


'''simple docstring'''






import math
import os
from copy import deepcopy

import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer

from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed


lowercase							='true'





def lowerCamelCase__      (						__lowerCamelCase	:							str	,   __lowerCamelCase	:							Tuple=8_2	,   __lowerCamelCase	:							int=1_6							):







   '''simple docstring'''




   set_seed(4_2							)
   _UpperCAmelCase       :       Any							   =RegressionModel()
   _UpperCAmelCase       :       int							   =deepcopy(__lowerCamelCase							)
   _UpperCAmelCase       :       Union[str, Any]							   =RegressionDataset(length=__lowerCamelCase							)
   _UpperCAmelCase       :       Tuple							   =DataLoader(__lowerCamelCase	,   batch_size=__lowerCamelCase							)
   model.to(accelerator.device							)
   _UpperCAmelCase   ,	_UpperCAmelCase       :       List[Any]							   =accelerator.prepare(__lowerCamelCase	,   __lowerCamelCase							)
   return model, ddp_model, dataloader





def lowerCamelCase__      (						__lowerCamelCase	:							Accelerator	,   __lowerCamelCase	:							Dict=False							):







   '''simple docstring'''




   _UpperCAmelCase       :       List[Any]							   =AutoTokenizer.from_pretrained('hf-internal-testing/mrpc-bert-base-cased'							)
   _UpperCAmelCase       :       List[Any]							   =load_dataset('glue'	,   'mrpc'	,   split='validation'							)

   def tokenize_function(__lowerCamelCase	:							Any							):
      _UpperCAmelCase       :       Tuple							   =tokenizer(examples['sentence1']	,   examples['sentence2']	,   truncation=__lowerCamelCase	,   max_length=__lowerCamelCase							)
      return outputs

   with accelerator.main_process_first():
      _UpperCAmelCase       :       List[Any]							   =dataset.map(
          __lowerCamelCase	,   batched=__lowerCamelCase	,   remove_columns=['idx', 'sentence1', 'sentence2']	,   )

   _UpperCAmelCase       :       Optional[int]							   =tokenized_datasets.rename_column('label'	,   'labels'							)

   def collate_fn(__lowerCamelCase	:							List[str]							):
      if use_longest:
         return tokenizer.pad(__lowerCamelCase	,   padding='longest'	,   return_tensors='pt'							)
      return tokenizer.pad(__lowerCamelCase	,   padding='max_length'	,   max_length=1_2_8	,   return_tensors='pt'							)

   return DataLoader(__lowerCamelCase	,   shuffle=__lowerCamelCase	,   collate_fn=__lowerCamelCase	,   batch_size=1_6							)





def lowerCamelCase__      (						__lowerCamelCase	:							List[Any]	,   __lowerCamelCase	:							Any							):







   '''simple docstring'''




   _UpperCAmelCase       :       List[Any]							   =Accelerator(dispatch_batches=__lowerCamelCase	,   split_batches=__lowerCamelCase							)
   _UpperCAmelCase       :       str							   =get_dataloader(__lowerCamelCase	,   not dispatch_batches							)
   _UpperCAmelCase       :       List[Any]							   =AutoModelForSequenceClassification.from_pretrained(
       'hf-internal-testing/mrpc-bert-base-cased'	,   return_dict=__lowerCamelCase							)
   _UpperCAmelCase   ,	_UpperCAmelCase       :       Optional[int]							   =accelerator.prepare(__lowerCamelCase	,   __lowerCamelCase							)
   return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator





def lowerCamelCase__      (						__lowerCamelCase	:							str	,   __lowerCamelCase	:							List[Any]	,   __lowerCamelCase	:							List[Any]							):







   '''simple docstring'''




   _UpperCAmelCase       :       Optional[int]							   =[]
   for batch in dataloader:
      _UpperCAmelCase   ,	_UpperCAmelCase       :       Tuple							   =batch.values()
      with torch.no_grad():
         _UpperCAmelCase       :       Optional[int]							   =model(__lowerCamelCase							)
         _UpperCAmelCase   ,	_UpperCAmelCase       :       Optional[int]							   =accelerator.gather_for_metrics((logit, target)							)
         logits_and_targets.append((logit, target)							)
   _UpperCAmelCase   ,	_UpperCAmelCase       :       int							   =[], []
   for logit, targ in logits_and_targets:
      logits.append(__lowerCamelCase							)
      targs.append(__lowerCamelCase							)
   _UpperCAmelCase   ,	_UpperCAmelCase       :       Tuple							   =torch.cat(__lowerCamelCase							), torch.cat(__lowerCamelCase							)
   return logits, targs





def lowerCamelCase__      (						__lowerCamelCase	:							Accelerator	,   __lowerCamelCase	:							Tuple=8_2	,   __lowerCamelCase	:							List[Any]=False	,   __lowerCamelCase	:							Optional[int]=False	,   __lowerCamelCase	:							str=1_6							):







   '''simple docstring'''




   _UpperCAmelCase   ,	_UpperCAmelCase   ,	_UpperCAmelCase       :       Optional[int]							   =get_basic_setup(__lowerCamelCase	,   __lowerCamelCase	,   __lowerCamelCase							)
   _UpperCAmelCase   ,	_UpperCAmelCase       :       List[Any]							   =generate_predictions(__lowerCamelCase	,   __lowerCamelCase	,   __lowerCamelCase							)
   assert (
       len(__lowerCamelCase							) == num_samples
   ), f"Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(__lowerCamelCase							)}"





def lowerCamelCase__      (						__lowerCamelCase	:							bool = False	,   __lowerCamelCase	:							bool = False							):







   '''simple docstring'''




   _UpperCAmelCase       :       List[str]							   =evaluate.load('glue'	,   'mrpc'							)
   _UpperCAmelCase   ,	_UpperCAmelCase       :       str							   =get_mrpc_setup(__lowerCamelCase	,   __lowerCamelCase							)
   # First do baseline
   _UpperCAmelCase   ,	_UpperCAmelCase   ,	_UpperCAmelCase       :       Dict							   =setup['no']
   model.to(__lowerCamelCase							)
   model.eval()
   for batch in dataloader:
      batch.to(__lowerCamelCase							)
      with torch.inference_mode():
         _UpperCAmelCase       :       int							   =model(**__lowerCamelCase							)
      _UpperCAmelCase       :       Tuple							   =outputs.logits.argmax(dim=-1							)
      metric.add_batch(predictions=__lowerCamelCase	,   references=batch['labels']							)
   _UpperCAmelCase       :       Dict							   =metric.compute()

   # Then do distributed
   _UpperCAmelCase   ,	_UpperCAmelCase   ,	_UpperCAmelCase       :       Any							   =setup['ddp']
   model.eval()
   for batch in dataloader:
      with torch.inference_mode():
         _UpperCAmelCase       :       List[Any]							   =model(**__lowerCamelCase							)
      _UpperCAmelCase       :       str							   =outputs.logits.argmax(dim=-1							)
      _UpperCAmelCase       :       Tuple							   =batch['labels']
      _UpperCAmelCase   ,	_UpperCAmelCase       :       int							   =accelerator.gather_for_metrics((preds, references)							)
      metric.add_batch(predictions=__lowerCamelCase	,   references=__lowerCamelCase							)
   _UpperCAmelCase       :       Union[str, Any]							   =metric.compute()

   for key in "accuracy f1".split():
      assert math.isclose(
          baseline[key]	,   distributed[key]							), f"Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n"





def lowerCamelCase__      (						):







   '''simple docstring'''




   _UpperCAmelCase       :       int							   =Accelerator(split_batches=__lowerCamelCase	,   dispatch_batches=__lowerCamelCase							)
   if accelerator.is_local_main_process:
      datasets.utils.logging.set_verbosity_warning()
      transformers.utils.logging.set_verbosity_warning()
   else:
      datasets.utils.logging.set_verbosity_error()
      transformers.utils.logging.set_verbosity_error()
   # These are a bit slower so they should only be ran on the GPU or TPU
   if torch.cuda.is_available() or is_tpu_available():
      if accelerator.is_local_main_process:
         print('**Testing gather_for_metrics**'							)
      for split_batches in [True, False]:
         for dispatch_batches in [True, False]:
            if accelerator.is_local_main_process:
               print(f"With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`"							)
            test_mrpc(__lowerCamelCase	,   __lowerCamelCase							)
            accelerator.state._reset_state()
   if accelerator.is_local_main_process:
      print('**Test torch metrics**'							)
   for split_batches in [True, False]:
      for dispatch_batches in [True, False]:
         _UpperCAmelCase       :       int							   =Accelerator(split_batches=__lowerCamelCase	,   dispatch_batches=__lowerCamelCase							)
         if accelerator.is_local_main_process:
            print(f"With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99"							)
         test_torch_metrics(__lowerCamelCase	,   9_9							)
         accelerator.state._reset_state()
   if accelerator.is_local_main_process:
      print('**Test last batch is not dropped when perfectly divisible**'							)
   _UpperCAmelCase       :       Optional[Any]							   =Accelerator()
   test_torch_metrics(__lowerCamelCase	,   5_1_2							)
   accelerator.state._reset_state()





def lowerCamelCase__      (						__lowerCamelCase	:							Tuple							):







   '''simple docstring'''




   main()


if __name__ == "__main__":
 main()





'''simple docstring'''






from typing import Any





def lowerCamelCase__      (						__lowerCamelCase	:							list	,   __lowerCamelCase	:							list	,   __lowerCamelCase	:							dict	,   __lowerCamelCase	:							dict	,   __lowerCamelCase	:							dict	,   ):







   '''simple docstring'''




   _validation(
       __lowerCamelCase	,   __lowerCamelCase	,   __lowerCamelCase	,   __lowerCamelCase	,   __lowerCamelCase	,   )
   # Creates data structures and fill initial step
   _UpperCAmelCase       :       dict							   ={}
   _UpperCAmelCase       :       dict							   ={}
   for state in states_space:
      _UpperCAmelCase       :       int							   =observations_space[0]
      _UpperCAmelCase       :       int							   =(
          initial_probabilities[state] * emission_probabilities[state][observation]
      )
      _UpperCAmelCase       :       int							   =None

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

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

         _UpperCAmelCase       :       List[Any]							   =arg_max

    # The final observation
   _UpperCAmelCase       :       int							   =observations_space[len(__lowerCamelCase							) - 1]

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

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

   return result





def lowerCamelCase__      (						__lowerCamelCase	:							Any	,   __lowerCamelCase	:							Any	,   __lowerCamelCase	:							Any	,   __lowerCamelCase	:							Any	,   __lowerCamelCase	:							Any	,   ):







   '''simple docstring'''




   _validate_not_empty(
       __lowerCamelCase	,   __lowerCamelCase	,   __lowerCamelCase	,   __lowerCamelCase	,   __lowerCamelCase	,   )
   _validate_lists(__lowerCamelCase	,   __lowerCamelCase							)
   _validate_dicts(
       __lowerCamelCase	,   __lowerCamelCase	,   __lowerCamelCase							)





def lowerCamelCase__      (						__lowerCamelCase	:							Any	,   __lowerCamelCase	:							Any	,   __lowerCamelCase	:							Any	,   __lowerCamelCase	:							Any	,   __lowerCamelCase	:							Any	,   ):







   '''simple docstring'''




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





def lowerCamelCase__      (						__lowerCamelCase	:							Any	,   __lowerCamelCase	:							Any							):







   '''simple docstring'''




   _validate_list(__lowerCamelCase	,   'observations_space'							)
   _validate_list(__lowerCamelCase	,   'states_space'							)





def lowerCamelCase__      (						__lowerCamelCase	:							Any	,   __lowerCamelCase	:							str							):







   '''simple docstring'''




   if not isinstance(_object	,   __lowerCamelCase							):
      _UpperCAmelCase       :       Any							   =f"{var_name} must be a list"
      raise ValueError(__lowerCamelCase							)
   else:
      for x in _object:
         if not isinstance(__lowerCamelCase	,   __lowerCamelCase							):
            _UpperCAmelCase       :       Optional[int]							   =f"{var_name} must be a list of strings"
            raise ValueError(__lowerCamelCase							)





def lowerCamelCase__      (						__lowerCamelCase	:							Any	,   __lowerCamelCase	:							Any	,   __lowerCamelCase	:							Any	,   ):







   '''simple docstring'''




   _validate_dict(__lowerCamelCase	,   'initial_probabilities'	,   __lowerCamelCase							)
   _validate_nested_dict(__lowerCamelCase	,   'transition_probabilities'							)
   _validate_nested_dict(__lowerCamelCase	,   'emission_probabilities'							)





def lowerCamelCase__      (						__lowerCamelCase	:							Any	,   __lowerCamelCase	:							str							):







   '''simple docstring'''




   _validate_dict(_object	,   __lowerCamelCase	,   __lowerCamelCase							)
   for x in _object.values():
      _validate_dict(__lowerCamelCase	,   __lowerCamelCase	,   __lowerCamelCase	,   __lowerCamelCase							)





def lowerCamelCase__      (						__lowerCamelCase	:							Any	,   __lowerCamelCase	:							str	,   __lowerCamelCase	:							type	,   __lowerCamelCase	:							bool = False							):







   '''simple docstring'''




   if not isinstance(_object	,   __lowerCamelCase							):
      _UpperCAmelCase       :       List[str]							   =f"{var_name} must be a dict"
      raise ValueError(__lowerCamelCase							)
   if not all(isinstance(__lowerCamelCase	,   __lowerCamelCase							) for x in _object							):
      _UpperCAmelCase       :       str							   =f"{var_name} all keys must be strings"
      raise ValueError(__lowerCamelCase							)
   if not all(isinstance(__lowerCamelCase	,   __lowerCamelCase							) for x in _object.values()							):
      _UpperCAmelCase       :       int							   ='nested dictionary ' if nested else ''
      _UpperCAmelCase       :       Optional[int]							   =f"{var_name} {nested_text}all values must be {value_type.__name__}"
      raise ValueError(__lowerCamelCase							)


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

 testmod()

