





import argparse
from collections import OrderedDict
from pathlib import Path

import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F

from transformers import DetrImageProcessor, TableTransformerConfig, TableTransformerForObjectDetection
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase__  :     Optional[Any]   			=logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
UpperCAmelCase__  :     Tuple   			=[]
for i in range(6):
				# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
				rename_keys.append(
				    (F"transformer.encoder.layers.{i}.self_attn.out_proj.weight", F"encoder.layers.{i}.self_attn.out_proj.weight")
				)
				rename_keys.append(
				    (F"transformer.encoder.layers.{i}.self_attn.out_proj.bias", F"encoder.layers.{i}.self_attn.out_proj.bias")
				)
				rename_keys.append((F"transformer.encoder.layers.{i}.linear1.weight", F"encoder.layers.{i}.fc1.weight"))
				rename_keys.append((F"transformer.encoder.layers.{i}.linear1.bias", F"encoder.layers.{i}.fc1.bias"))
				rename_keys.append((F"transformer.encoder.layers.{i}.linear2.weight", F"encoder.layers.{i}.fc2.weight"))
				rename_keys.append((F"transformer.encoder.layers.{i}.linear2.bias", F"encoder.layers.{i}.fc2.bias"))
				rename_keys.append(
				    (F"transformer.encoder.layers.{i}.norm1.weight", F"encoder.layers.{i}.self_attn_layer_norm.weight")
				)
				rename_keys.append((F"transformer.encoder.layers.{i}.norm1.bias", F"encoder.layers.{i}.self_attn_layer_norm.bias"))
				rename_keys.append((F"transformer.encoder.layers.{i}.norm2.weight", F"encoder.layers.{i}.final_layer_norm.weight"))
				rename_keys.append((F"transformer.encoder.layers.{i}.norm2.bias", F"encoder.layers.{i}.final_layer_norm.bias"))
				# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
				rename_keys.append(
				    (F"transformer.decoder.layers.{i}.self_attn.out_proj.weight", F"decoder.layers.{i}.self_attn.out_proj.weight")
				)
				rename_keys.append(
				    (F"transformer.decoder.layers.{i}.self_attn.out_proj.bias", F"decoder.layers.{i}.self_attn.out_proj.bias")
				)
				rename_keys.append(
				    (
				        F"transformer.decoder.layers.{i}.multihead_attn.out_proj.weight",
				        F"decoder.layers.{i}.encoder_attn.out_proj.weight",
				    )
				)
				rename_keys.append(
				    (
				        F"transformer.decoder.layers.{i}.multihead_attn.out_proj.bias",
				        F"decoder.layers.{i}.encoder_attn.out_proj.bias",
				    )
				)
				rename_keys.append((F"transformer.decoder.layers.{i}.linear1.weight", F"decoder.layers.{i}.fc1.weight"))
				rename_keys.append((F"transformer.decoder.layers.{i}.linear1.bias", F"decoder.layers.{i}.fc1.bias"))
				rename_keys.append((F"transformer.decoder.layers.{i}.linear2.weight", F"decoder.layers.{i}.fc2.weight"))
				rename_keys.append((F"transformer.decoder.layers.{i}.linear2.bias", F"decoder.layers.{i}.fc2.bias"))
				rename_keys.append(
				    (F"transformer.decoder.layers.{i}.norm1.weight", F"decoder.layers.{i}.self_attn_layer_norm.weight")
				)
				rename_keys.append((F"transformer.decoder.layers.{i}.norm1.bias", F"decoder.layers.{i}.self_attn_layer_norm.bias"))
				rename_keys.append(
				    (F"transformer.decoder.layers.{i}.norm2.weight", F"decoder.layers.{i}.encoder_attn_layer_norm.weight")
				)
				rename_keys.append(
				    (F"transformer.decoder.layers.{i}.norm2.bias", F"decoder.layers.{i}.encoder_attn_layer_norm.bias")
				)
				rename_keys.append((F"transformer.decoder.layers.{i}.norm3.weight", F"decoder.layers.{i}.final_layer_norm.weight"))
				rename_keys.append((F"transformer.decoder.layers.{i}.norm3.bias", F"decoder.layers.{i}.final_layer_norm.bias"))

# convolutional projection + query embeddings + layernorm of encoder + layernorm of decoder + class and bounding box heads
rename_keys.extend(
    [
        ('''input_proj.weight''', '''input_projection.weight'''),
        ('''input_proj.bias''', '''input_projection.bias'''),
        ('''query_embed.weight''', '''query_position_embeddings.weight'''),
        ('''transformer.encoder.norm.weight''', '''encoder.layernorm.weight'''),
        ('''transformer.encoder.norm.bias''', '''encoder.layernorm.bias'''),
        ('''transformer.decoder.norm.weight''', '''decoder.layernorm.weight'''),
        ('''transformer.decoder.norm.bias''', '''decoder.layernorm.bias'''),
        ('''class_embed.weight''', '''class_labels_classifier.weight'''),
        ('''class_embed.bias''', '''class_labels_classifier.bias'''),
        ('''bbox_embed.layers.0.weight''', '''bbox_predictor.layers.0.weight'''),
        ('''bbox_embed.layers.0.bias''', '''bbox_predictor.layers.0.bias'''),
        ('''bbox_embed.layers.1.weight''', '''bbox_predictor.layers.1.weight'''),
        ('''bbox_embed.layers.1.bias''', '''bbox_predictor.layers.1.bias'''),
        ('''bbox_embed.layers.2.weight''', '''bbox_predictor.layers.2.weight'''),
        ('''bbox_embed.layers.2.bias''', '''bbox_predictor.layers.2.bias'''),
    ]
)





def        _lowercase   (  _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase			)  ->					int:
				lowerCamelCase			=state_dict.pop(_a			)
				lowerCamelCase			=val





def        _lowercase   (  _UpperCAmelCase			)  ->					List[Any]:
				lowerCamelCase			=OrderedDict()
				for key, value in state_dict.items():
								if "backbone.0.body" in key:
												lowerCamelCase			=key.replace("""backbone.0.body"""     , """backbone.conv_encoder.model"""			)
												lowerCamelCase			=value
								else:
												lowerCamelCase			=value

				return new_state_dict





def        _lowercase   (  _UpperCAmelCase			)  ->					Optional[Any]:
				lowerCamelCase			=""

				# first: transformer encoder
				for i in range(6			):
								# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
								lowerCamelCase			=state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""			)
								lowerCamelCase			=state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias"""			)
								# next, add query, keys and values (in that order) to the state dict
								lowerCamelCase			=in_proj_weight[:2_56, :]
								lowerCamelCase			=in_proj_bias[:2_56]
								lowerCamelCase			=in_proj_weight[2_56:5_12, :]
								lowerCamelCase			=in_proj_bias[2_56:5_12]
								lowerCamelCase			=in_proj_weight[-2_56:, :]
								lowerCamelCase			=in_proj_bias[-2_56:]
				# next: transformer decoder (which is a bit more complex because it also includes cross-attention)
				for i in range(6			):
								# read in weights + bias of input projection layer of self-attention
								lowerCamelCase			=state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""			)
								lowerCamelCase			=state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias"""			)
								# next, add query, keys and values (in that order) to the state dict
								lowerCamelCase			=in_proj_weight[:2_56, :]
								lowerCamelCase			=in_proj_bias[:2_56]
								lowerCamelCase			=in_proj_weight[2_56:5_12, :]
								lowerCamelCase			=in_proj_bias[2_56:5_12]
								lowerCamelCase			=in_proj_weight[-2_56:, :]
								lowerCamelCase			=in_proj_bias[-2_56:]
								# read in weights + bias of input projection layer of cross-attention
								lowerCamelCase			=state_dict.pop(
								    F"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""			)
								lowerCamelCase			=state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias"""			)
								# next, add query, keys and values (in that order) of cross-attention to the state dict
								lowerCamelCase			=in_proj_weight_cross_attn[:2_56, :]
								lowerCamelCase			=in_proj_bias_cross_attn[:2_56]
								lowerCamelCase			=in_proj_weight_cross_attn[2_56:5_12, :]
								lowerCamelCase			=in_proj_bias_cross_attn[2_56:5_12]
								lowerCamelCase			=in_proj_weight_cross_attn[-2_56:, :]
								lowerCamelCase			=in_proj_bias_cross_attn[-2_56:]





def        _lowercase   (  _UpperCAmelCase     , _UpperCAmelCase			)  ->					Dict:
				lowerCamelCase			=image.size
				lowerCamelCase			=max(_a     , _a			)
				lowerCamelCase			=8_00 if "detection" in checkpoint_url else 10_00
				lowerCamelCase			=target_max_size / current_max_size
				lowerCamelCase			=image.resize((int(round(scale * width			)			), int(round(scale * height			)			))			)

				return resized_image





def        _lowercase   (  _UpperCAmelCase			)  ->					Tuple:
				lowerCamelCase			=F.to_tensor(_a			)
				lowerCamelCase			=F.normalize(_a     , mean=[0.4_8_5, 0.4_5_6, 0.4_0_6]     , std=[0.2_2_9, 0.2_2_4, 0.2_2_5]			)
				return image





@torch.no_grad()
def        _lowercase   (  _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase			)  ->					List[str]:
				logger.info("""Converting model..."""			)

				# load original state dict
				lowerCamelCase			=torch.hub.load_state_dict_from_url(_a     , map_location="""cpu"""			)
				# rename keys
				for src, dest in rename_keys:
								rename_key(_a     , _a     , _a			)
				lowerCamelCase			=rename_backbone_keys(_a			)
				# query, key and value matrices need special treatment
				read_in_q_k_v(_a			)
				# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
				lowerCamelCase			="model."
				for key in state_dict.copy().keys():
								if not key.startswith("""class_labels_classifier"""			) and not key.startswith("""bbox_predictor"""			):
												lowerCamelCase			=state_dict.pop(_a			)
												lowerCamelCase			=val
    # create HuggingFace model and load state dict
				lowerCamelCase			=TableTransformerConfig(
				    backbone="""resnet18"""     , mask_loss_coefficient=1     , dice_loss_coefficient=1     , ce_loss_coefficient=1     , bbox_loss_coefficient=5     , giou_loss_coefficient=2     , eos_coefficient=0.4     , class_cost=1     , bbox_cost=5     , giou_cost=2     , )

				if "detection" in checkpoint_url:
								lowerCamelCase			=15
								lowerCamelCase			=2
								lowerCamelCase			={0: "table", 1: "table rotated"}
								lowerCamelCase			=idalabel
								lowerCamelCase			={v: k for k, v in idalabel.items()}
				else:
								lowerCamelCase			=1_25
								lowerCamelCase			=6
								lowerCamelCase			={
								    0: "table",
								    1: "table column",
								    2: "table row",
								    3: "table column header",
								    4: "table projected row header",
								    5: "table spanning cell",
								}
								lowerCamelCase			=idalabel
								lowerCamelCase			={v: k for k, v in idalabel.items()}

				lowerCamelCase			=DetrImageProcessor(
				    format="""coco_detection"""     , max_size=8_00 if """detection""" in checkpoint_url else 10_00			)
				lowerCamelCase			=TableTransformerForObjectDetection(_a			)
				model.load_state_dict(_a			)
				model.eval()

				# verify our conversion
				lowerCamelCase			="example_pdf.png" if "detection" in checkpoint_url else "example_table.png"
				lowerCamelCase			=hf_hub_download(repo_id="""nielsr/example-pdf"""     , repo_type="""dataset"""     , filename=_a			)
				lowerCamelCase			=Image.open(_a			).convert("""RGB"""			)
				lowerCamelCase			=normalize(resize(_a     , _a			)			).unsqueeze(0			)

				lowerCamelCase			=model(_a			)

				if "detection" in checkpoint_url:
								lowerCamelCase			=(1, 15, 3)
								lowerCamelCase			=torch.tensor(
								    [[-6.7_8_9_7, -1_6.9_9_8_5, 6.7_9_3_7], [-8.0_1_8_6, -2_2.2_1_9_2, 6.9_6_7_7], [-7.3_1_1_7, -2_1.0_7_0_8, 7.4_0_5_5]]			)
								lowerCamelCase			=torch.tensor([[0.4_8_6_7, 0.1_7_6_7, 0.6_7_3_2], [0.6_7_1_8, 0.4_4_7_9, 0.3_8_3_0], [0.4_7_1_6, 0.1_7_6_0, 0.6_3_6_4]]			)

				else:
								lowerCamelCase			=(1, 1_25, 7)
								lowerCamelCase			=torch.tensor(
								    [[-1_8.1_4_3_0, -8.3_2_1_4, 4.8_2_7_4], [-1_8.4_6_8_5, -7.1_3_6_1, -4.2_6_6_7], [-2_6.3_6_9_3, -9.3_4_2_9, -4.9_9_6_2]]			)
								lowerCamelCase			=torch.tensor([[0.4_9_8_3, 0.5_5_9_5, 0.9_4_4_0], [0.4_9_1_6, 0.6_3_1_5, 0.5_9_5_4], [0.6_1_0_8, 0.8_6_3_7, 0.1_1_3_5]]			)

				assert outputs.logits.shape == expected_shape
				assert torch.allclose(outputs.logits[0, :3, :3]     , _a     , atol=1e-4			)
				assert torch.allclose(outputs.pred_boxes[0, :3, :3]     , _a     , atol=1e-4			)
				print("""Looks ok!"""			)

				if pytorch_dump_folder_path is not None:
								# Save model and image processor
								logger.info(F"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."""			)
								Path(_a			).mkdir(exist_ok=_a			)
								model.save_pretrained(_a			)
								image_processor.save_pretrained(_a			)

				if push_to_hub:
								# Push model to HF hub
								logger.info("""Pushing model to the hub..."""			)
								lowerCamelCase			=(
								    "microsoft/table-transformer-detection"
								    if "detection" in checkpoint_url
								    else "microsoft/table-transformer-structure-recognition"
								)
								model.push_to_hub(_a			)
								image_processor.push_to_hub(_a			)


if __name__ == "__main__":
				UpperCAmelCase__  :     Optional[int]   			=argparse.ArgumentParser()

				parser.add_argument(
				    '''--checkpoint_url''',
				    default='''https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth''',
				    type=str,
				    choices=[
				        '''https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth''',
				        '''https://pubtables1m.blob.core.windows.net/model/pubtables1m_structure_detr_r18.pth''',
				    ],
				    help='''URL of the Table Transformer checkpoint you\'d like to convert.''',
				)
				parser.add_argument(
				    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
				)
				parser.add_argument(
				    '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
				)
				UpperCAmelCase__  :     Dict   			=parser.parse_args()
				convert_table_transformer_checkpoint(args.checkpoint_url, 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


UpperCAmelCase__  :     Optional[int]   			=logging.getLogger(__name__)

UpperCAmelCase__  :     Tuple   			=50  # max width of layer names
UpperCAmelCase__  :     List[str]   			=70  # max width of quantizer names





def        _lowercase   (  _UpperCAmelCase			)  ->					List[str]:
				lowerCamelCase			=parser.add_argument_group("""quant_trainer arguments"""			)
				group.add_argument("""--wprec"""     , type=_UpperCAmelCase     , default=8     , help="""weight precision"""			)
				group.add_argument("""--aprec"""     , type=_UpperCAmelCase     , 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=_UpperCAmelCase     , nargs="""+"""     , help="""disable quantizers by keyword"""			)
				group.add_argument("""--quant-disable-layer-module"""     , type=_UpperCAmelCase     , help="""disable quantizers by keyword under layer."""			)
				group.add_argument("""--quant-enable-layer-module"""     , type=_UpperCAmelCase     , 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=_UpperCAmelCase     , type=_UpperCAmelCase     , 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=_UpperCAmelCase     , 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   (  _UpperCAmelCase			)  ->					Dict:

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

				lowerCamelCase			=QuantDescriptor(num_bits=args.aprec     , calib_method=_UpperCAmelCase			)
				lowerCamelCase			=QuantDescriptor(num_bits=args.wprec     , axis=(None if args.quant_per_tensor else (0,))			)
				quant_nn.QuantLinear.set_default_quant_desc_input(_UpperCAmelCase			)
				quant_nn.QuantLinear.set_default_quant_desc_weight(_UpperCAmelCase			)





def        _lowercase   (  _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase=False     , _UpperCAmelCase=False			)  ->					int:
				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(_UpperCAmelCase     , ["""embeddings"""]     , which="""weight"""     , _disabled=_UpperCAmelCase			)

								if args.quant_disable:
												set_quantizer_by_name(_UpperCAmelCase     , [""""""]     , _disabled=_UpperCAmelCase			)

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

								if args.quant_disable_layer_module:
												set_quantizer_by_name(_UpperCAmelCase     , [r"""layer.\d+.""" + args.quant_disable_layer_module]     , _disabled=_UpperCAmelCase			)

								if args.quant_enable_layer_module:
												set_quantizer_by_name(_UpperCAmelCase     , [r"""layer.\d+.""" + args.quant_enable_layer_module]     , _disabled=_UpperCAmelCase			)

								if args.recalibrate_weights:
												recalibrate_weights(_UpperCAmelCase			)

								if args.fuse_qkv:
												fuse_qkv(_UpperCAmelCase     , _UpperCAmelCase			)

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

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





def        _lowercase   (  _UpperCAmelCase			)  ->					Optional[Any]:
				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   (  _UpperCAmelCase     , _UpperCAmelCase			)  ->					Optional[int]:
				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(_UpperCAmelCase			)





def        _lowercase   (  _UpperCAmelCase     , _UpperCAmelCase			)  ->					Tuple:

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

								lowerCamelCase			=max(_UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase			)
								qq._amax.fill_(_UpperCAmelCase			)
								qk._amax.fill_(_UpperCAmelCase			)
								qv._amax.fill_(_UpperCAmelCase			)
								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   (  _UpperCAmelCase     , _UpperCAmelCase			)  ->					int:

				for name, mod in model.named_modules():
								if name.endswith(""".output.dense"""			) and not name.endswith("""attention.output.dense"""			):
												lowerCamelCase			=mod._input_quantizer._amax.data.detach().item()
												mod._input_quantizer._amax.data.detach().clamp_(max=_UpperCAmelCase			)
												lowerCamelCase			=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   (  _UpperCAmelCase			)  ->					Dict:

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





def        _lowercase   (  _UpperCAmelCase			)  ->					List[str]:

				for name, mod in model.named_modules():
								if hasattr(_UpperCAmelCase     , """_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)
												lowerCamelCase			=set() if mod._weight_quantizer.axis is None else set(mod._weight_quantizer.axis			)
												lowerCamelCase			=set(range(len(mod.weight.size()			)			)			) - axis_set
												lowerCamelCase			=pytorch_quantization.utils.reduce_amax(mod.weight     , axis=_UpperCAmelCase     , keepdims=_UpperCAmelCase			).detach()
												logger.info(F"""RECALIB: {name:{name_width}} {mod._weight_quantizer._amax.flatten()} -> {amax.flatten()}"""			)
												lowerCamelCase			=amax





def        _lowercase   (  _UpperCAmelCase     , _UpperCAmelCase=25     , _UpperCAmelCase=1_80     , _UpperCAmelCase=None			)  ->					Dict:

				if ignore is None:
								lowerCamelCase			=[]
				elif not isinstance(_UpperCAmelCase     , _UpperCAmelCase			):
								lowerCamelCase			=[ignore]

				lowerCamelCase			=0
				for name, mod in model.named_modules():
								if not hasattr(_UpperCAmelCase     , """weight"""			):
												continue
								lowerCamelCase			=max(_UpperCAmelCase     , len(_UpperCAmelCase			)			)

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





def        _lowercase   (  _UpperCAmelCase			)  ->					Dict:
				lowerCamelCase			=0
				for name, mod in model.named_modules():
								if isinstance(_UpperCAmelCase     , pytorch_quantization.nn.TensorQuantizer			):
												print(F"""{name:80} {mod}"""			)
												count += 1
				print(F"""{count} TensorQuantizers found in model"""			)





def        _lowercase   (  _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase			)  ->					Union[str, Any]:
				lowerCamelCase			=getattr(_UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase			)
				if quantizer_mod is not None:
								assert hasattr(_UpperCAmelCase     , _UpperCAmelCase			)
								setattr(_UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase			)
				else:
								logger.warning(F"""{name} has no {quantizer}"""			)





def        _lowercase   (  _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase="both"     , **_UpperCAmelCase			)  ->					List[str]:
				lowerCamelCase			=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(_UpperCAmelCase     , _UpperCAmelCase     , """_input_quantizer"""     , _UpperCAmelCase     , _UpperCAmelCase			)
								if which in ["weight", "both"]:
												set_quantizer(_UpperCAmelCase     , _UpperCAmelCase     , """_weight_quantizer"""     , _UpperCAmelCase     , _UpperCAmelCase			)
				logger.info(_UpperCAmelCase			)





def        _lowercase   (  _UpperCAmelCase     , _UpperCAmelCase     , **_UpperCAmelCase			)  ->					int:

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





