


"""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 			A__	:


     """simple docstring"""

     def __init__(	self:						Optional[int]     ,		__a:						str = "cpu"     ,		__a:						str = "openai/clip-vit-large-patch14"	)->    None:
           lowerCamelCase					:					List[str]        =	device
           lowerCamelCase					:					Tuple        =	CLIPTokenizerFast.from_pretrained(__a	)
           lowerCamelCase					:					Dict        =	[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]
           lowerCamelCase					:					Dict        =	[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]
           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 		a__						(	self:						Tuple     ,		__a:						str	)->    List[str]:
           lowerCamelCase					:					List[str]        =	self.resize(__a	)
           lowerCamelCase					:					str        =	self.center_crop(__a	)
           lowerCamelCase					:					int        =	self.normalize(__a	)
           return images







     def __call__(	self:						int     ,		__a:						Optional[Any]=None     ,		__a:						Dict=None     ,		**__a:						int	)->    Tuple:
           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 			A__	(						nn.Module):


     """simple docstring"""

     def __init__(	self:						List[str]     ,		__a:						Optional[Any]=10     ,		__a:						Dict=0.01     ,		__a:						Optional[Any]=None     ,		__a:						List[Any]=None     ,		__a:						Any=None     ,		__a:						int=None     ,		__a:						Tuple=None     ,		__a:						int=None     ,		__a:						Union[str, Any]=False     ,		__a:						str=True     ,		__a:						List[str]="image"     ,		__a:						Any=True     ,		__a:						Dict=False     ,		__a:						str=False     ,		__a:						Any=False     ,		)->    None:
           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 		a__						(	self:						int     ,		__a:						Optional[Any]=None     ,		__a:						List[Any]=None     ,		__a:						List[Any]=5     ,		__a:						List[str]=True	)->    List[Any]:
           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 		a__						(	self:						Optional[Any]     ,		__a:						Tuple=None     ,		__a:						Any=None	)->    Optional[int]:
           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 		a__						(	self:						Optional[Any]     ,		__a:						Dict	)->    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 		a__						(	self:						str     ,		__a:						Optional[int]     ,		__a:						List[str]     ,		__a:						List[Any]=None	)->    Optional[Any]:
           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 		a__						(	self:						Any     ,		__a:						Tuple     ,		__a:						Tuple     ,		__a:						int	)->    str:
           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 		a__						(	self:						Optional[int]     ,		__a:						List[str]     ,		__a:						Optional[int]     ,		__a:						Dict	)->    int:
           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 		a__						(	self:						Optional[Any]     ,		__a:						Optional[int]     ,		__a:						Optional[int]     ,		__a:						Tuple	)->    List[Any]:
           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 		a__						(	self:						str     ,		__a:						Union[str, Any]	)->    Dict:
           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 		a__						(	self:						List[Any]     ,		__a:						int     ,		__a:						Any=None     ,		__a:						Tuple=None     ,		__a:						Tuple=True     ,		__a:						Any=False     ,		__a:						Any=True     ,		__a:						List[str]=True     ,		__a:						int=None     ,		)->    Union[str, Any]:
           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'	)	)








"""simple docstring"""


import argparse
import pickle

import numpy as np
import torch
from torch import nn

from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging


logging.set_verbosity_info()




def   snake_case		(     UpperCamelCase__       :						Optional[Any]			,				UpperCamelCase__       :						Tuple			,				UpperCamelCase__       :						Optional[Any]=None    )   ->      Tuple:
      # set parameter of one layer
      assert torch_layer.weight.shape == weight.shape, F'{torch_layer} layer.weight does not match'
      lowerCamelCase					:					Dict        =	nn.Parameter(UpperCamelCase__    )
      if bias is not None:
            assert torch_layer.bias.shape == bias.shape, F'{torch_layer} layer.bias does not match'
            lowerCamelCase					:					Any        =	nn.Parameter(UpperCamelCase__    )




def   snake_case		(     UpperCamelCase__       :						int			,				UpperCamelCase__       :						Any			,				UpperCamelCase__       :						Tuple    )   ->      Union[str, Any]:
      # set torch weights for 1-to-1 comparison
      lowerCamelCase					:					Dict        =	np.asarray(weights[0]    )
      lowerCamelCase					:					List[Any]        =	np.asarray(weights[1]    )
      lowerCamelCase					:					List[str]        =	np.asarray(weights[2]    )

      set_param(
          torch_layer.self_attention.query_key			,				torch.tensor(UpperCamelCase__    ).transpose(1			,				2    ).contiguous().view(-1			,				UpperCamelCase__    )			,				)
      set_param(
          torch_layer.self_attention.value			,				torch.tensor(UpperCamelCase__    ).transpose(1			,				2    ).contiguous().view(-1			,				UpperCamelCase__    )			,				)
      set_param(
          torch_layer.output.dense			,				torch.tensor(UpperCamelCase__    ).view(-1			,				UpperCamelCase__    ).contiguous().transpose(0			,				1    )			,				)




def   snake_case		(     UpperCamelCase__       :						Optional[Any]			,				UpperCamelCase__       :						Optional[Any]			,				UpperCamelCase__       :						Any    )   ->      List[Any]:
      # set torch weights for 1-to-1 comparison
      lowerCamelCase					:					Tuple        =	np.asarray(weights[0]    )
      lowerCamelCase					:					Any        =	np.asarray(weights[1]    )
      lowerCamelCase					:					List[Any]        =	np.asarray(weights[2]    )
      lowerCamelCase					:					List[str]        =	np.asarray(weights[3]    )

      set_param(
          torch_layer.self_attention.query			,				torch.tensor(UpperCamelCase__    ).transpose(1			,				2    ).contiguous().view(-1			,				UpperCamelCase__    )			,				)
      set_param(
          torch_layer.self_attention.key			,				torch.tensor(UpperCamelCase__    ).transpose(1			,				2    ).contiguous().view(-1			,				UpperCamelCase__    )			,				)
      set_param(
          torch_layer.self_attention.value			,				torch.tensor(UpperCamelCase__    ).transpose(1			,				2    ).contiguous().view(-1			,				UpperCamelCase__    )			,				)
      set_param(
          torch_layer.output.dense			,				torch.tensor(UpperCamelCase__    ).view(-1			,				UpperCamelCase__    ).contiguous().transpose(0			,				1    )			,				)




def   snake_case		(     UpperCamelCase__       :						Dict			,				UpperCamelCase__       :						Optional[int]			,				UpperCamelCase__       :						Any    )   ->      Optional[Any]:
      # layernorm 1
      lowerCamelCase					:					str        =	weights[0][0][0]
      lowerCamelCase					:					Optional[int]        =	np.asarray(layer_norm_a[0]    )
      lowerCamelCase					:					Tuple        =	np.asarray(layer_norm_a[1]    )
      set_param(
          torch_block.attention.layer_norm			,				torch.tensor(UpperCamelCase__    )			,				torch.tensor(UpperCamelCase__    )			,				)

      # lsh weights + output
      lowerCamelCase					:					List[Any]        =	weights[0][1]
      if len(UpperCamelCase__    ) < 4:
            set_layer_weights_in_torch_lsh(UpperCamelCase__			,				torch_block.attention			,				UpperCamelCase__    )
      else:
            set_layer_weights_in_torch_local(UpperCamelCase__			,				torch_block.attention			,				UpperCamelCase__    )

      # intermediate weighs
      lowerCamelCase					:					int        =	weights[2][0][1][2]

      # Chunked Feed Forward
      if len(UpperCamelCase__    ) == 4:
            lowerCamelCase					:					Dict        =	intermediate_weights[2]

      # layernorm 2
      lowerCamelCase					:					Optional[int]        =	np.asarray(intermediate_weights[0][0]    )
      lowerCamelCase					:					Tuple        =	np.asarray(intermediate_weights[0][1]    )
      set_param(
          torch_block.feed_forward.layer_norm			,				torch.tensor(UpperCamelCase__    )			,				torch.tensor(UpperCamelCase__    )			,				)

      # intermediate dense
      lowerCamelCase					:					Optional[Any]        =	np.asarray(intermediate_weights[1][0]    )
      lowerCamelCase					:					Union[str, Any]        =	np.asarray(intermediate_weights[1][1]    )
      set_param(
          torch_block.feed_forward.dense.dense			,				torch.tensor(UpperCamelCase__    ).transpose(0			,				1    ).contiguous()			,				torch.tensor(UpperCamelCase__    )			,				)

      # intermediate out
      lowerCamelCase					:					Optional[int]        =	np.asarray(intermediate_weights[4][0]    )
      lowerCamelCase					:					List[Any]        =	np.asarray(intermediate_weights[4][1]    )
      set_param(
          torch_block.feed_forward.output.dense			,				torch.tensor(UpperCamelCase__    ).transpose(0			,				1    ).contiguous()			,				torch.tensor(UpperCamelCase__    )			,				)




def   snake_case		(     UpperCamelCase__       :						Optional[Any]			,				UpperCamelCase__       :						List[str]			,				UpperCamelCase__       :						int    )   ->      List[Any]:
      # reformer model
      lowerCamelCase					:					List[Any]        =	torch_model.reformer

      # word embeds
      lowerCamelCase					:					Union[str, Any]        =	np.asarray(weights[1]    )
      set_param(
          torch_model_reformer.embeddings.word_embeddings			,				torch.tensor(UpperCamelCase__    )			,				)

      if isinstance(weights[3]			,				UpperCamelCase__    ):
            lowerCamelCase					:					Union[str, Any]        =	torch_model_reformer.embeddings.position_embeddings
            for emb_idx in range(len(position_embeddings.weights    )    ):
                  lowerCamelCase					:					str        =	np.asarray(weights[3][emb_idx][0]    )
                  assert (
                      position_embeddings.weights[emb_idx].shape == emb_weights.shape
                  ), F'{position_embeddings[emb_idx]} emb does not match'
                  lowerCamelCase					:					Dict        =	nn.Parameter(torch.tensor(UpperCamelCase__    )    )

      lowerCamelCase					:					int        =	weights[5]
      assert len(torch_model_reformer.encoder.layers    ) * 4 == len(
          UpperCamelCase__    ), "HF and trax model do not have the same number of layers"
      for layer_idx, layer in enumerate(torch_model_reformer.encoder.layers    ):
            lowerCamelCase					:					Dict        =	trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
            set_block_weights_in_torch(UpperCamelCase__			,				UpperCamelCase__			,				UpperCamelCase__    )

      # output layer norm
      lowerCamelCase					:					Any        =	np.asarray(weights[7][0]    )
      lowerCamelCase					:					List[str]        =	np.asarray(weights[7][1]    )
      set_param(
          torch_model_reformer.encoder.layer_norm			,				torch.tensor(UpperCamelCase__    )			,				torch.tensor(UpperCamelCase__    )			,				)

      # output embeddings
      lowerCamelCase					:					List[Any]        =	np.asarray(weights[9][0]    )
      lowerCamelCase					:					Optional[int]        =	np.asarray(weights[9][1]    )
      set_param(
          torch_model.lm_head.decoder			,				torch.tensor(UpperCamelCase__    ).transpose(0			,				1    ).contiguous()			,				torch.tensor(UpperCamelCase__    )			,				)




def   snake_case		(     UpperCamelCase__       :						str			,				UpperCamelCase__       :						int			,				UpperCamelCase__       :						str    )   ->      Optional[int]:
      # Initialise PyTorch model
      lowerCamelCase					:					Union[str, Any]        =	ReformerConfig.from_json_file(UpperCamelCase__    )
      print(F'Building PyTorch model from configuration: {config}'    )
      lowerCamelCase					:					str        =	ReformerModelWithLMHead(UpperCamelCase__    )

      with open(UpperCamelCase__			,				"""rb"""    ) as f:
            lowerCamelCase					:					str        =	pickle.load(UpperCamelCase__    )["""weights"""]

      set_model_weights_in_torch(UpperCamelCase__			,				UpperCamelCase__			,				config.hidden_size    )

      # Save pytorch-model
      print(F'Save PyTorch model to {pytorch_dump_path}'    )
      torch.save(model.state_dict()			,				UpperCamelCase__    )


if __name__ == "__main__":
    __lowerCamelCase      :Dict	    =		argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '--trax_model_pkl_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
    )
    parser.add_argument(
        '--config_file',
        default=None,
        type=str,
        required=True,
        help=(
            'The config json file corresponding to the pre-trained Reformer model. \n'
            'This specifies the model architecture.'
        ),
    )
    parser.add_argument(
        '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
    )
    __lowerCamelCase      :Optional[int]	    =		parser.parse_args()
    convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)




