import inspect
import unittest

from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
      import torch

      from transformers import MobileNetVaForImageClassification, MobileNetVaModel
      from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
      from PIL import Image

      from transformers import MobileNetVaImageProcessor





class      lowerCAmelCase						(					_a			):



     def 			a__  (					self       ):
            _A=	self.config_class(**self.inputs_dict       )
            self.parent.assertTrue(hasattr(lowerCAmelCase__				,      'tf_padding'       )       )
            self.parent.assertTrue(hasattr(lowerCAmelCase__				,      'depth_multiplier'       )       )





class      lowerCAmelCase						:



     def __init__(					self				,      lowerCAmelCase__				,      lowerCAmelCase__=13				,      lowerCAmelCase__=3				,      lowerCAmelCase__=32				,      lowerCAmelCase__=0.25				,      lowerCAmelCase__=8				,      lowerCAmelCase__=True				,      lowerCAmelCase__=1024				,      lowerCAmelCase__=32				,      lowerCAmelCase__="relu6"				,      lowerCAmelCase__=0.1				,      lowerCAmelCase__=0.02				,      lowerCAmelCase__=True				,      lowerCAmelCase__=True				,      lowerCAmelCase__=10				,      lowerCAmelCase__=None				,      ):
            _A=	parent
            _A=	batch_size
            _A=	num_channels
            _A=	image_size
            _A=	depth_multiplier
            _A=	min_depth
            _A=	tf_padding
            _A=	int(last_hidden_size * depth_multiplier       )
            _A=	output_stride
            _A=	hidden_act
            _A=	classifier_dropout_prob
            _A=	use_labels
            _A=	is_training
            _A=	num_labels
            _A=	initializer_range
            _A=	scope



     def 			a__  (					self       ):
            _A=	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

            _A=	None
            _A=	None
            if self.use_labels:
                   _A=	ids_tensor([self.batch_size]				,      self.num_labels       )
                   _A=	ids_tensor([self.batch_size, self.image_size, self.image_size]				,      self.num_labels       )

            _A=	self.get_config()

            return config, pixel_values, labels, pixel_labels



     def 			a__  (					self       ):
            return MobileNetVaConfig(
                num_channels=self.num_channels				,      image_size=self.image_size				,      depth_multiplier=self.depth_multiplier				,      min_depth=self.min_depth				,      tf_padding=self.tf_padding				,      hidden_act=self.hidden_act				,      classifier_dropout_prob=self.classifier_dropout_prob				,      initializer_range=self.initializer_range				,      )



     def 			a__  (					self				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__       ):
            _A=	MobileNetVaModel(config=lowerCAmelCase__       )
            model.to(lowerCAmelCase__       )
            model.eval()
            _A=	model(lowerCAmelCase__       )
            self.parent.assertEqual(
                result.last_hidden_state.shape				,      (
                    self.batch_size,
                    self.last_hidden_size,
                    self.image_size // self.output_stride,
                    self.image_size // self.output_stride,
                )				,      )



     def 			a__  (					self				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__       ):
            _A=	self.num_labels
            _A=	MobileNetVaForImageClassification(lowerCAmelCase__       )
            model.to(lowerCAmelCase__       )
            model.eval()
            _A=	model(lowerCAmelCase__				,      labels=lowerCAmelCase__       )
            self.parent.assertEqual(result.logits.shape				,      (self.batch_size, self.num_labels)       )





     def 			a__  (					self       ):
            _A=	self.prepare_config_and_inputs()
            _A,			_A,			_A,			_A=	config_and_inputs
            _A=	{'pixel_values': pixel_values}
            return config, inputs_dict





@require_torch
class      lowerCAmelCase						(					_a			,		_a			,		unittest.TestCase			):
     _SCREAMING_SNAKE_CASE     :     Any		=(MobileNetVaModel, MobileNetVaForImageClassification) if is_torch_available() else ()
     _SCREAMING_SNAKE_CASE     :     List[Any]		=(
         {"""feature-extraction""": MobileNetVaModel, """image-classification""": MobileNetVaForImageClassification}
         if is_torch_available()
         else {}
     )

     _SCREAMING_SNAKE_CASE     :     List[Any]		=False
     _SCREAMING_SNAKE_CASE     :     int		=False
     _SCREAMING_SNAKE_CASE     :     Optional[int]		=False
     _SCREAMING_SNAKE_CASE     :     Optional[int]		=False



     def 			a__  (					self       ):
            _A=	MobileNetVaModelTester(self       )
            _A=	MobileNetVaConfigTester(self				,      config_class=lowerCAmelCase__				,      has_text_modality=lowerCAmelCase__       )



     def 			a__  (					self       ):
            self.config_tester.run_common_tests()



     @unittest.skip(reason='MobileNetV1 does not use inputs_embeds'       )
     def 			a__  (					self       ):
            pass



     @unittest.skip(reason='MobileNetV1 does not support input and output embeddings'       )
     def 			a__  (					self       ):
            pass



     @unittest.skip(reason='MobileNetV1 does not output attentions'       )
     def 			a__  (					self       ):
            pass



     def 			a__  (					self       ):
            _A,			_A=	self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                   _A=	model_class(lowerCAmelCase__       )
                   _A=	inspect.signature(model.forward       )
                   # signature.parameters is an OrderedDict => so arg_names order is deterministic
                   _A=	[*signature.parameters.keys()]

                   _A=	['pixel_values']
                   self.assertListEqual(arg_names[:1]				,      lowerCAmelCase__       )



     def 			a__  (					self       ):
            _A=	self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*lowerCAmelCase__       )



     def 			a__  (					self       ):
            def check_hidden_states_output(lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__       ):
                   _A=	model_class(lowerCAmelCase__       )
                   model.to(lowerCAmelCase__       )
                   model.eval()

                   with torch.no_grad():
                          _A=	model(**self._prepare_for_class(lowerCAmelCase__				,      lowerCAmelCase__       )       )

                   _A=	outputs.hidden_states

                   _A=	26
                   self.assertEqual(len(lowerCAmelCase__       )				,      lowerCAmelCase__       )

            _A,			_A=	self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                   _A=	True
                   check_hidden_states_output(lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__       )

                   # check that output_hidden_states also work using config
                   del inputs_dict["output_hidden_states"]
                   _A=	True

                   check_hidden_states_output(lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__       )



     def 			a__  (					self       ):
            _A=	self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase__       )





     @slow
     def 			a__  (					self       ):
            for model_name in MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                   _A=	MobileNetVaModel.from_pretrained(lowerCAmelCase__       )
                   self.assertIsNotNone(lowerCAmelCase__       )


def 							UpperCamelCase				(    )		->						Optional[Any]:




       '''simple docstring'''






       _A=	Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'   )
       return image





@require_torch
@require_vision
class      lowerCAmelCase						(					unittest.TestCase			):



     @cached_property
     def 			a__  (					self       ):
            return (
                MobileNetVaImageProcessor.from_pretrained('google/mobilenet_v1_1.0_224'       ) if is_vision_available() else None
            )





     @slow
     def 			a__  (					self       ):
            _A=	MobileNetVaForImageClassification.from_pretrained('google/mobilenet_v1_1.0_224'       ).to(lowerCAmelCase__       )

            _A=	self.default_image_processor
            _A=	prepare_img()
            _A=	image_processor(images=lowerCAmelCase__				,      return_tensors='pt'       ).to(lowerCAmelCase__       )

            # forward pass
            with torch.no_grad():
                   _A=	model(**lowerCAmelCase__       )

            # verify the logits
            _A=	torch.Size((1, 1001)       )
            self.assertEqual(outputs.logits.shape				,      lowerCAmelCase__       )

            _A=	torch.tensor([-4.1739, -1.1233, 3.1205]       ).to(lowerCAmelCase__       )

            self.assertTrue(torch.allclose(outputs.logits[0, :3]				,      lowerCAmelCase__				,      atol=1E-4       )       )
#
# This a `torch.distributed` diagnostics script that checks that all GPUs in the cluster (one or
# many nodes) can talk to each other via nccl and allocate gpu memory.
#
# To run first adjust the number of processes and nodes:
#
# python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py
#
# You may need to add --master_addr $MASTER_ADDR --master_port $MASTER_PORT if using a custom addr:port
#
# You can also use the rdzv API: --rdzv_endpoint $MASTER_ADDR:$MASTER_PORT --rdzv_backend c10d
#
# use torch.distributed.launch instead of torch.distributed.run for torch < 1.9
#
# If you get a hanging in `barrier` calls you have some network issues, you may try to debug this with:
#
# NCCL_DEBUG=INFO python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py
#
# which should tell you what's going on behind the scenes.
#
#
# This script can be run via `srun` in the SLURM environment as well. Here is a SLURM script that
# runs on 2 nodes of 4 gpus per node:
#
# #SBATCH --job-name=test-nodes        # name
# #SBATCH --nodes=2                    # nodes
# #SBATCH --ntasks-per-node=1          # crucial - only 1 task per dist per node!
# #SBATCH --cpus-per-task=10           # number of cores per tasks
# #SBATCH --gres=gpu:4                 # number of gpus
# #SBATCH --time 0:05:00               # maximum execution time (HH:MM:SS)
# #SBATCH --output=%x-%j.out           # output file name
#
# GPUS_PER_NODE=4
# MASTER_ADDR=$(scontrol show hostnames $SLURM_JOB_NODELIST | head -n 1)
# MASTER_PORT=6000
#
# srun --jobid $SLURM_JOBID bash -c 'python -m torch.distributed.run \
# --nproc_per_node $GPUS_PER_NODE --nnodes $SLURM_NNODES --node_rank $SLURM_PROCID \
# --master_addr $MASTER_ADDR --master_port $MASTER_PORT \
# torch-distributed-gpu-test.py'
#

import fcntl
import os
import socket

import torch
import torch.distributed as dist

def 							UpperCamelCase				(    *lowerCAmelCase_   )		->						Dict:




       '''simple docstring'''






       with open(lowerCAmelCase_       ,					'r'   ) as fh:
              fcntl.flock(lowerCAmelCase_       ,					fcntl.LOCK_EX   )
              try:
                     print(*lowerCAmelCase_   )
              finally:
                     fcntl.flock(lowerCAmelCase_       ,					fcntl.LOCK_UN   )


UpperCAmelCase_								=							int(os.environ['''LOCAL_RANK'''])
torch.cuda.set_device(local_rank)
UpperCAmelCase_								=							torch.device('''cuda''', local_rank)
UpperCAmelCase_								=							socket.gethostname()

UpperCAmelCase_								=							F"[{hostname}-{local_rank}]"

try:
      # test distributed
      dist.init_process_group('''nccl''')
      dist.all_reduce(torch.ones(1).to(device), op=dist.ReduceOp.SUM)
      dist.barrier()

      # test cuda is available and can allocate memory
      torch.cuda.is_available()
      torch.ones(1).cuda(local_rank)

      # global rank
      UpperCAmelCase_								=							dist.get_rank()
      UpperCAmelCase_								=							dist.get_world_size()

      printflock(F"{gpu} is OK (global rank: {rank}/{world_size})")

      dist.barrier()
      if rank == 0:
            printflock(F"pt={torch.__version__}, cuda={torch.version.cuda}, nccl={torch.cuda.nccl.version()}")

except Exception:
      printflock(F"{gpu} is broken")
      raise