"""simple docstring"""





import argparse
from pathlib import Path

import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version

from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse('''0.12.2'''):
			raise Exception('''requires fairseq >= 0.12.2''')
if version.parse(fairseq.__version__) > version.parse('''2'''):
			raise Exception('''requires fairseq < v2''')

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

lowerCAmelCase_     			=						'''Hello, World!'''
lowerCAmelCase_     			=						'''en_XX'''



def   lowerCamelCase_(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE	)->     Optional[Any]:
							_SCREAMING_SNAKE_CASE		:    int      	=     Path("""data_bin"""	)
							_SCREAMING_SNAKE_CASE		:    Optional[int]      	=     FairseqXmodModel.from_pretrained(
							    model_name_or_path=str(Path(__SCREAMING_SNAKE_CASE	).parent	)  ,						checkpoint_file=Path(__SCREAMING_SNAKE_CASE	).name  ,						_name="""xmod_base"""  ,						arch="""xmod_base"""  ,						task="""multilingual_masked_lm"""  ,						data_name_or_path=str(__SCREAMING_SNAKE_CASE	)  ,						bpe="""sentencepiece"""  ,						sentencepiece_model=str(Path(__SCREAMING_SNAKE_CASE	).parent / """sentencepiece.bpe.model"""	)  ,						src_dict=str(data_dir / """dict.txt"""	)  ,						)
							xmod.eval()  # disable dropout
							print(__SCREAMING_SNAKE_CASE	)

							_SCREAMING_SNAKE_CASE		:    Optional[Any]      	=     xmod.model.encoder.sentence_encoder
							_SCREAMING_SNAKE_CASE		:    Any      	=     XmodConfig(
							    vocab_size=xmod_sent_encoder.embed_tokens.num_embeddings  ,						hidden_size=xmod.cfg.model.encoder_embed_dim  ,						num_hidden_layers=xmod.cfg.model.encoder_layers  ,						num_attention_heads=xmod.cfg.model.encoder_attention_heads  ,						intermediate_size=xmod.cfg.model.encoder_ffn_embed_dim  ,						max_position_embeddings=514  ,						type_vocab_size=1  ,						layer_norm_eps=1e-5  ,						pre_norm=xmod.cfg.model.encoder_normalize_before  ,						adapter_reduction_factor=getattr(xmod.cfg.model  ,						"""bottleneck"""  ,						2	)  ,						adapter_layer_norm=xmod.cfg.model.adapter_layer_norm  ,						adapter_reuse_layer_norm=xmod.cfg.model.adapter_reuse_layer_norm  ,						ln_before_adapter=xmod.cfg.model.ln_before_adapter  ,						languages=xmod.cfg.model.languages  ,						)
							if classification_head:
														_SCREAMING_SNAKE_CASE		:    Dict      	=     xmod.model.classification_heads["""mnli"""].out_proj.weight.shape[0]

							print("""Our X-MOD config:"""  ,						__SCREAMING_SNAKE_CASE	)

							_SCREAMING_SNAKE_CASE		:    int      	=     XmodForSequenceClassification(__SCREAMING_SNAKE_CASE	) if classification_head else XmodForMaskedLM(__SCREAMING_SNAKE_CASE	)
							model.eval()

							# Now let's copy all the weights.
							# Embeddings
							_SCREAMING_SNAKE_CASE		:    Dict      	=     xmod_sent_encoder.embed_tokens.weight
							_SCREAMING_SNAKE_CASE		:    Optional[Any]      	=     xmod_sent_encoder.embed_positions.weight
							_SCREAMING_SNAKE_CASE		:    Union[str, Any]      	=     torch.zeros_like(
							    model.roberta.embeddings.token_type_embeddings.weight	)  # just zero them out b/c xmod doesn't use them.

							_SCREAMING_SNAKE_CASE		:    Optional[Any]      	=     xmod_sent_encoder.layernorm_embedding.weight
							_SCREAMING_SNAKE_CASE		:    Tuple      	=     xmod_sent_encoder.layernorm_embedding.bias

							for i in range(config.num_hidden_layers	):
														# Encoder: start of layer
														_SCREAMING_SNAKE_CASE		:    List[Any]      	=     model.roberta.encoder.layer[i]
														_SCREAMING_SNAKE_CASE		:    Optional[int]      	=     xmod_sent_encoder.layers[i]

														# self attention
														_SCREAMING_SNAKE_CASE		:    Optional[int]      	=     layer.attention.self
														if not (
														    xmod_layer.self_attn.k_proj.weight.data.shape
														    == xmod_layer.self_attn.q_proj.weight.data.shape
														    == xmod_layer.self_attn.v_proj.weight.data.shape
														    == torch.Size((config.hidden_size, config.hidden_size)	)
														):
																					raise AssertionError("""Dimensions of self-attention weights do not match."""	)

														_SCREAMING_SNAKE_CASE		:    int      	=     xmod_layer.self_attn.q_proj.weight
														_SCREAMING_SNAKE_CASE		:    Optional[int]      	=     xmod_layer.self_attn.q_proj.bias
														_SCREAMING_SNAKE_CASE		:    Optional[Any]      	=     xmod_layer.self_attn.k_proj.weight
														_SCREAMING_SNAKE_CASE		:    str      	=     xmod_layer.self_attn.k_proj.bias
														_SCREAMING_SNAKE_CASE		:    Any      	=     xmod_layer.self_attn.v_proj.weight
														_SCREAMING_SNAKE_CASE		:    str      	=     xmod_layer.self_attn.v_proj.bias

														# self-attention output
														_SCREAMING_SNAKE_CASE		:    Optional[Any]      	=     layer.attention.output
														if self_output.dense.weight.shape != xmod_layer.self_attn.out_proj.weight.shape:
																					raise AssertionError("""Dimensions of self-attention output weights do not match."""	)
														_SCREAMING_SNAKE_CASE		:    List[str]      	=     xmod_layer.self_attn.out_proj.weight
														_SCREAMING_SNAKE_CASE		:    str      	=     xmod_layer.self_attn.out_proj.bias
														_SCREAMING_SNAKE_CASE		:    Union[str, Any]      	=     xmod_layer.self_attn_layer_norm.weight
														_SCREAMING_SNAKE_CASE		:    List[Any]      	=     xmod_layer.self_attn_layer_norm.bias

														# intermediate
														_SCREAMING_SNAKE_CASE		:    Any      	=     layer.intermediate
														if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
																					raise AssertionError("""Dimensions of intermediate weights do not match."""	)
														_SCREAMING_SNAKE_CASE		:    Any      	=     xmod_layer.fca.weight
														_SCREAMING_SNAKE_CASE		:    Union[str, Any]      	=     xmod_layer.fca.bias

														# output
														_SCREAMING_SNAKE_CASE		:    int      	=     layer.output
														if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
																					raise AssertionError("""Dimensions of feed-forward weights do not match."""	)
														_SCREAMING_SNAKE_CASE		:    Union[str, Any]      	=     xmod_layer.fca.weight
														_SCREAMING_SNAKE_CASE		:    List[Any]      	=     xmod_layer.fca.bias
														_SCREAMING_SNAKE_CASE		:    Any      	=     xmod_layer.final_layer_norm.weight
														_SCREAMING_SNAKE_CASE		:    List[str]      	=     xmod_layer.final_layer_norm.bias
														if bert_output.adapter_layer_norm is not None:
																					_SCREAMING_SNAKE_CASE		:    Any      	=     xmod_layer.adapter_layer_norm.weight
																					_SCREAMING_SNAKE_CASE		:    Optional[Any]      	=     xmod_layer.adapter_layer_norm.bias

														if sorted(bert_output.adapter_modules.keys()	) != sorted(xmod_layer.adapter_modules.keys()	):
																					raise AssertionError("""Lists of language adapters do not match."""	)
														for lang_code, adapter in xmod_layer.adapter_modules.items():
																					_SCREAMING_SNAKE_CASE		:    Tuple      	=     bert_output.adapter_modules[lang_code]
																					_SCREAMING_SNAKE_CASE		:    int      	=     xmod_layer.adapter_modules[lang_code]
																					_SCREAMING_SNAKE_CASE		:    str      	=     from_adapter.fca.weight
																					_SCREAMING_SNAKE_CASE		:    Optional[int]      	=     from_adapter.fca.bias
																					_SCREAMING_SNAKE_CASE		:    Tuple      	=     from_adapter.fca.weight
																					_SCREAMING_SNAKE_CASE		:    Dict      	=     from_adapter.fca.bias

														# end of layer

							if xmod_sent_encoder.layer_norm is not None:
														_SCREAMING_SNAKE_CASE		:    List[str]      	=     xmod_sent_encoder.layer_norm.weight
														_SCREAMING_SNAKE_CASE		:    Dict      	=     xmod_sent_encoder.layer_norm.bias

							if classification_head:
														_SCREAMING_SNAKE_CASE		:    Dict      	=     xmod.model.classification_heads["""mnli"""].dense.weight
														_SCREAMING_SNAKE_CASE		:    Tuple      	=     xmod.model.classification_heads["""mnli"""].dense.bias
														_SCREAMING_SNAKE_CASE		:    Union[str, Any]      	=     xmod.model.classification_heads["""mnli"""].out_proj.weight
														_SCREAMING_SNAKE_CASE		:    int      	=     xmod.model.classification_heads["""mnli"""].out_proj.bias
							else:
														# LM Head
														_SCREAMING_SNAKE_CASE		:    Optional[Any]      	=     xmod.model.encoder.lm_head.dense.weight
														_SCREAMING_SNAKE_CASE		:    List[Any]      	=     xmod.model.encoder.lm_head.dense.bias
														_SCREAMING_SNAKE_CASE		:    List[str]      	=     xmod.model.encoder.lm_head.layer_norm.weight
														_SCREAMING_SNAKE_CASE		:    int      	=     xmod.model.encoder.lm_head.layer_norm.bias
														_SCREAMING_SNAKE_CASE		:    str      	=     xmod.model.encoder.lm_head.weight
														_SCREAMING_SNAKE_CASE		:    Union[str, Any]      	=     xmod.model.encoder.lm_head.bias

							# Let's check that we get the same results.
							_SCREAMING_SNAKE_CASE		:    List[str]      	=     xmod.encode(__SCREAMING_SNAKE_CASE	).unsqueeze(0	)  # batch of size 1
							model.roberta.set_default_language(__SCREAMING_SNAKE_CASE	)

							_SCREAMING_SNAKE_CASE		:    str      	=     model(__SCREAMING_SNAKE_CASE	)[0]
							if classification_head:
														_SCREAMING_SNAKE_CASE		:    Union[str, Any]      	=     xmod.model.classification_heads["""mnli"""](xmod.extract_features(__SCREAMING_SNAKE_CASE	)	)
							else:
														_SCREAMING_SNAKE_CASE		:    str      	=     xmod.model(__SCREAMING_SNAKE_CASE  ,						lang_id=[SAMPLE_LANGUAGE]	)[0]
							print(our_output.shape  ,						their_output.shape	)
							_SCREAMING_SNAKE_CASE		:    Optional[int]      	=     torch.max(torch.abs(our_output - their_output	)	).item()
							print(F"""max_absolute_diff = {max_absolute_diff}"""	)  # ~ 1e-7
							_SCREAMING_SNAKE_CASE		:    Optional[int]      	=     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"""	)

							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__":
			lowerCAmelCase_     			=						argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '''--xmod_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.'''
			)
			lowerCAmelCase_     			=						parser.parse_args()
			convert_xmod_checkpoint_to_pytorch(
			    args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
			)




"""simple docstring"""

import os
from glob import glob

import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn

from transformers import CLIPModel, CLIPTokenizerFast
from utils import get_device, get_timestamp, show_pil
class   _UpperCAmelCase				:




      def __init__(				self ,							lowercase_ = "cpu" ,							lowercase_ = "openai/clip-vit-large-patch14"				)    ->       None:
          UpperCAmelCase          =       device
          UpperCAmelCase          =       CLIPTokenizerFast.from_pretrained(lowercase_				)
          UpperCAmelCase          =       [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]
          UpperCAmelCase          =       [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]
          UpperCAmelCase          =       torchvision.transforms.Normalize(self.image_mean ,							self.image_std				)
          UpperCAmelCase          =       torchvision.transforms.Resize(2_2_4				)
          UpperCAmelCase          =       torchvision.transforms.CenterCrop(2_2_4				)




      def      a_   (				self ,							lowercase_				)    ->       Any:
          UpperCAmelCase          =       self.resize(lowercase_				)
          UpperCAmelCase          =       self.center_crop(lowercase_				)
          UpperCAmelCase          =       self.normalize(lowercase_				)
          return images




      def __call__(				self ,							lowercase_=None ,							lowercase_=None ,							**lowercase_				)    ->       Union[str, Any]:
          UpperCAmelCase          =       self.tokenizer(text=lowercase_ ,							**lowercase_				)
          UpperCAmelCase          =       self.preprocess_img(lowercase_				)
          UpperCAmelCase          =       {key: value.to(self.device				) for (key, value) in encoding.items()}
          return encoding

class   _UpperCAmelCase				(		nn.Module				):




      def __init__(				self ,							lowercase_=1_0 ,							lowercase_=0.0_1 ,							lowercase_=None ,							lowercase_=None ,							lowercase_=None ,							lowercase_=None ,							lowercase_=None ,							lowercase_=None ,							lowercase_=False ,							lowercase_=True ,							lowercase_="image" ,							lowercase_=True ,							lowercase_=False ,							lowercase_=False ,							lowercase_=False ,							)    ->       None:
          super().__init__()
          UpperCAmelCase          =       None
          UpperCAmelCase          =       device if device else get_device()
          if vqgan:
              UpperCAmelCase          =       vqgan
          else:
              UpperCAmelCase          =       load_vqgan(self.device ,							conf_path=lowercase_ ,							ckpt_path=lowercase_				)
          self.vqgan.eval()
          if clip:
              UpperCAmelCase          =       clip
          else:
              UpperCAmelCase          =       CLIPModel.from_pretrained('openai/clip-vit-base-patch32'				)
          self.clip.to(self.device				)
          UpperCAmelCase          =       ProcessorGradientFlow(device=self.device				)

          UpperCAmelCase          =       iterations
          UpperCAmelCase          =       lr
          UpperCAmelCase          =       log
          UpperCAmelCase          =       make_grid
          UpperCAmelCase          =       return_val
          UpperCAmelCase          =       quantize
          UpperCAmelCase          =       self.vqgan.decoder.z_shape




      def      a_   (				self ,							lowercase_=None ,							lowercase_=None ,							lowercase_=5 ,							lowercase_=True				)    ->       Dict:
          UpperCAmelCase          =       []
          if output_path is None:
              UpperCAmelCase          =       './animation.gif'
          if input_path is None:
              UpperCAmelCase          =       self.save_path
          UpperCAmelCase          =       sorted(glob(input_path + '/*'				)				)
          if not len(lowercase_				):
              raise ValueError(
                  'No images found in save path, aborting (did you pass save_intermediate=True to the generate'
                  ' function?)'				)
          if len(lowercase_				) == 1:
              print('Only one image found in save path, (did you pass save_intermediate=True to the generate function?)'				)
          UpperCAmelCase          =       total_duration / len(lowercase_				)
          UpperCAmelCase          =       [frame_duration] * len(lowercase_				)
          if extend_frames:
              UpperCAmelCase          =       1.5
              UpperCAmelCase          =       3
          for file_name in paths:
              if file_name.endswith('.png'				):
                  images.append(imageio.imread(lowercase_				)				)
          imageio.mimsave(lowercase_ ,							lowercase_ ,							duration=lowercase_				)
          print(F"gif saved to {output_path}"				)




      def      a_   (				self ,							lowercase_=None ,							lowercase_=None				)    ->       List[Any]:
          if not (path or img):
              raise ValueError('Input either path or tensor'				)
          if img is not None:
              raise NotImplementedError
          UpperCAmelCase          =       preprocess(Image.open(lowercase_				) ,							target_image_size=2_5_6				).to(self.device				)
          UpperCAmelCase          =       preprocess_vqgan(lowercase_				)
          UpperCAmelCase      ,				*UpperCAmelCase          =       self.vqgan.encode(lowercase_				)
          return z




      def      a_   (				self ,							lowercase_				)    ->       Optional[int]:
          UpperCAmelCase          =       self.latent.detach().requires_grad_()
          UpperCAmelCase          =       base_latent + transform_vector
          if self.quantize:
              UpperCAmelCase      ,				*UpperCAmelCase          =       self.vqgan.quantize(lowercase_				)
          else:
              UpperCAmelCase          =       trans_latent
          return self.vqgan.decode(lowercase_				)




      def      a_   (				self ,							lowercase_ ,							lowercase_ ,							lowercase_=None				)    ->       str:
          UpperCAmelCase          =       self.clip_preprocessor(text=lowercase_ ,							images=lowercase_ ,							return_tensors='pt' ,							padding=lowercase_				)
          UpperCAmelCase          =       self.clip(**lowercase_				)
          UpperCAmelCase          =       clip_outputs.logits_per_image
          if weights is not None:
              UpperCAmelCase          =       similarity_logits * weights
          return similarity_logits.sum()




      def      a_   (				self ,							lowercase_ ,							lowercase_ ,							lowercase_				)    ->       List[Any]:
          UpperCAmelCase          =       self._get_clip_similarity(pos_prompts['prompts'] ,							lowercase_ ,							weights=(1 / pos_prompts['weights'])				)
          if neg_prompts:
              UpperCAmelCase          =       self._get_clip_similarity(neg_prompts['prompts'] ,							lowercase_ ,							weights=neg_prompts['weights']				)
          else:
              UpperCAmelCase          =       torch.tensor([1] ,							device=self.device				)
          UpperCAmelCase          =       -torch.log(lowercase_				) + torch.log(lowercase_				)
          return loss




      def      a_   (				self ,							lowercase_ ,							lowercase_ ,							lowercase_				)    ->       Tuple:
          UpperCAmelCase          =       torch.randn_like(self.latent ,							requires_grad=lowercase_ ,							device=self.device				)
          UpperCAmelCase          =       torch.optim.Adam([vector] ,							lr=self.lr				)

          for i in range(self.iterations				):
              optim.zero_grad()
              UpperCAmelCase          =       self._add_vector(lowercase_				)
              UpperCAmelCase          =       loop_post_process(lowercase_				)
              UpperCAmelCase          =       self._get_CLIP_loss(lowercase_ ,							lowercase_ ,							lowercase_				)
              print('CLIP loss' ,							lowercase_				)
              if self.log:
                  wandb.log({'CLIP Loss': clip_loss}				)
              clip_loss.backward(retain_graph=lowercase_				)
              optim.step()
              if self.return_val == "image":
                  yield custom_to_pil(transformed_img[0]				)
              else:
                  yield vector




      def      a_   (				self ,							lowercase_ ,							lowercase_ ,							lowercase_				)    ->       Dict:
          wandb.init(reinit=lowercase_ ,							project='face-editor'				)
          wandb.config.update({'Positive Prompts': positive_prompts}				)
          wandb.config.update({'Negative Prompts': negative_prompts}				)
          wandb.config.update({'lr': self.lr, 'iterations': self.iterations}				)
          if image_path:
              UpperCAmelCase          =       Image.open(lowercase_				)
              UpperCAmelCase          =       image.resize((2_5_6, 2_5_6)				)
              wandb.log('Original Image' ,							wandb.Image(lowercase_				)				)




      def      a_   (				self ,							lowercase_				)    ->       Tuple:
          if not prompts:
              return []
          UpperCAmelCase          =       []
          UpperCAmelCase          =       []
          if isinstance(lowercase_ ,							lowercase_				):
              UpperCAmelCase          =       [prompt.strip() for prompt in prompts.split('|'				)]
          for prompt in prompts:
              if isinstance(lowercase_ ,							(tuple, list)				):
                  UpperCAmelCase          =       prompt[0]
                  UpperCAmelCase          =       float(prompt[1]				)
              elif ":" in prompt:
                  UpperCAmelCase      ,				UpperCAmelCase          =       prompt.split(':'				)
                  UpperCAmelCase          =       float(lowercase_				)
              else:
                  UpperCAmelCase          =       prompt
                  UpperCAmelCase          =       1.0
              processed_prompts.append(lowercase_				)
              weights.append(lowercase_				)
          return {
              "prompts": processed_prompts,
              "weights": torch.tensor(lowercase_ ,							device=self.device				),
          }




      def      a_   (				self ,							lowercase_ ,							lowercase_=None ,							lowercase_=None ,							lowercase_=True ,							lowercase_=False ,							lowercase_=True ,							lowercase_=True ,							lowercase_=None ,							)    ->       List[str]:
          if image_path:
              UpperCAmelCase          =       self._get_latent(lowercase_				)
          else:
              UpperCAmelCase          =       torch.randn(self.latent_dim ,							device=self.device				)
          if self.log:
              self._init_logging(lowercase_ ,							lowercase_ ,							lowercase_				)

          assert pos_prompts, "You must provide at least one positive prompt."
          UpperCAmelCase          =       self.process_prompts(lowercase_				)
          UpperCAmelCase          =       self.process_prompts(lowercase_				)
          if save_final and save_path is None:
              UpperCAmelCase          =       os.path.join('./outputs/' ,							'_'.join(pos_prompts['prompts']				)				)
          if not os.path.exists(lowercase_				):
              os.makedirs(lowercase_				)
          else:
              UpperCAmelCase          =       save_path + '_' + get_timestamp()
              os.makedirs(lowercase_				)
          UpperCAmelCase          =       save_path

          UpperCAmelCase          =       self.vqgan.decode(self.latent				)[0]
          if show_intermediate:
              print('Original Image'				)
              show_pil(custom_to_pil(lowercase_				)				)

          UpperCAmelCase          =       loop_post_process(lowercase_				)
          for iter, transformed_img in enumerate(self._optimize_CLIP(lowercase_ ,							lowercase_ ,							lowercase_				)				):
              if show_intermediate:
                  show_pil(lowercase_				)
              if save_intermediate:
                  transformed_img.save(os.path.join(self.save_path ,							F"iter_{iter:03d}.png"				)				)
              if self.log:
                  wandb.log({'Image': wandb.Image(lowercase_				)}				)
          if show_final:
              show_pil(lowercase_				)
          if save_final:
              transformed_img.save(os.path.join(self.save_path ,							F"iter_{iter:03d}_final.png"				)				)



