'''simple docstring'''





import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
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 TvltImageProcessor






def SCREAMING_SNAKE_CASE__   (							snake_case    :							int							,     snake_case    :							str=10							,     snake_case    :							Optional[int]=10							,     snake_case    :							List[Any]=False							,     snake_case    :							Optional[Any]=False   )					->							str:







     """simple docstring"""
     a :				Any				=      []
     for i in range(image_processor_tester.num_frames   ):
          video.append(np.random.randint(255							,     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
          a :				Any				=      [Image.fromarray(np.moveaxis(snake_case							,     0							,     -1   )   ) for frame in video]

     if torchify:
          a :				List[str]				=      [torch.from_numpy(snake_case   ) for frame in video]

     return video






def SCREAMING_SNAKE_CASE__   (							snake_case    :							Union[str, Any]							,     snake_case    :							Optional[int]=False							,     snake_case    :							Union[str, Any]=False							,     snake_case    :							Dict=False   )					->							Dict:







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

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

     return video_inputs



class    UpperCamelCase (    unittest.TestCase    ):







    """simple docstring"""





    def __init__( self			:		Optional[int]				,				UpperCAmelCase_			:		List[Any]				,				UpperCAmelCase_			:		Dict=7				,				UpperCAmelCase_			:		Union[str, Any]=3				,				UpperCAmelCase_			:		str=4				,				UpperCAmelCase_			:		Any=1_8				,				UpperCAmelCase_			:		List[str]=3_0				,				UpperCAmelCase_			:		List[str]=4_0_0				,				UpperCAmelCase_			:		List[Any]=True				,				UpperCAmelCase_			:		str=None				,				UpperCAmelCase_			:		Dict=True				,				UpperCAmelCase_			:		List[Any]=[0.5, 0.5, 0.5]				,				UpperCAmelCase_			:		Union[str, Any]=[0.5, 0.5, 0.5]				,				UpperCAmelCase_			:		Tuple=True				,				UpperCAmelCase_			:		Tuple=None				,				):


         """simple docstring"""







         a :				Optional[int]				=      size if size is not None else {'shortest_edge': 1_8}
         a :				Union[str, Any]				=      crop_size if crop_size is not None else {'height': 1_8, 'width': 1_8}

         a :				List[Any]				=      parent
         a :				Optional[Any]				=      batch_size
         a :				Tuple				=      num_channels
         a :				Dict				=      num_frames
         a :				Dict				=      image_size
         a :				Optional[Any]				=      min_resolution
         a :				List[Any]				=      max_resolution
         a :				Dict				=      do_resize
         a :				List[Any]				=      size
         a :				Dict				=      do_normalize
         a :				Optional[Any]				=      image_mean
         a :				List[str]				=      image_std
         a :				Tuple				=      do_center_crop
         a :				List[Any]				=      crop_size







    def 					SCREAMING_SNAKE_CASE_			( self			:		Tuple):


         """simple docstring"""







         return {
             "image_mean": self.image_mean,
             "image_std": self.image_std,
             "do_normalize": self.do_normalize,
             "do_resize": self.do_resize,
             "size": self.size,
             "do_center_crop": self.do_center_crop,
             "crop_size": self.crop_size,
         }






@require_torch
@require_vision
class    UpperCamelCase (    a_  ,			unittest.TestCase    ):







    """simple docstring"""





    A			:  Optional[Any]            =       TvltImageProcessor if is_vision_available() else None





    def 					SCREAMING_SNAKE_CASE_			( self			:		List[Any]):


         """simple docstring"""







         a :				Optional[Any]				=      TvltImageProcessorTester(self)





    @property
    def 					SCREAMING_SNAKE_CASE_			( self			:		Tuple):


         """simple docstring"""







         return self.image_processor_tester.prepare_image_processor_dict()





    def 					SCREAMING_SNAKE_CASE_			( self			:		Tuple):


         """simple docstring"""







         a :				int				=      self.image_processing_class(**self.image_processor_dict)
         self.assertTrue(hasattr(__lowercase				,				'image_mean'))
         self.assertTrue(hasattr(__lowercase				,				'image_std'))
         self.assertTrue(hasattr(__lowercase				,				'do_normalize'))
         self.assertTrue(hasattr(__lowercase				,				'do_resize'))
         self.assertTrue(hasattr(__lowercase				,				'do_center_crop'))
         self.assertTrue(hasattr(__lowercase				,				'size'))





    def 					SCREAMING_SNAKE_CASE_			( self			:		Tuple):


         """simple docstring"""







         a :				Optional[Any]				=      self.image_processing_class(**self.image_processor_dict)
         # create random PIL videos
         a :				Dict				=      prepare_video_inputs(self.image_processor_tester				,				equal_resolution=__lowercase)
         for video in video_inputs:
              self.assertIsInstance(__lowercase				,				__lowercase)
              self.assertIsInstance(video[0]				,				Image.Image)

         # Test not batched input
         a :				Any				=      image_processor(video_inputs[0]				,				return_tensors='pt').pixel_values
         self.assertEqual(
             encoded_videos.shape				,				(
                 1,
                 self.image_processor_tester.num_frames,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size['height'],
                 self.image_processor_tester.crop_size['width'],
             )				,				)

         # Test batched
         a :				List[Any]				=      image_processor(__lowercase				,				return_tensors='pt').pixel_values
         self.assertEqual(
             encoded_videos.shape				,				(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_frames,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size['height'],
                 self.image_processor_tester.crop_size['width'],
             )				,				)





    def 					SCREAMING_SNAKE_CASE_			( self			:		List[Any]):


         """simple docstring"""







         a :				Dict				=      self.image_processing_class(**self.image_processor_dict)
         # create random numpy tensors
         a :				Union[str, Any]				=      prepare_video_inputs(self.image_processor_tester				,				equal_resolution=__lowercase				,				numpify=__lowercase)
         for video in video_inputs:
              self.assertIsInstance(__lowercase				,				__lowercase)
              self.assertIsInstance(video[0]				,				np.ndarray)

         # Test not batched input
         a :				Union[str, Any]				=      image_processor(video_inputs[0]				,				return_tensors='pt').pixel_values
         self.assertEqual(
             encoded_videos.shape				,				(
                 1,
                 self.image_processor_tester.num_frames,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size['height'],
                 self.image_processor_tester.crop_size['width'],
             )				,				)

         # Test batched
         a :				Tuple				=      image_processor(__lowercase				,				return_tensors='pt').pixel_values
         self.assertEqual(
             encoded_videos.shape				,				(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_frames,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size['height'],
                 self.image_processor_tester.crop_size['width'],
             )				,				)







    def 					SCREAMING_SNAKE_CASE_			( self			:		Tuple):


         """simple docstring"""







         a :				str				=      self.image_processing_class(**self.image_processor_dict)
         # create random PyTorch tensors
         a :				Union[str, Any]				=      prepare_video_inputs(self.image_processor_tester				,				equal_resolution=__lowercase				,				torchify=__lowercase)
         for video in video_inputs:
              self.assertIsInstance(__lowercase				,				__lowercase)
              self.assertIsInstance(video[0]				,				torch.Tensor)

         # Test not batched input
         a :				Optional[Any]				=      image_processor(video_inputs[0]				,				return_tensors='pt').pixel_values
         self.assertEqual(
             encoded_videos.shape				,				(
                 1,
                 self.image_processor_tester.num_frames,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size['height'],
                 self.image_processor_tester.crop_size['width'],
             )				,				)

         # Test batched
         a :				Tuple				=      image_processor(__lowercase				,				return_tensors='pt').pixel_values
         self.assertEqual(
             encoded_videos.shape				,				(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_frames,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size['height'],
                 self.image_processor_tester.crop_size['width'],
             )				,				)







# as due to their complexity multi-gpu tests could impact other tests, and to aid debug we have those in a separate module.

import os
import sys

from transformers.testing_utils import TestCasePlus, execute_subprocess_async, get_gpu_count, require_torch_gpu, slow

from .utils import load_json


class 				__SCREAMING_SNAKE_CASE							(		lowerCamelCase  ):

					@classmethod
					def 	lowercase_							(				cls					):







						'''simple docstring'''




						return cls





					@slow
					@require_torch_gpu
					def 	lowercase_							(				self					):







						'''simple docstring'''




						lowerCamelCase_							:		Dict							   =							self.get_auto_remove_tmp_dir()
						lowerCamelCase_							:		Tuple							   =							f"\n            --model_name Helsinki-NLP/opus-mt-en-ro\n            --save_dir {output_dir}\n            --data_dir {self.test_file_dir_str}/test_data/wmt_en_ro\n            --num_beams 2\n            --task translation\n        ".split()

						# we want this test to run even if there is only one GPU, but if there are more we use them all
						lowerCamelCase_							:		List[Any]							   =							get_gpu_count()
						lowerCamelCase_							:		Union[str, Any]							   =							f"\n            -m torch.distributed.launch\n            --nproc_per_node={n_gpu}\n            {self.test_file_dir}/run_distributed_eval.py\n        ".split()
						lowerCamelCase_							:		int							   =							[sys.executable] + distributed_args + args
						execute_subprocess_async(__lowercase ,       env=self.get_env()					)

						lowerCamelCase_							:		Any							   =							os.path.join(__lowercase ,       'test_bleu.json'					)
						lowerCamelCase_							:		List[Any]							   =							load_json(__lowercase					)
						# print(metrics)
						self.assertGreaterEqual(metrics['bleu'] ,       25					)
