

"""simple docstring"""




import ast
import difflib
from collections.abc import Mapping
from typing import Any, Callable, Dict





class      __UpperCamelCase					(  A__		):
		pass

def   __SCREAMING_SNAKE_CASE		(		lowercase__     ,				lowercase__     ,				lowercase__=None     ,				lowercase__=False ):
					"""simple docstring"""







					try:
										A     =   ast.parse(lowercase__ )
					except SyntaxError as e:
										print("The code generated by the agent is not valid.\n"     ,				lowercase__ )
										return
					if state is None:
										A     =   {}
					A     =   None
					for idx, node in enumerate(expression.body ):
										try:
															A     =   evaluate_ast(lowercase__     ,				lowercase__     ,				lowercase__ )
										except InterpretorError as e:
															A     =   F"""Evaluation of the code stopped at line {idx} before the end because of the following error"""
															if chat_mode:
																				msg += (
																				    F""". Copy paste the following error message and send it back to the agent:\nI get an error: '{e}'"""
																				)
															else:
																				msg += F""":\n{e}"""
															print(lowercase__ )
															break
										if line_result is not None:
															A     =   line_result

					return result
def   __SCREAMING_SNAKE_CASE		(		lowercase__     ,				lowercase__     ,				lowercase__ ):
					"""simple docstring"""







					if isinstance(lowercase__     ,				ast.Assign ):
										# Assignement -> we evaluate the assignement which should update the state
										# We return the variable assigned as it may be used to determine the final result.
										return evaluate_assign(lowercase__     ,				lowercase__     ,				lowercase__ )
					elif isinstance(lowercase__     ,				ast.Call ):
										# Function call -> we return the value of the function call
										return evaluate_call(lowercase__     ,				lowercase__     ,				lowercase__ )
					elif isinstance(lowercase__     ,				ast.Constant ):
										# Constant -> just return the value
										return expression.value
					elif isinstance(lowercase__     ,				ast.Dict ):
										# Dict -> evaluate all keys and values
										A     =   [evaluate_ast(lowercase__     ,				lowercase__     ,				lowercase__ ) for k in expression.keys]
										A     =   [evaluate_ast(lowercase__     ,				lowercase__     ,				lowercase__ ) for v in expression.values]
										return dict(zip(lowercase__     ,				lowercase__ ) )
					elif isinstance(lowercase__     ,				ast.Expr ):
										# Expression -> evaluate the content
										return evaluate_ast(expression.value     ,				lowercase__     ,				lowercase__ )
					elif isinstance(lowercase__     ,				ast.For ):
										# For loop -> execute the loop
										return evaluate_for(lowercase__     ,				lowercase__     ,				lowercase__ )
					elif isinstance(lowercase__     ,				ast.FormattedValue ):
										# Formatted value (part of f-string) -> evaluate the content and return
										return evaluate_ast(expression.value     ,				lowercase__     ,				lowercase__ )
					elif isinstance(lowercase__     ,				ast.If ):
										# If -> execute the right branch
										return evaluate_if(lowercase__     ,				lowercase__     ,				lowercase__ )
					elif hasattr(lowercase__     ,				"Index" ) and isinstance(lowercase__     ,				ast.Index ):
										return evaluate_ast(expression.value     ,				lowercase__     ,				lowercase__ )
					elif isinstance(lowercase__     ,				ast.JoinedStr ):
										return "".join([str(evaluate_ast(lowercase__     ,				lowercase__     ,				lowercase__ ) ) for v in expression.values] )
					elif isinstance(lowercase__     ,				ast.List ):
										# List -> evaluate all elements
										return [evaluate_ast(lowercase__     ,				lowercase__     ,				lowercase__ ) for elt in expression.elts]
					elif isinstance(lowercase__     ,				ast.Name ):
										# Name -> pick up the value in the state
										return evaluate_name(lowercase__     ,				lowercase__     ,				lowercase__ )
					elif isinstance(lowercase__     ,				ast.Subscript ):
										# Subscript -> return the value of the indexing
										return evaluate_subscript(lowercase__     ,				lowercase__     ,				lowercase__ )
					else:
										# For now we refuse anything else. Let's add things as we need them.
										raise InterpretorError(F"""{expression.__class__.__name__} is not supported.""" )
def   __SCREAMING_SNAKE_CASE		(		lowercase__     ,				lowercase__     ,				lowercase__ ):
					"""simple docstring"""
					A     =   assign.targets
					A     =   evaluate_ast(assign.value     ,				lowercase__     ,				lowercase__ )

					if len(lowercase__ ) == 1:
										A     =   result
					else:
										if len(lowercase__ ) != len(lowercase__ ):
															raise InterpretorError(F"""Expected {len(lowercase__ )} values but got {len(lowercase__ )}.""" )
										for var_name, r in zip(lowercase__     ,				lowercase__ ):
															A     =   r
					return result
def   __SCREAMING_SNAKE_CASE		(		lowercase__     ,				lowercase__     ,				lowercase__ ):
					"""simple docstring"""







					if not isinstance(call.func     ,				ast.Name ):
										raise InterpretorError(
										    F"""It is not permitted to evaluate other functions than the provided tools (tried to execute {call.func} of """
										    F"""type {type(call.func )}.""" )
					A     =   call.func.id
					if func_name not in tools:
										raise InterpretorError(
										    F"""It is not permitted to evaluate other functions than the provided tools (tried to execute {call.func.id}).""" )

					A     =   tools[func_name]
					# Todo deal with args
					A     =   [evaluate_ast(lowercase__     ,				lowercase__     ,				lowercase__ ) for arg in call.args]
					A     =   {keyword.arg: evaluate_ast(keyword.value     ,				lowercase__     ,				lowercase__ ) for keyword in call.keywords}
					return func(*lowercase__     ,				**lowercase__ )
def   __SCREAMING_SNAKE_CASE		(		lowercase__     ,				lowercase__     ,				lowercase__ ):
					"""simple docstring"""
					A     =   evaluate_ast(subscript.slice     ,				lowercase__     ,				lowercase__ )
					A     =   evaluate_ast(subscript.value     ,				lowercase__     ,				lowercase__ )
					if isinstance(lowercase__     ,				(list, tuple) ):
										return value[int(lowercase__ )]
					if index in value:
										return value[index]
					if isinstance(lowercase__     ,				lowercase__ ) and isinstance(lowercase__     ,				lowercase__ ):
										A     =   difflib.get_close_matches(lowercase__     ,				list(value.keys() ) )
										if len(lowercase__ ) > 0:
															return value[close_matches[0]]

					raise InterpretorError(F"""Could not index {value} with '{index}'.""" )
def   __SCREAMING_SNAKE_CASE		(		lowercase__     ,				lowercase__     ,				lowercase__ ):
					"""simple docstring"""







					if name.id in state:
										return state[name.id]
					A     =   difflib.get_close_matches(name.id     ,				list(state.keys() ) )
					if len(lowercase__ ) > 0:
										return state[close_matches[0]]
					raise InterpretorError(F"""The variable `{name.id}` is not defined.""" )
def   __SCREAMING_SNAKE_CASE		(		lowercase__     ,				lowercase__     ,				lowercase__ ):
					"""simple docstring"""







					if len(condition.ops ) > 1:
										raise InterpretorError("Cannot evaluate conditions with multiple operators" )

					A     =   evaluate_ast(condition.left     ,				lowercase__     ,				lowercase__ )
					A     =   condition.ops[0]
					A     =   evaluate_ast(condition.comparators[0]     ,				lowercase__     ,				lowercase__ )

					if isinstance(lowercase__     ,				ast.Eq ):
										return left == right
					elif isinstance(lowercase__     ,				ast.NotEq ):
										return left != right
					elif isinstance(lowercase__     ,				ast.Lt ):
										return left < right
					elif isinstance(lowercase__     ,				ast.LtE ):
										return left <= right
					elif isinstance(lowercase__     ,				ast.Gt ):
										return left > right
					elif isinstance(lowercase__     ,				ast.GtE ):
										return left >= right
					elif isinstance(lowercase__     ,				ast.Is ):
										return left is right
					elif isinstance(lowercase__     ,				ast.IsNot ):
										return left is not right
					elif isinstance(lowercase__     ,				ast.In ):
										return left in right
					elif isinstance(lowercase__     ,				ast.NotIn ):
										return left not in right
					else:
										raise InterpretorError(F"""Operator not supported: {comparator}""" )
def   __SCREAMING_SNAKE_CASE		(		lowercase__     ,				lowercase__     ,				lowercase__ ):
					"""simple docstring"""
					A     =   None
					if evaluate_condition(if_statement.test     ,				lowercase__     ,				lowercase__ ):
										for line in if_statement.body:
															A     =   evaluate_ast(lowercase__     ,				lowercase__     ,				lowercase__ )
															if line_result is not None:
																				A     =   line_result
					else:
										for line in if_statement.orelse:
															A     =   evaluate_ast(lowercase__     ,				lowercase__     ,				lowercase__ )
															if line_result is not None:
																				A     =   line_result
					return result
def   __SCREAMING_SNAKE_CASE		(		lowercase__     ,				lowercase__     ,				lowercase__ ):
					"""simple docstring"""
					A     =   None
					A     =   evaluate_ast(for_loop.iter     ,				lowercase__     ,				lowercase__ )
					for counter in iterator:
										A     =   counter
										for expression in for_loop.body:
															A     =   evaluate_ast(lowercase__     ,				lowercase__     ,				lowercase__ )
															if line_result is not None:
																				A     =   line_result
					return result












'''simple docstring'''


import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


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

_lowerCamelCase					:						List[Any]					     =							['UtteranceLevel']




@torch.no_grad()
def  __a			(  UpperCAmelCase							,      UpperCAmelCase							,      UpperCAmelCase							,      UpperCAmelCase     )   ->Union[str, Any]:



	"""simple docstring"""







	A					=   torch.load(UpperCAmelCase							,      map_location="""cpu"""     )
	if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
		raise NotImplementedError(f"""The supported s3prl models are {SUPPORTED_MODELS}"""     )

	A					=   checkpoint["""Downstream"""]

	A					=   HubertConfig.from_pretrained(UpperCAmelCase     )
	A					=   HubertForSequenceClassification.from_pretrained(UpperCAmelCase							,      config=UpperCAmelCase     )
	A					=   WavaVecaFeatureExtractor.from_pretrained(
	    UpperCAmelCase							,      return_attention_mask=UpperCAmelCase							,      do_normalize=UpperCAmelCase     )

	if hf_congfig.use_weighted_layer_sum:
		A					=   checkpoint["""Featurizer"""]["""weights"""]

	A					=   downstream_dict["""projector.weight"""]
	A					=   downstream_dict["""projector.bias"""]
	A					=   downstream_dict["""model.post_net.linear.weight"""]
	A					=   downstream_dict["""model.post_net.linear.bias"""]

	hf_feature_extractor.save_pretrained(UpperCAmelCase     )
	hf_model.save_pretrained(UpperCAmelCase     )


if __name__ == "__main__":
				_lowerCamelCase					:						List[str]					     =							argparse.ArgumentParser()
				parser.add_argument(
				    '--base_model_name', default=None, type=str, help='Name of the huggingface pretrained base model.'
				)
				parser.add_argument('--config_path', default=None, type=str, help='Path to the huggingface classifier config.')
				parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to the s3prl checkpoint.')
				parser.add_argument('--model_dump_path', default=None, type=str, help='Path to the final converted model.')
				_lowerCamelCase					:						List[Any]					     =							parser.parse_args()
				convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)

