
'''simple docstring'''
import argparse
import json
from pathlib import Path

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

from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging


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






def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE				):
							# initialize config
							if "resnet-50" in model_name:
														_snake_case       =  ResNetConfig.from_pretrained("""microsoft/resnet-50"""				)
							elif "resnet-101" in model_name:
														_snake_case       =  ResNetConfig.from_pretrained("""microsoft/resnet-101"""				)
							else:
														raise ValueError("""Model name should include either resnet50 or resnet101"""				)

							_snake_case       =  DetrConfig(use_timm_backbone=_SCREAMING_SNAKE_CASE							, backbone_config=_SCREAMING_SNAKE_CASE				)

							# set label attributes
							_snake_case       =  """panoptic""" in model_name
							if is_panoptic:
														_snake_case       =  250
							else:
														_snake_case       =  91
														_snake_case       =  """huggingface/label-files"""
														_snake_case       =  """coco-detection-id2label.json"""
														_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()}

							return config, is_panoptic






def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE				):
							# here we list all keys to be renamed (original name on the left, our name on the right)
							_snake_case       =  []

							# stem
							# fmt: off
							rename_keys.append(("""backbone.0.body.conv1.weight""", """backbone.conv_encoder.model.embedder.embedder.convolution.weight""")				)
							rename_keys.append(("""backbone.0.body.bn1.weight""", """backbone.conv_encoder.model.embedder.embedder.normalization.weight""")				)
							rename_keys.append(("""backbone.0.body.bn1.bias""", """backbone.conv_encoder.model.embedder.embedder.normalization.bias""")				)
							rename_keys.append(("""backbone.0.body.bn1.running_mean""", """backbone.conv_encoder.model.embedder.embedder.normalization.running_mean""")				)
							rename_keys.append(("""backbone.0.body.bn1.running_var""", """backbone.conv_encoder.model.embedder.embedder.normalization.running_var""")				)
							# stages
							for stage_idx in range(len(config.backbone_config.depths				)				):
														for layer_idx in range(config.backbone_config.depths[stage_idx]				):
																					# shortcut
																					if layer_idx == 0:
																												rename_keys.append(
																												    (
																												        f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight""",
																												        f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight""",
																												    )				)
																												rename_keys.append(
																												    (
																												        f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight""",
																												        f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight""",
																												    )				)
																												rename_keys.append(
																												    (
																												        f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias""",
																												        f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias""",
																												    )				)
																												rename_keys.append(
																												    (
																												        f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean""",
																												        f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean""",
																												    )				)
																												rename_keys.append(
																												    (
																												        f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var""",
																												        f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var""",
																												    )				)
																					# 3 convs
																					for i in range(3				):
																												rename_keys.append(
																												    (
																												        f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight""",
																												        f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight""",
																												    )				)
																												rename_keys.append(
																												    (
																												        f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight""",
																												        f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight""",
																												    )				)
																												rename_keys.append(
																												    (
																												        f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias""",
																												        f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias""",
																												    )				)
																												rename_keys.append(
																												    (
																												        f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean""",
																												        f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean""",
																												    )				)
																												rename_keys.append(
																												    (
																												        f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var""",
																												        f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var""",
																												    )				)
    # fmt: on

							for i in range(config.encoder_layers				):
														# 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}.multihead_attn.out_proj.weight""",
														        f"""decoder.layers.{i}.encoder_attn.out_proj.weight""",
														    )				)
														rename_keys.append(
														    (
														        f"""transformer.decoder.layers.{i}.multihead_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""")				)

							# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
							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"""),
							    ]				)

							return rename_keys






def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				):
							_snake_case       =  state_dict.pop(_SCREAMING_SNAKE_CASE				)
							_snake_case       =  val






def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE=False				):
							_snake_case       =  """"""
							if is_panoptic:
														_snake_case       =  """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)
														_snake_case       =  state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""				)
														_snake_case       =  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
														_snake_case       =  in_proj_weight[:256, :]
														_snake_case       =  in_proj_bias[:256]
														_snake_case       =  in_proj_weight[256:512, :]
														_snake_case       =  in_proj_bias[256:512]
														_snake_case       =  in_proj_weight[-256:, :]
														_snake_case       =  in_proj_bias[-256:]
							# next: transformer decoder (which is a bit more complex because it also includes cross-attention)
							for i in range(6				):
														# read in weights + bias of input projection layer of self-attention
														_snake_case       =  state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""				)
														_snake_case       =  state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias"""				)
														# next, add query, keys and values (in that order) to the state dict
														_snake_case       =  in_proj_weight[:256, :]
														_snake_case       =  in_proj_bias[:256]
														_snake_case       =  in_proj_weight[256:512, :]
														_snake_case       =  in_proj_bias[256:512]
														_snake_case       =  in_proj_weight[-256:, :]
														_snake_case       =  in_proj_bias[-256:]
														# read in weights + bias of input projection layer of cross-attention
														_snake_case       =  state_dict.pop(
														    f"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""				)
														_snake_case       =  state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias"""				)
														# next, add query, keys and values (in that order) of cross-attention to the state dict
														_snake_case       =  in_proj_weight_cross_attn[:256, :]
														_snake_case       =  in_proj_bias_cross_attn[:256]
														_snake_case       =  in_proj_weight_cross_attn[256:512, :]
														_snake_case       =  in_proj_bias_cross_attn[256:512]
														_snake_case       =  in_proj_weight_cross_attn[-256:, :]
														_snake_case       =  in_proj_bias_cross_attn[-256:]






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 im






@torch.no_grad()
def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE=None							, _SCREAMING_SNAKE_CASE=False				):
							_snake_case,							_snake_case       =  get_detr_config(_SCREAMING_SNAKE_CASE				)

							# load original model from torch hub
							_snake_case       =  {
							    """detr-resnet-50""": """detr_resnet50""",
							    """detr-resnet-101""": """detr_resnet101""",
							}
							logger.info(f"""Converting model {model_name}..."""				)
							_snake_case       =  torch.hub.load("""facebookresearch/detr"""							, model_name_to_original_name[model_name]							, pretrained=_SCREAMING_SNAKE_CASE				).eval()
							_snake_case       =  detr.state_dict()
							# rename keys
							for src, dest in create_rename_keys(_SCREAMING_SNAKE_CASE				):
														if is_panoptic:
																					_snake_case       =  """detr.""" + src
														rename_key(_SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				)
							# query, key and value matrices need special treatment
							read_in_q_k_v(_SCREAMING_SNAKE_CASE							, is_panoptic=_SCREAMING_SNAKE_CASE				)
							# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
							_snake_case       =  """detr.model.""" if is_panoptic else """model."""
							for key in state_dict.copy().keys():
														if is_panoptic:
																					if (
																					    key.startswith("""detr"""				)
																					    and not key.startswith("""class_labels_classifier"""				)
																					    and not key.startswith("""bbox_predictor"""				)
																					):
																												_snake_case       =  state_dict.pop(_SCREAMING_SNAKE_CASE				)
																												_snake_case       =  val
																					elif "class_labels_classifier" in key or "bbox_predictor" in key:
																												_snake_case       =  state_dict.pop(_SCREAMING_SNAKE_CASE				)
																												_snake_case       =  val
																					elif key.startswith("""bbox_attention"""				) or key.startswith("""mask_head"""				):
																												continue
																					else:
																												_snake_case       =  state_dict.pop(_SCREAMING_SNAKE_CASE				)
																												_snake_case       =  val
														else:
																					if not key.startswith("""class_labels_classifier"""				) and not key.startswith("""bbox_predictor"""				):
																												_snake_case       =  state_dict.pop(_SCREAMING_SNAKE_CASE				)
																												_snake_case       =  val

    # finally, create HuggingFace model and load state dict
							_snake_case       =  DetrForSegmentation(_SCREAMING_SNAKE_CASE				) if is_panoptic else DetrForObjectDetection(_SCREAMING_SNAKE_CASE				)
							model.load_state_dict(_SCREAMING_SNAKE_CASE				)
							model.eval()

							# verify our conversion on an image
							_snake_case       =  """coco_panoptic""" if is_panoptic else """coco_detection"""
							_snake_case       =  DetrImageProcessor(format=_SCREAMING_SNAKE_CASE				)

							_snake_case       =  processor(images=prepare_img()							, return_tensors="""pt"""				)
							_snake_case       =  encoding["""pixel_values"""]

							_snake_case       =  detr(_SCREAMING_SNAKE_CASE				)
							_snake_case       =  model(_SCREAMING_SNAKE_CASE				)

							assert torch.allclose(outputs.logits							, original_outputs["""pred_logits"""]							, atol=1E-3				)
							assert torch.allclose(outputs.pred_boxes							, original_outputs["""pred_boxes"""]							, atol=1E-3				)
							if is_panoptic:
														assert torch.allclose(outputs.pred_masks							, original_outputs["""pred_masks"""]							, atol=1E-4				)
							print("""Looks ok!"""				)

							if pytorch_dump_folder_path is not None:
														# 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				)
														processor.save_pretrained(_SCREAMING_SNAKE_CASE				)

							if push_to_hub:
														# Upload model and image processor to the hub
														logger.info("""Uploading PyTorch model and image processor to the hub..."""				)
														model.push_to_hub(f"""nielsr/{model_name}"""				)
														processor.push_to_hub(f"""nielsr/{model_name}"""				)


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

				parser.add_argument(
				    '--model_name',
				    default='detr-resnet-50',
				    type=str,
				    choices=['detr-resnet-50', 'detr-resnet-101'],
				    help='Name of the 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.'
				)
				parser.add_argument('--push_to_hub', action='store_true', help='Whether to push the model to the hub or not.')
				__lowerCAmelCase												=	parser.parse_args()
				convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)

'''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]}"""  )