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   _lowerCAmelCase		:







							def __init__(				self     :    Optional[int]	,					__snake_case     :    str = "cpu"	,					__snake_case     :    str = "openai/clip-vit-large-patch14"		):
										lowerCamelCase						:List[str]						  =     device
										lowerCamelCase						:Tuple						  =     CLIPTokenizerFast.from_pretrained(__a		)
										lowerCamelCase						:Dict						  =     [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]
										lowerCamelCase						:Dict						  =     [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]
										lowerCamelCase						:Tuple						  =     torchvision.transforms.Normalize(self.image_mean	,					self.image_std		)
										lowerCamelCase						:List[Any]						  =     torchvision.transforms.Resize(224		)
										lowerCamelCase						:Dict						  =     torchvision.transforms.CenterCrop(224		)







							def 				snake_case  (				self     :    Tuple	,					__snake_case     :    str		):
										lowerCamelCase						:List[str]						  =     self.resize(__a		)
										lowerCamelCase						:str						  =     self.center_crop(__a		)
										lowerCamelCase						:int						  =     self.normalize(__a		)
										return images







							def __call__(				self     :    int	,					__snake_case     :    Optional[Any]=None	,					__snake_case     :    Dict=None	,					**__snake_case     :    int		):
										lowerCamelCase						:str						  =     self.tokenizer(text=__a	,					**__a		)
										lowerCamelCase						:Optional[int]						  =     self.preprocess_img(__a		)
										lowerCamelCase						:str						  =     {key: value.to(self.device		) for (key, value) in encoding.items()}
										return encoding


class   _lowerCAmelCase		(     nn.Module				):







							def __init__(				self     :    List[str]	,					__snake_case     :    Optional[Any]=10	,					__snake_case     :    Dict=0.0_1	,					__snake_case     :    Optional[Any]=None	,					__snake_case     :    List[Any]=None	,					__snake_case     :    Any=None	,					__snake_case     :    int=None	,					__snake_case     :    Tuple=None	,					__snake_case     :    int=None	,					__snake_case     :    Union[str, Any]=False	,					__snake_case     :    str=True	,					__snake_case     :    List[str]="image"	,					__snake_case     :    Any=True	,					__snake_case     :    Dict=False	,					__snake_case     :    str=False	,					__snake_case     :    Any=False	,					):
										super().__init__()
										lowerCamelCase						:Any						  =     None
										lowerCamelCase						:Optional[int]						  =     device if device else get_device()
										if vqgan:
													lowerCamelCase						:List[Any]						  =     vqgan
										else:
													lowerCamelCase						:Union[str, Any]						  =     load_vqgan(self.device	,					conf_path=__a	,					ckpt_path=__a		)
										self.vqgan.eval()
										if clip:
													lowerCamelCase						:str						  =     clip
										else:
													lowerCamelCase						:Tuple						  =     CLIPModel.from_pretrained('''openai/clip-vit-base-patch32'''		)
										self.clip.to(self.device		)
										lowerCamelCase						:Any						  =     ProcessorGradientFlow(device=self.device		)

										lowerCamelCase						:List[Any]						  =     iterations
										lowerCamelCase						:str						  =     lr
										lowerCamelCase						:Optional[int]						  =     log
										lowerCamelCase						:List[Any]						  =     make_grid
										lowerCamelCase						:Optional[int]						  =     return_val
										lowerCamelCase						:Optional[int]						  =     quantize
										lowerCamelCase						:List[Any]						  =     self.vqgan.decoder.z_shape







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







							def 				snake_case  (				self     :    Optional[Any]	,					__snake_case     :    Tuple=None	,					__snake_case     :    Any=None		):
										if not (path or img):
													raise ValueError('''Input either path or tensor'''		)
										if img is not None:
													raise NotImplementedError
										lowerCamelCase						:Tuple						  =     preprocess(Image.open(__a		)	,					target_image_size=256		).to(self.device		)
										lowerCamelCase						:Dict						  =     preprocess_vqgan(__a		)
										lowerCamelCase						:List[str]						  =     self.vqgan.encode(__a		)
										return z







							def 				snake_case  (				self     :    Optional[Any]	,					__snake_case     :    Dict		):
										lowerCamelCase						:List[str]						  =     self.latent.detach().requires_grad_()
										lowerCamelCase						:int						  =     base_latent + transform_vector
										if self.quantize:
													lowerCamelCase						:Union[str, Any]						  =     self.vqgan.quantize(__a		)
										else:
													lowerCamelCase						:Optional[Any]						  =     trans_latent
										return self.vqgan.decode(__a		)







							def 				snake_case  (				self     :    str	,					__snake_case     :    Optional[int]	,					__snake_case     :    List[str]	,					__snake_case     :    List[Any]=None		):
										lowerCamelCase						:Optional[int]						  =     self.clip_preprocessor(text=__a	,					images=__a	,					return_tensors='''pt'''	,					padding=__a		)
										lowerCamelCase						:Optional[int]						  =     self.clip(**__a		)
										lowerCamelCase						:Optional[int]						  =     clip_outputs.logits_per_image
										if weights is not None:
													lowerCamelCase						:List[str]						  =     similarity_logits * weights
										return similarity_logits.sum()







							def 				snake_case  (				self     :    Any	,					__snake_case     :    Tuple	,					__snake_case     :    Tuple	,					__snake_case     :    int		):
										lowerCamelCase						:List[Any]						  =     self._get_clip_similarity(pos_prompts['''prompts''']	,					__a	,					weights=(1 / pos_prompts['''weights'''])		)
										if neg_prompts:
													lowerCamelCase						:Optional[Any]						  =     self._get_clip_similarity(neg_prompts['''prompts''']	,					__a	,					weights=neg_prompts['''weights''']		)
										else:
													lowerCamelCase						:int						  =     torch.tensor([1]	,					device=self.device		)
										lowerCamelCase						:Dict						  =     -torch.log(__a		) + torch.log(__a		)
										return loss







							def 				snake_case  (				self     :    Optional[int]	,					__snake_case     :    List[str]	,					__snake_case     :    Optional[int]	,					__snake_case     :    Dict		):
										lowerCamelCase						:Tuple						  =     torch.randn_like(self.latent	,					requires_grad=__a	,					device=self.device		)
										lowerCamelCase						:int						  =     torch.optim.Adam([vector]	,					lr=self.lr		)

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







							def 				snake_case  (				self     :    Optional[Any]	,					__snake_case     :    Optional[int]	,					__snake_case     :    Optional[int]	,					__snake_case     :    Tuple		):
										wandb.init(reinit=__a	,					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:
													lowerCamelCase						:Dict						  =     Image.open(__a		)
													lowerCamelCase						:Optional[Any]						  =     image.resize((256, 256)		)
													wandb.log('''Original Image'''	,					wandb.Image(__a		)		)







							def 				snake_case  (				self     :    str	,					__snake_case     :    Union[str, Any]		):
										if not prompts:
													return []
										lowerCamelCase						:Dict						  =     []
										lowerCamelCase						:str						  =     []
										if isinstance(__a	,					__a		):
													lowerCamelCase						:int						  =     [prompt.strip() for prompt in prompts.split('''|'''		)]
										for prompt in prompts:
													if isinstance(__a	,					(tuple, list)		):
																lowerCamelCase						:str						  =     prompt[0]
																lowerCamelCase						:Optional[Any]						  =     float(prompt[1]		)
													elif ":" in prompt:
																lowerCamelCase						:str						  =     prompt.split(''':'''		)
																lowerCamelCase						:int						  =     float(__a		)
													else:
																lowerCamelCase						:Union[str, Any]						  =     prompt
																lowerCamelCase						:Optional[int]						  =     1.0
													processed_prompts.append(__a		)
													weights.append(__a		)
										return {
										    "prompts": processed_prompts,
										    "weights": torch.tensor(__a	,					device=self.device		),
										}







							def 				snake_case  (				self     :    List[Any]	,					__snake_case     :    int	,					__snake_case     :    Any=None	,					__snake_case     :    Tuple=None	,					__snake_case     :    Tuple=True	,					__snake_case     :    Any=False	,					__snake_case     :    Any=True	,					__snake_case     :    List[str]=True	,					__snake_case     :    int=None	,					):
										if image_path:
													lowerCamelCase						:str						  =     self._get_latent(__a		)
										else:
													lowerCamelCase						:List[str]						  =     torch.randn(self.latent_dim	,					device=self.device		)
										if self.log:
													self._init_logging(__a	,					__a	,					__a		)

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

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

										lowerCamelCase						:Optional[int]						  =     loop_post_process(__a		)
										for iter, transformed_img in enumerate(self._optimize_CLIP(__a	,					__a	,					__a		)		):
													if show_intermediate:
																show_pil(__a		)
													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(__a		)}		)
										if show_final:
													show_pil(__a		)
										if save_final:
													transformed_img.save(os.path.join(self.save_path	,					F"iter_{iter:03d}_final.png"		)		)




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()
A__           =							logging.get_logger(__name__)

A__           =							"""Hello, World!"""
A__           =							"""en_XX"""





def  _lowerCamelCase					(   a_				: str						,			a_				: str						,			a_				: bool):
			lowerCamelCase						:int						  =     Path('''data_bin''')
			lowerCamelCase						:Union[str, Any]						  =     FairseqXmodModel.from_pretrained(
			    model_name_or_path=str(Path(a_).parent)						,			checkpoint_file=Path(a_).name						,			_name='''xmod_base'''						,			arch='''xmod_base'''						,			task='''multilingual_masked_lm'''						,			data_name_or_path=str(a_)						,			bpe='''sentencepiece'''						,			sentencepiece_model=str(Path(a_).parent / '''sentencepiece.bpe.model''')						,			src_dict=str(data_dir / '''dict.txt''')						,			)
			xmod.eval()  # disable dropout
			print(a_)

			lowerCamelCase						:Any						  =     xmod.model.encoder.sentence_encoder
			lowerCamelCase						:List[str]						  =     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=5_14						,			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:
						lowerCamelCase						:Dict						  =     xmod.model.classification_heads['''mnli'''].out_proj.weight.shape[0]

			print('''Our X-MOD config:'''						,			a_)

			lowerCamelCase						:List[Any]						  =     XmodForSequenceClassification(a_) if classification_head else XmodForMaskedLM(a_)
			model.eval()

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

			lowerCamelCase						:List[Any]						  =     xmod_sent_encoder.layernorm_embedding.weight
			lowerCamelCase						:Optional[int]						  =     xmod_sent_encoder.layernorm_embedding.bias

			for i in range(config.num_hidden_layers):
						# Encoder: start of layer
						lowerCamelCase						:Union[str, Any]						  =     model.roberta.encoder.layer[i]
						lowerCamelCase						:List[str]						  =     xmod_sent_encoder.layers[i]

						# self attention
						lowerCamelCase						: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.''')

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

						# self-attention output
						lowerCamelCase						:Optional[int]						  =     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.''')
						lowerCamelCase						:List[Any]						  =     xmod_layer.self_attn.out_proj.weight
						lowerCamelCase						:Union[str, Any]						  =     xmod_layer.self_attn.out_proj.bias
						lowerCamelCase						:str						  =     xmod_layer.self_attn_layer_norm.weight
						lowerCamelCase						:List[Any]						  =     xmod_layer.self_attn_layer_norm.bias

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

						# output
						lowerCamelCase						:List[str]						  =     layer.output
						if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
									raise AssertionError('''Dimensions of feed-forward weights do not match.''')
						lowerCamelCase						:str						  =     xmod_layer.fca.weight
						lowerCamelCase						:int						  =     xmod_layer.fca.bias
						lowerCamelCase						:List[Any]						  =     xmod_layer.final_layer_norm.weight
						lowerCamelCase						:List[str]						  =     xmod_layer.final_layer_norm.bias
						if bert_output.adapter_layer_norm is not None:
									lowerCamelCase						:List[str]						  =     xmod_layer.adapter_layer_norm.weight
									lowerCamelCase						:int						  =     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():
									lowerCamelCase						:Optional[int]						  =     bert_output.adapter_modules[lang_code]
									lowerCamelCase						:Dict						  =     xmod_layer.adapter_modules[lang_code]
									lowerCamelCase						:List[Any]						  =     from_adapter.fca.weight
									lowerCamelCase						:List[Any]						  =     from_adapter.fca.bias
									lowerCamelCase						:Dict						  =     from_adapter.fca.weight
									lowerCamelCase						:Optional[Any]						  =     from_adapter.fca.bias

						# end of layer

			if xmod_sent_encoder.layer_norm is not None:
						lowerCamelCase						:Dict						  =     xmod_sent_encoder.layer_norm.weight
						lowerCamelCase						:List[Any]						  =     xmod_sent_encoder.layer_norm.bias

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

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

			lowerCamelCase						:Any						  =     model(a_)[0]
			if classification_head:
						lowerCamelCase						:Dict						  =     xmod.model.classification_heads['''mnli'''](xmod.extract_features(a_))
			else:
						lowerCamelCase						:int						  =     xmod.model(a_						,			lang_id=[SAMPLE_LANGUAGE])[0]
			print(our_output.shape						,			their_output.shape)
			lowerCamelCase						:List[str]						  =     torch.max(torch.abs(our_output - their_output)).item()
			print(F"max_absolute_diff = {max_absolute_diff}")  # ~ 1e-7
			lowerCamelCase						:str						  =     torch.allclose(a_						,			a_						,			atol=1e-3)
			print('''Do both models output the same tensors?'''						,			'''🔥''' if success else '''💩''')
			if not success:
						raise Exception('''Something went wRoNg''')

			Path(a_).mkdir(parents=a_						,			exist_ok=a_)
			print(F"Saving model to {pytorch_dump_folder_path}")
			model.save_pretrained(a_)


if __name__ == "__main__":
			A__           =							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."""
			)
			A__           =							parser.parse_args()
			convert_xmod_checkpoint_to_pytorch(
			    args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
			)



