



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__	      =       ["""text""", """image""", """audio"""]


def 		_UpperCAmelCase				(      snake_case			):





					"""simple docstring"""

					_lowerCAmelCase						=    []

					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((5_12, 5_12)			)			)
										elif input_type == "audio":
															inputs.append(torch.ones(30_00			)			)
										elif isinstance(snake_case	,						snake_case			):
															inputs.append(create_inputs(snake_case			)			)
										else:
															raise ValueError(F'Invalid type requested: {input_type}'			)

					return inputs


def 		_UpperCAmelCase				(      snake_case			):





					"""simple docstring"""

					_lowerCAmelCase						=    []

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

					return output_types





@is_tool_test
class 					__lowerCAmelCase :




				def 							snake_case   (							self		):



									"""simple docstring"""





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

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

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




				def 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase						=    create_inputs(self.tool.inputs		)
									_lowerCAmelCase						=    self.tool(*_snake_case		)

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

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




				def 							snake_case   (							self		):



									"""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 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase						=    create_inputs(self.tool.inputs		)
									_lowerCAmelCase						=    self.tool(*_snake_case		)

									if not isinstance(_snake_case						,				_snake_case		):
														_lowerCAmelCase						=    [outputs]

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

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




				def 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase						=    create_inputs(self.tool.inputs		)

									_lowerCAmelCase						=    []

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

									if not isinstance(_snake_case						,				_snake_case		):
														_lowerCAmelCase						=    [outputs]

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







import argparse

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase       =							logging.get_logger(__name__)
def 			_a (      SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE     ):


		"""simple docstring"""




		lowercase__            =      RobertaPreLayerNormConfig.from_pretrained(
		    SCREAMING_SNAKE_CASE						,				architectures=['''RobertaPreLayerNormForMaskedLM''']     )

		# convert state_dict
		lowercase__            =      torch.load(hf_hub_download(repo_id=SCREAMING_SNAKE_CASE						,				filename='''pytorch_model.bin'''     )     )
		lowercase__            =      {}
		for tensor_key, tensor_value in original_state_dict.items():
				# The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
				if tensor_key.startswith('''roberta.'''     ):
						lowercase__            =      '''roberta_prelayernorm.''' + tensor_key[len('''roberta.'''     ) :]

				# The original implementation contains weights which are not used, remove them from the state_dict
				if tensor_key.endswith('''.self.LayerNorm.weight'''     ) or tensor_key.endswith('''.self.LayerNorm.bias'''     ):
						continue

				lowercase__            =      tensor_value

		lowercase__            =      RobertaPreLayerNormForMaskedLM.from_pretrained(
		    pretrained_model_name_or_path=SCREAMING_SNAKE_CASE						,				config=SCREAMING_SNAKE_CASE						,				state_dict=SCREAMING_SNAKE_CASE     )
		model.save_pretrained(SCREAMING_SNAKE_CASE     )

		# convert tokenizer
		lowercase__            =      AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE     )
		tokenizer.save_pretrained(SCREAMING_SNAKE_CASE     )


if __name__ == "__main__":
			lowerCAmelCase       =							argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '--checkpoint-repo',
			    default=None,
			    type=str,
			    required=True,
			    help='Path the official PyTorch dump, e.g. \'andreasmadsen/efficient_mlm_m0.40\'.',
			)
			parser.add_argument(
			    '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
			)
			lowerCAmelCase       =							parser.parse_args()
			convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)


