
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     UpperCAmelCase						:



   def __init__(self						:   Optional[int]       ,		snake_case__						:   str       ,		snake_case__						:   Tuple=13       ,		snake_case__						:   Optional[Any]=7       ,		snake_case__						:   List[str]=True       ,		snake_case__						:   int=True       ,		snake_case__						:   Dict=True       ,		snake_case__						:   int=True       ,		snake_case__						:   Tuple=99       ,		snake_case__						:   Any=64       ,		snake_case__						:   Any=32       ,		snake_case__						:   int=5       ,		snake_case__						:   str=4       ,		snake_case__						:   Any=37       ,		snake_case__						:   Any="gelu"       ,		snake_case__						:   List[str]=0.1       ,		snake_case__						:   Union[str, Any]=0.1       ,		snake_case__						:   Optional[Any]=5_12       ,		snake_case__						:   List[Any]=16       ,		snake_case__						:   int=2       ,		snake_case__						:   Optional[int]=0.02       ,		snake_case__						:   Dict=3       ,		snake_case__						:   str=4       ,		snake_case__						:   Any=None       ,		)						->      Optional[Any]:


          '''simple docstring'''


          snake_case			:  List[Any]     							= parent
          snake_case			:  Tuple     							= batch_size
          snake_case			:  List[str]     							= seq_length
          snake_case			:  Dict     							= is_training
          snake_case			:  List[str]     							= use_input_mask
          snake_case			:  List[Any]     							= use_token_type_ids
          snake_case			:  Optional[Any]     							= use_labels
          snake_case			:  List[Any]     							= vocab_size
          snake_case			:  int     							= hidden_size
          snake_case			:  Optional[Any]     							= embedding_size
          snake_case			:  str     							= num_hidden_layers
          snake_case			:  List[str]     							= num_attention_heads
          snake_case			:  str     							= intermediate_size
          snake_case			:  Union[str, Any]     							= hidden_act
          snake_case			:  Optional[Any]     							= hidden_dropout_prob
          snake_case			:  Optional[int]     							= attention_probs_dropout_prob
          snake_case			:  Any     							= max_position_embeddings
          snake_case			:  Union[str, Any]     							= type_vocab_size
          snake_case			:  Dict     							= type_sequence_label_size
          snake_case			:  Dict     							= initializer_range
          snake_case			:  Any     							= num_labels
          snake_case			:  Any     							= num_choices
          snake_case			:  List[str]     							= scope



   def 							_SCREAMING_SNAKE_CASE  (self						:   List[Any]					)						->      Optional[int]:


          '''simple docstring'''


          snake_case			:  List[Any]     							= ids_tensor([self.batch_size, self.seq_length]       ,		self.vocab_size					)

          snake_case			:  Union[str, Any]     							= None
          if self.use_input_mask:
                 snake_case			:  str     							= random_attention_mask([self.batch_size, self.seq_length]					)

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

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

          snake_case			:  Union[str, Any]     							= self.get_config()

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



   def 							_SCREAMING_SNAKE_CASE  (self						:   int					)						->      Any:


          '''simple docstring'''


          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=snake_case__       ,		initializer_range=self.initializer_range       ,		)



   def 							_SCREAMING_SNAKE_CASE  (self						:   Optional[int]       ,		snake_case__						:   Union[str, Any]       ,		snake_case__						:   str       ,		snake_case__						:   Optional[Any]       ,		snake_case__						:   int       ,		snake_case__						:   Dict       ,		snake_case__						:   Tuple       ,		snake_case__						:   List[str]					)						->      List[str]:


          '''simple docstring'''


          snake_case			:  Optional[Any]     							= MobileBertModel(config=snake_case__					)
          model.to(snake_case__					)
          model.eval()
          snake_case			:  Dict     							= model(snake_case__       ,		attention_mask=snake_case__       ,		token_type_ids=snake_case__					)
          snake_case			:  List[Any]     							= model(snake_case__       ,		token_type_ids=snake_case__					)
          snake_case			:  Any     							= model(snake_case__					)

          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 							_SCREAMING_SNAKE_CASE  (self						:   Union[str, Any]       ,		snake_case__						:   List[Any]       ,		snake_case__						:   int       ,		snake_case__						:   List[str]       ,		snake_case__						:   Union[str, Any]       ,		snake_case__						:   Dict       ,		snake_case__						:   Tuple       ,		snake_case__						:   Union[str, Any]					)						->      int:


          '''simple docstring'''


          snake_case			:  List[str]     							= MobileBertForMaskedLM(config=snake_case__					)
          model.to(snake_case__					)
          model.eval()
          snake_case			:  Tuple     							= model(snake_case__       ,		attention_mask=snake_case__       ,		token_type_ids=snake_case__       ,		labels=snake_case__					)
          self.parent.assertEqual(result.logits.shape       ,		(self.batch_size, self.seq_length, self.vocab_size)					)



   def 							_SCREAMING_SNAKE_CASE  (self						:   str       ,		snake_case__						:   int       ,		snake_case__						:   Dict       ,		snake_case__						:   List[str]       ,		snake_case__						:   Optional[int]       ,		snake_case__						:   int       ,		snake_case__						:   Any       ,		snake_case__						:   str					)						->      Optional[Any]:


          '''simple docstring'''


          snake_case			:  Tuple     							= MobileBertForNextSentencePrediction(config=snake_case__					)
          model.to(snake_case__					)
          model.eval()
          snake_case			:  List[Any]     							= model(
              snake_case__       ,		attention_mask=snake_case__       ,		token_type_ids=snake_case__       ,		labels=snake_case__       ,		)
          self.parent.assertEqual(result.logits.shape       ,		(self.batch_size, 2)					)



   def 							_SCREAMING_SNAKE_CASE  (self						:   List[Any]       ,		snake_case__						:   List[str]       ,		snake_case__						:   Dict       ,		snake_case__						:   Dict       ,		snake_case__						:   Any       ,		snake_case__						:   List[Any]       ,		snake_case__						:   Tuple       ,		snake_case__						:   int					)						->      Optional[Any]:


          '''simple docstring'''


          snake_case			:  Dict     							= MobileBertForPreTraining(config=snake_case__					)
          model.to(snake_case__					)
          model.eval()
          snake_case			:  str     							= model(
              snake_case__       ,		attention_mask=snake_case__       ,		token_type_ids=snake_case__       ,		labels=snake_case__       ,		next_sentence_label=snake_case__       ,		)
          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 							_SCREAMING_SNAKE_CASE  (self						:   List[str]       ,		snake_case__						:   str       ,		snake_case__						:   str       ,		snake_case__						:   Union[str, Any]       ,		snake_case__						:   Any       ,		snake_case__						:   Tuple       ,		snake_case__						:   Dict       ,		snake_case__						:   str					)						->      Optional[int]:


          '''simple docstring'''


          snake_case			:  List[str]     							= MobileBertForQuestionAnswering(config=snake_case__					)
          model.to(snake_case__					)
          model.eval()
          snake_case			:  int     							= model(
              snake_case__       ,		attention_mask=snake_case__       ,		token_type_ids=snake_case__       ,		start_positions=snake_case__       ,		end_positions=snake_case__       ,		)
          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 							_SCREAMING_SNAKE_CASE  (self						:   Tuple       ,		snake_case__						:   Optional[Any]       ,		snake_case__						:   Dict       ,		snake_case__						:   Any       ,		snake_case__						:   Optional[Any]       ,		snake_case__						:   List[Any]       ,		snake_case__						:   Optional[int]       ,		snake_case__						:   List[str]					)						->      Tuple:


          '''simple docstring'''


          snake_case			:  List[str]     							= self.num_labels
          snake_case			:  Tuple     							= MobileBertForSequenceClassification(snake_case__					)
          model.to(snake_case__					)
          model.eval()
          snake_case			:  List[str]     							= model(snake_case__       ,		attention_mask=snake_case__       ,		token_type_ids=snake_case__       ,		labels=snake_case__					)
          self.parent.assertEqual(result.logits.shape       ,		(self.batch_size, self.num_labels)					)



   def 							_SCREAMING_SNAKE_CASE  (self						:   str       ,		snake_case__						:   Union[str, Any]       ,		snake_case__						:   Optional[int]       ,		snake_case__						:   List[Any]       ,		snake_case__						:   Union[str, Any]       ,		snake_case__						:   List[Any]       ,		snake_case__						:   Dict       ,		snake_case__						:   List[str]					)						->      int:


          '''simple docstring'''


          snake_case			:  Any     							= self.num_labels
          snake_case			:  Optional[Any]     							= MobileBertForTokenClassification(config=snake_case__					)
          model.to(snake_case__					)
          model.eval()
          snake_case			:  int     							= model(snake_case__       ,		attention_mask=snake_case__       ,		token_type_ids=snake_case__       ,		labels=snake_case__					)
          self.parent.assertEqual(result.logits.shape       ,		(self.batch_size, self.seq_length, self.num_labels)					)



   def 							_SCREAMING_SNAKE_CASE  (self						:   Any       ,		snake_case__						:   Optional[Any]       ,		snake_case__						:   Union[str, Any]       ,		snake_case__						:   int       ,		snake_case__						:   Union[str, Any]       ,		snake_case__						:   List[str]       ,		snake_case__						:   List[str]       ,		snake_case__						:   Any					)						->      Union[str, Any]:


          '''simple docstring'''


          snake_case			:  Dict     							= self.num_choices
          snake_case			:  Tuple     							= MobileBertForMultipleChoice(config=snake_case__					)
          model.to(snake_case__					)
          model.eval()
          snake_case			:  Dict     							= input_ids.unsqueeze(1					).expand(-1       ,		self.num_choices       ,		-1					).contiguous()
          snake_case			:  List[Any]     							= token_type_ids.unsqueeze(1					).expand(-1       ,		self.num_choices       ,		-1					).contiguous()
          snake_case			:  Dict     							= input_mask.unsqueeze(1					).expand(-1       ,		self.num_choices       ,		-1					).contiguous()
          snake_case			:  Any     							= model(
              snake_case__       ,		attention_mask=snake_case__       ,		token_type_ids=snake_case__       ,		labels=snake_case__       ,		)
          self.parent.assertEqual(result.logits.shape       ,		(self.batch_size, self.num_choices)					)



   def 							_SCREAMING_SNAKE_CASE  (self						:   List[str]					)						->      Tuple:


          '''simple docstring'''


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






@require_torch
class     UpperCAmelCase						(		A_			,A_			,unittest.TestCase					):
   A__   :						List[str]			=	(
       (
           MobileBertModel,
           MobileBertForMaskedLM,
           MobileBertForMultipleChoice,
           MobileBertForNextSentencePrediction,
           MobileBertForPreTraining,
           MobileBertForQuestionAnswering,
           MobileBertForSequenceClassification,
           MobileBertForTokenClassification,
       )
       if is_torch_available()
       else ()
   )
   A__   :						int			=	(
       {
           "feature-extraction": MobileBertModel,
           "fill-mask": MobileBertForMaskedLM,
           "question-answering": MobileBertForQuestionAnswering,
           "text-classification": MobileBertForSequenceClassification,
           "token-classification": MobileBertForTokenClassification,
           "zero-shot": MobileBertForSequenceClassification,
       }
       if is_torch_available()
       else {}
   )
   A__   :						int			=	True



   def 							_SCREAMING_SNAKE_CASE  (self						:   Any       ,		snake_case__						:   List[Any]       ,		snake_case__						:   Optional[int]       ,		snake_case__						:   Optional[Any]=False					)						->      List[str]:


          '''simple docstring'''


          snake_case			:  List[Any]     							= super()._prepare_for_class(snake_case__       ,		snake_case__       ,		return_labels=snake_case__					)

          if return_labels:
                 if model_class in get_values(snake_case__					):
                        snake_case			:  Tuple     							= torch.zeros(
                            (self.model_tester.batch_size, self.model_tester.seq_length)       ,		dtype=torch.long       ,		device=snake_case__					)
                        snake_case			:  Optional[Any]     							= torch.zeros(
                            self.model_tester.batch_size       ,		dtype=torch.long       ,		device=snake_case__					)
          return inputs_dict



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


          '''simple docstring'''


          snake_case			:  Dict     							= MobileBertModelTester(self					)
          snake_case			:  Optional[int]     							= ConfigTester(self       ,		config_class=snake_case__       ,		hidden_size=37					)



   def 							_SCREAMING_SNAKE_CASE  (self						:   Optional[Any]					)						->      Optional[int]:


          '''simple docstring'''


          self.config_tester.run_common_tests()



   def 							_SCREAMING_SNAKE_CASE  (self						:   Optional[Any]					)						->      Dict:


          '''simple docstring'''


          snake_case			:  Union[str, Any]     							= self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_mobilebert_model(*snake_case__					)



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


          '''simple docstring'''


          snake_case			:  Optional[Any]     							= self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_mobilebert_for_masked_lm(*snake_case__					)



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


          '''simple docstring'''


          snake_case			:  Any     							= self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_mobilebert_for_multiple_choice(*snake_case__					)



   def 							_SCREAMING_SNAKE_CASE  (self						:   List[str]					)						->      int:


          '''simple docstring'''


          snake_case			:  str     							= self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*snake_case__					)



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


          '''simple docstring'''


          snake_case			:  Any     							= self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_mobilebert_for_pretraining(*snake_case__					)



   def 							_SCREAMING_SNAKE_CASE  (self						:   List[str]					)						->      Optional[int]:


          '''simple docstring'''


          snake_case			:  Dict     							= self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_mobilebert_for_question_answering(*snake_case__					)



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


          '''simple docstring'''


          snake_case			:  Optional[int]     							= self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_mobilebert_for_sequence_classification(*snake_case__					)



   def 							_SCREAMING_SNAKE_CASE  (self						:   List[str]					)						->      Optional[int]:


          '''simple docstring'''


          snake_case			:  List[str]     							= self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_mobilebert_for_token_classification(*snake_case__					)



def      UpperCamelCase   (       __lowerCamelCase	:					Union[str, Any]       ):
       return torch.tensor(
           __lowerCamelCase							,   dtype=torch.long							,   device=__lowerCamelCase							,   )


__lowerCamelCase				=			1e-3






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



   @slow
   def 							_SCREAMING_SNAKE_CASE  (self						:   Dict					)						->      int:


          '''simple docstring'''


          snake_case			:  Optional[Any]     							= MobileBertModel.from_pretrained("google/mobilebert-uncased"					).to(snake_case__					)
          snake_case			:  str     							= _long_tensor([[1_01, 71_10, 10_05, 10_56, 20_23, 1_13_33, 1_74_13, 10_29, 1_02]]					)
          with torch.no_grad():
                 snake_case			:  Dict     							= model(snake_case__					)[0]
          snake_case			:  str     							= torch.Size((1, 9, 5_12)					)
          self.assertEqual(output.shape       ,		snake_case__					)
          snake_case			:  List[str]     							= torch.tensor(
              [
                  [
                      [-2.4_73_65_26e07, 8.2_69_16_56e04, 1.6_52_18_38e05],
                      [-5.7_54_17_04e-01, 3.9_05_60_22e00, 4.4_01_15_07e00],
                      [2.6_04_73_59e00, 1.5_67_76_52e00, -1.7_32_41_88e-01],
                  ]
              ]       ,		device=snake_case__       ,		)

          # 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
          snake_case			:  Optional[Any]     							= torch.all((expected_slice / output[..., :3, :3]) >= 1 - TOLERANCE					)
          snake_case			:  List[str]     							= torch.all((expected_slice / output[..., :3, :3]) <= 1 + TOLERANCE					)

          self.assertTrue(lower_bound and upper_bound					)







import argparse
from pathlib import Path

import torch
from packaging import version
from torch.onnx import export

from diffusers import AutoencoderKL


__lowerCamelCase				=			version.parse(version.parse(torch.__version__).base_version) < version.parse("""1.11""")


def      UpperCamelCase   (       __lowerCamelCase	:					List[Any]							,   __lowerCamelCase	:					tuple							,   __lowerCamelCase	:					Path							,   __lowerCamelCase	:					Tuple							,   __lowerCamelCase	:					Any							,   __lowerCamelCase	:					Dict							,   __lowerCamelCase	:					str							,   __lowerCamelCase	:					Optional[Any]=False							,   ):
       output_path.parent.mkdir(parents=__lowerCamelCase							,   exist_ok=__lowerCamelCase       )
       # PyTorch deprecated the `enable_onnx_checker` and `use_external_data_format` arguments in v1.11,
       # so we check the torch version for backwards compatibility
       if is_torch_less_than_1_11:
              export(
                  __lowerCamelCase							,   __lowerCamelCase							,   f=output_path.as_posix()							,   input_names=__lowerCamelCase							,   output_names=__lowerCamelCase							,   dynamic_axes=__lowerCamelCase							,   do_constant_folding=__lowerCamelCase							,   use_external_data_format=__lowerCamelCase							,   enable_onnx_checker=__lowerCamelCase							,   opset_version=__lowerCamelCase							,   )
       else:
              export(
                  __lowerCamelCase							,   __lowerCamelCase							,   f=output_path.as_posix()							,   input_names=__lowerCamelCase							,   output_names=__lowerCamelCase							,   dynamic_axes=__lowerCamelCase							,   do_constant_folding=__lowerCamelCase							,   opset_version=__lowerCamelCase							,   )


@torch.no_grad()
def      UpperCamelCase   (       __lowerCamelCase	:					str							,   __lowerCamelCase	:					str							,   __lowerCamelCase	:					int							,   __lowerCamelCase	:					bool = False       ):
       snake_case			:  Optional[int]     							= torch.floataa if fpaa else torch.floataa
       if fpaa and torch.cuda.is_available():
              snake_case			:  str     							= "cuda"
       elif fpaa and not torch.cuda.is_available():
              raise ValueError("`float16` model export is only supported on GPUs with CUDA"       )
       else:
              snake_case			:  Tuple     							= "cpu"
       snake_case			:  Union[str, Any]     							= Path(__lowerCamelCase       )

       # VAE DECODER
       snake_case			:  Any     							= AutoencoderKL.from_pretrained(model_path + "/vae"       )
       snake_case			:  Tuple     							= vae_decoder.config.latent_channels
       # forward only through the decoder part
       snake_case			:  Tuple     							= vae_decoder.decode
       onnx_export(
           __lowerCamelCase							,   model_args=(
               torch.randn(1							,   __lowerCamelCase							,   25							,   25       ).to(device=__lowerCamelCase							,   dtype=__lowerCamelCase       ),
               False,
           )							,   output_path=output_path / "vae_decoder" / "model.onnx"							,   ordered_input_names=["latent_sample", "return_dict"]							,   output_names=["sample"]							,   dynamic_axes={
               "latent_sample": {0: "batch", 1: "channels", 2: "height", 3: "width"},
           }							,   opset=__lowerCamelCase							,   )
       del vae_decoder


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

   parser.add_argument(
       """--model_path""",
       type=str,
       required=True,
       help="""Path to the `diffusers` checkpoint to convert (either a local directory or on the Hub).""",
   )

   parser.add_argument("""--output_path""", type=str, required=True, help="""Path to the output model.""")
   parser.add_argument(
       """--opset""",
       default=14,
       type=int,
       help="""The version of the ONNX operator set to use.""",
   )
   parser.add_argument("""--fp16""", action="""store_true""", default=False, help="""Export the models in `float16` mode""")

   __lowerCamelCase				=			parser.parse_args()
   print(args.output_path)
   convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
   print("""SD: Done: ONNX""")





