

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    _a						(unittest.TestCase    ):


					'''simple docstring'''






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






					def   __A      (			self      ):
						A__							:   str					       =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

						A__							:   Tuple					       =		self.get_config()

						return config, pixel_values






					def   __A      (			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   __A      (			self   ,	A__   ,	A__      ):
						A__							:   Dict					       =		FlaxRegNetModel(config=A__      )
						A__							:   Any					       =		model(A__      )

						# 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   __A      (			self   ,	A__   ,	A__      ):
						A__							:   Any					       =		self.num_labels
						A__							:   List[str]					       =		FlaxRegNetForImageClassification(config=A__      )
						A__							:   int					       =		model(A__      )
						self.parent.assertEqual(result.logits.shape   ,	(self.batch_size, self.num_labels)      )






					def   __A      (			self      ):
						A__							:   Union[str, Any]					       =		self.prepare_config_and_inputs()
						A__       ,       A__							:   Tuple					       =		config_and_inputs
						A__							:   Dict					       =		{"""pixel_values""": pixel_values}
						return config, inputs_dict



@require_flax
class    _a						(__magic_name__		,       unittest.TestCase    ):


					'''simple docstring'''






					UpperCAmelCase__:       Dict  							=	(FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()

					UpperCAmelCase__:       str  							=	False
					UpperCAmelCase__:       str  							=	False
					UpperCAmelCase__:       Optional[Any]  							=	False






					def   __A      (			self      ):
						A__							:   int					       =		FlaxRegNetModelTester(self      )
						A__							:   Dict					       =		ConfigTester(self   ,	config_class=A__   ,	has_text_modality=A__      )






					def   __A      (			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   __A      (			self      ):
						return






					def   __A      (			self      ):
						A__							:   Dict					       =		self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*A__      )






					def   __A      (			self      ):
						A__							:   List[Any]					       =		self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*A__      )






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






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






					def   __A      (			self      ):
						A__       ,       A__							:   int					       =		self.model_tester.prepare_config_and_inputs_for_common()

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

							A__							:   Optional[Any]					       =		["""pixel_values"""]
							self.assertListEqual(arg_names[:1]   ,	A__      )






					def   __A      (			self      ):
						def check_hidden_states_output(A__   ,	A__   ,	A__      ):
							A__							:   List[str]					       =		model_class(A__      )

							A__							:   Any					       =		model(**self._prepare_for_class(A__   ,	A__      )      )

							A__							:   int					       =		outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

							A__							:   str					       =		self.model_tester.num_stages
							self.assertEqual(len(A__      )   ,	expected_num_stages + 1      )

						A__       ,       A__							:   str					       =		self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
							A__							:   Dict					       =		True
							check_hidden_states_output(A__   ,	A__   ,	A__      )

							# check that output_hidden_states also work using config
							del inputs_dict["output_hidden_states"]
							A__							:   Any					       =		True

							check_hidden_states_output(A__   ,	A__   ,	A__      )






					def   __A      (			self      ):
						A__       ,       A__							:   Tuple					       =		self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
							with self.subTest(model_class.__name__      ):
								A__							:   List[Any]					       =		self._prepare_for_class(A__   ,	A__      )
								A__							:   Any					       =		model_class(A__      )

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

								with self.subTest("""JIT Enabled"""      ):
									A__							:   Optional[int]					       =		model_jitted(**A__      ).to_tuple()

								with self.subTest("""JIT Disabled"""      ):
									with jax.disable_jit():
										A__							:   str					       =		model_jitted(**A__      ).to_tuple()

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



def 						UpperCamelCase ()					->				Union[str, Any]:
	A__							:   Union[str, Any]					       =		Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
	return image



@require_flax
class    _a						(unittest.TestCase    ):


					'''simple docstring'''






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






					@slow
					def   __A      (			self      ):
						A__							:   Tuple					       =		FlaxRegNetForImageClassification.from_pretrained("""facebook/regnet-y-040"""      )

						A__							:   Optional[int]					       =		self.default_image_processor
						A__							:   Optional[int]					       =		prepare_img()
						A__							:   List[str]					       =		image_processor(images=A__   ,	return_tensors="""np"""      )

						A__							:   Union[str, Any]					       =		model(**A__      )

						# verify the logits
						A__							:   List[str]					       =		(1, 1000)
						self.assertEqual(outputs.logits.shape   ,	A__      )

						A__							:   int					       =		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]   ,	A__   ,	atol=1e-4      )      )






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


A_	:			Optional[int]  	=      {'configuration_fnet': ['FNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FNetConfig']}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A_	:			Optional[Any]  	=      ['FNetTokenizer']

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A_	:			Tuple  	=      ['FNetTokenizerFast']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A_	:			List[Any]  	=      [
	    'FNET_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'FNetForMaskedLM',
	    'FNetForMultipleChoice',
	    'FNetForNextSentencePrediction',
	    'FNetForPreTraining',
	    'FNetForQuestionAnswering',
	    'FNetForSequenceClassification',
	    'FNetForTokenClassification',
	    'FNetLayer',
	    'FNetModel',
	    'FNetPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig

	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_fnet import FNetTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_fnet_fast import FNetTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_fnet import (
		    FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
		    FNetForMaskedLM,
		    FNetForMultipleChoice,
		    FNetForNextSentencePrediction,
		    FNetForPreTraining,
		    FNetForQuestionAnswering,
		    FNetForSequenceClassification,
		    FNetForTokenClassification,
		    FNetLayer,
		    FNetModel,
		    FNetPreTrainedModel,
		)


else:
	import sys

	A_	:			Optional[Any]  	=      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



