





import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()

class 		SCREAMING_SNAKE_CASE__				(       unittest.TestCase  ):


      def    a (self       :      Tuple      ):





         """simple docstring"""



         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()


      @property
      def    a (self       :      Optional[Any]      ):





         """simple docstring"""



         __snake_case							       =   1
         __snake_case							       =   3
         __snake_case							       =   (32, 32)

         __snake_case							       =   floats_tensor((batch_size, num_channels) + sizes				,			rng=random.Random(0      )      ).to(a__      )
         return image


      @property
      def    a (self       :      List[str]      ):





         """simple docstring"""



         torch.manual_seed(0      )
         __snake_case							       =   UNetaDConditionModel(
             block_out_channels=(32, 32, 64)				,			layers_per_block=2				,			sample_size=32				,			in_channels=7				,			out_channels=4				,			down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''', '''CrossAttnDownBlock2D''')				,			up_block_types=('''CrossAttnUpBlock2D''', '''CrossAttnUpBlock2D''', '''UpBlock2D''')				,			cross_attention_dim=32				,			attention_head_dim=8				,			use_linear_projection=a__				,			only_cross_attention=(True, True, False)				,			num_class_embeds=100				,			)
         return model


      @property
      def    a (self       :      List[str]      ):





         """simple docstring"""



         torch.manual_seed(0      )
         __snake_case							       =   AutoencoderKL(
             block_out_channels=[32, 32, 64]				,			in_channels=3				,			out_channels=3				,			down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''', '''DownEncoderBlock2D''']				,			up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''', '''UpDecoderBlock2D''']				,			latent_channels=4				,			)
         return model


      @property
      def    a (self       :      int      ):





         """simple docstring"""



         torch.manual_seed(0      )
         __snake_case							       =   CLIPTextConfig(
             bos_token_id=0				,			eos_token_id=2				,			hidden_size=32				,			intermediate_size=37				,			layer_norm_eps=1E-05				,			num_attention_heads=4				,			num_hidden_layers=5				,			pad_token_id=1				,			vocab_size=1000				,			hidden_act='''gelu'''				,			projection_dim=512				,			)
         return CLIPTextModel(a__      )


      def    a (self       :      Tuple      ):





         """simple docstring"""



         __snake_case							       =   '''cpu'''  # ensure determinism for the device-dependent torch.Generator
         __snake_case							       =   self.dummy_cond_unet_upscale
         __snake_case							       =   DDPMScheduler()
         __snake_case							       =   DDIMScheduler(prediction_type='''v_prediction'''      )
         __snake_case							       =   self.dummy_vae
         __snake_case							       =   self.dummy_text_encoder
         __snake_case							       =   CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''      )

         __snake_case							       =   self.dummy_image.cpu().permute(0				,			2				,			3				,			1      )[0]
         __snake_case							       =   Image.fromarray(np.uinta(a__      )      ).convert('''RGB'''      ).resize((64, 64)      )

         # make sure here that pndm scheduler skips prk
         __snake_case							       =   StableDiffusionUpscalePipeline(
             unet=a__				,			low_res_scheduler=a__				,			scheduler=a__				,			vae=a__				,			text_encoder=a__				,			tokenizer=a__				,			max_noise_level=350				,			)
         __snake_case							       =   sd_pipe.to(a__      )
         sd_pipe.set_progress_bar_config(disable=a__      )

         __snake_case							       =   '''A painting of a squirrel eating a burger'''
         __snake_case							       =   torch.Generator(device=a__      ).manual_seed(0      )
         __snake_case							       =   sd_pipe(
             [prompt]				,			image=a__				,			generator=a__				,			guidance_scale=6.0				,			noise_level=20				,			num_inference_steps=2				,			output_type='''np'''				,			)

         __snake_case							       =   output.images

         __snake_case							       =   torch.Generator(device=a__      ).manual_seed(0      )
         __snake_case							       =   sd_pipe(
             [prompt]				,			image=a__				,			generator=a__				,			guidance_scale=6.0				,			noise_level=20				,			num_inference_steps=2				,			output_type='''np'''				,			return_dict=a__				,			)[0]

         __snake_case							       =   image[0, -3:, -3:, -1]
         __snake_case							       =   image_from_tuple[0, -3:, -3:, -1]

         __snake_case							       =   low_res_image.size[0] * 4
         assert image.shape == (1, expected_height_width, expected_height_width, 3)
         __snake_case							       =   np.array([0.3_1_1_3, 0.3_9_1_0, 0.4_2_7_2, 0.4_8_5_9, 0.5_0_6_1, 0.4_6_5_2, 0.5_3_6_2, 0.5_7_1_5, 0.5_6_6_1]      )

         assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-2
         assert np.abs(image_from_tuple_slice.flatten() - expected_slice      ).max() < 1E-2


      def    a (self       :      str      ):





         """simple docstring"""



         __snake_case							       =   '''cpu'''  # ensure determinism for the device-dependent torch.Generator
         __snake_case							       =   self.dummy_cond_unet_upscale
         __snake_case							       =   DDPMScheduler()
         __snake_case							       =   DDIMScheduler(prediction_type='''v_prediction'''      )
         __snake_case							       =   self.dummy_vae
         __snake_case							       =   self.dummy_text_encoder
         __snake_case							       =   CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''      )

         __snake_case							       =   self.dummy_image.cpu().permute(0				,			2				,			3				,			1      )[0]
         __snake_case							       =   Image.fromarray(np.uinta(a__      )      ).convert('''RGB'''      ).resize((64, 64)      )

         # make sure here that pndm scheduler skips prk
         __snake_case							       =   StableDiffusionUpscalePipeline(
             unet=a__				,			low_res_scheduler=a__				,			scheduler=a__				,			vae=a__				,			text_encoder=a__				,			tokenizer=a__				,			max_noise_level=350				,			)
         __snake_case							       =   sd_pipe.to(a__      )
         sd_pipe.set_progress_bar_config(disable=a__      )

         __snake_case							       =   '''A painting of a squirrel eating a burger'''
         __snake_case							       =   sd_pipe(
             2 * [prompt]				,			image=2 * [low_res_image]				,			guidance_scale=6.0				,			noise_level=20				,			num_inference_steps=2				,			output_type='''np'''				,			)
         __snake_case							       =   output.images
         assert image.shape[0] == 2

         __snake_case							       =   torch.Generator(device=a__      ).manual_seed(0      )
         __snake_case							       =   sd_pipe(
             [prompt]				,			image=a__				,			generator=a__				,			num_images_per_prompt=2				,			guidance_scale=6.0				,			noise_level=20				,			num_inference_steps=2				,			output_type='''np'''				,			)
         __snake_case							       =   output.images
         assert image.shape[0] == 2


      @unittest.skipIf(torch_device != '''cuda'''				,			'''This test requires a GPU'''      )
      def    a (self       :      List[Any]      ):





         """simple docstring"""



         __snake_case							       =   self.dummy_cond_unet_upscale
         __snake_case							       =   DDPMScheduler()
         __snake_case							       =   DDIMScheduler(prediction_type='''v_prediction'''      )
         __snake_case							       =   self.dummy_vae
         __snake_case							       =   self.dummy_text_encoder
         __snake_case							       =   CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''      )

         __snake_case							       =   self.dummy_image.cpu().permute(0				,			2				,			3				,			1      )[0]
         __snake_case							       =   Image.fromarray(np.uinta(a__      )      ).convert('''RGB'''      ).resize((64, 64)      )

         # put models in fp16, except vae as it overflows in fp16
         __snake_case							       =   unet.half()
         __snake_case							       =   text_encoder.half()

         # make sure here that pndm scheduler skips prk
         __snake_case							       =   StableDiffusionUpscalePipeline(
             unet=a__				,			low_res_scheduler=a__				,			scheduler=a__				,			vae=a__				,			text_encoder=a__				,			tokenizer=a__				,			max_noise_level=350				,			)
         __snake_case							       =   sd_pipe.to(a__      )
         sd_pipe.set_progress_bar_config(disable=a__      )

         __snake_case							       =   '''A painting of a squirrel eating a burger'''
         __snake_case							       =   torch.manual_seed(0      )
         __snake_case							       =   sd_pipe(
             [prompt]				,			image=a__				,			generator=a__				,			num_inference_steps=2				,			output_type='''np'''				,			).images

         __snake_case							       =   low_res_image.size[0] * 4
         assert image.shape == (1, expected_height_width, expected_height_width, 3)






@slow
@require_torch_gpu
class 		SCREAMING_SNAKE_CASE__				(       unittest.TestCase  ):


      def    a (self       :      Optional[int]      ):





         """simple docstring"""



         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()


      def    a (self       :      List[Any]      ):





         """simple docstring"""



         __snake_case							       =   load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
             '''/sd2-upscale/low_res_cat.png'''      )
         __snake_case							       =   load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'''
             '''/upsampled_cat.npy'''      )

         __snake_case							       =   '''stabilityai/stable-diffusion-x4-upscaler'''
         __snake_case							       =   StableDiffusionUpscalePipeline.from_pretrained(a__      )
         pipe.to(a__      )
         pipe.set_progress_bar_config(disable=a__      )
         pipe.enable_attention_slicing()

         __snake_case							       =   '''a cat sitting on a park bench'''

         __snake_case							       =   torch.manual_seed(0      )
         __snake_case							       =   pipe(
             prompt=a__				,			image=a__				,			generator=a__				,			output_type='''np'''				,			)
         __snake_case							       =   output.images[0]

         assert image.shape == (512, 512, 3)
         assert np.abs(expected_image - image      ).max() < 1E-3


      def    a (self       :      int      ):





         """simple docstring"""



         __snake_case							       =   load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
             '''/sd2-upscale/low_res_cat.png'''      )
         __snake_case							       =   load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'''
             '''/upsampled_cat_fp16.npy'''      )

         __snake_case							       =   '''stabilityai/stable-diffusion-x4-upscaler'''
         __snake_case							       =   StableDiffusionUpscalePipeline.from_pretrained(
             a__				,			torch_dtype=torch.floataa				,			)
         pipe.to(a__      )
         pipe.set_progress_bar_config(disable=a__      )
         pipe.enable_attention_slicing()

         __snake_case							       =   '''a cat sitting on a park bench'''

         __snake_case							       =   torch.manual_seed(0      )
         __snake_case							       =   pipe(
             prompt=a__				,			image=a__				,			generator=a__				,			output_type='''np'''				,			)
         __snake_case							       =   output.images[0]

         assert image.shape == (512, 512, 3)
         assert np.abs(expected_image - image      ).max() < 5E-1


      def    a (self       :      str      ):





         """simple docstring"""



         torch.cuda.empty_cache()
         torch.cuda.reset_max_memory_allocated()
         torch.cuda.reset_peak_memory_stats()

         __snake_case							       =   load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
             '''/sd2-upscale/low_res_cat.png'''      )

         __snake_case							       =   '''stabilityai/stable-diffusion-x4-upscaler'''
         __snake_case							       =   StableDiffusionUpscalePipeline.from_pretrained(
             a__				,			torch_dtype=torch.floataa				,			)
         pipe.to(a__      )
         pipe.set_progress_bar_config(disable=a__      )
         pipe.enable_attention_slicing(1      )
         pipe.enable_sequential_cpu_offload()

         __snake_case							       =   '''a cat sitting on a park bench'''

         __snake_case							       =   torch.manual_seed(0      )
         __snake_case							       =   pipe(
             prompt=a__				,			image=a__				,			generator=a__				,			num_inference_steps=5				,			output_type='''np'''				,			)

         __snake_case							       =   torch.cuda.max_memory_allocated()
         # make sure that less than 2.9 GB is allocated
         assert mem_bytes < 2.9 * 10**9







'''simple docstring'''







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)
A     =logging.getLogger(__name__)


A     ='Hello world! cécé herlolip'


A     =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 						snake_case_							(_a	:							List[Any]    ,   _a	:							Any     ):
    UpperCAmelCase			       =		BertAbsConfig(
        temp_dir='''.'''    ,   finetune_bert=_a    ,   large=_a    ,   share_emb=_a    ,   use_bert_emb=_a    ,   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    ,   )
    UpperCAmelCase			       =		torch.load(_a    ,   lambda _a    ,   _a     : storage     )
    UpperCAmelCase			       =		AbsSummarizer(_a    ,   torch.device('''cpu'''     )    ,   _a     )
    original.eval()

    UpperCAmelCase			       =		BertAbsSummarizer(_a    ,   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'''     )
    UpperCAmelCase			       =		BertTokenizer.from_pretrained('''bert-base-uncased'''     )

    # prepare the model inputs
    UpperCAmelCase			       =		tokenizer.encode('''This is sample éàalj\'-.'''     )
    encoder_input_ids.extend([tokenizer.pad_token_id] * (5_1_2 - len(_a     ))     )
    UpperCAmelCase			       =		torch.tensor(_a     ).unsqueeze(0     )
    UpperCAmelCase			       =		tokenizer.encode('''This is sample 3 éàalj\'-.'''     )
    decoder_input_ids.extend([tokenizer.pad_token_id] * (5_1_2 - len(_a     ))     )
    UpperCAmelCase			       =		torch.tensor(_a     ).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
    UpperCAmelCase			       =		encoder_input_ids
    UpperCAmelCase			       =		decoder_input_ids
    UpperCAmelCase			       =		UpperCAmelCase			       =		None
    UpperCAmelCase			       =		None
    UpperCAmelCase			       =		UpperCAmelCase			       =		None
    UpperCAmelCase			       =		UpperCAmelCase			       =		None
    UpperCAmelCase			       =		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
    UpperCAmelCase			       =		original(_a    ,   _a    ,   _a    ,   _a    ,   _a    ,   _a    ,   _a     )[0]
    UpperCAmelCase			       =		original.generator(_a     )

    UpperCAmelCase			       =		new_model(
        _a    ,   _a    ,   _a    ,   _a    ,   _a     )[0]
    UpperCAmelCase			       =		new_model.generator(_a     )

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

    UpperCAmelCase			       =		torch.allclose(_a    ,   _a    ,   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__":
   A     =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.',
   )
   A     =parser.parse_args()

   convert_bertabs_checkpoints(
       args.bertabs_checkpoint_path,
       args.pytorch_dump_folder_path,
   )




