




"""simple docstring"""




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


lowerCAmelCase							:			List[str]     							=							["""text""", """image""", """audio"""]



def     a__ (				snake_case__				)						->					str:
			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     a__ (				snake_case__				)						->					str:
			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     __magic_name__							:






				'''simple docstring'''





				def    _lowerCAmelCase				(       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		, _a ):
													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    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					create_inputs(self.tool.inputs )
							lowerCamelCase			      =					self.tool(*_a )

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

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





				def    _lowerCAmelCase				(       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    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					create_inputs(self.tool.inputs )
							lowerCamelCase			      =					self.tool(*_a )

							if not isinstance(_a		, _a ):
										lowerCamelCase			      =					[outputs]

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

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





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					create_inputs(self.tool.inputs )

							lowerCamelCase			      =					[]

							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			      =					self.tool(*_a )

							if not isinstance(_a		, _a ):
										lowerCamelCase			      =					[outputs]

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









"""simple docstring"""




import argparse
from pathlib import Path

import torch

from transformers import OPTConfig, OPTModel
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase							:			Dict     							=							logging.get_logger(__name__)



def     a__ (				snake_case__				)						->					Dict:
			lowerCamelCase			      =					torch.load(snake_case__       , map_location="""cpu"""				)
			if "model" in sd.keys():
						lowerCamelCase			      =					torch.load(snake_case__       , map_location="""cpu"""				)["""model"""]

			# pop unnecessary weights
			lowerCamelCase			      =					[
			    """decoder.version""",
			    """decoder.output_projection.weight""",
			]
			for key in keys_to_delete:
						if key in sd:
									sd.pop(snake_case__				)

			lowerCamelCase			      =					{
			    """decoder.project_in_dim.weight""": """decoder.project_in.weight""",
			    """decoder.project_out_dim.weight""": """decoder.project_out.weight""",
			    """decoder.layer_norm.weight""": """decoder.final_layer_norm.weight""",
			    """decoder.layer_norm.bias""": """decoder.final_layer_norm.bias""",
			}
			for old_key, new_key in keys_to_rename.items():
						if old_key in sd:
									lowerCamelCase			      =					sd.pop(snake_case__				)

			lowerCamelCase			      =					list(sd.keys()				)
			for key in keys:
						if ".qkv_proj." in key:
									lowerCamelCase			      =					sd[key]
									# We split QKV in separate Q,K,V

									lowerCamelCase			      =					key.replace(""".qkv_proj."""       , """.q_proj."""				)
									lowerCamelCase			      =					key.replace(""".qkv_proj."""       , """.k_proj."""				)
									lowerCamelCase			      =					key.replace(""".qkv_proj."""       , """.v_proj."""				)

									lowerCamelCase			      =					value.shape[0]
									assert depth % 3 == 0
									# `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
									# https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
									lowerCamelCase      ,   lowerCamelCase      ,   lowerCamelCase			      =					torch.split(snake_case__       , depth // 3       , dim=0				)

									lowerCamelCase			      =					q
									lowerCamelCase			      =					k
									lowerCamelCase			      =					v
									del sd[key]

			return sd



@torch.no_grad()
def     a__ (				snake_case__       , snake_case__       , snake_case__=None				)						->					Tuple:
			lowerCamelCase			      =					load_checkpoint(snake_case__				)

			if config is not None:
						lowerCamelCase			      =					OPTConfig.from_pretrained(snake_case__				)
			else:
						lowerCamelCase			      =					OPTConfig()

			lowerCamelCase			      =					OPTModel(snake_case__				).half().eval()
			model.load_state_dict(snake_case__				)

			# Check results
			Path(snake_case__				).mkdir(exist_ok=snake_case__				)
			model.save_pretrained(snake_case__				)


if __name__ == "__main__":
					lowerCAmelCase							:			List[str]     							=							argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--fairseq_path""",
					    type=str,
					    help=(
					        """path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:"""
					        """ https://huggingface.co/models?other=opt_metasq"""
					    ),
					)
					parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
					parser.add_argument("""--hf_config""", default=None, type=str, help="""Define HF config.""")
					lowerCAmelCase							:			Optional[Any]     							=							parser.parse_args()
					convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)



