder_chunk_size > 0   ,	chunk_size=mega_original_args['mega_args'].encoder_chunk_size   ,	truncation=mega_original_args['mega_args'].truncation_length   ,	normalization_type=mega_original_args['mega_args'].normalization_type   ,	normalize_before_mega=UpperCAmelCase   ,	norm_affine=UpperCAmelCase   ,	use_feature_dropout=mega_original_args['mega_args'].feature_dropout   ,	relative_positional_bias=mega_original_args['mega_args'].rel_pos_bias   ,	max_positions=mega_original_args['mega_args'].max_source_positions   ,	nffn_hidden_size=mega_original_args['mega_args'].encoder_ffn_embed_dim   ,	normalize_before_ffn=mega_original_args['mega_args'].normalize_before   ,	nffn_activation_dropout_prob=0.0   ,	add_token_type_embeddings=UpperCAmelCase   ,	add_lm_hidden_dense_layer=UpperCAmelCase   ,	)

      __A													=      MegaForMaskedLM(UpperCAmelCase						).eval()

      # the originl checkpoint just uses nn.Embedding for the word embeddings
      # we use a wrapper module for embeddings to add support for positional embeddings
      __A													=      original_mlm.mega.embedding_layer.weight

      # modify the state dictionary of the original checkpoint to account for naming issues in the Hugging Face
      # ecosystem -- any names containing "beta" or "gamma" aren't safe to use and are renamed upon _load_pretrained,
      # also renaming previously confusing parameter names
      __A													=      original_mlm.mega.encoders.state_dict()
      __A													=      {}
      for module_name in original_state_dict.keys():
            __A													=      None
            # have to handle gamma, beta, and alpha differently due to their use
            # in multiple modules within the original repository;
            # beta is used in EMA, MovingAverageGatedAttention, and RotaryRelativePositionalBias, and must be renamed due to flax/tf weights
            # the EMA sublayer was renamed from "move" to "ema_gate" for readability, so that is also done here
            if "beta" in module_name:
                  # EMA sub-layers were always called "move" in the original repo
                  if "move.beta" in module_name:
                        __A													=      module_name.replace('move.beta'   ,	'ema_gate.ema_expansion_matrix'						)
                  elif "mega_layer.beta" in module_name:
                        __A													=      module_name.replace('beta'   ,	'qk_bias'						)
                  else:
                        __A													=      module_name.replace('beta'   ,	'b_param'						)
        # beta is used in EMA and MovingAverageGatedAttention, and must be renamed due to flax/tf weights
            elif "gamma" in module_name:
                  if "move.gamma" in module_name:
                        __A													=      module_name.replace('move.gamma'   ,	'ema_gate.kernel_projection_matrix'						)
                  elif "mega_layer.gamma" in module_name:
                        __A													=      module_name.replace('gamma'   ,	'qk_weight'						)
                  else:
                        __A													=      module_name.replace('gamma'   ,	'g_param'						)
        # alpha is used in EMA and positional bias; renaming to improve readability
            elif "move.alpha" in module_name:
                  __A													=      module_name.replace('move.alpha'   ,	'ema_gate.decay_factor'						)
            # delta is only used in EMA; renaming to improve readability
            elif "move.delta" in module_name:
                  __A													=      module_name.replace('move.delta'   ,	'ema_gate.damping_factor'						)
            # omega is only used in EMA; renaming to improve readability
            elif "omega" in module_name:
                  __A													=      module_name.replace('move.omega'   ,	'ema_gate.residual_weight'						)

            if new_module_name:
                  __A													=      new_module_name

      if len(UpperCAmelCase						) != 0:
            print(f'Renaming these keys: {updated_keys.keys()}'						)
      else:
            print('No need to rename state dict entries'						)
      for old, new in updated_keys.items():
            __A													=      original_state_dict.pop(UpperCAmelCase						)

      # now attempt to load the state dictionary with updated names
      # note that we now call it `mega.layers` instead of `mega.encoders` due to hugging face style
      print('HF Mega encoder:'   ,	hf_mlm.mega.layers.load_state_dict(UpperCAmelCase						)						)

      # load the MLM head weights directly
      print(
          'HF Mega MLM layer:'   ,	hf_mlm.mlm_head.load_state_dict(
              torch.load(os.path.join(UpperCAmelCase   ,	'mlm_head_weights.pt'						)   ,	map_location='cpu'						)						)   ,	)

      # test on a randomly generated input sequence
      __A													=      torch.randint(0   ,	hf_config.vocab_size   ,	size=(4, 2_5_6)						)
      __A													=      torch.ones_like(UpperCAmelCase						)
      # mask a few tokens to make sure masking is applied appropriately :)
      __A													=      0

      # run forward passes
      __A													=      original_mlm(UpperCAmelCase   ,	UpperCAmelCase   ,	batch_first=UpperCAmelCase   ,	ignore_mask_value=0						)
      __A													=      hf_mlm(UpperCAmelCase   ,	UpperCAmelCase						)[0]

      # print shapes and diff
      print(f'original output {original_output.shape}'						)
      print(f'hf output {hf_output.shape}'						)
      print(f'max diff: {(original_output - hf_output).max()}'						)  # 0.0
      __A													=      torch.allclose(UpperCAmelCase   ,	UpperCAmelCase   ,	atol=1e-3						)

      if success:
            print('Yay!'						)
            hf_mlm.save_pretrained(UpperCAmelCase						)
      else:
            raise RuntimeError(f'Something\'s broken :(\nOriginal:\n{original_output}\n\nHF\n{hf_output}\n{hf_mlm}'						)

      if includes_tokenizer:
            print('Transferring tokenizer'						)
            __A													=      AutoTokenizer.from_pretrained(UpperCAmelCase						)
            tokenizer.save_pretrained(UpperCAmelCase						)


if __name__ == "__main__":
 a__		: str  							=							argparse.ArgumentParser()

 parser.add_argument(
     "--pretrained_checkpoint_path",
     default=None,
     type=str,
     required=True,
     help="Point to the directory containing your model weights using the official Mega repo",
 )

 parser.add_argument(
     "--output_path", default=None, type=str, required=True, help="Location to save the Hugging Face version"
 )

 parser.add_argument(
     "--includes_tokenizer",
     action="store_true",
     help="Use this flag if there is a Hugging Face tokenizer in the original checkpoint repo",
 )

 a__		: Tuple  							=							parser.parse_args()

 convert_checkpoint_to_huggingface(args.pretrained_checkpoint_path, args.output_path, args.includes_tokenizer)







"""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     (   _lowerCAmelCase						,       _lowerCAmelCase						,       unittest.TestCase							):
				UpperCAmelCase	=      StableDiffusionParadigmsPipeline
				UpperCAmelCase	=      TEXT_TO_IMAGE_PARAMS
				UpperCAmelCase	=      TEXT_TO_IMAGE_BATCH_PARAMS
				UpperCAmelCase	=      TEXT_TO_IMAGE_IMAGE_PARAMS
				UpperCAmelCase	=      TEXT_TO_IMAGE_IMAGE_PARAMS







				def   lowercase_		(    self    :List[Any]  )      ->					Tuple:




									"""simple docstring"""



									torch.manual_seed(0  )
									lowerCamelCase__		:   int  				=					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=__UpperCAmelCase      ,)
									lowerCamelCase__		:   List[Any]  				=					DDIMParallelScheduler(
									    beta_start=0.00_085      ,beta_end=0.012      ,beta_schedule='''scaled_linear'''      ,clip_sample=__UpperCAmelCase      ,set_alpha_to_one=__UpperCAmelCase      ,)
									torch.manual_seed(0  )
									lowerCamelCase__		:   Dict  				=					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=1_28      ,)
									torch.manual_seed(0  )
									lowerCamelCase__		:   Any  				=					CLIPTextConfig(
									    bos_token_id=0      ,eos_token_id=2      ,hidden_size=32      ,intermediate_size=37      ,layer_norm_eps=1E-05      ,num_attention_heads=4      ,num_hidden_layers=5      ,pad_token_id=1      ,vocab_size=10_00      ,hidden_act='''gelu'''      ,projection_dim=5_12      ,)
									lowerCamelCase__		:   Tuple  				=					CLIPTextModel(__UpperCAmelCase  )
									lowerCamelCase__		:   int  				=					CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''  )

									lowerCamelCase__		:   List[Any]  				=					{
									    '''unet''': unet,
									    '''scheduler''': scheduler,
									    '''vae''': vae,
									    '''text_encoder''': text_encoder,
									    '''tokenizer''': tokenizer,
									    '''safety_checker''': None,
									    '''feature_extractor''': None,
									}
									return components







				def   lowercase_		(    self    :Tuple      ,__UpperCAmelCase    :int      ,__UpperCAmelCase    :Dict=0  )      ->					Optional[int]:




									"""simple docstring"""



									if str(__UpperCAmelCase  ).startswith('''mps'''  ):
														lowerCamelCase__		:   Dict  				=					torch.manual_seed(__UpperCAmelCase  )
									else:
														lowerCamelCase__		:   List[str]  				=					torch.Generator(device=__UpperCAmelCase  ).manual_seed(__UpperCAmelCase  )
									lowerCamelCase__		:   Optional[int]  				=					{
									    '''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   lowercase_		(    self    :Optional[int]  )      ->					str:




									"""simple docstring"""



									lowerCamelCase__		:   Any  				=					'''cpu'''  # ensure determinism for the device-dependent torch.Generator
									lowerCamelCase__		:   int  				=					self.get_dummy_components()
									lowerCamelCase__		:   int  				=					StableDiffusionParadigmsPipeline(**__UpperCAmelCase  )
									lowerCamelCase__		:   Union[str, Any]  				=					sd_pipe.to(__UpperCAmelCase  )
									sd_pipe.set_progress_bar_config(disable=__UpperCAmelCase  )

									lowerCamelCase__		:   Dict  				=					self.get_dummy_inputs(__UpperCAmelCase  )
									lowerCamelCase__		:   Tuple  				=					sd_pipe(**__UpperCAmelCase  ).images
									lowerCamelCase__		:   List[Any]  				=					image[0, -3:, -3:, -1]
									assert image.shape == (1, 64, 64, 3)

									lowerCamelCase__		:   Tuple  				=					np.array([0.4_773, 0.5_417, 0.4_723, 0.4_925, 0.5_631, 0.4_752, 0.5_240, 0.4_935, 0.5_023]  )

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







				def   lowercase_		(    self    :int  )      ->					str:




									"""simple docstring"""



									lowerCamelCase__		:   Tuple  				=					'''cpu'''  # ensure determinism for the device-dependent torch.Generator
									lowerCamelCase__		:   Optional[int]  				=					self.get_dummy_components()
									torch.manual_seed(0  )
									lowerCamelCase__		:   Any  				=					DDPMParallelScheduler()
									torch.manual_seed(0  )
									lowerCamelCase__		:   str  				=					StableDiffusionParadigmsPipeline(**__UpperCAmelCase  )
									lowerCamelCase__		:   Tuple  				=					sd_pipe.to(__UpperCAmelCase  )
									sd_pipe.set_progress_bar_config(disable=__UpperCAmelCase  )

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

									lowerCamelCase__		:   Optional[int]  				=					np.array([0.3_573, 0.4_420, 0.4_960, 0.4_799, 0.3_796, 0.3_879, 0.4_819, 0.4_365, 0.4_468]  )

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







				def   lowercase_		(    self    :List[str]  )      ->					Dict:




									"""simple docstring"""



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







				def   lowercase_		(    self    :Any  )      ->					str:




									"""simple docstring"""



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







				def   lowercase_		(    self    :int  )      ->					List[str]:




									"""simple docstring"""



									lowerCamelCase__		:   Optional[Any]  				=					'''cpu'''  # ensure determinism for the device-dependent torch.Generator
									lowerCamelCase__		:   str  				=					self.get_dummy_components()
									lowerCamelCase__		:   int  				=					StableDiffusionParadigmsPipeline(**__UpperCAmelCase  )
									lowerCamelCase__		:   List[Any]  				=					sd_pipe.to(__UpperCAmelCase  )
									sd_pipe.set_progress_bar_config(disable=__UpperCAmelCase  )

									lowerCamelCase__		:   int  				=					self.get_dummy_inputs(__UpperCAmelCase  )
									lowerCamelCase__		:   int  				=					'''french fries'''
									lowerCamelCase__		:   Any  				=					sd_pipe(**__UpperCAmelCase      ,negative_prompt=__UpperCAmelCase  )
									lowerCamelCase__		:   Optional[Any]  				=					output.images
									lowerCamelCase__		:   str  				=					image[0, -3:, -3:, -1]

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

									lowerCamelCase__		:   Tuple  				=					np.array([0.4_771, 0.5_420, 0.4_683, 0.4_918, 0.5_636, 0.4_725, 0.5_230, 0.4_923, 0.5_015]  )

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





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







				def   lowercase_		(    self    :Union[str, Any]  )      ->					Optional[int]:




									"""simple docstring"""



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







				def   lowercase_		(    self    :Optional[int]      ,__UpperCAmelCase    :List[str]=0  )      ->					str:




									"""simple docstring"""



									lowerCamelCase__		:   str  				=					torch.Generator(device=__UpperCAmelCase  ).manual_seed(__UpperCAmelCase  )
									lowerCamelCase__		:   List[Any]  				=					{
									    '''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   lowercase_		(    self    :Optional[Any]  )      ->					Optional[int]:




									"""simple docstring"""



									lowerCamelCase__		:   Optional[Any]  				=					'''stabilityai/stable-diffusion-2-base'''
									lowerCamelCase__		:   Tuple  				=					DDIMParallelScheduler.from_pretrained(__UpperCAmelCase      ,subfolder='''scheduler'''  )
									lowerCamelCase__		:   Optional[Any]  				=					StableDiffusionParadigmsPipeline.from_pretrained(__UpperCAmelCase      ,scheduler=__UpperCAmelCase      ,safety_checker=__UpperCAmelCase  )
									pipe.to(__UpperCAmelCase  )
									pipe.set_progress_bar_config(disable=__UpperCAmelCase  )
									pipe.enable_attention_slicing()

									lowerCamelCase__		:   Optional[Any]  				=					self.get_inputs()
									lowerCamelCase__		:   Tuple  				=					pipe(**__UpperCAmelCase  ).images
									lowerCamelCase__		:   List[Any]  				=					image[0, -3:, -3:, -1].flatten()

									assert image.shape == (1, 5_12, 5_12, 3)

									lowerCamelCase__		:   List[str]  				=					np.array([0.9_622, 0.9_602, 0.9_748, 0.9_591, 0.9_630, 0.9_691, 0.9_661, 0.9_631, 0.9_741]  )

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






