






import hashlib
import unittest

from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image
else:


   class 			SCREAMING_SNAKE_CASE      :
        '''simple docstring'''


        @staticmethod
        def 					_A							(					*UpperCAmelCase_							:						Dict       ,			**UpperCAmelCase_							:						List[Any]   ):
          pass


def 	lowerCamelCase__      (  lowercase					):

  """simple docstring"""






  SCREAMING_SNAKE_CASE							:   List[Any]						    =     hashlib.mda(image.tobytes()					)
  return m.hexdigest()


@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 			SCREAMING_SNAKE_CASE      (      unittest.TestCase       ):
     '''simple docstring'''


     UpperCamelCase_				:		List[str]								=						MODEL_FOR_DEPTH_ESTIMATION_MAPPING


     def 					_A							(					self							:						Dict       ,			UpperCAmelCase_							:						Dict       ,			UpperCAmelCase_							:						List[Any]       ,			UpperCAmelCase_							:						List[Any]   ):
       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     DepthEstimationPipeline(model=UpperCAmelCase_       ,			image_processor=UpperCAmelCase_   )
       return depth_estimator, [
           "./tests/fixtures/tests_samples/COCO/000000039769.png",
           "./tests/fixtures/tests_samples/COCO/000000039769.png",
       ]


     def 					_A							(					self							:						Union[str, Any]       ,			UpperCAmelCase_							:						str       ,			UpperCAmelCase_							:						Union[str, Any]   ):
       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     depth_estimator("./tests/fixtures/tests_samples/COCO/000000039769.png"   )
       self.assertEqual({"predicted_depth": ANY(torch.Tensor   ), "depth": ANY(Image.Image   )}       ,			UpperCAmelCase_   )
       import datasets

       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     datasets.load_dataset("hf-internal-testing/fixtures_image_utils"       ,			"image"       ,			split="test"   )
       SCREAMING_SNAKE_CASE							:   Any						    =     depth_estimator(
           [
               Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"   ),
               "http://images.cocodataset.org/val2017/000000039769.jpg",
               # RGBA
               dataset[0]["file"],
               # LA
               dataset[1]["file"],
               # L
               dataset[2]["file"],
           ]   )
       self.assertEqual(
           [
               {"predicted_depth": ANY(torch.Tensor   ), "depth": ANY(Image.Image   )},
               {"predicted_depth": ANY(torch.Tensor   ), "depth": ANY(Image.Image   )},
               {"predicted_depth": ANY(torch.Tensor   ), "depth": ANY(Image.Image   )},
               {"predicted_depth": ANY(torch.Tensor   ), "depth": ANY(Image.Image   )},
               {"predicted_depth": ANY(torch.Tensor   ), "depth": ANY(Image.Image   )},
           ]       ,			UpperCAmelCase_       ,			)


     @require_tf
     @unittest.skip("Depth estimation is not implemented in TF"   )
     def 					_A							(					self							:						Any   ):
       pass


     @slow
     @require_torch
     def 					_A							(					self							:						str   ):
       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     "Intel/dpt-large"
       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     pipeline("depth-estimation"       ,			model=UpperCAmelCase_   )
       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     depth_estimator("http://images.cocodataset.org/val2017/000000039769.jpg"   )
       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     hashimage(outputs["depth"]   )

       # This seems flaky.
       # self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
       self.assertEqual(nested_simplify(outputs["predicted_depth"].max().item()   )       ,			29.304   )
       self.assertEqual(nested_simplify(outputs["predicted_depth"].min().item()   )       ,			2.662   )






     @require_torch
     def 					_A							(					self							:						Union[str, Any]   ):
       self.skipTest("There is not hf-internal-testing tiny model for either GLPN nor DPT"   )














'''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


__UpperCamelCase   :							int         =   getLogger(__name__)


__UpperCamelCase   :							int         =   """cuda""" if torch.cuda.is_available() else """cpu"""



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						List[str],       SCREAMING_SNAKE_CASE__:						str,       SCREAMING_SNAKE_CASE__:						str,       SCREAMING_SNAKE_CASE__:						int = 8,       SCREAMING_SNAKE_CASE__:						str = DEFAULT_DEVICE,       SCREAMING_SNAKE_CASE__:						Any=False,       SCREAMING_SNAKE_CASE__:						Tuple="summarization",       SCREAMING_SNAKE_CASE__:						List[Any]=None,       **SCREAMING_SNAKE_CASE__:						int,       )	->      Dict:

   """simple docstring"""
   __a					  =     Path(SCREAMING_SNAKE_CASE__     ).open('w',       encoding='utf-8'     )
   __a					  =     str(SCREAMING_SNAKE_CASE__     )
   __a					  =     AutoModelForSeqaSeqLM.from_pretrained(SCREAMING_SNAKE_CASE__     ).to(SCREAMING_SNAKE_CASE__     )
   if fpaa:
      __a					  =     model.half()

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

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



def  __UpperCAmelCase						(					)	->      List[str]:

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



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						Optional[int]=True     )	->      List[Any]:

   """simple docstring"""
   __a					  =     argparse.ArgumentParser()
   parser.add_argument('model_name',       type=SCREAMING_SNAKE_CASE__,       help='like facebook/bart-large-cnn,t5-base, etc.'     )
   parser.add_argument('input_path',       type=SCREAMING_SNAKE_CASE__,       help='like cnn_dm/test.source'     )
   parser.add_argument('save_path',       type=SCREAMING_SNAKE_CASE__,       help='where to save summaries'     )
   parser.add_argument('--reference_path',       type=SCREAMING_SNAKE_CASE__,       required=SCREAMING_SNAKE_CASE__,       help='like cnn_dm/test.target'     )
   parser.add_argument('--score_path',       type=SCREAMING_SNAKE_CASE__,       required=SCREAMING_SNAKE_CASE__,       default='metrics.json',       help='where to save metrics'     )
   parser.add_argument('--device',       type=SCREAMING_SNAKE_CASE__,       required=SCREAMING_SNAKE_CASE__,       default=SCREAMING_SNAKE_CASE__,       help='cuda, cuda:1, cpu etc.'     )
   parser.add_argument(
       '--prefix',       type=SCREAMING_SNAKE_CASE__,       required=SCREAMING_SNAKE_CASE__,       default=SCREAMING_SNAKE_CASE__,       help='will be added to the begininng of src examples'     )
   parser.add_argument('--task',       type=SCREAMING_SNAKE_CASE__,       default='summarization',       help='used for task_specific_params + metrics'     )
   parser.add_argument('--bs',       type=SCREAMING_SNAKE_CASE__,       default=8,       required=SCREAMING_SNAKE_CASE__,       help='batch size'     )
   parser.add_argument(
       '--n_obs',       type=SCREAMING_SNAKE_CASE__,       default=-1,       required=SCREAMING_SNAKE_CASE__,       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=SCREAMING_SNAKE_CASE__,       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					  =     parser.parse_known_args()
   __a					  =     parse_numeric_n_bool_cl_kwargs(SCREAMING_SNAKE_CASE__     )
   if parsed_args and verbose:
      print(f"""parsed the following generate kwargs: {parsed_args}"""     )
   __a					  =     [' ' + x.rstrip() if 't5' in args.model_name else x.rstrip() for x in open(args.input_path     ).readlines()]
   if args.n_obs > 0:
      __a					  =     examples[: args.n_obs]
   Path(args.save_path     ).parent.mkdir(exist_ok=SCREAMING_SNAKE_CASE__     )

   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'     )

   __a					  =     generate_summaries_or_translations(
       SCREAMING_SNAKE_CASE__,       args.save_path,       args.model_name,       batch_size=args.bs,       device=args.device,       fpaa=args.fpaa,       task=args.task,       prefix=args.prefix,       **SCREAMING_SNAKE_CASE__,       )

   if args.reference_path is None:
      return {}

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

   if args.dump_args:
      scores.update(SCREAMING_SNAKE_CASE__     )
   if args.info:
      __a					  =     args.info

   if verbose:
      print(SCREAMING_SNAKE_CASE__     )

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