"""simple docstring"""



import unittest

from transformers import load_tool
from transformers.tools.agent_types import AGENT_TYPE_MAPPING

from .test_tools_common import ToolTesterMixin, output_types
class 	__UpperCAmelCase						(unittest.TestCase       ,      __A    ):







			'''simple docstring'''





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										A__					:    Dict														= load_tool("""translation"""       )
										self.tool.setup()
										A__					:    List[Any]														= load_tool("""translation"""      ,			remote=snake_case_       )





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										A__					:    Optional[Any]														= self.tool("""Hey, what's up?"""      ,			src_lang="""English"""      ,			tgt_lang="""French"""       )
										self.assertEqual(snake_case_      ,			"""- Hé, comment ça va?"""       )





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										A__					:    Dict														= self.remote_tool("""Hey, what's up?"""      ,			src_lang="""English"""      ,			tgt_lang="""French"""       )
										self.assertEqual(snake_case_      ,			"""- Hé, comment ça va?"""       )





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										A__					:    str														= self.tool(text="""Hey, what's up?"""      ,			src_lang="""English"""      ,			tgt_lang="""French"""       )
										self.assertEqual(snake_case_      ,			"""- Hé, comment ça va?"""       )





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										A__					:    Tuple														= self.remote_tool(text="""Hey, what's up?"""      ,			src_lang="""English"""      ,			tgt_lang="""French"""       )
										self.assertEqual(snake_case_      ,			"""- Hé, comment ça va?"""       )





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										A__					:    str														= ["""Hey, what's up?""", """English""", """Spanish"""]
										A__					:    List[Any]														= self.tool(*snake_case_       )

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

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





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										A__					:    List[str]														= ["""Hey, what's up?""", """English""", """Spanish"""]
										A__					:    int														= self.tool(*snake_case_       )

										if not isinstance(snake_case_      ,			snake_case_       ):
																	A__					:    Dict														= [outputs]

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

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





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										A__					:    Dict														= ["""Hey, what's up?""", """English""", """Spanish"""]

										A__					:    Optional[Any]														= []

										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
										A__					:    Any														= self.tool(*snake_case_       )

										if not isinstance(snake_case_      ,			snake_case_       ):
																	A__					:    Optional[int]														= [outputs]

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






"""simple docstring"""



import os
import random
from pathlib import Path
from typing import List

import numpy as np
import torch
from torch.cuda.amp import GradScaler

from .utils import (
    MODEL_NAME,
    OPTIMIZER_NAME,
    RNG_STATE_NAME,
    SCALER_NAME,
    SCHEDULER_NAME,
    get_pretty_name,
    is_tpu_available,
    is_xpu_available,
    save,
)


if is_tpu_available(check_device=False):
				import torch_xla.core.xla_model as xm

from .logging import get_logger
from .state import PartialState


_UpperCamelCase         =		get_logger(__name__)

def    _A(		lowerCAmelCase ,     lowerCAmelCase ,     lowerCAmelCase ,     lowerCAmelCase ,     lowerCAmelCase ,     lowerCAmelCase = None ,     ):
							# Model states
							for i, state in enumerate(lowerCAmelCase       ):
														A__					:    Tuple														= F'''{MODEL_NAME}.bin''' if i == 0 else F'''{MODEL_NAME}_{i}.bin'''
														A__					:    Union[str, Any]														= os.path.join(lowerCAmelCase ,     lowerCAmelCase       )
														save(lowerCAmelCase ,     lowerCAmelCase       )
														logger.info(F'''Model weights saved in {output_model_file}'''       )
							# Optimizer states
							for i, opt in enumerate(lowerCAmelCase       ):
														A__					:    Tuple														= opt.state_dict()
														A__					:    Union[str, Any]														= F'''{OPTIMIZER_NAME}.bin''' if i == 0 else F'''{OPTIMIZER_NAME}_{i}.bin'''
														A__					:    Tuple														= os.path.join(lowerCAmelCase ,     lowerCAmelCase       )
														save(lowerCAmelCase ,     lowerCAmelCase       )
														logger.info(F'''Optimizer state saved in {output_optimizer_file}'''       )
							# Scheduler states
							for i, scheduler in enumerate(lowerCAmelCase       ):
														A__					:    List[str]														= scheduler.state_dict()
														A__					:    Dict														= F'''{SCHEDULER_NAME}.bin''' if i == 0 else F'''{SCHEDULER_NAME}_{i}.bin'''
														A__					:    Optional[int]														= os.path.join(lowerCAmelCase ,     lowerCAmelCase       )
														save(lowerCAmelCase ,     lowerCAmelCase       )
														logger.info(F'''Scheduler state saved in {output_scheduler_file}'''       )
							# GradScaler state
							if scaler is not None:
														A__					:    Dict														= scaler.state_dict()
														A__					:    Optional[int]														= os.path.join(lowerCAmelCase ,     lowerCAmelCase       )
														torch.save(lowerCAmelCase ,     lowerCAmelCase       )
														logger.info(F'''Gradient scaler state saved in {output_scaler_file}'''       )
							# Random number generator states
							A__					:    Tuple														= {}
							A__					:    Any														= F'''{RNG_STATE_NAME}_{process_index}.pkl'''
							A__					:    Any														= random.getstate()
							A__					:    Any														= np.random.get_state()
							A__					:    int														= torch.get_rng_state()
							if is_xpu_available():
														A__					:    List[str]														= torch.xpu.get_rng_state_all()
							else:
														A__					:    Union[str, Any]														= torch.cuda.get_rng_state_all()
							if is_tpu_available():
														A__					:    str														= xm.get_rng_state()
							A__					:    str														= os.path.join(lowerCAmelCase ,     lowerCAmelCase       )
							torch.save(lowerCAmelCase ,     lowerCAmelCase       )
							logger.info(F'''Random states saved in {output_states_file}'''       )
							return output_dir

def    _A(		lowerCAmelCase ,     lowerCAmelCase ,     lowerCAmelCase ,     lowerCAmelCase ,     lowerCAmelCase ,     lowerCAmelCase=None ,     lowerCAmelCase=None ,     **lowerCAmelCase ,     ):
							if map_location not in [None, "cpu", "on_device"]:
														raise TypeError(
														    """Unsupported optimizer map location passed, please choose one of `None`, `'cpu'`, or `'on_device'`"""       )
							if map_location is None:
														A__					:    Union[str, Any]														= """cpu"""
							elif map_location == "on_device":
														A__					:    str														= PartialState().device
							# Model states
							for i, model in enumerate(lowerCAmelCase       ):
														A__					:    List[str]														= F'''{MODEL_NAME}.bin''' if i == 0 else F'''{MODEL_NAME}_{i}.bin'''
														A__					:    Optional[Any]														= os.path.join(lowerCAmelCase ,     lowerCAmelCase       )
														models[i].load_state_dict(torch.load(lowerCAmelCase ,     map_location=lowerCAmelCase       ) ,     **lowerCAmelCase       )
							logger.info("""All model weights loaded successfully"""       )

							# Optimizer states
							for i, opt in enumerate(lowerCAmelCase       ):
														A__					:    str														= F'''{OPTIMIZER_NAME}.bin''' if i == 0 else F'''{OPTIMIZER_NAME}_{i}.bin'''
														A__					:    Union[str, Any]														= os.path.join(lowerCAmelCase ,     lowerCAmelCase       )
														A__					:    List[str]														= torch.load(lowerCAmelCase ,     map_location=lowerCAmelCase       )
														optimizers[i].load_state_dict(lowerCAmelCase       )
							logger.info("""All optimizer states loaded successfully"""       )

							# Scheduler states
							for i, scheduler in enumerate(lowerCAmelCase       ):
														A__					:    Any														= F'''{SCHEDULER_NAME}.bin''' if i == 0 else F'''{SCHEDULER_NAME}_{i}.bin'''
														A__					:    Optional[int]														= os.path.join(lowerCAmelCase ,     lowerCAmelCase       )
														scheduler.load_state_dict(torch.load(lowerCAmelCase       )       )
							logger.info("""All scheduler states loaded successfully"""       )

							# GradScaler state
							if scaler is not None:
														A__					:    Optional[Any]														= os.path.join(lowerCAmelCase ,     lowerCAmelCase       )
														scaler.load_state_dict(torch.load(lowerCAmelCase       )       )
														logger.info("""GradScaler state loaded successfully"""       )

							# Random states
							try:
														A__					:    Union[str, Any]														= torch.load(os.path.join(lowerCAmelCase ,     F'''{RNG_STATE_NAME}_{process_index}.pkl'''       )       )
														random.setstate(states["""random_state"""]       )
														np.random.set_state(states["""numpy_random_seed"""]       )
														torch.set_rng_state(states["""torch_manual_seed"""]       )
														if is_xpu_available():
																					torch.xpu.set_rng_state_all(states["""torch_xpu_manual_seed"""]       )
														else:
																					torch.cuda.set_rng_state_all(states["""torch_cuda_manual_seed"""]       )
														if is_tpu_available():
																					xm.set_rng_state(states["""xm_seed"""]       )
														logger.info("""All random states loaded successfully"""       )
							except Exception:
														logger.info("""Could not load random states"""       )

def    _A(		lowerCAmelCase ,     lowerCAmelCase ,     lowerCAmelCase = 0       ):
							A__					:    int														= Path(lowerCAmelCase       ) / F'''custom_checkpoint_{index}.pkl'''
							logger.info(F'''Saving the state of {get_pretty_name(lowerCAmelCase       )} to {save_location}'''       )
							torch.save(obj.state_dict() ,     lowerCAmelCase       )



def    _A(		lowerCAmelCase ,     lowerCAmelCase ,     lowerCAmelCase = 0       ):
							A__					:    List[str]														= F'''{path}/custom_checkpoint_{index}.pkl'''
							logger.info(F'''Loading the state of {get_pretty_name(lowerCAmelCase       )} from {load_location}'''       )
							obj.load_state_dict(torch.load(lowerCAmelCase ,     map_location="""cpu"""       )       )





