

import warnings
from typing import Dict

import numpy as np

from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline


if is_tf_available():
						from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

if is_torch_available():
						from ..models.auto.modeling_auto import MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING







def 			A_      (    _UpperCAmelCase    ):
						return 1.0 / (1.0 + np.exp(-_outputs    ))







def 			A_      (    _UpperCAmelCase    ):
						SCREAMING_SNAKE_CASE_: List[Any]  		=       np.max(_outputs				,	axis=-1				,	keepdims=_UpperCAmelCase    )
						SCREAMING_SNAKE_CASE_: Dict  		=       np.exp(_outputs - maxes    )
						return shifted_exp / shifted_exp.sum(axis=-1				,	keepdims=_UpperCAmelCase    )



class   __lowercase				( UpperCAmelCase_	):




			"""simple docstring"""







			_UpperCAmelCase   :							List[Any]										=     '''sigmoid'''
			_UpperCAmelCase   :							str										=     '''softmax'''
			_UpperCAmelCase   :							List[Any]										=     '''none'''







@add_end_docstrings(
    UpperCAmelCase_		,       r'''
        return_all_scores (`bool`, *optional*, defaults to `False`):
            Whether to return all prediction scores or just the one of the predicted class.
        function_to_apply (`str`, *optional*, defaults to `"default"`):
            The function to apply to the model outputs in order to retrieve the scores. Accepts four different values:

            - `"default"`: if the model has a single label, will apply the sigmoid function on the output. If the model
              has several labels, will apply the softmax function on the output.
            - `"sigmoid"`: Applies the sigmoid function on the output.
            - `"softmax"`: Applies the softmax function on the output.
            - `"none"`: Does not apply any function on the output.
    '''		,       )
class   __lowercase				( UpperCAmelCase_	):




			"""simple docstring"""







			_UpperCAmelCase   :							Optional[int]										=     False
			_UpperCAmelCase   :							Tuple										=     ClassificationFunction.NONE



			def __init__( self  :     Union[str, Any]   ,						**lowerCAmelCase__  :     int):
									super().__init__(**lowerCAmelCase__)

									self.check_model_type(
									    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
									    if self.framework == "tf"
									    else MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING)



			def        _SCREAMING_SNAKE_CASE			( self  :     Any   ,						lowerCAmelCase__  :     List[Any]=None   ,						lowerCAmelCase__  :     int=None   ,						lowerCAmelCase__  :     List[str]=""   ,						**lowerCAmelCase__  :     Union[str, Any]):
									# Using "" as default argument because we're going to use `top_k=None` in user code to declare
									# "No top_k"
									SCREAMING_SNAKE_CASE_: int  		=       tokenizer_kwargs

									SCREAMING_SNAKE_CASE_: Dict  		=       {}
									if hasattr(self.model.config   ,						"return_all_scores") and return_all_scores is None:
															SCREAMING_SNAKE_CASE_: List[str]  		=       self.model.config.return_all_scores

									if isinstance(lowerCAmelCase__   ,						lowerCAmelCase__) or top_k is None:
															SCREAMING_SNAKE_CASE_: Any  		=       top_k
															SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       False
									elif return_all_scores is not None:
															warnings.warn(
															    "`return_all_scores` is now deprecated,  if want a similar functionality use `top_k=None` instead of"
															    " `return_all_scores=True` or `top_k=1` instead of `return_all_scores=False`."   ,						lowerCAmelCase__   ,						)
															if return_all_scores:
																					SCREAMING_SNAKE_CASE_: int  		=       None
															else:
																					SCREAMING_SNAKE_CASE_: str  		=       1

									if isinstance(lowerCAmelCase__   ,						lowerCAmelCase__):
															SCREAMING_SNAKE_CASE_: List[str]  		=       ClassificationFunction[function_to_apply.upper()]

									if function_to_apply is not None:
															SCREAMING_SNAKE_CASE_: List[Any]  		=       function_to_apply
									return preprocess_params, {}, postprocess_params



			def __call__( self  :     int   ,						*lowerCAmelCase__  :     List[str]   ,						**lowerCAmelCase__  :     Union[str, Any]):
									SCREAMING_SNAKE_CASE_: Tuple  		=       super().__call__(*lowerCAmelCase__   ,						**lowerCAmelCase__)
									# TODO try and retrieve it in a nicer way from _sanitize_parameters.
									SCREAMING_SNAKE_CASE_: Tuple  		=       "top_k" not in kwargs
									if isinstance(args[0]   ,						lowerCAmelCase__) and _legacy:
															# This pipeline is odd, and return a list when single item is run
															return [result]
									else:
															return result



			def        _SCREAMING_SNAKE_CASE			( self  :     Any   ,						lowerCAmelCase__  :     Optional[Any]   ,						**lowerCAmelCase__  :     Dict):
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       self.framework
									if isinstance(lowerCAmelCase__   ,						lowerCAmelCase__):
															return self.tokenizer(**lowerCAmelCase__   ,						return_tensors=lowerCAmelCase__   ,						**lowerCAmelCase__)
									elif isinstance(lowerCAmelCase__   ,						lowerCAmelCase__) and len(lowerCAmelCase__) == 1 and isinstance(inputs[0]   ,						lowerCAmelCase__) and len(inputs[0]) == 2:
															# It used to be valid to use a list of list of list for text pairs, keeping this path for BC
															return self.tokenizer(
															    text=inputs[0][0]   ,						text_pair=inputs[0][1]   ,						return_tensors=lowerCAmelCase__   ,						**lowerCAmelCase__)
									elif isinstance(lowerCAmelCase__   ,						lowerCAmelCase__):
															# This is likely an invalid usage of the pipeline attempting to pass text pairs.
															raise ValueError(
															    "The pipeline received invalid inputs, if you are trying to send text pairs, you can try to send a"
															    " dictionary `{\"text\": \"My text\", \"text_pair\": \"My pair\"}` in order to send a text pair.")
									return self.tokenizer(lowerCAmelCase__   ,						return_tensors=lowerCAmelCase__   ,						**lowerCAmelCase__)



			def        _SCREAMING_SNAKE_CASE			( self  :     int   ,						lowerCAmelCase__  :     List[Any]):
									return self.model(**lowerCAmelCase__)




			def        _SCREAMING_SNAKE_CASE			( self  :     List[Any]   ,						lowerCAmelCase__  :     Tuple   ,						lowerCAmelCase__  :     Union[str, Any]=None   ,						lowerCAmelCase__  :     Dict=1   ,						lowerCAmelCase__  :     Optional[Any]=True):
									# `_legacy` is used to determine if we're running the naked pipeline and in backward
									# compatibility mode, or if running the pipeline with `pipeline(..., top_k=1)` we're running
									# the more natural result containing the list.
									# Default value before `set_parameters`
									if function_to_apply is None:
															if self.model.config.problem_type == "multi_label_classification" or self.model.config.num_labels == 1:
																					SCREAMING_SNAKE_CASE_: List[Any]  		=       ClassificationFunction.SIGMOID
															elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
																					SCREAMING_SNAKE_CASE_: Optional[Any]  		=       ClassificationFunction.SOFTMAX
															elif hasattr(self.model.config   ,						"function_to_apply") and function_to_apply is None:
																					SCREAMING_SNAKE_CASE_: Tuple  		=       self.model.config.function_to_apply
															else:
																					SCREAMING_SNAKE_CASE_: List[str]  		=       ClassificationFunction.NONE

									SCREAMING_SNAKE_CASE_: List[Any]  		=       model_outputs["logits"][0]
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       outputs.numpy()

									if function_to_apply == ClassificationFunction.SIGMOID:
															SCREAMING_SNAKE_CASE_: List[str]  		=       sigmoid(lowerCAmelCase__)
									elif function_to_apply == ClassificationFunction.SOFTMAX:
															SCREAMING_SNAKE_CASE_: Tuple  		=       softmax(lowerCAmelCase__)
									elif function_to_apply == ClassificationFunction.NONE:
															SCREAMING_SNAKE_CASE_: List[str]  		=       outputs
									else:
															raise ValueError(F"Unrecognized `function_to_apply` argument: {function_to_apply}")

									if top_k == 1 and _legacy:
															return {"label": self.model.config.idalabel[scores.argmax().item()], "score": scores.max().item()}

									SCREAMING_SNAKE_CASE_: Dict  		=       [
									    {"label": self.model.config.idalabel[i], "score": score.item()} for i, score in enumerate(lowerCAmelCase__)
									]
									if not _legacy:
															dict_scores.sort(key=lambda lowerCAmelCase__: x["score"]   ,						reverse=lowerCAmelCase__)
															if top_k is not None:
																					SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       dict_scores[:top_k]
									return dict_scores










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


snake_case							:       List[Any]     			=      logging.getLogger(__name__)

snake_case							:       Optional[int]     			=      50  # max width of layer names
snake_case							:       Any     			=      70  # max width of quantizer names






def 							__lowercase   ( __lowerCAmelCase							:				Tuple   ):
							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 							__lowercase   ( __lowerCAmelCase							:				Union[str, Any]   ):

							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 							__lowercase   ( __lowerCAmelCase							:				Optional[Any]					,    __lowerCAmelCase							:				List[Any]					,    __lowerCAmelCase							:				Union[str, Any]=False					,    __lowerCAmelCase							:				Dict=False   ):
							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 							__lowercase   ( __lowerCAmelCase							:				Optional[int]   ):
							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 							__lowercase   ( __lowerCAmelCase							:				Union[str, Any]					,    __lowerCAmelCase							:				List[Any]   ):
							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 							__lowercase   ( __lowerCAmelCase							:				Optional[Any]					,    __lowerCAmelCase							:				str   ):

							def fusea(__lowerCAmelCase							:				Dict					,    __lowerCAmelCase							:				Optional[int]					,    __lowerCAmelCase							:				List[str]   ):
														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 							__lowercase   ( __lowerCAmelCase							:				Tuple					,    __lowerCAmelCase							:				List[Any]   ):

							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 							__lowercase   ( __lowerCAmelCase							:				Optional[Any]   ):

							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 							__lowercase   ( __lowerCAmelCase							:				Union[str, Any]   ):

							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 							__lowercase   ( __lowerCAmelCase							:				Tuple					,    __lowerCAmelCase							:				Optional[int]=2_5					,    __lowerCAmelCase							:				List[Any]=1_8_0					,    __lowerCAmelCase							:				Tuple=None   ):

							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 							__lowercase   ( __lowerCAmelCase							:				Dict   ):
							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 							__lowercase   ( __lowerCAmelCase							:				List[Any]					,    __lowerCAmelCase							:				Optional[int]					,    __lowerCAmelCase							:				Optional[Any]					,    __lowerCAmelCase							:				int					,    __lowerCAmelCase							:				Dict   ):
							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 							__lowercase   ( __lowerCAmelCase							:				Optional[Any]					,    __lowerCAmelCase							:				Union[str, Any]					,    __lowerCAmelCase							:				Optional[int]="both"					,    **__lowerCAmelCase							:				str   ):
							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 							__lowercase   ( __lowerCAmelCase							:				Any					,    __lowerCAmelCase							:				Tuple					,    **__lowerCAmelCase							:				Optional[Any]   ):

							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   )




