
import argparse
import math
import os

import torch
from neural_compressor.utils.pytorch import load
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, StableDiffusionPipeline, UNetaDConditionModel





def 	__lowerCamelCase							():
					SCREAMING_SNAKE_CASE    			=       argparse.ArgumentParser()
					parser.add_argument(
					    "-m"      , "--pretrained_model_name_or_path"      , type=lowercase__      , default=lowercase__      , required=lowercase__      , help="Path to pretrained model or model identifier from huggingface.co/models."      , )
					parser.add_argument(
					    "-c"      , "--caption"      , type=lowercase__      , default="robotic cat with wings"      , help="Text used to generate images."      , )
					parser.add_argument(
					    "-n"      , "--images_num"      , type=lowercase__      , default=4      , help="How much images to generate."      , )
					parser.add_argument(
					    "-s"      , "--seed"      , type=lowercase__      , default=4_2      , help="Seed for random process."      , )
					parser.add_argument(
					    "-ci"      , "--cuda_id"      , type=lowercase__      , default=0      , help="cuda_id."      , )
					SCREAMING_SNAKE_CASE    			=       parser.parse_args()
					return args





def 	__lowerCamelCase							(UpperCAmelCase__     :							List[str]      , UpperCAmelCase__     :							Tuple      , UpperCAmelCase__     :							Dict  ):
					if not len(lowercase__  ) == rows * cols:
										raise ValueError("The specified number of rows and columns are not correct."  )

					SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       imgs[0].size
					SCREAMING_SNAKE_CASE    			=       Image.new("RGB"      , size=(cols * w, rows * h)  )
					SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       grid.size

					for i, img in enumerate(lowercase__  ):
										grid.paste(lowercase__      , box=(i % cols * w, i // cols * h)  )
					return grid





def 	__lowerCamelCase							(UpperCAmelCase__     :							str      , UpperCAmelCase__     :							List[str]="robotic cat with wings"      , UpperCAmelCase__     :							Optional[int]=7.5      , UpperCAmelCase__     :							Any=5_0      , UpperCAmelCase__     :							Dict=1      , UpperCAmelCase__     :							int=4_2      , ):
					SCREAMING_SNAKE_CASE    			=       torch.Generator(pipeline.device  ).manual_seed(lowercase__  )
					SCREAMING_SNAKE_CASE    			=       pipeline(
					    lowercase__      , guidance_scale=lowercase__      , num_inference_steps=lowercase__      , generator=lowercase__      , num_images_per_prompt=lowercase__      , ).images
					SCREAMING_SNAKE_CASE    			=       int(math.sqrt(lowercase__  )  )
					SCREAMING_SNAKE_CASE    			=       image_grid(lowercase__      , rows=_rows      , cols=num_images_per_prompt // _rows  )
					return grid, images


_lowerCamelCase					:   Optional[int]							=		parse_args()
# Load models and create wrapper for stable diffusion
_lowerCamelCase					:   Dict							=		CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder='''tokenizer''')
_lowerCamelCase					:   List[str]							=		CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder='''text_encoder''')
_lowerCamelCase					:   Tuple							=		AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder='''vae''')
_lowerCamelCase					:   str							=		UNetaDConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder='''unet''')

_lowerCamelCase					:   List[str]							=		StableDiffusionPipeline.from_pretrained(
    args.pretrained_model_name_or_path, text_encoder=text_encoder, vae=vae, unet=unet, tokenizer=tokenizer
)
_lowerCamelCase					:   Any							=		lambda images, clip_input: (images, False)
if os.path.exists(os.path.join(args.pretrained_model_name_or_path, '''best_model.pt''')):
							_lowerCamelCase					:   List[str]							=		load(args.pretrained_model_name_or_path, model=unet)
							unet.eval()
							setattr(pipeline, '''unet''', unet)
else:
							_lowerCamelCase					:   Optional[int]							=		unet.to(torch.device('''cuda''', args.cuda_id))
_lowerCamelCase					:   Tuple							=		pipeline.to(unet.device)
_lowerCamelCase				,						_lowerCamelCase					:   List[str]							=		generate_images(pipeline, prompt=args.caption, num_images_per_prompt=args.images_num, seed=args.seed)
grid.save(os.path.join(args.pretrained_model_name_or_path, '''{}.png'''.format('''_'''.join(args.caption.split()))))
_lowerCamelCase					:   Optional[int]							=		os.path.join(args.pretrained_model_name_or_path, '''_'''.join(args.caption.split()))
os.makedirs(dirname, exist_ok=True)
for idx, image in enumerate(images):
							image.save(os.path.join(dirname, '''{}.png'''.format(idx + 1)))





import argparse
import logging
from collections import namedtuple

import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer  # The authors' implementation

from transformers import BertTokenizer


logging.basicConfig(level=logging.INFO)
_lowerCamelCase					:   Optional[int]							=		logging.getLogger(__name__)


_lowerCamelCase					:   Optional[int]							=		'''Hello world! cécé herlolip'''


_lowerCamelCase					:   List[Any]							=		namedtuple(
    '''BertAbsConfig''',
    [
        '''temp_dir''',
        '''large''',
        '''use_bert_emb''',
        '''finetune_bert''',
        '''encoder''',
        '''share_emb''',
        '''max_pos''',
        '''enc_layers''',
        '''enc_hidden_size''',
        '''enc_heads''',
        '''enc_ff_size''',
        '''enc_dropout''',
        '''dec_layers''',
        '''dec_hidden_size''',
        '''dec_heads''',
        '''dec_ff_size''',
        '''dec_dropout''',
    ],
)





def 	__lowerCamelCase							(UpperCAmelCase__     :							str      , UpperCAmelCase__     :							Optional[int]  ):
					SCREAMING_SNAKE_CASE    			=       BertAbsConfig(
					    temp_dir="."      , finetune_bert=UpperCAmelCase__      , large=UpperCAmelCase__      , share_emb=UpperCAmelCase__      , use_bert_emb=UpperCAmelCase__      , encoder="bert"      , max_pos=5_1_2      , enc_layers=6      , enc_hidden_size=5_1_2      , enc_heads=8      , enc_ff_size=5_1_2      , enc_dropout=0.2      , dec_layers=6      , dec_hidden_size=7_6_8      , dec_heads=8      , dec_ff_size=2_0_4_8      , dec_dropout=0.2      , )
					SCREAMING_SNAKE_CASE    			=       torch.load(UpperCAmelCase__      , lambda UpperCAmelCase__      , UpperCAmelCase__  : storage  )
					SCREAMING_SNAKE_CASE    			=       AbsSummarizer(UpperCAmelCase__      , torch.device("cpu"  )      , UpperCAmelCase__  )
					original.eval()

					SCREAMING_SNAKE_CASE    			=       BertAbsSummarizer(UpperCAmelCase__      , torch.device("cpu"  )  )
					new_model.eval()

					# -------------------
					# Convert the weights
					# -------------------

					logging.info("convert the model"  )
					new_model.bert.load_state_dict(original.bert.state_dict()  )
					new_model.decoder.load_state_dict(original.decoder.state_dict()  )
					new_model.generator.load_state_dict(original.generator.state_dict()  )

					# ----------------------------------
					# Make sure the outpus are identical
					# ----------------------------------

					logging.info("Make sure that the models' outputs are identical"  )
					SCREAMING_SNAKE_CASE    			=       BertTokenizer.from_pretrained("bert-base-uncased"  )

					# prepare the model inputs
					SCREAMING_SNAKE_CASE    			=       tokenizer.encode("This is sample éàalj'-."  )
					encoder_input_ids.extend([tokenizer.pad_token_id] * (5_1_2 - len(UpperCAmelCase__  ))  )
					SCREAMING_SNAKE_CASE    			=       torch.tensor(UpperCAmelCase__  ).unsqueeze(0  )
					SCREAMING_SNAKE_CASE    			=       tokenizer.encode("This is sample 3 éàalj'-."  )
					decoder_input_ids.extend([tokenizer.pad_token_id] * (5_1_2 - len(UpperCAmelCase__  ))  )
					SCREAMING_SNAKE_CASE    			=       torch.tensor(UpperCAmelCase__  ).unsqueeze(0  )

					# failsafe to make sure the weights reset does not affect the
					# loaded weights.
					assert torch.max(torch.abs(original.generator[0].weight - new_model.generator[0].weight  )  ) == 0

					# forward pass
					SCREAMING_SNAKE_CASE    			=       encoder_input_ids
					SCREAMING_SNAKE_CASE    			=       decoder_input_ids
					SCREAMING_SNAKE_CASE    			=       SCREAMING_SNAKE_CASE    			=       None
					SCREAMING_SNAKE_CASE    			=       None
					SCREAMING_SNAKE_CASE    			=       SCREAMING_SNAKE_CASE    			=       None
					SCREAMING_SNAKE_CASE    			=       SCREAMING_SNAKE_CASE    			=       None
					SCREAMING_SNAKE_CASE    			=       None

					# The original model does not apply the geneator layer immediatly but rather in
					# the beam search (where it combines softmax + linear layer). Since we already
					# apply the softmax in our generation process we only apply the linear layer here.
					# We make sure that the outputs of the full stack are identical
					SCREAMING_SNAKE_CASE    			=       original(UpperCAmelCase__      , UpperCAmelCase__      , UpperCAmelCase__      , UpperCAmelCase__      , UpperCAmelCase__      , UpperCAmelCase__      , UpperCAmelCase__  )[0]
					SCREAMING_SNAKE_CASE    			=       original.generator(UpperCAmelCase__  )

					SCREAMING_SNAKE_CASE    			=       new_model(
					    UpperCAmelCase__      , UpperCAmelCase__      , UpperCAmelCase__      , UpperCAmelCase__      , UpperCAmelCase__  )[0]
					SCREAMING_SNAKE_CASE    			=       new_model.generator(UpperCAmelCase__  )

					SCREAMING_SNAKE_CASE    			=       torch.max(torch.abs(output_converted_model - output_original_model  )  ).item()
					print("Maximum absolute difference beween weights: {:.2f}".format(UpperCAmelCase__  )  )
					SCREAMING_SNAKE_CASE    			=       torch.max(torch.abs(output_converted_generator - output_original_generator  )  ).item()
					print("Maximum absolute difference beween weights: {:.2f}".format(UpperCAmelCase__  )  )

					SCREAMING_SNAKE_CASE    			=       torch.allclose(UpperCAmelCase__      , UpperCAmelCase__      , atol=1e-3  )
					if are_identical:
										logging.info("all weights are equal up to 1e-3"  )
					else:
										raise ValueError("the weights are different. The new model is likely different from the original one."  )

					# The model has been saved with torch.save(model) and this is bound to the exact
					# directory structure. We save the state_dict instead.
					logging.info("saving the model's state dictionary"  )
					torch.save(
					    new_model.state_dict()      , "./bertabs-finetuned-cnndm-extractive-abstractive-summarization/pytorch_model.bin"  )


if __name__ == "__main__":
							_lowerCamelCase					:   Union[str, Any]							=		argparse.ArgumentParser()
							parser.add_argument(
							    '''--bertabs_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.''',
							)
							_lowerCamelCase					:   Any							=		parser.parse_args()

							convert_bertabs_checkpoints(
							    args.bertabs_checkpoint_path,
							    args.pytorch_dump_folder_path,
							)




