



from __future__ import annotations

import inspect
import unittest
from typing import List, Tuple

from transformers import RegNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
    import tensorflow as tf

    from transformers import TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST, TFRegNetForImageClassification, TFRegNetModel


if is_vision_available():
    from PIL import Image

    from transformers import AutoImageProcessor



class 				lowerCAmelCase_		:
       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,			)     ->		Any:
          UpperCamelCase    :  Optional[Any]							=	parent
          UpperCamelCase    :  Optional[Any]							=	batch_size
          UpperCamelCase    :  Optional[Any]							=	image_size
          UpperCamelCase    :  str							=	num_channels
          UpperCamelCase    :  List[Any]							=	embeddings_size
          UpperCamelCase    :  Dict							=	hidden_sizes
          UpperCamelCase    :  Tuple							=	depths
          UpperCamelCase    :  Any							=	is_training
          UpperCamelCase    :  Tuple							=	use_labels
          UpperCamelCase    :  Union[str, Any]							=	hidden_act
          UpperCamelCase    :  List[Any]							=	num_labels
          UpperCamelCase    :  Any							=	scope
          UpperCamelCase    :  int							=	len(__a       )
       def 					snake_case_       (			self       )     ->		Optional[int]:
          UpperCamelCase    :  Any							=	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

          UpperCamelCase    :  Tuple							=	None
          if self.use_labels:
             UpperCamelCase    :  Tuple							=	ids_tensor([self.batch_size],			self.num_labels       )

          UpperCamelCase    :  int							=	self.get_config()
          return config, pixel_values, labels
       def 					snake_case_       (			self       )     ->		Optional[int]:
          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,			)
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )     ->		Any:
          UpperCamelCase    :  Union[str, Any]							=	TFRegNetModel(config=__a       )
          UpperCamelCase    :  Any							=	model(__a,			training=__a       )
          # expected last hidden states: B, C, H // 32, W // 32
          self.parent.assertEqual(
              result.last_hidden_state.shape,			(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32),			)
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )     ->		List[str]:
          UpperCamelCase    :  Dict							=	self.num_labels
          UpperCamelCase    :  Optional[Any]							=	TFRegNetForImageClassification(__a       )
          UpperCamelCase    :  Optional[Any]							=	model(__a,			labels=__a,			training=__a       )
          self.parent.assertEqual(result.logits.shape,			(self.batch_size, self.num_labels)       )






       def 					snake_case_       (			self       )     ->		str:
          UpperCamelCase    :  List[Any]							=	self.prepare_config_and_inputs()
          UpperCamelCase    :  Optional[Any]							=	config_and_inputs
          UpperCamelCase    :  Union[str, Any]							=	{"pixel_values": pixel_values}
          return config, inputs_dict






@require_tf
class 				lowerCAmelCase_		(  a__       ,    a__       ,    unittest.TestCase							):
       UpperCAmelCase__				:					Dict        =  (TFRegNetModel, TFRegNetForImageClassification) if is_tf_available() else ()
       UpperCAmelCase__				:					str        =  (
           {"feature-extraction": TFRegNetModel, "image-classification": TFRegNetForImageClassification}
           if is_tf_available()
           else {}
       )

       UpperCAmelCase__				:					List[str]        =  False
       UpperCAmelCase__				:					List[str]        =  False
       UpperCAmelCase__				:					List[str]        =  False
       UpperCAmelCase__				:					Dict        =  False
       UpperCAmelCase__				:					Any        =  False
       def 					snake_case_       (			self       )     ->		Dict:
          UpperCamelCase    :  List[str]							=	TFRegNetModelTester(self       )
          UpperCamelCase    :  Optional[Any]							=	ConfigTester(self,			config_class=__a,			has_text_modality=__a       )
       def 					snake_case_       (			self       )     ->		Optional[Any]:
          return
       @unittest.skip(reason='RegNet does not use inputs_embeds'       )
       def 					snake_case_       (			self       )     ->		Dict:
          pass
       @unittest.skipIf(
           not is_tf_available() or len(tf.config.list_physical_devices('GPU'       )       ) == 0,			reason='TF does not support backprop for grouped convolutions on CPU.',			)
       @slow
       def 					snake_case_       (			self       )     ->		Any:
          super().test_keras_fit()
       @unittest.skip(reason='RegNet does not support input and output embeddings'       )
       def 					snake_case_       (			self       )     ->		Union[str, Any]:
          pass
       def 					snake_case_       (			self       )     ->		List[str]:
          UpperCamelCase    :  Optional[Any]							=	self.model_tester.prepare_config_and_inputs_for_common()

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

             UpperCamelCase    :  List[Any]							=	["pixel_values"]
             self.assertListEqual(arg_names[:1],			__a       )
       def 					snake_case_       (			self       )     ->		Optional[int]:
          UpperCamelCase    :  int							=	self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*__a       )
       def 					snake_case_       (			self       )     ->		List[Any]:

          def check_hidden_states_output(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       ):
             UpperCamelCase    :  Any							=	model_class(__a       )
             UpperCamelCase    :  Optional[int]							=	model(**self._prepare_for_class(__a,			__a       ),			training=__a       )

             UpperCamelCase    :  Any							=	outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

             UpperCamelCase    :  Optional[int]							=	self.model_tester.num_stages
             self.assertEqual(len(__a       ),			expected_num_stages + 1       )

             # RegNet's feature maps are of shape (batch_size, num_channels, height, width)
             self.assertListEqual(
                 list(hidden_states[0].shape[-2:]       ),			[self.model_tester.image_size // 2, self.model_tester.image_size // 2],			)

          UpperCamelCase    :  Dict							=	self.model_tester.prepare_config_and_inputs_for_common()
          UpperCamelCase    :  str							=	["basic", "bottleneck"]
          for model_class in self.all_model_classes:
             for layer_type in layers_type:
                UpperCamelCase    :  str							=	layer_type
                UpperCamelCase    :  Optional[Any]							=	True
                check_hidden_states_output(__a,			__a,			__a       )

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

                check_hidden_states_output(__a,			__a,			__a       )
       def 					snake_case_       (			self       )     ->		Dict:
          UpperCamelCase    :  int							=	self.model_tester.prepare_config_and_inputs_for_common()

          def check_equivalence(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_={}       ):
             UpperCamelCase    :  Union[str, Any]							=	model(__a,			return_dict=__a,			**__a       )
             UpperCamelCase    :  Any							=	model(__a,			return_dict=__a,			**__a       ).to_tuple()

             def recursive_check(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       ):
                if isinstance(__a,			(List, Tuple)       ):
                   for tuple_iterable_value, dict_iterable_value in zip(__a,			__a       ):
                      recursive_check(__a,			__a       )
                elif tuple_object is None:
                   return
                else:
                   self.assertTrue(
                       all(tf.equal(__a,			__a       )       ),			msg=(
                           'Tuple and dict output are not equal. Difference:'
                           F""" {tf.math.reduce_max(tf.abs(tuple_object - dict_object       )       )}"""
                       ),			)

                recursive_check(__a,			__a       )

          for model_class in self.all_model_classes:
             UpperCamelCase    :  str							=	model_class(__a       )

             UpperCamelCase    :  int							=	self._prepare_for_class(__a,			__a       )
             UpperCamelCase    :  int							=	self._prepare_for_class(__a,			__a       )
             check_equivalence(__a,			__a,			__a       )

             UpperCamelCase    :  Any							=	self._prepare_for_class(__a,			__a,			return_labels=__a       )
             UpperCamelCase    :  Optional[int]							=	self._prepare_for_class(__a,			__a,			return_labels=__a       )
             check_equivalence(__a,			__a,			__a       )

             UpperCamelCase    :  List[str]							=	self._prepare_for_class(__a,			__a       )
             UpperCamelCase    :  List[Any]							=	self._prepare_for_class(__a,			__a       )
             check_equivalence(__a,			__a,			__a,			{'output_hidden_states': True}       )

             UpperCamelCase    :  Tuple							=	self._prepare_for_class(__a,			__a,			return_labels=__a       )
             UpperCamelCase    :  List[Any]							=	self._prepare_for_class(__a,			__a,			return_labels=__a       )
             check_equivalence(__a,			__a,			__a,			{'output_hidden_states': True}       )
       def 					snake_case_       (			self       )     ->		Tuple:
          UpperCamelCase    :  Tuple							=	self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*__a       )






       @slow
       def 					snake_case_       (			self       )     ->		List[Any]:

          for model_name in TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             UpperCamelCase    :  int							=	TFRegNetModel.from_pretrained(__a       )
             self.assertIsNotNone(__a       )






def 		UpperCamelCase	(   )	->     Optional[int]:
   UpperCamelCase    :  int							=	Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'	)
   return image



@require_tf
@require_vision
class 				lowerCAmelCase_		(  unittest.TestCase							):
       @cached_property
       def 					snake_case_       (			self       )     ->		Optional[Any]:
          return (
              AutoImageProcessor.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]       )
              if is_vision_available()
              else None
          )






       @slow
       def 					snake_case_       (			self       )     ->		Dict:
          UpperCamelCase    :  Tuple							=	TFRegNetForImageClassification.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]       )

          UpperCamelCase    :  Optional[Any]							=	self.default_image_processor
          UpperCamelCase    :  Dict							=	prepare_img()
          UpperCamelCase    :  List[Any]							=	image_processor(images=__a,			return_tensors='tf'       )

          # forward pass
          UpperCamelCase    :  Optional[int]							=	model(**__a,			training=__a       )

          # verify the logits
          UpperCamelCase    :  Dict							=	tf.TensorShape((1, 1000)       )
          self.assertEqual(outputs.logits.shape,			__a       )

          UpperCamelCase    :  int							=	tf.constant([-0.41_80, -1.50_51, -3.48_36]       )

          tf.debugging.assert_near(outputs.logits[0, :3],			__a,			atol=1e-4       )








from PIL import Image






def 				A				(					_lowerCamelCase  ):





							'''simple docstring'''




							_lowerCAmelCase   ,    _lowerCAmelCase     :							int    =    image.size
							_lowerCAmelCase     :							Any    =    0
							_lowerCAmelCase     :							Tuple    =    image.load()
							for i in range(_lowerCamelCase  ):
														for j in range(_lowerCamelCase  ):
																					_lowerCAmelCase     :							Union[str, Any]    =    pixels[j, i]
																					mean += pixel
							mean //= width * height

							for j in range(_lowerCamelCase  ):
														for i in range(_lowerCamelCase  ):
																					_lowerCAmelCase     :							Optional[Any]    =    255 if pixels[i, j] > mean else 0
							return image


if __name__ == "__main__":
						_snake_case		=  mean_threshold(Image.open("path_to_image").convert("L"))
						image.save("output_image_path")
