






'''simple docstring'''
import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch

from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search

from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_KEYS


logging.basicConfig(level=logging.DEBUG)
a_					=	logging.getLogger()





def 	_a(					UpperCamelCase__			:  Path,		UpperCamelCase__			:  list							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__       :			List[str]													='''\n'''.join(UpperCamelCase__							)
      Path(UpperCamelCase__							).open('''w'''							).writelines(UpperCamelCase__							)


a_					=	'patrickvonplaten/t5-tiny-random'
a_					=	'sshleifer/bart-tiny-random'
a_					=	'sshleifer/tiny-mbart'

a_					=	logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks







class        __SCREAMING_SNAKE_CASE (     lowerCamelCase			):




 def        __magic_name__		( self		:							Dict   , __lowercase		:							Optional[int] )		->  str:
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=Path(self.get_auto_remove_tmp_dir() ) / '''utest_input.source'''
       SCREAMING_SNAKE_CASE__       :			str													=input_file_name.parent / '''utest_output.txt'''
       assert not output_file_name.exists()
       SCREAMING_SNAKE_CASE__       :			Any													=[''' New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County.''']
       _dump_articles(__lowercase   , __lowercase )

       SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=str(Path(self.get_auto_remove_tmp_dir() ) / '''scores.json''' )
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													='''translation_en_to_de''' if model == T5_TINY else '''summarization'''
       SCREAMING_SNAKE_CASE__       :			Optional[int]													=F"\n            run_eval_search.py\n            {model}\n            {input_file_name}\n            {output_file_name}\n            --score_path {score_path}\n            --task {task}\n            --num_beams 2\n            --length_penalty 2.0\n            ".split()

       with patch.object(__lowercase   , '''argv'''   , __lowercase ):
             run_generate()
             assert Path(__lowercase ).exists()
             # os.remove(Path(output_file_name))




 def        __magic_name__		( self		:							Tuple )		->  str:
       self.run_eval_tester(__lowercase )




 @parameterized.expand([BART_TINY, MBART_TINY] )
 @slow
 def        __magic_name__		( self		:							Dict   , __lowercase		:							Tuple )		->  int:
       self.run_eval_tester(__lowercase )




 @parameterized.expand([T5_TINY, MBART_TINY] )
 @slow
 def        __magic_name__		( self		:							Tuple   , __lowercase		:							Optional[Any] )		->  Any:
       SCREAMING_SNAKE_CASE__       :			List[Any]													=Path(self.get_auto_remove_tmp_dir() ) / '''utest_input.source'''
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=input_file_name.parent / '''utest_output.txt'''
       assert not output_file_name.exists()

       SCREAMING_SNAKE_CASE__       :			int													={
           '''en''': ['''Machine learning is great, isn\'t it?''', '''I like to eat bananas''', '''Tomorrow is another great day!'''],
           '''de''': [
               '''Maschinelles Lernen ist großartig, oder?''',
               '''Ich esse gerne Bananen''',
               '''Morgen ist wieder ein toller Tag!''',
           ],
       }

       SCREAMING_SNAKE_CASE__       :			Tuple													=Path(self.get_auto_remove_tmp_dir() )
       SCREAMING_SNAKE_CASE__       :			Any													=str(tmp_dir / '''scores.json''' )
       SCREAMING_SNAKE_CASE__       :			List[Any]													=str(tmp_dir / '''val.target''' )
       _dump_articles(__lowercase   , text['''en'''] )
       _dump_articles(__lowercase   , text['''de'''] )
       SCREAMING_SNAKE_CASE__       :			List[str]													='''translation_en_to_de''' if model == T5_TINY else '''summarization'''
       SCREAMING_SNAKE_CASE__       :			List[Any]													=F"\n            run_eval_search.py\n            {model}\n            {str(__lowercase )}\n            {str(__lowercase )}\n            --score_path {score_path}\n            --reference_path {reference_path}\n            --task {task}\n            ".split()
       testargs.extend(['''--search''', '''num_beams=1:2 length_penalty=0.9:1.0'''] )

       with patch.object(__lowercase   , '''argv'''   , __lowercase ):
             with CaptureStdout() as cs:
                   run_search()
             SCREAMING_SNAKE_CASE__       :			Tuple													=[''' num_beams | length_penalty''', model, '''Best score args''']
             SCREAMING_SNAKE_CASE__       :			str													=['''Info''']
             if "translation" in task:
                   expected_strings.append('''bleu''' )
             else:
                   expected_strings.extend(__lowercase )
             for w in expected_strings:
                   assert w in cs.out
             for w in un_expected_strings:
                   assert w not in cs.out
             assert Path(__lowercase ).exists()
             os.remove(Path(__lowercase ) )







'''simple docstring'''
from typing import Dict, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging


if is_torch_available():
  import torch


a_					=	logging.get_logger(__name__)






class        __SCREAMING_SNAKE_CASE (     lowerCamelCase			):
 snake_case_												=							["""pixel_values"""]




 def __init__( self		:							List[str]   , __lowercase		:							bool = True   , __lowercase		:							Optional[Dict[str, int]] = None   , __lowercase		:							PILImageResampling = PILImageResampling.BILINEAR   , __lowercase		:							bool = True   , __lowercase		:							Dict[str, int] = None   , __lowercase		:							bool = True   , __lowercase		:							Union[int, float] = 1 / 2_55   , __lowercase		:							bool = True   , __lowercase		:							Optional[Union[float, List[float]]] = None   , __lowercase		:							Optional[Union[float, List[float]]] = None   , **__lowercase		:							List[Any]   , )		->  None:
       super().__init__(**__lowercase )
       SCREAMING_SNAKE_CASE__       :			Any													=size if size is not None else {'''shortest_edge''': 2_56}
       SCREAMING_SNAKE_CASE__       :			str													=get_size_dict(__lowercase   , default_to_square=__lowercase )
       SCREAMING_SNAKE_CASE__       :			Dict													=crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
       SCREAMING_SNAKE_CASE__       :			Tuple													=get_size_dict(__lowercase   , param_name='''crop_size''' )
       SCREAMING_SNAKE_CASE__       :			int													=do_resize
       SCREAMING_SNAKE_CASE__       :			Dict													=size
       SCREAMING_SNAKE_CASE__       :			List[str]													=resample
       SCREAMING_SNAKE_CASE__       :			List[Any]													=do_center_crop
       SCREAMING_SNAKE_CASE__       :			str													=crop_size
       SCREAMING_SNAKE_CASE__       :			List[str]													=do_rescale
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=rescale_factor
       SCREAMING_SNAKE_CASE__       :			List[str]													=do_normalize
       SCREAMING_SNAKE_CASE__       :			Any													=image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=image_std if image_std is not None else IMAGENET_STANDARD_STD




 def        __magic_name__		( self		:							List[Any]   , __lowercase		:							np.ndarray   , __lowercase		:							Dict[str, int]   , __lowercase		:							PILImageResampling = PILImageResampling.BICUBIC   , __lowercase		:							Optional[Union[str, ChannelDimension]] = None   , **__lowercase		:							Optional[Any]   , )		->  np.ndarray:
       SCREAMING_SNAKE_CASE__       :			str													=get_size_dict(__lowercase   , default_to_square=__lowercase )
       if "shortest_edge" not in size:
             raise ValueError(F"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}" )
       SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=get_resize_output_image_size(__lowercase   , size=size['''shortest_edge''']   , default_to_square=__lowercase )
       return resize(__lowercase   , size=__lowercase   , resample=__lowercase   , data_format=__lowercase   , **__lowercase )




 def        __magic_name__		( self		:							int   , __lowercase		:							np.ndarray   , __lowercase		:							Dict[str, int]   , __lowercase		:							Optional[Union[str, ChannelDimension]] = None   , **__lowercase		:							Any   , )		->  np.ndarray:
       SCREAMING_SNAKE_CASE__       :			List[Any]													=get_size_dict(__lowercase )
       if "height" not in size or "width" not in size:
             raise ValueError(F"The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}" )
       return center_crop(__lowercase   , size=(size['''height'''], size['''width'''])   , data_format=__lowercase   , **__lowercase )




 def        __magic_name__		( self		:							Optional[Any]   , __lowercase		:							np.ndarray   , __lowercase		:							float   , __lowercase		:							Optional[Union[str, ChannelDimension]] = None   , **__lowercase		:							Tuple )		->  np.ndarray:
       return rescale(__lowercase   , scale=__lowercase   , data_format=__lowercase   , **__lowercase )




 def        __magic_name__		( self		:							Dict   , __lowercase		:							np.ndarray   , __lowercase		:							Union[float, List[float]]   , __lowercase		:							Union[float, List[float]]   , __lowercase		:							Optional[Union[str, ChannelDimension]] = None   , **__lowercase		:							str   , )		->  np.ndarray:
       return normalize(__lowercase   , mean=__lowercase   , std=__lowercase   , data_format=__lowercase   , **__lowercase )




 def        __magic_name__		( self		:							List[Any]   , __lowercase		:							ImageInput   , __lowercase		:							Optional[bool] = None   , __lowercase		:							Dict[str, int] = None   , __lowercase		:							PILImageResampling = None   , __lowercase		:							bool = None   , __lowercase		:							Dict[str, int] = None   , __lowercase		:							Optional[bool] = None   , __lowercase		:							Optional[float] = None   , __lowercase		:							Optional[bool] = None   , __lowercase		:							Optional[Union[float, List[float]]] = None   , __lowercase		:							Optional[Union[float, List[float]]] = None   , __lowercase		:							Optional[Union[str, TensorType]] = None   , __lowercase		:							Union[str, ChannelDimension] = ChannelDimension.FIRST   , **__lowercase		:							int   , )		->  Dict:
       SCREAMING_SNAKE_CASE__       :			List[Any]													=do_resize if do_resize is not None else self.do_resize
       SCREAMING_SNAKE_CASE__       :			int													=size if size is not None else self.size
       SCREAMING_SNAKE_CASE__       :			Optional[int]													=get_size_dict(__lowercase   , default_to_square=__lowercase )
       SCREAMING_SNAKE_CASE__       :			Any													=resample if resample is not None else self.resample
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=do_center_crop if do_center_crop is not None else self.do_center_crop
       SCREAMING_SNAKE_CASE__       :			List[str]													=crop_size if crop_size is not None else self.crop_size
       SCREAMING_SNAKE_CASE__       :			int													=get_size_dict(__lowercase   , param_name='''crop_size''' )
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=do_rescale if do_rescale is not None else self.do_rescale
       SCREAMING_SNAKE_CASE__       :			int													=rescale_factor if rescale_factor is not None else self.rescale_factor
       SCREAMING_SNAKE_CASE__       :			List[str]													=do_normalize if do_normalize is not None else self.do_normalize
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=image_mean if image_mean is not None else self.image_mean
       SCREAMING_SNAKE_CASE__       :			Any													=image_std if image_std is not None else self.image_std

       SCREAMING_SNAKE_CASE__       :			str													=make_list_of_images(__lowercase )

       if not valid_images(__lowercase ):
             raise ValueError(
                 '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
                 '''torch.Tensor, tf.Tensor or jax.ndarray.''' )

       if do_resize and size is None:
             raise ValueError('''Size must be specified if do_resize is True.''' )

       if do_center_crop and crop_size is None:
             raise ValueError('''Crop size must be specified if do_center_crop is True.''' )

       if do_rescale and rescale_factor is None:
             raise ValueError('''Rescale factor must be specified if do_rescale is True.''' )

       if do_normalize and (image_mean is None or image_std is None):
             raise ValueError('''Image mean and std must be specified if do_normalize is True.''' )

       # All transformations expect numpy arrays.
       SCREAMING_SNAKE_CASE__       :			Dict													=[to_numpy_array(__lowercase ) for image in images]

       if do_resize:
             SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=[self.resize(image=__lowercase   , size=__lowercase   , resample=__lowercase ) for image in images]

       if do_center_crop:
             SCREAMING_SNAKE_CASE__       :			List[Any]													=[self.center_crop(image=__lowercase   , size=__lowercase ) for image in images]

       if do_rescale:
             SCREAMING_SNAKE_CASE__       :			List[Any]													=[self.rescale(image=__lowercase   , scale=__lowercase ) for image in images]

       if do_normalize:
             SCREAMING_SNAKE_CASE__       :			Tuple													=[self.normalize(image=__lowercase   , mean=__lowercase   , std=__lowercase ) for image in images]

       SCREAMING_SNAKE_CASE__       :			str													=[to_channel_dimension_format(__lowercase   , __lowercase ) for image in images]

       SCREAMING_SNAKE_CASE__       :			Any													={'''pixel_values''': images}
       return BatchFeature(data=__lowercase   , tensor_type=__lowercase )




 def        __magic_name__		( self		:							Optional[int]   , __lowercase		:							Tuple   , __lowercase		:							List[Tuple] = None )		->  Any:
       SCREAMING_SNAKE_CASE__       :			Optional[int]													=outputs.logits

       # Resize logits and compute semantic segmentation maps
       if target_sizes is not None:
             if len(__lowercase ) != len(__lowercase ):
                   raise ValueError(
                       '''Make sure that you pass in as many target sizes as the batch dimension of the logits''' )

             if is_torch_tensor(__lowercase ):
                   SCREAMING_SNAKE_CASE__       :			int													=target_sizes.numpy()

             SCREAMING_SNAKE_CASE__       :			List[Any]													=[]

             for idx in range(len(__lowercase ) ):
                   SCREAMING_SNAKE_CASE__       :			Any													=torch.nn.functional.interpolate(
                       logits[idx].unsqueeze(dim=0 )   , size=target_sizes[idx]   , mode='''bilinear'''   , align_corners=__lowercase )
                   SCREAMING_SNAKE_CASE__       :			List[str]													=resized_logits[0].argmax(dim=0 )
                   semantic_segmentation.append(__lowercase )
       else:
             SCREAMING_SNAKE_CASE__       :			Any													=logits.argmax(dim=1 )
             SCREAMING_SNAKE_CASE__       :			Optional[int]													=[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]

       return semantic_segmentation