



'''simple docstring'''






import tempfile
import unittest

import numpy as np
import torch

from diffusers import DiffusionPipeline
from diffusers.models.attention_processor import Attention, AttnAddedKVProcessor



class 				lowercase			(    unittest.TestCase							):


 """simple docstring"""


 def _snake_case					(				self    ,a_ = False				)						->  Union[str, Any]:
        _UpperCAmelCase    :		Union[str, Any]     	=					10

        if only_cross_attention:
               _UpperCAmelCase    :		Union[str, Any]     	=					12
        else:
               # when only cross attention is not set, the cross attention dim must be the same as the query dim
               _UpperCAmelCase    :		List[str]     	=					query_dim

        return {
            "query_dim": query_dim,
            "cross_attention_dim": cross_attention_dim,
            "heads": 2,
            "dim_head": 4,
            "added_kv_proj_dim": 6,
            "norm_num_groups": 1,
            "only_cross_attention": only_cross_attention,
            "processor": AttnAddedKVProcessor(),
        }







 def _snake_case					(				self    ,a_    ,a_				)						->  List[Any]:
        _UpperCAmelCase    :		Union[str, Any]     	=					2

        _UpperCAmelCase    :		Optional[int]     	=					torch.rand(snake_case__    ,snake_case__    ,3    ,2				)
        _UpperCAmelCase    :		str     	=					torch.rand(snake_case__    ,4    ,snake_case__				)
        _UpperCAmelCase    :		List[str]     	=					None

        return {
            "hidden_states": hidden_states,
            "encoder_hidden_states": encoder_hidden_states,
            "attention_mask": attention_mask,
        }







 def _snake_case					(				self				)						->  Union[str, Any]:
        torch.manual_seed(0				)

        _UpperCAmelCase    :		List[Any]     	=					self.get_constructor_arguments(only_cross_attention=snake_case__				)
        _UpperCAmelCase    :		Optional[int]     	=					Attention(**snake_case__				)

        self.assertTrue(attn.to_k is not None				)
        self.assertTrue(attn.to_v is not None				)

        _UpperCAmelCase    :		List[Any]     	=					self.get_forward_arguments(
            query_dim=constructor_args["""query_dim"""]    ,added_kv_proj_dim=constructor_args["""added_kv_proj_dim"""]				)

        _UpperCAmelCase    :		int     	=					attn(**snake_case__				)

        # only self attention

        torch.manual_seed(0				)

        _UpperCAmelCase    :		Union[str, Any]     	=					self.get_constructor_arguments(only_cross_attention=snake_case__				)
        _UpperCAmelCase    :		Any     	=					Attention(**snake_case__				)

        self.assertTrue(attn.to_k is None				)
        self.assertTrue(attn.to_v is None				)

        _UpperCAmelCase    :		Optional[Any]     	=					self.get_forward_arguments(
            query_dim=constructor_args["""query_dim"""]    ,added_kv_proj_dim=constructor_args["""added_kv_proj_dim"""]				)

        _UpperCAmelCase    :		Optional[Any]     	=					attn(**snake_case__				)

        self.assertTrue((only_cross_attn_out != self_and_cross_attn_out).all()				)







class 				lowercase			(    unittest.TestCase							):


 """simple docstring"""


 def _snake_case					(				self				)						->  str:
        _UpperCAmelCase    :		List[Any]     	=					DiffusionPipeline.from_pretrained("""hf-internal-testing/tiny-stable-diffusion-pipe"""    ,safety_checker=snake_case__				)

        _UpperCAmelCase    :		Tuple     	=					pipe(
            """foo"""    ,num_inference_steps=2    ,generator=torch.Generator("""cpu"""				).manual_seed(0				)    ,output_type="""np"""    ,).images

        # the initial conversion succeeds
        _UpperCAmelCase    :		Tuple     	=					DiffusionPipeline.from_pretrained(
            """hf-internal-testing/tiny-stable-diffusion-pipe"""    ,device_map="""sequential"""    ,safety_checker=snake_case__				)

        _UpperCAmelCase    :		Optional[int]     	=					pipe(
            """foo"""    ,num_inference_steps=2    ,generator=torch.Generator("""cpu"""				).manual_seed(0				)    ,output_type="""np"""    ,).images

        with tempfile.TemporaryDirectory() as tmpdir:
               # save the converted model
               pipe.save_pretrained(snake_case__				)

               # can also load the converted weights
               _UpperCAmelCase    :		Dict     	=					DiffusionPipeline.from_pretrained(snake_case__    ,device_map="""sequential"""    ,safety_checker=snake_case__				)

        _UpperCAmelCase    :		Optional[int]     	=					pipe(
            """foo"""    ,num_inference_steps=2    ,generator=torch.Generator("""cpu"""				).manual_seed(0				)    ,output_type="""np"""    ,).images

        self.assertTrue(np.allclose(snake_case__    ,snake_case__    ,atol=1E-5				)				)
        self.assertTrue(np.allclose(snake_case__    ,snake_case__    ,atol=1E-5				)				)





"""simple docstring"""
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


lowercase		:    Dict			=       getLogger(__name__)


lowercase		:    Dict			=       'cuda' if torch.cuda.is_available() else 'cpu'


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


					'''simple docstring'''
					__a:    str		     =       Path(lowerCamelCase_			).open("""w"""						,					encoding="""utf-8"""			)
					__a:    Tuple		     =       str(lowerCamelCase_			)
					__a:    List[Any]		     =       AutoModelForSeqaSeqLM.from_pretrained(lowerCamelCase_			).to(lowerCamelCase_			)
					if fpaa:
										__a:    List[str]		     =       model.half()

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

					__a:    str		     =       time.time()
					# update config with task specific params
					use_task_specific_params(lowerCamelCase_						,					lowerCamelCase_			)
					if prefix is None:
										__a:    Tuple		     =       prefix or getattr(model.config						,					"""prefix"""						,					""""""			) or """"""
					for examples_chunk in tqdm(list(chunks(lowerCamelCase_						,					lowerCamelCase_			)			)			):
										__a:    Tuple		     =       [prefix + text for text in examples_chunk]
										__a:    Optional[int]		     =       tokenizer(lowerCamelCase_						,					return_tensors="""pt"""						,					truncation=lowerCamelCase_						,					padding="""longest"""			).to(lowerCamelCase_			)
										__a:    Any		     =       model.generate(
										    input_ids=batch.input_ids						,					attention_mask=batch.attention_mask						,					**lowerCamelCase_						,					)
										__a:    Optional[int]		     =       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()
					__a:    Any		     =       int(time.time() - start_time			)  # seconds
					__a:    Optional[int]		     =       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_     (						lowerCamelCase_=True			)  ->      Any:


					'''simple docstring'''
					__a:    Any		     =       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
					__a     ,					__a:    int		     =       parser.parse_known_args()
					__a:    List[Any]		     =       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:
										__a:    Tuple		     =       [""" """ + x.rstrip() if """t5""" in args.model_name else x.rstrip() for x in f.readlines()]
					if args.n_obs > 0:
										__a:    Any		     =       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."""			)
					__a:    Tuple		     =       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
					__a:    Optional[Any]		     =       calculate_bleu if """translation""" in args.task else calculate_rouge
					__a:    Union[str, Any]		     =       [x.rstrip() for x in open(args.save_path			).readlines()]
					__a:    List[str]		     =       [x.rstrip() for x in open(args.reference_path			).readlines()][: len(lowerCamelCase_			)]
					__a:    dict		     =       score_fn(lowerCamelCase_						,					lowerCamelCase_			)
					scores.update(lowerCamelCase_			)

					if args.dump_args:
										scores.update(lowerCamelCase_			)
					if args.info:
										__a:    Optional[Any]		     =       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)
