

import inspect
import unittest
from math import floor

from transformers import CvtConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
      import torch

      from transformers import CvtForImageClassification, CvtModel
      from transformers.models.cvt.modeling_cvt import CVT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
      from PIL import Image

      from transformers import AutoImageProcessor

class    _a (       UpperCAmelCase__							):




      """simple docstring"""

      def 						__A     (					self				:							List[str]     ):
            A_		   =							self.config_class(**self.inputs_dict     )
            self.parent.assertTrue(hasattr(__lowercase		,       "embed_dim"     )     )
            self.parent.assertTrue(hasattr(__lowercase		,       "num_heads"     )     )




class    _a :




      """simple docstring"""

      def __init__(					self				:							Dict		,       UpperCAmelCase				:							Dict		,       UpperCAmelCase				:							str=13		,       UpperCAmelCase				:							List[Any]=64		,       UpperCAmelCase				:							Any=3		,       UpperCAmelCase				:							Optional[int]=[16, 48, 96]		,       UpperCAmelCase				:							List[Any]=[1, 3, 6]		,       UpperCAmelCase				:							int=[1, 2, 10]		,       UpperCAmelCase				:							Union[str, Any]=[7, 3, 3]		,       UpperCAmelCase				:							Dict=[4, 2, 2]		,       UpperCAmelCase				:							str=[2, 1, 1]		,       UpperCAmelCase				:							List[str]=[2, 2, 2]		,       UpperCAmelCase				:							Union[str, Any]=[False, False, True]		,       UpperCAmelCase				:							str=[0.0, 0.0, 0.0]		,       UpperCAmelCase				:							List[str]=0.02		,       UpperCAmelCase				:							Union[str, Any]=1E-12		,       UpperCAmelCase				:							Dict=True		,       UpperCAmelCase				:							Any=True		,       UpperCAmelCase				:							str=2		,       ):
            A_		   =							parent
            A_		   =							batch_size
            A_		   =							image_size
            A_		   =							patch_sizes
            A_		   =							patch_stride
            A_		   =							patch_padding
            A_		   =							is_training
            A_		   =							use_labels
            A_		   =							num_labels
            A_		   =							num_channels
            A_		   =							embed_dim
            A_		   =							num_heads
            A_		   =							stride_kv
            A_		   =							depth
            A_		   =							cls_token
            A_		   =							attention_drop_rate
            A_		   =							initializer_range
            A_		   =							layer_norm_eps





      def 						__A     (					self				:							Optional[Any]     ):
            A_		   =							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

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

            A_		   =							self.get_config()
            return config, pixel_values, labels





      def 						__A     (					self				:							str     ):
            return CvtConfig(
                image_size=self.image_size		,       num_labels=self.num_labels		,       num_channels=self.num_channels		,       embed_dim=self.embed_dim		,       num_heads=self.num_heads		,       patch_sizes=self.patch_sizes		,       patch_padding=self.patch_padding		,       patch_stride=self.patch_stride		,       stride_kv=self.stride_kv		,       depth=self.depth		,       cls_token=self.cls_token		,       attention_drop_rate=self.attention_drop_rate		,       initializer_range=self.initializer_range		,       )





      def 						__A     (					self				:							str		,       UpperCAmelCase				:							Tuple		,       UpperCAmelCase				:							Tuple		,       UpperCAmelCase				:							Any     ):
            A_		   =							CvtModel(config=__lowercase     )
            model.to(__lowercase     )
            model.eval()
            A_		   =							model(__lowercase     )
            A_		   =							(self.image_size, self.image_size)
            A_   ,			A_		   =							image_size[0], image_size[1]
            for i in range(len(self.depth     )     ):
                  A_		   =							floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1     )
                  A_		   =							floor(((width + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1     )
            self.parent.assertEqual(result.last_hidden_state.shape		,       (self.batch_size, self.embed_dim[-1], height, width)     )





      def 						__A     (					self				:							Any		,       UpperCAmelCase				:							int		,       UpperCAmelCase				:							List[str]		,       UpperCAmelCase				:							int     ):
            A_		   =							self.num_labels
            A_		   =							CvtForImageClassification(__lowercase     )
            model.to(__lowercase     )
            model.eval()
            A_		   =							model(__lowercase		,       labels=__lowercase     )
            self.parent.assertEqual(result.logits.shape		,       (self.batch_size, self.num_labels)     )







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




@require_torch
class    _a (       UpperCAmelCase__			,							UpperCAmelCase__			,							unittest.TestCase							):




      """simple docstring"""

      _lowerCamelCase    :       Tuple							=    (CvtModel, CvtForImageClassification) if is_torch_available() else ()
      _lowerCamelCase    :       Optional[Any]							=    (
          {'feature-extraction': CvtModel, 'image-classification': CvtForImageClassification}
          if is_torch_available()
          else {}
      )

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





      def 						__A     (					self				:							Dict     ):
            A_		   =							CvtModelTester(self     )
            A_		   =							ConfigTester(self		,       config_class=__lowercase		,       has_text_modality=__lowercase		,       hidden_size=37     )





      def 						__A     (					self				:							Optional[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 						__A     (					self				:							Optional[int]     ):
            return





      @unittest.skip(reason="Cvt does not output attentions"     )
      def 						__A     (					self				:							str     ):
            pass





      @unittest.skip(reason="Cvt does not use inputs_embeds"     )
      def 						__A     (					self				:							Union[str, Any]     ):
            pass





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





      def 						__A     (					self				:							Tuple     ):
            A_   ,			A_		   =							self.model_tester.prepare_config_and_inputs_for_common()

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

                  A_		   =							["pixel_values"]
                  self.assertListEqual(arg_names[:1]		,       __lowercase     )





      def 						__A     (					self				:							Tuple     ):
            A_		   =							self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*__lowercase     )





      def 						__A     (					self				:							Tuple     ):






            def check_hidden_states_output(UpperCAmelCase				:							str		,       UpperCAmelCase				:							Tuple		,       UpperCAmelCase				:							str     ):
                  A_		   =							model_class(__lowercase     )
                  model.to(__lowercase     )
                  model.eval()

                  with torch.no_grad():
                        A_		   =							model(**self._prepare_for_class(__lowercase		,       __lowercase     )     )

                  A_		   =							outputs.hidden_states

                  A_		   =							len(self.model_tester.depth     )
                  self.assertEqual(len(__lowercase     )		,       __lowercase     )

                  # verify the first hidden states (first block)
                  self.assertListEqual(
                      list(hidden_states[0].shape[-3:]     )		,       [
                          self.model_tester.embed_dim[0],
                          self.model_tester.image_size // 4,
                          self.model_tester.image_size // 4,
                      ]		,       )

            A_   ,			A_		   =							self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                  A_		   =							True
                  check_hidden_states_output(__lowercase		,       __lowercase		,       __lowercase     )

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

                  check_hidden_states_output(__lowercase		,       __lowercase		,       __lowercase     )





      def 						__A     (					self				:							List[str]     ):
            A_		   =							self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_image_classification(*__lowercase     )





      @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."     )
      def 						__A     (					self				:							List[Any]     ):
            pass







      @slow
      def 						__A     (					self				:							Any     ):






            for model_name in CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                  A_		   =							CvtModel.from_pretrained(__lowercase     )
                  self.assertIsNotNone(__lowercase     )




def 	__snake_case		(      ):



      """simple docstring"""

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



@require_torch
@require_vision
class    _a (       unittest.TestCase							):




      """simple docstring"""

      @cached_property
      def 						__A     (					self				:							Any     ):
            return AutoImageProcessor.from_pretrained(CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]     )







      @slow
      def 						__A     (					self				:							Union[str, Any]     ):
            A_		   =							CvtForImageClassification.from_pretrained(CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]     ).to(__lowercase     )

            A_		   =							self.default_image_processor
            A_		   =							prepare_img()
            A_		   =							image_processor(images=__lowercase		,       return_tensors="pt"     ).to(__lowercase     )

            # forward pass
            with torch.no_grad():
                  A_		   =							model(**__lowercase     )

            # verify the logits
            A_		   =							torch.Size((1, 1000)     )
            self.assertEqual(outputs.logits.shape		,       __lowercase     )

            A_		   =							torch.tensor([0.9_285, 0.9_015, -0.3_150]     ).to(__lowercase     )

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





import numpy
class 		UpperCAmelCase   :







						'''simple docstring'''





						def __init__(						self							:     Union[str, Any]       ,	__lowercase							:     numpy.ndarray       ,	__lowercase							:     numpy.ndarray						):






							"""simple docstring"""






							snake_case_		    =     input_array

							# Random initial weights are assigned where first argument is the
							# number of nodes in previous layer and second argument is the
							# number of nodes in the next layer.

							# Random initial weights are assigned.
							# self.input_array.shape[1] is used to represent number of nodes in input layer.
							# First hidden layer consists of 4 nodes.
							snake_case_		    =     numpy.random.rand(
							    self.input_array.shape[1]       ,	4						)

							# Random initial values for the first hidden layer.
							# First hidden layer has 4 nodes.
							# Second hidden layer has 3 nodes.
							snake_case_		    =     numpy.random.rand(
							    4       ,	3						)

							# Random initial values for the second hidden layer.
							# Second hidden layer has 3 nodes.
							# Output layer has 1 node.
							snake_case_		    =     numpy.random.rand(3       ,	1						)

							# Real output values provided.
							snake_case_		    =     output_array

							# Predicted output values by the neural network.
							# Predicted_output array initially consists of zeroes.
							snake_case_		    =     numpy.zeros(output_array.shape						)






						def        snake_case__						(						self							:     Optional[Any]						):






							"""simple docstring"""






							snake_case_		    =     sigmoid(
							    numpy.dot(self.input_array       ,	self.input_layer_and_first_hidden_layer_weights						)						)

							# layer_between_first_hidden_layer_and_second_hidden_layer is the layer
							# connecting the first hidden set of nodes with the second hidden set of nodes.
							snake_case_		    =     sigmoid(
							    numpy.dot(
							        self.layer_between_input_and_first_hidden_layer       ,	self.first_hidden_layer_and_second_hidden_layer_weights       ,	)						)

							# layer_between_second_hidden_layer_and_output is the layer connecting
							# second hidden layer with the output node.
							snake_case_		    =     sigmoid(
							    numpy.dot(
							        self.layer_between_first_hidden_layer_and_second_hidden_layer       ,	self.second_hidden_layer_and_output_layer_weights       ,	)						)

							return self.layer_between_second_hidden_layer_and_output






						def        snake_case__						(						self							:     Any						):






							"""simple docstring"""






							snake_case_		    =     numpy.dot(
							    self.layer_between_first_hidden_layer_and_second_hidden_layer.T       ,	2
							    * (self.output_array - self.predicted_output)
							    * sigmoid_derivative(self.predicted_output						)       ,	)
							snake_case_		    =     numpy.dot(
							    self.layer_between_input_and_first_hidden_layer.T       ,	numpy.dot(
							        2
							        * (self.output_array - self.predicted_output)
							        * sigmoid_derivative(self.predicted_output						)       ,	self.second_hidden_layer_and_output_layer_weights.T       ,	)
							    * sigmoid_derivative(
							        self.layer_between_first_hidden_layer_and_second_hidden_layer						)       ,	)
							snake_case_		    =     numpy.dot(
							    self.input_array.T       ,	numpy.dot(
							        numpy.dot(
							            2
							            * (self.output_array - self.predicted_output)
							            * sigmoid_derivative(self.predicted_output						)       ,	self.second_hidden_layer_and_output_layer_weights.T       ,	)
							        * sigmoid_derivative(
							            self.layer_between_first_hidden_layer_and_second_hidden_layer						)       ,	self.first_hidden_layer_and_second_hidden_layer_weights.T       ,	)
							    * sigmoid_derivative(self.layer_between_input_and_first_hidden_layer						)       ,	)

							self.input_layer_and_first_hidden_layer_weights += (
							    updated_input_layer_and_first_hidden_layer_weights
							)
							self.first_hidden_layer_and_second_hidden_layer_weights += (
							    updated_first_hidden_layer_and_second_hidden_layer_weights
							)
							self.second_hidden_layer_and_output_layer_weights += (
							    updated_second_hidden_layer_and_output_layer_weights
							)






						def        snake_case__						(						self							:     Optional[Any]       ,	__lowercase							:     numpy.ndarray       ,	__lowercase							:     int       ,	__lowercase							:     bool						):






							"""simple docstring"""






							for iteration in range(1       ,	iterations + 1						):
								snake_case_		    =     self.feedforward()
								self.back_propagation()
								if give_loss:
									snake_case_		    =     numpy.mean(numpy.square(output - self.feedforward()						)						)
									print(f"Iteration {iteration} Loss: {loss}"						)






						def        snake_case__						(						self							:     Union[str, Any]       ,	__lowercase							:     numpy.ndarray						):






							"""simple docstring"""






							snake_case_		    =     input_arr

							snake_case_		    =     sigmoid(
							    numpy.dot(self.array       ,	self.input_layer_and_first_hidden_layer_weights						)						)

							snake_case_		    =     sigmoid(
							    numpy.dot(
							        self.layer_between_input_and_first_hidden_layer       ,	self.first_hidden_layer_and_second_hidden_layer_weights       ,	)						)

							snake_case_		    =     sigmoid(
							    numpy.dot(
							        self.layer_between_first_hidden_layer_and_second_hidden_layer       ,	self.second_hidden_layer_and_output_layer_weights       ,	)						)

							return int(self.layer_between_second_hidden_layer_and_output > 0.6						)







def 						lowerCamelCase__    (		_A							):







	'''simple docstring'''





	return 1 / (1 + numpy.exp(-value							))


def 						lowerCamelCase__    (		_A							):







	'''simple docstring'''





	return (value) * (1 - (value))


def 						lowerCamelCase__    (		):







	'''simple docstring'''





	snake_case_		    =     numpy.array(
	    (
	        [0, 0, 0],
	        [0, 0, 1],
	        [0, 1, 0],
	        [0, 1, 1],
	        [1, 0, 0],
	        [1, 0, 1],
	        [1, 1, 0],
	        [1, 1, 1],
	    )     ,	dtype=numpy.floataa     ,	)

	# True output values for the given input values.
	snake_case_		    =     numpy.array(([0], [1], [1], [0], [1], [0], [0], [1])     ,	dtype=numpy.floataa							)

	# Calling neural network class.
	snake_case_		    =     TwoHiddenLayerNeuralNetwork(
	    input_array=_A     ,	output_array=_A							)

	# Calling training function.
	# Set give_loss to True if you want to see loss in every iteration.
	neural_network.train(output=_A     ,	iterations=10     ,	give_loss=_A							)

	return neural_network.predict(numpy.array(([1, 1, 1])     ,	dtype=numpy.floataa							)							)


if __name__ == "__main__":
		example()
