



import os
import tempfile
import unittest
import uuid
from pathlib import Path

from transformers.testing_utils import get_tests_dir, require_soundfile, require_torch, require_vision
from transformers.tools.agent_types import AgentAudio, AgentImage, AgentText
from transformers.utils import is_soundfile_availble, is_torch_available, is_vision_available


if is_torch_available():
			import torch

if is_soundfile_availble():
			import soundfile as sf

if is_vision_available():
			from PIL import Image






def 			lowerCAmelCase__	(					lowerCamelCase_	:	int=""):





				'''simple docstring'''

				lowerCAmelCase__  :       List[Any]				=  tempfile.mkdtemp()
				return os.path.join(lowerCamelCase_ ,str(uuid.uuida()) + suffix)



@require_soundfile
@require_torch
class  lowerCamelCase__    (							unittest.TestCase):




		'''simple docstring'''



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

						"""simple docstring"""






						lowerCAmelCase__  :       List[str]				=  torch.rand(12	,dtype=torch.floataa				) - 0.5
						lowerCAmelCase__  :       Union[str, Any]				=  AgentAudio(lowerCamelCase_				)
						lowerCAmelCase__  :       Any				=  str(agent_type.to_string()				)

						# Ensure that the tensor and the agent_type's tensor are the same
						self.assertTrue(torch.allclose(lowerCamelCase_	,agent_type.to_raw()	,atol=1e-4				)				)

						del agent_type

						# Ensure the path remains even after the object deletion
						self.assertTrue(os.path.exists(lowerCamelCase_				)				)

						# Ensure that the file contains the same value as the original tensor
						lowerCAmelCase__  :       Union[str, Any]				=  sf.read(lowerCamelCase_				)
						self.assertTrue(torch.allclose(lowerCamelCase_	,torch.tensor(lowerCamelCase_				)	,atol=1e-4				)				)



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

						"""simple docstring"""






						lowerCAmelCase__  :       str				=  torch.rand(12	,dtype=torch.floataa				) - 0.5
						lowerCAmelCase__  :       List[str]				=  get_new_path(suffix='''.wav'''				)
						sf.write(lowerCamelCase_	,lowerCamelCase_	,1_60_00				)

						lowerCAmelCase__  :       Optional[int]				=  AgentAudio(lowerCamelCase_				)

						self.assertTrue(torch.allclose(lowerCamelCase_	,agent_type.to_raw()	,atol=1e-4				)				)
						self.assertEqual(agent_type.to_string()	,lowerCamelCase_				)



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




		'''simple docstring'''



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

						"""simple docstring"""






						lowerCAmelCase__  :       Tuple				=  torch.randint(0	,2_56	,(64, 64, 3)				)
						lowerCAmelCase__  :       Union[str, Any]				=  AgentImage(lowerCamelCase_				)
						lowerCAmelCase__  :       Union[str, Any]				=  str(agent_type.to_string()				)

						# Ensure that the tensor and the agent_type's tensor are the same
						self.assertTrue(torch.allclose(lowerCamelCase_	,agent_type._tensor	,atol=1e-4				)				)

						self.assertIsInstance(agent_type.to_raw()	,Image.Image				)

						# Ensure the path remains even after the object deletion
						del agent_type
						self.assertTrue(os.path.exists(lowerCamelCase_				)				)



		def 	lowerCAmelCase__   (self				)  ->    Tuple:

						"""simple docstring"""






						lowerCAmelCase__  :       str				=  Path(get_tests_dir('''fixtures/tests_samples/COCO'''				)				) / """000000039769.png"""
						lowerCAmelCase__  :       Optional[int]				=  Image.open(lowerCamelCase_				)
						lowerCAmelCase__  :       str				=  AgentImage(lowerCamelCase_				)

						self.assertTrue(path.samefile(agent_type.to_string()				)				)
						self.assertTrue(image == agent_type.to_raw()				)

						# Ensure the path remains even after the object deletion
						del agent_type
						self.assertTrue(os.path.exists(lowerCamelCase_				)				)



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

						"""simple docstring"""






						lowerCAmelCase__  :       Optional[int]				=  Path(get_tests_dir('''fixtures/tests_samples/COCO'''				)				) / """000000039769.png"""
						lowerCAmelCase__  :       List[str]				=  Image.open(lowerCamelCase_				)
						lowerCAmelCase__  :       Tuple				=  AgentImage(lowerCamelCase_				)

						self.assertFalse(path.samefile(agent_type.to_string()				)				)
						self.assertTrue(image == agent_type.to_raw()				)

						# Ensure the path remains even after the object deletion
						del agent_type
						self.assertTrue(os.path.exists(lowerCamelCase_				)				)



class  lowerCamelCase__    (							unittest.TestCase):




		'''simple docstring'''



		def 	lowerCAmelCase__   (self				)  ->    Tuple:

						"""simple docstring"""






						lowerCAmelCase__  :       Any				=  """Hey!"""
						lowerCAmelCase__  :       Tuple				=  AgentText(lowerCamelCase_				)

						self.assertEqual(lowerCamelCase_	,agent_type.to_string()				)
						self.assertEqual(lowerCamelCase_	,agent_type.to_raw()				)
						self.assertEqual(lowerCamelCase_	,lowerCamelCase_				)






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				)

