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


__lowercase							 =					logging.getLogger(__name__)

__lowercase							 =					50  # max width of layer names
__lowercase							 =					70  # max width of quantizer names



def lowerCamelCase  (   SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    __UpperCamelCase     :List[Any]			  =		parser.add_argument_group('''quant_trainer arguments'''	)
    group.add_argument('''--wprec'''  ,			type=SCREAMING_SNAKE_CASE  ,			default=8  ,			help='''weight precision'''	)
    group.add_argument('''--aprec'''  ,			type=SCREAMING_SNAKE_CASE  ,			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=SCREAMING_SNAKE_CASE  ,			nargs='''+'''  ,			help='''disable quantizers by keyword'''	)
    group.add_argument('''--quant-disable-layer-module'''  ,			type=SCREAMING_SNAKE_CASE  ,			help='''disable quantizers by keyword under layer.'''	)
    group.add_argument('''--quant-enable-layer-module'''  ,			type=SCREAMING_SNAKE_CASE  ,			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=SCREAMING_SNAKE_CASE  ,			type=SCREAMING_SNAKE_CASE  ,			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=SCREAMING_SNAKE_CASE  ,			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 lowerCamelCase  (   SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







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

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



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE=False  ,			SCREAMING_SNAKE_CASE=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(SCREAMING_SNAKE_CASE  ,			['''embeddings''']  ,			which='''weight'''  ,			_disabled=SCREAMING_SNAKE_CASE	)

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

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

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

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

        if args.recalibrate_weights:
            recalibrate_weights(SCREAMING_SNAKE_CASE	)

        if args.fuse_qkv:
            fuse_qkv(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)

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

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



def lowerCamelCase  (   SCREAMING_SNAKE_CASE	):
    '''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 lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''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(SCREAMING_SNAKE_CASE	)



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







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

        __UpperCamelCase     :Any			  =		max(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)
        qq._amax.fill_(SCREAMING_SNAKE_CASE	)
        qk._amax.fill_(SCREAMING_SNAKE_CASE	)
        qv._amax.fill_(SCREAMING_SNAKE_CASE	)
        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 lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







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



def lowerCamelCase  (   SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







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



def lowerCamelCase  (   SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    for name, mod in model.named_modules():
        if hasattr(SCREAMING_SNAKE_CASE  ,			'''_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)
            __UpperCamelCase     :Tuple			  =		set() if mod._weight_quantizer.axis is None else set(mod._weight_quantizer.axis	)
            __UpperCamelCase     :List[str]			  =		set(range(len(mod.weight.size()	)	)	) - axis_set
            __UpperCamelCase     :Tuple			  =		pytorch_quantization.utils.reduce_amax(mod.weight  ,			axis=SCREAMING_SNAKE_CASE  ,			keepdims=SCREAMING_SNAKE_CASE	).detach()
            logger.info(f"""RECALIB: {name:{name_width}} {mod._weight_quantizer._amax.flatten()} -> {amax.flatten()}"""	)
            __UpperCamelCase     :List[Any]			  =		amax



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE=25  ,			SCREAMING_SNAKE_CASE=180  ,			SCREAMING_SNAKE_CASE=None	):
    '''simple docstring'''







    if ignore is None:
        __UpperCamelCase     :Optional[int]			  =		[]
    elif not isinstance(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
        __UpperCamelCase     :Optional[Any]			  =		[ignore]

    __UpperCamelCase     :str			  =		0
    for name, mod in model.named_modules():
        if not hasattr(SCREAMING_SNAKE_CASE  ,			'''weight'''	):
            continue
        __UpperCamelCase     :Any			  =		max(SCREAMING_SNAKE_CASE  ,			len(SCREAMING_SNAKE_CASE	)	)

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



def lowerCamelCase  (   SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    __UpperCamelCase     :str			  =		0
    for name, mod in model.named_modules():
        if isinstance(SCREAMING_SNAKE_CASE  ,			pytorch_quantization.nn.TensorQuantizer	):
            print(f"""{name:80} {mod}"""	)
            count += 1
    print(f"""{count} TensorQuantizers found in model"""	)



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







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



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE="both"  ,			**SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    __UpperCamelCase     :Tuple			  =		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(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			'''_input_quantizer'''  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)
        if which in ["weight", "both"]:
            set_quantizer(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			'''_weight_quantizer'''  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)
    logger.info(SCREAMING_SNAKE_CASE	)



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			**SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







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






# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch

from ..models.auto import AutoModelForSequenceClassification, AutoTokenizer
from .base import PipelineTool


class 	lowerCamelCase_     (   UpperCAmelCase_ ):






    '''simple docstring'''






    a__	:						Optional[Any]						=      """facebook/bart-large-mnli"""
    a__	:						int						=      (
        """This is a tool that classifies an English text using provided labels. It takes two inputs: `text`, which """
        """should be the text to classify, and `labels`, which should be the list of labels to use for classification. """
        """It returns the most likely label in the list of provided `labels` for the input text."""
    )
    a__	:						Optional[Any]						=      """text_classifier"""
    a__	:						Any						=      AutoTokenizer
    a__	:						str						=      AutoModelForSequenceClassification

    a__	:						str						=      ["""text""", ["""text"""]]
    a__	:						Optional[int]						=      ["""text"""]







    def 			UpperCamelCase__   (	self)       ->	Union[str, Any]:
        super().setup()
        __UpperCamelCase     :int			  =		self.model.config
        __UpperCamelCase     :Optional[Any]			  =		-1
        for idx, label in config.idalabel.items():
            if label.lower().startswith('''entail'''):
                __UpperCamelCase     :List[Any]			  =		int(__lowercase)
        if self.entailment_id == -1:
            raise ValueError('''Could not determine the entailment ID from the model config, please pass it at init.''')







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase)       ->	Union[str, Any]:
        __UpperCamelCase     :Any			  =		labels
        return self.pre_processor(
            [text] * len(__lowercase) , [f"""This example is {label}""" for label in labels] , return_tensors='''pt''' , padding='''max_length''' , )







    def 			UpperCamelCase__   (	self , __lowercase)       ->	Optional[Any]:
        __UpperCamelCase     :List[Any]			  =		outputs.logits
        __UpperCamelCase     :Any			  =		torch.argmax(logits[:, 2]).item()
        return self._labels[label_id]





