






"""simple docstring"""






import logging
import re

import pytorch_quantization
import pytorch_quantization.nn as quant_nn
import torch
from pytorch_quantization import calib
from pytorch_quantization.tensor_quant import QuantDescriptor


UpperCAmelCase_   : Optional[Any]   			= logging.getLogger(__name__)

UpperCAmelCase_   : Any   			= 50  # max width of layer names
UpperCAmelCase_   : List[Any]   			= 70  # max width of quantizer names



def 					_A			(__a							) ->       Optional[Any]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		parser.add_argument_group('''quant_trainer arguments'''							)
    group.add_argument('''--wprec'''    ,				type=__a    ,				default=8    ,				help='''weight precision'''							)
    group.add_argument('''--aprec'''    ,				type=__a    ,				default=8    ,				help='''activation precision'''							)
    group.add_argument('''--quant-per-tensor'''    ,				action='''store_true'''    ,				help='''per tensor weight scaling'''							)
    group.add_argument('''--quant-disable'''    ,				action='''store_true'''    ,				help='''disable all quantizers'''							)
    group.add_argument('''--quant-disable-embeddings'''    ,				action='''store_true'''    ,				help='''disable all embeddings quantizers'''							)
    group.add_argument('''--quant-disable-keyword'''    ,				type=__a    ,				nargs='''+'''    ,				help='''disable quantizers by keyword'''							)
    group.add_argument('''--quant-disable-layer-module'''    ,				type=__a    ,				help='''disable quantizers by keyword under layer.'''							)
    group.add_argument('''--quant-enable-layer-module'''    ,				type=__a    ,				help='''enable quantizers by keyword under layer'''							)
    group.add_argument('''--calibrator'''    ,				default='''max'''    ,				help='''which quantization range calibrator to use'''							)
    group.add_argument('''--percentile'''    ,				default=__a    ,				type=__a    ,				help='''percentile for PercentileCalibrator'''							)
    group.add_argument('''--fuse-qkv'''    ,				action='''store_true'''    ,				help='''use the same scale factor for qkv'''							)
    group.add_argument('''--clip-gelu'''    ,				metavar='''N'''    ,				type=__a    ,				help='''clip gelu output maximum value to N'''							)
    group.add_argument(
        '''--recalibrate-weights'''    ,				action='''store_true'''    ,				help=(
            '''recalibrate weight amaxes by taking the max of the weights.'''
            ''' amaxes will be computed with the current quantization granularity (axis).'''
        )    ,				)



def 					_A			(__a							) ->       List[str]:
    """simple docstring"""






    if args.calibrator == "max":
        SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		'''max'''
    elif args.calibrator == "percentile":
        if args.percentile is None:
            raise ValueError('''Specify --percentile when using percentile calibrator'''							)
        SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		'''histogram'''
    elif args.calibrator == "mse":
        SCREAMING_SNAKE_CASE_      :  List[str]    						=		'''histogram'''
    else:
        raise ValueError(f'Invalid calibrator {args.calibrator}'							)

    SCREAMING_SNAKE_CASE_      :  List[str]    						=		QuantDescriptor(num_bits=args.aprec    ,				calib_method=__a							)
    SCREAMING_SNAKE_CASE_      :  Dict    						=		QuantDescriptor(num_bits=args.wprec    ,				axis=(None if args.quant_per_tensor else (0,))							)
    quant_nn.QuantLinear.set_default_quant_desc_input(__a							)
    quant_nn.QuantLinear.set_default_quant_desc_weight(__a							)



def 					_A			(__a    ,				__a    ,				__a=False    ,				__a=False							) ->       Optional[Any]:
    """simple docstring"""






    logger.info('''Configuring Model for Quantization'''							)
    logger.info(f'using quantization package {pytorch_quantization.__file__}'							)

    if not calib:
        if args.quant_disable_embeddings:
            set_quantizer_by_name(__a    ,				['''embeddings''']    ,				which='''weight'''    ,				_disabled=__a							)

        if args.quant_disable:
            set_quantizer_by_name(__a    ,				['''''']    ,				_disabled=__a							)

        if args.quant_disable_keyword:
            set_quantizer_by_name(__a    ,				args.quant_disable_keyword    ,				_disabled=__a							)

        if args.quant_disable_layer_module:
            set_quantizer_by_name(__a    ,				[R'''layer.\d+.''' + args.quant_disable_layer_module]    ,				_disabled=__a							)

        if args.quant_enable_layer_module:
            set_quantizer_by_name(__a    ,				[R'''layer.\d+.''' + args.quant_enable_layer_module]    ,				_disabled=__a							)

        if args.recalibrate_weights:
            recalibrate_weights(__a							)

        if args.fuse_qkv:
            fuse_qkv(__a    ,				__a							)

    if args.clip_gelu:
        clip_gelu(__a    ,				args.clip_gelu							)

    # if args.local_rank in [-1, 0] and not calib:
    print_quant_summary(__a							)



def 					_A			(__a							) ->       int:
    """simple docstring"""






    logger.info('''Enabling Calibration'''							)
    for name, module in model.named_modules():
        if name.endswith('''_quantizer'''							):
            if module._calibrator is not None:
                module.disable_quant()
                module.enable_calib()
            else:
                module.disable()
            logger.info(f'{name:80}: {module}'							)



def 					_A			(__a    ,				__a							) ->       Optional[int]:
    """simple docstring"""






    logger.info('''Loading calibrated amax'''							)
    for name, module in model.named_modules():
        if name.endswith('''_quantizer'''							):
            if module._calibrator is not None:
                if isinstance(module._calibrator    ,				calib.MaxCalibrator							):
                    module.load_calib_amax()
                else:
                    module.load_calib_amax('''percentile'''    ,				percentile=args.percentile							)
                module.enable_quant()
                module.disable_calib()
            else:
                module.enable()
    model.cuda()
    print_quant_summary(__a							)



def 					_A			(__a    ,				__a							) ->       Optional[int]:
    """simple docstring"""






    def fusea(__a    ,				__a    ,				__a							):
        for mod in [qq, qk, qv]:
            if not hasattr(__a    ,				'''_amax'''							):
                print('''          WARNING: NO AMAX BUFFER'''							)
                return
        SCREAMING_SNAKE_CASE_      :  List[Any]    						=		qq._amax.detach().item()
        SCREAMING_SNAKE_CASE_      :  Dict    						=		qk._amax.detach().item()
        SCREAMING_SNAKE_CASE_      :  List[str]    						=		qv._amax.detach().item()

        SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		max(__a    ,				__a    ,				__a							)
        qq._amax.fill_(__a							)
        qk._amax.fill_(__a							)
        qv._amax.fill_(__a							)
        logger.info(f'          q={q:5.2f} k={k:5.2f} v={v:5.2f} -> {amax:5.2f}'							)

    for name, mod in model.named_modules():
        if name.endswith('''.attention.self'''							):
            logger.info(f'FUSE_QKV: {name:{name_width}}'							)
            fusea(mod.matmul_q_input_quantizer    ,				mod.matmul_k_input_quantizer    ,				mod.matmul_v_input_quantizer							)
            if args.quant_per_tensor:
                fusea(mod.query._weight_quantizer    ,				mod.key._weight_quantizer    ,				mod.value._weight_quantizer							)



def 					_A			(__a    ,				__a							) ->       List[str]:
    """simple docstring"""






    for name, mod in model.named_modules():
        if name.endswith('''.output.dense'''							) and not name.endswith('''attention.output.dense'''							):
            SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		mod._input_quantizer._amax.data.detach().item()
            mod._input_quantizer._amax.data.detach().clamp_(max=__a							)
            SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		mod._input_quantizer._amax.data.detach().item()
            logger.info(f'CLIP_GELU: {name:{name_width}} amax: {amax_init:5.2f} -> {amax:5.2f}'							)



def 					_A			(__a							) ->       Dict:
    """simple docstring"""






    for name, mod in model.named_modules():
        if hasattr(__a    ,				'''_weight_quantizer'''							) and mod._weight_quantizer.axis is not None:
            SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		mod.weight.shape[0]
            SCREAMING_SNAKE_CASE_      :  Tuple    						=		mod._weight_quantizer._amax.detach()
            SCREAMING_SNAKE_CASE_      :  str    						=		torch.ones(__a    ,				dtype=amax.dtype    ,				device=amax.device							) * amax
            print(f'expanding {name} {amax} -> {mod._weight_quantizer._amax}'							)



def 					_A			(__a							) ->       List[Any]:
    """simple docstring"""






    for name, mod in model.named_modules():
        if hasattr(__a    ,				'''_weight_quantizer'''							):
            if not hasattr(mod.weight_quantizer    ,				'''_amax'''							):
                print('''RECALIB: {name:{name_width}} WARNING: NO AMAX BUFFER'''							)
                continue

            # determine which axes to reduce across
            # e.g. a 4D tensor quantized per axis 0 should reduce over (1,2,3)
            SCREAMING_SNAKE_CASE_      :  int    						=		set() if mod._weight_quantizer.axis is None else set(mod._weight_quantizer.axis							)
            SCREAMING_SNAKE_CASE_      :  Tuple    						=		set(range(len(mod.weight.size()							)							)							) - axis_set
            SCREAMING_SNAKE_CASE_      :  Any    						=		pytorch_quantization.utils.reduce_amax(mod.weight    ,				axis=__a    ,				keepdims=__a							).detach()
            logger.info(f'RECALIB: {name:{name_width}} {mod._weight_quantizer._amax.flatten()} -> {amax.flatten()}'							)
            SCREAMING_SNAKE_CASE_      :  Dict    						=		amax



def 					_A			(__a    ,				__a=25    ,				__a=1_80    ,				__a=None							) ->       List[str]:
    """simple docstring"""






    if ignore is None:
        SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		[]
    elif not isinstance(__a    ,				__a							):
        SCREAMING_SNAKE_CASE_      :  int    						=		[ignore]

    SCREAMING_SNAKE_CASE_      :  List[Any]    						=		0
    for name, mod in model.named_modules():
        if not hasattr(__a    ,				'''weight'''							):
            continue
        SCREAMING_SNAKE_CASE_      :  List[str]    						=		max(__a    ,				len(__a							)							)

    for name, mod in model.named_modules():
        SCREAMING_SNAKE_CASE_      :  int    						=		getattr(__a    ,				'''_input_quantizer'''    ,				__a							)
        SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		getattr(__a    ,				'''_weight_quantizer'''    ,				__a							)
        if not hasattr(__a    ,				'''weight'''							):
            continue
        if type(__a							) in ignore:
            continue
        if [True for s in ignore if type(__a							) is str and s in name]:
            continue
        SCREAMING_SNAKE_CASE_      :  List[str]    						=		f'Act:{input_q.extra_repr()}'
        SCREAMING_SNAKE_CASE_      :  Tuple    						=		f'Wgt:{weight_q.extra_repr()}'
        SCREAMING_SNAKE_CASE_      :  List[Any]    						=		f'{name:{name_width}} {act_str} {wgt_str}'
        if len(__a							) <= line_width:
            logger.info(__a							)
        else:
            logger.info(f'{name:{name_width}} {act_str}'							)
            logger.info(f'{"  ":{name_width}} {wgt_str}'							)



def 					_A			(__a							) ->       List[Any]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		0
    for name, mod in model.named_modules():
        if isinstance(__a    ,				pytorch_quantization.nn.TensorQuantizer							):
            print(f'{name:80} {mod}'							)
            count += 1
    print(f'{count} TensorQuantizers found in model'							)



def 					_A			(__a    ,				__a    ,				__a    ,				__a    ,				__a							) ->       Tuple:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  Any    						=		getattr(__a    ,				__a    ,				__a							)
    if quantizer_mod is not None:
        assert hasattr(__a    ,				__a							)
        setattr(__a    ,				__a    ,				__a							)
    else:
        logger.warning(f'{name} has no {quantizer}'							)



def 					_A			(__a    ,				__a    ,				__a="both"    ,				**__a							) ->       Tuple:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		f'Warning: changing {which} quantizers of {name:{qname_width}}'
    for k, v in kwargs.items():
        s += f' {k}={v}'
        if which in ["input", "both"]:
            set_quantizer(__a    ,				__a    ,				'''_input_quantizer'''    ,				__a    ,				__a							)
        if which in ["weight", "both"]:
            set_quantizer(__a    ,				__a    ,				'''_weight_quantizer'''    ,				__a    ,				__a							)
    logger.info(__a							)





def 					_A			(__a    ,				__a    ,				**__a							) ->       List[str]:
    """simple docstring"""






    for name, mod in model.named_modules():
        if hasattr(__a    ,				'''_input_quantizer'''							) or hasattr(__a    ,				'''_weight_quantizer'''							):
            for n in names:
                if re.search(__a    ,				__a							):
                    set_quantizers(__a    ,				__a    ,				**__a							)
        elif name.endswith('''_quantizer'''							):
            for n in names:
                if re.search(__a    ,				__a							):
                    SCREAMING_SNAKE_CASE_      :  Dict    						=		f'Warning: changing {name:{name_width}}'
                    for k, v in kwargs.items():
                        s += f' {k}={v}'
                        setattr(__a    ,				__a    ,				__a							)
                    logger.info(__a							)




"""simple docstring"""





# Logistic Regression from scratch

# In[62]:

# In[63]:

# importing all the required libraries

import numpy as np
from matplotlib import pyplot as plt
from sklearn import datasets



def       lowerCamelCase__		(			__snake_case	)							->		Tuple:


					"""simple docstring"""





					return 1 / (1 + np.exp(-z	))



def       lowerCamelCase__		(			__snake_case,     __snake_case	)							->		List[str]:


					"""simple docstring"""





					return (-y * np.log(__snake_case	) - (1 - y) * np.log(1 - h	)).mean()



def       lowerCamelCase__		(			__snake_case,     __snake_case,     __snake_case	)							->		int:


					"""simple docstring"""





					_UpperCamelCase							 =  np.dot(__snake_case,     __snake_case	)
					return np.sum(y * scores - np.log(1 + np.exp(__snake_case	)	)	)



def       lowerCamelCase__		(			__snake_case,     __snake_case,     __snake_case,     __snake_case=7_00_00	)							->		Union[str, Any]:


					"""simple docstring"""





					_UpperCamelCase							 =  np.zeros(x.shape[1]	)

					for iterations in range(__snake_case	):
										_UpperCamelCase							 =  np.dot(__snake_case,     __snake_case	)
										_UpperCamelCase							 =  sigmoid_function(__snake_case	)
										_UpperCamelCase							 =  np.dot(x.T,     h - y	) / y.size
										_UpperCamelCase							 =  theta - alpha * gradient  # updating the weights
										_UpperCamelCase							 =  np.dot(__snake_case,     __snake_case	)
										_UpperCamelCase							 =  sigmoid_function(__snake_case	)
										_UpperCamelCase							 =  cost_function(__snake_case,     __snake_case	)
										if iterations % 1_00 == 0:
															print(F'''loss: {j} \t'''	)  # printing the loss after every 100 iterations
					return theta


# In[68]:

if __name__ == "__main__":
				_a														=		datasets.load_iris()
				_a														=		iris.data[:, :2]
				_a														=		(iris.target != 0) * 1

				_a														=		0.1
				_a														=		logistic_reg(alpha, x, y, max_iterations=7_0000)
				print("""theta: """, theta)  # printing the theta i.e our weights vector



				def       lowerCamelCase__		(			__snake_case	)							->		Tuple:


									"""simple docstring"""





									return sigmoid_function(
									    np.dot(__snake_case,     __snake_case	)	)  # predicting the value of probability from the logistic regression algorithm

				plt.figure(figsize=(10, 6))
				plt.scatter(x[y == 0][:, 0], x[y == 0][:, 1], color="""b""", label="""0""")
				plt.scatter(x[y == 1][:, 0], x[y == 1][:, 1], color="""r""", label="""1""")
				((_a)							,							(_a))														=		(x[:, 0].min(), x[:, 0].max())
				((_a)							,							(_a))														=		(x[:, 1].min(), x[:, 1].max())
				((_a)							,							(_a))														=		np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
				_a														=		np.c_[xxa.ravel(), xxa.ravel()]
				_a														=		predict_prob(grid).reshape(xxa.shape)
				plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors="""black""")

				plt.legend()
				plt.show()






