
'''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 	__lowercase				(  unittest.TestCase    ):

			def __init__(self			,   A			,   A=3			,   A=3_2			,   A=3			,   A=1_0			,   A=[1_0, 2_0, 3_0, 4_0]			,   A=[1, 1, 2, 1]			,   A=True			,   A=True			,   A="relu"			,   A=3			,   A=None			,   ):
								lowerCamelCase_					:      int     =		parent
								lowerCamelCase_					:      Tuple     =		batch_size
								lowerCamelCase_					:      Optional[int]     =		image_size
								lowerCamelCase_					:      Optional[int]     =		num_channels
								lowerCamelCase_					:      int     =		embeddings_size
								lowerCamelCase_					:      List[str]     =		hidden_sizes
								lowerCamelCase_					:      Union[str, Any]     =		depths
								lowerCamelCase_					:      Optional[Any]     =		is_training
								lowerCamelCase_					:      str     =		use_labels
								lowerCamelCase_					:      Optional[Any]     =		hidden_act
								lowerCamelCase_					:      str     =		num_labels
								lowerCamelCase_					:      List[str]     =		scope
								lowerCamelCase_					:      int     =		len(_A      )

			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      Tuple     =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

								lowerCamelCase_					:      List[str]     =		self.get_config()

								return config, pixel_values

			def        UpperCAmelCase__ (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        UpperCAmelCase__ (self			,   A			,   A      ):
								lowerCamelCase_					:      int     =		FlaxRegNetModel(config=_A      )
								lowerCamelCase_					:      Tuple     =		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 // 3_2, self.image_size // 3_2)			,   )

			def        UpperCAmelCase__ (self			,   A			,   A      ):
								lowerCamelCase_					:      Dict     =		self.num_labels
								lowerCamelCase_					:      List[str]     =		FlaxRegNetForImageClassification(config=_A      )
								lowerCamelCase_					:      Dict     =		model(_A      )
								self.parent.assertEqual(result.logits.shape			,   (self.batch_size, self.num_labels)      )






			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      Dict     =		self.prepare_config_and_inputs()
								lowerCamelCase_					:      Dict     =		config_and_inputs
								lowerCamelCase_					:      Dict     =		{'pixel_values': pixel_values}
								return config, inputs_dict

@require_flax
class 	__lowercase				(  snake_case__      ,     unittest.TestCase    ):
			lowerCamelCase			:		List[str]	      =			(FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()

			lowerCamelCase			:		List[str]	      =			False
			lowerCamelCase			:		Optional[int]	      =			False
			lowerCamelCase			:		List[str]	      =			False

			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      str     =		FlaxRegNetModelTester(self      )
								lowerCamelCase_					:      Any     =		ConfigTester(self			,   config_class=_A			,   has_text_modality=_A      )

			def        UpperCAmelCase__ (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        UpperCAmelCase__ (self      ):
								return

			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      Tuple     =		self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*_A      )

			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      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        UpperCAmelCase__ (self      ):
								pass

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

			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      List[str]     =		self.model_tester.prepare_config_and_inputs_for_common()

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

													lowerCamelCase_					:      List[str]     =		['pixel_values']
													self.assertListEqual(arg_names[:1]			,   _A      )

			def        UpperCAmelCase__ (self      ):
								def check_hidden_states_output(A			,   A			,   A      ):
													lowerCamelCase_					:      int     =		model_class(_A      )

													lowerCamelCase_					:      str     =		model(**self._prepare_for_class(_A			,   _A      )      )

													lowerCamelCase_					:      Dict     =		outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

													lowerCamelCase_					:      int     =		self.model_tester.num_stages
													self.assertEqual(len(_A      )			,   expected_num_stages + 1      )

								lowerCamelCase_					:      List[Any]     =		self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
													lowerCamelCase_					:      Optional[int]     =		True
													check_hidden_states_output(_A			,   _A			,   _A      )

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

													check_hidden_states_output(_A			,   _A			,   _A      )






			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      List[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_					:      int     =		self._prepare_for_class(_A			,   _A      )
																		lowerCamelCase_					:      Tuple     =		model_class(_A      )

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

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

																		with self.subTest('''JIT Disabled'''      ):
																							with jax.disable_jit():
																												lowerCamelCase_					:      Optional[int]     =		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 lowercase_	(   )     ->							Optional[Any]:
					'''simple docstring'''

					lowerCamelCase_					:      Union[str, Any]     =		Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''				)
					return image






@require_flax
class 	__lowercase				(  unittest.TestCase    ):

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






			@slow
			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      str     =		FlaxRegNetForImageClassification.from_pretrained('''facebook/regnet-y-040'''      )

								lowerCamelCase_					:      List[str]     =		self.default_image_processor
								lowerCamelCase_					:      str     =		prepare_img()
								lowerCamelCase_					:      Any     =		image_processor(images=_A			,   return_tensors='''np'''      )

								lowerCamelCase_					:      Dict     =		model(**_A      )

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

								lowerCamelCase_					:      Tuple     =		jnp.array([-0.41_80, -1.50_51, -3.48_36]      )

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




'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


__lowercase							:   int					=     {}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowercase							:   Any					=     ['''BartphoTokenizer''']

if TYPE_CHECKING:
					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_bartpho import BartphoTokenizer

else:
					import sys

					__lowercase							:   Union[str, Any]					=     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


