


'''simple docstring'''





import argparse
import json
from collections import OrderedDict
from pathlib import Path

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

from transformers import (
    SegformerConfig,
    SegformerForImageClassification,
    SegformerForSemanticSegmentation,
    SegformerImageProcessor,
)
from transformers.utils import logging


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






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: Optional[Any],_SCREAMING_SNAKE_CASE					: Dict=False ):






			"""simple docstring"""




			__A=	OrderedDict()
			for key, value in state_dict.items():
						if encoder_only and not key.startswith('head' ):
									__A=	'''segformer.encoder.''' + key
						if key.startswith('backbone' ):
									__A=	key.replace('backbone','segformer.encoder' )
						if "patch_embed" in key:
									# replace for example patch_embed1 by patch_embeddings.0
									__A=	key[key.find('patch_embed' ) + len('patch_embed' )]
									__A=	key.replace(f"""patch_embed{idx}""",f"""patch_embeddings.{int(__a )-1}""" )
						if "norm" in key:
									__A=	key.replace('norm','layer_norm' )
						if "segformer.encoder.layer_norm" in key:
									# replace for example layer_norm1 by layer_norm.0
									__A=	key[key.find('segformer.encoder.layer_norm' ) + len('segformer.encoder.layer_norm' )]
									__A=	key.replace(f"""layer_norm{idx}""",f"""layer_norm.{int(__a )-1}""" )
						if "layer_norm1" in key:
									__A=	key.replace('layer_norm1','layer_norm_1' )
						if "layer_norm2" in key:
									__A=	key.replace('layer_norm2','layer_norm_2' )
						if "block" in key:
									# replace for example block1 by block.0
									__A=	key[key.find('block' ) + len('block' )]
									__A=	key.replace(f"""block{idx}""",f"""block.{int(__a )-1}""" )
						if "attn.q" in key:
									__A=	key.replace('attn.q','attention.self.query' )
						if "attn.proj" in key:
									__A=	key.replace('attn.proj','attention.output.dense' )
						if "attn" in key:
									__A=	key.replace('attn','attention.self' )
						if "fc1" in key:
									__A=	key.replace('fc1','dense1' )
						if "fc2" in key:
									__A=	key.replace('fc2','dense2' )
						if "linear_pred" in key:
									__A=	key.replace('linear_pred','classifier' )
						if "linear_fuse" in key:
									__A=	key.replace('linear_fuse.conv','linear_fuse' )
									__A=	key.replace('linear_fuse.bn','batch_norm' )
						if "linear_c" in key:
									# replace for example linear_c4 by linear_c.3
									__A=	key[key.find('linear_c' ) + len('linear_c' )]
									__A=	key.replace(f"""linear_c{idx}""",f"""linear_c.{int(__a )-1}""" )
						if key.startswith('head' ):
									__A=	key.replace('head','classifier' )
						__A=	value

			return new_state_dict






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: str,_SCREAMING_SNAKE_CASE					: Union[str, Any] ):






			"""simple docstring"""




			for i in range(config.num_encoder_blocks ):
						for j in range(config.depths[i] ):
									# read in weights + bias of keys and values (which is a single matrix in the original implementation)
									__A=	state_dict.pop(f"""segformer.encoder.block.{i}.{j}.attention.self.kv.weight""" )
									__A=	state_dict.pop(f"""segformer.encoder.block.{i}.{j}.attention.self.kv.bias""" )
									# next, add keys and values (in that order) to the state dict
									__A=	kv_weight[
									    : config.hidden_sizes[i], :
									]
									__A=	kv_bias[: config.hidden_sizes[i]]
									__A=	kv_weight[
									    config.hidden_sizes[i] :, :
									]
									__A=	kv_bias[
									    config.hidden_sizes[i] :
									]






def 						UpperCAmelCase__(				):






			"""simple docstring"""




			__A=	'''http://images.cocodataset.org/val2017/000000039769.jpg'''
			__A=	Image.open(requests.get(__a,stream=__a ).raw )

			return image






@torch.no_grad()
def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: Dict,_SCREAMING_SNAKE_CASE					: str,_SCREAMING_SNAKE_CASE					: Optional[int] ):






			"""simple docstring"""




			__A=	SegformerConfig()
			__A=	False

			# set attributes based on model_name
			__A=	'''huggingface/label-files'''
			if "segformer" in model_name:
						__A=	model_name[len('segformer.' ) : len('segformer.' ) + 2]
						if "ade" in model_name:
									__A=	150
									__A=	'''ade20k-id2label.json'''
									__A=	(1, 150, 128, 128)
						elif "city" in model_name:
									__A=	19
									__A=	'''cityscapes-id2label.json'''
									__A=	(1, 19, 128, 128)
						else:
									raise ValueError(f"""Model {model_name} not supported""" )
			elif "mit" in model_name:
						__A=	True
						__A=	model_name[4:6]
						__A=	1000
						__A=	'''imagenet-1k-id2label.json'''
						__A=	(1, 1000)
			else:
						raise ValueError(f"""Model {model_name} not supported""" )

			# set config attributes
			__A=	json.load(open(hf_hub_download(__a,__a,repo_type='dataset' ),'r' ) )
			__A=	{int(__a ): v for k, v in idalabel.items()}
			__A=	idalabel
			__A=	{v: k for k, v in idalabel.items()}
			if size == "b0":
						pass
			elif size == "b1":
						__A=	[64, 128, 320, 512]
						__A=	256
			elif size == "b2":
						__A=	[64, 128, 320, 512]
						__A=	768
						__A=	[3, 4, 6, 3]
			elif size == "b3":
						__A=	[64, 128, 320, 512]
						__A=	768
						__A=	[3, 4, 18, 3]
			elif size == "b4":
						__A=	[64, 128, 320, 512]
						__A=	768
						__A=	[3, 8, 27, 3]
			elif size == "b5":
						__A=	[64, 128, 320, 512]
						__A=	768
						__A=	[3, 6, 40, 3]
			else:
						raise ValueError(f"""Size {size} not supported""" )

			# load image processor (only resize + normalize)
			__A=	SegformerImageProcessor(
			    image_scale=(512, 512),keep_ratio=__a,align=__a,do_random_crop=__a )

			# prepare image
			__A=	prepare_img()
			__A=	image_processor(images=__a,return_tensors='pt' ).pixel_values

			logger.info(f"""Converting model {model_name}...""" )

			# load original state dict
			if encoder_only:
						__A=	torch.load(__a,map_location=torch.device('cpu' ) )
			else:
						__A=	torch.load(__a,map_location=torch.device('cpu' ) )['''state_dict''']

			# rename keys
			__A=	rename_keys(__a,encoder_only=__a )
			if not encoder_only:
						del state_dict["decode_head.conv_seg.weight"]
						del state_dict["decode_head.conv_seg.bias"]

			# key and value matrices need special treatment
			read_in_k_v(__a,__a )

			# create HuggingFace model and load state dict
			if encoder_only:
						__A=	False
						__A=	SegformerForImageClassification(__a )
			else:
						__A=	SegformerForSemanticSegmentation(__a )
			model.load_state_dict(__a )
			model.eval()

			# forward pass
			__A=	model(__a )
			__A=	outputs.logits

			# set expected_slice based on model name
			# ADE20k checkpoints
			if model_name == "segformer.b0.512x512.ade.160k":
						__A=	torch.tensor(
						    [
						        [[-4.63_10, -5.52_32, -6.23_56], [-5.19_21, -6.14_44, -6.59_96], [-5.44_24, -6.27_90, -6.75_74]],
						        [[-12.13_91, -13.31_22, -13.95_54], [-12.87_32, -13.93_52, -14.35_63], [-12.94_38, -13.82_26, -14.25_13]],
						        [[-12.51_34, -13.46_86, -14.49_15], [-12.86_69, -14.43_43, -14.77_58], [-13.25_23, -14.58_19, -15.06_94]],
						    ] )
			elif model_name == "segformer.b1.512x512.ade.160k":
						__A=	torch.tensor(
						    [
						        [[-7.58_20, -8.72_31, -8.32_15], [-8.06_00, -10.35_29, -10.03_04], [-7.52_08, -9.41_03, -9.62_39]],
						        [[-12.69_18, -13.89_94, -13.71_37], [-13.31_96, -15.75_23, -15.47_89], [-12.93_43, -14.87_57, -14.96_89]],
						        [[-11.19_11, -11.94_21, -11.32_43], [-11.33_42, -13.68_39, -13.35_81], [-10.39_09, -12.18_32, -12.48_58]],
						    ] )
			elif model_name == "segformer.b2.512x512.ade.160k":
						__A=	torch.tensor(
						    [
						        [[-11.81_73, -14.38_50, -16.31_28], [-14.56_48, -16.58_04, -18.65_68], [-14.72_23, -15.73_87, -18.42_18]],
						        [[-15.72_90, -17.91_71, -19.44_23], [-18.31_05, -19.94_48, -21.46_61], [-17.92_96, -18.64_97, -20.79_10]],
						        [[-15.07_83, -17.03_36, -18.27_89], [-16.87_71, -18.68_70, -20.16_12], [-16.24_54, -17.14_26, -19.50_55]],
						    ] )
			elif model_name == "segformer.b3.512x512.ade.160k":
						__A=	torch.tensor(
						    [
						        [[-9.08_78, -10.20_81, -10.18_91], [-9.31_44, -10.79_41, -10.98_43], [-9.22_94, -10.38_55, -10.57_04]],
						        [[-12.23_16, -13.90_68, -13.61_02], [-12.91_61, -14.37_02, -14.32_35], [-12.52_33, -13.71_74, -13.79_32]],
						        [[-14.62_75, -15.24_90, -14.97_27], [-14.34_00, -15.96_87, -16.28_27], [-14.14_84, -15.40_33, -15.89_37]],
						    ] )
			elif model_name == "segformer.b4.512x512.ade.160k":
						__A=	torch.tensor(
						    [
						        [[-12.31_44, -13.24_47, -14.08_02], [-13.36_14, -14.58_16, -15.61_17], [-13.33_40, -14.44_33, -16.22_19]],
						        [[-19.27_81, -20.41_28, -20.75_06], [-20.61_53, -21.65_66, -22.09_98], [-19.98_00, -21.04_30, -22.14_94]],
						        [[-18.87_39, -19.78_04, -21.18_34], [-20.12_33, -21.67_65, -23.29_44], [-20.03_15, -21.26_41, -23.69_44]],
						    ] )
			elif model_name == "segformer.b5.640x640.ade.160k":
						__A=	torch.tensor(
						    [
						        [[-9.55_24, -12.08_35, -11.73_48], [-10.52_29, -13.64_46, -14.56_62], [-9.58_42, -12.88_51, -13.94_14]],
						        [[-15.34_32, -17.53_23, -17.08_18], [-16.33_30, -18.92_55, -19.21_01], [-15.13_40, -17.78_48, -18.39_71]],
						        [[-12.60_72, -14.94_86, -14.66_31], [-13.76_29, -17.09_07, -17.77_45], [-12.78_99, -16.16_95, -17.16_71]],
						    ] )
			# Cityscapes checkpoints
			elif model_name == "segformer.b0.1024x1024.city.160k":
						__A=	torch.tensor(
						    [
						        [[-11.92_95, -13.40_57, -14.81_06], [-13.34_31, -14.81_79, -15.37_81], [-14.28_36, -15.59_42, -16.15_88]],
						        [[-11.49_06, -12.80_67, -13.65_64], [-13.11_89, -14.05_00, -14.15_43], [-13.87_48, -14.51_36, -14.87_89]],
						        [[0.53_74, 0.10_67, -0.47_42], [0.11_41, -0.22_55, -0.70_99], [-0.30_00, -0.59_24, -1.31_05]],
						    ] )
			elif model_name == "segformer.b0.512x1024.city.160k":
						__A=	torch.tensor(
						    [
						        [[-7.82_17, -9.87_67, -10.17_17], [-9.44_38, -10.90_58, -11.40_47], [-9.79_39, -12.34_95, -12.10_79]],
						        [[-7.15_14, -9.53_36, -10.08_60], [-9.77_76, -11.68_22, -11.84_39], [-10.14_11, -12.76_55, -12.89_72]],
						        [[0.30_21, 0.08_05, -0.23_10], [-0.03_28, -0.16_05, -0.27_14], [-0.14_08, -0.54_77, -0.69_76]],
						    ] )
			elif model_name == "segformer.b0.640x1280.city.160k":
						__A=	torch.tensor(
						    [
						        [
						            [-1.1_3_7_2e0_1, -1.2_7_8_7e0_1, -1.3_4_7_7e0_1],
						            [-1.2_5_3_6e0_1, -1.4_1_9_4e0_1, -1.4_4_0_9e0_1],
						            [-1.3_2_1_7e0_1, -1.4_8_8_8e0_1, -1.5_3_2_7e0_1],
						        ],
						        [
						            [-1.4_7_9_1e0_1, -1.7_1_2_2e0_1, -1.8_2_7_7e0_1],
						            [-1.7_1_6_3e0_1, -1.9_1_9_2e0_1, -1.9_5_3_3e0_1],
						            [-1.7_8_9_7e0_1, -1.9_9_9_1e0_1, -2.0_3_1_5e0_1],
						        ],
						        [
						            [7.6_7_2_3e-0_1, 4.1_9_2_1e-0_1, -7.7_8_7_8e-0_2],
						            [4.7_7_7_2e-0_1, 9.5_5_5_7e-0_3, -2.8_0_8_2e-0_1],
						            [3.6_0_3_2e-0_1, -2.4_8_2_6e-0_1, -5.1_1_6_8e-0_1],
						        ],
						    ] )
			elif model_name == "segformer.b0.768x768.city.160k":
						__A=	torch.tensor(
						    [
						        [[-9.49_59, -11.30_87, -11.74_79], [-11.00_25, -12.65_40, -12.33_19], [-11.40_64, -13.04_87, -12.99_05]],
						        [[-9.89_05, -11.30_84, -12.08_54], [-11.17_26, -12.76_98, -12.95_83], [-11.59_85, -13.32_78, -14.17_74]],
						        [[0.22_13, 0.01_92, -0.24_66], [-0.17_31, -0.42_13, -0.48_74], [-0.31_26, -0.65_41, -1.13_89]],
						    ] )
			elif model_name == "segformer.b1.1024x1024.city.160k":
						__A=	torch.tensor(
						    [
						        [[-13.57_48, -13.91_11, -12.65_00], [-14.35_00, -15.36_83, -14.23_28], [-14.75_32, -16.04_24, -15.60_87]],
						        [[-17.16_51, -15.87_25, -12.96_53], [-17.25_80, -17.37_18, -14.82_23], [-16.60_58, -16.87_83, -16.74_52]],
						        [[-3.64_56, -3.02_09, -1.42_03], [-3.07_97, -3.19_59, -2.00_00], [-1.87_57, -1.92_17, -1.69_97]],
						    ] )
			elif model_name == "segformer.b2.1024x1024.city.160k":
						__A=	torch.tensor(
						    [
						        [[-16.09_76, -16.48_56, -17.39_62], [-16.62_34, -19.03_42, -19.76_85], [-16.09_00, -18.06_61, -19.11_80]],
						        [[-18.47_50, -18.84_88, -19.50_74], [-19.40_30, -22.15_70, -22.59_77], [-19.11_91, -20.84_86, -22.37_83]],
						        [[-4.51_78, -5.50_37, -6.51_09], [-5.08_84, -7.21_74, -8.03_34], [-4.41_56, -5.81_17, -7.29_70]],
						    ] )
			elif model_name == "segformer.b3.1024x1024.city.160k":
						__A=	torch.tensor(
						    [
						        [[-14.20_81, -14.47_32, -14.19_77], [-14.58_67, -16.44_23, -16.63_56], [-13.44_41, -14.96_85, -16.86_96]],
						        [[-14.45_76, -14.70_73, -15.04_51], [-15.08_16, -17.62_37, -17.98_73], [-14.42_13, -16.01_99, -18.59_92]],
						        [[-4.73_49, -4.95_88, -5.09_66], [-4.32_10, -6.93_25, -7.25_91], [-3.43_12, -4.74_84, -7.19_17]],
						    ] )
			elif model_name == "segformer.b4.1024x1024.city.160k":
						__A=	torch.tensor(
						    [
						        [[-11.77_37, -11.95_26, -11.32_73], [-13.66_92, -14.45_74, -13.88_78], [-13.89_37, -14.69_24, -15.93_45]],
						        [[-14.67_06, -14.53_30, -14.13_06], [-16.15_02, -16.81_80, -16.42_69], [-16.83_38, -17.89_39, -20.17_46]],
						        [[1.04_91, 0.82_89, 1.03_10], [1.10_44, 0.52_19, 0.80_55], [1.08_99, 0.69_26, 0.55_90]],
						    ] )
			elif model_name == "segformer.b5.1024x1024.city.160k":
						__A=	torch.tensor(
						    [
						        [[-12.56_41, -13.47_77, -13.06_84], [-13.95_87, -15.89_83, -16.65_57], [-13.31_09, -15.73_50, -16.31_41]],
						        [[-14.70_74, -15.43_52, -14.59_44], [-16.63_53, -18.16_63, -18.61_20], [-15.17_02, -18.03_29, -18.15_47]],
						        [[-1.79_90, -2.09_51, -1.77_84], [-2.63_97, -3.82_45, -3.96_86], [-1.52_64, -2.81_26, -2.93_16]],
						    ] )
			else:
						__A=	logits.argmax(-1 ).item()
						print('Predicted class:',model.config.idalabel[predicted_class_idx] )

			# verify logits
			if not encoder_only:
						assert logits.shape == expected_shape
						assert torch.allclose(logits[0, :3, :3, :3],__a,atol=1e-2 )

			# finally, 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 __name__ == "__main__":
					UpperCAmelCase__  			=				argparse.ArgumentParser()

					parser.add_argument(
					    '''--model_name''',
					    default='''segformer.b0.512x512.ade.160k''',
					    type=str,
					    help='''Name of the model you\'d like to convert.''',
					)
					parser.add_argument(
					    '''--checkpoint_path''', default=None, type=str, help='''Path to the original PyTorch checkpoint (.pth file).'''
					)
					parser.add_argument(
					    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
					)
					UpperCAmelCase__  			=				parser.parse_args()
					convert_segformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)






import argparse
import collections

import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints

from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()



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

					"""simple docstring"""

					return params[F"""{prefix}/{prefix}/relpos_bias/rel_embedding"""][:, i, :]



def   __UpperCAmelCase					(      __a :     List[Any]						,__a :     Optional[int]						,__a :     int						,__a :     List[str]="attention"      )     ->	List[str]:

					"""simple docstring"""

					_a      :       str   =						np.ascontiguousarray(params[F"""{prefix}/{prefix}/{layer_name}/key/kernel"""][:, i, :, :]      )
					_a      :       Tuple   =						k_tmp.reshape(k_tmp.shape[0]						,k_tmp.shape[1] * k_tmp.shape[2]      )
					_a      :       Any   =						np.ascontiguousarray(params[F"""{prefix}/{prefix}/{layer_name}/out/kernel"""][:, i, :, :]      )
					_a      :       Dict   =						o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1]						,o_tmp.shape[2]      )
					_a      :       Union[str, Any]   =						np.ascontiguousarray(params[F"""{prefix}/{prefix}/{layer_name}/query/kernel"""][:, i, :, :]      )
					_a      :       Any   =						q_tmp.reshape(q_tmp.shape[0]						,q_tmp.shape[1] * q_tmp.shape[2]      )
					_a      :       Tuple   =						np.ascontiguousarray(params[F"""{prefix}/{prefix}/{layer_name}/value/kernel"""][:, i, :, :]      )
					_a      :       int   =						v_tmp.reshape(v_tmp.shape[0]						,v_tmp.shape[1] * v_tmp.shape[2]      )
					return k, o, q, v



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

					"""simple docstring"""

					if split_mlp_wi:
										_a      :       Union[str, Any]   =						params[F"""{prefix}/{prefix}/mlp/wi_0/kernel"""][:, i, :]
										_a      :       Union[str, Any]   =						params[F"""{prefix}/{prefix}/mlp/wi_1/kernel"""][:, i, :]
										_a      :       List[str]   =						(wi_a, wi_a)
					else:
										_a      :       List[str]   =						params[F"""{prefix}/{prefix}/mlp/wi/kernel"""][:, i, :]

					_a      :       Optional[int]   =						params[F"""{prefix}/{prefix}/mlp/wo/kernel"""][:, i, :]
					return wi, wo



def   __UpperCAmelCase					(      __a :     List[Any]						,__a :     Optional[Any]						,__a :     Union[str, Any]						,__a :     str      )     ->	List[str]:

					"""simple docstring"""

					return params[F"""{prefix}/{prefix}/{layer_name}/scale"""][:, i]



def   __UpperCAmelCase					(      __a :     dict						,*, __a :     int						,__a :     bool						,__a :     bool = False      )     ->	Any:

					"""simple docstring"""

					_a      :       Dict   =						traverse_util.flatten_dict(variables['''target''']      )
					_a      :       Any   =						{'''/'''.join(__a      ): v for k, v in old.items()}

					# v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
					_a      :       Optional[int]   =						'''encoder/encoder/mlp/wi_0/kernel''' in old
					print('''Split MLP:'''						,__a      )

					_a      :       Tuple   =						collections.OrderedDict()

					# Shared embeddings.
					_a      :       Any   =						old['''token_embedder/embedding''']

					# Encoder.
					for i in range(__a      ):
										# Block i, layer 0 (Self Attention).
										_a      :       Optional[Any]   =						tax_layer_norm_lookup(__a						,__a						,'''encoder'''						,'''pre_attention_layer_norm'''      )
										_a ,   _a ,   _a ,   _a      :       List[str]   =						tax_attention_lookup(__a						,__a						,'''encoder'''						,'''attention'''      )
										_a      :       List[str]   =						layer_norm
										_a      :       Optional[Any]   =						k.T
										_a      :       str   =						o.T
										_a      :       List[Any]   =						q.T
										_a      :       Tuple   =						v.T

										# Block i, layer 1 (MLP).
										_a      :       str   =						tax_layer_norm_lookup(__a						,__a						,'''encoder'''						,'''pre_mlp_layer_norm'''      )
										_a ,   _a      :       Any   =						tax_mlp_lookup(__a						,__a						,'''encoder'''						,__a      )
										_a      :       str   =						layer_norm
										if split_mlp_wi:
															_a      :       List[Any]   =						wi[0].T
															_a      :       Any   =						wi[1].T
										else:
															_a      :       Any   =						wi.T
										_a      :       Optional[Any]   =						wo.T
										if scalable_attention:
															# convert the rel_embedding of each layer
															_a      :       Dict   =						tax_relpos_bias_lookup(
															    __a						,__a						,'''encoder'''      ).T

					_a      :       List[str]   =						old['''encoder/encoder_norm/scale''']

					if not scalable_attention:
										_a      :       List[Any]   =						tax_relpos_bias_lookup(
										    __a						,0						,'''encoder'''      ).T
										_a      :       Optional[Any]   =						tax_relpos_bias_lookup(
										    __a						,0						,'''decoder'''      ).T

					if not is_encoder_only:
										# Decoder.
										for i in range(__a      ):
															# Block i, layer 0 (Self Attention).
															_a      :       Union[str, Any]   =						tax_layer_norm_lookup(__a						,__a						,'''decoder'''						,'''pre_self_attention_layer_norm'''      )
															_a ,   _a ,   _a ,   _a      :       Optional[Any]   =						tax_attention_lookup(__a						,__a						,'''decoder'''						,'''self_attention'''      )
															_a      :       Optional[Any]   =						layer_norm
															_a      :       Dict   =						k.T
															_a      :       str   =						o.T
															_a      :       str   =						q.T
															_a      :       List[str]   =						v.T

															# Block i, layer 1 (Cross Attention).
															_a      :       Any   =						tax_layer_norm_lookup(__a						,__a						,'''decoder'''						,'''pre_cross_attention_layer_norm'''      )
															_a ,   _a ,   _a ,   _a      :       str   =						tax_attention_lookup(__a						,__a						,'''decoder'''						,'''encoder_decoder_attention'''      )
															_a      :       Optional[Any]   =						layer_norm
															_a      :       Optional[int]   =						k.T
															_a      :       Dict   =						o.T
															_a      :       str   =						q.T
															_a      :       int   =						v.T

															# Block i, layer 2 (MLP).
															_a      :       Optional[int]   =						tax_layer_norm_lookup(__a						,__a						,'''decoder'''						,'''pre_mlp_layer_norm'''      )
															_a ,   _a      :       Tuple   =						tax_mlp_lookup(__a						,__a						,'''decoder'''						,__a      )
															_a      :       Optional[Any]   =						layer_norm
															if split_mlp_wi:
																				_a      :       List[str]   =						wi[0].T
																				_a      :       List[Any]   =						wi[1].T
															else:
																				_a      :       Dict   =						wi.T
															_a      :       str   =						wo.T

															if scalable_attention:
																				# convert the rel_embedding of each layer
																				_a      :       Tuple   =						tax_relpos_bias_lookup(__a						,__a						,'''decoder'''      ).T

										_a      :       Tuple   =						old['''decoder/decoder_norm/scale''']

										# LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
										if "decoder/logits_dense/kernel" in old:
															_a      :       Any   =						old['''decoder/logits_dense/kernel'''].T

					return new



def   __UpperCAmelCase					(      __a :     Dict						,__a :     bool      )     ->	Tuple:

					"""simple docstring"""

					_a      :       Tuple   =						collections.OrderedDict([(k, torch.from_numpy(v.copy()      )) for (k, v) in converted_params.items()]      )

					# Add what is missing.
					if "encoder.embed_tokens.weight" not in state_dict:
										_a      :       Any   =						state_dict['''shared.weight''']

					if not is_encoder_only:
										if "decoder.embed_tokens.weight" not in state_dict:
															_a      :       Optional[int]   =						state_dict['''shared.weight''']

										if "lm_head.weight" not in state_dict:  # For old 1.0 models.
															print('''Using shared word embeddings as lm_head.'''      )
															_a      :       str   =						state_dict['''shared.weight''']

					return state_dict



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

					"""simple docstring"""

					_a      :       List[str]   =						checkpoints.load_tax_checkpoint(__a      )
					_a      :       str   =						convert_tax_to_pytorch(
					    __a						,num_layers=config.num_layers						,is_encoder_only=__a						,scalable_attention=__a      )
					_a      :       str   =						make_state_dict(__a						,__a      )
					model.load_state_dict(__a						,strict=__a      )



def   __UpperCAmelCase					(      __a :     List[Any]						,__a :     Any						,__a :     Union[str, Any]						,__a :     bool = False						,__a :     bool = False						,)     ->	Optional[Any]:

					"""simple docstring"""

					_a      :       List[str]   =						MTaConfig.from_json_file(__a      )
					print(F"""Building PyTorch model from configuration: {config}"""      )
					# Non-v1.1 checkpoints could also use T5Model, but this works for all.
					# The v1.0 checkpoints will simply have an LM head that is the word embeddings.
					if is_encoder_only:
										_a      :       Any   =						UMTaEncoderModel(__a      )
					else:
										_a      :       Tuple   =						UMTaForConditionalGeneration(__a      )

					# Load weights from tf checkpoint
					load_tax_weights_in_ta(__a						,__a						,__a						,__a						,__a      )

					# Save pytorch-model
					print(F"""Save PyTorch model to {pytorch_dump_path}"""      )
					model.save_pretrained(__a      )

					# Verify that we can load the checkpoint.
					model.from_pretrained(__a      )
					print('''Done'''      )


if __name__ == "__main__":
			a__												=						argparse.ArgumentParser(description='''Converts a native T5X checkpoint into a PyTorch checkpoint.''')
			# Required parameters
			parser.add_argument(
			    '''--t5x_checkpoint_path''', default=None, type=str, required=True, help='''Path to the T5X checkpoint.'''
			)
			parser.add_argument(
			    '''--config_file''',
			    default=None,
			    type=str,
			    required=True,
			    help='''The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.''',
			)
			parser.add_argument(
			    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
			)
			parser.add_argument(
			    '''--is_encoder_only''', action='''store_true''', help='''Check if the model is encoder-decoder model''', default=False
			)
			parser.add_argument(
			    '''--scalable_attention''',
			    action='''store_true''',
			    help='''Whether the model uses scaled attention (umt5 model)''',
			    default=False,
			)
			a__												=						parser.parse_args()
			convert_tax_checkpoint_to_pytorch(
			    args.tax_checkpoint_path,
			    args.config_file,
			    args.pytorch_dump_path,
			    args.is_encoder_only,
			    args.scalable_attention,
			)



