




"""simple docstring"""







import inspect
import unittest

from transformers import ResNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
    import jax
    import jax.numpy as jnp

    from transformers.models.resnet.modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel

if is_vision_available():
    from PIL import Image

    from transformers import AutoImageProcessor

class 							snake_case_ (				unittest.TestCase ):







   """simple docstring"""




   def __init__(		self					,				lowerCamelCase_					,				lowerCamelCase_=3					,				lowerCamelCase_=3_2					,				lowerCamelCase_=3					,				lowerCamelCase_=1_0					,				lowerCamelCase_=[1_0, 2_0, 3_0, 4_0]					,				lowerCamelCase_=[1, 1, 2, 1]					,				lowerCamelCase_=True					,				lowerCamelCase_=True					,				lowerCamelCase_="relu"					,				lowerCamelCase_=3					,				lowerCamelCase_=None					,				)							->      str:
       UpperCamelCase										=  parent
       UpperCamelCase										=  batch_size
       UpperCamelCase										=  image_size
       UpperCamelCase										=  num_channels
       UpperCamelCase										=  embeddings_size
       UpperCamelCase										=  hidden_sizes
       UpperCamelCase										=  depths
       UpperCamelCase										=  is_training
       UpperCamelCase										=  use_labels
       UpperCamelCase										=  hidden_act
       UpperCamelCase										=  num_labels
       UpperCamelCase										=  scope
       UpperCamelCase										=  len(lowerCamelCase_)







   def      UpperCAmelCase__ (		self)							->      Optional[int]:
       UpperCamelCase										=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

       UpperCamelCase										=  self.get_config()

       return config, pixel_values







   def      UpperCAmelCase__ (		self)							->      str:
       return ResNetConfig(
           num_channels=self.num_channels					,				embeddings_size=self.embeddings_size					,				hidden_sizes=self.hidden_sizes					,				depths=self.depths					,				hidden_act=self.hidden_act					,				num_labels=self.num_labels					,				image_size=self.image_size					,				)







   def      UpperCAmelCase__ (		self					,				lowerCamelCase_					,				lowerCamelCase_)							->      List[Any]:
       UpperCamelCase										=  FlaxResNetModel(config=lowerCamelCase_)
       UpperCamelCase										=  model(lowerCamelCase_)

       # Output shape (b, c, h, w)
       self.parent.assertEqual(
           result.last_hidden_state.shape					,				(self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2)					,				)







   def      UpperCAmelCase__ (		self					,				lowerCamelCase_					,				lowerCamelCase_)							->      Optional[Any]:
       UpperCamelCase										=  self.num_labels
       UpperCamelCase										=  FlaxResNetForImageClassification(config=lowerCamelCase_)
       UpperCamelCase										=  model(lowerCamelCase_)
       self.parent.assertEqual(result.logits.shape					,				(self.batch_size, self.num_labels))







   def      UpperCAmelCase__ (		self)							->      Union[str, Any]:
       UpperCamelCase										=  self.prepare_config_and_inputs()
       UpperCamelCase					,  UpperCamelCase										=  config_and_inputs
       UpperCamelCase										=  {'''pixel_values''': pixel_values}
       return config, inputs_dict

@require_flax
class 							snake_case_ (				lowerCamelCase_  ,   unittest.TestCase ):







   """simple docstring"""




   A_										=					(FlaxResNetModel, FlaxResNetForImageClassification) if is_flax_available() else ()

   A_										=					False
   A_										=					False
   A_										=					False







   def      UpperCAmelCase__ (		self)							->      None:
       UpperCamelCase										=  FlaxResNetModelTester(self)
       UpperCamelCase										=  ConfigTester(self					,				config_class=lowerCamelCase_					,				has_text_modality=lowerCamelCase_)







   def      UpperCAmelCase__ (		self)							->      List[str]:
       self.create_and_test_config_common_properties()
       self.config_tester.create_and_test_config_to_json_string()
       self.config_tester.create_and_test_config_to_json_file()
       self.config_tester.create_and_test_config_from_and_save_pretrained()
       self.config_tester.create_and_test_config_with_num_labels()
       self.config_tester.check_config_can_be_init_without_params()
       self.config_tester.check_config_arguments_init()







   def      UpperCAmelCase__ (		self)							->      Optional[int]:
       return







   def      UpperCAmelCase__ (		self)							->      Dict:
       UpperCamelCase										=  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*lowerCamelCase_)







   def      UpperCAmelCase__ (		self)							->      Dict:
       UpperCamelCase										=  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*lowerCamelCase_)







   @unittest.skip(reason='''ResNet does not use inputs_embeds''')
   def      UpperCAmelCase__ (		self)							->      List[str]:
       pass







   @unittest.skip(reason='''ResNet does not support input and output embeddings''')
   def      UpperCAmelCase__ (		self)							->      Any:
       pass







   def      UpperCAmelCase__ (		self)							->      str:
       UpperCamelCase					,  UpperCamelCase										=  self.model_tester.prepare_config_and_inputs_for_common()

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

           UpperCamelCase										=  ['''pixel_values''']
           self.assertListEqual(arg_names[:1]					,				lowerCamelCase_)







   def      UpperCAmelCase__ (		self)							->      Any:
       def check_hidden_states_output(lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_):
           UpperCamelCase										=  model_class(lowerCamelCase_)

           UpperCamelCase										=  model(**self._prepare_for_class(lowerCamelCase_					,				lowerCamelCase_))

           UpperCamelCase										=  outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

           UpperCamelCase										=  self.model_tester.num_stages
           self.assertEqual(len(lowerCamelCase_)					,				expected_num_stages + 1)







   @unittest.skip(reason='''ResNet does not use feedforward chunking''')
   def      UpperCAmelCase__ (		self)							->      List[Any]:
       pass







   def      UpperCAmelCase__ (		self)							->      str:
       UpperCamelCase					,  UpperCamelCase										=  self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
           with self.subTest(model_class.__name__):
               UpperCamelCase										=  self._prepare_for_class(lowerCamelCase_					,				lowerCamelCase_)
               UpperCamelCase										=  model_class(lowerCamelCase_)

               @jax.jit
               def model_jitted(lowerCamelCase_					,				**lowerCamelCase_):
                   return model(pixel_values=lowerCamelCase_					,				**lowerCamelCase_)

               with self.subTest('''JIT Enabled'''):
                   UpperCamelCase										=  model_jitted(**lowerCamelCase_).to_tuple()

               with self.subTest('''JIT Disabled'''):
                   with jax.disable_jit():
                       UpperCamelCase										=  model_jitted(**lowerCamelCase_).to_tuple()

               self.assertEqual(len(lowerCamelCase_)					,				len(lowerCamelCase_))
               for jitted_output, output in zip(lowerCamelCase_					,				lowerCamelCase_):
                   self.assertEqual(jitted_output.shape					,				output.shape)





def     __snake_case   (							):


    """simple docstring"""







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


@require_flax
class 							snake_case_ (				unittest.TestCase ):







   """simple docstring"""




   @cached_property
   def      UpperCAmelCase__ (		self)							->      Dict:
       return AutoImageProcessor.from_pretrained('''microsoft/resnet-50''') if is_vision_available() else None







   @slow
   def      UpperCAmelCase__ (		self)							->      List[Any]:
       UpperCamelCase										=  FlaxResNetForImageClassification.from_pretrained('''microsoft/resnet-50''')

       UpperCamelCase										=  self.default_image_processor
       UpperCamelCase										=  prepare_img()
       UpperCamelCase										=  image_processor(images=lowerCamelCase_					,				return_tensors='''np''')

       UpperCamelCase										=  model(**lowerCamelCase_)

       # verify the logits
       UpperCamelCase										=  (1, 1_0_0_0)
       self.assertEqual(outputs.logits.shape					,				lowerCamelCase_)

       UpperCamelCase										=  jnp.array([-11.1069, -9.7877, -8.3777])

       self.assertTrue(jnp.allclose(outputs.logits[0, :3]					,				lowerCamelCase_					,				atol=1e-4))





"""simple docstring"""







import os
import sys
from pathlib import Path

import pytest

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node

from .utils import execute_subprocess_async, get_torch_dist_unique_port, require_torch





def     __snake_case   (							):


    """simple docstring"""







    UpperCamelCase										=  Dataset.from_dict({'''i''': range(17						)}						)
    UpperCamelCase										=  len(_lowercase						)
    UpperCamelCase										=  3
    UpperCamelCase										=  [
        split_dataset_by_node(_lowercase ,rank=_lowercase ,world_size=_lowercase						) for rank in range(_lowercase						)
    ]
    assert sum(len(_lowercase						) for ds in datasets_per_rank						) == full_size
    assert len({tuple(x.values()						) for ds in datasets_per_rank for x in ds}						) == full_size





def     __snake_case   (							):


    """simple docstring"""







    def gen():
        return ({"i": i} for i in range(17						))

    UpperCamelCase										=  3
    UpperCamelCase										=  IterableDataset.from_generator(_lowercase						)
    UpperCamelCase										=  len(list(_lowercase						)						)
    UpperCamelCase										=  [
        split_dataset_by_node(_lowercase ,rank=_lowercase ,world_size=_lowercase						) for rank in range(_lowercase						)
    ]
    assert sum(len(list(_lowercase						)						) for ds in datasets_per_rank						) == full_size
    assert len({tuple(x.values()						) for ds in datasets_per_rank for x in ds}						) == full_size





@pytest.mark.parametrize('''shards_per_node''' ,[1, 2, 3]						)
def     __snake_case   (							_lowercase						):


    """simple docstring"""







    def gen(_lowercase						):
        for shard in shards:
            yield from ({"i": i, "shard": shard} for i in range(17						))

    UpperCamelCase										=  3
    UpperCamelCase										=  shards_per_node * world_size
    UpperCamelCase										=  {'''shards''': [f'shard_{shard_idx}.txt' for shard_idx in range(_lowercase						)]}
    UpperCamelCase										=  IterableDataset.from_generator(_lowercase ,gen_kwargs=_lowercase						)
    UpperCamelCase										=  len(list(_lowercase						)						)
    assert full_ds.n_shards == world_size * shards_per_node
    UpperCamelCase										=  [
        split_dataset_by_node(_lowercase ,rank=_lowercase ,world_size=_lowercase						) for rank in range(_lowercase						)
    ]
    assert [ds.n_shards for ds in datasets_per_rank] == [shards_per_node] * world_size
    assert sum(len(list(_lowercase						)						) for ds in datasets_per_rank						) == full_size
    assert len({tuple(x.values()						) for ds in datasets_per_rank for x in ds}						) == full_size





def     __snake_case   (							):


    """simple docstring"""







    def gen():
        return ({"i": i} for i in range(17						))

    UpperCamelCase										=  2
    UpperCamelCase										=  IterableDataset.from_generator(_lowercase						)
    UpperCamelCase										=  len(list(_lowercase						)						)

    UpperCamelCase										=  split_dataset_by_node(_lowercase ,rank=0 ,world_size=_lowercase						).shuffle(seed=42						)
    assert len(list(_lowercase						)						) == 1 + full_size // world_size
    with pytest.raises(_lowercase						):
        split_dataset_by_node(_lowercase ,rank=0 ,world_size=_lowercase						).shuffle()

    UpperCamelCase										=  split_dataset_by_node(full_ds.shuffle(seed=42						) ,rank=0 ,world_size=_lowercase						)
    assert len(list(_lowercase						)						) == 1 + full_size // world_size
    with pytest.raises(_lowercase						):
        split_dataset_by_node(full_ds.shuffle() ,rank=0 ,world_size=_lowercase						)





@pytest.mark.parametrize('''streaming''' ,[False, True]						)
@require_torch
@pytest.mark.skipif(os.name == '''nt''' ,reason='''execute_subprocess_async doesn\'t support windows'''						)
@pytest.mark.integration
def     __snake_case   (							_lowercase						):


    """simple docstring"""







    UpperCamelCase										=  2
    UpperCamelCase										=  get_torch_dist_unique_port()
    UpperCamelCase										=  Path(_lowercase						).resolve().parent / '''distributed_scripts''' / '''launch_torch_distributed.py'''
    UpperCamelCase										=  f'\n        -m torch.distributed.launch\n        --nproc_per_node={nproc_per_node}\n        --master_port={master_port}\n        {test_script}\n    '.split()
    UpperCamelCase										=  f'\n        --streaming={streaming}\n    '.split()
    UpperCamelCase										=  [sys.executable] + distributed_args + args
    execute_subprocess_async(_lowercase ,env=os.environ.copy()						)





@pytest.mark.parametrize(
    '''nproc_per_node, num_workers''' ,[
        (2, 2),  # each node has 2 shards and each worker has 1 shards
        (3, 2),  # each node uses all the shards but skips examples, and each worker has 2 shards
    ] ,)
@require_torch
@pytest.mark.skipif(os.name == '''nt''' ,reason='''execute_subprocess_async doesn\'t support windows'''						)
@pytest.mark.integration
def     __snake_case   (							_lowercase ,_lowercase						):


    """simple docstring"""







    UpperCamelCase										=  True
    UpperCamelCase										=  get_torch_dist_unique_port()
    UpperCamelCase										=  Path(_lowercase						).resolve().parent / '''distributed_scripts''' / '''launch_torch_distributed.py'''
    UpperCamelCase										=  f'\n        -m torch.distributed.launch\n        --nproc_per_node={nproc_per_node}\n        --master_port={master_port}\n        {test_script}\n    '.split()
    UpperCamelCase										=  f'\n        --streaming={streaming}\n        --num_workers={num_workers}\n    '.split()
    UpperCamelCase										=  [sys.executable] + distributed_args + args
    execute_subprocess_async(_lowercase ,env=os.environ.copy()						)