



from pathlib import Path
from typing import List

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText


if is_torch_available():
			import torch

if is_vision_available():
			from PIL import Image


__snake_case       :	int            =['text', 'image', 'audio']






def 			lowerCAmelCase__	(					lowerCamelCase_	:	Any):





				'''simple docstring'''

				lowerCAmelCase__  :       Union[str, Any]				=  []

				for input_type in input_types:
								if input_type == "text":
												inputs.append('''Text input''')
								elif input_type == "image":
												inputs.append(
												    Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO''')) / '''000000039769.png''').resize((512, 512)))
								elif input_type == "audio":
												inputs.append(torch.ones(3000))
								elif isinstance(UpperCAmelCase__ ,UpperCAmelCase__):
												inputs.append(create_inputs(UpperCAmelCase__))
								else:
												raise ValueError(f"""Invalid type requested: {input_type}""")

				return inputs






def 			lowerCAmelCase__	(					lowerCamelCase_	:	Optional[int]):





				'''simple docstring'''

				lowerCAmelCase__  :       Tuple				=  []

				for output in outputs:
								if isinstance(UpperCAmelCase__ ,(str, AgentText)):
												output_types.append('''text''')
								elif isinstance(UpperCAmelCase__ ,(Image.Image, AgentImage)):
												output_types.append('''image''')
								elif isinstance(UpperCAmelCase__ ,(torch.Tensor, AgentAudio)):
												output_types.append('''audio''')
								else:
												raise ValueError(f"""Invalid output: {output}""")

				return output_types



@is_tool_test
class  lowerCamelCase__    :




		'''simple docstring'''



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

						"""simple docstring"""






						self.assertTrue(hasattr(self.tool	,'''inputs'''				)				)
						self.assertTrue(hasattr(self.tool	,'''outputs'''				)				)

						lowerCAmelCase__  :       Dict				=  self.tool.inputs
						for _input in inputs:
										if isinstance(_input	,_A				):
														for __input in _input:
																		self.assertTrue(__input in authorized_types				)
										else:
														self.assertTrue(_input in authorized_types				)

						lowerCAmelCase__  :       List[str]				=  self.tool.outputs
						for _output in outputs:
										self.assertTrue(_output in authorized_types				)



		def 	lowerCAmelCase__   (self				)  ->    int:

						"""simple docstring"""






						lowerCAmelCase__  :       Tuple				=  create_inputs(self.tool.inputs				)
						lowerCAmelCase__  :       Dict				=  self.tool(*_A				)

						# There is a single output
						if len(self.tool.outputs				) == 1:
										lowerCAmelCase__  :       int				=  [outputs]

						self.assertListEqual(output_types(_A				)	,self.tool.outputs				)



		def 	lowerCAmelCase__   (self				)  ->    Tuple:

						"""simple docstring"""






						self.assertTrue(hasattr(self.tool	,'''description'''				)				)
						self.assertTrue(hasattr(self.tool	,'''default_checkpoint'''				)				)
						self.assertTrue(self.tool.description.startswith('''This is a tool that'''				)				)



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

						"""simple docstring"""






						lowerCAmelCase__  :       List[str]				=  create_inputs(self.tool.inputs				)
						lowerCAmelCase__  :       Tuple				=  self.tool(*_A				)

						if not isinstance(_A	,_A				):
										lowerCAmelCase__  :       Union[str, Any]				=  [outputs]

						self.assertEqual(len(_A				)	,len(self.tool.outputs				)				)

						for output, output_type in zip(_A	,self.tool.outputs				):
										lowerCAmelCase__  :       Optional[int]				=  AGENT_TYPE_MAPPING[output_type]
										self.assertTrue(isinstance(_A	,_A				)				)



		def 	lowerCAmelCase__   (self				)  ->    Union[str, Any]:

						"""simple docstring"""






						lowerCAmelCase__  :       int				=  create_inputs(self.tool.inputs				)

						lowerCAmelCase__  :       Optional[Any]				=  []

						for _input, input_type in zip(_A	,self.tool.inputs				):
										if isinstance(_A	,_A				):
														_inputs.append([AGENT_TYPE_MAPPING[_input_type](_input				) for _input_type in input_type]				)
										else:
														_inputs.append(AGENT_TYPE_MAPPING[input_type](_input				)				)

        # Should not raise an error
						lowerCAmelCase__  :       Optional[Any]				=  self.tool(*_A				)

						if not isinstance(_A	,_A				):
										lowerCAmelCase__  :       Optional[int]				=  [outputs]

						self.assertEqual(len(_A				)	,len(self.tool.outputs				)				)






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				)

