

import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, parse_numeric_n_bool_cl_kwargs, use_task_specific_params


SCREAMING_SNAKE_CASE		:Any				  =    getLogger(__name__)


SCREAMING_SNAKE_CASE		:Tuple				  =    """cuda""" if torch.cuda.is_available() else """cpu"""




def  lowerCAmelCase(			SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ = 8 ,	SCREAMING_SNAKE_CASE_ = DEFAULT_DEVICE ,	SCREAMING_SNAKE_CASE_=False ,	SCREAMING_SNAKE_CASE_="summarization" ,	SCREAMING_SNAKE_CASE_=None ,	**SCREAMING_SNAKE_CASE_ ,	)->	Dict:



    """simple docstring"""






    UpperCamelCase_      =	Path(__lowerCamelCase ).open("w" ,	encoding="utf-8" )
    UpperCamelCase_      =	str(__lowerCamelCase )
    UpperCamelCase_      =	AutoModelForSeqaSeqLM.from_pretrained(__lowerCamelCase ).to(__lowerCamelCase )
    if fpaa:
        UpperCamelCase_      =	model.half()

    UpperCamelCase_      =	AutoTokenizer.from_pretrained(__lowerCamelCase )
    logger.info(f"Inferred tokenizer type: {tokenizer.__class__}" )  # if this is wrong, check config.model_type.

    UpperCamelCase_      =	time.time()
    # update config with task specific params
    use_task_specific_params(__lowerCamelCase ,	__lowerCamelCase )
    if prefix is None:
        UpperCamelCase_      =	prefix or getattr(model.config ,	"prefix" ,	"" ) or """"""
    for examples_chunk in tqdm(list(chunks(__lowerCamelCase ,	__lowerCamelCase ) ) ):
        UpperCamelCase_      =	[prefix + text for text in examples_chunk]
        UpperCamelCase_      =	tokenizer(__lowerCamelCase ,	return_tensors="pt" ,	truncation=__lowerCamelCase ,	padding="longest" ).to(__lowerCamelCase )
        UpperCamelCase_      =	model.generate(
            input_ids=batch.input_ids ,	attention_mask=batch.attention_mask ,	**__lowerCamelCase ,	)
        UpperCamelCase_      =	tokenizer.batch_decode(__lowerCamelCase ,	skip_special_tokens=__lowerCamelCase ,	clean_up_tokenization_spaces=__lowerCamelCase )
        for hypothesis in dec:
            fout.write(hypothesis + "\n" )
            fout.flush()
    fout.close()
    UpperCamelCase_      =	int(time.time() - start_time )  # seconds
    UpperCamelCase_      =	len(__lowerCamelCase )
    return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs ,	4 )}




def  lowerCAmelCase(			)->	str:



    """simple docstring"""






    return datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S" )




def  lowerCAmelCase(			SCREAMING_SNAKE_CASE_=True )->	Dict:



    """simple docstring"""






    UpperCamelCase_      =	argparse.ArgumentParser()
    parser.add_argument("model_name" ,	type=__lowerCamelCase ,	help="like facebook/bart-large-cnn,t5-base, etc." )
    parser.add_argument("input_path" ,	type=__lowerCamelCase ,	help="like cnn_dm/test.source" )
    parser.add_argument("save_path" ,	type=__lowerCamelCase ,	help="where to save summaries" )
    parser.add_argument("--reference_path" ,	type=__lowerCamelCase ,	required=__lowerCamelCase ,	help="like cnn_dm/test.target" )
    parser.add_argument("--score_path" ,	type=__lowerCamelCase ,	required=__lowerCamelCase ,	default="metrics.json" ,	help="where to save metrics" )
    parser.add_argument("--device" ,	type=__lowerCamelCase ,	required=__lowerCamelCase ,	default=__lowerCamelCase ,	help="cuda, cuda:1, cpu etc." )
    parser.add_argument(
        "--prefix" ,	type=__lowerCamelCase ,	required=__lowerCamelCase ,	default=__lowerCamelCase ,	help="will be added to the begininng of src examples" )
    parser.add_argument("--task" ,	type=__lowerCamelCase ,	default="summarization" ,	help="used for task_specific_params + metrics" )
    parser.add_argument("--bs" ,	type=__lowerCamelCase ,	default=8 ,	required=__lowerCamelCase ,	help="batch size" )
    parser.add_argument(
        "--n_obs" ,	type=__lowerCamelCase ,	default=-1 ,	required=__lowerCamelCase ,	help="How many observations. Defaults to all." )
    parser.add_argument("--fp16" ,	action="store_true" )
    parser.add_argument("--dump-args" ,	action="store_true" ,	help="print the custom hparams with the results" )
    parser.add_argument(
        "--info" ,	nargs="?" ,	type=__lowerCamelCase ,	const=datetime_now() ,	help=(
            "use in conjunction w/ --dump-args to print with the results whatever other info you'd like, e.g."
            " lang=en-ru. If no value is passed, the current datetime string will be used."
        ) ,	)
    # Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate
    UpperCamelCase_      =	parser.parse_known_args()
    UpperCamelCase_      =	parse_numeric_n_bool_cl_kwargs(__lowerCamelCase )
    if parsed_args and verbose:
        print(f"parsed the following generate kwargs: {parsed_args}" )
    with open(args.input_path ) as f:
        UpperCamelCase_      =	[""" """ + x.rstrip() if """t5""" in args.model_name else x.rstrip() for x in f.readlines()]
    if args.n_obs > 0:
        UpperCamelCase_      =	examples[: args.n_obs]
    Path(args.save_path ).parent.mkdir(exist_ok=__lowerCamelCase )
    if args.reference_path is None and Path(args.score_path ).exists():
        warnings.warn(f"score_path {args.score_path} will be overwritten unless you type ctrl-c." )
    UpperCamelCase_      =	generate_summaries_or_translations(
        __lowerCamelCase ,	args.save_path ,	args.model_name ,	batch_size=args.bs ,	device=args.device ,	fpaa=args.fpaa ,	task=args.task ,	prefix=args.prefix ,	**__lowerCamelCase ,	)

    if args.reference_path is None:
        return {}

    # Compute scores
    UpperCamelCase_      =	calculate_bleu if """translation""" in args.task else calculate_rouge
    UpperCamelCase_      =	[x.rstrip() for x in open(args.save_path ).readlines()]
    UpperCamelCase_      =	[x.rstrip() for x in open(args.reference_path ).readlines()][: len(__lowerCamelCase )]
    UpperCamelCase_      =	score_fn(__lowerCamelCase ,	__lowerCamelCase )
    scores.update(__lowerCamelCase )

    if args.dump_args:
        scores.update(__lowerCamelCase )
    if args.info:
        UpperCamelCase_      =	args.info

    if verbose:
        print(__lowerCamelCase )

    if args.score_path is not None:
        json.dump(__lowerCamelCase ,	open(args.score_path ,	"w" ) )

    return scores


if __name__ == "__main__":
 # Usage for MT:
 # python run_eval.py MODEL_NAME $DATA_DIR/test.source $save_dir/test_translations.txt --reference_path $DATA_DIR/test.target --score_path $save_dir/test_bleu.json  --task translation $@
 run_generate(verbose=True)








import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()






class 	__magic_name__				(       snake_case ,			unittest.TestCase							):
     UpperCamelCase_  :Any								=			KandinskyPipeline
     UpperCamelCase_  :str								=			[
         """prompt""",
         """image_embeds""",
         """negative_image_embeds""",
     ]
     UpperCamelCase_  :Union[str, Any]								=			["""prompt""", """negative_prompt""", """image_embeds""", """negative_image_embeds"""]
     UpperCamelCase_  :int								=			[
         """generator""",
         """height""",
         """width""",
         """latents""",
         """guidance_scale""",
         """negative_prompt""",
         """num_inference_steps""",
         """return_dict""",
         """guidance_scale""",
         """num_images_per_prompt""",
         """output_type""",
         """return_dict""",
     ]
     UpperCamelCase_  :Optional[Any]								=			False






     @property
     def   UpperCAmelCase_							(							self  )->    Union[str, Any]:
         return 32






     @property
     def   UpperCAmelCase_							(							self  )->    Optional[Any]:
         return 32






     @property
     def   UpperCAmelCase_							(							self  )->    str:
         return self.time_input_dim






     @property
     def   UpperCAmelCase_							(							self  )->    Union[str, Any]:
         return self.time_input_dim * 4






     @property
     def   UpperCAmelCase_							(							self  )->    List[str]:
         return 100






     @property
     def   UpperCAmelCase_							(							self  )->    List[Any]:
         UpperCamelCase_      =	XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base"  )
         return tokenizer






     @property
     def   UpperCAmelCase_							(							self  )->    int:
         torch.manual_seed(0  )
         UpperCamelCase_      =	MCLIPConfig(
             numDims=self.cross_attention_dim		,	transformerDimensions=self.text_embedder_hidden_size		,	hidden_size=self.text_embedder_hidden_size		,	intermediate_size=37		,	num_attention_heads=4		,	num_hidden_layers=5		,	vocab_size=1_005		,	)

         UpperCamelCase_      =	MultilingualCLIP(_lowercase  )
         UpperCamelCase_      =	text_encoder.eval()

         return text_encoder






     @property
     def   UpperCAmelCase_							(							self  )->    List[str]:
         torch.manual_seed(0  )

         UpperCamelCase_      =	{
             "in_channels": 4,
             # Out channels is double in channels because predicts mean and variance
             "out_channels": 8,
             "addition_embed_type": "text_image",
             "down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
             "up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
             "mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
             "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
             "layers_per_block": 1,
             "encoder_hid_dim": self.text_embedder_hidden_size,
             "encoder_hid_dim_type": "text_image_proj",
             "cross_attention_dim": self.cross_attention_dim,
             "attention_head_dim": 4,
             "resnet_time_scale_shift": "scale_shift",
             "class_embed_type": None,
         }

         UpperCamelCase_      =	UNetaDConditionModel(**_lowercase  )
         return model






     @property
     def   UpperCAmelCase_							(							self  )->    Dict:
         return {
             "block_out_channels": [32, 64],
             "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
             "in_channels": 3,
             "latent_channels": 4,
             "layers_per_block": 1,
             "norm_num_groups": 8,
             "norm_type": "spatial",
             "num_vq_embeddings": 12,
             "out_channels": 3,
             "up_block_types": [
                 "AttnUpDecoderBlock2D",
                 "UpDecoderBlock2D",
             ],
             "vq_embed_dim": 4,
         }






     @property
     def   UpperCAmelCase_							(							self  )->    Tuple:
         torch.manual_seed(0  )
         UpperCamelCase_      =	VQModel(**self.dummy_movq_kwargs  )
         return model






     def   UpperCAmelCase_							(							self  )->    Dict:
         UpperCamelCase_      =	self.dummy_text_encoder
         UpperCamelCase_      =	self.dummy_tokenizer
         UpperCamelCase_      =	self.dummy_unet
         UpperCamelCase_      =	self.dummy_movq

         UpperCamelCase_      =	DDIMScheduler(
             num_train_timesteps=1_000		,	beta_schedule="linear"		,	beta_start=0.00_085		,	beta_end=0.012		,	clip_sample=_lowercase		,	set_alpha_to_one=_lowercase		,	steps_offset=1		,	prediction_type="epsilon"		,	thresholding=_lowercase		,	)

         UpperCamelCase_      =	{
             "text_encoder": text_encoder,
             "tokenizer": tokenizer,
             "unet": unet,
             "scheduler": scheduler,
             "movq": movq,
         }
         return components






     def   UpperCAmelCase_							(							self		,	_lowercase		,	_lowercase=0  )->    Union[str, Any]:
         UpperCamelCase_      =	floats_tensor((1, self.cross_attention_dim)		,	rng=random.Random(_lowercase  )  ).to(_lowercase  )
         UpperCamelCase_      =	floats_tensor((1, self.cross_attention_dim)		,	rng=random.Random(seed + 1  )  ).to(_lowercase  )
         if str(_lowercase  ).startswith("mps"  ):
             UpperCamelCase_      =	torch.manual_seed(_lowercase  )
         else:
             UpperCamelCase_      =	torch.Generator(device=_lowercase  ).manual_seed(_lowercase  )
         UpperCamelCase_      =	{
             "prompt": "horse",
             "image_embeds": image_embeds,
             "negative_image_embeds": negative_image_embeds,
             "generator": generator,
             "height": 64,
             "width": 64,
             "guidance_scale": 4.0,
             "num_inference_steps": 2,
             "output_type": "np",
         }
         return inputs






     def   UpperCAmelCase_							(							self  )->    Tuple:
         UpperCamelCase_      =	"cpu"

         UpperCamelCase_      =	self.get_dummy_components()

         UpperCamelCase_      =	self.pipeline_class(**_lowercase  )
         UpperCamelCase_      =	pipe.to(_lowercase  )

         pipe.set_progress_bar_config(disable=_lowercase  )

         UpperCamelCase_      =	pipe(**self.get_dummy_inputs(_lowercase  )  )
         UpperCamelCase_      =	output.images

         UpperCamelCase_      =	pipe(
             **self.get_dummy_inputs(_lowercase  )		,	return_dict=_lowercase		,	)[0]

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

         assert image.shape == (1, 64, 64, 3)

         UpperCamelCase_      =	np.array(
             [0.328_663, 1.0, 0.23_216_873, 1.0, 0.92_717_564, 0.4_639_046, 0.96_894_777, 0.31_713_378, 0.6_293_953]  )

         assert (
             np.abs(image_slice.flatten() - expected_slice  ).max() < 1e-2
         ), F" expected_slice {expected_slice}, but got {image_slice.flatten()}"
         assert (
             np.abs(image_from_tuple_slice.flatten() - expected_slice  ).max() < 1e-2
         ), F" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"






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






     def   UpperCAmelCase_							(							self  )->    Tuple:
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()






     def   UpperCAmelCase_							(							self  )->    str:
         UpperCamelCase_      =	load_numpy(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
             "/kandinsky/kandinsky_text2img_cat_fp16.npy"  )

         UpperCamelCase_      =	KandinskyPriorPipeline.from_pretrained(
             "kandinsky-community/kandinsky-2-1-prior"		,	torch_dtype=torch.floataa  )
         pipe_prior.to(_lowercase  )

         UpperCamelCase_      =	KandinskyPipeline.from_pretrained("kandinsky-community/kandinsky-2-1"		,	torch_dtype=torch.floataa  )
         UpperCamelCase_      =	pipeline.to(_lowercase  )
         pipeline.set_progress_bar_config(disable=_lowercase  )

         UpperCamelCase_      =	"red cat, 4k photo"

         UpperCamelCase_      =	torch.Generator(device="cuda"  ).manual_seed(0  )
         UpperCamelCase_     ,					UpperCamelCase_      =	pipe_prior(
             _lowercase		,	generator=_lowercase		,	num_inference_steps=5		,	negative_prompt=""		,	).to_tuple()

         UpperCamelCase_      =	torch.Generator(device="cuda"  ).manual_seed(0  )
         UpperCamelCase_      =	pipeline(
             _lowercase		,	image_embeds=_lowercase		,	negative_image_embeds=_lowercase		,	generator=_lowercase		,	num_inference_steps=100		,	output_type="np"		,	)

         UpperCamelCase_      =	output.images[0]

         assert image.shape == (512, 512, 3)

         assert_mean_pixel_difference(_lowercase		,	_lowercase  )
