

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


_a		       =   'true'







def _a (       SCREAMING_SNAKE_CASE		:			Dict    ,					SCREAMING_SNAKE_CASE		:			Any=82    ,					SCREAMING_SNAKE_CASE		:			str=16		)    ->    List[Any]:

 """simple docstring"""






 set_seed(42		)
 __lowerCAmelCase:   Union[str, Any]   	=      RegressionModel()
 __lowerCAmelCase:   Optional[int]   	=      deepcopy(__snake_case		)
 __lowerCAmelCase:   Tuple   	=      RegressionDataset(length=__snake_case		)
 __lowerCAmelCase:   Any   	=      DataLoader(__snake_case    ,					batch_size=__snake_case		)
 model.to(accelerator.device		)
 __lowerCAmelCase	,							__lowerCAmelCase:   List[str]   	=      accelerator.prepare(__snake_case    ,					__snake_case		)
 return model, ddp_model, dataloader







def _a (       SCREAMING_SNAKE_CASE		:			Accelerator    ,					SCREAMING_SNAKE_CASE		:			List[str]=False		)    ->    int:

 """simple docstring"""






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

 def tokenize_function(SCREAMING_SNAKE_CASE		:			Optional[int]		):
  __lowerCAmelCase:   List[Any]   	=      tokenizer(examples['sentence1']    ,					examples['sentence2']    ,					truncation=__snake_case    ,					max_length=__snake_case		)
  return outputs

 with accelerator.main_process_first():
  __lowerCAmelCase:   Tuple   	=      dataset.map(
      __snake_case    ,					batched=__snake_case    ,					remove_columns=['idx', 'sentence1', 'sentence2']    ,					)

 __lowerCAmelCase:   str   	=      tokenized_datasets.rename_column('label'    ,					'labels'		)

 def collate_fn(SCREAMING_SNAKE_CASE		:			str		):
  if use_longest:
   return tokenizer.pad(__snake_case    ,					padding='longest'    ,					return_tensors='pt'		)
  return tokenizer.pad(__snake_case    ,					padding='max_length'    ,					max_length=1_28    ,					return_tensors='pt'		)

 return DataLoader(__snake_case    ,					shuffle=__snake_case    ,					collate_fn=__snake_case    ,					batch_size=16		)







def _a (       SCREAMING_SNAKE_CASE		:			List[Any]    ,					SCREAMING_SNAKE_CASE		:			Dict		)    ->    int:

 """simple docstring"""






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







def _a (       SCREAMING_SNAKE_CASE		:			Any    ,					SCREAMING_SNAKE_CASE		:			int    ,					SCREAMING_SNAKE_CASE		:			int		)    ->    str:

 """simple docstring"""






 __lowerCAmelCase:   Any   	=      []
 for batch in dataloader:
  __lowerCAmelCase	,							__lowerCAmelCase:   Dict   	=      batch.values()
  with torch.no_grad():
   __lowerCAmelCase:   List[Any]   	=      model(__snake_case		)
   __lowerCAmelCase	,							__lowerCAmelCase:   Optional[Any]   	=      accelerator.gather_for_metrics((logit, target)		)
   logits_and_targets.append((logit, target)		)
 __lowerCAmelCase	,							__lowerCAmelCase:   Optional[Any]   	=      [], []
 for logit, targ in logits_and_targets:
  logits.append(__snake_case		)
  targs.append(__snake_case		)
 __lowerCAmelCase	,							__lowerCAmelCase:   List[Any]   	=      torch.cat(__snake_case		), torch.cat(__snake_case		)
 return logits, targs







def _a (       SCREAMING_SNAKE_CASE		:			Accelerator    ,					SCREAMING_SNAKE_CASE		:			int=82    ,					SCREAMING_SNAKE_CASE		:			str=False    ,					SCREAMING_SNAKE_CASE		:			int=False    ,					SCREAMING_SNAKE_CASE		:			Optional[int]=16		)    ->    Tuple:

 """simple docstring"""






 __lowerCAmelCase	,							__lowerCAmelCase	,							__lowerCAmelCase:   Optional[Any]   	=      get_basic_setup(__snake_case    ,					__snake_case    ,					__snake_case		)
 __lowerCAmelCase	,							__lowerCAmelCase:   Any   	=      generate_predictions(__snake_case    ,					__snake_case    ,					__snake_case		)
 assert (
     len(__snake_case		) == num_samples
 ), f'''Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(__snake_case		)}'''







def _a (       SCREAMING_SNAKE_CASE		:			bool = False    ,					SCREAMING_SNAKE_CASE		:			bool = False		)    ->    Tuple:

 """simple docstring"""






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

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

 """simple docstring"""






 __lowerCAmelCase:   Optional[int]   	=      Accelerator(split_batches=__snake_case    ,					dispatch_batches=__snake_case		)
 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(__snake_case    ,					__snake_case		)
    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]:
   __lowerCAmelCase:   Any   	=      Accelerator(split_batches=__snake_case    ,					dispatch_batches=__snake_case		)
   if accelerator.is_local_main_process:
    print(f'''With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99'''		)
   test_torch_metrics(__snake_case    ,					99		)
   accelerator.state._reset_state()
 if accelerator.is_local_main_process:
  print('**Test last batch is not dropped when perfectly divisible**'		)
 __lowerCAmelCase:   List[str]   	=      Accelerator()
 test_torch_metrics(__snake_case    ,					5_12		)
 accelerator.state._reset_state()







def _a (       SCREAMING_SNAKE_CASE		:			str		)    ->    int:

 """simple docstring"""






 main()


if __name__ == "__main__":
  main()













"""simple docstring"""



import unittest

from transformers import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
    from PIL import Image
else:

    class        a     :







        @staticmethod
        def 			UpperCamelCase_      (			*_lowerCamelCase							,		**_lowerCamelCase       ):
          pass

@is_pipeline_test
@require_torch
@require_vision
class        a     (   unittest.TestCase	):
    UpperCAmelCase_ :     Union[str, Any]	     =MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase       ):
      lowercase							    =       pipeline('visual-question-answering'							,		model='hf-internal-testing/tiny-vilt-random-vqa'       )
      lowercase							    =       [
          {
              'image': Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'       ),
              'question': 'How many cats are there?',
          },
          {
              'image': './tests/fixtures/tests_samples/COCO/000000039769.png',
              'question': 'How many cats are there?',
          },
      ]
      return vqa_pipeline, examples







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase							,		_lowerCamelCase       ):
      lowercase							    =       vqa_pipeline(_lowerCamelCase							,		top_k=1       )
      self.assertEqual(
          _lowerCamelCase							,		[
              [{'score': ANY(_lowerCamelCase       ), 'answer': ANY(_lowerCamelCase       )}],
              [{'score': ANY(_lowerCamelCase       ), 'answer': ANY(_lowerCamelCase       )}],
          ]							,		)







    @require_torch
    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       pipeline('visual-question-answering'							,		model='hf-internal-testing/tiny-vilt-random-vqa'       )
      lowercase							    =       './tests/fixtures/tests_samples/COCO/000000039769.png'
      lowercase							    =       'How many cats are there?'

      lowercase							    =       vqa_pipeline(image=_lowerCamelCase							,		question='How many cats are there?'							,		top_k=2       )
      self.assertEqual(
          _lowerCamelCase							,		[{'score': ANY(_lowerCamelCase       ), 'answer': ANY(_lowerCamelCase       )}, {'score': ANY(_lowerCamelCase       ), 'answer': ANY(_lowerCamelCase       )}]       )

      lowercase							    =       vqa_pipeline({'image': image, 'question': question}							,		top_k=2       )
      self.assertEqual(
          _lowerCamelCase							,		[{'score': ANY(_lowerCamelCase       ), 'answer': ANY(_lowerCamelCase       )}, {'score': ANY(_lowerCamelCase       ), 'answer': ANY(_lowerCamelCase       )}]       )







    @slow
    @require_torch
    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       pipeline('visual-question-answering'							,		model='dandelin/vilt-b32-finetuned-vqa'       )
      lowercase							    =       './tests/fixtures/tests_samples/COCO/000000039769.png'
      lowercase							    =       'How many cats are there?'

      lowercase							    =       vqa_pipeline(image=_lowerCamelCase							,		question=_lowerCamelCase							,		top_k=2       )
      self.assertEqual(
          nested_simplify(_lowerCamelCase							,		decimals=4       )							,		[{'score': 0.8_7_9_9, 'answer': '2'}, {'score': 0.2_9_6, 'answer': '1'}]       )

      lowercase							    =       vqa_pipeline({'image': image, 'question': question}							,		top_k=2       )
      self.assertEqual(
          nested_simplify(_lowerCamelCase							,		decimals=4       )							,		[{'score': 0.8_7_9_9, 'answer': '2'}, {'score': 0.2_9_6, 'answer': '1'}]       )

      lowercase							    =       vqa_pipeline(
          [{'image': image, 'question': question}, {'image': image, 'question': question}]							,		top_k=2       )
      self.assertEqual(
          nested_simplify(_lowerCamelCase							,		decimals=4       )							,		[[{'score': 0.8_7_9_9, 'answer': '2'}, {'score': 0.2_9_6, 'answer': '1'}]] * 2							,		)







    @require_tf
    @unittest.skip('Visual question answering not implemented in TF'       )
    def 			UpperCamelCase_      (			self       ):
      pass


