



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 ViTImageProcessor



class  lowerCamelCase__    (							unittest.TestCase):




		'''simple docstring'''



		def __init__(self	,__lowerCamelCase	,__lowerCamelCase=13	,__lowerCamelCase=3	,__lowerCamelCase=2_24	,__lowerCamelCase=30	,__lowerCamelCase=4_00	,__lowerCamelCase=True	,__lowerCamelCase=None	,__lowerCamelCase=True	,__lowerCamelCase=[0.5, 0.5, 0.5]	,__lowerCamelCase=[0.5, 0.5, 0.5]	,)  ->    Dict:

						"""simple docstring"""






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



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

						"""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,
						}



@require_torch
@require_vision
class  lowerCamelCase__    (							lowerCamelCase__   ,       unittest.TestCase):




		'''simple docstring'''



		snake_case_			=ViTImageProcessor if is_vision_available() else None



		def 	lowerCAmelCase__   (self				)  ->    str:

						"""simple docstring"""






						lowerCAmelCase__  :       Dict				=  EfficientFormerImageProcessorTester(self				)



		@property
		def 	lowerCAmelCase__   (self				)  ->    str:

						"""simple docstring"""






						return self.image_proc_tester.prepare_image_processor_dict()



		def 	lowerCAmelCase__   (self				)  ->    Any:

						"""simple docstring"""






						lowerCAmelCase__  :       int				=  self.image_processing_class(**self.image_processor_dict				)
						self.assertTrue(hasattr(__lowerCamelCase	,'''image_mean'''				)				)
						self.assertTrue(hasattr(__lowerCamelCase	,'''image_std'''				)				)
						self.assertTrue(hasattr(__lowerCamelCase	,'''do_normalize'''				)				)
						self.assertTrue(hasattr(__lowerCamelCase	,'''do_resize'''				)				)
						self.assertTrue(hasattr(__lowerCamelCase	,'''size'''				)				)



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

						"""simple docstring"""






						pass



		def 	lowerCAmelCase__   (self				)  ->    List[str]:

						"""simple docstring"""






						lowerCAmelCase__  :       int				=  self.image_processing_class(**self.image_processor_dict				)
						# create random PIL images
						lowerCAmelCase__  :       List[str]				=  prepare_image_inputs(self.image_proc_tester	,equal_resolution=__lowerCamelCase				)
						for image in image_inputs:
										self.assertIsInstance(__lowerCamelCase	,Image.Image				)

						# Test not batched input
						lowerCAmelCase__  :       Tuple				=  image_processor(image_inputs[0]	,return_tensors='''pt'''				).pixel_values
						self.assertEqual(
						    encoded_images.shape	,(
						        1,
						        self.image_proc_tester.num_channels,
						        self.image_proc_tester.size['''height'''],
						        self.image_proc_tester.size['''width'''],
						    )	,)

						# Test batched
						lowerCAmelCase__  :       List[Any]				=  image_processor(__lowerCamelCase	,return_tensors='''pt'''				).pixel_values
						self.assertEqual(
						    encoded_images.shape	,(
						        self.image_proc_tester.batch_size,
						        self.image_proc_tester.num_channels,
						        self.image_proc_tester.size['''height'''],
						        self.image_proc_tester.size['''width'''],
						    )	,)



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

						"""simple docstring"""






						lowerCAmelCase__  :       Union[str, Any]				=  self.image_processing_class(**self.image_processor_dict				)
						# create random numpy tensors
						lowerCAmelCase__  :       Union[str, Any]				=  prepare_image_inputs(self.image_proc_tester	,equal_resolution=__lowerCamelCase	,numpify=__lowerCamelCase				)
						for image in image_inputs:
										self.assertIsInstance(__lowerCamelCase	,np.ndarray				)

						# Test not batched input
						lowerCAmelCase__  :       int				=  image_processor(image_inputs[0]	,return_tensors='''pt'''				).pixel_values
						self.assertEqual(
						    encoded_images.shape	,(
						        1,
						        self.image_proc_tester.num_channels,
						        self.image_proc_tester.size['''height'''],
						        self.image_proc_tester.size['''width'''],
						    )	,)

						# Test batched
						lowerCAmelCase__  :       int				=  image_processor(__lowerCamelCase	,return_tensors='''pt'''				).pixel_values
						self.assertEqual(
						    encoded_images.shape	,(
						        self.image_proc_tester.batch_size,
						        self.image_proc_tester.num_channels,
						        self.image_proc_tester.size['''height'''],
						        self.image_proc_tester.size['''width'''],
						    )	,)



		def 	lowerCAmelCase__   (self				)  ->    Any:

						"""simple docstring"""






						lowerCAmelCase__  :       Optional[Any]				=  self.image_processing_class(**self.image_processor_dict				)
						# create random PyTorch tensors
						lowerCAmelCase__  :       List[str]				=  prepare_image_inputs(self.image_proc_tester	,equal_resolution=__lowerCamelCase	,torchify=__lowerCamelCase				)
						for image in image_inputs:
										self.assertIsInstance(__lowerCamelCase	,torch.Tensor				)

						# Test not batched input
						lowerCAmelCase__  :       Tuple				=  image_processor(image_inputs[0]	,return_tensors='''pt'''				).pixel_values
						self.assertEqual(
						    encoded_images.shape	,(
						        1,
						        self.image_proc_tester.num_channels,
						        self.image_proc_tester.size['''height'''],
						        self.image_proc_tester.size['''width'''],
						    )	,)

						# Test batched
						lowerCAmelCase__  :       Union[str, Any]				=  image_processor(__lowerCamelCase	,return_tensors='''pt'''				).pixel_values
						self.assertEqual(
						    encoded_images.shape	,(
						        self.image_proc_tester.batch_size,
						        self.image_proc_tester.num_channels,
						        self.image_proc_tester.size['''height'''],
						        self.image_proc_tester.size['''width'''],
						    )	,)






import io
import json
import unittest

from parameterized import parameterized

from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu


__snake_case       :	Optional[int]            =get_tests_dir() + '/test_data/fsmt/fsmt_val_data.json'
with io.open(filename, 'r', encoding='utf-8') as f:
							__snake_case       :	Optional[Any]            =json.load(f)



@require_torch
class  lowerCamelCase__    (							unittest.TestCase):




		'''simple docstring'''



		def 	lowerCAmelCase__   (self	,__lowerCamelCase				)  ->    Dict:

						"""simple docstring"""






						return FSMTTokenizer.from_pretrained(__lowerCamelCase				)



		def 	lowerCAmelCase__   (self	,__lowerCamelCase				)  ->    Optional[int]:

						"""simple docstring"""






						lowerCAmelCase__  :       Optional[Any]				=  FSMTForConditionalGeneration.from_pretrained(__lowerCamelCase				).to(__lowerCamelCase				)
						if torch_device == "cuda":
										model.half()
						return model



		@parameterized.expand(
		    [
		        ['''en-ru''', 26.0],
		        ['''ru-en''', 22.0],
		        ['''en-de''', 22.0],
		        ['''de-en''', 29.0],
		    ]				)
		@slow
		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase				)  ->    Any:

						"""simple docstring"""






						lowerCAmelCase__  :       List[str]				=  f"""facebook/wmt19-{pair}"""
						lowerCAmelCase__  :       int				=  self.get_tokenizer(__lowerCamelCase				)
						lowerCAmelCase__  :       Optional[int]				=  self.get_model(__lowerCamelCase				)

						lowerCAmelCase__  :       List[Any]				=  bleu_data[pair]['''src''']
						lowerCAmelCase__  :       List[Any]				=  bleu_data[pair]['''tgt''']

						lowerCAmelCase__  :       List[str]				=  tokenizer(__lowerCamelCase	,return_tensors='''pt'''	,truncation=__lowerCamelCase	,padding='''longest'''				).to(__lowerCamelCase				)
						lowerCAmelCase__  :       List[str]				=  model.generate(
						    input_ids=batch.input_ids	,num_beams=8	,)
						lowerCAmelCase__  :       Tuple				=  tokenizer.batch_decode(
						    __lowerCamelCase	,skip_special_tokens=__lowerCamelCase	,clean_up_tokenization_spaces=__lowerCamelCase				)
						lowerCAmelCase__  :       Union[str, Any]				=  calculate_bleu(__lowerCamelCase	,__lowerCamelCase				)
						print(__lowerCamelCase				)
						self.assertGreaterEqual(scores['''bleu''']	,__lowerCamelCase				)

