


"""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


lowerCamelCase_       :       Dict      						=  logging.getLogger(__name__)

lowerCamelCase_       :       List[str]      						=  5_0  # max width of layer names
lowerCamelCase_       :       List[Any]      						=  7_0  # max width of quantizer names
def _A						(						lowercase		):







    """simple docstring"""
    a					=parser.add_argument_group('''quant_trainer arguments'''		)
    group.add_argument('''--wprec'''	,   type=lowerCAmelCase__	,   default=8	,   help='''weight precision'''		)
    group.add_argument('''--aprec'''	,   type=lowerCAmelCase__	,   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=lowerCAmelCase__	,   nargs='''+'''	,   help='''disable quantizers by keyword'''		)
    group.add_argument('''--quant-disable-layer-module'''	,   type=lowerCAmelCase__	,   help='''disable quantizers by keyword under layer.'''		)
    group.add_argument('''--quant-enable-layer-module'''	,   type=lowerCAmelCase__	,   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=lowerCAmelCase__	,   type=lowerCAmelCase__	,   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=lowerCAmelCase__	,   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						(						lowercase		):







    """simple docstring"""



    if args.calibrator == "max":
        a					='''max'''
    elif args.calibrator == "percentile":
        if args.percentile is None:
            raise ValueError('''Specify --percentile when using percentile calibrator'''		)
        a					='''histogram'''
    elif args.calibrator == "mse":
        a					='''histogram'''
    else:
        raise ValueError(f'''Invalid calibrator {args.calibrator}'''		)

    a					=QuantDescriptor(num_bits=args.aprec	,   calib_method=lowerCAmelCase__		)
    a					=QuantDescriptor(num_bits=args.wprec	,   axis=(None if args.quant_per_tensor else (0,))		)
    quant_nn.QuantLinear.set_default_quant_desc_input(lowerCAmelCase__		)
    quant_nn.QuantLinear.set_default_quant_desc_weight(lowerCAmelCase__		)
def _A						(						lowercase	,   lowercase	,   lowercase=False	,   lowercase=False		):







    """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(lowerCAmelCase__	,   ['''embeddings''']	,   which='''weight'''	,   _disabled=lowerCAmelCase__		)

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

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

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

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

        if args.recalibrate_weights:
            recalibrate_weights(lowerCAmelCase__		)

        if args.fuse_qkv:
            fuse_qkv(lowerCAmelCase__	,   lowerCAmelCase__		)

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

    # if args.local_rank in [-1, 0] and not calib:
    print_quant_summary(lowerCAmelCase__		)
def _A						(						lowercase		):







    """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						(						lowercase	,   lowercase		):







    """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(lowerCAmelCase__		)
def _A						(						lowercase	,   lowercase		):







    """simple docstring"""



    def fusea(lowercase	,   lowercase	,   lowercase		):
        for mod in [qq, qk, qv]:
            if not hasattr(lowerCAmelCase__	,   '''_amax'''		):
                print('''          WARNING: NO AMAX BUFFER'''		)
                return
        a					=qq._amax.detach().item()
        a					=qk._amax.detach().item()
        a					=qv._amax.detach().item()

        a					=max(lowerCAmelCase__	,   lowerCAmelCase__	,   lowerCAmelCase__		)
        qq._amax.fill_(lowerCAmelCase__		)
        qk._amax.fill_(lowerCAmelCase__		)
        qv._amax.fill_(lowerCAmelCase__		)
        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						(						lowercase	,   lowercase		):







    """simple docstring"""



    for name, mod in model.named_modules():
        if name.endswith('''.output.dense'''		) and not name.endswith('''attention.output.dense'''		):
            a					=mod._input_quantizer._amax.data.detach().item()
            mod._input_quantizer._amax.data.detach().clamp_(max=lowerCAmelCase__		)
            a					=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						(						lowercase		):







    """simple docstring"""



    for name, mod in model.named_modules():
        if hasattr(lowerCAmelCase__	,   '''_weight_quantizer'''		) and mod._weight_quantizer.axis is not None:
            a					=mod.weight.shape[0]
            a					=mod._weight_quantizer._amax.detach()
            a					=torch.ones(lowerCAmelCase__	,   dtype=amax.dtype	,   device=amax.device		) * amax
            print(f'''expanding {name} {amax} -> {mod._weight_quantizer._amax}'''		)
def _A						(						lowercase		):







    """simple docstring"""



    for name, mod in model.named_modules():
        if hasattr(lowerCAmelCase__	,   '''_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)
            a					=set() if mod._weight_quantizer.axis is None else set(mod._weight_quantizer.axis		)
            a					=set(range(len(mod.weight.size()		)		)		) - axis_set
            a					=pytorch_quantization.utils.reduce_amax(mod.weight	,   axis=lowerCAmelCase__	,   keepdims=lowerCAmelCase__		).detach()
            logger.info(f'''RECALIB: {name:{name_width}} {mod._weight_quantizer._amax.flatten()} -> {amax.flatten()}'''		)
            a					=amax
def _A						(						lowercase	,   lowercase=25	,   lowercase=1_80	,   lowercase=None		):







    """simple docstring"""



    if ignore is None:
        a					=[]
    elif not isinstance(lowerCAmelCase__	,   lowerCAmelCase__		):
        a					=[ignore]

    a					=0
    for name, mod in model.named_modules():
        if not hasattr(lowerCAmelCase__	,   '''weight'''		):
            continue
        a					=max(lowerCAmelCase__	,   len(lowerCAmelCase__		)		)

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







    """simple docstring"""
    a					=0
    for name, mod in model.named_modules():
        if isinstance(lowerCAmelCase__	,   pytorch_quantization.nn.TensorQuantizer		):
            print(f'''{name:80} {mod}'''		)
            count += 1
    print(f'''{count} TensorQuantizers found in model'''		)
def _A						(						lowercase	,   lowercase	,   lowercase	,   lowercase	,   lowercase		):







    """simple docstring"""
    a					=getattr(lowerCAmelCase__	,   lowerCAmelCase__	,   lowerCAmelCase__		)
    if quantizer_mod is not None:
        assert hasattr(lowerCAmelCase__	,   lowerCAmelCase__		)
        setattr(lowerCAmelCase__	,   lowerCAmelCase__	,   lowerCAmelCase__		)
    else:
        logger.warning(f'''{name} has no {quantizer}'''		)
def _A						(						lowercase	,   lowercase	,   lowercase="both"	,   **lowercase		):







    """simple docstring"""
    a					=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(lowerCAmelCase__	,   lowerCAmelCase__	,   '''_input_quantizer'''	,   lowerCAmelCase__	,   lowerCAmelCase__		)
        if which in ["weight", "both"]:
            set_quantizer(lowerCAmelCase__	,   lowerCAmelCase__	,   '''_weight_quantizer'''	,   lowerCAmelCase__	,   lowerCAmelCase__		)
    logger.info(lowerCAmelCase__		)





def _A						(						lowercase	,   lowercase	,   **lowercase		):







    """simple docstring"""



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



"""simple docstring"""







from __future__ import annotations






class 				__A    :






 """simple docstring"""




 def __init__(		self			,   __A = 0						) ->					Dict:
     a					=key


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A						) ->					list[str]:
     assert isinstance(__A			,   __A						) and isinstance(__A			,   __A						)

     a					=key or self.__key or 1

     # make sure key is an appropriate size
     key %= 255

     return [chr(ord(__A						) ^ key						) for ch in content]


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A						) ->					list[str]:
     assert isinstance(__A			,   __A						) and isinstance(__A			,   __A						)

     a					=key or self.__key or 1

     # make sure key is an appropriate size
     key %= 255

     return [chr(ord(__A						) ^ key						) for ch in content]


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A = 0						) ->					str:
     assert isinstance(__A			,   __A						) and isinstance(__A			,   __A						)

     a					=key or self.__key or 1

     # make sure key can be any size
     while key > 255:
         key -= 255

     # This will be returned
     a					=''''''

     for ch in content:
         ans += chr(ord(__A						) ^ key						)

     return ans


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A = 0						) ->					str:
     assert isinstance(__A			,   __A						) and isinstance(__A			,   __A						)

     a					=key or self.__key or 1

     # make sure key can be any size
     while key > 255:
         key -= 255

     # This will be returned
     a					=''''''

     for ch in content:
         ans += chr(ord(__A						) ^ key						)

     return ans


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A = 0						) ->					bool:
     assert isinstance(__A			,   __A						) and isinstance(__A			,   __A						)

     try:
         with open(__A						) as fin, open('''encrypt.out'''			,   '''w+'''						) as fout:
             # actual encrypt-process
             for line in fin:
                 fout.write(self.encrypt_string(__A			,   __A						)						)

     except OSError:
         return False

     return True





 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A						) ->					bool:
     assert isinstance(__A			,   __A						) and isinstance(__A			,   __A						)

     try:
         with open(__A						) as fin, open('''decrypt.out'''			,   '''w+'''						) as fout:
             # actual encrypt-process
             for line in fin:
                 fout.write(self.decrypt_string(__A			,   __A						)						)

     except OSError:
         return False

     return True


# Tests
# crypt = XORCipher()
# key = 67

# # test encrypt
# print(crypt.encrypt("hallo welt",key))
# # test decrypt
# print(crypt.decrypt(crypt.encrypt("hallo welt",key), key))

# # test encrypt_string
# print(crypt.encrypt_string("hallo welt",key))

# # test decrypt_string
# print(crypt.decrypt_string(crypt.encrypt_string("hallo welt",key),key))

# if (crypt.encrypt_file("test.txt",key)):
#       print("encrypt successful")
# else:
#       print("encrypt unsuccessful")

# if (crypt.decrypt_file("encrypt.out",key)):
#       print("decrypt successful")
# else:
#       print("decrypt unsuccessful")