

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


a__												=						logging.getLogger(__name__)

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



def   __UpperCAmelCase					(      __a :     Optional[Any]      )     ->	Dict:

					"""simple docstring"""

					_a      :       List[str]   =						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   __UpperCAmelCase					(      __a :     Union[str, Any]      )     ->	str:

					"""simple docstring"""

					if args.calibrator == "max":
										_a      :       int   =						'''max'''
					elif args.calibrator == "percentile":
										if args.percentile is None:
															raise ValueError('''Specify --percentile when using percentile calibrator'''      )
										_a      :       List[str]   =						'''histogram'''
					elif args.calibrator == "mse":
										_a      :       Dict   =						'''histogram'''
					else:
										raise ValueError(F"""Invalid calibrator {args.calibrator}"""      )

					_a      :       Any   =						QuantDescriptor(num_bits=args.aprec						,calib_method=__a      )
					_a      :       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   __UpperCAmelCase					(      __a :     List[Any]						,__a :     Union[str, Any]						,__a :     List[str]=False						,__a :     Tuple=False      )     ->	str:

					"""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   __UpperCAmelCase					(      __a :     str      )     ->	Dict:

					"""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   __UpperCAmelCase					(      __a :     int						,__a :     Union[str, Any]      )     ->	Optional[Any]:

					"""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   __UpperCAmelCase					(      __a :     Any						,__a :     int      )     ->	Tuple:

					"""simple docstring"""

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

										_a      :       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   __UpperCAmelCase					(      __a :     Any						,__a :     Any      )     ->	Any:

					"""simple docstring"""

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



def   __UpperCAmelCase					(      __a :     List[str]      )     ->	str:

					"""simple docstring"""

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



def   __UpperCAmelCase					(      __a :     Optional[Any]      )     ->	Dict:

					"""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)
															_a      :       Dict   =						set() if mod._weight_quantizer.axis is None else set(mod._weight_quantizer.axis      )
															_a      :       Tuple   =						set(range(len(mod.weight.size()      )      )      ) - axis_set
															_a      :       str   =						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()}"""      )
															_a      :       int   =						amax



def   __UpperCAmelCase					(      __a :     Union[str, Any]						,__a :     int=25						,__a :     str=180						,__a :     Union[str, Any]=None      )     ->	str:

					"""simple docstring"""

					if ignore is None:
										_a      :       Optional[int]   =						[]
					elif not isinstance(__a						,__a      ):
										_a      :       Tuple   =						[ignore]

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

					for name, mod in model.named_modules():
										_a      :       Tuple   =						getattr(__a						,'''_input_quantizer'''						,__a      )
										_a      :       int   =						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
										_a      :       List[str]   =						F"""Act:{input_q.extra_repr()}"""
										_a      :       int   =						F"""Wgt:{weight_q.extra_repr()}"""
										_a      :       int   =						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   __UpperCAmelCase					(      __a :     str      )     ->	Optional[Any]:

					"""simple docstring"""

					_a      :       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   __UpperCAmelCase					(      __a :     Any						,__a :     Union[str, Any]						,__a :     Tuple						,__a :     Dict						,__a :     List[Any]      )     ->	int:

					"""simple docstring"""

					_a      :       int   =						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   __UpperCAmelCase					(      __a :     Union[str, Any]						,__a :     Optional[Any]						,__a :     Any="both"						,**__a :     Optional[Any]      )     ->	str:

					"""simple docstring"""

					_a      :       List[str]   =						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   __UpperCAmelCase					(      __a :     str						,__a :     Dict						,**__a :     Union[str, Any]      )     ->	Any:

					"""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      ):
																									_a      :       List[str]   =						F"""Warning: changing {name:{name_width}}"""
																									for k, v in kwargs.items():
																														s += F""" {k}={v}"""
																														setattr(__a						,__a						,__a      )
																									logger.info(__a      )






import json
import os
import unittest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class 							UpperCAmelCase_						(     __lowercase							,			unittest.TestCase  ):
							"""simple docstring"""







							UpperCAmelCase__			:		List[Any]             =       MgpstrTokenizer
							UpperCAmelCase__			:		int             =       False
							UpperCAmelCase__			:		Union[str, Any]             =       {}
							UpperCAmelCase__			:		List[Any]             =       False


							def 							__lowercase							(     self			)       ->       Any:
												super().setUp()

												# fmt: off
												_a      :       Tuple   =						['''[GO]''', '''[s]''', '''0''', '''1''', '''2''', '''3''', '''4''', '''5''', '''6''', '''7''', '''8''', '''9''', '''a''', '''b''', '''c''', '''d''', '''e''', '''f''', '''g''', '''h''', '''i''', '''j''', '''k''', '''l''', '''m''', '''n''', '''o''', '''p''', '''q''', '''r''', '''s''', '''t''', '''u''', '''v''', '''w''', '''x''', '''y''', '''z''']
												# fmt: on
												_a      :       Optional[int]   =						dict(zip(_a			,				range(len(_a			)			)			)			)

												_a      :       Any   =						os.path.join(self.tmpdirname			,				VOCAB_FILES_NAMES['''vocab_file''']			)
												with open(self.vocab_file			,				'''w'''			,				encoding='''utf-8'''			) as fp:
																	fp.write(json.dumps(_a			) + '''\n'''			)


							def 							__lowercase							(     self			,				**_a			)       ->       Dict:
												return MgpstrTokenizer.from_pretrained(self.tmpdirname			,				**_a			)


							def 							__lowercase							(     self			,				_a			)       ->       Tuple:
												_a      :       List[str]   =						'''tester'''
												_a      :       Optional[Any]   =						'''tester'''
												return input_text, output_text


							@unittest.skip('''MGP-STR always lower cases letters.'''			)
							def 							__lowercase							(     self			)       ->       Any:
												pass


							def 							__lowercase							(     self			)       ->       Any:
												_a      :       Union[str, Any]   =						self.get_tokenizers(do_lower_case=_a			)
												for tokenizer in tokenizers:
																	with self.subTest(F"""{tokenizer.__class__.__name__}"""			):
																						_a      :       int   =						'''[SPECIAL_TOKEN]'''

																						tokenizer.add_special_tokens({'''cls_token''': special_token}			)
																						_a      :       Tuple   =						tokenizer.encode([special_token]			,				add_special_tokens=_a			)
																						self.assertEqual(len(_a			)			,				1			)

																						_a      :       Tuple   =						tokenizer.decode(_a			,				skip_special_tokens=_a			)
																						self.assertTrue(special_token not in decoded			)


							def 							__lowercase							(     self			)       ->       Tuple:
												_a      :       List[Any]   =						self.get_tokenizers()
												for tokenizer in tokenizers:
																	with self.subTest(F"""{tokenizer.__class__.__name__}"""			):
																						_a ,   _a      :       int   =						self.get_input_output_texts(_a			)

																						_a      :       List[str]   =						tokenizer.tokenize(_a			)
																						_a      :       Optional[int]   =						tokenizer.convert_tokens_to_ids(_a			)
																						_a      :       Tuple   =						tokenizer.encode(_a			,				add_special_tokens=_a			)
																						self.assertListEqual(_a			,				_a			)

																						_a      :       Optional[int]   =						tokenizer.convert_ids_to_tokens(_a			)
																						self.assertNotEqual(len(_a			)			,				0			)
																						_a      :       int   =						tokenizer.decode(_a			)
																						self.assertIsInstance(_a			,				_a			)

																						self.assertEqual(text_a.replace(''' '''			,				''''''			)			,				_a			)


							@unittest.skip('''MGP-STR tokenizer only handles one sequence.'''			)
							def 							__lowercase							(     self			)       ->       List[str]:
												pass




							@unittest.skip('''inputs cannot be pretokenized in MgpstrTokenizer'''			)
							def 							__lowercase							(     self			)       ->       Optional[Any]:
												pass



