


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


A			:						Tuple        =  ['text', 'image', 'audio']






def 					UpperCamelCase   (      __magic_name__	:				List[str]	)  ->    Optional[Any]:
		"""simple docstring"""




		lowercase__	     =						[]

		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(__magic_name__       ,       __magic_name__	):
						inputs.append(create_inputs(__magic_name__	)	)
				else:
						raise ValueError(f'''Invalid type requested: {input_type}'''	)

		return inputs






def 					UpperCamelCase   (      __magic_name__	:				List	)  ->    Tuple:
		"""simple docstring"""




		lowercase__	     =						[]

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

		return output_types







@is_tool_test
class 				A       :
					'''simple docstring'''






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




							"""simple docstring"""


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

							lowercase__	     =						self.tool.inputs
							for _input in inputs:
									if isinstance(_input ,   _UpperCAmelCase						):
											for __input in _input:
													self.assertTrue(__input in authorized_types						)
									else:
											self.assertTrue(_input in authorized_types						)

							lowercase__	     =						self.tool.outputs
							for _output in outputs:
									self.assertTrue(_output in authorized_types						)

					def     lowerCamelCase__	(self       :		List[Any]						)		->  int:




							"""simple docstring"""


							lowercase__	     =						create_inputs(self.tool.inputs						)
							lowercase__	     =						self.tool(*_UpperCAmelCase						)

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

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

					def     lowerCamelCase__	(self       :		Any						)		->  Any:




							"""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[int]						)		->  List[str]:




							"""simple docstring"""


							lowercase__	     =						create_inputs(self.tool.inputs						)
							lowercase__	     =						self.tool(*_UpperCAmelCase						)

							if not isinstance(_UpperCAmelCase ,   _UpperCAmelCase						):
									lowercase__	     =						[outputs]

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

							for output, output_type in zip(_UpperCAmelCase ,   self.tool.outputs						):
									lowercase__	     =						AGENT_TYPE_MAPPING[output_type]
									self.assertTrue(isinstance(_UpperCAmelCase ,   _UpperCAmelCase						)						)


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




							"""simple docstring"""


							lowercase__	     =						create_inputs(self.tool.inputs						)

							lowercase__	     =						[]

							for _input, input_type in zip(_UpperCAmelCase ,   self.tool.inputs						):
									if isinstance(_UpperCAmelCase ,   _UpperCAmelCase						):
											_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
							lowercase__	     =						self.tool(*_UpperCAmelCase						)

							if not isinstance(_UpperCAmelCase ,   _UpperCAmelCase						):
									lowercase__	     =						[outputs]

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





import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration






def 					UpperCamelCase   (      __magic_name__	:				Optional[Any]	)  ->    Tuple:
		"""simple docstring"""




		lowercase__	     =						[
		    """encoder.version""",
		    """decoder.version""",
		    """model.encoder.version""",
		    """model.decoder.version""",
		    """_float_tensor""",
		    """decoder.output_projection.weight""",
		]
		for k in ignore_keys:
				state_dict.pop(__magic_name__       ,       __magic_name__	)






def 					UpperCamelCase   (      __magic_name__	:				int	)  ->    Union[str, Any]:
		"""simple docstring"""




		lowercase__     ,      lowercase__	     =						emb.weight.shape
		lowercase__	     =						nn.Linear(__magic_name__       ,       __magic_name__       ,       bias=__magic_name__	)
		lowercase__	     =						emb.weight.data
		return lin_layer






def 					UpperCamelCase   (      __magic_name__	:				str       ,       __magic_name__	:				str="facebook/mbart-large-en-ro"       ,       __magic_name__	:				Any=False       ,       __magic_name__	:				int=False	)  ->    Optional[int]:
		"""simple docstring"""




		lowercase__	     =						torch.load(__magic_name__       ,       map_location="""cpu"""	)["""model"""]
		remove_ignore_keys_(__magic_name__	)
		lowercase__	     =						state_dict["""encoder.embed_tokens.weight"""].shape[0]

		lowercase__	     =						MBartConfig.from_pretrained(__magic_name__       ,       vocab_size=__magic_name__	)
		if mbart_aa and finetuned:
				lowercase__	     =						"""relu"""

		lowercase__	     =						state_dict["""decoder.embed_tokens.weight"""]
		lowercase__	     =						MBartForConditionalGeneration(__magic_name__	)
		model.model.load_state_dict(__magic_name__	)

		if finetuned:
				lowercase__	     =						make_linear_from_emb(model.model.shared	)

		return model


if __name__ == "__main__":
	A			:						Optional[Any]        =  argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    'fairseq_path', type=str, help='bart.large, bart.large.cnn or a path to a model.pt on local filesystem.'
	)
	parser.add_argument('pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
	parser.add_argument(
	    '--hf_config',
	    default='facebook/mbart-large-cc25',
	    type=str,
	    help='Which huggingface architecture to use: mbart-large',
	)
	parser.add_argument('--mbart_50', action='store_true', help='whether the model is mMART-50 checkpoint')
	parser.add_argument('--finetuned', action='store_true', help='whether the model is a fine-tuned checkpoint')
	A			:						Any        =  parser.parse_args()
	A			:						str        =  convert_fairseq_mbart_checkpoint_from_disk(
	    args.fairseq_path, hf_config_path=args.hf_config, finetuned=args.finetuned, mbart_aa=args.mbart_aa
	)
	model.save_pretrained(args.pytorch_dump_folder_path)

