


"""simple docstring"""
import argparse
import os
import shutil
from pathlib import Path

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

from diffusers import OnnxRuntimeModel, OnnxStableDiffusionPipeline, StableDiffusionPipeline


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





def 				lowerCAmelCase__      (     _UpperCamelCase    :			int	,     _UpperCamelCase    :			tuple	,     _UpperCamelCase    :			Path	,     _UpperCamelCase    :			Optional[Any]	,     _UpperCamelCase    :			int	,     _UpperCamelCase    :			str	,     _UpperCamelCase    :			str	,     _UpperCamelCase    :			Optional[int]=False	,     )   ->			Optional[int]:





						"""simple docstring"""






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





@torch.no_grad()
def 				lowerCAmelCase__      (     _UpperCamelCase    :			str	,     _UpperCamelCase    :			str	,     _UpperCamelCase    :			int	,     _UpperCamelCase    :			bool = False   )   ->			Any:





						"""simple docstring"""






						snake_case        =     torch.floataa if fpaa else torch.floataa
						if fpaa and torch.cuda.is_available():
												snake_case        =     """cuda"""
						elif fpaa and not torch.cuda.is_available():
												raise ValueError('`float16` model export is only supported on GPUs with CUDA'   )
						else:
												snake_case        =     """cpu"""
						snake_case        =     StableDiffusionPipeline.from_pretrained(lowercase__	,     torch_dtype=lowercase__   ).to(lowercase__   )
						snake_case        =     Path(lowercase__   )

						# TEXT ENCODER
						snake_case        =     pipeline.text_encoder.config.max_position_embeddings
						snake_case        =     pipeline.text_encoder.config.hidden_size
						snake_case        =     pipeline.tokenizer(
						    'A sample prompt'	,     padding='max_length'	,     max_length=pipeline.tokenizer.model_max_length	,     truncation=lowercase__	,     return_tensors='pt'	,     )
						onnx_export(
						    pipeline.text_encoder	,     model_args=(text_input.input_ids.to(device=lowercase__	,     dtype=torch.intaa   ))	,     output_path=output_path / 'text_encoder' / 'model.onnx'	,     ordered_input_names=['input_ids']	,     output_names=['last_hidden_state', 'pooler_output']	,     dynamic_axes={
						        'input_ids': {0: 'batch', 1: 'sequence'},
						    }	,     opset=lowercase__	,     )
						del pipeline.text_encoder

						# UNET
						snake_case        =     pipeline.unet.config.in_channels
						snake_case        =     pipeline.unet.config.sample_size
						snake_case        =     output_path / """unet""" / """model.onnx"""
						onnx_export(
						    pipeline.unet	,     model_args=(
						        torch.randn(2	,     lowercase__	,     lowercase__	,     lowercase__   ).to(device=lowercase__	,     dtype=lowercase__   ),
						        torch.randn(2   ).to(device=lowercase__	,     dtype=lowercase__   ),
						        torch.randn(2	,     lowercase__	,     lowercase__   ).to(device=lowercase__	,     dtype=lowercase__   ),
						        False,
						    )	,     output_path=lowercase__	,     ordered_input_names=['sample', 'timestep', 'encoder_hidden_states', 'return_dict']	,     output_names=['out_sample']	,     dynamic_axes={
						        'sample': {0: 'batch', 1: 'channels', 2: 'height', 3: 'width'},
						        'timestep': {0: 'batch'},
						        'encoder_hidden_states': {0: 'batch', 1: 'sequence'},
						    }	,     opset=lowercase__	,     use_external_data_format=lowercase__	,     )
						snake_case        =     str(unet_path.absolute().as_posix()   )
						snake_case        =     os.path.dirname(lowercase__   )
						snake_case        =     onnx.load(lowercase__   )
						# clean up existing tensor files
						shutil.rmtree(lowercase__   )
						os.mkdir(lowercase__   )
						# collate external tensor files into one
						onnx.save_model(
						    lowercase__	,     lowercase__	,     save_as_external_data=lowercase__	,     all_tensors_to_one_file=lowercase__	,     location='weights.pb'	,     convert_attribute=lowercase__	,     )
						del pipeline.unet

						# VAE ENCODER
						snake_case        =     pipeline.vae
						snake_case        =     vae_encoder.config.in_channels
						snake_case        =     vae_encoder.config.sample_size
						# need to get the raw tensor output (sample) from the encoder
						snake_case        =     lambda _UpperCamelCase	,     _UpperCamelCase   : vae_encoder.encode(lowercase__	,     lowercase__   )[0].sample()
						onnx_export(
						    lowercase__	,     model_args=(
						        torch.randn(1	,     lowercase__	,     lowercase__	,     lowercase__   ).to(device=lowercase__	,     dtype=lowercase__   ),
						        False,
						    )	,     output_path=output_path / 'vae_encoder' / 'model.onnx'	,     ordered_input_names=['sample', 'return_dict']	,     output_names=['latent_sample']	,     dynamic_axes={
						        'sample': {0: 'batch', 1: 'channels', 2: 'height', 3: 'width'},
						    }	,     opset=lowercase__	,     )

						# VAE DECODER
						snake_case        =     pipeline.vae
						snake_case        =     vae_decoder.config.latent_channels
						snake_case        =     vae_decoder.config.out_channels
						# forward only through the decoder part
						snake_case        =     vae_encoder.decode
						onnx_export(
						    lowercase__	,     model_args=(
						        torch.randn(1	,     lowercase__	,     lowercase__	,     lowercase__   ).to(device=lowercase__	,     dtype=lowercase__   ),
						        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=lowercase__	,     )
						del pipeline.vae

						# SAFETY CHECKER
						if pipeline.safety_checker is not None:
												snake_case        =     pipeline.safety_checker
												snake_case        =     safety_checker.config.vision_config.num_channels
												snake_case        =     safety_checker.config.vision_config.image_size
												snake_case        =     safety_checker.forward_onnx
												onnx_export(
												    pipeline.safety_checker	,     model_args=(
												        torch.randn(
												            1	,     lowercase__	,     lowercase__	,     lowercase__	,     ).to(device=lowercase__	,     dtype=lowercase__   ),
												        torch.randn(1	,     lowercase__	,     lowercase__	,     lowercase__   ).to(device=lowercase__	,     dtype=lowercase__   ),
												    )	,     output_path=output_path / 'safety_checker' / 'model.onnx'	,     ordered_input_names=['clip_input', 'images']	,     output_names=['out_images', 'has_nsfw_concepts']	,     dynamic_axes={
												        'clip_input': {0: 'batch', 1: 'channels', 2: 'height', 3: 'width'},
												        'images': {0: 'batch', 1: 'height', 2: 'width', 3: 'channels'},
												    }	,     opset=lowercase__	,     )
												del pipeline.safety_checker
												snake_case        =     OnnxRuntimeModel.from_pretrained(output_path / 'safety_checker'   )
												snake_case        =     pipeline.feature_extractor
						else:
												snake_case        =     None
												snake_case        =     None

						snake_case        =     OnnxStableDiffusionPipeline(
						    vae_encoder=OnnxRuntimeModel.from_pretrained(output_path / 'vae_encoder'   )	,     vae_decoder=OnnxRuntimeModel.from_pretrained(output_path / 'vae_decoder'   )	,     text_encoder=OnnxRuntimeModel.from_pretrained(output_path / 'text_encoder'   )	,     tokenizer=pipeline.tokenizer	,     unet=OnnxRuntimeModel.from_pretrained(output_path / 'unet'   )	,     scheduler=pipeline.scheduler	,     safety_checker=lowercase__	,     feature_extractor=lowercase__	,     requires_safety_checker=safety_checker is not None	,     )

						onnx_pipeline.save_pretrained(lowercase__   )
						print('ONNX pipeline saved to'	,     lowercase__   )

						del pipeline
						del onnx_pipeline
						snake_case        =     OnnxStableDiffusionPipeline.from_pretrained(lowercase__	,     provider='CPUExecutionProvider'   )
						print('ONNX pipeline is loadable'   )


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE__   =					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")

					SCREAMING_SNAKE_CASE__   =					parser.parse_args()

					convert_models(args.model_path, args.output_path, args.opset, args.fpaa)



"""simple docstring"""
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 					lowerCAmelCase_      :



				"""simple docstring"""

				def __init__(			self						,      lowerCAmelCase						,      lowerCAmelCase=13						,      lowerCAmelCase=7						,      lowerCAmelCase=True						,      lowerCAmelCase=True						,      lowerCAmelCase=True						,      lowerCAmelCase=True						,      lowerCAmelCase=99						,      lowerCAmelCase=32						,      lowerCAmelCase=2						,      lowerCAmelCase=4						,      lowerCAmelCase=37						,      lowerCAmelCase="gelu"						,      lowerCAmelCase=0.1						,      lowerCAmelCase=0.1						,      lowerCAmelCase=5_12						,      lowerCAmelCase=16						,      lowerCAmelCase=2						,      lowerCAmelCase=0.02						,      lowerCAmelCase=False						,      lowerCAmelCase=True						,      lowerCAmelCase="None"						,      lowerCAmelCase=3						,      lowerCAmelCase=4						,      lowerCAmelCase=None						,      ):






										"""simple docstring"""

										snake_case        =     parent
										snake_case        =     batch_size
										snake_case        =     seq_length
										snake_case        =     is_training
										snake_case        =     use_input_mask
										snake_case        =     use_token_type_ids
										snake_case        =     use_labels
										snake_case        =     vocab_size
										snake_case        =     hidden_size
										snake_case        =     num_hidden_layers
										snake_case        =     num_attention_heads
										snake_case        =     intermediate_size
										snake_case        =     hidden_act
										snake_case        =     hidden_dropout_prob
										snake_case        =     attention_probs_dropout_prob
										snake_case        =     max_position_embeddings
										snake_case        =     type_vocab_size
										snake_case        =     type_sequence_label_size
										snake_case        =     initializer_range
										snake_case        =     num_labels
										snake_case        =     num_choices
										snake_case        =     relative_attention
										snake_case        =     position_biased_input
										snake_case        =     pos_att_type
										snake_case        =     scope

				def 						snake_case	(			self      ):






										"""simple docstring"""

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

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

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

										snake_case        =     None
										snake_case        =     None
										snake_case        =     None
										if self.use_labels:
																snake_case        =     ids_tensor([self.batch_size]						,      self.type_sequence_label_size      )
																snake_case        =     ids_tensor([self.batch_size, self.seq_length]						,      self.num_labels      )

										snake_case        =     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=lowerCAmelCase						,      )

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

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase      ):






										"""simple docstring"""

										snake_case        =     TFDebertaVaModel(config=lowerCAmelCase      )
										snake_case        =     {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}

										snake_case        =     [input_ids, input_mask]
										snake_case        =     model(lowerCAmelCase      )

										snake_case        =     model(lowerCAmelCase      )

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

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase      ):






										"""simple docstring"""

										snake_case        =     TFDebertaVaForMaskedLM(config=lowerCAmelCase      )
										snake_case        =     {
										    'input_ids': input_ids,
										    'attention_mask': input_mask,
										    'token_type_ids': token_type_ids,
										}
										snake_case        =     model(lowerCAmelCase      )
										self.parent.assertEqual(result.logits.shape						,      (self.batch_size, self.seq_length, self.vocab_size)      )

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase      ):






										"""simple docstring"""

										snake_case        =     self.num_labels
										snake_case        =     TFDebertaVaForSequenceClassification(config=lowerCAmelCase      )
										snake_case        =     {
										    'input_ids': input_ids,
										    'attention_mask': input_mask,
										    'token_type_ids': token_type_ids,
										}

										snake_case        =     model(lowerCAmelCase      )
										self.parent.assertEqual(result.logits.shape						,      (self.batch_size, self.num_labels)      )

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase      ):






										"""simple docstring"""

										snake_case        =     self.num_labels
										snake_case        =     TFDebertaVaForTokenClassification(config=lowerCAmelCase      )
										snake_case        =     {
										    'input_ids': input_ids,
										    'attention_mask': input_mask,
										    'token_type_ids': token_type_ids,
										}
										snake_case        =     model(lowerCAmelCase      )
										self.parent.assertEqual(result.logits.shape						,      (self.batch_size, self.seq_length, self.num_labels)      )

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase      ):






										"""simple docstring"""

										snake_case        =     TFDebertaVaForQuestionAnswering(config=lowerCAmelCase      )
										snake_case        =     {
										    'input_ids': input_ids,
										    'attention_mask': input_mask,
										    'token_type_ids': token_type_ids,
										}

										snake_case        =     model(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 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     self.prepare_config_and_inputs()
										(
										    (
										    snake_case
										)   ,(
										    snake_case
										)   ,(
										    snake_case
										)   ,(
										    snake_case
										)   ,(
										    snake_case
										)   ,(
										    snake_case
										)   ,(
										    snake_case
										)   ,
										)        =     config_and_inputs
										snake_case        =     {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
										return config, inputs_dict





@require_tf
class 					lowerCAmelCase_      (     lowerCAmelCase       ,      lowerCAmelCase       ,      unittest.TestCase   ):



				"""simple docstring"""
				_lowerCAmelCase       :      Optional[int]     					=  (
				    (
				        TFDebertaVaModel,
				        TFDebertaVaForMaskedLM,
				        TFDebertaVaForQuestionAnswering,
				        TFDebertaVaForSequenceClassification,
				        TFDebertaVaForTokenClassification,
				    )
				    if is_tf_available()
				    else ()
				)
				_lowerCAmelCase       :      Any     					=  (
				    {
				        """feature-extraction""": TFDebertaVaModel,
				        """fill-mask""": TFDebertaVaForMaskedLM,
				        """question-answering""": TFDebertaVaForQuestionAnswering,
				        """text-classification""": TFDebertaVaForSequenceClassification,
				        """token-classification""": TFDebertaVaForTokenClassification,
				        """zero-shot""": TFDebertaVaForSequenceClassification,
				    }
				    if is_tf_available()
				    else {}
				)

				_lowerCAmelCase       :      Optional[Any]     					=  False
				_lowerCAmelCase       :      List[Any]     					=  False

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     TFDebertaVaModelTester(self      )
										snake_case        =     ConfigTester(self						,      config_class=lowerCAmelCase						,      hidden_size=37      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										self.config_tester.run_common_tests()

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*lowerCAmelCase      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_masked_lm(*lowerCAmelCase      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_question_answering(*lowerCAmelCase      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_sequence_classification(*lowerCAmelCase      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_token_classification(*lowerCAmelCase      )

				@slow
				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     TFDebertaVaModel.from_pretrained('kamalkraj/deberta-v2-xlarge'      )
										self.assertIsNotNone(lowerCAmelCase      )





@require_tf
class 					lowerCAmelCase_      (     unittest.TestCase   ):



				"""simple docstring"""

				@unittest.skip(reason='Model not available yet'      )
				def 						snake_case	(			self      ):






										"""simple docstring"""

										pass

				@slow
				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     TFDebertaVaModel.from_pretrained('kamalkraj/deberta-v2-xlarge'      )
										snake_case        =     tf.constant([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]      )
										snake_case        =     tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]      )
										snake_case        =     model(lowerCAmelCase						,      attention_mask=lowerCAmelCase      )[0]

										snake_case        =     tf.constant(
										    [[[0.23_56, 0.19_48, 0.03_69], [-0.10_63, 0.35_86, -0.51_52], [-0.63_99, -0.02_59, -0.25_25]]]      )
										tf.debugging.assert_near(output[:, 1:4, 1:4]						,      lowerCAmelCase						,      atol=1E-4      )


