

import json
import os
import tempfile

from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available


if is_torch_available():
		import numpy as np
		import torch

if is_vision_available():
		from PIL import Image







def _a			(	SCREAMING_SNAKE_CASE		:      int   , SCREAMING_SNAKE_CASE		:      Optional[int]=False   , SCREAMING_SNAKE_CASE		:      Optional[Any]=False   , SCREAMING_SNAKE_CASE		:      int=False		)   ->   int:

	"""simple docstring"""






	assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

	__lowerCAmelCase:       Dict            =				[]
	for i in range(image_processor_tester.batch_size		):
		if equal_resolution:
			__lowerCAmelCase:       Any            =				image_processor_tester.max_resolution
		else:
			# To avoid getting image width/height 0
			__lowerCAmelCase:       Optional[Any]            =				image_processor_tester.min_resolution
			if getattr(SCREAMING_SNAKE_CASE   , 'size_divisor'   , SCREAMING_SNAKE_CASE		):
				# If `size_divisor` is defined, the image needs to have width/size >= `size_divisor`
				__lowerCAmelCase:       str            =				max(image_processor_tester.size_divisor   , SCREAMING_SNAKE_CASE		)
			__lowerCAmelCase     ,							__lowerCAmelCase:       Optional[int]            =				np.random.choice(np.arange(SCREAMING_SNAKE_CASE   , image_processor_tester.max_resolution		)   , 2		)
		image_inputs.append(
		    np.random.randint(2_55   , size=(image_processor_tester.num_channels, width, height)   , dtype=np.uinta		)		)

	if not numpify and not torchify:
		# PIL expects the channel dimension as last dimension
		__lowerCAmelCase:       Union[str, Any]            =				[Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE   , 0   , -1		)		) for image in image_inputs]

	if torchify:
		__lowerCAmelCase:       Tuple            =				[torch.from_numpy(SCREAMING_SNAKE_CASE		) for image in image_inputs]

	return image_inputs







def _a			(	SCREAMING_SNAKE_CASE		:      Tuple   , SCREAMING_SNAKE_CASE		:      Any=10   , SCREAMING_SNAKE_CASE		:      List[str]=10   , SCREAMING_SNAKE_CASE		:      Any=False   , SCREAMING_SNAKE_CASE		:      Tuple=False		)   ->   Any:

	"""simple docstring"""






	__lowerCAmelCase:       List[str]            =				[]
	for i in range(image_processor_tester.num_frames		):
		video.append(np.random.randint(2_55   , size=(image_processor_tester.num_channels, width, height)   , dtype=np.uinta		)		)

	if not numpify and not torchify:
		# PIL expects the channel dimension as last dimension
		__lowerCAmelCase:       Optional[Any]            =				[Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE   , 0   , -1		)		) for frame in video]

	if torchify:
		__lowerCAmelCase:       Any            =				[torch.from_numpy(SCREAMING_SNAKE_CASE		) for frame in video]

	return video







def _a			(	SCREAMING_SNAKE_CASE		:      Optional[int]   , SCREAMING_SNAKE_CASE		:      Optional[int]=False   , SCREAMING_SNAKE_CASE		:      Union[str, Any]=False   , SCREAMING_SNAKE_CASE		:      List[Any]=False		)   ->   Dict:

	"""simple docstring"""






	assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

	__lowerCAmelCase:       Union[str, Any]            =				[]
	for i in range(image_processor_tester.batch_size		):
		if equal_resolution:
			__lowerCAmelCase:       int            =				image_processor_tester.max_resolution
		else:
			__lowerCAmelCase     ,							__lowerCAmelCase:       str            =				np.random.choice(
			    np.arange(image_processor_tester.min_resolution   , image_processor_tester.max_resolution		)   , 2		)
			__lowerCAmelCase:       Union[str, Any]            =				prepare_video(
			    image_processor_tester=SCREAMING_SNAKE_CASE   , width=SCREAMING_SNAKE_CASE   , height=SCREAMING_SNAKE_CASE   , numpify=SCREAMING_SNAKE_CASE   , torchify=SCREAMING_SNAKE_CASE   , )
		video_inputs.append(SCREAMING_SNAKE_CASE		)

	return video_inputs






class 							A_			:
					_lowercase      :					List[Any]       =     None


					def   UpperCAmelCase  (							self      :		Union[str, Any] )							->     Dict:
						__lowerCAmelCase:       Tuple            =				self.image_processing_class(**self.image_processor_dict )
						__lowerCAmelCase:       Union[str, Any]            =				json.loads(image_processor.to_json_string() )
						for key, value in self.image_processor_dict.items():
							self.assertEqual(obj[key]      ,  UpperCAmelCase )


					def   UpperCAmelCase  (							self      :		List[Any] )							->     List[str]:
						__lowerCAmelCase:       List[str]            =				self.image_processing_class(**self.image_processor_dict )

						with tempfile.TemporaryDirectory() as tmpdirname:
							__lowerCAmelCase:       List[str]            =				os.path.join(UpperCAmelCase      ,  'image_processor.json' )
							image_processor_first.to_json_file(UpperCAmelCase )
							__lowerCAmelCase:       Optional[Any]            =				self.image_processing_class.from_json_file(UpperCAmelCase )

						self.assertEqual(image_processor_second.to_dict()      ,  image_processor_first.to_dict() )


					def   UpperCAmelCase  (							self      :		str )							->     List[str]:
						__lowerCAmelCase:       int            =				self.image_processing_class(**self.image_processor_dict )

						with tempfile.TemporaryDirectory() as tmpdirname:
							__lowerCAmelCase:       int            =				image_processor_first.save_pretrained(UpperCAmelCase )[0]
							check_json_file_has_correct_format(UpperCAmelCase )
							__lowerCAmelCase:       List[str]            =				self.image_processing_class.from_pretrained(UpperCAmelCase )

						self.assertEqual(image_processor_second.to_dict()      ,  image_processor_first.to_dict() )


					def   UpperCAmelCase  (							self      :		List[Any] )							->     Optional[int]:
						__lowerCAmelCase:       Any            =				self.image_processing_class()
						self.assertIsNotNone(UpperCAmelCase )


					@require_torch
					@require_vision
					def   UpperCAmelCase  (							self      :		Union[str, Any] )							->     int:







						if self.test_cast_dtype is not None:
							# Initialize image_processor
							__lowerCAmelCase:       List[Any]            =				self.image_processing_class(**self.image_processor_dict )

							# create random PyTorch tensors
							__lowerCAmelCase:       List[Any]            =				prepare_image_inputs(self.image_processor_tester      ,  equal_resolution=UpperCAmelCase      ,  torchify=UpperCAmelCase )

							__lowerCAmelCase:       Union[str, Any]            =				image_processor(UpperCAmelCase      ,  return_tensors='pt' )
							# for layoutLM compatiblity
							self.assertEqual(encoding.pixel_values.device      ,  torch.device('cpu' ) )
							self.assertEqual(encoding.pixel_values.dtype      ,  torch.floataa )

							__lowerCAmelCase:       Optional[int]            =				image_processor(UpperCAmelCase      ,  return_tensors='pt' ).to(torch.floataa )
							self.assertEqual(encoding.pixel_values.device      ,  torch.device('cpu' ) )
							self.assertEqual(encoding.pixel_values.dtype      ,  torch.floataa )

							__lowerCAmelCase:       Any            =				image_processor(UpperCAmelCase      ,  return_tensors='pt' ).to('cpu'      ,  torch.bfloataa )
							self.assertEqual(encoding.pixel_values.device      ,  torch.device('cpu' ) )
							self.assertEqual(encoding.pixel_values.dtype      ,  torch.bfloataa )

							with self.assertRaises(UpperCAmelCase ):
								__lowerCAmelCase:       Any            =				image_processor(UpperCAmelCase      ,  return_tensors='pt' ).to(torch.bfloataa      ,  'cpu' )

							# Try with text + image feature
							__lowerCAmelCase:       int            =				image_processor(UpperCAmelCase      ,  return_tensors='pt' )
							encoding.update({'input_ids': torch.LongTensor([[1, 2, 3], [4, 5, 6]] )} )
							__lowerCAmelCase:       Dict            =				encoding.to(torch.floataa )

							self.assertEqual(encoding.pixel_values.device      ,  torch.device('cpu' ) )
							self.assertEqual(encoding.pixel_values.dtype      ,  torch.floataa )
							self.assertEqual(encoding.input_ids.dtype      ,  torch.long )








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


_a			=       getLogger(__name__)


_a			=       '''cuda''' if torch.cuda.is_available() else '''cpu'''







def _a			(	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		:      Union[str, Any]=False   , SCREAMING_SNAKE_CASE		:      Tuple="summarization"   , SCREAMING_SNAKE_CASE		:      int=None   , **SCREAMING_SNAKE_CASE		:      Optional[Any]   , )   ->   Dict:

 """simple docstring"""






 __lowerCAmelCase:       Union[str, Any]            =				Path(SCREAMING_SNAKE_CASE		).open('w'   , encoding='utf-8'		)
 __lowerCAmelCase:       Tuple            =				str(SCREAMING_SNAKE_CASE		)
 __lowerCAmelCase:       Optional[int]            =				AutoModelForSeqaSeqLM.from_pretrained(SCREAMING_SNAKE_CASE		).to(SCREAMING_SNAKE_CASE		)
 if fpaa:
  __lowerCAmelCase:       List[Any]            =				model.half()

 __lowerCAmelCase:       Dict            =				AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE		)
 logger.info(f'''Inferred tokenizer type: {tokenizer.__class__}'''		)  # if this is wrong, check config.model_type.

 __lowerCAmelCase:       Any            =				time.time()
 # update config with task specific params
 use_task_specific_params(SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE		)
 if prefix is None:
  __lowerCAmelCase:       Optional[Any]            =				prefix or getattr(model.config   , 'prefix'   , ''		) or ''
 for examples_chunk in tqdm(list(chunks(SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE		)		)		):
  __lowerCAmelCase:       str            =				[prefix + text for text in examples_chunk]
  __lowerCAmelCase:       Optional[Any]            =				tokenizer(SCREAMING_SNAKE_CASE   , return_tensors='pt'   , truncation=SCREAMING_SNAKE_CASE   , padding='longest'		).to(SCREAMING_SNAKE_CASE		)
  __lowerCAmelCase:       List[str]            =				model.generate(
      input_ids=batch.input_ids   , attention_mask=batch.attention_mask   , **SCREAMING_SNAKE_CASE   , )
  __lowerCAmelCase:       Dict            =				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()
 __lowerCAmelCase:       str            =				int(time.time() - start_time		)  # seconds
 __lowerCAmelCase:       Tuple            =				len(SCREAMING_SNAKE_CASE		)
 return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs   , 4		)}







def _a			(	)   ->   Dict:

 """simple docstring"""






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







def _a			(	SCREAMING_SNAKE_CASE		:      Tuple=True		)   ->   int:

 """simple docstring"""






 __lowerCAmelCase:       str            =				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
 __lowerCAmelCase     ,							__lowerCAmelCase:       Union[str, Any]            =				parser.parse_known_args()
 __lowerCAmelCase:       Union[str, Any]            =				parse_numeric_n_bool_cl_kwargs(SCREAMING_SNAKE_CASE		)
 if parsed_args and verbose:
  print(f'''parsed the following generate kwargs: {parsed_args}'''		)
 with open(args.input_path		) as f:
  __lowerCAmelCase:       Optional[int]            =				[' ' + x.rstrip() if 't5' in args.model_name else x.rstrip() for x in f.readlines()]
 if args.n_obs > 0:
  __lowerCAmelCase:       Tuple            =				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.'''		)
 __lowerCAmelCase:       Tuple            =				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
 __lowerCAmelCase:       Dict            =				calculate_bleu if 'translation' in args.task else calculate_rouge
 __lowerCAmelCase:       Optional[Any]            =				[x.rstrip() for x in open(args.save_path		).readlines()]
 __lowerCAmelCase:       Union[str, Any]            =				[x.rstrip() for x in open(args.reference_path		).readlines()][: len(SCREAMING_SNAKE_CASE		)]
 __lowerCAmelCase:       dict            =				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:
  __lowerCAmelCase:       Any            =				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)





