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 	__snake_case	:





      '''simple docstring'''






      def __init__(					self     :				Optional[int]    ,  _UpperCamelCase     :				Dict    ,  _UpperCamelCase     :				Tuple=13    ,  _UpperCamelCase     :				List[Any]=7    ,  _UpperCamelCase     :				Dict=True    ,  _UpperCamelCase     :				str=True    ,  _UpperCamelCase     :				Any=True    ,  _UpperCamelCase     :				Any=True    ,  _UpperCamelCase     :				str=99    ,  _UpperCamelCase     :				Any=32    ,  _UpperCamelCase     :				Union[str, Any]=2    ,  _UpperCamelCase     :				str=4    ,  _UpperCamelCase     :				Tuple=37    ,  _UpperCamelCase     :				int="gelu"    ,  _UpperCamelCase     :				Optional[Any]=0.1    ,  _UpperCamelCase     :				Any=0.1    ,  _UpperCamelCase     :				Tuple=512    ,  _UpperCamelCase     :				Union[str, Any]=16    ,  _UpperCamelCase     :				List[str]=2    ,  _UpperCamelCase     :				Dict=0.0_2    ,  _UpperCamelCase     :				Optional[Any]=False    ,  _UpperCamelCase     :				List[str]=True    ,  _UpperCamelCase     :				List[Any]="None"    ,  _UpperCamelCase     :				int=3    ,  _UpperCamelCase     :				Any=4    ,  _UpperCamelCase     :				Tuple=None    ,  )				->Any:


          """simple docstring"""


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



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


          """simple docstring"""


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

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

          _lowerCamelCase     :		List[Any] 						=						None
          if self.use_token_type_ids:
              _lowerCamelCase     :		Optional[Any] 						=						ids_tensor([self.batch_size, self.seq_length]    ,  self.type_vocab_size)

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

          _lowerCamelCase     :		int 						=						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=_UpperCamelCase    ,  )

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



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


          """simple docstring"""


          _lowerCamelCase     :		Tuple 						=						TFDebertaVaModel(config=_UpperCamelCase)
          _lowerCamelCase     :		int 						=						{"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}

          _lowerCamelCase     :		Dict 						=						[input_ids, input_mask]
          _lowerCamelCase     :		Optional[int] 						=						model(_UpperCamelCase)

          _lowerCamelCase     :		Dict 						=						model(_UpperCamelCase)

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



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


          """simple docstring"""


          _lowerCamelCase     :		List[Any] 						=						TFDebertaVaForMaskedLM(config=_UpperCamelCase)
          _lowerCamelCase     :		Union[str, Any] 						=						{
              """input_ids""": input_ids,
              """attention_mask""": input_mask,
              """token_type_ids""": token_type_ids,
          }
          _lowerCamelCase     :		Any 						=						model(_UpperCamelCase)
          self.parent.assertEqual(result.logits.shape    ,  (self.batch_size, self.seq_length, self.vocab_size))



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


          """simple docstring"""


          _lowerCamelCase     :		Optional[int] 						=						self.num_labels
          _lowerCamelCase     :		List[str] 						=						TFDebertaVaForSequenceClassification(config=_UpperCamelCase)
          _lowerCamelCase     :		Optional[Any] 						=						{
              """input_ids""": input_ids,
              """attention_mask""": input_mask,
              """token_type_ids""": token_type_ids,
          }

          _lowerCamelCase     :		Optional[int] 						=						model(_UpperCamelCase)
          self.parent.assertEqual(result.logits.shape    ,  (self.batch_size, self.num_labels))



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


          """simple docstring"""


          _lowerCamelCase     :		str 						=						self.num_labels
          _lowerCamelCase     :		Optional[int] 						=						TFDebertaVaForTokenClassification(config=_UpperCamelCase)
          _lowerCamelCase     :		List[Any] 						=						{
              """input_ids""": input_ids,
              """attention_mask""": input_mask,
              """token_type_ids""": token_type_ids,
          }
          _lowerCamelCase     :		Any 						=						model(_UpperCamelCase)
          self.parent.assertEqual(result.logits.shape    ,  (self.batch_size, self.seq_length, self.num_labels))



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


          """simple docstring"""


          _lowerCamelCase     :		str 						=						TFDebertaVaForQuestionAnswering(config=_UpperCamelCase)
          _lowerCamelCase     :		List[str] 						=						{
              """input_ids""": input_ids,
              """attention_mask""": input_mask,
              """token_type_ids""": token_type_ids,
          }

          _lowerCamelCase     :		int 						=						model(_UpperCamelCase)
          self.parent.assertEqual(result.start_logits.shape    ,  (self.batch_size, self.seq_length))
          self.parent.assertEqual(result.end_logits.shape    ,  (self.batch_size, self.seq_length))




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


          """simple docstring"""


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





@require_tf
class 	__snake_case	( __lowerCAmelCase ,  __lowerCAmelCase ,  unittest.TestCase		):





      '''simple docstring'''






      _snake_case    		=       (
          (
              TFDebertaVaModel,
              TFDebertaVaForMaskedLM,
              TFDebertaVaForQuestionAnswering,
              TFDebertaVaForSequenceClassification,
              TFDebertaVaForTokenClassification,
          )
          if is_tf_available()
          else ()
      )
      _snake_case    		=       (
          {
              'feature-extraction': TFDebertaVaModel,
              'fill-mask': TFDebertaVaForMaskedLM,
              'question-answering': TFDebertaVaForQuestionAnswering,
              'text-classification': TFDebertaVaForSequenceClassification,
              'token-classification': TFDebertaVaForTokenClassification,
              'zero-shot': TFDebertaVaForSequenceClassification,
          }
          if is_tf_available()
          else {}
      )

      _snake_case    		=       False
      _snake_case    		=       False



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


          """simple docstring"""


          _lowerCamelCase     :		Optional[Any] 						=						TFDebertaVaModelTester(self)
          _lowerCamelCase     :		Dict 						=						ConfigTester(self    ,  config_class=_UpperCamelCase    ,  hidden_size=37)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				int)				->str:


          """simple docstring"""


          self.config_tester.run_common_tests()



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


          """simple docstring"""


          _lowerCamelCase     :		List[str] 						=						self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*_UpperCamelCase)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Any)				->int:


          """simple docstring"""


          _lowerCamelCase     :		List[str] 						=						self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_masked_lm(*_UpperCamelCase)



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


          """simple docstring"""


          _lowerCamelCase     :		Dict 						=						self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_question_answering(*_UpperCamelCase)



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


          """simple docstring"""


          _lowerCamelCase     :		str 						=						self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_sequence_classification(*_UpperCamelCase)



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


          """simple docstring"""


          _lowerCamelCase     :		Dict 						=						self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_token_classification(*_UpperCamelCase)




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


          """simple docstring"""


          _lowerCamelCase     :		Any 						=						TFDebertaVaModel.from_pretrained("""kamalkraj/deberta-v2-xlarge""")
          self.assertIsNotNone(_UpperCamelCase)





@require_tf
class 	__snake_case	( unittest.TestCase		):





      '''simple docstring'''






      @unittest.skip(reason="""Model not available yet""")
      def 	_SCREAMING_SNAKE_CASE    (					self     :				str)				->Tuple:


          """simple docstring"""


          pass




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


          """simple docstring"""


          _lowerCamelCase     :		Union[str, Any] 						=						TFDebertaVaModel.from_pretrained("""kamalkraj/deberta-v2-xlarge""")
          _lowerCamelCase     :		Optional[Any] 						=						tf.constant([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]])
          _lowerCamelCase     :		Optional[int] 						=						tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])
          _lowerCamelCase     :		Optional[int] 						=						model(_UpperCamelCase    ,  attention_mask=_UpperCamelCase)[0]

          _lowerCamelCase     :		Dict 						=						tf.constant(
              [[[0.2_3_5_6, 0.1_9_4_8, 0.0_3_6_9], [-0.1_0_6_3, 0.3_5_8_6, -0.5_1_5_2], [-0.6_3_9_9, -0.0_2_5_9, -0.2_5_2_5]]])
          tf.debugging.assert_near(output[:, 1:4, 1:4]    ,  _UpperCamelCase    ,  atol=1E-4)





import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


lowerCAmelCase			:							Tuple     =version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")







def 						A__  (					__A   , __A   , __A   , __A   , __A   , __A   , __A   , __A=False   , ):







    '''simple docstring'''
    output_path.parent.mkdir(parents=__A   , exist_ok=__A	)
    # 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(
            __A   , __A   , f=output_path.as_posix()   , input_names=__A   , output_names=__A   , dynamic_axes=__A   , do_constant_folding=__A   , use_external_data_format=__A   , enable_onnx_checker=__A   , opset_version=__A   , )
    else:
        export(
            __A   , __A   , f=output_path.as_posix()   , input_names=__A   , output_names=__A   , dynamic_axes=__A   , do_constant_folding=__A   , opset_version=__A   , )







@torch.no_grad()
def 						A__  (					__A   , __A   , __A   , __A = False	):







    '''simple docstring'''
    _lowerCamelCase     :		Tuple 						=						torch.floataa if fpaa else torch.floataa
    if fpaa and torch.cuda.is_available():
        _lowerCamelCase     :		str 						=						"""cuda"""
    elif fpaa and not torch.cuda.is_available():
        raise ValueError("""`float16` model export is only supported on GPUs with CUDA"""	)
    else:
        _lowerCamelCase     :		List[str] 						=						"""cpu"""
    _lowerCamelCase     :		Dict 						=						Path(__A	)

    # VAE DECODER
    _lowerCamelCase     :		Optional[Any] 						=						AutoencoderKL.from_pretrained(model_path + """/vae"""	)
    _lowerCamelCase     :		List[str] 						=						vae_decoder.config.latent_channels
    # forward only through the decoder part
    _lowerCamelCase     :		Tuple 						=						vae_decoder.decode
    onnx_export(
        __A   , model_args=(
            torch.randn(1   , __A   , 25   , 25	).to(device=__A   , dtype=__A	),
            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=__A   , )
    del vae_decoder


if __name__ == "__main__":
    lowerCAmelCase			:							Optional[int]     =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			:							Optional[Any]     =parser.parse_args()
    print(args.output_path)
    convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
    print("SD: Done: ONNX")




