



import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileViTConfig,
    MobileViTForImageClassification,
    MobileViTForSemanticSegmentation,
    MobileViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
UpperCamelCase_										=					logging.get_logger(__name__)







def 	lowerCamelCase_   (  _a    :		int  ):



    '''simple docstring'''




    UpperCAmelCase_			:    List[str]   						= MobileViTConfig()

    # size of the architecture
    if "mobilevit_s" in mobilevit_name:
        UpperCAmelCase_			:    int   						= [144, 192, 240]
        UpperCAmelCase_			:    Union[str, Any]   						= [16, 32, 64, 96, 128, 160, 640]
    elif "mobilevit_xs" in mobilevit_name:
        UpperCAmelCase_			:    Any   						= [96, 120, 144]
        UpperCAmelCase_			:    int   						= [16, 32, 48, 64, 80, 96, 384]
    elif "mobilevit_xxs" in mobilevit_name:
        UpperCAmelCase_			:    List[Any]   						= [64, 80, 96]
        UpperCAmelCase_			:    Optional[Any]   						= [16, 16, 24, 48, 64, 80, 320]
        UpperCAmelCase_			:    List[str]   						= 0.0_5
        UpperCAmelCase_			:    Tuple   						= 2.0

    if mobilevit_name.startswith("""deeplabv3_"""  ):
        UpperCAmelCase_			:    List[Any]   						= 512
        UpperCAmelCase_			:    Any   						= 16
        UpperCAmelCase_			:    List[Any]   						= 21
        UpperCAmelCase_			:    List[str]   						= """pascal-voc-id2label.json"""
    else:
        UpperCAmelCase_			:    str   						= 1000
        UpperCAmelCase_			:    Optional[Any]   						= """imagenet-1k-id2label.json"""

    UpperCAmelCase_			:    Optional[int]   						= """huggingface/label-files"""
    UpperCAmelCase_			:    Dict   						= json.load(open(hf_hub_download(__lowerCAmelCase					,						__lowerCAmelCase					,						repo_type="""dataset"""  )					,						"""r"""  )  )
    UpperCAmelCase_			:    int   						= {int(__lowerCAmelCase  ): v for k, v in idalabel.items()}
    UpperCAmelCase_			:    Optional[Any]   						= idalabel
    UpperCAmelCase_			:    Any   						= {v: k for k, v in idalabel.items()}

    return config







def 	lowerCamelCase_   (  _a    :		Any					,						_a    :		Any=False  ):



    '''simple docstring'''




    for i in range(1					,						6  ):
        if F'''layer_{i}.''' in name:
            UpperCAmelCase_			:    Dict   						= name.replace(F'''layer_{i}.'''					,						F'''encoder.layer.{i - 1}.'''  )

    if "conv_1." in name:
        UpperCAmelCase_			:    List[Any]   						= name.replace("""conv_1."""					,						"""conv_stem."""  )
    if ".block." in name:
        UpperCAmelCase_			:    Tuple   						= name.replace(""".block."""					,						"""."""  )

    if "exp_1x1" in name:
        UpperCAmelCase_			:    int   						= name.replace("""exp_1x1"""					,						"""expand_1x1"""  )
    if "red_1x1" in name:
        UpperCAmelCase_			:    Optional[int]   						= name.replace("""red_1x1"""					,						"""reduce_1x1"""  )
    if ".local_rep.conv_3x3." in name:
        UpperCAmelCase_			:    List[str]   						= name.replace(""".local_rep.conv_3x3."""					,						""".conv_kxk."""  )
    if ".local_rep.conv_1x1." in name:
        UpperCAmelCase_			:    Any   						= name.replace(""".local_rep.conv_1x1."""					,						""".conv_1x1."""  )
    if ".norm." in name:
        UpperCAmelCase_			:    Any   						= name.replace(""".norm."""					,						""".normalization."""  )
    if ".conv." in name:
        UpperCAmelCase_			:    int   						= name.replace(""".conv."""					,						""".convolution."""  )
    if ".conv_proj." in name:
        UpperCAmelCase_			:    Optional[Any]   						= name.replace(""".conv_proj."""					,						""".conv_projection."""  )

    for i in range(0					,						2  ):
        for j in range(0					,						4  ):
            if F'''.{i}.{j}.''' in name:
                UpperCAmelCase_			:    List[Any]   						= name.replace(F'''.{i}.{j}.'''					,						F'''.{i}.layer.{j}.'''  )

    for i in range(2					,						6  ):
        for j in range(0					,						4  ):
            if F'''.{i}.{j}.''' in name:
                UpperCAmelCase_			:    Dict   						= name.replace(F'''.{i}.{j}.'''					,						F'''.{i}.'''  )
                if "expand_1x1" in name:
                    UpperCAmelCase_			:    List[Any]   						= name.replace("""expand_1x1"""					,						"""downsampling_layer.expand_1x1"""  )
                if "conv_3x3" in name:
                    UpperCAmelCase_			:    Optional[Any]   						= name.replace("""conv_3x3"""					,						"""downsampling_layer.conv_3x3"""  )
                if "reduce_1x1" in name:
                    UpperCAmelCase_			:    int   						= name.replace("""reduce_1x1"""					,						"""downsampling_layer.reduce_1x1"""  )

    for i in range(2					,						5  ):
        if F'''.global_rep.{i}.weight''' in name:
            UpperCAmelCase_			:    Any   						= name.replace(F'''.global_rep.{i}.weight'''					,						""".layernorm.weight"""  )
        if F'''.global_rep.{i}.bias''' in name:
            UpperCAmelCase_			:    int   						= name.replace(F'''.global_rep.{i}.bias'''					,						""".layernorm.bias"""  )

    if ".global_rep." in name:
        UpperCAmelCase_			:    List[str]   						= name.replace(""".global_rep."""					,						""".transformer."""  )
    if ".pre_norm_mha.0." in name:
        UpperCAmelCase_			:    Optional[Any]   						= name.replace(""".pre_norm_mha.0."""					,						""".layernorm_before."""  )
    if ".pre_norm_mha.1.out_proj." in name:
        UpperCAmelCase_			:    Dict   						= name.replace(""".pre_norm_mha.1.out_proj."""					,						""".attention.output.dense."""  )
    if ".pre_norm_ffn.0." in name:
        UpperCAmelCase_			:    Optional[int]   						= name.replace(""".pre_norm_ffn.0."""					,						""".layernorm_after."""  )
    if ".pre_norm_ffn.1." in name:
        UpperCAmelCase_			:    int   						= name.replace(""".pre_norm_ffn.1."""					,						""".intermediate.dense."""  )
    if ".pre_norm_ffn.4." in name:
        UpperCAmelCase_			:    Any   						= name.replace(""".pre_norm_ffn.4."""					,						""".output.dense."""  )
    if ".transformer." in name:
        UpperCAmelCase_			:    Union[str, Any]   						= name.replace(""".transformer."""					,						""".transformer.layer."""  )

    if ".aspp_layer." in name:
        UpperCAmelCase_			:    int   						= name.replace(""".aspp_layer."""					,						"""."""  )
    if ".aspp_pool." in name:
        UpperCAmelCase_			:    str   						= name.replace(""".aspp_pool."""					,						"""."""  )
    if "seg_head." in name:
        UpperCAmelCase_			:    Optional[int]   						= name.replace("""seg_head."""					,						"""segmentation_head."""  )
    if "segmentation_head.classifier.classifier." in name:
        UpperCAmelCase_			:    List[str]   						= name.replace("""segmentation_head.classifier.classifier."""					,						"""segmentation_head.classifier."""  )

    if "classifier.fc." in name:
        UpperCAmelCase_			:    Tuple   						= name.replace("""classifier.fc."""					,						"""classifier."""  )
    elif (not base_model) and ("segmentation_head." not in name):
        UpperCAmelCase_			:    Dict   						= """mobilevit.""" + name

    return name







def 	lowerCamelCase_   (  _a    :		Tuple					,						_a    :		int					,						_a    :		List[Any]=False  ):



    '''simple docstring'''




    if base_model:
        UpperCAmelCase_			:    int   						= """"""
    else:
        UpperCAmelCase_			:    Union[str, Any]   						= """mobilevit."""

    for key in orig_state_dict.copy().keys():
        UpperCAmelCase_			:    Union[str, Any]   						= orig_state_dict.pop(__lowerCAmelCase  )

        if key[:8] == "encoder.":
            UpperCAmelCase_			:    str   						= key[8:]

        if "qkv" in key:
            UpperCAmelCase_			:    Union[str, Any]   						= key.split("""."""  )
            UpperCAmelCase_			:    Dict   						= int(key_split[0][6:]  ) - 1
            UpperCAmelCase_			:    Any   						= int(key_split[3]  )
            UpperCAmelCase_			:    str   						= model.get_submodule(F'''{model_prefix}encoder.layer.{layer_num}'''  )
            UpperCAmelCase_			:    Dict   						= layer.transformer.layer[transformer_num].attention.attention.all_head_size
            UpperCAmelCase_			:    int   						= (
                F'''{model_prefix}encoder.layer.{layer_num}.transformer.layer.{transformer_num}.attention.attention.'''
            )
            if "weight" in key:
                UpperCAmelCase_			:    str   						= val[:dim, :]
                UpperCAmelCase_			:    Optional[Any]   						= val[dim : dim * 2, :]
                UpperCAmelCase_			:    Tuple   						= val[-dim:, :]
            else:
                UpperCAmelCase_			:    Optional[Any]   						= val[:dim]
                UpperCAmelCase_			:    int   						= val[dim : dim * 2]
                UpperCAmelCase_			:    Tuple   						= val[-dim:]
        else:
            UpperCAmelCase_			:    Optional[int]   						= val

    return orig_state_dict







def 	lowerCamelCase_   (  ):



    '''simple docstring'''




    UpperCAmelCase_			:    Optional[int]   						= """http://images.cocodataset.org/val2017/000000039769.jpg"""
    UpperCAmelCase_			:    Dict   						= Image.open(requests.get(__lowerCAmelCase					,						stream=__lowerCAmelCase  ).raw  )
    return im







@torch.no_grad()
def 	lowerCamelCase_   (  _a    :		Dict					,						_a    :		str					,						_a    :		List[Any]					,						_a    :		int=False  ):



    '''simple docstring'''




    UpperCAmelCase_			:    List[Any]   						= get_mobilevit_config(__lowerCAmelCase  )

    # load original state_dict
    UpperCAmelCase_			:    Dict   						= torch.load(__lowerCAmelCase					,						map_location="""cpu"""  )

    # load 🤗 model
    if mobilevit_name.startswith("""deeplabv3_"""  ):
        UpperCAmelCase_			:    Optional[Any]   						= MobileViTForSemanticSegmentation(__lowerCAmelCase  ).eval()
    else:
        UpperCAmelCase_			:    str   						= MobileViTForImageClassification(__lowerCAmelCase  ).eval()

    UpperCAmelCase_			:    int   						= convert_state_dict(__lowerCAmelCase					,						__lowerCAmelCase  )
    model.load_state_dict(__lowerCAmelCase  )

    # Check outputs on an image, prepared by MobileViTImageProcessor
    UpperCAmelCase_			:    List[str]   						= MobileViTImageProcessor(crop_size=config.image_size					,						size=config.image_size + 32  )
    UpperCAmelCase_			:    List[str]   						= image_processor(images=prepare_img()					,						return_tensors="""pt"""  )
    UpperCAmelCase_			:    Any   						= model(**__lowerCAmelCase  )
    UpperCAmelCase_			:    Optional[Any]   						= outputs.logits

    if mobilevit_name.startswith("""deeplabv3_"""  ):
        assert logits.shape == (1, 21, 32, 32)

        if mobilevit_name == "deeplabv3_mobilevit_s":
            UpperCAmelCase_			:    List[Any]   						= torch.tensor(
                [
                    [[6.2_0_6_5, 6.1_2_9_2, 6.2_0_7_0], [6.1_0_7_9, 6.1_2_5_4, 6.1_7_4_7], [6.0_0_4_2, 6.1_0_7_1, 6.1_0_3_4]],
                    [[-6.9_2_5_3, -6.8_6_5_3, -7.0_3_9_8], [-7.3_2_1_8, -7.3_9_8_3, -7.3_6_7_0], [-7.1_9_6_1, -7.2_4_8_2, -7.1_5_6_9]],
                    [[-4.4_7_2_3, -4.4_3_4_8, -4.3_7_6_9], [-5.3_6_2_9, -5.4_6_3_2, -5.4_5_9_8], [-5.1_5_8_7, -5.3_4_0_2, -5.5_0_5_9]],
                ]  )
        elif mobilevit_name == "deeplabv3_mobilevit_xs":
            UpperCAmelCase_			:    str   						= torch.tensor(
                [
                    [[5.4_4_4_9, 5.5_7_3_3, 5.6_3_1_4], [5.1_8_1_5, 5.3_9_3_0, 5.5_9_6_3], [5.1_6_5_6, 5.4_3_3_3, 5.4_8_5_3]],
                    [[-9.4_4_2_3, -9.7_7_6_6, -9.6_7_1_4], [-9.1_5_8_1, -9.5_7_2_0, -9.5_5_1_9], [-9.1_0_0_6, -9.6_4_5_8, -9.5_7_0_3]],
                    [[-7.7_7_2_1, -7.3_7_1_6, -7.1_5_8_3], [-8.4_5_9_9, -8.0_6_2_4, -7.7_9_4_4], [-8.4_1_7_2, -7.8_3_6_6, -7.5_0_2_5]],
                ]  )
        elif mobilevit_name == "deeplabv3_mobilevit_xxs":
            UpperCAmelCase_			:    List[Any]   						= torch.tensor(
                [
                    [[6.9_8_1_1, 6.9_7_4_3, 7.3_1_2_3], [7.1_7_7_7, 7.1_9_3_1, 7.3_9_3_8], [7.5_6_3_3, 7.8_0_5_0, 7.8_9_0_1]],
                    [[-1_0.5_5_3_6, -1_0.2_3_3_2, -1_0.2_9_2_4], [-1_0.2_3_3_6, -9.8_6_2_4, -9.5_9_6_4], [-1_0.8_8_4_0, -1_0.8_1_5_8, -1_0.6_6_5_9]],
                    [[-3.4_9_3_8, -3.0_6_3_1, -2.8_6_2_0], [-3.4_2_0_5, -2.8_1_3_5, -2.6_8_7_5], [-3.4_1_7_9, -2.7_9_4_5, -2.8_7_5_0]],
                ]  )
        else:
            raise ValueError(F'''Unknown mobilevit_name: {mobilevit_name}'''  )

        assert torch.allclose(logits[0, :3, :3, :3]					,						__lowerCAmelCase					,						atol=1E-4  )
    else:
        assert logits.shape == (1, 1000)

        if mobilevit_name == "mobilevit_s":
            UpperCAmelCase_			:    Optional[int]   						= torch.tensor([-0.9_8_6_6, 0.2_3_9_2, -1.1_2_4_1]  )
        elif mobilevit_name == "mobilevit_xs":
            UpperCAmelCase_			:    Optional[int]   						= torch.tensor([-2.4_7_6_1, -0.9_3_9_9, -1.9_5_8_7]  )
        elif mobilevit_name == "mobilevit_xxs":
            UpperCAmelCase_			:    Any   						= torch.tensor([-1.9_3_6_4, -1.2_3_2_7, -0.4_6_5_3]  )
        else:
            raise ValueError(F'''Unknown mobilevit_name: {mobilevit_name}'''  )

        assert torch.allclose(logits[0, :3]					,						__lowerCAmelCase					,						atol=1E-4  )

    Path(__lowerCAmelCase  ).mkdir(exist_ok=__lowerCAmelCase  )
    print(F'''Saving model {mobilevit_name} to {pytorch_dump_folder_path}'''  )
    model.save_pretrained(__lowerCAmelCase  )
    print(F'''Saving image processor to {pytorch_dump_folder_path}'''  )
    image_processor.save_pretrained(__lowerCAmelCase  )

    if push_to_hub:
        UpperCAmelCase_			:    Union[str, Any]   						= {
            """mobilevit_s""": """mobilevit-small""",
            """mobilevit_xs""": """mobilevit-x-small""",
            """mobilevit_xxs""": """mobilevit-xx-small""",
            """deeplabv3_mobilevit_s""": """deeplabv3-mobilevit-small""",
            """deeplabv3_mobilevit_xs""": """deeplabv3-mobilevit-x-small""",
            """deeplabv3_mobilevit_xxs""": """deeplabv3-mobilevit-xx-small""",
        }

        print("""Pushing to the hub..."""  )
        UpperCAmelCase_			:    int   						= model_mapping[mobilevit_name]
        image_processor.push_to_hub(__lowerCAmelCase					,						organization="""apple"""  )
        model.push_to_hub(__lowerCAmelCase					,						organization="""apple"""  )


if __name__ == "__main__":
      UpperCamelCase_										=					argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '''--mobilevit_name''',
          default='''mobilevit_s''',
          type=str,
          help=(
              '''Name of the MobileViT model you\'d like to convert. Should be one of \'mobilevit_s\', \'mobilevit_xs\','''
              ''' \'mobilevit_xxs\', \'deeplabv3_mobilevit_s\', \'deeplabv3_mobilevit_xs\', \'deeplabv3_mobilevit_xxs\'.'''
          ),
      )
      parser.add_argument(
          '''--checkpoint_path''', required=True, type=str, help='''Path to the original state dict (.pt file).'''
      )
      parser.add_argument(
          '''--pytorch_dump_folder_path''', required=True, type=str, help='''Path to the output PyTorch model directory.'''
      )
      parser.add_argument(
          '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
      )

      UpperCamelCase_										=					parser.parse_args()
      convert_movilevit_checkpoint(
          args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
      )




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   )




