





"""simple docstring"""
import unittest

import numpy as np
import torch
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyVaaPriorPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()






class 					_SCREAMING_SNAKE_CASE(   UpperCamelCase_    ,	unittest.TestCase  ):
       SCREAMING_SNAKE_CASE_   :      str							=  KandinskyVaaPriorPipeline
       SCREAMING_SNAKE_CASE_   :      str							=  ['''prompt''']
       SCREAMING_SNAKE_CASE_   :      int							=  ['''prompt''', '''negative_prompt''']
       SCREAMING_SNAKE_CASE_   :      Any							=  [
           '''num_images_per_prompt''',
           '''generator''',
           '''num_inference_steps''',
           '''latents''',
           '''negative_prompt''',
           '''guidance_scale''',
           '''output_type''',
           '''return_dict''',
       ]
       SCREAMING_SNAKE_CASE_   :      List[str]							=  False




       @property
       def     _UpperCamelCase     (						self							)			->      Optional[Any]:





             """simple docstring"""






             return 32




       @property
       def     _UpperCamelCase     (						self							)			->      int:





             """simple docstring"""






             return 32




       @property
       def     _UpperCamelCase     (						self							)			->      Optional[int]:





             """simple docstring"""






             return self.time_input_dim




       @property
       def     _UpperCamelCase     (						self							)			->      Any:





             """simple docstring"""






             return self.time_input_dim * 4




       @property
       def     _UpperCamelCase     (						self							)			->      str:





             """simple docstring"""






             return 1_00




       @property
       def     _UpperCamelCase     (						self							)			->      List[str]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''							)
             return tokenizer




       @property
       def     _UpperCamelCase     (						self							)			->      Optional[int]:





             """simple docstring"""






             torch.manual_seed(0							)
             __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  CLIPTextConfig(
                 bos_token_id=0		,eos_token_id=2		,hidden_size=self.text_embedder_hidden_size		,projection_dim=self.text_embedder_hidden_size		,intermediate_size=37		,layer_norm_eps=1E-05		,num_attention_heads=4		,num_hidden_layers=5		,pad_token_id=1		,vocab_size=10_00		,)
             return CLIPTextModelWithProjection(UpperCamelCase__							)




       @property
       def     _UpperCamelCase     (						self							)			->      int:





             """simple docstring"""






             torch.manual_seed(0							)

             __SCREAMING_SNAKE_CASE   :List[str]						=  {
                 '''num_attention_heads''': 2,
                 '''attention_head_dim''': 12,
                 '''embedding_dim''': self.text_embedder_hidden_size,
                 '''num_layers''': 1,
             }

             __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  PriorTransformer(**UpperCamelCase__							)
             # clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  nn.Parameter(torch.ones(model.clip_std.shape							)							)
             return model




       @property
       def     _UpperCamelCase     (						self							)			->      Tuple:





             """simple docstring"""






             torch.manual_seed(0							)
             __SCREAMING_SNAKE_CASE   :List[Any]						=  CLIPVisionConfig(
                 hidden_size=self.text_embedder_hidden_size		,image_size=2_24		,projection_dim=self.text_embedder_hidden_size		,intermediate_size=37		,num_attention_heads=4		,num_channels=3		,num_hidden_layers=5		,patch_size=14		,)

             __SCREAMING_SNAKE_CASE   :Dict						=  CLIPVisionModelWithProjection(UpperCamelCase__							)
             return model




       @property
       def     _UpperCamelCase     (						self							)			->      Optional[int]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :Tuple						=  CLIPImageProcessor(
                 crop_size=2_24		,do_center_crop=UpperCamelCase__		,do_normalize=UpperCamelCase__		,do_resize=UpperCamelCase__		,image_mean=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]		,image_std=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]		,resample=3		,size=2_24		,)

             return image_processor




       def     _UpperCamelCase     (						self							)			->      List[str]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  self.dummy_prior
             __SCREAMING_SNAKE_CASE   :List[Any]						=  self.dummy_image_encoder
             __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  self.dummy_text_encoder
             __SCREAMING_SNAKE_CASE   :Optional[int]						=  self.dummy_tokenizer
             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  self.dummy_image_processor

             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  UnCLIPScheduler(
                 variance_type='''fixed_small_log'''		,prediction_type='''sample'''		,num_train_timesteps=10_00		,clip_sample=UpperCamelCase__		,clip_sample_range=1_0.0		,)

             __SCREAMING_SNAKE_CASE   :List[Any]						=  {
                 '''prior''': prior,
                 '''image_encoder''': image_encoder,
                 '''text_encoder''': text_encoder,
                 '''tokenizer''': tokenizer,
                 '''scheduler''': scheduler,
                 '''image_processor''': image_processor,
             }

             return components




       def     _UpperCamelCase     (						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__=0							)			->      int:





             """simple docstring"""






             if str(UpperCamelCase__							).startswith('''mps'''							):
                   __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  torch.manual_seed(UpperCamelCase__							)
             else:
                   __SCREAMING_SNAKE_CASE   :Optional[Any]						=  torch.Generator(device=UpperCamelCase__							).manual_seed(UpperCamelCase__							)
             __SCREAMING_SNAKE_CASE   :Optional[int]						=  {
                 '''prompt''': '''horse''',
                 '''generator''': generator,
                 '''guidance_scale''': 4.0,
                 '''num_inference_steps''': 2,
                 '''output_type''': '''np''',
             }
             return inputs




       def     _UpperCamelCase     (						self							)			->      Optional[int]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :Any						=  '''cpu'''

             __SCREAMING_SNAKE_CASE   :List[Any]						=  self.get_dummy_components()

             __SCREAMING_SNAKE_CASE   :List[str]						=  self.pipeline_class(**UpperCamelCase__							)
             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  pipe.to(UpperCamelCase__							)

             pipe.set_progress_bar_config(disable=UpperCamelCase__							)

             __SCREAMING_SNAKE_CASE   :Any						=  pipe(**self.get_dummy_inputs(UpperCamelCase__							)							)
             __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  output.image_embeds

             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  pipe(
                 **self.get_dummy_inputs(UpperCamelCase__							)		,return_dict=UpperCamelCase__		,)[0]

             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  image[0, -10:]
             __SCREAMING_SNAKE_CASE   :Optional[int]						=  image_from_tuple[0, -10:]

             assert image.shape == (1, 32)

             __SCREAMING_SNAKE_CASE   :Tuple						=  np.array(
                 [-0.0_5_3_2, 1.7_1_2_0, 0.3_6_5_6, -1.0_8_5_2, -0.8_9_4_6, -1.1_7_5_6, 0.4_3_4_8, 0.2_4_8_2, 0.5_1_4_6, -0.1_1_5_6]							)

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




       @skip_mps
       def     _UpperCamelCase     (						self							)			->      Dict:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :Tuple						=  torch_device == '''cpu'''
             __SCREAMING_SNAKE_CASE   :Any						=  True
             __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  False

             self._test_inference_batch_single_identical(
                 test_max_difference=UpperCamelCase__		,relax_max_difference=UpperCamelCase__		,test_mean_pixel_difference=UpperCamelCase__		,)




       @skip_mps
       def     _UpperCamelCase     (						self							)			->      Optional[Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :Optional[int]						=  torch_device == '''cpu'''
             __SCREAMING_SNAKE_CASE   :int						=  False

             self._test_attention_slicing_forward_pass(
                 test_max_difference=UpperCamelCase__		,test_mean_pixel_difference=UpperCamelCase__		,)
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


__UpperCamelCase		:       Any									=     getLogger(__name__)


__UpperCamelCase		:       int									=     'cuda' if torch.cuda.is_available() else 'cpu'
def       A	(    _lowercase		,  _lowercase		,  _lowercase		,  _lowercase = 8		,  _lowercase = DEFAULT_DEVICE		,  _lowercase=False		,  _lowercase="summarization"		,  _lowercase=None		,  **_lowercase		,  ):
						SCREAMING_SNAKE_CASE							:    List[str]							=							Path(_lowercase						).open('''w'''		,  encoding='''utf-8'''						)
						SCREAMING_SNAKE_CASE							:    int							=							str(_lowercase						)
						SCREAMING_SNAKE_CASE							:    Any							=							AutoModelForSeqaSeqLM.from_pretrained(_lowercase						).to(_lowercase						)
						if fpaa:
												SCREAMING_SNAKE_CASE							:    Dict							=							model.half()

						SCREAMING_SNAKE_CASE							:    Optional[Any]							=							AutoTokenizer.from_pretrained(_lowercase						)
						logger.info(f"""Inferred tokenizer type: {tokenizer.__class__}"""						)  # if this is wrong, check config.model_type.

						SCREAMING_SNAKE_CASE							:    str							=							time.time()
						# update config with task specific params
						use_task_specific_params(_lowercase		,  _lowercase						)
						if prefix is None:
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							prefix or getattr(model.config		,  '''prefix'''		,  ''''''						) or ''''''
						for examples_chunk in tqdm(list(chunks(_lowercase		,  _lowercase						)						)						):
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							[prefix + text for text in examples_chunk]
												SCREAMING_SNAKE_CASE							:    Dict							=							tokenizer(_lowercase		,  return_tensors='''pt'''		,  truncation=_lowercase		,  padding='''longest'''						).to(_lowercase						)
												SCREAMING_SNAKE_CASE							:    str							=							model.generate(
												    input_ids=batch.input_ids		,  attention_mask=batch.attention_mask		,  **_lowercase		,  )
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							tokenizer.batch_decode(_lowercase		,  skip_special_tokens=_lowercase		,  clean_up_tokenization_spaces=_lowercase						)
												for hypothesis in dec:
																		fout.write(hypothesis + '''\n'''						)
																		fout.flush()
						fout.close()
						SCREAMING_SNAKE_CASE							:    Tuple							=							int(time.time() - start_time						)  # seconds
						SCREAMING_SNAKE_CASE							:    str							=							len(_lowercase						)
						return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs		,  4						)}
def       A	(    ):
						return datetime.datetime.now().strftime('''%Y-%m-%d %H:%M:%S'''						)
def       A	(    _lowercase=True						):
						SCREAMING_SNAKE_CASE							:    int							=							argparse.ArgumentParser()
						parser.add_argument('''model_name'''		,  type=_lowercase		,  help='''like facebook/bart-large-cnn,t5-base, etc.'''						)
						parser.add_argument('''input_path'''		,  type=_lowercase		,  help='''like cnn_dm/test.source'''						)
						parser.add_argument('''save_path'''		,  type=_lowercase		,  help='''where to save summaries'''						)
						parser.add_argument('''--reference_path'''		,  type=_lowercase		,  required=_lowercase		,  help='''like cnn_dm/test.target'''						)
						parser.add_argument('''--score_path'''		,  type=_lowercase		,  required=_lowercase		,  default='''metrics.json'''		,  help='''where to save metrics'''						)
						parser.add_argument('''--device'''		,  type=_lowercase		,  required=_lowercase		,  default=_lowercase		,  help='''cuda, cuda:1, cpu etc.'''						)
						parser.add_argument(
						    '''--prefix'''		,  type=_lowercase		,  required=_lowercase		,  default=_lowercase		,  help='''will be added to the begininng of src examples'''						)
						parser.add_argument('''--task'''		,  type=_lowercase		,  default='''summarization'''		,  help='''used for task_specific_params + metrics'''						)
						parser.add_argument('''--bs'''		,  type=_lowercase		,  default=8		,  required=_lowercase		,  help='''batch size'''						)
						parser.add_argument(
						    '''--n_obs'''		,  type=_lowercase		,  default=-1		,  required=_lowercase		,  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=_lowercase		,  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
						SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE							:    Optional[Any]							=							parser.parse_known_args()
						SCREAMING_SNAKE_CASE							:    Optional[Any]							=							parse_numeric_n_bool_cl_kwargs(_lowercase						)
						if parsed_args and verbose:
												print(f"""parsed the following generate kwargs: {parsed_args}"""						)
						SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							[''' ''' + x.rstrip() if '''t5''' in args.model_name else x.rstrip() for x in open(args.input_path						).readlines()]
						if args.n_obs > 0:
												SCREAMING_SNAKE_CASE							:    Any							=							examples[: args.n_obs]
						Path(args.save_path						).parent.mkdir(exist_ok=_lowercase						)

						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."""						)

						if args.device == "cpu" and args.fpaa:
												# this mix leads to RuntimeError: "threshold_cpu" not implemented for 'Half'
												raise ValueError('''Can\'t mix --fp16 and --device cpu'''						)

						SCREAMING_SNAKE_CASE							:    List[str]							=							generate_summaries_or_translations(
						    _lowercase		,  args.save_path		,  args.model_name		,  batch_size=args.bs		,  device=args.device		,  fpaa=args.fpaa		,  task=args.task		,  prefix=args.prefix		,  **_lowercase		,  )

						if args.reference_path is None:
												return {}

						# Compute scores
						SCREAMING_SNAKE_CASE							:    Dict							=							calculate_bleu if '''translation''' in args.task else calculate_rouge
						SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							[x.rstrip() for x in open(args.save_path						).readlines()]
						SCREAMING_SNAKE_CASE							:    Optional[int]							=							[x.rstrip() for x in open(args.reference_path						).readlines()][: len(_lowercase						)]
						SCREAMING_SNAKE_CASE							:    dict							=							score_fn(_lowercase		,  _lowercase						)
						scores.update(_lowercase						)

						if args.dump_args:
												scores.update(_lowercase						)
						if args.info:
												SCREAMING_SNAKE_CASE							:    Tuple							=							args.info

						if verbose:
												print(_lowercase						)

						if args.score_path is not None:
												json.dump(_lowercase		,  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)





