


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()
A			:						List[Any]        =  logging.get_logger(__name__)






def 					UpperCamelCase   (      __magic_name__	:				Union[str, Any]       ,       __magic_name__	:				Any=False	)  ->    Tuple:
		"""simple docstring"""




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

		return new_state_dict






def 					UpperCamelCase   (      __magic_name__	:				Any       ,       __magic_name__	:				Union[str, Any]	)  ->    int:
		"""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)
						lowercase__	     =						state_dict.pop(f'''segformer.encoder.block.{i}.{j}.attention.self.kv.weight'''	)
						lowercase__	     =						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
						lowercase__	     =						kv_weight[
						    : config.hidden_sizes[i], :
						]
						lowercase__	     =						kv_bias[: config.hidden_sizes[i]]
						lowercase__	     =						kv_weight[
						    config.hidden_sizes[i] :, :
						]
						lowercase__	     =						kv_bias[
						    config.hidden_sizes[i] :
						]






def 					UpperCamelCase   (      )  ->    List[str]:
		"""simple docstring"""




		lowercase__	     =						"""http://images.cocodataset.org/val2017/000000039769.jpg"""
		lowercase__	     =						Image.open(requests.get(__magic_name__       ,       stream=__magic_name__	).raw	)

		return image






@torch.no_grad()
def 					UpperCamelCase   (      __magic_name__	:				List[Any]       ,       __magic_name__	:				Dict       ,       __magic_name__	:				Union[str, Any]	)  ->    Optional[Any]:
		"""simple docstring"""




		lowercase__	     =						SegformerConfig()
		lowercase__	     =						False

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

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

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

		# prepare image
		lowercase__	     =						prepare_img()
		lowercase__	     =						image_processor(images=__magic_name__       ,       return_tensors="""pt"""	).pixel_values

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

		# load original state dict
		if encoder_only:
				lowercase__	     =						torch.load(__magic_name__       ,       map_location=torch.device("""cpu"""	)	)
		else:
				lowercase__	     =						torch.load(__magic_name__       ,       map_location=torch.device("""cpu"""	)	)["""state_dict"""]

		# rename keys
		lowercase__	     =						rename_keys(__magic_name__       ,       encoder_only=__magic_name__	)
		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(__magic_name__       ,       __magic_name__	)

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

		# forward pass
		lowercase__	     =						model(__magic_name__	)
		lowercase__	     =						outputs.logits

		# set expected_slice based on model name
		# ADE20k checkpoints
		if model_name == "segformer.b0.512x512.ade.160k":
				lowercase__	     =						torch.tensor(
				    [
				        [[-4.6_3_1_0, -5.5_2_3_2, -6.2_3_5_6], [-5.1_9_2_1, -6.1_4_4_4, -6.5_9_9_6], [-5.4_4_2_4, -6.2_7_9_0, -6.7_5_7_4]],
				        [[-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":
				lowercase__	     =						torch.tensor(
				    [
				        [[-7.5_8_2_0, -8.7_2_3_1, -8.3_2_1_5], [-8.0_6_0_0, -1_0.3_5_2_9, -1_0.0_3_0_4], [-7.5_2_0_8, -9.4_1_0_3, -9.6_2_3_9]],
				        [[-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":
				lowercase__	     =						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":
				lowercase__	     =						torch.tensor(
				    [
				        [[-9.0_8_7_8, -1_0.2_0_8_1, -1_0.1_8_9_1], [-9.3_1_4_4, -1_0.7_9_4_1, -1_0.9_8_4_3], [-9.2_2_9_4, -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":
				lowercase__	     =						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":
				lowercase__	     =						torch.tensor(
				    [
				        [[-9.5_5_2_4, -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], [-9.5_8_4_2, -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":
				lowercase__	     =						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]],
				        [[0.5_3_7_4, 0.1_0_6_7, -0.4_7_4_2], [0.1_1_4_1, -0.2_2_5_5, -0.7_0_9_9], [-0.3_0_0_0, -0.5_9_2_4, -1.3_1_0_5]],
				    ]	)
		elif model_name == "segformer.b0.512x1024.city.160k":
				lowercase__	     =						torch.tensor(
				    [
				        [[-7.8_2_1_7, -9.8_7_6_7, -1_0.1_7_1_7], [-9.4_4_3_8, -1_0.9_0_5_8, -1_1.4_0_4_7], [-9.7_9_3_9, -1_2.3_4_9_5, -1_2.1_0_7_9]],
				        [[-7.1_5_1_4, -9.5_3_3_6, -1_0.0_8_6_0], [-9.7_7_7_6, -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]],
				        [[0.3_0_2_1, 0.0_8_0_5, -0.2_3_1_0], [-0.0_3_2_8, -0.1_6_0_5, -0.2_7_1_4], [-0.1_4_0_8, -0.5_4_7_7, -0.6_9_7_6]],
				    ]	)
		elif model_name == "segformer.b0.640x1280.city.160k":
				lowercase__	     =						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":
				lowercase__	     =						torch.tensor(
				    [
				        [[-9.4_9_5_9, -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]],
				        [[-9.8_9_0_5, -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]],
				        [[0.2_2_1_3, 0.0_1_9_2, -0.2_4_6_6], [-0.1_7_3_1, -0.4_2_1_3, -0.4_8_7_4], [-0.3_1_2_6, -0.6_5_4_1, -1.1_3_8_9]],
				    ]	)
		elif model_name == "segformer.b1.1024x1024.city.160k":
				lowercase__	     =						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]],
				        [[-3.6_4_5_6, -3.0_2_0_9, -1.4_2_0_3], [-3.0_7_9_7, -3.1_9_5_9, -2.0_0_0_0], [-1.8_7_5_7, -1.9_2_1_7, -1.6_9_9_7]],
				    ]	)
		elif model_name == "segformer.b2.1024x1024.city.160k":
				lowercase__	     =						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]],
				        [[-4.5_1_7_8, -5.5_0_3_7, -6.5_1_0_9], [-5.0_8_8_4, -7.2_1_7_4, -8.0_3_3_4], [-4.4_1_5_6, -5.8_1_1_7, -7.2_9_7_0]],
				    ]	)
		elif model_name == "segformer.b3.1024x1024.city.160k":
				lowercase__	     =						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]],
				        [[-4.7_3_4_9, -4.9_5_8_8, -5.0_9_6_6], [-4.3_2_1_0, -6.9_3_2_5, -7.2_5_9_1], [-3.4_3_1_2, -4.7_4_8_4, -7.1_9_1_7]],
				    ]	)
		elif model_name == "segformer.b4.1024x1024.city.160k":
				lowercase__	     =						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]],
				        [[1.0_4_9_1, 0.8_2_8_9, 1.0_3_1_0], [1.1_0_4_4, 0.5_2_1_9, 0.8_0_5_5], [1.0_8_9_9, 0.6_9_2_6, 0.5_5_9_0]],
				    ]	)
		elif model_name == "segformer.b5.1024x1024.city.160k":
				lowercase__	     =						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]],
				        [[-1.7_9_9_0, -2.0_9_5_1, -1.7_7_8_4], [-2.6_3_9_7, -3.8_2_4_5, -3.9_6_8_6], [-1.5_2_6_4, -2.8_1_2_6, -2.9_3_1_6]],
				    ]	)
		else:
				lowercase__	     =						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]       ,       __magic_name__       ,       atol=1E-2	)

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


if __name__ == "__main__":
	A			:						List[str]        =  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.'
	)
	A			:						int        =  parser.parse_args()
	convert_segformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)





'''simple docstring'''







from __future__ import annotations

import unittest

from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
       import tensorflow as tf

       from transformers.models.xglm.modeling_tf_xglm import (
           TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
           TFXGLMForCausalLM,
           TFXGLMModel,
       )
@require_tf
class 						UpperCamelCase__       :



     """simple docstring"""






     __magic_name__           =     XGLMConfig
     __magic_name__           =     {}
     __magic_name__           =     "gelu"







     def __init__(			self   ,						snake_case__   ,						snake_case__=14   ,						snake_case__=7   ,						snake_case__=True   ,						snake_case__=True   ,						snake_case__=True   ,						snake_case__=99   ,						snake_case__=32   ,						snake_case__=2   ,						snake_case__=4   ,						snake_case__=37   ,						snake_case__="gelu"   ,						snake_case__=0.1   ,						snake_case__=0.1   ,						snake_case__=512   ,						snake_case__=0.02   ,						):

            '''simple docstring'''

            _lowerCAmelCase     :				List[Any]       =							parent
            _lowerCAmelCase     :				Tuple       =							batch_size
            _lowerCAmelCase     :				Optional[Any]       =							seq_length
            _lowerCAmelCase     :				Dict       =							is_training
            _lowerCAmelCase     :				str       =							use_input_mask
            _lowerCAmelCase     :				Tuple       =							use_labels
            _lowerCAmelCase     :				Union[str, Any]       =							vocab_size
            _lowerCAmelCase     :				Any       =							d_model
            _lowerCAmelCase     :				Dict       =							num_hidden_layers
            _lowerCAmelCase     :				Optional[Any]       =							num_attention_heads
            _lowerCAmelCase     :				Tuple       =							ffn_dim
            _lowerCAmelCase     :				Tuple       =							activation_function
            _lowerCAmelCase     :				int       =							activation_dropout
            _lowerCAmelCase     :				Optional[Any]       =							attention_dropout
            _lowerCAmelCase     :				Union[str, Any]       =							max_position_embeddings
            _lowerCAmelCase     :				List[Any]       =							initializer_range
            _lowerCAmelCase     :				List[str]       =							None
            _lowerCAmelCase     :				Any       =							0
            _lowerCAmelCase     :				Any       =							2
            _lowerCAmelCase     :				Optional[Any]       =							1







     def a						(			self ):

            '''simple docstring'''

            return XGLMConfig.from_pretrained('facebook/xglm-564M' )







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Optional[Any]       =							tf.clip_by_value(
                ids_tensor([self.batch_size, self.seq_length]   ,						self.vocab_size )   ,						clip_value_min=0   ,						clip_value_max=3 )

            _lowerCAmelCase     :				str       =							None
            if self.use_input_mask:
                   _lowerCAmelCase     :				int       =							random_attention_mask([self.batch_size, self.seq_length] )

            _lowerCAmelCase     :				str       =							self.get_config()

            _lowerCAmelCase     :				Union[str, Any]       =							floats_tensor([self.num_hidden_layers, self.num_attention_heads]   ,						2 )

            return (
                config,
                input_ids,
                input_mask,
                head_mask,
            )







     def a						(			self ):

            '''simple docstring'''

            return XGLMConfig(
                vocab_size=self.vocab_size   ,						d_model=self.hidden_size   ,						num_layers=self.num_hidden_layers   ,						attention_heads=self.num_attention_heads   ,						ffn_dim=self.ffn_dim   ,						activation_function=self.activation_function   ,						activation_dropout=self.activation_dropout   ,						attention_dropout=self.attention_dropout   ,						max_position_embeddings=self.max_position_embeddings   ,						initializer_range=self.initializer_range   ,						use_cache=snake_case__   ,						bos_token_id=self.bos_token_id   ,						eos_token_id=self.eos_token_id   ,						pad_token_id=self.pad_token_id   ,						return_dict=snake_case__   ,						)







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				int       =							self.prepare_config_and_inputs()

            (
                (
                _lowerCAmelCase
            )      ,  (
                _lowerCAmelCase
            )      ,  (
                _lowerCAmelCase
            )      ,  (
                _lowerCAmelCase
            )      ,  
            )     :				Dict       =							config_and_inputs

            _lowerCAmelCase     :				Optional[int]       =							{
                'input_ids': input_ids,
                'head_mask': head_mask,
            }

            return config, inputs_dict
@require_tf
class 						UpperCamelCase__       (			SCREAMING_SNAKE_CASE_     ,       SCREAMING_SNAKE_CASE_     ,       unittest.TestCase						):



     """simple docstring"""






     __magic_name__           =     (TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
     __magic_name__           =     (TFXGLMForCausalLM,) if is_tf_available() else ()
     __magic_name__           =     (
         {"feature-extraction": TFXGLMModel, "text-generation": TFXGLMForCausalLM} if is_tf_available() else {}
     )
     __magic_name__           =     False
     __magic_name__           =     False
     __magic_name__           =     False







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Dict       =							TFXGLMModelTester(self )
            _lowerCAmelCase     :				Dict       =							ConfigTester(self   ,						config_class=snake_case__   ,						n_embd=37 )







     def a						(			self ):

            '''simple docstring'''

            self.config_tester.run_common_tests()







     @slow
     def a						(			self ):

            '''simple docstring'''

            for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                   _lowerCAmelCase     :				Optional[Any]       =							TFXGLMModel.from_pretrained(snake_case__ )
                   self.assertIsNotNone(snake_case__ )







     @unittest.skip(reason='Currently, model embeddings are going to undergo a major refactor.' )
     def a						(			self ):

            '''simple docstring'''

            super().test_resize_token_embeddings()
@require_tf
class 						UpperCamelCase__       (			unittest.TestCase						):



     """simple docstring"""






     @slow
     def a						(			self   ,						snake_case__=True ):

            '''simple docstring'''

            _lowerCAmelCase     :				str       =							TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M' )
            _lowerCAmelCase     :				str       =							tf.convert_to_tensor([[2, 268, 9865]]   ,						dtype=tf.intaa )  # The dog
            # </s> The dog is a very friendly dog. He is very affectionate and loves to play with other
            # fmt: off
            _lowerCAmelCase     :				str       =							[2, 268, 9865, 67, 11, 1988, 5_7252, 9865, 5, 984, 67, 1988, 21_3838, 1658, 53, 7_0446, 33, 6657, 278, 1581]
            # fmt: on
            _lowerCAmelCase     :				Optional[int]       =							model.generate(snake_case__   ,						do_sample=snake_case__   ,						num_beams=1 )
            if verify_outputs:
                   self.assertListEqual(output_ids[0].numpy().tolist()   ,						snake_case__ )







     @slow
     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				int       =							XGLMTokenizer.from_pretrained('facebook/xglm-564M' )
            _lowerCAmelCase     :				int       =							TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M' )

            tf.random.set_seed(0 )
            _lowerCAmelCase     :				Optional[int]       =							tokenizer('Today is a nice day and'   ,						return_tensors='tf' )
            _lowerCAmelCase     :				Dict       =							tokenized.input_ids
            # forces the generation to happen on CPU, to avoid GPU-related quirks (and assure same output regardless of the available devices)
            with tf.device(':/CPU:0' ):
                   _lowerCAmelCase     :				Dict       =							model.generate(snake_case__   ,						do_sample=snake_case__   ,						seed=[7, 0] )
            _lowerCAmelCase     :				List[Any]       =							tokenizer.decode(output_ids[0]   ,						skip_special_tokens=snake_case__ )

            _lowerCAmelCase     :				Tuple       =							(
                'Today is a nice day and warm evening here over Southern Alberta!! Today when they closed schools due'
            )
            self.assertEqual(snake_case__   ,						snake_case__ )







     @slow
     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Any       =							TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M' )
            _lowerCAmelCase     :				int       =							XGLMTokenizer.from_pretrained('facebook/xglm-564M' )

            _lowerCAmelCase     :				Tuple       =							'left'

            # use different length sentences to test batching
            _lowerCAmelCase     :				Optional[int]       =							[
                'This is an extremelly long sentence that only exists to test the ability of the model to cope with '
                'left-padding, such as in batched generation. The output for the sequence below should be the same '
                'regardless of whether left padding is applied or not. When',
                'Hello, my dog is a little',
            ]

            _lowerCAmelCase     :				Tuple       =							tokenizer(snake_case__   ,						return_tensors='tf'   ,						padding=snake_case__ )
            _lowerCAmelCase     :				Any       =							inputs['input_ids']

            _lowerCAmelCase     :				Any       =							model.generate(input_ids=snake_case__   ,						attention_mask=inputs['attention_mask']   ,						max_new_tokens=12 )

            _lowerCAmelCase     :				Dict       =							tokenizer(sentences[0]   ,						return_tensors='tf' ).input_ids
            _lowerCAmelCase     :				Union[str, Any]       =							model.generate(input_ids=snake_case__   ,						max_new_tokens=12 )

            _lowerCAmelCase     :				int       =							tokenizer(sentences[1]   ,						return_tensors='tf' ).input_ids
            _lowerCAmelCase     :				Optional[Any]       =							model.generate(input_ids=snake_case__   ,						max_new_tokens=12 )

            _lowerCAmelCase     :				Optional[Any]       =							tokenizer.batch_decode(snake_case__   ,						skip_special_tokens=snake_case__ )
            _lowerCAmelCase     :				Any       =							tokenizer.decode(output_non_padded[0]   ,						skip_special_tokens=snake_case__ )
            _lowerCAmelCase     :				Optional[Any]       =							tokenizer.decode(output_padded[0]   ,						skip_special_tokens=snake_case__ )

            _lowerCAmelCase     :				List[Any]       =							[
                'This is an extremelly long sentence that only exists to test the ability of the model to cope with '
                'left-padding, such as in batched generation. The output for the sequence below should be the same '
                'regardless of whether left padding is applied or not. When left padding is applied, the sequence will be '
                'a single',
                'Hello, my dog is a little bit of a shy one, but he is very friendly',
            ]
            self.assertListEqual(snake_case__   ,						snake_case__ )
            self.assertListEqual(snake_case__   ,						[non_padded_sentence, padded_sentence] )

