






"""simple docstring"""



import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


A__       :				List[Any] 					=					version.parse(version.parse(torch.__version__).base_version) < version.parse('1.11')

def  _snake_case				(     lowerCamelCase__	:    Optional[Any]				,						lowerCamelCase__	:    List[str]				,						lowerCamelCase__	:    Dict				,						lowerCamelCase__	:    Dict				,						lowerCamelCase__	:    Optional[Any]				,						lowerCamelCase__	:    Any				,						lowerCamelCase__	:    List[Any]				,						lowerCamelCase__	:    str=False				,						)				->	int:
							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  _snake_case				(     lowerCamelCase__	:    int				,						lowerCamelCase__	:    Union[str, Any]				,						lowerCamelCase__	:    int				,						lowerCamelCase__	:    Tuple = False  )				->	List[Any]:
							lowerCamelCase_       :							Tuple									=torch.floataa if fpaa else torch.floataa
							if fpaa and torch.cuda.is_available():
														lowerCamelCase_       :							Union[str, Any]									="""cuda"""
							elif fpaa and not torch.cuda.is_available():
														raise ValueError("`float16` model export is only supported on GPUs with CUDA"  )
							else:
														lowerCamelCase_       :							Any									="""cpu"""
							lowerCamelCase_       :							Any									=Path(_lowerCAmelCase  )

							# VAE DECODER
							lowerCamelCase_       :							Union[str, Any]									=AutoencoderKL.from_pretrained(model_path + "/vae"  )
							lowerCamelCase_       :							Any									=vae_decoder.config.latent_channels
							# forward only through the decoder part
							lowerCamelCase_       :							Optional[int]									=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__":
						A__       :				Tuple 					=					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')

						A__       :				Tuple 					=					parser.parse_args()
						print(args.output_path)
						convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
						print('SD: Done: ONNX')








import unittest

import numpy as np

from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
     import jax.numpy as jnp

     from transformers.models.albert.modeling_flax_albert import (
         FlaxAlbertForMaskedLM,
         FlaxAlbertForMultipleChoice,
         FlaxAlbertForPreTraining,
         FlaxAlbertForQuestionAnswering,
         FlaxAlbertForSequenceClassification,
         FlaxAlbertForTokenClassification,
         FlaxAlbertModel,
     )






class SCREAMING_SNAKE_CASE__     (		unittest.TestCase    ):




    '''simple docstring'''






    def __init__(       self,		lowerCamelCase__,		lowerCamelCase__=13,		lowerCamelCase__=7,		lowerCamelCase__=True,		lowerCamelCase__=True,		lowerCamelCase__=True,		lowerCamelCase__=True,		lowerCamelCase__=99,		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__=4,		):
        A :       List[str]          =   parent
        A :       Optional[int]          =   batch_size
        A :       Union[str, Any]          =   seq_length
        A :       Any          =   is_training
        A :       List[str]          =   use_attention_mask
        A :       Union[str, Any]          =   use_token_type_ids
        A :       Any          =   use_labels
        A :       str          =   vocab_size
        A :       Union[str, Any]          =   hidden_size
        A :       str          =   num_hidden_layers
        A :       List[Any]          =   num_attention_heads
        A :       Optional[int]          =   intermediate_size
        A :       Optional[Any]          =   hidden_act
        A :       Dict          =   hidden_dropout_prob
        A :       List[Any]          =   attention_probs_dropout_prob
        A :       Optional[int]          =   max_position_embeddings
        A :       int          =   type_vocab_size
        A :       str          =   type_sequence_label_size
        A :       List[Any]          =   initializer_range
        A :       str          =   num_choices






    def 			_lowerCAmelCase					(       self		):
        A :       Optional[int]          =   ids_tensor([self.batch_size, self.seq_length],		self.vocab_size		)

        A :       Union[str, Any]          =   None
        if self.use_attention_mask:
            A :       Optional[int]          =   random_attention_mask([self.batch_size, self.seq_length]		)

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

        A :       Optional[int]          =   AlbertConfig(
            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=lowerCamelCase__,		initializer_range=self.initializer_range,		)

        return config, input_ids, token_type_ids, attention_mask






    def 			_lowerCAmelCase					(       self		):
        A :       Dict          =   self.prepare_config_and_inputs()
        A     ,							A     ,							A     ,							A :       str          =   config_and_inputs
        A :       Union[str, Any]          =   {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
        return config, inputs_dict






@require_flax
class SCREAMING_SNAKE_CASE__     (		SCREAMING_SNAKE_CASE__ ,						unittest.TestCase    ):




    '''simple docstring'''






    __lowerCamelCase			: Any					=      (
        (
            FlaxAlbertModel,
            FlaxAlbertForPreTraining,
            FlaxAlbertForMaskedLM,
            FlaxAlbertForMultipleChoice,
            FlaxAlbertForQuestionAnswering,
            FlaxAlbertForSequenceClassification,
            FlaxAlbertForTokenClassification,
            FlaxAlbertForQuestionAnswering,
        )
        if is_flax_available()
        else ()
    )






    def 			_lowerCAmelCase					(       self		):
        A :       Dict          =   FlaxAlbertModelTester(self		)






    @slow
    def 			_lowerCAmelCase					(       self		):
        for model_class_name in self.all_model_classes:
            A :       Dict          =   model_class_name.from_pretrained("""albert-base-v2"""		)
            A :       Optional[int]          =   model(np.ones((1, 1)		)		)
            self.assertIsNotNone(lowerCamelCase__		)






@require_flax
class SCREAMING_SNAKE_CASE__     (		unittest.TestCase    ):




    '''simple docstring'''






    @slow
    def 			_lowerCAmelCase					(       self		):
        A :       Dict          =   FlaxAlbertModel.from_pretrained("""albert-base-v2"""		)
        A :       List[str]          =   np.array([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]		)
        A :       str          =   np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]		)
        A :       Optional[int]          =   model(lowerCamelCase__,		attention_mask=lowerCamelCase__		)[0]
        A :       str          =   (1, 11, 768)
        self.assertEqual(output.shape,		lowerCamelCase__		)
        A :       Optional[int]          =   np.array(
            [[[-0.6513, 1.5035, -0.2766], [-0.6515, 1.5046, -0.2780], [-0.6512, 1.5049, -0.2784]]]		)

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


