
from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import ResNetConfig
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 TFResNetForImageClassification, TFResNetModel
			from transformers.models.resnet.modeling_tf_resnet import TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
			from PIL import Image

			from transformers import AutoImageProcessor







class   lowerCamelCase	:





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





					def 	UpperCAmelCase(self   :					Optional[int]     )		->		Any:
									snake_case				  =      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

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

									snake_case				  =      self.get_config()

									return config, pixel_values, labels





					def 	UpperCAmelCase(self   :					Union[str, Any]     )		->		Tuple:
									return ResNetConfig(
									    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   :					Union[str, Any]       ,					_A   :					str       ,					_A   :					int       ,					_A   :					Union[str, Any]     )		->		Any:
									snake_case				  =      TFResNetModel(config=_lowercase     )
									snake_case				  =      model(_lowercase     )
									# 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 // 3_2, self.image_size // 3_2)       ,					)





					def 	UpperCAmelCase(self   :					str       ,					_A   :					Tuple       ,					_A   :					List[str]       ,					_A   :					Union[str, Any]     )		->		List[Any]:
									snake_case				  =      self.num_labels
									snake_case				  =      TFResNetForImageClassification(_lowercase     )
									snake_case				  =      model(_lowercase       ,					labels=_lowercase     )
									self.parent.assertEqual(result.logits.shape       ,					(self.batch_size, self.num_labels)     )





					def 	UpperCAmelCase(self   :					Any     )		->		Optional[Any]:
									snake_case				  =      self.prepare_config_and_inputs()
									snake_case				  =      config_and_inputs
									snake_case				  =      {"""pixel_values""": pixel_values}
									return config, inputs_dict







@require_tf
class   lowerCamelCase	(						__snake_case							,				__snake_case							,				unittest.TestCase						):
					UpperCAmelCase__       :							Optional[Any]     =	(TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
					UpperCAmelCase__       :							List[str]     =	(
					    {"feature-extraction": TFResNetModel, "image-classification": TFResNetForImageClassification}
					    if is_tf_available()
					    else {}
					)

					UpperCAmelCase__       :							List[Any]     =	False
					UpperCAmelCase__       :							Optional[int]     =	False
					UpperCAmelCase__       :							Union[str, Any]     =	False
					UpperCAmelCase__       :							Dict     =	False
					UpperCAmelCase__       :							int     =	False





					def 	UpperCAmelCase(self   :					List[Any]     )		->		Optional[Any]:
									snake_case				  =      TFResNetModelTester(self     )
									snake_case				  =      ConfigTester(self       ,					config_class=_lowercase       ,					has_text_modality=_lowercase     )





					def 	UpperCAmelCase(self   :					Any     )		->		List[Any]:
									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   :					Union[str, Any]     )		->		Tuple:
									return





					@unittest.skip(reason="ResNet does not use inputs_embeds"     )
					def 	UpperCAmelCase(self   :					int     )		->		Optional[int]:
									pass





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





					def 	UpperCAmelCase(self   :					int     )		->		Tuple:
									snake_case				  =      self.model_tester.prepare_config_and_inputs_for_common()

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

													snake_case				  =      ["""pixel_values"""]
													self.assertListEqual(arg_names[:1]       ,					_lowercase     )





					def 	UpperCAmelCase(self   :					str     )		->		Dict:
									snake_case				  =      self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*_lowercase     )





					def 	UpperCAmelCase(self   :					Union[str, Any]     )		->		Tuple:

									def check_hidden_states_output(_A   :					Union[str, Any]       ,					_A   :					Union[str, Any]       ,					_A   :					List[str]     ):
													snake_case				  =      model_class(_lowercase     )
													snake_case				  =      model(**self._prepare_for_class(_lowercase       ,					_lowercase     )     )

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

													snake_case				  =      self.model_tester.num_stages
													self.assertEqual(len(_lowercase     )       ,					expected_num_stages + 1     )

													# ResNet'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 // 4, self.model_tester.image_size // 4]       ,					)

									snake_case				  =      self.model_tester.prepare_config_and_inputs_for_common()
									snake_case				  =      ["""basic""", """bottleneck"""]
									for model_class in self.all_model_classes:
													for layer_type in layers_type:
																	snake_case				  =      layer_type
																	snake_case				  =      True
																	check_hidden_states_output(_lowercase       ,					_lowercase       ,					_lowercase     )

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

																	check_hidden_states_output(_lowercase       ,					_lowercase       ,					_lowercase     )





					def 	UpperCAmelCase(self   :					Union[str, Any]     )		->		List[str]:
									snake_case				  =      self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*_lowercase     )





					@slow
					def 	UpperCAmelCase(self   :					Optional[int]     )		->		Dict:

									for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													snake_case				  =      TFResNetModel.from_pretrained(_lowercase     )
													self.assertIsNotNone(_lowercase     )



def 		lowercase_    (     )							->							Optional[int]:
				"""simple docstring"""

				snake_case				  =      Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"	)
				return image







@require_tf
@require_vision
class   lowerCamelCase	(						unittest.TestCase						):





					@cached_property
					def 	UpperCAmelCase(self   :					Optional[int]     )		->		List[str]:
									return (
									    AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]     )
									    if is_vision_available()
									    else None
									)





					@slow
					def 	UpperCAmelCase(self   :					str     )		->		List[Any]:
									snake_case				  =      TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]     )

									snake_case				  =      self.default_image_processor
									snake_case				  =      prepare_img()
									snake_case				  =      image_processor(images=_lowercase       ,					return_tensors="tf"     )

									# forward pass
									snake_case				  =      model(**_lowercase     )

									# verify the logits
									snake_case				  =      tf.TensorShape((1, 1_0_0_0)     )
									self.assertEqual(outputs.logits.shape       ,					_lowercase     )

									snake_case				  =      tf.constant([-11.10_69, -9.78_77, -8.37_77]     )

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



def 		lowercase_    (     A__				,	A__	)							->							List[Any]:
    """simple docstring"""

    snake_case				  =      [0 for i in range(r + 1	)]
    # nc0 = 1
    snake_case				  =      1
    for i in range(1				,	n + 1	):
        # to compute current row from previous row.
        snake_case				  =      min(A__				,	A__	)
        while j > 0:
            c[j] += c[j - 1]
            j -= 1
    return c[r]





print(binomial_coefficient(n=10, r=5))

