


'''simple docstring'''







from __future__ import annotations

import unittest

from transformers import DebertaVaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

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


if is_tf_available():
  import tensorflow as tf

  from transformers import (
      TFDebertaVaForMaskedLM,
      TFDebertaVaForQuestionAnswering,
      TFDebertaVaForSequenceClassification,
      TFDebertaVaForTokenClassification,
      TFDebertaVaModel,
  )
class 					lowercase_     :


    def __init__(     self  ,					a  ,					a=13  ,					a=7  ,					a=True  ,					a=True  ,					a=True  ,					a=True  ,					a=99  ,					a=32  ,					a=2  ,					a=4  ,					a=37  ,					a="gelu"  ,					a=0.1  ,					a=0.1  ,					a=5_12  ,					a=16  ,					a=2  ,					a=0.02  ,					a=False  ,					a=True  ,					a="None"  ,					a=3  ,					a=4  ,					a=None  ,					):
          UpperCamelCase__										=				parent
          UpperCamelCase__										=				batch_size
          UpperCamelCase__										=				seq_length
          UpperCamelCase__										=				is_training
          UpperCamelCase__										=				use_input_mask
          UpperCamelCase__										=				use_token_type_ids
          UpperCamelCase__										=				use_labels
          UpperCamelCase__										=				vocab_size
          UpperCamelCase__										=				hidden_size
          UpperCamelCase__										=				num_hidden_layers
          UpperCamelCase__										=				num_attention_heads
          UpperCamelCase__										=				intermediate_size
          UpperCamelCase__										=				hidden_act
          UpperCamelCase__										=				hidden_dropout_prob
          UpperCamelCase__										=				attention_probs_dropout_prob
          UpperCamelCase__										=				max_position_embeddings
          UpperCamelCase__										=				type_vocab_size
          UpperCamelCase__										=				type_sequence_label_size
          UpperCamelCase__										=				initializer_range
          UpperCamelCase__										=				num_labels
          UpperCamelCase__										=				num_choices
          UpperCamelCase__										=				relative_attention
          UpperCamelCase__										=				position_biased_input
          UpperCamelCase__										=				pos_att_type
          UpperCamelCase__										=				scope


    def    __a							(     self    ):
          UpperCamelCase__										=				ids_tensor([self.batch_size, self.seq_length]  ,					self.vocab_size    )

          UpperCamelCase__										=				None
          if self.use_input_mask:
                UpperCamelCase__										=				random_attention_mask([self.batch_size, self.seq_length]    )

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

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

          UpperCamelCase__										=				DebertaVaConfig(
              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  ,					relative_attention=self.relative_attention  ,					position_biased_input=self.position_biased_input  ,					initializer_range=self.initializer_range  ,					return_dict=a  ,					)

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


    def    __a							(     self  ,					a  ,					a  ,					a  ,					a  ,					a  ,					a  ,					a    ):
          UpperCamelCase__										=				TFDebertaVaModel(config=a    )
          UpperCamelCase__										=				{"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}

          UpperCamelCase__										=				[input_ids, input_mask]
          UpperCamelCase__										=				model(a    )

          UpperCamelCase__										=				model(a    )

          self.parent.assertEqual(result.last_hidden_state.shape  ,					(self.batch_size, self.seq_length, self.hidden_size)    )


    def    __a							(     self  ,					a  ,					a  ,					a  ,					a  ,					a  ,					a  ,					a    ):
          UpperCamelCase__										=				TFDebertaVaForMaskedLM(config=a    )
          UpperCamelCase__										=				{
              "input_ids": input_ids,
              "attention_mask": input_mask,
              "token_type_ids": token_type_ids,
          }
          UpperCamelCase__										=				model(a    )
          self.parent.assertEqual(result.logits.shape  ,					(self.batch_size, self.seq_length, self.vocab_size)    )


    def    __a							(     self  ,					a  ,					a  ,					a  ,					a  ,					a  ,					a  ,					a    ):
          UpperCamelCase__										=				self.num_labels
          UpperCamelCase__										=				TFDebertaVaForSequenceClassification(config=a    )
          UpperCamelCase__										=				{
              "input_ids": input_ids,
              "attention_mask": input_mask,
              "token_type_ids": token_type_ids,
          }

          UpperCamelCase__										=				model(a    )
          self.parent.assertEqual(result.logits.shape  ,					(self.batch_size, self.num_labels)    )


    def    __a							(     self  ,					a  ,					a  ,					a  ,					a  ,					a  ,					a  ,					a    ):
          UpperCamelCase__										=				self.num_labels
          UpperCamelCase__										=				TFDebertaVaForTokenClassification(config=a    )
          UpperCamelCase__										=				{
              "input_ids": input_ids,
              "attention_mask": input_mask,
              "token_type_ids": token_type_ids,
          }
          UpperCamelCase__										=				model(a    )
          self.parent.assertEqual(result.logits.shape  ,					(self.batch_size, self.seq_length, self.num_labels)    )


    def    __a							(     self  ,					a  ,					a  ,					a  ,					a  ,					a  ,					a  ,					a    ):
          UpperCamelCase__										=				TFDebertaVaForQuestionAnswering(config=a    )
          UpperCamelCase__										=				{
              "input_ids": input_ids,
              "attention_mask": input_mask,
              "token_type_ids": token_type_ids,
          }

          UpperCamelCase__										=				model(a    )
          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    __a							(     self    ):
          UpperCamelCase__										=				self.prepare_config_and_inputs()
          (
              (
              UpperCamelCase__
          )					,	(
              UpperCamelCase__
          )					,	(
              UpperCamelCase__
          )					,	(
              UpperCamelCase__
          )					,	(
              UpperCamelCase__
          )					,	(
              UpperCamelCase__
          )					,	(
              UpperCamelCase__
          )					,	
          )										=				config_and_inputs
          UpperCamelCase__										=				{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
          return config, inputs_dict

@require_tf
class 					lowercase_     (    a__  ,						a__  ,						unittest.TestCase   ):
    __UpperCAmelCase   			=	(
        (
            TFDebertaVaModel,
            TFDebertaVaForMaskedLM,
            TFDebertaVaForQuestionAnswering,
            TFDebertaVaForSequenceClassification,
            TFDebertaVaForTokenClassification,
        )
        if is_tf_available()
        else ()
    )
    __UpperCAmelCase   			=	(
        {
            'feature-extraction': TFDebertaVaModel,
            'fill-mask': TFDebertaVaForMaskedLM,
            'question-answering': TFDebertaVaForQuestionAnswering,
            'text-classification': TFDebertaVaForSequenceClassification,
            'token-classification': TFDebertaVaForTokenClassification,
            'zero-shot': TFDebertaVaForSequenceClassification,
        }
        if is_tf_available()
        else {}
    )

    __UpperCAmelCase   			=	False
    __UpperCAmelCase   			=	False


    def    __a							(     self    ):
          UpperCamelCase__										=				TFDebertaVaModelTester(self    )
          UpperCamelCase__										=				ConfigTester(self  ,					config_class=a  ,					hidden_size=37    )


    def    __a							(     self    ):
          self.config_tester.run_common_tests()


    def    __a							(     self    ):
          UpperCamelCase__										=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*a    )


    def    __a							(     self    ):
          UpperCamelCase__										=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_masked_lm(*a    )


    def    __a							(     self    ):
          UpperCamelCase__										=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_question_answering(*a    )


    def    __a							(     self    ):
          UpperCamelCase__										=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_sequence_classification(*a    )


    def    __a							(     self    ):
          UpperCamelCase__										=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_token_classification(*a    )


    @slow
    def    __a							(     self    ):
          UpperCamelCase__										=				TFDebertaVaModel.from_pretrained("kamalkraj/deberta-v2-xlarge"    )
          self.assertIsNotNone(a    )

@require_tf
class 					lowercase_     (    unittest.TestCase   ):


    @unittest.skip(reason="Model not available yet"    )
    def    __a							(     self    ):
          pass


    @slow
    def    __a							(     self    ):
          UpperCamelCase__										=				TFDebertaVaModel.from_pretrained("kamalkraj/deberta-v2-xlarge"    )
          UpperCamelCase__										=				tf.constant([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]    )
          UpperCamelCase__										=				tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]    )
          UpperCamelCase__										=				model(a  ,					attention_mask=a    )[0]

          UpperCamelCase__										=				tf.constant(
              [[[0.2356, 0.1948, 0.0369], [-0.1063, 0.3586, -0.5152], [-0.6399, -0.0259, -0.2525]]]    )
          tf.debugging.assert_near(output[:, 1:4, 1:4]  ,					a  ,					atol=1e-4    )











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 YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging


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





def __UpperCamelCase			(       _A  ):
						lowerCAmelCase_      			=  YolosConfig()

						# size of the architecture
						if "yolos_ti" in yolos_name:
												lowerCAmelCase_      			=  192
												lowerCAmelCase_      			=  768
												lowerCAmelCase_      			=  12
												lowerCAmelCase_      			=  3
												lowerCAmelCase_      			=  [800, 1333]
												lowerCAmelCase_      			=  False
						elif yolos_name == "yolos_s_dWr":
												lowerCAmelCase_      			=  330
												lowerCAmelCase_      			=  14
												lowerCAmelCase_      			=  6
												lowerCAmelCase_      			=  1320
						elif "yolos_s" in yolos_name:
												lowerCAmelCase_      			=  384
												lowerCAmelCase_      			=  1536
												lowerCAmelCase_      			=  12
												lowerCAmelCase_      			=  6
						elif "yolos_b" in yolos_name:
												lowerCAmelCase_      			=  [800, 1344]

						lowerCAmelCase_      			=  91
						lowerCAmelCase_      			=  '''huggingface/label-files'''
						lowerCAmelCase_      			=  '''coco-detection-id2label.json'''
						lowerCAmelCase_      			=  json.load(open(hf_hub_download(_A ,  _A ,  repo_type='''dataset'''  ) ,  '''r'''  )  )
						lowerCAmelCase_      			=  {int(_A  ): v for k, v in idalabel.items()}
						lowerCAmelCase_      			=  idalabel
						lowerCAmelCase_      			=  {v: k for k, v in idalabel.items()}

						return config





def __UpperCamelCase			(       _A ,  _A ,  _A = False  ):
						for i in range(config.num_hidden_layers  ):
												# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
												lowerCAmelCase_      			=  state_dict.pop(f"blocks.{i}.attn.qkv.weight"  )
												lowerCAmelCase_      			=  state_dict.pop(f"blocks.{i}.attn.qkv.bias"  )
												# next, add query, keys and values (in that order) to the state dict
												lowerCAmelCase_      			=  in_proj_weight[: config.hidden_size, :]
												lowerCAmelCase_      			=  in_proj_bias[: config.hidden_size]
												lowerCAmelCase_      			=  in_proj_weight[
												    config.hidden_size : config.hidden_size * 2, :
												]
												lowerCAmelCase_      			=  in_proj_bias[
												    config.hidden_size : config.hidden_size * 2
												]
												lowerCAmelCase_      			=  in_proj_weight[-config.hidden_size :, :]
												lowerCAmelCase_      			=  in_proj_bias[-config.hidden_size :]





def __UpperCamelCase			(       _A  ):
						if "backbone" in name:
												lowerCAmelCase_      			=  name.replace('''backbone''' ,  '''vit'''  )
						if "cls_token" in name:
												lowerCAmelCase_      			=  name.replace('''cls_token''' ,  '''embeddings.cls_token'''  )
						if "det_token" in name:
												lowerCAmelCase_      			=  name.replace('''det_token''' ,  '''embeddings.detection_tokens'''  )
						if "mid_pos_embed" in name:
												lowerCAmelCase_      			=  name.replace('''mid_pos_embed''' ,  '''encoder.mid_position_embeddings'''  )
						if "pos_embed" in name:
												lowerCAmelCase_      			=  name.replace('''pos_embed''' ,  '''embeddings.position_embeddings'''  )
						if "patch_embed.proj" in name:
												lowerCAmelCase_      			=  name.replace('''patch_embed.proj''' ,  '''embeddings.patch_embeddings.projection'''  )
						if "blocks" in name:
												lowerCAmelCase_      			=  name.replace('''blocks''' ,  '''encoder.layer'''  )
						if "attn.proj" in name:
												lowerCAmelCase_      			=  name.replace('''attn.proj''' ,  '''attention.output.dense'''  )
						if "attn" in name:
												lowerCAmelCase_      			=  name.replace('''attn''' ,  '''attention.self'''  )
						if "norm1" in name:
												lowerCAmelCase_      			=  name.replace('''norm1''' ,  '''layernorm_before'''  )
						if "norm2" in name:
												lowerCAmelCase_      			=  name.replace('''norm2''' ,  '''layernorm_after'''  )
						if "mlp.fc1" in name:
												lowerCAmelCase_      			=  name.replace('''mlp.fc1''' ,  '''intermediate.dense'''  )
						if "mlp.fc2" in name:
												lowerCAmelCase_      			=  name.replace('''mlp.fc2''' ,  '''output.dense'''  )
						if "class_embed" in name:
												lowerCAmelCase_      			=  name.replace('''class_embed''' ,  '''class_labels_classifier'''  )
						if "bbox_embed" in name:
												lowerCAmelCase_      			=  name.replace('''bbox_embed''' ,  '''bbox_predictor'''  )
						if "vit.norm" in name:
												lowerCAmelCase_      			=  name.replace('''vit.norm''' ,  '''vit.layernorm'''  )

						return name





def __UpperCamelCase			(       _A ,  _A  ):
						for key in orig_state_dict.copy().keys():
												lowerCAmelCase_      			=  orig_state_dict.pop(_A  )

												if "qkv" in key:
																		lowerCAmelCase_      			=  key.split('''.'''  )
																		lowerCAmelCase_      			=  int(key_split[2]  )
																		lowerCAmelCase_      			=  model.vit.encoder.layer[layer_num].attention.attention.all_head_size
																		if "weight" in key:
																								lowerCAmelCase_      			=  val[:dim, :]
																								lowerCAmelCase_      			=  val[
																								    dim : dim * 2, :
																								]
																								lowerCAmelCase_      			=  val[-dim:, :]
																		else:
																								lowerCAmelCase_      			=  val[:dim]
																								lowerCAmelCase_      			=  val[dim : dim * 2]
																								lowerCAmelCase_      			=  val[-dim:]
												else:
																		lowerCAmelCase_      			=  val

						return orig_state_dict





def __UpperCamelCase			(       ):
						lowerCAmelCase_      			=  '''http://images.cocodataset.org/val2017/000000039769.jpg'''
						lowerCAmelCase_      			=  Image.open(requests.get(_A ,  stream=_A  ).raw  )
						return im





@torch.no_grad()
def __UpperCamelCase			(       _A ,  _A ,  _A ,  _A = False  ):
						lowerCAmelCase_      			=  get_yolos_config(_A  )

						# load original state_dict
						lowerCAmelCase_      			=  torch.load(_A ,  map_location='''cpu'''  )['''model''']

						# load 🤗 model
						lowerCAmelCase_      			=  YolosForObjectDetection(_A  )
						model.eval()
						lowerCAmelCase_      			=  convert_state_dict(_A ,  _A  )
						model.load_state_dict(_A  )

						# Check outputs on an image, prepared by YolosImageProcessor
						lowerCAmelCase_      			=  800 if yolos_name != '''yolos_ti''' else 512
						lowerCAmelCase_      			=  YolosImageProcessor(format='''coco_detection''' ,  size=_A  )
						lowerCAmelCase_      			=  image_processor(images=prepare_img() ,  return_tensors='''pt'''  )
						lowerCAmelCase_      			=  model(**_A  )
						lowerCAmelCase_			,					lowerCAmelCase_      			=  outputs.logits, outputs.pred_boxes

						lowerCAmelCase_			,					lowerCAmelCase_      			=  None, None
						if yolos_name == "yolos_ti":
												lowerCAmelCase_      			=  torch.tensor(
												    [[-3_9.5_0_2_2, -1_1.9_8_2_0, -1_7.6_8_8_8], [-2_9.9_5_7_4, -9.9_7_6_9, -1_7.7_6_9_1], [-4_2.3_2_8_1, -2_0.7_2_0_0, -3_0.6_2_9_4]]  )
												lowerCAmelCase_      			=  torch.tensor(
												    [[0.4_0_2_1, 0.0_8_3_6, 0.7_9_7_9], [0.0_1_8_4, 0.2_6_0_9, 0.0_3_6_4], [0.1_7_8_1, 0.2_0_0_4, 0.2_0_9_5]]  )
						elif yolos_name == "yolos_s_200_pre":
												lowerCAmelCase_      			=  torch.tensor(
												    [[-2_4.0_2_4_8, -1_0.3_0_2_4, -1_4.8_2_9_0], [-4_2.0_3_9_2, -1_6.8_2_0_0, -2_7.4_3_3_4], [-2_7.2_7_4_3, -1_1.8_1_5_4, -1_8.7_1_4_8]]  )
												lowerCAmelCase_      			=  torch.tensor(
												    [[0.2_5_5_9, 0.5_4_5_5, 0.4_7_0_6], [0.2_9_8_9, 0.7_2_7_9, 0.1_8_7_5], [0.7_7_3_2, 0.4_0_1_7, 0.4_4_6_2]]  )
						elif yolos_name == "yolos_s_300_pre":
												lowerCAmelCase_      			=  torch.tensor(
												    [[-3_6.2_2_2_0, -1_4.4_3_8_5, -2_3.5_4_5_7], [-3_5.6_9_7_0, -1_4.7_5_8_3, -2_1.3_9_3_5], [-3_1.5_9_3_9, -1_3.6_0_4_2, -1_6.8_0_4_9]]  )
												lowerCAmelCase_      			=  torch.tensor(
												    [[0.7_6_1_4, 0.2_3_1_6, 0.4_7_2_8], [0.7_1_6_8, 0.4_4_9_5, 0.3_8_5_5], [0.4_9_9_6, 0.1_4_6_6, 0.9_9_9_6]]  )
						elif yolos_name == "yolos_s_dWr":
												lowerCAmelCase_      			=  torch.tensor(
												    [[-4_2.8_6_6_8, -2_4.1_0_4_9, -4_1.1_6_9_0], [-3_4.7_4_5_6, -1_4.1_2_7_4, -2_4.9_1_9_4], [-3_3.7_8_9_8, -1_2.1_9_4_6, -2_5.6_4_9_5]]  )
												lowerCAmelCase_      			=  torch.tensor(
												    [[0.5_5_8_7, 0.2_7_7_3, 0.0_6_0_5], [0.5_0_0_4, 0.3_0_1_4, 0.9_9_9_4], [0.4_9_9_9, 0.1_5_4_8, 0.9_9_9_4]]  )
						elif yolos_name == "yolos_base":
												lowerCAmelCase_      			=  torch.tensor(
												    [[-4_0.6_0_6_4, -2_4.3_0_8_4, -3_2.6_4_4_7], [-5_5.1_9_9_0, -3_0.7_7_1_9, -3_5.5_8_7_7], [-5_1.4_3_1_1, -3_3.3_5_0_7, -3_5.6_4_6_2]]  )
												lowerCAmelCase_      			=  torch.tensor(
												    [[0.5_5_5_5, 0.2_7_9_4, 0.0_6_5_5], [0.9_0_4_9, 0.2_6_6_4, 0.1_8_9_4], [0.9_1_8_3, 0.1_9_8_4, 0.1_6_3_5]]  )
						else:
												raise ValueError(f"Unknown yolos_name: {yolos_name}"  )

						assert torch.allclose(logits[0, :3, :3] ,  _A ,  atol=1E-4  )
						assert torch.allclose(pred_boxes[0, :3, :3] ,  _A ,  atol=1E-4  )

						Path(_A  ).mkdir(exist_ok=_A  )
						print(f"Saving model {yolos_name} to {pytorch_dump_folder_path}"  )
						model.save_pretrained(_A  )
						print(f"Saving image processor to {pytorch_dump_folder_path}"  )
						image_processor.save_pretrained(_A  )

						if push_to_hub:
												lowerCAmelCase_      			=  {
												    '''yolos_ti''': '''yolos-tiny''',
												    '''yolos_s_200_pre''': '''yolos-small''',
												    '''yolos_s_300_pre''': '''yolos-small-300''',
												    '''yolos_s_dWr''': '''yolos-small-dwr''',
												    '''yolos_base''': '''yolos-base''',
												}

												print('''Pushing to the hub...'''  )
												lowerCAmelCase_      			=  model_mapping[yolos_name]
												image_processor.push_to_hub(_A ,  organization='''hustvl'''  )
												model.push_to_hub(_A ,  organization='''hustvl'''  )


if __name__ == "__main__":
						_A     =			argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '''--yolos_name''',
						    default='''yolos_s_200_pre''',
						    type=str,
						    help=(
						        '''Name of the YOLOS model you\'d like to convert. Should be one of \'yolos_ti\', \'yolos_s_200_pre\','''
						        ''' \'yolos_s_300_pre\', \'yolos_s_dWr\', \'yolos_base\'.'''
						    ),
						)
						parser.add_argument(
						    '''--checkpoint_path''', default=None, type=str, help='''Path to the original state dict (.pth file).'''
						)
						parser.add_argument(
						    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
						)
						parser.add_argument(
						    '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
						)

						_A     =			parser.parse_args()
						convert_yolos_checkpoint(args.yolos_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)




