






import json
import os
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

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

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image

 from transformers import ImageGPTImageProcessor

class 		lowerCAmelCase     ( unittest.TestCase			):

     '''simple docstring'''

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


            """simple docstring"""

            __lowercase      :							Optional[Any]				=     size if size is not None else {"height": 18, "width": 18}
            __lowercase      :							Optional[int]				=     parent
            __lowercase      :							Dict				=     batch_size
            __lowercase      :							Optional[Any]				=     num_channels
            __lowercase      :							List[str]				=     image_size
            __lowercase      :							Optional[int]				=     min_resolution
            __lowercase      :							str				=     max_resolution
            __lowercase      :							Dict				=     do_resize
            __lowercase      :							Optional[int]				=     size
            __lowercase      :							List[str]				=     do_normalize



     def       lowerCAmelCase				(  self				:	Optional[int]							)			->  Tuple:


            """simple docstring"""

            return {
                # here we create 2 clusters for the sake of simplicity
                "clusters": np.asarray(
                    [
                        [0.8866443634033203, 0.6618829369544983, 0.3891746401786804],
                        [-0.6042559146881104, -0.02295008860528469, 0.5423797369003296],
                    ]							),
                "do_resize": self.do_resize,
                "size": self.size,
                "do_normalize": self.do_normalize,
            }







@require_torch
@require_vision
class 		lowerCAmelCase     ( _snake_case      ,       unittest.TestCase			):

     '''simple docstring'''

     _A						:			List[Any]									=      ImageGPTImageProcessor if is_vision_available() else None



     def       lowerCAmelCase				(  self				:	List[Any]							)			->  List[Any]:


            """simple docstring"""

            __lowercase      :							List[str]				=     ImageGPTImageProcessingTester(self							)



     @property
     def       lowerCAmelCase				(  self				:	List[Any]							)			->  Dict:


            """simple docstring"""

            return self.image_processor_tester.prepare_image_processor_dict()



     def       lowerCAmelCase				(  self				:	int							)			->  str:


            """simple docstring"""

            __lowercase      :							Optional[Any]				=     self.image_processing_class(**self.image_processor_dict							)
            self.assertTrue(hasattr(snake_case_		,     """clusters"""							)							)
            self.assertTrue(hasattr(snake_case_		,     """do_resize"""							)							)
            self.assertTrue(hasattr(snake_case_		,     """size"""							)							)
            self.assertTrue(hasattr(snake_case_		,     """do_normalize"""							)							)



     def       lowerCAmelCase				(  self				:	List[str]							)			->  List[Any]:


            """simple docstring"""

            __lowercase      :							List[Any]				=     self.image_processing_class.from_dict(self.image_processor_dict							)
            self.assertEqual(image_processor.size		,     {"""height""": 18, """width""": 18}							)

            __lowercase      :							Union[str, Any]				=     self.image_processing_class.from_dict(self.image_processor_dict		,     size=42							)
            self.assertEqual(image_processor.size		,     {"""height""": 42, """width""": 42}							)



     def       lowerCAmelCase				(  self				:	Optional[Any]							)			->  Any:


            """simple docstring"""

            __lowercase      :							Dict				=     self.image_processing_class(**self.image_processor_dict							)
            __lowercase      :							Dict				=     json.loads(image_processor.to_json_string()							)
            for key, value in self.image_processor_dict.items():
                   if key == "clusters":
                          self.assertTrue(np.array_equal(snake_case_		,     obj[key]							)							)
                   else:
                          self.assertEqual(obj[key]		,     snake_case_							)



     def       lowerCAmelCase				(  self				:	int							)			->  Optional[int]:


            """simple docstring"""

            __lowercase      :							Optional[Any]				=     self.image_processing_class(**self.image_processor_dict							)

            with tempfile.TemporaryDirectory() as tmpdirname:
                   __lowercase      :							List[Any]				=     os.path.join(snake_case_		,     """image_processor.json"""							)
                   image_processor_first.to_json_file(snake_case_							)
                   __lowercase      :							str				=     self.image_processing_class.from_json_file(snake_case_							).to_dict()

            __lowercase      :							Optional[int]				=     image_processor_first.to_dict()
            for key, value in image_processor_first.items():
                   if key == "clusters":
                          self.assertTrue(np.array_equal(snake_case_		,     image_processor_second[key]							)							)
                   else:
                          self.assertEqual(image_processor_first[key]		,     snake_case_							)



     def       lowerCAmelCase				(  self				:	int							)			->  Dict:


            """simple docstring"""

            __lowercase      :							Tuple				=     self.image_processing_class(**self.image_processor_dict							)

            with tempfile.TemporaryDirectory() as tmpdirname:
                   image_processor_first.save_pretrained(snake_case_							)
                   __lowercase      :							Any				=     self.image_processing_class.from_pretrained(snake_case_							).to_dict()

            __lowercase      :							List[str]				=     image_processor_first.to_dict()
            for key, value in image_processor_first.items():
                   if key == "clusters":
                          self.assertTrue(np.array_equal(snake_case_		,     image_processor_second[key]							)							)
                   else:
                          self.assertEqual(image_processor_first[key]		,     snake_case_							)



     @unittest.skip("""ImageGPT requires clusters at initialization"""							)
     def       lowerCAmelCase				(  self				:	str							)			->  Any:


            """simple docstring"""

            pass







def       snake_case_		(     ):
       __lowercase      :							Optional[int]				=     load_dataset("""hf-internal-testing/fixtures_image_utils"""       ,       split="""test"""      )

       __lowercase      :							List[str]				=     Image.open(dataset[4]["""file"""]      )
       __lowercase      :							List[str]				=     Image.open(dataset[5]["""file"""]      )

       __lowercase      :							Union[str, Any]				=     [imagea, imagea]

       return images



@require_vision
@require_torch
class 		lowerCAmelCase     ( unittest.TestCase			):

     '''simple docstring'''

     @slow
     def       lowerCAmelCase				(  self				:	str							)			->  str:


            """simple docstring"""

            __lowercase      :							Optional[Any]				=     ImageGPTImageProcessor.from_pretrained("""openai/imagegpt-small"""							)

            __lowercase      :							Any				=     prepare_images()

            # test non-batched
            __lowercase      :							int				=     image_processing(images[0]		,     return_tensors="""pt"""							)

            self.assertIsInstance(encoding.input_ids		,     torch.LongTensor							)
            self.assertEqual(encoding.input_ids.shape		,     (1, 1024)							)

            __lowercase      :							Optional[Any]				=     [306, 191, 191]
            self.assertEqual(encoding.input_ids[0, :3].tolist()		,     snake_case_							)

            # test batched
            __lowercase      :							Dict				=     image_processing(snake_case_		,     return_tensors="""pt"""							)

            self.assertIsInstance(encoding.input_ids		,     torch.LongTensor							)
            self.assertEqual(encoding.input_ids.shape		,     (2, 1024)							)

            __lowercase      :							Any				=     [303, 13, 13]
            self.assertEqual(encoding.input_ids[1, -3:].tolist()		,     snake_case_							)







import fire

from utils import calculate_rouge, save_json
def       snake_case_		(     lowerCAmelCase_       :						Any       ,       lowerCAmelCase_       :						str       ,       lowerCAmelCase_       :						str=None       ,       **lowerCAmelCase_       :						str      ):
							__lowercase      :							Tuple				=     [x.strip() for x in open(lowerCAmelCase_      ).readlines()]
							__lowercase      :							Dict				=     [x.strip() for x in open(lowerCAmelCase_      ).readlines()][: len(lowerCAmelCase_      )]
							__lowercase      :							Tuple				=     calculate_rouge(lowerCAmelCase_       ,       lowerCAmelCase_       ,       **lowerCAmelCase_      )
							if save_path is not None:
														save_json(lowerCAmelCase_       ,       lowerCAmelCase_       ,       indent=lowerCAmelCase_      )
							return metrics  # these print nicely


if __name__ == "__main__":
	fire.Fire(calculate_rouge_path)