

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 _UpperCamelCase							(							unittest.TestCase      ):
			'''simple docstring'''







			def __init__(	self							:       List[Any]					,					a							:       Dict					,					a							:       Tuple=3					,					a							:       Any=32					,					a							:       str=3					,					a							:       Optional[int]=10					,					a							:       Union[str, Any]=[10, 20, 30, 40]					,					a							:       Any=[1, 1, 2, 1]					,					a							:       str=True					,					a							:       Dict=True					,					a							:       Optional[Any]="relu"					,					a							:       List[Any]=3					,					a							:       Any=None					,					)							->				int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				parent
				SCREAMING_SNAKE_CASE      :					Optional[int]    =				batch_size
				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				image_size
				SCREAMING_SNAKE_CASE      :					str    =				num_channels
				SCREAMING_SNAKE_CASE      :					List[str]    =				embeddings_size
				SCREAMING_SNAKE_CASE      :					Optional[int]    =				hidden_sizes
				SCREAMING_SNAKE_CASE      :					Tuple    =				depths
				SCREAMING_SNAKE_CASE      :					Optional[int]    =				is_training
				SCREAMING_SNAKE_CASE      :					Tuple    =				use_labels
				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				hidden_act
				SCREAMING_SNAKE_CASE      :					Any    =				num_labels
				SCREAMING_SNAKE_CASE      :					List[str]    =				scope
				SCREAMING_SNAKE_CASE      :					Any    =				len(a		)
			def 							__UpperCamelCase       (	self							:       Union[str, Any]		)							->				int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

				SCREAMING_SNAKE_CASE      :					Optional[int]    =				self.get_config()

				return config, pixel_values
			def 							__UpperCamelCase       (	self							:       Union[str, Any]		)							->				str:

				"""simple docstring"""

				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							:       Optional[Any]					,					a							:       List[str]					,					a							:       int		)							->				int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Dict    =				FlaxRegNetModel(config=a		)
				SCREAMING_SNAKE_CASE      :					List[str]    =				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 							__UpperCamelCase       (	self							:       List[Any]					,					a							:       List[Any]					,					a							:       int		)							->				Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				self.num_labels
				SCREAMING_SNAKE_CASE      :					List[Any]    =				FlaxRegNetForImageClassification(config=a		)
				SCREAMING_SNAKE_CASE      :					Tuple    =				model(a		)
				self.parent.assertEqual(result.logits.shape					,					(self.batch_size, self.num_labels)		)






			def 							__UpperCamelCase       (	self							:       Any		)							->				Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					int    =				self.prepare_config_and_inputs()
				SCREAMING_SNAKE_CASE   ,SCREAMING_SNAKE_CASE      :					Dict    =				config_and_inputs
				SCREAMING_SNAKE_CASE      :					str    =				{"pixel_values": pixel_values}
				return config, inputs_dict






@require_flax
class _UpperCamelCase							(							__A							,		unittest.TestCase      ):
			'''simple docstring'''







			lowerCamelCase__							=(FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()

			lowerCamelCase__							=False
			lowerCamelCase__							=False
			lowerCamelCase__							=False
			def 							__UpperCamelCase       (	self							:       Dict		)							->				None:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Optional[int]    =				FlaxRegNetModelTester(self		)
				SCREAMING_SNAKE_CASE      :					Dict    =				ConfigTester(self					,					config_class=a					,					has_text_modality=a		)
			def 							__UpperCamelCase       (	self							:       int		)							->				Any:

				"""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							:       List[str]		)							->				Optional[int]:

				"""simple docstring"""

				return
			def 							__UpperCamelCase       (	self							:       Optional[Any]		)							->				List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*a		)
			def 							__UpperCamelCase       (	self							:       List[str]		)							->				Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Union[str, 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							:       List[str]		)							->				Union[str, Any]:

				"""simple docstring"""

				pass
			@unittest.skip(reason="RegNet does not support input and output embeddings"		)
			def 							__UpperCamelCase       (	self							:       List[str]		)							->				List[str]:

				"""simple docstring"""

				pass
			def 							__UpperCamelCase       (	self							:       int		)							->				Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   ,SCREAMING_SNAKE_CASE      :					Dict    =				self.model_tester.prepare_config_and_inputs_for_common()

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

					SCREAMING_SNAKE_CASE      :					Any    =				["pixel_values"]
					self.assertListEqual(arg_names[:1]					,					a		)
			def 							__UpperCamelCase       (	self							:       Tuple		)							->				List[str]:

				"""simple docstring"""

				def check_hidden_states_output(a							:       List[str]					,					a							:       Tuple					,					a							:       Optional[int]		):
					SCREAMING_SNAKE_CASE      :					Dict    =				model_class(a		)

					SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				model(**self._prepare_for_class(a					,					a		)		)

					SCREAMING_SNAKE_CASE      :					Optional[int]    =				outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

					SCREAMING_SNAKE_CASE      :					int    =				self.model_tester.num_stages
					self.assertEqual(len(a		)					,					expected_num_stages + 1		)

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

				for model_class in self.all_model_classes:
					SCREAMING_SNAKE_CASE      :					List[Any]    =				True
					check_hidden_states_output(a					,					a					,					a		)

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

					check_hidden_states_output(a					,					a					,					a		)






			def 							__UpperCamelCase       (	self							:       Optional[Any]		)							->				Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   ,SCREAMING_SNAKE_CASE      :					Dict    =				self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
					with self.subTest(model_class.__name__		):
						SCREAMING_SNAKE_CASE      :					List[str]    =				self._prepare_for_class(a					,					a		)
						SCREAMING_SNAKE_CASE      :					Optional[Any]    =				model_class(a		)

						@jax.jit
						def model_jitted(a							:       Optional[int]					,					**a							:       str		):
							return model(pixel_values=a					,					**a		)

						with self.subTest("JIT Enabled"		):
							SCREAMING_SNAKE_CASE      :					str    =				model_jitted(**a		).to_tuple()

						with self.subTest("JIT Disabled"		):
							with jax.disable_jit():
								SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				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        lowerCamelCase__       (    ):
	SCREAMING_SNAKE_CASE      :					Optional[int]    =				Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
	return image






@require_flax
class _UpperCamelCase							(							unittest.TestCase      ):
			'''simple docstring'''







			@cached_property
			def 							__UpperCamelCase       (	self							:       Union[str, Any]		)							->				Dict:

				"""simple docstring"""

				return AutoImageProcessor.from_pretrained("facebook/regnet-y-040"		) if is_vision_available() else None






			@slow
			def 							__UpperCamelCase       (	self							:       Optional[Any]		)							->				Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Any    =				FlaxRegNetForImageClassification.from_pretrained("facebook/regnet-y-040"		)

				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				self.default_image_processor
				SCREAMING_SNAKE_CASE      :					Any    =				prepare_img()
				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				image_processor(images=a					,					return_tensors="np"		)

				SCREAMING_SNAKE_CASE      :					int    =				model(**a		)

				# verify the logits
				SCREAMING_SNAKE_CASE      :					str    =				(1, 1000)
				self.assertEqual(outputs.logits.shape					,					a		)

				SCREAMING_SNAKE_CASE      :					Any    =				jnp.array([-0.4180, -1.5051, -3.4836]		)

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






'''simple docstring'''




import random






def        _lowerCAmelCase			(  __snake_case  : int ,							__snake_case  : float ,							__snake_case  : bool = False			)     ->     dict:
     __A						: dict      =       {i: [] for i in range(__snake_case			)}

     # if probability is greater or equal than 1, then generate a complete graph
     if probability >= 1:
          return complete_graph(__snake_case			)
     # if probability is lower or equal than 0, then return a graph without edges
     if probability <= 0:
          return graph

     # for each couple of nodes, add an edge from u to v
     # if the number randomly generated is greater than probability probability
     for i in range(__snake_case			):
          for j in range(i + 1 ,							__snake_case			):
               if random.random() < probability:
                    graph[i].append(__snake_case			)
                    if not directed:
                         # if the graph is undirected, add an edge in from j to i, either
                         graph[j].append(__snake_case			)
     return graph






def        _lowerCAmelCase			(  __snake_case  : int			)     ->     dict:
     return {
         i: [j for j in range(__snake_case			) if i != j] for i in range(__snake_case			)
     }


if __name__ == "__main__":
  import doctest

  doctest.testmod()