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

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

						SCREAMING_SNAKE_CASE__													= self.get_config()
						return config, pixel_values, labels
	def      A_				(			self							:   List[str]							):
						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      A_				(			self							:   Dict       ,   UpperCAmelCase_							:   Any       ,   UpperCAmelCase_							:   Tuple       ,   UpperCAmelCase_							:   Any							):
						SCREAMING_SNAKE_CASE__													= TFRegNetModel(config=UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= model(UpperCAmelCase_       ,   training=UpperCAmelCase_							)
						# 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      A_				(			self							:   List[str]       ,   UpperCAmelCase_							:   Union[str, Any]       ,   UpperCAmelCase_							:   Tuple       ,   UpperCAmelCase_							:   Dict							):
						SCREAMING_SNAKE_CASE__													= self.num_labels
						SCREAMING_SNAKE_CASE__													= TFRegNetForImageClassification(UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= model(UpperCAmelCase_       ,   labels=UpperCAmelCase_       ,   training=UpperCAmelCase_							)
						self.parent.assertEqual(result.logits.shape       ,   (self.batch_size, self.num_labels)							)





	def      A_				(			self							:   List[str]							):
						SCREAMING_SNAKE_CASE__													= self.prepare_config_and_inputs()
						SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__													= config_and_inputs
						SCREAMING_SNAKE_CASE__													= {'pixel_values': pixel_values}
						return config, inputs_dict





@require_tf
class 						lowercase__  (						_UpperCAmelCase     ,       _UpperCAmelCase     ,       unittest.TestCase  ):
	A__    :   Any				       =(TFRegNetModel, TFRegNetForImageClassification) if is_tf_available() else ()
	A__    :   int				       =(
	    {"""feature-extraction""": TFRegNetModel, """image-classification""": TFRegNetForImageClassification}
	    if is_tf_available()
	    else {}
	)

	A__    :   Any				       =False
	A__    :   Optional[Any]				       =False
	A__    :   Dict				       =False
	A__    :   List[str]				       =False
	A__    :   Union[str, Any]				       =False
	def      A_				(			self							:   List[str]							):
						SCREAMING_SNAKE_CASE__													= TFRegNetModelTester(self							)
						SCREAMING_SNAKE_CASE__													= ConfigTester(self       ,   config_class=UpperCAmelCase_       ,   has_text_modality=UpperCAmelCase_							)
	def      A_				(			self							:   Union[str, Any]							):
						return
	@unittest.skip(reason='RegNet does not use inputs_embeds'							)
	def      A_				(			self							:   Tuple							):
						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      A_				(			self							:   Any							):
						super().test_keras_fit()
	@unittest.skip(reason='RegNet does not support input and output embeddings'							)
	def      A_				(			self							:   str							):
						pass
	def      A_				(			self							:   str							):
						SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__													= self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
											SCREAMING_SNAKE_CASE__													= model_class(UpperCAmelCase_							)
											SCREAMING_SNAKE_CASE__													= inspect.signature(model.call							)
											# signature.parameters is an OrderedDict => so arg_names order is deterministic
											SCREAMING_SNAKE_CASE__													= [*signature.parameters.keys()]

											SCREAMING_SNAKE_CASE__													= ['pixel_values']
											self.assertListEqual(arg_names[:1]       ,   UpperCAmelCase_							)
	def      A_				(			self							:   Union[str, Any]							):
						SCREAMING_SNAKE_CASE__													= self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*UpperCAmelCase_							)
	def      A_				(			self							:   Dict							):
						def check_hidden_states_output(UpperCAmelCase_							:   Dict       ,   UpperCAmelCase_							:   Any       ,   UpperCAmelCase_							:   Any							):
											SCREAMING_SNAKE_CASE__													= model_class(UpperCAmelCase_							)
											SCREAMING_SNAKE_CASE__													= model(**self._prepare_for_class(UpperCAmelCase_       ,   UpperCAmelCase_							)       ,   training=UpperCAmelCase_							)

											SCREAMING_SNAKE_CASE__													= outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

											SCREAMING_SNAKE_CASE__													= self.model_tester.num_stages
											self.assertEqual(len(UpperCAmelCase_							)       ,   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]       ,   )

						SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__													= self.model_tester.prepare_config_and_inputs_for_common()
						SCREAMING_SNAKE_CASE__													= ['basic', 'bottleneck']
						for model_class in self.all_model_classes:
											for layer_type in layers_type:
																SCREAMING_SNAKE_CASE__													= layer_type
																SCREAMING_SNAKE_CASE__													= True
																check_hidden_states_output(UpperCAmelCase_       ,   UpperCAmelCase_       ,   UpperCAmelCase_							)

																# check that output_hidden_states also work using config
																del inputs_dict["output_hidden_states"]
																SCREAMING_SNAKE_CASE__													= True

																check_hidden_states_output(UpperCAmelCase_       ,   UpperCAmelCase_       ,   UpperCAmelCase_							)
	def      A_				(			self							:   Tuple							):
						SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__													= self.model_tester.prepare_config_and_inputs_for_common()

						def check_equivalence(UpperCAmelCase_							:   Optional[int]       ,   UpperCAmelCase_							:   List[Any]       ,   UpperCAmelCase_							:   Union[str, Any]       ,   UpperCAmelCase_							:   List[str]={}							):
											SCREAMING_SNAKE_CASE__													= model(UpperCAmelCase_       ,   return_dict=UpperCAmelCase_       ,   **UpperCAmelCase_							)
											SCREAMING_SNAKE_CASE__													= model(UpperCAmelCase_       ,   return_dict=UpperCAmelCase_       ,   **UpperCAmelCase_							).to_tuple()

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

																recursive_check(UpperCAmelCase_       ,   UpperCAmelCase_							)

						for model_class in self.all_model_classes:
											SCREAMING_SNAKE_CASE__													= model_class(UpperCAmelCase_							)

											SCREAMING_SNAKE_CASE__													= self._prepare_for_class(UpperCAmelCase_       ,   UpperCAmelCase_							)
											SCREAMING_SNAKE_CASE__													= self._prepare_for_class(UpperCAmelCase_       ,   UpperCAmelCase_							)
											check_equivalence(UpperCAmelCase_       ,   UpperCAmelCase_       ,   UpperCAmelCase_							)

											SCREAMING_SNAKE_CASE__													= self._prepare_for_class(UpperCAmelCase_       ,   UpperCAmelCase_       ,   return_labels=UpperCAmelCase_							)
											SCREAMING_SNAKE_CASE__													= self._prepare_for_class(UpperCAmelCase_       ,   UpperCAmelCase_       ,   return_labels=UpperCAmelCase_							)
											check_equivalence(UpperCAmelCase_       ,   UpperCAmelCase_       ,   UpperCAmelCase_							)

											SCREAMING_SNAKE_CASE__													= self._prepare_for_class(UpperCAmelCase_       ,   UpperCAmelCase_							)
											SCREAMING_SNAKE_CASE__													= self._prepare_for_class(UpperCAmelCase_       ,   UpperCAmelCase_							)
											check_equivalence(UpperCAmelCase_       ,   UpperCAmelCase_       ,   UpperCAmelCase_       ,   {'output_hidden_states': True}							)

											SCREAMING_SNAKE_CASE__													= self._prepare_for_class(UpperCAmelCase_       ,   UpperCAmelCase_       ,   return_labels=UpperCAmelCase_							)
											SCREAMING_SNAKE_CASE__													= self._prepare_for_class(UpperCAmelCase_       ,   UpperCAmelCase_       ,   return_labels=UpperCAmelCase_							)
											check_equivalence(UpperCAmelCase_       ,   UpperCAmelCase_       ,   UpperCAmelCase_       ,   {'output_hidden_states': True}							)
	def      A_				(			self							:   List[Any]							):
						SCREAMING_SNAKE_CASE__													= self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase_							)





	@slow
	def      A_				(			self							:   Dict							):
						for model_name in TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											SCREAMING_SNAKE_CASE__													= TFRegNetModel.from_pretrained(UpperCAmelCase_							)
											self.assertIsNotNone(UpperCAmelCase_							)






def 							_lowercase							(     )		->		Union[str, Any]:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__													= Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'		)
					return image




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





	@slow
	def      A_				(			self							:   Optional[int]							):
						SCREAMING_SNAKE_CASE__													= TFRegNetForImageClassification.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]							)

						SCREAMING_SNAKE_CASE__													= self.default_image_processor
						SCREAMING_SNAKE_CASE__													= prepare_img()
						SCREAMING_SNAKE_CASE__													= image_processor(images=UpperCAmelCase_       ,   return_tensors='tf'							)

						# forward pass
						SCREAMING_SNAKE_CASE__													= model(**UpperCAmelCase_       ,   training=UpperCAmelCase_							)

						# verify the logits
						SCREAMING_SNAKE_CASE__													= tf.TensorShape((1, 1000)							)
						self.assertEqual(outputs.logits.shape       ,   UpperCAmelCase_							)

						SCREAMING_SNAKE_CASE__													= tf.constant([-0.4_180, -1.5_051, -3.4_836]							)

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







import os






def 							_lowercase							(     )		->		List[str]:
					'''simple docstring'''


					with open(os.path.dirname(UpperCamelCase_		) + '/p022_names.txt'		) as file:
										SCREAMING_SNAKE_CASE__													= str(file.readlines()[0]		)
										SCREAMING_SNAKE_CASE__													= names.replace('"'	,	''		).split(','		)

					names.sort()

					SCREAMING_SNAKE_CASE__													= 0
					SCREAMING_SNAKE_CASE__													= 0

					for i, name in enumerate(UpperCamelCase_		):
										for letter in name:
															name_score += ord(UpperCamelCase_		) - 64

										total_score += (i + 1) * name_score
										SCREAMING_SNAKE_CASE__													= 0
					return total_score


if __name__ == "__main__":
				print(solution())





