


'''simple docstring'''





import warnings
from dataclasses import dataclass, field
from typing import Optional, Tuple

from .training_args import TrainingArguments
from .utils import cached_property, is_tf_available, logging, requires_backends


lowerCAmelCase__						:List[Any]         =							logging.get_logger(__name__)

if is_tf_available():
		import tensorflow as tf

@dataclass
class 	__a   (       UpperCAmelCase    ):
		_a			:						Any    			=			'tf'
		_a			:						Optional[str]    			=			field(
		    default=UpperCAmelCase					,   metadata={'help': 'Name of TPU'}					,   )

		_a			:						Optional[str]    			=			field(
		    default=UpperCAmelCase					,   metadata={'help': 'Zone of TPU'}					,   )

		_a			:						Optional[str]    			=			field(
		    default=UpperCAmelCase					,   metadata={'help': 'Name of Cloud TPU-enabled project'}					,   )

		_a			:						float    			=			field(
		    default=1.0					,   metadata={'help': 'Power for the Polynomial decay LR scheduler.'}					,   )

		_a			:						bool    			=			field(default=UpperCAmelCase					,   metadata={'help': 'Whether to activate the XLA compilation or not'}    )
		@cached_property
		def 			UpperCAmelCase__       (    self		)				-> Tuple["tf.distribute.Strategy", int]:




									"""simple docstring"""





									requires_backends(self    ,					['tf']		)
									logger.info('Tensorflow: setting up strategy'		)

									_UpperCAmelCase					=    tf.config.list_physical_devices('GPU'		)

									# Set to float16 at first
									if self.fpaa:
																tf.keras.mixed_precision.set_global_policy('mixed_float16'		)

									if self.no_cuda:
																_UpperCAmelCase					=    tf.distribute.OneDeviceStrategy(device='/cpu:0'		)
									else:
																try:
																							if self.tpu_name:
																														_UpperCAmelCase					=    tf.distribute.cluster_resolver.TPUClusterResolver(
																														    self.tpu_name    ,					zone=self.tpu_zone    ,					project=self.gcp_project		)
																							else:
																														_UpperCAmelCase					=    tf.distribute.cluster_resolver.TPUClusterResolver()
																except ValueError:
																							if self.tpu_name:
																														raise RuntimeError(f'''Couldn\'t connect to TPU {self.tpu_name}!'''		)
																							else:
																														_UpperCAmelCase					=    None

																if tpu:
																							# Set to bfloat16 in case of TPU
																							if self.fpaa:
																														tf.keras.mixed_precision.set_global_policy('mixed_bfloat16'		)

																							tf.config.experimental_connect_to_cluster(_SCREAMING_SNAKE_CASE		)
																							tf.tpu.experimental.initialize_tpu_system(_SCREAMING_SNAKE_CASE		)

																							_UpperCAmelCase					=    tf.distribute.TPUStrategy(_SCREAMING_SNAKE_CASE		)

																elif len(_SCREAMING_SNAKE_CASE		) == 0:
																							_UpperCAmelCase					=    tf.distribute.OneDeviceStrategy(device='/cpu:0'		)
																elif len(_SCREAMING_SNAKE_CASE		) == 1:
																							_UpperCAmelCase					=    tf.distribute.OneDeviceStrategy(device='/gpu:0'		)
																elif len(_SCREAMING_SNAKE_CASE		) > 1:
																							# If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
																							_UpperCAmelCase					=    tf.distribute.MirroredStrategy()
																else:
																							raise ValueError('Cannot find the proper strategy, please check your environment properties.'		)

									return strategy
		@property
		def 			UpperCAmelCase__       (    self		)				-> "tf.distribute.Strategy":




									"""simple docstring"""





									requires_backends(self    ,					['tf']		)
									return self._setup_strategy
		@property
		def 			UpperCAmelCase__       (    self		)				-> int:




									"""simple docstring"""





									requires_backends(self    ,					['tf']		)
									return self._setup_strategy.num_replicas_in_sync
		@property
		def 			UpperCAmelCase__       (    self		)				-> Optional[int]:




									"""simple docstring"""





									return False  # TF Logging is handled by Keras not the Trainer
		@property
		def 			UpperCAmelCase__       (    self		)				-> int:




									"""simple docstring"""





									if self.per_gpu_train_batch_size:
																logger.warning(
																    'Using deprecated `--per_gpu_train_batch_size` argument which will be removed in a future '
																    'version. Using `--per_device_train_batch_size` is preferred.'		)
									_UpperCAmelCase					=    self.per_gpu_train_batch_size or self.per_device_train_batch_size
									return per_device_batch_size * self.n_replicas
		@property
		def 			UpperCAmelCase__       (    self		)				-> int:




									"""simple docstring"""





									if self.per_gpu_eval_batch_size:
																logger.warning(
																    'Using deprecated `--per_gpu_eval_batch_size` argument which will be removed in a future '
																    'version. Using `--per_device_eval_batch_size` is preferred.'		)
									_UpperCAmelCase					=    self.per_gpu_eval_batch_size or self.per_device_eval_batch_size
									return per_device_batch_size * self.n_replicas






		@property
		def 			UpperCAmelCase__       (    self		)				-> int:




									"""simple docstring"""





									requires_backends(self    ,					['tf']		)
									warnings.warn(
									    'The n_gpu argument is deprecated and will be removed in a future version, use n_replicas instead.'    ,					_SCREAMING_SNAKE_CASE    ,					)
									return self._setup_strategy.num_replicas_in_sync




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 	__a   (       unittest.TestCase    ):
		def __init__(    self    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE=3    ,					_SCREAMING_SNAKE_CASE=32    ,					_SCREAMING_SNAKE_CASE=3    ,					_SCREAMING_SNAKE_CASE=10    ,					_SCREAMING_SNAKE_CASE=[10, 20, 30, 40]    ,					_SCREAMING_SNAKE_CASE=[1, 1, 2, 1]    ,					_SCREAMING_SNAKE_CASE=True    ,					_SCREAMING_SNAKE_CASE=True    ,					_SCREAMING_SNAKE_CASE="relu"    ,					_SCREAMING_SNAKE_CASE=3    ,					_SCREAMING_SNAKE_CASE=None    ,					)				-> Tuple:




									"""simple docstring"""





									_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(_SCREAMING_SNAKE_CASE		)
		def 			UpperCAmelCase__       (    self		)				-> List[Any]:




									"""simple docstring"""





									_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		)				-> Union[str, Any]:




									"""simple docstring"""





									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    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE		)				-> Optional[Any]:




									"""simple docstring"""





									_UpperCAmelCase					=    FlaxResNetModel(config=_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase					=    model(_SCREAMING_SNAKE_CASE		)

									# Output shape (b, c, h, w)
									self.parent.assertEqual(
									    result.last_hidden_state.shape    ,					(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)    ,					)
		def 			UpperCAmelCase__       (    self    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE		)				-> Any:




									"""simple docstring"""





									_UpperCAmelCase					=    self.num_labels
									_UpperCAmelCase					=    FlaxResNetForImageClassification(config=_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase					=    model(_SCREAMING_SNAKE_CASE		)
									self.parent.assertEqual(result.logits.shape    ,					(self.batch_size, self.num_labels)		)






		def 			UpperCAmelCase__       (    self		)				-> Any:




									"""simple docstring"""





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



@require_flax
class 	__a   (       UpperCAmelCase					,   unittest.TestCase    ):
		_a			:						Any    			=			(FlaxResNetModel, FlaxResNetForImageClassification) if is_flax_available() else ()

		_a			:						Dict    			=			False
		_a			:						Optional[Any]    			=			False
		_a			:						Optional[Any]    			=			False
		def 			UpperCAmelCase__       (    self		)				-> None:




									"""simple docstring"""





									_UpperCAmelCase					=    FlaxResNetModelTester(self		)
									_UpperCAmelCase					=    ConfigTester(self    ,					config_class=_SCREAMING_SNAKE_CASE    ,					has_text_modality=_SCREAMING_SNAKE_CASE		)
		def 			UpperCAmelCase__       (    self		)				-> str:




									"""simple docstring"""





									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		)				-> int:




									"""simple docstring"""





									return
		def 			UpperCAmelCase__       (    self		)				-> Tuple:




									"""simple docstring"""





									_UpperCAmelCase					=    self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE		)
		def 			UpperCAmelCase__       (    self		)				-> List[Any]:




									"""simple docstring"""





									_UpperCAmelCase					=    self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*_SCREAMING_SNAKE_CASE		)
		@unittest.skip(reason='ResNet does not use inputs_embeds'		)
		def 			UpperCAmelCase__       (    self		)				-> Optional[Any]:




									"""simple docstring"""





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




									"""simple docstring"""





									pass
		def 			UpperCAmelCase__       (    self		)				-> Dict:




									"""simple docstring"""





									_UpperCAmelCase	, _UpperCAmelCase					=    self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
																_UpperCAmelCase					=    model_class(_SCREAMING_SNAKE_CASE		)
																_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]    ,					_SCREAMING_SNAKE_CASE		)
		def 			UpperCAmelCase__       (    self		)				-> int:




									"""simple docstring"""





									def check_hidden_states_output(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE		):
																_UpperCAmelCase					=    model_class(_SCREAMING_SNAKE_CASE		)

																_UpperCAmelCase					=    model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE		)		)

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

																_UpperCAmelCase					=    self.model_tester.num_stages
																self.assertEqual(len(_SCREAMING_SNAKE_CASE		)    ,					expected_num_stages + 1		)
		@unittest.skip(reason='ResNet does not use feedforward chunking'		)
		def 			UpperCAmelCase__       (    self		)				-> List[Any]:




									"""simple docstring"""





									pass






		def 			UpperCAmelCase__       (    self		)				-> Union[str, Any]:




									"""simple docstring"""





									_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(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE		)
																							_UpperCAmelCase					=    model_class(_SCREAMING_SNAKE_CASE		)

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

																							with self.subTest('JIT Enabled'		):
																														_UpperCAmelCase					=    model_jitted(**_SCREAMING_SNAKE_CASE		).to_tuple()

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

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



def  lowerCAmelCase__				(    )						->	Optional[int]:


							'''simple docstring'''





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







@require_flax
class 	__a   (       unittest.TestCase    ):
		@cached_property
		def 			UpperCAmelCase__       (    self		)				-> List[str]:




									"""simple docstring"""





									return AutoImageProcessor.from_pretrained('microsoft/resnet-50'		) if is_vision_available() else None






		@slow
		def 			UpperCAmelCase__       (    self		)				-> List[str]:




									"""simple docstring"""





									_UpperCAmelCase					=    FlaxResNetForImageClassification.from_pretrained('microsoft/resnet-50'		)

									_UpperCAmelCase					=    self.default_image_processor
									_UpperCAmelCase					=    prepare_img()
									_UpperCAmelCase					=    image_processor(images=_SCREAMING_SNAKE_CASE    ,					return_tensors='np'		)

									_UpperCAmelCase					=    model(**_SCREAMING_SNAKE_CASE		)

									# verify the logits
									_UpperCAmelCase					=    (1, 1000)
									self.assertEqual(outputs.logits.shape    ,					_SCREAMING_SNAKE_CASE		)

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

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