



'''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()
__lowerCAmelCase												=	logging.get_logger(__name__)






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

							return new_state_dict






def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				):
							# for each of the encoder blocks:
							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)
																					_snake_case       =  state_dict.pop(f"""segformer.encoder.block.{i}.{j}.attention.self.kv.weight"""				)
																					_snake_case       =  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
																					_snake_case       =  kv_weight[
																					    : config.hidden_sizes[i], :
																					]
																					_snake_case       =  kv_bias[: config.hidden_sizes[i]]
																					_snake_case       =  kv_weight[
																					    config.hidden_sizes[i] :, :
																					]
																					_snake_case       =  kv_bias[
																					    config.hidden_sizes[i] :
																					]






def  __SCREAMING_SNAKE_CASE				(  ):
							_snake_case       =  """http://images.cocodataset.org/val2017/000000039769.jpg"""
							_snake_case       =  Image.open(requests.get(_SCREAMING_SNAKE_CASE							, stream=_SCREAMING_SNAKE_CASE				).raw				)

							return image






@torch.no_grad()
def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				):
							_snake_case       =  SegformerConfig()
							_snake_case       =  False

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

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

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

							# prepare image
							_snake_case       =  prepare_img()
							_snake_case       =  image_processor(images=_SCREAMING_SNAKE_CASE							, return_tensors="""pt"""				).pixel_values

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

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

							# rename keys
							_snake_case       =  rename_keys(_SCREAMING_SNAKE_CASE							, encoder_only=_SCREAMING_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(_SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				)

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

							# forward pass
							_snake_case       =  model(_SCREAMING_SNAKE_CASE				)
							_snake_case       =  outputs.logits

							# set expected_slice based on model name
							# ADE20k checkpoints
							if model_name == "segformer.b0.512x512.ade.160k":
														_snake_case       =  torch.tensor(
														    [
														        [[-4.6310, -5.5232, -6.2356], [-5.1921, -6.1444, -6.5996], [-5.4424, -6.2790, -6.7574]],
														        [[-12.1391, -13.3122, -13.9554], [-12.8732, -13.9352, -14.3563], [-12.9438, -13.8226, -14.2513]],
														        [[-12.5134, -13.4686, -14.4915], [-12.8669, -14.4343, -14.7758], [-13.2523, -14.5819, -15.0694]],
														    ]				)
							elif model_name == "segformer.b1.512x512.ade.160k":
														_snake_case       =  torch.tensor(
														    [
														        [[-7.5820, -8.7231, -8.3215], [-8.0600, -10.3529, -10.0304], [-7.5208, -9.4103, -9.6239]],
														        [[-12.6918, -13.8994, -13.7137], [-13.3196, -15.7523, -15.4789], [-12.9343, -14.8757, -14.9689]],
														        [[-11.1911, -11.9421, -11.3243], [-11.3342, -13.6839, -13.3581], [-10.3909, -12.1832, -12.4858]],
														    ]				)
							elif model_name == "segformer.b2.512x512.ade.160k":
														_snake_case       =  torch.tensor(
														    [
														        [[-11.8173, -14.3850, -16.3128], [-14.5648, -16.5804, -18.6568], [-14.7223, -15.7387, -18.4218]],
														        [[-15.7290, -17.9171, -19.4423], [-18.3105, -19.9448, -21.4661], [-17.9296, -18.6497, -20.7910]],
														        [[-15.0783, -17.0336, -18.2789], [-16.8771, -18.6870, -20.1612], [-16.2454, -17.1426, -19.5055]],
														    ]				)
							elif model_name == "segformer.b3.512x512.ade.160k":
														_snake_case       =  torch.tensor(
														    [
														        [[-9.0878, -10.2081, -10.1891], [-9.3144, -10.7941, -10.9843], [-9.2294, -10.3855, -10.5704]],
														        [[-12.2316, -13.9068, -13.6102], [-12.9161, -14.3702, -14.3235], [-12.5233, -13.7174, -13.7932]],
														        [[-14.6275, -15.2490, -14.9727], [-14.3400, -15.9687, -16.2827], [-14.1484, -15.4033, -15.8937]],
														    ]				)
							elif model_name == "segformer.b4.512x512.ade.160k":
														_snake_case       =  torch.tensor(
														    [
														        [[-12.3144, -13.2447, -14.0802], [-13.3614, -14.5816, -15.6117], [-13.3340, -14.4433, -16.2219]],
														        [[-19.2781, -20.4128, -20.7506], [-20.6153, -21.6566, -22.0998], [-19.9800, -21.0430, -22.1494]],
														        [[-18.8739, -19.7804, -21.1834], [-20.1233, -21.6765, -23.2944], [-20.0315, -21.2641, -23.6944]],
														    ]				)
							elif model_name == "segformer.b5.640x640.ade.160k":
														_snake_case       =  torch.tensor(
														    [
														        [[-9.5524, -12.0835, -11.7348], [-10.5229, -13.6446, -14.5662], [-9.5842, -12.8851, -13.9414]],
														        [[-15.3432, -17.5323, -17.0818], [-16.3330, -18.9255, -19.2101], [-15.1340, -17.7848, -18.3971]],
														        [[-12.6072, -14.9486, -14.6631], [-13.7629, -17.0907, -17.7745], [-12.7899, -16.1695, -17.1671]],
														    ]				)
							# Cityscapes checkpoints
							elif model_name == "segformer.b0.1024x1024.city.160k":
														_snake_case       =  torch.tensor(
														    [
														        [[-11.9295, -13.4057, -14.8106], [-13.3431, -14.8179, -15.3781], [-14.2836, -15.5942, -16.1588]],
														        [[-11.4906, -12.8067, -13.6564], [-13.1189, -14.0500, -14.1543], [-13.8748, -14.5136, -14.8789]],
														        [[0.5374, 0.1067, -0.4742], [0.1141, -0.2255, -0.7099], [-0.3000, -0.5924, -1.3105]],
														    ]				)
							elif model_name == "segformer.b0.512x1024.city.160k":
														_snake_case       =  torch.tensor(
														    [
														        [[-7.8217, -9.8767, -10.1717], [-9.4438, -10.9058, -11.4047], [-9.7939, -12.3495, -12.1079]],
														        [[-7.1514, -9.5336, -10.0860], [-9.7776, -11.6822, -11.8439], [-10.1411, -12.7655, -12.8972]],
														        [[0.3021, 0.0805, -0.2310], [-0.0328, -0.1605, -0.2714], [-0.1408, -0.5477, -0.6976]],
														    ]				)
							elif model_name == "segformer.b0.640x1280.city.160k":
														_snake_case       =  torch.tensor(
														    [
														        [
														            [-1.1_372E01, -1.2_787E01, -1.3_477E01],
														            [-1.2_536E01, -1.4_194E01, -1.4_409E01],
														            [-1.3_217E01, -1.4_888E01, -1.5_327E01],
														        ],
														        [
														            [-1.4_791E01, -1.7_122E01, -1.8_277E01],
														            [-1.7_163E01, -1.9_192E01, -1.9_533E01],
														            [-1.7_897E01, -1.9_991E01, -2.0_315E01],
														        ],
														        [
														            [7.6_723E-01, 4.1_921E-01, -7.7_878E-02],
														            [4.7_772E-01, 9.5_557E-03, -2.8_082E-01],
														            [3.6_032E-01, -2.4_826E-01, -5.1_168E-01],
														        ],
														    ]				)
							elif model_name == "segformer.b0.768x768.city.160k":
														_snake_case       =  torch.tensor(
														    [
														        [[-9.4959, -11.3087, -11.7479], [-11.0025, -12.6540, -12.3319], [-11.4064, -13.0487, -12.9905]],
														        [[-9.8905, -11.3084, -12.0854], [-11.1726, -12.7698, -12.9583], [-11.5985, -13.3278, -14.1774]],
														        [[0.2213, 0.0192, -0.2466], [-0.1731, -0.4213, -0.4874], [-0.3126, -0.6541, -1.1389]],
														    ]				)
							elif model_name == "segformer.b1.1024x1024.city.160k":
														_snake_case       =  torch.tensor(
														    [
														        [[-13.5748, -13.9111, -12.6500], [-14.3500, -15.3683, -14.2328], [-14.7532, -16.0424, -15.6087]],
														        [[-17.1651, -15.8725, -12.9653], [-17.2580, -17.3718, -14.8223], [-16.6058, -16.8783, -16.7452]],
														        [[-3.6456, -3.0209, -1.4203], [-3.0797, -3.1959, -2.0000], [-1.8757, -1.9217, -1.6997]],
														    ]				)
							elif model_name == "segformer.b2.1024x1024.city.160k":
														_snake_case       =  torch.tensor(
														    [
														        [[-16.0976, -16.4856, -17.3962], [-16.6234, -19.0342, -19.7685], [-16.0900, -18.0661, -19.1180]],
														        [[-18.4750, -18.8488, -19.5074], [-19.4030, -22.1570, -22.5977], [-19.1191, -20.8486, -22.3783]],
														        [[-4.5178, -5.5037, -6.5109], [-5.0884, -7.2174, -8.0334], [-4.4156, -5.8117, -7.2970]],
														    ]				)
							elif model_name == "segformer.b3.1024x1024.city.160k":
														_snake_case       =  torch.tensor(
														    [
														        [[-14.2081, -14.4732, -14.1977], [-14.5867, -16.4423, -16.6356], [-13.4441, -14.9685, -16.8696]],
														        [[-14.4576, -14.7073, -15.0451], [-15.0816, -17.6237, -17.9873], [-14.4213, -16.0199, -18.5992]],
														        [[-4.7349, -4.9588, -5.0966], [-4.3210, -6.9325, -7.2591], [-3.4312, -4.7484, -7.1917]],
														    ]				)
							elif model_name == "segformer.b4.1024x1024.city.160k":
														_snake_case       =  torch.tensor(
														    [
														        [[-11.7737, -11.9526, -11.3273], [-13.6692, -14.4574, -13.8878], [-13.8937, -14.6924, -15.9345]],
														        [[-14.6706, -14.5330, -14.1306], [-16.1502, -16.8180, -16.4269], [-16.8338, -17.8939, -20.1746]],
														        [[1.0491, 0.8289, 1.0310], [1.1044, 0.5219, 0.8055], [1.0899, 0.6926, 0.5590]],
														    ]				)
							elif model_name == "segformer.b5.1024x1024.city.160k":
														_snake_case       =  torch.tensor(
														    [
														        [[-12.5641, -13.4777, -13.0684], [-13.9587, -15.8983, -16.6557], [-13.3109, -15.7350, -16.3141]],
														        [[-14.7074, -15.4352, -14.5944], [-16.6353, -18.1663, -18.6120], [-15.1702, -18.0329, -18.1547]],
														        [[-1.7990, -2.0951, -1.7784], [-2.6397, -3.8245, -3.9686], [-1.5264, -2.8126, -2.9316]],
														    ]				)
							else:
														_snake_case       =  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]							, _SCREAMING_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(_SCREAMING_SNAKE_CASE				).mkdir(exist_ok=_SCREAMING_SNAKE_CASE				)
							model.save_pretrained(_SCREAMING_SNAKE_CASE				)
							image_processor.save_pretrained(_SCREAMING_SNAKE_CASE				)


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

'''simple docstring'''
import unittest

from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch

				from transformers import (
				    DebertaForMaskedLM,
				    DebertaForQuestionAnswering,
				    DebertaForSequenceClassification,
				    DebertaForTokenClassification,
				    DebertaModel,
				)
				from transformers.models.deberta.modeling_deberta import DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST


class       _lowerCAmelCase   (   __snake_case				):







							'''simple docstring'''

							def __init__(self  ,  UpperCAmelCase  ,  UpperCAmelCase=13  ,  UpperCAmelCase=7  ,  UpperCAmelCase=True  ,  UpperCAmelCase=True  ,  UpperCAmelCase=True  ,  UpperCAmelCase=True  ,  UpperCAmelCase=99  ,  UpperCAmelCase=32  ,  UpperCAmelCase=5  ,  UpperCAmelCase=4  ,  UpperCAmelCase=37  ,  UpperCAmelCase="gelu"  ,  UpperCAmelCase=0.1  ,  UpperCAmelCase=0.1  ,  UpperCAmelCase=512  ,  UpperCAmelCase=16  ,  UpperCAmelCase=2  ,  UpperCAmelCase=0.02  ,  UpperCAmelCase=False  ,  UpperCAmelCase=True  ,  UpperCAmelCase="None"  ,  UpperCAmelCase=3  ,  UpperCAmelCase=4  ,  UpperCAmelCase=None  ,  )					->       Optional[Any]:
														_snake_case       =  parent
														_snake_case       =  batch_size
														_snake_case       =  seq_length
														_snake_case       =  is_training
														_snake_case       =  use_input_mask
														_snake_case       =  use_token_type_ids
														_snake_case       =  use_labels
														_snake_case       =  vocab_size
														_snake_case       =  hidden_size
														_snake_case       =  num_hidden_layers
														_snake_case       =  num_attention_heads
														_snake_case       =  intermediate_size
														_snake_case       =  hidden_act
														_snake_case       =  hidden_dropout_prob
														_snake_case       =  attention_probs_dropout_prob
														_snake_case       =  max_position_embeddings
														_snake_case       =  type_vocab_size
														_snake_case       =  type_sequence_label_size
														_snake_case       =  initializer_range
														_snake_case       =  num_labels
														_snake_case       =  num_choices
														_snake_case       =  relative_attention
														_snake_case       =  position_biased_input
														_snake_case       =  pos_att_type
														_snake_case       =  scope

							def 	lowercase							(self  )					->       List[Any]:
														_snake_case       =  ids_tensor([self.batch_size, self.seq_length]  ,  self.vocab_size  )

														_snake_case       =  None
														if self.use_input_mask:
																					_snake_case       =  ids_tensor([self.batch_size, self.seq_length]  ,  vocab_size=2  )

														_snake_case       =  None
														if self.use_token_type_ids:
																					_snake_case       =  ids_tensor([self.batch_size, self.seq_length]  ,  self.type_vocab_size  )

														_snake_case       =  None
														_snake_case       =  None
														_snake_case       =  None
														if self.use_labels:
																					_snake_case       =  ids_tensor([self.batch_size]  ,  self.type_sequence_label_size  )
																					_snake_case       =  ids_tensor([self.batch_size, self.seq_length]  ,  self.num_labels  )
																					_snake_case       =  ids_tensor([self.batch_size]  ,  self.num_choices  )

														_snake_case       =  self.get_config()

														return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels

							def 	lowercase							(self  )					->       int:
														return DebertaConfig(
														    vocab_size=self.vocab_size  ,  hidden_size=self.hidden_size  ,  num_hidden_layers=self.num_hidden_layers  ,  num_attention_heads=self.num_attention_heads  ,  intermediate_size=self.intermediate_size  ,  hidden_act=self.hidden_act  ,  hidden_dropout_prob=self.hidden_dropout_prob  ,  attention_probs_dropout_prob=self.attention_probs_dropout_prob  ,  max_position_embeddings=self.max_position_embeddings  ,  type_vocab_size=self.type_vocab_size  ,  initializer_range=self.initializer_range  ,  relative_attention=self.relative_attention  ,  position_biased_input=self.position_biased_input  ,  pos_att_type=self.pos_att_type  ,  )

							def 	lowercase							(self  )					->       int:
														_snake_case       =  self.get_config()
														_snake_case       =  300
														return config

							def 	lowercase							(self  ,  UpperCAmelCase  )					->       Dict:
														self.parent.assertListEqual(list(result.loss.size()  )  ,  []  )

							def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  )					->       Optional[Any]:
														_snake_case       =  DebertaModel(config=UpperCAmelCase  )
														model.to(UpperCAmelCase  )
														model.eval()
														_snake_case       =  model(UpperCAmelCase  ,  attention_mask=UpperCAmelCase  ,  token_type_ids=UpperCAmelCase  )[0]
														_snake_case       =  model(UpperCAmelCase  ,  token_type_ids=UpperCAmelCase  )[0]
														_snake_case       =  model(UpperCAmelCase  )[0]

														self.parent.assertListEqual(list(sequence_output.size()  )  ,  [self.batch_size, self.seq_length, self.hidden_size]  )

							def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  )					->       str:
														_snake_case       =  DebertaForMaskedLM(config=UpperCAmelCase  )
														model.to(UpperCAmelCase  )
														model.eval()
														_snake_case       =  model(UpperCAmelCase  ,  attention_mask=UpperCAmelCase  ,  token_type_ids=UpperCAmelCase  ,  labels=UpperCAmelCase  )

														self.parent.assertEqual(result.logits.shape  ,  (self.batch_size, self.seq_length, self.vocab_size)  )

							def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  )					->       List[str]:
														_snake_case       =  self.num_labels
														_snake_case       =  DebertaForSequenceClassification(UpperCAmelCase  )
														model.to(UpperCAmelCase  )
														model.eval()
														_snake_case       =  model(UpperCAmelCase  ,  attention_mask=UpperCAmelCase  ,  token_type_ids=UpperCAmelCase  ,  labels=UpperCAmelCase  )
														self.parent.assertListEqual(list(result.logits.size()  )  ,  [self.batch_size, self.num_labels]  )
														self.check_loss_output(UpperCAmelCase  )

							def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  )					->       Any:
														_snake_case       =  self.num_labels
														_snake_case       =  DebertaForTokenClassification(config=UpperCAmelCase  )
														model.to(UpperCAmelCase  )
														model.eval()
														_snake_case       =  model(UpperCAmelCase  ,  attention_mask=UpperCAmelCase  ,  token_type_ids=UpperCAmelCase  ,  labels=UpperCAmelCase  )
														self.parent.assertEqual(result.logits.shape  ,  (self.batch_size, self.seq_length, self.num_labels)  )

							def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  )					->       Any:
														_snake_case       =  DebertaForQuestionAnswering(config=UpperCAmelCase  )
														model.to(UpperCAmelCase  )
														model.eval()
														_snake_case       =  model(
														    UpperCAmelCase  ,  attention_mask=UpperCAmelCase  ,  token_type_ids=UpperCAmelCase  ,  start_positions=UpperCAmelCase  ,  end_positions=UpperCAmelCase  ,  )
														self.parent.assertEqual(result.start_logits.shape  ,  (self.batch_size, self.seq_length)  )
														self.parent.assertEqual(result.end_logits.shape  ,  (self.batch_size, self.seq_length)  )





							def 	lowercase							(self  )					->       Tuple:
														_snake_case       =  self.prepare_config_and_inputs()
														(
														    (
														    _snake_case
														),							(
														    _snake_case
														),							(
														    _snake_case
														),							(
														    _snake_case
														),							(
														    _snake_case
														),							(
														    _snake_case
														),							(
														    _snake_case
														),							
														)       =  config_and_inputs
														_snake_case       =  {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
														return config, inputs_dict


@require_torch
class       _lowerCAmelCase   (   __snake_case					,							__snake_case					,							unittest.TestCase				):







							'''simple docstring'''

							lowerCAmelCase_ 						=						(
							    (
							        DebertaModel,
							        DebertaForMaskedLM,
							        DebertaForSequenceClassification,
							        DebertaForTokenClassification,
							        DebertaForQuestionAnswering,
							    )
							    if is_torch_available()
							    else ()
							)
							lowerCAmelCase_ 						=						(
							    {
							        "feature-extraction": DebertaModel,
							        "fill-mask": DebertaForMaskedLM,
							        "question-answering": DebertaForQuestionAnswering,
							        "text-classification": DebertaForSequenceClassification,
							        "token-classification": DebertaForTokenClassification,
							        "zero-shot": DebertaForSequenceClassification,
							    }
							    if is_torch_available()
							    else {}
							)

							lowerCAmelCase_ 						=						True
							lowerCAmelCase_ 						=						False
							lowerCAmelCase_ 						=						False
							lowerCAmelCase_ 						=						False
							lowerCAmelCase_ 						=						False

							def 	lowercase							(self  )					->       Any:
														_snake_case       =  DebertaModelTester(self  )
														_snake_case       =  ConfigTester(self  ,  config_class=UpperCAmelCase  ,  hidden_size=37  )

							def 	lowercase							(self  )					->       List[str]:
														self.config_tester.run_common_tests()

							def 	lowercase							(self  )					->       Tuple:
														_snake_case       =  self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_deberta_model(*UpperCAmelCase  )

							def 	lowercase							(self  )					->       str:
														_snake_case       =  self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_deberta_for_sequence_classification(*UpperCAmelCase  )

							def 	lowercase							(self  )					->       int:
														_snake_case       =  self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_deberta_for_masked_lm(*UpperCAmelCase  )

							def 	lowercase							(self  )					->       Any:
														_snake_case       =  self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_deberta_for_question_answering(*UpperCAmelCase  )

							def 	lowercase							(self  )					->       str:
														_snake_case       =  self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_deberta_for_token_classification(*UpperCAmelCase  )





							@slow
							def 	lowercase							(self  )					->       Tuple:
														for model_name in DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																					_snake_case       =  DebertaModel.from_pretrained(UpperCAmelCase  )
																					self.assertIsNotNone(UpperCAmelCase  )


@require_torch
@require_sentencepiece
@require_tokenizers
class       _lowerCAmelCase   (   unittest.TestCase				):







							'''simple docstring'''

							@unittest.skip(reason="""Model not available yet"""  )
							def 	lowercase							(self  )					->       Any:
														pass





							@slow
							def 	lowercase							(self  )					->       Dict:
														_snake_case       =  DebertaModel.from_pretrained("""microsoft/deberta-base"""  )

														_snake_case       =  torch.tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]  )
														_snake_case       =  torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]  )
														with torch.no_grad():
																					_snake_case       =  model(UpperCAmelCase  ,  attention_mask=UpperCAmelCase  )[0]
														# compare the actual values for a slice.
														_snake_case       =  torch.tensor(
														    [[[-0.5986, -0.8055, -0.8462], [1.4484, -0.9348, -0.8059], [0.3123, 0.0032, -1.4131]]]  )
														self.assertTrue(torch.allclose(output[:, 1:4, 1:4]  ,  UpperCAmelCase  ,  atol=1e-4  )  ,  f"""{output[:, 1:4, 1:4]}"""  )