"""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, prepare_image_inputs


if is_torch_available():
	import torch

if is_vision_available():
	from PIL import Image

	from transformers import OwlViTImageProcessor






class    __UpperCAmelCase						(	unittest.TestCase	):

				def __init__(      self	, _lowerCamelCase	, _lowerCamelCase=7	, _lowerCamelCase=3	, _lowerCamelCase=18	, _lowerCamelCase=30	, _lowerCamelCase=400	, _lowerCamelCase=True	, _lowerCamelCase=None	, _lowerCamelCase=True	, _lowerCamelCase=None	, _lowerCamelCase=True	, _lowerCamelCase=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]	, _lowerCamelCase=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]	, _lowerCamelCase=True	, ):
										lowerCamelCase__					=parent
										lowerCamelCase__					=batch_size
										lowerCamelCase__					=num_channels
										lowerCamelCase__					=image_size
										lowerCamelCase__					=min_resolution
										lowerCamelCase__					=max_resolution
										lowerCamelCase__					=do_resize
										lowerCamelCase__					=size if size is not None else {"height": 18, "width": 18}
										lowerCamelCase__					=do_center_crop
										lowerCamelCase__					=crop_size if crop_size is not None else {"height": 18, "width": 18}
										lowerCamelCase__					=do_normalize
										lowerCamelCase__					=image_mean
										lowerCamelCase__					=image_std
										lowerCamelCase__					=do_convert_rgb






				def 		_a	(      self      ):
										return {
										    "do_resize": self.do_resize,
										    "size": self.size,
										    "do_center_crop": self.do_center_crop,
										    "crop_size": self.crop_size,
										    "do_normalize": self.do_normalize,
										    "image_mean": self.image_mean,
										    "image_std": self.image_std,
										    "do_convert_rgb": self.do_convert_rgb,
										}






@require_torch
@require_vision
class    __UpperCAmelCase						(	__lowerCAmelCase      ,      unittest.TestCase	):
				A__      :   Tuple  							=       OwlViTImageProcessor if is_vision_available() else None

				def 		_a	(      self      ):
										lowerCamelCase__					=OwlViTImageProcessingTester(self      )

				@property
				def 		_a	(      self      ):
										return self.image_processor_tester.prepare_image_processor_dict()

				def 		_a	(      self      ):
										lowerCamelCase__					=self.image_processing_class(**self.image_processor_dict      )
										self.assertTrue(hasattr(_lowerCamelCase	, "do_resize"      )      )
										self.assertTrue(hasattr(_lowerCamelCase	, "size"      )      )
										self.assertTrue(hasattr(_lowerCamelCase	, "do_center_crop"      )      )
										self.assertTrue(hasattr(_lowerCamelCase	, "center_crop"      )      )
										self.assertTrue(hasattr(_lowerCamelCase	, "do_normalize"      )      )
										self.assertTrue(hasattr(_lowerCamelCase	, "image_mean"      )      )
										self.assertTrue(hasattr(_lowerCamelCase	, "image_std"      )      )
										self.assertTrue(hasattr(_lowerCamelCase	, "do_convert_rgb"      )      )

				def 		_a	(      self      ):
										lowerCamelCase__					=self.image_processing_class.from_dict(self.image_processor_dict      )
										self.assertEqual(image_processor.size	, {"height": 18, "width": 18}      )
										self.assertEqual(image_processor.crop_size	, {"height": 18, "width": 18}      )

										lowerCamelCase__					=self.image_processing_class.from_dict(self.image_processor_dict	, size=42	, crop_size=84      )
										self.assertEqual(image_processor.size	, {"height": 42, "width": 42}      )
										self.assertEqual(image_processor.crop_size	, {"height": 84, "width": 84}      )

				def 		_a	(      self      ):
										# Initialize image_processing
										lowerCamelCase__					=self.image_processing_class(**self.image_processor_dict      )
										# create random PIL images
										lowerCamelCase__					=prepare_image_inputs(self.image_processor_tester	, equal_resolution=_lowerCamelCase      )

										for image in image_inputs:
																self.assertIsInstance(_lowerCamelCase	, Image.Image      )

										# Test not batched input
										lowerCamelCase__					=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.crop_size["height"],
										        self.image_processor_tester.crop_size["width"],
										    )	, )

										# Test batched
										lowerCamelCase__					=image_processing(_lowerCamelCase	, 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.crop_size["height"],
										        self.image_processor_tester.crop_size["width"],
										    )	, )

				def 		_a	(      self      ):
										# Initialize image_processing
										lowerCamelCase__					=self.image_processing_class(**self.image_processor_dict      )
										# create random numpy tensors
										lowerCamelCase__					=prepare_image_inputs(self.image_processor_tester	, equal_resolution=_lowerCamelCase	, numpify=_lowerCamelCase      )
										for image in image_inputs:
																self.assertIsInstance(_lowerCamelCase	, np.ndarray      )

										# Test not batched input
										lowerCamelCase__					=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.crop_size["height"],
										        self.image_processor_tester.crop_size["width"],
										    )	, )

										# Test batched
										lowerCamelCase__					=image_processing(_lowerCamelCase	, 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.crop_size["height"],
										        self.image_processor_tester.crop_size["width"],
										    )	, )






				def 		_a	(      self      ):
										# Initialize image_processing
										lowerCamelCase__					=self.image_processing_class(**self.image_processor_dict      )
										# create random PyTorch tensors
										lowerCamelCase__					=prepare_image_inputs(self.image_processor_tester	, equal_resolution=_lowerCamelCase	, torchify=_lowerCamelCase      )
										for image in image_inputs:
																self.assertIsInstance(_lowerCamelCase	, torch.Tensor      )

										# Test not batched input
										lowerCamelCase__					=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.crop_size["height"],
										        self.image_processor_tester.crop_size["width"],
										    )	, )

										# Test batched
										lowerCamelCase__					=image_processing(_lowerCamelCase	, 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.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 	lowerCAmelCase__(       __lowercase			):






       '''simple docstring'''


       @classmethod
       def 			UpperCamelCase_	(					cls							)	->   Tuple:
            return cls







       @slow
       @require_torch_gpu
       def 			UpperCamelCase_	(					self							)	->   Any:
            _SCREAMING_SNAKE_CASE				:		List[str]  			=							self.get_auto_remove_tmp_dir()
            _SCREAMING_SNAKE_CASE				:		List[Any]  			=							F"""
            --model_name Helsinki-NLP/opus-mt-en-ro
            --save_dir {output_dir}
            --data_dir {self.test_file_dir_str}/test_data/wmt_en_ro
            --num_beams 2
            --task translation
        """.split()

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

            _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							os.path.join(__lowerCamelCase			,  "test_bleu.json"							)
            _SCREAMING_SNAKE_CASE				:		List[str]  			=							load_json(__lowerCamelCase							)
            # print(metrics)
            self.assertGreaterEqual(metrics["bleu"]			,  2_5							)