


'''simple docstring'''




import inspect
import unittest

from transformers import RegNetConfig, 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.regnet.modeling_flax_regnet import FlaxRegNetForImageClassification, FlaxRegNetModel

if is_vision_available():
				from PIL import Image

				from transformers import AutoImageProcessor



class  lowerCAmelCase				(				unittest.TestCase							):



	def __init__(					self ,					snake_case__ ,					snake_case__=3 ,					snake_case__=32 ,					snake_case__=3 ,					snake_case__=10 ,					snake_case__=[10, 20, 30, 40] ,					snake_case__=[1, 1, 2, 1] ,					snake_case__=True ,					snake_case__=True ,					snake_case__="relu" ,					snake_case__=3 ,					snake_case__=None ,					):
		lowerCAmelCase					:    Any      							= parent
		lowerCAmelCase					:    Tuple      							= batch_size
		lowerCAmelCase					:    Optional[int]      							= image_size
		lowerCAmelCase					:    List[str]      							= num_channels
		lowerCAmelCase					:    Any      							= embeddings_size
		lowerCAmelCase					:    Optional[Any]      							= hidden_sizes
		lowerCAmelCase					:    List[str]      							= depths
		lowerCAmelCase					:    Union[str, Any]      							= is_training
		lowerCAmelCase					:    Any      							= use_labels
		lowerCAmelCase					:    Any      							= hidden_act
		lowerCAmelCase					:    str      							= num_labels
		lowerCAmelCase					:    Dict      							= scope
		lowerCAmelCase					:    Optional[int]      							= len(snake_case__    )



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Optional[int]      							= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

		lowerCAmelCase					:    Any      							= self.get_config()

		return config, pixel_values



	def 				lowercase     (					self    ):
		return RegNetConfig(
		    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 				lowercase     (					self ,					snake_case__ ,					snake_case__    ):
		lowerCAmelCase					:    Union[str, Any]      							= FlaxRegNetModel(config=snake_case__    )
		lowerCAmelCase					:    Dict      							= model(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 				lowercase     (					self ,					snake_case__ ,					snake_case__    ):
		lowerCAmelCase					:    int      							= self.num_labels
		lowerCAmelCase					:    List[str]      							= FlaxRegNetForImageClassification(config=snake_case__    )
		lowerCAmelCase					:    Tuple      							= model(snake_case__    )
		self.parent.assertEqual(result.logits.shape ,					(self.batch_size, self.num_labels)    )



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    int      							= self.prepare_config_and_inputs()
		lowerCAmelCase					:    Tuple      							= config_and_inputs
		lowerCAmelCase					:    Dict      							= {'pixel_values': pixel_values}
		return config, inputs_dict



@require_flax
class  lowerCAmelCase				(				a      ,				unittest.TestCase							):
	_lowerCamelCase   :					Dict			      = (FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()

	_lowerCamelCase   :					List[str]			      = False
	_lowerCamelCase   :					Optional[Any]			      = False
	_lowerCamelCase   :					Optional[Any]			      = False



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    int      							= FlaxRegNetModelTester(self    )
		lowerCAmelCase					:    Optional[int]      							= ConfigTester(self ,					config_class=snake_case__ ,					has_text_modality=snake_case__    )



	def 				lowercase     (					self    ):
		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 				lowercase     (					self    ):
		return



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    str      							= self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_model(*snake_case__    )



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Dict      							= self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_for_image_classification(*snake_case__    )



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



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



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Optional[Any]      							= self.model_tester.prepare_config_and_inputs_for_common()

		for model_class in self.all_model_classes:
			lowerCAmelCase					:    Dict      							= model_class(snake_case__    )
			lowerCAmelCase					:    List[Any]      							= inspect.signature(model.__call__    )
			# signature.parameters is an OrderedDict => so arg_names order is deterministic
			lowerCAmelCase					:    List[str]      							= [*signature.parameters.keys()]

			lowerCAmelCase					:    List[Any]      							= ['pixel_values']
			self.assertListEqual(arg_names[:1] ,					snake_case__    )



	def 				lowercase     (					self    ):
		def check_hidden_states_output(snake_case__ ,					snake_case__ ,					snake_case__    ):
			lowerCAmelCase					:    List[str]      							= model_class(snake_case__    )

			lowerCAmelCase					:    Optional[Any]      							= model(**self._prepare_for_class(snake_case__ ,					snake_case__    )    )

			lowerCAmelCase					:    str      							= outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

			lowerCAmelCase					:    Tuple      							= self.model_tester.num_stages
			self.assertEqual(len(snake_case__    ) ,					expected_num_stages + 1    )

		lowerCAmelCase					:    Dict      							= self.model_tester.prepare_config_and_inputs_for_common()

		for model_class in self.all_model_classes:
			lowerCAmelCase					:    Tuple      							= True
			check_hidden_states_output(snake_case__ ,					snake_case__ ,					snake_case__    )

			# check that output_hidden_states also work using config
			del inputs_dict["output_hidden_states"]
			lowerCAmelCase					:    Optional[int]      							= True

			check_hidden_states_output(snake_case__ ,					snake_case__ ,					snake_case__    )



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Optional[Any]      							= self.model_tester.prepare_config_and_inputs_for_common()

		for model_class in self.all_model_classes:
			with self.subTest(model_class.__name__    ):
				lowerCAmelCase					:    List[str]      							= self._prepare_for_class(snake_case__ ,					snake_case__    )
				lowerCAmelCase					:    Tuple      							= model_class(snake_case__    )

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

				with self.subTest('JIT Enabled'    ):
					lowerCAmelCase					:    Optional[int]      							= model_jitted(**snake_case__    ).to_tuple()

				with self.subTest('JIT Disabled'    ):
					with jax.disable_jit():
						lowerCAmelCase					:    Union[str, Any]      							= model_jitted(**snake_case__    ).to_tuple()

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


def 			__UpperCamelCase    (    )    ->      List[str]:




	"""simple docstring"""





	lowerCAmelCase					:    Optional[Any]      							= Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
	return image






@require_flax
class  lowerCAmelCase				(				unittest.TestCase							):



	@cached_property
	def 				lowercase     (					self    ):
		return AutoImageProcessor.from_pretrained('facebook/regnet-y-040'    ) if is_vision_available() else None



	@slow
	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Optional[Any]      							= FlaxRegNetForImageClassification.from_pretrained('facebook/regnet-y-040'    )

		lowerCAmelCase					:    Optional[Any]      							= self.default_image_processor
		lowerCAmelCase					:    int      							= prepare_img()
		lowerCAmelCase					:    Any      							= image_processor(images=snake_case__ ,					return_tensors='np'    )

		lowerCAmelCase					:    Dict      							= model(**snake_case__    )

		# verify the logits
		lowerCAmelCase					:    Union[str, Any]      							= (1, 1000)
		self.assertEqual(outputs.logits.shape ,					snake_case__    )

		lowerCAmelCase					:    List[str]      							= jnp.array([-0.4_1_8_0, -1.5_0_5_1, -3.4_8_3_6]    )

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









'''simple docstring'''




from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_lowerCAmelCase  :		Tuple		     =	logging.get_logger(__name__)

_lowerCAmelCase  :		List[str]		     =	{
    'facebook/xmod-base': 'https://huggingface.co/facebook/xmod-base/resolve/main/config.json',
    'facebook/xmod-large-prenorm': 'https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json',
    'facebook/xmod-base-13-125k': 'https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json',
    'facebook/xmod-base-30-125k': 'https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json',
    'facebook/xmod-base-30-195k': 'https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json',
    'facebook/xmod-base-60-125k': 'https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json',
    'facebook/xmod-base-60-265k': 'https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json',
    'facebook/xmod-base-75-125k': 'https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json',
    'facebook/xmod-base-75-269k': 'https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json',
}



class  lowerCAmelCase				(				a							):
	_lowerCamelCase   :					int			      = """xmod"""



	def __init__(					self ,					snake_case__=3_0522 ,					snake_case__=768 ,					snake_case__=12 ,					snake_case__=12 ,					snake_case__=3072 ,					snake_case__="gelu" ,					snake_case__=0.1 ,					snake_case__=0.1 ,					snake_case__=512 ,					snake_case__=2 ,					snake_case__=0.0_2 ,					snake_case__=1e-1_2 ,					snake_case__=1 ,					snake_case__=0 ,					snake_case__=2 ,					snake_case__="absolute" ,					snake_case__=True ,					snake_case__=None ,					snake_case__=False ,					snake_case__=2 ,					snake_case__=False ,					snake_case__=True ,					snake_case__=True ,					snake_case__=("en_XX",) ,					snake_case__=None ,					**snake_case__ ,					):
		super().__init__(pad_token_id=snake_case__ ,					bos_token_id=snake_case__ ,					eos_token_id=snake_case__ ,					**snake_case__    )

		lowerCAmelCase					:    Dict      							= vocab_size
		lowerCAmelCase					:    Optional[Any]      							= hidden_size
		lowerCAmelCase					:    int      							= num_hidden_layers
		lowerCAmelCase					:    List[Any]      							= num_attention_heads
		lowerCAmelCase					:    List[Any]      							= hidden_act
		lowerCAmelCase					:    Optional[int]      							= intermediate_size
		lowerCAmelCase					:    Optional[int]      							= hidden_dropout_prob
		lowerCAmelCase					:    Optional[Any]      							= attention_probs_dropout_prob
		lowerCAmelCase					:    str      							= max_position_embeddings
		lowerCAmelCase					:    int      							= type_vocab_size
		lowerCAmelCase					:    List[Any]      							= initializer_range
		lowerCAmelCase					:    Any      							= layer_norm_eps
		lowerCAmelCase					:    Dict      							= position_embedding_type
		lowerCAmelCase					:    Optional[Any]      							= use_cache
		lowerCAmelCase					:    Union[str, Any]      							= classifier_dropout
		lowerCAmelCase					:    int      							= pre_norm
		lowerCAmelCase					:    Optional[Any]      							= adapter_reduction_factor
		lowerCAmelCase					:    Any      							= adapter_layer_norm
		lowerCAmelCase					:    Dict      							= adapter_reuse_layer_norm
		lowerCAmelCase					:    Any      							= ln_before_adapter
		lowerCAmelCase					:    Optional[Any]      							= list(snake_case__    )
		lowerCAmelCase					:    List[Any]      							= default_language



class  lowerCAmelCase				(				a							):



	@property
	def 				lowercase     (					self    ):
		if self.task == "multiple-choice":
			lowerCAmelCase					:    List[Any]      							= {0: 'batch', 1: 'choice', 2: 'sequence'}
		else:
			lowerCAmelCase					:    Optional[int]      							= {0: 'batch', 1: 'sequence'}
		return OrderedDict(
		    [
		        ('input_ids', dynamic_axis),
		        ('attention_mask', dynamic_axis),
		    ]    )
