



"""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()
lowercase__       =	logging.get_logger(__name__)





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

							return new_state_dict





def    __magic_name__     ( _lowerCamelCase					:			str	,							_lowerCamelCase					:			Optional[int]       ):






							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		:		Any						=							state_dict.pop(F'''segformer.encoder.block.{i}.{j}.attention.self.kv.weight'''       )
																					__a		:		Dict						=							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		:		Optional[int]						=							kv_weight[
																					    : config.hidden_sizes[i], :
																					]
																					__a		:		List[Any]						=							kv_bias[: config.hidden_sizes[i]]
																					__a		:		Tuple						=							kv_weight[
																					    config.hidden_sizes[i] :, :
																					]
																					__a		:		Dict						=							kv_bias[
																					    config.hidden_sizes[i] :
																					]





def    __magic_name__     ( ):
							__a		:		Optional[Any]						=							"""http://images.cocodataset.org/val2017/000000039769.jpg"""
							__a		:		Optional[int]						=							Image.open(requests.get(snake_case__	,							stream=snake_case__       ).raw       )

							return image





@torch.no_grad()
def    __magic_name__     ( _lowerCamelCase					:			Union[str, Any]	,							_lowerCamelCase					:			Tuple	,							_lowerCamelCase					:			Dict       ):
							__a		:		Any						=							SegformerConfig()
							__a		:		Optional[Any]						=							False

							# set attributes based on model_name
							__a		:		Optional[int]						=							"""huggingface/label-files"""
							if "segformer" in model_name:
														__a		:		str						=							model_name[len("""segformer."""       ) : len("""segformer."""       ) + 2]
														if "ade" in model_name:
																					__a		:		int						=							1_5_0
																					__a		:		Any						=							"""ade20k-id2label.json"""
																					__a		:		Any						=							(1, 1_5_0, 1_2_8, 1_2_8)
														elif "city" in model_name:
																					__a		:		Optional[int]						=							1_9
																					__a		:		Tuple						=							"""cityscapes-id2label.json"""
																					__a		:		Optional[Any]						=							(1, 1_9, 1_2_8, 1_2_8)
														else:
																					raise ValueError(F'''Model {model_name} not supported'''       )
							elif "mit" in model_name:
														__a		:		Optional[int]						=							True
														__a		:		Optional[int]						=							model_name[4:6]
														__a		:		List[str]						=							1_0_0_0
														__a		:		Tuple						=							"""imagenet-1k-id2label.json"""
														__a		:		Dict						=							(1, 1_0_0_0)
							else:
														raise ValueError(F'''Model {model_name} not supported'''       )

							# set config attributes
							__a		:		Tuple						=							json.load(open(hf_hub_download(snake_case__	,							snake_case__	,							repo_type="""dataset"""       )	,							"""r"""       )       )
							__a		:		str						=							{int(snake_case__       ): v for k, v in idalabel.items()}
							__a		:		Union[str, Any]						=							idalabel
							__a		:		Union[str, Any]						=							{v: k for k, v in idalabel.items()}
							if size == "b0":
														pass
							elif size == "b1":
														__a		:		Dict						=							[6_4, 1_2_8, 3_2_0, 5_1_2]
														__a		:		Optional[int]						=							2_5_6
							elif size == "b2":
														__a		:		List[str]						=							[6_4, 1_2_8, 3_2_0, 5_1_2]
														__a		:		Any						=							7_6_8
														__a		:		List[str]						=							[3, 4, 6, 3]
							elif size == "b3":
														__a		:		Optional[int]						=							[6_4, 1_2_8, 3_2_0, 5_1_2]
														__a		:		List[Any]						=							7_6_8
														__a		:		List[str]						=							[3, 4, 1_8, 3]
							elif size == "b4":
														__a		:		Tuple						=							[6_4, 1_2_8, 3_2_0, 5_1_2]
														__a		:		Optional[int]						=							7_6_8
														__a		:		str						=							[3, 8, 2_7, 3]
							elif size == "b5":
														__a		:		Tuple						=							[6_4, 1_2_8, 3_2_0, 5_1_2]
														__a		:		Optional[Any]						=							7_6_8
														__a		:		Optional[int]						=							[3, 6, 4_0, 3]
							else:
														raise ValueError(F'''Size {size} not supported'''       )

							# load image processor (only resize + normalize)
							__a		:		int						=							SegformerImageProcessor(
							    image_scale=(5_1_2, 5_1_2)	,							keep_ratio=snake_case__	,							align=snake_case__	,							do_random_crop=snake_case__       )

							# prepare image
							__a		:		int						=							prepare_img()
							__a		:		List[str]						=							image_processor(images=snake_case__	,							return_tensors="""pt"""       ).pixel_values

							logger.info(F'''Converting model {model_name}...'''       )

							# load original state dict
							if encoder_only:
														__a		:		int						=							torch.load(snake_case__	,							map_location=torch.device("""cpu"""       )       )
							else:
														__a		:		Union[str, Any]						=							torch.load(snake_case__	,							map_location=torch.device("""cpu"""       )       )["""state_dict"""]

							# rename keys
							__a		:		Any						=							rename_keys(snake_case__	,							encoder_only=snake_case__       )
							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(snake_case__	,							snake_case__       )

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

							# forward pass
							__a		:		str						=							model(snake_case__       )
							__a		:		Optional[int]						=							outputs.logits

							# set expected_slice based on model name
							# ADE20k checkpoints
							if model_name == "segformer.b0.512x512.ade.160k":
														__a		:		List[str]						=							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]],
														        [[-1_2.1_3_9_1, -1_3.3_1_2_2, -1_3.9_5_5_4], [-1_2.8_7_3_2, -1_3.9_3_5_2, -1_4.3_5_6_3], [-1_2.9_4_3_8, -1_3.8_2_2_6, -1_4.2_5_1_3]],
														        [[-1_2.5_1_3_4, -1_3.4_6_8_6, -1_4.4_9_1_5], [-1_2.8_6_6_9, -1_4.4_3_4_3, -1_4.7_7_5_8], [-1_3.2_5_2_3, -1_4.5_8_1_9, -1_5.0_6_9_4]],
														    ]       )
							elif model_name == "segformer.b1.512x512.ade.160k":
														__a		:		List[Any]						=							torch.tensor(
														    [
														        [[-7.58_20, -8.72_31, -8.32_15], [-8.06_00, -1_0.3_5_2_9, -1_0.0_3_0_4], [-7.52_08, -9.41_03, -9.62_39]],
														        [[-1_2.6_9_1_8, -1_3.8_9_9_4, -1_3.7_1_3_7], [-1_3.3_1_9_6, -1_5.7_5_2_3, -1_5.4_7_8_9], [-1_2.9_3_4_3, -1_4.8_7_5_7, -1_4.9_6_8_9]],
														        [[-1_1.1_9_1_1, -1_1.9_4_2_1, -1_1.3_2_4_3], [-1_1.3_3_4_2, -1_3.6_8_3_9, -1_3.3_5_8_1], [-1_0.3_9_0_9, -1_2.1_8_3_2, -1_2.4_8_5_8]],
														    ]       )
							elif model_name == "segformer.b2.512x512.ade.160k":
														__a		:		int						=							torch.tensor(
														    [
														        [[-1_1.8_1_7_3, -1_4.3_8_5_0, -1_6.3_1_2_8], [-1_4.5_6_4_8, -1_6.5_8_0_4, -1_8.6_5_6_8], [-1_4.7_2_2_3, -1_5.7_3_8_7, -1_8.4_2_1_8]],
														        [[-1_5.7_2_9_0, -1_7.9_1_7_1, -1_9.4_4_2_3], [-1_8.3_1_0_5, -1_9.9_4_4_8, -2_1.4_6_6_1], [-1_7.9_2_9_6, -1_8.6_4_9_7, -2_0.7_9_1_0]],
														        [[-1_5.0_7_8_3, -1_7.0_3_3_6, -1_8.2_7_8_9], [-1_6.8_7_7_1, -1_8.6_8_7_0, -2_0.1_6_1_2], [-1_6.2_4_5_4, -1_7.1_4_2_6, -1_9.5_0_5_5]],
														    ]       )
							elif model_name == "segformer.b3.512x512.ade.160k":
														__a		:		Tuple						=							torch.tensor(
														    [
														        [[-9.08_78, -1_0.2_0_8_1, -1_0.1_8_9_1], [-9.31_44, -1_0.7_9_4_1, -1_0.9_8_4_3], [-9.22_94, -1_0.3_8_5_5, -1_0.5_7_0_4]],
														        [[-1_2.2_3_1_6, -1_3.9_0_6_8, -1_3.6_1_0_2], [-1_2.9_1_6_1, -1_4.3_7_0_2, -1_4.3_2_3_5], [-1_2.5_2_3_3, -1_3.7_1_7_4, -1_3.7_9_3_2]],
														        [[-1_4.6_2_7_5, -1_5.2_4_9_0, -1_4.9_7_2_7], [-1_4.3_4_0_0, -1_5.9_6_8_7, -1_6.2_8_2_7], [-1_4.1_4_8_4, -1_5.4_0_3_3, -1_5.8_9_3_7]],
														    ]       )
							elif model_name == "segformer.b4.512x512.ade.160k":
														__a		:		Any						=							torch.tensor(
														    [
														        [[-1_2.3_1_4_4, -1_3.2_4_4_7, -1_4.0_8_0_2], [-1_3.3_6_1_4, -1_4.5_8_1_6, -1_5.6_1_1_7], [-1_3.3_3_4_0, -1_4.4_4_3_3, -1_6.2_2_1_9]],
														        [[-1_9.2_7_8_1, -2_0.4_1_2_8, -2_0.7_5_0_6], [-2_0.6_1_5_3, -2_1.6_5_6_6, -2_2.0_9_9_8], [-1_9.9_8_0_0, -2_1.0_4_3_0, -2_2.1_4_9_4]],
														        [[-1_8.8_7_3_9, -1_9.7_8_0_4, -2_1.1_8_3_4], [-2_0.1_2_3_3, -2_1.6_7_6_5, -2_3.2_9_4_4], [-2_0.0_3_1_5, -2_1.2_6_4_1, -2_3.6_9_4_4]],
														    ]       )
							elif model_name == "segformer.b5.640x640.ade.160k":
														__a		:		Optional[Any]						=							torch.tensor(
														    [
														        [[-9.55_24, -1_2.0_8_3_5, -1_1.7_3_4_8], [-1_0.5_2_2_9, -1_3.6_4_4_6, -1_4.5_6_6_2], [-9.58_42, -1_2.8_8_5_1, -1_3.9_4_1_4]],
														        [[-1_5.3_4_3_2, -1_7.5_3_2_3, -1_7.0_8_1_8], [-1_6.3_3_3_0, -1_8.9_2_5_5, -1_9.2_1_0_1], [-1_5.1_3_4_0, -1_7.7_8_4_8, -1_8.3_9_7_1]],
														        [[-1_2.6_0_7_2, -1_4.9_4_8_6, -1_4.6_6_3_1], [-1_3.7_6_2_9, -1_7.0_9_0_7, -1_7.7_7_4_5], [-1_2.7_8_9_9, -1_6.1_6_9_5, -1_7.1_6_7_1]],
														    ]       )
							# Cityscapes checkpoints
							elif model_name == "segformer.b0.1024x1024.city.160k":
														__a		:		Dict						=							torch.tensor(
														    [
														        [[-1_1.9_2_9_5, -1_3.4_0_5_7, -1_4.8_1_0_6], [-1_3.3_4_3_1, -1_4.8_1_7_9, -1_5.3_7_8_1], [-1_4.2_8_3_6, -1_5.5_9_4_2, -1_6.1_5_8_8]],
														        [[-1_1.4_9_0_6, -1_2.8_0_6_7, -1_3.6_5_6_4], [-1_3.1_1_8_9, -1_4.0_5_0_0, -1_4.1_5_4_3], [-1_3.8_7_4_8, -1_4.5_1_3_6, -1_4.8_7_8_9]],
														        [[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		:		Dict						=							torch.tensor(
														    [
														        [[-7.82_17, -9.87_67, -1_0.1_7_1_7], [-9.44_38, -1_0.9_0_5_8, -1_1.4_0_4_7], [-9.79_39, -1_2.3_4_9_5, -1_2.1_0_7_9]],
														        [[-7.15_14, -9.53_36, -1_0.0_8_6_0], [-9.77_76, -1_1.6_8_2_2, -1_1.8_4_3_9], [-1_0.1_4_1_1, -1_2.7_6_5_5, -1_2.8_9_7_2]],
														        [[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		:		List[Any]						=							torch.tensor(
														    [
														        [
														            [-1.1372E01, -1.2787E01, -1.3477E01],
														            [-1.2536E01, -1.4194E01, -1.4409E01],
														            [-1.3217E01, -1.4888E01, -1.5327E01],
														        ],
														        [
														            [-1.4791E01, -1.7122E01, -1.8277E01],
														            [-1.7163E01, -1.9192E01, -1.9533E01],
														            [-1.7897E01, -1.9991E01, -2.0315E01],
														        ],
														        [
														            [7.6723E-01, 4.1921E-01, -7.7878E-02],
														            [4.7772E-01, 9.5557E-03, -2.8082E-01],
														            [3.6032E-01, -2.4826E-01, -5.1168E-01],
														        ],
														    ]       )
							elif model_name == "segformer.b0.768x768.city.160k":
														__a		:		Union[str, Any]						=							torch.tensor(
														    [
														        [[-9.49_59, -1_1.3_0_8_7, -1_1.7_4_7_9], [-1_1.0_0_2_5, -1_2.6_5_4_0, -1_2.3_3_1_9], [-1_1.4_0_6_4, -1_3.0_4_8_7, -1_2.9_9_0_5]],
														        [[-9.89_05, -1_1.3_0_8_4, -1_2.0_8_5_4], [-1_1.1_7_2_6, -1_2.7_6_9_8, -1_2.9_5_8_3], [-1_1.5_9_8_5, -1_3.3_2_7_8, -1_4.1_7_7_4]],
														        [[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		:		Dict						=							torch.tensor(
														    [
														        [[-1_3.5_7_4_8, -1_3.9_1_1_1, -1_2.6_5_0_0], [-1_4.3_5_0_0, -1_5.3_6_8_3, -1_4.2_3_2_8], [-1_4.7_5_3_2, -1_6.0_4_2_4, -1_5.6_0_8_7]],
														        [[-1_7.1_6_5_1, -1_5.8_7_2_5, -1_2.9_6_5_3], [-1_7.2_5_8_0, -1_7.3_7_1_8, -1_4.8_2_2_3], [-1_6.6_0_5_8, -1_6.8_7_8_3, -1_6.7_4_5_2]],
														        [[-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		:		str						=							torch.tensor(
														    [
														        [[-1_6.0_9_7_6, -1_6.4_8_5_6, -1_7.3_9_6_2], [-1_6.6_2_3_4, -1_9.0_3_4_2, -1_9.7_6_8_5], [-1_6.0_9_0_0, -1_8.0_6_6_1, -1_9.1_1_8_0]],
														        [[-1_8.4_7_5_0, -1_8.8_4_8_8, -1_9.5_0_7_4], [-1_9.4_0_3_0, -2_2.1_5_7_0, -2_2.5_9_7_7], [-1_9.1_1_9_1, -2_0.8_4_8_6, -2_2.3_7_8_3]],
														        [[-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		:		Union[str, Any]						=							torch.tensor(
														    [
														        [[-1_4.2_0_8_1, -1_4.4_7_3_2, -1_4.1_9_7_7], [-1_4.5_8_6_7, -1_6.4_4_2_3, -1_6.6_3_5_6], [-1_3.4_4_4_1, -1_4.9_6_8_5, -1_6.8_6_9_6]],
														        [[-1_4.4_5_7_6, -1_4.7_0_7_3, -1_5.0_4_5_1], [-1_5.0_8_1_6, -1_7.6_2_3_7, -1_7.9_8_7_3], [-1_4.4_2_1_3, -1_6.0_1_9_9, -1_8.5_9_9_2]],
														        [[-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		:		List[Any]						=							torch.tensor(
														    [
														        [[-1_1.7_7_3_7, -1_1.9_5_2_6, -1_1.3_2_7_3], [-1_3.6_6_9_2, -1_4.4_5_7_4, -1_3.8_8_7_8], [-1_3.8_9_3_7, -1_4.6_9_2_4, -1_5.9_3_4_5]],
														        [[-1_4.6_7_0_6, -1_4.5_3_3_0, -1_4.1_3_0_6], [-1_6.1_5_0_2, -1_6.8_1_8_0, -1_6.4_2_6_9], [-1_6.8_3_3_8, -1_7.8_9_3_9, -2_0.1_7_4_6]],
														        [[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		:		Optional[Any]						=							torch.tensor(
														    [
														        [[-1_2.5_6_4_1, -1_3.4_7_7_7, -1_3.0_6_8_4], [-1_3.9_5_8_7, -1_5.8_9_8_3, -1_6.6_5_5_7], [-1_3.3_1_0_9, -1_5.7_3_5_0, -1_6.3_1_4_1]],
														        [[-1_4.7_0_7_4, -1_5.4_3_5_2, -1_4.5_9_4_4], [-1_6.6_3_5_3, -1_8.1_6_6_3, -1_8.6_1_2_0], [-1_5.1_7_0_2, -1_8.0_3_2_9, -1_8.1_5_4_7]],
														        [[-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		:		str						=							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]	,							snake_case__	,							atol=1E-2       )

							# finally, save model and image processor
							logger.info(F'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'''       )
							Path(snake_case__       ).mkdir(exist_ok=snake_case__       )
							model.save_pretrained(snake_case__       )
							image_processor.save_pretrained(snake_case__       )


if __name__ == "__main__":
	lowercase__       =	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."
	)
	lowercase__       =	parser.parse_args()
	convert_segformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)









import unittest

from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


lowerCamelCase__       		=  get_tests_dir("""fixtures/test_sentencepiece_no_bos.model""")




@require_sentencepiece
@require_tokenizers
class       snake_case__ (				lowercase_						,     unittest.TestCase):

       '''simple docstring'''
       lowerCamelCase				:					List[str]								=			PegasusTokenizer
       lowerCamelCase				:					Optional[int]								=			PegasusTokenizerFast
       lowerCamelCase				:					int								=			True
       lowerCamelCase				:					List[Any]								=			True



       def        __lowercase    (				self     )			->   Tuple:






            '''simple docstring'''





            super().setUp()

            # We have a SentencePiece fixture for testing
            __snake_case      :Optional[int] 							=    PegasusTokenizer(a__     )
            tokenizer.save_pretrained(self.tmpdirname     )



       @cached_property
       def        __lowercase    (				self     )			->   List[Any]:






            '''simple docstring'''





            return PegasusTokenizer.from_pretrained("""google/pegasus-large"""     )



       def        __lowercase    (				self      ,    **a__     )			->   PegasusTokenizer:






            '''simple docstring'''





            return PegasusTokenizer.from_pretrained(self.tmpdirname      ,    **a__     )



       def        __lowercase    (				self      ,    a__     )			->   Optional[int]:






            '''simple docstring'''





            return ("This is a test", "This is a test")



       def        __lowercase    (				self     )			->   Dict:






            '''simple docstring'''





            __snake_case      :Dict 							=    """</s>"""
            __snake_case      :Union[str, Any] 							=    1

            self.assertEqual(self.get_tokenizer()._convert_token_to_id(a__     )      ,    a__     )
            self.assertEqual(self.get_tokenizer()._convert_id_to_token(a__     )      ,    a__     )



       def        __lowercase    (				self     )			->   List[str]:






            '''simple docstring'''





            __snake_case      :Any 							=    list(self.get_tokenizer().get_vocab().keys()     )

            self.assertEqual(vocab_keys[0]      ,    """<pad>"""     )
            self.assertEqual(vocab_keys[1]      ,    """</s>"""     )
            self.assertEqual(vocab_keys[-1]      ,    """v"""     )
            self.assertEqual(len(a__     )      ,    11_03     )



       def        __lowercase    (				self     )			->   Union[str, Any]:






            '''simple docstring'''





            self.assertEqual(self.get_tokenizer().vocab_size      ,    11_03     )



       def        __lowercase    (				self     )			->   List[Any]:






            '''simple docstring'''





            __snake_case      :int 							=    self.rust_tokenizer_class.from_pretrained(self.tmpdirname     )
            __snake_case      :Tuple 							=    self.tokenizer_class.from_pretrained(self.tmpdirname     )
            __snake_case      :List[Any] 							=    (
                """Let's see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important"""
                """ </s> <pad> <pad> <pad>"""
            )
            __snake_case      :Tuple 							=    rust_tokenizer([raw_input_str]      ,    return_tensors=a__      ,    add_special_tokens=a__     ).input_ids[0]
            __snake_case      :Union[str, Any] 							=    py_tokenizer([raw_input_str]      ,    return_tensors=a__      ,    add_special_tokens=a__     ).input_ids[0]
            self.assertListEqual(a__      ,    a__     )



       def        __lowercase    (				self     )			->   Tuple:






            '''simple docstring'''





            __snake_case      :List[Any] 							=    self._large_tokenizer
            # <mask_1> masks whole sentence while <mask_2> masks single word
            __snake_case      :Any 							=    """<mask_1> To ensure a <mask_2> flow of bank resolutions."""
            __snake_case      :List[Any] 							=    [2, 4_13, 6_15, 1_14, 3, 19_71, 1_13, 16_79, 1_07_10, 1_07, 1]
            __snake_case      :Tuple 							=    tokenizer([raw_input_str]      ,    return_tensors=a__     ).input_ids[0]
            self.assertListEqual(a__      ,    a__     )



       def        __lowercase    (				self     )			->   List[Any]:






            '''simple docstring'''





            __snake_case      :Optional[int] 							=    self._large_tokenizer
            # The tracebacks for the following asserts are **better** without messages or self.assertEqual
            assert tokenizer.vocab_size == 9_61_03
            assert tokenizer.pad_token_id == 0
            assert tokenizer.eos_token_id == 1
            assert tokenizer.offset == 1_03
            assert tokenizer.unk_token_id == tokenizer.offset + 2 == 1_05
            assert tokenizer.unk_token == "<unk>"
            assert tokenizer.model_max_length == 10_24
            __snake_case      :Any 							=    """To ensure a smooth flow of bank resolutions."""
            __snake_case      :int 							=    [4_13, 6_15, 1_14, 22_91, 19_71, 1_13, 16_79, 1_07_10, 1_07, 1]
            __snake_case      :Any 							=    tokenizer([raw_input_str]      ,    return_tensors=a__     ).input_ids[0]
            self.assertListEqual(a__      ,    a__     )
            assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]     ) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]



       @require_torch
       def        __lowercase    (				self     )			->   Dict:






            '''simple docstring'''





            __snake_case      :str 							=    ["""This is going to be way too long.""" * 1_50, """short example"""]
            __snake_case      :int 							=    ["""not super long but more than 5 tokens""", """tiny"""]
            __snake_case      :str 							=    self._large_tokenizer(a__      ,    padding=a__      ,    truncation=a__      ,    return_tensors="""pt"""     )
            __snake_case      :Optional[int] 							=    self._large_tokenizer(
                text_target=a__      ,    max_length=5      ,    padding=a__      ,    truncation=a__      ,    return_tensors="""pt"""     )

            assert batch.input_ids.shape == (2, 10_24)
            assert batch.attention_mask.shape == (2, 10_24)
            assert targets["input_ids"].shape == (2, 5)
            assert len(a__     ) == 2  # input_ids, attention_mask.



       @slow
       def        __lowercase    (				self     )			->   Dict:






            '''simple docstring'''





            __snake_case      :Union[str, Any] 							=    {"""input_ids""": [[3_89_79, 1_43, 1_84_85, 6_06, 1_30, 2_66_69, 8_76_86, 1_21, 5_41_89, 11_29, 1_11, 2_66_69, 8_76_86, 1_21, 91_14, 1_47_87, 1_21, 1_32_49, 1_58, 5_92, 9_56, 1_21, 1_46_21, 3_15_76, 1_43, 6_26_13, 1_08, 96_88, 9_30, 4_34_30, 1_15_62, 6_26_13, 3_04, 1_08, 1_14_43, 8_97, 1_08, 93_14, 1_74_15, 6_33_99, 1_08, 1_14_43, 76_14, 1_83_16, 1_18, 42_84, 71_48, 1_24_30, 1_43, 14_00, 2_57_03, 1_58, 1_11, 42_84, 71_48, 1_17_72, 1_43, 2_12_97, 10_64, 1_58, 1_22, 2_04, 35_06, 17_54, 11_33, 1_47_87, 15_81, 1_15, 3_32_24, 44_82, 1_11, 13_55, 1_10, 2_91_73, 3_17, 5_08_33, 1_08, 2_01_47, 9_46_65, 1_11, 7_71_98, 1_07, 1], [1_10, 6_26_13, 1_17, 6_38, 1_12, 11_33, 1_21, 2_00_98, 13_55, 7_90_50, 1_38_72, 1_35, 15_96, 5_35_41, 13_52, 1_41, 1_30_39, 55_42, 1_24, 3_02, 5_18, 1_11, 2_68, 29_56, 1_15, 1_49, 44_27, 1_07, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1_39, 12_35, 27_99, 1_82_89, 1_77_80, 2_04, 1_09, 94_74, 12_96, 1_07, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
            # fmt: on

            self.tokenizer_integration_test_util(
                expected_encoding=a__      ,    model_name="""google/bigbird-pegasus-large-arxiv"""      ,    revision="""ba85d0851d708441f91440d509690f1ab6353415"""      ,    )




@require_sentencepiece
@require_tokenizers
class       snake_case__ (				lowercase_						,     unittest.TestCase):

       '''simple docstring'''
       lowerCamelCase				:					List[Any]								=			PegasusTokenizer
       lowerCamelCase				:					List[Any]								=			PegasusTokenizerFast
       lowerCamelCase				:					List[str]								=			True
       lowerCamelCase				:					List[Any]								=			True



       def        __lowercase    (				self     )			->   Dict:






            '''simple docstring'''





            super().setUp()

            # We have a SentencePiece fixture for testing
            __snake_case      :Optional[int] 							=    PegasusTokenizer(a__      ,    offset=0      ,    mask_token_sent=a__      ,    mask_token="""[MASK]"""     )
            tokenizer.save_pretrained(self.tmpdirname     )



       @cached_property
       def        __lowercase    (				self     )			->   Union[str, Any]:






            '''simple docstring'''





            return PegasusTokenizer.from_pretrained("""google/bigbird-pegasus-large-arxiv"""     )



       def        __lowercase    (				self      ,    **a__     )			->   PegasusTokenizer:






            '''simple docstring'''





            return PegasusTokenizer.from_pretrained(self.tmpdirname      ,    **a__     )



       def        __lowercase    (				self      ,    a__     )			->   str:






            '''simple docstring'''





            return ("This is a test", "This is a test")



       def        __lowercase    (				self     )			->   Optional[Any]:






            '''simple docstring'''





            __snake_case      :Tuple 							=    self.rust_tokenizer_class.from_pretrained(self.tmpdirname     )
            __snake_case      :Optional[int] 							=    self.tokenizer_class.from_pretrained(self.tmpdirname     )
            __snake_case      :Union[str, Any] 							=    (
                """Let's see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>"""
                """ <pad> <pad> <pad>"""
            )
            __snake_case      :Dict 							=    rust_tokenizer([raw_input_str]      ,    return_tensors=a__      ,    add_special_tokens=a__     ).input_ids[0]
            __snake_case      :List[Any] 							=    py_tokenizer([raw_input_str]      ,    return_tensors=a__      ,    add_special_tokens=a__     ).input_ids[0]
            self.assertListEqual(a__      ,    a__     )



       @require_torch
       def        __lowercase    (				self     )			->   str:






            '''simple docstring'''





            __snake_case      :Any 							=    ["""This is going to be way too long.""" * 10_00, """short example"""]
            __snake_case      :str 							=    ["""not super long but more than 5 tokens""", """tiny"""]
            __snake_case      :Optional[int] 							=    self._large_tokenizer(a__      ,    padding=a__      ,    truncation=a__      ,    return_tensors="""pt"""     )
            __snake_case      :Union[str, Any] 							=    self._large_tokenizer(
                text_target=a__      ,    max_length=5      ,    padding=a__      ,    truncation=a__      ,    return_tensors="""pt"""     )

            assert batch.input_ids.shape == (2, 40_96)
            assert batch.attention_mask.shape == (2, 40_96)
            assert targets["input_ids"].shape == (2, 5)
            assert len(a__     ) == 2  # input_ids, attention_mask.



       def        __lowercase    (				self     )			->   List[str]:






            '''simple docstring'''





            __snake_case      :Tuple 							=    (
                """This is an example string that is used to test the original TF implementation against the HF"""
                """ implementation"""
            )

            __snake_case      :Dict 							=    self._large_tokenizer(a__     ).input_ids

            self.assertListEqual(
                a__      ,    [1_82, 1_17, 1_42, 5_87, 42_11, 1_20, 1_17, 2_63, 1_12, 8_04, 1_09, 8_56, 2_50_16, 31_37, 4_64, 1_09, 2_69_55, 31_37, 1]      ,    )


