




"""simple docstring"""




from typing import Any



def     a__ (				snake_case__       , snake_case__       , snake_case__       , snake_case__       , snake_case__       , )						->					list:
			_validation(
			    snake_case__       , snake_case__       , snake_case__       , snake_case__       , snake_case__       , )
			# Creates data structures and fill initial step
			lowerCamelCase			      =					{}
			lowerCamelCase			      =					{}
			for state in states_space:
						lowerCamelCase			      =					observations_space[0]
						lowerCamelCase			      =					(
						    initial_probabilities[state] * emission_probabilities[state][observation]
						)
						lowerCamelCase			      =					None

			# Fills the data structure with the probabilities of
			# different transitions and pointers to previous states
			for o in range(1       , len(snake_case__				)				):
						lowerCamelCase			      =					observations_space[o]
						lowerCamelCase			      =					observations_space[o - 1]
						for state in states_space:
									# Calculates the argmax for probability function
									lowerCamelCase			      =					""""""
									lowerCamelCase			      =					-1
									for k_state in states_space:
												lowerCamelCase			      =					(
												    probabilities[(k_state, prior_observation)]
												    * transition_probabilities[k_state][state]
												    * emission_probabilities[state][observation]
												)
												if probability > max_probability:
															lowerCamelCase			      =					probability
															lowerCamelCase			      =					k_state

            # Update probabilities and pointers dicts
									lowerCamelCase			      =					(
									    probabilities[(arg_max, prior_observation)]
									    * transition_probabilities[arg_max][state]
									    * emission_probabilities[state][observation]
									)

									lowerCamelCase			      =					arg_max

    # The final observation
			lowerCamelCase			      =					observations_space[len(snake_case__				) - 1]

			# argmax for given final observation
			lowerCamelCase			      =					""""""
			lowerCamelCase			      =					-1
			for k_state in states_space:
						lowerCamelCase			      =					probabilities[(k_state, final_observation)]
						if probability > max_probability:
									lowerCamelCase			      =					probability
									lowerCamelCase			      =					k_state
			lowerCamelCase			      =					arg_max

			# Process pointers backwards
			lowerCamelCase			      =					last_state
			lowerCamelCase			      =					[]
			for o in range(len(snake_case__				) - 1       , -1       , -1				):
						result.append(snake_case__				)
						lowerCamelCase			      =					pointers[previous, observations_space[o]]
			result.reverse()

			return result



def     a__ (				snake_case__       , snake_case__       , snake_case__       , snake_case__       , snake_case__       , )						->					None:
			_validate_not_empty(
			    snake_case__       , snake_case__       , snake_case__       , snake_case__       , snake_case__       , )
			_validate_lists(snake_case__       , snake_case__				)
			_validate_dicts(
			    snake_case__       , snake_case__       , snake_case__				)



def     a__ (				snake_case__       , snake_case__       , snake_case__       , snake_case__       , snake_case__       , )						->					None:
			if not all(
			    [
			        observations_space,
			        states_space,
			        initial_probabilities,
			        transition_probabilities,
			        emission_probabilities,
			    ]				):
						raise ValueError("""There's an empty parameter"""				)



def     a__ (				snake_case__       , snake_case__				)						->					None:
			_validate_list(snake_case__       , """observations_space"""				)
			_validate_list(snake_case__       , """states_space"""				)



def     a__ (				snake_case__       , snake_case__				)						->					None:
			if not isinstance(_object       , snake_case__				):
						lowerCamelCase			      =					F'{var_name} must be a list'
						raise ValueError(snake_case__				)
			else:
						for x in _object:
									if not isinstance(snake_case__       , snake_case__				):
												lowerCamelCase			      =					F'{var_name} must be a list of strings'
												raise ValueError(snake_case__				)



def     a__ (				snake_case__       , snake_case__       , snake_case__       , )						->					None:
			_validate_dict(snake_case__       , """initial_probabilities"""       , snake_case__				)
			_validate_nested_dict(snake_case__       , """transition_probabilities"""				)
			_validate_nested_dict(snake_case__       , """emission_probabilities"""				)



def     a__ (				snake_case__       , snake_case__				)						->					None:
			_validate_dict(_object       , snake_case__       , snake_case__				)
			for x in _object.values():
						_validate_dict(snake_case__       , snake_case__       , snake_case__       , snake_case__				)



def     a__ (				snake_case__       , snake_case__       , snake_case__       , snake_case__ = False				)						->					None:
			if not isinstance(_object       , snake_case__				):
						lowerCamelCase			      =					F'{var_name} must be a dict'
						raise ValueError(snake_case__				)
			if not all(isinstance(snake_case__       , snake_case__				) for x in _object				):
						lowerCamelCase			      =					F'{var_name} all keys must be strings'
						raise ValueError(snake_case__				)
			if not all(isinstance(snake_case__       , snake_case__				) for x in _object.values()				):
						lowerCamelCase			      =					"""nested dictionary """ if nested else """"""
						lowerCamelCase			      =					F'{var_name} {nested_text}all values must be {value_type.__name__}'
						raise ValueError(snake_case__				)


if __name__ == "__main__":
					from doctest import testmod

					testmod()









"""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)



