





import json
import logging
import os
import sys
from pathlib import Path

import finetune_rag

from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    require_ray,
    require_torch_gpu,
    require_torch_multi_gpu,
)


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

lowercase_					     =	logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
class        SCREAMING_SNAKE_CASE     (UpperCAmelCase   ):


   def  SCREAMING_SNAKE_CASE_							(					self    :       List[str]    ,  a    :       List[str] )->     str:





     """simple docstring"""







     os.makedirs(a    ,  exist_ok=a )
     lowercase__			    =						{'source': 'What is love ?', 'target': 'life'}
     lowercase__			    =						{'train': 12, 'val': 2, 'test': 2}
     for split in ["train", "test", "val"]:
       for field in ["source", "target"]:
         lowercase__			    =						'\n'.join([contents[field]] * n_lines[split] )
         with open(os.path.join(a    ,  f"""{split}.{field}""" )    ,  'w' ) as f:
           f.write(a )


   def  SCREAMING_SNAKE_CASE_							(					self    :       Any    ,  a    :       int    ,  a    :       str = "pytorch" )->     int:





     """simple docstring"""







     lowercase__			    =						self.get_auto_remove_tmp_dir()
     lowercase__			    =						os.path.join(a    ,  'output' )
     lowercase__			    =						os.path.join(a    ,  'data' )
     self._create_dummy_data(data_dir=a )

     lowercase__			    =						f"""
                --data_dir {data_dir} \
                --output_dir {output_dir} \
                --model_name_or_path facebook/rag-sequence-base \
                --model_type rag_sequence \
                --do_train \
                --do_predict \
                --n_val -1 \
                --val_check_interval 1.0 \
                --train_batch_size 2 \
                --eval_batch_size 1 \
                --max_source_length 25 \
                --max_target_length 25 \
                --val_max_target_length 25 \
                --test_max_target_length 25 \
                --label_smoothing 0.1 \
                --dropout 0.1 \
                --attention_dropout 0.1 \
                --weight_decay 0.001 \
                --adam_epsilon 1e-08 \
                --max_grad_norm 0.1 \
                --lr_scheduler polynomial \
                --learning_rate 3e-04 \
                --num_train_epochs 1 \
                --warmup_steps 4 \
                --gradient_accumulation_steps 1 \
                --distributed-port 8787 \
                --use_dummy_dataset 1 \
                --distributed_retriever {distributed_retriever} \
            """.split()

     if gpus > 0:
       testargs.append(f"""--gpus={gpus}""" )
       if is_apex_available():
         testargs.append('--fp16' )
     else:
       testargs.append('--gpus=0' )
       testargs.append('--distributed_backend=ddp_cpu' )
       testargs.append('--num_processes=2' )

     lowercase__			    =						[sys.executable, str(Path(finetune_rag.__file__ ).resolve() )] + testargs
     execute_subprocess_async(a    ,  env=self.get_env() )

     lowercase__			    =						os.path.join(a    ,  'metrics.json' )
     with open(a ) as f:
       lowercase__			    =						json.load(a )
     return result


   @require_torch_gpu
   def  SCREAMING_SNAKE_CASE_							(					self    :       Optional[int] )->     Union[str, Any]:





     """simple docstring"""







     lowercase__			    =						self._run_finetune(gpus=1 )
     self.assertGreaterEqual(result['test'][0]['test_avg_em']    ,  0.2 )


   @require_torch_multi_gpu
   def  SCREAMING_SNAKE_CASE_							(					self    :       Any )->     int:





     """simple docstring"""







     lowercase__			    =						self._run_finetune(gpus=2 )
     self.assertGreaterEqual(result['test'][0]['test_avg_em']    ,  0.2 )


   @require_torch_gpu
   @require_ray
   def  SCREAMING_SNAKE_CASE_							(					self    :       Tuple )->     Optional[Any]:





     """simple docstring"""







     lowercase__			    =						self._run_finetune(gpus=1    ,  distributed_retriever='ray' )
     self.assertGreaterEqual(result['test'][0]['test_avg_em']    ,  0.2 )


   @require_torch_multi_gpu
   @require_ray
   def  SCREAMING_SNAKE_CASE_							(					self    :       List[Any] )->     str:





     """simple docstring"""







     lowercase__			    =						self._run_finetune(gpus=1    ,  distributed_retriever='ray' )
     self.assertGreaterEqual(result['test'][0]['test_avg_em']    ,  0.2 )









import unittest

import numpy as np

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

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
      import torch

if is_vision_available():
      from PIL import Image

      from transformers import DonutImageProcessor
class        SCREAMING_SNAKE_CASE     (unittest.TestCase   ):


   def __init__(					self    :       Any    ,  a    :       str    ,  a    :       List[Any]=7    ,  a    :       int=3    ,  a    :       int=18    ,  a    :       Optional[Any]=30    ,  a    :       Optional[int]=400    ,  a    :       int=True    ,  a    :       Tuple=None    ,  a    :       Optional[Any]=True    ,  a    :       str=False    ,  a    :       str=True    ,  a    :       int=True    ,  a    :       Tuple=[0.5, 0.5, 0.5]    ,  a    :       Any=[0.5, 0.5, 0.5]    ,  )->     Optional[int]:





     """simple docstring"""







     lowercase__			    =						parent
     lowercase__			    =						batch_size
     lowercase__			    =						num_channels
     lowercase__			    =						image_size
     lowercase__			    =						min_resolution
     lowercase__			    =						max_resolution
     lowercase__			    =						do_resize
     lowercase__			    =						size if size is not None else {'height': 18, 'width': 20}
     lowercase__			    =						do_thumbnail
     lowercase__			    =						do_align_axis
     lowercase__			    =						do_pad
     lowercase__			    =						do_normalize
     lowercase__			    =						image_mean
     lowercase__			    =						image_std


   def  SCREAMING_SNAKE_CASE_							(					self    :       Optional[Any] )->     Tuple:





     """simple docstring"""







     return {
         "do_resize": self.do_resize,
         "size": self.size,
         "do_thumbnail": self.do_thumbnail,
         "do_align_long_axis": self.do_align_axis,
         "do_pad": self.do_pad,
         "do_normalize": self.do_normalize,
         "image_mean": self.image_mean,
         "image_std": self.image_std,
     }






@require_torch
@require_vision
class        SCREAMING_SNAKE_CASE     (UpperCAmelCase    ,			unittest.TestCase   ):
   _UpperCamelCase					:	Optional[Any]				      = DonutImageProcessor if is_vision_available() else None


   def  SCREAMING_SNAKE_CASE_							(					self    :       int )->     List[Any]:





     """simple docstring"""







     lowercase__			    =						DonutImageProcessingTester(self )


   @property
   def  SCREAMING_SNAKE_CASE_							(					self    :       Union[str, Any] )->     Optional[int]:





     """simple docstring"""







     return self.image_processor_tester.prepare_image_processor_dict()


   def  SCREAMING_SNAKE_CASE_							(					self    :       Any )->     int:





     """simple docstring"""







     lowercase__			    =						self.image_processing_class(**self.image_processor_dict )
     self.assertTrue(hasattr(a    ,  'do_resize' ) )
     self.assertTrue(hasattr(a    ,  'size' ) )
     self.assertTrue(hasattr(a    ,  'do_thumbnail' ) )
     self.assertTrue(hasattr(a    ,  'do_align_long_axis' ) )
     self.assertTrue(hasattr(a    ,  'do_pad' ) )
     self.assertTrue(hasattr(a    ,  'do_normalize' ) )
     self.assertTrue(hasattr(a    ,  'image_mean' ) )
     self.assertTrue(hasattr(a    ,  'image_std' ) )


   def  SCREAMING_SNAKE_CASE_							(					self    :       Optional[Any] )->     Dict:





     """simple docstring"""







     lowercase__			    =						self.image_processing_class.from_dict(self.image_processor_dict )
     self.assertEqual(image_processor.size    ,  {'height': 18, 'width': 20} )

     lowercase__			    =						self.image_processing_class.from_dict(self.image_processor_dict    ,  size=42 )
     self.assertEqual(image_processor.size    ,  {'height': 42, 'width': 42} )

     # Previous config had dimensions in (width, height) order
     lowercase__			    =						self.image_processing_class.from_dict(self.image_processor_dict    ,  size=(42, 84) )
     self.assertEqual(image_processor.size    ,  {'height': 84, 'width': 42} )


   def  SCREAMING_SNAKE_CASE_							(					self    :       Optional[Any] )->     Dict:





     """simple docstring"""







     pass


   @is_flaky()
   def  SCREAMING_SNAKE_CASE_							(					self    :       str )->     Optional[int]:





     """simple docstring"""







     lowercase__			    =						self.image_processing_class(**self.image_processor_dict )
     # create random PIL images
     lowercase__			    =						prepare_image_inputs(self.image_processor_tester    ,  equal_resolution=a )
     for image in image_inputs:
       self.assertIsInstance(a    ,  Image.Image )

     # Test not batched input
     lowercase__			    =						image_processing(image_inputs[0]    ,  return_tensors='pt' ).pixel_values
     self.assertEqual(
         encoded_images.shape    ,  (
             1,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.size['height'],
             self.image_processor_tester.size['width'],
         )    ,  )

     # Test batched
     lowercase__			    =						image_processing(a    ,  return_tensors='pt' ).pixel_values
     self.assertEqual(
         encoded_images.shape    ,  (
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.size['height'],
             self.image_processor_tester.size['width'],
         )    ,  )


   @is_flaky()
   def  SCREAMING_SNAKE_CASE_							(					self    :       Optional[Any] )->     Tuple:





     """simple docstring"""







     lowercase__			    =						self.image_processing_class(**self.image_processor_dict )
     # create random numpy tensors
     lowercase__			    =						prepare_image_inputs(self.image_processor_tester    ,  equal_resolution=a    ,  numpify=a )
     for image in image_inputs:
       self.assertIsInstance(a    ,  np.ndarray )

     # Test not batched input
     lowercase__			    =						image_processing(image_inputs[0]    ,  return_tensors='pt' ).pixel_values
     self.assertEqual(
         encoded_images.shape    ,  (
             1,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.size['height'],
             self.image_processor_tester.size['width'],
         )    ,  )

     # Test batched
     lowercase__			    =						image_processing(a    ,  return_tensors='pt' ).pixel_values
     self.assertEqual(
         encoded_images.shape    ,  (
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.size['height'],
             self.image_processor_tester.size['width'],
         )    ,  )


   @is_flaky()
   def  SCREAMING_SNAKE_CASE_							(					self    :       List[str] )->     Dict:





     """simple docstring"""







     lowercase__			    =						self.image_processing_class(**self.image_processor_dict )
     # create random PyTorch tensors
     lowercase__			    =						prepare_image_inputs(self.image_processor_tester    ,  equal_resolution=a    ,  torchify=a )
     for image in image_inputs:
       self.assertIsInstance(a    ,  torch.Tensor )

     # Test not batched input
     lowercase__			    =						image_processing(image_inputs[0]    ,  return_tensors='pt' ).pixel_values
     self.assertEqual(
         encoded_images.shape    ,  (
             1,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.size['height'],
             self.image_processor_tester.size['width'],
         )    ,  )

     # Test batched
     lowercase__			    =						image_processing(a    ,  return_tensors='pt' ).pixel_values
     self.assertEqual(
         encoded_images.shape    ,  (
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.size['height'],
             self.image_processor_tester.size['width'],
         )    ,  )


