



"""simple docstring"""

from typing import Any




def __lowercase    (							_a	,    _a	,    _a	,    _a	,    _a	,    ):
    _validation(
        a_	,    a_	,    a_	,    a_	,    a_	,    )
    # Creates data structures and fill initial step
    snake_case_				:  dict       =			{}
    snake_case_				:  dict       =			{}
    for state in states_space:
        snake_case_				:  Dict       =			observations_space[0]
        snake_case_				:  Optional[int]       =			(
            initial_probabilities[state] * emission_probabilities[state][observation]
        )
        snake_case_				:  str       =			None

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

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

            snake_case_				:  int       =			arg_max

    # The final observation
    snake_case_				:  str       =			observations_space[len(a_					) - 1]

    # argmax for given final observation
    snake_case_				:  str       =			''''''
    snake_case_				:  Optional[Any]       =			-1
    for k_state in states_space:
        snake_case_				:  Union[str, Any]       =			probabilities[(k_state, final_observation)]
        if probability > max_probability:
            snake_case_				:  str       =			probability
            snake_case_				:  List[Any]       =			k_state
    snake_case_				:  Optional[Any]       =			arg_max

    # Process pointers backwards
    snake_case_				:  List[str]       =			last_state
    snake_case_				:  Any       =			[]
    for o in range(len(a_					) - 1	,    -1	,    -1					):
        result.append(a_					)
        snake_case_				:  List[Any]       =			pointers[previous, observations_space[o]]
    result.reverse()

    return result




def __lowercase    (							_a	,    _a	,    _a	,    _a	,    _a	,    ):
    _validate_not_empty(
        a_	,    a_	,    a_	,    a_	,    a_	,    )
    _validate_lists(a_	,    a_					)
    _validate_dicts(
        a_	,    a_	,    a_					)




def __lowercase    (							_a	,    _a	,    _a	,    _a	,    _a	,    ):
    if not all(
        [
            observations_space,
            states_space,
            initial_probabilities,
            transition_probabilities,
            emission_probabilities,
        ]					):
        raise ValueError('''There\'s an empty parameter'''					)




def __lowercase    (							_a	,    _a					):
    _validate_list(a_	,    '''observations_space'''					)
    _validate_list(a_	,    '''states_space'''					)




def __lowercase    (							_a	,    _a					):
    if not isinstance(_object	,    a_					):
        snake_case_				:  Dict       =			f"{var_name} must be a list"
        raise ValueError(a_					)
    else:
        for x in _object:
            if not isinstance(a_	,    a_					):
                snake_case_				:  List[str]       =			f"{var_name} must be a list of strings"
                raise ValueError(a_					)




def __lowercase    (							_a	,    _a	,    _a	,    ):
    _validate_dict(a_	,    '''initial_probabilities'''	,    a_					)
    _validate_nested_dict(a_	,    '''transition_probabilities'''					)
    _validate_nested_dict(a_	,    '''emission_probabilities'''					)




def __lowercase    (							_a	,    _a					):
    _validate_dict(_object	,    a_	,    a_					)
    for x in _object.values():
        _validate_dict(a_	,    a_	,    a_	,    a_					)




def __lowercase    (							_a	,    _a	,    _a	,    _a = False					):
    if not isinstance(_object	,    a_					):
        snake_case_				:  str       =			f"{var_name} must be a dict"
        raise ValueError(a_					)
    if not all(isinstance(a_	,    a_					) for x in _object					):
        snake_case_				:  Tuple       =			f"{var_name} all keys must be strings"
        raise ValueError(a_					)
    if not all(isinstance(a_	,    a_					) for x in _object.values()					):
        snake_case_				:  Dict       =			'''nested dictionary ''' if nested else ''''''
        snake_case_				:  Optional[int]       =			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"""

import math
import time
from typing import Dict, List, Optional

from torch.utils.data import Dataset

from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics


if is_torch_tpu_available(check_device=False):
     import torch_xla.core.xla_model as xm
     import torch_xla.debug.metrics as met




class  _UpperCAmelCase (    lowerCAmelCase__):

 def __init__(  self	: List[Any]						,  *lowercase_	: List[Any]						,  lowercase_	: Optional[Any]=None						,  lowercase_	: Tuple=None						,  **lowercase_	: Union[str, Any]       ):
     super().__init__(*lowercase_						,  **lowercase_       )
     snake_case_				:  str       =			eval_examples
     snake_case_				:  Optional[Any]       =			post_process_function

 def 		_snake_case      (  self	: Union[str, Any]						,  lowercase_	: Optional[Dataset] = None						,  lowercase_	: str=None						,  lowercase_	: Optional[List[str]] = None						,  lowercase_	: str = "eval"						,  **lowercase_	: Dict						,  ):
     snake_case_				:  int       =			gen_kwargs.copy()
     snake_case_				:  Dict       =			(
         gen_kwargs['''max_length'''] if gen_kwargs.get('''max_length'''       ) is not None else self.args.generation_max_length
     )
     snake_case_				:  Tuple       =			(
         gen_kwargs['''num_beams'''] if gen_kwargs.get('''num_beams'''       ) is not None else self.args.generation_num_beams
     )
     snake_case_				:  Optional[Any]       =			gen_kwargs

     snake_case_				:  Optional[int]       =			self.eval_dataset if eval_dataset is None else eval_dataset
     snake_case_				:  Optional[Any]       =			self.get_eval_dataloader(lowercase_       )
     snake_case_				:  Dict       =			self.eval_examples if eval_examples is None else eval_examples

     # Temporarily disable metric computation, we will do it in the loop here.
     snake_case_				:  Optional[int]       =			self.compute_metrics
     snake_case_				:  str       =			None
     snake_case_				:  int       =			time.time()
     snake_case_				:  Any       =			self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
     try:
         snake_case_				:  str       =			eval_loop(
             lowercase_						,  description='''Evaluation'''						,  prediction_loss_only=True if compute_metrics is None else None						,  ignore_keys=lowercase_						,  metric_key_prefix=lowercase_						,  )
     finally:
         snake_case_				:  Optional[int]       =			compute_metrics
     snake_case_				:  Dict       =			self.args.eval_batch_size * self.args.world_size
     if f"{metric_key_prefix}_jit_compilation_time" in output.metrics:
         start_time += output.metrics[f"{metric_key_prefix}_jit_compilation_time"]
     output.metrics.update(
         speed_metrics(
             lowercase_						,  lowercase_						,  num_samples=output.num_samples						,  num_steps=math.ceil(output.num_samples / total_batch_size       )						,  )       )

     if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
         # Only the main node write the results by default
         snake_case_				:  Tuple       =			self.post_process_function(lowercase_						,  lowercase_						,  lowercase_       )
         snake_case_				:  Optional[int]       =			self.compute_metrics(lowercase_       )

         # Prefix all keys with metric_key_prefix + '_'
         for key in list(metrics.keys()       ):
             if not key.startswith(f"{metric_key_prefix}_"       ):
                 snake_case_				:  int       =			metrics.pop(lowercase_       )

         metrics.update(output.metrics       )
     else:
         snake_case_				:  Tuple       =			output.metrics

     if self.args.should_log:
         # Only the main node log the results by default
         self.log(lowercase_       )

     if self.args.tpu_metrics_debug or self.args.debug:
         # tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
         xm.master_print(met.metrics_report()       )

     snake_case_				:  str       =			self.callback_handler.on_evaluate(self.args						,  self.state						,  self.control						,  lowercase_       )
     return metrics






 def 		_snake_case      (  self	: Any						,  lowercase_	: int						,  lowercase_	: List[str]						,  lowercase_	: str=None						,  lowercase_	: str = "test"						,  **lowercase_	: Dict       ):
     snake_case_				:  Dict       =			gen_kwargs.copy()

     snake_case_				:  Dict       =			self.get_test_dataloader(lowercase_       )

     # Temporarily disable metric computation, we will do it in the loop here.
     snake_case_				:  Tuple       =			self.compute_metrics
     snake_case_				:  int       =			None
     snake_case_				:  List[Any]       =			time.time()
     snake_case_				:  int       =			self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
     try:
         snake_case_				:  List[str]       =			eval_loop(
             lowercase_						,  description='''Prediction'''						,  prediction_loss_only=True if compute_metrics is None else None						,  ignore_keys=lowercase_						,  metric_key_prefix=lowercase_						,  )
     finally:
         snake_case_				:  str       =			compute_metrics

     snake_case_				:  int       =			self.args.eval_batch_size * self.args.world_size
     if f"{metric_key_prefix}_jit_compilation_time" in output.metrics:
         start_time += output.metrics[f"{metric_key_prefix}_jit_compilation_time"]
     output.metrics.update(
         speed_metrics(
             lowercase_						,  lowercase_						,  num_samples=output.num_samples						,  num_steps=math.ceil(output.num_samples / total_batch_size       )						,  )       )
     if self.post_process_function is None or self.compute_metrics is None:
         return output

     snake_case_				:  Tuple       =			self.post_process_function(lowercase_						,  lowercase_						,  lowercase_						,  '''predict'''       )
     snake_case_				:  Dict       =			self.compute_metrics(lowercase_       )

     # Prefix all keys with metric_key_prefix + '_'
     for key in list(metrics.keys()       ):
         if not key.startswith(f"{metric_key_prefix}_"       ):
             snake_case_				:  Union[str, Any]       =			metrics.pop(lowercase_       )
     metrics.update(output.metrics       )
     return PredictionOutput(predictions=predictions.predictions						,  label_ids=predictions.label_ids						,  metrics=lowercase_       )





