
import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory

from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
    convert,
    ensure_valid_input,
    generate_identified_filename,
    infer_shapes,
    quantize,
)
from transformers.testing_utils import require_tf, require_tokenizers, require_torch, slow





class   UpperCamelCase_      :







						'''simple docstring'''



						def        SCREAMING_SNAKE_CASE(			self							:Dict		,    lowerCAmelCase__							:Optional[Any]		,    lowerCAmelCase__							:Optional[Any]		,    lowerCAmelCase__							:str     )  ->Tuple:
										return None





class   UpperCamelCase_      :







						'''simple docstring'''



						def        SCREAMING_SNAKE_CASE(			self							:List[Any]		,    lowerCAmelCase__							:Dict		,    lowerCAmelCase__							:Tuple		,    lowerCAmelCase__							:str		,    lowerCAmelCase__							:List[Any]     )  ->int:
										return None





class   UpperCamelCase_      (      unittest.TestCase		):







						'''simple docstring'''



						UpperCamelCase      :  List[Any]				   =					[
						    # (model_name, model_kwargs)
						    ('''bert-base-cased''', {}),
						    ('''gpt2''', {'''use_cache''': False}),  # We don't support exporting GPT2 past keys anymore
						]





						@require_tf
						@slow
						def        SCREAMING_SNAKE_CASE(			self							:Tuple     )  ->Optional[Any]:
										for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
														self._test_export(lowerCAmelCase__		,    "tf"		,    12		,    **lowerCAmelCase__     )





						@require_torch
						@slow
						def        SCREAMING_SNAKE_CASE(			self							:str     )  ->List[Any]:
										for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
														self._test_export(lowerCAmelCase__		,    "pt"		,    12		,    **lowerCAmelCase__     )





						@require_torch
						@slow
						def        SCREAMING_SNAKE_CASE(			self							:Optional[int]     )  ->Any:
										from transformers import BertModel

										lowercase         =      ["[UNK]", "[SEP]", "[CLS]", "[PAD]", "[MASK]", "some", "other", "words"]
										with NamedTemporaryFile(mode="w+t"     ) as vocab_file:
														vocab_file.write("\n".join(lowerCAmelCase__     )     )
														vocab_file.flush()
														lowercase         =      BertTokenizerFast(vocab_file.name     )

										with TemporaryDirectory() as bert_save_dir:
														lowercase         =      BertModel(BertConfig(vocab_size=len(lowerCAmelCase__     )     )     )
														model.save_pretrained(lowerCAmelCase__     )
														self._test_export(lowerCAmelCase__		,    "pt"		,    12		,    lowerCAmelCase__     )





						@require_tf
						@slow
						def        SCREAMING_SNAKE_CASE(			self							:Any     )  ->Union[str, Any]:
										for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
														lowercase         =      self._test_export(lowerCAmelCase__		,    "tf"		,    12		,    **lowerCAmelCase__     )
														lowercase         =      quantize(Path(lowerCAmelCase__     )     )

														# Ensure the actual quantized model is not bigger than the original one
														if quantized_path.stat().st_size >= Path(lowerCAmelCase__     ).stat().st_size:
																		self.fail("Quantized model is bigger than initial ONNX model"     )





						@require_torch
						@slow
						def        SCREAMING_SNAKE_CASE(			self							:List[Any]     )  ->Union[str, Any]:
										for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
														lowercase         =      self._test_export(lowerCAmelCase__		,    "pt"		,    12		,    **lowerCAmelCase__     )
														lowercase         =      quantize(lowerCAmelCase__     )

														# Ensure the actual quantized model is not bigger than the original one
														if quantized_path.stat().st_size >= Path(lowerCAmelCase__     ).stat().st_size:
																		self.fail("Quantized model is bigger than initial ONNX model"     )





						def        SCREAMING_SNAKE_CASE(			self							:List[Any]		,    lowerCAmelCase__							:List[Any]		,    lowerCAmelCase__							:Any		,    lowerCAmelCase__							:Dict		,    lowerCAmelCase__							:Optional[Any]=None		,    **lowerCAmelCase__							:List[str]     )  ->List[Any]:
										try:
														# Compute path
														with TemporaryDirectory() as tempdir:
																		lowercase         =      Path(lowerCAmelCase__     ).joinpath("model.onnx"     )

														# Remove folder if exists
														if path.parent.exists():
																		path.parent.rmdir()

														# Export
														convert(lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__		,    **lowerCAmelCase__     )

														return path
										except Exception as e:
														self.fail(lowerCAmelCase__     )





						@require_torch
						@require_tokenizers
						@slow
						def        SCREAMING_SNAKE_CASE(			self							:Optional[int]     )  ->List[str]:
										from transformers import BertModel

										lowercase         =      BertModel(BertConfig.from_pretrained("lysandre/tiny-bert-random"     )     )
										lowercase         =      BertTokenizerFast.from_pretrained("lysandre/tiny-bert-random"     )
										self._test_infer_dynamic_axis(lowerCAmelCase__		,    lowerCAmelCase__		,    "pt"     )





						@require_tf
						@require_tokenizers
						@slow
						def        SCREAMING_SNAKE_CASE(			self							:Any     )  ->Optional[Any]:
										from transformers import TFBertModel

										lowercase         =      TFBertModel(BertConfig.from_pretrained("lysandre/tiny-bert-random"     )     )
										lowercase         =      BertTokenizerFast.from_pretrained("lysandre/tiny-bert-random"     )
										self._test_infer_dynamic_axis(lowerCAmelCase__		,    lowerCAmelCase__		,    "tf"     )





						def        SCREAMING_SNAKE_CASE(			self							:Dict		,    lowerCAmelCase__							:int		,    lowerCAmelCase__							:List[str]		,    lowerCAmelCase__							:Optional[int]     )  ->Union[str, Any]:
										lowercase         =      FeatureExtractionPipeline(lowerCAmelCase__		,    lowerCAmelCase__     )

										lowercase         =      ["input_ids", "token_type_ids", "attention_mask", "output_0", "output_1"]
										lowercase     ,	lowercase     ,	lowercase     ,	lowercase         =      infer_shapes(lowerCAmelCase__		,    lowerCAmelCase__     )

										# Assert all variables are present
										self.assertEqual(len(lowerCAmelCase__     )		,    len(lowerCAmelCase__     )     )
										self.assertTrue(all(var_name in shapes for var_name in variable_names     )     )
										self.assertSequenceEqual(variable_names[:3]		,    lowerCAmelCase__     )
										self.assertSequenceEqual(variable_names[3:]		,    lowerCAmelCase__     )

										# Assert inputs are {0: batch, 1: sequence}
										for var_name in ["input_ids", "token_type_ids", "attention_mask"]:
														self.assertDictEqual(shapes[var_name]		,    {0: "batch", 1: "sequence"}     )

										# Assert outputs are {0: batch, 1: sequence} and {0: batch}
										self.assertDictEqual(shapes["output_0"]		,    {0: "batch", 1: "sequence"}     )
										self.assertDictEqual(shapes["output_1"]		,    {0: "batch"}     )





						def        SCREAMING_SNAKE_CASE(			self							:Dict     )  ->List[str]:
										lowercase         =      ["input_ids", "attention_mask", "token_type_ids"]
										lowercase         =      {"input_ids": [1, 2, 3, 4], "attention_mask": [0, 0, 0, 0], "token_type_ids": [1, 1, 1, 1]}
										lowercase     ,	lowercase         =      ensure_valid_input(FuncContiguousArgs()		,    lowerCAmelCase__		,    lowerCAmelCase__     )

										# Should have exactly the same number of args (all are valid)
										self.assertEqual(len(lowerCAmelCase__     )		,    3     )

										# Should have exactly the same input names
										self.assertEqual(set(lowerCAmelCase__     )		,    set(lowerCAmelCase__     )     )

										# Parameter should be reordered according to their respective place in the function:
										# (input_ids, token_type_ids, attention_mask)
										self.assertEqual(lowerCAmelCase__		,    (tokens["input_ids"], tokens["token_type_ids"], tokens["attention_mask"])     )

										# Generated args are interleaved with another args (for instance parameter "past" in GPT2)
										lowercase     ,	lowercase         =      ensure_valid_input(FuncNonContiguousArgs()		,    lowerCAmelCase__		,    lowerCAmelCase__     )

										# Should have exactly the one arg (all before the one not provided "some_other_args")
										self.assertEqual(len(lowerCAmelCase__     )		,    1     )
										self.assertEqual(len(lowerCAmelCase__     )		,    1     )

										# Should have only "input_ids"
										self.assertEqual(inputs_args[0]		,    tokens["input_ids"]     )
										self.assertEqual(ordered_input_names[0]		,    "input_ids"     )





						def        SCREAMING_SNAKE_CASE(			self							:Any     )  ->List[Any]:
										lowercase         =      generate_identified_filename(Path("/home/something/my_fake_model.onnx"     )		,    "-test"     )
										self.assertEqual("/home/something/my_fake_model-test.onnx"		,    generated.as_posix()     )


import numpy as np

# Importing the Keras libraries and packages
import tensorflow as tf
from tensorflow.keras import layers, models

if __name__ == "__main__":
						# Initialising the CNN
						# (Sequential- Building the model layer by layer)
						_snake_case      :							Tuple     	=    models.Sequential()

						# Step 1 - Convolution
						# Here 64,64 is the length & breadth of dataset images and 3 is for the RGB channel
						# (3,3) is the kernel size (filter matrix)
						classifier.add(
						    layers.ConvaD(32, (3, 3), input_shape=(64, 64, 3), activation="relu")
						)

						# Step 2 - Pooling
						classifier.add(layers.MaxPoolingaD(pool_size=(2, 2)))

						# Adding a second convolutional layer
						classifier.add(layers.ConvaD(32, (3, 3), activation="relu"))
						classifier.add(layers.MaxPoolingaD(pool_size=(2, 2)))

						# Step 3 - Flattening
						classifier.add(layers.Flatten())

						# Step 4 - Full connection
						classifier.add(layers.Dense(units=1_28, activation="relu"))
						classifier.add(layers.Dense(units=1, activation="sigmoid"))

						# Compiling the CNN
						classifier.compile(
						    optimizer="adam", loss="binary_crossentropy", metrics=["accuracy"]
						)

						# Part 2 - Fitting the CNN to the images

						# Load Trained model weights

						# from keras.models import load_model
						# regressor=load_model('cnn.h5')

						_snake_case      :							Optional[int]     	=    tf.keras.preprocessing.image.ImageDataGenerator(
						    rescale=1.0 / 2_55, shear_range=0.2, zoom_range=0.2, horizontal_flip=True
						)

						_snake_case      :							Any     	=    tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 2_55)

						_snake_case      :							Dict     	=    train_datagen.flow_from_directory(
						    "dataset/training_set", target_size=(64, 64), batch_size=32, class_mode="binary"
						)

						_snake_case      :							Dict     	=    test_datagen.flow_from_directory(
						    "dataset/test_set", target_size=(64, 64), batch_size=32, class_mode="binary"
						)

						classifier.fit_generator(
						    training_set, steps_per_epoch=5, epochs=30, validation_data=test_set
						)

						classifier.save("cnn.h5")

						# Part 3 - Making new predictions

						_snake_case      :							int     	=    tf.keras.preprocessing.image.load_img(
						    "dataset/single_prediction/image.png", target_size=(64, 64)
						)
						_snake_case      :							Any     	=    tf.keras.preprocessing.image.img_to_array(test_image)
						_snake_case      :							int     	=    np.expand_dims(test_image, axis=0)
						_snake_case      :							Optional[Any]     	=    classifier.predict(test_image)
						# training_set.class_indices
						if result[0][0] == 0:
												_snake_case      :							str     	=    "Normal"
						if result[0][0] == 1:
												_snake_case      :							str     	=    "Abnormality detected"
