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


snake_case__					:      List[Any]				      =  '''true'''


def  _lowerCamelCase   (				lowerCamelCase_ :							int     ,					lowerCamelCase_ :							Any=82     ,					lowerCamelCase_ :							str=16       ):



     """simple docstring"""





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


def  _lowerCamelCase   (				lowerCamelCase_ :							Optional[Any]     ,					lowerCamelCase_ :							Tuple=False       ):



     """simple docstring"""





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

     def tokenize_function(lowerCamelCase_ :							Optional[Any]       ):
          UpperCAmelCase_							:       List[Any]       =				tokenizer(examples['sentence1']     ,					examples['sentence2']     ,					truncation=_lowerCamelCase     ,					max_length=_lowerCamelCase       )
          return outputs

     with accelerator.main_process_first():
          UpperCAmelCase_							:       Union[str, Any]       =				dataset.map(
              _lowerCamelCase     ,					batched=_lowerCamelCase     ,					remove_columns=['idx', 'sentence1', 'sentence2']     ,					)

     UpperCAmelCase_							:       int       =				tokenized_datasets.rename_column('label'     ,					'labels'       )

     def collate_fn(lowerCamelCase_ :							Any       ):
          if use_longest:
               return tokenizer.pad(_lowerCamelCase     ,					padding='longest'     ,					return_tensors='pt'       )
          return tokenizer.pad(_lowerCamelCase     ,					padding='max_length'     ,					max_length=128     ,					return_tensors='pt'       )

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


def  _lowerCamelCase   (				lowerCamelCase_ :							int     ,					lowerCamelCase_ :							Tuple       ):



     """simple docstring"""





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


def  _lowerCamelCase   (				lowerCamelCase_ :							Optional[int]     ,					lowerCamelCase_ :							List[str]     ,					lowerCamelCase_ :							Union[str, Any]       ):



     """simple docstring"""





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


def  _lowerCamelCase   (				lowerCamelCase_ :							List[str]     ,					lowerCamelCase_ :							Union[str, Any]=82     ,					lowerCamelCase_ :							int=False     ,					lowerCamelCase_ :							List[Any]=False     ,					lowerCamelCase_ :							Tuple=16       ):



     """simple docstring"""





     UpperCAmelCase_							:       Optional[int]       =				get_basic_setup(_lowerCamelCase     ,					_lowerCamelCase     ,					_lowerCamelCase       )
     UpperCAmelCase_							:       Optional[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_ :							int = False     ,					lowerCamelCase_ :							Tuple = False       ):



     """simple docstring"""





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

     # Then do distributed
     UpperCAmelCase_							:       Optional[int]       =				setup["""ddp"""]
     model.eval()
     for batch in dataloader:
          with torch.inference_mode():
               UpperCAmelCase_							:       Union[str, Any]       =				model(**_lowerCamelCase       )
          UpperCAmelCase_							:       Optional[Any]       =				outputs.logits.argmax(dim=-1       )
          UpperCAmelCase_							:       List[str]       =				batch["""labels"""]
          UpperCAmelCase_							:       Optional[Any]       =				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_							:       str       =				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_							:       Tuple       =				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     ,					99       )
               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     ,					512       )
     accelerator.state._reset_state()


def  _lowerCamelCase   (				lowerCamelCase_ :							List[str]       ):



     """simple docstring"""





     main()


if __name__ == "__main__":
 main()







'''simple docstring'''


import hashlib
import unittest

from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image
else:






 class 					__SCREAMING_SNAKE_CASE						:







   '''simple docstring'''






   @staticmethod
   def _UpperCamelCase					(			*snake_case_				,		**snake_case_						):




        '''simple docstring'''

        pass


def  _lowerCamelCase   (				lowerCamelCase_ :							Image       ):



     """simple docstring"""





     UpperCAmelCase_							:       Dict       =				hashlib.mda(image.tobytes()       )
     return m.hexdigest()






@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 					__SCREAMING_SNAKE_CASE						(	unittest.TestCase	):







  '''simple docstring'''






  lowerCamelCase_		:Optional[int]						   =		MODEL_FOR_DEPTH_ESTIMATION_MAPPING


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       str       =				DepthEstimationPipeline(model=snake_case_				,		image_processor=snake_case_						)
       return depth_estimator, [
           "./tests/fixtures/tests_samples/COCO/000000039769.png",
           "./tests/fixtures/tests_samples/COCO/000000039769.png",
       ]


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       Dict       =				depth_estimator('./tests/fixtures/tests_samples/COCO/000000039769.png'						)
       self.assertEqual({'predicted_depth': ANY(torch.Tensor						), 'depth': ANY(Image.Image						)}				,		snake_case_						)
       import datasets

       UpperCAmelCase_							:       Optional[int]       =				datasets.load_dataset('hf-internal-testing/fixtures_image_utils'				,		'image'				,		split='test'						)
       UpperCAmelCase_							:       Union[str, Any]       =				depth_estimator(
           [
               Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'						),
               'http://images.cocodataset.org/val2017/000000039769.jpg',
               # RGBA
               dataset[0]['file'],
               # LA
               dataset[1]['file'],
               # L
               dataset[2]['file'],
           ]						)
       self.assertEqual(
           [
               {'predicted_depth': ANY(torch.Tensor						), 'depth': ANY(Image.Image						)},
               {'predicted_depth': ANY(torch.Tensor						), 'depth': ANY(Image.Image						)},
               {'predicted_depth': ANY(torch.Tensor						), 'depth': ANY(Image.Image						)},
               {'predicted_depth': ANY(torch.Tensor						), 'depth': ANY(Image.Image						)},
               {'predicted_depth': ANY(torch.Tensor						), 'depth': ANY(Image.Image						)},
           ]				,		snake_case_				,		)


  @require_tf
  @unittest.skip('Depth estimation is not implemented in TF'						)
  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       pass


  @slow
  @require_torch
  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       Any       =				'Intel/dpt-large'
       UpperCAmelCase_							:       Dict       =				pipeline('depth-estimation'				,		model=snake_case_						)
       UpperCAmelCase_							:       Optional[int]       =				depth_estimator('http://images.cocodataset.org/val2017/000000039769.jpg'						)
       UpperCAmelCase_							:       Union[str, Any]       =				hashimage(outputs['depth']						)

       # This seems flaky.
       # self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
       self.assertEqual(nested_simplify(outputs['predicted_depth'].max().item()						)				,		29.3_04						)
       self.assertEqual(nested_simplify(outputs['predicted_depth'].min().item()						)				,		2.6_62						)






  @require_torch
  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       self.skipTest('There is not hf-internal-testing tiny model for either GLPN nor DPT'						)






