






import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging


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







def       lowercase__	(					A_:						Optional[Any]		)	->				int:







       """simple docstring"""






       __UpperCAmelCase     				=DPTConfig()

       if "large" in checkpoint_url:
              __UpperCAmelCase     				=1024
              __UpperCAmelCase     				=4096
              __UpperCAmelCase     				=24
              __UpperCAmelCase     				=16
              __UpperCAmelCase     				=[5, 11, 17, 23]
              __UpperCAmelCase     				=[256, 512, 1024, 1024]
              __UpperCAmelCase     				=(1, 384, 384)

       if "ade" in checkpoint_url:
              __UpperCAmelCase     				=True

              __UpperCAmelCase     				=150
              __UpperCAmelCase     				="""huggingface/label-files"""
              __UpperCAmelCase     				="""ade20k-id2label.json"""
              __UpperCAmelCase     				=json.load(open(cached_download(hf_hub_url(snake_case__  ,      snake_case__  ,      repo_type="""dataset"""		)		)  ,      """r"""		)		)
              __UpperCAmelCase     				={int(snake_case__		): v for k, v in idalabel.items()}
              __UpperCAmelCase     				=idalabel
              __UpperCAmelCase     				={v: k for k, v in idalabel.items()}
              __UpperCAmelCase     				=[1, 150, 480, 480]

       return config, expected_shape







def       lowercase__	(					A_:						List[Any]		)	->				List[str]:







       """simple docstring"""






       __UpperCAmelCase     				=["""pretrained.model.head.weight""", """pretrained.model.head.bias"""]
       for k in ignore_keys:
              state_dict.pop(snake_case__  ,      snake_case__		)







def       lowercase__	(					A_:						Any		)	->				Optional[int]:







       """simple docstring"""






       if (
           "pretrained.model" in name
           and "cls_token" not in name
           and "pos_embed" not in name
           and "patch_embed" not in name
       ):
              __UpperCAmelCase     				=name.replace("""pretrained.model"""  ,      """dpt.encoder"""		)
       if "pretrained.model" in name:
              __UpperCAmelCase     				=name.replace("""pretrained.model"""  ,      """dpt.embeddings"""		)
       if "patch_embed" in name:
              __UpperCAmelCase     				=name.replace("""patch_embed"""  ,      """patch_embeddings"""		)
       if "pos_embed" in name:
              __UpperCAmelCase     				=name.replace("""pos_embed"""  ,      """position_embeddings"""		)
       if "attn.proj" in name:
              __UpperCAmelCase     				=name.replace("""attn.proj"""  ,      """attention.output.dense"""		)
       if "proj" in name and "project" not in name:
              __UpperCAmelCase     				=name.replace("""proj"""  ,      """projection"""		)
       if "blocks" in name:
              __UpperCAmelCase     				=name.replace("""blocks"""  ,      """layer"""		)
       if "mlp.fc1" in name:
              __UpperCAmelCase     				=name.replace("""mlp.fc1"""  ,      """intermediate.dense"""		)
       if "mlp.fc2" in name:
              __UpperCAmelCase     				=name.replace("""mlp.fc2"""  ,      """output.dense"""		)
       if "norm1" in name:
              __UpperCAmelCase     				=name.replace("""norm1"""  ,      """layernorm_before"""		)
       if "norm2" in name:
              __UpperCAmelCase     				=name.replace("""norm2"""  ,      """layernorm_after"""		)
       if "scratch.output_conv" in name:
              __UpperCAmelCase     				=name.replace("""scratch.output_conv"""  ,      """head"""		)
       if "scratch" in name:
              __UpperCAmelCase     				=name.replace("""scratch"""  ,      """neck"""		)
       if "layer1_rn" in name:
              __UpperCAmelCase     				=name.replace("""layer1_rn"""  ,      """convs.0"""		)
       if "layer2_rn" in name:
              __UpperCAmelCase     				=name.replace("""layer2_rn"""  ,      """convs.1"""		)
       if "layer3_rn" in name:
              __UpperCAmelCase     				=name.replace("""layer3_rn"""  ,      """convs.2"""		)
       if "layer4_rn" in name:
              __UpperCAmelCase     				=name.replace("""layer4_rn"""  ,      """convs.3"""		)
       if "refinenet" in name:
              __UpperCAmelCase     				=int(name[len("""neck.refinenet"""		) : len("""neck.refinenet"""		) + 1]		)
              # tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
              __UpperCAmelCase     				=name.replace(F'''refinenet{layer_idx}'''  ,      F'''fusion_stage.layers.{abs(layer_idx-4		)}'''		)
       if "out_conv" in name:
              __UpperCAmelCase     				=name.replace("""out_conv"""  ,      """projection"""		)
       if "resConfUnit1" in name:
              __UpperCAmelCase     				=name.replace("""resConfUnit1"""  ,      """residual_layer1"""		)
       if "resConfUnit2" in name:
              __UpperCAmelCase     				=name.replace("""resConfUnit2"""  ,      """residual_layer2"""		)
       if "conv1" in name:
              __UpperCAmelCase     				=name.replace("""conv1"""  ,      """convolution1"""		)
       if "conv2" in name:
              __UpperCAmelCase     				=name.replace("""conv2"""  ,      """convolution2"""		)
       # readout blocks
       if "pretrained.act_postprocess1.0.project.0" in name:
              __UpperCAmelCase     				=name.replace("""pretrained.act_postprocess1.0.project.0"""  ,      """neck.reassemble_stage.readout_projects.0.0"""		)
       if "pretrained.act_postprocess2.0.project.0" in name:
              __UpperCAmelCase     				=name.replace("""pretrained.act_postprocess2.0.project.0"""  ,      """neck.reassemble_stage.readout_projects.1.0"""		)
       if "pretrained.act_postprocess3.0.project.0" in name:
              __UpperCAmelCase     				=name.replace("""pretrained.act_postprocess3.0.project.0"""  ,      """neck.reassemble_stage.readout_projects.2.0"""		)
       if "pretrained.act_postprocess4.0.project.0" in name:
              __UpperCAmelCase     				=name.replace("""pretrained.act_postprocess4.0.project.0"""  ,      """neck.reassemble_stage.readout_projects.3.0"""		)
       # resize blocks
       if "pretrained.act_postprocess1.3" in name:
              __UpperCAmelCase     				=name.replace("""pretrained.act_postprocess1.3"""  ,      """neck.reassemble_stage.layers.0.projection"""		)
       if "pretrained.act_postprocess1.4" in name:
              __UpperCAmelCase     				=name.replace("""pretrained.act_postprocess1.4"""  ,      """neck.reassemble_stage.layers.0.resize"""		)
       if "pretrained.act_postprocess2.3" in name:
              __UpperCAmelCase     				=name.replace("""pretrained.act_postprocess2.3"""  ,      """neck.reassemble_stage.layers.1.projection"""		)
       if "pretrained.act_postprocess2.4" in name:
              __UpperCAmelCase     				=name.replace("""pretrained.act_postprocess2.4"""  ,      """neck.reassemble_stage.layers.1.resize"""		)
       if "pretrained.act_postprocess3.3" in name:
              __UpperCAmelCase     				=name.replace("""pretrained.act_postprocess3.3"""  ,      """neck.reassemble_stage.layers.2.projection"""		)
       if "pretrained.act_postprocess4.3" in name:
              __UpperCAmelCase     				=name.replace("""pretrained.act_postprocess4.3"""  ,      """neck.reassemble_stage.layers.3.projection"""		)
       if "pretrained.act_postprocess4.4" in name:
              __UpperCAmelCase     				=name.replace("""pretrained.act_postprocess4.4"""  ,      """neck.reassemble_stage.layers.3.resize"""		)
       if "pretrained" in name:
              __UpperCAmelCase     				=name.replace("""pretrained"""  ,      """dpt"""		)
       if "bn" in name:
              __UpperCAmelCase     				=name.replace("""bn"""  ,      """batch_norm"""		)
       if "head" in name:
              __UpperCAmelCase     				=name.replace("""head"""  ,      """head.head"""		)
       if "encoder.norm" in name:
              __UpperCAmelCase     				=name.replace("""encoder.norm"""  ,      """layernorm"""		)
       if "auxlayer" in name:
              __UpperCAmelCase     				=name.replace("""auxlayer"""  ,      """auxiliary_head.head"""		)

       return name







def       lowercase__	(					A_:						Union[str, Any]  ,      A_:						str		)	->				List[str]:







       """simple docstring"""






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







def       lowercase__	(					)	->				Tuple:







       """simple docstring"""






       __UpperCAmelCase     				="""http://images.cocodataset.org/val2017/000000039769.jpg"""
       __UpperCAmelCase     				=Image.open(requests.get(snake_case__  ,      stream=snake_case__		).raw		)
       return im







@torch.no_grad()
def       lowercase__	(					A_:						Dict  ,      A_:						Optional[Any]  ,      A_:						List[Any]  ,      A_:						List[Any]		)	->				List[Any]:







       """simple docstring"""






       __UpperCAmelCase     				=get_dpt_config(snake_case__		)
       # load original state_dict from URL
       __UpperCAmelCase     				=torch.hub.load_state_dict_from_url(snake_case__  ,      map_location="""cpu"""		)
       # remove certain keys
       remove_ignore_keys_(snake_case__		)
       # rename keys
       for key in state_dict.copy().keys():
              __UpperCAmelCase     				=state_dict.pop(snake_case__		)
              __UpperCAmelCase     				=val
       # read in qkv matrices
       read_in_q_k_v(snake_case__  ,      snake_case__		)

       # load HuggingFace model
       __UpperCAmelCase     				=DPTForSemanticSegmentation(snake_case__		) if """ade""" in checkpoint_url else DPTForDepthEstimation(snake_case__		)
       model.load_state_dict(snake_case__		)
       model.eval()

       # Check outputs on an image
       __UpperCAmelCase     				=480 if """ade""" in checkpoint_url else 384
       __UpperCAmelCase     				=DPTImageProcessor(size=snake_case__		)

       __UpperCAmelCase     				=prepare_img()
       __UpperCAmelCase     				=image_processor(snake_case__  ,      return_tensors="""pt"""		)

       # forward pass
       __UpperCAmelCase     				=model(**snake_case__		).logits if """ade""" in checkpoint_url else model(**snake_case__		).predicted_depth

       # Assert logits
       __UpperCAmelCase     				=torch.tensor([[6.3_1_9_9, 6.3_6_2_9, 6.4_1_4_8], [6.3_8_5_0, 6.3_6_1_5, 6.4_1_6_6], [6.3_5_1_9, 6.3_1_7_6, 6.3_5_7_5]]		)
       if "ade" in checkpoint_url:
              __UpperCAmelCase     				=torch.tensor([[4.0_4_8_0, 4.2_4_2_0, 4.4_3_6_0], [4.3_1_2_4, 4.5_6_9_3, 4.8_2_6_1], [4.5_7_6_8, 4.8_9_6_5, 5.2_1_6_3]]		)
       assert outputs.shape == torch.Size(snake_case__		)
       assert (
           torch.allclose(outputs[0, 0, :3, :3]  ,      snake_case__  ,      atol=1e-4		)
           if "ade" in checkpoint_url
           else torch.allclose(outputs[0, :3, :3]  ,      snake_case__		)
       )

       Path(snake_case__		).mkdir(exist_ok=snake_case__		)
       print(F'''Saving model to {pytorch_dump_folder_path}'''		)
       model.save_pretrained(snake_case__		)
       print(F'''Saving image processor to {pytorch_dump_folder_path}'''		)
       image_processor.save_pretrained(snake_case__		)

       if push_to_hub:
              print("""Pushing model to hub..."""		)
              model.push_to_hub(
                  repo_path_or_name=Path(snake_case__  ,      snake_case__		)  ,      organization="""nielsr"""  ,      commit_message="""Add model"""  ,      use_temp_dir=snake_case__  ,      )
              image_processor.push_to_hub(
                  repo_path_or_name=Path(snake_case__  ,      snake_case__		)  ,      organization="""nielsr"""  ,      commit_message="""Add image processor"""  ,      use_temp_dir=snake_case__  ,      )


if __name__ == "__main__":
   __A					=	argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       "--checkpoint_url",
       default="https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt",
       type=str,
       help="URL of the original DPT checkpoint you'd like to convert.",
   )
   parser.add_argument(
       "--pytorch_dump_folder_path",
       default=None,
       type=str,
       required=True,
       help="Path to the output PyTorch model directory.",
   )
   parser.add_argument(
       "--push_to_hub",
       action="store_true",
   )
   parser.add_argument(
       "--model_name",
       default="dpt-large",
       type=str,
       help="Name of the model, in case you're pushing to the hub.",
   )

   __A					=	parser.parse_args()
   convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)












"""simple docstring"""
import unittest

from transformers import MobileBertConfig, is_torch_available
from transformers.models.auto import get_values
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, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers import (
							    MODEL_FOR_PRETRAINING_MAPPING,
							    MobileBertForMaskedLM,
							    MobileBertForMultipleChoice,
							    MobileBertForNextSentencePrediction,
							    MobileBertForPreTraining,
							    MobileBertForQuestionAnswering,
							    MobileBertForSequenceClassification,
							    MobileBertForTokenClassification,
							    MobileBertModel,
							)



class   SCREAMING_SNAKE_CASE	:





	"""simple docstring"""




	def __init__(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_=13  ,  lowerCAmelCase_=7  ,  lowerCAmelCase_=True  ,  lowerCAmelCase_=True  ,  lowerCAmelCase_=True  ,  lowerCAmelCase_=True  ,  lowerCAmelCase_=99  ,  lowerCAmelCase_=64  ,  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_=3  ,  lowerCAmelCase_=4  ,  lowerCAmelCase_=None  ,  ):
		A_		:    Optional[Any]						     = parent
		A_		:    List[str]						     = batch_size
		A_		:    List[str]						     = seq_length
		A_		:    List[str]						     = is_training
		A_		:    Optional[Any]						     = use_input_mask
		A_		:    Dict						     = use_token_type_ids
		A_		:    Any						     = use_labels
		A_		:    Tuple						     = vocab_size
		A_		:    Optional[Any]						     = hidden_size
		A_		:    Tuple						     = embedding_size
		A_		:    Tuple						     = num_hidden_layers
		A_		:    Any						     = num_attention_heads
		A_		:    Optional[int]						     = intermediate_size
		A_		:    Union[str, Any]						     = hidden_act
		A_		:    Optional[int]						     = hidden_dropout_prob
		A_		:    Optional[Any]						     = attention_probs_dropout_prob
		A_		:    str						     = max_position_embeddings
		A_		:    str						     = type_vocab_size
		A_		:    Optional[Any]						     = type_sequence_label_size
		A_		:    List[str]						     = initializer_range
		A_		:    str						     = num_labels
		A_		:    Dict						     = num_choices
		A_		:    Any						     = scope





	def  lowerCamelCase(self				):
		A_		:    Any						     = ids_tensor([self.batch_size, self.seq_length]  ,  self.vocab_size				)

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

		A_		:    int						     = None
		if self.use_token_type_ids:
			A_		:    Union[str, Any]						     = ids_tensor([self.batch_size, self.seq_length]  ,  self.type_vocab_size				)

		A_		:    Union[str, Any]						     = None
		A_		:    Union[str, Any]						     = None
		A_		:    List[str]						     = None
		if self.use_labels:
			A_		:    Dict						     = ids_tensor([self.batch_size]  ,  self.type_sequence_label_size				)
			A_		:    Union[str, Any]						     = ids_tensor([self.batch_size, self.seq_length]  ,  self.num_labels				)
			A_		:    Dict						     = ids_tensor([self.batch_size]  ,  self.num_choices				)

		A_		:    Tuple						     = self.get_config()

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





	def  lowerCamelCase(self				):
		return MobileBertConfig(
		    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  ,  embedding_size=self.embedding_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  ,  is_decoder=lowerCAmelCase_  ,  initializer_range=self.initializer_range  ,  )





	def  lowerCamelCase(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_				):
		A_		:    str						     = MobileBertModel(config=lowerCAmelCase_				)
		model.to(lowerCAmelCase_				)
		model.eval()
		A_		:    Optional[int]						     = model(lowerCAmelCase_  ,  attention_mask=lowerCAmelCase_  ,  token_type_ids=lowerCAmelCase_				)
		A_		:    Union[str, Any]						     = model(lowerCAmelCase_  ,  token_type_ids=lowerCAmelCase_				)
		A_		:    int						     = model(lowerCAmelCase_				)

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





	def  lowerCamelCase(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_				):
		A_		:    Optional[int]						     = MobileBertForMaskedLM(config=lowerCAmelCase_				)
		model.to(lowerCAmelCase_				)
		model.eval()
		A_		:    List[Any]						     = 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  lowerCamelCase(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_				):
		A_		:    Optional[int]						     = MobileBertForNextSentencePrediction(config=lowerCAmelCase_				)
		model.to(lowerCAmelCase_				)
		model.eval()
		A_		:    Tuple						     = model(
		    lowerCAmelCase_  ,  attention_mask=lowerCAmelCase_  ,  token_type_ids=lowerCAmelCase_  ,  labels=lowerCAmelCase_  ,  )
		self.parent.assertEqual(result.logits.shape  ,  (self.batch_size, 2)				)





	def  lowerCamelCase(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_				):
		A_		:    int						     = MobileBertForPreTraining(config=lowerCAmelCase_				)
		model.to(lowerCAmelCase_				)
		model.eval()
		A_		:    Union[str, Any]						     = model(
		    lowerCAmelCase_  ,  attention_mask=lowerCAmelCase_  ,  token_type_ids=lowerCAmelCase_  ,  labels=lowerCAmelCase_  ,  next_sentence_label=lowerCAmelCase_  ,  )
		self.parent.assertEqual(result.prediction_logits.shape  ,  (self.batch_size, self.seq_length, self.vocab_size)				)
		self.parent.assertEqual(result.seq_relationship_logits.shape  ,  (self.batch_size, 2)				)





	def  lowerCamelCase(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_				):
		A_		:    Union[str, Any]						     = MobileBertForQuestionAnswering(config=lowerCAmelCase_				)
		model.to(lowerCAmelCase_				)
		model.eval()
		A_		:    Tuple						     = 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  lowerCamelCase(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_				):
		A_		:    int						     = self.num_labels
		A_		:    Optional[int]						     = MobileBertForSequenceClassification(lowerCAmelCase_				)
		model.to(lowerCAmelCase_				)
		model.eval()
		A_		:    int						     = model(lowerCAmelCase_  ,  attention_mask=lowerCAmelCase_  ,  token_type_ids=lowerCAmelCase_  ,  labels=lowerCAmelCase_				)
		self.parent.assertEqual(result.logits.shape  ,  (self.batch_size, self.num_labels)				)





	def  lowerCamelCase(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_				):
		A_		:    str						     = self.num_labels
		A_		:    int						     = MobileBertForTokenClassification(config=lowerCAmelCase_				)
		model.to(lowerCAmelCase_				)
		model.eval()
		A_		:    str						     = 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  lowerCamelCase(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_				):
		A_		:    str						     = self.num_choices
		A_		:    int						     = MobileBertForMultipleChoice(config=lowerCAmelCase_				)
		model.to(lowerCAmelCase_				)
		model.eval()
		A_		:    Tuple						     = input_ids.unsqueeze(1				).expand(-1  ,  self.num_choices  ,  -1				).contiguous()
		A_		:    str						     = token_type_ids.unsqueeze(1				).expand(-1  ,  self.num_choices  ,  -1				).contiguous()
		A_		:    Optional[int]						     = input_mask.unsqueeze(1				).expand(-1  ,  self.num_choices  ,  -1				).contiguous()
		A_		:    Union[str, Any]						     = model(
		    lowerCAmelCase_  ,  attention_mask=lowerCAmelCase_  ,  token_type_ids=lowerCAmelCase_  ,  labels=lowerCAmelCase_  ,  )
		self.parent.assertEqual(result.logits.shape  ,  (self.batch_size, self.num_choices)				)





	def  lowerCamelCase(self				):
		A_		:    Optional[Any]						     = self.prepare_config_and_inputs()
		(
		    (
		    A_
		)     ,  (
		    A_
		)     ,  (
		    A_
		)     ,  (
		    A_
		)     ,  (
		    A_
		)     ,  (
		    A_
		)     ,  (
		    A_
		)     ,  
		)		:    Dict						     = config_and_inputs
		A_		:    Any						     = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
		return config, inputs_dict






@require_torch
class   SCREAMING_SNAKE_CASE	(					_SCREAMING_SNAKE_CASE							,							_SCREAMING_SNAKE_CASE							,							unittest.TestCase    ):





	"""simple docstring"""




	_A							:					Dict 			=					(
	    (
	        MobileBertModel,
	        MobileBertForMaskedLM,
	        MobileBertForMultipleChoice,
	        MobileBertForNextSentencePrediction,
	        MobileBertForPreTraining,
	        MobileBertForQuestionAnswering,
	        MobileBertForSequenceClassification,
	        MobileBertForTokenClassification,
	    )
	    if is_torch_available()
	    else ()
	)
	_A							:					Union[str, Any] 			=					(
	    {
	        """feature-extraction""": MobileBertModel,
	        """fill-mask""": MobileBertForMaskedLM,
	        """question-answering""": MobileBertForQuestionAnswering,
	        """text-classification""": MobileBertForSequenceClassification,
	        """token-classification""": MobileBertForTokenClassification,
	        """zero-shot""": MobileBertForSequenceClassification,
	    }
	    if is_torch_available()
	    else {}
	)
	_A							:					Union[str, Any] 			=					True





	def  lowerCamelCase(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_=False				):
		A_		:    Optional[int]						     = super()._prepare_for_class(lowerCAmelCase_  ,  lowerCAmelCase_  ,  return_labels=lowerCAmelCase_				)

		if return_labels:
			if model_class in get_values(lowerCAmelCase_				):
				A_		:    List[Any]						     = torch.zeros(
				    (self.model_tester.batch_size, self.model_tester.seq_length)  ,  dtype=torch.long  ,  device=lowerCAmelCase_				)
				A_		:    Optional[int]						     = torch.zeros(
				    self.model_tester.batch_size  ,  dtype=torch.long  ,  device=lowerCAmelCase_				)
		return inputs_dict





	def  lowerCamelCase(self				):
		A_		:    Optional[int]						     = MobileBertModelTester(self				)
		A_		:    Optional[Any]						     = ConfigTester(self  ,  config_class=lowerCAmelCase_  ,  hidden_size=37				)





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





	def  lowerCamelCase(self				):
		A_		:    Optional[Any]						     = self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_mobilebert_model(*lowerCAmelCase_				)





	def  lowerCamelCase(self				):
		A_		:    Tuple						     = self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_mobilebert_for_masked_lm(*lowerCAmelCase_				)





	def  lowerCamelCase(self				):
		A_		:    Optional[Any]						     = self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_mobilebert_for_multiple_choice(*lowerCAmelCase_				)





	def  lowerCamelCase(self				):
		A_		:    Optional[int]						     = self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*lowerCAmelCase_				)





	def  lowerCamelCase(self				):
		A_		:    Dict						     = self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_mobilebert_for_pretraining(*lowerCAmelCase_				)





	def  lowerCamelCase(self				):
		A_		:    Any						     = self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_mobilebert_for_question_answering(*lowerCAmelCase_				)





	def  lowerCamelCase(self				):
		A_		:    Dict						     = self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_mobilebert_for_sequence_classification(*lowerCAmelCase_				)





	def  lowerCamelCase(self				):
		A_		:    Optional[Any]						     = self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_mobilebert_for_token_classification(*lowerCAmelCase_				)






def       __UpperCamelCase						(		snake_case__       ):
	return torch.tensor(
	    snake_case__ , dtype=torch.long , device=snake_case__ , )


_lowerCAmelCase       =				1e-3




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





	"""simple docstring"""




	@slow
	def  lowerCamelCase(self				):
		A_		:    Union[str, Any]						     = MobileBertModel.from_pretrained("""google/mobilebert-uncased"""				).to(lowerCAmelCase_				)
		A_		:    Any						     = _long_tensor([[101, 7110, 1005, 1056, 2023, 11333, 17413, 1029, 102]]				)
		with torch.no_grad():
			A_		:    Dict						     = model(lowerCAmelCase_				)[0]
		A_		:    Any						     = torch.Size((1, 9, 512)				)
		self.assertEqual(output.shape  ,  lowerCAmelCase_				)
		A_		:    Optional[int]						     = torch.tensor(
		    [
		        [
		            [-2.4_7_3_6_5_2_6e0_7, 8.2_6_9_1_6_5_6e0_4, 1.6_5_2_1_8_3_8e0_5],
		            [-5.7_5_4_1_7_0_4e-0_1, 3.9_0_5_6_0_2_2e0_0, 4.4_0_1_1_5_0_7e0_0],
		            [2.6_0_4_7_3_5_9e0_0, 1.5_6_7_7_6_5_2e0_0, -1.7_3_2_4_1_8_8e-0_1],
		        ]
		    ]  ,  device=lowerCAmelCase_  ,  )

		# MobileBERT results range from 10e0 to 10e8. Even a 0.0000001% difference with a value of 10e8 results in a
		# ~1 difference, it's therefore not a good idea to measure using addition.
		# Here, we instead divide the expected result with the result in order to obtain ~1. We then check that the
		# result is held between bounds: 1 - TOLERANCE < expected_result / result < 1 + TOLERANCE
		A_		:    List[str]						     = torch.all((expected_slice / output[..., :3, :3]) >= 1 - TOLERANCE				)
		A_		:    Any						     = torch.all((expected_slice / output[..., :3, :3]) <= 1 + TOLERANCE				)

		self.assertTrue(lower_bound and upper_bound				)

