


'''simple docstring'''

import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DiffusionPipeline,
    EulerDiscreteScheduler,
    StableDiffusionXLPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()







class 			UpperCAmelCase			(							SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__		,      unittest.TestCase):
 _lowerCamelCase			:       Union[str, Any]            =       StableDiffusionXLPipeline
 _lowerCamelCase			:       Tuple            =       TEXT_TO_IMAGE_PARAMS
 _lowerCamelCase			:       List[Any]            =       TEXT_TO_IMAGE_BATCH_PARAMS
 _lowerCamelCase			:       str            =       TEXT_TO_IMAGE_IMAGE_PARAMS
 _lowerCamelCase			:       Optional[Any]            =       TEXT_TO_IMAGE_IMAGE_PARAMS

 def 		lowercase_    (			self			:						Union[str, Any]       ):





    """simple docstring"""



    torch.manual_seed(0       )
    UpperCamelCase__			    =    UNetaDConditionModel(
        block_out_channels=(32, 64),       layers_per_block=2,       sample_size=32,       in_channels=4,       out_channels=4,       down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),       up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),       attention_head_dim=(2, 4),       use_linear_projection=a_,       addition_embed_type="text_time",       addition_time_embed_dim=8,       transformer_layers_per_block=(1, 2),       projection_class_embeddings_input_dim=80,       cross_attention_dim=64,       )
    UpperCamelCase__			    =    EulerDiscreteScheduler(
        beta_start=0.00_085,       beta_end=0.012,       steps_offset=1,       beta_schedule="scaled_linear",       timestep_spacing="leading",       )
    torch.manual_seed(0       )
    UpperCamelCase__			    =    AutoencoderKL(
        block_out_channels=[32, 64],       in_channels=3,       out_channels=3,       down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],       up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],       latent_channels=4,       sample_size=128,       )
    torch.manual_seed(0       )
    UpperCamelCase__			    =    CLIPTextConfig(
        bos_token_id=0,       eos_token_id=2,       hidden_size=32,       intermediate_size=37,       layer_norm_eps=1e-0_5,       num_attention_heads=4,       num_hidden_layers=5,       pad_token_id=1,       vocab_size=1000,       hidden_act="gelu",       projection_dim=32,       )
    UpperCamelCase__			    =    CLIPTextModel(a_       )
    UpperCamelCase__			    =    CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip",       local_files_only=a_       )

    UpperCamelCase__			    =    CLIPTextModelWithProjection(a_       )
    UpperCamelCase__			    =    CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip",       local_files_only=a_       )

    UpperCamelCase__			    =    {
        "unet": unet,
        "scheduler": scheduler,
        "vae": vae,
        "text_encoder": text_encoder,
        "tokenizer": tokenizer,
        "text_encoder_2": text_encoder_a,
        "tokenizer_2": tokenizer_a,
        # "safety_checker": None,
        # "feature_extractor": None,
    }
    return components

 def 		lowercase_    (			self			:						Optional[int],       a_			:						int,       a_			:						Union[str, Any]=0       ):





    """simple docstring"""



    if str(a_       ).startswith("mps"       ):
       UpperCamelCase__			    =    torch.manual_seed(a_       )
    else:
       UpperCamelCase__			    =    torch.Generator(device=a_       ).manual_seed(a_       )
    UpperCamelCase__			    =    {
        "prompt": "A painting of a squirrel eating a burger",
        "generator": generator,
        "num_inference_steps": 2,
        "guidance_scale": 5.0,
        "output_type": "numpy",
    }
    return inputs

 def 		lowercase_    (			self			:						List[Any]       ):





    """simple docstring"""



    UpperCamelCase__			    =    "cpu"  # ensure determinism for the device-dependent torch.Generator
    UpperCamelCase__			    =    self.get_dummy_components()
    UpperCamelCase__			    =    StableDiffusionXLPipeline(**a_       )
    UpperCamelCase__			    =    sd_pipe.to(a_       )
    sd_pipe.set_progress_bar_config(disable=a_       )

    UpperCamelCase__			    =    self.get_dummy_inputs(a_       )
    UpperCamelCase__			    =    sd_pipe(**a_       ).images
    UpperCamelCase__			    =    image[0, -3:, -3:, -1]

    assert image.shape == (1, 64, 64, 3)
    UpperCamelCase__			    =    np.array([0.5_873, 0.6_128, 0.4_797, 0.5_122, 0.5_674, 0.4_639, 0.5_227, 0.5_149, 0.4_747]       )

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

 def 		lowercase_    (			self			:						Union[str, Any]       ):





    """simple docstring"""



    UpperCamelCase__			    =    self.get_dummy_components()
    UpperCamelCase__			    =    StableDiffusionXLPipeline(**a_       )
    UpperCamelCase__			    =    sd_pipe.to(a_       )
    UpperCamelCase__			    =    sd_pipe.to(a_       )
    sd_pipe.set_progress_bar_config(disable=a_       )

    # forward without prompt embeds
    UpperCamelCase__			    =    self.get_dummy_inputs(a_       )
    UpperCamelCase__			    =    3 * ["this is a negative prompt"]
    UpperCamelCase__			    =    negative_prompt
    UpperCamelCase__			    =    3 * [inputs["prompt"]]

    UpperCamelCase__			    =    sd_pipe(**a_       )
    UpperCamelCase__			    =    output.images[0, -3:, -3:, -1]

    # forward with prompt embeds
    UpperCamelCase__			    =    self.get_dummy_inputs(a_       )
    UpperCamelCase__			    =    3 * ["this is a negative prompt"]
    UpperCamelCase__			    =    3 * [inputs.pop("prompt"       )]

    (
        (
        UpperCamelCase__
    )    ,		(
        UpperCamelCase__
    )    ,		(
        UpperCamelCase__
    )    ,		(
        UpperCamelCase__
    )    ,		
    )			    =    sd_pipe.encode_prompt(a_,       negative_prompt=a_       )

    UpperCamelCase__			    =    sd_pipe(
        **a_,       prompt_embeds=a_,       negative_prompt_embeds=a_,       pooled_prompt_embeds=a_,       negative_pooled_prompt_embeds=a_,       )
    UpperCamelCase__			    =    output.images[0, -3:, -3:, -1]

    # make sure that it's equal
    assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()       ).max() < 1e-4

 def 		lowercase_    (			self			:						Tuple       ):





    """simple docstring"""



    super().test_attention_slicing_forward_pass(expected_max_diff=3e-3       )

 def 		lowercase_    (			self			:						Dict       ):





    """simple docstring"""



    super().test_inference_batch_single_identical(expected_max_diff=3e-3       )







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

 def 		lowercase_    (			self			:						List[str]       ):





    """simple docstring"""



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

 def 		lowercase_    (			self			:						Tuple,       a_			:						Optional[Any],       a_			:						Optional[Any]="cpu",       a_			:						Dict=torch.floataa,       a_			:						Union[str, Any]=0       ):





    """simple docstring"""



    UpperCamelCase__			    =    torch.Generator(device=a_       ).manual_seed(a_       )
    UpperCamelCase__			    =    np.random.RandomState(a_       ).standard_normal((1, 4, 64, 64)       )
    UpperCamelCase__			    =    torch.from_numpy(a_       ).to(device=a_,       dtype=a_       )
    UpperCamelCase__			    =    {
        "prompt": "a photograph of an astronaut riding a horse",
        "latents": latents,
        "generator": generator,
        "num_inference_steps": 3,
        "guidance_scale": 7.5,
        "output_type": "numpy",
    }
    return inputs

 def 		lowercase_    (			self			:						Dict       ):





    """simple docstring"""



    UpperCamelCase__			    =    DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-base"       )
    pipe.to(a_       )
    pipe.set_progress_bar_config(disable=a_       )

    UpperCamelCase__			    =    self.get_inputs(a_       )
    UpperCamelCase__			    =    pipe(**a_       ).images
    UpperCamelCase__			    =    image[0, -3:, -3:, -1].flatten()

    assert image.shape == (1, 512, 512, 3)
    UpperCamelCase__			    =    np.array([0.49_493, 0.47_896, 0.40_798, 0.54_214, 0.53_212, 0.48_202, 0.47_656, 0.46_329, 0.48_506]       )
    assert np.abs(image_slice - expected_slice       ).max() < 7e-3

import argparse
import logging
import os
import sys
from collections import namedtuple

import torch
from modeling_bertabs import BertAbs, build_predictor
from torch.utils.data import DataLoader, SequentialSampler
from tqdm import tqdm

from transformers import BertTokenizer

from .utils_summarization import (
    CNNDMDataset,
    build_mask,
    compute_token_type_ids,
    encode_for_summarization,
    truncate_or_pad,
)


a__				:      Any    		=     logging.getLogger(__name__)
logging.basicConfig(stream=sys.stdout, level=logging.INFO)


a__				:      Optional[int]    		=     namedtuple('''Batch''', ['''document_names''', '''batch_size''', '''src''', '''segs''', '''mask_src''', '''tgt_str'''])







def 				UpperCAmelCase_(    a__   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				List[str]				      = BertTokenizer.from_pretrained('''bert-base-uncased''' ,						do_lower_case=a__   )
    SCREAMING_SNAKE_CASE				:				List[Any]				      = BertAbs.from_pretrained('''remi/bertabs-finetuned-extractive-abstractive-summarization'''   )
    model.to(args.device   )
    model.eval()

    SCREAMING_SNAKE_CASE				:				int				      = {
        '''BOS''': tokenizer.vocab['''[unused0]'''],
        '''EOS''': tokenizer.vocab['''[unused1]'''],
        '''PAD''': tokenizer.vocab['''[PAD]'''],
    }

    if args.compute_rouge:
        SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = []
        SCREAMING_SNAKE_CASE				:				Dict				      = []

        import nltk
        import rouge

        nltk.download('''punkt'''   )
        SCREAMING_SNAKE_CASE				:				Tuple				      = rouge.Rouge(
            metrics=['''rouge-n''', '''rouge-l'''] ,						max_n=2 ,						limit_length=a__ ,						length_limit=args.beam_size ,						length_limit_type='''words''' ,						apply_avg=a__ ,						apply_best=a__ ,						alpha=0.5 ,						weight_factor=1.2 ,						stemming=a__ ,						)

    # these (unused) arguments are defined to keep the compatibility
    # with the legacy code and will be deleted in a next iteration.
    SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = ''''''
    SCREAMING_SNAKE_CASE				:				Tuple				      = ''''''

    SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = build_data_iterator(a__ ,						a__   )
    SCREAMING_SNAKE_CASE				:				str				      = build_predictor(a__ ,						a__ ,						a__ ,						a__   )

    logger.info('''***** Running evaluation *****'''   )
    logger.info('''  Number examples = %d''' ,						len(data_iterator.dataset   )   )
    logger.info('''  Batch size = %d''' ,						args.batch_size   )
    logger.info(''''''   )
    logger.info('''***** Beam Search parameters *****'''   )
    logger.info('''  Beam size = %d''' ,						args.beam_size   )
    logger.info('''  Minimum length = %d''' ,						args.min_length   )
    logger.info('''  Maximum length = %d''' ,						args.max_length   )
    logger.info('''  Alpha (length penalty) = %.2f''' ,						args.alpha   )
    logger.info('''  Trigrams %s be blocked''' ,						('''will''' if args.block_trigram else '''will NOT''')   )

    for batch in tqdm(a__   ):
        SCREAMING_SNAKE_CASE				:				Tuple				      = predictor.translate_batch(a__   )
        SCREAMING_SNAKE_CASE				:				Optional[Any]				      = predictor.from_batch(a__   )
        SCREAMING_SNAKE_CASE				:				List[str]				      = [format_summary(a__   ) for t in translations]
        save_summaries(a__ ,						args.summaries_output_dir ,						batch.document_names   )

        if args.compute_rouge:
            reference_summaries += batch.tgt_str
            generated_summaries += summaries

    if args.compute_rouge:
        SCREAMING_SNAKE_CASE				:				List[Any]				      = rouge_evaluator.get_scores(a__ ,						a__   )
        SCREAMING_SNAKE_CASE				:				Dict				      = format_rouge_scores(a__   )
        save_rouge_scores(a__   )
        print(a__   )







def 				UpperCAmelCase_(    a__ ,						a__ ,						a__   ):



    """simple docstring"""



    for summary, document_name in zip(a__ ,						a__   ):
        # Prepare the summary file's name
        if "." in document_name:
            SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = '''.'''.join(document_name.split('''.'''   )[:-1]   )
            SCREAMING_SNAKE_CASE				:				int				      = document_name.split('''.'''   )[-1]
            SCREAMING_SNAKE_CASE				:				Tuple				      = bare_document_name + '''_summary.''' + extension
        else:
            SCREAMING_SNAKE_CASE				:				Optional[int]				      = document_name + '''_summary'''

        SCREAMING_SNAKE_CASE				:				List[Any]				      = os.path.join(a__ ,						a__   )
        with open(a__ ,						'''w'''   ) as output:
            output.write(a__   )







def 				UpperCAmelCase_(    a__   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE				:				Any				      = translation
    SCREAMING_SNAKE_CASE				:				int				      = (
        raw_summary.replace('''[unused0]''' ,						''''''   )
        .replace('''[unused3]''' ,						''''''   )
        .replace('''[PAD]''' ,						''''''   )
        .replace('''[unused1]''' ,						''''''   )
        .replace(r''' +''' ,						''' '''   )
        .replace(''' [unused2] ''' ,						'''. '''   )
        .replace('''[unused2]''' ,						''''''   )
        .strip()
    )

    return summary







def 				UpperCAmelCase_(    a__   ):



    """simple docstring"""



    return """\n
****** ROUGE SCORES ******

** ROUGE 1
F1        >> {:.3f}
Precision >> {:.3f}
Recall    >> {:.3f}

** ROUGE 2
F1        >> {:.3f}
Precision >> {:.3f}
Recall    >> {:.3f}

** ROUGE L
F1        >> {:.3f}
Precision >> {:.3f}
Recall    >> {:.3f}""".format(
        scores['''rouge-1''']['''f'''] ,						scores['''rouge-1''']['''p'''] ,						scores['''rouge-1''']['''r'''] ,						scores['''rouge-2''']['''f'''] ,						scores['''rouge-2''']['''p'''] ,						scores['''rouge-2''']['''r'''] ,						scores['''rouge-l''']['''f'''] ,						scores['''rouge-l''']['''p'''] ,						scores['''rouge-l''']['''r'''] ,						)







def 				UpperCAmelCase_(    a__   ):



    """simple docstring"""



    with open('''rouge_scores.txt''' ,						'''w'''   ) as output:
        output.write(a__   )







def 				UpperCAmelCase_(    a__ ,						a__   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				List[str]				      = load_and_cache_examples(a__ ,						a__   )
    SCREAMING_SNAKE_CASE				:				List[str]				      = SequentialSampler(a__   )

    def collate_fn(a__   ):
        return collate(a__ ,						a__ ,						block_size=512 ,						device=args.device   )

    SCREAMING_SNAKE_CASE				:				Dict				      = DataLoader(
        a__ ,						sampler=a__ ,						batch_size=args.batch_size ,						collate_fn=a__ ,						)

    return iterator







def 				UpperCAmelCase_(    a__ ,						a__   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				Optional[int]				      = CNNDMDataset(args.documents_dir   )
    return dataset







def 				UpperCAmelCase_(    a__ ,						a__ ,						a__ ,						a__   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				str				      = [x for x in data if not len(x[1]   ) == 0]  # remove empty_files
    SCREAMING_SNAKE_CASE				:				int				      = [name for name, _, _ in data]
    SCREAMING_SNAKE_CASE				:				List[str]				      = [''' '''.join(a__   ) for _, _, summary_list in data]

    SCREAMING_SNAKE_CASE				:				Dict				      = [encode_for_summarization(a__ ,						a__ ,						a__   ) for _, story, summary in data]
    SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = torch.tensor(
        [truncate_or_pad(a__ ,						a__ ,						tokenizer.pad_token_id   ) for story, _ in encoded_text]   )
    SCREAMING_SNAKE_CASE				:				List[Any]				      = compute_token_type_ids(a__ ,						tokenizer.cls_token_id   )
    SCREAMING_SNAKE_CASE				:				int				      = build_mask(a__ ,						tokenizer.pad_token_id   )

    SCREAMING_SNAKE_CASE				:				Optional[int]				      = Batch(
        document_names=a__ ,						batch_size=len(a__   ) ,						src=encoded_stories.to(a__   ) ,						segs=encoder_token_type_ids.to(a__   ) ,						mask_src=encoder_mask.to(a__   ) ,						tgt_str=a__ ,						)

    return batch







def 				UpperCAmelCase_(    a__ ,						a__   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				Optional[Any]				      = summary_tokens.to('''cpu'''   ).numpy()
    SCREAMING_SNAKE_CASE				:				str				      = tokenizer.decode(a__   )
    SCREAMING_SNAKE_CASE				:				int				      = summary.split('''.'''   )
    SCREAMING_SNAKE_CASE				:				Dict				      = [s + '''.''' for s in sentences]
    return sentences







def 				UpperCAmelCase_(    ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				str				      = argparse.ArgumentParser()
    parser.add_argument(
        '''--documents_dir''' ,						default=a__ ,						type=a__ ,						required=a__ ,						help='''The folder where the documents to summarize are located.''' ,						)
    parser.add_argument(
        '''--summaries_output_dir''' ,						default=a__ ,						type=a__ ,						required=a__ ,						help='''The folder in wich the summaries should be written. Defaults to the folder where the documents are''' ,						)
    parser.add_argument(
        '''--compute_rouge''' ,						default=a__ ,						type=a__ ,						required=a__ ,						help='''Compute the ROUGE metrics during evaluation. Only available for the CNN/DailyMail dataset.''' ,						)
    # EVALUATION options
    parser.add_argument(
        '''--no_cuda''' ,						default=a__ ,						type=a__ ,						help='''Whether to force the execution on CPU.''' ,						)
    parser.add_argument(
        '''--batch_size''' ,						default=4 ,						type=a__ ,						help='''Batch size per GPU/CPU for training.''' ,						)
    # BEAM SEARCH arguments
    parser.add_argument(
        '''--min_length''' ,						default=50 ,						type=a__ ,						help='''Minimum number of tokens for the summaries.''' ,						)
    parser.add_argument(
        '''--max_length''' ,						default=200 ,						type=a__ ,						help='''Maixmum number of tokens for the summaries.''' ,						)
    parser.add_argument(
        '''--beam_size''' ,						default=5 ,						type=a__ ,						help='''The number of beams to start with for each example.''' ,						)
    parser.add_argument(
        '''--alpha''' ,						default=0.95 ,						type=a__ ,						help='''The value of alpha for the length penalty in the beam search.''' ,						)
    parser.add_argument(
        '''--block_trigram''' ,						default=a__ ,						type=a__ ,						help='''Whether to block the existence of repeating trigrams in the text generated by beam search.''' ,						)
    SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = parser.parse_args()

    # Select device (distibuted not available)
    SCREAMING_SNAKE_CASE				:				List[Any]				      = torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu'''   )

    # Check the existence of directories
    if not args.summaries_output_dir:
        SCREAMING_SNAKE_CASE				:				Tuple				      = args.documents_dir

    if not documents_dir_is_valid(args.documents_dir   ):
        raise FileNotFoundError(
            '''We could not find the directory you specified for the documents to summarize, or it was empty. Please'''
            ''' specify a valid path.'''   )
    os.makedirs(args.summaries_output_dir ,						exist_ok=a__   )

    evaluate(a__   )







def 				UpperCAmelCase_(    a__   ):



    """simple docstring"""



    if not os.path.exists(a__   ):
        return False

    SCREAMING_SNAKE_CASE				:				List[str]				      = os.listdir(a__   )
    if len(a__   ) == 0:
        return False

    return True


if __name__ == "__main__":
      main()
