

import argparse
import pathlib

import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import RobertaConfig, RobertaForMaskedLM, RobertaForSequenceClassification
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse('''0.9.0'''):
  raise Exception('''requires fairseq >= 0.9.0''')


logging.set_verbosity_info()
_a	 =     logging.get_logger(__name__)

_a	 =     '''Hello world! cécé herlolip'''







def _a				( SCREAMING_SNAKE_CASE	:    List[str]					,      SCREAMING_SNAKE_CASE	:    Union[str, Any]					,      SCREAMING_SNAKE_CASE	:    str	)					->					Union[str, Any]:

 """simple docstring"""






 __lowerCAmelCase:     List[Any]     			=   FairseqRobertaModel.from_pretrained(SCREAMING_SNAKE_CASE	)
 roberta.eval()  # disable dropout
 __lowerCAmelCase:     List[str]     			=   roberta.model.encoder.sentence_encoder
 __lowerCAmelCase:     Union[str, Any]     			=   RobertaConfig(
     vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings					,      hidden_size=roberta.args.encoder_embed_dim					,      num_hidden_layers=roberta.args.encoder_layers					,      num_attention_heads=roberta.args.encoder_attention_heads					,      intermediate_size=roberta.args.encoder_ffn_embed_dim					,      max_position_embeddings=5_14					,      type_vocab_size=1					,      layer_norm_eps=1E-5					,      )
 if classification_head:
  __lowerCAmelCase:     List[Any]     			=   roberta.model.classification_heads['mnli'].out_proj.weight.shape[0]
 print('Our BERT config:'					,      SCREAMING_SNAKE_CASE	)

 __lowerCAmelCase:     Any     			=   RobertaForSequenceClassification(SCREAMING_SNAKE_CASE	) if classification_head else RobertaForMaskedLM(SCREAMING_SNAKE_CASE	)
 model.eval()

 # Now let's copy all the weights.
 # Embeddings
 __lowerCAmelCase:     Any     			=   roberta_sent_encoder.embed_tokens.weight
 __lowerCAmelCase:     int     			=   roberta_sent_encoder.embed_positions.weight
 __lowerCAmelCase:     List[Any]     			=   torch.zeros_like(
     model.roberta.embeddings.token_type_embeddings.weight	)  # just zero them out b/c RoBERTa doesn't use them.
 __lowerCAmelCase:     List[Any]     			=   roberta_sent_encoder.emb_layer_norm.weight
 __lowerCAmelCase:     Optional[int]     			=   roberta_sent_encoder.emb_layer_norm.bias

 for i in range(config.num_hidden_layers	):
  # Encoder: start of layer
  __lowerCAmelCase:     str     			=   model.roberta.encoder.layer[i]
  __lowerCAmelCase:     Optional[Any]     			=   roberta_sent_encoder.layers[i]

  # self attention
  __lowerCAmelCase:     Optional[int]     			=   layer.attention.self
  assert (
      roberta_layer.self_attn.k_proj.weight.data.shape
      == roberta_layer.self_attn.q_proj.weight.data.shape
      == roberta_layer.self_attn.v_proj.weight.data.shape
      == torch.Size((config.hidden_size, config.hidden_size)	)
  )

  __lowerCAmelCase:     Optional[int]     			=   roberta_layer.self_attn.q_proj.weight
  __lowerCAmelCase:     Tuple     			=   roberta_layer.self_attn.q_proj.bias
  __lowerCAmelCase:     Optional[Any]     			=   roberta_layer.self_attn.k_proj.weight
  __lowerCAmelCase:     Optional[int]     			=   roberta_layer.self_attn.k_proj.bias
  __lowerCAmelCase:     Tuple     			=   roberta_layer.self_attn.v_proj.weight
  __lowerCAmelCase:     Dict     			=   roberta_layer.self_attn.v_proj.bias

  # self-attention output
  __lowerCAmelCase:     int     			=   layer.attention.output
  assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
  __lowerCAmelCase:     Union[str, Any]     			=   roberta_layer.self_attn.out_proj.weight
  __lowerCAmelCase:     int     			=   roberta_layer.self_attn.out_proj.bias
  __lowerCAmelCase:     Dict     			=   roberta_layer.self_attn_layer_norm.weight
  __lowerCAmelCase:     int     			=   roberta_layer.self_attn_layer_norm.bias

  # intermediate
  __lowerCAmelCase:     List[str]     			=   layer.intermediate
  assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
  __lowerCAmelCase:     Optional[int]     			=   roberta_layer.fca.weight
  __lowerCAmelCase:     Optional[Any]     			=   roberta_layer.fca.bias

  # output
  __lowerCAmelCase:     Union[str, Any]     			=   layer.output
  assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
  __lowerCAmelCase:     Union[str, Any]     			=   roberta_layer.fca.weight
  __lowerCAmelCase:     Dict     			=   roberta_layer.fca.bias
  __lowerCAmelCase:     int     			=   roberta_layer.final_layer_norm.weight
  __lowerCAmelCase:     List[str]     			=   roberta_layer.final_layer_norm.bias
  # end of layer

 if classification_head:
  __lowerCAmelCase:     Any     			=   roberta.model.classification_heads['mnli'].dense.weight
  __lowerCAmelCase:     Optional[int]     			=   roberta.model.classification_heads['mnli'].dense.bias
  __lowerCAmelCase:     Any     			=   roberta.model.classification_heads['mnli'].out_proj.weight
  __lowerCAmelCase:     str     			=   roberta.model.classification_heads['mnli'].out_proj.bias
 else:
  # LM Head
  __lowerCAmelCase:     str     			=   roberta.model.encoder.lm_head.dense.weight
  __lowerCAmelCase:     Optional[Any]     			=   roberta.model.encoder.lm_head.dense.bias
  __lowerCAmelCase:     Optional[int]     			=   roberta.model.encoder.lm_head.layer_norm.weight
  __lowerCAmelCase:     List[str]     			=   roberta.model.encoder.lm_head.layer_norm.bias
  __lowerCAmelCase:     int     			=   roberta.model.encoder.lm_head.weight
  __lowerCAmelCase:     Optional[int]     			=   roberta.model.encoder.lm_head.bias

 # Let's check that we get the same results.
 __lowerCAmelCase:     Dict     			=   roberta.encode(SCREAMING_SNAKE_CASE	).unsqueeze(0	)  # batch of size 1

 __lowerCAmelCase:     int     			=   model(SCREAMING_SNAKE_CASE	)[0]
 if classification_head:
  __lowerCAmelCase:     Optional[Any]     			=   roberta.model.classification_heads['mnli'](roberta.extract_features(SCREAMING_SNAKE_CASE	)	)
 else:
  __lowerCAmelCase:     Optional[Any]     			=   roberta.model(SCREAMING_SNAKE_CASE	)[0]
 print(our_output.shape					,      their_output.shape	)
 __lowerCAmelCase:     Optional[Any]     			=   torch.max(torch.abs(our_output - their_output	)	).item()
 print(f'''max_absolute_diff = {max_absolute_diff}'''	)  # ~ 1e-7
 __lowerCAmelCase:     Any     			=   torch.allclose(SCREAMING_SNAKE_CASE					,      SCREAMING_SNAKE_CASE					,      atol=1E-3	)
 print('Do both models output the same tensors?'					,      '🔥' if success else '💩'	)
 if not success:
  raise Exception('Something went wRoNg'	)

 pathlib.Path(SCREAMING_SNAKE_CASE	).mkdir(parents=SCREAMING_SNAKE_CASE					,      exist_ok=SCREAMING_SNAKE_CASE	)
 print(f'''Saving model to {pytorch_dump_folder_path}'''	)
 model.save_pretrained(SCREAMING_SNAKE_CASE	)


if __name__ == "__main__":
  _a	 =     argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '''--roberta_checkpoint_path''', default=None, type=str, required=True, help='''Path the official PyTorch dump.'''
  )
  parser.add_argument(
      '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
  )
  parser.add_argument(
      '''--classification_head''', action='''store_true''', help='''Whether to convert a final classification head.'''
  )
  _a	 =     parser.parse_args()
  convert_roberta_checkpoint_to_pytorch(
      args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
  )








from typing import Union

import torch
from PIL import Image
from torchvision import transforms as tfms
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)






class 							A_				(	snake_case__      ):


     def __init__(							self    :	List[Any]    ,						UpperCAmelCase    :	AutoencoderKL    ,						UpperCAmelCase    :	CLIPTextModel    ,						UpperCAmelCase    :	CLIPTokenizer    ,						UpperCAmelCase    :	UNetaDConditionModel    ,						UpperCAmelCase    :	Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler]    ,						)							->			Tuple:
      super().__init__()

      self.register_modules(vae=UpperCAmelCase    ,						text_encoder=UpperCAmelCase    ,						tokenizer=UpperCAmelCase    ,						unet=UpperCAmelCase    ,						scheduler=UpperCAmelCase      )


     def 						UpperCAmelCase						(							self    :	str    ,						UpperCAmelCase    :	Dict      )							->			Any:
      with torch.no_grad():
       __lowerCAmelCase:     Tuple     			=   self.vae.encode(tfms.ToTensor()(UpperCAmelCase      ).unsqueeze(0      ).to(self.device      ) * 2 - 1      )
       __lowerCAmelCase:     Tuple     			=   0.18215 * latent.latent_dist.sample()
      return latent


     def 						UpperCAmelCase						(							self    :	List[Any]    ,						UpperCAmelCase    :	str      )							->			int:
      __lowerCAmelCase:     Optional[Any]     			=   (1 / 0.18215) * latent
      with torch.no_grad():
       __lowerCAmelCase:     str     			=   self.vae.decode(UpperCAmelCase      ).sample
      __lowerCAmelCase:     Any     			=   (img / 2 + 0.5).clamp(0    ,						1      )
      __lowerCAmelCase:     Optional[Any]     			=   img.detach().cpu().permute(0    ,						2    ,						3    ,						1      ).numpy()
      __lowerCAmelCase:     List[str]     			=   (img * 2_5_5).round().astype('uint8'      )
      return Image.fromarray(img[0]      )


     def 						UpperCAmelCase						(							self    :	Optional[Any]    ,						UpperCAmelCase    :	Tuple      )							->			str:
      __lowerCAmelCase:     Dict     			=   self.tokenizer(
          UpperCAmelCase    ,						padding='max_length'    ,						max_length=self.tokenizer.model_max_length    ,						truncation=UpperCAmelCase    ,						return_tensors='pt'    ,						)

      __lowerCAmelCase:     Any     			=   self.text_encoder(text_input.input_ids.to(self.device      )      )[0]

      __lowerCAmelCase:     List[str]     			=   self.tokenizer(
          ''    ,						padding='max_length'    ,						max_length=self.tokenizer.model_max_length    ,						truncation=UpperCAmelCase    ,						return_tensors='pt'    ,						)

      __lowerCAmelCase:     str     			=   self.text_encoder(uncond_input.input_ids.to(self.device      )      )[0]

      return torch.cat([uncond_embedding, text_embedding]      )


     def __call__(							self    :	Optional[int]    ,						UpperCAmelCase    :	Image.Image    ,						UpperCAmelCase    :	str    ,						UpperCAmelCase    :	float = 0.3    ,						UpperCAmelCase    :	float = 0.6    ,						UpperCAmelCase    :	float = 0.5    ,						UpperCAmelCase    :	int = 4_2    ,						UpperCAmelCase    :	int = 5_0    ,						UpperCAmelCase    :	float = 7.5    ,						)							->			Image.Image:
      __lowerCAmelCase:     Optional[int]     			=   steps - int(kmin * steps      )
      __lowerCAmelCase:     Dict     			=   steps - int(kmax * steps      )

      __lowerCAmelCase:     Union[str, Any]     			=   self.prep_text(UpperCAmelCase      )

      self.scheduler.set_timesteps(UpperCAmelCase      )

      __lowerCAmelCase			,							__lowerCAmelCase:     str     			=   img.size
      __lowerCAmelCase:     List[Any]     			=   self.encode(UpperCAmelCase      )

      torch.manual_seed(UpperCAmelCase      )
      __lowerCAmelCase:     Optional[int]     			=   torch.randn(
          (1, self.unet.config.in_channels, height // 8, width // 8)    ,						).to(self.device      )

      __lowerCAmelCase:     int     			=   self.scheduler.add_noise(
          UpperCAmelCase    ,						UpperCAmelCase    ,						timesteps=self.scheduler.timesteps[tmax]    ,						)

      __lowerCAmelCase:     Tuple     			=   torch.cat([latents] * 2      )

      __lowerCAmelCase:     int     			=   self.scheduler.scale_model_input(UpperCAmelCase    ,						self.scheduler.timesteps[tmax]      )

      with torch.no_grad():
       __lowerCAmelCase:     List[str]     			=   self.unet(
           UpperCAmelCase    ,						self.scheduler.timesteps[tmax]    ,						encoder_hidden_states=UpperCAmelCase    ,						).sample

      __lowerCAmelCase			,							__lowerCAmelCase:     Optional[Any]     			=   pred.chunk(2      )
      __lowerCAmelCase:     Union[str, Any]     			=   pred_uncond + guidance_scale * (pred_text - pred_uncond)

      __lowerCAmelCase:     List[str]     			=   self.scheduler.step(UpperCAmelCase    ,						self.scheduler.timesteps[tmax]    ,						UpperCAmelCase      ).prev_sample

      for i, t in enumerate(tqdm(self.scheduler.timesteps      )      ):
       if i > tmax:
        if i < tmin:  # layout generation phase
         __lowerCAmelCase:     Optional[Any]     			=   self.scheduler.add_noise(
             UpperCAmelCase    ,						UpperCAmelCase    ,						timesteps=UpperCAmelCase    ,						)

         __lowerCAmelCase:     List[Any]     			=   (mix_factor * latents) + (
             1 - mix_factor
         ) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
         __lowerCAmelCase:     Optional[Any]     			=   torch.cat([input] * 2      )

        else:  # content generation phase
         __lowerCAmelCase:     str     			=   torch.cat([latents] * 2      )

        __lowerCAmelCase:     List[Any]     			=   self.scheduler.scale_model_input(UpperCAmelCase    ,						UpperCAmelCase      )

        with torch.no_grad():
         __lowerCAmelCase:     Any     			=   self.unet(
             UpperCAmelCase    ,						UpperCAmelCase    ,						encoder_hidden_states=UpperCAmelCase    ,						).sample

        __lowerCAmelCase			,							__lowerCAmelCase:     Optional[int]     			=   pred.chunk(2      )
        __lowerCAmelCase:     Union[str, Any]     			=   pred_uncond + guidance_scale * (pred_text - pred_uncond)

        __lowerCAmelCase:     List[str]     			=   self.scheduler.step(UpperCAmelCase    ,						UpperCAmelCase    ,						UpperCAmelCase      ).prev_sample

      return self.decode(UpperCAmelCase      )





