


'''simple docstring'''
from typing import Any




def 	_A  (       A							,A							,A							,A							,A							,)  ->				list:
	_validation(
	    A							,A							,A							,A							,A							,)
	# Creates data structures and fill initial step
	lowercase						:		dict   					=		{}
	lowercase						:		dict   					=		{}
	for state in states_space:
		lowercase						:		List[str]   					=		observations_space[0]
		lowercase						:		Optional[Any]   					=		(
		    initial_probabilities[state] * emission_probabilities[state][observation]
		)
		lowercase						:		str   					=		None

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

            # Update probabilities and pointers dicts
			lowercase						:		Union[str, Any]   					=		(
			    probabilities[(arg_max, prior_observation)]
			    * transition_probabilities[arg_max][state]
			    * emission_probabilities[state][observation]
			)

			lowercase						:		List[Any]   					=		arg_max

    # The final observation
	lowercase						:		Any   					=		observations_space[len(A							) - 1]

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

	# Process pointers backwards
	lowercase						:		Any   					=		last_state
	lowercase						:		int   					=		[]
	for o in range(len(A							) - 1							,-1							,-1							):
		result.append(A							)
		lowercase						:		Optional[Any]   					=		pointers[previous, observations_space[o]]
	result.reverse()

	return result




def 	_A  (       A							,A							,A							,A							,A							,)  ->				None:
	_validate_not_empty(
	    A							,A							,A							,A							,A							,)
	_validate_lists(A							,A							)
	_validate_dicts(
	    A							,A							,A							)




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




def 	_A  (       A							,A							)  ->				None:
	_validate_list(A							,"observations_space"							)
	_validate_list(A							,"states_space"							)




def 	_A  (       A							,A							)  ->				None:
	if not isinstance(_object							,A							):
		lowercase						:		Any   					=		F'''{var_name} must be a list'''
		raise ValueError(A							)
	else:
		for x in _object:
			if not isinstance(A							,A							):
				lowercase						:		Dict   					=		F'''{var_name} must be a list of strings'''
				raise ValueError(A							)




def 	_A  (       A							,A							,A							,)  ->				None:
	_validate_dict(A							,"initial_probabilities"							,A							)
	_validate_nested_dict(A							,"transition_probabilities"							)
	_validate_nested_dict(A							,"emission_probabilities"							)




def 	_A  (       A							,A							)  ->				None:
	_validate_dict(_object							,A							,A							)
	for x in _object.values():
		_validate_dict(A							,A							,A							,A							)




def 	_A  (       A							,A							,A							,A = False							)  ->				None:
	if not isinstance(_object							,A							):
		lowercase						:		Any   					=		F'''{var_name} must be a dict'''
		raise ValueError(A							)
	if not all(isinstance(A							,A							) for x in _object							):
		lowercase						:		Union[str, Any]   					=		F'''{var_name} all keys must be strings'''
		raise ValueError(A							)
	if not all(isinstance(A							,A							) for x in _object.values()							):
		lowercase						:		List[Any]   					=		"nested dictionary " if nested else ""
		lowercase						:		Optional[Any]   					=		F'''{var_name} {nested_text}all values must be {value_type.__name__}'''
		raise ValueError(A							)


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

					testmod()






'''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[Any]             =				["""text""", """image""", """audio"""]




def 	_A  (       A							)  ->				Dict:
	lowercase						:		str   					=		[]

	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_1_2, 5_1_2)							)							)
		elif input_type == "audio":
			inputs.append(torch.ones(3_0_0_0							)							)
		elif isinstance(A							,A							):
			inputs.append(create_inputs(A							)							)
		else:
			raise ValueError(F'''Invalid type requested: {input_type}'''							)

	return inputs




def 	_A  (       A							)  ->				str:
	lowercase						:		Tuple   					=		[]

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

	return output_types






@is_tool_test
class        _UpperCamelCase	:


				'''simple docstring'''


				def 	a__		(     self							)    ->  Optional[Any]:
					self.assertTrue(hasattr(self.tool							,      "inputs"							)							)
					self.assertTrue(hasattr(self.tool							,      "outputs"							)							)

					lowercase						:		Optional[Any]   					=		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							)

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

				def 	a__		(     self							)    ->  Any:
					lowercase						:		Any   					=		create_inputs(self.tool.inputs							)
					lowercase						:		Tuple   					=		self.tool(*a_							)

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

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

				def 	a__		(     self							)    ->  List[str]:
					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 	a__		(     self							)    ->  int:
					lowercase						:		str   					=		create_inputs(self.tool.inputs							)
					lowercase						:		str   					=		self.tool(*a_							)

					if not isinstance(a_							,      a_							):
						lowercase						:		Union[str, Any]   					=		[outputs]

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

					for output, output_type in zip(a_							,      self.tool.outputs							):
						lowercase						:		List[str]   					=		AGENT_TYPE_MAPPING[output_type]
						self.assertTrue(isinstance(a_							,      a_							)							)


				def 	a__		(     self							)    ->  Optional[int]:
					lowercase						:		int   					=		create_inputs(self.tool.inputs							)

					lowercase						:		str   					=		[]

					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
					lowercase						:		Optional[int]   					=		self.tool(*a_							)

					if not isinstance(a_							,      a_							):
						lowercase						:		str   					=		[outputs]

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


