
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 (
    MobileViTConfig,
    MobileViTForImageClassification,
    MobileViTForSemanticSegmentation,
    MobileViTImageProcessor,
)
from transformers.utils import logging


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


def 							__A(lowerCAmelCase )		->				Tuple:

  """simple docstring"""






  _UpperCamelCase						    =	MobileViTConfig()

  # size of the architecture
  if "mobilevit_s" in mobilevit_name:
    _UpperCamelCase						    =	[1_4_4, 1_9_2, 2_4_0]
    _UpperCamelCase						    =	[1_6, 3_2, 6_4, 9_6, 1_2_8, 1_6_0, 6_4_0]
  elif "mobilevit_xs" in mobilevit_name:
    _UpperCamelCase						    =	[9_6, 1_2_0, 1_4_4]
    _UpperCamelCase						    =	[1_6, 3_2, 4_8, 6_4, 8_0, 9_6, 3_8_4]
  elif "mobilevit_xxs" in mobilevit_name:
    _UpperCamelCase						    =	[6_4, 8_0, 9_6]
    _UpperCamelCase						    =	[1_6, 1_6, 2_4, 4_8, 6_4, 8_0, 3_2_0]
    _UpperCamelCase						    =	0.05
    _UpperCamelCase						    =	2.0

  if mobilevit_name.startswith("""deeplabv3_""" ):
    _UpperCamelCase						    =	5_1_2
    _UpperCamelCase						    =	1_6
    _UpperCamelCase						    =	2_1
    _UpperCamelCase						    =	"""pascal-voc-id2label.json"""
  else:
    _UpperCamelCase						    =	1_0_0_0
    _UpperCamelCase						    =	"""imagenet-1k-id2label.json"""

  _UpperCamelCase						    =	"""huggingface/label-files"""
  _UpperCamelCase						    =	json.load(open(hf_hub_download(lowerCAmelCase						,  lowerCAmelCase						,  repo_type="""dataset""" )						,  """r""" ) )
  _UpperCamelCase						    =	{int(lowerCAmelCase ): v for k, v in idalabel.items()}
  _UpperCamelCase						    =	idalabel
  _UpperCamelCase						    =	{v: k for k, v in idalabel.items()}

  return config


def 							__A(lowerCAmelCase						,  lowerCAmelCase=False )		->				List[Any]:

  """simple docstring"""






  for i in range(1						,  6 ):
    if F'layer_{i}.' in name:
      _UpperCamelCase						    =	name.replace(F'layer_{i}.'						,  F'encoder.layer.{i - 1}.' )

  if "conv_1." in name:
    _UpperCamelCase						    =	name.replace("""conv_1."""						,  """conv_stem.""" )
  if ".block." in name:
    _UpperCamelCase						    =	name.replace(""".block."""						,  """.""" )

  if "exp_1x1" in name:
    _UpperCamelCase						    =	name.replace("""exp_1x1"""						,  """expand_1x1""" )
  if "red_1x1" in name:
    _UpperCamelCase						    =	name.replace("""red_1x1"""						,  """reduce_1x1""" )
  if ".local_rep.conv_3x3." in name:
    _UpperCamelCase						    =	name.replace(""".local_rep.conv_3x3."""						,  """.conv_kxk.""" )
  if ".local_rep.conv_1x1." in name:
    _UpperCamelCase						    =	name.replace(""".local_rep.conv_1x1."""						,  """.conv_1x1.""" )
  if ".norm." in name:
    _UpperCamelCase						    =	name.replace(""".norm."""						,  """.normalization.""" )
  if ".conv." in name:
    _UpperCamelCase						    =	name.replace(""".conv."""						,  """.convolution.""" )
  if ".conv_proj." in name:
    _UpperCamelCase						    =	name.replace(""".conv_proj."""						,  """.conv_projection.""" )

  for i in range(0						,  2 ):
    for j in range(0						,  4 ):
      if F'.{i}.{j}.' in name:
        _UpperCamelCase						    =	name.replace(F'.{i}.{j}.'						,  F'.{i}.layer.{j}.' )

  for i in range(2						,  6 ):
    for j in range(0						,  4 ):
      if F'.{i}.{j}.' in name:
        _UpperCamelCase						    =	name.replace(F'.{i}.{j}.'						,  F'.{i}.' )
        if "expand_1x1" in name:
          _UpperCamelCase						    =	name.replace("""expand_1x1"""						,  """downsampling_layer.expand_1x1""" )
        if "conv_3x3" in name:
          _UpperCamelCase						    =	name.replace("""conv_3x3"""						,  """downsampling_layer.conv_3x3""" )
        if "reduce_1x1" in name:
          _UpperCamelCase						    =	name.replace("""reduce_1x1"""						,  """downsampling_layer.reduce_1x1""" )

  for i in range(2						,  5 ):
    if F'.global_rep.{i}.weight' in name:
      _UpperCamelCase						    =	name.replace(F'.global_rep.{i}.weight'						,  """.layernorm.weight""" )
    if F'.global_rep.{i}.bias' in name:
      _UpperCamelCase						    =	name.replace(F'.global_rep.{i}.bias'						,  """.layernorm.bias""" )

  if ".global_rep." in name:
    _UpperCamelCase						    =	name.replace(""".global_rep."""						,  """.transformer.""" )
  if ".pre_norm_mha.0." in name:
    _UpperCamelCase						    =	name.replace(""".pre_norm_mha.0."""						,  """.layernorm_before.""" )
  if ".pre_norm_mha.1.out_proj." in name:
    _UpperCamelCase						    =	name.replace(""".pre_norm_mha.1.out_proj."""						,  """.attention.output.dense.""" )
  if ".pre_norm_ffn.0." in name:
    _UpperCamelCase						    =	name.replace(""".pre_norm_ffn.0."""						,  """.layernorm_after.""" )
  if ".pre_norm_ffn.1." in name:
    _UpperCamelCase						    =	name.replace(""".pre_norm_ffn.1."""						,  """.intermediate.dense.""" )
  if ".pre_norm_ffn.4." in name:
    _UpperCamelCase						    =	name.replace(""".pre_norm_ffn.4."""						,  """.output.dense.""" )
  if ".transformer." in name:
    _UpperCamelCase						    =	name.replace(""".transformer."""						,  """.transformer.layer.""" )

  if ".aspp_layer." in name:
    _UpperCamelCase						    =	name.replace(""".aspp_layer."""						,  """.""" )
  if ".aspp_pool." in name:
    _UpperCamelCase						    =	name.replace(""".aspp_pool."""						,  """.""" )
  if "seg_head." in name:
    _UpperCamelCase						    =	name.replace("""seg_head."""						,  """segmentation_head.""" )
  if "segmentation_head.classifier.classifier." in name:
    _UpperCamelCase						    =	name.replace("""segmentation_head.classifier.classifier."""						,  """segmentation_head.classifier.""" )

  if "classifier.fc." in name:
    _UpperCamelCase						    =	name.replace("""classifier.fc."""						,  """classifier.""" )
  elif (not base_model) and ("segmentation_head." not in name):
    _UpperCamelCase						    =	"""mobilevit.""" + name

  return name


def 							__A(lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase=False )		->				str:

  """simple docstring"""






  if base_model:
    _UpperCamelCase						    =	""""""
  else:
    _UpperCamelCase						    =	"""mobilevit."""

  for key in orig_state_dict.copy().keys():
    _UpperCamelCase						    =	orig_state_dict.pop(lowerCAmelCase )

    if key[:8] == "encoder.":
      _UpperCamelCase						    =	key[8:]

    if "qkv" in key:
      _UpperCamelCase						    =	key.split(""".""" )
      _UpperCamelCase						    =	int(key_split[0][6:] ) - 1
      _UpperCamelCase						    =	int(key_split[3] )
      _UpperCamelCase						    =	model.get_submodule(F'{model_prefix}encoder.layer.{layer_num}' )
      _UpperCamelCase						    =	layer.transformer.layer[transformer_num].attention.attention.all_head_size
      _UpperCamelCase						    =	(
          F'{model_prefix}encoder.layer.{layer_num}.transformer.layer.{transformer_num}.attention.attention.'
      )
      if "weight" in key:
        _UpperCamelCase						    =	val[:dim, :]
        _UpperCamelCase						    =	val[dim : dim * 2, :]
        _UpperCamelCase						    =	val[-dim:, :]
      else:
        _UpperCamelCase						    =	val[:dim]
        _UpperCamelCase						    =	val[dim : dim * 2]
        _UpperCamelCase						    =	val[-dim:]
    else:
      _UpperCamelCase						    =	val

  return orig_state_dict


def 							__A()		->				Dict:

  """simple docstring"""






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


@torch.no_grad()
def 							__A(lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase=False )		->				List[Any]:

  """simple docstring"""






  _UpperCamelCase						    =	get_mobilevit_config(lowerCAmelCase )

  # load original state_dict
  _UpperCamelCase						    =	torch.load(lowerCAmelCase						,  map_location="""cpu""" )

  # load 🤗 model
  if mobilevit_name.startswith("""deeplabv3_""" ):
    _UpperCamelCase						    =	MobileViTForSemanticSegmentation(lowerCAmelCase ).eval()
  else:
    _UpperCamelCase						    =	MobileViTForImageClassification(lowerCAmelCase ).eval()

  _UpperCamelCase						    =	convert_state_dict(lowerCAmelCase						,  lowerCAmelCase )
  model.load_state_dict(lowerCAmelCase )

  # Check outputs on an image, prepared by MobileViTImageProcessor
  _UpperCamelCase						    =	MobileViTImageProcessor(crop_size=config.image_size						,  size=config.image_size + 3_2 )
  _UpperCamelCase						    =	image_processor(images=prepare_img()						,  return_tensors="""pt""" )
  _UpperCamelCase						    =	model(**lowerCAmelCase )
  _UpperCamelCase						    =	outputs.logits

  if mobilevit_name.startswith("""deeplabv3_""" ):
    assert logits.shape == (1, 2_1, 3_2, 3_2)

    if mobilevit_name == "deeplabv3_mobilevit_s":
      _UpperCamelCase						    =	torch.tensor(
          [
              [[6.2065, 6.1292, 6.2070], [6.1079, 6.1254, 6.1747], [6.0042, 6.1071, 6.1034]],
              [[-6.9253, -6.8653, -7.0398], [-7.3218, -7.3983, -7.3670], [-7.1961, -7.2482, -7.1569]],
              [[-4.4723, -4.4348, -4.3769], [-5.3629, -5.4632, -5.4598], [-5.1587, -5.3402, -5.5059]],
          ] )
    elif mobilevit_name == "deeplabv3_mobilevit_xs":
      _UpperCamelCase						    =	torch.tensor(
          [
              [[5.4449, 5.5733, 5.6314], [5.1815, 5.3930, 5.5963], [5.1656, 5.4333, 5.4853]],
              [[-9.4423, -9.7766, -9.6714], [-9.1581, -9.5720, -9.5519], [-9.1006, -9.6458, -9.5703]],
              [[-7.7721, -7.3716, -7.1583], [-8.4599, -8.0624, -7.7944], [-8.4172, -7.8366, -7.5025]],
          ] )
    elif mobilevit_name == "deeplabv3_mobilevit_xxs":
      _UpperCamelCase						    =	torch.tensor(
          [
              [[6.9811, 6.9743, 7.3123], [7.1777, 7.1931, 7.3938], [7.5633, 7.8050, 7.8901]],
              [[-10.5536, -10.2332, -10.2924], [-10.2336, -9.8624, -9.5964], [-10.8840, -10.8158, -10.6659]],
              [[-3.4938, -3.0631, -2.8620], [-3.4205, -2.8135, -2.6875], [-3.4179, -2.7945, -2.8750]],
          ] )
    else:
      raise ValueError(F'Unknown mobilevit_name: {mobilevit_name}' )

    assert torch.allclose(logits[0, :3, :3, :3]						,  lowerCAmelCase						,  atol=1e-4 )
  else:
    assert logits.shape == (1, 1_0_0_0)

    if mobilevit_name == "mobilevit_s":
      _UpperCamelCase						    =	torch.tensor([-0.9866, 0.2392, -1.1241] )
    elif mobilevit_name == "mobilevit_xs":
      _UpperCamelCase						    =	torch.tensor([-2.4761, -0.9399, -1.9587] )
    elif mobilevit_name == "mobilevit_xxs":
      _UpperCamelCase						    =	torch.tensor([-1.9364, -1.2327, -0.4653] )
    else:
      raise ValueError(F'Unknown mobilevit_name: {mobilevit_name}' )

    assert torch.allclose(logits[0, :3]						,  lowerCAmelCase						,  atol=1e-4 )

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

  if push_to_hub:
    _UpperCamelCase						    =	{
        """mobilevit_s""": """mobilevit-small""",
        """mobilevit_xs""": """mobilevit-x-small""",
        """mobilevit_xxs""": """mobilevit-xx-small""",
        """deeplabv3_mobilevit_s""": """deeplabv3-mobilevit-small""",
        """deeplabv3_mobilevit_xs""": """deeplabv3-mobilevit-x-small""",
        """deeplabv3_mobilevit_xxs""": """deeplabv3-mobilevit-xx-small""",
    }

    print("""Pushing to the hub...""" )
    _UpperCamelCase						    =	model_mapping[mobilevit_name]
    image_processor.push_to_hub(lowerCAmelCase						,  organization="""apple""" )
    model.push_to_hub(lowerCAmelCase						,  organization="""apple""" )


if __name__ == "__main__":
  lowerCamelCase__	=							argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--mobilevit_name",
      default="mobilevit_s",
      type=str,
      help=(
          "Name of the MobileViT model you'd like to convert. Should be one of 'mobilevit_s', 'mobilevit_xs',"
          " 'mobilevit_xxs', 'deeplabv3_mobilevit_s', 'deeplabv3_mobilevit_xs', 'deeplabv3_mobilevit_xxs'."
      ),
  )
  parser.add_argument(
      "--checkpoint_path", required=True, type=str, help="Path to the original state dict (.pt file)."
  )
  parser.add_argument(
      "--pytorch_dump_folder_path", required=True, 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."
  )

  lowerCamelCase__	=							parser.parse_args()
  convert_movilevit_checkpoint(
      args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
  )





import os
import tempfile
import unittest

from transformers import NezhaConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch

		from transformers import (
		    MODEL_FOR_PRETRAINING_MAPPING,
		    NezhaForMaskedLM,
		    NezhaForMultipleChoice,
		    NezhaForNextSentencePrediction,
		    NezhaForPreTraining,
		    NezhaForQuestionAnswering,
		    NezhaForSequenceClassification,
		    NezhaForTokenClassification,
		    NezhaModel,
		)
		from transformers.models.nezha.modeling_nezha import NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST







class        lowerCAmelCase__      :

						def __init__(			self       ,							a       ,							a=13       ,							a=7       ,							a=True       ,							a=True       ,							a=True       ,							a=True       ,							a=99       ,							a=32       ,							a=5       ,							a=4       ,							a=37       ,							a="gelu"       ,							a=0.1       ,							a=0.1       ,							a=1_28       ,							a=32       ,							a=16       ,							a=2       ,							a=0.02       ,							a=3       ,							a=4       ,							a=None       ,							)					->      Optional[int]:


								'''simple docstring'''

								_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						    =	scope

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


								'''simple docstring'''

								_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						    =	ids_tensor([self.batch_size]       ,							self.num_choices				)

								_UpperCamelCase						    =	self.get_config()

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

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


								'''simple docstring'''

								return NezhaConfig(
								    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       ,							is_decoder=a       ,							initializer_range=self.initializer_range       ,							)

						def  A_				(			self				)					->      int:


								'''simple docstring'''

								(
								    (
								    _UpperCamelCase
								)    ,	(
								    _UpperCamelCase
								)    ,	(
								    _UpperCamelCase
								)    ,	(
								    _UpperCamelCase
								)    ,	(
								    _UpperCamelCase
								)    ,	(
								    _UpperCamelCase
								)    ,	(
								    _UpperCamelCase
								)    ,	
								)						    =	self.prepare_config_and_inputs()

								_UpperCamelCase						    =	True
								_UpperCamelCase						    =	floats_tensor([self.batch_size, self.seq_length, self.hidden_size]				)
								_UpperCamelCase						    =	ids_tensor([self.batch_size, self.seq_length]       ,							vocab_size=2				)

								return (
								    config,
								    input_ids,
								    token_type_ids,
								    input_mask,
								    sequence_labels,
								    token_labels,
								    choice_labels,
								    encoder_hidden_states,
								    encoder_attention_mask,
								)

						def  A_				(			self       ,							a       ,							a       ,							a       ,							a       ,							a       ,							a       ,							a				)					->      Union[str, Any]:


								'''simple docstring'''

								_UpperCamelCase						    =	NezhaModel(config=a				)
								model.to(a				)
								model.eval()
								_UpperCamelCase						    =	model(a       ,							attention_mask=a       ,							token_type_ids=a				)
								_UpperCamelCase						    =	model(a       ,							token_type_ids=a				)
								_UpperCamelCase						    =	model(a				)
								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  A_				(			self       ,							a       ,							a       ,							a       ,							a       ,							a       ,							a       ,							a       ,							a       ,							a       ,							)					->      List[Any]:


								'''simple docstring'''

								_UpperCamelCase						    =	True
								_UpperCamelCase						    =	NezhaModel(a				)
								model.to(a				)
								model.eval()
								_UpperCamelCase						    =	model(
								    a       ,							attention_mask=a       ,							token_type_ids=a       ,							encoder_hidden_states=a       ,							encoder_attention_mask=a       ,							)
								_UpperCamelCase						    =	model(
								    a       ,							attention_mask=a       ,							token_type_ids=a       ,							encoder_hidden_states=a       ,							)
								_UpperCamelCase						    =	model(a       ,							attention_mask=a       ,							token_type_ids=a				)
								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  A_				(			self       ,							a       ,							a       ,							a       ,							a       ,							a       ,							a       ,							a				)					->      Dict:


								'''simple docstring'''

								_UpperCamelCase						    =	NezhaForMaskedLM(config=a				)
								model.to(a				)
								model.eval()
								_UpperCamelCase						    =	model(a       ,							attention_mask=a       ,							token_type_ids=a       ,							labels=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				)					->      Optional[int]:


								'''simple docstring'''

								_UpperCamelCase						    =	NezhaForNextSentencePrediction(config=a				)
								model.to(a				)
								model.eval()
								_UpperCamelCase						    =	model(
								    a       ,							attention_mask=a       ,							token_type_ids=a       ,							labels=a       ,							)
								self.parent.assertEqual(result.logits.shape       ,							(self.batch_size, 2)				)

						def  A_				(			self       ,							a       ,							a       ,							a       ,							a       ,							a       ,							a       ,							a				)					->      Dict:


								'''simple docstring'''

								_UpperCamelCase						    =	NezhaForPreTraining(config=a				)
								model.to(a				)
								model.eval()
								_UpperCamelCase						    =	model(
								    a       ,							attention_mask=a       ,							token_type_ids=a       ,							labels=a       ,							next_sentence_label=a       ,							)
								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  A_				(			self       ,							a       ,							a       ,							a       ,							a       ,							a       ,							a       ,							a				)					->      List[str]:


								'''simple docstring'''

								_UpperCamelCase						    =	NezhaForQuestionAnswering(config=a				)
								model.to(a				)
								model.eval()
								_UpperCamelCase						    =	model(
								    a       ,							attention_mask=a       ,							token_type_ids=a       ,							start_positions=a       ,							end_positions=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       ,							a       ,							a       ,							a       ,							a       ,							a       ,							a       ,							a				)					->      Optional[int]:


								'''simple docstring'''

								_UpperCamelCase						    =	self.num_labels
								_UpperCamelCase						    =	NezhaForSequenceClassification(a				)
								model.to(a				)
								model.eval()
								_UpperCamelCase						    =	model(a       ,							attention_mask=a       ,							token_type_ids=a       ,							labels=a				)
								self.parent.assertEqual(result.logits.shape       ,							(self.batch_size, self.num_labels)				)

						def  A_				(			self       ,							a       ,							a       ,							a       ,							a       ,							a       ,							a       ,							a				)					->      Dict:


								'''simple docstring'''

								_UpperCamelCase						    =	self.num_labels
								_UpperCamelCase						    =	NezhaForTokenClassification(config=a				)
								model.to(a				)
								model.eval()
								_UpperCamelCase						    =	model(a       ,							attention_mask=a       ,							token_type_ids=a       ,							labels=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				)					->      Any:


								'''simple docstring'''

								_UpperCamelCase						    =	self.num_choices
								_UpperCamelCase						    =	NezhaForMultipleChoice(config=a				)
								model.to(a				)
								model.eval()
								_UpperCamelCase						    =	input_ids.unsqueeze(1				).expand(-1       ,							self.num_choices       ,							-1				).contiguous()
								_UpperCamelCase						    =	token_type_ids.unsqueeze(1				).expand(-1       ,							self.num_choices       ,							-1				).contiguous()
								_UpperCamelCase						    =	input_mask.unsqueeze(1				).expand(-1       ,							self.num_choices       ,							-1				).contiguous()
								_UpperCamelCase						    =	model(
								    a       ,							attention_mask=a       ,							token_type_ids=a       ,							labels=a       ,							)
								self.parent.assertEqual(result.logits.shape       ,							(self.batch_size, self.num_choices)				)







						def  A_				(			self				)					->      Tuple:


								'''simple docstring'''

								_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_torch
class        lowerCAmelCase__      (   __lowercase		,	__lowercase		,	__lowercase		,	unittest.TestCase	):
						UpperCamelCase_							: int	=		(
						    (
						        NezhaModel,
						        NezhaForMaskedLM,
						        NezhaForMultipleChoice,
						        NezhaForNextSentencePrediction,
						        NezhaForPreTraining,
						        NezhaForQuestionAnswering,
						        NezhaForSequenceClassification,
						        NezhaForTokenClassification,
						    )
						    if is_torch_available()
						    else ()
						)
						UpperCamelCase_							: Dict	=		(
						    {
						        "feature-extraction": NezhaModel,
						        "fill-mask": NezhaForMaskedLM,
						        "question-answering": NezhaForQuestionAnswering,
						        "text-classification": NezhaForSequenceClassification,
						        "token-classification": NezhaForTokenClassification,
						        "zero-shot": NezhaForSequenceClassification,
						    }
						    if is_torch_available()
						    else {}
						)
						UpperCamelCase_							: Dict	=		True

						def  A_				(			self       ,							a       ,							a       ,							a=False				)					->      List[Any]:


								'''simple docstring'''

								_UpperCamelCase						    =	super()._prepare_for_class(a       ,							a       ,							return_labels=a				)

								if return_labels:
										if model_class in get_values(a				):
												_UpperCamelCase						    =	torch.zeros(
												    (self.model_tester.batch_size, self.model_tester.seq_length)       ,							dtype=torch.long       ,							device=a				)
												_UpperCamelCase						    =	torch.zeros(
												    self.model_tester.batch_size       ,							dtype=torch.long       ,							device=a				)
								return inputs_dict

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


								'''simple docstring'''

								_UpperCamelCase						    =	NezhaModelTester(self				)
								_UpperCamelCase						    =	ConfigTester(self       ,							config_class=a       ,							hidden_size=37				)

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


								'''simple docstring'''

								self.config_tester.run_common_tests()

						def  A_				(			self				)					->      Any:


								'''simple docstring'''

								_UpperCamelCase						    =	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*a				)

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


								'''simple docstring'''

								_UpperCamelCase						    =	self.model_tester.prepare_config_and_inputs_for_decoder()
								self.model_tester.create_and_check_model_as_decoder(*a				)

						def  A_				(			self				)					->      Dict:


								'''simple docstring'''

								(
								    (
								    _UpperCamelCase
								)    ,	(
								    _UpperCamelCase
								)    ,	(
								    _UpperCamelCase
								)    ,	(
								    _UpperCamelCase
								)    ,	(
								    _UpperCamelCase
								)    ,	(
								    _UpperCamelCase
								)    ,	(
								    _UpperCamelCase
								)    ,	(
								    _UpperCamelCase
								)    ,	(
								    _UpperCamelCase
								)    ,	
								)						    =	self.model_tester.prepare_config_and_inputs_for_decoder()

								_UpperCamelCase						    =	None

								self.model_tester.create_and_check_model_as_decoder(
								    a       ,							a       ,							a       ,							a       ,							a       ,							a       ,							a       ,							a       ,							a       ,							)

						def  A_				(			self				)					->      str:


								'''simple docstring'''

								_UpperCamelCase						    =	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_masked_lm(*a				)

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


								'''simple docstring'''

								_UpperCamelCase						    =	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_multiple_choice(*a				)

						def  A_				(			self				)					->      str:


								'''simple docstring'''

								_UpperCamelCase						    =	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_next_sequence_prediction(*a				)

						def  A_				(			self				)					->      int:


								'''simple docstring'''

								_UpperCamelCase						    =	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_pretraining(*a				)

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


								'''simple docstring'''

								_UpperCamelCase						    =	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_question_answering(*a				)

						def  A_				(			self				)					->      Any:


								'''simple docstring'''

								_UpperCamelCase						    =	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_sequence_classification(*a				)

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


								'''simple docstring'''

								_UpperCamelCase						    =	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_token_classification(*a				)

						@slow
						def  A_				(			self				)					->      List[Any]:


								'''simple docstring'''

								for model_name in NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										_UpperCamelCase						    =	NezhaModel.from_pretrained(a				)
										self.assertIsNotNone(a				)







						@slow
						@require_torch_gpu
						def  A_				(			self				)					->      Optional[Any]:


								'''simple docstring'''

								_UpperCamelCase    ,	_UpperCamelCase						    =	self.model_tester.prepare_config_and_inputs_for_common()
								for model_class in self.all_model_classes:
										# NezhaForMultipleChoice behaves incorrectly in JIT environments.
										if model_class == NezhaForMultipleChoice:
												return

										_UpperCamelCase						    =	True
										_UpperCamelCase						    =	model_class(config=a				)

										_UpperCamelCase						    =	self._prepare_for_class(a       ,							a				)
										_UpperCamelCase						    =	torch.jit.trace(
										    a       ,							(inputs_dict["""input_ids"""].to("""cpu"""				), inputs_dict["""attention_mask"""].to("""cpu"""				))				)

										with tempfile.TemporaryDirectory() as tmp:
												torch.jit.save(a       ,							os.path.join(a       ,							"""bert.pt"""				)				)
												_UpperCamelCase						    =	torch.jit.load(os.path.join(a       ,							"""bert.pt"""				)       ,							map_location=a				)
												loaded(inputs_dict["""input_ids"""].to(a				)       ,							inputs_dict["""attention_mask"""].to(a				)				)







@require_torch
class        lowerCAmelCase__      (   unittest.TestCase	):

						@slow
						def  A_				(			self				)					->      List[str]:


								'''simple docstring'''

								_UpperCamelCase						    =	NezhaModel.from_pretrained("""sijunhe/nezha-cn-base"""				)
								_UpperCamelCase						    =	torch.tensor([[0, 1, 2, 3, 4, 5]]				)
								_UpperCamelCase						    =	torch.tensor([[0, 1, 1, 1, 1, 1]]				)
								with torch.no_grad():
										_UpperCamelCase						    =	model(a       ,							attention_mask=a				)[0]
								_UpperCamelCase						    =	torch.Size((1, 6, 7_68)				)
								self.assertEqual(output.shape       ,							a				)
								_UpperCamelCase						    =	torch.tensor([[[0.0685, 0.2441, 0.1102], [0.0600, 0.1906, 0.1349], [0.0221, 0.0819, 0.0586]]]				)

								self.assertTrue(torch.allclose(output[:, 1:4, 1:4]       ,							a       ,							atol=1e-4				)				)







						@slow
						def  A_				(			self				)					->      List[str]:


								'''simple docstring'''

								_UpperCamelCase						    =	NezhaForMaskedLM.from_pretrained("""sijunhe/nezha-cn-base"""				)
								_UpperCamelCase						    =	torch.tensor([[0, 1, 2, 3, 4, 5]]				)
								_UpperCamelCase						    =	torch.tensor([[1, 1, 1, 1, 1, 1]]				)
								with torch.no_grad():
										_UpperCamelCase						    =	model(a       ,							attention_mask=a				)[0]
								_UpperCamelCase						    =	torch.Size((1, 6, 2_11_28)				)
								self.assertEqual(output.shape       ,							a				)
								_UpperCamelCase						    =	torch.tensor(
								    [[-2.7939, -1.7902, -2.2189], [-2.8585, -1.8908, -2.3723], [-2.6499, -1.7750, -2.2558]]				)

								self.assertTrue(torch.allclose(output[:, 1:4, 1:4]       ,							a       ,							atol=1e-4				)				)



