





'''simple docstring'''


import gc
import importlib.metadata
import tempfile
import unittest

from packaging import version

from transformers import (
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    BitsAndBytesConfig,
    pipeline,
)
from transformers.testing_utils import (
    is_torch_available,
    require_accelerate,
    require_bitsandbytes,
    require_torch,
    require_torch_gpu,
    require_torch_multi_gpu,
    slow,
)







def    _lowerCamelCase  (__lowerCamelCase  :     List[Any]     )   ->    Any:

			'''simple docstring'''





			if model.config.model_type == "gpt2":
						return model.transformer.h[0].mlp.c_fc
			return model.transformer.h[0].mlp.dense_ah_to_h


if is_torch_available():
		import torch
		import torch.nn as nn






		class       UpperCamelCase__  (		nn.Module     ):







			def __init__(				self			:   Optional[int] , lowerCamelCase			:   nn.Module , lowerCamelCase			:   int		):




						'''simple docstring'''
						super().__init__()
						a__     = module
						a__     = nn.Sequential(
						    nn.Linear(module.in_features , lowerCAmelCase__ , bias=lowerCAmelCase__		) , nn.Linear(lowerCAmelCase__ , module.out_features , bias=lowerCAmelCase__		) , )
						a__     = (2.0 / (5 * min(module.in_features , module.out_features		))) ** 0.5
						nn.init.normal_(self.adapter[0].weight , std=lowerCAmelCase__		)
						nn.init.zeros_(self.adapter[1].weight		)
						self.adapter.to(module.weight.device		)







			def 						__a   (				self			:   Dict , lowerCamelCase			:   Tuple , *lowerCamelCase			:   List[str] , **lowerCamelCase			:   List[str]		):




						'''simple docstring'''
						return self.module(lowerCAmelCase__ , *lowerCAmelCase__ , **lowerCAmelCase__		) + self.adapter(lowerCAmelCase__		)






@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class       UpperCamelCase__  (		unittest.TestCase     ):
	# We keep the constants inside the init function and model loading inside setUp function

	# We need to test on relatively large models (aka >1b parameters otherwise the quantiztion may not work as expected)
	# Therefore here we use only bloom-1b3 to test our module
	lowerCAmelCase__					:			str								=						"bigscience/bloom-1b7"

	# Constant values
	lowerCAmelCase__					:			Union[str, Any]								=						2.109_659_552_692_574

	lowerCAmelCase__					:			str								=						"Hello my name is"
	lowerCAmelCase__					:			Optional[Any]								=						set()
	EXPECTED_OUTPUTS.add("Hello my name is John and I am a professional photographer. I"     )
	EXPECTED_OUTPUTS.add("Hello my name is John.\nI am a friend of your father.\n"     )
	EXPECTED_OUTPUTS.add("Hello my name is John Doe, I am a student at the University"     )
	lowerCAmelCase__					:			int								=						10







	def 						__a   (				self			:   List[Any]		):




				'''simple docstring'''
				# Models and tokenizer
				a__     = AutoTokenizer.from_pretrained(self.model_name		)






class       UpperCamelCase__  (		_snake_case     ):







	def 						__a   (				self			:   Union[str, Any]		):




				'''simple docstring'''
				super().setUp()

				# Models and tokenizer
				a__     = AutoModelForCausalLM.from_pretrained(
				    self.model_name , torch_dtype=torch.floataa , device_map="auto"		)
				a__     = AutoModelForCausalLM.from_pretrained(self.model_name , load_in_abit=lowerCAmelCase__ , device_map="auto"		)







	def 						__a   (				self			:   Optional[Any]		):




				'''simple docstring'''
				del self.model_fpaa
				del self.model_abit

				gc.collect()
				torch.cuda.empty_cache()







	def 						__a   (				self			:   Any		):




				'''simple docstring'''
				a__     = self.model_abit.config

				self.assertTrue(hasattr(lowerCAmelCase__ , "quantization_config"		)		)

				a__     = config.to_dict()
				a__     = config.to_diff_dict()

				a__     = config.to_json_string()







	def 						__a   (				self			:   Union[str, Any]		):




				'''simple docstring'''
				from bitsandbytes.nn import Paramsabit

				a__     = self.model_fpaa.get_memory_footprint()
				a__     = self.model_abit.get_memory_footprint()

				self.assertAlmostEqual(mem_fpaa / mem_abit , self.EXPECTED_RELATIVE_DIFFERENCE		)
				a__     = get_some_linear_layer(self.model_abit		)
				self.assertTrue(linear.weight.__class__ == Paramsabit		)







	def 						__a   (				self			:   Tuple		):




				'''simple docstring'''
				from transformers import TaPreTrainedModel

				self.model_fpaa.get_memory_footprint()
				self.model_abit.get_memory_footprint()

				for name, module in self.model_abit.named_modules():
							if isinstance(lowerCAmelCase__ , torch.nn.Linear		):
										if name not in ["lm_head"] + TaPreTrainedModel._keep_in_fpaa_modules:
													# 4-bit parameters are packed in uint8 variables
													self.assertTrue(module.weight.dtype == torch.uinta		)







	def 						__a   (				self			:   Optional[Any]		):




				'''simple docstring'''
				a__     = self.tokenizer(self.input_text , return_tensors="pt"		)
				a__     = self.model_abit.generate(input_ids=encoded_input["input_ids"].to(0		) , max_new_tokens=1_0		)

				self.assertIn(self.tokenizer.decode(output_sequences[0] , skip_special_tokens=lowerCAmelCase__		) , self.EXPECTED_OUTPUTS		)







	def 						__a   (				self			:   Dict		):




				'''simple docstring'''
				a__     = BitsAndBytesConfig()
				a__     = True

				a__     = AutoModelForCausalLM.from_pretrained(
				    self.model_name , quantization_config=lowerCAmelCase__ , device_map="auto"		)

				a__     = self.tokenizer(self.input_text , return_tensors="pt"		)
				a__     = model_abit_from_config.generate(
				    input_ids=encoded_input["input_ids"].to(0		) , max_new_tokens=1_0		)

				self.assertIn(self.tokenizer.decode(output_sequences[0] , skip_special_tokens=lowerCAmelCase__		) , self.EXPECTED_OUTPUTS		)







	def 						__a   (				self			:   str		):




				'''simple docstring'''
				with self.assertRaises(lowerCAmelCase__		), tempfile.TemporaryDirectory() as tmpdirname:
							self.model_abit.save_pretrained(lowerCAmelCase__		)







	def 						__a   (				self			:   int		):




				'''simple docstring'''
				a__     = BitsAndBytesConfig()

				with self.assertRaises(lowerCAmelCase__		):
							a__     = AutoModelForCausalLM.from_pretrained(
							    self.model_name , quantization_config=lowerCAmelCase__ , load_in_abit=lowerCAmelCase__ , device_map="auto" , bnb_abit_quant_type="nf4" , )







	def 						__a   (				self			:   Any		):




				'''simple docstring'''
				with self.assertRaises(lowerCAmelCase__		):
							# Tries with `str`
							self.model_abit.to("cpu"		)

				with self.assertRaises(lowerCAmelCase__		):
							# Tries with a `dtype``
							self.model_abit.to(torch.floataa		)

				with self.assertRaises(lowerCAmelCase__		):
							# Tries with a `device`
							self.model_abit.to(torch.device("cuda:0"		)		)

				with self.assertRaises(lowerCAmelCase__		):
							# Tries with a `device`
							self.model_abit.float()

				with self.assertRaises(lowerCAmelCase__		):
							# Tries with a `device`
							self.model_abit.half()

				# Test if we did not break anything
				a__     = self.tokenizer(self.input_text , return_tensors="pt"		)

				a__     = self.model_fpaa.to(torch.floataa		)
				a__     = self.model_fpaa.generate(input_ids=encoded_input["input_ids"].to(0		) , max_new_tokens=1_0		)

				# Check this does not throw an error
				a__     = self.model_fpaa.to("cpu"		)

				# Check this does not throw an error
				a__     = self.model_fpaa.half()

				# Check this does not throw an error
				a__     = self.model_fpaa.float()







	def 						__a   (				self			:   Any		):




				'''simple docstring'''
				a__     = AutoModelForSeqaSeqLM.from_pretrained("t5-small" , load_in_abit=lowerCAmelCase__ , device_map="auto"		)
				self.assertTrue(model.decoder.block[0].layer[2].DenseReluDense.wo.weight.dtype == torch.floataa		)






@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class       UpperCamelCase__  (		unittest.TestCase     ):







	@classmethod
	def 						__a   (				cls			:   str		):




				'''simple docstring'''
				a__     = "t5-small"
				a__     = "google/flan-t5-small"  # flan-t5 uses dense-act instead of dense-relu-dense
				a__     = AutoTokenizer.from_pretrained(cls.model_name		)
				a__     = "Translate in German: Hello, my dog is cute"







	def 						__a   (				self			:   Optional[int]		):




				'''simple docstring'''
				gc.collect()
				torch.cuda.empty_cache()







	def 						__a   (				self			:   List[str]		):




				'''simple docstring'''
				from transformers import TaForConditionalGeneration

				a__     = TaForConditionalGeneration._keep_in_fpaa_modules
				a__     = None

				# test with `t5-small`
				a__     = TaForConditionalGeneration.from_pretrained(self.model_name , load_in_abit=lowerCAmelCase__ , device_map="auto"		)
				a__     = self.tokenizer(self.input_text , return_tensors="pt"		).to(0		)
				a__     = model.generate(**lowerCAmelCase__		)

				# test with `flan-t5-small`
				a__     = TaForConditionalGeneration.from_pretrained(
				    self.dense_act_model_name , load_in_abit=lowerCAmelCase__ , device_map="auto"		)
				a__     = self.tokenizer(self.input_text , return_tensors="pt"		).to(0		)
				a__     = model.generate(**lowerCAmelCase__		)
				a__     = modules







	def 						__a   (				self			:   Any		):




				'''simple docstring'''
				import bitsandbytes as bnb

				from transformers import TaForConditionalGeneration

				# test with `t5-small`
				a__     = TaForConditionalGeneration.from_pretrained(self.model_name , load_in_abit=lowerCAmelCase__ , device_map="auto"		)

				# there was a bug with decoders - this test checks that it is fixed
				self.assertTrue(isinstance(model.decoder.block[0].layer[0].SelfAttention.q , bnb.nn.Linearabit		)		)

				a__     = self.tokenizer(self.input_text , return_tensors="pt"		).to(0		)
				a__     = model.generate(**lowerCAmelCase__		)

				# test with `flan-t5-small`
				a__     = TaForConditionalGeneration.from_pretrained(
				    self.dense_act_model_name , load_in_abit=lowerCAmelCase__ , device_map="auto"		)
				a__     = self.tokenizer(self.input_text , return_tensors="pt"		).to(0		)
				a__     = model.generate(**lowerCAmelCase__		)






class       UpperCamelCase__  (		_snake_case     ):







	def 						__a   (				self			:   str		):




				'''simple docstring'''
				super().setUp()
				# model_name
				a__     = "bigscience/bloom-560m"
				a__     = "t5-small"

				# Different types of model

				a__     = AutoModel.from_pretrained(self.model_name , load_in_abit=lowerCAmelCase__ , device_map="auto"		)
				# Sequence classification model
				a__     = AutoModelForSequenceClassification.from_pretrained(
				    self.model_name , load_in_abit=lowerCAmelCase__ , device_map="auto"		)
				# CausalLM model
				a__     = AutoModelForCausalLM.from_pretrained(self.model_name , load_in_abit=lowerCAmelCase__ , device_map="auto"		)
				# Seq2seq model
				a__     = AutoModelForSeqaSeqLM.from_pretrained(
				    self.seq_to_seq_name , load_in_abit=lowerCAmelCase__ , device_map="auto"		)







	def 						__a   (				self			:   Optional[int]		):




				'''simple docstring'''
				del self.base_model
				del self.sequence_model
				del self.model_abit
				del self.seq_to_seq_model

				gc.collect()
				torch.cuda.empty_cache()







	def 						__a   (				self			:   Any		):




				'''simple docstring'''
				from bitsandbytes.nn import Paramsabit

				self.assertTrue(self.base_model.h[-1].mlp.dense_ah_to_h.weight.__class__ == Paramsabit		)

				# Other heads should be nn.Parameter
				self.assertTrue(self.model_abit.lm_head.weight.__class__ == torch.nn.Parameter		)
				self.assertTrue(self.sequence_model.score.weight.__class__ == torch.nn.Parameter		)
				self.assertTrue(self.seq_to_seq_model.lm_head.weight.__class__ == torch.nn.Parameter		)






class       UpperCamelCase__  (		_snake_case     ):







	def 						__a   (				self			:   Dict		):




				'''simple docstring'''
				super().setUp()







	def 						__a   (				self			:   Optional[Any]		):




				'''simple docstring'''
				del self.pipe

				gc.collect()
				torch.cuda.empty_cache()







	def 						__a   (				self			:   Optional[Any]		):




				'''simple docstring'''
				a__     = pipeline(
				    "text-generation" , model=self.model_name , model_kwargs={"device_map": "auto", "load_in_4bit": True, "torch_dtype": torch.floataa} , max_new_tokens=self.MAX_NEW_TOKENS , )

				# Real second forward pass
				a__     = self.pipe(self.input_text		)
				self.assertIn(pipeline_output[0]["generated_text"] , self.EXPECTED_OUTPUTS		)






@require_torch_multi_gpu
class       UpperCamelCase__  (		_snake_case     ):







	def 						__a   (				self			:   List[str]		):




				'''simple docstring'''
				super().setUp()







	def 						__a   (				self			:   List[Any]		):




				'''simple docstring'''
				a__     = AutoModelForCausalLM.from_pretrained(
				    self.model_name , load_in_abit=lowerCAmelCase__ , device_map="balanced"		)

				# Check correct device map
				self.assertEqual(set(model_parallel.hf_device_map.values()		) , {0, 1}		)

				# Check that inference pass works on the model
				a__     = self.tokenizer(self.input_text , return_tensors="pt"		)

				# Second real batch
				a__     = model_parallel.generate(input_ids=encoded_input["input_ids"].to(0		) , max_new_tokens=1_0		)
				self.assertIn(self.tokenizer.decode(output_parallel[0] , skip_special_tokens=lowerCAmelCase__		) , self.EXPECTED_OUTPUTS		)






class       UpperCamelCase__  (		_snake_case     ):







	def 						__a   (				self			:   Dict		):




				'''simple docstring'''
				a__     = "facebook/opt-350m"
				super().setUp()







	def 						__a   (				self			:   Tuple		):




				'''simple docstring'''
				if version.parse(importlib.metadata.version("bitsandbytes"		)		) < version.parse("0.37.0"		):
							return

				# Step 1: freeze all parameters
				a__     = AutoModelForCausalLM.from_pretrained(self.model_name , load_in_abit=lowerCAmelCase__		)

				self.assertEqual(set(model.hf_device_map.values()		) , {torch.cuda.current_device()}		)

				for param in model.parameters():
							a__     = False  # freeze the model - train adapters later
							if param.ndim == 1:
										# cast the small parameters (e.g. layernorm) to fp32 for stability
										a__     = param.data.to(torch.floataa		)

										 # Step 2: add adapters
				for _, module in model.named_modules():
							if "OPTAttention" in repr(type(lowerCAmelCase__		)		):
										a__     = LoRALayer(module.q_proj , rank=1_6		)
										a__     = LoRALayer(module.k_proj , rank=1_6		)
										a__     = LoRALayer(module.v_proj , rank=1_6		)

										 # Step 3: dummy batch
				a__     = self.tokenizer("Test batch " , return_tensors="pt"		).to(0		)

				# Step 4: Check if the gradient is not None
				with torch.cuda.amp.autocast():
							a__     = model.forward(**lowerCAmelCase__		)
							out.logits.norm().backward()

				for module in model.modules():
							if isinstance(lowerCAmelCase__ , lowerCAmelCase__		):
										self.assertTrue(module.adapter[1].weight.grad is not None		)
										self.assertTrue(module.adapter[1].weight.grad.norm().item() > 0		)
							elif isinstance(lowerCAmelCase__ , nn.Embedding		):
										self.assertTrue(module.weight.grad is None		)






class       UpperCamelCase__  (		_snake_case     ):
	lowerCAmelCase__					:			Optional[int]								=						"gpt2-xl"
	lowerCAmelCase__					:			str								=						3.3_191_854_854_152_187










'''simple docstring'''


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)
		lowerCAmelCase_   :   str       =					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=128, 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')

		lowerCAmelCase_   :   Any       =					tf.keras.preprocessing.image.ImageDataGenerator(
		    rescale=1.0 / 255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True
		)

		lowerCAmelCase_   :   List[Any]       =					tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 255)

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

		lowerCAmelCase_   :   Any       =					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

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



