






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 (
    ConditionalDetrConfig,
    ConditionalDetrForObjectDetection,
    ConditionalDetrForSegmentation,
    ConditionalDetrImageProcessor,
)
from transformers.utils import logging


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

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

     # q, k, v projections in self/cross-attention in decoder for conditional DETR
     rename_keys.append(
         (f"""transformer.decoder.layers.{i}.sa_qcontent_proj.weight""", f"""decoder.layers.{i}.sa_qcontent_proj.weight""")
     )
     rename_keys.append(
         (f"""transformer.decoder.layers.{i}.sa_kcontent_proj.weight""", f"""decoder.layers.{i}.sa_kcontent_proj.weight""")
     )
     rename_keys.append(
         (f"""transformer.decoder.layers.{i}.sa_qpos_proj.weight""", f"""decoder.layers.{i}.sa_qpos_proj.weight""")
     )
     rename_keys.append(
         (f"""transformer.decoder.layers.{i}.sa_kpos_proj.weight""", f"""decoder.layers.{i}.sa_kpos_proj.weight""")
     )
     rename_keys.append((f"""transformer.decoder.layers.{i}.sa_v_proj.weight""", f"""decoder.layers.{i}.sa_v_proj.weight"""))
     rename_keys.append(
         (f"""transformer.decoder.layers.{i}.ca_qcontent_proj.weight""", f"""decoder.layers.{i}.ca_qcontent_proj.weight""")
     )
     # rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.weight", f"decoder.layers.{i}.ca_qpos_proj.weight"))
     rename_keys.append(
         (f"""transformer.decoder.layers.{i}.ca_kcontent_proj.weight""", f"""decoder.layers.{i}.ca_kcontent_proj.weight""")
     )
     rename_keys.append(
         (f"""transformer.decoder.layers.{i}.ca_kpos_proj.weight""", f"""decoder.layers.{i}.ca_kpos_proj.weight""")
     )
     rename_keys.append((f"""transformer.decoder.layers.{i}.ca_v_proj.weight""", f"""decoder.layers.{i}.ca_v_proj.weight"""))
     rename_keys.append(
         (f"""transformer.decoder.layers.{i}.ca_qpos_sine_proj.weight""", f"""decoder.layers.{i}.ca_qpos_sine_proj.weight""")
     )

     rename_keys.append(
         (f"""transformer.decoder.layers.{i}.sa_qcontent_proj.bias""", f"""decoder.layers.{i}.sa_qcontent_proj.bias""")
     )
     rename_keys.append(
         (f"""transformer.decoder.layers.{i}.sa_kcontent_proj.bias""", f"""decoder.layers.{i}.sa_kcontent_proj.bias""")
     )
     rename_keys.append((f"""transformer.decoder.layers.{i}.sa_qpos_proj.bias""", f"""decoder.layers.{i}.sa_qpos_proj.bias"""))
     rename_keys.append((f"""transformer.decoder.layers.{i}.sa_kpos_proj.bias""", f"""decoder.layers.{i}.sa_kpos_proj.bias"""))
     rename_keys.append((f"""transformer.decoder.layers.{i}.sa_v_proj.bias""", f"""decoder.layers.{i}.sa_v_proj.bias"""))
     rename_keys.append(
         (f"""transformer.decoder.layers.{i}.ca_qcontent_proj.bias""", f"""decoder.layers.{i}.ca_qcontent_proj.bias""")
     )
     # rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.bias", f"decoder.layers.{i}.ca_qpos_proj.bias"))
     rename_keys.append(
         (f"""transformer.decoder.layers.{i}.ca_kcontent_proj.bias""", f"""decoder.layers.{i}.ca_kcontent_proj.bias""")
     )
     rename_keys.append((f"""transformer.decoder.layers.{i}.ca_kpos_proj.bias""", f"""decoder.layers.{i}.ca_kpos_proj.bias"""))
     rename_keys.append((f"""transformer.decoder.layers.{i}.ca_v_proj.bias""", f"""decoder.layers.{i}.ca_v_proj.bias"""))
     rename_keys.append(
         (f"""transformer.decoder.layers.{i}.ca_qpos_sine_proj.bias""", f"""decoder.layers.{i}.ca_qpos_sine_proj.bias""")
     )

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
# for conditional DETR, also convert reference point head and query scale MLP
rename_keys.extend(
    [
        ("input_proj.weight", "input_projection.weight"),
        ("input_proj.bias", "input_projection.bias"),
        ("query_embed.weight", "query_position_embeddings.weight"),
        ("transformer.decoder.norm.weight", "decoder.layernorm.weight"),
        ("transformer.decoder.norm.bias", "decoder.layernorm.bias"),
        ("class_embed.weight", "class_labels_classifier.weight"),
        ("class_embed.bias", "class_labels_classifier.bias"),
        ("bbox_embed.layers.0.weight", "bbox_predictor.layers.0.weight"),
        ("bbox_embed.layers.0.bias", "bbox_predictor.layers.0.bias"),
        ("bbox_embed.layers.1.weight", "bbox_predictor.layers.1.weight"),
        ("bbox_embed.layers.1.bias", "bbox_predictor.layers.1.bias"),
        ("bbox_embed.layers.2.weight", "bbox_predictor.layers.2.weight"),
        ("bbox_embed.layers.2.bias", "bbox_predictor.layers.2.bias"),
        ("transformer.decoder.ref_point_head.layers.0.weight", "decoder.ref_point_head.layers.0.weight"),
        ("transformer.decoder.ref_point_head.layers.0.bias", "decoder.ref_point_head.layers.0.bias"),
        ("transformer.decoder.ref_point_head.layers.1.weight", "decoder.ref_point_head.layers.1.weight"),
        ("transformer.decoder.ref_point_head.layers.1.bias", "decoder.ref_point_head.layers.1.bias"),
        ("transformer.decoder.query_scale.layers.0.weight", "decoder.query_scale.layers.0.weight"),
        ("transformer.decoder.query_scale.layers.0.bias", "decoder.query_scale.layers.0.bias"),
        ("transformer.decoder.query_scale.layers.1.weight", "decoder.query_scale.layers.1.weight"),
        ("transformer.decoder.query_scale.layers.1.bias", "decoder.query_scale.layers.1.bias"),
        ("transformer.decoder.layers.0.ca_qpos_proj.weight", "decoder.layers.0.ca_qpos_proj.weight"),
        ("transformer.decoder.layers.0.ca_qpos_proj.bias", "decoder.layers.0.ca_qpos_proj.bias"),
    ]
)
def 						_a    (      a :List[Any]	,							a :int	,							a :List[Any] )							->			Dict:
 a		  =      state_dict.pop(UpperCamelCase__ )
 a		  =      val
def 						_a    (      a :int )							->			Union[str, Any]:
 a		  =      OrderedDict()
 for key, value in state_dict.items():
  if "backbone.0.body" in key:
   a		  =      key.replace('''backbone.0.body'''	,							'''backbone.conv_encoder.model''' )
   a		  =      value
  else:
   a		  =      value

 return new_state_dict
def 						_a    (      a :int	,							a :Dict=False )							->			Union[str, Any]:
 a		  =      ''''''
 if is_panoptic:
  a		  =      '''conditional_detr.'''

 # first: transformer encoder
 for i in range(6 ):
  # read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
  a		  =      state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight""" )
  a		  =      state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias""" )
  # next, add query, keys and values (in that order) to the state dict
  a		  =      in_proj_weight[:256, :]
  a		  =      in_proj_bias[:256]
  a		  =      in_proj_weight[256:512, :]
  a		  =      in_proj_bias[256:512]
  a		  =      in_proj_weight[-256:, :]
  a		  =      in_proj_bias[-256:]
def 						_a    (      )							->			Dict:
 a		  =      '''http://images.cocodataset.org/val2017/000000039769.jpg'''
 a		  =      Image.open(requests.get(UpperCamelCase__	,							stream=UpperCamelCase__ ).raw )

 return im
@torch.no_grad()
def 						_a    (      a :Union[str, Any]	,							a :str )							->			List[str]:
 a		  =      ConditionalDetrConfig()
 # set backbone and dilation attributes
 if "resnet101" in model_name:
  a		  =      '''resnet101'''
 if "dc5" in model_name:
  a		  =      True
 a		  =      '''panoptic''' in model_name
 if is_panoptic:
  a		  =      250
 else:
  a		  =      91
  a		  =      '''huggingface/label-files'''
  a		  =      '''coco-detection-id2label.json'''
  a		  =      json.load(open(hf_hub_download(UpperCamelCase__	,							UpperCamelCase__	,							repo_type='''dataset''' )	,							'''r''' ) )
  a		  =      {int(UpperCamelCase__ ): v for k, v in idalabel.items()}
  a		  =      idalabel
  a		  =      {v: k for k, v in idalabel.items()}

 # load image processor
 a		  =      '''coco_panoptic''' if is_panoptic else '''coco_detection'''
 a		  =      ConditionalDetrImageProcessor(format=UpperCamelCase__ )

 # prepare image
 a		  =      prepare_img()
 a		  =      image_processor(images=UpperCamelCase__	,							return_tensors='''pt''' )
 a		  =      encoding['''pixel_values''']

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

 # load original model from torch hub
 a		  =      torch.hub.load('''DeppMeng/ConditionalDETR'''	,							UpperCamelCase__	,							pretrained=UpperCamelCase__ ).eval()
 a		  =      conditional_detr.state_dict()
 # rename keys
 for src, dest in rename_keys:
  if is_panoptic:
   a		  =      '''conditional_detr.''' + src
  rename_key(UpperCamelCase__	,							UpperCamelCase__	,							UpperCamelCase__ )
 a		  =      rename_backbone_keys(UpperCamelCase__ )
 # query, key and value matrices need special treatment
 read_in_q_k_v(UpperCamelCase__	,							is_panoptic=UpperCamelCase__ )
 # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
 a		  =      '''conditional_detr.model.''' if is_panoptic else '''model.'''
 for key in state_dict.copy().keys():
  if is_panoptic:
   if (
       key.startswith('''conditional_detr''' )
       and not key.startswith('''class_labels_classifier''' )
       and not key.startswith('''bbox_predictor''' )
   ):
    a		  =      state_dict.pop(UpperCamelCase__ )
    a		  =      val
   elif "class_labels_classifier" in key or "bbox_predictor" in key:
    a		  =      state_dict.pop(UpperCamelCase__ )
    a		  =      val
   elif key.startswith('''bbox_attention''' ) or key.startswith('''mask_head''' ):
    continue
   else:
    a		  =      state_dict.pop(UpperCamelCase__ )
    a		  =      val
  else:
   if not key.startswith('''class_labels_classifier''' ) and not key.startswith('''bbox_predictor''' ):
    a		  =      state_dict.pop(UpperCamelCase__ )
    a		  =      val
    # finally, create HuggingFace model and load state dict
 a		  =      ConditionalDetrForSegmentation(UpperCamelCase__ ) if is_panoptic else ConditionalDetrForObjectDetection(UpperCamelCase__ )
 model.load_state_dict(UpperCamelCase__ )
 model.eval()
 model.push_to_hub(repo_id=UpperCamelCase__	,							organization='''DepuMeng'''	,							commit_message='''Add model''' )
 # verify our conversion
 a		  =      conditional_detr(UpperCamelCase__ )
 a		  =      model(UpperCamelCase__ )
 assert torch.allclose(outputs.logits	,							original_outputs['''pred_logits''']	,							atol=1e-4 )
 assert torch.allclose(outputs.pred_boxes	,							original_outputs['''pred_boxes''']	,							atol=1e-4 )
 if is_panoptic:
  assert torch.allclose(outputs.pred_masks	,							original_outputs['''pred_masks''']	,							atol=1e-4 )

 # Save model and image processor
 logger.info(F"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}...""" )
 Path(UpperCamelCase__ ).mkdir(exist_ok=UpperCamelCase__ )
 model.save_pretrained(UpperCamelCase__ )
 image_processor.save_pretrained(UpperCamelCase__ )


if __name__ == "__main__":
     UpperCAmelCase__				   =		argparse.ArgumentParser()

     parser.add_argument(
         "--model_name",
         default="conditional_detr_resnet50",
         type=str,
         help="Name of the CONDITIONAL_DETR model you'd like to convert.",
     )
     parser.add_argument(
         "--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
     )
     UpperCAmelCase__				   =		parser.parse_args()
     convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)








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 (       __magic_name__ ):

			"""simple docstring"""







			def __init__(      self			,						lowerCamelCase__			,						lowerCamelCase__=13			,						lowerCamelCase__=7			,						lowerCamelCase__=True			,						lowerCamelCase__=True			,						lowerCamelCase__=True			,						lowerCamelCase__=True			,						lowerCamelCase__=99			,						lowerCamelCase__=32			,						lowerCamelCase__=5			,						lowerCamelCase__=4			,						lowerCamelCase__=37			,						lowerCamelCase__="gelu"			,						lowerCamelCase__=0.1			,						lowerCamelCase__=0.1			,						lowerCamelCase__=512			,						lowerCamelCase__=16			,						lowerCamelCase__=2			,						lowerCamelCase__=0.02			,						lowerCamelCase__=False			,						lowerCamelCase__=True			,						lowerCamelCase__="None"			,						lowerCamelCase__=3			,						lowerCamelCase__=4			,						lowerCamelCase__=None			,						)    ->      int:



							'''simple docstring'''
							__lowerCamelCase		       =    parent
							__lowerCamelCase		       =    batch_size
							__lowerCamelCase		       =    seq_length
							__lowerCamelCase		       =    is_training
							__lowerCamelCase		       =    use_input_mask
							__lowerCamelCase		       =    use_token_type_ids
							__lowerCamelCase		       =    use_labels
							__lowerCamelCase		       =    vocab_size
							__lowerCamelCase		       =    hidden_size
							__lowerCamelCase		       =    num_hidden_layers
							__lowerCamelCase		       =    num_attention_heads
							__lowerCamelCase		       =    intermediate_size
							__lowerCamelCase		       =    hidden_act
							__lowerCamelCase		       =    hidden_dropout_prob
							__lowerCamelCase		       =    attention_probs_dropout_prob
							__lowerCamelCase		       =    max_position_embeddings
							__lowerCamelCase		       =    type_vocab_size
							__lowerCamelCase		       =    type_sequence_label_size
							__lowerCamelCase		       =    initializer_range
							__lowerCamelCase		       =    num_labels
							__lowerCamelCase		       =    num_choices
							__lowerCamelCase		       =    relative_attention
							__lowerCamelCase		       =    position_biased_input
							__lowerCamelCase		       =    pos_att_type
							__lowerCamelCase		       =    scope


			def       lowercase_    (      self							)    ->      str:



							'''simple docstring'''
							__lowerCamelCase		       =    ids_tensor([self.batch_size, self.seq_length]			,						self.vocab_size							)

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

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

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

							__lowerCamelCase		       =    self.get_config()

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


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



							'''simple docstring'''
							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							)    ->      List[str]:



							'''simple docstring'''
							__lowerCamelCase		       =    self.get_config()
							__lowerCamelCase		       =    300
							return config


			def       lowercase_    (      self			,						lowerCamelCase__							)    ->      Optional[int]:



							'''simple docstring'''
							self.parent.assertListEqual(list(result.loss.size()							)			,						[]							)


			def       lowercase_    (      self			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__							)    ->      Any:



							'''simple docstring'''
							__lowerCamelCase		       =    DebertaModel(config=lowerCamelCase__							)
							model.to(lowerCamelCase__							)
							model.eval()
							__lowerCamelCase		       =    model(lowerCamelCase__			,						attention_mask=lowerCamelCase__			,						token_type_ids=lowerCamelCase__							)[0]
							__lowerCamelCase		       =    model(lowerCamelCase__			,						token_type_ids=lowerCamelCase__							)[0]
							__lowerCamelCase		       =    model(lowerCamelCase__							)[0]

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


			def       lowercase_    (      self			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__							)    ->      Tuple:



							'''simple docstring'''
							__lowerCamelCase		       =    DebertaForMaskedLM(config=lowerCamelCase__							)
							model.to(lowerCamelCase__							)
							model.eval()
							__lowerCamelCase		       =    model(lowerCamelCase__			,						attention_mask=lowerCamelCase__			,						token_type_ids=lowerCamelCase__			,						labels=lowerCamelCase__							)

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


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



							'''simple docstring'''
							__lowerCamelCase		       =    self.num_labels
							__lowerCamelCase		       =    DebertaForSequenceClassification(lowerCamelCase__							)
							model.to(lowerCamelCase__							)
							model.eval()
							__lowerCamelCase		       =    model(lowerCamelCase__			,						attention_mask=lowerCamelCase__			,						token_type_ids=lowerCamelCase__			,						labels=lowerCamelCase__							)
							self.parent.assertListEqual(list(result.logits.size()							)			,						[self.batch_size, self.num_labels]							)
							self.check_loss_output(lowerCamelCase__							)


			def       lowercase_    (      self			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__							)    ->      Optional[int]:



							'''simple docstring'''
							__lowerCamelCase		       =    self.num_labels
							__lowerCamelCase		       =    DebertaForTokenClassification(config=lowerCamelCase__							)
							model.to(lowerCamelCase__							)
							model.eval()
							__lowerCamelCase		       =    model(lowerCamelCase__			,						attention_mask=lowerCamelCase__			,						token_type_ids=lowerCamelCase__			,						labels=lowerCamelCase__							)
							self.parent.assertEqual(result.logits.shape			,						(self.batch_size, self.seq_length, self.num_labels)							)


			def       lowercase_    (      self			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__							)    ->      Dict:



							'''simple docstring'''
							__lowerCamelCase		       =    DebertaForQuestionAnswering(config=lowerCamelCase__							)
							model.to(lowerCamelCase__							)
							model.eval()
							__lowerCamelCase		       =    model(
							    lowerCamelCase__			,						attention_mask=lowerCamelCase__			,						token_type_ids=lowerCamelCase__			,						start_positions=lowerCamelCase__			,						end_positions=lowerCamelCase__			,						)
							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							)    ->      Union[str, Any]:



							'''simple docstring'''
							__lowerCamelCase		       =    self.prepare_config_and_inputs()
							(
							    (
							    __lowerCamelCase
							)       ,						(
							    __lowerCamelCase
							)       ,						(
							    __lowerCamelCase
							)       ,						(
							    __lowerCamelCase
							)       ,						(
							    __lowerCamelCase
							)       ,						(
							    __lowerCamelCase
							)       ,						(
							    __lowerCamelCase
							)       ,						
							)		       =    config_and_inputs
							__lowerCamelCase		       =    {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
							return config, inputs_dict







@require_torch
class     __lowerCAmelCase (       __magic_name__			,				__magic_name__			,				unittest.TestCase ):

			"""simple docstring"""







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

			snake_case_   		=       True
			snake_case_   		=       False
			snake_case_   		=       False
			snake_case_   		=       False
			snake_case_   		=       False


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



							'''simple docstring'''
							__lowerCamelCase		       =    DebertaModelTester(self							)
							__lowerCamelCase		       =    ConfigTester(self			,						config_class=lowerCamelCase__			,						hidden_size=37							)


			def       lowercase_    (      self							)    ->      Optional[int]:



							'''simple docstring'''
							self.config_tester.run_common_tests()


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



							'''simple docstring'''
							__lowerCamelCase		       =    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_deberta_model(*lowerCamelCase__							)


			def       lowercase_    (      self							)    ->      Optional[int]:



							'''simple docstring'''
							__lowerCamelCase		       =    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_deberta_for_sequence_classification(*lowerCamelCase__							)


			def       lowercase_    (      self							)    ->      Dict:



							'''simple docstring'''
							__lowerCamelCase		       =    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_deberta_for_masked_lm(*lowerCamelCase__							)


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



							'''simple docstring'''
							__lowerCamelCase		       =    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_deberta_for_question_answering(*lowerCamelCase__							)


			def       lowercase_    (      self							)    ->      str:



							'''simple docstring'''
							__lowerCamelCase		       =    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_deberta_for_token_classification(*lowerCamelCase__							)






			@slow
			def       lowercase_    (      self							)    ->      Optional[int]:



							'''simple docstring'''
							for model_name in DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											__lowerCamelCase		       =    DebertaModel.from_pretrained(lowerCamelCase__							)
											self.assertIsNotNone(lowerCamelCase__							)







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

			"""simple docstring"""







			@unittest.skip(reason='Model not available yet'							)
			def       lowercase_    (      self							)    ->      Union[str, Any]:



							'''simple docstring'''
							pass






			@slow
			def       lowercase_    (      self							)    ->      Tuple:



							'''simple docstring'''
							__lowerCamelCase		       =    DebertaModel.from_pretrained('microsoft/deberta-base'							)

							__lowerCamelCase		       =    torch.tensor([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]							)
							__lowerCamelCase		       =    torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]							)
							with torch.no_grad():
											__lowerCamelCase		       =    model(lowerCamelCase__			,						attention_mask=lowerCamelCase__							)[0]
							# compare the actual values for a slice.
							__lowerCamelCase		       =    torch.tensor(
							    [[[-0.59_86, -0.80_55, -0.84_62], [1.44_84, -0.93_48, -0.80_59], [0.31_23, 0.00_32, -1.41_31]]]							)
							self.assertTrue(torch.allclose(output[:, 1:4, 1:4]			,						lowerCamelCase__			,						atol=1e-4							)			,						f"""{output[:, 1:4, 1:4]}"""							)


